- Dalai Lama says he hopes to live for another 40 years Reuters
- At 90, the Dalai Lama braces for final showdown with Beijing: his reincarnation CNN
- Dalai Lama says he will be reincarnated, Trust will identify successor Dawn
- Statement Affirming the Continuation of the Institution of Dalai Lama The Office of His Holiness The Dalai Lama
- Indian minister backs Dalai Lama’s position on successor, contradicting China Reuters
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Dalai Lama says he hopes to live for another 40 years – Reuters
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World’s largest Legoland opens to tourists in Shanghai
SHANGHAI — Visitors were welcomed by a giant Lego man over 26 meters (85 feet) tall named Dada as they arrived at the new Legoland resort in Shanghai.
The Legoland resort, which opened Saturday, is the first in China. It is the largest Legoland in the world and was built with 85 million Lego bricks.
The resort was developed in conjunction with the Shanghai government by Merlin Entertainments and the LEGO Group.
Among the main attractions in the resort is Miniland, which replicates well-known sights from across the world using Lego bricks. It features sights from across China like Beijing’s Temple of Heaven and Shanghai’s the Bund waterfront. There’s also a boat tour through a historical Chinese water town built with Lego bricks.
Visitors were greeted by performances featuring Legoland characters. Tickets range from $44 (319 yuan) to $84 (599 yuan).
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From Microbial Homeostasis to Systemic Pathogenesis: A Narrative Revie
Introduction
The human body constitutes a complex symbiotic ecosystem. Microbial communities colonizing distinct anatomical sites form dynamic cross-kingdom networks that are essential for maintaining physiological homeostasis. Among these, the gut microbiota (GM) represents the functionally most sophisticated microbial consortium, whose metabolic potential exceeds the human genome by two orders of magnitude. Through continuous bidirectional molecular crosstalk—encompassing metabolite exchange, epigenetic regulation, and genetic material transfer (eg, horizontal gene transfer)—the GM orchestrates fundamental biological processes ranging from nutrient metabolism and immune maturation to neuroendocrine modulation, effectively serving as a multifunctional virtual endocrine organ.1–3
Accumulating evidence underscores the dual role of the gut microbiota as both a guardian of mucosal health and an instigator of systemic diseases.4,5 Its metabolic arsenal—encompassing xenobiotic detoxification, secondary bile acid biotransformation, and synthesis of neuroactive tryptophan derivatives—establishes the gut microbiota as a core dynamic regulator of host homeostasis.6–8 Nevertheless, the delicate equilibrium of host-microbiota symbiosis remains vulnerable to modern ecological perturbations. Epidemiological studies have consistently documented associations between gut dysbiosis and multifactorial pathological conditions, including metabolic disorders characterized by insulin resistance, neurological diseases involving gut-brain axis dysregulation, and autoimmune disorders accompanied by mucosal immune abnormalities.9,10 Paradoxically, while clinical investigations continue to identify disease-associated microbiome signatures across populations, the elucidation of causal mechanisms remains hindered by interindividual genomic variations, lifestyle-dependent microbial adaptations, and multidimensional confounding factors influencing host-microbe crosstalk.11
This review systematically synthesizes cutting-edge research findings to comprehensively elucidate the multidimensional mechanistic roles of gut microbiota across four major disease spectra: neurological disorders (Alzheimer’s disease, Parkinson’s disease, Huntington’s disease), mental health disorders (depression, schizophrenia, bipolar disorder), metabolic diseases (obesity, diabetes mellitus, postmenopausal osteoporosis, gout) and tumorigenesis processes (lung cancer, breast cancer, prostate cancer). Through cross-spectrum comparative analysis, we strive to uncover evolutionarily conserved microbe-host interaction paradigms, thereby constructing a translational medicine framework bridging mechanistic interpretation and therapeutic innovation.
Gut Microbiota and Neurological Disorders
The bidirectional interplay between the gastrointestinal (GI) tract and the central nervous system (CNS) has long been a cornerstone of medical research. This relationship, termed the gut-brain axis, exemplifies the profound interdependence of these systems.12 Growing evidence suggests that the composition of the GM undergoes marked changes in various neurological disorders, with these changes closely associated with the relative abundance of specific microorganisms.13 However, the GM is not only closely associated with gastrointestinal disorders but also linked to a range of neurological disorders.14 Factors such as stress, mode of delivery, probiotic effects, biological clock regulation, dietary habits, and occupational and environmental exposures have been implicated in the bidirectional interactions between the GM and brain function, commonly referred to as the “microbiota-gut-brain axis.” The microbiota plays a crucial role in the bidirectional communication within this axis, influencing both gut and brain function.15,16 Research employing rodent models has demonstrated that gut microbiota significantly influences neuroinflammation, neurodevelopment, emotional regulation, and behavioral outcomes.17–19 The GI tract and CNS are continuously exposed to a diverse array of signaling stimuli, both environmental and intrinsic to the body. These stimuli play a crucial role in maintaining the intricate balance necessary for optimal functioning of both systems.20,21 While C-fiber mediated viscerosensory transmission via vagal and sympathetic afferents was traditionally considered the principal pathway for gut-brain communication,22,23 contemporary research has identified gut microbiota-derived metabolites as essential signaling mediators in this axis.24 Notably, microbial dysbiosis characterized by reduced butyrate-producing taxa has been mechanistically linked to inflammatory bowel diseases (IBD), primarily through disruption of gut-vascular barrier integrity and subsequent bacterial translocation.25,26
Intestinal Microbiota Imbalance and Alzheimer’s Disease
Alzheimer’s disease (AD) is a neurodegenerative disorder and the predominant cause of dementia among older adults.27 Activation of microglia and imbalance in neuronal calcium homeostasis, triggered by amyloid β-protein (Aβ) deposition, are considered key mechanisms in AD development.28 The GM performs vital physiological functions in the human body by activating pattern recognition receptors (PRRs) on innate and adaptive immune cells through constant interaction with the host immune system.29 Notably, intracerebral LPS administration in mouse models has been associated with elevated amyloid-beta (Aβ) levels in the hippocampus, correlating with cognitive deficits.30 These findings provide significant evidence for LPS’s role in promoting amyloid fibril formation, indicating that intestinal inflammation may play a pivotal role in the pathogenesis of AD (Figure 1).31 Dubosiella enrichment has been shown to mitigate AD progression through palmitoleic acid biosynthesis, with this anti-inflammatory lipid mediator demonstrating neuroprotective efficacy against neural metabolic dysregulation.32 Recent studies have revealed a close link between the worsening of systemic inflammation, neuroinflammatory processes, and the increase in proinflammatory GM. Considering that an imbalance in GM can trigger a decrease in microglial activity, the microbiota’s pathological activation may contribute to the progression of AD. Notably, specific gut microbiota generate nitric oxide (NO) and activate microglia, contributing to the progression of AD pathology (Figure 1).33–35 Acute and chronic viral infections activate microglia, leading to cytokine release and neuroinflammation. This neuroinflammation can influence the pathological processes of amyloid-beta (Aβ) and tau proteins.36–38
Figure 1 LPS exposure significantly elevates hippocampal β-amyloid (Aβ) levels, experimentally confirming that neuroinflammation directly drives core AD pathology. Gut microbiota dysbiosis manifests as increased abundance of Helicobacter spp. and decreased abundance of Spirochaetes phylum. This imbalance aggravates AD pathogenesis through triple pathways: Specific microbiota generate nitric oxide activating microglia → promoting neuroinflammation and aberrant Aβ/Tau deposition; Upregulated intestinal NLRP3 inflammasome expression → triggering systemic inflammatory cascades; Dysregulated aryl hydrocarbon receptor (AhR) signaling activation→ compromising blood-brain barrier integrity. Prebiotic intervention increases Lactobacillus spp. abundance, enhancing biosynthesis of palmitoleic acid and butyric acid, which delay AD progression through anti-inflammatory properties and neurometabolic regulation.
Calhm2-Mediated Gut-Brain Axis Dysregulation in AD Pathogenesis
In a 5xFAD mouse model harboring five familial AD mutations, elevated expression of calcium homeostasis regulator protein 2 (Calhm2) was significantly reduced by either conventional or conditional microglial cell-specific knockdown, leading to a marked reduction in amyloid plaque deposition, neuroinflammation, and cognitive deficits, thereby identifying Calhm2 as a potential therapeutic target for AD (Figure 1).39 The study identified that systemic changes resulting from gut microbiota dysbiosis, caused by reduced endogenous melatonin (EMR), may contribute to the development of AD and obesity.40,41 Research has shown that periodontitis contributes to the development of AD through mechanisms involving the ingestion of salivary microbiota and communication between the GM and the brain in transgenic mouse models.42 Microbe-derived metabolites from the GM have been found in the cerebrospinal fluid of AD patients. These metabolites correlate with AD biomarkers, such as phosphorylated tau and the tau/Aβ42 ratio, suggesting that the gut microbiota contributes to the pathogenesis of AD.43–46
Substantial differences in the GM composition were detected using 16S rRNA sequencing of fecal samples between APP transgenic mice and wild-type models.47 The transgenic AD mouse model exhibited distinct GM profiles. Studies on germ-free mice demonstrated that amyloid plaques and neuroinflammation were absent in the absence of microbes. A strong correlation between GM dysregulation and AD-associated neuroinflammation was identified, with elevated expression of aberrant intestinal NLRP3 being positively correlated with the activation of peripheral inflammatory vesicles.48 As AD progressed, peripheral inflammatory vesicles progressively exacerbated neuroinflammation. Significant changes in the GM composition were observed in young and old 5xFAD mice, characterized by an increased abundance of Helicobacter and a decreased abundance of thick-walled bacterial phyla. It was revealed that the prebiotic mannan oligosaccharide (MOS) increased the abundance of Lactobacillus and decreased the abundance of Spirochaetes (Figure 1). Furthermore, MOS increased the production of butyric acid and the levels of associated microorganisms (Table 1). Ecological imbalance of the GM exacerbates AD pathology by activating the aromatic hydrocarbon receptor (AhR) signaling, damaging blood-brain barrier (BBB) integrity.48–50
Table 1 Gut Microbiota and Neurological Disorders
The Gut Microbiome and Parkinson’s Disease
Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by the abnormal deposition of α-synuclein (α-syn) within nigrostriatal dopaminergic neurons, resulting in subsequent motor deficits and gastrointestinal dysfunction. The abnormal deposition of α-synuclein results in the accumulation of eosinophilic cytoplasmic inclusions, termed Lewy bodies.30,51,56 The hallmark clinical manifestation of PD is motor dysfunction, characterized by muscle rigidity, resting tremor, bradykinesia, and postural instability.57–59 The neurodegenerative pathogenesis of PD is primarily driven by the progressive accumulation of misfolded α-synuclein within the CNS. Progressive dopaminergic neuronal degeneration underlies the strong interplay between motor and non-motor symptoms in PD.60 Non-motor symptoms encompass neuropsychiatric manifestations (eg, depression, dementia) and gastrointestinal disturbances, including constipation, sialorrhea, bowel dysfunction, nausea, and dysphagia.
Gut Microbiota-Mediated α-Synuclein Pathology in PD Pathogenesis
PD is increasingly recognized as a multisystemic disorder arising from the interplay of genetic susceptibility, environmental factors, and age-related decline. Research indicates that gastrointestinal dysfunction in PD patients is strongly associated with gut microbiota dysbiosis and pathological α-synuclein aggregation within the enteric nervous system.61–63 Emerging evidence suggests that PD pathogenesis originates in the gastrointestinal tract, mediated by bidirectional host-microbiome interactions. While the gut-brain axis hypothesis provides a compelling framework, it’s important to note that the temporal relationship between gut dysbiosis and PD onset remains debated. Some longitudinal studies have failed to demonstrate consistent microbial changes preceding clinical diagnosis. Notable microbial shifts include elevated abundances of Akkermansia spp., Bifidobacterium spp., and Lactobacillus spp., alongside decreased colonization by Bacteroides spp. And Enterococcus faecalis. The reported microbial alterations show considerable variability across populations, with recent systematic reviews highlighting geographical and methodological factors as major contributors to observed discrepancies.Furthermore, fecal microbiota transplantation suppressed the TLR4/MyD88/NF-κB signaling pathway in both the substantia nigra and colon of rotenone-induced PD mouse models51–54 (Table 1). Translational caution is warranted as rodent PD models, while valuable for mechanistic studies, often utilize acute toxin exposures that differ fundamentally from human disease progression patterns.Up to 80% of PD patients exhibit gastrointestinal dysfunction, notably constipation, often preceding motor symptom onset by several years.64–66 Idiopathic constipation represents a significant comorbidity in PD and is associated with neurodegenerative alterations in the enteric nervous system.52–54 The presence of pathological α-synuclein aggregates in the enteric nervous system is hypothesized to represent an early biomarker of PD, preceding motor symptom manifestation.57,58,60 The specificity of enteric α-syn as a PD biomarker requires further validation, given its reported presence in other neurodegenerative conditions and aging populations.This pathological process is associated with chronic constipation and structural/functional alterations in the gastrointestinal tract wall. The GM has been implicated in disrupting enteric neuronal homeostasis, potentially driving pathological α-synuclein aggregation.67,68 These alterations are detectable during prodromal PD stages and proposed as potential biomarkers antecedent to motor symptom manifestation.57,58,60 Numerous studies have explored the gut microbiota-PD relationship, focusing on microbial composition and disease progression. For instance, a 2020 cohort study identified a marked reduction in Prevotella spp. abundance and diminished representation of non-Enterobacteriaceae taxa in PD patient fecal samples.52–54 Current human studies face methodological challenges, particularly regarding standardization of microbiota analysis protocols and adequate control for confounding variables like medication use and dietary habits.
Composition of the Gut Microbiome and Huntington’s Disease
Huntington’s disease (HD) is an autosomal dominant neurodegenerative disorder characterized by motor, cognitive, and psychiatric symptoms, with disease progression modulated by diverse environmental factors. The HTT gene, encoding the huntingtin protein, is mapped to chromosome 4. Pathogenic expansion of the CAG trinucleotide repeat in exon 1 of the HTT gene drives HD pathogenesis, historically termed Huntington’s chorea. The huntingtin protein is ubiquitously expressed, including in the central nervous system and peripheral tissues such as skeletal muscle and the intestinal tract. Notably, mutant huntingtin (mHTT) expression in the gastrointestinal tract induces dysmotility, chronic diarrhea, and malabsorptive pathophysiology.69,70 In-depth investigations have revealed that mutant HTT may disrupt GM homeostasis, inducing dysbiosis—a microbial imbalance—which could exacerbate HD progression. Dysbiosis has been clinically documented in HD patients and experimentally linked to mutant HTT aggregation, behavioral abnormalities, and reduced lifespan in animal models. In summary, these findings collectively suggest that mutant HTT may act through GM disruption as a central pathogenic mechanism in HD development.55
In HD, Aeromonas enterocolitica and Pilocarpus have been associated with interleukin-4 (IL-4) and interleukin-6 (IL-6) concentrations, respectively.69 In HD patients, α-diversity and β-diversity were increased compared with healthy controls. It should be noted that the reported increases in microbial diversity metrics show interstudy variability, with some cohort studies reporting contradictory trends depending on disease stage and medication regimens. In HD patients, cells that interact directly or indirectly with the GM are activated. Although further studies are needed, the hyperactivation of natural immune cells in the gut of HD patients may play a key role in gut dysbiosis. This suggests that intestinal dysbiosis in HD patients may be closely related to the overactivation of immune cells in the gut.70 While compelling, the causal relationship between immune cell activation and dysbiosis requires further elucidation, as current evidence cannot exclude the possibility of reverse causality or shared environmental triggers.Through integrated metagenomic and metabolomic analyses, Qian et al demonstrated that, at the genus level, Bacillus-like bacteria, Fusobacterium, Paracoccidioides, Zeligia, Bifidobacterium, and Christensenella exhibited increased abundance, whereas Treponema (Tear Spirochetes), Roseburia, Clostridium, Ruminococcus,Brucella,Butyricicoccus,Agaricus,Phocaeicola,Coprococcus, and Fusicatenibacter showed notably reduced abundance in individuals with HD. Subsequent metabolomic profiling identified dimethisterone, propylparaben, vanillin, tulipolide, p-hydroxymandelic acid, and heptasaponin as potential diagnostic or prognostic biomarkers for HD55 (Table 1). The specificity of these metabolites as HD biomarkers warrants rigorous evaluation, given their known involvement in other neurodegenerative and inflammatory conditions.
Gut Microbiota and Mental Health Disorders
The brain-gut axis is regulated through neuroendocrine systems (eg, HPA axis), immune signaling, and bidirectional neural pathways.71 The HPA axis regulates immune responses through precise modulation of pro- and anti-inflammatory cytokine production. Conversely, neuromodulatory processes are predominantly mediated by the autonomic nervous system (ANS) — comprising parasympathetic (eg, vagal), sensory, and sympathetic fibers — and the enteric nervous system (ENS).72 The ENS governs the functions of muscles, mucous membranes, and blood vessels within the GI tract, thereby regulating its overall activity.73 Notably, more than 30 different neurotransmitters are involved in its function.74,75 The ENS is histologically distinct from the peripheral nervous system (PNS), as its neuronal components lack ensheathment by connective tissue collagen or Schwann cells. Instead, these components are ensheathed by specialized glial cells phenotypically analogous to astrocytes in the CNS.76
The enteric nervous system primarily consists of the Meissner’s plexus, located in the submucosa of the intestinal mucosa, and the Auerbach’s plexus, located between the circular and longitudinal muscularis propria.77 Due to its location, the ENS maintains close connections with the systemic immune defenses of both the gut-associated lymphoid tissues (GALT) and the mucosal-associated lymphoid tissues (MALT) through numerous neurotransmitters and cytokines. Neurotransmitters released by the enteric nervous system bind to receptors on Peyer’s patches and lymphocytes. GALT, primarily composed of immune system lymphocytes, accounts for 70% of the total and plays a key role in the immune response to external antigens.78–80 Meanwhile, microorganisms in the gut, including specific species of bacteria and fungi, synthesize and secrete various neurotransmitters that transmit signals to the GALT and ENS.81 Hormonal regulation of brain-gut communication is primarily mediated by the HPA axis, also known as the stress axis, which regulates the stress response. The hypothalamus releases corticoliberin and antidiuretic hormone, which initiate a hormonal cascade along the HPA axis, prompting the anterior pituitary to release corticotropin (ACTH). ACTH travels through the bloodstream to the adrenal cortex, where it stimulates the secretion of glucocorticoids, including cortisol.82–84 Existing research suggests a close association between intestinal flora and depression, schizophrenia, and bipolar disorder.
Gut Microbiota and Depression
Depression is a common psychological disorder characterized by persistent feelings of sadness and apathy, typically lasting at least two weeks. The development of this disorder is influenced by a combination of genetic and environmental factors, including major life changes, family problems, and chronic health distress.12,85 Depression is a leading cause of long-term disability and suicide worldwide. Major depressive disorder remains a leading cause of disability among psychiatric disorders worldwide. Increasing numbers of preclinical and clinical studies are focusing on the GM, including the composition of microorganisms and changes in their functions, such as metabolite production. These changes are referred to as dysbiosis and are strongly associated with the onset and development of depression, regulated through the gut-brain axis.80
Table 2 Gut Microbiota and Mental Health Disorders
The balance within Bacteroidetes was disrupted, evidenced by an increase in Bacteroidetes spp. abundance and a decrease in Brucella spp. and Streptococcus spp. colonies (Figure 2). A significant association exists between short-chain fatty acids and depression, with low levels observed in patients with major depressive disorder (MDD). However, supplementation with these fatty acids, particularly butyrate, can exert an antidepressant effect by enhancing intestinal permeability and improving the responsiveness of the HPA axis80 (Table 2). It has also been demonstrated90 that dietary constituents, including probiotics (eg, Lactobacillus and Bifidobacterium), prebiotics (eg, dietary fiber and α-lactalbumin), synthetic prebiotics, postbiotics (eg, short-chain fatty acids), dairy products, and spices (eg, fruits, vegetables, and herbs), protect against mental disorders by enhancing beneficial gut microbiota and inhibiting harmful microbiota. In addition, Saccharomyces boulardii improves gut health, reduces depressive-like behaviors, decreases HPA axis hyperactivity, and alters the gut microbiota in hemiplegic spastic cerebral palsy (CP) rats.91 Skonieczna-Zydecka et al92 conducted an short-chain fatty acids(SCFAs) profiling study on 116 females aged 52.0 (±4.7) years and found that 40.52% of the participants had depression. The results showed that depressed patients had lower levels of propionic acid and higher levels of isocaproic acid compared to healthy controls. In the early stages of MDD, changes in the microbiota may occur, potentially triggering the onset of MDD. Over time, pathological changes in MDD can affect the intestinal environment, further exacerbating the ecological imbalance (Figure 2).
Figure 2 Altered gut microbiota composition in depression and proposed bidirectional gut-brain axis mechanisms. In patients with depression, the relative abundance of Bacteroides increases, whereas the abundances of Brucella and Streptococcus decrease. Arrows depict key interactions: (Top-down, Brain→Gut) Cerebral inflammation under depression promotes gut microbiota dysbiosis via neuro-endocrine pathways; (Bottom-up, Gut→Brain) Gut dysbiosis exacerbates central nervous system inflammation; (Lateral, Synergy) Sustained gut-brain axis interactions drive the observed Bacteroides proliferation and reduction of Brucella and Streptococcus.
Multi-Target Microbiota Interventions for Depression via Gut-Brain Axis Modulation
Beyond probiotics, prebiotics, and dietary fibers that positively modulate depression pathogenesis, emerging research demonstrates that Clostridium butyricum reverses gut dysbiosis in inflammatory depression model mice, concurrently reducing proinflammatory cytokine levels and producing antidepressant-like behavioral improvements.93 This finding aligns with clinical observations—adolescent depression patients exhibit significantly reduced abundance of short-chain fatty acid-producing bacteria (eg, Faecalibacterium, Blautia, Collinsella) in fecal samples, with restoration trends following sertraline intervention.18 Mechanistic studies further reveal that proline supplementation exacerbates depression phenotypes by promoting microbial translocation, while human microbiota transplantation confirms this process involves prefrontal cortex GABA metabolic dysregulation and aberrant extracellular matrix gene expression.94 Notably, Faecalibacterium prausnitzii not only serves as a potential diagnostic biomarker,95 but its functional impairment (eg, via fecal microbiota transplantation from methylmercury-exposed mice) can induce depression-anxiety comorbidity,96 highlighting the precision intervention value of microbiota modulation. Therapeutic strategy research transcends conventional paradigms: selective regulation of intestinal epithelial serotonin reuptake transporters specifically ameliorates mood-related behaviors;97 the natural compound Icariside II (ICS II) enhances gut barrier integrity by enriching Akkermansia and Ligilactobacillus;98 metformin reprograms serotonin metabolism via the microbiota-gut-brain axis;99 and indole-3-propionic acid (IPA) inhibits ferroptosis through the NRF2/System xc-/GPX4 pathway, disrupting the depression-myocardial ischemia-reperfusion injury comorbidity100—jointly establishing a multi-target intervention network.
Gut Microbiome and Schizophrenia
Emerging research explores the gut-brain axis bidirectional mechanisms.101,102 Changes in microbial composition have been strongly linked to a broad spectrum of diseases, ranging from localized gastrointestinal disorders to respiratory, cardiovascular, and neurological disorders.103–105 The microbiota has shown sensitivity to a wide range of intrinsic and extrinsic factors, including genetics,106 modes of transmission,107 dietary habits,108 and infections and their treatment modalities109 (Figure 3). Schizophrenia(SCZ) is a chronic and highly disruptive mental disorder characterized by abnormalities in mental functioning as well as behavioral deficits that show a high degree of individual variability.86 A recent study, building on the traditional technique of gene-set enrichment analysis, employed data from a published study involving 33,426 SCZ patients and 32,541 healthy controls with genome-wide association study (GWAS) data. The study identified associations between specific microbial genera and SCZ, such as Desulfovibrio spp. and Mycobacterium spp., suggesting that these microbes may contribute to the pathogenesis of SCZ.110 While this GWAS approach reveals important associations, the field is increasingly adopting longitudinal metagenomic sequencing to determine whether microbial changes precede symptom onset – a critical criterion for establishing causality. It should be emphasized that such observational data cannot exclude reverse causation, particularly given evidence that antipsychotic medications directly alter gut microbiota composition. Future studies combining microbial strain-level analysis with host immune profiling may better disentangle microbial drivers from disease consequences.
Figure 3 Multifactorial disruption of gut microbiota homeostasis and its vicious cycle with CNS disorders via the gut-brain axis. Key factors (infections, therapeutics, diet, genetics, transmission patterns) collectively induce gut microbiota dysbiosis (arrows encircling intestine). This imbalance: Triggers intestinal barrier disruption: Pathobiont translocation (arrow downward) promotes systemic inflammation; Activates gut-brain signaling (arrow rightward): Dysregulated microbial metabolites modulate CNS function via neuroendocrine/immune pathways; Drives CNS dysfunction (arrow downward): Resultant neuroinflammation and neuronal abnormalities further perturb gut microbiota, reinforcing a pathological cycle that exacerbates depression, schizophrenia, and bipolar disorder.
Studies examining the impact of the GM on SCZ spectrum disorders are generally limited by small sample sizes.111 These studies have generally reported changes in microbial diversity in patients with SCZ compared to healthy controls, though findings remain inconsistent.112 In a study involving 64 patients with SCZ and 53 healthy controls, 12 significantly different microbiota biomarkers were identified. In a study involving 64 patients with SCZ and 53 healthy controls, 12 significantly different microbiota biomarkers were identified.113 However, this cross-sectional study had a small sample size and did not account for the effects of antipsychotic medication on the GM.
Dynamic Microbial Dysbiosis in SCZ: Clinical Correlates and Therapeutic Implications
A study found elevated levels of Lactobacillus in individuals at high risk for SCZ.106 Conversely, another study found decreased levels of Lactobacillus in patients with first-episode psychosis.114 The study found that in the population of first-episode psychotic patients, microbial composition, particularly Lactobacillaceae, was strongly associated with disease severity. Follow-up after one year showed that patients with significant differences in microbial composition had lower remission rates compared to healthy controls, who exhibited higher remission rates.87 Additionally, the study observed a decrease in Bifidobacterium and E. coli levels in patients. After 24 weeks of risperidone treatment, these levels increased, while Lactobacillus levels declined. A psychiatric pathology study involving patients with a disease duration of more than 10 years (age range: 12–56 years) found that decreased levels of Ruminococcaceae (Clostridiaceae) were associated with a reduction in negative symptoms. The study also observed that an increase in depressive symptoms was associated with a higher presence of Mycobacterium anisopliae.88 Although a healthy microbiome typically exhibits a high degree of diversity,115,116 in patients with SCZ, the oropharyngeal microbiome exhibited lower biodiversity compared to controls.117 Additionally, studies on oropharyngeal phages (viruses) have shown elevated levels of phages and Lactobacillus acidophilus in patients with SCZ, which are positively correlated with an increased risk of co-morbid immune disorders compared to lower levels in non-psychiatric controls.118
Gut Barrier Breakdown and Microbial Translocation in Schizophrenia Pathogenesis
RNA sequencing analysis revealed that gut-derived microbial products were more likely to enter the systemic circulation in patients with SCZ compared to non-psychotic controls. Moreover, the study observed an increased microbial diversity in the patients’ blood. Furthermore, levels of genes associated with Chlamydia were significantly elevated in individuals with SCZ compared to healthy controls.119 These findings may enhance our understanding of the pathophysiological mechanisms underlying SCZ. Additionally, other clinical studies have explored the permeability of blood biomarkers related to gastrointestinal tract infiltration.111,120 The serological surrogate marker soluble cluster of differentiation (sCD)14 was found to be significantly more prevalent in SCZ patients, with a 3.1-fold increase in the risk of bacterial translocation compared to healthy controls. Furthermore, sCD14 and lipopolysaccharide-binding proteins were significantly correlated with C-reactive protein levels in SCZ patients, suggesting shared inflammatory pathways. This implies that a compromised intestinal barrier may facilitate the entry of microbes and other markers into systemic circulation, thereby triggering a low-grade inflammatory state.120 Zhuocan Li et al observed significant alterations in the microbial composition of patients with SCZ. Specifically, several microbial taxa exhibited a consistent upregulation, including Aspergillus, Gram-negative bacilli, Lactobacillaceae, Enterobacteriaceae, and Aspergillus spp. Concurrently, five taxa demonstrated consistent downregulation in patients, including Fusobacterium, E. faecalis, Bacillus roseus, and two species of acidophilus. This microbial distribution pattern may reflect specific features of the microbial environment in SCZ patients. Moreover, GM alterations in SCZ patients are marked by a decrease in anti-inflammatory butyrate-producing genera and an increase in specific opportunistic bacterial genera and probiotics86 (Table 2).
Bipolar Disorder
Bipolar disorder (BD), also referred to as manic-depressive disorder, is a serious mental illness characterized by mitochondrial dysfunction, oxidative stress, and abnormal calcium signaling. It is primarily marked by the simultaneous occurrence of two extreme mood states: mania and depression. BD affects 45 million people worldwide. According to the National Institute of Mental Health, up to 50% of individuals with BD do not receive adequate mental health treatment, leading to over 2 million untreated cases in the United States. The progression of the disease, including relapse and worsening of bipolar symptoms, has become an increasing concern. These trends highlight the need for further research into potential preventive strategies for BD treatment.
Changes in the composition of the GM have been found to contribute to the development of neurological disorders, including bipolar affective disorder.121–123 Studies have shown that stress, including social stress, can affect the composition of the GM. Moreover, bidirectional communication between the gut and the CNS plays a crucial role in the response to stress.124–126 The body’s stress response manifests in immune modulation, including cytokine release, and is closely associated with stress exposure and impaired gut barrier function. Experimental and clinical studies demonstrate that elevated stress levels correlate with increased intestinal permeability.127,128 Furthermore, emerging evidence suggests that the blood-brain barrier (BBB)’s integrity is modulated by GM composition, where dysbiosis may induce BBB compromised integrity.129 Research evidence indicates that specific gut bacterial taxa may contribute to weight dysregulation pathology in BD through modulation of lipid metabolism, energy homeostasis, and amino acid pathways.130 Recent experimental studies further demonstrate that Roseburia intestinalis significantly increases production of the microbial metabolite homovanillic acid (HVA) by promoting Bifidobacterium longum colonization and proliferation. This metabolite suppresses synaptic autophagic hyperactivation by antagonizing aberrant degradation of LC3-II and SQSTM1/p62 proteins in hippocampal neurons, thereby preserving presynaptic membrane integrity and functional stability.131 However, current evidence remains insufficient to establish causal-temporal relationships between gut microbiome alterations and BD, necessitating further elucidation of bidirectional mechanisms and potential confounders through longitudinal cohort studies or experimental models.
Multi-Omics Reveals Tryptophan-Serotonin Axis Dysregulation in BD
Painold et al conducted a study that investigated the relationship between gut microbiota and BD89 (Table 2). The gut-brain axis association underscores the potential role of specific GM as psychobiotic agents capable of influencing neurological function. Beyond cytokines, Lai et al (2023) explored the relationship between key neurotransmitter precursors—notably tryptophan—and BD. Utilizing shotgun metagenomic sequencing, the authors compared GM composition and genes linked to tryptophan (Trp) biosynthesis/metabolism across 25 BD patients and 28 healthy controls. Their findings demonstrated that BD patients displayed dysregulated tryptophan hydroxylase and aromatic aminotransferase activity, resulting in diminished Trp biosynthesis and, consequently, reduced serotonin production.132 Shotgun metagenomics and longitudinal studies are propelling mechanistic research on the GM-bipolar disorder relationship into deeper dimensions. Shotgun methodology overcomes limitations of conventional 16S sequencing by precisely identifying aberrant functional genes. Longitudinal investigations dynamically track GM fluctuations to delineate causal relationships: multi-timepoint analyses capture temporal patterns linking microbial shifts with mood episodes and drug responses, while integrated metabolomics constructs “microbe-metabolite-symptom” dynamic networks, thereby revealing targeted intervention windows. These methodological innovations are driving a paradigm shift from correlational observations toward mechanistic exploration and clinical translation.
Gut Microbiota and Metabolic Diseases
Metabolic disorders represent a mounting global health challenge, driven by their soaring prevalence. The GM orchestrates pivotal interactions with the host via the synthesis of diverse metabolites, originating from both exogenous dietary substrates and endogenous host-derived compounds. Dysbiosis of the GM—alterations in its composition and functional capacity—is strongly implicated in the pathogenesis of metabolic disorders. Specific metabolites synthesized by gut microbiota—including bile acids, short-chain fatty acids, branched-chain amino acids, trimethylamine N-oxide, tryptophan, and indole derivatives—play critical roles in the pathogenesis of metabolic disorders. Over the past two decades, the global prevalence of metabolic disorders has surged, primarily attributed to excessive caloric intake and sedentary lifestyles. Metabolic disorders comprise a spectrum of interconnected pathologies, such as obesity, nonalcoholic steatohepatitis (NASH), dyslipidemia, impaired glucose tolerance, insulin resistance, hypertension. The co-occurrence of these conditions synergistically exacerbates cardiovascular disease -related morbidity and mortality.
Obesity
Obesity, defined as excessive adiposity relative to height, is recognized by major international health organizations as a defining epidemic of the 21st century.133 Obese individuals demonstrate reduced GM diversity relative to lean individuals, giving rise to a distinct profile of microbial metabolites that modulate systemic energy homeostasis and glucagon-like peptide-1 (GLP-1) secretion, thereby influencing metabolic dysfunction.134 High-fat diets have been demonstrated to perturb GM composition and promote the development of GLP-1 resistance through disruption of enteric neuronal nitric oxide synthase (nNOS) activity, thereby compromising intestinal regulation of energy homeostasis and disrupting gut-brain axis signaling pathways.135 The GM are central regulators of energy homeostasis, synthesizing SCFAs and liberating energy via dietary fiber fermentation. Furthermore, the GM augments intestinal nutrient absorption by stimulating intestinal villi angioneogenesis and suppressing adipokine-mediated lipoprotein lipase (LPL) activity during fasting, thereby facilitating triglyceride deposition in adipose tissue.133
Lactobacillus spp., key members of the small intestinal microbiota, have been demonstrated to modulate intestinal epithelial cells (IECs), thereby attenuating early-life diet-induced obesity. A Lactobacillus-derived metabolite, phenyl lactic acid (PLA), confers protection against metabolic dysfunction induced by early-life antibiotic exposure and high-fat diet (HFD) consumption via upregulation of peroxisome proliferator-activated receptor gamma (PPAR-γ) expression in small intestinal epithelial cells (SI IECs) (Table 3). 136
Table 3 Gut Microbiota and Metabolic Diseases
Diabetes
Emerging preclinical evidence has established a causal link between GM dysbiosis and the emergence of insulin resistance, a hallmark mechanism driving the pathogenesis of type 2 diabetes mellitus (T2DM).144,145 This relationship encompasses multifactorial mechanisms, such as endotoxemia, compromised intestinal barrier integrity, dysregulated bile acid metabolism, and perturbed brown adipose tissue (BAT) distribution.146 Elevated abundances of mucin-degrading Akkermansia spp. correlate with enhanced glucose homeostasis in individuals with early-stage T2DM.147 These findings underscore the therapeutic potential of targeted modulation of GM composition to ameliorate metabolic dysfunction.
A synbiotic formulation containing Bifidobacterium bifidum and Lactobacillus acidophilus markedly lowered fasting blood glucose levels in T2DM patients, as evidenced by a randomized controlled trial.148 Restoration of gut microbiota composition to a profile akin to healthy controls improved glycemic control, highlighting the therapeutic relevance of microbial modulation.137 Patients with T2DM demonstrate markedly diminished GM alpha diversity and microbial abundance relative to healthy controls.138,149,150 Individuals with prediabetes and T2DM exhibit distinct metabolic signatures and GM compositional profiles, reflecting progressive dysbiosis across clinical stages.138,150 Compared to healthy controls, patients with T2D exhibit reduced levels of butyrate-producing bacteria, including Bifidobacterium, Akkermansia, and E. faecalis139,149,151 as well as decreased levels of Thick-walled bacteria, Clostridiaceae, and Streptococcus pepticus. Additionally, a significant reduction in Brucella spp. was negatively correlated with HbA1c and glucose levels in patients with T2D140 (Table 3).
During the onset of T2D, an increase in the phylum Anabaena and a decrease in the phylum Thickettsia have been identified.138,150 In addition, there is a trend toward increased levels of Actinobacteria and Anabaena phyla,139 as well as Lactobacillus138,150 in patients with T2D. A diminished abundance of Lactobacillaceae has been observed in T2DM patients, and this reduction is associated with impaired glucagon-like peptide-1 (GLP-1) sensitivity.152 Notably, elevated ratios of Mycobacterium avium to Mycobacterium smegmatis and Prevotella to Bacteroides fragilis were observed in T2DM patients, exhibiting a positive association with fasting blood glucose concentrations.153
In patients with type 1 diabetes (T1D), reduced proportions of Bifidobacterium and thick-walled bacilli phyla, as well as a downward trend in the Bacteroides phylum, have been observed. Conversely, elevated levels of Dora spp. (family Trichoderma) in patients with T2D are associated with chronic inflammation and may serve as indicators for high-risk T2D populations.149 Collectively, these findings indicate that the microbiome plays a critical role in the pathogenesis of T2D. Bilen et al reported elevated abundances of S. aureus and S. epidermidis in the conjunctiva of T2D patients relative to controls, whereas animal models demonstrated that microbial dysbiosis correlated with heightened treatment resistance.154 These findings underscore the significant role of the microbiome in T2D progression.155
Postmenopausal Osteoporosis
Osteoporosis is a condition characterized by low bone mass and/or poor bone quality, which may progress to skeletal fractures that occur spontaneously or with minimal impact.156 It is characterized by a reduction in trabecular bone volume and degradation of the microstructure of the medullary cavity.157 Postmenopausal osteoporosis (PMO) is a condition resulting from estrogen deficiency, leading to a decrease in bone mass and deterioration of bone microstructure, which subsequently increases the risk of fragility fractures.158 Menopause is a significant predisposing factor for osteoporosis in women, with the prevalence of the condition in women aged 50 and older projected to reach 13.6 million by 2030.159 As the correlation between the gut and bone becomes increasingly evident, numerous therapeutic studies for postmenopausal osteoporosis are emerging, focusing on GM modulation as a potential therapeutic approach.The administration of Lactobacillus rhamnosus GG has been shown to alleviate osteoporosis in de-ovulated rats through modulation of the Th17/Treg balance and gut microbiota composition.141 Furthermore, LGG treatment was found to ameliorate estrogen deficiency-induced inflammation and mucosal damage, while enhancing the expression of GLP-2 receptor (GLP-2R) and tight junction proteins (Table 3). 16S rRNA sequencing revealed a significant increase in the ratio of Thick-walled phylum to Anthrobacterium phylum during estrogen deficiency. Additionally, significant changes in the composition of the dominant intestinal microbiota were observed.141
Significant associations were identified between GM communities, particularly within the Burkholderia order, and an increase in osteoclasts, along with a reduced risk of PMO.160 Studies have found that fecal samples collected from osteoporosis patients and healthy individuals show differences in the composition of the GM community, as analyzed by 16S rRNA gene sequencing. The results indicated that, at the phylum level, the Aspergillus and Fusarium groups were significantly more abundant in the osteoporosis (ON) group than in the normal control (NC) group, while the Synergistic group was significantly less abundant. At the genus level, Roseburia, Clostridia_UCG.014, Agathobacter, Dialister, and Lactobacillus showed significant differences between the OP and NC groups, as well as between the ON and NC groups. These findings suggest that gut flora dysregulation is associated with impaired host urate degradation and systemic inflammation, and could serve as a non-invasive diagnostic marker for gout.161
Gout
The incidence of hyperuricemia (HUA) and gout continues to rise, representing a growing public health concern.162 Studies have shown that alterations in the composition and metabolism of the GM lead to abnormal uric acid degradation, increased uric acid production, release of proinflammatory mediators, and impairment of the intestinal barrier, all of which contribute to the development of gout.163
A metagenomic analysis of 307 stool samples from 102 gout patients and 86 healthy controls revealed significant differences between the GM of gout patients and healthy controls. The relative abundance of Prevotella, Fusobacterium, and Lactobacillus was increased in gout patients, whereas Enterobacteriaceae and butyrate-producing bacteria were decreased142 (Table 3). Additional studies have demonstrated bidirectional causality between the GM and host urate metabolism, with host-microbiota crosstalk playing a crucial role in patients with hyperuricemia. Alterations in the GM not only influence host urate metabolism but also serve as a prognostic indicator of urate metabolism disorders.143, Hyperuricemia, a precursor to gout, is commonly observed in other metabolic disorders associated with microbiota dysbiosis. A study analyzed the gut microbiota of hyperuricemic patients using 16S ribosomal RNA sequencing on fecal samples to assess microbial dysbiosis, including richness, diversity, composition, and the relative abundance of microbial taxa. The cohort consisted of 1,392 subjects (mean age 61.3 years, 57.4% female, 17.2% with hyperuricemia) from rural areas. Compared to patients with normouricemia, hyperuricemic patients exhibited reduced microbial abundance and diversity, altered microbiota composition, and a lower relative abundance of the genus Synechococcus.164
Gut Microbiota and Cancer
Cancer metastasis is the leading cause of death among cancer patients. Recent studies have identified the intratumoral microbiota as an integral component of tumors, with evidence suggesting its functional regulation of various aspects of metastasis.165 Tumor tissues from various origins harbor intratumoral microbial components, which are closely associated with cancer onset, progression, and therapeutic efficacy. The oral microbiota may contribute to cancer development and progression through mechanisms such as DNA mutations, activation of oncogenic pathways, promotion of chronic inflammation, modulation of the complement system, and facilitation of metastasis.166 There is increasing evidence that the GM modulates the efficacy and toxicity of cancer therapies, particularly immunotherapy and its immune-related adverse effects. Adverse reactions to immunotherapy in patients receiving antibiotics further support the significant role of the microbiota.167 Studies have identified 11 causal relationships between GM genetics and cancer, including one involving the genus Bifidobacterium. Additionally, 17 strong associations between genetic factors in the GM and cancer have been observed.168 Imbalances in GM homeostasis have now been linked to several cancers.
Lung Cancer
It has been demonstrated that the interaction between the human microbiota and lung cancer represents a complex, multifactorial relationship, with several pathways linking the microbiota, thereby supporting the existence of the gut-lung axis (GLA).169 There are intricate communication pathways between the gut and lung microbiota, with this connection extending beyond the lymphatic and blood circulatory systems.170 The lung microbiota can influence the composition and function of the GM via the blood circulation.171 Aberrant activity of the GM is closely associated with the onset and progression of various respiratory diseases, including COPD, cystic fibrosis, respiratory infections, and asthma.172 This suggests a bidirectional regulation of the gut-lung axis, indicating a complex biological interaction, with lung diseases often associated with intestinal dysbiosis and immune-inflammatory responses, where GM and its metabolites play a direct or indirect role in immune regulation.173 Intratumoral injection of the butyrate-producing bacterium Roseburia promotes subcutaneous tumor growth, suggesting that the intratumoral microbiome may have potential prognostic and therapeutic value.174 An increasing body of evidence suggests that the intratumoral microbiota may serve as diagnostic, prognostic, and therapeutic targets for emerging biomarkers.175
Table 4 Gut Microbiota and Cancer
A 16S rRNA sequencing analysis of surgically resected tissue samples from patients with non-small cell lung cancer (NSCLC) and benign lung diseases revealed significant differences in the relative abundance of lung microbiota, as well as in α- and β-diversity between the two groups. At the genus level, significant differences in the abundance of 13 taxa were observed between squamous cell carcinoma and adenocarcinoma of the lung.180 Modulation of the intestinal microbiota has been shown to influence the anti-lung cancer response in mouse models, with the administration of probiotics and fecal microbiota transplants enhancing the effects of antitumor therapies. Supplementation with bacterial species, such as mucinophilic Akkermansia, which are known to be reduced in lung cancer patients, may offer a potential strategy to enhance the efficacy of these therapeutic interventions176 (Table 4). The oral microbiota can be utilized in the prevention and treatment of lung cancer and to mitigate the side effects of anticancer therapies by modulating the balance of the oral microbiota.181 Studies have shown that lung adenocarcinomas are enriched with Bacillus and Castorius, whereas lung squamous carcinoma is enriched with Brucella abortus. The microbial community is altered in patients with lung cancer, and its diversity may be associated with the disease site and pathology.182 Overall, immune interactions within the gut-lung axis are bidirectional and complex, involving multiple interactions between the microbial components of both the intestinal and lung microbiota, with immune effects occurring both locally and distally. Disruptions in this axis may lead to adverse outcomes, including the promotion of cancer development, pathogen colonization, tissue damage, and increased susceptibility to infection.170
Microbial regulatory mechanisms offer novel opportunities for precision oncology in lung cancer. Tetrahydrobiopterin from Bacillus sp. SVD06 specifically induces apoptosis in human lung adenocarcinoma cells (A549).183 Separately, RNase Binase secreted by Bacillus intermedius selectively targets A549 cells while triggering apoptosis programs, demonstrating negligible toxicity toward normal lung epithelial cells (LEK).184 Notably, Coagulococcus species may influence chemotherapy resistance in lung adenocarcinoma by modulating DNA repair pathways.185 In animal models, Wistar rats bearing synthetic squamous cell carcinomas maintain normal immune responses to sheep red blood cells and inactivated Brucella abortus during tumor progression. However, serum-detected immunosuppressive factors correlate with localized lymphocyte suppression and diminished antitumor immunity.186
Breast Cancer
Advances in modern sequencing and metagenomics technologies have enabled a deeper understanding of the tumor microbiome, allowing for comprehensive characterization of tissues such as the breast. Breast cancer (BC) is the most common cancer among women and the leading cause of cancer-related deaths in women worldwide.187 Mastitis is a condition characterized by engorgement, swelling, and inflammation of the mammary gland, typically resulting from infection by pathogenic microorganisms.177 Emerging studies identify octamer-binding transcription factor 1 (OCT1) as a novel independent prognostic biomarker in estrogen receptor-positive breast cancer (ER+ BC).188 Separately, poly(ADP-ribose) polymerase (PARP) inhibitors demonstrate favorable efficacy and safety in Phase I–II clinical trials for metastatic triple-negative breast cancer (TNBC) (Figure 4).189
Figure 4 In estrogen receptor-positive breast cancer (ER⁺ BC), octamer-binding transcription factor 1 (OCT1) serves as a novel independent prognostic biomarker. Conversely, poly(ADP-ribose) polymerase (PARP) inhibitors demonstrate significant efficacy and safety in metastatic triple-negative breast cancer (TNBC). Notably, TNBC patients exhibit elevated gut Clostridiales abundance with increased circulating trimethylamine N-oxide (TMAO). Mechanistically, TMAO activates the PERK endoplasmic reticulum stress pathway, inducing tumor cell heat shock response and enhancing CD8⁺ T cell-mediated anti-tumor immunity. Strikingly, oral broad-spectrum antibiotics suppress mammary tumor growth while reducing Clostridiales abundance, corroborating the causal role of gut microbiota in TNBC immunomodulation.
A multi-omics analysis of triple-negative breast cancer (TNBC) patients revealed that Clostridiales spp. and the related metabolite trimethylamine N-oxide (TMAO) were more abundant in tumors with an activated immune microenvironment. TMAO induced a thermomorphic response in tumor cells through activation of the endoplasmic reticulum stress kinase PERK, thereby enhancing CD8+ T cell-mediated TNBC antitumor immunity in vivo (Figure 4).190 The microbiota in the mammary gland differs between malignant tumors and normal tissues. Aerosolized antibiotics have been shown to reduce the growth of mammary tumors in mice and significantly limit lung metastasis. Oral absorbable antibiotics also reduced mammary tumors. In ampicillin-treated nodes, the immune microenvironment exhibited M1 features and enhanced T-cell/macrophage infiltration.191
Some evidence suggests the presence of a unique microbial community in breast tissue, previously considered sterile. Additionally, breast tumors harbor distinct microbial communities that differ from those of normal breast tissue, and these microbial communities may originate from the gut microbiota.187 A variety of factors can impact the gut microbiota, including, but not limited to, age, ethnicity, body mass index (BMI), physical activity level, dietary habits, concurrent medications, and antibiotic use.192–194 For example, the abundance of mucinophilic Akkermansia increases with dietary shifts toward fiber-rich foods and has been correlated with body composition in some BC patients.195 In addition, a prospective, randomized intervention trial conducted by Wastyk et al revealed a correlation between the intake of high-fiber or fermented foods and immune responses.196 Enrichment in n-3 polyunsaturated fatty acids (PUFA) has been associated with a reduced risk of BC in offspring. Using C57BL/6 pregnant mice, it has been demonstrated that the alpha-diversity of the GM in n-3 Sup-FO and n-3 Sup-FSO offspring was significantly higher than that in n-3 Def offspring after maternal supplementation with n-3 PUFA. The relative abundance of Akkermansia, Lactobacillus, and Mucispirillum was observed to be higher in the n-3 Sup-FO and n-3 Sup-FSO offspring groups compared to the control group at all ages. Moreover, maternal n-3 Def diet was associated with reduced abundance of Lactobacillus, Bifidobacterium, and Pasteurella in the 7-week-old offspring. The n-3 Sup-FO and n-3 Sup-FSO groups were also found to be more diverse than the control group in the n-3 Sup-FO group.197
Dietary patterns modulate the mammary microbiota. Fecal transplantation has been shown to alter both the gut and mammary tumor microbiota, suggesting a link between the gut and mammary microbiota. Recent studies have demonstrated that high-density lipoprotein (HDL) cholesterol increases serum levels of bacterial lipopolysaccharides (LPS), and that fecal transplantation, controlling for dietary source, reduced LPS bioavailability in animals fed a high-fat diet (HFD).198 A study revealed changes in the gut microbiota of mastitis rats, characterized by an increased abundance of the Aspergillus phylum. Mammary tissue showed elevated levels of arachidonic acid metabolites and norepinephrine. The development of adenitis leads to changes in the microbiota and alterations in the metabolic profiles of various biological samples, including colon contents, plasma, and mammary tissue (Table 4). Major manifestations include disturbances in bile acid metabolism, amino acid metabolism, and arachidonic acid metabolism.177
Prostate Cancer
Prostate cancer remains the most common non-cutaneous malignancy among male patients and one of the leading causes of cancer-related deaths worldwide. Increasing evidence suggests that the microbiota may play a crucial role in carcinogenesis and in modulating the efficacy and activity of anticancer therapies (eg, chemotherapy, immune checkpoint inhibitors, targeted therapies) across various hematologic and solid tumors.199 Dysbiosis of the bladder microbiota has been linked to various urologic disorders.200 Recent studies of the urinary microbiota have challenged the long-held belief that urine is sterile, as the urinary microbiota has been linked to the development of bladder and prostate cancers, similar to the role of the gut microbiota in cancer development.201
Using the inverse variance weighting or Wald ratio method, it was demonstrated that Bifidobacterium (p = 0.030), Actinobacterium (phylum p = 0.037, class p = 0.041), and Ruminococcus groups (p = 0.018) were associated with an increased risk of BCa, while Allisonella (p = 0.004, p = 0.038) was associated with a reduced risk of BCa and PCa, respectively.178 Lactobacillus and Bifidobacterium probiotic mixtures enhanced the antitumor effects through the gut-tumor immune response axis179 (Table 4). Compared to healthy controls, the urinary microbiota composition in patients with genitourinary cancers exhibited significant differences. Lactic acid-producing bacteria, such as Bifidobacterium spp. and Lactobacillus spp., may enhance the efficacy of Bacillus Calmette-Guerin (BCG) therapy in bladder cancer.
Conclusion
Gut dysbiosis, as a cross-disease hub linking neurodegenerative disorders, psychiatric conditions, metabolic syndromes, and malignancies, demonstrates increasing clinical significance. In neurodegenerative contexts: Alzheimer’s disease patients exhibit exacerbated amyloid-beta deposition via microglial inflammatory activation triggered by gut microbial metabolites; Parkinson’s disease models reveal that enteropathic α-synuclein pathological dissemination precedes motor symptom onset, while microbiota-targeted interventions significantly alleviate neuroinflammation. Within psychiatric disorders: Depressed patients show reduced short-chain fatty acid SCFAs levels closely associated with hypothalamic-pituitary-adrenal (HPA) axis hyperactivity. Specific probiotics and natural compounds restore synaptic plasticity through gut-brain axis signaling repair. Metabolic disease research demonstrates: Diabetic patients’ decreased butyrate-producing bacteria directly correlate with insulin resistance, with microbiota modulation strategies partially reversing glucose metabolic abnormalities. Regarding tumor microenvironment regulation: Gut microbiota influences immune checkpoint inhibitor efficacy through metabolic reprogramming, particularly demonstrating enhanced anti-tumor immunity potential in breast and triple-negative lung cancers.
At the metabolism-immune interface, microbial metabolites modulate systemic inflammatory states through receptor-mediated immunocyte differentiation. In neural signaling, enteropathic proteins influence central nervous functions via vagal nerve pathways. Regarding gut-brain axis regulation, microbial dysbiosis directly compromises intestinal barrier integrity, subsequently affecting distal organs through circulatory dissemination. These mechanisms reveal concerted multi-target effects of microbe-host interactions in disease pathogenesis. However, gut microbiota-disease interplay exhibits complex bidirectionality: Fecal microbiota transplantation (FMT) studies demonstrate that colonizing germ-free mice with patient-derived microbiota only partially recapitulates disease phenotypes, suggesting dysbiosis may represent a secondary outcome of genetic-environmental interactions. Longitudinal metabolomics profiling further reveals that altered tryptophan/kynurenine ratios during disease progression precede microbial structural shifts, implying host metabolic derangements may drive ecological remodeling of the microbiota.
Outlook
Future gut microbiota research must transcend traditional correlative approaches by focusing on three innovation axes directly aligned with disease spectra: Firstly, developing spatiotemporal metabolite tracking technologies to precisely map real-time trajectories of effector molecules (eg, short-chain fatty acids, LPS) along the gut-brain axis signaling pathway. This will capture organ-specific epigenetic imprints in neurodegenerative contexts—such as microglial activation in Alzheimer’s disease and enteropathic α-synuclein dissemination in Parkinson’s disease—and synaptic plasticity impairments in psychiatric disorders like depression. Secondly, constructing microbiota-host causal inference models through longitudinal metabolomic monitoring across the four disease dimensions. This approach will delineate temporal relationships between critical metabolic events—including tryptophan dysregulation in psychiatric disorders and insulin sensitivity modulation in metabolic diseases—and microbial structural shifts. It will further differentiate functional weights of driver strains (eg, checkpoint regulator microbes in malignancies) from commensal bacteria. Ultimately, advancing clinical translation of targeted interventions: Optimizing synthetic microbial community transplantation for disease-specific applications in neuroinflammation (Parkinson’s models), metabolic dysregulation (diabetic insulin resistance), and tumor immunity (breast cancer estrogen metabolism); Engineering metabolite-directed delivery systems to restore intestinal barrier integrity (foundational for gut-brain axis repair in psychiatric disorders) while synergizing with vagal nerve pathways to improve neural function. This integrated strategy will enable precise ecological recalibration from “Microbial Homeostasis to Systemic Pathogenesis”.
Data Sharing Statement
The data analyzed in this review are derived from previously published studies, which are cited in the text. Readers are referred to the original publications for access to the data.
Acknowledgments
We sincerely apologize to all colleagues whose important work could not be cited in this review owing to space limitations, especially many prominent and pioneer work in the neurodegenerative diseases and neuroinflammation field.
Funding
This work was supported by the Outstanding Youth Scientific Research Program for Universities in Anhui Province (2024AH020014), the National Natural Science Foundation of China (82072890 and 31701288), the Natural Science Foundation of Guangdong Province (2020A1515010113) and the Key Scientific Research Projects of Universities in Anhui Province (2024AH051965).
Disclosure
The authors declare that there are no competing interests associated with this work.
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170. Zhao Y, Liu Y, Li S. Role of lung and gut microbiota on lung cancer pathogenesis. J Cancer Res Clin Oncol. 2021;147:2177–2186. doi:10.1007/s00432-021-03644-0
171. Goto T. Microbiota and lung cancer. Semin Cancer Biol. 2022;86:1–10. doi:10.1016/j.semcancer.2022.07.006
172. Bingula R, Filaire M, Radosevic-Robin N. Desired Turbulence? Gut-Lung Axis, Immunity, and Lung Cancer. J Oncol. 2017;2017:5035371. doi:10.1155/2017/5035371
173. Ma P-J, Wang -M-M, Wang Y. Gut microbiota: a new insight into lung diseases. Biomed Pharmacother Biomedecine Pharmacother. 2022;155:113810. doi:10.1016/j.biopha.2022.113810
174. Ma Y, Chen H, Li H. Intratumor microbiome-derived butyrate promotes lung cancer metastasis. Cell Rep Med. 2024;5(4):101488. doi:10.1016/j.xcrm.2024.101488
175. Liu W, Xu J, Pi Z. Untangling the web of intratumor microbiota in lung cancer. Biochim Biophys Acta Rev Cancer. 2023;1878(6):189025. doi:10.1016/j.bbcan.2023.189025
176. Corrêa RO, Castro PR, Moser R. Butyrate: connecting the gut-lung axis to the management of pulmonary disorders. Front Nutr. 2022;9:1011732. doi:10.3389/fnut.2022.1011732
177. Chen K. Enhanced protein-metabolite correlation analysis: to investigate the association between Staphylococcus aureus mastitis and metabolic immune pathways. FASEB J off Publ Fed Am Soc Exp Biol. 2024;38:e23587.
178. Mingdong W, Xiang G, Yongjun Q, Mingshuai W, Hao P. Causal associations between gut microbiota and urological tumors: a two-sample Mendelian randomization study. BMC Cancer. 2023;23(1):854. doi:10.1186/s12885-023-11383-3
179. Miyake M, Oda Y, Owari T. Probiotics enhances anti-tumor immune response induced by gemcitabine plus cisplatin chemotherapy for urothelial cancer. Cancer Sci. 2023;114(3):1118–1130. doi:10.1111/cas.15666
180. Zheng X, Lu X, Hu Y. Distinct respiratory microbiota associates with lung cancer clinicopathological characteristics. Front Oncol. 2023;13:847182. doi:10.3389/fonc.2023.847182
181. Ma Q, Li X, Jiang H, et al. Mechanisms underlying the effects, and clinical applications, of oral microbiota in lung cancer: current challenges and prospects. Crit Rev Microbiol. 2024;50(5):631–652. doi:10.1080/1040841X.2023.2247493
182. Sun Y, Liu Y, Li J. Characterization of Lung and Oral Microbiomes in Lung Cancer Patients Using Culturomics and 16S rRNA Gene Sequencing. Microbiol Spectr. 2023;11(3):e0031423. doi:10.1128/spectrum.00314-23
183. Mahendran R, Selvaraj SP, Dhanapal AR. Tetrahydrobiopterin from cyanide-degrading bacterium Bacillus pumilus strain SVD06 induces apoptosis in human lung adenocarcinoma cell (A549). Biotechnol Appl Biochem. 2023;70(6):2052–2068. doi:10.1002/bab.2509
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185. Ting Y, Wang Y-S, Liao E-C, Chou H-C, Chan H-L. Investigate the relationship between Bacillus coagulans and its inhibition of chemotherapy-induced lung cancer resistance. Biotechnol Appl Biochem. 2024;71(6):1453–1478. doi:10.1002/bab.2641
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187. Song X, Wei C, Li X. The Relationship Between Microbial Community and Breast Cancer. Front Cell Infect Microbiol. 2022;12:849022. doi:10.3389/fcimb.2022.849022
188. Kawiak A. Molecular research and treatment of breast cancer. Int J Mol Sci. 2022;23(17):9617. doi:10.3390/ijms23179617
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190. Wang H, Rong X, Zhao G. The microbial metabolite trimethylamine N-oxide promotes antitumor immunity in triple-negative breast cancer. Cell Metab. 2022;34(4):581–594.e8. doi:10.1016/j.cmet.2022.02.010
191. Bernardo G, Le Noci V, Ottaviano E. Reduction of Staphylococcus epidermidis in the mammary tumor microbiota induces antitumor immunity and decreases breast cancer aggressiveness. Cancer Lett. 2023;555:216041. doi:10.1016/j.canlet.2022.216041
192. Hong BS, Lee KP. A systematic review of the biological mechanisms linking physical activity and breast cancer. Phys Act Nutr. 2020;24(3):25–31. doi:10.20463/pan.2020.0018
193. Routy B, Jackson T, Mählmann L. Melanoma and microbiota: current understanding and future directions. Cancer Cell. 2024;42(1):16–34. doi:10.1016/j.ccell.2023.12.003
194. Arifuzzaman M, Collins N, Guo C-J, Artis D. Nutritional regulation of microbiota-derived metabolites: implications for immunity and inflammation. Immunity. 2024;57(1):14–27. doi:10.1016/j.immuni.2023.12.009
195. Frugé AD, Van der Pol W, Rogers LQ, Morrow CD, Tsuruta Y, Demark-Wahnefried W. Fecal Akkermansia muciniphila Is Associated with Body Composition and Microbiota Diversity in Overweight and Obese Women with Breast Cancer Participating in a Presurgical Weight Loss Trial. J Acad Nutr Diet. 2020;120(4):650–659. doi:10.1016/j.jand.2018.08.164
196. Wastyk HC, Fragiadakis GK, Perelman D. Gut-microbiota-targeted diets modulate human immune status. Cell. 2021;184(16):4137–4153.e14. doi:10.1016/j.cell.2021.06.019
197. Li J, Wan Y, Zheng Z. Maternal n-3 polyunsaturated fatty acids restructure gut microbiota of offspring mice and decrease their susceptibility to mammary gland cancer. Food Funct. 2021;12(17):8154–8168. doi:10.1039/D1FO00906K
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The Sky Today on Saturday, July 5: Neptune stands still
The distant planet Neptune stands still against the background stars of Pisces in the early-morning sky, still visible close to Saturn.
This contrast-enhanced image of Neptune was snapped by Voyager 2 in 1989. Credit: NASA/JPL
- On a specified date, Neptune appears stationary in the constellation Pisces, positioned approximately 1° north of Saturn.
- The planetary pair, Saturn (magnitude 0.9) and Neptune (magnitude 7.7), are observable in the southeastern sky before sunrise, with Saturn easily visible to the naked eye and Neptune requiring binoculars or a telescope.
- Saturn’s rings are approximately 40” across, and several of its moons, including Titan, are also visible through a telescope.
Neptune stands stationary against the background stars of Pisces the Fish at 11 A.M. EDT. The solar system’s most distant planet is visible in the early-morning sky, now just 1° north of the planet Saturn.
Catch the planetary pair a few hours before sunrise in the southeast, standing about 35° high at 3:30 A.M. local daylight time. They are below and slightly to the left of the Circlet asterism in Pisces. Saturn is easily visible without optical aid at magnitude 0.9, offering a bright signpost to find magnitude 7.7 Neptune, which falls below the detectability threshold of the naked eye. Instead, you can use binoculars or any telescope to find the planet, visible in the same field of view as Saturn. Neptune’s tiny disk spans just 2” at its great distance — can you tell that this “flat,” bluish-gray star is not a star at all?
Through a telescope eyepiece, Saturn shows off its lovely rings, now 40” from end to end. Several smaller, 10th-magnitude moons hover near the disk of the planet, while mid-8th-magnitude Titan, the planet’s largest moon, lies about 2.5’ west of the ringed world.
Sunrise: 5:38 A.M.
Sunset: 8:32 P.M.
Moonrise: 4:15 P.M.
Moonset: 1:26 A.M.
Moon Phase: Waxing gibbous (76%)
*Times for sunrise, sunset, moonrise, and moonset are given in local time from 40° N 90° W. The Moon’s illumination is given at 12 P.M. local time from the same location.For a look ahead at more upcoming sky events, check out our full Sky This Week column.
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Six killed, several injured in bus-trawler collision
Pakistan
Police say that the cause of the accident is being determined
MUZAFFARGARH (Dunya News) – A tragic road accident near Langer Sarai in Muzaffargarh resulted in a collision between a passenger bus and a trailer, leaving six people dead and eighteen injured.
According to a police spokesperson, the deceased include two men, two women, and two children, while the bus driver also lost his life in the accident. The bus was traveling from Jhang to Alipur when the incident occurred.
The Muzaffargarh police spokesperson stated that upon receiving the report of the accident, Rescue 1122 teams promptly reached the site and, after providing first aid, transferred the injured to a nearby hospital.
The District Traffic Officer, DSP, and SHO, along with police personnel, remained present at the scene and assessed the situation.
Police say that the cause of the accident is being determined, and investigations are underway. Initial information suggests that the bus collided head-on with an oncoming trailer.
The police have cleared the accident site and restored the flow of traffic. After identification and completion of legal formalities, the bodies are being handed over to their families.
Officials expressed deep sorrow over the incident and extended condolences to the affected families.
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EU to stockpile critical minerals amid geopolitical risks, FT says – Reuters
- EU to stockpile critical minerals amid geopolitical risks, FT says Reuters
- Building an Electrified World: The Strategic Role of Critical Materials Nasdaq
- Europe Is Playing Catch-Up in the Race for Critical Minerals Yahoo
- Harnessing Africa’s bargaining power in the critical minerals race African Business
- EU plans to stockpile critical minerals amid geopolitical risks, Financial Times reports TradingView
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Lesley Garrett ‘honoured’ to sing in Bedford Proms 2025
Alex PopeBBC News, Bedfordshire
Bedford Proms
Lesley Garrett said she loves singing in open-air concerts as it was a wonderful way to connect with the audience Opera singer and performer Lesley Garrett said she was “honoured” to be invited back to an outdoor concert series to restart a Proms event.
Garrett, 70, will perform at Proms in the Park, alongside Russell Watson, as part of the Bedford Summer Sessions on Sunday 6 July.
The event was last held in the town in 2023 and has returned after requests from members of the public, organisers said.
Garrett said it could be the last Proms she performs in, but “I will give it my all, which is still considerable”.
Martin McKay
The Proms event has been held in Bedford many times over the years and the event in 2021 marked its 25th anniversary “Singing isn’t something I do for a living, it’s what I am”, she said.
“I do it because that’s what I was born to do.”
“It’s an exciting time in my life. I no longer have to prove anything. I’m not looking to grow my career, but enjoy the legacy of being in the profession for 45 years, as I started in 1980.”
After the Proms, her next role will be Heidi Schiller in Stephen Sondheim’s Follies for the Northern Ireland Opera Company in September.
Then she will help plan a November concert for Bantam of the Opera, a choir she is involved with, before undergoing a hip replacement.
Welsh National Opera
Lesley Garrett performing in The Merry Widow Garrett said music was in her soul, and she would “carry on until I can no longer perform”.
“The big criteria is whether I’m still good enough – I still have singing lessons every week with Joy Mammen, my original singing teacher,” she said.
“We will then decide together to hang up those chords. I would hate to start disappointing people. You never know if the next one is going to be the last.”
Garrett last came to the town to sing in 2018 and said she could not wait to return to Bedford to perform in the Proms with her “old friend” Russell Watson.
“If it’s my last Proms, I’m thrilled it’s going to be with him,” she continued.
“I’m honoured to be asked to restart the Bedford Proms, I will give it my all, which is still considerable.”
Bedford Proms
Anyone coming to the Proms is encouraged to bring their own food, drink and flags Mark Harrison, promoter at Cuffe & Taylor, said the absence of the Proms from the Summer Sessions in 2024 “left many feeling disappointed”.
“We have listened to the general public’s wishes, and we are delighted that we have been able to bring it back for 2025,” he said.
The last Proms was held in 2023, in a slightly different format as West End Proms.
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Japan 0-0 Wales: Watch, listen & follow live
Japan team news: Leitch to lead inexperienced sidepublished at 05:42 British Summer Time
Japan v Wales (06:00 BST)
Image source, Getty ImagesImage caption, Michael Leitch has featured in four World Cups and played 87 internationals
Eddie Jones has named eight uncapped internationals in his matchday squad, with around 15 players unavailable.
Michael Leitch, 36, will captain the side and is the only member of the squad who has more than 50 caps.
Prop Yota Kamimori and wing Kippei Ishida, who Jones says can be a Japan version of South Africa double World Cup winner Cheslin Kolbe, will both win first caps, while there are six new potential caps on the bench.
Toulouse scrum-half Naoto Saito, who was part of the squad that won the French Top 14 title last weekend, is not involved today, but will be available for the second Test in Kobe.
Japan: Takuro Matsunaga; Kippei Ishida, Dylan Riley, Shogo Nakano, Malo Tuitama; Seungsin Lee, Shinobu Fujiwara; Yota Kamimori, Mamoru Harada, Shuhei Takeuchi, Epineri Uluiviti, Warner Deans, Michael Leitch (capt), Jack Cornelsen, Amato Fakatava.
Replacements: Hayate Era, Sena Kimura, Keijiro Tamefusa, Waisake Raratubua, Ben Gunter, Shuntaro Kitamura, Ichigo Nakakusu, Halatoa Vailea.
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Identification of Shared Biomarkers in Chronic Kidney Disease and Diab
Introduction
The global prevalence of chronic kidney disease (CKD) has been rising steadily, currently affecting approximately 10.8% of the total population.1 CKD is characterized by abnormalities in kidney structure or function persisting for over 3 months, affecting overall health. A key indicator is a glomerular filtration rate (GFR) of less than 60 mL/ (min·1.73 m²), accompanied by at least one of the following markers of kidney injury: albuminuria, abnormal urinary sediment (eg, hematuria), electrolyte disturbances due to renal tubule dysfunction, histological abnormalities, structural changes in imaging, or a history of kidney transplantation.2 CKD often presents gradually, with subtle or atypical symptoms in the early stages, making timely detection challenging. At onset, typical indicators include hypertension, hyperglycemia, and microalbuminuria, which are not highly sensitive to standard diagnostic tests, contributing to a poor clinical prognosis.
As the condition advances, it can evolve into nephrotic syndrome, chronic nephritis, or acute nephritis, with some patients progressing to end-stage renal disease (ESRD). The long waiting period for kidney transplantation, due to limited donor availability, results in many patients with ESRD relying on dialysis for survival. Over 60% of these patients undergo dialysis for more than a year, with approximately 23% needing long-term dialysis for over 3 years.3 This not only imposes a significant physiological, psychological, and financial burden but also affects the quality of life.
Treatment for CKD primarily focuses on slowing nephron damage, managing hyperfiltration, addressing complications, and providing renal replacement therapy. However, hemodialysis often leads to poor functional outcomes, including uremia-related malnutrition and muscle wasting, and carries risks of infection and vascular complications, further compromising patient quality of life. While kidney transplantation offers improved quality of life, recipients face persistent CKD-related symptoms and complications from immunosuppressive therapies.4 Consequently, more effective and scientifically-based methods are needed for the diagnosis and treatment of CKD.
Diabetic nephropathy (DN) is a common and serious microvascular complication of diabetes, particularly prevalent in type 2 diabetes mellitus (T2DM), primarily induced by hyperglycemia. Its clinical manifestations include proteinuria, progressive renal dysfunction, hypertension, and edema. In China, approximately 20% to 40% of patients with diabetes are affected by DN, with most cases in the early, asymptomatic stages.5 Current research suggests that the pathogenesis of DN is closely associated with hyperglycemia, the accumulation of advanced glycosylation end products, as well as inflammatory and immune responses.6 Currently, DN has surpassed glomerulonephritis as the leading cause of new cases of CKD in China.7 As DN advances, patients face an increased risk of developing CKD due to factors such as RAAS activation and microvascular damage induced by sustained hyperglycemia and hypertension.8 Early screening and prompt diagnosis of DN are essential to prevent progression to CKD and end-stage nephropathy. However, the precise mechanisms by which DN contributes to CKD remain unclear. Thus, studying DN is vital not only for understanding the underlying mechanisms of CKD but also for identifying new therapeutic targets for both DN and CKD prevention and treatment.
Single-cell RNA sequencing (scRNA-seq) is a cutting-edge high-throughput sequencing technique that enables the analysis of gene expression profiles at the single-cell level. By analyzing cellular composition, gene enrichment pathways, and intercellular communication, scRNA-seq offers insights into the underlying pathological processes of diseases. Recently, scRNA-seq has gained widespread use due to its sensitivity, accuracy, and efficiency. Unlike traditional sequencing methods, scRNA-seq enables detailed analysis of the cellular spectrum, identification of specific cell types, and mapping of gene expression patterns in heterogeneous cell samples. This allows for the study of gene expression at the single-cell level, providing a microscopic view of disease progression.9 The application of scRNA-seq in kidney research is promising, as it enhances understanding of cellular heterogeneity in CKD and DN, as well as identifying the potential mechanisms between these conditions. Additionally, scRNA-seq can help elucidate the correlation between CKD and DN, offering valuable insights for identifying biomarkers that can predict disease progression and inform patient-specific treatment strategies.10
In this study, biomarkers associated with DN and CKD were identified through single-cell analysis, differential expression analysis, and protein-protein interaction (PPI) network construction. A comprehensive bioinformatics approach was utilized, including cell communication analysis, pseudotime series analysis, and gene set enrichment analysis (GSEA). The mechanisms of these biomarkers were examined, with an emphasis on key cell types and immune responses in patients with DN and CKD. This research provides a theoretical foundation and novel perspectives for studying disease associations, advancing diagnostic methods, and identifying therapeutic targets. Additionally, cellular-level validation of biomarker expression offers valuable insights for distinguishing between these two conditions.
Materials and Methods
Single-Cell RNA-Sequencing
Nine blood samples were collected for scRNA-seq, including three control samples, three CKD samples (All were stage 5 chronic kidney disease), and three DN samples (One sample was stage 4 chronic kidney disease, and the rest were stage 5 chronic kidney disease, and all were stage 5 diabetic nephropathy). Sample grouping information is shown in Supplementary Table 1.
Patients with DN were selected based on the 2020 Kidney Disease: Improving Global Outcomes (KDIGO) Guidelines, using the following criteria: (1) A urine albumin-creatinine ratio (UACR) of ≥ 30 mg/g, measured at least twice over a 3 to 6-month period, with other factors excluded; (2) An estimated glomerular filtration rate (eGFR) of < 60mL • min-1 • (1.73 m2) −1 persisting for more than 3 months; (3) Renal biopsy results indicating pathological changes consistent with DN. Patients with CKD met the 2020 KDIGO Guidelines for CKD diagnosis in the absence of diabetes. The exclusion criteria were: (1) Severe infections; (2) Malignant tumors; (3) Active autoimmune diseases; (4) Concurrent cardiovascular or cerebrovascular events; (5) Pregnancy. This study was approved by our hospital’s Ethics Committee, and informed consent was obtained from all participants.
Chronic Kidney Disease is classified into stages based on GFR and albuminuria, as outlined by the KDIGO guidelines. The KDIGO 2012 Classification includes: Stage 1: GFR ≥ 90 mL/min/1.73 m² with evidence of kidney damage (eg, albuminuria, structural abnormalities, or genetic disorders); Stage 2: GFR 60–89 mL/min/1.73 m² with kidney damage; Stage 3a: GFR 45–59 mL/min/1.73 m²; Stage 3b: GFR 30–44 mL/min/1.73 m²; Stage 4: GFR 15–29 mL/min/1.73 m²; Stage 5: GFR < 15 mL/min/1.73 m² or kidney failure requiring dialysis/transplantation.11
DN progression is classified based on GFR and albuminuria, integrating criteria from both diabetes and CKD guidelines. The KDIGO framework is widely used, with modifications specific to Diabetic Kidney Disease. Stage 1: GFR > 90 mL/min/1.73 m² (elevated due to renal hyperfiltration), early glomerular hypertrophy and hyperfiltration; Stage 2: GFR normal or mildly elevated (≥ 90 mL/min/1.73 m²), persistently elevated albumin-to-creatinine ratio (ACR 30–300 mg/g, moderately increased), kidney structural damage (eg, glomerular basement membrane thickening); Stage 3: GFR 60–89 mL/min/1.73 m² (CKD Stage 2), ACR ≥ 300 mg/g (severely increased), with clinical signs of hypertension and progressive proteinuria; Stage 4: GFR 15–59 mL/min/1.73 m² (CKD Stages 3–4), persistent ACR ≥ 300 mg/g, complications include declining kidney function, edema, and cardiovascular risks; Stage 5: kidney failure, GFR < 15 mL/min/1.73 m² (CKD Stage 5), requiring dialysis or transplantation.12
Peripheral blood mononuclear cells (PBMCs) were isolated from peripheral blood samples using Ficoll density gradient centrifugation. Cell viability, assessed using AO/PI double fluorescent staining on a Countstar Rigel (S2) instrument, was required to exceed 85%. Following quality inspection, the single-cell suspension met the quality control criteria and proceeded with library construction, adhering to the SOP “ChromiumNextGEMSingleCell3_3.1_rev_d” from 10x Genomics. The Illumina Nova-seq 6000 PE150 platform was employed for sequencing the single-cell library.
Data Filtering
Sequencing data were initially examined for data volume, sequencing base quality, and sequencing saturation, followed by sequence statistics analysis using CellRanger (v 7.0).13 Single-cell analysis was then conducted on the RNA-sequencing dataset using the Seurat package (v 3.1.5).14 A Seurat object was created with parameters min.cells = 100 and min.features = 100 to filter out low-quality cells. Next, the scDblFinder package (v 1.17.2) was applied to identify and eliminate doublet cells.15 Cell screening criteria were as follows: library size exceeding 500 but below the 95th percentile (10,000 cells), gene counts below the 95th percentile (10,000 cells), and mitochondrial content restricted to less than 10%. Gene expression in each cell was normalized using the LogNormalize method.
Principal Component Analysis (PCA) and Cell Annotation
The FindVariableFeatures function with the variance-stabilizing transformation (vst) method was employed to identify genes exhibiting significant variation across cells. From this analysis, the top 2000 genes with the highest variability were selected. To minimize the effects of differing sequencing batches, data from the nine samples were integrated. The FindIntegrationAnchors function was used to identify anchors from a set of Seurat objects, and the IntegrateData function was then applied to merge the samples based on these anchors. PCA was applied to scale and reduce the data dimensionality. Principal components (PCs) with higher rankings in PCA encapsulate more diverse and valuable differential features. An elbow plot was constructed to identify the appropriate number of PCs for clustering analysis. Cells were clustered in an unsupervised manner using the FindNeighbors and FindClusters functions (resolution = 1). t-distributed stochastic neighbor embedding (t-SNE) was used to visualize cell clusters. Marker genes for each cluster were identified and annotated by comparing them with known cell type marker genes from the CellMarker database (http://biocc.hrbmu.edu.cn/CellMarker/) for cell annotation. A bar chart illustrating cell proportions across samples was generated to represent the distribution of cells within each sample.
Cell Correlation Analysis
The relationship between different cells in the PCA space was examined by constructing cluster dendrograms based on PCA dimensions, with the aim of analyzing the Euclidean distances between the cells. Additionally, the correlation among various cell types was evaluated using the average gene expression data.
GSEA and Gene Set Variation Analysis (GSVA)
Differential expression analysis was conducted for each cell type, comparing control samples with DN and CKD samples. The log2FoldChange (FC) values for each gene were sorted in descending order for each cell type. GSEA was then performed using the clusterProfiler package (v 3.16.0), with the Kyoto Encyclopedia of Genes and Genomes (KEGG) gene set as the background (|Normalized Enrichment Scores (NES)| > 1, NOM p < 0.05).16 The GSVA package (v 1.46.0) was used to compute GSVA scores across all samples for different cell types based on the h.all.v2022.3.Hs.symbols.gmt gene set.17 The limma package (v 3.54.0) was then applied to assess the statistical significance in pathway differences between control samples and CKD or DN samples (p < 0.05).18
Cell Communication and Pseudotime Analysis
CellPhoneDB analysis was performed separately on the control, CKD, and DN samples. The receptor-ligand pairs were filtered with a threshold of p < 0.05 and a minimum mean expression value > 1. Key cells were selected based on annotated cell types according to literature reports.19,20 To examine the differentiation status of key cells at different periods, pseudotime analysis was conducted using Monocle (v 2.14.0), providing insights into the progression of cellular differentiation over time.21
Identification of Candidate Genes
To identify candidate genes, differentially expressed genes (DEGs) in key cell types were compared between control and DN samples, and between control and CKD samples. DEGs were selected based on the following criteria: |average log2FC| > 0.25, pct > 0.1, and adj.p < 0.05. The DEGs identified between control and DN samples were designated as DEGs1, while those between control and CKD samples were designated as DEGs2. A Venn diagram tool (http://bioinformatics.psb.ugent.be/webtools/Venn/), was used to find the intersection of DEGs1 and DEGs2, resulting in a list of candidate genes shared by both DN and CKD groups. Gene Ontology (GO) and KEGG pathway analyses were subsequently performed using the clusterProfiler package (v 3.16.0) to investigate the shared functions and pathways of these candidate genes.16
PPI Network Analysis
To explore interactions among candidate genes, a PPI network was constructed using the STRING database (https://string-db.org) with a confidence threshold of 0.4. Proteins not connected from the main network were excluded, allowing the focus to remain on the hub genes. The MCODE function in Cytoscape (v 3.10.1) was then used to analyze sub-networks of these hub genes, specifically highlighting the TOP1 sub-network based on the following parameters: degree cutoff = 2, node score cutoff = 0.2, K-core = 2, and max depth = 100).22 The CytoHubba plugin was used to rank hub genes according to four scoring methods (MCC, MNC, Closeness, and Degree). The top 10 genes from each scoring method were selected and genes that consistently appeared across all four methods were designated as biomarkers.
Enrichment Analysis and Gene Co-Expression Network of Biomarkers
The correlation coefficients between gene expression in the control versus DN samples and control versus CKD samples were calculated and ranked. This ranking allowed for further GSEA analysis with thresholds set at (|NES| > 1 and adj.p < 0.05). GO and KEGG gene sets were used as background for this analysis. The GeneMANIA database (http://genemania.org) was employed to predict genes that interact with the biomarkers and explore their associated biological functions, facilitating the construction of a gene co-expression network.
To investigate the activity of upstream pathways associated with the biomarkers, their corresponding pathways were retrieved using the SPEED2 database (https://speed2.sys-bio.net/). The activities of these enriched upstream pathways were quantified using the Bates test and subsequently ranked.
Biomarker-Drug-Disease Network
The Comparative Toxicogenomics Database (CTD) (http://ctdbase.org/) was used to predict drugs targeting the biomarkers and to screen relationship pairs in human species. Additionally, the CTD database provided the diseases associated with the identified drugs, which were then visualized in a biomarker-drug-disease network.
Expression Analysis of Biomarkers
The “polygenic query” function in GTEx (v 8, https://www.gtexportal.org/home/) was utilized to analyze the expression of biomarkers across different cells and tissues. The expression levels of biomarkers in the annotated cells were assessed, followed by a comparison of expression differences between the control, CKD, and DN groups. Additionally, the expression patterns of biomarkers were analyzed throughout pseudotime based on previous pseudotime analysis results.
Statistical Analysis
Data processing and analysis were performed using R software (version 4.2.1). In the bioinformatics analysis, the Wilcoxon rank-sum test was used to examine the differences between the 2 groups. A P-value less than 0.05 was regarded as statistically significant. In addition, the bioinformatics tools and databases used in this study are shown in Supplementary Table 2.
Results
Annotation of 10 Cell Types
The quality of sequencing was assessed, with a Q30 value for all samples exceeding 74%, and the Q20 value surpassing 82% (Supplementary Table 3). Moreover, the sequencing saturation for all samples was greater than 81%, with a mapping rate of over 85% (Supplementary Tables 4 and 5). These results confirm that the sequencing quality of all samples was high, making them suitable for further analysis. After filtering out low-quality cells, the initial cell count of 61,935 was reduced to 28,938 (Supplementary Figure 1). To minimize computational load, the top 2000 most variable genes were selected for PCA (Supplementary Figure 2). The genes from the top nine PCs are shown in Figure 1A, and the top 20 PCs, chosen based on the elbow plot, were used for unsupervised clustering (Figure 1B). A total of 27 clusters were identified, and 10 cell types were annotated: natural killer (NK) T cells, T cells, NK cells, monocytes, B cells, macrophages, mast cells, dendritic cells, and plasma cells (Figure 1C and D, Supplementary Figure 3). T cells, NK T cells, and monocytes were most prevalent across the samples (Figure 1E).
Figure 1 Continued.
Figure 1 Annotation of cluster subtypes and unsupervised clustering analysis of single-cell samples. (A and B) PCA analysis and elbow plot for determining the optimal PCs. (C and D) Heatmap of cell clustering based on genes involved in t-SNE dimensionality reduction across the samples. (E) Proportion of cell clusters in control, DN, and CKD samples.
Abbreviations: PCA, Principal component analysis; PCs, Principal components; t-SNE, t-distributed stochastic neighbor embedding; DN, Diabetic nephropathy; CKD, Chronic kidney disease.
Pathway Similarities Between CKD and DN Across Cell Types
The dendrogram showed that cells in close proximity demonstrate higher similarity. Notably, NK cells and NK T cells showed a greater degree of similarity to each other, followed by a closer resemblance to T cells (Figure 2A). A strong positive correlation was also observed between NK cells and NK T cells (Figure 2B). The DEGs in dendritic cells between control and CKD as well as DN samples were enriched in pathways such as the proteasome, endometrial cancer, and apoptosis. In macrophages, the NOD-like receptor signaling pathway was the enriched pathway, while oxidative phosphorylation, non-alcoholic fatty liver disease, and valine, leucine, and isoleucine degradation were the key pathways in mast cells. The enriched pathways for the nine cell types are shown in Supplementary Figure 4 Plasma cells from DN samples were not analyzed due to an insufficient sample size). Significant differences were observed in most pathways between DN and normal samples in T cells, NK T cells, monocytes, B cells, and NK cells. Similarly, notable pathway differences were found between CKD and control samples in T cells, NK T cells, monocytes, and NK cells. The pathway activities in T cells, NK T cells, monocytes, and NK cells were found to be elevated in both CKD and DN, with similar activation patterns suggesting a resemblance between the two conditions (Figure 2C and D).
Figure 2 CKD and DN samples enriched in pathways of nine cell types. (A) Clustering tree diagram. (B) Heatmap showing cell correlation. (C and D) GSVA analysis of cell subgroups between the control and CKD groups, and the control and DN group groups.
Abbreviations: CKD, Chronic kidney disease; DN, Diabetic nephropathy; GSVA, Gene set variation analysis.
Disruption and Imbalance of Cell Communication in DN and CKD
Cell communication patterns varied markedly between conditions, with a significant increase in cell communication frequency observed in CKD compared to the control group, while the frequency of cell communication was significantly reduced in DN (Figure 3A–F). This indicates that the occurrence of DN and CKD may be associated with disruptions and imbalances in cell communication.
Figure 3 Disrupted and imbalanced cell communication in DN and CKD. (A, C, E) Heatmaps displaying the relationship between the selected CKD and DN genes and their corresponding expression pathways in the control group, along with changes in gene expression levels. (B, D, F) Cell communication trajectories for control, CKD, and DN samples.
Abbreviations: DN, Diabetic nephropathy; CKD, Chronic kidney disease.
Identification of 119 Candidate Genes Associated with Both CKD and DN
Analysis of myeloid cell subtypes, including monocytes, macrophages, mast cells, and dendritic cells, identified these as key cell types involved in both CKD and DN. Differential expression analysis revealed 297 DEGs (DEGs1) between the control and CKD samples and 277 DEGs (DEGs2) between the control and DN samples in these cell types (Figure 4A and B). Upon intersecting these datasets, 119 candidate genes associated with both CKD and DN were obtained (Figure 4C). The involvement of these candidate genes in disease was linked to several KEGG pathways, such as viral life cycle, measles, malaria, and B cell receptor signaling. Additionally, GO functions related to these genes included negative regulation of MAP kinase activity, toll-like receptor 4 signaling pathway, immunological synapse, platelet alpha granule lumen, amyloid-beta binding, and chemokine receptor binding (Figure 4D and E).
Figure 4 Screening of key genes. (A and B) Manhattan plots illustrating differentially expressed genes across each chromosome for CKD vs control and DN vs control, respectively (left to right). The y-axis represents -log10(p) values, and the x-axis represents chromosomes, visualizing gene expression across the genome. (C) Intersection of candidate genes relevant to both CKD and DN. (D) KEGG and GO analysis of candidate genes. (E) Bubble plot showing distinct enrichment items, with each node representing a specific biological function. KEGG pathways identified include viral life cycles, viral protein-cytokine receptor interactions, measles, B-cell receptor signaling, African trypanosomiasis, malaria, phagosome, cell adhesion molecules, antigen processing and presentation, and hematopoietic cell lineage.
Abbreviations: DN, Diabetic nephropathy; CKD, Chronic kidney disease; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology.
MX1, IRF7, STAT1, and ISG15 Were Identified as Biomarkers
From the initial 119 candidate genes, 21 genes corresponding to discrete proteins were excluded, resulting in a PPI network consisting of 98 proteins (Figure 5A). The top subnetwork identified by the MCODE function included 25 nodes and 267 edges, revealing strong interactions among the proteins (Figure 5B). By intersecting the top 10 genes obtained from the MCC, MNC, Closeness, and Degree scores in the cytoHubba plugin, four biomarkers were identified: MX1, IRF7, STAT1, and ISG15 (Figure 5C). A gene co-expression network was constructed, identifying 20 genes interacting with these biomarkers, primarily involved in functions such as response to type I interferon, cellular response to type I interferon, and viral response (Figure 5D). Additionally, pathway analysis showed that the JAK-STAT, TLR, and TNFa signaling pathways displayed elevated biological activity, while the Hippo and Wnt pathways showed down-regulation in their activities (Figure 5E).
Figure 5 Associations between key genes and biomarkers in the sample. (A and B) Interaction analysis of 119 candidate genes using a PPI network constructed using STRING (https://string-db.org), with a confidence score of 0.4, identifying 21 discrete proteins and a network comprising 98 interacting proteins. The network contains 98 nodes and 569 edges, visualized using Cytoscape (version 3.10.1). (C) Biomarker identification through MCC, MNC, Closeness, and Degree scores using the cytoHubba plugin, examining the expression activity of four biomarkers across 16 major cell communication signaling pathways. (D) GeneMANIA network analysis, displaying the four biomarker genes in the inner circle, with the outer circle showing other genes related to them. Each gene color denotes its biological pathway, with a high correlation density indicating essential biological functions and significant interactions with other genes. (E) Upstream pathway analysis of the biomarkers.
Abbreviations: PPI, protein-protein interaction; MNC, Maximum Neighborhood Component; MCC, Maximal Clique Centrality; STRING, Search Tool for the Retrieval of Interacting Genes/Proteins.
Lysosome Pathway Enrichment of MX1, IRF7, STAT1, and ISG15
To further explore the functions and pathways associated with the identified biomarkers, GSEA analysis was conducted. The results indicated that MX1 was enriched in pathways such as lysosome, degradation of other glycans, and glutathione metabolism in both CKD and DN (Supplementary Figure 5A and B). IRF7 showed involvement in lysosome-related processes, and was additionally enriched in pathways associated with Vibrio cholerae and Leishmania infections in both CKD and DN (Supplementary Figure 5C and D). STAT1 was linked to lysosome, insulin signaling pathway, Fc gamma R (FcγR)-mediated phagocytosis, and other pathways across CKD and DN (Supplementary Figure 5E and F). Lysosome, systemic lupus erythematosus, glutathione metabolism, and other pathways were enriched by ISG15 in both CKD and DN (Supplementary Figure 5G and H). All biomarkers showed enrichment in the lysosome pathway. Moreover, in CKD, MX1, IRF7, STAT1, and ISG15 were all enriched in the oxidative phosphorylation pathway. In DN, they were all enriched in the FcγR mediated phagocytosis pathway. In addition, a biomarker-drug-disease network was constructed, comprising 165 nodes, including the four biomarkers, 151 drugs (eg, acetylcysteine, acrolein, and alpha-pinene), and 10 diseases (eg, diabetes mellitus, diabetes complications, and diabetic angiopathies), with a total of 635 interaction pairs (Supplementary Figure 6).
Elevated Expression of MX1 and IRF7 in Dendritic Cells
The expression levels of the biomarkers MX1, STAT1, and ISG15 were highest in the neuronal cells of the esophagus muscularis, while expression data for IRF7 was unavailable (Figure 6A). The expression levels of the biomarkers in each cell type across different group samples (control, CKD, DN) are shown in Figure 6B. IRF7 showed high expression in dendritic cells in all samples, and MX1 exhibited elevated expression specifically in dendritic cells, particularly in DN and CKD samples (Figure 6C). In contrast, STAT1 and ISG15 were widely expressed in macrophages, monocytes, NK cells, and NK T cells. Notably, these four biomarkers showed significant differential expression in NK cells, T cells, B cells, and NK T cells (Figure 6D–G). The pseudotime analysis highlighted that the significantly elevated expression of MX1 and IRF7 in dendritic cells is associated with myeloid cells differentiation into dendritic cells (Figure 7A–G).
Figure 6 Connection between expression levels of biomarkers and each cell type. (A) Expression levels of biomarkers across different tissues and cells. (B) Expression levels of biomarkers in individual cell types. (C–G) Differential expression of the four biomarkers in each cell type, presented in boxplots. These results show significant differences in the expressions of MX1, STAT1, ISG15, and IRF7 in NK cells, T cells, B cells, and NKT cells. *p<0.05, there is evidence of significant difference; **p<0.01; ***p<0.005; ****p<0.001, with strong evidence of significant difference (P value was used as the standard for figure (D–G) screening of differentially expressed genes).
Abbreviations: ns, no significant difference.
Figure 7 Pseudotime analysis showing the differentiation states of cells. Branch points indicate potential decision points in cellular processes. (A–C) Each point represents a cell, with cells exhibiting similar cellular states grouped together. Branch points in the pseudotime trajectory indicate potential decision points in the cell’s biological process (eg, four branch points in this analysis). Cells are color-labeled according to pseudotime, state, and group. Integrating biomarker expression data, the differentiation states of genes related to disease progression can be identified. (D–G) Pseudotime analysis of biomarkers. The results reveal that MX1 and IRF7 genes show significantly higher expression levels in dendritic cells, suggesting that MX1 and IRF7 may play a role in the differentiation of myeloid cells into dendritic cells. In contrast, the other two biomarkers (STAT1 and ISG15) did not exhibit significant differences in expression, indicating they may have a lesser impact on this differentiation process.
Discussion
T2DM and CKD are both widespread chronic diseases. The most common microvascular complication of T2DM is DN, which is the leading cause of CKD.23 Currently, the diagnosis of these two conditions relies on traditional markers such as estimated glomerular filtration rate (eGFR), urinary albumin measurement, and creatinine levels, especially in the absence of renal biopsy.24 Early diagnosis of CKD and DN is often subjective due to the lack of non-invasive biomarkers.25 This limitation complicates the design of clinical trials, impeding efforts to identify effective treatments, facilitate early detection, and ensure timely diagnosis. Additionally, reducing cardiovascular mortality and slowing the progression to ESRD remain significant unmet medical needs for patients with CKD and DN.26
This study offers new insights into the molecular mechanisms underlying DN and CKD, by identifying common biomarkers and exploring the biological processes involved. Using scRNA-seq technology, we analyzed cell-specific gene expression changes across control, CKD, and DN groups, identifying differentially expressed genes within distinct cell subpopulations. Through further examination of the signaling pathways within these cell clusters, this study provides a theoretical foundation for understanding biomarkers related to the progression of CKD and DN, their roles in immune response, and their potential as therapeutic targets.
In this study, four biomarkers—MX1, IRF7, STAT1, and ISG15—were identified through differential expression analysis and the PPI network analysis, showing notable expression in both DN and CKD samples.
Interferon-stimulated gene 15 (ISG15) is a 15kD ubiquitin-like protein induced by the binding of interferon-α (IFN-α) to the promoters of interferon (IFN) regulatory factor (IRF) and the interferon-stimulated response element. ISG15 has been implicated in several intracellular processes, including autophagy, apoptosis, and signal transduction. As a cytokine, it activates Janus kinase (JAK) and the JAK/STAT signaling pathway, which mediates various physiological and pathological responses, such as cell proliferation, differentiation, apoptosis, and immune regulation.27 Recent studies indicate that JAK/STAT pathway activation exacerbates renal fibrosis and glomerulosclerosis, while ISG15 overexpression contributes to systemic inflammation and CKD.28,29 At the same time, ISG15 can modify viral proteins or host proteins, thereby inhibiting viral replication.30 ISG15 modification can directly interfere with the life cycle of the virus.31 And the resistance of ISG15-deficient cells to paramyxovirus is reduced, indicating their direct antiviral activity.32 In oral squamous cell carcinoma, tumor cell-derived ISG15 promotes fibroblast recruitment, promoting tumor growth and metastasis through CD11a-dependent glycolytic reprogramming.33 In pancreatic and renal clear cell carcinoma, ISG15 levels are elevated and high expression is associated with adverse clinical outcomes.34,35 ISG15 can also promote tumor cell migration and immunosuppression by inducing the macrophage M2-like phenotype.36 To sum up, ISG15 has shown a pleiotropic effect in antiviral immunity, tumor progression and metastasis, and tumor microenvironment regulation. Its complex functional mechanism in different pathophysiological processes provides rich and promising research directions for the development of prevention and treatment strategies for related diseases.
This study found that MX1 was specifically highly expressed in DN and CKD samples. The MX1 gene encodes an interferon-induced protein that is involved in the cell’s antiviral immune response.37 In the context of diabetes, persistent hyperglycemia and lipid metabolism disorders may activate MX1, leading to chronic inflammation, which may promote pathological changes in DN.38 In CKD, MX1 may reduce the inflammatory response caused by viral infection by inhibiting viral replication, thereby producing a protective effect on CKD.39,40 Additionally, the degree of methylation of the MX1 gene promoter is correlated with the severity of COVID-19 and there may be gender differences.41 This suggests that the expression level of MX1 can be used as an early indicator of viral infection, and its gene polymorphism is also related to the risk of autoimmune disease, and is of great value in the judgment of infection type, individualized treatment and disease risk assessment.
Signal transducer and activator of transcription 1 (STAT1) is a cytoplasmic transcription factor activated by various stimuli, regulating the human immune system.42 Moreover, STAT1 mediates interferon signaling pathways and plays a crucial role in antiviral (eg, HBV, HCV, HIV) and antibacterial (eg, Mycobacterium tuberculosis) immune responses. Its phosphorylation levels reflect the progression of infection.43–45 Additionally, STAT1 gain-of-function mutations are associated with chronic mucosal skin candidiasis and systemic lupus erythematosus (SLE), while loss-of-function mutations lead to severe immunodeficiency.46,47 STAT1 exhibits a dual role in cancer: high expression in prostate and breast cancers may indicate a better prognosis, but it may also promote immune escape in some solid tumors.48 These findings suggest that STAT1 could serve as a monitoring indicator for infectious disease progression, a molecular diagnostic marker for autoimmune diseases, and a potential target for tumor prognosis evaluation. Regulating STAT1 may provide novel strategies for precise treatment of related diseases.
IRF7 (Interferon Regulatory Factor 7) is a key member of the IRF family, playing a pivotal role in innate immunity and antiviral responses.49,50 Studies have shown that IRF7 expression correlates with disease activity in SLE patients, with elevated mRNA levels positively correlated with serum IFN levels, IFN scores, and SLEDAI scores.51,52 In acute myeloid leukemia, inhibiting TOX exerts anti-tumor effects by upregulating IRF7 expression.53 Furthermore, IRF7 mediates the transcription of MCP-1, an obesity-related molecule.54 These findings indicate that changes in IRF7 expression are linked to disease development and may serve as a potential biomarker for diagnosis.
MX1, IRF7, STAT1, and ISG15 are core regulatory molecules in the type I interferon (IFN-α/β) response pathway, interacting with each other in complex ways. STAT1, as a central signal node, is phosphorylated by JAK kinase under IFN-γ or IFN-α/β stimulation. This results in the formation of a dimer that translocates to the nucleus and directly activates IRF7 transcription.55 IRF7, in turn, amplifies IFN-α/β production, creating a positive feedback loop, and induces MX1 and ISG15 expression.56 MX1, an antiviral effector protein, relies on the STAT1-IRF7 axis, while ISG15 stabilizes STAT1 and IRF7 proteins via ubiquitination (ISGylation) and enhances their transcriptional activity.57 Additionally, ISG15 regulates MX1 oligomerization through non-covalent binding, impacting its antiviral function.58 In CKD and DN, these complex interactions may lead to the oversecretion of pro-inflammatory factors (eg, TNF-α, IL-6) and dysregulated cytotoxic immune responses, accelerating tissue damage.
Furthermore, exploring the relationship between MX1, IRF7, STAT1, and ISG15 and traditional renal function markers (eg, serum creatinine, urea nitrogen, and urine protein) is valuable. Serum creatinine levels are influenced by muscle metabolism and glomerular filtration capacity,59 urea nitrogen reflects protein metabolism and renal excretion function,60 and urinary protein indicates impaired glomerular filtration barrier.61 In contrast, MX1, IRF7, STAT1, and ISG15 are more involved in immunomodulation and inflammatory response pathways.62,63 In kidney disease progression, local inflammation in the kidney and immune cell activation64,65 can alter the expression of these biomarkers. In early CKD, the immune-inflammatory response begins subtly, and the expression of MX1, IRF7, etc., may change before traditional markers like serum creatinine or urea nitrogen significantly deviate. At this stage, urine protein may also remain at critical levels.66–69 This suggests that biomarkers such as MX1 could serve as early warning indicators, complementing traditional markers. As the disease progresses and traditional markers become more abnormal, these novel biomarkers may increase, providing a more comprehensive basis for disease assessment.
GSEA results indicated that all four biomarkers were enriched in the Oxidative Phosphorylation pathway in CKD, while in DN, they were enriched in the FcγR-mediated Phagocytosis pathway. Kidney cells typically rely on Oxidative Phosphorylation to maintain physiological functions like reabsorption and secretion, processes requiring substantial energy.70 However, in CKD, kidney cell metabolic pathways are altered, and their dependence on Oxidative Phosphorylation is enhanced.71 Oxidative Phosphorylation is a major source of intracellular reactive oxygen species (ROS). In CKD, metabolic disturbances and inflammatory responses may overactivate this pathway, resulting in excessive ROS production.71 Excessive ROS can damage cellular proteins, lipids, and DNA, triggering oxidative stress and accelerating renal fibrosis and functional decline.72 FcγR-mediated phagocytosis is crucial for immune function, facilitating the uptake and clearance of phagocytes (eg, macrophages, neutrophils) that recognize target cells or granules bound to antibodies.73 In early DN, immune complexes may accumulate in renal tissues, activating the complement system and inflammatory responses, thereby exacerbating kidney damage.74 If phagocytosis fails to clear target cells completely, residual antigens may continue to stimulate the immune response, leading to chronic inflammation and renal fibrosis.75 These findings highlight the differences in enriched pathways between CKD and DN, shedding light on the distinct pathogenesis and progression of each disease. This insight is crucial for understanding disease mechanisms and developing targeted therapeutic strategies.
This study found that IRF7 expression was elevated in dendritic cells (DCs). In CKD and DN, immune cells like DCs are continuously activated during disease progression.76 Plasmacytoid DCs (pDCs) are the primary producers of IFN-I,77 and in pDCs, IRF7 expression is regulated by NFATC3, which enhances IFN production.78 Upon stimulation by TLR7 or TLR9, IRF7 is activated, promoting IFN-α secretion.79 High IRF7 expression in DCs strengthens their antiviral immune response,50,80 enhances antigen presentation, and regulates the Th1 immune response and inflammatory factor release.81–84 In CKD and DN, abnormal IRF7 expression may contribute to disease progression via two mechanisms: excessive activation can cause persistent inflammation, macrophage infiltration, and proinflammatory factor release (eg, TNF-α, IL-6), exacerbating fibrosis;38,85,86 additionally, IRF7 may worsen podocyte damage and glomerular basement membrane thickening under metabolic stress (eg, high sugar or glycosylation end product stimulation).87 Notably, the high-sugar environment in DN amplifies IRF7-mediated inflammation, creating a vicious “metabolic-inflammatory” cycle.88 Thus, understanding IRF7’s role in DCs provides key insights into the pathogenesis of CKD and DN, offering future directions for developing targeted therapies to block disease progression.74–77
Our study also highlights that STAT1 and ISG15 are widely expressed in macrophages, monocytes, NK cells, and NKT cells. In macrophages, IFN-γ stimulates the phosphorylation of STAT1 by JAK1/JAK2, promoting STAT1 dimerization, nuclear translocation, and the expression of pro-inflammatory genes such as IRF1 and CXCL10, enhancing M1 polarization and antibacterial function.89,90 ISG15 regulates cytokine expression (eg, TNF-α, IL-6) by activating the JAK-STAT pathway, modulating immune response intensity.91 In monocytes, STAT1 enhances the inflammatory response via the TLR/MyD88 pathway,92 while ISG15 may regulate cell migration and phagocytosis through NF-κB signaling.58,93 For NK and NKT cells, STAT1 mediates the IFN-γ feedback loop, promoting granzyme and perforin expression,94,95 while ISG15 supports IFN-γ production and cytotoxic function by stabilizing STAT1/STAT4.56,96 Free ISG15 also enhances NK cell cytotoxicity via LFA-1 receptors.57 Importantly, STAT1 can compete with STAT3 for DNA binding to regulate immune balance, but over-activation may lead to chronic inflammation and is associated with autoimmune and metabolic diseases like diabetic nephropathy.55,97 These findings underscore the core role of STAT1 and ISG15 in the innate immune system, offering new insights into immune cell activation and laying the foundation for targeted therapies in inflammatory and metabolic diseases.90
Furthermore, sodium-glucose cotransporter 2 inhibitors have been shown to interfere with the polarization of DCs by reducing receptor pairing between M2 macrophages and T cells. In this study, cell communication analysis of DN and CKD groups revealed that B cells, NK cells, T cells, and monocytes exhibited the closest interactions. The four biomarkers, MX1, IRF7, STAT1, and ISG15, were widely expressed in these cell populations. These findings suggest that these biomarkers play central roles in the immune response and the progression of CKD and DN. These biomarkers could serve as valuable targets for predicting disease progression.
However, this study has certain limitations. First, single-cell sequencing technology presents challenges such as high costs and time consumption, making it difficult to fully assess the accuracy of these markers in disease evaluation. More importantly, the specific functional mechanisms, clinical translational potential, and diagnostic value of these biomarkers require systematic validation in independent cohorts. Additionally, the dynamic changes in relevant signaling pathways during CKD and DN progression, their correlation with disease stage and severity, and the clinical application value of these markers still need further investigation. To address these limitations, future research will focus on enhancing the clinical applicability of single-cell sequencing, including cost reduction, time efficiency, and improved accuracy and reliability. Interdisciplinary collaboration will be promoted to facilitate its clinical use. We plan to explore the impact of biomarkers on disease-related cell behavior and physiological processes through cell culture and animal models (eg, PDO, PDX). This will involve verifying whether biomarker regulation can reverse disease phenotypes and elucidate underlying mechanisms. Concurrently, large-scale, multicenter clinical samples will be collected to investigate the MX1/IRF7/STAT1/ISG15 pathway using gene-editing technologies. Furthermore, we will compare biomarker expression across CKD, DN, and other renal diseases to assess specificity. In terms of clinical translation, we will collaborate with the Clinical Center for Nephrology to examine the correlation between biomarkers, disease staging, and treatment response. Clinical measurement techniques such as ELISA, mass spectrometry, and immunohistochemistry will be used to correlate biomarker expression with clinical data. Samples from different geographic regions will be collected to assess the generalizability of our findings.
Conclusion
In this study, through single-cell RNA sequencing and the application of a series of bioinformatics methods, four biomarkers (MX1, IRF7, STAT1, ISG15) in CKD and DN were identified. During the clinical diagnosis process, detecting the expression levels of biomarkers in patients may serve as a means of auxiliary diagnosis for CKD and DN, and also as an important basis for predicting disease progression. Meanwhile, in the clinical treatment of CKD and DN, these biomarkers can be considered as therapeutic targets.
Data Sharing Statement
All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.
Ethics Approval and Consent to Participate
This study was conducted with approval from the Ethics Committee of The Second Hospital Affiliated to Kunming Medical University (PJ-2021-36). This study was conducted in accordance with the declaration of Helsinki. Written informed consent was obtained from all participants.
Acknowledgments
We would like to acknowledge the hard and dedicated work of all the staff that implemented the intervention and evaluation components of the study.
Funding
Yunnan Revitalization Talent Support Program (Youth talent project: NO.YNWR-QNBJ-2020-269) and (Famous doctors project: NO.YNWR-MY-2019-075). Reserve Talents Project for Young and Middle-aged Academic and Technical leaders in Yunnan Province (202005AC160024). Yunnan Fundamental Research Kunming Medical University Joint Projects (grant NO. 202201AY070001-101). National Clinical Research Center of Chronic Kidney Disease, the Second Affiliated Hospital of Kunming Medical University.(Project Number: GF2020003). The Second Affiliated Hospital of Kunming Medical University talent echelon cultivation project-Academic leader (RCTDXS-202303).
Disclosure
The authors declare that they have no competing interests.
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