Multiplexed single-cell characterization of alternative polyadenylation regulators.

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity regulated by the cleavage and polyadenylation (CPA) machinery. To better understand how these proteins govern polyA site choice, we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a framework to detect perturbation-dependent changes in polyadenylation and characterize modules of co-regulated polyA sites. We find groups of intronic polyA sites regulated by distinct components of the nuclear RNA life cycle, including elongation, splicing, termination, and surveillance. We train and validate a deep neural network (APARENT-Perturb) for tandem polyA site usage, delineating a cis-regulatory code that predicts perturbation response and reveals interactions between regulatory complexes. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation.

Accurate detection of short and long active ORFs using Ribo-seq data.

MOTIVATION: Ribo-seq, a technique for deep-sequencing ribosome-protected mRNA fragments, has enabled transcriptome-wide monitoring of translation in vivo. It has opened avenues for re-evaluating the coding potential of open reading frames (ORFs), including many short ORFs that were previously presumed to be non-translating. However, the detection of translating ORFs, specifically short ORFs, from Ribo-seq data, remains challenging due to its high heterogeneity and noise. RESULTS: We present ribotricer, a method for detecting actively translating ORFs by directly leveraging the three-nucleotide periodicity of Ribo-seq data. Ribotricer demonstrates higher accuracy and robustness compared with other methods at detecting actively translating ORFs including short ORFs on multiple published datasets across species inclusive of Arabidopsis, Caenorhabditis elegans, Drosophila, human, mouse, rat, yeast and zebrafish. AVAILABILITY AND IMPLEMENTATION: Ribotricer is available at https://github.com/smithlabcode/ribotricer. All analysis scripts and results are available at https://github.com/smithlabcode/ribotricer-results. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reaction Decoder Tool (RDT): extracting features from chemical reactions.

UNLABELLED: Extracting chemical features like Atom-Atom Mapping (AAM), Bond Changes (BCs) and Reaction Centres from biochemical reactions helps us understand the chemical composition of enzymatic reactions. Reaction Decoder is a robust command line tool, which performs this task with high accuracy. It supports standard chemical input/output exchange formats i.e. RXN/SMILES, computes AAM, highlights BCs and creates images of the mapped reaction. This aids in the analysis of metabolic pathways and the ability to perform comparative studies of chemical reactions based on these features. AVAILABILITY AND IMPLEMENTATION: This software is implemented in Java, supported on Windows, Linux and Mac OSX, and freely available at https://github.com/asad/ReactionDecoder CONTACT: : asad@ebi.ac.uk or s9asad@gmail.com.

Dictionary learning for integrative, multimodal and scalable single-cell analysis.

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

Genome-wide translational response of Candida albicans to fluconazole treatment.

Azoles are commonly used for the treatment of fungal infections, and the ability of human fungal pathogens to rapidly respond to azole treatment is critical for the development of antifungal resistance. While the roles of genetic mutations, chromosomal rearrangements, and transcriptional mechanisms in azole resistance have been well-characterized, very little is known about post-transcriptional and translational mechanisms that drive this process. In addition, most previous genome-wide studies have focused on transcriptional responses to azole treatment and likely serve as inaccurate proxies for changes in protein expression due to extensive post-transcriptional and translational regulation. In this study, we use ribosome profiling to provide the first picture of the global translational response of a major human fungal pathogen, Candida albicans, to treatment with fluconazole (Flu), one of the most widely used azole drugs. We identify sets of genes showing significantly altered translational efficiency, including genes associated with a variety of biological processes such as the cell cycle, DNA repair, cell wall/cell membrane biosynthesis, transport, signaling, DNA- and RNA-binding activities, and protein synthesis. We observe both similarities and differences among the most highly represented gene categories (as defined by gene ontology) that are regulated by fluconazole at the translational vs transcriptional levels. Importantly, however, very few genes that are translationally regulated by fluconazole are also controlled transcriptionally under this condition. Our findings suggest that C. albicans possesses distinct translational mechanisms that are important for the response to antifungal treatment, which could eventually be targeted by novel antifungal therapies. IMPORTANCE Azoles are one of the most commonly used drug classes to treat human fungal pathogens. While point mutations, chromosomal rearrangements, and transcriptional mechanisms that drive azole resistance have been well-characterized, we know very little about the role of translational mechanisms. In this study, we determined the global translational profile of genes that are expressed in the major human fungal pathogen Candida albicans in response to fluconazole, one of the most widely used azole drugs. We find both similarities and differences among the most highly represented categories of genes regulated by fluconazole at the transcriptional and translational levels. Interestingly, however, many of the specific genes that are regulated by fluconazole at the translational level do not appear to be controlled by transcriptional mechanisms under this condition. Our results suggest that distinct C. albicans translational mechanisms control the response to antifungals and could eventually be targeted in the development of new therapies.

CPA-Perturb-seq: Multiplexed single-cell characterization of alternative polyadenylation regulators.

Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity that is governed by the cleavage and polyadenylation (CPA) regulatory machinery. To better understand how these proteins govern polyA site choice we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 known CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a statistical framework to specifically identify perturbation-dependent changes in intronic and tandem polyadenylation, and discover modules of co-regulated polyA sites exhibiting distinct functional properties. By training a multi-task deep neural network (APARENT-Perturb) on our dataset, we delineate a cis-regulatory code that predicts responsiveness to perturbation and reveals interactions between distinct regulatory complexes. Finally, we leverage our framework to re-analyze published scRNA-seq datasets, identifying new regulators that affect the relative abundance of alternatively polyadenylated transcripts, and characterizing extensive cellular heterogeneity in 3' UTR length amongst antibody-producing cells. Our work highlights the potential for multiplexed single-cell perturbation screens to further our understanding of post-transcriptional regulation in vitro and in vivo.

Comparison and evaluation of statistical error models for scRNA-seq.

BACKGROUND: Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. Deconvolving these effects is a key challenge for preprocessing workflows. Recent work has demonstrated the importance and utility of count models for scRNA-seq analysis, but there is a lack of consensus on which statistical distributions and parameter settings are appropriate. RESULTS: Here, we analyze 59 scRNA-seq datasets that span a wide range of technologies, systems, and sequencing depths in order to evaluate the performance of different error models. We find that while a Poisson error model appears appropriate for sparse datasets, we observe clear evidence of overdispersion for genes with sufficient sequencing depth in all biological systems, necessitating the use of a negative binomial model. Moreover, we find that the degree of overdispersion varies widely across datasets, systems, and gene abundances, and argues for a data-driven approach for parameter estimation. CONCLUSIONS: Based on these analyses, we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis.

Global translational landscape of the Candida albicans morphological transition.

Candida albicans, a major human fungal pathogen associated with high mortality and/or morbidity rates in a wide variety of immunocompromised individuals, undergoes a reversible morphological transition from yeast to filamentous cells that is required for virulence. While previous studies have identified and characterized global transcriptional mechanisms important for driving this transition, as well as other virulence properties, in C. albicans and other pathogens, considerably little is known about the role of genome-wide translational mechanisms. Using ribosome profiling, we report the first global translational profile associated with C. albicans morphogenesis. Strikingly, many genes involved in pathogenesis, filamentation, and the response to stress show reduced translational efficiency (TE). Several of these genes are known to be strongly induced at the transcriptional level, suggesting that a translational fine-tuning mechanism is in place. We also identify potential upstream open reading frames (uORFs), associated with genes involved in pathogenesis, and novel ORFs, several of which show altered TE during filamentation. Using a novel bioinformatics method for global analysis of ribosome pausing that will be applicable to a wide variety of genetic systems, we demonstrate an enrichment of ribosome pausing sites in C. albicans genes associated with protein synthesis and cell wall functions. Altogether, our results suggest that the C. albicans morphological transition, and most likely additional virulence processes in fungal pathogens, is associated with widespread global alterations in TE that do not simply reflect changes in transcript levels. These alterations affect the expression of many genes associated with processes essential for virulence and pathogenesis.

Musashi1 Contribution to Glioblastoma Development via Regulation of a Network of DNA Replication, Cell Cycle and Division Genes.

RNA-binding proteins (RBPs) function as master regulators of gene expression. Alterations in their levels are often observed in tumors with numerous oncogenic RBPs identified in recent years. Musashi1 (Msi1) is an RBP and stem cell gene that controls the balance between self-renewal and differentiation. High Msi1 levels have been observed in multiple tumors including glioblastoma and are often associated with poor patient outcomes and tumor growth. A comprehensive genomic analysis identified a network of cell cycle/division and DNA replication genes and established these processes as Msi1's core regulatory functions in glioblastoma. Msi1 controls this gene network via two mechanisms: direct interaction and indirect regulation mediated by the transcription factors E2F2 and E2F8. Moreover, glioblastoma lines with Msi1 knockout (KO) displayed increased sensitivity to cell cycle and DNA replication inhibitors. Our results suggest that a drug combination strategy (Msi1 + cell cycle/DNA replication inhibitors) could be a viable route to treat glioblastoma.

A Protein Microarray-Based Investigation of Cerebrospinal Fluid Reveals Distinct Autoantibody Signature in Low and High-Grade Gliomas.

Gliomas are one of the most aggressive primary brain tumors arising from neural progenitor cells. Delayed diagnosis, invasive biopsy, and diagnostic challenges stems the need for specific, minimally-invasive, and early diagnostic biomarkers. Tumor-associated (TA) autoantibodies are measurable in the biofluids long before the onset of the symptoms, suggesting their role in early diagnosis and clinical management of the patients. In the current study, cerebrospinal fluid (CSF) samples from patients with low-grade glioma (LGG) and the Glioblastoma multiforme (GBM) that characterizes advanced disease were compared with healthy control samples to identify putative TA autoantibodies, using protein microarrays. The CSF samples from LGGs (n = 10), GBM (n = 7) were compared with the control CSF samples (n = 6). Proteins showing significant antigenic response were cross-verified. Proteins NOL4 (a cancer-testis antigen) and KALRN showed an antigenic response in the CSF of GBM patients, whereas, UTP4 and CCDC28A showed an antigenic response in low grade gliomas when compared with the control samples. TA autoantibodies identified in this study from the CSF of the patients could supplement current screening modalities. Further validation of these TA autoantibodies on a larger clinical cohort could provide cues towards relevance of these proteins in early diagnosis of the disease.

Publisher Correction: Genomic analyses of early responses to radiation in glioblastoma reveal new alterations at transcription, splicing, and translation levels.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Genomic analyses of early responses to radiation inglioblastoma reveal new alterations at transcription,splicing, and translation levels.

High-dose radiation is the main component of glioblastoma therapy. Unfortunately, radio-resistance is a common problem and a major contributor to tumor relapse. Understanding the molecular mechanisms driving response to radiation is critical for identifying regulatory routes that could be targeted to improve treatment response. We conducted an integrated analysis in the U251 and U343 glioblastoma cell lines to map early alterations in the expression of genes at three levels: transcription, splicing, and translation in response to ionizing radiation. Changes at the transcriptional level were the most prevalent response. Downregulated genes are strongly associated with cell cycle and DNA replication and linked to a coordinated module of expression. Alterations in this group are likely driven by decreased expression of the transcription factor FOXM1 and members of the E2F family. Genes involved in RNA regulatory mechanisms were affected at the mRNA, splicing, and translation levels, highlighting their importance in radiation-response. We identified a number of oncogenic factors, with an increased expression upon radiation exposure, including BCL6, RRM2B, IDO1, FTH1, APIP, and LRIG2 and lncRNAs NEAT1 and FTX. Several of these targets have been previously implicated in radio-resistance. Therefore, antagonizing their effects post-radiation could increase therapeutic efficacy. Our integrated analysis provides a comprehensive view of early response to radiation in glioblastoma. We identify new biological processes involved in altered expression of various oncogenic factors and suggest new target options to increase radiation sensitivity and prevent relapse.

pysradb: A Python package to query next-generation sequencing metadata and data from NCBI Sequence Read Archive.

The NCBI Sequence Read Archive (SRA) is the primary archive of next-generation sequencing datasets. SRA makes metadata and raw sequencing data available to the research community to encourage reproducibility and to provide avenues for testing novel hypotheses on publicly available data. However, methods to programmatically access this data are limited. We introduce the Python package, pysradb, which provides a collection of command line methods to query and download metadata and data from SRA, utilizing the curated metadata database available through the SRAdb project. We demonstrate the utility of pysradb on multiple use cases for searching and downloading SRA datasets. It is available freely at https://github.com/saketkc/pysradb.

Mapping person-to-person variation in viral mutations that escape polyclonal serum targeting influenza hemagglutinin.

A longstanding question is how influenza virus evolves to escape human immunity, which is polyclonal and can target many distinct epitopes. Here, we map how all amino-acid mutations to influenza's major surface protein affect viral neutralization by polyclonal human sera. The serum of some individuals is so focused that it selects single mutations that reduce viral neutralization by over an order of magnitude. However, different viral mutations escape the sera of different individuals. This individual-to-individual variation in viral escape mutations is not present among ferrets that have been infected just once with a defined viral strain. Our results show how different single mutations help influenza virus escape the immunity of different members of the human population, a phenomenon that could shape viral evolution and disease susceptibility.

Evaluation of autoantibody signatures in meningioma patients using human proteome arrays.

Meningiomas are one of the most common tumors of the Central nervous system (CNS). This study aims to identify the autoantibody biomarkers in meningiomas using high-density human proteome arrays (~17,000 full-length recombinant human proteins). Screening of sera from 15 unaffected healthy individuals, 10 individuals with meningioma grade I and 5 with meningioma grade II was performed. This comprehensive proteomics based investigation revealed the dysregulation of 489 and 104 proteins in grades I and II of meningioma, respectively, along with the enrichment of several signalling pathways, which might play a crucial role in the manifestation of the disease. Autoantibody targets like IGHG4, CRYM, EFCAB2, STAT6, HDAC7A and CCNB1 were significantly dysregulated across both the grades. Further, we compared this to the tissue proteome and gene expression profile from GEO database. Previously reported upregulated proteins from meningioma tissue-based proteomics obtained from high-resolution mass spectrometry demonstrated an aggravated autoimmune response, emphasizing the clinical relevance of these targets. Some of these targets like SELENBP1 were tested for their presence in tumor tissue using immunoblotting. In the light of highly invasive diagnostic modalities employed to diagnose CNS tumors like meningioma, these autoantibody markers offer a minimally invasive diagnostic platform which could be pursued further for clinical translation.

Autoantibody Profiling of Glioma Serum Samples to Identify Biomarkers Using Human Proteome Arrays.

The heterogeneity and poor prognosis associated with gliomas, makes biomarker identification imperative. Here, we report autoantibody signatures across various grades of glioma serum samples and sub-categories of glioblastoma multiforme using Human Proteome chips containing ~17000 full-length human proteins. The deduced sets of classifier proteins helped to distinguish Grade II, III and IV samples from the healthy subjects with 88, 89 and 94% sensitivity and 87, 100 and 73% specificity, respectively. Proteins namely, SNX1, EYA1, PQBP1 and IGHG1 showed dysregulation across various grades. Sub-classes of GBM, based on its proximity to the sub-ventricular zone, have been reported to have different prognostic outcomes. To this end, we identified dysregulation of NEDD9, a protein involved in cell migration, with probable prognostic potential. Another subcategory of patients where the IDH1 gene is mutated, are known to have better prognosis as compared to patients carrying the wild type gene. On a comparison of these two cohorts, we found STUB1 and YWHAH proteins dysregulated in Grade II glioma patients. In addition to common pathways associated with tumourigenesis, we found enrichment of immunoregulatory and cytoskeletal remodelling pathways, emphasizing the need to explore biochemical alterations arising due to autoimmune responses in glioma.

Anatomy of BioJS, an open source community for the life sciences.

BioJS is an open source software project that develops visualization tools for different types of biological data. Here we report on the factors that influenced the growth of the BioJS user and developer community, and outline our strategy for building on this growth. The lessons we have learned on BioJS may also be relevant to other open source software projects.