scTEA-db: a comprehensive database of novel terminal exon isoforms identified from human single cell transcriptomes.

The usage of alternative terminal exons results in messenger RNA (mRNA) isoforms that differ in their 3' untranslated regions (3' UTRs) and often also in their protein-coding sequences. Alternative 3' UTRs contain different sets of cis-regulatory elements known to regulate mRNA stability, translation and localization, all of which are vital to cell identity and function. In previous work, we revealed that ∼25 percent of the experimentally observed RNA 3' ends are located within regions currently annotated as intronic, indicating that many 3' end isoforms remain to be uncovered. Also, the inclusion of not yet annotated terminal exons is more tissue specific compared to the already annotated ones. Here, we present the single cell-based Terminal Exon Annotation database (scTEA-db, www.scTEA-db.org) that provides the community with 12 063 so far not yet annotated terminal exons and associated transcript isoforms identified by analysing 53 069 publicly available single cell transcriptomes. Our scTEA-db web portal offers an array of features to find and explore novel terminal exons belonging to 5538 human genes, 110 of which are known cancer drivers. In summary, scTEA-db provides the foundation for studying the biological role of large numbers of so far not annotated terminal exon isoforms in cell identity and function.

Germline MBD4 deficiency causes a multi-tumor predisposition syndrome.

We report an autosomal recessive, multi-organ tumor predisposition syndrome, caused by bi-allelic loss-of-function germline variants in the base excision repair (BER) gene MBD4. We identified five individuals with bi-allelic MBD4 variants within four families and these individuals had a personal and/or family history of adenomatous colorectal polyposis, acute myeloid leukemia, and uveal melanoma. MBD4 encodes a glycosylase involved in repair of G:T mismatches resulting from deamination of 5'-methylcytosine. The colorectal adenomas from MBD4-deficient individuals showed a mutator phenotype attributable to mutational signature SBS1, consistent with the function of MBD4. MBD4-deficient polyps harbored somatic mutations in similar driver genes to sporadic colorectal tumors, although AMER1 mutations were more common and KRAS mutations less frequent. Our findings expand the role of BER deficiencies in tumor predisposition. Inclusion of MBD4 in genetic testing for polyposis and multi-tumor phenotypes is warranted to improve disease management.

Alternative cleavage and polyadenylation in health and disease.

Most human genes have multiple sites at which RNA 3' end cleavage and polyadenylation can occur, enabling the expression of distinct transcript isoforms under different conditions. Novel methods to sequence RNA 3' ends have generated comprehensive catalogues of polyadenylation (poly(A)) sites; their analysis using innovative computational methods has revealed how poly(A) site choice is regulated by core RNA 3' end processing factors, such as cleavage factor I and cleavage and polyadenylation specificity factor, as well as by other RNA-binding proteins, particularly splicing factors. Here, we review the experimental and computational methods that have enabled the global mapping of mRNA and of long non-coding RNA 3' ends, quantification of the resulting isoforms and the discovery of regulators of alternative cleavage and polyadenylation (APA). We highlight the different types of APA-derived isoforms and their functional differences, and illustrate how APA contributes to human diseases, including cancer and haematological, immunological and neurological diseases.

PolyASite 2.0: a consolidated atlas of polyadenylation sites from 3' end sequencing.

Generated by 3' end cleavage and polyadenylation at alternative polyadenylation (poly(A)) sites, alternative terminal exons account for much of the variation between human transcript isoforms. More than a dozen protocols have been developed so far for capturing and sequencing RNA 3' ends from a variety of cell types and species. In previous studies, we have used these data to uncover novel regulatory signals and cell type-specific isoforms. Here we present an update of the PolyASite (https://polyasite.unibas.ch) resource of poly(A) sites, constructed from publicly available human, mouse and worm 3' end sequencing datasets by enforcing uniform quality measures, including the flagging of putative internal priming sites. Through integrated processing of all data, we identified and clustered sites that are closely spaced and share polyadenylation signals, as these are likely the result of stochastic variations in processing. For each cluster, we identified the representative - most frequently processed - site and estimated the relative use in the transcriptome across all samples. We have established a modern web portal for efficient finding, exploration and export of data. Database generation is fully automated, greatly facilitating incorporation of new datasets and the updating of underlying genome resources.

Terminal exon characterization with TECtool reveals an abundance of cell-specific isoforms.

Sequencing of RNA 3' ends has uncovered numerous sites that do not correspond to the termination sites of known transcripts. Through their 3' untranslated regions, protein-coding RNAs interact with RNA-binding proteins and microRNAs, which regulate many properties, including RNA stability and subcellular localization. We developed the terminal exon characterization (TEC) tool ( http://tectool.unibas.ch ), which can be used with RNA-sequencing data from any species for which a genome annotation that includes sites of RNA cleavage and polyadenylation is available. We discovered hundreds of previously unknown isoforms and cell-type-specific terminal exons in human cells. Ribosome profiling data revealed that many of these isoforms were translated. By applying TECtool to single-cell sequencing data, we found that the newly identified isoforms were expressed in subpopulations of cells. Thus, TECtool enables the identification of previously unknown isoforms in well-studied cell systems and in rare cell types.

A comprehensive analysis of 3' end sequencing data sets reveals novel polyadenylation signals and the repressive role of heterogeneous ribonucleoprotein C on cleavage and polyadenylation.

Alternative polyadenylation (APA) is a general mechanism of transcript diversification in mammals, which has been recently linked to proliferative states and cancer. Different 3' untranslated region (3' UTR) isoforms interact with different RNA-binding proteins (RBPs), which modify the stability, translation, and subcellular localization of the corresponding transcripts. Although the heterogeneity of pre-mRNA 3' end processing has been established with high-throughput approaches, the mechanisms that underlie systematic changes in 3' UTR lengths remain to be characterized. Through a uniform analysis of a large number of 3' end sequencing data sets, we have uncovered 18 signals, six of which are novel, whose positioning with respect to pre-mRNA cleavage sites indicates a role in pre-mRNA 3' end processing in both mouse and human. With 3' end sequencing we have demonstrated that the heterogeneous ribonucleoprotein C (HNRNPC), which binds the poly(U) motif whose frequency also peaks in the vicinity of polyadenylation (poly(A)) sites, has a genome-wide effect on poly(A) site usage. HNRNPC-regulated 3' UTRs are enriched in ELAV-like RBP 1 (ELAVL1) binding sites and include those of the CD47 gene, which participate in the recently discovered mechanism of 3' UTR-dependent protein localization (UDPL). Our study thus establishes an up-to-date, high-confidence catalog of 3' end processing sites and poly(A) signals, and it uncovers an important role of HNRNPC in regulating 3' end processing. It further suggests that U-rich elements mediate interactions with multiple RBPs that regulate different stages in a transcript's life cycle.

Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction.

miRNAs are small RNAs that regulate gene expression post-transcriptionally. By repressing the translation and promoting the degradation of target mRNAs, miRNAs may reduce the cell-to-cell variability in protein expression, induce correlations between target expression levels, and provide a layer through which targets can influence each other's expression as "competing RNAs" (ceRNAs). However, experimental evidence for these behaviors is limited. Combining mathematical modeling with RNA sequencing of individual human embryonic kidney cells in which the expression of two distinct miRNAs was induced over a wide range, we have inferred parameters describing the response of hundreds of miRNA targets to miRNA induction. Individual targets have widely different response dynamics, and only a small proportion of predicted targets exhibit high sensitivity to miRNA induction. Our data reveal for the first time the response parameters of the entire network of endogenous miRNA targets to miRNA induction, demonstrating that miRNAs correlate target expression and at the same time increase the variability in expression of individual targets across cells. The approach is generalizable to other miRNAs and post-transcriptional regulators to improve the understanding of gene expression dynamics in individual cell types.

ISMARA: automated modeling of genomic signals as a democracy of regulatory motifs.

Accurate reconstruction of the regulatory networks that control gene expression is one of the key current challenges in molecular biology. Although gene expression and chromatin state dynamics are ultimately encoded by constellations of binding sites recognized by regulators such as transcriptions factors (TFs) and microRNAs (miRNAs), our understanding of this regulatory code and its context-dependent read-out remains very limited. Given that there are thousands of potential regulators in mammals, it is not practical to use direct experimentation to identify which of these play a key role for a particular system of interest. We developed a methodology that models gene expression or chromatin modifications in terms of genome-wide predictions of regulatory sites and completely automated it into a web-based tool called ISMARA (Integrated System for Motif Activity Response Analysis). Given only gene expression or chromatin state data across a set of samples as input, ISMARA identifies the key TFs and miRNAs driving expression/chromatin changes and makes detailed predictions regarding their regulatory roles. These include predicted activities of the regulators across the samples, their genome-wide targets, enriched gene categories among the targets, and direct interactions between the regulators. Applying ISMARA to data sets from well-studied systems, we show that it consistently identifies known key regulators ab initio. We also present a number of novel predictions including regulatory interactions in innate immunity, a master regulator of mucociliary differentiation, TFs consistently disregulated in cancer, and TFs that mediate specific chromatin modifications.

Exploiting the multiplexing capabilities of tandem mass tags for high-throughput estimation of cellular protein abundances by mass spectrometry.

The generation of dynamic models of biological processes critically depends on the determination of precise cellular concentrations of biomolecules. Measurements of system-wide absolute protein levels are particularly valuable information in systems biology. Recently, mass spectrometry based proteomics approaches have been developed to estimate protein concentrations on a proteome-wide scale. However, for very complex proteomes, fractionation steps are required, increasing samples number and instrument analysis time. As a result, the number of full proteomes that can be routinely analyzed is limited. Here we combined absolute quantification strategies with the multiplexing capabilities of isobaric tandem mass tags to determine cellular protein abundances in a high throughput and proteome-wide scale even for highly complex biological systems, such as a whole human cell line. We generated two independent data sets to demonstrate the power of the approach regarding sample throughput, dynamic range, quantitative precision and accuracy as well as proteome coverage in comparison to existing mass spectrometry based strategies.

Embryonic stem cell-specific microRNAs contribute to pluripotency by inhibiting regulators of multiple differentiation pathways.

The findings that microRNAs (miRNAs) are essential for early development in many species and that embryonic miRNAs can reprogram somatic cells into induced pluripotent stem cells suggest that these miRNAs act directly on transcriptional and chromatin regulators of pluripotency. To elucidate the transcription regulatory networks immediately downstream of embryonic miRNAs, we extended the motif activity response analysis approach that infers the regulatory impact of both transcription factors (TFs) and miRNAs from genome-wide expression states. Applying this approach to multiple experimental data sets generated from mouse embryonic stem cells (ESCs) that did or did not express miRNAs of the ESC-specific miR-290-295 cluster, we identified multiple TFs that are direct miRNA targets, some of which are known to be active during cell differentiation. Our results provide new insights into the transcription regulatory network downstream of ESC-specific miRNAs, indicating that these miRNAs act on cell cycle and chromatin regulators at several levels and downregulate TFs that are involved in the innate immune response.

MAPP unravels frequent co-regulation of splicing and polyadenylation by RNA-binding proteins and their dysregulation in cancer.

Maturation of eukaryotic pre-mRNAs via splicing and polyadenylation is modulated across cell types and conditions by a variety of RNA-binding proteins (RBPs). Although there exist over 1,500 RBPs in human cells, their binding motifs and functions still remain to be elucidated, especially in the complex environment of tissues and in the context of diseases. To overcome the lack of methods for the systematic and automated detection of sequence motif-guided pre-mRNA processing regulation from RNA sequencing (RNA-Seq) data we have developed MAPP (Motif Activity on Pre-mRNA Processing). Applying MAPP to RBP knock-down experiments reveals that many RBPs regulate both splicing and polyadenylation of nascent transcripts by acting on similar sequence motifs. MAPP not only infers these sequence motifs, but also unravels the position-dependent impact of the RBPs on pre-mRNA processing. Interestingly, all investigated RBPs that act on both splicing and 3' end processing exhibit a consistently repressive or activating effect on both processes, providing a first glimpse on the underlying mechanism. Applying MAPP to normal and malignant brain tissue samples unveils that the motifs bound by the PTBP1 and RBFOX RBPs coordinately drive the oncogenic splicing program active in glioblastomas demonstrating that MAPP paves the way for characterizing pre-mRNA processing regulators under physiological and pathological conditions.

Analysis of 10,478 cancer genomes identifies candidate driver genes and opportunities for precision oncology.

Tumor genomic profiling is increasingly seen as a prerequisite to guide the treatment of patients with cancer. To explore the value of whole-genome sequencing (WGS) in broadening the scope of cancers potentially amenable to a precision therapy, we analysed whole-genome sequencing data on 10,478 patients spanning 35 cancer types recruited to the UK 100,000 Genomes Project. We identified 330 candidate driver genes, including 74 that are new to any cancer. We estimate that approximately 55% of patients studied harbor at least one clinically relevant mutation, predicting either sensitivity or resistance to certain treatments or clinical trial eligibility. By performing computational chemogenomic analysis of cancer mutations we identify additional targets for compounds that represent attractive candidates for future clinical trials. This study represents one of the most comprehensive efforts thus far to identify cancer driver genes in the real world setting and assess their impact on informing precision oncology.

Whole genome sequencing refines stratification and therapy of patients with clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, but a comprehensive description of its genomic landscape is lacking. We report the whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project, providing for a detailed description of the somatic mutational landscape of ccRCC. We identify candidate driver genes, which as well as emphasising the major role of epigenetic regulation in ccRCC highlight additional biological pathways extending opportunities for therapeutic interventions. Genomic characterisation identified patients with divergent clinical outcome; higher number of structural copy number alterations associated with poorer prognosis, whereas VHL mutations were independently associated with a better prognosis. The observations that higher T-cell infiltration is associated with better overall survival and that genetically predicted immune evasion is not common supports the rationale for immunotherapy. These findings should inform personalised surveillance and treatment strategies for ccRCC patients.

Genomic evolution shapes prostate cancer disease type.

The development of cancer is an evolutionary process involving the sequential acquisition of genetic alterations that disrupt normal biological processes, enabling tumor cells to rapidly proliferate and eventually invade and metastasize to other tissues. We investigated the genomic evolution of prostate cancer through the application of three separate classification methods, each designed to investigate a different aspect of tumor evolution. Integrating the results revealed the existence of two distinct types of prostate cancer that arise from divergent evolutionary trajectories, designated as the Canonical and Alternative evolutionary disease types. We therefore propose the evotype model for prostate cancer evolution wherein Alternative-evotype tumors diverge from those of the Canonical-evotype through the stochastic accumulation of genetic alterations associated with disruptions to androgen receptor DNA binding. Our model unifies many previous molecular observations, providing a powerful new framework to investigate prostate cancer disease progression.

Investigating the Human Host-ssRNA Virus Interaction Landscape Using the SMEAGOL Toolbox.

Viruses have evolved numerous mechanisms to exploit the molecular machinery of their host cells, including the broad spectrum of host RNA-binding proteins (RBPs). However, the RBP interactomes of most viruses are largely unknown. To shed light on the interaction landscape of RNA viruses with human host cell RBPs, we have analysed 197 single-stranded RNA (ssRNA) viral genome sequences and found that the majority of ssRNA virus genomes are significantly enriched or depleted in motifs for specific human RBPs, suggesting selection pressure on these interactions. To facilitate tailored investigations and the analysis of genomes sequenced in future, we have released our methodology as a fast and user-friendly computational toolbox named SMEAGOL. Our resources will contribute to future studies of specific ssRNA virus-host cell interactions and support the identification of antiviral drug targets.

The International Virus Bioinformatics Meeting 2022.

The International Virus Bioinformatics Meeting 2022 took place online, on 23-25 March 2022, and has attracted about 380 participants from all over the world. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The participants created a highly interactive scientific environment even without physical face-to-face interactions. This meeting is a focal point to gain an insight into the state-of-the-art of the virus bioinformatics research landscape and to interact with researchers in the forefront as well as aspiring young scientists. The meeting featured eight invited and 18 contributed talks in eight sessions on three days, as well as 52 posters, which were presented during three virtual poster sessions. The main topics were: SARS-CoV-2, viral emergence and surveillance, virus-host interactions, viral sequence analysis, virus identification and annotation, phages, and viral diversity. This report summarizes the main research findings and highlights presented at the meeting.

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor.

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.

Genome-wide bioinformatic analyses predict key host and viral factors in SARS-CoV-2 pathogenesis.

The novel betacoronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a worldwide pandemic (COVID-19) after emerging in Wuhan, China. Here we analyzed public host and viral RNA sequencing data to better understand how SARS-CoV-2 interacts with human respiratory cells. We identified genes, isoforms and transposable element families that are specifically altered in SARS-CoV-2-infected respiratory cells. Well-known immunoregulatory genes including CSF2, IL32, IL-6 and SERPINA3 were differentially expressed, while immunoregulatory transposable element families were upregulated. We predicted conserved interactions between the SARS-CoV-2 genome and human RNA-binding proteins such as the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) and eukaryotic initiation factor 4 (eIF4b). We also identified a viral sequence variant with a statistically significant skew associated with age of infection, that may contribute to intracellular host-pathogen interactions. These findings can help identify host mechanisms that can be targeted by prophylactics and/or therapeutics to reduce the severity of COVID-19.

Whole-genome analysis of Nigerian patients with breast cancer reveals ethnic-driven somatic evolution and distinct genomic subtypes.

Black women across the African diaspora experience more aggressive breast cancer with higher mortality rates than white women of European ancestry. Although inter-ethnic germline variation is known, differential somatic evolution has not been investigated in detail. Analysis of deep whole genomes of 97 breast cancers, with RNA-seq in a subset, from women in Nigeria in comparison with The Cancer Genome Atlas (n = 76) reveal a higher rate of genomic instability and increased intra-tumoral heterogeneity as well as a unique genomic subtype defined by early clonal GATA3 mutations with a 10.5-year younger age at diagnosis. We also find non-coding mutations in bona fide drivers (ZNF217 and SYPL1) and a previously unreported INDEL signature strongly associated with African ancestry proportion, underscoring the need to expand inclusion of diverse populations in biomedical research. Finally, we demonstrate that characterizing tumors for homologous recombination deficiency has significant clinical relevance in stratifying patients for potentially life-saving therapies.