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bioRxiv2025

Trans-acting mutations reveal non-nuclear modulators of both intrinsic and extrinsic gene expression noise in a eukaryote.

Gene expression regulation is a stochastic process that can be modified by mutations not only in a deterministic manner but also in a probabilistic way, for example by changing the extent of cell-to-cell variability in gene expression (also called "gene expression noise"). Although systematic studies have successfully analyzed the properties of cis-acting mutations that modulate expression noise at their own locus, less is known about the type of genetic changes that alter expression noise in trans (at loci distant from the mutation). Here, we applied genetic mapping on yeast strains (Saccharomyces cerevisiae) generated by random mutagenesis and identified three mutations (in chs1, yme2 and msh1 genes) that changed the expression noise of a reporter gene in the nuclear genome regulated by the yeast TDH3 promoter. These mutations affected either extrinsic noise (variability due to cell-specific factors), intrinsic noise (inherent variability within each cell) or both, and their effects were found to not be specific to the TDH3 promoter. Surprisingly, all three mutations targeted proteins located outside of the nucleus: Yme2 and Msh1 being involved in the maintenance of mitochondrial genome integrity, and Chs1 being necessary for the repair of cell-wall defects in freshly-born daughter cells. Our results reveal that mitochondrial state can modulate the extent of intrinsic expression noise of eukaryotic nuclear genes.

genetics↗

bioRxiv2016

Quantifying the degree of sharing of genetic and non-genetic causes of gene expression variability across four tissues.

Gene expression can provide biological mechanisms which underlie genetic associations with complex traits and diseases, but often the most relevant tissue for the trait is inaccessible and a proxy is the only alternative. Here, we investigate shared and tissue specific patterns of variability in expression in multiple tissues, to quantify the degree of sharing of causes (genetic or non-genetic) of variability in gene expression among tissues. Using gene expression in ~800 female twins from the TwinsUK cohort in skin, fat, whole blood and lymphoblastoid cell lines (LCLs), we identified 9166 significant cis-eQTLs in fat, 9551 in LCLs, 8731 in skin and 5313 in blood (1% FDR). We observed up to 80% of cis-eQTLs are shared in pairs of tissues. In addition, the cis genetic correlation between tissues is > 90% for 35% of the genes, indicating for these genes a largely tissue-shared component of cis regulation. However, variance components show that cis genetic signals explain only a small fraction of the variation in expression, with from 67-87% of the variance explained by environmental factors, and 53% of the genetic effects occurring in trans. We observe a trans genetic correlation of 0 for all genes except a few which show correlation between fat and skin expression. The environmental effects are also observed to be entirely tissue specific, despite related tissues largely sharing exposures. These results demonstrate that patterns of gene expression are largely tissue specific, strongly supporting the need to study higher order regulatory interactions in the appropriate tissue context with large samples sizes and diversity of environmental contexts.

Genomics↗

bioRxiv2024

Optimizing Cost-Effective Gene Expression Phenotyping Approaches in Cattle Using 3' mRNA Sequencing

BackgroundGenetic and genomic selection programs require large numbers of phenotypes observed for animals in shared environments. Direct measurements of phenotypes like meat quality, methane emission, and disease susceptibility are difficult and expensive to measure at scale but are critically important to livestock production. Our work leans on our understanding of the "Central Dogma" of molecular genetics to leverage molecular intermediates as cheaply-measured proxies of organism-level phenotypes. The rapidly declining cost of next-generation sequencing presents opportunities for population-level molecular phenotyping. While the cost of whole transcriptome sequencing has declined recently, its required sequencing depth still makes it an expensive choice for wide-scale molecular phenotyping. We aim to optimize 3' mRNA sequencing (3' mRNA-Seq) approaches for collecting cost-effective proxy molecular phenotypes for cattle from easy-to-collect tissue samples (i.e., whole blood). We used matched 3' mRNA-Seq samples for 15 Holstein male calves in a heat stress trail to identify the 1) best library preparation kit (Takara SMART-Seq v4 3' DE and Lexogen QuantSeq) and 2) optimal sequencing depth (0.5 to 20 million reads/sample) to capture gene expression phenotypes most cost-effectively. ResultsTakara SMART-Seq v4 3' DE outperformed Lexogen QuantSeq libraries across all metrics: number of quality reads, expressed genes, informative genes, differentially expressed genes, and 3' biased intragenic variants. Serial downsampling analyses identified that as few as 8.0 million reads per sample could effectively capture most of the between-sample variation in gene expression. However, progressively more reads did provide marginal increases in recall across metrics. These 3' mRNA-Seq reads can also capture animal genotypes that could be used as the basis for downstream imputation. The 10 million read downsampled groups called an average of 104,386 SNPs and 20,131 INDELs, many of which segregate at moderate minor allele frequencies in the population. ConclusionThis work demonstrates that 3' mRNA-Seq with Takara SMART-Seq v4 3' DE can provide an incredibly cost-effective (<$25/sample) approach to quantifying molecular phenotypes (gene expression) while discovering sufficient variation for use in genotype imputation. Ongoing work is evaluating the accuracy of imputation and the ability of much larger datasets to predict individual animal phenotypes.

genomics↗

bioRxiv2021

The BLMP-1 transcription factor promotes oscillatory gene expression to achieve timely molting

Gene expression oscillators can coordinate developmental events in space and time. In C. elegans, a gene expression oscillator directs rhythmic accumulation of [~]25% of the transcriptome, and thus thousands of transcripts, presumably to control molting, a process of rhythmic skin regeneration. Recently, a reverse genetic screen identified several transcription factors important for molting. Here, we characterize one of these, BLMP-1, orthologous to the mammalian transcription repressor PRDM1. We find it to be important for timely molting, and oscillatory gene expression. We propose a dual function for BLMP-1 in shaping oscillatory gene expression and coupling it to a set of direct targets, which ensures cuticular integrity. With mammalian PRDM1/BLIMP1 promoting regular cycles of postnatal hair follicle regeneration, our findings point to the possible existence of a fundamentally conserved clock mechanism in control of rhythmic skin regeneration.

developmental biology↗

bioRxiv2020

Alpha-synuclein expression supports interferon stimulated gene expression in neurons

The protein alpha-synuclein (asyn) is predominantly expressed in neurons and is associated with neurodegenerative diseases like Parkinsons disease (PD); yet, a functional role for asyn in neurons is not clearly established. We have previously shown that asyn expression is up-regulated following viral infection in neurons and is critical for host immune responses to RNA virus infections. Here, we investigate the mechanisms underlying asyn-dependent immune responses to RNA virus infection in the brain. Using asyn knock-out (KO) mice and human neuronal models, we show that asyn is required for expression of the full repertoire of interferon-stimulated genes (ISGs) in neurons following acute RNA virus infection. Furthermore, treatment of asyn KO human neurons with poly I:C or type I interferon also fail to induce expression of the full complement of ISGs suggesting that asyn plays an important role in modulating neuronal innate immune responses. In brain tissue, asyn-dependent ISG expression is independent of microglia activation and supports activation of infiltrating lymphocytes following viral challenge. We also show that virus infections lead to accumulation of phosphorylated S129 asyn in human and non-human primate neuronal tissues. In a model of pS129 asyn pathology, we found that infection with West Nile virus increases microglia activation but does not significantly alter pS129 asyn pathology in the mouse model. Taken together, our results establish asyn as a novel, neuron-specific modulator of innate immunity by a mechanism that promotes interferon-stimulated gene expression and links responses to virus infection with formation of phosphorylated S129-asyn in neuronal tissue.

neuroscience↗

bioRxiv2021

A data-driven method to learn a jump diffusion process from aggregate biological gene expression data

Dynamic models of gene expression are urgently required. Different from trajectory inference and RNA velocity, our method reveals gene dynamics by learning a jump diffusion process for modeling the biological process directly. The algorithm needs aggregate gene expression data as input and outputs the parameters of the jump diffusion process. The learned jump diffusion process can predict population distributions of gene expression at any developmental stage, achieve long-time trajectories for individual cells, and offer a novel approach to computing RNA velocity. Moreover, it studies biological systems from a stochastic dynamics perspective. Gene expression data at a time point, which is a snapshot of a cellular process, is treated as an empirical marginal distribution of a stochastic process. The Wasserstein distance between the empirical distribution and predicted distribution by the jump diffusion process is minimized to learn the dynamics. For the learned jump diffusion equation, its trajectories correspond to the development process of cells and stochasticity determines the heterogeneity of cells. Its instantaneous rate of state change can be taken as "RNA velocity", and the changes in scales and orientations of clusters can be noticed too. We demonstrate that our method can recover the underlying nonlinear dynamics better compared to parametric models and diffusion processes driven by Brownian motion for both synthetic and real world datasets. Our method is also robust to perturbations of data because it only involves population expectations.

bioinformatics↗

bioRxiv2024

Time restricted feeding with or without ketosis influences metabolism-related gene expression in a tissue-specific manner in aged rats

Many of the hallmarks of aging involve alterations in cellular and organismal metabolism. One pathway with the potential to impact several traditional markers of impaired function with aging is the PI3K/AKT metabolic pathway. Regulation of this pathway includes many aspects of cellular function, including protein synthesis, proliferation and survival, as well as many downstream targets, including mTOR and FOXOs. Importantly, this pathway is pivotal to the function of every organ system in the human body. Thus, we investigated the expression of several genes along this pathway in multiple organs, including the brain, liver and skeletal muscle, in aged subjects that had been on different experimental diets to regulate metabolic function since mid-life. Specifically, rats were fed a control ad lib diet (AL), a time restricted feeding diet (cTRF), or a time restricted feeding diet with ketogenic macronutrients (kTRF) for the majority of their adult lives (from 8-25 months). We previously reported that regardless of macronutrient ratio, TRF-fed rats in both macronutrient groups required significantly less training to acquire a biconditional association task than their ad lib fed counterparts. The current experiments expand on this work by quantifying metabolism-related gene expression across tissues and interrogating for potential relationships with cognitive performance. AKT expression was significantly reduced in kTRF fed rats within liver and muscle tissue. However, AKT expression within the perirhinal cortex (PER) was higher in kTRF rats with the best cognitive performance. Within CA3, higher levels of FOXO1 gene expression correlated with poorer cognitive performance in ad libitum fed rats. Together, these data demonstrate diet- and tissue-specific alterations in metabolism-related gene expression and their correlation with cognitive status.

neuroscience↗

bioRxiv2018

Forecasting when cells die during antibiotic exposure using stochastic gene expression

Antibiotic killing does not occur at a single, precise time for all cells within a population. Variability in time to death can be caused by stochastic expression of genes, resulting in differences in endogenous stress-resistance levels between individual cells in a population. This variability can be part of a bet-hedging strategy where cells leverage noise to ensure a subset of the population can tolerate the drug, while decreasing the overall cost of expressing resistance genes. We asked whether single-cell differences in gene expression prior to antibiotic addition were related to cell survival times after antibiotic exposure for a range of genes of diverse function. We quantified the time to death of single cells under antibiotic exposure in combination with expression of reporters. For some reporters, the time to cell death had a strong relationship with the initial expression level of the genes. Reporters that could forecast cell fate included stress response genes, but also genes involved in a variety of other cellular processes like metabolism. Our results highlight the single-cell level non-uniformity of antibiotic killing and also provide examples of key genes where cell-to-cell variation in expression prior to antibiotic exposure is strongly linked to extended durations of antibiotic survival.

systems biology↗

bioRxiv2023

Tau alters global gene expression affecting chromatin in the early stages of Alzheimer's disease

Recent research revealed that Tau plays critical roles in various neuronal functions. Our previous work demonstrated that destabilization and nuclear delocalization of Tau can alter the expression of glutamatergic genes, thereby mediating early neuronal damage. Upon analysing gene coexpression of AD temporal regions at different stages and the transcriptional output in differentiated neuroblastoma cells, we discovered that changes in Tau availability are linked to global alterations in gene expression that affect multiple neuronal pathways. Comparison with the human AD temporal region showed that the Tau-dependent modulation of gene expression closely resembles an intermediate stage of AD that precedes the definitive AD condition. Furthermore, we identified the chromatin remodelling pathway as being significantly affected by Tau in both our cellular model and AD brains, with reductions in heterochromatin markers. We propose that Tau is able to globally affect the neuronal transcriptome and that its availability is linked to changes in gene expression during intermediate stages of AD development. In addition, we found that the epigenetic pathway is strongly affected by Tau during the progression of AD.

neuroscience↗

bioRxiv2022

DNA-protein quasi-mapping for rapid differential gene expression analysis in non-model organisms

BackgroundConventional differential gene expression analysis pipelines for non-model organisms require computationally expensive transcriptome assembly. We recently proposed an alternative strategy of directly aligning RNA-seq reads to a protein database, and demonstrated drastic improvements in speed, memory usage, and accuracy in identifying differentially expressed genes. ResultHere we report a further speed-up by replacing DNA-protein alignment by quasi-mapping, making our pipeline > 1000 x faster than assembly-based approach, and still more accurate. We also compare quasi-mapping to other mapping techniques, and show that it is faster but at the cost of sensitivity. ConclusionWe provide a quick-and-dirty differential gene expression analysis pipeline for non-model organisms without a reference transcriptome, which directly quasi-maps RNA-seq reads to a reference protein database, avoiding computationally expensive transcriptome assembly.

bioinformatics↗

bioRxiv2024

Gene Expression Reactomes Across Species Do Not Correlate with Gene Structural Similarity

Mammalian animal model species for preclinical drug testing share a large number of genes with similar names and/or protein primary structures, but gene expression patterns in different tissues and in response to different drugs have not been previously systematically characterized. Here, we experimentally measure the gene expression responses (reactomes) of each gene in the mouse and rat genomes in 10 different organs to 3 different drugs. Surprisingly, we observe that gene reactomes across species generally do not correlate with structural similarity. Thus, we propose an alternative functional cross-species gene mapping approach, based on organ-specific gene expression response to drug dosing.

molecular biology↗

bioRxiv2024

Associations Between Prenatal Vitamin D and Placental Gene Expression

BackgroundVitamin D is a hormone regulating gene transcription. Prenatal vitamin D has been linked to immune and vascular function in the placenta, a key organ of pregnancy. To date, studies of vitamin D and placental gene expression have focused on a limited number of candidate genes. Transcriptome-wide RNA sequencing can provide a more complete representation of the placental effects of vitamin D. ObjectiveWe investigated the association between prenatal vitamin D levels and placental gene expression in a large, prospective pregnancy cohort. MethodsParticipants were recruited in Shelby County, Tennessee in the Conditions Affecting Neurocognitive Development and Learning in Early childhood (CANDLE) study. Vitamin D level (plasma total 25-hydroxyvitatmin D, [25(OH)D]) was measured at mid-pregnancy (16-28 weeks gestation) and delivery. Placenta samples were collected at birth. RNA was isolated and sequenced. We identified differentially expressed genes (DEGs) using adjusted linear regression models. We also conducted weighted gene co-expression network analysis (WGCNA). ResultsThe median 25(OH)D of participants was 21.8 ng/mL at mid-pregnancy (N=774, IQR: 15.4-26.5 ng/mL) and 23.6 ng/mL at delivery (N=753, IQR: 16.8-29.1 ng/mL). Placental expression of 25 DEGs was associated with 25(OH)D at mid-pregnancy, but no DEG was associated with 25(OH)D at delivery. DEGs were related to energy metabolism, cytoskeletal function, and RNA transcription. Using WGCNA, we identified 2 gene modules whose expression was associated with 25(OH)D at mid-pregnancy and 1 module associated with 25(OH)D at delivery. These modules were enriched for genes related to mitochondrial and cytoskeletal function, and were regulated by transcription factors including ARNT2, BHLHE40, FOSL2, JUND, and NFKB1. ConclusionsOur results indicate that 25(OH)D during mid-pregnancy, but not at delivery, is associated with placental gene expression at birth. Future research is needed to investigate a potential role of vitamin D in programming placental mitochondrial metabolism, intracellular transport, and transcriptional regulation during pregnancy.

developmental biology↗

bioRxiv2021

Developmental Alcohol Exposure in Drosophila: Effects on Adult Phenotypes and Gene Expression in the Brain

Fetal alcohol exposure can lead to developmental abnormalities, intellectual disability, and behavioral changes, collectively termed fetal alcohol spectrum disorder (FASD). In 2015, the Centers for Disease Control found that 1 in 10 pregnant women report alcohol use and more than 3 million women in the USA are at risk of exposing their developing baby to alcohol. Drosophila melanogaster is an excellent genetic model to study developmental effects of alcohol exposure because many individuals of the same genotype can be reared rapidly and economically under controlled environmental conditions. Flies exposed to alcohol undergo physiological and behavioral changes that resemble human alcohol-related phenotypes. Here, we show that adult flies that developed on ethanol-supplemented medium have decreased viability, reduced sensitivity to ethanol, and disrupted sleep and activity patterns. To assess the effects of exposure to alcohol during development on brain gene expression, we performed single cell RNA sequencing and resolved cell clusters with differentially expressed genes which represent distinct neuronal and glial populations. Differential gene expression showed extensive sexual dimorphism with little overlap between males and females. Gene expression differences following developmental alcohol exposure were similar to previously reported differential gene expression following cocaine consumption, suggesting that common neural substrates respond to both drugs. Genes associated with glutathione metabolism, lipid transport, glutamate and GABA metabolism, and vision feature in sexually dimorphic global multi-cluster interaction networks. Our results provide a blueprint for translational studies on alcohol-induced effects on gene expression in the brain that may contribute to or result from FASD in human populations.

genetics↗

bioRxiv2023

SEAMoD: A fully interpretable neural network for cis-regulatory analysis of differentially expressed genes

A common way to investigate gene regulatory mechanisms is to identify differentially expressed genes using transcriptomics, find their candidate enhancers using epigenomics, and search for over-represented transcription factor (TF) motifs in these enhancers using bioinformatics tools. A related follow-up task is to model gene expression as a function of enhancer sequences and rank TF motifs by their contribution to such models, thus prioritizing among regulators. We present a new computational tool called SEAMoD that performs the above tasks of motif finding and sequence-to-expression modeling simultaneously. It trains a convolutional neural network model to relate enhancer sequences to differential expression in one or more biological conditions. The model uses TF motifs to interpret the sequences, learning these motifs and their relative importance to each biological condition from data. It also utilizes epigenomic information in the form of activity scores of putative enhancers and automatically searches for the most promising enhancer for each gene. Compared to existing neural network models of non-coding sequences, SEAMoD uses far fewer parameters, requires far less training data, and emphasizes biological interpretability. We used SEAMoD to understand regulatory mechanisms underlying the differentiation of neural stem cell (NSC) derived from mouse forebrain. We profiled gene expression and histone modifications in NSC and three differentiated cell types and used SEAMoD to model differential expression of nearly 12,000 genes with an accuracy of 81%, in the process identifying the Olig2, E2f family TFs, Foxo3, and Tcf4 as key transcriptional regulators of the differentiation process.

bioinformatics↗

bioRxiv2024

Pervasive tissue-, genetic background-, and allele-specific gene expression effects in Drosophila melanogaster

The pervasiveness of gene expression variation and its contribution to phenotypic variation and evolution is well known. This gene expression variation is context dependent, with differences in regulatory architecture often associated with intrinsic and environmental factors, and is modulated by regulatory elements that can act in cis (linked) or in trans (unlinked) relative to the genes they affect. So far, little is known about how this genetic variation affects the evolution of regulatory architecture among closely related tissues during population divergence. To address this question, we analyzed gene expression in the midgut, hindgut, and Malpighian tubule as well as microbiome composition in the two gut tissues in four Drosophila melanogaster strains and their F1 hybrids from two divergent populations: one from the derived, European range and one from the ancestral, African range. In both the transcriptome and microbiome data, we detected extensive tissue- and genetic background-specific effects, including effects of genetic background on overall tissue specificity. Tissue-specific effects were typically stronger than genetic background-specific effects, although the two gut tissues were not more similar to each other than to the Malpighian tubules. An examination of allele specific expression revealed that, while both cis and trans effects were more tissue-specific in genes expressed differentially between populations than genes with conserved expression, trans effects were more tissue-specific than cis effects. Despite there being highly variable regulatory architecture, this observation was robust across tissues and genetic backgrounds, suggesting that the expression of trans variation can be spatially fine-tuned as well as or better than cis variation during population divergence and yielding new insights into cis and trans regulatory evolution. Author SummaryGenetic variants regulating gene expression can act in cis (linked) or in trans (unlinked) relative to the genes they affect and are thought to be important during adaptation because they can spatially and temporally fine-tune gene expression. In this study, we used the fruit fly Drosophila melanogaster to compare gene expression between inbred parental strains and their offspring in order to characterize the basis of gene expression regulation and inheritance. We examined gene expression in three tissues (midgut, hindgut, and Malpighian tubule) and four genetic backgrounds stemming from Europe and the ancestral range in Africa. Additionally, we characterized the bacterial community composition in the two gut tissues. We detected extensive tissue- and genetic background-specific effects on gene expression and bacterial community composition, although tissue-specific effects were typically stronger than genetic background effects. Genes with cis and trans regulatory effects were more tissue-specific than genes with conserved expression, while those with trans effects were more tissue-specific than those with cis effects. These results suggest that the expression of trans variation can be spatially fine-tuned as well as (or better than) cis variation as populations diverge from one another. Our study yields novel insight into the genetic basis of gene regulatory evolution.

evolutionary biology↗

bioRxiv2020

Hyperbolic geometry of gene expression

Understanding the patterns of gene expression is key to elucidating the differences between cell types and across disease conditions. The overwhelmingly large number of genes involved generally makes this problem intractable. Yet, we find that gene expression patterns in five different data datasets can all be described using a small number of variables. These variables describe differences between cells according to a hyperbolic metric. We reach this conclusion by developing methods that, starting with an initial assumption of a Euclidean geometry, can detect the presence of other geometries in the data. The Euclidean metric is used in most of current studies of gene expression, primarily because it is difficult to use other non-linear metrics in high dimensional spaces. The hyperbolic metric is much more suitable for describing data produced by a hierarchically organized network, which is relevant for many biological processes. We find that the hyperbolic effects, but not the space dimensionality, increase with the number of genes that are taken into account. The hyperbolic curvature was the smallest for mouse embryonic stem cells, stronger for mouse kidney, lung and brain cells, and reached the largest value in a set of human cells integrated from multiple sources. We show that taking into account hyperbolic geometry strongly improves the visualization of gene expression data compared to leading visualization methods. These results demonstrate the advantages of knowing the underlying geometry when analyzing high-dimensional data.

bioinformatics↗

bioRxiv2020

Disentangling intrinsic and extrinsic gene expression noise in growing cells

Gene expression is a stochastic process. Despite the increase of protein numbers in growing cells, the protein concentrations are often found to be confined within small ranges throughout the cell cycle. Considering the time trajectory of protein concentration as a random walker in the concentration space, an effective restoring force (with a corresponding "spring constant") must exist to prevent the divergence of concentration due to random uctuations. In this work, we prove that the magnitude of the effective spring constant is directly related to the fraction of intrinsic noise in the total protein concentration noise. We show that one can infer the magnitude of intrinsic, extrinsic, and measurement noises of gene expression solely based on time-resolved data of protein concentration, without any a priori knowledge of the underlying gene expression dynamics. We apply this method to experimental data of single-cell bacterial gene expression. The results allow us to estimate the average protein number and the translation burst parameter.

genetics↗

bioRxiv2019

Channel Capacity Computations for Unregulated and Autoregulated Gene Expression

How living cells can reliably process biochemical cues in the presence of molecular noise is not fully understood. Here we investigate the fidelity of information transfer in the expression of a single gene. We use the established model of gene expression to examine how precisely the protein levels can be controlled by two distinct mechanisms: (i) the transcription rate of the gene, or (ii) the translation rate for the corresponding mRNA. The fidelity of gene expression is quantified with the information-theoretic notion of information capacity. Derived information capacity formulae reveal that transcriptional control generally provides a tangibly higher capacity as compared to the translational control. We next introduce negative feedback regulation in gene expression, where the protein directly inhibits its own transcription. While negative feedback reduces noise in the level of the protein for a given input signal, it also decreases the input-to-output sensitivity. Our results show that the combined effect of these two opposing forces is a reduced capacity in the presence of feedback. In summary, our analysis presents the first analytical quantification of information transfer in simple gene expression models, which provides insight into the fidelity of basic gene expression control mechanisms.

systems biology↗