Gene-specific patterns of expression variation across organs and species

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.

Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster

Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper results in cell and tissue damage ranging in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes respond to nonessential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the heavy metal stress response. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource using a combination of differential expression analysis and expression quantitative trait locus mapping. Differential expression analysis revealed clear patterns of tissue-specific expression. Tissue and treatment specific responses to copper stress were also detected using expression quantitative trait locus mapping. Expression quantitative trait locus associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited both genotype-by-tissue and genotype-by-treatment effects on gene expression under copper stress, illuminating tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight candidate genes for future functional characterization.

Investigating the impact of inflammatory response-related genes on renal fibrosis diagnosis: a machine learning-based study with experimental validation

Renal fibrosis plays a crucial role in the progression of renal diseases, yet the lack of effective diagnostic markers poses challenges in scientific and clinical practices. In this study, we employed machine learning techniques to identify potential biomarkers for renal fibrosis. Utilizing two datasets from the GEO database, we applied LASSO, SVM-RFE and RF algorithms to screen for differentially expressed genes related to inflammatory responses between the renal fibrosis group and the control group. As a result, we identified four genes (CCL5, IFITM1, RIPK2, and TNFAIP6) as promising diagnostic indicators for renal fibrosis. These genes were further validated through in vivo experiments and immunohistochemistry, demonstrating their utility as reliable markers for assessing renal fibrosis. Additionally, we conducted a comprehensive analysis to explore the relationship between these candidate biomarkers, immunity, and drug sensitivity. Integrating these findings, we developed a nomogram with a high discriminative ability, achieving a concordance index of 0.933, enabling the prediction of disease risk in patients with renal fibrosis. Overall, our study presents a predictive model for renal fibrosis and highlights the significance of four potential biomarkers, facilitating clinical diagnosis and personalized treatment. This finding presents valuable insights for advancing precision medicine approaches in the management of renal fibrosis.Communicated by Ramaswamy H. Sarma.

Tempo and mode of gene expression evolution in the brain across primates

Primate evolution has led to a remarkable diversity of behavioral specializations and pronounced brain size variation among species (Barton, 2012; DeCasien and Higham, 2019; Powell et al., 2017). Gene expression provides a promising opportunity for studying the molecular basis of brain evolution, but it has been explored in very few primate species to date (e.g. Khaitovich et al., 2005; Khrameeva et al., 2020; Ma et al., 2022; Somel et al., 2009). To understand the landscape of gene expression evolution across the primate lineage, we generated and analyzed RNA-seq data from four brain regions in an unprecedented eighteen species. Here, we show a remarkable level of variation in gene expression among hominid species, including humans and chimpanzees, despite their relatively recent divergence time from other primates. We found that individual genes display a wide range of expression dynamics across evolutionary time reflective of the diverse selection pressures acting on genes within primate brain tissue. Using our samples that represent a 190-fold difference in primate brain size, we identified genes with variation in expression most correlated with brain size. Our study extensively broadens the phylogenetic context of what is known about the molecular evolution of the brain across primates and identifies novel candidate genes for the study of genetic regulation of brain evolution.

CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis

SUMMARY

High-throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), a Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics.

AVAILABILITY AND IMPLEMENTATION

https://github.com/lasseignelab/CoSIA.

Building an optimal predictive model for imputing tissue-specific gene expression by combining genotype and whole-blood transcriptome data

Accurate imputation of tissue-specific gene expression can be a powerful tool for understanding the biological mechanisms underlying human complex traits. Existing imputation methods can be grouped into two categories according to the types of predictors used. The first category uses genotype data, while the second category uses whole-blood expression data. Both data types can be easily collected from blood, avoiding invasive tissue biopsies. In this study, we attempted to build an optimal predictive model for imputing tissue-specific gene expression by combining the genotype and whole-blood expression data. We first evaluated the imputation performance of each standalone model (using genotype data [GEN model] and using whole-blood expression data [WBE model]) using their respective data types across 47 human tissues. The WBE model outperformed the GEN model in most tissues by a large gain. Then, we developed several combined models that leverage both types of predictors to further improve imputation performance. We tried various strategies, including utilizing a merged dataset of the two data types (MERGED models) and integrating the imputation outcomes of the two standalone models (inverse variance-weighted [IVW] models). We found that one of the MERGED models noticeably outperformed the standalone models. This model involved a fixed ratio between the two regularization penalty factors for the two predictor types so that the contribution of the whole-blood transcriptome is upweighted compared with the genotype. Our study suggests that one can improve the imputation of tissue-specific gene expression by combining the genotype and whole-blood expression, but the improvement can be largely dependent on the combination strategy chosen.

CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis

High throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), an Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics.

Gene expression variation underlying tissue-specific responses to copper stress in Drosophila melanogaster

Copper is one of a handful of biologically necessary heavy metals that is also a common environmental pollutant. Under normal conditions, copper ions are required for many key physiological processes. However, in excess, copper quickly results in cell and tissue damage that can range in severity from temporary injury to permanent neurological damage. Because of its biological relevance, and because many conserved copper-responsive genes also respond to other non-essential heavy metal pollutants, copper resistance in Drosophila melanogaster is a useful model system with which to investigate the genetic control of the response to heavy metal stress. Because heavy metal toxicity has the potential to differently impact specific tissues, we genetically characterized the control of the gene expression response to copper stress in a tissue-specific manner in this study. We assessed the copper stress response in head and gut tissue of 96 inbred strains from the Drosophila Synthetic Population Resource (DSPR) using a combination of differential expression analysis and expression quantitative trait locus (eQTL) mapping. Differential expression analysis revealed clear patterns of tissue-specific expression, primarily driven by a more pronounced gene expression response in gut tissue. eQTL mapping of gene expression under control and copper conditions as well as for the change in gene expression following copper exposure (copper response eQTL) revealed hundreds of genes with tissue-specific local cis-eQTL and many distant trans-eQTL. eQTL associated with MtnA, Mdr49, Mdr50, and Sod3 exhibited genotype by environment effects on gene expression under copper stress, illuminating several tissue- and treatment-specific patterns of gene expression control. Together, our data build a nuanced description of the roles and interactions between allelic and expression variation in copper-responsive genes, provide valuable insight into the genomic architecture of susceptibility to metal toxicity, and highlight many candidate genes for future functional characterization.

Comparative microRNA profiling of Trypanosoma cruzi infected human cells

Introduction

Trypanosoma cruzi, the causative agent of Chagas disease, can infect almost any nucleated cell in the mammalian host. Although previous studies have described the transcriptomic changes that occur in host cells during parasite infection, the understanding of the role of post-transcriptional regulation in this process is limited. MicroRNAs, a class of short non-coding RNAs, are key players in regulating gene expression at the post-transcriptional level, and their involvement in the host-T. cruzi interplay is a growing area of research. However, to our knowledge, there are no comparative studies on the microRNA changes that occur in different cell types in response to T. cruzi infection.

Methods and results

Here we investigated microRNA changes in epithelial cells, cardiomyocytes and macrophages infected with T. cruzi for 24 hours, using small RNA sequencing followed by careful bioinformatics analysis. We show that, although microRNAs are highly cell type-specific, a signature of three microRNAs -miR-146a, miR-708 and miR-1246, emerges as consistently responsive to T. cruzi infection across representative human cell types. T. cruzi lacks canonical microRNA-induced silencing mechanisms and we confirm that it does not produce any small RNA that mimics known host microRNAs. We found that macrophages show a broad response to parasite infection, while microRNA changes in epithelial and cardiomyocytes are modest. Complementary data indicated that cardiomyocyte response may be greater at early time points of infection.

Conclusions

Our findings emphasize the significance of considering microRNA changes at the cellular level and complement previous studies conducted at higher organizational levels, such as heart samples. While miR-146a has been previously implicated in T. cruzi infection, similarly to its involvement in many other immunological responses, miR-1246 and miR-708 are demonstrated here for the first time. Given their expression in multiple cell types, we anticipate our work as a starting point for future investigations into their role in the post-transcriptional regulation of T. cruzi infected cells and their potential as biomarkers for Chagas disease.

Gene expression variability in long-term survivors of childhood cancer and cancer-free controls in response to ionizing irradiation

BACKGROUND

Differential expression analysis is usually adjusted for variation. However, most studies that examined the expression variability (EV) have used computations affected by low expression levels and did not examine healthy tissue. This study aims to calculate and characterize an unbiased EV in primary fibroblasts of childhood cancer survivors and cancer-free controls (N0) in response to ionizing radiation.

METHODS

Human skin fibroblasts of 52 donors with a first primary neoplasm in childhood (N1), 52 donors with at least one second primary neoplasm (N2 +), as well as 52 N0 were obtained from the KiKme case-control study and exposed to a high (2 Gray) and a low dose (0.05 Gray) of X-rays and sham- irradiation (0 Gray). Genes were then classified as hypo-, non-, or hyper-variable per donor group and radiation treatment, and then examined for over-represented functional signatures.

RESULTS

We found 22 genes with considerable EV differences between donor groups, of which 11 genes were associated with response to ionizing radiation, stress, and DNA repair. The largest number of genes exclusive to one donor group and variability classification combination were all detected in N0: hypo-variable genes after 0 Gray (n = 49), 0.05 Gray (n = 41), and 2 Gray (n = 38), as well as hyper-variable genes after any dose (n = 43). While after 2 Gray positive regulation of cell cycle was hypo-variable in N0, (regulation of) fibroblast proliferation was over-represented in hyper-variable genes of N1 and N2+. In N2+, 30 genes were uniquely classified as hyper-variable after the low dose and were associated with the ERK1/ERK2 cascade. For N1, no exclusive gene sets with functions related to the radiation response were detected in our data.

CONCLUSION

N2+ showed high degrees of variability in pathways for the cell fate decision after genotoxic insults that may lead to the transfer and multiplication of DNA-damage via proliferation, where apoptosis and removal of the damaged genome would have been appropriate. Such a deficiency could potentially lead to a higher vulnerability towards side effects of exposure to high doses of ionizing radiation, but following low-dose applications employed in diagnostics, as well.

A neurodevelopmental epigenetic programme mediated by SMARCD3-DAB1-Reelin signalling is hijacked to promote medulloblastoma metastasis

How abnormal neurodevelopment relates to the tumour aggressiveness of medulloblastoma (MB), the most common type of embryonal tumour, remains elusive. Here we uncover a neurodevelopmental epigenomic programme that is hijacked to induce MB metastatic dissemination. Unsupervised analyses of integrated publicly available datasets with our newly generated data reveal that SMARCD3 (also known as BAF60C) regulates Disabled 1 (DAB1)-mediated Reelin signalling in Purkinje cell migration and MB metastasis by orchestrating cis-regulatory elements at the DAB1 locus. We further identify that a core set of transcription factors, enhancer of zeste homologue 2 (EZH2) and nuclear factor I X (NFIX), coordinates with the cis-regulatory elements at the SMARCD3 locus to form a chromatin hub to control SMARCD3 expression in the developing cerebellum and in metastatic MB. Increased SMARCD3 expression activates Reelin-DAB1-mediated Src kinase signalling, which results in a MB response to Src inhibition. These data deepen our understanding of how neurodevelopmental programming influences disease progression and provide a potential therapeutic option for patients with MB.

The evolution of ovary-biased gene expression in Hawaiian Drosophila

With detailed data on gene expression accessible from an increasingly broad array of species, we can test the extent to which our developmental genetic knowledge from model organisms predicts expression patterns and variation across species. But to know when differences in gene expression across species are significant, we first need to know how much evolutionary variation in gene expression we expect to observe. Here we provide an answer by analyzing RNAseq data across twelve species of Hawaiian Drosophilidae flies, focusing on gene expression differences between the ovary and other tissues. We show that over evolutionary time, there exists a cohort of ovary specific genes that is stable and that largely corresponds to described expression patterns from laboratory model Drosophila species. Our results also provide a demonstration of the prediction that, as phylogenetic distance increases, variation between species overwhelms variation between tissue types. Using ancestral state reconstruction of expression, we describe the distribution of evolutionary changes in tissue-biased expression, and use this to identify gains and losses of ovary-biased expression across these twelve species. We then use this distribution to calculate the evolutionary correlation in expression changes between genes, and demonstrate that genes with known interactions in D. melanogaster are significantly more correlated in their evolution than genes with no or unknown interactions. Finally, we use this correlation matrix to infer new networks of genes that share evolutionary trajectories, and we present these results as a dataset of new testable hypotheses about genetic roles and interactions in the function and evolution of the Drosophila ovary.

Gpx4, Selenov, and Txnrd3 Are Three Most Testis-Abundant Selenogenes Resistant to Dietary Selenium Concentrations and Actively Expressed During Reproductive Ages in Rats

Almost all selenogenes are expressed in the testis, and those have the highest and constant expressions will be the primary candidates for functional analysis of selenium (Se) in male reproduction. This study aimed to profile the mRNA expressions of the testis-abundant selenogenes of rat models in responses to growth and dietary Se concentrations. Forty-eight weaning SD male rats were fed Se deficient basal diet (BD) for 5 weeks and then randomly grouped (n = 12/group) for being fed BD or BD plus 0.25, 3, or 5 mg Se/kg for 4 more weeks before sacrifice. Abundances of selenogenomic mRNAs in the liver and testis were determined with relative qPCR and those of the testis-abundant selenogenes in 13 kinds of tissues were assayed with a molecular beacon-based qPCR. Spatiotemporal expressions of rat selenogenome were also analyzed with the RNA-Seq transcriptomic data published by NCBI. mRNA abundances of glutathione peroxidase 4 (Gpx4), nuclear Gpx4 (nGpx4), selenoprotein V (Selenov), and thioredoxin reductase 3 (Txnrd3) in the testis were significantly higher than that in any other tissues (P < 0.05). Moreover, testicular mRNA abundances of Gpx4, Selenov, and Txnrd3 were not affected by levels of dietary Se supplementation (P > 0.05), and much higher at 6-21 weeks old than at 2 and 104 weeks old (P < 0.05). The result showed that Gpx4, Selenov, and Txnrd3 were most highly expressed in the testis of rats especially at reproductive ages and resistant to the impact of dietary Se levels, which suggested their specific importance in male reproduction.

Stochastic Modeling of Gene Expression Evolution Uncovers Tissue- and Sex-Specific Properties of Expression Evolution in the Drosophila Genus

Gene expression evolution is typically modeled with the stochastic Ornstein-Uhlenbeck (OU) process. It has been suggested that the estimation of within-species variations using replicated data can increase the predictive power of such models, but this hypothesis has not been fully tested. We developed EvoGeneX, a computationally efficient implementation of the OU-based method that models within-species variation. Using extensive simulations, we show that modeling within-species variations and appropriate selection of species improve the performance of the model. Further, to facilitate a comparative analysis of expression evolution, we introduce a formal measure of evolutionary expression divergence for a group of genes using the rate and the asymptotic level of divergence. With these tools in hand, we performed the first-ever analysis of the evolution of gene expression across different body-parts, species, and sexes of the Drosophila genus. We observed that genes with adaptive expression evolution tend to be body-part specific, whereas the genes with constrained evolution tend to be shared across body-parts. Among the neutrally evolving gene expression patterns, gonads in both sexes have higher expression divergence relative to other tissues and the male gonads have even higher divergence than the female gonads. Among the evolutionarily constrained genes, the gonads show different divergence patterns, where the male gonads, and not the female gonads, show less constrained divergence than other body-parts. Finally, we show interesting examples of adaptive expression evolution, including adaptation of odor binding proteins.

A Novel Sparse Graph-Regularized Singular Value Decomposition Model and Its Application to Genomic Data Analysis

Learning the gene coexpression pattern is a central challenge for high-dimensional gene expression analysis. Recently, sparse singular value decomposition (SVD) has been used to achieve this goal. However, this model ignores the structural information between variables (e.g., a gene network). The typical graph-regularized penalty can be used to incorporate such prior graph information to achieve more accurate discovery and better interpretability. However, the existing approach fails to consider the opposite effect of variables with negative correlations. In this article, we propose a novel sparse graph-regularized SVD model with absolute operator (AGSVD) for high-dimensional gene expression pattern discovery. The key of AGSVD is to impose a novel graph-regularized penalty ( | u|^T L| u| ). However, such a penalty is a nonconvex and nonsmooth function, so it brings new challenges to model solving. We show that the nonconvex problem can be efficiently handled in a convex fashion by adopting an alternating optimization strategy. The simulation results on synthetic data show that our method is more effective than the existing SVD-based ones. In addition, the results on several real gene expression data sets show that the proposed methods can discover more biologically interpretable expression patterns by incorporating the prior gene network.

What are housekeeping genes?

The concept of "housekeeping gene" has been used for four decades but remains loosely defined. Housekeeping genes are commonly described as "essential for cellular existence regardless of their specific function in the tissue or organism", and "stably expressed irrespective of tissue type, developmental stage, cell cycle state, or external signal". However, experimental support for the tenet that gene essentiality is linked to stable expression across cell types, conditions, and organisms has been limited. Here we use genome-scale functional genomic screens together with bulk and single-cell sequencing technologies to test this link and optimize a quantitative and experimentally validated definition of housekeeping gene. Using the optimized definition, we identify, characterize, and provide as resources, housekeeping gene lists extracted from several human datasets, and 10 other animal species that include primates, chicken, and C. elegans. We find that stably expressed genes are not necessarily essential, and that the individual genes that are essential and stably expressed can considerably differ across organisms; yet the pathways enriched among these genes are conserved. Further, the level of conservation of housekeeping genes across the analyzed organisms captures their taxonomic groups, showing evolutionary relevance for our definition. Therefore, we present a quantitative and experimentally supported definition of housekeeping genes that can contribute to better understanding of their unique biological and evolutionary characteristics.

Transcriptome analysis reveals high tumor heterogeneity with respect to re-activation of stemness and proliferation programs

Significant alterations in signaling pathways and transcriptional regulatory programs together represent major hallmarks of many cancers. These, among all, include the reactivation of stemness, which is registered by the expression of pathways that are active in the embryonic stem cells (ESCs). Here, we assembled gene sets that reflect the stemness and proliferation signatures and used them to analyze a large panel of RNA-seq data from The Cancer Genome Atlas (TCGA) Consortium in order to specifically assess the expression of stemness-related and proliferation-related genes across a collection of different tumor types. We introduced a metric that captures the collective similarity of the expression profile of a tumor to that of ESCs, which showed that stemness and proliferation signatures vary greatly between different tumor types. We also observed a high degree of intertumoral heterogeneity in the expression of stemness- and proliferation-related genes, which was associated with increased hazard ratios in a fraction of tumors and mirrored by high intratumoral heterogeneity and a remarkable stemness capacity in metastatic lesions across cancer cells in single cell RNA-seq datasets. Taken together, these results indicate that the expression of stemness signatures is highly heterogeneous and cannot be used as a universal determinant of cancer. This calls into question the universal validity of diagnostic tests that are based on stem cell markers.

Comparative multi-tissue profiling reveals extensive tissue-specificity in transcriptome reprogramming during thermal adaptation

Thermal adaptation is an extensively used intervention for enhancing or suppressing thermogenic and mitochondrial activity in adipose tissues. As such, it has been suggested as a potential lifestyle intervention for body weight maintenance. While the metabolic consequences of thermal acclimation are not limited to the adipose tissues, the impact on the rest of the tissues in context of their gene expression profile remains unclear. Here, we provide a systematic characterization of the effects in a comparative multi-tissue RNA sequencing approach following exposure of mice to 10 °C, 22 °C, or 34 °C in a panel of organs consisting of spleen, bone marrow, spinal cord, brain, hypothalamus, ileum, liver, quadriceps, subcutaneous-, visceral- and brown adipose tissues. We highlight that transcriptional responses to temperature alterations exhibit a high degree of tissue-specificity both at the gene level and at GO enrichment gene sets, and show that the tissue-specificity is not directed by the distinct basic gene expression pattern exhibited by the various organs. Our study places the adaptation of individual tissues to different temperatures in a whole-organism framework and provides integrative transcriptional analysis necessary for understanding the temperature-mediated biological programming.

Humans, mice and most other mammals are constantly exposed to fluctuations in the temperature of their environment. These fluctuations cause striking metabolic effects in the body, for example, exposure to cold promotes burning of calories to generate heat, thereby reducing how much fat accumulates in the body. On the other hand, warmer temperatures strengthen the bones and protect against a bone disease known as osteoporosis. As such, it has been suggested that exposure to alternating warm or cold temperatures could be a potential lifestyle intervention that conveys various benefits to our health.

Our body stores fat in tissues known as adipose tissues, which are found all over the body including under the skin and around our major organs and muscles. Exposure to cold triggers changes in the activities of some genes in the adipose tissues to burn more calories. But it remains unclear how temperature affects the activities of other organs with respect to their expression of genes in the whole-body context.

Hadadi, Spiljar et al. used an RNA sequencing approach to study the activities of genes in various tissues of mice exposed to cold (10°C), room temperature (22°C), or mild warm (34°C). The experiments revealed numerous genes whose levels were different in the various organs and temperatures tested.

Overall, adipose tissues experienced the biggest changes in gene levels between different temperatures, followed by tissues involved in immune responses, and the brain and spinal cord tissues. Each organ changed gene expression levels in its own way. , and this was not due to the different intimate gene expression profile between the various organs.

These findings improve our understanding of how changes in temperature affect mammals by putting the responses of individual tissues into the context of the whole body. Hadadi, Spiljar et al. also generated a web-based, free-to-use application to allow others to view and further analyze the data collected in this work for gene levels in the various organs of interest.

Evolution of gene expression across species and specialized zooids in Siphonophora

Siphonophores are complex colonial animals, consisting of asexually produced bodies (zooids) that are functionally specialized for specific tasks, including feeding, swimming, and sexual reproduction. Though this extreme functional specialization has captivated biologists for generations, its genomic underpinnings remain unknown. We use RNA-seq to investigate gene expression patterns in five zooids and one specialized tissue across seven siphonophore species. Analyses of gene expression across species present several challenges, including identification of comparable expression changes on gene trees with complex histories of speciation, duplication, and loss. We examine gene expression within species, conduct classical analyses examining expression patterns between species, and introduce species branch filtering, which allows us to examine the evolution of expression across species in a phylogenetic framework. Within and across species, we identified hundreds of zooid-specific and species-specific genes, as well as a number of putative transcription factors showing differential expression in particular zooids and developmental stages. We found that gene expression patterns tended to be largely consistent in zooids with the same function across species, but also some large lineage-specific shifts in gene expression. Our findings show that patterns of gene expression have the potential to define zooids in colonial organisms. Traditional analyses of the evolution of gene expression focus on the tips of gene phylogenies, identifying large-scale expression patterns that are zooid or species variable. The new explicit phylogenetic approach we propose here focuses on branches (not tips) offering a deeper evolutionary perspective into specific changes in gene expression within zooids along all branches of the gene (and species) trees.

Comparative analysis of transcriptomic profiles among ascidians, zebrafish, and mice: Insights from tissue-specific gene expression

Tissue/organ-specific genes (TSGs) are important not only for understanding organ development and function, but also for investigating the evolutionary lineages of organs in animals. Here, we investigate the TSGs of 9 adult tissues of an ascidian, Ciona intestinalis Type A (Ciona robusta), which lies in the important position of being the sister group of vertebrates. RNA-seq and qRT-PCR identified the Ciona TSGs in each tissue, and BLAST searches identified their homologs in zebrafish and mice. Tissue distributions of the vertebrate homologs were analyzed and clustered using public RNA-seq data for 12 zebrafish and 30 mouse tissues. Among the vertebrate homologs of the Ciona TSGs in the neural complex, 48% and 63% showed high expression in the zebrafish and mouse brain, respectively, suggesting that the central nervous system is evolutionarily conserved in chordates. In contrast, vertebrate homologs of Ciona TSGs in the ovary, pharynx, and intestine were not consistently highly expressed in the corresponding tissues of vertebrates, suggesting that these organs have evolved in Ciona-specific lineages. Intriguingly, more TSG homologs of the Ciona stomach were highly expressed in the vertebrate liver (17-29%) and intestine (22-33%) than in the mouse stomach (5%). Expression profiles for these genes suggest that the biological roles of the Ciona stomach are distinct from those of their vertebrate counterparts. Collectively, Ciona tissues were categorized into 3 groups: i) high similarity to the corresponding vertebrate tissues (neural complex and heart), ii) low similarity to the corresponding vertebrate tissues (ovary, pharynx, and intestine), and iii) low similarity to the corresponding vertebrate tissues, but high similarity to other vertebrate tissues (stomach, endostyle, and siphons). The present study provides transcriptomic catalogs of adult ascidian tissues and significant insights into the evolutionary lineages of the brain, heart, and digestive tract of chordates.

Proteomic Portraits Reveal Evolutionarily Conserved and Divergent Responses to Spinal Cord Injury

Despite the emergence of promising therapeutic approaches in preclinical studies, the failure of large-scale clinical trials leaves clinicians without effective treatments for acute spinal cord injury (SCI). These trials are hindered by their reliance on detailed neurological examinations to establish outcomes, which inflate the time and resources required for completion. Moreover, therapeutic development takes place in animal models whose relevance to human injury remains unclear. Here, we address these challenges through targeted proteomic analyses of cerebrospinal fluid and serum samples from 111 patients with acute SCI and, in parallel, a large animal (porcine) model of SCI. We develop protein biomarkers of injury severity and recovery, including a prognostic model of neurological improvement at 6 months with an area under the receiver operating characteristic curve of 0.91, and validate these in an independent cohort. Through cross-species proteomic analyses, we dissect evolutionarily conserved and divergent aspects of the SCI response and establish the cerebrospinal fluid abundance of glial fibrillary acidic protein as a biochemical outcome measure in both humans and pigs. Our work opens up new avenues to catalyze translation by facilitating the evaluation of novel SCI therapies, while also providing a resource from which to direct future preclinical efforts.

• Targeted proteomic analysis of CSF and serum samples from 111 acute SCI patients.

• Single- and multiprotein biomarkers of injury severity and recovery.

• Parallel proteomic analysis in a large animal model identifies conserved biomarkers.

• Evolutionary conservation and divergence of the proteomic response to SCI.

Mechanistic role of antioxidants in rescuing delayed gastric emptying in high fat diet induced diabetic female mice

Diabetic gastroparesis (DG) exhibits delayed gastric emptying (GE) due to impaired gastric non-adrenergic, non-cholinergic (NANC) relaxation. These defects are due to loss or reduction of nuclear factor (erythroid-derived 2)-like 2 (Nrf2) that causes reduced expression and/or dimerization of neuronal nitric oxide synthase alpha (nNOSα) gene expression and function. We investigated the effect of potent Nrf2 activators (cinnamaldehyde [CNM] & curcumin [CUR]) on GE in obesity-induced diabetic female mice. We fed adult female homozygous Nfe2l2-/- (Nrf2 KO) and wild-type (WT) female mice with either a high-fat diet (HFD) or a normal diet (ND) for a period of 16 weeks. Groups of HFD mice were fed with CUR or CNM either at 6th or 10th week respectively. Our results demonstrate that supplementation of CNM or CUR restored impaired nitrergic relaxation and attenuated delayed GE in HFD fed mice. Supplementation of CNM or CUR normalized altered gastric antrum protein expression of (1) p-ERK/p-JNK/MAPK/p-GSK-3β, (2) BH4 (Cofactor of nNOS) biosynthesis enzyme GCH-1 and the GSH/GSSG ratio, (3) nNOSα protein & dimerization and soluble guanylate cyclase (sGC), (4) AhR and ER expression, (5) inflammatory cytokines (TNF α, IL-1β, IL-6), (6)TLR-4, as well as (7) reduced oxidative stress markers in WT but not in Nrf2 KO obesity-induced chronic diabetic female mice. Immunoprecipitation experiments revealed an interaction between nNOS and Nrf2 proteins. Our results conclude that Nrf2 activation restores nitrergic-mediated gastric motility and GE by normalizing inflammation and oxidative stress induced by obesity-induced chronic diabetes.

An ancient, conserved gene regulatory network led to the rise of oral venom systems

Although oral venom systems are ecologically important characters, how they originated is still unclear. In this study, we show that oral venom systems likely originated from a gene regulatory network conserved across amniotes. This network, which we term the “metavenom network,” comprises over 3,000 housekeeping genes coexpressed with venom and play a role in protein folding and modification. Comparative transcriptomics revealed that the network is conserved between venom glands of snakes and salivary glands of mammals. This suggests that while these tissues have evolved different functions, they share a common regulatory core, that persisted since their common ancestor. We propose several evolutionary mechanisms that can utilize this common regulatory core to give rise to venomous animals from their nonvenomous ancestors.

Oral venom systems evolved multiple times in numerous vertebrates enabling the exploitation of unique predatory niches. Yet how and when they evolved remains poorly understood. Up to now, most research on venom evolution has focused strictly on the toxins. However, using toxins present in modern day animals to trace the origin of the venom system is difficult, since they tend to evolve rapidly, show complex patterns of expression, and were incorporated into the venom arsenal relatively recently. Here we focus on gene regulatory networks associated with the production of toxins in snakes, rather than the toxins themselves. We found that overall venom gland gene expression was surprisingly well conserved when compared to salivary glands of other amniotes. We characterized the “metavenom network,” a network of ∼3,000 nonsecreted housekeeping genes that are strongly coexpressed with the toxins, and are primarily involved in protein folding and modification. Conserved across amniotes, this network was coopted for venom evolution by exaptation of existing members and the recruitment of new toxin genes. For instance, starting from this common molecular foundation, Heloderma lizards, shrews, and solenodon, evolved venoms in parallel by overexpression of kallikreins, which were common in ancestral saliva and induce vasodilation when injected, causing circulatory shock. Derived venoms, such as those of snakes, incorporated novel toxins, though still rely on hypotension for prey immobilization. These similarities suggest repeated cooption of shared molecular machinery for the evolution of oral venom in mammals and reptiles, blurring the line between truly venomous animals and their ancestors.

A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies

Comparative functional genomics offers a powerful approach to study species evolution. To date, the majority of these studies have focused on the transcriptome in mammalian and yeast phylogenies. Here, we present a novel multi-species proteomic dataset and a computational pipeline to systematically compare the protein levels across multiple plant species. Globally we find that protein levels diverge according to phylogenetic distance but is more constrained than the mRNA level. Module-level comparative analysis of groups of proteins shows that proteins that are more highly expressed tend to be more conserved. To interpret the evolutionary patterns of conservation and divergence, we develop a novel network-based integrative analysis pipeline that combines publicly available transcriptomic datasets to define co-expression modules. Our analysis pipeline can be used to relate the changes in protein levels to different species-specific phenotypic traits. We present a case study with the rhizobia-legume symbiosis process that supports the role of autophagy in this symbiotic association.

Energy Shortage in Human and Mouse Models of SLC4A11-Associated Corneal Endothelial Dystrophies

Purpose

To elucidate the molecular events in solute carrier family 4 member 11 (SLC4A11)-deficient corneal endothelium that lead to the endothelial dysfunction that characterizes the dystrophies associated with SLC4A11 mutations, congenital hereditary endothelial dystrophy (CHED) and Fuchs endothelial corneal dystrophy 4.

Methods

Comparative transcriptomic analysis (CTA) was performed in primary human corneal endothelial cells (pHCEnC) and murine corneal endothelial cells (MCEnC) with normal and reduced levels of SLC4A11 (SLC4A11 KD pHCEnC) and Slc4a11 (Slc4a11^−/− MCEnC), respectively. Validation of differentially expressed genes was performed using immunofluorescence staining of CHED corneal endothelium, as well as western blot and quantitative PCR analysis of SLC4A11 KD pHCEnC and Slc4a11^−/− MCEnC. Functional analyses were performed to investigate potential functional changes associated with the observed transcriptomic alterations.

Results

CTA revealed inhibition of cell metabolism and ion transport function as well as mitochondrial dysfunction, leading to reduced adenosine triphosphate (ATP) production, in SLC4A11 KD pHCEnC and Slc4a11^−/− MCEnC. Co-localization of SNARE protein STX17 with mitochondria marker COX4 was observed in CHED corneal endothelium, as was activation of AMPK–p53/ULK1 in both SLC4A11 KD pHCEnC and Slc4a11^−/− MCEnC, providing additional evidence of mitochondrial dysfunction and mitophagy. Reduced Na⁺-dependent HCO₃⁻ transport activity and altered NH₄Cl-induced membrane potential changes were observed in Slc4a11^−/− MCEnC.

Conclusions

Reduced steady-state ATP levels and subsequent activation of the AMPK–p53 pathway provide a link between the metabolic functional deficit and transcriptome alterations, as well as evidence of insufficient ATP to maintain the Na⁺/K⁺-ATPase corneal endothelial pump as the cause of the edema that characterizes SLC4A11-associated corneal endothelial dystrophies.

Amalgamated cross-species transcriptomes reveal organ-specific propensity in gene expression evolution

The origins of multicellular physiology are tied to evolution of gene expression. Genes can shift expression as organisms evolve, but how ancestral expression influences altered descendant expression is not well understood. To examine this, we amalgamate 1,903 RNA-seq datasets from 182 research projects, including 6 organs in 21 vertebrate species. Quality control eliminates project-specific biases, and expression shifts are reconstructed using gene-family-wise phylogenetic Ornstein-Uhlenbeck models. Expression shifts following gene duplication result in more drastic changes in expression properties than shifts without gene duplication. The expression properties are tightly coupled with protein evolutionary rate, depending on whether and how gene duplication occurred. Fluxes in expression patterns among organs are nonrandom, forming modular connections that are reshaped by gene duplication. Thus, if expression shifts, ancestral expression in some organs induces a strong propensity for expression in particular organs in descendants. Regardless of whether the shifts are adaptive or not, this supports a major role for what might be termed preadaptive pathways of gene expression evolution.

A randomised controlled trial of innovative specialised meat product for patients with cardiovascular and metabolic disorders

Cardiovascular diseases remain one of the leading causes of death globally. A lot of dietary patterns for CVD prevention have been proposed, but special attention is paid to functional foods. Bioactive proteins and peptides from animal sources are also considered tools for the prevention of CVDs. Here, 40 overweight or obese adult men and women aged between 61 and 66 years, with a body-mass index between 28 and 61 kg.m-2, were enrolled into a randomised controlled trial of new meat products for specialised nutrition. Participants in the control group (n = 20) consumed a standard hyponatric low-calorie diet for 28-30 days (10 days inpatient and 18-20 days outpatient), and in the experimental group – a low-calorie diet and 100g developed meat product (ratio of the porcine aorta to hearts 1:3) per day. Total cholesterol, triglyceride, cholesterol low-density lipoprotein, and cholesterol high-density lipoprotein levels were measured in the serum; from this, the atherogenic index was calculated. The positive effect of developed meat products on the serum lipid profile of patients during the trial was mild but noticeable. A significant reduction in cholesterol levels was noticed in the experimental group, by 18.2% and 14.0% after 7 – 10 and 28 – 30 days, respectively, while the cholesterol level in the control group returned to its original level after 28 – 30 days of dieting. The difference between the control and experimental groups was not significant, while data in the percentiles were. Therefore, it is more preferable to use a developed product as a component in diet therapy for hyperlipidaemic humans for over 28 – 30 days. Pronounced effects of the product could be linked to the unique proteome and peptidome of heart and aorta tissues based on organ-specific gene expression and the presence of tissue-specific substances.

Large-scale analysis of human gene expression variability associates highly variable drug targets with lower drug effectiveness and safety

Motivation

The effectiveness of drugs tends to vary between patients. One of the well-known reasons for this phenomenon is genetic polymorphisms in drug target genes among patients. Here, we propose that differences in expression levels of drug target genes across individuals can also contribute to this phenomenon.

Results

To explore this hypothesis, we analyzed the expression variability of protein-coding genes, and particularly drug target genes, across individuals. For this, we developed a novel variability measure, termed local coefficient of variation (LCV), which ranks the expression variability of each gene relative to genes with similar expression levels. Unlike commonly used methods, LCV neutralizes expression levels biases without imposing any distribution over the variation and is robust to data incompleteness. Application of LCV to RNA-sequencing profiles of 19 human tissues and to target genes of 1076 approved drugs revealed that drug target genes were significantly more variable than protein-coding genes. Analysis of 113 drugs with available effectiveness scores showed that drugs targeting highly variable genes tended to be less effective in the population. Furthermore, comparison of approved drugs to drugs that were withdrawn from the market showed that withdrawn drugs targeted significantly more variable genes than approved drugs. Last, upon analyzing gender differences we found that the variability of drug target genes was similar between men and women. Altogether, our results suggest that expression variability of drug target genes could contribute to the variable responsiveness and effectiveness of drugs, and is worth considering during drug treatment and development.

Availability and implementation

LCV is available as a python script in GitHub (https://github.com/eyalsim/LCV).

Supplementary information

Supplementary data are available at Bioinformatics online.

Human brown adipose tissue: Classical brown rather than brite/beige?

NEW FINDINGS

What is the topic of this review? It has been suggested that human brown adipose tissue (BAT) is more similar to the brite/beige adipose tissue of mice than to classical BAT of mice. The basis of this is discussed in relationship to the physiological conditions of standard experimental mice. What advances does it highlight? We highlight that, provided mouse adipose tissues are examined under physiological conditions closer to those prevalent for most humans, the gene expression profile of mouse classical BAT is more similar to that of human BAT than is the profile of mouse brite/beige adipose tissue. Human BAT is therefore not different in nature from classical mouse BAT.

ABSTRACT

Since the presence of brown adipose tissue (BAT) was established in adult humans some 13 years ago, its physiological significance and molecular characteristics have been discussed. In particular, it has been proposed that the mouse adipose tissue depot most closely resembling and molecularly parallel to human BAT is not classical mouse BAT. Instead, so-called brite or beige adipose tissue, which is characteristically observed in the inguinal 'white' adipose tissue depot of mice, has been proposed to be the closest mouse equivalent of human BAT. We summarize here the published evidence examining this question. We emphasize the differences in tissue appearance and tissue transcriptomes from 'standard' mice [young, chow fed and, in effect semi-cold exposed (20°C)] versus 'physiologically humanized' mice [middle-aged, high-fat diet-fed mice living at thermoneutrality (30°C)]. We find that in the physiologically humanized mice, classical BAT displays molecular and cellular characteristics that are more akin to human BAT than are those of brite/beige adipose tissues from either standard or physiologically humanized mice. We suggest, therefore, that mouse BAT is the more relevant tissue for translational studies. This is an invited summary of a presentation given at Physiology 2019 (Aberdeen).

Evolutionary Patterns of Sex-Biased Genes in Three Species of Haplodiploid Insects

Females and males often differ obviously in morphology and behavior, and the differences between sexes are the result of natural selection and/or sexual selection. To a great extent, the differences between the two sexes are the result of differential gene expression. In haplodiploid insects, this phenomenon is obvious, since males develop from unfertilized zygotes and females develop from fertilized zygotes. Whiteflies of the Bemisia tabaci species complex are typical haplodiploid insects, and some species of this complex are important pests of many crops worldwide. Here, we report the transcriptome profiles of males and females in three species of this whitefly complex. Between-species comparisons revealed that non-sex-biased genes display higher variation than male-biased or female-biased genes. Sex-biased genes evolve at a slow rate in protein coding sequences and gene expression and have a pattern of evolution that differs from those of social haplodiploid insects and diploid animals. Genes with high evolutionary rates are more related to non-sex-biased traits-such as nutrition, immune system, and detoxification-than to sex-biased traits, indicating that the evolution of protein coding sequences and gene expression has been mainly driven by non-sex-biased traits.

Embryonic Thermal Manipulation Affects the Antioxidant Response to Post-Hatch Thermal Exposure in Broiler Chickens

Simple Summary

The broiler chicken is one of the most important livestock species in the world, as it occupies a major role in the modern human diet. Due to uneven artificial selection pressures, the broiler has increased in size over the past few decades at the expense of its ability to withstand oxidative damage, the latter of which is often a byproduct of thermal stress. In order to attenuate the effects of heat stress, thermal manipulation (TM), which involves changes in incubation temperature at certain points of embryonic development, is increasingly being presented as a way in which to improve broiler thermotolerance. Therefore, the objective of this study was to investigate how TM might affect broiler response to post-hatch thermal stress in the context of the genes that help combat oxidative damage, namely the catalase, NADPH oxidase 4 (NOX4), and superoxide dismutase 2 (SOD2) genes. Expression of all three aforementioned genes differed significantly between TM and control chickens after exposure to cold and heat stress. Conclusively, TM may act as a viable mode of preventative treatment for broilers at risk of thermally induced oxidative stress.

Abstract

Thermal stress is a major source of oxidative damage in the broiler chicken (Gallus gallus domesticus) due to the latter’s impaired metabolic function. While heat stress has been extensively studied in broilers, the effects of cold stress on broiler physiologic and oxidative function are still relatively unknown. The present study aimed to understand how thermal manipulation (TM) might affect a broiler’s oxidative response to post-hatch thermal stress in terms of the mRNA expression of the catalase, NADPH oxidase 4 (NOX4), and superoxide dismutase 2 (SOD2) genes. During embryonic days 10 to 18, TM was carried out by raising the temperature to 39 °C at 65% relative humidity for 18 h/day. To induce heat stress, room temperature was raised from 21 to 35 °C during post-hatch days (PD) 28 to 35, while cold stress was induced during PD 32 to 37 by lowering the room temperature from 21 to 16 °C. At the end of the thermal stress periods, a number of chickens were euthanized to extract hepatic and splenic tissue from the heat-stressed group and cardiac, hepatic, muscular, and splenic tissue from the cold-stressed group. Catalase, NOX4, and SOD2 expression in the heart, liver, and spleen were decreased in TM chickens compared to controls after both cold and heat stress. In contrast, the expression levels of these genes in the breast muscles of the TM group were increased or not affected. Moreover, TM chicks possessed an increased body weight (BW) and decreased cloacal temperature (T^C) compared to controls on PD 37. In addition, TM led to increased BW and lower T^C after both cold and heat stress. Conclusively, our findings suggest that TM has a significant effect on the oxidative function of thermally stressed broilers.

Integrated chemical and transcriptomic analyses unveils synthetic characteristics of different medicinal root parts of Angelica sinensis

Objective

Why are different medicinal parts including heads, bodies and tails of Angelicae Sinensis Radix (ASR) distinct in pharmaceutical activities? Here we explored their discrepancy in chemical constituents and transcriptome.

Methods

ASR were separated into three medicinal parts: heads (rootstocks with petiole traces of ASR), bodies (taproots of ASR) and tails (lateral roots of ASR), and chemical and transcriptomic analyses were conducted simultaneously.

Results

High performance liquid chromatography (HPLC) fingerprint results showed that five widely used active ingredients (ferulic acid, senkyunolide H, senkyunolide A, n-butylphathlide, and ligustilide) were distributed unevenly in the three ASR medicinal parts. Partial least squares-discriminant analysis (PLS-DA) demonstrated that the heads can be differentiated from the two other root parts due to different amounts of the main components. However, the content of ferulic acid (a main quality marker) was significantly higher in tails than in the heads and bodies. The transcriptome analysis found that 25,062, 10,148 and 29,504 unigenes were specifically expressed in the heads, bodies and tails, respectively. WGCNA analysis identified 17 co-expression modules, which were constructed from the 19,198 genes in the nine samples of ASR. Additionally, we identified 28 unigenes involved in two phenylpropanoid biosynthesis (PB) pathways about ferulic acid metabolism pathways, of which 17 unigenes (60.7%) in the PB pathway were highly expressed in the tails. The expression levels of PAL, C3H, and CQT transcripts were significantly higher in the tails than in other root parts. RT-qPCR analysis confirmed that PAL, C3H, and CQT genes were predominantly expressed in the tail parts, especially PAL, whose expression was more than doubled as compared with that in other root parts.

Conclusion

Chemical and transcriptomic analyses revealed the distribution contents and pivotal transcripts of the ferulic acid biosynthesis-related pathways. The spatial gene expression pattern partially explained the discrepancy of integral medicinal activities of three medicinal root parts.

Multi-species annotation of transcriptome and chromatin structure in domesticated animals

BACKGROUND

Comparative genomics studies are central in identifying the coding and non-coding elements associated with complex traits, and the functional annotation of genomes is a critical step to decipher the genotype-to-phenotype relationships in livestock animals. As part of the Functional Annotation of Animal Genomes (FAANG) action, the FR-AgENCODE project aimed to create reference functional maps of domesticated animals by profiling the landscape of transcription (RNA-seq), chromatin accessibility (ATAC-seq) and conformation (Hi-C) in species representing ruminants (cattle, goat), monogastrics (pig) and birds (chicken), using three target samples related to metabolism (liver) and immunity (CD4+ and CD8+ T cells).

RESULTS

RNA-seq assays considerably extended the available catalog of annotated transcripts and identified differentially expressed genes with unknown function, including new syntenic lncRNAs. ATAC-seq highlighted an enrichment for transcription factor binding sites in differentially accessible regions of the chromatin. Comparative analyses revealed a core set of conserved regulatory regions across species. Topologically associating domains (TADs) and epigenetic A/B compartments annotated from Hi-C data were consistent with RNA-seq and ATAC-seq data. Multi-species comparisons showed that conserved TAD boundaries had stronger insulation properties than species-specific ones and that the genomic distribution of orthologous genes in A/B compartments was significantly conserved across species.

CONCLUSIONS

We report the first multi-species and multi-assay genome annotation results obtained by a FAANG project. Beyond the generation of reference annotations and the confirmation of previous findings on model animals, the integrative analysis of data from multiple assays and species sheds a new light on the multi-scale selective pressure shaping genome organization from birds to mammals. Overall, these results emphasize the value of FAANG for research on domesticated animals and reinforces the importance of future meta-analyses of the reference datasets being generated by this community on different species.

Automated minute scale RNA-seq of pluripotent stem cell differentiation reveals early divergence of human and mouse gene expression kinetics

Pluripotent stem cells retain the developmental timing of their species of origin in vitro, an observation that suggests the existence of a cell-intrinsic developmental clock, yet the nature and machinery of the clock remain a mystery. We hypothesize that one possible component may lie in species-specific differences in the kinetics of transcriptional responses to differentiation signals. Using a liquid-handling robot, mouse and human pluripotent stem cells were exposed to identical neural differentiation conditions and sampled for RNA-sequencing at high frequency, every 4 or 10 minutes, for the first 10 hours of differentiation to test for differences in transcriptomic response rates. The majority of initial transcriptional responses occurred within a rapid window in the first minutes of differentiation for both human and mouse stem cells. Despite similarly early onsets of gene expression changes, we observed shortened and condensed gene expression patterns in mouse pluripotent stem cells compared to protracted trends in human pluripotent stem cells. Moreover, the speed at which individual genes were upregulated, as measured by the slopes of gene expression changes over time, was significantly faster in mouse compared to human cells. These results suggest that downstream transcriptomic response kinetics to signaling cues are faster in mouse versus human cells, and may offer a partial account for the vast differences in developmental rates across species.

Measurement of Organ-Specific and Acute-Phase Blood Protein Levels in Early Lyme Disease

Lyme disease results from infection of humans with the spirochete Borrelia burgdorferi. The first and most common clinical manifestation is the circular, inflamed skin lesion referred to as erythema migrans; later manifestations result from infections of other body sites. Laboratory diagnosis of Lyme disease can be challenging in patients with erythema migrans because of the time delay in the development of specific diagnostic antibodies against Borrelia. Reliable blood biomarkers for the early diagnosis of Lyme disease in patients with erythema migrans are needed. Here, we performed selected reaction monitoring, a targeted mass spectrometry-based approach, to measure selected proteins that (1) are known to be predominantly expressed in one organ (i.e., organ-specific blood proteins) and whose blood concentrations may change as a result of Lyme disease, or (2) are involved in acute immune responses. In a longitudinal cohort of 40 Lyme disease patients and 20 healthy controls, we identified 10 proteins with significantly altered serum levels in patients at the time of diagnosis, and we also developed a 10-protein panel identified through multivariate analysis. In an independent cohort of patients with erythema migrans, six of these proteins, APOA4, C9, CRP, CST6, PGLYRP2, and S100A9, were confirmed to show significantly altered serum levels in patients at time of presentation. Nine of the 10 proteins from the multivariate panel were also verified in the second cohort. These proteins, primarily innate immune response proteins or proteins specific to liver, skin, or white blood cells, may serve as candidate blood biomarkers requiring further validation to aid in the laboratory diagnosis of early Lyme disease.

Deep Learning Implicitly Handles Tissue Specific Phenomena to Predict Tumor DNA Accessibility and Immune Activity

DNA accessibility is a key dynamic feature of chromatin regulation that can potentiate transcriptional events and tumor progression. To gain insight into chromatin state across existing tumor data, we improved neural network models for predicting accessibility from DNA sequence and extended them to incorporate a global set of RNA sequencing gene expression inputs. Our expression-informed model expanded the application domain beyond specific tissue types to tissues not present in training and achieved consistently high accuracy in predicting DNA accessibility at promoter and promoter flank regions. We then leveraged our new tool by analyzing the DNA accessibility landscape of promoters across The Cancer Genome Atlas. We show that in lung adenocarcinoma the accessibility perspective uniquely highlights immune pathways inversely correlated with a more open chromatin state and that accessibility patterns learned from even a single tumor type can discriminate immune inflammation across many cancers, often with direct relation to patient prognosis.

Edge-group sparse PCA for network-guided high dimensional data analysis

Motivation

Principal component analysis (PCA) has been widely used to deal with high-dimensional gene expression data. In this study, we proposed an Edge-group Sparse PCA (ESPCA) model by incorporating the group structure from a prior gene network into the PCA framework for dimension reduction and feature interpretation. ESPCA enforces sparsity of principal component (PC) loadings through considering the connectivity of gene variables in the prior network. We developed an alternating iterative algorithm to solve ESPCA. The key of this algorithm is to solve a new k-edge sparse projection problem and a greedy strategy has been adapted to address it. Here we adopted ESPCA for analyzing multiple gene expression matrices simultaneously. By incorporating prior knowledge, our method can overcome the drawbacks of sparse PCA and capture some gene modules with better biological interpretations.

Results

We evaluated the performance of ESPCA using a set of artificial datasets and two real biological datasets (including TCGA pan-cancer expression data and ENCODE expression data), and compared their performance with PCA and sparse PCA. The results showed that ESPCA could identify more biologically relevant genes, improve their biological interpretations and reveal distinct sample characteristics.

Availability and implementation

An R package of ESPCA is available at http://page.amss.ac.cn/shihua.zhang/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Using mouse genetics to understand human skeletal disease

Technological advances have enabled the study of the human genome in incredible detail with relative ease. However, our ability to interpret the functional significance of the millions of genetic variants present within each individual is limited. As a result, the confident assignment of disease-causing variant calls remains a significant challenge. Here we explore how mouse genetics can help address this deficit in functional genomic understanding. Underpinned by marked genetic correspondence, skeletal biology shows inter-species similarities which provide important opportunities to use data from mouse models to direct research into the genetic basis of skeletal pathophysiology. In this article we outline critical resources that may be used to establish genotype/phenotype relationships in skeletal tissue, identify genes with established skeletal effects and define the transcriptome of critical skeletal cell types. Finally, we outline how these mouse resources might be utilized to progress from a list of human sequence variants toward plausible gene candidates that contribute to skeletal disease.

Human brown adipose tissue is phenocopied by classical brown adipose tissue in physiologically humanized mice

Human and rodent brown adipose tissues (BAT) appear morphologically and molecularly different. Here we compare human BAT with both classical brown and brite/beige adipose tissues of 'physiologically humanized' mice: middle-aged mice living under conditions approaching human thermal and nutritional conditions, that is, prolonged exposure to thermoneutral temperature (approximately 30 °C) and to an energy-rich (high-fat, high-sugar) diet. We find that the morphological, cellular and molecular characteristics (both marker and adipose-selective gene expression) of classical brown fat, but not of brite/beige fat, of these physiologically humanized mice are notably similar to human BAT. We also demonstrate, both in silico and experimentally, that in physiologically humanized mice only classical BAT possesses a high thermogenic potential. These observations suggest that classical rodent BAT is the tissue of choice for translational studies aimed at recruiting human BAT to counteract the development of obesity and its comorbidities.

Comparative Transcriptome Analyses Reveal the Role of Conserved Function in Electric Organ Convergence Across Electric Fishes

The independent origins of multiple electric organs (EOs) of fish are fascinating examples of convergent evolution. However, comparative transcriptomics of different electric fish lineages are scarce. In this study, we found that the gene expression of EOs and skeletal muscles from three lineages (Mormyroidea, Siluriformes, and Gymnotiformes) tended to cluster together based on the species of origin, irrespective of the organ from which they are derived. A pairwise comparison of differentially expressed genes (DEGs) revealed that no less than half of shared DEGs exhibited parallel expression differentiation, indicating conserved directionality of differential expression either in or between lineages, but only a few shared DEGs were identified across all focal species. Nevertheless, the functional enrichment analysis of DEGs indicated that there were more parallel gene expression changes at the level of pathways and biological functions. Therefore, we may conclude that there is no parallel evolution of the entire transcriptomes of EOs among different lineages. Further, our results support the hypothesis that it is not different genes but conserved biological functions that play a crucial role in the convergence of complex phenotypes. This study provides insight into the genetic basis underlying the EO convergent evolution; however, more studies in different cases will be needed to demonstrate whether this pattern can be extended to other cases to derive a general rule for convergent evolution.

Computational translation of genomic responses from experimental model systems to humans

The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human “Translation Problems” defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.

Empirical comparison of genomic responses in mouse models and human disease contexts is not sufficient for addressing the challenge of prospective translation from mouse models to human disease contexts. We address this challenge by developing a semi-supervised machine learning approach that combines supervised modeling of mouse datasets with unsupervised modeling of human disease-context datasets to predict human in vivo differentially expressed genes and enriched pathways. Semi-supervised training of a feed forward neural network was the most efficacious model for translating experimentally derived mouse biological associations to the human in vivo disease context. We find that computational generalization of signaling insights substantially improves upon direct generalization of mouse experimental insights and argue that such approaches can facilitate more clinically impactful translation of insights from preclinical studies in model systems to patients.

Recent advances in functional genome analysis

At the beginning of this century, the Human Genome Project produced the first drafts of the human genome sequence. Following this, large-scale functional genomics studies were initiated to understand the molecular basis underlying the translation of the instructions encoded in the genome into the biological traits of organisms. Instrumental in the ensuing revolution in functional genomics were the rapid advances in massively parallel sequencing technologies as well as the development of a wide diversity of protocols that make use of these technologies to understand cellular behavior at the molecular level. Here, we review recent advances in functional genomic methods, discuss some of their current capabilities and limitations, and briefly sketch future directions within the field.

Tissue-Specific Profiling of Oxidative Stress-Associated Transcriptome in a Healthy Mouse Model

Oxidative stress is a common phenomenon and is linked to a wide range of diseases and pathological processes including aging. Tissue-specific variation in redox signaling and cellular responses to oxidative stress may be associated with vulnerability especially to age-related and chronic diseases. In order to provide a basis for tissue-specific difference, we examined the tissue-specific transcriptional features of 101 oxidative stress-associated genes in 10 different tissues and organs of healthy mice under physiological conditions. Microarray analysis results, which were consistent with quantitative polymerase chain reaction (qPCR) results, showed that catalase, Gpx3, and Gpx4 were most highly regulated in the liver, kidney, and testes. We also found the tissue-specific gene expression of SOD1 (liver and kidney), SOD2 (heart and muscle), and SOD3 (lung and kidney). The current results will serve as a reference for animal models and help advance our understanding of tissue-specific variability in oxidative stress-associated pathogenesis.

The Arctic charr (Salvelinus alpinus) genome and transcriptome assembly

Arctic charr have a circumpolar distribution, persevere under extreme environmental conditions, and reach ages unknown to most other salmonids. The Salvelinus genus is primarily composed of species with genomes that are structured more like the ancestral salmonid genome than most Oncorhynchus and Salmo species of sister genera. It is thought that this aspect of the genome may be important for local adaptation (due to increased recombination) and anadromy (the migration of fish from saltwater to freshwater). In this study, we describe the generation of a new genetic map, the sequencing and assembly of the Arctic charr genome (GenBank accession: GCF_002910315.2) using the newly created genetic map and a previous genetic map, and present several analyses of the Arctic charr genes and genome assembly. The newly generated genetic map consists of 8,574 unique genetic markers and is similar to previous genetic maps with the exception of three major structural differences. The N50, identified BUSCOs, repetitive DNA content, and total size of the Arctic charr assembled genome are all comparable to other assembled salmonid genomes. An analysis to identify orthologous genes revealed that a large number of orthologs could be identified between salmonids and many appear to have highly conserved gene expression profiles between species. Comparing orthologous gene expression profiles may give us a better insight into which genes are more likely to influence species specific phenotypes.

Characterisation and functional predictions of canine long non-coding RNAs

Long non-coding RNAs (lncRNAs) are a family of heterogeneous RNAs that play major roles in multiple biological processes. We recently identified an extended repertoire of more than 10,000 lncRNAs of the domestic dog however, predicting their biological functionality remains challenging. In this study, we have characterised the expression profiles of 10,444 canine lncRNAs in 26 distinct tissue types, representing various anatomical systems. We showed that lncRNA expressions are mainly clustered by tissue type and we highlighted that 44% of canine lncRNAs are expressed in a tissue-specific manner. We further demonstrated that tissue-specificity correlates with specific families of canine transposable elements. In addition, we identified more than 900 conserved dog-human lncRNAs for which we show their overall reproducible expression patterns between dog and human through comparative transcriptomics. Finally, co-expression analyses of lncRNA and neighbouring protein-coding genes identified more than 3,400 canine lncRNAs, suggesting that functional roles of these lncRNAs act as regulatory elements. Altogether, this genomic and transcriptomic integrative study of lncRNAs constitutes a major resource to investigate genotype to phenotype relationships and biomedical research in the dog species.

Whole-Body Single-Cell Sequencing Reveals Transcriptional Domains in the Annelid Larval Body

Animal bodies comprise diverse arrays of cells. To characterize cellular identities across an entire body, we have compared the transcriptomes of single cells randomly picked from dissociated whole larvae of the marine annelid Platynereis dumerilii. We identify five transcriptionally distinct groups of differentiated cells, each expressing a unique set of transcription factors and effector genes that implement cellular phenotypes. Spatial mapping of cells into a cellular expression atlas, and wholemount in situ hybridization of group-specific genes reveals spatially coherent transcriptional domains in the larval body, comprising, for example, apical sensory-neurosecretory cells versus neural/epidermal surface cells. These domains represent new, basic subdivisions of the annelid body based entirely on differential gene expression, and are composed of multiple, transcriptionally similar cell types. They do not represent clonal domains, as revealed by developmental lineage analysis. We propose that the transcriptional domains that subdivide the annelid larval body represent families of related cell types that have arisen by evolutionary diversification. Their possible evolutionary conservation makes them a promising tool for evo-devo research.

Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states

Distinguishing cell states based only on gene expression data remains a challenging task. This is true even for analyses within a species. In cross-species comparisons, the results obtained by different groups have varied widely. Here, we integrate RNA-seq data from more than 40 cell and tissue types of four mammalian species to identify sets of associated genes as indicators for specific cell states in each species. We employ a statistical method, TROM, to identify both protein-coding and non-coding indicators. Next, we map the cell states within each species and also between species using these indicator genes. We recapitulate known phenotypic similarity between related cell and tissue types and reveal molecular basis for their similarity. We also report novel associations between several tissues and cell types with functional support. Moreover, our identified conserved associated genes are found to be a good resource for studying cell differentiation and reprogramming. Lastly, long non-coding RNAs can serve well as associated genes to indicate cell states. We further infer the biological functions of those non-coding associated genes based on their co-expressed protein-coding genes. This study demonstrates that combining statistical modeling with public RNA-seq data can be powerful for improving our understanding of cell identity control.