Biology subjects

Senior, A.

Publications and source records attributed to Senior, A..

10 recordsLinked to original sources

bioRxiv2025

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model

Deep learning models that predict functional genomic measurements from DNA sequence are powerful tools for deciphering the genetic regulatory code. Existing methods trade off between input sequence length and prediction resolution, thereby limiting their modality scope and performance. We present AlphaGenome, which takes as input 1 megabase of DNA sequence and predicts thousands of functional genomic tracks up to single base pair resolution across diverse modalities - including gene expression, transcription initiation, chromatin accessibility, histone modifications, transcription factor binding, chro- matin contact maps, splice site usage, and splice junction coordinates and strength. Trained on human and mouse genomes, AlphaGenome matches or exceeds the strongest respective available external models on 24 out of 26 evaluations on variant effect prediction. AlphaGenomes ability to simultaneously score variant effects across all modalities accurately recapitulates the mechanisms of clinically-relevant variants near the TAL1 oncogene. To facilitate broader use, we provide tools for making genome track and variant effect predictions from sequence.

bioinformatics↗

bioRxiv2025

Dietary macronutrients modulate the proteome of brown adipose tissue in males and their female offspring

Brown adipose tissue (BAT) dissipates energy as heat, not only under cold exposure but also in the dissipation of excess ingested energy. Therefore, enhancing BAT activity is a potential avenue to combat weight gain. Dietary macronutrient composition influences BAT size and has recently been shown to influence BAT size of daughters through the patriline of C57BL/6J mice. However, the effects of macronutrient composition and any paternal effects on BAT function have yet to be characterised. Using the Geometric Framework for Nutrition, we investigated the effects of macronutrient composition on the BAT proteome in male mice and intergenerational effects in their offspring. In fathers, >50% of the proteome was affected by macronutrient composition, with distinct clusters of proteins that responded in similar ways. We identified two clusters with inverse patterns that correlated with BAT mass. Notably, UCP1 was reduced on low fat diets that promoted increased BAT mass, while there were increased levels of proteins involved in protein turnover on those same diets. The same diets also led to a reduction in proteins involved in purine biosynthesis (often UCP1 inhibitors). We did not find any effects of paternal diet on the BAT proteome in sons, but paternal protein intake negatively affected basigin expression in daughters - a protein that regulates UCP1 transcription. Our results highlight that dietary macronutrient composition in males remodels the protein expression landscape of BAT, and pre-conceptionally reprograms BAT expression profiles of female offspring.

molecular biology↗

bioRxiv2024

Interpreting prediction intervals and distributions for decoding biological generality in meta-analyses

Despite the importance of identifying predictable regularities for knowledge transfer across contexts, the generality of ecological and evolutionary findings is yet to be systematically quantified. We present the first large-scale evaluation of generality using new metrics. By focusing on biologically relevant study levels, we show that generalization is not uncommon. Overall, 20% of meta-analyses will produce a non-zero effect 95% of the time in future replication studies with a 70% probability of observing meaningful effects in study-level contexts. We argue that the misconception that generalization is exceedingly rare is due to conflating within-study and between-study variances in ecological and evolutionary meta-analyses, which results from focusing too much on total heterogeneity (the sum of within-study and between-study variances). We encourage using our proposed approach to elucidate general patterns underpinning ecological and evolutionary phenomena.

ecology↗

bioRxiv2024

High fat low carbohydrate diet is linked to protection against CNS autoimmunity

Multiple sclerosis (MS) is a common central nervous system (CNS) autoimmune disease, and diets and nutrients are emerging as critical contributing factors. However, a comprehensive understanding of their impacts and the underlying mechanisms involved is lacking. Harnessing state-of-the-art nutritional geometry analytical methods, we first revealed that globally, increased carbohydrate supply was associated with increased MS disease burden, while fat supply had an opposite effect. Furthermore, in a preclinical MS mouse model, experimental autoimmune encephalomyelitis (EAE), we found that an isocaloric diet high in carbohydrate aggravated EAE, while a diet enriched in fat was fully protective. This was reflected by reduced neuroinflammation and skewing towards anti-inflammatory phenotypes, which involved transcriptomic, epigenetic and immunometabolic changes. We showcased that manipulating diets is a potentially efficient and cost-effective approach to prevent and/or ameliorate EAE. This exhibits translational potentials for intervention/prevention of MS and possibly other autoimmune diseases.

immunology↗

bioRxiv2024

Bivariate multilevel meta-analysis of log response ratio and standardized mean difference for robust and reproducible environmental and biological sciences

Meta-analytic modelling plays a pivotal role in synthesizing research and informing relevant policies. Yet researchers face many analytical challenges. In environmental and biological sciences, one of the most common yet unrecognised issues is the selection between two common effect size metrics, log response ratio (lnRR) and standardized mean difference (SMD); these two are the most popular and alternative effect sizes. Having to choose between them creates room for analytical flexibility, which is susceptible to researcher degrees of freedom. Another common issue is failure to deal with statistical dependence between effect sizes, resulting in invalid inferences on evidence. We propose addressing these two issues through the joint synthesis (dual use) of lnRR and SMD. Using 75 meta-analyses, including 3,887 environmental/biological primary studies ([~]20,000 effect sizes), we show a high false positive rate (40%) in conventional meta-analytic practices (random-effects model) compared to the proposed bivariate multilevel meta-analysis of lnRR and SMD along with robust variance estimation. Relying solely on either lnRR or SMD results in non-trivial discrepancies in detecting statistically significant effects (18%) and occasional inconsistencies in sign (9%). Discrepancies in interpreting effect size, heterogeneity, and publication bias are prevalent between models using lnRR and SMD (e.g., 52% for publication bias). In contrast, bivariate synthesis of lnRR and SMD yields substantial information gain, reducing standard error in effect size estimates by 29%, equivalent to adding 40 additional effect sizes. We present a user-friendly website with a step-by-step implementation guide. Our proposed robust approach aspires to improve meta-analytic modelling using lnRR and SMD in environmental and biological evidence synthesis, amplifying their reproducibility and credibility.

ecology↗

bioRxiv2024

Optimised proteomic analysis of insulin granules from MIN6 β-cells identifies Scamp3, a novel regulator of insulin secretion and content

Pancreatic {beta}-cells in the islets of Langerhans are key to maintaining glucose homeostasis, by secreting the peptide hormone insulin. Insulin is packaged within vesicles named insulin secretory granules (ISGs), that have recently been considered to have intrinsic structures and proteins that regulate insulin granule maturation, trafficking, and secretion. Previously, studies have identified a handful of novel ISG-associated proteins using different separation techniques. Here, this study combines an optimized ISG isolation technique and mass spectrometry-based proteomics, with an unbiased protein correlation profiling and targeted machine learning approach to uncover 211 ISG-associated proteins. Five of these proteins: Syntaxin-7, Synaptophysin, Synaptotagmin-13, Zinc transporter ZIP8 and SCAMP3 have not been previously ISG-associated. Through colocalization analysis of confocal imaging we validate the association of these proteins to the ISG in MIN6 and human {beta}-cells. We further validate the role for one (SCAMP3) in regulating insulin storage and secretion from {beta}-cells for the first time. SCAMP3 knock-down INS-1 cells show a reduction in insulin content and dysfunctional insulin secretion. These data provide the basis for future investigation into {beta}-cell biology and the regulation of insulin secretion. Article HighlightsO_LIWhy did we undertake this study? We undertook this study to optimize insulin granule isolation techniques alongside enhanced proteomics analyses to establish the first published murine insulin granule proteome. C_LIO_LIWhat is the specific question(s) we wanted to answer? We aimed to specifically answer and investigate what proteins are present on insulin granules from MIN6 cells to further our understanding of insulin granule biogenesis, trafficking, and secretion. C_LIO_LIWhat did we find? We find and validate the presence of 5 novel insulin granule-associated proteins. C_LIO_LIWhat are the implications of our findings? An extensive proteomics analysis of MIN6 insulin granules and implicate Scamp3 as a novel protein that regulates insulin content and secretion in beta-cells. C_LI

cell biology↗

bioRxiv2024

Single-cell analysis identifies distinct macrophage phenotypes associated with pro-disease and pro-resolving functions in the endometriotic niche

Endometriosis negatively impacts the health-related quality of life of 190 million women worldwide. Novel advances in non-hormonal treatments for this debilitating condition are desperately needed. Macrophages play a vital role in the pathophysiology of endometriosis and represent a promising therapeutic target. In the current study, we revealed the full transcriptomic complexity of endometriosis-associated macrophage subpopulations using single-cell analyses in a preclinical mouse model of experimental endometriosis. We have identified two key lesion-resident populations that resemble i) tumour-associated macrophages (characterized by expression of Folr2, Mrc1, Gas6 and Ccl8+) that promoted expression of Col1a1 and Tgfb1 in human endometrial stromal cells and increased angiogenic meshes in human umbilical vein endothelial cells, and ii) scar-associated macrophages (Mmp12, Cd9, Spp1, Trem2+) that exhibited a phenotype associated with fibrosis and matrix remodelling. We also described a population of pro-resolving large peritoneal macrophages (LpM) that align with a lipid-associated macrophage phenotype (Apoe, Saa3, Pid1) concomitant with altered lipid metabolism and cholesterol efflux. Gain of function experiments using an Apoe mimetic resulted in decreased lesion size and fibrosis, and modification of peritoneal macrophage populations in the preclinical model. Using cross-species analysis of mouse and human single-cell datasets, we determined the concordance of peritoneal and lesion-resident macrophage subpopulations, identifying key similarities and differences in transcriptomic phenotypes. Ultimately, we envisage that these findings will inform the design and use of specific macrophage-targeted therapies and open new avenues for the treatment of endometriosis.

immunology↗

bioRxiv2020

Dietary protein increases T cell independent sIgA production through changes in gut microbiota-derived extracellular vesicles

Secretory IgA (sIgA) is a key mucosal component ensuring host-microbiota mutualism. Using nutritional geometry modelling in mice fed 10 different macronutrient-defined, isocaloric diets, we identified dietary protein as the major driver of sIgA production. Protein-driven sIgA induction was not mediated by T cell-dependent pathways or changes in gut microbiota composition. Instead, the microbiota of high-protein fed mice produced significantly higher quantities of extracellular vesicles (EV), compared to those of mice fed high-carbohydrate or high-fat diets. These EV activated TLR4 to increase the epithelial expression of IgA-inducing cytokine, APRIL, B cell chemokine, CCL28, and the IgA transporter, PIGR. We showed that succinate, produced in high concentrations by microbiota of high-protein fed animals, increased the generation of reactive oxygen species by bacteria, which in turn promoted EV production. This is the first report establishing a causal link between dietary macronutrient composition, gut microbial EV release and host sIgA response.

immunology↗

bioRxiv2020

Sex and Power: sexual dimorphism in trait variability and its eco-evolutionary and statistical implications

Biomedical and clinical sciences are experiencing a renewed interest in the fact that males and females differ in many anatomic, physiological, and behavioral traits. Sex differences in trait variability, however, are yet to receive similar recognition. In medical science, mammalian females are assumed to have higher trait variability due to estrous cycles (the estrus-mediated variability hypothesis); historically in biomedical research, females have been excluded for this reason. Contrastingly, evolutionary theory and associated data support the greater male variability hypothesis. Here, we test these competing hypotheses in 218 traits measured in >26,900 mice, using meta-analysis methods. Neither hypothesis could universally explain patterns in trait variability. Sex-bias in variability was trait-dependent. While greater male variability was found in morphological traits, females were much more variable in immunological traits. Sex-specific variability has eco-evolutionary ramifications including sex-dependent responses to climate change, as well as statistical implications including power analysis considering sex difference in variance.

evolutionary biology↗

bioRxiv2019

Genome-wide analysis of diet and gene interactions in Drosophila uncovers the glucose transporter, CG4607, as a diet-responsive gene.

Genetic and environmental factors play a major role in metabolic health. However, they do not act in isolation, as a change in an environmental factor such as diet may exert different effects based on an individuals genotype. Here, we sought to understand how such gene-diet interactions influenced nutrient storage and utilisation, a major determinant of metabolic disease. We subjected the Drosophila Genetic Reference Panel (DGRP), comprising 200 genetically divergent inbred fly strains, to diets varying in sugar, fat and protein. We assessed starvation resistance, a holistic phenotype of nutrient storage and utilisation that can be robustly measured. Diet influenced the starvation resistance of each strain, but this effect varied markedly between strains. This demonstrates that genetics plays a major role in the response to diet. Furthermore, heritability analysis revealed that the greatest variability arose from diets either high in sugar or high in protein. To uncover the genetic underpinnings of this variation, we mapped 1,239 diet-responsive SNPs in 534 genes, 325 of which have human orthologues. Using whole-body knockdown, we confirmed that 30 candidate genes were required for glucose tolerance, storage and utilization. In particular, we characterised CG4607, a GLUT6/GLUT8 homolog, as a key protein involved in sugar tolerance. Overall, this provides strong evidence that genetics is a major contributor to how individuals respond to diets of varying nutrient composition. It is likely that a similar principle may be applied to metabolic disease in higher organisms thus supporting the case for nutrigenomics as an important health strategy.

genetics↗