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Age at exposure is a critical modifier of the risk of radiation-induced cancer. However, the effects of age on radiation-induced carcinogenesis remain poorly understood. In this study, we focused on tissue stem cells using Lgr5-eGFP-ires-CreERT2 mice to compare radiation-induced DNA damage responses between Lgr5+ and Lgr5- intestinal stem cells. Three-dimensional immunostaining analyses demonstrated that radiation induced apoptosis and the mitotic index more efficiently in adult Lgr5- stem cells than in adult Lgr5+ stem cells but not in infants, regardless of Lgr5 expression. Supporting this evidence, rapid and transient p53 activation occurred after irradiation in adult intestinal crypts but not in infants. RNA sequencing revealed greater variability in gene expression in adult Lgr5+ stem cells than in infant Lgr5+ stem cells after irradiation. Notably, the cell cycle and DNA repair pathways were more enriched in adult stem cells than in infant stem cells after irradiation. Our findings suggest that radiation-induced DNA damage responses in mouse intestinal crypts differ between infants and adults, potentially contributing to the age-dependent susceptibility to radiation carcinogenesis. [ABSTRACT FROM AUTHOR]

Different genes show different levels of expression variability. For example, highly expressed genes tend to exhibit less expression variability. Genes whose promoters have TATA box and initiator motifs tend to have increased expression variability. On the other hand, DNA methylation of transcriptional units, or gene body DNA methylation, is associated with reduced gene expression variability in many species. Interestingly, some insect lineages, most notably Diptera including the canonical model insect Drosophila melanogaster, have lost DNA methylation. Therefore, it is of interest to determine whether genomic features similarly influence gene expression variability in lineages with and without DNA methylation. We analyzed recently generated large-scale data sets in D. melanogaster and honey bee (Apis mellifera) to investigate these questions. Our analysis shows that increased gene expression levels are consistently associated with reduced expression variability in both species, while the presence of TATA box is consistently associated with increased gene expression variability. In contrast, initiator motifs and gene lengths have weak effects limited to some data sets. Importantly, we show that a sequence characteristics indicative of gene body DNA methylation is strongly and negatively associate with gene expression variability in honey bees, while it shows no such association in D. melanogaster. These results suggest the evolutionary loss of DNA methylation in some insect lineages has reshaped the molecular mechanisms concerning the regulation of gene expression variability.

Abstract Background Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process. Results In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (scRT-qPCR and scRNA-seq). Conclusions We identified that the gene transcription profiles of differentiating sister cells are more similar to each other than to those of non-related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister cells than between self-renewing sister cells. Furthermore, a progressive increase in this divergence from first generation to second generation was observed when comparing differentiating cousin cells to self renewing cousin cells. Our results are in favor of a gradual erasure of transcriptional memory during the differentiation process.

Background: Cell differentiation requires the integration of two opposite processes, a stabilizing cellular memory, especially at the transcriptional scale, and a burst of gene expression variability which follows the differentiation induction. Therefore, the actual capacity of a cell to undergo phenotypic change during a differentiation process relies upon a modification in this balance which favors change-inducing gene expression variability. However, there are no experimental data providing insight on how fast the transcriptomes of identical cells would diverge on the scale of the very first two cell divisions during the differentiation process. Results: In order to quantitatively address this question, we developed different experimental methods to recover the transcriptomes of related cells, after one and two divisions, while preserving the information about their lineage at the scale of a single cell division. We analyzed the transcriptomes of related cells from two differentiation biological systems (human CD34+ cells and T2EC chicken primary erythrocytic progenitors) using two different single-cell transcriptomics technologies (scRT-qPCR and scRNA-seq). Conclusions: We identified that the gene transcription profiles of differentiating sister cells are more similar to each other than to those of non-related cells of the same type, sharing the same environment and undergoing similar biological processes. More importantly, we observed greater discrepancies between differentiating sister cells than between self-renewing sister cells. Furthermore, a progressive increase in this divergence from first generation to second generation was observed when comparing differentiating cousin cells to self renewing cousin cells. Our results are in favor of a gradual erasure of transcriptional memory during the differentiation process. [ABSTRACT FROM AUTHOR]

We performed single-cell RNA sequencing (scRNA-seq) on a population of 5,000 Tetrahymena thermophila, using the 10x Genomics 3′ gene expression analysis, to investigate gene expression variability within this clonal population. Initially, we estimated the 3ʹ-untranslated regions (3′ UTRs), which were absent in existing annotation files but are crucial for the 10x Genomics 3′ gene expression analysis, using the peaks2utr method. This allowed us to create a modified annotation file, which was then utilized in our scRNA-seq analysis. Our analysis revealed significant gene expression variability within the population, even after removing the effect of cell phase-related features. This variability predominantly appeared in six distinct clusters. Through gene ontology and KEGG pathway enrichment analyses, we identified that these were primarily associated with ribosomal proteins, proteins specific to mitochondria, proteins involved in peroxisome-specific carbon metabolism, cytoskeletal proteins, motor proteins, and immobilized antigens.

CD8 T cell proper differentiation during antiviral responses relies on metabolic adaptations. Herein, we investigated global metabolic activity in single CD8 T cells along an in vivo response by estimating metabolic fluxes from single-cell RNA-sequencing data. The approach was validated by the observation of metabolic variations known from experimental studies on global cell populations, while adding temporally detailed information and unravelling yet undescribed sections of CD8 T cell metabolism that are affected by cellular differentiation. Furthermore, inter-cellular variability in gene expression level, highlighted by single cell data, and heterogeneity of metabolic activity 4 days post-infection, revealed a new transition stage accompanied by a metabolic switch in activated cells differentiating into full-blown effectors.

Abstract Heat-imposed crop failure is often attributed to reduced thermotolerance of floral tissues; however, the underlying mechanism remains unknown. Here, we demonstrate that m6A RNA methylation increases in Arabidopsis flowers and negatively regulates gene expression variability. Stochastic gene expression provides flexibility to cope with environmental stresses. We find that reduced transcriptional fluctuation is associated with compromised activation of heat-responsive genes. Moreover, disruption of an RNA demethylase AtALKBH10B leads to lower gene expression variability, suppression of heat-activated genes, and strong reduction of plant fertility. Our work proposes a novel role for RNA methylation in the bet-hedging strategy of heat stress response.

Control of organ morphology is a fundamental feature of living organisms. There is, however, observable variation in organ size and shape within a given genotype. Taking the sepal of Arabidopsis as a model, we investigated whether we can use variability of gene expression alongside variability of organ morphology to identify gene regulatory networks potentially involved in organ size and shape determination. We produced a dataset composed of morphological parameters and genome-wide transcriptome obtained from 27 individual sepals from wild-type plants with nearly identical genetic backgrounds, environment, and developmental stage. Sepals exhibited appreciable variability in both morphology and transcriptome, with response to stimulus genes and cell-wall related genes displaying high variability in expression. We additionally identified five modules of co-expressed genes which correlated significantly with morphology, revealing biologically relevant gene regulatory networks. Interestingly, cell-wall related genes were overrepresented in two of the top three modules. Overall, our work highlights the benefit of using coupled variation in gene expression and phenotype in wild-type plants to shed light on the mechanisms underlying organ size and shape determination. Although causality between gene expression and sepal morphology has not been established, our approach opens the way to informed analysis for mutant characterization and functional studies.

Different genes show different levels of expression variability. For example, highly expressed genes tend to exhibit less expression variability. Genes whose promoters have TATA box and initiator motifs tend to have increased expression variability. On the other hand, DNA methylation of transcriptional units, or gene body DNA methylation, is associated with reduced gene expression variability in many species. Interestingly, some insect lineages, most notably Diptera including the canonical model insect Drosophila melanogaster, have lost DNA methylation. Therefore, it is of interest to determine whether genomic features similarly influence gene expression variability in lineages with and without DNA methylation. We analyzed recently generated large-scale data sets in D. melanogaster and honey bee (Apis mellifera) to investigate these questions. Our analysis shows that increased gene expression levels are consistently associated with reduced expression variability in both species, while the presence of TATA box is consistently associated with increased gene expression variability. In contrast, initiator motifs and gene lengths have weak effects limited to some data sets. Importantly, we show that a sequence characteristics indicative of gene body DNA methylation is strongly and negatively associate with gene expression variability in honey bees, while it shows no such association in D. melanogaster. These results suggest the evolutionary loss of DNA methylation in some insect lineages has reshaped the molecular mechanisms concerning the regulation of gene expression variability. [ABSTRACT FROM AUTHOR]

Circadian genes regulate several physiological functions such as circadian rhythm and metabolism and participate in the cytogenesis and progression of various malignancies. The abnormal expression of these genes in non-small cell lung cancer (NSCLC) is closely related to the clinicopathological features of NSCLC and may promote or inhibit NSCLC progression. Circadian rhythm disorders and clock gene abnormalities may increase the risk of lung cancer in some populations. We collected 15 circadian genes in NSCLC, namely PER1, PER2, PER3, TIMELESS, Cry1, Cry2, CLOCK, BMAL1/ARNTL-1, ARNTL2, NPAS2, NR1D1(REV-ERB), DEC1, DEC2, RORα, and RORγ, and determined their relationships with the clinicopathological features of patients and the potential mechanisms promoting or inhibiting NSCLC progression. We also summarized the studies on circadian rhythm disorders and circadian genes associated with lung cancer risk. The present study aimed to provide theoretical support for the future exploration of new therapeutic targets and for the primary prevention of NSCLC from the perspective of circadian genes. Interpretation of circadian rhythms in lung cancer could guide further lung cancer mechanism research and drug development that could lead to more effective treatments and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Purpose: Inherited variants in the cancer susceptibility genes, BRCA1 and BRCA2 account for up to 5% of breast cancers. Multiple gene expression studies have analysed gene expression patterns that maybe associated with BRCA12 pathogenic variant status; however, results from these studies lack consensus. These studies have focused on the differences in population means to identified genes associated with BRCA1/2-carriers with little consideration for gene expression variability, which is also under genetic control and is a feature of cellular function. Methods: We measured differential gene expression variability in three of the largest familial breast cancer datasets and a 2116 breast cancer meta-cohort. Additionally, we used RNA in situ hybridisation to confirm expression variability of EN1 in an independent cohort of more than 500 breast tumours. Results: BRCA1-associated breast tumours exhibited a 22.8% (95% CI 22.3–23.2) increase in transcriptome-wide gene expression variability compared to BRCAx tumours. Additionally, 40 genes were associated with BRCA1-related breast cancers that had ChIP-seq data suggestive of enriched EZH2 binding. Of these, two genes (EN1 and IGF2BP3) were significantly variable in both BRCA1-associated and basal-like breast tumours. RNA in situ analysis of EN1 supported a significant (p = 6.3 × 10−04) increase in expression variability in BRCA1-associated breast tumours. Conclusion: Our novel results describe a state of increased gene expression variability in BRCA1-related and basal-like breast tumours. Furthermore, genes with increased variability may be driven by changes in DNA occupancy of epigenetic effectors. The variation in gene expression is replicable and led to the identification of novel associations between genes and disease phenotypes. [ABSTRACT FROM AUTHOR]

Inter-individual variation in gene expression has been shown to be heritable and is often associated with differences in disease susceptibility between individuals. Many studies focused on mapping associations between genetic and gene regulatory variation, yet much less attention has been paid to the evolutionary processes that shape the observed differences in gene regulation between individuals in humans or any other primate. To begin addressing this gap, we performed a comparative analysis of gene expression variability and expression quantitative trait loci (eQTLs) in humans and chimpanzees, using gene expression data from primary heart samples. We found that expression variability in both species is often determined by non-genetic sources, such as cell-type heterogeneity. However, we also provide evidence that inter-individual variation in gene regulation can be genetically controlled, and that the degree of such variability is generally conserved in humans and chimpanzees. In particular, we found a significant overlap of orthologous genes associated with eQTLs in both species. We conclude that gene expression variability in humans and chimpanzees often evolves under similar evolutionary pressures.

Transcriptome sequencing is a powerful technique to study molecular changes that underlie the differences in physiological conditions and disease progression. A typical question that is posed in such studies is finding genes with significant changes between sample groups. In this respect expression variability is regarded as a nuisance factor that is primarily of technical origin and complicates the data analysis. However, it is becoming apparent that the biological variation in gene expression might be an important molecular phenotype that can affect physiological parameters. In this review we explore the recent literature on technical and biological variability in gene expression, sources of expression variability, (epi-)genetic hallmarks, and evolutionary constraints in genes with robust and variable gene expression. We provide an overview of recent findings on effects of external cues, such as diet and aging, on expression variability and on other biological phenomena that can be linked to it. We discuss metrics and tools that were developed for quantification of expression variability and highlight the importance of future studies in this direction. To assist the adoption of expression variability analysis, we also provide a detailed description and computer code, which can easily be utilized by other researchers. We also provide a reanalysis of recently published data to highlight the value of the analysis method. [ABSTRACT FROM AUTHOR]

The application of statistics has been instrumental in clarifying our understanding of the genome. While insights have been derived for almost all levels of genome function, most importantly, statistics has had the greatest impact on improving our knowledge of transcriptional regulation. But the drive to extract the most meaningful inferences from big data can often force us to overlook the fundamental role that statistics plays, and specifically, the basic assumptions that we make about big data. Normality is a statistical property that is often swept up into an assumption that we may or may not be consciously aware of making. This review highlights the inherent value of non-normal distributions to big data analysis by discussing use cases of non-normality that focus on gene expression data. Collectively, these examples help to motivate the premise of why at this stage, now more than ever, non-normality is important for learning about gene regulation, transcriptomics, and more. [ABSTRACT FROM AUTHOR]

Identifying the pathways that control a cellular phenotype is the first step to building a mechanistic model. Recent examples in developmental biology, cancer genomics, and neurological disease have demonstrated how changes in the variability of gene expression can highlight important genes that are under different degrees of regulatory control. Simple statistical tests exist to identify differentially-variable genes; however, methods for investigating how changes in gene expression variability in the context of pathways and gene sets are under-explored. Here we present pathVar, a new method that provides functional interpretation of gene expression variability changes at the level of pathways and gene sets. pathVar is based on a multinomial exact test, or an asymptotic Chi-squared test as a more computationally-efficient alternative. The method can be used for gene expression studies from any technology platform in all biological settings either with a single phenotypic group, or two-group comparisons. To demonstrate its utility, we applied the method to a diverse set of diseases, species and samples. Results from pathVar are benchmarked against analyses based on average expression and two methods of GSEA, and demonstrate that analyses using both statistics are useful for understanding transcriptional regulation. We also provide recommendations for the choice of variability statistic that have been informed through analyses on simulations and real data. Based on the datasets selected, we show how pathVar can be used to gain insight into expression variability of single cell versus bulk samples, different stem cell populations, and cancer versus normal tissue comparisons.

ABSTRACT Antibiotic-resistant bacteria are an increasingly serious public health concern, as strains emerge that demonstrate resistance to almost all available treatments. One factor that contributes to the crisis is the adaptive ability of bacteria, which exhibit remarkable phenotypic and gene expression heterogeneity in order to gain a survival advantage in damaging environments. This high degree of variability in gene expression across biological populations makes it a challenging task to identify key regulators of bacterial adaptation. Here, we research the regulation of adaptive resistance by investigating transcriptome profiles of Escherichia coli upon adaptation to disparate toxins, including antibiotics and biofuels. We locate potential target genes via conventional gene expression analysis as well as using a new analysis technique examining differential gene expression variability. By investigating trends across the diverse adaptation conditions, we identify a focused set of genes with conserved behavior, including those involved in cell motility, metabolism, membrane structure, and transport, and several genes of unknown function. To validate the biological relevance of the observed changes, we synthetically perturb gene expression using clustered regularly interspaced short palindromic repeat (CRISPR)-dCas9. Manipulation of select genes in combination with antibiotic treatment promotes adaptive resistance as demonstrated by an increased degree of antibiotic tolerance and heterogeneity in MICs. We study the mechanisms by which identified genes influence adaptation and find that select differentially variable genes have the potential to impact metabolic rates, mutation rates, and motility. Overall, this work provides evidence for a complex nongenetic response, encompassing shifts in gene expression and gene expression variability, which underlies adaptive resistance. IMPORTANCE Even initially sensitive bacteria can rapidly thwart antibiotic treatment through stress response processes known as adaptive resistance. Adaptive resistance fosters transient tolerance increases and the emergence of mutations conferring heritable drug resistance. In order to extend the applicable lifetime of new antibiotics, we must seek to hinder the occurrence of bacterial adaptive resistance; however, the regulation of adaptation is difficult to identify due to immense heterogeneity emerging during evolution. This study specifically seeks to generate heterogeneity by adapting bacteria to different stresses and then examines gene expression trends across the disparate populations in order to pinpoint key genes and pathways associated with adaptive resistance. The targets identified here may eventually inform strategies for impeding adaptive resistance and prolonging the effectiveness of antibiotic treatment.

Gene expression quantification can be a useful tool in studying the properties of bacterial biofilms. Unfortunately, techniques such as RNA extraction, cDNA synthesis, and quantitative PCR (qPCR) can introduce variability into mRNA transcript measurements, obscuring biologically relevant results. Here we sought to identify the steps that impair accurate gene expression quantification from Staphylococcus epidermidis biofilm samples. We devised an experimental setup that could be used to determine the contribution of each experimental step to the variability of mRNA transcript measurement. Among factors tested, biofilm growth contributed the most bias to gene expression quantification. Additional experiments demonstrated that pooling biofilms together reduced this variability, resulting in more accurate gene expression analysis results. We therefore recommend pooling in order to reduce the variability associated with gene expression quantification from biofilm samples.

Synthetic lethal interactions (SLIs) are robust mechanisms that provide cells with the ability to remain viable despite having mutations in genes critical to the DNA damage response, a core cellular process. Studies in model organisms such as S. cerevisiae showed that thousands of genes important in maintaining DNA integrity cooperated in a SLI network. Two genes participate in a SLI when a mutation in one gene has no effect on the cell, but mutations in both interacting genes are lethal. Furthermore in C. elegans , a mutation in a critical gene that is important for development induced a change in expression variability in the synthetic lethal interactor. In cancer, targeting SLIs shows promise in selectively killing cancer cells. For example, targeting PARP1 is an effective treatment for BRCA1 / 2 − breast and ovarian cancers . Although PARP1 is already identified as having a SLI with BRCA1 / 2 −, computationally searching for other genes that cooperate in the SLI network could highlight genes that may have promise for being a cancer-specific drug target. Using RNA sequencing data for ovarian cancer patients with BRCA2 mutations and the R Bioconductor package pathVar , we showed that genes whose expression changes to an invariant, stable expression state are likely candidates for SLIs with BRCA2. Our results highlight the interactions between the genes with predicted SLIs and protein-coding genes that are functionally important in the DNA damage response. The method of analyzing expression variability to computationally identify genes with SLIs can be applied to query SLIs in other tumor types. [ABSTRACT FROM AUTHOR]

Reproducibility in organ size and shape is a fascinating trait of living organisms. The mechanisms underlying such robustness remain, however, to be elucidated. Taking the sepal of Arabidopsis as a model, we investigated whether variability of gene expression plays a role in variation of organ size and shape. Previous work from our team identified cell-wall related genes as being enriched among the genes whose expression is highly variable. We then hypothesized that the variation of measured morphological parameters in cell-wall related single knockout mutants could be correlated with the variation in gene expression of the corresponding gene (the knocked-out gene) in wild-type plants. We analyzed sepal size and shape from 16 cell-wall mutants and found that sepal size variability correlates positively, not with gene expression variation, but with mean gene expression of the corresponding gene in wild type. These findings support a contribution of cell-wall related genes to the robustness of sepal size.

Abstract: A repeated measures microarray design with 22 healthy, non-smoking volunteers (aging 32±5years) was set up to study transcriptome profiles in whole blood samples. The results indicate that repeatable data can be obtained with high within-subject correlation. Probes that could discriminate between individuals are associated with immune and inflammatory functions. When investigating possible time trends in the microarray data, we have found no differential expression within a sampling period (within-season effect). Differential expression was observed between sampling seasons and the data suggest a weak response of genes related to immune system functioning. Finally, a high number of probes showed significant season-specific expression variability within subjects. Expression variability increased in springtime and there was an association of the probe list with immune system functioning. Our study suggests that the blood transcriptome of healthy individuals is reproducible over a time period of several months. [Copyright &y& Elsevier]

Gene expression is the most fundamental biological process, which is essential for phenotypic variation. It is regulated by various external (environment and evolution) and internal (genetic) factors. The level of gene expression depends on promoter architecture, along with other ex-ternal factors. Presence of sequence motifs, such as transcription factor binding sites (TFBSs) and TATA-box, or DNA methylation in vertebrates has been implicated in the regulation of expression of some genes in eukaryotes, but a large number of genes lack these sequences. On the other hand, several experimental and computational studies have shown that promoter sequences possess some special structural properties, such as low stability, less bendability, low nucleosome occupancy, and more curvature, which are prevalent across all organisms. These structural features may play role in transcription initiation and regulation of gene expression. We have studied the relationship between the structural features of promoter DNA, promoter directionality and gene expression variability in S. cerevisiae. This relationship has been ana-lyzed for seven different measures of gene expression variability, along with two different reg-ulatory effect measures. We find that a few of the variability measures of gene expression are linked to DNA structural properties, nucleosome occupancy, TATA-box presence, and bidir-ectionality of promoter regions. Interestingly, gene responsiveness is most intimately correlated with DNA structural features and promoter architecture. [ABSTRACT FROM AUTHOR]

Abstract: Using the Génolevures sequencing data, we developed an expression micro-array for the yeast Kluyveromyces lactis consisting of 482 genes, mainly involved in central metabolism, compound transport facilitators and stress response. The array was validated using the LAC/GAL system. By comparing gene expression in the laboratory reference strain CBS2359 and in an industrial strain B1, we demonstrated the influence of two carbon sources, glucose and lactose, on the expression of genes involved in the respiratory and in the fermentative metabolic pathways. We also showed that the two strains, although both originating from dairies, display unexpected differences in gene expression on each type of carbon source. [Copyright &y& Elsevier]

Transcriptional responses to the appropriate temporal pattern of action potential firing are essential for long-term adaption of neuronal properties to the functional activity of neural circuits and environmental experience. However, standard transcriptome analysis methods can be too limited in identifying critical aspects that coordinate temporal coding of action potential firing with transcriptome response. A Pearson correlation analysis was applied to determine how pairs of genes in the mouse dorsal root ganglion (DRG) neurons are coordinately expressed in response to stimulation producing the same number of action potentials by two different temporal patterns. Analysis of 4728 distinct gene-pairs related to calcium signaling, 435,711 pairs of transcription factors, 820 pairs of voltage-gated ion channels, and 86,862 pairs of calcium signaling genes with transcription factors indicated that genes become coordinately activated by distinct action potential firing patterns and this depends on the duration of stimulation. Moreover, a measure of expression variance revealed that the control of transcripts abundances is sensitive to the pattern of stimulation. Thus, action potentials impact intracellular signaling and the transcriptome in dynamic manner that not only alter gene expression levels significantly (as previously reported) but also affects the control of their expression fluctuations and profoundly remodel the transcriptional networks. [ABSTRACT FROM AUTHOR]

Background Single-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level is intrinsically stochastic and noisy. Yet, on the cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how can their expression stability be assessed? We have previously proposed a computational framework for ranking expression stability of genes in single cells for scRNA-seq data normalization and integration. In this study, we perform detailed evaluation and characterization of SEGs derived from this framework. Results Here, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets and the "Mouse Atlas" dataset are reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across diverse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in individual cells. Conclusions SEGs identified in this study have immediate utility both for understanding variation and stability of single-cell transcriptomes and for practical applications such as scRNA-seq data normalization. Our framework for calculating gene stability index, "scSEGIndex," is incorporated into the scMerge Bioconductor R package (https://sydneybiox.github.io/scMerge/reference/scSEGIndex.html) and can be used for identifying genes with stable expression in scRNA-seq datasets. [ABSTRACT FROM AUTHOR]

Summary Microbial populations can maximize fitness in dynamic environments through bet hedging, a process wherein a subpopulation assumes a phenotype not optimally adapted to the present environment but well adapted to an environment likely to be encountered. Here, we show that oxygen induces fluctuating expression of the trimethylamine oxide (TMAO) respiratory system of Escherichia coli , diversifying the cell population and enabling a bet-hedging strategy that permits growth following oxygen loss. This regulation by oxygen affects the variance in gene expression but leaves the mean unchanged. We show that the oxygen-sensitive transcription factor IscR is the key regulator of variability. Oxygen causes IscR to repress expression of a TMAO-responsive signaling system, allowing stochastic effects to have a strong effect on the output of the system and resulting in heterogeneous expression of the TMAO reduction machinery. This work reveals a mechanism through which cells regulate molecular noise to enhance fitness. [ABSTRACT FROM AUTHOR]