Biology subjects

Mendenhall, A. R.

Publications and source records attributed to Mendenhall, A. R..

7 recordsLinked to original sources

bioRxiv2025

An Explainable AI Framework for Identifying Universal Aging Signatures in Cell Embeddings

Aging is a complex biological process marked by progressive physiological decline and increased disease vulnerability. Single-cell RNA sequencing offers unprecedented resolution for studying aging, yet isolating aging-related signatures remains challenging because gene expression is primarily shaped by other factors such as cell type, tissue, and sex. We present ACE (Aging Cell Embeddings), an explainable deep generative framework that disentangles aging-related gene expression changes from background biological variation. ACE employs two latent representations: one capturing aging-related signatures and another representing non-aging-related variation in the data. Through explainable AI, ACE identifies key genes and pathways associated with aging amid dominant non-aging-related variations. Applied to large-scale mouse, fly, and human datasets, ACE uncovers aging signatures both within specific tissue-cell-type contexts and across all tissues and cell types, enabling accurate prediction of biological age. Moreover, ACE identifies aging genes conserved across species, highlighting its ability to reveal shared biological mechanisms of aging. Experimental RNAi knockdowns in C. elegans validate ACEs findings, confirming its ability to prioritize novel aging genes affecting lifespan. ACE reveals key pathways involved in proteostasis, immune regulation, and extracellular matrix remodeling, and identifies Uba52 through the cross-species model as an important aging gene, whose knockdown in C. elegans significantly shortens lifespan. By providing interpretable and generalizable aging embeddings, ACE establishes a foundation for cross-species single-cell aging studies and translational geroscience.

bioinformatics↗

bioRxiv2025

Diverse Patterns of Allele-Specific Expression in Healthy Human Tissues

Differences in gene sequence and gene expression underlie variation in traits. However, even monozygotic twins do not express their genes in the same way, develop divergence in traits, and succumb to distinct chronic diseases. During development, epigenetic silencing programs cause diversity in allele expression, resulting in differences in traits and chronic disease risk. To quantify human autosomal allele expression between individuals, we analyzed human allele-specific expression data from the GTEx project. For hundreds of genes, some individuals will express the gene biallelically, while many others may only express one allele or extreme bias towards one allele. We found gene-specific patterns of interindividual variation in allele bias. We found that some individuals have more genome-wide monoallelic/biased expression than others. Individuals also had distinct combinations of allele expression bias. These differences can underlie variation in traits, idiopathic or incompletely penetrant traits/diseases, and chronic diseases. Significance/ImpactAllele-specific expression can affect cancer, immune response, and genetic disease. This work reveals 1) gene-specific multimodal patterns of interindividual variation in allele bias, 2) that individuals can maintain bias across tissues, and 3) that different individuals have distinct combinations of silenced alleles. These different patterns and the weighted classifications demonstrate how allele bias manifests between individuals; there are individuals and tissues with more biased/non-Mendelian expression and some tissues have more age-related changes in which alleles are silenced.

genomics↗

bioRxiv2024

Maternal histone methyltransferases antagonistically regulate monoallelic expression in C. elegans

Undefined epigenetic programs act to probabilistically silence individual autosomal alleles, generating unique individuals, even from genetic clones. This sort of random monoallelic expression can explain variation in traits and diseases that differences in genes and environments cannot. Here, we developed the nematode Caenorhabditis elegans to study monoallelic expression in whole tissues, and defined a developmental genetic regulation pathway. We found maternal H3K9 histone methyltransferase (HMT) SET-25/SUV39/G9a works with HPL-2/HP1 and LIN-61/L3MBTL2 to randomly silence alleles in the intestinal progenitor E-cell of 8-cell embryos to cause monoallelic expression. SET-25 was antagonized by another maternal H3K9 HMT, MET-2/SETDB1, which works with LIN-65/ATF7ZIP and ARLE-14/ARL14EP to prevent monoallelic expression. The HMT-catalytic SET domains of both MET-2 and SET-25 were required for regulating monoallelic expression. Our data support a model wherein SET-25 and MET-2 regulate histones during development to generate patterns of somatic monoallelic expression that are persistent but not heritable.

systems biology↗

bioRxiv2022

Oxidative Stress Induced Senescence Gives Rise to Transcriptionally Distinct Physiological States

As people age, the risk of cardiovascular disease, diabetes, cancer, and Alzheimers disease increases, making age itself the greatest risk factor for many human diseases. Thus, understanding the aging process can have profound consequences for human health. One striking feature of the aging process is the accumulation of senescent cells with age. When cells become damaged, they can enter a state of senescence which is a permanent cell cycle exit associated with the secretion of inflammatory cytokines. In mouse models of aging, the destruction of senescent cells with senolytic drugs delays age-associated decline and extends healthy lifespan. Yet, despite the wealth of accumulated knowledge, we do not entirely understand the biology of senescent cells. Prior work has shown that senescence is associated with increased variation in gene expression, suggesting that there may exist distinct transcriptional signatures of senescence. Understanding the different transcriptional physiological states of senescent cells would allow us to better treat them with cell-type-specific senolytic drugs. Here, we performed large-scale single-cell RNA-sequencing time series experiments to understand the development of transcriptional heterogeneity among senescent cell types. Our approach allowed us to observe and classify different transcriptional signatures of senescent cells as they emerged through time. We found that upon entering oxidative stress-induced senescence, separate subpopulations of cells were reproducibly adopting two distinct transcriptional states, one of which was associated with stress response and the second one with tissue remodeling. Our data suggest that a combination of senolytic drugs may be needed to more effectively eliminate senescent cells by targeting physiologically distinct sub-populations.

cell biology↗

bioRxiv2020

Methods for High-Fidelity Spectral Data Collection for Generating Ground Truth Data for Simulated Tissues

Our research is focused on creating and simulating hyper-realistic artificial human tissue analogues. Generation and simulation of macroscopic biological material depends upon accurate ground-truth data on spectral properties of materials. Here, we developed methods for high fidelity spectral data collection using two differently colored simulated skin tissue samples and a portable spectral imaging camera. Using the standard procedure, we developed, we quantified the reproducibility of the spectral image signatures of the two synthetic skin samples under natural and artificial lighting conditions commonly found in clinical settings. We found high coefficients of determination for all measures taken under the same lighting. As expected, we found the spectral image signature of each sample was dependent on the illumination source. Our results confirm that illumination spectra data should be included with spectral image data. The high-fidelity methods for spectral image data collection we developed here should facilitate accurate collection of spectral image signature data for gross biological samples and synthetic materials collected under the same illumination source.

bioengineering↗

bioRxiv2019

Introns Control Intrinsic Noise

Introns are noncoding DNA elements found in between coding exons of most genes in most metazoans. Introns are known to both increase expression levels and provide means for making different protein products from the same gene via alternative splicing. It is thought that evolution has selected for these functions of introns. Here we present evidence for an additional reason why so many metazoan genes have introns, and why some do not. We found that introns control stochasticity in metazoan gene expression. They do this by controlling stochastic autosomal allele bias. We determined this by measuring the expression of differently colored reporter alleles with and without introns. We find that introns control stochastic autosomal allele biases using a 5-position dependent mechanism. Intron control of allele expression noise provides an additional mechanism to consider when pondering why so many metazoan genes have introns and why some have lost them.

systems biology↗

bioRxiv2018

Differences in protein dosage underlie nongenetic differences in traits

Phenotypic expression of many traits varies among isogenic individuals in homogeneous environments. Intrinsic variation in the protein chaperone system affects a wide variety of traits in diverse biological systems. In C. elegans, expression of hsp-16.2 chaperone biomarkers predicts the penetrance of mutations and lifespan after heat shock. But the physiological mechanisms by which cells express different amounts of the biomarker were unknown. Here, we used an in vivo microscopy approach to dissect the mechanisms of cell-to-cell variation in hsp-16.2 biomarker expression, focusing on the intestines, which generate most signal. We found both intrinsic noise and signaling noise are low. The major axis of cell-to-cell variation in gene expression is composed of general differences in protein dosage. Thus, hsp-16.2 biomarkers reveal states of high or low effective dosages for many genes. It is possible that natural variation in protein dosage or chaperone activity may account for missing heritability of some traits.

systems biology↗