Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions.

RNA sequencing offers unique insights into transcriptome diversity, and a plethora of tools have been developed to analyze alternative splicing. One important task is to detect changes in the relative transcript abundance in differential transcript usage (DTU) analysis. The choice of the right analysis tool is non-trivial and depends on experimental factors such as the availability of single- or paired-end and bulk or single-cell data. To help users select the most promising tool for their task, we performed a comprehensive benchmark of DTU detection tools. We cover a wide array of experimental settings, using simulated bulk and single-cell RNA-seq data as well as real transcriptomics datasets, including time-series data. Our results suggest that DEXSeq, edgeR, and LimmaDS are better choices for paired-end data, while DSGseq and DEXSeq can be used for single-end data. In single-cell simulation settings, we showed that satuRn performs better than DTUrtle. In addition, we showed that Spycone is optimal for time series DTU/IS analysis based on the evidence provided using GO terms enrichment analysis.

Network medicine-based epistasis detection in complex diseases: ready for quantum computing.

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs)1-3. Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL is the first application that demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD.

Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/ .

Alternative splicing impacts microRNA regulation within coding regions.

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind to target sites in different gene regions and regulate post-transcriptional gene expression. Approximately 95% of human multi-exon genes can be spliced alternatively, which enables the production of functionally diverse transcripts and proteins from a single gene. Through alternative splicing, transcripts might lose the exon with the miRNA target site and become unresponsive to miRNA regulation. To check this hypothesis, we studied the role of miRNA target sites in both coding and non-coding regions using six cancer data sets from The Cancer Genome Atlas (TCGA) and Parkinson's disease data from PPMI. First, we predicted miRNA target sites on mRNAs from their sequence using TarPmiR. To check whether alternative splicing interferes with this regulation, we trained linear regression models to predict miRNA expression from transcript expression. Using nested models, we compared the predictive power of transcripts with miRNA target sites in the coding regions to that of transcripts without target sites. Models containing transcripts with target sites perform significantly better. We conclude that alternative splicing does interfere with miRNA regulation by skipping exons with miRNA target sites within the coding region.

A YAP/TAZ-ARHGAP29-RhoA Signaling Axis Regulates Podocyte Protrusions and Integrin Adhesions.

Glomerular disease due to podocyte malfunction is a major factor in the pathogenesis of chronic kidney disease. Identification of podocyte-specific signaling pathways is therefore a prerequisite to characterizing relevant disease pathways and developing novel treatment approaches. Here, we employed loss of function studies for EPB41L5 (Yurt) as a central podocyte gene to generate a cell type-specific disease model. Loss of Yurt in fly nephrocytes caused protein uptake and slit diaphragm defects. Transcriptomic and proteomic analysis of human EPB41L5 knockout podocytes demonstrated impaired mechanotransduction via the YAP/TAZ signaling pathway. Further analysis of specific inhibition of the YAP/TAZ-TEAD transcription factor complex by TEADi led to the identification of ARGHAP29 as an EPB41L5 and YAP/TAZ-dependently expressed podocyte RhoGAP. Knockdown of ARHGAP29 caused increased RhoA activation, defective lamellipodia formation, and increased maturation of integrin adhesion complexes, explaining similar phenotypes caused by loss of EPB41L5 and TEADi expression in podocytes. Detection of increased levels of ARHGAP29 in early disease stages of human glomerular disease implies a novel negative feedback loop for mechanotransductive RhoA-YAP/TAZ signaling in podocyte physiology and disease.

Alternative splicing analysis benchmark with DICAST.

Alternative splicing is a major contributor to transcriptome and proteome diversity in health and disease. A plethora of tools have been developed for studying alternative splicing in RNA-seq data. Previous benchmarks focused on isoform quantification and mapping. They neglected event detection tools, which arguably provide the most detailed insights into the alternative splicing process. DICAST offers a modular and extensible framework for analysing alternative splicing integrating eleven splice-aware mapping and eight event detection tools. We benchmark all tools extensively on simulated as well as whole blood RNA-seq data. STAR and HISAT2 demonstrated the best balance between performance and run time. The performance of event detection tools varies widely with no tool outperforming all others. DICAST allows researchers to employ a consensus approach to consider the most successful tools jointly for robust event detection. Furthermore, we propose the first reporting standard to unify existing formats and to guide future tool development.

Systematic analysis of alternative splicing in time course data using Spycone.

MOTIVATION: During disease progression or organism development, alternative splicing may lead to isoform switches that demonstrate similar temporal patterns and reflect the alternative splicing co-regulation of such genes. Tools for dynamic process analysis usually neglect alternative splicing. RESULTS: Here, we propose Spycone, a splicing-aware framework for time course data analysis. Spycone exploits a novel IS detection algorithm and offers downstream analysis such as network and gene set enrichment. We demonstrate the performance of Spycone using simulated and real-world data of SARS-CoV-2 infection. AVAILABILITY AND IMPLEMENTATION: The Spycone package is available as a PyPI package. The source code of Spycone is available under the GPLv3 license at https://github.com/yollct/spycone and the documentation at https://spycone.readthedocs.io/en/latest/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Florigen and its homologs of FT/CETS/PEBP/RKIP/YbhB family may be the enzymes of small molecule metabolism: review of the evidence.

BACKGROUND: Flowering signals are sensed in plant leaves and transmitted to the shoot apical meristems, where the formation of flowers is initiated. Searches for a diffusible hormone-like signaling entity ("florigen") went on for many decades, until a product of plant gene FT was identified as the key component of florigen in the 1990s, based on the analysis of mutants, genetic complementation evidence, and protein and RNA localization studies. Sequence homologs of FT protein are found throughout prokaryotes and eukaryotes; some eukaryotic family members appear to bind phospholipids or interact with the components of the signal transduction cascades. Most FT homologs are known to share a constellation of five charged residues, three of which, i.e., two histidines and an aspartic acid, are located at the rim of a well-defined cavity on the protein surface. RESULTS: We studied molecular features of the FT homologs in prokaryotes and analyzed their genome context, to find tentative evidence connecting the bacterial FT homologs with small molecule metabolism, often involving substrates that contain sugar or ribonucleoside moieties. We argue that the unifying feature of this protein family, i.e., a set of charged residues conserved at the sequence and structural levels, is more likely to be an enzymatic active center than a catalytically inert ligand-binding site. CONCLUSIONS: We propose that most of FT-related proteins are enzymes operating on small diffusible molecules. Those metabolites may constitute an overlooked essential ingredient of the florigen signal.

Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases.

Alternative splicing (AS) is an important aspect of gene regulation. Nevertheless, its role in molecular processes and pathobiology is far from understood. A roadblock is that tools for the functional analysis of AS-set events are lacking. To mitigate this, we developed NEASE, a tool integrating pathways with structural annotations of protein-protein interactions to functionally characterize AS events. We show in four application cases how NEASE can identify pathways contributing to tissue identity and cell type development, and how it highlights splicing-related biomarkers. With a unique view on AS, NEASE generates unique and meaningful biological insights complementary to classical pathways analysis.

ASimulatoR: splice-aware RNA-Seq data simulation.

SUMMARY: A plethora of tools exist for RNA-Seq data analysis with a focus on alternative splicing (AS). However, appropriate data for their comparative evaluation is missing. The R package ASimulatoR simulates gold standard RNA-Seq datasets with fine-grained control over the distribution of AS events, which allow for evaluating alternative splicing tools, e.g. to study the effect of sequencing depth on the performance of AS event detection. AVAILABILITY AND IMPLEMENTATION: ASimulatoR is freely available at https://github.com/biomedbigdata/ASimulatoR as an R package under GPL-3 license.

DIGGER: exploring the functional role of alternative splicing in protein interactions.

Alternative splicing plays a major role in regulating the functional repertoire of the proteome. However, isoform-specific effects to protein-protein interactions (PPIs) are usually overlooked, making it impossible to judge the functional role of individual exons on a systems biology level. We overcome this barrier by integrating protein-protein interactions, domain-domain interactions and residue-level interactions information to lift exon expression analysis to a network level. Our user-friendly database DIGGER is available at https://exbio.wzw.tum.de/digger and allows users to seamlessly switch between isoform and exon-centric views of the interactome and to extract sub-networks of relevant isoforms, making it an essential resource for studying mechanistic consequences of alternative splicing.

Small Open Reading Frames, Non-Coding RNAs and Repetitive Elements in Bradyrhizobium japonicum USDA 110.

Small open reading frames (sORFs) and genes for non-coding RNAs are poorly investigated components of most genomes. Our analysis of 1391 ORFs recently annotated in the soybean symbiont Bradyrhizobium japonicum USDA 110 revealed that 78% of them contain less than 80 codons. Twenty-one of these sORFs are conserved in or outside Alphaproteobacteria and most of them are similar to genes found in transposable elements, in line with their broad distribution. Stabilizing selection was demonstrated for sORFs with proteomic evidence and bll1319_ISGA which is conserved at the nucleotide level in 16 alphaproteobacterial species, 79 species from other taxa and 49 other Proteobacteria. Further we used Northern blot hybridization to validate ten small RNAs (BjsR1 to BjsR10) belonging to new RNA families. We found that BjsR1 and BjsR3 have homologs outside the genus Bradyrhizobium, and BjsR5, BjsR6, BjsR7, and BjsR10 have up to four imperfect copies in Bradyrhizobium genomes. BjsR8, BjsR9, and BjsR10 are present exclusively in nodules, while the other sRNAs are also expressed in liquid cultures. We also found that the level of BjsR4 decreases after exposure to tellurite and iron, and this down-regulation contributes to survival under high iron conditions. Analysis of additional small RNAs overlapping with 3'-UTRs revealed two new repetitive elements named Br-REP1 and Br-REP2. These REP elements may play roles in the genomic plasticity and gene regulation and could be useful for strain identification by PCR-fingerprinting. Furthermore, we studied two potential toxin genes in the symbiotic island and confirmed toxicity of the yhaV homolog bll1687 but not of the newly annotated higB homolog blr0229_ISGA in E. coli. Finally, we revealed transcription interference resulting in an antisense RNA complementary to blr1853, a gene induced in symbiosis. The presented results expand our knowledge on sORFs, non-coding RNAs and repetitive elements in B. japonicum and related bacteria.

Nitrogen Fixation and Molecular Oxygen: Comparative Genomic Reconstruction of Transcription Regulation in Alphaproteobacteria.

Biological nitrogen fixation plays a crucial role in the nitrogen cycle. An ability to fix atmospheric nitrogen, reducing it to ammonium, was described for multiple species of Bacteria and Archaea. The transcriptional regulatory network for nitrogen fixation was extensively studied in several representatives of the class Alphaproteobacteria. This regulatory network includes the activator of nitrogen fixation NifA, working in tandem with the alternative sigma-factor RpoN as well as oxygen-responsive regulatory systems, one-component regulators FnrN/FixK and two-component system FixLJ. Here we used a comparative genomics approach for in silico study of the transcriptional regulatory network in 50 genomes of Alphaproteobacteria. We extended the known regulons and proposed the scenario for the evolution of the nitrogen fixation transcriptional network. The reconstructed network substantially expands the existing knowledge of transcriptional regulation in nitrogen-fixing microorganisms and can be used for genetic experiments, metabolic reconstruction, and evolutionary analysis.

Comparative genomics and evolution of regulons of the LacI-family transcription factors.

DNA-binding transcription factors (TFs) are essential components of transcriptional regulatory networks in bacteria. LacI-family TFs (LacI-TFs) are broadly distributed among certain lineages of bacteria. The majority of characterized LacI-TFs sense sugar effectors and regulate carbohydrate utilization genes. The comparative genomics approaches enable in silico identification of TF-binding sites and regulon reconstruction. To study the function and evolution of LacI-TFs, we performed genomics-based reconstruction and comparative analysis of their regulons. For over 1300 LacI-TFs from over 270 bacterial genomes, we predicted their cognate DNA-binding motifs and identified target genes. Using the genome context and metabolic subsystem analyses of reconstructed regulons, we tentatively assigned functional roles and predicted candidate effectors for 78 and 67% of the analyzed LacI-TFs, respectively. Nearly 90% of the studied LacI-TFs are local regulators of sugar utilization pathways, whereas the remaining 125 global regulators control large and diverse sets of metabolic genes. The global LacI-TFs include the previously known regulators CcpA in Firmicutes, FruR in Enterobacteria, and PurR in Gammaproteobacteria, as well as the three novel regulators-GluR, GapR, and PckR-that are predicted to control the central carbohydrate metabolism in three lineages of Alphaproteobacteria. Phylogenetic analysis of regulators combined with the reconstructed regulons provides a model of evolutionary diversification of the LacI protein family. The obtained genomic collection of in silico reconstructed LacI-TF regulons in bacteria is available in the RegPrecise database (http://regprecise.lbl.gov). It provides a framework for future structural and functional classification of the LacI protein family and identification of molecular determinants of the DNA and ligand specificity. The inferred regulons can be also used for functional gene annotation and reconstruction of sugar catabolic networks in diverse bacterial lineages.

Minimal genome encoding proteins with constrained amino acid repertoire.

Minimal bacterial gene set comprises the genetic elements needed for survival of engineered bacterium on a rich medium. This set is estimated to include 300-350 protein-coding genes. One way of simplifying an organism with such a minimal genome even further is to constrain the amino acid content of its proteins. In this study, comparative genomics approaches and the results of gene knockout experiments were used to extrapolate the minimal gene set of mollicutes, and bioinformatics combined with the knowledge-based analysis of the structure-function relationships in these proteins and their orthologs, paralogs and analogs was applied to examine the challenges of completely replacing the rarest residue, cysteine. Among several known functions of cysteine residues, their roles in the active centers of the enzymes responsible for deoxyribonucleoside synthesis and transfer RNA modification appear to be crucial, as no alternative chemistry is known for these reactions. Thus, drastic reduction of the content of the rarest amino acid in a minimal proteome appears to be possible, but its complete elimination is challenging.

Metagenomic analysis of hadopelagic microbial assemblages thriving at the deepest part of Mediterranean Sea, Matapan-Vavilov Deep.

The marine pelagic zone situated > 200 m below the sea level (bls) is the largest marine subsystem, comprising more than two-thirds of the oceanic volume. At the same time, it is one of the least explored ecosystems on Earth. Few large-scale environmental genomics studies have been undertaken to examine the phylogenetic diversity and functional gene repertoire of planktonic microbes present in mesopelagic and bathypelagic environments. Here, we present the description of the deep-sea microbial community thriving at > 4900 m depth in Matapan-Vavilov Deep (MVD). This canyon is the deepest site of Mediterranean Sea, with a deepest point located at approximately 5270 m, 56 km SW of city Pylos (Greece) in the Ionian Sea (36°34.00N, 21°07.44E). Comparative analysis of whole-metagenomic data revealed that unlike other deep-sea metagenomes, the prokaryotic diversity in MVD was extremely poor. The decline in the dark primary production rates, measured at 4908 m depth, was coincident with overwhelming dominance of copiotrophic Alteromonas macleodii'deep-ecotype' AltDE at the expense of other prokaryotes including those potentially involved in both autotrophic and anaplerotic CO(2) fixation. We also demonstrate the occurrence in deep-sea metagenomes of several clustered regularly interspaced short palindromic repeats systems.

Evolution of transcriptional regulation in closely related bacteria.

BACKGROUND: The exponential growth of the number of fully sequenced genomes at varying taxonomic closeness allows one to characterize transcriptional regulation using comparative-genomics analysis instead of time-consuming experimental methods. A transcriptional regulatory unit consists of a transcription factor, its binding site and a regulated gene. These units constitute a graph which contains so-called "network motifs", subgraphs of a given structure. Here we consider genomes of closely related Enterobacteriales and estimate the fraction of conserved network motifs and sites as well as positions under selection in various types of non-coding regions. RESULTS: Using a newly developed technique, we found that the highest fraction of positions under selection, approximately 50%, was observed in synvergon spacers (between consecutive genes from the same strand), followed by ~45% in divergon spacers (common 5'-regions), and ~10% in convergon spacers (common 3'-regions). The fraction of selected positions in functional regions was higher, 60% in transcription factor-binding sites and ~45% in terminators and promoters. Small, but significant differences were observed between Escherichia coli and Salmonella enterica. This fraction is similar to the one observed in eukaryotes.The conservation of binding sites demonstrated some differences between types of regulatory units. In E. coli, strains the interactions of the type "local transcriptional factor gene" turned out to be more conserved in feed-forward loops (FFLs) compared to non-motif interactions. The coherent FFLs tend to be less conserved than the incoherent FFLs. A natural explanation is that the former imply functional redundancy. CONCLUSIONS: A naïve hypothesis that FFL would be highly conserved turned out to be not entirely true: its conservation depends on its status in the transcriptional network and also from its usage. The fraction of positions under selection in intergenic regions of bacterial genomes is roughly similar to that of eukaryotes. Known regulatory sites explain 20±5% of selected positions.

Comparative genomics of ethanolamine utilization.

Ethanolamine can be used as a source of carbon and nitrogen by phylogenetically diverse bacteria. Ethanolamine-ammonia lyase, the enzyme that breaks ethanolamine into acetaldehyde and ammonia, is encoded by the gene tandem eutBC. Despite extensive studies of ethanolamine utilization in Salmonella enterica serovar Typhimurium, much remains to be learned about EutBC structure and catalytic mechanism, about the evolutionary origin of ethanolamine utilization, and about regulatory links between the metabolism of ethanolamine itself and the ethanolamine-ammonia lyase cofactor adenosylcobalamin. We used computational analysis of sequences, structures, genome contexts, and phylogenies of ethanolamine-ammonia lyases to address these questions and to evaluate recent data-mining studies that have suggested an association between bacterial food poisoning and the diol utilization pathways. We found that EutBC evolution included recruitment of a TIM barrel and a Rossmann fold domain and their fusion to N-terminal alpha-helical domains to give EutB and EutC, respectively. This fusion was followed by recruitment and occasional loss of auxiliary ethanolamine utilization genes in Firmicutes and by several horizontal transfers, most notably from the firmicute stem to the Enterobacteriaceae and from Alphaproteobacteria to Actinobacteria. We identified a conserved DNA motif that likely represents the EutR-binding site and is shared by the ethanolamine and cobalamin operons in several enterobacterial species, suggesting a mechanism for coupling the biosyntheses of apoenzyme and cofactor in these species. Finally, we found that the food poisoning phenotype is associated with the structural components of metabolosome more strongly than with ethanolamine utilization genes or with paralogous propanediol utilization genes per se.