Meta-analysis of RNA-seq expression data across species, tissues and studies

Transcriptomic analyses of differentially expressed human genes, micro RNAs and long-non-coding RNAs in severe, symptomatic and asymptomatic malaria infection

Malaria transmission and endemicity in Africa remains hugely disproportionate compared to the rest of the world. The complex life cycle of P. falciparum (Pf) between the vertebrate human host and the anopheline vector results in differential expression of genes within and between hosts. An in-depth understanding of Pf interaction with various human genes through regulatory elements will pave way for identification of newer tools in the arsenal for malaria control. Therefore, the regulatory elements (REs) involved in the over- or under-expression of various host immune genes hold the key to elucidating alternative control measures that can be applied for disease surveillance, prompt diagnosis and treatment. We carried out an RNAseq analysis to identify differentially expressed genes and network elucidation of non-coding RNAs and target genes associated with immune response in individuals with different clinical outcomes. Raw RNAseq datasets, retrieved for analyses include individuals with severe (Gambia-20), symptomatic (Burkina Faso-15), asymptomatic (Mali-16) malaria as well as uninfected controls (Tanzania-20; Mali-36). Of the total 107 datasets retrieved, we identified 5534 differentially expressed genes (DEGs) among disease and control groups. A peculiar pattern of DEGs was observed, with individuals presenting with severe/symptomatic malaria having the highest and most diverse upregulated genes, while a reverse phenomenon was recorded among asymptomatic and uninfected individuals. In addition, we identified 141 differentially expressed micro RNA (miRNA), of which 78 and 63 were upregulated and downregulated respectively. Interactome analysis revealed a moderate interaction between DEGs and miRNAs. Of all identified miRNA, five were unique (hsa-mir-32, hsa-mir-25, hsa-mir-221, hsa-mir-29 and hsa-mir-148) because of their connectivity to several genes, including hsa-mir-221 connected to 16 genes. Six-hundred and eight differentially expressed long non coding RNA (lncRNA) were also identified, including SLC7A11, LINC01524 among the upregulated ones. Our study provides important insight into host immune genes undergoing differential expression under different malaria conditions. It also identified unique miRNAs and lncRNAs that modify and/or regulate the expression of various immune genes. These regulatory elements we surmise, have the potential to serve a diagnostic purpose in discriminating between individuals with severe/symptomatic malaria and those with asymptomatic infection or uninfected, following further clinical validation from field isolates.

Cortical plasticity is associated with blood-brain barrier modulation

Brain microvessels possess the unique properties of a blood-brain barrier (BBB), tightly regulating the passage of molecules from the blood to the brain neuropil and vice versa. In models of brain injury, BBB dysfunction and the associated leakage of serum albumin to the neuropil have been shown to induce pathological plasticity, neuronal hyper-excitability, and seizures. The effect of neuronal activity on BBB function and whether it plays a role in plasticity in the healthy brain remain unclear. Here we show that neuronal activity induces modulation of microvascular permeability in the healthy brain and that it has a role in local network reorganization. Combining simultaneous electrophysiological recording and vascular imaging with transcriptomic analysis in rats, and functional and BBB-mapping MRI in human subjects, we show that prolonged stimulation of the limb induces a focal increase in BBB permeability in the corresponding somatosensory cortex that is associated with long-term synaptic plasticity. We further show that the increased microvascular permeability depends on neuronal activity and involves caveolae-mediated transcytosis and transforming growth factor β signaling. Our results reveal a role of BBB modulation in cortical plasticity in the healthy brain, highlighting the importance of neurovascular interactions for sensory experience and learning.

Single-cell transcriptomics is revolutionizing the improvement of plant biotechnology research: recent advances and future opportunities

Single-cell approaches are a promising way to obtain high-resolution transcriptomics data and have the potential to revolutionize the study of plant growth and development. Recent years have seen the advent of unprecedented technological advances in the field of plant biology to study the transcriptional information of individual cells by single-cell RNA sequencing (scRNA-seq). This review focuses on the modern advancements of single-cell transcriptomics in plants over the past few years. In addition, it also offers a new insight of how these emerging methods will expedite advance research in plant biotechnology in the near future. Lastly, the various technological hurdles and inherent limitations of single-cell technology that need to be conquered to develop such outstanding possible knowledge gain is critically analyzed and discussed.

Understanding species-specific and conserved RNA-protein interactions in vivo and in vitro

While evolution is often considered from a DNA- and protein-centric view, RNA-based regulation can also impact gene expression and protein sequences. Here we examined interspecies differences in RNA-protein interactions using the conserved neuronal RNA binding protein, Unkempt (UNK) as model. We find that roughly half of mRNAs bound in human are also bound in mouse. Unexpectedly, even when transcript-level binding was conserved across species differential motif usage was prevalent. To understand the biochemical basis of UNK-RNA interactions, we reconstituted the human and mouse UNK-RNA interactomes using a high-throughput biochemical assay. We uncover detailed features driving binding, show that in vivo patterns are captured in vitro, find that highly conserved sites are the strongest bound, and associate binding strength with downstream regulation. Furthermore, subtle sequence differences surrounding motifs are key determinants of species-specific binding. We highlight the complex features driving protein-RNA interactions and how these evolve to confer species-specific regulation.

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.

Unveiling the potential of proteomics in addressing food and feed safety challenges

The food and feed sector in Europe is rapidly evolving to address contemporary challenges, striving for fairer, safer, greener and more sustainable food systems. This includes the exploration of new protein sources for human consumption and animal feed such as protein derived from insects, algae or novel plant-derived proteins, and the re-evaluation of existing sources like processed animal protein (PAP). To generate reliable data on the diverse array of emerging protein sources for future food and feed safety assessments, a growing demand for the development and implementation of advanced analytical techniques exists. New approach methodologies (NAMs) including, mass spectrometry (MS)-based proteomics methods have been emerging as valuable techniques which potentially can be implemented in regulatory laboratory settings to complement conventional approaches in this realm. These MS-driven strategies have already proven their utility in diverse applications, including the detection of prohibited substances in feed, identification of allergens, differentiation of fish species in complex mixtures for fraud detection and the verification of novel foods and alternative protein sources. This EU-FORA programme was focused on three core objectives namely: (i) the training of the fellow in utilising MS-based proteomics for food and feed safety analyses, (ii) the involvement of the fellow in the development of standardised operating procedures (SOP) for targeted and non-targeted proteomic MS-based workflows for species and tissues specific PAP identification in a national reference laboratory (NRL) and (iii) the transfer and implementation of MS-based approaches and standardised protocols for PAP analysis at the fellow's home institution. Altogether, this programme facilitates the broadening and diversification of use of MS-based proteomic methodologies for reinforcing their significance within the domains of food and feed safety research and regulatory science applications.

Comparing morphological and secretory aspects of cephalic glands among the New World coral snakes brings novel insights on their biological roles

Oral and other cephalic glands have been surveyed by several studies with distinct purposes. Despite the wide diversity and medical relevance of the New World coral snakes, studies focusing on understanding the biological roles of the glands within this group are still scarce. Specifically, the venom glands of some coral snakes were previously investigated but all other cephalic glands remain uncharacterized. In this sense, performing morphological and molecular analysis of these glands may help better understand their biological role. Here, we studied the morphology of the venom, infralabial, rictal, and harderian glands of thirteen species of Micrurus and Micruroides euryxanthus. We also performed a molecular characterization of these glands from selected species of Micrurus using transcriptomic and proteomic approaches. We described substantial morphological variation in the cephalic glands of New World coral snakes and structural evidence for protein-secreting cells in the inferior rictal glands. Our molecular analysis revealed that the venom glands, as expected, are majorly devoted to toxin production, however, the infralabial and inferior rictal glands also expressed some toxin genes at low to medium levels, despite the marked morphological differences. On the other hand, the harderian glands were dominated by the expression of lipocalins, but do not produce toxins. Our integrative analysis, including the prediction of biological processes and pathways, helped decipher some important traits of cephalic glands and better understand their biology.

JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom

Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

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.

A stem cell zoo uncovers intracellular scaling of developmental tempo across mammals

Differential speeds in biochemical reactions have been proposed to be responsible for the differences in developmental tempo between mice and humans. However, the underlying mechanism controlling the species-specific kinetics remains to be determined. Using in vitro differentiation of pluripotent stem cells, we recapitulated the segmentation clocks of diverse mammalian species varying in body weight and taxa: marmoset, rabbit, cattle, and rhinoceros. Together with mouse and human, the segmentation clock periods of the six species did not scale with the animal body weight, but with the embryogenesis length. The biochemical kinetics of the core clock gene HES7 displayed clear scaling with the species-specific segmentation clock period. However, the cellular metabolic rates did not show an evident correlation. Instead, genes involving biochemical reactions showed an expression pattern that scales with the segmentation clock period. Altogether, our stem cell zoo uncovered general scaling laws governing species-specific developmental tempo.

Comparative three-dimensional genome architectures of adipose tissues provide insight into human-specific regulation of metabolic homeostasis

Elucidating the regulatory mechanisms of human adipose tissues (ATs) evolution is essential for understanding human-specific metabolic regulation, but the functional importance and evolutionary dynamics of three-dimensional (3D) genome organizations of ATs are not well defined. Here, we compared the 3D genome architectures of anatomically distinct ATs from humans and six representative mammalian models. We recognized evolutionarily conserved and human-specific chromatin conformation in ATs at multiple scales, including compartmentalization, topologically associating domain (TAD), and promoter-enhancer interactions (PEI), which have not been described previously. We found PEI are much more evolutionarily dynamic with respect to compartmentalization and topologically associating domain. Compared to conserved PEIs, human-specific PEIs are enriched for human-specific sequence, and the binding motifs of their potential mediators (transcription factors) are less conserved. Our data also demonstrated that genes involved in the evolutionary dynamics of chromatin organization have weaker transcriptional conservation than those associated with conserved chromatin organization. Furthermore, the genes involved in energy metabolism and the maintenance of metabolic homeostasis are enriched in human-specific chromatin organization, while housekeeping genes, health-related genes, and genetic variations are enriched in evolutionarily conserved compared to human-specific chromatin organization. Finally, we showed extensively divergent human-specific 3D genome organizations among one subcutaneous and three visceral ATs. Together, these findings provide a global overview of 3D genome architecture dynamics between ATs from human and mammalian models and new insights into understanding the regulatory evolution of human ATs.

Understanding the evolution of immune genes in jawed vertebrates

Driven by co-evolution with pathogens, host immunity continuously adapts to optimize defence against pathogens within a given environment. Recent advances in genetics, genomics and transcriptomics have enabled a more detailed investigation into how immunogenetic variation shapes the diversity of immune responses seen across domestic and wild animal species. However, a deeper understanding of the diverse molecular mechanisms that shape immunity within and among species is still needed to gain insight into-and generate evolutionary hypotheses on-the ultimate drivers of immunological differences. Here, we discuss current advances in our understanding of molecular evolution underpinning jawed vertebrate immunity. First, we introduce the immunome concept, a framework for characterizing genes involved in immune defence from a comparative perspective, then we outline how immune genes of interest can be identified. Second, we focus on how different selection modes are observed acting across groups of immune genes and propose hypotheses to explain these differences. We then provide an overview of the approaches used so far to study the evolutionary heterogeneity of immune genes on macro and microevolutionary scales. Finally, we discuss some of the current evidence as to how specific pathogens affect the evolution of different groups of immune genes. This review results from the collective discussion on the current key challenges in evolutionary immunology conducted at the ESEB 2021 Online Satellite Symposium: Molecular evolution of the vertebrate immune system, from the lab to natural populations.

Comparative single-cell transcriptomic analysis reveals differences in signaling pathways in gonadal primordial germ cells between chicken (Gallus gallus) and zebra finch (Taeniopygia guttata)

Primordial germ cells (PGCs) have been used in avian genetic resource conservation and transgenic animal production. Despite their potential applications to numerous avian taxa facing extinction due to habitat loss and degradation, research has largely focused on poultry, such as chickens, in part owing to the difficulty in obtaining intact PGCs from other species. Recently, phenotypic differences between PGCs of chicken and zebra finch, a wild bird with vocal learning, in early embryonic development have been reported. In this study, we used advanced single-cell RNA sequencing (scRNA-seq) technology to evaluate zebra finch and chicken PGCs and surrounding cells, and to identify species-specific characteristics. We constructed single-cell transcriptome landscapes of chicken gonadal PGCs for a comparison with previously reported scRNA-seq data for zebra finch. We identified interspecific differences in several signaling pathways in gonadal PGCs and somatic cells. In particular, NODAL and insulin signaling pathway activity levels were higher in zebra finch than in chickens, whereas activity levels of the downstream FGF signaling pathway, involved in the proliferation of chicken PGCs, were higher in chickens. This study is the first cross-species single-cell transcriptomic analysis targeting birds, revealing differences in germ cell development between phylogenetically distant Galliformes and Passeriformes. Our results provide a basis for understanding the reproductive physiology of avian germ cells and for utilizing PGCs in the restoration of endangered birds and the production of transgenic birds.

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.

Synthesizing genome regulation data with vote-counting

The increasing availability of high-throughput datasets allows amalgamating research information across a large body of genome regulation studies. Given the recent success of meta-analyses on transcriptional regulators, epigenetic marks, and enhancer:gene associations, we expect that such surveys will continue to provide novel and reproducible insights. However, meta-analyses are severely hampered by the diversity of available data, concurring protocols, an eclectic amount of bioinformatics tools, and myriads of conceivable parameter combinations. Such factors can easily bar life scientists from synthesizing omics data and substantially curb their interpretability. Despite statistical challenges of the method, we would like to emphasize the advantages of joining data from different sources through vote-counting and showcase examples that achieve a simple but highly intuitive data integration.

Genome-guided comparative in planta transcriptome analyses for identifying cross-species common virulence factors in bacterial phytopathogens

Plant bacterial disease is a complex outcome achieved through a combination of virulence factors that are activated during infection. However, the common virulence factors across diverse plant pathogens are largely uncharacterized. Here, we established a pan-genome shared across the following plant pathogens: Burkholderia glumae, Ralstonia solanacearum, and Xanthomonas oryzae pv. oryzae. By overlaying in planta transcriptomes onto the pan-genome, we investigated the expression profiles of common genes during infection. We found over 70% of identical patterns for genes commonly expressed by the pathogens in different plant hosts or infection sites. Co-expression patterns revealed the activation of a signal transduction cascade to recognize and respond to external changes within hosts. Using mutagenesis, we uncovered a relationship between bacterial virulence and functions highly conserved and shared in the studied genomes of the bacterial phytopathogens, including flagellar biosynthesis protein, C4-dicarboxylate ABC transporter, 2-methylisocitrate lyase, and protocatechuate 3,4-dioxygenase (PCD). In particular, the disruption of PCD gene led to attenuated virulence in all pathogens and significantly affected phytotoxin production in B. glumae. This PCD gene was ubiquitously distributed in most plant pathogens with high homology. In conclusion, our results provide cross-species in planta models for identifying common virulence factors, which can be useful for the protection of crops against diverse pathogens.

Tissue-specific impacts of aging and genetics on gene expression patterns in humans

Age is the primary risk factor for many common human diseases. Here, we quantify the relative contributions of genetics and aging to gene expression patterns across 27 tissues from 948 humans. We show that the predictive power of expression quantitative trait loci is impacted by age in many tissues. Jointly modelling the contributions of age and genetics to transcript level variation we find expression heritability (h²) is consistent among tissues while the contribution of aging varies by >20-fold with [Formula: see text] in 5 tissues. We find that while the force of purifying selection is stronger on genes expressed early versus late in life (Medawar's hypothesis), several highly proliferative tissues exhibit the opposite pattern. These non-Medawarian tissues exhibit high rates of cancer and age-of-expression-associated somatic mutations. In contrast, genes under genetic control are under relaxed constraint. Together, we demonstrate the distinct roles of aging and genetics on expression phenotypes.

Biomarker screening in preeclampsia: an RNA-sequencing approach based on data from multiple studies

OBJECTIVE

Biomarkers have become important in the prognosis and diagnosis of various diseases. High-throughput methods, such as RNA sequencing facilitate the detection of differentially expressed genes (DEGs), hence potential biomarker candidates. Individual studies suggest long lists of DEGs, hampering the identification of clinically relevant ones. Concerning preeclampsia - a major obstetric burden with high risk for adverse maternal and/or neonatal outcomes - limitations in diagnosis and prediction are still important issues. We, therefore, developed a workflow to facilitate the screening for biomarkers.

METHODS

On the basis of the tool DESeq2, a comprehensive workflow for identifying DEGs was established, analyzing data from several publicly available RNA-sequencing studies. We applied it to four RNA-sequencing datasets (one blood, three placenta) analyzing patients with preeclampsia and normotensive controls. We compared our results with other published approaches and evaluated their performance.

RESULTS

We identified 110 genes that are dysregulated in preeclampsia, observed in at least three of the studies analyzed, six even in all four studies. These included FLT-1, TREM-1, and FN1, which either represent established biomarkers at protein level, or promising candidates based on recent studies. For comparison, using a published meta-analysis approach, 5240 DEGs were obtained.

CONCLUSION

This study presents a data analysis workflow for preeclampsia biomarker screening, capable of identifying promising biomarker candidates, while drastically reducing the numbers of candidates. Moreover, we were also able to confirm its performance for heart failure. This approach can be applied to additional diseases for biomarker identification, and the set of DEGs identified in preeclampsia represents a resource for further studies.

Mouse models of COVID-19 recapitulate inflammatory pathways rather than gene expression

How well mouse models recapitulate the transcriptional profiles seen in humans remains debatable, with both conservation and diversity identified in various settings. Herein we use RNA-Seq data and bioinformatics approaches to analyze the transcriptional responses in SARS-CoV-2 infected lungs, comparing 4 human studies with the widely used K18-hACE2 mouse model, a model where hACE2 is expressed from the mouse ACE2 promoter, and a model that uses a mouse adapted virus and wild-type mice. Overlap of single copy orthologue differentially expressed genes (scoDEGs) between human and mouse studies was generally poor (≈15-35%). Rather than being associated with batch, sample treatment, viral load, lung damage or mouse model, the poor overlaps were primarily due to scoDEG expression differences between species. Importantly, analyses of immune signatures and inflammatory pathways illustrated highly significant concordances between species. As immunity and immunopathology are the focus of most studies, these mouse models can thus be viewed as representative and relevant models of COVID-19.

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

The increasingly large amount of proteomics data in the public domain enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contained 23 individual datasets, including a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively.

In all cases, we studied the distribution of canonical proteins between the different organs. The number of canonical proteins per dataset ranged from 273 (tendon) and 9,715 (liver) in mouse, and from 101 (tendon) and 6,130 (kidney) in rat. Then, we studied how protein abundances compared across different datasets and organs for both species. As a key point we carried out a comparative analysis of protein expression between mouse, rat and human tissues. We observed a high level of correlation of protein expression among orthologs between all three species in brain, kidney, heart and liver samples, whereas the correlation of protein expression was generally slightly lower between organs within the same species. Protein expression results have been integrated into the resource Expression Atlas for widespread dissemination.

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.

Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin

BACKGROUND

Evolutionary conservation is an invaluable tool for inferring functional significance in the genome, including regions that are crucial across many species and those that have undergone convergent evolution. Computational methods to test for sequence conservation are dominated by algorithms that examine the ability of one or more nucleotides to align across large evolutionary distances. While these nucleotide alignment-based approaches have proven powerful for protein-coding genes and some non-coding elements, they fail to capture conservation of many enhancers, distal regulatory elements that control spatial and temporal patterns of gene expression. The function of enhancers is governed by a complex, often tissue- and cell type-specific code that links combinations of transcription factor binding sites and other regulation-related sequence patterns to regulatory activity. Thus, function of orthologous enhancer regions can be conserved across large evolutionary distances, even when nucleotide turnover is high.

RESULTS

We present a new machine learning-based approach for evaluating enhancer conservation that leverages the combinatorial sequence code of enhancer activity rather than relying on the alignment of individual nucleotides. We first train a convolutional neural network model that can predict tissue-specific open chromatin, a proxy for enhancer activity, across mammals. Next, we apply that model to distinguish instances where the genome sequence would predict conserved function versus a loss of regulatory activity in that tissue. We present criteria for systematically evaluating model performance for this task and use them to demonstrate that our models accurately predict tissue-specific conservation and divergence in open chromatin between primate and rodent species, vastly out-performing leading nucleotide alignment-based approaches. We then apply our models to predict open chromatin at orthologs of brain and liver open chromatin regions across hundreds of mammals and find that brain enhancers associated with neuron activity have a stronger tendency than the general population to have predicted lineage-specific open chromatin.

CONCLUSION

The framework presented here provides a mechanism to annotate tissue-specific regulatory function across hundreds of genomes and to study enhancer evolution using predicted regulatory differences rather than nucleotide-level conservation measurements.

Spinal cord injury: a study protocol for a systematic review and meta-analysis of microRNA alterations

BACKGROUND

Spinal cord injury (SCI) is a devastating condition with no current neurorestorative treatments. Clinical trials have been hampered by a lack of meaningful diagnostic and prognostic markers of injury severity and neurologic recovery. Objective biomarkers and novel therapies for SCI represent urgent unmet clinical needs. Biomarkers of SCI that objectively stratify the severity of cord damage could expand the depth and scope of clinical trials and represent targets for the development of novel therapies for acute SCI. MicroRNAs (miRNAs) represent promising candidates both as informative molecules of injury severity and recovery, and as therapeutic targets. miRNAs are small, regulatory RNA molecules that are tissue-specific and evolutionarily conserved across species. miRNAs have been shown to represent powerful predictors of pathology, particularly with respect to neurologic disorders.

METHODS

Studies investigating miRNA alterations in all species of animal models and human studies of acute, traumatic SCI will be identified from PubMed, Embase, and Scopus. We aim to identify whether SCI is associated with a specific pattern of miRNA expression that is conserved across species, and whether SCI is associated with a tissue- or cell type-specific pattern of miRNA expression. The inclusion criteria for this study will include (1) studies published anytime, (2) including all species, and sexes with acute, traumatic SCI, (3) relating to the alteration of miRNA after SCI, using molecular-based detection platforms including qRT-PCR, microarray, and RNA-sequencing, (4) including statistically significant miRNA alterations in tissues, such as spinal cord, serum/plasma, and/or CSF, and (5) studies with a SHAM surgery group. Articles included in the review will have their titles, abstracts, and full texts reviewed by two independent authors. Random effects meta-regression will be performed, which allows for within-study and between-study variability, on the miRNA expression after SCI or SHAM surgery. We will analyze both the cumulative pooled dataset, as well as datasets stratified by species, tissue type, and timepoint to identify miRNA alterations that are specifically related to the injured spinal cord. We aim to identify SCI-related miRNA that are specifically altered both within a species, and those that are evolutionarily conserved across species, including humans. The analyses will provide a description of the evolutionarily conserved miRNA signature of the pathophysiological response to SCI.

DISCUSSION

Here, we present a protocol to perform a systematic review and meta-analysis to investigate the conserved inter- and intra-species miRNA changes that occur due to acute, traumatic SCI. This review seeks to serve as a valuable resource for the SCI community by establishing a rigorous and unbiased description of miRNA changes after SCI for the next generation of SCI biomarkers and therapeutic interventions.

TRIAL REGISTRATION

The protocol for the systematic review and meta-analysis has been registered through PROSPERO: CRD42021222552 .

Large-Scale Meta-Longitudinal Microbiome Data with a Known Batch Factor

Data contamination in meta-approaches where multiple biological samples are combined considerably affects the results of subsequent downstream analyses, such as differential abundance tests comparing multiple groups at a fixed time point. Little has been thoroughly investigated regarding the impact of the lurking variable of various batch sources, such as different days or different laboratories, in more complicated time series experimental designs, for instance, repeatedly measured longitudinal data and metadata. We highlight that the influence of batch factors is significant on subsequent downstream analyses, including longitudinal differential abundance tests, by performing a case study of microbiome time course data with two treatment groups and a simulation study of mimic microbiome longitudinal counts.

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.

Mineralized Cartilage and Bone-Like Tissues in Chondrichthyans Offer Potential Insights Into the Evolution and Development of Mineralized Tissues in the Vertebrate Endoskeleton

The impregnation of biominerals into the extracellular matrix of living organisms, a process termed biomineralization, gives rise to diverse mineralized (or calcified) tissues in vertebrates. Preservation of mineralized tissues in the fossil record has provided insights into the evolutionary history of vertebrates and their skeletons. However, current understanding of the vertebrate skeleton and of the processes underlying its formation is biased towards biomedical models such as the tetrapods mouse and chick. Chondrichthyans (sharks, skates, rays, and chimaeras) and osteichthyans are the only vertebrate groups with extant (living) representatives that have a mineralized skeleton, but the basal phylogenetic position of chondrichthyans could potentially offer unique insights into skeletal evolution. For example, bone is a vertebrate novelty, but the internal supporting skeleton (endoskeleton) of extant chondrichthyans is commonly described as lacking bone. The molecular and developmental basis for this assertion is yet to be tested. Subperichondral tissues in the endoskeleton of some chondrichthyans display mineralization patterns and histological and molecular features of bone, thereby challenging the notion that extant chondrichthyans lack endoskeletal bone. Additionally, the chondrichthyan endoskeleton demonstrates some unique features and others that are potentially homologous with other vertebrates, including a polygonal mineralization pattern, a trabecular mineralization pattern, and an unconstricted perichordal sheath. Because of the basal phylogenetic position of chondrichthyans among all other extant vertebrates with a mineralized skeleton, developmental and molecular studies of chondrichthyans are critical to flesh out the evolution of vertebrate skeletal tissues, but only a handful of such studies have been carried out to date. This review discusses morphological and molecular features of chondrichthyan endoskeletal tissues and cell types, ultimately emphasizing how comparative embryology and transcriptomics can reveal homology of mineralized skeletal tissues (and their cell types) between chondrichthyans and other vertebrates.

Annotation depth confounds direct comparison of gene expression across species

BACKGROUND

Comparisons of the molecular framework among organisms can be done on both structural and functional levels. One of the most common top-down approaches for functional comparisons is RNA sequencing. This estimation of organismal transcriptional responses is of interest for understanding evolution of molecular activity, which is used for answering a diversity of questions ranging from basic biology to pre-clinical species selection and translation. However, direct comparison between species is often hindered by evolutionary divergence in structure of molecular framework, as well as large difference in the depth of our understanding of the genetic background between humans and other species. Here, we focus on the latter. We attempt to understand how differences in transcriptome annotation affect direct gene abundance comparisons between species.

RESULTS

We examine and suggest some straightforward approaches for direct comparison given the current available tools and using a sample dataset from human, cynomolgus monkey, dog, rat and mouse with a common quantitation and normalization approach. In addition, we examine how variation in genome annotation depth and quality across species may affect these direct comparisons.

CONCLUSIONS

Our findings suggest that further efforts for better genome annotation or computational normalization tools may be of strong interest.

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.

An information-theoretic approach for measuring the distance of organ tissue samples using their transcriptomic signatures

Motivation

Recapitulating aspects of human organ functions using in vitro (e.g. plates, transwells, etc.), in vivo (e.g. mouse, rat, etc.), or ex vivo (e.g. organ chips, 3D systems, etc.) organ models is of paramount importance for drug discovery and precision medicine. It will allow us to identify potential side effects and test the effectiveness of new therapeutic approaches early in their design phase, and will inform the development of better disease models. Developing mathematical methods to reliably compare the ‘distance/similarity’ of organ models from/to the real human organ they represent is an understudied problem with important applications in biomedicine and tissue engineering.

Results

We introduce the Transcriptomic Signature Distance (TSD), an information-theoretic distance for assessing the transcriptomic similarity of two tissue samples, or two groups of tissue samples. In developing TSD, we are leveraging next-generation sequencing data as well as information retrieved from well-curated databases providing signature gene sets characteristic for human organs. We present the justification and mathematical development of the new distance and demonstrate its effectiveness and advantages in different scenarios of practical importance using several publicly available RNA-seq datasets.

Availability and Implementation

The computation of both TSD versions (simple and weighted) has been implemented in R and can be downloaded from https://github.com/Cod3B3nd3R/Transcriptomic-Signature-Distance.

Contact

dimitris.manatakis@emulatebio.com

Supplementary information

Supplementary data are available at Bioinformatics online.

A likelihood-based deconvolution of bulk gene expression data using single-cell references

Direct comparison of bulk gene expression profiles is complicated by distinct cell type mixtures in each sample which obscure whether observed differences are actually due to changes in expression levels themselves or simply due to differing cell type compositions. Single-cell technology has made it possible to measure gene expression in individual cells, achieving higher resolution at the expense of increased noise. If carefully incorporated, such single-cell data can be used to deconvolve bulk samples to yield accurate estimates of the true cell type proportions, thus enabling one to disentangle the effects of differential expression and cell type mixtures. Here, we propose a generative model and a likelihood-based inference method that uses asymptotic statistical theory and a novel optimization procedure to perform deconvolution of bulk RNA-seq data to produce accurate cell type proportion estimates. We demonstrate the effectiveness of our method, called RNA-Sieve, across a diverse array of scenarios involving real data and discuss extensions made uniquely possible by our probabilistic framework, including a demonstration of well-calibrated confidence intervals.

Vertebrate Lineages Exhibit Diverse Patterns of Transposable Element Regulation and Expression across Tissues

Transposable elements (TEs) comprise a major fraction of vertebrate genomes, yet little is known about their expression and regulation across tissues, and how this varies across major vertebrate lineages. We present the first comparative analysis integrating TE expression and TE regulatory pathway activity in somatic and gametic tissues for a diverse set of 12 vertebrates. We conduct simultaneous gene and TE expression analyses to characterize patterns of TE expression and TE regulation across vertebrates and examine relationships between these features. We find remarkable variation in the expression of genes involved in TE negative regulation across tissues and species, yet consistently high expression in germline tissues, particularly in testes. Most vertebrates show comparably high levels of TE regulatory pathway activity across gonadal tissues except for mammals, where reduced activity of TE regulatory pathways in ovarian tissues may be the result of lower relative germ cell densities. We also find that all vertebrate lineages examined exhibit remarkably high levels of TE-derived transcripts in somatic and gametic tissues, with recently active TE families showing higher expression in gametic tissues. Although most TE-derived transcripts originate from inactive ancient TE families (and are likely incapable of transposition), such high levels of TE-derived RNA in the cytoplasm may have secondary, unappreciated biological relevance.

Bipartite tight spectral clustering (BiTSC) algorithm for identifying conserved gene co-clusters in two species

MOTIVATION

Gene clustering is a widely used technique that has enabled computational prediction of unknown gene functions within a species. However, it remains a challenge to refine gene function prediction by leveraging evolutionarily conserved genes in another species. This challenge calls for a new computational algorithm to identify gene co-clusters in two species, so that genes in each co-cluster exhibit similar expression levels in each species and strong conservation between the species.

RESULTS

Here, we develop the bipartite tight spectral clustering (BiTSC) algorithm, which identifies gene co-clusters in two species based on gene orthology information and gene expression data. BiTSC novelly implements a formulation that encodes gene orthology as a bipartite network and gene expression data as node covariates. This formulation allows BiTSC to adopt and combine the advantages of multiple unsupervised learning techniques: kernel enhancement, bipartite spectral clustering, consensus clustering, tight clustering and hierarchical clustering. As a result, BiTSC is a flexible and robust algorithm capable of identifying informative gene co-clusters without forcing all genes into co-clusters. Another advantage of BiTSC is that it does not rely on any distributional assumptions. Beyond cross-species gene co-clustering, BiTSC also has wide applications as a general algorithm for identifying tight node co-clusters in any bipartite network with node covariates. We demonstrate the accuracy and robustness of BiTSC through comprehensive simulation studies. In a real data example, we use BiTSC to identify conserved gene co-clusters of Drosophila melanogaster and Caenorhabditis elegans, and we perform a series of downstream analysis to both validate BiTSC and verify the biological significance of the identified co-clusters.

AVAILABILITY AND IMPLEMENTATION

The Python package BiTSC is open-access and available at https://github.com/edensunyidan/BiTSC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comparative Analysis of Public RNA-Sequencing Data from Human Intestinal Enteroid (HIEs) Infected with Enteric RNA Viruses Identifies Universal and Virus-Specific Epithelial Responses

Acute gastroenteritis (AGE) has a significant disease burden on society. Noroviruses, rotaviruses, and astroviruses are important viral causes of AGE but are relatively understudied enteric pathogens. Recent developments in novel biomimetic human models of enteric disease are opening new possibilities for studying human-specific host-microbe interactions. Human intestinal enteroids (HIE), which are epithelium-only intestinal organoids derived from stem cells isolated from human intestinal biopsy tissues, have been successfully used to culture representative norovirus, rotavirus, and astrovirus strains. Previous studies investigated host-virus interactions at the intestinal epithelial interface by individually profiling the epithelial transcriptional response to a member of each virus family by RNA sequencing (RNA-seq). Despite differences in the tissue origin, enteric virus used, and hours post infection at which RNA was collected in each data set, the uniform analysis of publicly available datasets identified a conserved epithelial response to virus infection focused around "type I interferon production" and interferon-stimulated genes. Additionally, transcriptional changes specific to only one or two of the enteric viruses were also identified. This study can guide future explorations into common and unique aspects of the host response to virus infections in the human intestinal epithelium and demonstrates the promise of comparative RNA-seq analysis, even if performed under different experimental conditions, to discover universal and virus-specific genes and pathways responsible for antiviral host defense.

Expanded transcriptomic view of strawberry fruit ripening through meta-analysis

Strawberry is an important fruit crop and a model for studying non-climacteric fruit ripening. Fruit ripening and senescence influence strawberry fruit quality and postharvest storability, and have been intensively studied. However, genetic and physiological differences among cultivars preclude consensus understanding of these processes. We therefore performed a meta-analysis by mapping existing transcriptome data to the newly published and improved strawberry reference genome and extracted meta-differentially expressed genes (meta-DEGs) from six cultivars to provide an expanded transcriptomic view of strawberry ripening. We identified cultivar-specific transcriptome changes in anthocyanin biosynthesis-related genes and common changes in cell wall degradation, chlorophyll degradation, and starch metabolism-related genes during ripening. We also identified 483 meta-DEGs enriched in gene ontology categories related to photosynthesis and amino acid and fatty acid biosynthesis that had not been revealed in previous studies. We conclude that meta-analysis of existing transcriptome studies can effectively address fundamental questions in plant sciences.

Accelerated Evolution of Tissue-Specific Genes Mediates Divergence Amidst Gene Flow in European Green Lizards

The European green lizards of the Lacerta viridis complex consist of two closely related species, L. viridis and Lacerta bilineata that split less than 7 million years ago in the presence of gene flow. Recently, a third lineage, referred to as the “Adriatic” was described within the L. viridis complex distributed from Slovenia to Greece. However, whether gene flow between the Adriatic lineage and L. viridis or L. bilineata has occurred and the evolutionary processes involved in their diversification are currently unknown. We hypothesized that divergence occurred in the presence of gene flow between multiple lineages and involved tissue-specific gene evolution. In this study, we sequenced the whole genome of an individual of the Adriatic lineage and tested for the presence of gene flow amongst L. viridis, L. bilineata, and Adriatic. Additionally, we sequenced transcriptomes from multiple tissues to understand tissue-specific effects. The species tree supports that the Adriatic lineage is a sister taxon to L. bilineata. We detected gene flow between the Adriatic lineage and L. viridis suggesting that the evolutionary history of the L. viridis complex is likely shaped by gene flow. Interestingly, we observed topological differences between the autosomal and Z-chromosome phylogenies with a few fast evolving genes on the Z-chromosome. Genes highly expressed in the ovaries and strongly co-expressed in the brain experienced accelerated evolution presumably contributing to establishing reproductive isolation in the L. viridis complex.

Pharmacogenomics applied to recombinant human growth hormone responses in children with short stature

We present current knowledge concerning the pharmacogenomics of growth hormone therapy in children with short stature. We consider the evidence now emerging for the polygenic nature of response to recombinant human growth hormone (r-hGH). These data are related predominantly to the use of transcriptomic data for prediction. The impact of the complex interactions of developmental phenotype over childhood on response to r-hGH are discussed. Finally, the issues that need to be addressed in order to develop a clinical test are described.

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

Plant Co-expression Annotation Resource: a web server for identifying targets for genetically modified crop breeding pipelines

The development of genetically modified crops (GM) includes the discovery of candidate genes through bioinformatics analysis using genomics data, gene expression, and others. Proteins of unknown function (PUFs) are interesting targets for GM crops breeding pipelines for the novelty associated with such targets and also to avoid copyright protection. One method of inferring the putative function of PUFs is by relating them to factors of interest such as abiotic stresses using orthology and co-expression networks, in a guilt-by-association manner. In this regard, we have downloaded, analyzed, and processed genomics data of 53 angiosperms, totaling 1,862,010 genes and 2,332,974 RNA. Diamond and InterproScan were used to discover 72,266 PUFs for all organisms. RNA-seq datasets related to abiotic stresses were downloaded from NCBI/GEO. The RNA-seq data was used as input to the LSTrAP software to construct co-expression networks. LSTrAP also created clusters of transcripts with correlated expression, whose members are more probably related to the molecular mechanisms associated with abiotic stresses in the plants. Orthologous groups were created (OrhtoMCL) using all 2,332,974 proteins in order to associate PUFs to abiotic stress-related clusters of co-expression and therefore infer their function in a guilt-by-association manner. A freely available web resource named "Plant Co-expression Annotation Resource" ( https://www.machado.cnptia.embrapa.br/plantannot ), Plantannot, was created to provide indexed queries to search for PUF putatively associated with abiotic stresses. The web interface also allows browsing, querying, and retrieving of public genomics data from 53 plants. We hope Plantannot to be useful for researchers trying to obtain novel GM crops resistant to climate change hazards.

Single-Cell Transcriptome Analysis in Plants: Advances and Challenges

The rapid and enthusiastic adoption of single-cell RNA sequencing (scRNA-seq) has demonstrated that this technology is far more than just another way to perform transcriptome analysis. It is not an exaggeration to say that the advent of scRNA-seq is revolutionizing the details of whole-transcriptome snapshots from a tissue to a cell. With this disruptive technology, it is now possible to mine heterogeneity between tissue types and within cells like never before. This enables more rapid identification of rare and novel cell types, simultaneous characterization of multiple different cell types and states, more accurate and integrated understanding of their roles in life processes, and more. However, we are only at the beginning of unlocking the full potential of scRNA-seq applications. This is particularly true for plant sciences, where single-cell transcriptome profiling is in its early stage and has many exciting challenges to overcome. In this review, we compare and evaluate recent pioneering studies using the Arabidopsis root model, which has established new paradigms for scRNA-seq studies in plants. We also explore several new and promising single-cell analysis tools that are available to those wishing to study plant development and physiology at unprecedented resolution and scale. In addition, we propose some future directions on the use of scRNA-seq technology to tackle some of the critical challenges in plant research and breeding.

Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning-the branch of machine learning that concerns passing information from one domain to another-can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

Tissue-specific patterns of regulatory changes underlying gene expression differences among Ficedula flycatchers and their naturally occurring F1 hybrids

Changes in interacting cis- and trans-regulatory elements are important candidates for Dobzhansky-Muller hybrid incompatibilities and may contribute to hybrid dysfunction by giving rise to misexpression in hybrids. To gain insight into the molecular mechanisms and determinants of gene expression evolution in natural populations, we analyzed the transcriptome from multiple tissues of two recently diverged Ficedula flycatcher species and their naturally occurring F₁ hybrids. Differential gene expression analysis revealed that the extent of differentiation between species and the set of differentially expressed genes varied across tissues. Common to all tissues, a higher proportion of Z-linked genes than autosomal genes showed differential expression, providing evidence for a fast-Z effect. We further found clear signatures of hybrid misexpression in brain, heart, kidney, and liver. However, while testis showed the highest divergence of gene expression among tissues, it showed no clear signature of misexpression in F₁ hybrids, even though these hybrids were found to be sterile. It is therefore unlikely that incompatibilities between cis-trans regulatory changes explain the observed sterility. Instead, we found evidence that cis-regulatory changes play a significant role in the evolution of gene expression in testis, which illustrates the tissue-specific nature of cis-regulatory evolution bypassing constraints associated with pleiotropic effects of genes.

Cis and trans effects differentially contribute to the evolution of promoters and enhancers

BACKGROUND

Gene expression differences between species are driven by both cis and trans effects. Whereas cis effects are caused by genetic variants located on the same DNA molecule as the target gene, trans effects are due to genetic variants that affect diffusible elements. Previous studies have mostly assessed the impact of cis and trans effects at the gene level. However, how cis and trans effects differentially impact regulatory elements such as enhancers and promoters remains poorly understood. Here, we use massively parallel reporter assays to directly measure the transcriptional outputs of thousands of individual regulatory elements in embryonic stem cells and measure cis and trans effects between human and mouse.

RESULTS

Our approach reveals that cis effects are widespread across transcribed regulatory elements, and the strongest cis effects are associated with the disruption of motifs recognized by strong transcriptional activators. Conversely, we find that trans effects are rare but stronger in enhancers than promoters and are associated with a subset of transcription factors that are differentially expressed between human and mouse. While we find that cis-trans compensation is common within promoters, we do not see evidence of widespread cis-trans compensation at enhancers. Cis-trans compensation is inversely correlated with enhancer redundancy, suggesting that such compensation may often occur across multiple enhancers.

CONCLUSIONS

Our results highlight differences in the mode of evolution between promoters and enhancers in complex mammalian genomes and indicate that studying the evolution of individual regulatory elements is pivotal to understand the tempo and mode of gene expression evolution.

Insights into the skin of caecilian amphibians from gene expression profiles

Background

Gene expression profiles can provide insights into the molecular machinery behind tissue functions and, in turn, can further our understanding of environmental responses, and developmental and evolutionary processes. During vertebrate evolution, the skin has played a crucial role, displaying a wide diversity of essential functions. To unravel the molecular basis of skin specialisations and adaptations, we compared gene expression in the skin with eight other tissues in a phylogenetically and ecologically diverse species sample of one of the most neglected vertebrate groups, the caecilian amphibians (order Gymnophiona).

Results

The skin of the five studied caecilian species showed a distinct gene expression profile reflecting its developmental origin and showing similarities to other epithelial tissues. We identified 59 sequences with conserved enhanced expression in the skin that might be associated with caecilian dermal specialisations. Some of the up-regulated genes shared expression patterns with human skin and potentially are involved in skin functions across vertebrates. Variation trends in gene expression were detected between mid and posterior body skin suggesting different functions between body regions. Several candidate biologically active peptides were also annotated.

Conclusions

Our study provides the first atlas of differentially expressed sequences in caecilian tissues and a baseline to explore the molecular basis of the skin functions in caecilian amphibians, and more broadly in vertebrates.

Identification and characterization of male reproduction-related genes in pig (Sus scrofa) using transcriptome analysis

BACKGROUND

The systematic interrogation of reproduction-related genes was key to gain a comprehensive understanding of the molecular mechanisms underlying male reproductive traits in mammals. Here, based on the data collected from the NCBI SRA database, this study first revealed the genes involved in porcine male reproduction as well their uncharacterized transcriptional characteristics.

RESULTS

Results showed that the transcription of porcine genome was more widespread in testis than in other organs (the same for other mammals) and that testis had more tissue-specific genes (1210) than other organs. GO and GSEA analyses suggested that the identified test is-specific genes (TSGs) were associated with male reproduction. Subsequently, the transcriptional characteristics of porcine TSGs, which were conserved across different mammals, were uncovered. Data showed that 195 porcine TSGs shared similar expression patterns with other mammals (cattle, sheep, human and mouse), and had relatively higher transcription abundances and tissue specificity than low-conserved TSGs. Additionally, further analysis of the results suggested that alternative splicing, transcription factors binding, and the presence of other functionally similar genes were all involved in the regulation of porcine TSGs transcription.

CONCLUSIONS

Overall, this analysis revealed an extensive gene set involved in the regulation of porcine male reproduction and their dynamic transcription patterns. Data reported here provide valuable insights for a further improvement of the economic benefits of pigs as well as future treatments for male infertility.

Comparative Metatranscriptomics of Periodontitis Supports a Common Polymicrobial Shift in Metabolic Function and Identifies Novel Putative Disease-Associated ncRNAs

Periodontitis is an inflammatory disease that deteriorates bone supporting teeth afflicting ∼743 million people worldwide. Bacterial communities associated with disease have been classified into red, orange, purple, blue, green, and yellow complexes based on their roles in the periodontal pocket. Previous metagenomic and metatranscriptomics analyses suggest a common shift in metabolic signatures in disease vs. healthy communities with up-regulated processes including pyruvate fermentation, histidine degradation, amino acid metabolism, TonB-dependent receptors. In this work, we examine existing metatranscriptome datasets to identify the commonly differentially expressed transcripts and potential underlying RNA regulatory mechanisms behind the metabolic shifts. Raw RNA-seq reads from three studies (including 49 healthy and 48 periodontitis samples) were assembled into transcripts de novo. Analyses revealed 859 differentially expressed (DE) transcripts, 675 more- and 174 less-expressed. Only ∼20% of the DE transcripts originate from the pathogenic red/orange complexes, and ∼50% originate from organisms unaffiliated with a complex. Comparison of expression profiles revealed variations among disease samples; while specific metabolic processes are commonly up-regulated, the underlying organisms are diverse both within and across disease associated communities. Surveying DE transcripts for known ncRNAs from the Rfam database identified a large number of tRNAs and tmRNAs as well as riboswitches (FMN, glycine, lysine, and SAM) in more prevalent transcripts and the cobalamin riboswitch in both more and less prevalent transcripts. In silico discovery identified many putative ncRNAs in DE transcripts. We report 15 such putative ncRNAs having promising covariation in the predicted secondary structure and interesting genomic context. Seven of these are antisense of ribosomal proteins that are novel and may involve maintaining ribosomal protein stoichiometry during the disease associated metabolic shift. Our findings describe the role of organisms previously unaffiliated with disease and identify the commonality in progression of disease across three metatranscriptomic studies. We find that although the communities are diverse between individuals, the switch in metabolic signatures characteristic of disease is typically achieved through the contributions of several community members. Furthermore, we identify many ncRNAs (both known and putative) which may facilitate the metabolic shifts associated with periodontitis.

The Earth BioGenome project: opportunities and challenges for plant genomics and conservation

Sequencing them all. That is the ambitious goal of the recently launched Earth BioGenome project (Proceedings of the National Academy of Sciences of the United States of America, 115, 4325-4333), which aims to produce reference genomes for all eukaryotic species within the next decade. In this perspective, we discuss the opportunities of this project with a plant focus, but highlight also potential limitations. This includes the question of how to best capture all plant diversity, as the green taxon is one of the most complex clades in the tree of life, with over 300 000 species. For this, we highlight four key points: (i) the unique biological insights that could be gained from studying plants, (ii) their apparent underrepresentation in sequencing efforts given the number of threatened species, (iii) the necessity of phylogenomic methods that are aware of differences in genome complexity and quality, and (iv) the accounting for within-species genetic diversity and the historical aspect of conservation genetics.

A novel estimator of between-study variance in random-effects models

BACKGROUND

With the rapid development of high-throughput sequencing technologies, many datasets on the same biological subject are generated. A meta-analysis is an approach that combines results from different studies on the same topic. The random-effects model in a meta-analysis enables the modeling of differences between studies by incorporating the between-study variance.

RESULTS

This paper proposes a moments estimator of the between-study variance that represents the across-study variation. A new random-effects method (DSLD2), which involves two-step estimation starting with the DSL estimate and the [Formula: see text] in the second step, is presented. The DSLD2 method is compared with 6 other meta-analysis methods based on effect sizes across 8 aspects under three hypothesis settings. The results show that DSLD2 is a suitable method for identifying differentially expressed genes under the first hypothesis. The DSLD2 method is also applied to Alzheimer's microarray datasets. The differentially expressed genes detected by the DSLD2 method are significantly enriched in neurological diseases.

CONCLUSIONS

The results from both simulationes and an application show that DSLD2 is a suitable method for detecting differentially expressed genes under the first hypothesis.

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.

TreeExp2: An Integrated Framework for Phylogenetic Transcriptome Analysis

Recent innovations of next-generation sequencing such as RNA-seq have generated an enormous amount of comparative transcriptome data, which have shed lights on our understanding of the complexity of transcriptional regulatory systems. Despite numerous RNA-seq analyses, statistical methods and computational tools designed for phylogenetic transcriptome analysis and evolution have not been well developed. In response to this need, we developed software TreeExp2 specifically for RNA-seq data. The R-package TreeExp2 has implemented a suite of advanced, recently developed methods for transcriptome evolutionary analysis. Its main functions include the ancestral transcriptome inference, estimation of the strength of expression conservation, new expression distance, and the relative expression rate test. TreeExp2 provides an integrated, statistically sound framework for phylogenetic transcriptome analysis. It will considerably enhance our analytical capability for exploring the evolution and selection at the transcriptome level. The current version of TreeExp2 is available under GPLv3 license at the Github developer site https://github.com/jingwyang/TreeExp; last accessed November 12, 2019, and its online tutorial which describes the biological theories in details and fully worked case studies with real data can be found at https://jingwyang.github.io/TreeExp-Tutorial; last accessed November 12, 2019.