Category: 3. Business

  • Surrey couple visit 164 RNLI lifeboat stations

    Surrey couple visit 164 RNLI lifeboat stations

    Stuart Maisner

    BBC News, South East

    Allan Thornhill Helen and Allan Thornhill wearing life jackets on board the lifeboat at RNLI Buckie in ScotlandAllan Thornhill

    The couple boarded the lifeboat at RNLI Buckie in north-east Scotland

    A couple from Surrey, who are on a two-year mission to visit all 238 RNLI lifeboat stations in the UK and Ireland, have completed more than two thirds of their challenge.

    Allan and Helen Thornhill, from Smallfield, are raising funds for the 200-year-old life-saving charity.

    They have visited 164 locations since June 2024 and plan to finish at the RNLI’s headquarters, Poole in Dorset, in summer 2026.

    Mr Thornhill said: “It’s a great milestone to have reached and it makes the finish line seem in sight.”

    The couple started their challenge visiting Teddington RNLI, south-west London, on 1 June 2024.

    In 2025 they have so far visited Cornwall, Pembrokeshire, Northumberland, Yorkshire and Lincolnshire as well as eastern Scotland and Orkney and Shetland.

    Mr Thornhill said: “The highlight for me was visiting the most northerly lifeboat station in the UK in Shetland.

    “Longhope in Orkney was also stunning with a turquoise blue sea.”

    He added: “The Cornwall coast was also absolutely spectacular – one of most attractive we have visited so far.”

    Allan Thornhill Allan and Helen Thornhill standing on a jetty next to the RNLI lifeboat in Fowey in CornwallAllan Thornhill

    The couple visited the RNLI lifeboat station in Fowey in Cornwall

    Ms Thornhill has a fear of flying and had not been on a plane for 15 years.

    That all changed when the sea was too rough to travel to the Scilly Isles and they had no choice but to go by air.

    She said: “I don’t really remember the flight as I had my eyes shut and was crying my eyes out.”

    The pair have been undertaking their charity challenge in holidays from their jobs.

    Ms Thornhill said: “Pembrokeshire was stunning. When we arrived in Cardigan we actually witnessed the lifeboat launch on a rescue.

    “They successfully towed a small boat back to harbour.

    “It made us realise what our challenge is all about.”

    The couple hit their original fundraising target of £2,380 in June and now aim to double that by the end of the challenge.

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  • Cool Britannia: the heat pumps keeping pace with the UK’s wild weather | Heat pumps

    Cool Britannia: the heat pumps keeping pace with the UK’s wild weather | Heat pumps

    When David Tester, 56, installed a heat pump in his home in the winter of 2022, freezing temperatures descended on the country alongside a series of named storms. It was a world away from this summer’s heatwaves, which have pushed Britain to the brink of drought.

    But Tester’s choice of heat pump design has meant that his three-bedroom 1930s semi in West Sussex has remained at a comfortable temperature despite Britain’s increasingly volatile weather. The air-to-air heat pump provides heating in the winter but acts like an air conditioner in the summer months.

    “The heat pump has worked very well in both seasons and because I run it on electricity from my solar panels, it basically provides cooling for free,” he said.

    Heat pumps remain rare in Britain, but the recent spells of hotter than average temperatures may have the unexpected consequence of boosting the government’s ambition to replace Britain’s boilers.

    The government recently set out plans to include air-to-air heat pumps, which can act as air conditioners, in the same grant scheme that offers £7,500 to households that replace their gas boiler with “wet system” heat pumps.

    So far, the government has prioritised promoting “hydronic” heat pumps, in large part because they make use of the existing central heating pipes and radiators, which most households currently use to warm their homes.

    They work like a fridge in reverse by using electricity to capture and amplify even small amounts of heat outside a home to raise the water temperature of the central heating system. The warmed water travels through pipes and radiators to heat a home in the same way as water heated by a gas boiler.

    But these are far from the most common choice in homes across Europe, according to Jan Rosenow, an academic and a programme director at the Regulatory Assistance Project (RAP), which regularly analyses heat pump systems.

    “In most homes across Europe air-to-air heat pumps are the most popular option. We see the same thing in China, and the same in the US. It may seem new for UK homes, but really it is the dominant technology,” he said.

    Cold comfort

    In Britain air-to-air heat pumps are already beginning to roll out across public buildings, offices and shopping centres. Andrew Sissons, a deputy director at Nesta, a charity that undertakes research into home heating innovation, believes that smaller homes and flats in particular could soon benefit from combined heating and cooling too.

    Air heat pumps use the same basic principles as hydronic system heat pumps, but instead of heating water that is pumped through the home, they heat the air. In the summer a refrigerant is used to provide cooling.

    “Demand for air-to-air heat pumps could be significant,” Sissons said. “They provide cooling – which is more in demand as our summers get hotter – and the cost of units is far lower too. They are also relatively simple to install because they are usually wall mounted so they don’t require as much outdoor space.”

    The catch? They don’t usually provide hot water, meaning a separate low-carbon solution would be needed to replace a traditional gas boiler system entirely.

    In West Sussex, Tester has kept his gas boiler to heat the water used in his underfloor heating system on the first floor – but uses it sparingly for a few hours in the morning and evening during the winter months, while the air-to-air heat pump tackles the majority of the heating requirements. In summer, the pump runs on solar power to provide cooling.

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    Guilt-free cooling?

    The promise of cooler home temperatures through Britain’s increasingly warm summers does not sit easily with many. Home air conditioning is rare in Britain, unlike in southern Europe, and there are growing concerns over the impact of cooling on the UK’s energy demand and carbon targets as the climate crisis intensifies.

    The International Energy Agency has warned that growing demand for cooling could put a strain on electricity grids and climate goals. Cooling accounts for about 10% of global electricity demand today, but it is set to double by 2050 as the climate gets hotter.

    The global energy watchdog noted that in 2023 one power plant in China burned about 800 tonnes of coal in just one hour to help keep Shanghai residents cool during a summer heatwave.

    But British households should have no reservations about installing an air-to-air heat pump to use for cooling purposes too, according to Sissons.

    There are two main reasons for this: first, the number of days when cooling would be required are far lower than the number of days when the heat pump will make significant carbon savings by providing warmth in place of a gas boiler. These carbon savings would easily outweigh the extra carbon demand from cooling a home in summer, he said, not least because the UK’s electricity grid is increasingly green.

    Second, there is a strong correlation between the days when cooling will be in high demand, and the days when the UK is generating large amounts of solar power, Sissons said. Winter evenings are still likely to be the most taxing for the UK electricity system, even if there is a significant uptake in summer air conditioning in the years ahead.

    These calculations already form part of the extensive forecasts produced by the National Energy System Operator to determine how much energy Britain will need in the future – and how it can meet this requirement.

    The system operator’s modelling accounts for a huge increase in electricity use by the end of the decade, including demand from electric vehicles, heat pumps and data centres. It also forecasts more energy-efficient buildings and appliances combined with batteries which can provide extra electricity at times of peak demand.

    The modelling shows that a virtually zero carbon electricity grid could power Britain by the end of the decade – even with growing demand – and be used by the industry and government policymakers to help the UK achieve its clean power goals.

    Even so, homes can take steps to dull the effect of a surge in demand for cooling. The Energy Saving Trust recommends using passive cooling methods before fitting either an air conditioning unit or a heat pump with a cooling function to dampen the impact of higher energy demand. These include using window shades and improving ventilation to minimise the amount of energy used to cool a home.

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  • Effectiveness of Personalized Nutrition on Management Diabetes Mellitu

    Effectiveness of Personalized Nutrition on Management Diabetes Mellitu

    Introduction

    In 2021, the global prevalence of diabetes reached 6.1% of the world’s population, with approximately 529 million people, with the majority of them (96%) being diabetes mellitus type 2.1 Diabetes mellitus type 2 caused 66.3 million Disability-Adjusted Life Years (DALYs) in 2019.2, In 2021, the prevalence of prediabetes, specifically impaired glucose tolerance (IGT) and impaired fasting glucose (IFG), was 9.1% (464 million) and 5.8% (298 million), respectively, among adults aged 20–79 years worldwide. By 2045, the global prevalence of IGT and IFG is projected to increase to 10.0% (638 million) and 6.5% (414 million). Additionally, prediabetes can progress to diabetes in up to 50% of cases within 5 years.3 Nutrition plays a key role in managing symptoms and preventing progression of both diabetes and prediabetes. Nutritional interventions can help maintain glycemic targets, manage body weight, and improve cardiovascular risk factors (such as blood pressure and lipid profile) in people with diabetes and prediabetes.4

    Recent findings highlight that metabolic responses to diets can vary between individuals.5 This variability was evident in a randomized controlled trial involving 609 overweight adults, which demonstrated significant variation in weight loss, with a range of about 40 kg among individuals in both the low-fat and low-carbohydrate diet groups over 12 months.6 These differences are thought to be driven by fundamental factors that influence our individual responses to food and the biological effects of its consumption include the human genome, the epigenome, the microbiome, and variations between individuals in environmental exposures and lifestyle habits.5

    Over the past two decades, a nutritional intervention strategy known as personalized nutrition has emerged. Personalized nutrition has developed based on the understanding that each individual’s response to a nutritional intervention can significantly differ.4 Personalized nutrition can predict an individual’s unique response to a nutrient using machine learning. Personalized nutrition utilizes data based on anthropometry, food composition, metabolism, genotype-phenotype, and microbiome to generate its nutritional recommendation algorithms.4

    Several randomized controlled trials (RCTs) have been conducted on personalized nutrition in addressing diabetes mellitus type 2 and prediabetes. However, far too little attention has been paid to synthesizing the effectiveness of personalized nutrition compared to a control diet in managing these conditions. Therefore, a systematic review is needed to synthesize the effectiveness of personalized nutrition compared to a control diet in managing diabetes mellitus type 2 and prediabetes.

    Methodology

    Protocol and Registration

    We performed a systematic review following the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions7 and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 Checklist8 (Supplementary 1, and the Synthesis Without Meta-analysis (SWiM)9 (Supplementary 2). The study protocol has been registered at International Prospective Register of Systematic Reviews (PROSPERO), Number Centre for Reviews and Dissemination (CRD) 42024532817.

    Eligibility Criteria

    The inclusion criteria for study selection in this systematic review were (detailed on Supplementary 3):

    1. Studies on adults (aged > 18 years) diagnosed with diabetes mellitus type 2 and prediabetes.
    2. Studies that apply personalized nutrition interventions to manage diabetes mellitus type 2 and prediabetes. There are some differences in defining the term personalized nutrition, but in this review, personalized nutrition is defined according to the American Nutrition Association, which is a dietary recommendation approach based on individual differences in response to nutrition, nutritional status, eating patterns, meal timing, and environmental exposures. These differences arise due to variations in biochemistry, metabolism, genetics, and microbiome.10
    3. Studies that provide data on the effectiveness of personalized nutrition compared to control diet.
    4. Studies published in full-text format in English.
    5. Study design using RCTs.

    The exclusion criteria for study selection in this systematic review were (detailed on Supplementary 3):

    1. Studies conducted on children, adolescents, and animal subjects.
    2. Diseases unrelated to diabetes mellitus type 2 and prediabetes.
    3. Inadequate data in the study.
    4. Study designs using qualitative methods, case series, narrative reviews, systematic reviews, and observational studies (cohort, case-control, and cross-sectional studies).

    Information Sources and Search Strategy

    The study search sources were PubMed, Cochrane CENTRAL, and Medline (Ovid). We searched those published up to March 31, 2024, conducted on humans, with a randomized controlled trial design, and written in English. The literature search strategy was carried out using all Medical Subject Headings (MeSH) terms and text words for each concept in the search, combined with “OR”. Subsequently, the concepts of diabetes mellitus type 2 and prediabetes were merged using “OR” to form the fourth concept. The personalized nutrition concept and the fourth concept were combined using “AND”. A detailed searching strategy is listed in Supplementary 4. The retrieved studies were exported to the EndNote 21 citation manager.

    Selection Process

    Two reviewers Elisa Fauziyatul Munawaroh (EFM) and Andri Wijayakesuma (AW) independently screened the literature from all sources. In case of disagreement, it was resolved through consensus. A third reviewer, Dani Dani (DD) was involved if consensus could not be reached. The first stage assessed abstracts and titles using Covidence software. The second stage assessed the full texts using Covidence software.

    Data Collection Process and Data Items

    Two reviewers (EFM and AW) independently extracted data from a subset of the studies (20%). The inter-rater agreement between the two reviewers was high ≥95%. Any disagreements were resolved by reaching a consensus. Subsequently, EFM extracted data from all included studies. Data extracted onto Table of Study Characteristics included: author, year of publication, country, study design, participant type (age, gender), number of participants, description of the intervention, description of the comparison, intervention and follow-up duration, and measured outcomes.

    Study Risk of Bias Assessment

    Two reviewers (EFM and AW) assessed the quality of the included studies. In case of disagreement, it was resolved through consensus. If consensus could not be reached, DD was involved. The study quality assessment used Cochrane’s ROB 2 (Risk of Bias 2). ROB 2 consists of five domains of bias that are evaluated: the randomization process, the effect of the intervention, missing data, outcome measurement, and result reporting. The quality assessment results were then classified into high risk, some concerns, or low risk.7

    Synthesis Methods

    1. A meta-analysis was not considered appropriate for this review due to the significant heterogeneity until 99%, suggesting that the studies varied greatly in their participants, interventions, or measurement methods. Despite after we do subgroup analysis the source of heterogeneity could not be explained. A meta-analysis was not considered appropriate for this review also due to the limited number of studies and lacked enough comparable data of the included studies (often 2 RCTs).
    2. We used the Synthesis Without Meta-analysis (SWiM) method for synthesis instead of meta-analysis. SWiM was used in this systematic review to examine the quantitative effects of interventions. The synthesis included a structured summary of effect estimates as well as the combination of p values to evaluate the overall statistical significance. P value combining was conducted using Fisher’s method.7,9
    3. We analyzed studies by separating the risk of bias. The visually display results used the box and whisker plot.
    4. The review includes quantitative data on effect sizes, using the median mean difference (MD) as the measurement. Median MD was calculated for RCTs that provided necessary follow-up data for each outcome.
    5. Following standard Cochrane methodology, we created a Table Summary of Findings.7 We considered it important to summarize the following outcomes.

    1. HbA1c (%)
    2. Fasting Blood Glucose (mmol/L)
    3. PPGR (mg/dlxh)
    4. Body Weight (%)
    5. Energy Intake (% of energy)
    6. Carbohydrate Intake (% of energy)
    7. Gut Microbiome Richness and Diversity (species)

    Certainty Assessment

    The quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. The quality of evidence was evaluated by considering the risk of bias, inconsistency, indirectness, imprecision, and publication bias. Each domain was classified as not serious, serious, or very serious, except for the publication bias domain, which was classified as undetected or strongly suspected. GRADEpro Software was used to guide the evidence assessment for each outcome.11 The results of the evidence quality assessment were classified as high, moderate, low, or very low. The results of the evidence quality assessment are presented in the Table Summary of Findings.

    Results

    Study Selection

    The search results from three electronic databases, Medline, Cochrane CENTRAL, and PubMed, yielded 5061 relevant studies (Supplementary 5). After automatically excluding non-RCTs and duplicates using Covidence software, as well as manual exclusions, 3211 studies remained for the title and abstract screening. Based on title and abstract screening, 3200 studies were excluded, and eleven studies were included for full-text screening. From the full-text screening, three studies were excluded, and eight studies were included for review. Although our review included eight articles, only five distinct RCTs were represented. One RCT was reported in three separate articles: Ben-Yacov et al 2021;12 Ben-Yacov et al 2023;13 Shoer et al 2023.14 Another RCT was reported in two articles: Kharmats et al 202315 and Popp et al, 2022.16 The remaining three RCTs were each reported in a single article: Joshi et al 2023;17 Karvela et al 2024;18 and Rein et al, 2022.19 Figure 1 presents the PRISMA diagram flow illustrating the selection process of the included studies.

    Figure 1 PRISMA Diagram Flow.

    Study Characteristics

    The study characteristics table is presented in Table 1, and the studies included were conducted in Israel, India, United of Kingdom (UK), and the United States of America(USA). From these eight studies, there were only five clinical trials. Four of the five clinical trials used an individually randomized controlled trial design.12,16–18 Only one study used a crossover randomized controlled trial design.19 The total number of participants was 919 individuals with prediabetes or diabetes mellitus type 2. The five personalized nutrition clinical trials included in this review consistently used clinical data, anthropometric measurements, and blood biomarkers to predict participants’ responses to a particular food. In addition to these data, four of the clinical trials also utilized microbiome data.12,16–19 However, only one clinical trial used genotype data.18 The duration of the interventions ranged from 2 weeks to 1 year. The comparisons consisted of a Mediterranean diet, a low-fat diet, and a diet according to recommendations from nutrition experts (medical nutrition therapy).

    Table 1 Table of Study Characteristics

    Risk of Bias in Studies

    Supplementary 6 shows the quality assessment results of the eight studies evaluated based on Cochrane Risk of Bias 2. Two studies were rated as having a high risk of bias because the allocation was not concealed,17,18 and six were rated as having some concern because of bias due to deviation from intended interventions and reporting results.12–16,19

    Results of Synthesis and Certainty of Evidence

    Based on Table 2, which summarizes the findings on the effectiveness of personalized nutrition compared to a control diet for managing diabetes mellitus type 2 and prediabetes, the following is the data analysis for each outcome (quantitative synthesis detailed on Supplementary 7):

    Table 2 Summary of Findings on the Effectiveness of Personalized Nutrition Compared to Control Diet for Managing Diabetes Mellitus Type 2 and Prediabetes

    Effectiveness of Personalized Nutrition on HbA1c

    • The synthesis of the summarized effect estimate of HbA1c values from four studies (Ben-Yacov et al 2021;12 Joshi et al 2023;17 Karvela et al 2024;18 Kharmats et al 202315) showed a reduction in HbA1c levels in the personalized nutrition group, with a median mean difference (MD) of −0.925% compared to the control diet in diabetes mellitus type 2 and prediabetes patients. The combined p-value synthesis from these four studies indicated that this reduction was significant (p < 0.0, 4 studies) showed in Figure 2.
    • When the synthesis was limited to studies with some concerns risk of bias (Ben-Yacov et al 2021;12 Kharmats et al 202315), decreased HbA1c levels in the personalized nutrition group were still observed, with a median MD of −0.035% compared to the control diet in diabetes mellitus type 2 and prediabetes patients. The combined p-value also remained significant (p < 0.0, 2 studies) showed in Figure 2.

    Figure 2 Box-and-whisker plots of estimate of mean differences for HbA1c and separately by the risk of bias. Data mean differences HbA1c of some concern risk bias studies (Blue). Data mean differences HbA1c of high-risk bias studies (Orange).

    Effectiveness of Personalized Nutrition on Fasting Blood Glucose

    • The synthesis results using the p-combining from both studies (Ben-Yacov et al, 202112 and Karvela et al, 202418) found that personalized nutrition did not significantly reduce fasting blood glucose compared to control diet in prediabetes patients (p = 0.12, 2 studies).
    • The synthesis was limited to studies with some concerns risk of bias (Ben-Yacov et al 202112) found that personalized nutrition also did not significantly reduce fasting blood glucose compared to control diet in prediabetes patients.

    Effectiveness of Personalized Nutrition on PPGR

    • The synthesis results using the p-combining from both studies (Ben-Yacov et al, 202112 and Rein et al, 202219), which are some concern risks of bias studies, found that personalized nutrition significantly reduces PPGR compared to control diet in diabetes mellitus type 2 and prediabetes patients (p < 0.0, 2 studies).
    • The synthesis of the summarized effect estimates of PPGR values from two studies (Ben-Yacov et al, 202112 and Rein et al, 202219), showed personalized nutrition reduces postprandial glucose response (PPGR) with a median mean difference of −14.85 mg/dlxh compared to the control diet in diabetes mellitus type 2 and prediabetes patients.

    Effectiveness of Personalized Nutrition on Body Weight

    • Based on the results, the p-combining from Ben-Yacov et al, 2021;12 Joshi et al 202317 and Popp et al, 202216 that are some concern risks of bias and high-risk bias studies, personalized nutrition significantly reduced body weight compared to the control diet (p < 0.0, 3 studies). The median mean difference in body weight reduction was –0.58% in favor of personalized nutrition.
    • But, when limited to studies with some concern of bias (Ben-Yacov et al, 202112 and Popp et al, 202216) personalized nutrition did not significantly reduce body weight in diabetes mellitus type 2 and prediabetes compared to control diet (p = 0.06, 2 studies).

    Effectiveness of Personalized Nutrition on Energy Intake

    • The synthesis results using the p-combining method from both studies (Ben-Yacov et al 2021;12 Popp et al 202216) showed that personalized nutrition did not significantly reduce energy intake compared to the control diet in diabetes mellitus type 2 and prediabetes (p = 0.06, 2 studies).

    Effectiveness of Personalized Nutrition on Carbohydrate Intake

    • The synthesis results using the p-combining method of two studies (Ben-Yacov et al 2021;12 Popp et al 202216) concluded that personalized nutrition was significantly reduced by carbohydrate intake compared to control diets (p = 0.02, 2 studies) in diabetes mellitus type 2 and prediabetes.
    • The synthesis of the summarized effect estimates of carbohydrates intake from two studies (Ben-Yacov et al 2021;12 Popp et al 202216) showed personalized nutrition reduces carbohydrate intake with a median mean difference of −10.8% of energy compared to the control diet in diabetes mellitus type 2 and prediabetes patients.

    Effectiveness of Personalized Nutrition on Gut Microbiome

    • The one study (Ben-Yacov et al 2021;12 Ben-Yacov et al 2023;13 Shoer et al 202314) concluded that personalized nutrition significantly increased gut microbiome richness and diversity compared to baseline in prediabetes patients (p = 0.007). Meanwhile, the control diet only showed a significant impact on gut microbiome diversity in prediabetes patients (p = 0.18).

    Discussion

    The goal of a nutritional intervention in diabetes mellitus type 2, according to the American Diabetes Association (ADA) guidelines, is to improve HbA1c levels, blood pressure, and cholesterol, achieve and maintain target body weight, and prevent diabetes complications.20 Elevated HbA1c significant challenges for individuals with diabetes mellitus type 2. These factors serve as key indicators for assessing glycemic control.21 Personalized nutrition significantly reduces HbA1c levels compared to control diets with a median mean difference of −0.925%–0.035% in diabetes mellitus type 2 and prediabetes. The significant reduction in HbA1c in personalized nutrition is likely due to personalized nutrition significantly reduced carbohydrate intake, with a median mean difference of −10.8% compared to control diets. This is consistent with other randomized controlled trial studies, which state that HbA1c levels in a low-carbohydrate diet (8.5%) decreased significantly (p < 0.05) compared to those in a low-fat diet (4%) for 3 months.21

    Personalized nutrition also significantly reduces PPGR, with a median mean difference of −14.85 mg/dlxh. This suggests that personalized nutrition effectively modulates glycemic responses after meals. This aligns with its primary goal of improving short-term glucose metabolism in diabetes mellitus type 2 and prediabetes.

    The reduction in HbA1c is consistent with a decrease in PPGR but does not align with a reduction in fasting blood glucose levels. The difference in effects between HbA1c and fasting blood glucose may be due to HbA1c being more influenced by postprandial blood glucose than fasting blood glucose. This is because the participants recruited in the studies are controlled prediabetes and diabetes mellitus patients. This is supported by the study by Monnier and Colette, which found that in controlled diabetes patients (with low HbA1c), postprandial glucose is more dominant in affecting HbA1c. Conversely, in uncontrolled diabetes patients (with high HbA1c), fasting blood glucose is more dominant. Specifically, the study found that in patients with HbA1c less than 7.3%, postprandial glucose contributes about 70% to the effect on HbA1c. In contrast, in patients with HbA1c greater than 10.2%, the contribution of postprandial glucose drops to 30%, making fasting blood glucose a more dominant factor.22

    Weight loss in diabetes mellitus type 2 and prediabetes aims to improve clinical benefits and reduce disease progression. The weight loss target for diabetes is 5%, while for prediabetes it is 7–10%.20 The combined analysis of studies with varying levels of bias-including those with some concerns and high risk – show that personalized nutrition statistically significantly reduce body weight compared to control diets. The median mean difference in body weight reduction was –0.58% in favor of personalized nutrition. This suggests that while there is some indication that personalized nutrition may be effective in promoting weight loss. However, this finding should be interpreted with caution as it involves studies with a high risk of bias.

    When focusing only on studies with some risk of bias, personalized nutrition did not lead to a significant reduction in body weight among individuals with diabetes mellitus type 2 or prediabetes. This is reinforced by the study by Rein et al, 2022, which found no significant difference in weight loss between high- and low-adherence groups to personalized nutrition.19 Moreover, this review found no significant reduction in energy intake between personalized nutrition and control groups, which may explain the lack of substantial weight loss. In line with other personalized nutrition studies, the Food4Me study conducted on healthy adults in Europe showed no significant difference in body weight after 6 months between the personalized and non-personalized diets groups.23 The Preventomics study, conducted on overweight and obese individuals, also showed that personalized nutrition after ten weeks did not significantly affect weight loss compared to the control diet.24

    The previous systematic review mentioned that nutritional interventions can improve metabolic parameters in type 2 diabetes mellitus by improving gut microbiota.25 One of the RCTs in this review further assessed the effectiveness of personalized nutrition on gut microbiota conditions in prediabetic patients. The study found that personalized nutrition, when compared to baseline, significantly increased gut microbiome richness and diversity. Meanwhile, the control diet only showed a significant impact on gut microbiome diversity.12–14 Although further investigation is still needed, the study mentioned that two bacterial species, UBA11774 sp003507655 (from the Lachnospiraceae family) and UBA11471 sp000434215 (from the Bacteroidales order), mediated the personalized nutrition for clinical improvements in HbA1C, triglycerides, and HDL.13

    Personalized nutrition aligns with some goals of nutritional interventions for diabetes mellitus type 2 and prediabetes, such as significantly reducing HbA1c, PPGR level, carbohydrate intake and a possible reduction in body weight compared to control diets as well as significantly increased gut microbiome richness and diversity compared to baseline. However, personalized nutrition does not significantly affect fasting blood glucose in individuals with prediabetes, nor does it significantly affect energy intake in individuals with diabetes mellitus type 2 and prediabetes, compared to control diets. The partial achievement of intervention goals may be because machine learning algorithms in personalized nutrition mainly predict glucose response. This approach may overlook other metabolic responses, which are crucial for clinical improvement and disease progression in prediabetes and diabetes mellitus type 2.23 So, an essential aspect for future research is that personalized nutrition is expected to predict glycemic responses and other metabolic responses comprehensively.

    The strengths of this review are: first, the selection of studies and quality assessment were conducted by two independent reviewers. Second, the search strategy was carried out across three electronic databases. However, the limitations of this review include the fact that only studies published in English were included and this synthesis was not based on a meta-analysis. Therefore, caution is needed when interpreting these pieces of evidence.

    The quality of evidence in this review ranges from low to very low, so interpreting the results should be done with caution. Several factors contribute to this. First, two studies were categorized as high risk of bias due to allocation processes not being adequately concealed, potentially leading to selection bias. Second, inconsistency is often rated as serious due to high heterogeneity among studies. Third, some studies frequently categorize imprecision as serious due to small sample sizes.

    The studies were conducted in Israel, the United States, the United Kingdom, and India. This may limit generalizability as populations in other countries might have different genetic, lifestyle, cultural, and diabetes mellitus type 2 or prediabetes characteristics. Additionally, these studies primarily involved participants with controlled prediabetes or diabetes mellitus type 2, so the findings may not be generalized to individuals with uncontrolled diabetes mellitus type 2. Furthermore, studies on the effectiveness of personalized nutrition on fasting blood glucose and gut microbiomes parameters were only conducted in prediabetic patients, thus limiting generalizability to other patient groups.

    Conclusion

    This systematic review found that personalized nutrition interventions in individuals with diabetes mellitus type 2 and prediabetes yielded favorable effects on some glycemic outcomes, particularly HbA1c, postprandial glucose response (PPGR), reduction in carbohydrate intake and a possible reduction in body weight compared to control diets. Additionally, one study demonstrated improvements in gut microbiome richness and diversity compared to baseline. These results suggest that personalized nutrition may be a useful in glycemic management for people at risk of or living with diabetes. However, no significant effect of personalized nutrition was found on fasting blood glucose in individuals with prediabetes, nor on energy intake in individuals with diabetes melltius type 2 and prediabetes. This may be due to the current design of personalized nutrition algorithms, which are primarily focused on optimizing glycemic control rather than broader metabolic outcomes. Overall, health-care professionals and policymakers may increasingly consider integrating personalized nutrition approaches into diabetes clinical guidelines and management programs.

    The overall certainty of evidence in this review was rated from low to very low, largely due to some studies were categorized as high-risk bias, heterogeneity and small sample sizes. Future synthesis should aim to address these limitations by conducting larger, high-quality trials with standardized interventions and outcome measures to better evaluate the efficacy and applicability of personalized nutrition in diverse clinical settings.

    Abbreviations

    ADA, American Diabetes Association; ALG, Academic Leadership Grant; ALT, Alanine Aminotransferase; APC, Article Processing Charge; AST, Aspartate Aminotransferase; AW, Andi Wijayakesuma; BMI, Body Mass Index; CGM, Continuous Glucose Monitor; CONGA, Continuous Overall Net Glycemic Action; CRD, Centre for Reviews and Dissemination; DALYs, Disability-Adjusted Life Years; DD, Dani Dani; DM, Diabetes Mellitus; DMDH, Dewi Marhaeni Diah Herawati; DNA, Deoxyribonucleic Acid; EFM, Elisa Fauziyatul Munawaroh; GRADE, Grading of Recommendations Assessment, Development, and Evaluation; HbA1C, Hemoglobin A1C; HDL, High-Density Lipoprotein; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; LDL, Low-Density Lipoprotein; MAGE, Mean Amplitude Of Glycemic Excursion; MD, Mean Difference; MeSH, Medical Subject Headings; NICE, National Institute for Health and Care Excellence; PPGR, Postprandial Glucose Response; PPT, Personalized Postprandial Targeting; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PROSPERO, International Prospective Register of Systematic Reviews; RCTs, Randomized Controlled Trials; ROB 2, Risk Of Bias 2; SWiM, Synthesis Without Meta-Analysis; UK, United of Kingdom; USA, United States of America.

    Data Sharing Statement

    The original contributions presented in the study are included in the supplementary material, further inquiries can be directed to the corresponding author.

    Funding

    The research was funded by Academic Leadership Grant (ALG) number 14344/UN6.3.1/PT.00/2024 for DMDH. The Article Processing Charge (APC) was funded by the Directorate of Research and Community Service at Universitas Padjadjaran.

    Disclosure

    The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

    References

    1. Ong KL, Stafford LK, McLaughlin SA, et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the global burden of disease study 2021. Lancet. 2023;402(10397):203–234. doi:10.1016/S0140-6736(23)01301-6

    2. SafiriSaeid, KaramzadNahid, KaufmanJay S., BellArielle Wilder, NejadghaderiSeyed Aria, SullmanMark J. M., Moradi-LakehMaziar, CollinsGary, Kolahi Ali-Asghar. Prevalence, Deaths and Disability-Adjusted-Life-Years (DALYs) Due to Type 2 Diabetes and Its Attributable Risk Factors in 204 Countries and Territories, 1990-2019: Results From the Global Burden of Disease Study 2019 Frontiers in Endocrinology. ; 2022, February 25 13 838027. Available from doi:10.3389/fendo.2022.838027

    3. Rooney MR, Fang M, Ogurtsova K, et al. Global Prevalence of Prediabetes. Diabetes Care. 2023;46(7):1388–1394. doi:10.2337/dc22-2376

    4. Berry SE, Valdes AM, Drew DA, et al. Human postprandial responses to food and potential for precision nutrition. Nat Med. 2020;26(6):964–973. doi:10.1038/s41591-020-0934-0

    5. Bashiardes S, Godneva A, Elinav E, Segal E. Towards utilization of the human genome and microbiome for personalized nutrition. Curr Opin Biotechnol. 2018;51:57–63. doi:10.1016/j.copbio.2017.11.013

    6. Gardner CD, Trepanowski JF, Gobbo LCD, et al. Effect of low-fat vs low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion the DIETFITS randomized clinical trial. JAMA. 2018;319(7):667–679. doi:10.1001/jama.2018.0245

    7. Cochrane. Cochrane handbook for systematic reviews of interventions. 2019. Available from: https://onlinelibrary.wiley.com/doi/. Accessed July 18, 2025.

    8. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372. doi:10.1136/bmj.n71

    9. Campbell M, McKenzie JE, Sowden A, et al. Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline. BMJ. 2020;368. doi:10.1136/bmj.l6890

    10. Bush CL, Blumberg JB, El-Sohemy A, et al. Toward the definition of personalized nutrition: a proposal by the American Nutrition Association. J Am Coll Nutr. 2020;39(1):5–15. doi:10.1080/07315724.2019.1685332

    11. GRADEpro GDT. GRADEpro Guideline Development Tool [Software]. McMaster University and Evidence Prime.

    12. Ben-Yacov O, Godneva A, Rein M, et al. Personalized postprandial glucose response–targeting diet versus Mediterranean diet for glycemic control in prediabetes. Diabetes Care. 2021;44(9):1980–1991. doi:10.2337/DC21-0162

    13. Ben-Yacov O, Godneva A, Rein M, et al. Gut microbiome modulates the effects of a personalised postprandial-targeting (PPT) diet on cardiometabolic markers: a diet intervention in pre-diabetes. Gut. 2023;72(8):1486–1496. doi:10.1136/gutjnl-2022-329201

    14. Shoer S, Shilo S, Godneva A, et al. Impact of dietary interventions on pre-diabetic oral and gut microbiome, metabolites and cytokines. Nat Commun. 2023;14(1). doi:10.1038/s41467-023-41042-x

    15. Kharmats AY, Popp C, Hu L, et al. A randomized clinical trial comparing low-fat with precision nutrition–based diets for weight loss: impact on glycemic variability and Hba1c. Am J Clin Nutr. 2023;118(2):443–451. doi:10.1016/j.ajcnut.2023.05.026

    16. Popp CJ, Hu L, Kharmats AY, et al. Effect of a personalized diet to reduce postprandial glycemic response vs a low-fat diet on weight loss in adults with abnormal glucose metabolism and obesity: a randomized clinical Trial. JAMA Network Open. 2022;5(9):E2233760. doi:10.1001/jamanetworkopen.2022.33760

    17. Joshi S, Shamanna P, Dharmalingam M, et al. Digital Twin-enabled personalized nutrition improves metabolic dysfunction-associated fatty liver disease in type 2 diabetes: results of a 1-year randomized controlled study. Endocr Pract. 2023;29(12):960–970. doi:10.1016/j.eprac.2023.08.016

    18. Karvela M, Golden CT, Bell N, et al. Assessment of the impact of a personalised nutrition intervention in impaired glucose regulation over 26 weeks: a randomised controlled trial. Sci Rep. 2024;14(1). doi:10.1038/s41598-024-55105-6

    19. Rein M, Ben-Yacov O, Godneva A, et al. Effects of personalized diets by prediction of glycemic responses on glycemic control and metabolic health in newly diagnosed T2DM: a randomized dietary intervention pilot trial. BMC Med. 2022;20(1). doi:10.1186/s12916-022-02254-y

    20. Evert AB, Dennison M, Gardner CD, et al. Nutrition therapy for adults with diabetes or prediabetes: a consensus report. Diabetes Care. 2019;42(5):731–754. doi:10.2337/dci19-0014

    21. Wang LL, Wang Q, Hong Y, et al. The effect of low-carbohydrate diet on glycemic control in patients with type 2 diabetes mellitus. Nutrients. 2018;10(6):661. doi:10.3390/nu10060661

    22. Monnier L, Colette C. Contributions of fasting and postprandial glucose to hemoglobin A1c. Endocr Pract. 2006;12:42–46. doi:10.4158/EP.12.S1.42

    23. Bermingham KM, Linenberg I, Polidori L, et al. Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial. Nat Med. 2024;30(7):1888–1897. doi:10.1038/s41591-024-02951-6

    24. Aldubayan MA, Pigsborg K, Gormsen SMO, et al. A double-blinded, randomized, parallel intervention to evaluate biomarker-based nutrition plans for weight loss: the PREVENTOMICS study. Clin Nutr. 2022;41(8):1834–1844. doi:10.1016/j.clnu.2022.06.032

    25. Xu X, Zhang F, Ren J, et al. Dietary intervention improves metabolic levels in patients with type 2 diabetes through the gut microbiota: a systematic review and meta-analysis. Front Nutr. 2023;10. doi:10.3389/fnut.2023.1243095

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  • Clinical and Molecular Insights into Anti-MDA5 Antibody-Positive Derma

    Clinical and Molecular Insights into Anti-MDA5 Antibody-Positive Derma

    Introduction

    Dermatomyositis (DM) represents a heterogeneous group of autoimmune inflammatory myopathies characterized by varying involvement of skeletal muscles, skin, and multiple organ systems.1 Among the recognized myositis-specific autoantibodies, anti-melanoma differentiation-associated gene 5 (MDA5) antibody-positive dermatomyositis (MDA5-DM) has recently garnered substantial clinical and research interest due to its distinct clinical phenotype and poor prognosis.2

    MDA5-DM is predominantly characterized by rapidly progressive interstitial lung disease (RP-ILD), which frequently precipitates severe respiratory failure and carries a high short-term mortality rate.3,4 Early in its clinical course, however, MDA5-DM often manifests with nonspecific symptoms such as low-grade fever, dry cough, or subtle cutaneous eruptions, leading to frequent misdiagnosis as infectious diseases, allergic dermatitis, or other connective tissue disorders. This diagnostic ambiguity results in delayed confirmation and initiation of appropriate immunosuppressive therapy, which may exacerbate disease progression and worsen patient outcomes.

    Serum biomarkers such as Krebs von den Lungen-6 (KL-6) and ferritin, together with the presence of RP-ILD, have been previously identified as important prognostic factors in MDA5-DM.5–7 Concurrently, advances in high-throughput sequencing techniques, particularly transcriptomics, have significantly expanded our understanding of the underlying immune-mediated mechanisms driving this disease. Emerging evidence implicates signaling pathways involving interleukin-17 (IL-17), Toll-like receptors, and chemokine networks as pivotal contributors to the intense inflammatory milieu and rapid disease progression characteristic of MDA5-DM.8,9

    Despite these advances, comprehensive investigations that integrate both detailed clinical phenotyping and molecular analyses remain scarce. There is a critical need to elucidate core molecular pathways underlying disease pathogenesis, improve risk stratification of high-risk patients, and inform the development of personalized therapeutic strategies.

    In this context, our single-center retrospective study enrolled 29 confirmed MDA5-DM patients to systematically delineate their clinical features, misdiagnosis patterns, and key prognostic factors, with a particular focus on serum KL-6 levels. By leveraging publicly available transcriptomic datasets (GSE143323), we further explored differentially expressed genes and immune-inflammatory pathway enrichments to unravel potential molecular mechanisms contributing to the rapid progression of MDA5-DM. Our findings aim to provide a robust theoretical framework and practical guidance for early diagnosis, precise risk assessment, and tailored immunotherapy in this challenging autoimmune condition.

    Materials and Methods

    Study Design and Participants

    From January 2023 to June 2024, 29 patients diagnosed with MDA5-DM were identified at the Departments of Rheumatology and Immunology, Affiliated Hospital 2 of Nantong University. All participants fulfilled the 2017 EULAR/ACR classification criteria for dermatomyositis and tested positive for anti-MDA5 antibodies via immunoblot assay.10 Patients with incomplete data or lost to follow-up were excluded. All patients were followed up until December 31, 2024. This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Affiliated Hospital 2 of Nantong University (Approval No. 2024KT426).

    Clinical Data Collection

    Comprehensive clinical data were extracted from inpatient records, outpatient visits, and telephone follow-ups, encompassing the following domains:

    Demographics

    Age at onset, sex, and relevant comorbidities such as hypertension and diabetes mellitus.

    Initial Presentation and Diagnostic Timeline

    Department of initial consultation, preliminary diagnosis, time interval from first visit to definitive diagnosis, and documented misdiagnoses, including the nature of incorrect diagnoses.

    Clinical Manifestations

    Detailed documentation of dermatologic findings (eg, Gottron’s papules, V-sign rash, heliotrope rash, fingertip ulcers), muscular involvement (eg, myalgia, proximal muscle weakness), and respiratory symptoms (eg, dry cough, dyspnea, hypoxemia).

    Laboratory Parameters

    Complete blood counts including absolute lymphocyte counts; muscle enzymes including creatine kinase (CK) and lactate dehydrogenase (LDH); inflammatory markers such as C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and serum ferritin; systematic serum tumor-marker testing (CEA, CA-125, CA-199, NSE) was attempted but limited by incomplete data collection across the cohort; antinuclear antibody (ANA), Anti-extractable nuclear antigen antibodies (ENA), anti-MDA5 antibody titers graded semi-quantitatively (+, ++, +++); and Krebs von den Lungen-6 (KL-6) levels.

    Radiologic and Pulmonary Function Assessment

    High-resolution computed tomography (HRCT) was reviewed independently by two board-certified thoracic radiologists blinded to clinical data. Each scan was classified according to the predominant interstitial-lung-disease pattern: (i) nonspecific interstitial pneumonia-like (NSIP-like), (ii) organizing pneumonia-like (OP-like), (iii) usual interstitial pneumonia-like (UIP-like), (iv) diffuse alveolar damage (DAD), or (v) mixed/overlapping.

    Treatment Regimens and Outcomes

    Details on glucocorticoid and immunosuppressant use, occurrence of major adverse events such as respiratory failure, mortality, and survival status at latest follow-up.

    Definitions

    Anti-MDA5 Antibody Detection

    Anti-MDA5 antibodies were detected via line immunoblot assay, with antibody levels classified as +, ++, or +++ according to band intensity.

    Rapidly Progressive ILD (RP-ILD)

    RP-ILD was defined as the occurrence of any of the following within 3 months of respiratory symptom onset:Acute and progressive worsening of dyspnea requiring hospitalization or supplemental oxygen,Pulmonary function impairment, evidenced by a decline of >10% in forced vital capacity (FVC) or >15% in diffusion capacity for carbon monoxide (DLCO),An increase of >20% in the extent of interstitial abnormalities on HRCT,Arterial blood gas analysis indicating respiratory failure or a decrease in partial pressure of oxygen (PaO2) exceeding 10 mmHg (1 mmHg = 0.133 kPa).Alternative causes, such as severe infection, were excluded.11,12

    Missing Data Management

    For minor missingness in KL-6 measurements (n=4), multiple imputation was implemented incorporating covariates including age, sex, and RP-ILD status to reduce bias.

    Statistical Methods

    Data Preprocessing

    All variables underwent rigorous data encoding and quality control. Continuous variables conforming to normal distribution were described as mean ± standard deviation (eqn ± s), while non-normally distributed data were represented by median and interquartile range (IQR). Categorical variables were expressed as counts and percentages. Missing data, comprising less than 10% of the dataset, were addressed via multiple imputation using the “mice” package in R to mitigate potential biases due to incomplete observations.

    Transcriptomic Differential Expression Analysis

    To elucidate molecular abnormalities in immune-inflammatory pathways associated with MDA5-DM, publicly available RNA-sequencing data (GSE143323) were obtained from the Gene Expression Omnibus (GEO). Extensive literature searches confirmed that GSE143323 remains the most comprehensive and well-characterized MDA5-DM transcriptomic dataset available in public repositories. The dataset included muscle tissue samples from 36 dermatomyositis patients and 20 healthy controls. After performing quality assessments and excluding outliers with aberrant sequencing depth or expression profiles, raw data were normalized via the normalizeBetweenArrays function in R. Differentially expressed genes (DEGs) were identified using the limma package with thresholds set at |log2 fold change| ≥ 1 and p-value < 0.05, followed by adjustment for multiple comparisons using the Benjamini-Hochberg false discovery rate method. Functional enrichment analyses for Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were conducted using the clusterProfiler package, considering p.adjust < 0.05 as statistically significant. Visualization of results employed volcano plots, bubble plots, and bar charts.

    Survival Analysis and Prognostic Modeling

    LASSO Regression

    Candidate prognostic variables, including KL-6, ferritin, and RP-ILD status, were incorporated in LASSO regression models. The optimal regularization parameter (λ) was selected via 10-fold cross-validation to minimize overfitting and collinearity.

    Univariate Cox Proportional Hazards Regression

    Prognostic variables highlighted by LASSO were further analyzed in univariate Cox models to estimate hazard ratios (HR) with 95% confidence intervals (CI), assessing their association with all-cause mortality or severe respiratory failure.

    Kaplan–Meier Survival Analysis

    Study population was stratified based on serum KL-6 levels and the presence of RP-ILD; survival curves were generated and compared using Log rank tests. Statistical significance was defined as two-sided p < 0.05.

    Software and Statistical Tools

    Data imputation was performed with the mice package in R version 4.4.3. LASSO regression was conducted using the glmnet package. Survival analyses and Kaplan-Meier plots were generated using SPSS version 26.0. All tests were two-tailed, with p-values less than 0.05 considered statistically significant.

    Results

    Demographic Characteristics and Diagnostic Challenges

    A total of 29 patients diagnosed with MDA5-DM were enrolled in the study. The cohort had a mean age of 52.1 ± 13.6 years, with females accounting for 55.2% (Table 1). Fever was observed in 65.5% of patients, paralleled by an equal proportion (65.5%) presenting documented infections. Notably, 65.5% of patients tested positive for the anti-Ro-52 antibody. During their clinical course, a majority (79.3%) received Janus kinase (JAK) inhibitor therapy.

    Table 1 Demographic and Clinical Data of the 29 Patients

    Among these patients, initial misdiagnosis was common, affecting 18 individuals (62.1%). The most frequent misclassifications included pulmonary infections, idiopathic interstitial pneumonia, and various inflammatory dermatologic diseases, underscoring the diagnostic complexity of early-stage MDA5-DM. The median diagnostic delay was 37 days (interquartile range: 8 to 96 days), potentially compromising timely management and prognosis (Table 2).

    Table 2 Initial Department Visits and Misdiagnosis Profiles (n = 29)

    Clinical Manifestations and Pulmonary Involvement

    ILD was nearly ubiquitous, identified in 28 of 29 patients (96.6%), with RP-ILD observed in 12 patients (41.4%). Dominant respiratory symptoms encompassed dry cough, dyspnea, and hypoxemia. HRCT typically revealed ground-glass opacities and/or extensive consolidations. When available, pulmonary function tests demonstrated markedly reduced diffusing capacity. Comparative analysis of inflammatory markers revealed significantly lower lymphocyte counts in RP-ILD patients compared to Conventional ILD (C-ILD) patients (p = 0.018), as shown in Figure 1. Additionally, RP-ILD patients demonstrated significantly elevated CRP (p = 0.039) and ferritin (p = 0.0032) levels compared to those with C-ILD, further supporting the hyperinflammatory nature of rapidly progressive disease (Figure 1). Dermatological manifestations were prevalent in 96.6% of patients, including Gottron’s papules, heliotrope rash, and violaceous papules on the dorsal hands. During follow-up, 5 patients (17.2%) succumbed, highlighting the grave prognosis associated with MDA5-DM. Mortality was notably elevated in patients with RP-ILD, indicating a strong association between this subtype and adverse clinical outcomes.

    Figure 1 Comparative analysis of inflammatory markers between RP-ILD and C-ILD patients.Variables including CRP (A), D-Dimer (B), ESR (C), Hemoglobin (D), KL-6 (E), LDH (F), Lymphocyte (G), Monocyte (H), Neutrophil (I), Platelet (J), Serum Ferritin (K), and WBC (L) were compared between RP-ILD (yellow) and C-ILD (blue) groups. Statistical significance determined by Mann–Whitney U-test. Significant differences are marked with * (p < 0.05) and ** (p < 0.01).

    Survival Analysis and Prognostic Significance of KL-6

    LASSO regression analysis identified serum KL-6 level as the predominant prognostic biomarker predicting patient survival (Figures 2). Elevated KL-6 was independently associated with an increased hazard of mortality (hazard ratio [HR] = 2.96, 95% confidence interval [CI]: 1.44–6.15, p < 0.01). This association was further supported by Kaplan-Meier survival curves, which demonstrated significantly diminished survival probabilities in patients with high KL-6 compared to those with lower levels (Log rank test, p < 0.05) (Figure 3A). Notably, this survival disparity was especially evident in patients diagnosed with RP-ILD (Figure 3B).

    Figure 2 Variable selection by LASSO method. (A) Coefficients of all predictors gradually approached zero through 10-fold cross-validation. (B) Coefficients of 16 variables were non-zero at the leftmost dashed line (λ = λ.min). Min refers to the minimum value of λ.

    Figure 3 Kaplan-Meier survival curves comparing survival between different groups. (A) Survival curves based on high vs low KL6 levels, showing a significant difference with a p-value of 0.012. (B) Survival curves comparing RP-ILD and C-ILD groups, with a significant difference observed (p = 0.0034).

    Transcriptomic Profiling

    Differential Gene Expression Analysis

    Transcriptomic data (GSE143323) comprising muscle biopsies from 36 dermatomyositis patients and 20 healthy controls were analyzed. After rigorous quality control and exclusion of outliers, differential expression analysis was performed using the limma and DESeq2 packages. With thresholds set at |log2 fold change| ≥ 1 and Benjamini-Hochberg adjusted p-value < 0.05, we identified 182 differentially expressed genes (DEGs), including 141 upregulated and 41 downregulated (Figure 4).

    Figure 4 Volcano plot of differential gene expression analysis. Genes with log2 fold change ≥ 1 and p-value < 0.05 are shown in red, indicating significantly upregulated genes. Genes with log2 fold change ≤ −1 and p-value < 0.05 are shown in blue, indicating significantly downregulated genes. Genes with no significant difference are shown in grey.

    KEGG Pathway Enrichment

    KEGG enrichment analysis revealed significant overrepresentation of multiple immune and inflammatory signaling pathways among the DEGs. The most prominently enriched pathways included cytokine-cytokine receptor interactions (16 genes, p.adjust < 0.01), viral protein interaction pathways (12 genes, p.adjust < 0.01), and chemokine signaling (9 genes, p.adjust < 0.01). Additional significantly enriched pathways comprised IL-17 signaling and Toll-like receptor signaling pathways (Figure 5).

    Figure 5 KEGG pathway enrichment analysis of differentially expressed genes.Bar plot showing significantly enriched pathways ranked by adjusted p-value (p.adjust). Dot plot visualizing the same pathways, with dot size representing gene count and color intensity indicating the statistical significance (p.adjust).

    Gene Ontology Enrichment

    Gene Ontology enrichment analysis indicated significant involvement of biological processes such as neutrophil chemotaxis, T-cell activation, and broader immune responses. Molecular function and cellular component categories highlighted modulation of inflammatory mediator activity and immune receptor localization. Collectively, these transcriptomic findings corroborate the notion of a heightened and coordinated inflammatory milieu underlying the pathogenesis of MDA5-DM (Figure 6).

    Figure 6 Circular visualization of enriched GO terms. The circle plot displays the top 18 enriched GO terms, categorized into Biological Process (BP), Molecular Function (MF), and Cellular Component (CC).

    Discussion

    This single-center retrospective study systematically evaluated the clinical characteristics, diagnostic challenges, and prognostic indicators of MDA5-DM, complemented by transcriptomic analyses to explore the underlying immunopathological mechanisms. Our findings highlight a notably high misdiagnosis rate and substantial diagnostic delay, with approximately one-third of patients developing RP-ILD, which was strongly associated with increased mortality.13 Serum KL-6 emerged as an independent and robust prognostic biomarker, while transcriptomic data revealed significant activation of IL-17, Toll-like receptor, and other cytokine-mediated signaling pathways, shedding light on the molecular drivers of disease aggressiveness.

    Diagnostic Challenges and the Importance of Early Recognition

    We observed a high misdiagnosis rate of 62.1%, with the majority of patients initially presenting to non-rheumatology specialties. This underscores the heterogeneous and frequently nonspecific clinical manifestations of MDA5-DM, which are often mistaken for pulmonary infections, idiopathic interstitial pneumonia, or inflammatory dermatologic conditions.14 Such diagnostic inaccuracies contribute to delayed definitive diagnosis and postponement of immunosuppressive therapy initiation, thereby accelerating the onset of RP-ILD and adversely impacting prognosis. Prior studies have similarly underscored the critical importance of minimizing diagnostic delays to improve overall survival.15 Clinicians should maintain a high index of suspicion for MDA5-DM in patients presenting with respiratory symptoms alongside atypical cutaneous manifestations and promptly perform anti-MDA5 antibody testing to facilitate early diagnosis and timely intervention.Recent studies have highlighted how cutaneous manifestations serve as early indicators of systemic immune dysregulation in inflammatory dermatomyopathies, emphasizing the importance of integrated dermatologic-rheumatologic evaluation in suspected cases. Early recognition of these manifestations is crucial for timely diagnosis and intervention, potentially preventing progression to life-threatening complications such as RP-ILD.

    Prognostic Biomarkers and Risk Stratification

    Employing LASSO regression modeling, serum KL-6 was identified as the principal prognostic indicator. Subsequent univariate Cox proportional hazards and Kaplan–Meier survival analyses consistently confirmed that elevated KL-6 levels were strongly associated with increased mortality risk. As a mucin-like glycoprotein expressed by regenerating alveolar type II epithelial cells, KL-6 serves as an established biomarker for alveolar epithelial injury and active pulmonary inflammation.16 The prognostic significance was particularly pronounced in RP-ILD patients, where elevated KL-6 combined with rapid disease progression showed markedly diminished survival outcomes.

    Our comparative analysis revealed distinct inflammatory profiles differentiating RP-ILD from conventional ILD patterns. RP-ILD patients demonstrated significantly lower lymphocyte counts, elevated CRP, and increased ferritin levels, supporting the hyperinflammatory nature of rapidly progressive disease. However, despite previous studies proposing ferritin and other inflammatory markers as prognostic indicators,17,18 our survival analysis did not demonstrate statistically significant mortality associations, likely reflecting our limited sample size and patient heterogeneity.

    The 17.2% mortality rate observed in our cohort underscores the severe clinical trajectory of MDA5-DM, particularly in patients with concurrent RP-ILD and KL-6 elevation. The lymphopenia observed in RP-ILD patients corroborates previous findings establishing lymphocyte count as a severity marker, potentially reflecting the intense systemic inflammatory response characteristic of this phenotype.

    Based on these findings, KL-6 measurement should be incorporated into initial evaluation of patients with respiratory symptoms and radiographic pulmonary inflammation when MDA5-DM is suspected. The combination of persistent KL-6 elevation with rising ferritin and progressive HRCT abnormalities may identify highest-risk patients warranting treatment intensification, as serial KL-6 monitoring could provide earlier progression detection than traditional parameters alone. However, definitive threshold values require validation in larger prospective cohorts, and clinical implementation should be guided by institution-specific protocols developed through multidisciplinary specialist consultation. Future research should prioritize developing validated composite scoring systems integrating KL-6 with clinical, radiographic, and additional biomarker parameters to optimize personalized MDA5-DM management strategies.

    Insights From Transcriptomic Profiling and Molecular Pathogenesis

    Our integrative transcriptomic analysis of muscle biopsy samples revealed a coordinated activation of immune-inflammatory networks that may explain the aggressive phenotype characteristic of MDA5-DM. The analysis identified three interconnected pathways that form a self-perpetuating inflammatory circuit underlying disease pathogenesis.

    The predominant enrichment of cytokine-cytokine receptor interactions reflects the systemic hyperinflammatory state characteristic of MDA5-DM.19,20 Among these cytokine networks, IL-6 plays a particularly central role in orchestrating inflammatory cascades and has been recognized for its central role in various inflammatory conditions,21 with particular relevance to autoimmune pathogenesis. This finding is mechanistically significant as muscle inflammation serves as a central source of circulating inflammatory mediators, contributing to the multi-organ involvement that defines this condition.22 The extensive cytokine network activation provides a molecular foundation for understanding the severe systemic manifestations observed in MDA5-DM patients.

    Concurrently, IL-17 signaling pathway enrichment suggests prominent Th17-mediated inflammatory activation with direct implications for pulmonary pathology. Although our analysis utilized muscle tissue, the IL-17 pathway activation observed likely has broader implications for the devastating RP-ILD phenotype. Emerging evidence demonstrates IL-17’s crucial role in pulmonary fibrosis through enhanced neutrophil recruitment and fibroblast proliferation,23,24 potentially representing a mechanistic link between muscle inflammation and the severe pulmonary manifestations in our cohort.

    The enrichment of TLR pathways, particularly involving TLR3/7 components, is mechanistically significant given MDA5’s function as a cytoplasmic RNA sensor.25–27 Enhanced recognition of nucleic acid damage-associated molecular patterns (DAMPs) creates self-amplifying inflammatory circuits that perpetuate both muscle and pulmonary pathology. This mechanism explains the refractory nature of MDA5-DM and its tendency toward rapid progression.

    These findings support an integrated pathogenic model where (1) initial MDA5 activation triggers innate immune responses through TLR signaling, (2) this leads to adaptive immune polarization toward Th17 responses, and (3) the resulting cytokine storm amplifies systemic inflammation and tissue damage. This integrated circuit explains why MDA5-DM demonstrates both rapid progression and resistance to conventional immunosuppression.

    Our analysis also revealed JAK-STAT signaling pathway enrichment, providing direct therapeutic relevance.28 Janus kinase (JAK) inhibitors have emerged as promising agents for refractory MDA5-DM, with recent studies demonstrating efficacy in improving pulmonary function and controlling systemic inflammation.29–31 At our institution, 79.3% of patients received JAK inhibitor therapy, reflecting our center’s treatment protocols that incorporate this mechanistic rationale into clinical practice.

    The transcriptomic findings support therapeutic strategies that simultaneously target multiple nodes of this inflammatory network through IL-17 signaling inhibition, TLR pathway modulation, and JAK-STAT pathway blockade. While our study was not powered to definitively assess treatment outcomes, the mechanistic insights provide a molecular framework for understanding why multi-target approaches may be necessary for this complex autoimmune condition. However, we acknowledge the limitation of extrapolating muscle-derived molecular signatures to lung pathophysiology and emphasize the need for direct validation in pulmonary tissues or bronchoalveolar compartments.

    Future prospective studies and randomized controlled trials are essential to validate these therapeutic targets and optimize combination strategies based on individual molecular profiles, ultimately advancing precision medicine approaches in MDA5-DM management.

    Study Limitations and Future Directions

    Several limitations should be noted. The retrospective design and relatively small sample size at a single center limit the generalizability and statistical power of our findings. While comprehensive tumor marker analysis was limited by incomplete data collection across our cohort, this represents an acknowledged limitation of our retrospective design. Additionally, although our radiologists identified diverse ILD patterns (NSIP, OP, UIP, DAD, and overlapping patterns), our sample size of 29 patients precluded meaningful statistical analysis across multiple radiographic subgroups. The insufficient statistical power to detect radiographic pattern-biomarker associations limits our ability to provide clinically actionable insights regarding imaging predictors of disease severity or treatment response. Future multicenter studies should prioritize establishing critical radiographic-biomarker correlations to advance precision medicine in MDA5-DM, with particular emphasis on collecting samples stratified by ILD progression status to identify distinct molecular signatures.

    Composite Prognostic Score Development

    Future research should focus on developing an “MDA5-ILD Risk Score” incorporating KL-6 levels, HRCT patterns, oxygen saturation trajectory, lymphocyte count, and selected gene expression markers. While requiring validation in larger cohorts, such a composite tool could substantially improve personalized treatment strategies and guide therapeutic intensity decisions in clinical practice.

    Conclusion

    This study reveals that MDA5-DM presents significant diagnostic challenges with a 62.1% misdiagnosis rate and substantial mortality risk predominantly driven by RP-ILD. Serum KL-6 emerged as a robust independent prognostic biomarker, warranting integration into clinical risk stratification protocols. Transcriptomic analysis illuminated critical immune-inflammatory cascades, particularly cytokine networks and IL-17 signaling, offering mechanistic insights and potential therapeutic targets.

    These findings establish KL-6 as a clinically applicable prognostic tool while highlighting the need for early recognition and aggressive management of high-risk patients. Future multicenter prospective studies are essential to validate these biomarker findings and develop composite prognostic models incorporating clinical, radiographic, and molecular parameters to advance precision medicine approaches in MDA5-DM.

    Data Sharing Statement

    The data supporting the findings of this study are available from the corresponding author on reasonable request.

    Ethics Declarations

    The study was approved by the Ethics Committee of Affiliated Hospital 2 of Nantong University (Approval No. 2024KT426). As this was a retrospective analysis of anonymized clinical data, the requirement for written informed consent was waived by the Ethics Committee. All patient information was de-identified to ensure confidentiality.

    Author Contributions

    Yunli Ren and Tianqi Wu contributed equally to this work as co-first authors. All authors have made a substantial contribution to the work reported, be it in conception, design, conduct, acquisition of data, analysis and interpretation, or all of these; have been involved in drafting, revising, or critically reviewing the article; have given final approval for the version to be published; have agreed on the journal to which the article will be submitted; and agree to accept responsibility for all aspects of the work.

    Funding

    There is no funding to report.

    Disclosure

    The authors declare that they have no competing interests in this work.

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    7. Tsuji H, Nakashima R, Mimori T. Perspectives in the treatment of interstitial lung disease accompanied with anti-melanoma differentiation-associated gene 5-positive dermatomyositis. Int J Rheum Dis. 2024;27(5):e15201. doi:10.1111/1756-185X.15201

    8. Xu S, Hu X, Wang J, et al. Polymyositis and dermatomyositis biomarkers. Clin Chim Acta. 2023;547:117443. doi:10.1016/j.cca.2023.117443

    9. Ichimura Y, Konishi R, Shobo M, et al. Autoimmunity against melanoma differentiation-associated gene 5 induces interstitial lung disease mimicking dermatomyositis in mice. Proc Natl Acad Sci U S A. 2024;121(16):e2313070121. doi:10.1073/pnas.2313070121

    10. Lundberg IE, Tjärnlund A, Bottai M, et al. European league against rheumatism/American college of rheumatology classification criteria for adult and juvenile idiopathic inflammatory myopathies and their major subgroups. Arthritis Rheumatol. 2017;69(12):2271–2282. doi:10.1002/art.40320

    11. Xu L, You H, Wang L, et al. Identification of three different phenotypes in anti-melanoma differentiation-associated gene 5 antibody-positive dermatomyositis patients: implications for prediction of rapidly progressive interstitial lung disease. Arthritis Rheumatol. 2023;75(4):609–619. doi:10.1002/art.42308

    12. Moghadam-Kia S, Oddis CV, Sato S, Kuwana M, Aggarwal R. Anti-melanoma differentiation-associated gene 5 is associated with rapidly progressive lung disease and poor survival in us patients with amyopathic and myopathic dermatomyositis. Arthritis Care Res. 2016;68(5):689–694. doi:10.1002/acr.22728

    13. Shappley C, Paik JJ, Saketkoo LA. Myositis-related interstitial lung diseases: diagnostic features, treatment, and complications. Curr Treatm Opt Rheumatol. 2019;5(1):56–83. doi:10.1007/s40674-018-0110-6

    14. Xu X, Chen B, Huang J, Yang J, Li M. Seborrheic dermatitis-distributed rash in dermatomyositis is associated with progressive interstitial lung disease. J Am Acad Dermatol. 2021;84(6):1694–1696. doi:10.1016/j.jaad.2020.12.021

    15. Li Y, Wu J, Wu J, et al. Predictors of poor outcome of Anti-MDA5-associated rapidly progressive interstitial lung disease in a Chinese cohort with dermatomyositis. J Immunol Res. 2020;2020:2024869. doi:10.1155/2020/2024869

    16. Ye Y, Fu Q, Wang R, Guo Q, Bao C. Serum KL-6 level is a prognostic marker in patients with anti-MDA5 antibody-positive dermatomyositis associated with interstitial lung disease. J Clin Lab Anal. 2019;33(8):e22978. doi:10.1002/jcla.22978

    17. Shirakashi M, Nakashima R, Tsuji H, et al. Efficacy of plasma exchange in anti-MDA5-positive dermatomyositis with interstitial lung disease under combined immunosuppressive treatment. Rheumatology. 2020;59(11):3284–3292. doi:10.1093/rheumatology/keaa123

    18. Lian X, Zou J, Guo Q, et al. Mortality risk prediction in amyopathic dermatomyositis associated with interstitial lung disease: the FLAIR model. Chest. 2020;158(4):1535–1545. doi:10.1016/j.chest.2020.04.057

    19. Ye Y, Chen Z, Jiang S, et al. Single-cell profiling reveals distinct adaptive immune hallmarks in MDA5+ dermatomyositis with therapeutic implications. Nat Commun. 2022;13(1):6458. doi:10.1038/s41467-022-34145-4

    20. Zhang Y, Hu W, Li T, et al. Shared and distinctive inflammation-related protein profiling in idiopathic inflammatory myopathy with/without Anti-MDA5 autoantibodies. J Inflamm Res. 2025;18:6009–6024. doi:10.2147/JIR.S509777

    21. Niculet E, Chioncel V, Elisei AM, et al. Multifactorial expression of IL-6 with update on COVID-19 and the therapeutic strategies of its blockade (Review). Exp Ther Med. 2021;21(3):263. doi:10.3892/etm.2021.9693

    22. Allenbach Y, Uzunhan Y, Toquet S, et al. Different phenotypes in dermatomyositis associated with anti-MDA5 antibody: study of 121 cases. Neurology. 2020;95(1):e70–e78. doi:10.1212/WNL.0000000000009727

    23. Kwon OC, Lee EJ, Chang EJ, et al. IL-17A+GM-CSF+ neutrophils are the major infiltrating cells in interstitial lung disease in an autoimmune arthritis model. Front Immunol. 2018;9:1544. doi:10.3389/fimmu.2018.01544

    24. Zhang J, Wang D, Wang L, et al. Profibrotic effect of IL-17A and elevated IL-17RA in idiopathic pulmonary fibrosis and rheumatoid arthritis-associated lung disease support a direct role for IL-17A/IL-17RA in human fibrotic interstitial lung disease. Am J Physiol Lung Cell Mol Physiol. 2019;316(3):L487–L497. doi:10.1152/ajplung.00301.2018

    25. Wang K, Zhao J, Wu W, et al. RNA-containing immune complexes formed by anti-melanoma differentiation associated gene 5 autoantibody are potent inducers of IFN-α. Front Immunol. 2021;12:743704. doi:10.3389/fimmu.2021.743704

    26. Khan MI, Nur SM, Adhami V, Mukhtar H. Epigenetic regulation of RNA sensors: sentinels of immune response. Semin Cancer Biol. 2022;83:413–421. doi:10.1016/j.semcancer.2020.12.028

    27. Sultan H, Wu J, Kumai T, Salazar AM, Celis E. Role of MDA5 and interferon-I in dendritic cells for T cell expansion by anti-tumor peptide vaccines in mice. Cancer Immunol Immunother. 2018;67(7):1091–1103. doi:10.1007/s00262-018-2164-6

    28. Gasparotto M, Franco C, Zanatta E, et al. The interferon in idiopathic inflammatory myopathies: different signatures and new therapeutic perspectives. A literature review. Autoimmun Rev. 2023;22(6):103334. doi:10.1016/j.autrev.2023.103334

    29. Damsky W, Peterson D, Ramseier J, et al. The emerging role of Janus kinase inhibitors in the treatment of autoimmune and inflammatory diseases. J Allergy Clin Immunol. 2021;147(3):814–826. doi:10.1016/j.jaci.2020.10.022

    30. McPherson M, Economidou S, Liampas A, Zis P, Parperis K. Management of MDA-5 antibody positive clinically amyopathic dermatomyositis associated interstitial lung disease: a systematic review. Semin Arthritis Rheum. 2022;53:151959. doi:10.1016/j.semarthrit.2022.151959

    31. Yoshida T, Nakashima RA. Melanoma differentiation-associated gene 5 antibody positive dermatomyositis: recent progress in pathophysiology and treatment. Curr Rheumatol Rep. 2025;27(1):23. doi:10.1007/s11926-025-01188-7

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  • A chimeric peptide derived from a bacterial effector protein attenuate

    A chimeric peptide derived from a bacterial effector protein attenuate

    Introduction

    Toll-like receptors are crucial components of innate immunity, which is the first line of defence against invaded microorganisms. TLRs are expressed in a wide range of host cells, including macrophages, dendritic cells, B cells, epithelial and fibroblast cells. TLRs constitute an extracellular leucine-rich repeat (LRRs) that are involved in recognizing various pathogen-associated molecular patterns (PAMPs). TLRs harbour an intracellular domain termed the TIR domain, which is highly conserved and interacts with the TIR domain-containing adaptor proteins upon activation. There are four TIR domain-containing adaptor proteins such as TIRAP (TIR-domain-containing adaptor protein (TIRAP), Myeloid differentiation primary response protein 88 (MyD88), TIR domain-containing adaptor protein inducing interferon-β (TRIF) and TRIF-related adaptor molecule (TRAM) that are involved in the initiation of TLR signalling.1 The recognition of PAMPs by the TLRs triggers their activation, followed by selective recruitment of the TIR domain-containing adaptor proteins. This initiates a signalling cascade that results in expression of various pro-inflammatory cytokines, chemokines and anti-microbial peptides.2

    Dysregulated TLR activation can produce excess pro-inflammatory cytokines, resulting in whole-body inflammation. The aberrant activation of TLRs has been attributed to the pathogenesis of various inflammatory disorders, including sepsis, atherosclerosis, diabetes, and arthritis.3,4 The dysregulated activation of TLR4/2 by PAMPs results in excess production of pro-inflammatory cytokines, free radicals, and recruitment of other inflammatory mediators, leading to a systemic syndrome with tissue injury, increased vascular permeability, and multi-organ failure.5,6 TLR4 and TLR2 play crucial roles in the pathogenesis of inflammatory disorders like sepsis, making them promising therapeutic targets. However, no specific drugs are currently approved for sepsis treatment. Moreover, antibacterial therapy can worsen inflammation by releasing PAMPs that activate TLR4 and 2. While immunosuppressive agents like steroids can reduce systemic inflammation, their long-term use leads to severe side effects and heightened susceptibility to secondary or opportunistic infections.7

    Many pathogenic microorganisms encode effector proteins that subvert the TLR signalling to attenuate the production of pro-inflammatory cytokines.8 Bacterial pathogens such as Brucella, Salmonella, E. coli etc. encode effector proteins that mimic the TIR domain of eukaryotes to interfere with the TLR signalling.9,10 TcpB is a cell-permeable, TIR domain-containing protein from the intracellular bacterial pathogen Brucella, which inhibits NF-κB activation and the production of proinflammatory cytokines mediated by TLR2 and TLR4.11–13 The cell permeability of TcpB is attributed to its cationic amino acid-rich N-terminal phosphoinositide phosphate (PIP)-binding motif. Mutation of key residues within this motif significantly reduced the affinity of TcpB for PIPs.14 The C-terminal TIR domain of TcpB is responsible for disrupting TLR2/4 signalling, thereby downregulating the production of pro-inflammatory cytokines. The BB-loop region in the TIR domain of TcpB has been reported to be essential for interfering with the TLR2/4-mediated signalling pathway.14 TcpB interacts with the TLR2/4 adaptor proteins, MyD88 and TIRAP and promotes the enhanced ubiquitination and degradation of TIRAP.10,12,14,15 Although TcpB does not possess inherent ubiquitin ligase activity, it has been shown to interact with the host protein CLIP170, which facilitates the ubiquitination and subsequent degradation of TIRAP.16 Collectively, these findings suggest that TcpB may serve as a promising candidate for the development of peptide-based, cell-permeable therapeutics targeting TLR2/4 signalling.

    Therapeutic proteins are widely used to treat human diseases but face challenges such as immunogenicity and proteolytic instability. To overcome these limitations, peptide sequences are being explored as alternatives, gaining significant interest in the pharmaceutical industry.17,18 Peptide drugs exhibit enhanced potency, specificity, and low incidence of toxicity.19,20 Further, the conjugation of peptides with other drugs can improve the target selectivity and bioavailability and facilitate the delivery of cell impermeable cytosolic cargos.21–24 Therefore, peptide mimetics became one of the thrust areas of drug discovery with unprecedented potential.

    The unregulated use of antibiotics has led to the rapid emergence of antibiotic-resistant bacteria, significantly impacting global health. Treating intracellular bacterial infections poses a particular challenge due to the limited cellular availability of antibiotics at therapeutic doses.25 Given that the discovery of new antibiotics is both time-consuming and financially demanding, alternative strategies are urgently required to combat antimicrobial resistance.26,27 A promising approach involves modifying existing antibiotics to enhance their pharmacokinetic properties.18,24

    In this study we generated functional peptides from the TcpB protein of Brucella, followed by analysing their cell permeability and anti-inflammatory properties. The chimeric peptide, TB4-BBL2 targeted TIRAP and MYD88 to disrupt TLR2/4 signaling and significantly dampened cytokine induction in a mouse model of endotoxemia. Furthermore, we demonstrated that conjugating gentamicin with the peptides derived from TcpB enhanced its cellular availability, resulting in improved permeability, increased killing of intracellular bacteria, and suppression of proinflammatory cytokines in both macrophages and mice.

    Materials and Methods

    Sequence Analysis

    The TIR domain of TcpB was aligned with that of mouse TIRAP, using the Clustal Omega to identify the BB loop region of TcpB. The crystal structures of TIR domains of TcpB (pdb_00004c7m) and TIRAP (pdb_00002y92) were obtained from PDB and superimposed using Chimaera software.28,29

    Designing and Synthesis of Peptides from TcpB

    Various peptides were designed from the N-terminal PIP-binding and C-terminal BB-loop regions of TcpB (Table 1). The peptides were synthesised without or with a 5-carboxyfluorescein (FAM) tag at the N-terminus from commercial sources with >95% purity by HPLC. The lyophilized peptides were resuspended in 1X DPBS (Lonza) and stored in aliquots in the −80 freezer for further experiments.

    Table 1 Details of Peptides Used in the Study

    Cell Culture

    RAW264.7 (ATCC), HEK293T (ATCC) and immortalized Bone Marrow-Derived Macrophages from mice (iBMDM; a gift from Petr Broz, University of Lausanne) were cultured in Dulbecco’s Modified Eagle Medium (DMEM, Sigma) supplemented with 10% Fetal Bovine Serum (FBS, Sigma) and 1% penicillin-streptomycin solution at 37°C in the humidified atmosphere of 5% CO2. The iBMDMs were differentiated using m-CSF (20 ng/mL; Biolegend) or 10% culture supernatant of L929 cells for 48 hours and further maintained in Dulbecco’s Modified Eagle Medium supplemented with 10% Fetal Bovine Serum (FBS, Sigma) and 1% penicillin-streptomycin solution at 37°C.30

    Fluorescence Microscopy Analysis of Peptide-Treated Cells

    RAW264.7 cells (50,000 cells) were seeded into glass bottom petri plates (Eppendorf) or 24-well plates (50,000 cells/wells) and allowed to adhere overnight. Cells were then treated with 100 µM of FAM-labelled peptides for 2 hours. Subsequently, the cells were washed twice with 1X DPBS and treated with 0.1% of Trypsin EDTA (Sigma) for 5 minutes at 37°C to remove extracellular and membrane-bound peptides. Cells were then washed twice with 1X DPBS, followed by fixing the cells with 4% paraformaldehyde in PBS for 20 minutes at room temperature. Next, the nuclei of the cells were stained with Hoechst (Thermo Fisher Scientific) and plates were analysed using a fluorescent microscope (Carl Zeiss) at 20 X magnification. For confocal microscopy, the fixed cells were mounted in Prolong Gold anti-fading agent with DAPI (Thermo Fisher Scientific) and the cells were analysed using a laser confocal microscope (Leica) at 63x magnification. A total of 15 representative fields per sample were examined to assess peptide internalization.

    Flow Cytometry Analysis of Peptide-Treated Cells

    RAW264.7 cells were seeded into a 12-well plate (75,000 cells/well) and allowed to adhere overnight. Next, the cells were treated with FAM-labelled peptides for 2 hours, followed by treatment with 0.1% Trypsin-EDTA for 5 minutes at 37°C to remove the extracellular peptides. Subsequently the cells were washed with 1X DPBS and fixed with 4% paraformaldehyde as described before. The cells were collected and resuspended in 500 μL of ice-cold 1X PBS supplemented with 0.05% trypan blue (Sigma), followed by quantifying the fluorescence using flow cytometry (BD Fortessa). A total of 50,000 events were acquired and examined per sample using BD Fortessa flow cytometer. The data were analyzed using FlowJo software (BD Biosciences). Mean fluorescence intensity (MFI) of FAM signal was calculated to quantify peptide internalization. Statistical significance between groups was determined using one-way ANOVA.

    Peptide Internalization Assays with Inhibitors

    To perform the cold inhibition, RAW264.7 cells (50,000 cells) were seeded into glass bottom petri plates and allowed to adhere overnight. The cells were then treated with 50 µM of FAM-labelled peptides at 4°C for 2 hours, followed by processing the cells for microscopy and flow cytometry analysis as described before. To perform chemical inhibition, RAW264.7 cells were treated with indicated concentrations of various inhibitors such as Nocodazole (blocks microtubule polymerization, Sigma), Wortmannin (inhibitor of phosphoinositide 3-kinase involved in transcytosis, Sigma), methylated-β-cyclodextrin (MβCD, a caveolae-mediated endocytosis inhibitor by depleting cholesterol, Sigma), Cytochalasin D (inhibits macropinocytosis, Sigma), and Chlorpromazine (inhibits clathrin-mediated endocytosis, Sigma) for 30 minutes at 37°C, followed by treatment with FAM-labelled peptides (50 µM) for 2 hours. The cells were then processed for microscopy and flow cytometry analysis as described before.

    Peptide Treatment and Stimulation of Macrophages with LPS

    RAW264.7 or iBMDMs were seeded in 24-well plates (50,000 cells/well) and allowed to adhere overnight. Cells were then treated with various concentrations of peptides for 1 hour, followed by stimulation of cells with Lipopolysaccharide (LPS, 200 ng/mL, Sigma). The cells or culture supernatants were harvested at various times post-LPS stimulation, followed by quantification of TNF-α and IL-6 levels using qRT-PCR and ELISA.

    Quantification of Pro-Inflammatory Cytokines

    The mRNA levels of pro-inflammatory cytokines in cells or harvested mice organs were examined by isolating the total RNA and performing qRT-PCR. Briefly, cells or mice organs (spleen, liver, lungs, and kidneys) were harvested and homogenized in RNAiso PLUS (Takara), followed by total RNA extraction and cDNA synthesis using the Prime Script™ RT Reagent kit (Takara) according to the manufacturer’s instructions. Next, the qRT-PCR was performed using the primers specific for TNF-α, IL-6, and GAPDH. The relative gene expression was determined using the comparative 2−ΔΔCt method using CFX Maestro Software (Bio-Rad). Data were normalized with the endogenous control, GAPDH.

    To quantify pro-inflammatory cytokines in the serum, blood was collected from mice by cardiac puncture, followed by separation of the serum. The levels of TNF-α and IL-6 in serum or culture supernatants were quantified using the DuoSet ELISA kit (R&D Systems), following the manufacturer’s protocol.

    Quantification of the Secreted Lactate Dehydrogenase (LDH)

    To examine cytotoxicity through quantifying the secreted LDH, immortalised BMDM cells were seeded into 24-well plates and allowed to adhere overnight. The cells were then treated with indicated concentrations of various inhibitors or peptides, and the supernatants were harvested at 5 hours post-treatment. The secreted LDH in the culture supernatants was quantified using the LDH Cytotoxicity detection kit (Takara Bio) as per the manufacturer’s instructions. The clarified supernatant obtained from cells lysed with 0.1% Triton X 100 was used as the positive control.

    NF-κB Reporter Assay

    RAW264.7 cells were seeded into a 12-well plate (75,000 cells/well) and allowed to adhere overnight. Cells were then transfected with pLuc-NF-κB (1 μg/well, Stratagene) and pRL-TK (200 ng/well, Promega) using Xfect transfection reagent (Takara Bio) according to the manufacturer’s instructions. Twenty-four hours post-transfection, cells were treated with increasing concentrations of TB4-BBL2 peptide, followed by induction with LPS for 5 hours. Subsequently, the cells were lysed with 1X Passive Lysis Buffer at 4°C for 30 minutes, followed by clarification of the lysate by centrifugation. Firefly and Renilla luciferase activities were then quantified using the Dual-Luciferase Reporter Assay System (Promega), according to the manufacturer’s instructions. Firefly luminescence was normalized to Renilla luminescence, and the data were expressed as the fold change in NF-κB activation between LPS-stimulated and unstimulated cells.

    Detection of Reactive Oxygen Species (ROS) and Nitric Oxide (NO) in TB4-BBL2-Treated Mouse Macrophages

    RAW264.7 cells were seeded in a 12-well plate (75,000 cells/well) in phenol red-free DMEM supplemented with 10% FBS and 1% penicillin-streptomycin solution and allowed to adhere overnight. The cells were treated with TB4-BBL2 for 1 hour, followed by LPS induction (200 ng/mL) for twenty-four hours. Subsequently, the culture supernatant was collected, and the level of NO was measured using the Griess assay kit (Invitrogen) as per the manufacturer’s instruction. To examine the level of ROS, the cells were treated with the membrane-permeable redox-sensitive probe, 2′,7′-dicholorodihydroxyfluroscein diacetate (H2DCFDA) (10 μM, Invitrogen) for 30 minutes at 37°C. Next, the cells were washed thrice with 1X PBS and analyzed immediately for DCF formation using the multimode reader (PerkinElmer) with excitation at 495 nm and emission at 517 nm.

    Co-Immunoprecipitation Experiments

    HEK293T (1 X 106) cells were transfected with FLAG-TIRAP/ MYD88/ USP8/ EV with the Turbofect reagent (Thermo Fisher Scientific). The FLAG-tagged empty vector (EV) was used as a negative control, as it does not express any fusion protein capable of specifically interacting with the FAM-labelled peptides, thereby providing a measure of non-specific background fluorescence. Twenty-four hours post transfections, cells were lysed in the lysis buffer containing 20 mM Tris [pH 8.0], 150 mM NaCl, 1% Triton X100, 1 mM EDTA and 1X protease inhibitor cocktail (Pierce) at 4°C for 20 minutes. The lysates were clarified by centrifugation at 12,000 rpm for 20 min at 4°C. Next, the lysates were incubated with 50 μM of FAM-labelled TB4/TAT/TB4-BBL2 for 4 hours on a rotator at 4°C. Subsequently, 5 μg of anti-FLAG antibody (Sigma) was added to the lysates and incubated overnight at 4°C on the rotator. Next, the lysates were mixed with protein G plus agarose beads (Santa Cruz Biotechnology) and incubated for 2 hours at 4°C. Subsequently, agarose beads were washed three times with 1X IP buffer (20 mM Tris [pH8.0], 150 mM NaCl, 1% Triton X100) and re-suspended in 200 μL of 1X PBS. The samples were then transferred to 96-well Optiplate (PerkinElmer) and the fluorescence intensity was measured using the multimode reader (PerkinElmer) at an excitation of 490 nm and the emission at 520 nm.

    Microscale Thermophoresis (MST) Analysis

    To examine the interaction between TIRAP and TB4-BBL2 peptide using MST, we cloned and expressed murine TIRAP in E. coli as a fusion protein with maltose-binding protein (MBP). Subsequently, the recombinant MBP-TIRAP was purified in native condition using amylose affinity chromatography. The interaction between MBP-TIRAP and the FAM-labelled TB4-BBL2 was then examined using MST. The constant amount (100 nM) of FAM-labeled TB4-BBL2 was mixed with the increasing concentrations of MBP-TIRAP or MBP alone (0.0153 nM to 500 nM) in 1X DPBS (Sigma) containing 0.05% Tween-20. Ten minutes post-incubation at room temperature, the samples were loaded into MonolithTM standard-treated capillaries (Monolith TM NT.115 hydrophilic capillaries). MBP alone was used as the negative control (non-binder). During the experiment, the LED power of the instrument was kept at 40%, and MST power was maintained at a medium level. The binding experiments were performed in triplicate (n = 3 independent replicates), and the binding curve was obtained at 25°C using Monolith NT.115 instrument (NanoTemper Technologies). The Kd value was fitted using MO. Affinity analysis was performed using Affinity Analysis software (NanoTemper Technologies) and the final Kd value is reported as the mean ± standard deviation (SD).

    Detection of Protein Degradation by Immunoblotting

    RAW264.7 cells were seeded in a 12-well plate (75,000 cells/well) and allowed to adhere overnight. The cells were treated with 10 and 50 μM of TB4-BBL2 peptide and 50 μM of TIR peptide (cell-impermeable) for 5 hours. Subsequently, the cells were washed with ice-cold 1X DPBS and treated with 0.1% Trypsin-EDTA for 5 minutes at 37°C. Cells were washed again with 1X DPBS, followed by lysing the cells in radioimmunoprecipitation assay buffer (RIPA; 10 mM Tris HCL pH 8, 1mM EDTA, 1% Triton X 100, 0.1% SDS, 40 mM NaCl) with the protease inhibitor cocktail (Pierce). The lysate was processed for immunoblotting as described previously.31 The immunoblots were probed with primary antibodies against TIRAP, MYD88, TLR2 (1:2000; Cell Signaling Technology), and TRIF (1:1000; Santa Cruz Biotechnology), followed by incubation with an HRP-conjugated secondary antibody.

    Pulse-Chase Analysis

    To perform pulse-chase analysis with cycloheximide, RAW264.7 cells were treated with TB4-BBL2 for 3 hours and washed with 1X DPBS. Next, the cells were treated with cycloheximide (1 mg/mL; Sigma), followed by harvesting the cells at 1-, 2-, and 3-hours post-treatment. The cells were then lysed and subjected to immunoblotting, as described before.

    Dosage Tolerance Experiments with the TB4-BBL2 Peptide

    To determine the dosage tolerance, 15 male BALB/c mice were divided into 5 groups (3 mice/group) according to the dosages (5, 10, 20, and 40 mg/kg) of the TB4-BBL2 peptide, and PBS alone was used as the vehicle control. The mice were injected intravenously with the indicated doses of TB4-BBL2 peptide in sterile PBS and observed for toxicity indicators until 5 days post-injection, where the parameters were recorded every 6 hours. After 5 days, blood and organs were harvested from mice for detailed examination. Serum was separated from blood and analysed for pro-inflammatory cytokine levels using ELISA as described before.

    Induction of Endotoxemia in Mice and Analysing the Effect of TB4-BBL2 Peptide

    To optimise the dosage and analyse the effect of pre-treatment with TB4-BBL2 peptide, 25 mice were randomly distributed into 5 groups (5 mice/group) according to the dosages and controls (20, 30 and 40 mg/kg; PBS+LPS and PBS alone). Next, the mice were injected intravenously with the indicated concentrations of TB4-BBL2 peptide. One hour later, mice were injected intraperitoneally with E. coli LPS (25 mg/kg; Sigma). Two hours post-LPS treatment, blood was collected from the mice through the cardiac puncture, followed by euthanizing the mice and harvesting of the liver, spleen, kidney, and lungs. Serum was separated from the collected blood and the levels of pro-inflammatory cytokines in the serum and organs were quantified by ELISA and qRT-PCR, respectively, as described before.

    To analyse the effect of post-treatment with TB4-BBL2, 15 mice (5 mice/group) were randomly distributed into 3 groups (PBS, PBS+LPS and TB4-BBL2 peptide+LPS). Next, the mice were treated intraperitoneally with E. coli LPS (25 mg/kg) for 1 hour, followed by administration of TB4-BBL2 peptide (30 mg/kg). The mice were sacrificed 2 hours post-TB4-BBL2 peptide delivery, and the samples were harvested and processed as described before.

    To analyse the effect of simultaneous administration of LPS and TB4-BBL2, 15 mice (5 mice/group) were randomly distributed into 3 groups (PBS, PBS+LPS and 30 mg/kg of TB4-BBL2 peptide+LPS). The mice were administered with LPS (25 mg/kg) intraperitoneally, followed by treatment with TB4-BBL2 peptide (30 mg/kg) intravenously. The mice were sacrificed 3 hours post-injections, and the samples were harvested and processed as described before.

    Histopathology Studies

    The liver, lungs, kidneys, and spleen were harvested from peptide/LPS-treated mice and fixed with 10% neutral buffer saline for 24 hours. The tissues were processed for dehydration, clearing, and impregnation using isopropanol gradients and xylene. The tissues were then embedded in parafilm blocks, followed by cutting sections using a microtome (Leica microtome). The tissue sections were stained with haematoxylin and eosin dyes (Sigma) and analysed using a light microscope (Zeiss).

    Conjugation of Gentamicin with cTB4 or cTB4-BBL2

    cTB4 or cTB4-BBL2 (peptides with cysteine at the N-terminus) were synthesized and conjugated with the broad-spectrum antibiotic, gentamicin, using SMCC as the cross-linker, to generate gentamicin-conjugated cTB4 (cTB4-G) and gentamicin conjugated cTB4-BBL2 (cTB4-BBL2-G). SMCC forms stable amide bonds with the gentamicin amino group and stable thioether bonds with the N-terminal thiol group in cTB4 or cTB4-BBL2, thereby facilitating gentamicin peptide conjugation. SMCC was dissolved in DMSO at a concentration of 25 mg/mL to prepare a stock solution. It was then diluted to the appropriate concentrations and incubated with gentamicin in PBS, used as the conjugation buffer, at room temperature for two hours. Subsequently, peptides were added to the solution and incubated for two more hours at room temperature. To estimate the percentage of conjugation through free thiol group estimation, DTNB/ Ellman’s reagent was used.

    Estimation of Free Thiol Group

    Conjugated and unconjugated cTB4 were serially diluted and incubated with the Ellman’s reagent (DNTB) for 30 minutes at room temperature in a microtiter plate. After incubation, the absorbance was measured at 412 nm. The DNTB detects the free sulfhydryl groups (thiols) in a solution. DNTB reacts with free thiol groups (-SH) to form a mixed disulfide and 5-thio-2-nitrobenzoic acid (TNB), which can be quantified at 412 nm. Consequently, a higher absorbance value at 412 nm indicates a greater presence of free thiol groups in unconjugated cTB4. In contrast, a lower optical density at 412 nm suggests effective conjugation between cTB4 and the SMCC-linked Gentamicin.

    Gentamicin Efficiency Assay

    To assess the bactericidal efficiency of conjugated gentamicin, a gentamicin efficiency assay was performed following the method of Gomarasca et al, (2017). Primary cultures of B. neotomae and S. typhimurium were diluted at ratios of 1:5 and 1:100, respectively, in fresh growth medium, followed by the addition of gentamicin-cTB4 conjugate (cTB4-G). As controls, individual components of the conjugation reaction were added, such as gentamicin (50 μg/mL), DMSO (15 μL), and SMCC (150 μg/mL). The optical density of the bacterial cultures was recorded at OD600 after 4 and 16 hours of treatment to monitor the bacterial growth. To evaluate the antibacterial properties of the conjugates in vitro, macrophages were infected with Brucella or Salmonella and subsequently treated with either controls or conjugates at the indicated time points. Following treatment, the macrophages were lysed, and intracellular bacterial loads were quantified by CFU assays.

    To Examine the Effect of cTB4-BBL2-Gentamicin Conjugate in Mice Infected with B. melitensis

    To evaluate the in vivo effect of cTB4-BBL2-Gentamicin in the B. melitensis infected mice, 8-week-old female BALB/c mice (6 mice per group) were infected intraperitoneally with B. melitensis (2×105 CFU per mouse) in 100 μL of 1X PBS. Ten days post-infection, each infected mouse was treated intraperitoneally with one of the following: PBS, gentamicin (0.2 mg), cTB4-G (0.2 mg gentamicin conjugated with 0.4 mg of cTB4 using 0.4 mg of SMCC), or cTB4-BBL2-G (0.2 mg gentamicin conjugated with 0.4 mg of cTB4-BBL2 using 0.4 mg of SMCC) for 3 consecutive days. Fourteen days post-infection, blood was collected from the mice through cardiac puncture, followed by euthanizing the mice, harvesting the organs, and CFU analysis to quantify the splenic load of B. melitensis. Serum was isolated from blood to quantify secretedTNF-α levels by ELISA. The quantification of mRNA levels of TNF-α and IL-6 in the spleen, liver, kidney, and lungs was carried out through qRT-PCR as previously described.

    Ethical Statement

    Six to eight-week-old BALB/c mice (20–25 g) were obtained from and housed at the small animal experimentation facility of the National Institute of Animal Biotechnology (NIAB), Hyderabad, with food and water ad libitum. The experimental protocols were approved by the Institutional Biosafety Committee (Approval number: IBSC/Jul2020/NIAB/GR01) and Institutional Animal Ethics Committee (Approval number: IAEC/2019/NIAB/35/GKR). All procedures were conducted in accordance with the guidelines of the Committee for Control and Supervision of Experiments on Animals (CCSEA), Government of India, for the care and use of laboratory animals.

    Statistical Analysis

    The GraphPad Prism 6.0 software was used for statistical analysis of experimental data. Data are represented with the mean ± standard deviation (SD) or standard error of the mean (SEM) as mentioned in respective figure legends. For pairwise comparison, statistical significance was determined by unpaired t-tests (two-tailed). A one-way analysis of variance (ANOVA) test was used to analyse the statistical significance of data including more than two samples. A p-value less than 0.05 (p < 0.05) was considered statistically significant. The significance levels were indicated as follows: n.s. (not significant), p > 0.05; *: p < 0.05; **: p < 0.01; ***: p < 0.001; ****: p < 0.0001. All experiments were performed independently at least three times.

    Results

    Designing of Cell-Permeable and Anti-Inflammatory Peptides from TcpB Protein

    TcpB protein consists of an N-terminal PIP-binding motif and a TIR domain at its C-terminus (Figure 1A). The PIP-binding motif is rich in arginine and lysine residues, which is reported to bind to many species of PIPs.14 The association of TcpB with the plasma membrane and its internalization by macrophages has been previously reported.11,14 Furthermore, the membrane binding property of TcpB was attributed to its N-terminal fragment.11,14 Considering these facts, we sought to identify the shortest peptide, which confers the cell-permeability of TcpB. To achieve this, we designed various peptides from the N-terminal PIP-binding motif of TcpB and synthesised the peptides with the FAM label at their N-terminus (Table 1). The TIR domain of TcpB interferes with the TLR2/4 signalling where the BB-loop region is essential for this activity.14 The TIR domain of TcpB shares considerable amino acid similarity to that of other TIR domain-containing eukaryotic proteins, including the TLR2/4 adaptor protein, TIRAP. To identify the exact region of BB-loop in the TcpB, we aligned the TIR domain sequences of TcpB and TIRAP (Figure 1B). In agreement with the sequence similarity, the BB-loop of TcpB and TIRAP exhibited high structural superimposition (Figure 1C–E). Next, we designed peptides from the BB-loop region of TcpB for further studies (Table 1).

    Figure 1 BB loop in the TIR domain of TcpB and TIRAP share multiple conserved residues. (A) A schematic illustration of domain structure of TcpB protein. TcpB harbours an N-terminal PIP-binding motif and C-terminal TIR domain with the BB-loop. (B) Pair-wise sequence alignment showing the conserved amino acid residues among the TIR domain of TcpB and TIRAP (mouse). (C and D) Ribbon diagram showing the BB-loop region of TcpB (blue) and TIRAP (yellow) in the TIR domain. (E) Structural superimposition of TIR domain of TcpB and TIRAP showing the alignment of the BB-loop.

    Peptide from the PIP-Binding Motif of TcpB is Cell Permeable and Internalized Through Cholesterol-Dependent Endocytosis

    We synthesized peptides from the PIP-binding motif of TcpB with FAM label to examine their internalization using fluorescent microscopy and flow cytometry. RAW264.7 mouse macrophage cell lines were treated with 100 μM of FAM-labelled peptides, followed by fixing the cells and analysing them using laser confocal microscopy and flow cytometry. The peptide derived from the TAT protein of HIV and its mutant version, where lysine and arginine residues were mutated to Alanine (TATala), were used as the positive and negative controls, respectively.32 The confocal microscopy analyses showed the accumulation of TcpB-derived peptides, designated here as TB1, TB2, TB3, and TB4, in the macrophages as efficiently as the positive control, TAT peptide (Figure 2A). The truncated versions of the TB4 peptide viz. TB5, TB6, and TB7 peptides showed diminished cell permeability compared to TB4 (Figure 2A and B). Subsequently, we performed a flow cytometry analysis of peptide-treated macrophages that also showed the internalization of TB4 as effective as TAT peptide (Figure 2B). Further, we observed that the replacement of lysine residues with alanine in the cell-permeable peptide affected its uptake as the mutant version of the TB2 (TB2ala) peptide failed to accumulate in the treated macrophages (Figure 2A). Based on the peptide internalization studies, the TB4 peptide with 7 amino acids was the shortest peptide derived from TcpB, displaying efficient cell permeability. Subsequently, we analysed the permeability of TB4 in various cell types. TB4 was efficiently internalised by N9 microglial cells and human monocyte-derived macrophages (THP1) (Appendix Figure S1A). We did not observe internalization of the TB4 peptide by the human embryonic kidney cell line, HEK293 (Appendix Figure S1A).

    Figure 2 Generation of cell permeable peptides from the PIP-binding motif of TcpB (A) Uptake of TcpB derived peptides by macrophages. RAW264.7 cells were treated with FAM-labelled peptides from the PIP-binding motif of TcpB for 2 hours, followed by processing the cells for fluorescent microscopy. The green fluorescence inside the cells shows the internalised peptides and the nuclei stained with DAPI (blue). The cells were imaged using a laser confocal microscope at 63×. Scale bar, 50 µm. Image represents 10 different fields captured from cells treated with peptide. (B) Quantification of internalized peptides in RAW264.7 cells by flow cytometry. Fifty-thousand cells per sample were analyzed for quantification of the fluorescence signal. (C) Internalization of TB4 peptide in the presence of cellular uptake inhibitors. RAW264.7 cells were treated with various inhibitors, followed by treatment with FAM-TB4 peptide. The cells were treated with FAM-TB4 peptide at 4°C for 2 hours for cold inhibition. The fluorescent signal from the internalized FAM-TB4 peptide was quantified by flow cytometry where 50,000 cells per sample were analysed for quantification. (D) Fluorescent microscopy images of RAW264.7 cells treated with FAM-TB4 in the presence of Nocodazole, MβCD, or cold temperature treatment. The cells were imaged using a fluorescence microscope at 63×. Scale bar, 50 µm. Image represents 15 different fields captured from cells treated with inhibitors and TB4. (E and F) Flow cytometry analysis of RAW264.7 cells treated with increasing concentrations of MβCD (E) and Nocodazole (F), followed by treatment with FAM-TB4 peptide. The data are presented as the mean ± SEM from at least two independent experiments (n.s, P > 0.05;*, P< 0.05; **, P< 0.01; ***, P < 0.001; ****, P < 0.0001).

    Next, we sought to examine the mechanism by which macrophages internalise the TB4 peptide. The accumulation of TB4 was affected when the peptide uptake assay was performed at 4°C, indicating that internalization of TB4 peptide occurs through ATP-dependent endocytosis and not through passive translocation (Figure 2C). To examine the mechanism of internalization, we treated cells with various compounds that inhibit cellular uptake of macromolecules through endocytosis. The cytotoxicity of the compounds was examined to determine the treatment dose of inhibitors (Appendix Figure S1B). Treatment of cells with wortmannin (inhibitor of phosphoinositide 3-kinase mediated transcytosis), cytochalasin D (inhibits micropinocytosis), chlorpromazine (inhibits clathrin-mediated endocytosis) did not impact the uptake of TB4 (Figure 2C). In contrast, treatment of cells with nocodazole (blocks microtubule polymerization) and methylated-β-cyclodextrin (inhibits caveolae-mediated endocytosis through cholesterol depletion) affected the uptake of TB4 peptide by macrophages (Figure 2C and D). To corroborate these findings, we treated cells with increasing concentrations of methylated-β-cyclodextrin or nocodazole, followed by analysing the uptake of the TB4 peptide. We observed a dose-dependent inhibition of TB4 internalization by the compound-treated macrophages (Figure 2E and F). Collectively, our experimental data indicate that the cells internalize TB4 peptide through active endocytosis that requires cholesterol-rich lipid rafts.

    Generation of a Chimeric Peptide with Cell Permeability and Anti-Inflammatory Properties

    The TIR domain and intact BB loop are required for the anti-inflammatory property of TcpB.33 To verify this, we overexpressed the TIR domain of TcpB in macrophages, followed by analyzing its efficiency in suppressing LPS-induced pro-inflammatory cytokines. RAW264.7 cells were transfected with the TIR domain of TcpB, followed by treatment of cells with LPS. We observed suppression of LPS-induced production of pro-inflammatory cytokines, TNF-α, and IL-6 in the macrophages transfected with the TIR domain of TcpB (Figure 3A and B). Next, we designed peptides from the BB-loop region of TcpB, designated as BBL, and fused them with the cell-permeable peptide TB4 at the N-terminus (TB4-BBL). The TB4-BBL peptide carried the entire BB loop region of TcpB (Table 1). We also generated TB4-BBL1, which carried a shorter BB-loop, and TB4-BBL2 with an extended BB-loop region (Appendix Figure S2A). We synthesized two control peptides viz. TBX2, the cell-permeable anti-inflammatory peptide from TIRAP and TB4-BX2 where the anti-inflammatory peptide from TIRAP has been fused with the TB4 peptide.32 Next, we analysed the cell permeability of TcpB and TIRAP-derived peptides and all the peptides were efficiently internalized by the macrophages (Appendix Figure S2B). To examine, whether the peptides induce any cytotoxicity, iBMDMs were treated with cell-permeable peptides and analysed the release of LDH in the culture supernatants. None of the peptides induced any cytotoxicity in the treated macrophages (Figure 3C). Next, we examined the efficiency of TcpB and TIRAP-derived peptides to suppress the LPS-induced production of pro-inflammatory cytokines in macrophages. The peptide-treated macrophages were induced with LPS, followed by analysing the production of TNF-α and IL-6 by qRT-PCR and ELISA. We found that TB4-BBL peptides could suppress the induction of pro-inflammatory cytokines as efficiently as the control peptides (Figure 3D, Appendix Figure S2C-E). Since the TB4-BBL2 peptide was identified as the shortest peptide exhibiting maximum efficacy in suppressing pro-inflammatory cytokines secretion, along with minimal cytotoxicity and efficient cell-permeability, this peptide was selected for further experiments.

    Figure 3 Continued.

    Figure 3 Generation of cell permeable, anti-inflammatory peptide from TcpB. (A and B) The TIR domain of TcpB suppresses LPS-induced pro-inflammatory cytokines in macrophages. RAW264.7 cells were transfected with eukaryotic expression plasmid harboring the TIR domain of TcpB, followed by inducing the cells with LPS. The levels of secreted TNF-α (A) and IL-6 (B) were quantified by ELISA. (C) LDH assay to determine the cytotoxicity of TcpB or TIRAP-derived peptides. Immortalised BMDM cells were treated with 100 μM of indicated peptides for 5 hours, followed by quantifying the LDH released into the culture supernatants. (D) The chimeric peptides from TcpB or TIRAP suppress LPS-induced production of TNF-α. RAW264.7 cells were treated with the indicated peptides, followed by the induction of pro-inflammatory cytokines by LPS. The levels of TNF-α in the LPS-induced cells were quantified by ELISA. (E) ELISA showing the levels of LPS-induced TNF- α in RAW264.7 cells treated with increasing concentrations of TB4-BBL2. (F) qPCR data showing the LPS-induced TNF- α levels in RAW264.7 cells treated with increasing concentrations of TB4-BBL2. (G&H) ELISA (G) and q-RT-PCR (H) data showing the levels of LPS-induced IL-6 in RAW264.7 cells treated with increasing concentrations of TB4-BBL2. (I) TB4-BBL2 suppresses TLR2/4/9-mediated production of TNF-α. RAW264.7 cells were treated with increasing concentrations of TB4-BBL2, followed by the induction of pro-inflammatory cytokines by various TLR ligands; Pam3-CSK-TLR2, ODN-TLR9, Poly I:C-TLR3. The levels of TNF-α in the supernatant were quantified by ELISA. (J) Attenuation of LPS-induced NF-κB activation by TB4-BBL2 peptide in macrophages. RAW264.7 cells were transfected with luciferase reporter plasmids and treated with increasing concentrations of TB4-BBL2 peptide. Twenty-four hours post-transfection, cells were induced with LPS, followed by quantification of luciferase activity. The data are represented as relative luminescence units of NF-κB activation in the uninduced vs LPS-induced cells. (K) Efficacy of TB4-BBL2 peptide to attenuate TNF-α, which is induced prior, after, or simultaneous treatment with LPS. RAW264.7 cells were first treated with TB4-BBL2 peptide, followed by LPS induction or first induced with LPS, followed by peptide treatment or treated with peptide and LPS together. The levels of secreted TNF-α in the culture supernatants were quantified by ELISA. (L) TB4-BBL2 peptide attenuates LPS-induced production of NO in macrophages. RAW264.7 cells were treated with increasing concentrations of TB4-BBL2, followed by induction with LPS and quantification of NO levels by Griess assay. (M) Attenuation of LPS-induced ROS by TB4-BBL2 peptide. RAW264.7 cells were treated with increasing concentrations of TB4-BBL2 peptide, followed by inducing cells with LPS. Subsequently, the cells were treated with H2DCFDA for 30 minutes, and the formation of DCF was measured using a multi-mode reader with excitation at 492 nm and emission at 517 nm. The data are presented as the mean ± SEM from at least three independent experiments (n.s, P > 0.05;*, P< 0.05; **, P< 0.01; ***, P < 0.001; ****, P < 0.0001).

    Abbreviations: EV, Empty vector; CA, Cell alone.

    Macrophages treated with increasing concentrations of TB4-BBL2 showed a dose-dependent suppression of TNF-α and IL-6, confirming its anti-inflammatory property (Figure 3E–H, Appendix Figure S2F and G). To evaluate the efficacy of TB4-BBL2 in modulating TLR-driven inflammatory responses, RAW264.7 cells were pre-treated with increasing concentrations of TB4-BBL2, followed by stimulation with TLR ligands- Pam3CSK4 (TLR2 agonist), ODN (TLR9 agonist), and Poly I:C (TLR3 agonist). The levels of TNF-α in the supernatant were quantified by ELISA. TB4-BBL2 treatment resulted in a dose-dependent suppression of TNF-α production induced by TLR2/9 agonists (Figure 3I). Next, we examined the efficacy of TB4-BBL2 to suppress LPS-induced NF-κB activation, which drives the expression of pro-inflammatory cytokines in macrophages. RAW264.7 cells were transfected with the luciferase reporter plasmids, followed by treatment with various concentrations of TB4-BBL2 peptide. The luciferase activity, driven by NF-κB activation, was quantified after stimulating the cells with LPS. The TB4-BBL2 peptide could efficiently suppress the activation of NF-κB in a dose-dependent manner (Figure 3J).

    Next, we analysed the TB4-BBL2-mediated suppression of pro-inflammatory cytokines in macrophages upon pre- or post-LPS treatment. The TB4-BBL2-treated macrophages were induced with LPS, or LPS-induced macrophages were treated with TB4-BBL2. The TB4-BBL2 peptide could efficiently suppress the production of TNF-α in both conditions (Figure 3K). Simultaneous treatment of macrophages with LPS and TB4-BBL2 showed decreased efficiency compared to the conditions mentioned above (Figure 3K). In addition to pro-inflammatory cytokines, LPS induces the production of reactive oxygen species (ROS) and nitric oxide (NO) in endotoxemia. Therefore, we sought to examine the levels of LPS-induced ROS and NO in TB4-BBL2-treated macrophages. RAW264.7 cells were stimulated with LPS, followed by treating the cells with the TB4-BBL2 peptide and quantifying ROS and NO. The TB4-BBL2 peptide could efficiently suppress the LPS-induced generation of ROS and NO in the macrophages (Figure 3L and M). Our experimental data suggest that macrophages uptake the TB4-BBL2, and the internalized peptide could efficiently suppress LPS-induced activation of NF-κB, production of pro-inflammatory cytokines, ROS, and NO.

    TB4-BBL2 Targets TIRAP and MYD88 to Negatively Regulate LPS Signalling

    Since the TB4-BBL2 peptide interferes with LPS signalling, we wished to examine its mechanism of action. TcpB has been reported to interact with TLR2/4 adaptor proteins, TIRAP, and MYD88.10,12 TcpB-mediated ubiquitination and degradation of TIRAP have also been reported12,14 Therefore, we examined whether the TB4-BBL2 peptide interacts with TIRAP or MYD88 by co-immunoprecipitation. FLAG-EV was used as an experimental negative control because it does not produce any functional protein that could interact with the peptides, thus serving as a baseline for background fluorescence. We used an unrelated host protein, USP8, and TB4 or TAT peptide as controls to ensure the specificity of TB4-BBL2 interaction. HEK293T cells over-expressing FLAG-EV/TIRAP/MYD88/USP8 were lysed, followed by incubating the lysates with FAM-labelled TB4-BBL2/TB4/TAT peptide. Subsequently, FLAG-TIRAP/MYD88/USP8 was immunoprecipitated using the anti-FLAG antibody, and co-immunoprecipitation of FAM-labeled peptide was detected by measuring the fluorescence. We observed enhanced fluorescence in the co-immunoprecipitate of TIRAP or MYD88 with TB4-BBL2 peptide compared to the controls (Figure 4A). Minimal fluorescence levels were detected in the co-immunoprecipitation of TIRAP/MYD88 with TB4 and TAT peptide, indicating the specificity of TIRAP and MYD88 interaction with the TB4-BBL2 peptide. Additionally, minimal fluorescence was observed in the co-immunoprecipitation assays involving FLAG-EV or FLAG-USP8 with TB4-BBL2, further highlighting the strong and specific interaction between the TB4-BBL2 peptide and TIRAP/MYD88. To reconfirm the interaction further, we examined the binding affinity between FAM-TB4-BBL2 and purified recombinant MBP-TIRAP protein or MBP alone using a microscale thermophoresis assay. A Kd value of 99.6 nM ± 64.0 nM was observed between FAM-TB4-BBL2 and MBP-TIRAP compared to the MBP alone, indicating a positive interaction (Figure 4B). Collectively, our experimental data suggest that TB4-BBL2 specifically interacts with the TLR2/4 adaptor proteins TIRAP and MYD88.

    Figure 4 TB4-BBL2 interacts with TIRAP/MYD88 and induces their proteasomal degradation. (A) Lysates of HEK293T cells overexpressing Flag-TIRAP/MYD88/USP8/empty vector (EV) were incubated with FAM-labelled TAT/TB4/TB4-BBL2 as indicated in the figure, followed by immunoprecipitation of Flag-tagged proteins. Immunoprecipitation of TIRAP or MYD88 from the lysate incubated with FAM-TB4-BBL2 showed significantly higher fluorescence intensity, indicating co-immunoprecipitation of FAM-TB4-BBL2 due to its interaction with TIRAP or MYD88. (B) MST analysis to confirm the interaction between TIRAP and TB4-BBL2. The purified MBP-TIRAP was titrated over a concentration range of 500 nM to 0.01 nM while maintaining a constant concentration of 100 nM for the FAM-TB4-BBL2. The resulting thermophoresis data showed a dissociation constant (Kd) of 99.6± 64.0 nM for MBP-TIRAP and TB4-BBL2 interaction (mean ± SD, n = 3 biological independent experiments). (C) TB4-BBL2 promotes the degradation of TIRAP and MYD88. RAW264.7 cells were treated with indicated concentrations of TB4-BBL2 or TIR peptide (Table 1). Five hours post-treatment, cells were harvested, followed by immunoblotting and detection of endogenous TIRAP and MYD88. The right side of the panel shows the densitometry of TIRAP and MYD88 bands. (D) TB4-BBL2 does not alter the levels of TRIF and TLR2. RAW264.7 cells were treated with indicated concentrations of TB4-BBL2. Five hours post-treatment, cells were harvested, followed by immunoblotting and detection of endogenous proteins, TIRAP, MYD88, TRIF and TLR2. The right side of the panel shows the densitometry of TIRAP and MYD88 bands. (E and F) Pulse-chase analysis using cycloheximide. RAW264.7 cells were treated with TB4-BBL2 for three hours, followed by treatment with cycloheximide. Subsequently, the cells were harvested at the indicated time points and subjected to immunoblotting. The levels of endogenous TIRAP, MYD88, TRIF and TLR2 were detected in TB4-BBL2 treated cells with increasing time points in the presence or absence of cycloheximide. The right side of the panels shows the densitometry of the TIRAP, MYD88, TRIF and TLR2 bands. Beta actin served as a loading control for all the immunoblots. The immunoblots are representative of three independent experiments. The data are presented as the mean ± SEM from at least three independent experiments (n.s, P > 0.05; ***, P < 0.001; ****, P < 0.0001).

    Abbreviation: CHX, cycloheximide.

    Given that TB4-BBL2 interacts with TIRAP and MYD88, we sought to examine whether TB4-BBL2 induces the degradation of its binding partners. RAW264.7 cells were treated with TB4-BBL2 peptide, followed by analysing the endogenous levels of TIRAP and MYD88. We observed an enhanced degradation of endogenous TIRAP and MYD88 with TB4-BBL2 treatment (Figure 4C and D). We also treated macrophages with the TIR peptide alone, which is impermeable to the cells. The TIR peptide did not induce degradation of TIRAP or MYD88, suggesting that cell penetration of the peptide is essential for promoting their degradation (Figure 4C). To further validate these findings, the cells were treated with increasing concentrations of TB4-BBL2 and the levels of TIRAP, and MYD88 were assessed. A similar assay was performed for TLR2 and another adaptor protein TRIF to evaluate the specificity of TB4-BBL2. We observed a concentration-dependent reduction in TIRAP and MYD88 levels with TB4-BBL2 treatment, while TLR2 and TRIF levels remained unchanged (Figure 4D). These results suggest that TB4-BBL2 specifically targets the degradation of the adaptor proteins TIRAP and MYD88, which explains our previous observation that TB4-BBL2 treatment did not affect TLR3-TRIF–induced TNF-α production.

    Next, we performed a pulse-chase analysis where RAW264.7 cells were treated with TB4-BBL2 in the presence or absence of cycloheximide that can inhibit the synthesis of fresh proteins. The treated cells were harvested at various time points, followed by immunoblotting to detect the endogenous levels of TIRAP/MYD88/TRIF/TLR2. The cycloheximide-treated cells showed enhanced degradation of TIRAP and MYD88 by TB4-BBL2 compared to untreated cells (Figure 4E and F). The levels of TRIF and TLR2 remained unchanged in both cycloheximide-treated and untreated cells in the presence of TB4-BBL2 (Figure 4E and F). Taken together, our experimental data shows that TB4-BBL2 peptide specifically interacts with and degrades TIRAP and MYD88 to suppress the LPS-induced TLR4 signalling.

    TB4-BBL2 Peptide Attenuates LPS-Induced Endotoxemia in Mice

    Given that TB4-BBL2 attenuates LPS-induced production of pro-inflammatory cytokines, ROS, and NO in macrophages, we sought to examine its protection in LPS-induced endotoxemia in mice. First, we analysed any potential toxicity induced by TB4-BBL2 and its dosage tolerance in mice. BALB/c mice were treated with different concentrations of TB4-BBL2, followed by assessing various parameters for indication of toxicity. The peptide-treated mice showed no signs of toxicity except with the highest concentration (40 mg/kg) of TB4-BBL2 peptide, where the treated mice showed a slight splenomegaly (Figure 5A–C, Appendix Figure S3A-B).

    Figure 5 TB4-BBL2 suppresses production of LPS-induced pro-inflammatory cytokines in mice. (A) Pictographic representation of study design to analyze the toxicity of TB4-BBL2 peptide in mice. (B and C) Mice were treated with indicated concentrations of TB4-BBL2 peptide, and the toxicity parameters were evaluated for 5 days, followed by sacrifice of mice and necropsy. The spleen weight of peptide-treated mice was determined (B), and TNF-α level in the serum (C) was quantified using ELISA. (D) Pictographic representation of the experimental design for evaluating the efficacy of TB4-BBL2 peptide in the mice model of endotoxemia. (EN) TB4-BBL2 peptide attenuates LPS-induced production of pro-inflammatory cytokines in mice. Mice were treated with indicated doses of TB4-BBL2 peptide for 1 hour, followed by administration of LPS. Mice were sacrificed 2 hours post-LPS treatment, and the levels of TNF-α (E) and IL-6 (F) in the serum were quantified by ELISA and mRNA expression levels of TNF-α and IL-6 in the spleen (G and H), lungs (I and J), liver, (K and L) and kidney (M and N) were estimated by q-RT-PCR. The data are presented as the mean ± SEM of 3 mice per group (n.s, P > 0.05; *, P< 0.05; **, P< 0.01; ***, P < 0.001; ****, P < 0.0001).

    Next, we examined the effect of TB4-BBL2 on LPS-induced production of pro-inflammatory cytokines in mice. BALB/c mice were treated with increasing concentrations of TB4-BBL2, followed by inducing endotoxemia by administering LPS (Figure 5D). We found a dose-dependent suppression of pro-inflammatory cytokines in the TB4-BBL2 treated mice in the serum, lungs, spleen, liver, and kidneys (Figure 5E–N). Since 30 mg/kg of TB4-BBL2 peptide showed optimal protection, we selected this concentration for further experiments.

    Next, we analysed the efficacy of TB4-BBL2 peptide in pre- or -simultaneous induction of endotoxemia by LPS treatment (Figure 6A). Mice treated with LPS, followed by administration of TB4-BBL2 resulted in efficient suppression of pro-inflammatory cytokines (Figure 6B–K). Simultaneous treatment of mice with LPS and TB4-BBL2 peptide also showed protection but not to the extent of the experimental conditions described above (Figure 6B–K).

    Figure 6 The effect of TB4-BBL2 after LPS treatment or simultaneous treatment of mice with TB4-BBL2 and LPS. (A) Pictographic representation of the experimental design. (BK) The levels of TNF-α and IL-6 in the serum (B and C), spleen (D and E), lungs (F and G), liver (H and I), and kidneys (J and K) of mice in two experimental conditions such as TB4-BBL2 treatment after LPS administration or simultaneous delivery of TB4-BBL2 and LPS. The data are presented as the mean ± SEM of 5 mice per group (n.s, P > 0.05; *, P< 0.05; **, P< 0.01; ***, P < 0.001; ****, P < 0.0001).

    To evaluate the protective effects of the TB4-BBL2 peptide in endotoxemia, tissue sections from the spleen, lungs, liver, and kidney of control or LPS-treated mice, either untreated or treated with TB4-BBL2, were examined using H&E staining. Endotoxemia induced by LPS administration resulted in significant tissue injury, characterized by extensive inflammatory cell infiltration and the presence of necrotic foci. These pathological features were markedly reduced in the TB4-BBL2-treated group, highlighting its potential to mitigate tissue damage associated with endotoxemia (Appendix Figure S3C and D). Taken together, our experimental data indicate that TB4-BBL2 can efficiently suppress the production of pro-inflammatory cytokines in the mice model of endotoxemia without showing any signs of toxicity.

    Conjugation of Gentamicin with TcpB-Derived Peptide Enhances Its Intracellular Availability

    The antibiotic, gentamicin, displays a wide spectrum of bactericidal activity against several Gram-negative bacteria. However, it is semi-permeable to cells, limiting its application for treating infections caused by intracellular bacterial pathogens. In this study, we analyzed whether the conjugation of gentamicin with TcpB-derived peptides can improve its cell permeability. Gentamicin was conjugated to the added cysteine residue of TB4 using the cross-linker SMCC, and the efficiency of the conjugation process was evaluated using Ellman’s reagent, which detects free thiol groups of unconjugated peptides in the reaction mixture (Figure 7A). We observed an increasing concentration gradient of free thiol groups in unconjugated cTB4, whereas the negligible detection of thiol groups in cTB4-G indicated an efficient conjugation of cTB4 with gentamicin through SMCC (Figure 7B). Next, we evaluated the antimicrobial properties of cTB4-conjugated gentamicin against B. neotomae and S. typhimurium. We did not observe any difference in antibacterial properties between gentamicin alone and gentamicin-TB4 conjugate, suggesting that TB4 conjugation did not impair the activity of gentamicin (Figure 7C, Appendix Figure 4A). No significant growth inhibition was observed when the bacterial cultures were treated with DMSO or SMCC alone. We further analyzed the concentration of gentamicin-cTB4 required for optimal antibacterial activity by performing a gentamicin titration assay. Macrophages were infected with the highly infectious intracellular bacterial pathogen, B. melitensis and subsequently treated with various concentrations of gentamicin-TB4 conjugate or gentamicin alone. We found that 50 μg/mL of cTB4-G exhibited the maximum antibacterial potency as compared to gentamicin alone at both 12- and 24 hours post-treatment (Figure 7D). Furthermore, we observed a significant reduction of Salmonella/B. melitensis load in the infected macrophages with increasing treatment time (Figure 7E, Appendix Figure S4B-D).

    Figure 7 Conjugation of gentamicin with cTB4 enhances its cellular availability (A) Pictographic representation of conjugation of gentamicin with cTB4 using SMCC as the cross-linker. (B) Evaluating the efficiency of conjugation using Ellman’s reagent. The conjugated or unconjugated cTB4 was incubated with DNTB reagent, followed by measuring the OD at 412 nm. (C) Conjugation of gentamicin to cTB4 did not affect its antimicrobial activity. B. neotomae cultures were incubated with gentamicin alone or cTB4-gentamicin, followed by measuring OD 600 nm after sixteen hours (D) Antibacterial activity of increasing concentrations of cTB4-gentamicin against the intracellular localised B. melitensis. B. melitensis-infected macrophages were treated with indicated concentrations of cTB4-gentamicin, followed by the enumeration of CFU at 12 and 24 hours post-infection. (E) Antibacterial activity of cTB4-gentamicin against the intracellular localised B. melitensis at various treatment times. B. melitensis-infected macrophages were treated with cTB4-gentamicin conjugate for the indicated time points, followed by enumerating CFU. The data are presented as the mean ± SEM from at least two independent experiments (n.s, P > 0.05; *, P< 0.05; ****, P < 0.0001).

    Treatment of B. melitensis-Infected Mice with cTB4-BBL2-Gentamicin Results in Enhanced Bacterial Clearance and Suppression of Inflammatory Cytokines

    TB4-BBL2 peptide from TcpB protein exhibits excellent cell permeability and anti-inflammatory properties. Therefore, we conjugated gentamicin with cTB4-BBL2 to generate a hybrid drug (cTB4-BBL2-G) with cell permeability and antibacterial and anti-inflammatory properties. To evaluate its efficacy, we treated Brucella-infected macrophages with cTB4-BBL2-G, followed by enumerating the CFU and quantifying the levels of TNF-α. We observed enhanced elimination of intracellular B. melitensis (Figure 8A and B) as well as suppression of TNF-α in the infected macrophages treated with cTB4-BBL2-G (Figure 8C).

    Figure 8 Conjugating gentamicin with cTB4-BBL2 enhances cell permeability and imparts anti-inflammatory properties. (A and B) Macrophages were infected with B. melitensis for 12 hours (A) or 24 hours (B), followed by treatment with cTB4-BBL2-Gentamicin. The CFU was enumerated 12 or 24 hours post-treatment. (C) Treatment of B. melitensis infected macrophages with cTB4-BBL2-Gentamicin for 48 hours, followed by collection of supernatant and quantification of TNF-α by ELISA. (D) Schematic diagram showing the methodology used for examining the effect of cTB4-BBL2-Gentamicin treatment in B. melitensis infected mice. (E) Scatter plot showing the splenic load of B. melitensis in the mice treated with cTB4-BBL2-Gentamicin or indicated controls. (F) The levels of TNF-α in the serum of mice treated with TB4-BBL2-Gentamicin or indicated controls. (GJ) mRNA expression levels of TNF-α and IL-6 in the spleen (G), lungs (H), liver (I) and kidney (J) of cTB4-BBL2-Gentamicin treated mice or indicated controls by q-RT-PCR. The data are presented as the mean ± SEM of 6 mice per group (n.s, P > 0.05;*, P< 0.05; **, P< 0.01; ***, P < 0.001, ****, P < 0.0001).

    Next, we examined the antibacterial and anti-inflammatory efficacy of cTB4-BBL2-Gentamicin in the mice model of brucellosis. Ten days after infection with B. melitensis, BALB/c mice were treated with cTB4-BBL2-gentamicin or control treatments, including PBS, gentamicin alone, or cTB4-Gentamicin, for three consecutive days. On day 14 post-infection, the mice were sacrificed, and the bacterial load in the spleen was quantified by CFU enumeration (Figure 8D). We observed a significant reduction in B. melitensis load in the spleens of mice treated with c-TB4-Gentamicin or cTB4-BBL2-Gentamicin compared to those treated with gentamicin alone (Figure 8E). Further, we analysed the levels of TNF-α in the serum of mice in all experimental groups. Mice treated with cTB4-BBL2-Gentamicin exhibited significantly lower serum TNF-α levels compared to the other treatment groups (Figure 8F). Additionally, spleen, lungs, liver, and kidneys from each treatment group were examined for the expression of pro-inflammatory cytokine genes. In all analyzed organs, the cTB4-BBL2-Gentamicin treatment group showed a marked reduction in the expression levels of both TNF-α and IL-6 compared to the other groups (Figure 8G–J).

    Discussion

    Toll-like receptors play an indispensable role in the innate immunity of mammals by recognizing invaded microbial pathogens and inducing various protective immune responses, including adaptive immunity.34,35 There are twelve reported TLRs in mice, and ten in humans localized both on the cell surface and intracellular endolysosomal compartments of various cell types such as monocytes, macrophages, dendritic cells, neutrophils, B cells, T cells, and fibroblasts.36,37 The activation of TLRs by PAMPs leads to the recruitment of the TIR domain-containing adaptor proteins, and this molecular assembly activates various downstream proteins. The TLR signalling results in the production of pro-inflammatory cytokines, chemokines and many anti-microbial compounds such as nitrogen and oxygen free radicals and anti-microbial peptides. Even though TLRs play an essential role in defence against invaded microbial pathogens, their aberrant activation results in the pathogenesis of various inflammatory and infectious diseases. Therefore, the overactivation of TLR signalling is prevented by expressing multiple negative regulators of TLRs such as IRAK-M, SOCS1, Triad3A, and Toll-interacting protein.38–41 Some of these intracellular regulators are inherently present to regulate TLR activation at a physiological level, while others are upregulated through TLR signalling during infections.42 Since TLR triggers anti-microbial responses, many pathogenic microorganisms encode virulence factors that negatively regulate the TLR signalling. Many bacterial pathogens harbour proteases, acetyltransferases, kinases, deubiquitinases, or TIR-domain-containing proteins to subvert the TLR signalling.8 The food-borne pathogen Salmonella enteritidis secretes the TIR domain-containing protein, TlpA, to inhibit the TLR/IL1 induced NF-κB activation.43 Similarly, the uropathogenic E. coli CFT073 secretes the TIR domain protein, TcpC to block the MYD88-dependent signalling cascade.8,44 The intracellular bacterial pathogen, Brucella encodes the TIR domain-containing protein, TcpB that interferes with TLR2/4 signalling.10,13,14

    Dysregulated inflammatory pathways, including TLR signalling, result in many disorders ranging from acute conditions such as multi-organ failure to chronic conditions, including diabetes and cardiovascular diseases.45 TLR4 is activated by the PAMPS such as LPS and endogenous damage-associated molecular pattern viz. High Mobility Group Box Protein 1, Hyaluronan and HSP70.46,47 The variety of ligands for TLR4, encompassing both pathogen-related and endogenous substances, implies a potential association of this receptor with numerous disorders, including pathogen-associated diseases. The aberrant activation of TLR4 has been implicated in the onset of septicaemia and cardiovascular disorders.45 Therefore, TLR4 inhibitors represent promising therapeutic approaches to suppress the production of pro-inflammatory cytokines and other inflammatory mediators. The strategies to block the TLR include neutralizing TLR ligands, blocking the binding of ligands to TLRs or inhibitors that interfere with TLR signalling pathways.48 Many drugs are in the pipeline targeting TLR signaling pathways for infectious diseases caused by pathogens such as hepatitis C, Hepatitis B, anthrax, and influenza.49

    The microbial proteins that subvert the TLR4 signalling can serve as promising drugs for dampening inflammatory responses. Even though recombinant protein-based therapeutics have been proven effective in treating various clinical indications, they have many drawbacks, including physiochemical instability, immunogenicity, and suboptimal circulation half-life.50 In recent decades, peptides have emerged as a unique class of therapeutic agents with intrinsic properties and favourable pharmacodynamic profiles. Peptide-based drugs offer many advantages over protein-based therapeutics because of their high specificity, low toxicity, high diversity, and reduced immunogenicity.51 Furthermore, lower production cost and ease of production on a large scale using chemical synthesis make peptide-based therapeutics as attractive biopharmaceuticals over small molecule counterparts. From 2016 to 2022, there were 22 FDA-approved peptide-based drugs for a spectrum of diseases, such as hyperglycemia and lupus nephritis.52 Further, several hundreds of peptide-based therapeutics are currently being evaluated in clinical or pre-clinical trials.53 Hence, therapeutic peptides are promising candidates for developing novel interventions to treat inflammatory disorders.

    In this study, we designed and synthesized several peptides from the TcpB protein of Brucella, followed by the detailed characterization of these peptides. TcpB is a cell-permeable protein, and this property has been attributed to its PIP binding motif at the N-terminus. Therefore, we designed many peptides from the PIP-binding motif of TcpB and identified the shortest peptide, TB4, that retains the cell-permeable property of TcpB. Furthermore, the TB4 peptide demonstrated excellent cell permeability across various macrophage types. Such intracellular translocation of proteins or peptides typically occurs via diverse mechanisms, including endocytosis.54 The endocytosis through caveolae, which contain lipid rafts rich in cholesterol and phospholipids, mediates the internalization of various ligands, such as cholera toxin B and albumin.54 The pharmacological inhibitor of this pathway, MβCD, which depletes cholesterol from lipid rafts, affected the internalization of the TB4 peptide. This implies that TB4 is endocytosed through cholesterol-rich microdomains of the plasma membrane. The arginine and lysine residues in TB4 are essential for internalization as their replacement with alanine affected the cell permeability of TB4. The mutations of basic amino acids to alanine in the PIP-binding motif of TcpB have been reported to affect its property to bind to the phospholipids.11 Therefore, it can be assumed that the TB4 peptide binds to the phospholipids in the lipid raft to facilitate its internalization through endocytosis. The microtubule polymerization inhibitor, nocodazole, also affected the internalization of the TB4 peptide, suggesting the requirement of intact microtubule for endocytosis of TB4 as reported for uptake of other macromolecules.55,56 The reduced uptake of TB4 at 4 °C indicates that its internalization does not occur through direct translocation, but instead involves active uptake mechanisms specific to phagocytic cells. The cold inhibition of TB4 internalization further indicates the requirement of ATP, which is in agreement with the fact that endocytosis is an energy-dependent process.57,58

    The TIR domain is essential for TcpB to interfere with the TIRAP/MYD88 signaling. Overexpression of TIR domain in macrophages suppressed the LPS-induced production of pro-inflammatory cytokines. Previous studies have shown that the intact BB-loop of TcpB is required to inhibit TLR4 signalling.14 This encouraged us to design peptides from the BB-loop region of TcpB to identify a peptide that mimics the TLR suppression property of TcpB. Since the peptides derived from the TIR domain have no cell permeability, we conjugated them with the TB4 peptide. We evaluated the efficacy of various chimeric peptides and identified the shortest peptide, TB4-BBL2 that exhibited interference with the TIRAP/MYD88 signalling. The TB4-BBL2 peptide could efficiently suppress the NF-κB activation as well as LPS/Pam3-CSK and ODN-induced production of pro-inflammatory cytokines, ROS, and NO in macrophages. The cross-talk between NF-κB, TNF-α, and free radicals is known to play a crucial role in regulating inflammation.59

    TcpB has been reported to target the adaptor proteins of TLR4 viz. TIRAP and MYD88 to interfere with the TLR4 signalling. Therefore, we examined whether the TB4-BBL2 peptide mimics this property of TcpB. The co-immunoprecipitation of TB4-BBL2 with TIRAP and MYD88 suggests that the peptide binds to these adaptor proteins. To confirm the experimental data further, we used the MST technique to analyze the potential interaction between FAM-labelled TB4-BBL2 and recombinant MBP-TIRAP protein. The MST is a biophysical technique to quantitatively assess the interaction between biomolecules.60,61 The change in fluorescence intensity served as an indicator of the binding event occurring due to the establishment of a thermal gradient induced by infrared light. These variations in fluorescence, resulting from thermophoresis shifts induced by ligand binding, were employed to calculate equilibrium binding constants.62–64 The MST analysis showed a Kd value of 99.6 ± 64.0 nM between FAM-TB4-BBL2 and MBP-TIRAP compared to MBP alone. This indicates a positive interaction between FAM-TB4-BBL2 and MBP-TIRAP. The lower Kd value between the target TB4-BBL2 and ligand MBP-TIRAP indicates a positive interaction. TcpB has been reported to induce enhanced ubiquitination and degradation of TIRAP to deplete this adaptor protein to enable the negative regulation of TLR4 signalling.12 Interestingly, we found that the TB4-BBL2 peptide also promotes the targeted degradation of TIRAP and MYD88. However, further studies are needed to elucidate the precise mechanism by which TB4-BBL2 facilitates the degradation of these TLR adaptor proteins.

    Toxicity is a major limiting factor that compromises the use of proteins or peptide-based therapeutics. TB4-BBL2 did not induce any toxicity in the cells, as demonstrated by the negligible release of LDH by the peptide-treated cells. Further, we analysed the toxicity and dosage levels of TB4-BBL2 in mice. The mice tolerated peptide concentrations up to 40 mg/kg, with only slight splenomegaly observed at this higher dose of TB4-BBL2. Therefore, a dose of 30 mg/kg was selected to evaluate the efficacy of the TB4-BBL2 peptide in the mouse model of endotoxemia. LPS treatment in mice induces endotoxemia, characterized by elevated levels of pro-inflammatory cytokines and organ injury. We evaluated the effect of TB4-BBL2 under three conditions such as peptide treatment followed by LPS administration, simultaneous delivery of peptide and LPS and peptide treatment after LPS administration. Administration of the peptide, either prior to or following LPS challenge, significantly attenuated pro-inflammatory cytokine production in mice. The significant suppression of pro-inflammatory cytokines by TB4-BBL2 following endotoxemia induction highlights its therapeutic potential for disorders caused by aberrant activation of TLR4. Importantly, the demonstrated in vivo efficacy of TB4-BBL2 peptide underscores its translational potential, especially in acute inflammatory conditions such as sepsis and endotoxemia, where timely modulation of TLR4 signalling is critical. The protective effect of TB4-BBL2, even after inflammation has begun, emphasizes its translational potential as a prophylactic and post-exposure agent.

    Treating intracellular infection remains particularly challenging due to the poor cell permeability of conventional antimicrobial agents.25 Although some antimicrobials exhibit limited cell permeability, their higher doses and prolonged treatment can lead to severe side effects. Furthermore, the rapid emergence of antimicrobial resistance has significantly narrowed the range of effective antibiotics. As novel antibiotic discovery is costly and often yields limited success, improving the properties of existing antibiotics represents a practical and promising strategy.65 The intracellular niche shields pathogens, such as Brucella, Mycobacterium, and Salmonella from antimicrobial agents, contributing to their chronic persistence and frequent reinfections. To address the challenge of antibiotic delivery to intracellular pathogens, we explored modifying the aminoglycoside antibiotic gentamicin, which is known for its broad-spectrum activity but limited cellular uptake. Conjugation of gentamicin with the TcpB-derived peptide, TB4, enhanced its cellular uptake without compromising its antibacterial properties. Gentamicin alone exhibited limited efficacy against intracellular bacterial pathogens, however, its conjugation with the TB4 peptide significantly enhanced its ability to eliminate intracellular Brucella and Salmonella. In addition to reducing the intracellular burden of B. melitensis within macrophages, the hybrid drug TB4-BBL2-Gentamicin effectively attenuated the secretion of pro-inflammatory cytokines by the infected macrophages. Furthermore, this hybrid drug demonstrated in vivo efficacy in a murine model of brucellosis by significantly reducing the splenic burden of B. melitensis and suppressing the expression of pro-inflammatory cytokines in serum and various organs. These findings suggest that conjugating existing antibiotics with TcpB-derived peptides represents a promising strategy to overcome the poor membrane permeability, thereby restoring the efficacy of antibiotics compromised against intracellular pathogens. The desirable properties of TB4, including its small size, ease of synthesis, solubility, and efficient internalization via endocytosis, highlight its potential for wide applications in drug delivery. Moreover, anti-inflammatory peptides derived from TcpB can confer additional properties to antibiotics, beyond improving cell permeability and antimicrobial activity, thereby further enhancing the therapeutic efficacy of this strategy. In summary, our experimental data suggest that peptides derived from the TcpB protein possess significant translational potential for addressing a range of inflammatory disorders and for improving the pharmacokinetic properties of various therapeutic agents.

    Data Sharing Statement

    The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

    Acknowledgments

    We thank Ms. Rama Devi for assistance in flow cytometry, Mr. Shashikant Gawai for microscopy, and Dr. Jayant Hole and Mr. Raju at the Small Animal Facility of NIAB for help with mice experiments. We also acknowledge the BSL-3/ABSL-3 facility of UoH-NIAB for providing support with all B. melitensis-related experiments.

    Author Contributions

    GR conceived and designed the study. GR, BRN and BP wrote the paper. BRN and BP performed the experiments and analyzed the experimental data. All authors took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

    Funding

    We thank the Department of Science & Technology (DST), Ministry of Science and Technology, Government of India (Grant number: VI-D&P/563/206-17/TDT) for funding. We thank the National Institute of Animal Biotechnology (NIAB) for additional funding and experimental facilities. BRN acknowledges a research fellowship [647/(CSIR-UGC NET DEC. 2018)] from the University Grant Commission (UGC), Government of India. BP acknowledges a research fellowship (DST-INSPIRE IF210464) from Department of Science & Technology (DST), Ministry of Science and Technology, Government of India.

    Disclosure

    Dr Girish Radhakrishnan reports a patent 556338 licensed to National Institute of Animal Biotechnology and Department of Science and Technology. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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  • Understanding patient perspective in treatment and management of care

    Understanding patient perspective in treatment and management of care

    Introduction

    Drug development is an increasingly challenging process due to rising costs, strict regulatory requirements, a demanding market access environment, and high research and development attrition rates.1,2 New treatments should simultaneously improve patients’ quality of life while reducing overall healthcare costs.3 To facilitate the development of novel therapeutics that significantly improve quality of life over existing available treatments, the involvement of patient stakeholders (patients, families, caregivers, and advocacy organizations) as team members in the drug development process is becoming increasingly common in the pharmaceutical industry. Many regulatory agencies and associated committees and initiatives, like the European Medicines Agency (EMA), Scientific Advice Working Party (SAWP), Patient Engagement Advisory Committee (PEAC), Patient-Focused Drug Development (PFDD), and the United States Food and Drug Administration (FDA) have acknowledged the importance of patient-centric drug development.4–7 Furthermore, funding agencies and journal editors have recently begun to endorse patient engagement in patient-oriented research prior to funding drug development and publications.

    Engagement of patients across all phases of the drug development life cycle and systematic inclusion enables patients to be deeply involved in the interactions between advocacy organizations and bio-pharmaceutical companies to better understand and capture patient experience, burden of illness, and unmet clinical needs. Additionally, studies that incorporate accountability into the research process and empower patients to take on team-oriented roles re-define patient and stakeholder engagement by ensuring patients’ needs are understood early in drug development and incorporated into research priorities. Patient-focused medicines development includes consistently informing drug research, expanding access and awareness of clinical trial opportunities, improving clinical study design and outcomes, supporting a shared decision-making model, understanding tradeoffs of benefit/risk scenarios, and co-creation of meaningful and culturally appropriate study materials.8 Patient-focused clinical study design can also expand trial access to a pool of diverse patients who may consider clinical trials as a potential care option.9 Sub-optimal engagement can result in patients perceiving that clinical trial design is driven by “checking off boxes” rather than having a real mutual partnership.10–12

    In asthma, drug development and treatment are particularly challenging because of the complex and heterogeneous nature of the disease, which presents with varying clinical symptoms and treatment responses. Asthma triggers, response to medications, effectiveness of treatments, and asthma severity and exacerbations may vary from patient to patient and over the course of their disease.13 Hence, mutual collaboration between researchers and patients is instrumental to facilitate drug discovery and development. Moreover, collaborating with patients early in drug discovery process is crucial to design drugs that meet their unmet needs. Sanofi, a global biopharmaceutical company, and Allergy and Asthma Network (AAN), a US-based patient advocacy group, collaborated with patients with asthma from AAN’s patient community to better understand the challenges they face in the participation of clinical trials, medication adherence, their unmet medical needs, and the burdens impacting their quality of life.14 Engendering respect, trust, honesty, integrity, and accountability are tied to the mutual mission of putting patients first as key participants in the research process. When researchers understand patient needs and perspectives, they are better equipped to align their research efforts and decision-making structures to an integrated, patient-centric approach that is relevant to basic drug discovery.15 Pharmaceutical companies collaborating with patients living with asthma understand patient and caregiver lived experiences and can advance meaningful patient-centric research and development. The insights from these collaborations allowed Sanofi to better align research objectives with patients’ unmet medical needs, design better clinical studies to be patient-friendly and accessible, and ensured that the data generated in these studies reflect patients’ true health priorities.

    To advance patient-centric drug discovery and development in asthma, Sanofi, AAN, and members of AAN’s asthma patient community worked together through multiple structured interviews to better understand their perspectives and gaps in current asthma treatments, with an aim of accelerating the development of future therapies for asthma.

    Materials and Methods

    Literature Review

    A literature search was conducted using Medline® for English language articles published between July 2013 and July 2023. Published full-text articles regarding patient needs and expectations related to asthma treatment and patient-centric drug development in asthma were hand searched and included.

    Stakeholder Input

    To explore the unmet needs of patients living with asthma and understand their perspectives to guide future treatments, several virtual patient panels between patient advocates (n=5) and researchers (n=5) from Sanofi were conducted. All patients were contacted through the patient advocacy group AAN. They were selected based on their symptoms, disease severity, and treatment history. The selection was in alignment with the “Global Initiative for Asthma (GINA) clinical criteria for diagnosing asthma” to give a diverse perspective based on varying severity, and to reflect the diversity of lived experiences across geographies.16 A total of four virtual interviews were conducted with the patients, each of approximately 60 to 90 minutes in duration. The initial interviews included structured questions (Table 1), followed by open dialogues/transparent communication with patients about their experiences with asthma, treatment options, gaps, needs, and priorities.

    Table 1 Questions Used in the Patient Interview

    Results

    Results from the Virtual Interview Panels

    The interview panel included five patients of whom four had moderate-severe asthma as per the GINA clinical criteria and predominantly consisted of female patients (n=4/5) (Table 2).16

    Table 2 Patient Characteristics

    Unmet Needs of Patients with Asthma

    Based on patient panels led by advocates from the AAN, insights into patient experiences with asthma, unmet needs, priorities, adherence factors, treatment options, and treatment detractors were better understood. Key factors affecting asthma treatment adherence, gathered from patient input were side effects, lack of information about correct inhaler techniques, medication costs, misunderstanding medication dosing schedules, lack of communication between researchers and patients, lack of awareness about patient advocacy organizations and research led programs, and fear of discrimination or having concerns disregarded by healthcare providers (Table 3).

    Table 3 Key Factors Affecting Asthma Treatment Adherence

    Takeaways from Patient Interviews and Adopting a Patient-Centric Approach as a Way Forward

    The discussion with patient advocates emphasized that researchers must consider patients as partners to guide the decision-making processes and enable patient-centric principles across asthma research and treatment (Figures 1 and 2). Patient advocates have indicated that currently marketed medications have limitations, which may impede treatment adherence, and still do not achieve adequate asthma control. Of note, patients expect that new treatments should ease patient burden and treat the multiplicity of asthma symptoms, including the reduction of inflammation, relaxation of airways, and a reduction in response to asthma triggers with one treatment. For patients living with asthma, an ideal treatment would not require daily administration, must effectively control asthma symptoms within the least amount of elapsed time, and feature monthly dosing to eliminate the need for reliever inhalers or nebulizers in public. Furthermore, asthma medications must be cost-effective for all patients, regardless of their insurance status or coverage.

    Figure 1 Patient feedbacks.

    Figure 2 Takeaways from patient interviews.

    Discussion

    In this study, Sanofi collaborated with AAN, a patient advocacy group, to conduct interviews among patient stakeholders who have asthma to understand their perspectives about the disease and gaps in current asthma treatments, with an aim of developing novel and efficacious therapies. The patient stakeholders gave their inputs on factors affecting treatment adherence, unmet needs, and expectations for future treatments.

    Understanding patients’ unmet needs, their perspectives about current treatments, expectations from future treatments and challenges about participation in clinical trials is essential for the development of novel and efficacious drugs.1,2 The importance of involving patients early in drug development is emphasized by many regulatory agencies, funding agencies, and journal editors.4–7 Patient-focused drug development is particularly important in asthma because of the complex and heterogeneous nature of the disease.13

    In addition to patient interviews, evidence from published literature informed researchers at Sanofi about objective measures to link to prioritization decisions and the selection of novel drug targets and new research programs in asthma. It also helped researchers understand the unmet burden of disease, preferred formulations and modes of administration, biomarker and patient readiness considerations, standards of competing care (patient preference for differentiation), preferred therapies in the market (and gaps), adherence influencers, and patient burden from participation in clinical trials, among other factors.

    Expanding patient access to asthma clinical trial opportunities may also serve as a gateway to novel therapies for patients who otherwise may not have access to effectively manage their asthma and achieve a better health-related quality of life.17 Such opportunities can be difficult for patients who may not be aware of the availability of medical information, the rise of patient advocacy organizations, or the paradigm shift towards value-based healthcare.18 Enabling an ecosystem that integrates patients at each layer may help to decrease barriers, broaden access to clinical trials, and garner visibility on trial data and results in a patient-friendly manner.19

    The COVID-19 pandemic has also accelerated the pathway to new care settings, including digital technologies, which have the potential to help patients manage their asthma across different points in the care journey.20 These include digital applications and tools for maintaining a healthy lifestyle and monitoring tools with reminders to help patients take their medications on schedule while incorporating applications to track triggers (e.g., weather, air quality index, pollen, etc) moods, sleep, peak flow recordings, and nutrition; all of which have the potential to improve patients’ treatment adherence, quality of life, and health outcomes.21,22 Additionally, it would also be helpful to design broader symptom scales that collect data over a more extended period (e.g., weekly, monthly) as opposed to daily reporting linked to a numeric rating scale that evaluates asthma symptoms or trigger severity.23

    Developing opportunities for greater patient engagement, such as patient panels and advisory boards with patient advocacy groups can re-define patient and stakeholder engagement while providing patient-focused intelligence to inform, support, and facilitate product development.24 The cohesiveness of such relationships will allow more patients the opportunity to share their experiences and journeys and unlock greater health equity through meaningful interactions.25 Educating patients on treatment costs, including medication support programs, companion diagnostics, and routine examinations when at their healthcare provider’s office is advisable.26,27 Providing medication samples or valved-holding chambers/spacers for inhalers to people who can use them may also help to defray out-of-pocket costs. Increasing health literacy among patients with asthma also remains a crucial part of asthma management including achieving optimal symptom control.28 Other opportunities may include the development of a patient research partner course where the patient would learn about the expectations of participating in research with a research peer, creating a network of educated patients, and identifying other opportunities for researchers to engage patients. This will allow researchers and patients to partner to develop different types of studies.29

    Conclusion

    Asthma is a heterogeneous disease with each patient having their own unique treatment needs. As researchers may not often interact with patients living with asthma, a greater awareness and understanding of the needs and preferences of patients while focused on delivering personalized care, tailored to the needs of each patient, is required. Patient advocacy groups can bridge the conceptual gap between biopharmaceutical companies and patients to gather insights that emphasize patient experiences. Based on the input received from patient panels, the key factors affecting asthma treatment adherence are: medication side effects and cost, lack of knowledge among patients about correct inhaler techniques, dosing, dosing schedules, and lack of communication with advocacy groups and researchers. To overcome these limitations, patients suggested that new treatments could involve a single drug which reduces inflammation, relaxes airways and reduces response to triggers. Patients also suggested the drug be quick-acting and need not be taken daily to avoid the use of inhalers in public. The patient perspectives gleaned from this pilot study and previous research done on the topic would be valuable for researchers to design a novel drug to treat asthma and address patient unmet needs; however, further research involving more patients across different countries was deemed warranted. Sanofi has continued similar research across all disease areas.

    Abbreviations

    AAN, Allergy & Asthma Network; EMA, European Medicines Agency; FDA, Food and Drug Administration; PEAC, Patient Engagement Advisory Committee; PFDD, Patient-Focused Drug Development; SAWP, Scientific Advice Working Party.

    Ethics Approval and Informed Consent

    The study included a series of interviews and a workshop, hence there was no approval required from ethics committee. Informed consent and permission to publish results were obtained from all individual participants included in the study. The participants also provided consent to publish anonymized responses/direct quotes. The study was conducted in accordance with European Pharmaceutical Market Research Association (EPHMRA) guidelines.

    Consent for Publication

    All authors consented to publish the manuscript.

    Acknowledgments

    The medical writing and editorial assistance were provided by Rahul Nikam and Deepti Sharda from Sanofi. The authors would like to thank the patient advocates and advocacy organization (Allergy & Asthma Network) who participated in the interviews.

    Author Contributions

    AH, MJG, and DDG conceived, designed, and executed the study and acquired, analyzed, and interpreted the data. All authors contributed to data analysis, drafting or revising the article, have agreed on the journal to which the article will be submitted, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

    Funding

    The study was funded by Sanofi.

    Disclosure

    AH, MJG, MB, and EL are employees of Sanofi. DDG is an employee of Allergy & Asthma Network and reports grants from Sanofi. The authors report no other conflicts of interest in this work.

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