Molecular Evolution of Metazoan Hypoxia-inducing Factors

Most metazoans rely on aerobic energy production, which is dependent on adequate oxygen supply. In the case of reduced oxygen supply (hypoxia), the most profound changes in gene expression are mediated by transcription factors named hypoxia-inducible factors (HIF alpha). These proteins are post-translationally regulated by prolyl-4-hydroxylase (PHD) enzymes that are direct “sensors” of cellular oxygen levels. This thesis examines the molecular evolution of metazoan HIF systems. In early metazoans the HIF system emerged from pre-existing PHD oxygen sensors and early bHLH-PAS transcription factors. In invertebrates our analysis revealed an unexpected diversity of PHD genes and HIF alpha sequence characteristics. An early branching vertebrate, the epaulette shark (Hemiscyllium ocellatum) was chosen for sequencing and hypoxia preconditioning studies of HIF alpha and PHD genes. As no quantitative PCR reference genes were available, this thesis includes the first study of reference genes in cartilaginous fish species. Applying multiple statistical analysis we also discoveredthat commonly used reference gene software may perform poorly with some data sets. Novel reference genes allowed accurate measurements of the mRNAlevels of the studied target genes. Cartilaginous fishes have three genomic duplicates of both HIF alpha and PHD genes like mammals and teleost fishes. Combining functional divergence and selection analyses it was possible to describe how sequence changes in both HIF alpha and PHD duplicates may have contributed to the differential oxygen sensitivityof HIF alphas. Additionally, novel teleost HIF-1 alpha sequences were produced and used to reveal the molecular evolution of HIF-1 alpha in this lineage rich with hypoxia tolerant species.Siirretty Doriast

Dirichlet process mixture models for single-cell RNA-seq clustering

Clustering of cells based on gene expression is one of the major steps in single-cell RNA-sequencing (scRNA-seq) data analysis. One key challenge in cluster analysis is the unknown number of clusters and, for this issue, there is still no comprehensive solution. To enhance the process of defining meaningful cluster resolution, we compare Bayesian latent Dirichlet allocation (LDA) method to its non-parametric counterpart, hierarchical Dirichlet process (HDP) in the context of clustering scRNA-seq data. A potential main advantage of HDP is that it does not require the number of clusters as an input parameter from the user. While LDA has been used in single-cell data analysis, it has not been compared in detail with HDP. Here, we compare the cell clustering performance of LDA and HDP using four scRNA-seq datasets (immune cells, kidney, pancreas and decidua/placenta), with a specific focus on cluster numbers. Using both intrinsic (DB-index) and extrinsic (ARI) cluster quality measures, we show that the performance of LDA and HDP is dataset dependent. We describe a case where HDP produced a more appropriate clustering compared to the best performer from a series of LDA clusterings with different numbers of clusters. However, we also observed cases where the best performing LDA cluster numbers appropriately capture the main biological features while HDP tended to inflate the number of clusters. Overall, our study highlights the importance of carefully assessing the number of clusters when analyzing scRNA-seq data.</p

Computational strategies for single-cell multi-omics integration

Single-cell omics technologies are currently solving biological and medical problems that earlier have remained elusive, such as discovery of new cell types, cellular differentiation trajectories and communication networks across cells and tissues. Current advances especially in single-cell multi-omics hold high potential for breakthroughs by integration of multiple different omics layers. To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed. In this review, we first introduce recent developments in single-cell multi-omics in general and then focus on the available data integration strategies. The integration approaches are divided into three categories: early, intermediate, and late data integration. For each category, we describe the underlying conceptual principles and main characteristics, as well as provide examples of currently available tools and how they have been applied to analyze single-cell multi-omics data. Finally, we explore the challenges and prospective future directions of single-cell multi-omics data integration, including examples of adopting multi-view analysis approaches used in other disciplines to single-cell multi-omics.</p

Elasmobranch qPCR reference genes: a case study of hypoxia preconditioned epaulette sharks

<p>Abstract</p> <p>Background</p> <p>Elasmobranch fishes are an ancient group of vertebrates which have high potential as model species for research into evolutionary physiology and genomics. However, no comparative studies have established suitable reference genes for quantitative PCR (qPCR) in elasmobranchs for any physiological conditions. Oxygen availability has been a major force shaping the physiological evolution of vertebrates, especially fishes. Here we examined the suitability of 9 reference candidates from various functional categories after a single hypoxic insult or after hypoxia preconditioning in epaulette shark (<it>Hemiscyllium ocellatum</it>).</p> <p>Results</p> <p>Epaulette sharks were caught and exposed to hypoxia. Tissues were collected from 10 controls, 10 individuals with single hypoxic insult and 10 individuals with hypoxia preconditioning (8 hypoxic insults, 12 hours apart). We produced sequence information for reference gene candidates and monitored mRNA expression levels in four tissues: cerebellum, heart, gill and eye. The stability of the genes was examined with analysis of variance, geNorm and NormFinder. The best ranking genes in our study were <it>eukaryotic translation elongation factor 1 beta </it>(<it>eef1b</it>), <it>ubiquitin </it>(<it>ubq</it>) and <it>polymerase (RNA) II (DNA directed) polypeptide F </it>(<it>polr2f</it>). The performance of the <it>ribosomal protein L6 </it>(<it>rpl6</it>) was tissue-dependent. Notably, in one tissue the analysis of variance indicated statistically significant differences between treatments for genes that were ranked as the most stable candidates by reference gene software.</p> <p>Conclusions</p> <p>Our results indicate that <it>eef1b </it>and <it>ubq </it>are generally the most suitable reference genes for the conditions and tissues in the present epaulette shark studies. These genes could also be potential reference gene candidates for other physiological studies examining stress in elasmobranchs. The results emphasise the importance of inter-group variation in reference gene evaluation.</p

COVID-19-specific transcriptomic signature detectable in blood across multiple cohorts

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels

Differential ATAC-seq and ChIP-seq peak detection using ROTS

Changes in cellular chromatin states fine-tune transcriptional output and ultimately lead to phenotypic changes. Here we propose a novel application of our reproducibility-optimized test statistics (ROTS) to detect differential chromatin states (ATAC-seq) or differential chromatin modification states (ChIP-seq) between conditions. We compare the performance of ROTS to existing and widely used methods for ATAC-seq and ChIP-seq data using both synthetic and real datasets. Our results show that ROTS outperformed other commonly used methods when analyzing ATAC-seq data. ROTS also displayed the most accurate detection of small differences when modeling with synthetic data. We observed that two-step methods that require the use of a separate peak caller often more accurately called enrichment borders, whereas one-step methods without a separate peak calling step were more versatile in calling sub-peaks. The top ranked differential regions detected by the methods had marked correlation with transcriptional differences of the closest genes. Overall, our study provides evidence that ROTS is a useful addition to the available differential peak detection methods to study chromatin and performs especially well when applied to study differential chromatin states in ATAC-seq data. </p

Histone H3K4me3 breadth in hypoxia reveals endometrial core functions and stress adaptation linked to endometriosis

Trimethylation of histone H3 at lysine 4 (H3K4me3) is a marker of active promoters. Broad H3K4me3 promoter domains have been associated with cell type identity, but H3K4me3 dynamics upon cellular stress have not been well characterized. We assessed this by exposing endometrial stromal cells to hypoxia, which is a major cellular stress condition. We observed that hypoxia modifies the existing H3K4me3 marks and that promoter H3K4me3 breadth rather than height correlates with transcription. Broad H3K4me3 domains mark genes for endometrial core functions and are maintained or selectively extended upon hypoxia. Hypoxic extension of H3K4me3 breadth associates with stress adaptation genes relevant for the survival of endometrial cells including transcription factor KLF4, for which we found increased protein expression in the stroma of endometriosis lesions. These results substantiate the view on broad H3K4me3 as a marker of cell identity genes and reveal participation of H3K4me3 extension in cellular stress adaptation

Overexpression of Human Estrogen Biosynthetic Enzyme Hydroxysteroid (17beta) Dehydrogenase Type 1 Induces Adenomyosis-like Phenotype in Transgenic Mice

Hydroxysteroid (17beta) dehydrogenase type 1 (HSD17B1) is an enzyme that converts estrone to estradiol, while adenomyosis is an estrogen-dependent disease with poorly understood pathophysiology. In the present study, we show that mice universally over-expressing human estrogen biosynthetic enzyme HSD17B1 (HSD17B1TG mice) present with adenomyosis phenotype, characterized by histological and molecular evaluation. The first adenomyotic changes with endometrial glands partially or fully infiltrated into the myometrium appeared at the age of 5.5 months in HSD17B1TG females and became more prominent with increasing age. Preceding the phenotype, increased myometrial smooth muscle actin positivity and increased amount of glandular myofibroblast cells were observed in HSD17B1TG uteri. This was accompanied by transcriptomic upregulation of inflammatory and estrogen signaling pathways. Further, the genes upregulated in the HSD17B1TG uterus were enriched with genes previously observed to be induced in the human adenomyotic uterus, including several genes of the NFKB pathway. A 6-week-long HSD17B1 inhibitor treatment reduced the occurrence of the adenomyotic changes by 5-fold, whereas no effect was observed in the vehicle-treated HSD17B1TG mice, suggesting that estrogen is the main upstream regulator of adenomyosis-induced uterine signaling pathways. HSD17B1 is considered as a promising drug target to inhibit estrogen-dependent growth of endometrial disorders. The present data indicate that HSD17B1 over-expression in TG mice results in adenomyotic changes reversed by HSD17B1 inhibitor treatment and HSD17B1 is, thus, a potential novel drug target for adenomyosis.</p

Evolutionary origins of oxygen sensing in animals

Oxygen is required for aerobic energy production but its levels have to be tightly regulated to avoid deleterious effects. Thus, animals have evolved mechanisms to monitor and respond to fluctuations in oxygen availability. Here, the evolution of the HIF system is discussed in light of a report that reveals its presence in the simplest animal

Molecular evolution of the metazoan PHD-HIF oxygen-sensing system

Metazoans rely on aerobic energy production, which requires an adequate oxygen supply. During reduced oxygen supply (hypoxia), the most profound changes in gene expression are mediated by transcription factors known as hypoxia-inducible factors (HIFs). HIF alpha proteins are commonly posttranslationally regulated by prolyl-4-hydroxylase (PHD) enzymes, which are direct ‘‘sensors’ ’ of cellular oxygen levels. We examined the molecular evolution of the metazoan PHD– HIF oxygen-sensing system by constructing complete phylogenies for PHD and HIF alpha genes and used computational tools to characterize the molecular changes underlying the functional divergence of PHD and HIF alpha duplicates. The presence of PHDs in metazoan genomes predates the emergence of HIF alphas. Our analysis revealed an unexpected diversity of PHD genes and HIF alpha sequence characteristics in invertebrates, suggesting that the simple oxygen-sensing systems of Caenorhabditis and Drosophila may not be typical of other invertebrate bilaterians. We studied the early vertebrate evolution of the system by sequencing these genes in early-diverging cartilaginous fishes, elasmobranchs. Cartilaginous fishes appear to have three paralogs of both PHD and HIF alpha. The novel sequences were used as outgroups for a detailed molecular analysis of PHD and HIF alpha duplicates in a major air-breathing vertebrate lineage, the mammals, and a major water-breathing vertebrate lineage, the teleosts. In PHDs, functionally divergent amino acid sites were detected near the HIF alpha-binding channel and beta2beta3 loop that defines its substrate specificity. In HIF alphas