Category: 8. Health

According to McGill University, TissueTinker is using 3D bioprinting to revolutionize cancer drug testing by replacing outdated methods like animal trials and 2D cell cultures. Traditional models fail to mimic the complexity of human tumours, contributing to a staggering failure rate—over 90%—for cancer drugs that pass preclinical tests but flop in human trials.

TissueTinker, a recent McGill Innovation Fund (MIF) awardee, tackles this problem head-on. The startup creates miniaturized tumour models using 3D printing technology—specifically, bioink—to replicate both healthy and diseased human tissue side by side. These printed tumours are as small as 300 microns, the “sweet spot size,” according to co-founder Benjamin Ringler. “It’s large enough that it’s still valuable for testing purposes, but small enough to minimize resources.”

More than just small, these tumours are smart. Researchers can customize them to simulate specific tumour environments, gaining targeted insights into cancer behavior. “The ability to customize the tumour really allows researchers to gain deep, targeted insights into how cancer behaves at a micro level,” Ringler explained. This adaptability improves the predictive power of early-stage testing, reducing wasted investment in drugs that would otherwise fail in clinical trials.

“Because the testing environment more readily simulates the human body, researchers can better assess and understand whether or not their drug works before reaching clinical trial stages,” Ringler added. With development costs topping $1–2 billion per drug, this level of precision is not just a scientific advancement—it’s a financial necessity.

TissueTinker is scaling its technology, backed by the McGill Innovation Fund. “The MIF has provided tailored support, offering specific advice and helping us think critically about not just our next step, but our many steps down the road,” said Ringler. Alongside co-founders Madison Santos and Isabelle Dummer—experts in biomedical engineering and cell therapy—the team plans to expand their tumour model library and eventually license the platform.

“We’re not just solving a problem; we’re rethinking the way we approach cancer drug development,” said Ringler.

Thousands of differentially methylated CpGs characterize individual FTLD-TDP pathological subtypes

RRBS was performed to generate DNA methylation profiles from pairs of frozen post-mortem FCX and CER from FTLD-TDP patients (FTLD-TDP types A, B and C, GRN mutation carriers and C9orf72 repeat expansion carriers) and neuropathologically normal controls (Fig. 1A). After QC, 5,819,868 CpGs in FCX and 5,936,364 in CER were included in the analyses. 90% of the total number of retained CpGs overlapped between both tissues, with similar distributions with respects to genomic region, CpG island and regulatory element context (Fig. 1B). Differential methylation analysis was then performed at the CpG site level in both tissues, between each individual pathological subgroup and controls (Supp. Tables 2 and 3). Across all groups, we found 6,453 differentially methylated CpG sites (FDR < 0.05) in FCX and 7,018 in CER. In both brain regions, the majority of differentially methylated CpGs were in a gene body (61.1% in FCX and 54.1% in CER), followed by gene promoters (27.1% in FCX and 34.7% in CER), 3’-UTRs (5.9% in FCX and 4.1% in CER), 5’-UTRs (4.2% in FCX and 5.5% in CER), and a small proportion of intergenic CpGs (1.6% in both FCX and CER; Fig. 1C). In each tissue we found approximately the same number of CpGs to be hypo- and hypermethylated in FTLD-TDP patients, when compared to controls (Fig. 1D). Interestingly, the vast majority of differentially methylated CpGs we identified were unique to a disease subtype, with less than 10% of sites shared between two or more individual patient subgroups in both FCX (381 CpGs representing 6%; Fig. 1E) and CER (424 sites representing 6%; Fig. 1F). Of the overlapping CpGs in FCX, only six were found to be differentially methylated only in genetically unexplained groups of patients (TDP-A, TDP-B and TDP-C), annotated to CDH15, FN3KRP, HS1BP3, CYP2W1, NDUFAF6, TP53INP1 and ZIC3, whereas only two CpGs (within PLCB3 and UBE2A) were found differentially methylated across all pathological subtypes. In CER, no CpG sites were found in common between only genetically unexplained subgroups or all patients. Although we found that CpG positions were not commonly shared between disease groups, we did identify overlaps when analyzing the intersection of annotated genes from all differentially methylated CpGs. We found that 28.2% of genes overlapped between the different groups in FCX (1,327 genes; Supp. Figure 2 A) and 29.4% in CER (1,592 genes; Supp. Figure 2B). In FCX, the largest overlap was observed between TDP-A and all other disease subtypes, the majority being shared with TDP-GRN and TDP-B. Furthermore, we identified 25 genes in FCX and 20 in CER harboring differentially methylated CpG sites only within the sporadic patient groups (none of which was in common between both tissues), and 41 genes in FCX and 16 in CER where differentially methylated CpG sites were found across all patient groups, of which four were detected in both brain regions (HDAC4, PRDM16, PTPRN2 and RASA3, Supp. Tables 2 and 3). When analyzing the genes containing the most differentially methylated CpGs (≥ 5 CpGs) within each pathological subgroup, we found that in FCX, the TDP-A group had the highest number of such genes (N = 16), followed by TDP-GRN (N = 5), TDP-C (N = 5), TDP-B (N = 2) and finally TDP-C9 (N = 1) (Supp. Table 2). In CER however, we found the TDP-C9 group to have the highest number of such genes (N = 12), followed by TDP-A (N = 8), TDP-C (N = 7), and lastly TDP-GRN (N = 1) with none in TDP-B (Supp. Table 3). We next sought to investigate shared epigenetic mechanisms between patients, by combining groups of patients and comparing those to controls (genetically unexplained group ‘ABC’ including TDP-A/B/C and group ‘TDP’ including all TDP patients). We found that group ‘ABC’ only contributed 54 unique CpG sites in FCX and 108 in CER, representing 24 and 58 unique genes in FCX and CER, respectively (Supp. Tables 2 and 3). Group ‘TDP’ further contributed only a few additional unique CpGs with 13 in FCX and 8 in CER, representing 10 unique genes in FCX and 5 in CER, further supporting the specificity of findings to pathological subtypes, rather than shared disease mechanisms (Supp. Tables 2 and 3). Finally, to determine whether our findings are also brain region specific, we compared FCX to CER and found that only 64 CpG sites are common between brain regions across all disease groups (Supp. Tables 2 and 3). In terms of genes harboring differentially methylated CpGs, we also found a limited overlap between tissues, with 406 genes in TDP-A, 141 in TDP-B, 200 in TDP-C, 151 in TDP-GRN and 301 in TDP-C9, supporting the specificity of disease-associated methylation patterns not only to pathological subtypes but also to the brain region.

RRBS identifies differentially methylated CpGs in known FTLD genes

Next, we employed a targeted approach to investigate the presence of differentially methylated CpGs (FDR < 0.05) in both FCX and CER within known FTLD genes [8], including CHCHD10 [55], CHMP2B [56], CSF1R [57], C9orf72 [58, 59], FUS [60], GRN [61, 62], hnRNPA1 [63], hnRNPA2B1 [63], LRRK2 [64], MAPT [65], OPTN [66], SQSTM1 [67], TARDBP [5], TBK1 [66], TIA1 [68], UBQLN2 [69], VCP [70], as well as the recently implicated UNC13A [71,72,73], TNIP1 [73] and ANXA11 [74, 75]. We also included three additional genes previously reported to be differentially methylated in FTLD patients: SERPINA1 specifically in the C9orf72 repeat extension carrier group [76], and NFATC1 and OTUD4 which were reported across different FTLD pathological subtypes [29]. Overall, only few differentially methylated CpGs were found in these genes (Table 2); however, in the case of GRN and C9orf72 the previously identified differentially methylated regions in these genes were poorly covered in our study. Furthermore, and despite none of them overlapping with the previously reported CpG in intron 9, we did find that NFATC1 harbored numerous differentially methylated CpGs across multiple patient subgroups (Supp. Figure 3A). Of the differentially methylated CpGs in NFATC1 that we identified in the FCX, several showed high regulatory potential due to their location within the gene (promoter and both 5’- and 3’-UTRs). Given the previously reported finding that the expression of NFATC1 is increased in FCX from FTLD patients, we investigated NFATC1 expression in our previously generated bulk RNA sequencing dataset [10] and also found higher expression of NFATC1 in FCX from FTLD-TDP patients, when compared to controls (Supp. Figure 3B). We next tested the correlation between methylation levels at each differentially methylated CpG site in FCX and NFATC1 expression, in all FTLD-TDP patients for which both datasets were available, and found that methylation levels at the 5’-UTR CpG negatively correlated with the expression level of NFATC1 (r= -0.29; P = 0.0034; Supp. Figure 3C) suggesting that in addition to the previously reported intronic CpG, this 5’-UTR CpG may also play a role in regulating NFATC1 in FCX.

Promoter level differential methylation analysis identifies 12 promoter loci in FCX and 8 in CER

The single-base resolution of our data allows the investigation of individual CpG sites, much like array-based studies where methylation is profiled at single CpG sites and with only a few sites being profiled per gene; however, CpGs are most often clustered within CpG islands located in genomic areas with likely functional significance. As such, we sought to investigate whether aberrant methylation patterns are observed in CpG islands, in the brain of FTLD-TDP patients. For this, CpG sites were grouped into regions, and differential methylation analysis at the region level was performed. First, we included only loci located within gene promoters (defined by location ± 500 bp from the TSS) and performed differential methylation analysis in FCX and CER separately. We identified 12 differentially methylated regions (DMRs) in FCX and eight in CER, annotated to the promoters of 15 and 13 genes, respectively (Tables 3 and 4). In both tissues, we identified both hypo- and hypermethylated loci (67% hypo- and 33% hypermethylated in FCX; 50% hypo- and 50% hypermethylated in CER). None of the loci overlapped between brain regions and interestingly, promoter DMRs were mostly identified in genetically unexplained FTLD-TDP patients (subtypes TDP-A, TDP-B and TDP-C in FCX; subtype TDP-C in CER). Finally, in FCX only two loci were found in common between patient groups (TRIM34 and LINC01954) whereas in CER no shared loci were identified.

Genome wide region level analysis identifies hundreds of differentially methylated loci in FCX and CER

Next, we expanded our analyses beyond promoters to genome wide level, while still performing group comparisons in each brain region separately. From these analyses we identified hundreds of differentially methylated DMRs, with a total of 131 in FCX and 215 in CER across all patient groups, annotated to 123 and 203 genes, respectively (Fig. 2A and B; Supp. Fig. 4A and B; Supp. Tables 4 and 5). Of these, we found a similar proportion of hyper- and hypomethylated loci in both tissues, with most loci being hypomethylated (Fig. 2C; Supp. Tables 4 and 5). Regarding the genomic context of these loci in both tissues, the overwhelming majority was located within a gene body (75% in FCX and 80% in CER), followed by gene promoters (12% in FCX and 11% in CER), 3’-UTRs (9.5% in FCX and 6% in CER), and a small proportion in intergenic regions (2% in FCX and 1% in CER) and within 5’-UTRs (1.5% in FCX and 2% in CER; Fig. 2D; Supp. Tables 4 and 5). Akin to our findings from the CpG-level analyses, most DMRs are unique to pathological subtypes and thus, combining patient subgroups for analysis only contributed a limited amount of additional DMRs with three in FCX (annotated to PSMA6 in group ABC, and to NDUFA10 and SEMA3C in group TDP) and four in CER (annotated to FHL2, PDGFRA, and BLCAP in group ABC, and DHDDS in group TDP). In FCX, the strongest finding overall was a hypomethylated gene body DMR within GFPT2 (which spans exons 14 and part of the adjacent introns) in several group comparisons (TDP-B, TDP-C, TDP-GRN, group ABC, and group TDP; Supp. Table 4). Interestingly, and although not as strong as in FCX, GFPT2 is one of only five genes where DMRs were found in both FCX and CER (TDP-B; Table 5). We selected this locus to validate our RRBS finding, focusing on TDP-C which showed the strongest effect (logFC= -2.27; FDR = 1.2E-03; Supp. Figure 4C). We selected one highly methylated sample (> 80% methylation), one lowly methylated sample (< 20% methylation), as well as two samples with intermediate methylation per group (N = 4 TDP-C and N = 4 neuropathologically normal controls) based on methylation values across the region, measured by RRBS. Bisulfite sequencing (BS) targeted to the GFPT2 DMR showed at most a 10% difference in methylation level (range 1–10%) as compared to RRBS, with none of the samples changing their categorical classification of high/intermediate/low methylation, providing support and validation to our RRBS findings (Supp. Figure 4D).

Additionally, between the two DMR analyses (promoter and genome-wide), we identified only three loci in common, with one in FCX (overlapping PARVG/PARVB; Table 3 and Supp. Table 4), and two in CER (overlapping DHX33/DHX33-DT and a known CpG island within OTX2/OTX2-AS1; Table 4 and Supp. Table 5).

Finally, we investigated whether an impaired epigenetic machinery could represent a potential mechanism underlying the widespread DNA methylation changes we observed in FTLD-TDP patients. Using our previously generated bulk RNA sequencing dataset [10] we assessed expression levels of a subset of genes encoding for DNA methylation ‘writers’ or methyltransferase enzymes (DNMT1 responsible for methylation maintenance, and DNMT3A/B responsible for de novo methylation), as well as DNA methylation ‘erasers’ (TET1, TET2 and TET3, which are key players in the first step of the demethylation process), in FTLD-TDP patients and neuropathologically normal controls. Results from these analyses highlight expression changes in FCX in genes from both groups of DNA methylation regulators, namely DNMT1 (higher in FTLD-TDP; P = 4E-03) and TET3 (lower in FTLD-TDP: P = 2.7E-05), whereas in CER we found changes in TET1 (lower in FTLD-TDP; P = 1.3E-02) (Supp Fig. 5A). Furthermore, besides global changes across all FTLD-TDP patients, we also observed specific expression patterns of the assessed genes to some pathological subtypes (Supp Fig. 5B), suggesting that to some extent, differential expression of epigenetic machinery components may contribute to the methylation changes we observe with both pathological subtype and brain region specificity.

Enrichment analysis identifies distinct processes in TDP pathological subtypes

To gain insight into potential underlying functions or pathways in genetically unexplained FTLD-TDP patients (sporadic patient groups TDP-A, TDP-B, TDP-C and combined ‘ABC’) where we identified the most changes, we next performed Gene Ontology (GO) analyses focusing on the “Biological Process” (BP) and “Molecular Function” (MF) categories and using the differentially methylated genes from all analysis in each pathological group as input in FCX and CER separately (Supp. Tables 6 and 7). In the BP category, we identified 53 clusters of related terms in FCX and 52 in CER. In the MF category, we identified substantially less clusters with seven in FCX and eight in CER (Supp. Tables 6 and 7).

In the BP category, although we observed overall a large overlap of identified clusters (several related enriched terms that cluster together; Supp. Table 6), the top 3 processes are largely non-overlapping between pathological subtypes as well as tissue types (Fig. 3A). In TDP-A, terms related to nervous system and synapse development and regulation were the most significant in both FCX and CER (cluster 43; top GO term “Nervous system development”; 3.82E-10 in FCX and 7.11E-06 in CER). We further detect enrichment in FCX for terms related to regulation of phosphorylation, glycolysis, and protein modification (cluster 15; top GO term “Protein autophosphorylation”; P = 4.29E-06). Of note, and albeit not in the top 3, we identified two clusters that are not only unique to FCX but also to a specific pathological subtype. These included cluster 2 in TDP-A including terms related to DNA damage repair (top GO term “Recombinational repair”, P = 0.039), and cluster 37 in TDP-B including terms related to cholesterol biosynthesis (top GO term “Regulation of cholesterol biosynthetic process”, P = 0.011) (Supp. Table 6; Supp Fig. 6). In CER from TDP-B, we found the strongest enrichment in terms related to regulation of signaling pathways and transduction (cluster 31, top Go term “Regulation of signal transduction”; P = 6.64E-04). In TDP-C, we found an enrichment in terms related to protein localization and membrane receptor clustering in FCX (cluster 55; top GO term “Protein localization to membrane”; P = 1.01E-04), and to regulation of DNA-templated transcription in CER (cluster 1; top GO term “Positive regulation of transcription by RNA Polymerase II”; P = 2.27E-06). Across all groups in FCX, terms related to ion transport were highly enriched (cluster 51), whereas in the combined ABC group, we detected the strongest enrichment in terms related to protein and histone deubiquitination processes (cluster 52; top GO term “Protein K48-linked deubiquitination”; P = 3.25E-04).

Finally, in the MF category, we observed a large overlap of enriched clusters between pathological subtypes and across tissues (Supp. Tables 6 and 7). Importantly, we found two clusters in common between all TDP subtypes in both brain regions, namely terms related to binding to DNA and transcriptional regulatory regions (cluster 3), as well as ion channel and calcium transporter activity (cluster 13) (Fig. 3B; Supp Fig. 6).

Methylation levels at several DMRs correlate with gene expression levels

Given that altered gene expression is the most common and well-studied consequence of aberrant methylation, we next interrogated our previously generated bulk brain transcriptomic dataset [10] to assess correlations between methylation levels within all DMRs (from both promoter and genome-wide analyses) and the expression of the associated gene(s) for which expression was measured in FCX or CER. When several overlapping DMRs were identified within the same gene, they were merged into one single DMR with the coordinates of the largest region, whereas if several non-overlapping DMRs were identified within the same gene, they were treated as independent DMRs with correlations calculated for each. To increase statistical power, correlations were calculated including all study individuals (ALL; FTLD-TDP and controls combined) (Fig. 4; Supp. Tables 8 and 9). We found correlations between methylation and expression of the annotated gene for nine DMRs in FCX (CCDC169-SOHLH2, CAMTA1, DYSF, ICMT, LINC02139, NDUFA10, PDZD4, SPAG7 and WBP2NL; Fig. 4A) and 14 in CER (ARMC2, ATP2B3, BARHL1, BBS9, CSAG1, DEF8, MTAP, MYO15B, OTX2, PLD5, PLXNA3, PM20D1, PWWP3A and SORCS2; Fig. 4B). Interestingly, for four genes in FCX, we found that the correlations became stronger when including only FTLD-TDP patients, namely CAMTA1, PDZD4, WBP2NL, and DYSF, suggesting that disease environment may play a role in the methylation effect (Supp. Table 8). Next, for each of the 23 genes, we investigated whether differential expression was observed in the pathological subtypes where the DMR was identified, which was the case for nine genes: (i) five in FCX, namely CAMTA1 (lower expression in the TDP-A group; P = 1.9E-10); PDZD4 (lower expression in the TDP-GRN group; P = 4.12E-08); SPAG7 (lower expression in the TDP-GRN group; P = 5.6E-04); NDUFA10 (lower expression in all FTLD-TDP combined; P = 9.6E-04); and WBP2NL (higher expression in the TDP-A group; P = 0.011) (Fig. 5A); and (ii) four in CER, with three in the TDP-C group, namely ATP2B3 (lower expression in TDP-C; P = 5.9E-05); PLD5 and OTX2 (higher expression in TDP-C; P = 4.0E-03 and P = 0.034, respectively), and BBS9 in the TDP-C9 group (higher expression in TDP-C9; P = 2.1E-03) (Fig. 5B). No differential expression was observed for the other genes within the groups where the DMR was identified, compared to controls. In addition, for some genes we observed differential expression in pathological subtypes beyond those where the DMR was identified (Supp. Figure 7), suggesting that additional factors besides DNA methylation may modulate the expression of these genes. One such factor could be altered expression of epigenetic machinery components that regulate transcription via epigenetic modulation. To explore this hypothesis, we investigated whether the expression of a subset of genes encoding for methyl-CpG binding proteins (MBPs; namely MBD1, MBD2, MBD3 and MECP2), which bind to methylated DNA and recruit additional factors to modulate gene expression, was altered in FLTD-TDP patients. Results from these analyses show that in FTLD-TDP patients, MBD2 expression is increased in both FCX and CER (P = 1.6E-02 and P = 3.6E-03, respectively), as well as MBD3 in CER (P = 4.7E-03), as compared to neuropathologically normal controls (Supp Fig. 8), suggesting that differential expression of such components may play a role in the limited correlation between differentially methylated genes and their expression.

CAMTA1 expression is mediated by both methylation changes and TDP-43 levels

Its pivotal role in several processes such as regulating long-term memory [77] as well as neuronal development, maturation and survival [78], together with evidence of being a TDP-43 target [11, 79,80,81], made CAMTA1 an especially interesting and relevant finding in the context of FTLD-TDP pathology. As such, we selected this locus for further follow up. A closer inspection of the 185 bp CAMTA1 DMR revealed that it is located within intron 6 of CAMTA1 (NM_015215) in chromosome 1p36 (Supp Fig. 9A), and harbors several hypomethylated CpGs in the TDP-A group compared to controls (Supp. Figure 9B). First, to validate our CAMTA1 DMR finding, we investigated whether we could detect differential methylation at the CAMTA1 DMR, measured with an alternative technique to RRBS. For this, FCX DNA samples from TDP-A (N = 25) and control (N = 28) individuals overlapping with the RRBS study, were sequenced using ONT long-read sequencing, which also profiles CpG methylation. With ONT long-read sequencing we also confirmed the lower methylation levels in the TDP-A group compared to controls (logFC = -0.366; P = 0.0176; Fig. 6A). Next, also using ONT long-read sequencing, we sought to replicate this finding using an independent cohort of TDP-A (N = 80) and control (N = 22) samples, which corroborated the finding showing a hypomethylated DMR in TDP-A patients compared to controls (logFC = -0.276; P = 0.0363) (Fig. 6B). When combining the discovery and replication cohorts, a similar effect was observed (logFC = -0.27; P = 3.76E-03; Supp Fig. 9C). Next, using ONT sequencing data in the full cohort, we analyzed individual CpG sites within the CAMTA1 DMR to determine the most relevant CpGs driving the hypomethylation signal. We observed lower methylation in the TDP-A group at all CpGs measured in the locus, with CpG numbers 6, 7, 8 and 11 showing the strongest effect (Fig. 6C), suggesting that these sites have the highest predictive value as proxy for the methylation levels within the region. Finally, to confirm previous reports of CAMTA1 being a TDP-43 target, we used an additional transcriptomic dataset from TARDBP KD hiPSC-derived cortical neurons [50], which revealed a positive correlation between the expression of CAMTA1 and TARDBP genes, albeit just below significance using the limited data points available (r = 0.74, P = 0.057; Supp. Figure 9D), suggesting that CAMTA1 is indeed a TDP-43 target. To disentangle the relationship between the effects of TDP-43 dysfunction and methylation on the levels of CAMTA1, we next compared CAMTA1 levels within the group of TDP-A patients using stratification by methylation level, based on RRBS values across the CAMTA1 DMR (N = 20; comparing 10 samples with the highest methylation to 10 samples with the lowest methylation levels). This again showed lower CAMTA1 expression in the lower methylation group compared to the higher methylation group (P = 7.5E-03; Fig. 6D), suggesting that methylation changes at this DMR affect CAMTA1 expression independently and cumulatively to TDP-43 dysfunction.

Aberrant methylation at the CAMTA1 DMR alters expression of additional genes in the 1p36 locus

Mining the UCSC Genome Browser [82] revealed that this intronic DMR, which is not within a known CpG island, overlaps with an open chromatin region (defined by the DNaseI hypersensitivity clusters track from ENCODE V3), as well as several transcription factor binding sites (defined by the Transcription factor ChiP-seq clusters track from ENCODE V3), suggesting a high regulatory potential (Supp. Figure 10). Analyzing additional datasets aimed at profiling genome-wide regulatory elements (Roadmap Epigenomics [83], GeneHancer [84]) further revealed that the DMR overlaps an enhancer element (GH01J006404; GeneHancer) of which CAMTA1 is a predicted target (Supp. Figure 10). Broadening the analysis to the intron that harbors the DMR revealed a region rich in enhancer elements predicted to target several genes within the locus. Specifically in brain tissue [85], evidence supports the existence of enhancer elements in several brain regions predicted to target the neighboring gene VAMP3 (Supp. Figure 10). Given that methylation changes may alter chromatin conformation and thus affect the functioning of regulatory elements, we investigated whether aberrant methylation at the CAMTA1 DMR alters the expression of additional genes in the locus, besides CAMTA1. Testing all genes within 1 MB from the DMR, we found that methylation levels within the region correlate with the expression of VAMP3 (r_TDP = -0.3, P_TDP = 6.2E-03) and PARK7 (r_TDP=0.25, P_TDP=0.022) in FCX; however, only within TDP patients (Supp Table 10; Fig. 7A). When comparing TDP-A to controls, we found that only VAMP3 is differentially expressed in FCX (increased in the TDP-A group; P = 1.1E-03; Fig. 7B; Supp Fig. 11A) and that expression changes are also observed in additional pathological groups (Supp. Figure 11B). Furthermore, when investigating the effect of methylation on gene expression, within TDP-A patients stratified by methylation levels, we found that VAMP3 is differentially expressed between the two groups, with higher VAMP3 expression in the low methylation group (P = 0.015; Fig. 7C). Finally, querying the CLIPdb module of the POSTAR3 database [81] revealed no TDP-43 binding sites within VAMP3 in brain tissue, which is corroborated by our own transcriptomic dataset from TARDBP KD neurons (Supp. Figure 11C), suggesting that VAMP3 is not a TDP-43 target and that expression changes might be, at least in part, modulated by methylation changes at the CAMTA1 DMR. Taking ours and others’ findings together, we propose a working model for the CAMTA1 DMR and locus where on the one hand, in healthy brains, CAMTA1 levels are maintained both via nuclear TDP-43 (i.e. promoting adequate CAMTA1 splicing and expression through direct binding to the 5’-UTR), as well as correct gene body methylation. On the other hand, aggregation and subsequent accumulation of TDP-43 in the cytoplasm leads to TDP-43 loss-of-function and lower TDP-43-dependent CAMTA1 levels. In addition, and independently from TDP-43 dysfunction in TDP-A patients, hypomethylation within the CAMTA1 gene body alters chromatin availability and/or function of regulatory elements in the locus, further reducing CAMTA1 expression while activating nearby genes such as VAMP3. Dysfunction of both CAMTA1- and VAMP3-dependent mechanisms may contribute to neurodegeneration and the pathology observed in TDP-A patients. (Fig. 8).

A study of young people in Hong Kong found that individuals with higher levels of depressive symptoms and those prone to impulsive reactions were slightly more likely to skip breakfast. Breakfast skipping was also associated with anxiety, but the strength of this association was negligible. The research was published in Frontiers in Psychiatry.

Breakfast is the first meal of the day, typically eaten in the morning after a night’s sleep. People around the world eat different foods for breakfast depending on culture, tradition, and availability. In many Western countries, breakfast includes eggs, toast, cereal, fruit, or yogurt. In East Asia, breakfast often consists of rice, soup, pickled vegetables, or steamed buns. Some people prefer a light breakfast like a smoothie or coffee, while others opt for a hearty meal.

Breakfast is considered important because it helps replenish energy and provides essential nutrients after a long overnight fast. Studies have shown that eating breakfast can improve concentration, memory, and academic performance in children. It may also help regulate metabolism and support healthy weight management. Skipping breakfast has been associated with an increased risk of overeating later in the day and poorer overall diet quality. For many, breakfast is also a time to begin the day with a moment of calm or connection with family.

Study author Stephanie Ming Yin Wong and her colleagues aimed to explore patterns of breakfast consumption among youth in Hong Kong and to investigate the associations between breakfast skipping, impulsivity, and symptoms of depression and anxiety.

They analyzed data from the Hong Kong Youth Epidemiological Study of Mental Health (HK-YES), the first territory-wide household-based mental health study in Hong Kong specifically targeting young people aged 15 to 24. Data were collected between 2019 and 2022. Fifty-eight percent of participants were female.

This analysis included data from 3,154 participants, with an average age of 20 years. Participants answered questions about their breakfast habits and completed assessments of impulsivity (using the Barratt Impulsiveness Scale–11), depressive symptoms (Patient Health Questionnaire–9), anxiety symptoms (Generalized Anxiety Disorder Scale–7), and overall functioning (measured by self-reported productivity loss due to mental health problems and an interviewer-rated Social and Occupational Functioning Assessment Scale).

Results showed that 85% of participants consumed breakfast either daily or intermittently, while 15% regularly skipped breakfast. Individuals who skipped breakfast tended to be slightly more impulsive, particularly in terms of attentional control and self-control. They also reported slightly more severe depressive symptoms and marginally higher anxiety symptoms. Compared to peers who ate breakfast, those who skipped it reported just under one additional day of reduced productivity per month and slightly poorer social and occupational functioning.

“Breakfast skipping is associated with elevated depressive symptoms in young people, with impaired attentional control being an important mechanism in this relationship. Encouraging young people to build regular breakfast habits may be incorporated as part of future lifestyle interventions for mental disorders and be further emphasized in public health policies,” the study authors concluded.

The study sheds light on the links between breakfast-related habits and mental health. However, it should be noted that the reported associations were all very weak and detectable only because the sample was very large. Additionally, the study was exclusively conducted on residents of Hong Kong. Results on other cultural groups may differ.

The paper, “Breakfast skipping and depressive symptoms in an epidemiological youth sample in Hong Kong: the mediating role of reduced attentional control,” was authored by Stephanie Ming Yin Wong, Olivia Choi, Yi Nam Suen, Christy Lai Ming Hui, Edwin Ho Ming Lee, Sherry Kit Wa Chan, and Eric Yu Hai Chen.

Neurogenesis — a process whereby new neurons are created — is said to continue throughout one’s life, even as the rate is considered to slow down with age | Image used for representational purpose only
| Photo Credit: Getty Images/iStockphoto

In an interview on vaccine recommendations, health care professionals expressed growing concerns about the changing landscape of medical information dissemination. Laura Knockel, PharmD, BCACP, clinical associate professor at Iowa College of Pharmacy, emphasized the critical importance of relying on professional organizations and trusted health care providers for accurate vaccine information, stressing the rigorous safety testing of vaccines and the potential risks of misinformation. She warned that changes in vaccine recommendations could impact insurance coverage, patient access, and ultimately public health, particularly for vulnerable populations like low-income children. Further, she underscored the need for continued patient education, transparent communication, and a commitment to evidence-based medical guidance in an increasingly complex health care environment.

Drug Topics®: How will the trust of federal health entities be impacted for health care providers?

Laura Knockel, PharmD, BCACP: I think health care providers are going to struggle with where to go for accurate information. The first place we always looked was the CDC and the ACIP pages for that accurate information, but if we think just recently the COVID-19 recommendations changed, it was by done by a couple individuals on a video via a social media post rather than the traditional committee discussion, very transparent decision, and I’m really kind of concerned that that’s going to continue that way. So we need to find where to go to get that actual, accurate information. So I think leaning on professional organizations, the American Academy of Pediatrics [and] Infectious Diseases Society of America, are 2 good examples. A lot of these organizations have started to bulk up their vaccine resources or create specific vaccine resources for their clients, and it does seem to be accessible to the public. There may be some things behind a firewall, but I do think that their concern for getting out that correct, accurate, evidence-based recommendation is overriding their want to have it for their members only. So I really think that’s going to be one of the places that I’m going to lean on are those organizations.

Drug Topics: How can a pharmacist explain these changes to a patient worried about vaccine safety, especially if they heard conflicting messages?

Knockel: I’m encouraging patients to talk to trusted health care professionals and to not get their advice from social media or the internet or other strangers, focusing on the fact that vaccines have been studied before, during, and after FDA approval. I mean, they’re more rigorously tested than any other medications because we give them to healthy people, so we have a very, very low tolerance for risk for adverse events. So just really focusing on the fact that our vaccine safety program in the US is very robust even after FDA approval, and so hopefully that will help override some of the conflicting messages that they may be hearing.

Drug Topics: How do ACIP recommendations affect broader aspects of vaccine access and utilization, such as insurance reimbursement or public health programs?

Knockel: So right now, insurers are required to provide ACIP recommended vaccines at no cost to their patients, but if we narrow or remove a vaccine recommendation, that could lead to patients having to pay out of pocket for vaccines, which can cost hundreds of dollars per vaccine, and if a vaccine isn’t covered by insurance, a patient may be less likely to receive it. So if there’s not that demand from patients to have it, manufacturers may choose to stop making that vaccine, and so there’s just a real, huge vaccine access issue there if they aren’t even making the vaccine anymore, more of a public health look. If we look at Vaccines for Children, or VFC, it’s a federal program that provides free vaccines to low-income, underinsured children, and the ACIP specifically makes recommendations, and they vote on what vaccines should be covered by this VFC program. So if they change their recommendations for that, that’s only going to exacerbate these health inequalities that we have. So those are just 2 examples of putting up barriers to vaccination, when really we should be doing the opposite, making them more accessible and making them more convenient for our patients to receive.

Drug Topics: Is there anything else you would like to add?

Knockel: I guess my one piece would be what’s happening with vaccine policy at the federal level is irresponsible at best, and I would say extremely dangerous at worst, and can be overwhelming, especially when pharmacists have so many other demands on their time to try to keep track of all these updates that keep coming out. It’s almost like drinking from a fire hose, but I really think we need to stay up to date. Focus on educating the public and letting the patient, our patients, know the value of vaccines, and hopefully we can continue to keep our patients healthy.

READ MORE: Immunization Resource Center

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Stockholm County [Sweden], July 6 (ANI): People who have survived cancer as children are at higher risk of developing severe COVID 19, even decades after their diagnosis.

This is shown by a new study from Karolinska Institutet.

With medical science development in terms of research and technology, more and more children are surviving cancer. However, even long after treatment has ended, health risks may remain. In a new registry study, researchers investigated how adult childhood cancer survivors in Sweden and Denmark were affected by the COVID 19 pandemic.

The study included over 13,000 people who had been diagnosed with cancer before the age of 20 and who were at least 20 years old when the pandemic began. They were compared with both siblings and randomly selected individuals from the population of the same gender and year of birth.

The results show that childhood cancer survivors had a lower risk of contracting COVID 19, but were 58 per cent more likely to develop severe disease if they did become infected. Severe COVID 19 was defined as the patient receiving hospital care, intensive care or death related to the infection.

“It is important to understand that even though these individuals were not infected more often, the consequences were more serious when they did become ill,” says Javier Louro, postdoctoral researcher at the Institute of Environmental Medicine at Karolinska Institutet and first author of the study.

The differences in risk were particularly clear during periods of high transmission, such as when new virus variants such as Alpha and Omicron spread rapidly. In Sweden, where pandemic management was based more on recommendations than restrictions, the increase in risk was greater than in Denmark, which introduced early and strict measures.

“Our results suggest that childhood cancer survivors should be considered a risk group in future pandemics or other health crises. This could involve prioritising them for vaccination or offering special protection during periods of high transmission,” said Javier Louro. (ANI)

(This content is sourced from a syndicated feed and is published as received. The Tribune assumes no responsibility or liability for its accuracy, completeness, or content.)

Parkinson’s disease is a “brain disorder that causes unintended or uncontrollable movements, such as shaking, stiffness, and difficulty with balance and coordination,” according to the National Institute on Aging. Symptoms worsen over time, and people may eventually have trouble walking or talking. People with the disorder may also notice “mental and behavioral changes, sleep problems, depression, memory issues, and fatigue.”

Researchers at Lawson Research Institute (Lawson), the research arm of St. Joseph’s Health Care London, are investigating whether Ambroxol — a cough medicine used safely for decades in Europe — can slow dementia in people with Parkinson’s disease.

Published on June 30 in the prestigious JAMA Neurology, this 12-month clinical trial involving 55 participants with Parkinson’s disease dementia (PDD) monitored memory, psychiatric symptoms and GFAP, a blood marker linked to brain damage. Parkinson’s disease dementia causes memory loss, confusion, hallucinations and mood changes. About half of those diagnosed with Parkinson’s develop dementia within 10 years, profoundly affecting patients, families and the health care system.

Led by Cognitive Neurologist Dr. Stephen Pasternak, the study gave one group daily Ambroxol while the other group received a placebo. “Our goal was to change the course of Parkinson’s dementia,” says Pasternak. “This early trial offers hope and provides a strong foundation for larger studies.”

Key findings from the clinical trial include:

• Ambroxol was safe, well-tolerated and reached therapeutic levels in the brain
• Psychiatric symptoms worsened in the placebo group but remained stable in those taking Ambroxol.
• Participants with high-risk GBA1 gene variants showed improved cognitive performance on Ambroxol
• A marker of brain cell damage (GFAP) increased in the placebo group but stayed stable with Ambroxol, suggesting potential brain protection.

Although Ambroxol is approved in Europe for treating respiratory conditions and has a long-standing safety record — including use at high doses and during pregnancy — it is not approved for any use in Canada or the U.S.

“Current therapies for Parkinson’s disease and dementia address symptoms but do not stop the underlying disease,” explains Pasternak. “These findings suggest Ambroxol may protect brain function, especially in those genetically at risk. It offers a promising new treatment avenue where few currently exist.”

Ambroxol supports a key enzyme called glucocerebrosidase (GCase), which is produced by the GBA1 gene. In people with Parkinson’s disease, GCase levels are often low. When this enzyme doesn’t work properly, waste builds up in brain cells, leading to damage. Pasternak learned about Ambroxol during a fellowship at The Hospital for Sick Children (SickKids) in Toronto, where it was identified as a treatment for Gaucher disease — a rare genetic disorder in children caused by a deficiency of GCase.

He is now applying that research to explore whether boosting GCase with Ambroxol could help protect the brain in Parkinson’s-related diseases. “This research is vital because Parkinson’s dementia profoundly affects patients and families,” says Pasternak. “If a drug like Ambroxol can help, it could offer real hope and improve lives.”

Funded by the Weston Foundation, this study is an important step toward developing new treatments for Parkinson’s disease and other cognitive disorders, including dementia with Lewy bodies. Pasternak and his team plan to start a follow-up clinical trial focused specifically on cognition later this year.

Tokyo [Japan], July 6 (ANI): Gut bacteria are considered to be a key factor in many health-related issues. However, the number and variety of them are vast, as are the ways in which they interact with the body’s chemistry and each other.

For the first time, researchers from the University of Tokyo used a special kind of artificial intelligence called a Bayesian neural network to probe a dataset on gut bacteria in order to find relationships that current analytical tools could not reliably identify.

The human body comprises about 30 trillion to 40 trillion cells, but your intestines contain about 100 trillion gut bacteria. Technically, you’re carrying around more cells that aren’t you than are. Food for thought. And speaking of food, these gut bacteria are, of course, responsible for some aspects of digestion, though what’s surprising to some is how they can relate to many other aspects of human health as well.

The bacteria are incredibly varied and also produce and modify a bewildering number of different chemicals called metabolites. These act like molecular messengers, permeating your body, affecting everything from your immune system and metabolism to your brain function and mood. Needless to say, there’s much to gain by understanding gut bacteria.

“The problem is that we’re only beginning to understand which bacteria produce which human metabolites and how these relationships change in different diseases,” said Project Researcher Tung Dang from the Tsunoda lab in the Department of Biological Sciences, adding, “By accurately mapping these bacteria-chemical relationships, we could potentially develop personalized treatments. Imagine being able to grow a specific bacterium to produce beneficial human metabolites or designing targeted therapies that modify these metabolites to treat diseases.”

There are uncountably many and varied bacteria and metabolites, and therefore far more relationships between these things. Gathering data on this alone is a monumental undertaking, but unpicking that data to find interesting patterns that might betray some useful function is even more so. To do this, Dang and his team decided to explore the use of state-of-the art artificial intelligence (AI) tools.

“Our system, VBayesMM, automatically distinguishes the key players that significantly influence metabolites from the vast background of less relevant microbes, while also acknowledging uncertainty about the predicted relationships, rather than providing overconfident but potentially wrong answers,” said Dang. “When tested on real data from sleep disorder, obesity and cancer studies, our approach consistently outperformed existing methods and identified specific bacterial families that align with known biological processes, giving confidence that it discovers real biological relationships rather than meaningless statistical patterns.”

As VBayesMM can handle and communicate issues of uncertainty, it gives researchers more confidence than a tool which does not. Even though the system is optimized to cope with heavy analytical workloads, mining such huge datasets still comes with high computational cost; however, as time goes on, this will become less and less of a barrier to those wishing to use it. Other limitations at present include that the system benefits from having more data about the gut bacteria than the metabolites they produce; when there’s insufficient bacteria data, the accuracy drops. Also, VBayesMM assumes the microbes act independently, but in reality, gut bacteria interact in an incredibly complex number of ways.

“We plan to work with more comprehensive chemical datasets that capture the complete range of bacterial products, though this creates new challenges in determining whether chemicals come from bacteria, the human body or external sources like diet,” said Dang. “We also aim to make VBayesMM more robust when analyzing diverse patient populations, incorporating bacterial ‘family tree’ relationships to make better predictions, and further reducing the computational time needed for analysis. For clinical applications, the ultimate goal is identifying specific bacterial targets for treatments or dietary interventions that could actually help patients, moving from basic research toward practical medical applications.” (ANI)

(This content is sourced from a syndicated feed and is published as received. The Tribune assumes no responsibility or liability for its accuracy, completeness, or content.)

People who breeze through multiplication often chalk it up to good teachers or hard study. New evidence shows that some brains start the race to learn math with stronger internal wiring.

Researchers also found that a tiny dose of brain stimulation, an electrical buzz, can narrow the gap for those born with weaker brain wiring.

For the study, a five‑day experiment was led by Roi Cohen Kadosh at the University of Surrey, working with colleagues in Oxford, Toronto, and Stanford.

The research centered on 72 right‑handed adults who trained on calculation or memorization tasks while researchers watched activity in the frontoparietal network and applied gentle current to specific sites.

Brain stimulation may boost math skills

Long before electrodes enter the picture, studies show that robust traffic between the dorsolateral prefrontal cortex (dlPFC) and posterior parietal cortex (PPC) predicts sharper arithmetic gains in school‑age children and adults.

These front and back hubs share data with the hippocampus to shift a learner to quick fact retrieval.

People whose signals are faint across this route often stall at the procedural stage, echoing the classic Matthew effect in education, where early advantages snowball over time.

Testing brain stimulation for math education

Participants sat for baseline scans that gauged connectivity strength and local levels of the messenger chemicals GABA and glutamate, a well‑known marker pair for plasticity.

They then solved novel two‑operand problems either by learning an algorithm or by rote rehearsal. During practice, half received sham stimulation, a third received current over the left and right dlPFC, and the remainder over the PPC.

The team used transcranial random noise stimulation, a method introduced in 2008 that sprinkles high‑frequency currents over the scalp and temporarily boosts cortical excitability.

Random noise is thought to raise the signal‑to‑noise ratio for neurons that hover just below firing threshold, giving sluggish circuits a clearer pulse without overshooting in healthy tissue.

The device delivered less than a milliamp, about the tingle you feel from a nine‑volt battery on your tongue, and participants were blind to the condition.

Stimulation aids weak brain connections

Learners who started with feeble dlPFC‑PPC links but received frontal stimulation shaved reaction times on calculation problems by roughly six percent over five sessions, an edge the sham group never matched.

Those with naturally strong links showed no extra benefit and, in rare cases, slight interference when current was added.

The boost also hinged on neurochemistry. Improvement tracked with a drop in local GABA, hinting that the brain shifted into a plastic phase where change beats stability, but only when connectivity stayed modest rather than surging.

Efforts to improve math education

Drill trials, where answers were simply rehearsed, showed little or no gain from stimulation.

The authors suggest that memorization leans less on executive control and more on localized storage, so frontoparietal tuning adds limited value once the answer is locked in.

“So far, most efforts to improve education have focused on changing the environment, training teachers, redesigning curricula, while largely overlooking the learner’s neurobiology,” said Cohen Kadosh.

He added that addressing brain constraints directly could broaden access to diverse career pathways and reduce long‑term inequalities in income, health and well-being.

Brain stimulation may help math struggles

The results revive the idea that brief, well‑timed stimulation could pair with instruction to help stragglers close arithmetic gaps rather than languish under cumulative deficits.

Importantly, the benefit was selective, underscoring the need for screening tools that flag students with weak network strength before any device is applied.

Safety remains favourable at these intensities, but researchers warn against DIY use; stimulating the wrong region or at the wrong time could impair other skills or harden circuits prematurely.

Regulators are still drafting guidelines for non‑medical cognitive devices, and large‑scale school trials have yet to clear ethics boards. 

Broader implications of the research

Past work links higher math fluency in children to elevated parietal GABA, but the relation flips in adulthood, showing that the plasticity window moves with age.

This developmental switch reminds educators that interventions may need age‑specific dosing and targeting.

Animal studies and computational models further suggest that random noise can stabilize synapses once learning consolidates, offering a route to lock in gains without chronic stimulation.

Future projects will watch how long the boost lasts and whether repeated cycles can replace expensive tutoring for some learners.

The future of math education

While electrodes will never replace good teaching, they may act as scaffolds, lifting under‑connected brains so that practice sticks.

If larger trials replicate these findings and prove durable benefits, policy makers could consider targeted neuro-support alongside curriculum reform to help close the widening achievement gap that still defines math education.

The study is published in the journal PLOS Biology.

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