Gene expression variability: the other dimension in transcriptome analysis

Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

JOURNAL/nrgr/04.03/01300535-202412000-00028/figure1/v/2024-04-08T165401Z/r/image-tiff Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal cord injury. They can greatly affect nerve regeneration and functional recovery. However, there is still limited understanding of the peripheral immune inflammatory response in spinal cord injury. In this study, we obtained microRNA expression profiles from the peripheral blood of patients with spinal cord injury using high-throughput sequencing. We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus (GEO) database (GSE151371). We identified 54 differentially expressed microRNAs and 1656 differentially expressed genes using bioinformatics approaches. Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways, such as neutrophil extracellular trap formation pathway, T cell receptor signaling pathway, and nuclear factor-κB signal pathway, were abnormally activated or inhibited in spinal cord injury patient samples. We applied an integrated strategy that combines weighted gene co-expression network analysis, LASSO logistic regression, and SVM-RFE algorithm and identified three biomarkers associated with spinal cord injury: ANO10, BST1, and ZFP36L2. We verified the expression levels and diagnostic performance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve. Quantitative polymerase chain reaction results showed that ANO10 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients. We also constructed a small RNA-mRNA interaction network using Cytoscape. Additionally, we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal cord injury patients using the CIBERSORT tool. The proportions of naïve B cells, plasma cells, monocytes, and neutrophils were increased while the proportions of memory B cells, CD8+ T cells, resting natural killer cells, resting dendritic cells, and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects, and ANO10, BST1 and ZFP26L2 were closely related to the proportion of certain immune cell types. The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal cord injury and suggest that ANO10, BST1, and ZFP36L2 are potential biomarkers for spinal cord injury. The study was registered in the Chinese Clinical Trial Registry (registration No. ChiCTR2200066985, December 12, 2022).

SIEVE: One-stop differential expression, variability, and skewness analyses using RNA-Seq data

Motivation

RNA-Seq data analysis is commonly biased towards detecting differentially expressed genes and insufficiently conveys the complexity of gene expression changes between biological conditions. This bias arises because discrete models of RNA-Seq count data cannot fully characterize the mean, variance, and skewness of gene expression distribution using independent model parameters. A unified framework that simultaneously tests for differential expression, variability, and skewness is needed to realize the full potential of RNA-Seq data analysis in a systems biology context.

Results

We present SIEVE, a statistical methodology that provides the desired unified framework. SIEVE embraces a compositional data analysis framework that transforms discrete RNA-Seq counts to a continuous form with a distribution that is well-fitted by a skew-normal distribution. Simulation results show that SIEVE controls the false discovery rate and probability of Type II error better than existing methods for differential expression analysis. Analysis of the Mayo RNA-Seq dataset for Alzheimer's disease using SIEVE reveals that a gene set with significant expression difference in mean, standard deviation and skewness between the control and the Alzheimer's disease group strongly predicts a subject's disease state. Furthermore, functional enrichment analysis shows that relying solely on differentially expressed genes detects only a segment of a much broader spectrum of biological aspects associated with Alzheimer's disease. The latter aspects can only be revealed using genes that show differential variability and skewness. Thus, SIEVE enables fresh perspectives for understanding the intricate changes in gene expression that occur in complex diseases.

Availability

The SIEVE R package and source codes are available at https://github.com/Divo-Lee/SIEVE .

Quantifying transcriptome diversity: a review

Following the central dogma of molecular biology, gene expression heterogeneity can aid in predicting and explaining the wide variety of protein products, functions and, ultimately, heterogeneity in phenotypes. There is currently overlapping terminology used to describe the types of diversity in gene expression profiles, and overlooking these nuances can misrepresent important biological information. Here, we describe transcriptome diversity as a measure of the heterogeneity in (1) the expression of all genes within a sample or a single gene across samples in a population (gene-level diversity) or (2) the isoform-specific expression of a given gene (isoform-level diversity). We first overview modulators and quantification of transcriptome diversity at the gene level. Then, we discuss the role alternative splicing plays in driving transcript isoform-level diversity and how it can be quantified. Additionally, we overview computational resources for calculating gene-level and isoform-level diversity for high-throughput sequencing data. Finally, we discuss future applications of transcriptome diversity. This review provides a comprehensive overview of how gene expression diversity arises, and how measuring it determines a more complete picture of heterogeneity across proteins, cells, tissues, organisms and species.

Astrocytes control hippocampal synaptic plasticity through the vesicular-dependent release of D-serine

Astrocytes, the most abundant glial cells in the central nervous system (CNS), sense synaptic activity and respond through the release of gliotransmitters, a process mediated by intracellular Ca2+ level changes and SNARE-dependent mechanisms. Ionotropic N-methyl-D-aspartate (NMDA) receptors, which are activated by glutamate along with D-serine or glycine, play a crucial role in learning, memory, and synaptic plasticity. However, the precise impact of astrocyte-released D-serine on neuronal modulation remains insufficiently characterized. To address this, we have used the dominant negative SNARE (dnSNARE) mouse model, which selectively inhibits SNARE-dependent exocytosis from astrocytes. We recorded field excitatory postsynaptic potentials (fEPSPs) in CA3-CA1 synapses within hippocampal slices obtained from dnSNARE mice and wild-type (Wt) littermates. Our results demonstrate that hippocampal θ-burst long-term potentiation (LTP), a critical form of synaptic plasticity, is impaired in hippocampal slices from dnSNARE mice. Notably, this LTP impairment was rescued upon incubation with D-serine. To further investigate the involvement of astrocytes in D-serine-mediated mechanisms of LTP maintenance, we perfused hippocampal slices with L-serine - a substrate used by both neurons and astrocytes for D-serine production. The enhancement in LTP observed in dnSNARE mice was exclusively associated with D-serine presence, with no effects evident in the presence of L-serine. Additionally, both D- and L-serine reduced basal synaptic strength in the hippocampal slices of both Wt and dnSNARE mice. These results provide compelling evidence that distinct processes underlie the modulation of basal synaptic transmission and LTP through D-serine. Our findings underscore the pivotal contribution of astrocytes in D-serine-mediated processes that govern LTP establishment and basal transmission. This study not only provides essential insights into the intricate interplay between neurons and astrocytes but also emphasizes their collective role in shaping hippocampal synaptic function.

CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data

Identifying cell types is crucial for understanding the functional units of an organism. Machine learning has shown promising performance in identifying cell types, but many existing methods lack biological significance due to poor interpretability. However, it is of the utmost importance to understand what makes cells share the same function and form a specific cell type, motivating us to propose a biologically interpretable method. CellTICS prioritizes marker genes with cell-type-specific expression, using a hierarchy of biological pathways for neural network construction, and applying a multi-predictive-layer strategy to predict cell and sub-cell types. CellTICS usually outperforms existing methods in prediction accuracy. Moreover, CellTICS can reveal pathways that define a cell type or a cell type under specific physiological conditions, such as disease or aging. The nonlinear nature of neural networks enables us to identify many novel pathways. Interestingly, some of the pathways identified by CellTICS exhibit differential expression "variability" rather than differential expression across cell types, indicating that expression stochasticity within a pathway could be an important feature characteristic of a cell type. Overall, CellTICS provides a biologically interpretable method for identifying and characterizing cell types, shedding light on the underlying pathways that define cellular heterogeneity and its role in organismal function. CellTICS is available at https://github.com/qyyin0516/CellTICS.

Oyster aquaculture enhances sediment microbial diversity- Insights from a multi-omics study

The global aquaculture industry has grown substantially, with consequences for coastal ecology and biogeochemistry. Oyster aquaculture can alter the availability of resources for microbes that live in sediments as oysters move large quantities of organic material to the sediments via filter feeding, possibly leading to changes in the structure and function of sediment microbial communities. Here, we use a chronosequence approach to investigate the impacts of oyster farming on sediment microbial communities over 7 years of aquaculture activity in a temperate coastal system. We detected shifts in bacterial composition (16S rRNA amplicon sequencing), changes in gene expression (meta-transcriptomics), and variations in sediment elemental concentrations (sediment geochemistry) across different durations of oyster farming. Our results indicate that both the structure and function of bacterial communities vary between control (no oysters) and farm sites, with an overall increase in diversity and a shift towards anoxic tolerance in farm sites. However, little to no variation was observed in either structure or function with respect to farming duration suggesting these sediment microbial communities are resilient to change. We also did not find any significant impact of farming on heavy metal accumulation in the sediments. The minimal influence of long-term oyster farming on sediment bacterial function and biogeochemical processes as observed here can bear important consequences for establishing best practices for sustainable farming in these areas.

Importance

Sediment microbial communities drive a range of important ecosystem processes such as nutrient recycling and filtration. Oysters are well-known ecological engineers, and their presence is increasing as aquaculture expands in coastal waters globally. Determining how oyster aquaculture impacts sediment microbial processes is key to understanding current and future estuarine biogeochemical processes. Here, we use a multi-omics approach to study the effect of different durations of oyster farming on the structure and function of bacteria and elemental accumulation in the farm sediments. Our results indicate an increase in the diversity of bacterial communities in the farm sites with no such increases observed for elemental concentrations. Further, these effects persist across multiple years of farming with an increase of anoxic tolerant bacteria at farm sites. The multi-omics approach used in this study can serve as a valuable tool to facilitate understanding of the environmental impacts of oyster aquaculture.

Transcriptomic sequencing data illuminate insecticide-induced physiological stress mechanisms in aquatic non-target invertebrates

Pesticides are major agricultural stressors for freshwater species. Exposure to pesticides can disrupt the biotic integrity of freshwater ecosystems and impair associated ecosystem functions. Unfortunately, physiological mechanisms through which pesticides affect aquatic organisms are largely unknown. For example, the widely-used insecticide chlorantraniliprole is supposed to be highly selective for target pest species, i.e. Lepidoptera (butterflies), but its effect in aquatic non-target taxa is poorly studied. Using RNA-sequencing data, we quantified the insecticide effect on three aquatic invertebrate species: the caddisfly Lepidostoma basale, the mayfly Ephemera danica and the amphipod Gammarus pulex. Further, we tested how the insecticide-induced transcriptional response is modulated by biotic interaction between the two leaf-shredding species L. basale and G. pulex. While G. pulex was only weakly affected by chlorantraniliprole exposure, we detected strong transcriptional responses in L. basale and E. danica, implying that the stressor receptors are conserved between the target taxon Lepidoptera and other insect groups. We found in both insect species evidence for alterations of the developmental program. If transcriptional changes in the developmental program induce alterations in emergence phenology, pronounced effects on food web dynamics in a cross-ecosystem context are expected.

clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution

Background

Pathological conditions may result in certain genes having expression variance that differs markedly from that of the control. Finding such genes from gene expression data can provide invaluable candidates for therapeutic intervention. Under the dominant paradigm for modeling RNA-Seq gene counts using the negative binomial model, tests of differential variability are challenging to develop, owing to dependence of the variance on the mean.

Methods

Here, we describe clrDV, a statistical method for detecting genes that show differential variability between two populations. We present the skew-normal distribution for modeling gene-wise null distribution of centered log-ratio transformation of compositional RNA-seq data.

Results

Simulation results show that clrDV has false discovery rate and probability of Type II error that are on par with or superior to existing methodologies. In addition, its run time is faster than its closest competitors, and remains relatively constant for increasing sample size per group. Analysis of a large neurodegenerative disease RNA-Seq dataset using clrDV successfully recovers multiple gene candidates that have been reported to be associated with Alzheimer's disease.

SARS-CoV-2-associated organs failure and inflammation: a focus on the role of cellular and viral microRNAs

SARS-CoV-2 has been responsible for the recent pandemic all over the world, which has caused many complications. One of the hallmarks of SARS-CoV-2 infection is an induced immune dysregulation, in some cases resulting in cytokine storm syndrome, acute respiratory distress syndrome and many organs such as lungs, brain, and heart that are affected during the SARS-CoV-2 infection. Several physiological parameters are altered as a result of infection and cytokine storm. Among them, microRNAs (miRNAs) might reflect this poor condition since they play a significant role in immune cellular performance including inflammatory responses. Both host and viral-encoded miRNAs are crucial for the successful infection of SARS-CoV-2. For instance, dysregulation of miRNAs that modulate multiple genes expressed in COVID-19 patients with comorbidities (e.g., type 2 diabetes, and cerebrovascular disorders) could affect the severity of the disease. Therefore, altered expression levels of circulating miRNAs might be helpful to diagnose this illness and forecast whether a COVID-19 patient could develop a severe state of the disease. Moreover, a number of miRNAs could inhibit the expression of proteins, such as ACE2, TMPRSS2, spike, and Nsp12, involved in the life cycle of SARS-CoV-2. Accordingly, miRNAs represent potential biomarkers and therapeutic targets for this devastating viral disease. In the current study, we investigated modifications in miRNA expression and their influence on COVID-19 disease recovery, which may be employed as a therapy strategy to minimize COVID-19-related disorders.

BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures

Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportions of cell types. Expression deconvolution methods can help differentiate these scenarios. BEDwARS is a Bayesian deconvolution method designed to address differences between reference signatures of cell types and corresponding true signatures underlying bulk transcriptomic profiles. BEDwARS is more robust to noisy reference signatures and outperforms leading in-class methods for estimating cell type proportions and signatures. Application of BEDwARS to dihydropyridine dehydrogenase deficiency identified the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.

Regulatory roles of NAT10 in airway epithelial cell function and metabolism in pathological conditions

N-acetyltransferase 10 (NAT10), a nuclear acetyltransferase and a member of the GNAT family, plays critical roles in RNA stability and translation processes as well as cell proliferation. Little is known about regulatory effects of NAT10 in lung epithelial cell proliferation. We firstly investigated NTA10 mRNA expression in alveolar epithelial types I and II, basal, ciliated, club, and goblet/mucous epithelia from heathy and patients with chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, lung adenocarcinoma, para-tumor tissue, and systemic sclerosis, respectively. We selected A549 cells for representative of alveolar epithelia or H1299 and H460 cells as airway epithelia with different genetic backgrounds and studied dynamic responses of NAT10-down-regulated epithelia to high temperature, lipopolysaccharide, cigarette smoking extract (CSE), drugs, radiation, and phosphoinositide 3-kinase (PI3K) inhibitors at various doses. We also compared transcriptomic profiles between alveolar and airway epithelia, between cells with or without NAT10 down-regulation, between early and late stages, and between challenges. The present study demonstrated that NAT10 expression increased in human lung epithelia and varied among epithelial types, challenges, and diseases. Knockdown of NAT10 altered epithelial mitochondrial functions, dynamic responses to LPS, CSE, or PI3K inhibitors, and transcriptomic phenomes. NAT10 regulates biological phenomes, and behaviors are more complex and are dependent upon multiple signal pathways. Thus, NAT10-associated signal pathways can be a new alternative for understanding the disease and developing new biomarkers and targets.

Characterizing the landscape of gene expression variance in humans

Gene expression variance has been linked to organismal function and fitness but remains a commonly neglected aspect of molecular research. As a result, we lack a comprehensive understanding of the patterns of transcriptional variance across genes, and how this variance is linked to context-specific gene regulation and gene function. Here, we use 57 large publicly available RNA-seq data sets to investigate the landscape of gene expression variance. These studies cover a wide range of tissues and allowed us to assess if there are consistently more or less variable genes across tissues and data sets and what mechanisms drive these patterns. We show that gene expression variance is broadly similar across tissues and studies, indicating that the pattern of transcriptional variance is consistent. We use this similarity to create both global and within-tissue rankings of variation, which we use to show that function, sequence variation, and gene regulatory signatures contribute to gene expression variance. Low-variance genes are associated with fundamental cell processes and have lower levels of genetic polymorphisms, have higher gene-gene connectivity, and tend to be associated with chromatin states associated with transcription. In contrast, high-variance genes are enriched for genes involved in immune response, environmentally responsive genes, immediate early genes, and are associated with higher levels of polymorphisms. These results show that the pattern of transcriptional variance is not noise. Instead, it is a consistent gene trait that seems to be functionally constrained in human populations. Furthermore, this commonly neglected aspect of molecular phenotypic variation harbors important information to understand complex traits and disease.

The Precision Toxicology Initiative

The goal of PrecisionTox is to overcome conceptual barriers to replacing traditional mammalian chemical safety testing by accelerating the discovery of evolutionarily conserved toxicity pathways that are shared by descent among humans and more distantly related animals. An international consortium is systematically testing the toxicological effects of a diverse set of chemicals on a suite of five model species comprising fruit flies, nematodes, water fleas, and embryos of clawed frogs and zebrafish along with human cell lines. Multiple forms of omics and comparative toxicology data are integrated to map the evolutionary origins of biomolecular interactions, which are predictive of adverse health effects, to major branches of the animal phylogeny. These conserved elements of adverse outcome pathways (AOPs) and their biomarkers are expect to provide mechanistic insight useful for regulating groups of chemicals based on their shared modes of action. PrecisionTox also aims to quantify risk variation within populations by recognizing susceptibility as a heritable trait that varies with genetic diversity. This initiative incorporates legal experts and collaborates with risk managers to address specific needs within European chemicals legislation, including the uptake of new approach methodologies (NAMs) for setting precise regulatory limits on toxic chemicals.

Acute intrahippocampal administration of melanin-concentrating hormone impairs memory consolidation and decreases the expression of MCHR-1 and TrkB receptors

Interest in the role of melanin-concentrating hormone (MCH) in memory processes has increased in recent years, with some studies reporting memory-enhancing effects, while others report deleterious effects. Due to these discrepancies, this study seeks to provide new evidence about the role of MCH in memory consolidation and its relation with BDNF/TrkB system. To this end, in the first experiment, increased doses of MCH were acutely administered in both hippocampi to groups of male rats (25, 50, 200, and 500 ng). Microinjections were carried out immediately after finishing the sample trial of two hippocampal-dependent behavioral tasks: the Novel Object Recognition Test (NORT) and the modified Elevated Plus Maze (mEPM) test. Results indicated that a dose of 200 ng of MCH or higher impaired memory consolidation in both tasks. A second experiment was performed in which a dose of 200 ng of MCH was administered alone or co-administered with the MCHR-1 antagonist ATC-0175 at the end of the sample trial in the NORT. Results showed that MCH impaired memory consolidation, while the co-administration with ATC-0175 reverted this detrimental effect. Moreover, MCH induced a significant decrease in hippocampal MCHR-1 and TrkB expression with no modification in the expression of BDNF and NMDA receptor subunits NR1, NR2A, and NR2B. These results suggest that MCH in vivo elicits pro-amnesic effects in the rat hippocampus by decreasing the availability of its receptor and TrkB receptors, thus linking both endogenous systems to memory processes.

Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called "differential expression analysis" approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.

Progress in Discovering Transcriptional Noise in Aging

Increasing stochasticity is a key feature in the aging process. At the molecular level, in addition to genome instability, a well-recognized hallmark of aging, cell-to-cell variation in gene expression was first identified in mouse hearts. With the technological breakthrough in single-cell RNA sequencing, most studies performed in recent years have demonstrated a positive correlation between cell-to-cell variation and age in human pancreatic cells, as well as mouse lymphocytes, lung cells, and muscle stem cells during senescence in vitro. This phenomenon is known as the "transcriptional noise" of aging. In addition to the increasing evidence in experimental observations, progress also has been made to better define transcriptional noise. Traditionally, transcriptional noise is measured using simple statistical measurements, such as the coefficient of variation, Fano factor, and correlation coefficient. Recently, multiple novel methods have been proposed, e.g., global coordination level analysis, to define transcriptional noise based on network analysis of gene-to-gene coordination. However, remaining challenges include a limited number of wet-lab observations, technical noise in single-cell RNA sequencing, and the lack of a standard and/or optimal data analytical measurement of transcriptional noise. Here, we review the recent technological progress, current knowledge, and challenges to better understand transcriptional noise in aging.

Transcriptome and molecular regulatory mechanisms analysis of gills in the black tiger shrimp Penaeus monodon under chronic low-salinity stress

Background: Salinity is one of the main influencing factors in the culture environment and is extremely important for the survival, growth, development and reproduction of aquatic animals. Methods: In this study, a comparative transcriptome analysis (maintained for 45 days in three different salinities, 30 psu (HC group), 18 psu (MC group) and 3 psu (LC group)) was performed by high-throughput sequencing of economically cultured Penaeus monodon. P. monodon gill tissues from each treatment were collected for RNA-seq analysis to identify potential genes and pathways in response to low salinity stress. Results: A total of 64,475 unigenes were annotated in this study. There were 1,140 upregulated genes and 1,531 downregulated genes observed in the LC vs. HC group and 1,000 upregulated genes and 1,062 downregulated genes observed in the MC vs. HC group. In the LC vs. HC group, 583 DEGs significantly mapped to 37 signaling pathways, such as the NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway; in the MC vs. HC group, 444 DEGs significantly mapped to 28 signaling pathways, such as the MAPK signaling pathway, Hippo signaling pathway and calcium signaling pathway. These pathways were significantly associated mainly with signal transduction, immunity and metabolism. Conclusions: These results suggest that low salinity stress may affect regulatory mechanisms such as metabolism, immunity, and signal transduction in addition to osmolarity in P. monodon. The greater the difference in salinity, the more significant the difference in genes. This study provides some guidance for understanding the low-salt domestication culture of P. monodon.

Cathepsin L was involved in vascular aging by mediating phenotypic transformation of vascular cells

Vascular media and adventitia-induced remodeling plays an important role in vascular aging. However, the mechanism remains unclear. This study aims to investigate the mechanisms underlying vascular aging. Transcriptome analysis revealed that the expression of cathepsin L (CTSL) significantly decreased in arteries of old mice (24 months old) compared with that in arteries of young mice (4 months old), which was confirmed by immunohistochemistry and Western blot. The expression of CTSL in adventitia fibroblasts (AFs) and vascular smooth muscle cells (VSMCs) of aged mice was lower than that of young mice. Compared with wild-type control mice, CTSL knockout (CTSL ^{- /-}) mice had increased collagen deposition (fibrosis) and decreased telomerase activity and LC3Ⅱ/ LC3Ⅰratio. The expression of mammalian target of rapamycin (mTOR) and osteopontin (OPN) increased in aortas of CTSL^-/-mice compared with that in aortas of wild-type control mice. In vitro, lentivirus-mediated CTSL knockdown induced VSMCs senescence and AFs transformed into myofibroblasts (MFs). Rapamycin, a mTOR inhibitor, inhibited CTSL deficiency induced VSMCs senescence, osteopontin (OPN) secretion and AFs migration. In conclusion, the decreased level of CTSL with age may participate in vascular aging by promoting the phenotypic transformation of vascular cells.

m6A RNA methylation impairs gene expression variability and reproductive thermotolerance in Arabidopsis

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.

Sex differences in interindividual gene expression variability across human tissues

Understanding phenotypic sex differences has long been a goal of biology from both a medical and evolutionary perspective. Although much attention has been paid to mean differences in phenotype between the sexes, little is known about sex differences in phenotypic variability. To gain insight into sex differences in interindividual variability at the molecular level, we analyzed RNA-seq data from 43 tissues from the Genotype-Tissue Expression project (GTEx). Within each tissue, we identified genes that show sex differences in gene expression variability. We found that these sex-differentially variable (SDV) genes are associated with various important biological functions, including sex hormone response, immune response, and other signaling pathways. By analyzing single-cell RNA sequencing data collected from breast epithelial cells, we found that genes with sex differences in gene expression variability in breast tissue tend to be expressed in a cell-type-specific manner. We looked for an association between SDV expression and Graves' disease, a well-known heavily female-biased disease, and found a significant enrichment of Graves' associated genes among genes with higher variability in females in thyroid tissue. This suggests a possible role for SDV expression in sex-biased disease. We then examined the evolutionary constraints acting on genes with sex differences in variability and found that they exhibit evidence of increased selective constraint. Through analysis of sex-biased eQTL data, we found evidence that SDV expression may have a genetic basis. Finally, we propose a simple evolutionary model for the emergence of SDV expression from sex-specific constraints.

Mother–Fetus Immune Cross-Talk Coordinates “Extrinsic”/“Intrinsic” Embryo Gene Expression Noise and Growth Stability

Developmental instability (DI) is thought to be inversely related to a capacity of an organism to buffer its development against random genetic and environmental perturbations. DI is represented by a trait’s inter- and intra-individual variabilities. The inter-individual variability (inversely referred to as canalization) indicates the capability of organisms to reproduce a trait from individual to individual. The intra-individual variability reflects an organism’s capability to stabilize a trait internally under the same conditions, and, for symmetric traits, it is expressed as fluctuating asymmetry (FA). When representing a trait as a random variable conditioned on environmental fluctuations, it is clear that, in statistical terms, the DI partitions into “extrinsic” (canalization) and “intrinsic” (FA) components of a trait’s variance/noise. We established a simple statistical framework to dissect both parts of a symmetric trait variance/noise using a PCA (principal component analysis) projection of the left/right measurements on eigenvectors followed by GAMLSS (generalized additive models for location scale and shape) modeling of eigenvalues. The first eigenvalue represents “extrinsic” and the second—“intrinsic” DI components. We applied this framework to investigate the impact of mother–fetus major histocompatibility complex (MHC)-mediated immune cross-talk on gene expression noise and developmental stability. We showed that “intrinsic” gene noise for the entire transcriptional landscape could be estimated from a small subset of randomly selected genes. Using a diagnostic set of genes, we found that allogeneic MHC combinations tended to decrease “extrinsic” and “intrinsic” gene noise in C57BL/6J embryos developing in the surrogate NOD-SCID and BALB/c mothers. The “intrinsic” gene noise was negatively correlated with growth (embryonic mass) and the levels of placental growth factor (PLGF), but not vascular endothelial growth factor (VEGF). However, it was positively associated with phenotypic growth instability and noise in PLGF. In mammals, the mother–fetus MHC interaction plays a significant role in development, contributing to the fitness of the offspring. Our results demonstrate that a positive impact of distant MHC combinations on embryonic growth could be mediated by the reduction of “intrinsic” gene noise followed by the developmental stabilization of growth.

Mathematical model for the relationship between single-cell and bulk gene expression to clarify the interpretation of bulk gene expression data

BACKGROUND

Differential expression analysis is a standard approach in molecular biology. For example, genes whose expression levels differ between diseased and non-diseased samples are considered to be associated with that disease. On the other hand, differential variability analysis focuses on the differences of the variances of gene expression between sample groups. Although differential variability is also known to capture biological information, its interpretation remains unclear and controversial. Recent single-cell analyses have revealed that differences between sample groups can affect gene expression in a cellular subset-specific manner or by altering the proportion of a particular cellular subset. The aim of this study is to clarify the interpretation of mean and variance of bulk gene expression data.

METHOD

We developed a mathematical model in which the bulk gene expression value is proportional to the mean value of the single-cell gene expression profile. Based on this model, we performed theoretical, simulated and real single-cell RNA-seq data analyses.

RESULT AND CONCLUSION

We identified how differences in single-cell gene expression profiles affect the differences in the mean and the variance of bulk gene expression. It is shown that differential expression analysis of bulk expression data can overlook significant changes in gene expression at the single-cell level. Further, differential variability analysis capture the complex feature affected by different gene expression shifts for each subset, changes in the proportions of cellular subsets, and variation in single-cell distribution parameters among samples.

Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability

There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour-normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.

Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks

Methods for time series prediction and classification of gene regulatory networks (GRNs) from gene expression data have been treated separately so far. The recent emergence of attention-based recurrent neural network (RNN) models boosted the interpretability of RNN parameters, making them appealing for the understanding of gene interactions. In this work, we generated synthetic time series gene expression data from a range of archetypal GRNs and we relied on a dual attention RNN to predict the gene temporal dynamics. We show that the prediction is extremely accurate for GRNs with different architectures. Next, we focused on the attention mechanism of the RNN and, using tools from graph theory, we found that its graph properties allow one to hierarchically distinguish different architectures of the GRN. We show that the GRN responded differently to the addition of noise in the prediction by the RNN and we related the noise response to the analysis of the attention mechanism. In conclusion, this work provides a way to understand and exploit the attention mechanism of RNNs and it paves the way to RNN-based methods for time series prediction and inference of GRNs from gene expression data.

Identifying toggle genes from transcriptome-wide scatter: A new perspective for biological regulation

The study of gene expression variability, especially for cancer and cell differentiation studies, has become important. Here, we investigate transcriptome-wide scatter of 23 cell types and conditions across different levels of biological complexity. We focused on genes that act like toggle switches between pairwise replicates of the same cell type, i.e. genes expressed in one replicate and not expressed in the other, sometimes also referred as ON/OFF genes. The proportion of these toggle genes dramatically increases from unicellular to multicellular organization, especially for development and cancer cells. A relevant portion of toggle switches are non-coding genes: in unicellular systems the most represented classes are tRNA and rRNA, while multicellular systems more frequently show lncRNA, sncRNA and pseudogenes. Notably, disease associated microRNAs (miRNAs), pseudogenes and numerous uncharacterized transcripts are present in both development and cancer cells. On top of the known intrinsic and extrinsic factors, our work indicates toggle genes as a novel collective component creating transcriptome-wide variability. This requires further investigation for elucidating both evolutionary and disease processes.

CStone: A de novo transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure

With the exponential growth of sequence information stored over the last decade, including that of de novo assembled contigs from RNA-Seq experiments, quantification of chimeric sequences has become essential when assembling read data. In transcriptomics, de novo assembled chimeras can closely resemble underlying transcripts, but patterns such as those seen between co-evolving sites, or mapped read counts, become obscured. We have created a de Bruijn based de novo assembler for RNA-Seq data that utilizes a classification system to describe the complexity of underlying graphs from which contigs are created. Each contig is labelled with one of three levels, indicating whether or not ambiguous paths exist. A by-product of this is information on the range of complexity of the underlying gene families present. As a demonstration of CStones ability to assemble high-quality contigs, and to label them in this manner, both simulated and real data were used. For simulated data, ten million read pairs were generated from cDNA libraries representing four species, Drosophila melanogaster, Panthera pardus, Rattus norvegicus and Serinus canaria. These were assembled using CStone, Trinity and rnaSPAdes; the latter two being high-quality, well established, de novo assembers. For real data, two RNA-Seq datasets, each consisting of ≈30 million read pairs, representing two adult D. melanogaster whole-body samples were used. The contigs that CStone produced were comparable in quality to those of Trinity and rnaSPAdes in terms of length, sequence identity of aligned regions and the range of cDNA transcripts represented, whilst providing additional information on chimerism. Here we describe the details of CStones assembly and classification process, and propose that similar classification systems can be incorporated into other de novo assembly tools. Within a related side study, we explore the effects that chimera’s within reference sets have on the identification of differentially expression genes. CStone is available at: https://sourceforge.net/projects/cstone/.

Within transcriptome reference sets, non-chimeric sequences are representations of transcribed genes, while artificially generated chimeric ones are mosaics of two or more pieces of DNA incorrectly pieced together. One area where such sets are utilized is in the quantification of gene expression patterns; where RNA-Seq reads are mapped to the sequences within, and subsequent count values reflect expression levels. Artificial chimeras can have a negative impact on count values by erroneously increasing variation in relation to the reads being mapped. Reference sets can be created from de novo assembled contigs, but chimeras can be introduced during the assembly process via the required traversal of graphs, representing gene families, constructed from the RNA-Seq data. Graph complexity determines how likely chimeras will arise. We have created CStone, a de novo assembler that utilizes a classification system to describe such complexity. Contigs created by CStone are labelled in a manner that indicates whether or not they are non-chimeric. This encourages contig dependent results to be presented with increased objectivity by maintaining the context of ambiguity associated with the assembly process. CStone has been tested extensively. Additionally, we have quantified the relationship between chimeras within reference sets and the identification of differentially expressed genes.

Transcriptome-Wide Profile of 25-Hydroxyvitamin D3 in Primary Immune Cells from Human Peripheral Blood

Vitamin D₃ is an essential micronutrient mediating pleiotropic effects in multiple tissues and cell types via its metabolite 1α,25-dihydroxyvitamin D₃ (1,25(OH)₂D₃), which activates the transcription factor vitamin D receptor. In this study, we used peripheral blood mononuclear cells (PBMCs) obtained from five healthy adults and investigated transcriptome-wide, whether the precursor of 1,25(OH)₂D₃, 25-hydroxyvitamin D₃ (25(OH)D₃), has gene regulatory potential on its own. Applying thresholds of >2 in fold change of gene expression and <0.05 as a false discovery rate, in this ex vivo approach the maximal physiological concentration of 25(OH)D₃ (250 nM (nmol/L)) none of the study participants had a significant effect on their PBMC transcriptome. In contrast, 1000 and 10,000 nM 25(OH)D₃ regulated 398 and 477 genes, respectively, which is comparable to the 625 genes responding to 10 nM 1,25(OH)₂D₃. The majority of these genes displayed specificity to the tested individuals, but not to the vitamin D metabolite. Interestingly, the genes MYLIP (myosin regulatory light chain interacting protein) and ABCG1 (ATP binding cassette subfamily G member 1) showed to be specific targets of 10,000 nM 25(OH)D₃. In conclusion, 100- and 1000-fold higher 25(OH)D₃ concentrations than the reference 10 nM 1,25(OH)₂D₃ are able to affect the transcriptome of PBMCs with a profile comparable to that of 1,25(OH)₂D₃.

Histone H3K27 methylation perturbs transcriptional robustness and underpins dispensability of highly conserved genes in fungi

Epigenetic modifications are key regulators of gene expression and underpin genome integrity. Yet, how epigenetic changes affect the evolution and transcriptional robustness of genes remains largely unknown. Here, we show how the repressive histone mark H3K27me3 underpins the trajectory of highly conserved genes in fungi. We first performed transcriptomic profiling on closely related species of the plant pathogen Fusarium graminearum species complex. We determined transcriptional responsiveness of genes across environmental conditions to determine expression robustness. To infer evolutionary conservation, we used a framework of 23 species across the Fusarium genus including three species covered with histone methylation data. Gene expression variation is negatively correlated with gene conservation confirming that highly conserved genes show higher expression robustness. In contrast, genes marked by H3K27me3 do not show such associations. Furthermore, highly conserved genes marked by H3K27me3 encode smaller proteins, exhibit weaker codon usage bias, higher levels of hydrophobicity, show lower intrinsically disordered regions, and are enriched for functions related to regulation and membrane transport. The evolutionary age of conserved genes with H3K27me3 histone marks falls typically within the origins of the Fusarium genus. We show that highly conserved genes marked by H3K27me3 are more likely to be dispensable for survival during host infection. Lastly, we show that conserved genes exposed to repressive H3K27me3 marks across distantly related Fusarium fungi are associated with transcriptional perturbation at the microevolutionary scale. In conclusion, we show how repressive histone marks are entangled in the evolutionary fate of highly conserved genes across evolutionary timescales.

Identifying a novel biological mechanism for alcohol addiction associated with circRNA networks acting as potential miRNA sponges

Our lab and others have shown that chronic alcohol use leads to gene and miRNA expression changes across the mesocorticolimbic (MCL) system. Circular RNAs (circRNAs) are noncoding RNAs that form closed-loop structures and are reported to alter gene expression through miRNA sequestration, thus providing a potentially novel neurobiological mechanism for the development of alcohol dependence (AD). Genome-wide expression of circRNA was assessed in the nucleus accumbens (NAc) from 32 AD-matched cases/controls. Significant circRNAs (unadj. p ≤ 0.05) were identified via regression and clustered in circRNA networks via weighted gene co-expression network analysis (WGCNA). CircRNA interactions with previously generated mRNA and miRNA were detected via correlation and bioinformatic analyses. Significant circRNAs (N = 542) clustered in nine significant AD modules (FWER p ≤ 0.05), within which we identified 137 circRNA hubs. We detected 23 significant circRNA-miRNA-mRNA interactions (FDR ≤ 0.10). Among these, circRNA-406742 and miR-1200 significantly interact with the highest number of mRNA, including genes associated with neuronal functioning and alcohol addiction (HRAS, PRKCB, HOMER1, and PCLO). Finally, we integrate genotypic information that revealed 96 significant circRNA expression quantitative trait loci (eQTLs) (unadj. p ≤ 0.002) that showed significant enrichment within recent alcohol use disorder (AUD) and smoking genome-wide association study (GWAS). To our knowledge, this is the first study to examine the role of circRNA in the neuropathology of AD. We show that circRNAs impact mRNA expression by interacting with miRNA in the NAc of AD subjects. More importantly, we provide indirect evidence for the clinical importance of circRNA in the development of AUD by detecting a significant enrichment of our circRNA eQTLs among GWAS of substance abuse.

Cellular, molecular, and therapeutic characterization of pilocarpine-induced temporal lobe epilepsy

Animal models have expanded our understanding of temporal lobe epilepsy (TLE). However, translating these to cell-specific druggable hypotheses is not explored. Herein, we conducted an integrative insilico-analysis of an available transcriptomics dataset obtained from animals with pilocarpine-induced-TLE. A set of 119 genes with subtle-to-moderate impact predicted most forms of epilepsy with ~ 97% accuracy and characteristically mapped to upregulated homeostatic and downregulated synaptic pathways. The deconvolution of cellular proportions revealed opposing changes in diverse cell types. The proportion of nonneuronal cells increased whereas that of interneurons, except for those expressing vasoactive intestinal peptide (Vip), decreased, and pyramidal neurons of the cornu-ammonis (CA) subfields showed the highest variation in proportion. A probabilistic Bayesian-network demonstrated an aberrant and oscillating physiological interaction between nonneuronal cells involved in the blood–brain-barrier and Vip interneurons in driving seizures, and their role was evaluated insilico using transcriptomic changes induced by valproic-acid, which showed opposing effects in the two cell-types. Additionally, we revealed novel epileptic and antiepileptic mechanisms and predicted drugs using causal inference, outperforming the present drug repurposing approaches. These well-powered findings not only expand the understanding of TLE and seizure oscillation, but also provide predictive biomarkers of epilepsy, cellular and causal micro-circuitry changes associated with it, and a drug-discovery method focusing on these events.

Texturized P(VDF-TrFE)/BT membrane enhances bone neoformation in calvaria defects regardless of the association with photobiomodulation therapy in ovariectomized rats

OBJECTIVES

The purpose of this investigation was to evaluate in vivo the response of bone tissue to photobiomodulation when associated with texturized P(VDF-TrFE)/BT in calvaria defects of ovariectomized rats.

MATERIALS AND METHODS

Wistar Hannover rats were submitted to ovariectomy/control surgery. Calvaria bone defects of 5-mm diameter were performed after 90 days of ovariectomy. The animals were divided into OVX (without laser (L) and membrane), OVX + P(VDF-TrFE)/BT, OVX + P(VDF-TrFE)/BT + L, and OVX + PTFE + L. It was utilized a low-intensity gallium-aluminum-arsenide laser (GaAlAs) with 780-nm wavelength and 30-J/cm² energy density in 12 sessions (120 s). Thirty days after the bone defect the animals were euthanized for histological, microtomographic, and molecular evaluation. Quantitative analysis was analyzed by statistical software for p < 0.05.

RESULTS

Histological parameters showed bone tissue formation at the borders of all group defects. The association of photobiomodulation and texturized P(VDF-TrFE)/BT was not synergistic and did not show significant changes in morphometric analysis and biomarkers gene expression. Nevertheless, texturized P(VDF-TrFE)/BT membrane enhanced bone repair regardless of the association with photobiomodulation therapy, with an increase of connectivity density when compared to the OVX + PTFE + L group. The association of photobiomodulation therapy and PTFE was synergistic, increasing the expression of Runx2, Alp, Bsp, Bglap, Sp7, and Rankl, even though not enough to reflect significance in the morphometric parameters.

CONCLUSIONS

The utilization of texturized P (VDF-TrFE)/BT, regardless of the association with photobiomodulation therapy, enhanced bone repair in an experimental model of osteoporosis.

microRNA Heterogeneity, Innate-Immune Defense and the Efficacy of SARS-CoV-2 Infection-A Commentary

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a member of the genus Betacoronavirus in the family Coronaviridae, possesses an unusually large single-stranded viral RNA (ssvRNA) genome of about ~29,811 nucleotides (nt) that causes severe and acute respiratory distress and a highly lethal viral pneumonia known as COVID-19. COVID-19 also presents with multiple ancillary systemic diseases and often involves cardiovascular, inflammatory, and/or neurological complications. Pathological viral genomes consisting of ssvRNA, like cellular messenger RNA (mRNA), are susceptible to attack, destruction, neutralization, and/or modulation by naturally occurring small non-coding RNAs (sncRNAs) within the host cell, some of which are known as microRNAs (miRNAs). This paper proposes that the actions of the 2650 known human miRNAs and other sncRNAs form the basis for an under-recognized and unappreciated innate-immune regulator of ssvRNA viral genome activities and have implications for the efficiency of SARS-CoV-2 invasion, infection, and replication. Recent research indicates that both miRNA and mRNA abundance, speciation, and complexity varies widely amongst human individuals, and this may: (i) In part explain the variability in the innate-immune immunological and pathophysiological response of different human individuals to the initiation and progression of SARS-CoV-2 infection in multiple tissue types; and (ii) further support our understanding of human biochemical and genetic individuality and the variable resistance of individuals to ssvRNA-mediated viral infection and disease. This commentary will briefly address current findings and concepts in this fascinating research area of non-coding RNA and innate-immunity with special reference to natural host miRNAs, SARS-CoV-2, and the current COVID-19 pandemic.

Estimates of gene ensemble noise highlight critical pathways and predict disease severity in H1N1, COVID-19 and mortality in sepsis patients

Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is challenging. This stems from the heterogeneous response of individuals to disease and is reflected in the inter-individual variability of gene expression responses that obscures differential gene expression analysis. Here, we developed an alternative approach that could be applied to dissect the disease-associated molecular changes. We define gene ensemble noise as a measure that represents a variance for a collection of genes encoding for either members of known biological pathways or subunits of annotated protein complexes and calculated within an individual. The gene ensemble noise allows for the holistic identification and interpretation of gene expression disbalance on the level of gene networks and systems. By comparing gene expression data from COVID-19, H1N1, and sepsis patients we identified common disturbances in a number of pathways and protein complexes relevant to the sepsis pathology. Among others, these include the mitochondrial respiratory chain complex I and peroxisomes. This suggests a Warburg effect and oxidative stress as common hallmarks of the immune host-pathogen response. Finally, we showed that gene ensemble noise could successfully be applied for the prediction of clinical outcome namely, the mortality of patients. Thus, we conclude that gene ensemble noise represents a promising approach for the investigation of molecular mechanisms of pathology through a prism of alterations in the coherent expression of gene circuits.

PTSD is associated with accelerated transcriptional aging in World Trade Center responders

Posttraumatic stress disorder (PTSD) is associated with shortened lifespan and healthspan, which suggests accelerated aging. Emerging evidence suggests that methylation age may be accelerated in PTSD. It is important to examine whether transcriptional age is also accelerated because transcriptome is highly dynamic, associated with age-related outcomes, and may offer greater insight into the premature aging in PTSD. This study is the first reported investigation of the relationship between transcriptional age and PTSD. Using RNA-Seq data from our previous study on 324 World Trade Center responders (201 never had PTSD, 81 with current PTSD, and 42 with past PTSD), as well as a transcriptional age calculator (RNAAgeCalc) recently developed by our group, we found that responders with current PTSD, compared with responders without a PTSD diagnosis, showed accelerated transcriptional aging (p = 0.0077) after adjustment for chronological age and race. We compared our results to the epigenetic aging results computed from several epigenetic clock calculators on matching DNA methylation data. GrimAge methylation age acceleration was also associated with PTSD diagnosis (p = 0.0097), and the results remained significant after adjustment for the proportions of immune cell types. PhenoAge, Hannum, and Horvath methylation age acceleration were not reliably related to PTSD. Both epigenetic and transcriptional aging may provide biological insights into the mechanisms underpinning aging in PTSD.

RNA sequencing describes both population structure and plasticity-selection dynamics in a non-model fish

Background

Messenger RNA sequencing is becoming more common in studies of non-model species and is most often used for gene expression-based investigations. However, the method holds potential for numerous other applications as well—including analyses of alternative splicing, population structure, and signatures of selection. To maximize the utility of mRNA data sets, distinct analyses may be combined such as by exploring dynamics between gene expression with signatures of selection in the context of population structure. Here, we compare two published data sets describing two populations of a minnow species endemic to the San Francisco Estuary (Sacramento splittail, Pogonichthys macrolepidotus): a microsatellite data set showing population structure, and an mRNA whole transcriptome data set obtained after the two populations were exposed to a salinity challenge. We compared measures of population structure and genetic variation using single nucleotide polymorphisms (SNPs) called from mRNA from the whole transcriptome sequencing study with those patterns determined from microsatellites. For investigating plasticity and evolution, intra- and inter-population transcriptome plasticity was investigated with differential gene expression, differential exon usage, and gene expression variation. Outlier SNP analysis was also performed on the mRNA data set and signatures of selection and phenotypic plasticity were investigated on an individual-gene basis.

Results

We found that mRNA sequencing revealed patterns of population structure consistent with those found with microsatellites, but with lower magnitudes of genetic variation and population differentiation consistent with widespread purifying selection expected when using mRNA. In addition, within individual genes, phenotypic plasticity or signatures of selection were found in almost mutual exclusion (except heatr6, nfu1, slc22a6, sya, and mmp13).

Conclusions

These results show that an mRNA sequencing data set may have multiple uses, including describing population structure and for investigating the mechanistic interplay of evolution and plasticity in adaptation. MRNA sequencing thus complements traditional sequencing methods used for population genetics, in addition to its utility for describing phenotypic plasticity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07592-4.

Identification of 37 Heterogeneous Drug Candidates for Treatment of COVID-19 via a Rational Transcriptomics-Based Drug Repurposing Approach

A year after the initial outbreak, the COVID-19 pandemic caused by SARS-CoV-2 virus remains a serious threat to global health, while current treatment options are insufficient to bring major improvements. The aim of this study is to identify repurposable drug candidates with a potential to reverse transcriptomic alterations in the host cells infected by SARS-CoV-2. We have developed a rational computational pipeline to filter publicly available transcriptomic datasets of SARS-CoV-2-infected biosamples based on their responsiveness to the virus, to generate a list of relevant differentially expressed genes, and to identify drug candidates for repurposing using LINCS connectivity map. Pathway enrichment analysis was performed to place the results into biological context. We identified 37 structurally heterogeneous drug candidates and revealed several biological processes as druggable pathways. These pathways include metabolic and biosynthetic processes, cellular developmental processes, immune response and signaling pathways, with steroid metabolic process being targeted by half of the drug candidates. The pipeline developed in this study integrates biological knowledge with rational study design and can be adapted for future more comprehensive studies. Our findings support further investigations of some drugs currently in clinical trials, such as itraconazole and imatinib, and suggest 31 previously unexplored drugs as treatment options for COVID-19.

DNMT1 Promotes Genome Methylation and Early Embryo Development in Cockroaches

The influence of DNA methylation on gene behavior and its consequent phenotypic effects appear to be very important, but the details are not well understood. Insects offer a diversity of DNA methylation modes, making them an excellent lineage for comparative analyses. However, functional studies have tended to focus on quite specialized holometabolan species, such as wasps, bees, beetles, and flies. Here, we have studied DNA methylation in the hemimetabolan insect Blattella germanica. In this cockroach, a gene involved in DNA methylation, DNA methyltransferase 1 (DNMT1), is expressed in early embryogenesis. In our experiments, RNAi of DNMT1 reduces DNA methylation and impairs blastoderm formation. Using reduced representation bisulfite sequencing and transcriptome analyses, we observed that methylated genes are associated with metabolism and are highly expressed, whereas unmethylated genes are related to signaling and show low expression. Moreover, methylated genes show greater expression change and less expression variability than unmethylated genes.

Gene expression variability in human and chimpanzee populations share common determinants

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.