Biology subjects

Williams, E. G.

Publications and source records attributed to Williams, E. G..

13 recordsLinked to original sources

bioRxiv2026

Voluntary Exercise Differentially Affects High-Fat Induced Metabolic Phenotypes Across BXD Mouse Strains

Obesity and metabolic diseases such as diabetes reflect long-term interactions between diet, lifestyle, and genetics, variables that are hard to control in humans but can be systematically varied in mice, though behavioral variables like long-term exercise remain harder to model. Having previously phenotyped ~50 BXD strains, we identified BXD40, BXD43, and BXD100 as predisposed to voluntary wheel-running (~6 km/day at 24 weeks) with variable susceptibility to diet-induced obesity and diabetes. We followed these strains from 8-32 weeks on a 60% kcal/fat high-fat (HF) diet, with or without access to running wheels. Wheel usage declined from ~9 km/day in the first two weeks to a steady ~5 km/day thereafter yet still improved oral glucose tolerance by 35% over sedentary controls by 24 weeks. Despite equivalent body weight, runners ate 20% more relative to body weight, indicating the glucose benefit was independent of caloric intake. We next asked whether the gut microbiome contributes to this benefit. Long-term exercise produced only modest, cohort-dependent shifts in cecal microbial composition, though functional profiling revealed exercise-associated differences in carbohydrate- and glucose-metabolism pathways. Reciprocal bedding exchange between sedentary and running BXD43 cages reduced microbial differentiation in a taxon-specific manner and coincided with marked attenuation of the glucose phenotype, despite unchanged running behavior. Together, these findings validate BXD40, BXD43, and BXD100, alongside the canonical C57BL/6, as a model for studying diet, exercise, and genetics in metabolic disease, and implicate gut microbial taxa in shaping exercise's metabolic effects during HF feeding.

genetics↗

bioRxiv2025

Integrative Analysis of the Mouse Cecal Microbiome Across Diet, Age, and Metabolic State in the Diverse BXD Population

BackgroundThe gut microbiota adapts to and shapes the hosts metabolic state through affecting circulating metabolites and consequent gene regulatory networks, resulting in systemic influences in diverse organs via connections such as the gut-liver axis. Numerous variables such as diet, age, and host genetics modulate the composition of the gut microbiome, but their interactions and specific associative and mechanistic links to host molecular phenotypes remain incompletely unannotated. Integrated multi-omics approaches in genetically diverse populations offer an opportunity to dissect these interactions and identify predictive microbial signatures for host phenotypes, such as body weight, and molecular associations with gene expression pathways in gut and liver. ResultsWe sequenced, aligned, and integrated the cecal metagenome, metatranscriptome, and host transcriptome from 232 mice across 175 distinct cohorts according to a low fat chow diet (CD) or a high-fat diet (HF), four adult ages (between roughly 180 to 730 days of age), and 43 distinct genotypes (inbred BXD strains). Genetics and diet exerted the strongest influence on microbiota abundance and activity, followed by age. HF feeding significantly reduced diversity across all ages and all genotypes, altering >300 species. Machine learning models based on microbial profiles reliably predicted body weight within dietary group (AUC = 0.84 for CD, 0.79 for HF) and chronological age (AUC = 0.84), with model performance rising of age prediction rising to 0.95 when integrating top microbial features with liver proteomics. Network analyses of expression data revealed links between genes, pathways, and specific microbes, including a negative association between cecal Ido1 expression and short-chain fatty acid (SCFA)-producing Lachnospiraceae, suggesting dietary fat may modulate host tryptophan metabolism through microbiota shifts. ConclusionsWhole metagenome and metatranscriptome sequencing approaches have massively expanded the landscape of microbiome analysis compared to earlier short-read 16S analyses. The resulting datasets quantify hundreds of uniquely identifiable microbes, which can be used to create sets of highly predictive microbial biomarkers for aging and obesity. When trained on controlled mouse populations, these results demonstrate that microbiome profiling can achieve high predictive capacity (AUC = 0.95 with multi-omics integration) for complex readouts such as age and body weight (AUC = 0.84), even considering genetic and dietary variation, establishing a framework for biomarker development. While at present many bacteria are still functionally unannotated at the species level, multi-omics approaches -- including gene expression from the host tissues -- provide insights into the functional associations of specific taxa in the microbiome.

microbiology↗

bioRxiv2025

Genetic Modulation of Lifespan: Dynamic Effects, Sex Differences, and Body Weight Trade-offs

The dynamics of lifespan are shaped by DNA variants that exert effects at different ages. We have mapped genetic loci that modulate age-specific mortality using an actuarial approach. We started with an initial population of 6,438 pubescent siblings and ended with a survivorship of 559 mice that lived to at least 1100 days. Twenty-nine Vita loci dynamically modulate the mean lifespan of survivorships with strong age- and sex-specific effects. Fourteen have relatively steady effects on mortality while other loci act forcefully only early or late in life and with polarities of effects that invert. A distinct set of 19 Soma loci shape the negative correlation between weights of young adults with their life expectancies--much more strongly so in males than females. Another set of 11 Soma loci shape the positive correlation between weights at older ages with life expectancies. The Vita and Soma loci share 289 age-dependent epistatic interactions (LODs [≥]3.8) but fewer than 4% are common to both sexes. We provide two examples of how to move from maps toward potential mechanisms. Our findings provide an empirical bridge between evolutionary theories on aging and genetic and molecular causes. These loci and their interactions are key to begin to understand the impact of interventions that may extend healthy lifespan in mice and even in humans.

genetics↗

bioRxiv2024

Spatially tuneable multi-omics sequencing using light-driven combinatorial barcoding of molecules in tissues

Mapping the molecular identities and functions of cells within their spatial context is key to understanding the complex interplay within and between tissue neighbourhoods. A wide range of methods have recently enabled spatial profiling of cellular anatomical contexts, some offering single-cell resolution. These use different barcoding schemes to encode either the location or the identity of target molecules. However, all these technologies face a trade-off between spatial resolution, depth of profiling, and scalability. Here, we present Barcoding by Activated Linkage of Indexes (BALI), a method that uses light to write combinatorial spatial molecular barcodes directly onto target molecules in situ, enabling multi-omic profiling by next generation sequencing. A unique feature of BALI is that the user can define the number, size, and shape, and resolution of the spatial locations to be interrogated, with the potential to profile millions of distinct regions with subcellular precision. As a proof of concept, we used BALI to capture the transcriptome, chromatin accessibility, or both simultaneously, from distinct areas of the mouse brain in single tissue sections, demonstrating strong concordance with publicly available datasets. BALI therefore combines high spatial resolution, high throughput, histological compatibility, and workflow accessibility to enable powerful spatial multi-omic profiling.

genomics↗

bioRxiv2023

Polyglucosan body density in the aged mouse hippocampus is controlled by a novel modifier locus on chromosome 1

In aged humans and mice, aggregates of hypobranched glycogen molecules called polyglucosan bodies (PGBs) accumulate in hippocampal astrocytes. PGBs are known to drive cognitive decline in neurological diseases but remain largely unstudied in the context of typical brain aging. Here, we show that PGBs arise in autophagy-dysregulated astrocytes of the aged C57BL/6J mouse hippocampus. To map the genetic cause of age-related PGB accumulation, we quantified PGB burden in 32 fully sequenced BXD-recombinant inbred mouse strains, which display a 400-fold variation in hippocampal PGB burden at 16-18 months of age. A major modifier locus was mapped to chromosome 1 at 72-75 Mb, which we defined as the Pgb1 locus. To evaluate candidate genes and downstream mechanisms by which Pgb1 controls the aggregation of glycogen, extensive hippocampal transcriptomic and proteomic datasets were produced for aged mice of the BXD family. We utilized these datasets to identify Smarcal1 and Usp37 as potential regulators of PGB accumulation. To assess the effect of PGB burden on age-related cognitive decline, we performed phenome-wide association scans, transcriptomic analyses as well as conditioned fear memory and Y-maze testing. Importantly, we did not find any evidence suggesting a negative impact of PGBs on cognition. Taken together, our study demonstrates that the Pgb1 locus controls glycogen aggregation in astrocytes of the aged hippocampus without affecting age-related cognitive decline.

genetics↗

bioRxiv2023

Genetic and dietary modulators of the inflammatory response in the gastro-intestinal tract of the BXD mouse genetic reference population

Inflammatory gut disorders, including inflammatory bowel disease (IBD), can be impacted by dietary, environmental and genetic factors. While the incidence of IBD is increasing worldwide, we still lack a complete understanding of the gene-by-environment interactions underlying inflammation and IBD. Here, we profiled the colon transcriptome of 52 BXD mouse strains fed with a chow or high-fat diet (HFD) and identified a subset of BXD strains that exhibit an IBD-like transcriptome signature on HFD, indicating that an interplay of genetics and diet can significantly affect intestinal inflammation. Using gene co-expression analyses, we identified modules that are enriched for IBD-dysregulated genes and found that these IBD-related modules share cis-regulatory elements that are responsive to the STAT2, SMAD3, and REL transcription factors. We used module quantitative trait locus (ModQTL) analyses to identify genetic loci associated with the expression of these modules. Through a prioritization scheme involving systems genetics in the mouse and integration with external human datasets, we identified Muc4 and Epha6 as the top candidates mediating differences in HFD-driven intestinal inflammation. This work provides insights into the contribution of genetics and diet to IBD risk and identifies two candidate genes, MUC4 and EPHA6, that may mediate IBD susceptibility in humans.

systems biology↗

bioRxiv2021

GCN5 Maintains Muscle Integrity by Acetylating YY1 to Promote Dystrophin Expression

This work identifies a novel role for the acetyltransferase GCN5 in regulating muscle integrity through inhibition of DNA binding activity of the transcriptional repressor YY1. Here we report that in mice a muscle-specific knockout of GCN5 (Gcn5skm-/-) reduces the expression of key structural muscle proteins, including dystrophin, resulting in myopathy. Supporting our observation, a meta-analysis between the differential transcriptome of Gcn5skm-/- muscle and all available open-access data sets identified top correlations with musculoskeletal diseases in humans. GCN5 was found to acetylate YY1 at two residues (K392 and K393), which disrupts the interaction between the YY1 zinc-finger region and DNA. De/acetylation mimics for these YY1 post-translational modifications modulated muscle structural gene expression and DNA binding. Analysis of human GTEx data also found positive and negative correlations between fiber diameter and GCN5 and YY1 respectively. Collectively, our results demonstrate that GCN5 acetyltransferase activity regulates YY1 DNA binding and expression of dystrophin to modulate muscle integrity.

cell biology↗

bioRxiv2021

Towards a systematic characterization of protein complex function: a natural language processing and machine-learning framework

It is a general assumption of molecular biology that the ensemble of expressed molecules, their activities and interactions determine biological processes, cellular states and phenotypes. Quantitative abundance of transcripts, proteins and metabolites are now routinely measured with considerable depth via an array of "OMICS" technologies, and recently a number of methods have also been introduced for the parallel analysis of the abundance, subunit composition and cell state specific changes of protein complexes. In comparison to the measurement of the molecular entities in a cell, the determination of their function remains experimentally challenging and labor-intensive. This holds particularly true for determining the function of protein complexes, which constitute the core functional assemblies of the cell. Therefore, the tremendous progress in multi-layer molecular profiling has been slow to translate into increased functional understanding of biological processes, cellular states and phenotypes. In this study we describe PCfun, a computational framework for the systematic annotation of protein complex function using Gene Ontology (GO) terms. This work is built upon the use of word embedding-- natural language text embedded into continuous vector space that preserves semantic relationships-- generated from the machine reading of 1 million open access PubMed Central articles. PCfun leverages the embedding for rapid annotation of protein complex function by integrating two approaches: (1) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector, and (2) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing the statistical test for the enrichment of the top NN GO terms within the child terms of the predicted GO terms by RF models. Thus, PCfun amalgamates information learned from the gold-standard protein-complex database, CORUM, with the unbiased predictions obtained directly from the word embedding, thereby enabling PCfun to identify the potential functions of putative protein complexes. The documentation and examples of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.

bioinformatics↗

bioRxiv2020

The Molecular Landscape of the Aging Mouse Liver

Systems biology approaches often use inferred networks of gene expression and metabolite data to identify regulatory factors and pathways connected with phenotypic variance. Generally, study-specific multi-layer "Omics" datasets are used to contextualize generic molecular networks. In this regard separating upstream causal mechanisms, downstream biomarkers, and incidental correlations remains a significant challenge, yet it is essential for designing mechanistic experiments. To address this, we designed a study following a population of 2157 individuals from 89 isogenic BXD mouse strains across their lifespan to identify molecular interactions among genotype, environment, age (GxExA) and metabolic fitness. Each strain was separated into two cohorts, one fed low fat (6% cal/fat) and the other high fat (60% cal/fat) diets. Tissues were collected for 662 individuals (309 cohorts) diverging across age (7, 12, 18, and 24 months), diet, sex, and strain. Transcriptome, proteome, and metabolome data were generated for liver. Of these we identified linear relations among these molecular data with lifespan for the same genomes of mice (Roy et al. 2020), and we defined [~]1100 novel protein-coding genes associated with longevity. We knocked down the ortholog of Ctsd in C. elegans. The treatment reduced longevity both in wildtype and in mutant long-lived strains, thus validating the prediction. Next, to assess the molecular impact of GxExA on gene expression, the multi-omics data was parsed into metabolic networks where connectivity varied due to the independent variables. Differences in edge strengths connecting nodes in these molecular networks according to each variable enabled causal inference by using stability selection, with roughly 21% of novel gene-pathway connections being causally affected by diet and/or age. For instance, Chchd2 is activated by aging and drives changes in the proteasome, oxidative phosphorylation, and mitochondrial translation transcriptional networks. Together, we have developed a large multi-omics resource for studying aging in the liver, and a resource for turning standard associations into causal networks.

systems biology↗

bioRxiv2019

Influence of body weight at young adulthood on the epigenetic clock and lifespan in the BXD murine family

DNA methylation (DNAm) is shaped by genetic and environmental factors and modulated by aging. Here, we examine interrelations between epigenetic aging, body weight (BW), and lifespan in 12 inbred mouse strains from the BXD panel that exhibit over two-fold variation in longevity. Genome-wide DNAm was assayed in 70 liver specimens from mice ranging in age from 6 to 25 months that were maintained on normal chow or high fat diet (HFD). We defined subsets of CpG regions associated with age, BW at young adulthood, and strain-by-diet dependent life expectancy. The age associated differentially methylated CpG regions (age-DMRs) featured distinct genomic characteristics, with DNAm gains over time occurring in sites with high CpG density and low average methylation. CpG regions associated with BW were enriched in introns and generally showed lower methylation in mice with higher BW, and inversely correlated with gene expression such that mRNA was higher in mice with higher BW. Lifespan-associated regions featured no distinct genomic characteristics but were linked to genes involved in lifespan regulation, including the telomerase reverse transcriptase gene, Tert, which showed lower methylation and higher gene expression in long-lived strains. An epigenetic clock defined from the age-DMRs conveyed accelerated aging in mice belonging to strains with shorter lifespans. Both higher BW at young adulthood and HFD were associated with accelerated epigenetic aging. Our results highlight the age-accelerating effect of heavier body weight. Furthermore, the study demonstrates that the measure of epigenetic aging derived from age-DMRs can predict strain and diet-induced differences in lifespan.

genomics↗

bioRxiv2019

Modulation of longevity by diet, and youthful body weight, but not by weight gain after maturity

Diet and environment profoundly modulate lifespan. We measured longevity as a function of diet and weight gain across a genetically diverse family of mice. We followed 1348 females from two parental strains--C57BL/6J and DBA/2J--and 146 cohorts of BXD isogenic progeny strains (n = 73) across their lifespan on a low fat chow diet (CD, 18% calories from fat) and on a high fat diet (HFD, 60% calories from fat). On average, HFD shortens lifespan by 85 days or 12%, roughly equivalent to an 8-10 year decrease in humans. However, strain variation in the response of diet on longevity is remarkably high, ranging from a longevity loss of 54% in BXD65 to a gain of 37% in BXD8. Baseline weights and early weight gain are both associated with a mean decrease in longevity of [~]4 days/g. By 500 days-of-age, cases fed HFD gained four times as much weight as control on average. However, strain-specific variation was substantial, thus weight gain did not correlate well with lifespan. In summary, high fat had a strong negative effect on longevity, but genetic interactions effects were even stronger. This highlights the unequivocal importance of genetic differences in making dietary recommendations.

genetics↗

bioRxiv2019

The expanded BXD family of mice: A cohort for experimental systems genetics and precision medicine

The challenge of precision medicine is to model complex interactions among DNA variants, sets of phenotypes, and complex environmental factors and confounders. We have expanded the BXD family, creating a powerful and extensible test bed for experimental precision medicine and an ideal cohort to study gene-by-environmental interactions.\n\nThese BXD segregate for over 6 million variants, with a mean minor allele frequency close to 0.5. We have increased the family two-fold to 150 inbred strains, all derived from C57BL/6J and DBA/2J. We have also generated updated and comprehensive genotypes and an unrivaled deep phenome.\n\nApproximately 10,000 recombinations have been located, allowing precision of quantitative trait loci mapping of {+/-}2.0 Mb over much of the genome and {+/-}0.5 Mb for Mendelian loci. The BXD phenome includes more than 100 omics data sets and >7000 quantitative and clinical phenotypes, all of which is publicly available.\n\nThe BXD family is an enduring, collaborative, and replicable resource to test causal and mechanistic links between genomes and phenomes at many stages and under a wide variety of treatments and interventions.

genomics↗

bioRxiv2018

Genomic, Proteomic and Phenotypic Heterogeneity in HeLa Cells across Laboratories: Implications for Reproducibility of Research Results

The independent reproduction of research results is a cornerstone of experimental research, yet it is beset by numerous challenges, including the quality and veracity of reagents and materials. Much of life science research depends on life materials, including human tissue culture cells. In this study we aimed at determining the degree of variability in the molecular makeup and the ensuing phenotypic consequences in commonly used human tissue culture cells. We collected 14 stock HeLa aliquots from 13 different laboratories across the globe, cultured them in uniform conditions and profiled the genome-wide copy numbers, mRNAs, proteins and protein turnover rates via genomic techniques and SWATH mass spectrometry, respectively. We also phenotyped each cell line with respect to the ability of transfected Let7 mimics to modulate Salmonella infection.\n\nWe discovered significant heterogeneity between HeLa variants, especially between lines of the CCL2 and Kyoto variety. We also observed progressive divergence within a specific cell line over 50 successive passages. From the aggregate multi-omic datasets we quantified the response of the cells to genomic variability across the transcriptome and proteome. We discovered organelle-specific proteome remodeling and buffering of protein abundance by protein complex stoichiometry, mediated by the adaptation of protein turnover rates. By associating quantitative proteotype and phenotype measurements we identified protein patterns that explained the varying response of the different cell lines to Salmonella infection.\n\nAltogether the results indicate a striking degree of genomic variability, the rapid evolution of genomic variability in culture and its complex translation into distinctive expressed molecular and phenotypic patterns. The results have broad implications for the interpretation and reproducibility of research results obtained from HeLa cells and provide important basis for a general discussion of the value and requirements for communicating research results obtained from human tissue culture cells.

systems biology↗