MotivationAnnotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number.ResultsWe propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manually crafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre’s ability to capture the functional difference of enzyme isoforms.Availability and implementationThe server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre.Supplementary information Supplementary data are available at Bioinformatics online.

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. Highthroughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3′ RnA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of timeseries transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNcseq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

We have developed Lynx (http://lynx.ci.uchicago.edu)—a web-based database and a knowledge extraction engine, supporting annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Its underlying knowledge base (LynxKB) integrates various classes of information from >35 public databases and private collections, as well as manually curated data from our group and collaborators. Lynx provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization to assist the user in extracting meaningful knowledge from LynxKB and experimental data, whereas its service-oriented architecture provides public access to LynxKB and its analytical tools via user-friendly web services and interfaces.

Inhibition of B-cell receptor (BCR) signaling through the BTK inhibitor, ibrutinib, has generated a remarkable response in mantle cell lymphoma (MCL). However, approximately one third of patients do not respond well to the drug, and disease relapse on ibrutinib is nearly universal. Alternative therapeutic strategies aimed to prevent and overcome ibrutinib resistance are needed. We compared and contrasted the effects of selinexor, a selective inhibitor of nuclear export, with ibrutinib in six MCL cell lines that display differential intrinsic sensitivity to ibrutinib. We found that selinexor had a broader antitumor activity in MCL than ibrutinib. MCL cell lines resistant to ibrutinib remained sensitive to selinexor. We showed that selinexor induced apoptosis/cell-cycle arrest and XPO-1 knockdown also retarded cell growth. Furthermore, downregulation of the NFkB gene signature, as opposed to BCR signature, was a common feature that underlies the response of MCL to both selinexor and ibrutinib. Meanwhile, unaltered NFkB was associated with ibrutinib resistance. Mechnistically, selinexor induced nuclear retention of IkB that was accompanied by the reduction of DNA-binding activity of NFkB, suggesting that NFkB is trapped in an inhibitory complex. Coimmunoprecipitation confirmed that p65 of NFkB and IkB were physically associated. In primary MCL tumors, we further demonstrated that the number of cells with IkB nuclear retention was linearly correlated with the degree of apoptosis. Our data highlight the role of NFkB pathway in drug response to ibrutinib and selinexor and show the potential of using selinexor to prevent and overcome intrinsic ibrutinib resistance through NFkB inhibition.

BaxΔ2 is a functional pro-apoptotic Bax isoform having alterations in its N-terminus, but sharing the rest of its sequence with Baxα. BaxΔ2 is unable to target mitochondria due to the loss of helix α1. Instead, it forms cytosolic aggregates and activates caspase 8. However, the functional domain(s) responsible for BaxΔ2 behavior have remained elusive. Here we show that disruption of helix α1 makes Baxα mimic the behavior of BaxΔ2. However, the other alterations in the BaxΔ2 N-terminus have no significant impact on aggregation or cell death. We found that the hallmark BH3 domain is necessary but not sufficient for aggregation-mediated cell death. We also noted that the core region shared by Baxα and BaxΔ2 is required for the formation of large aggregates, which is essential for BaxΔ2 cytotoxicity. However, aggregation by itself is unable to trigger cell death without the C-terminus. Interestingly, the C-terminal helical conformation, not its primary sequence, appears to be critical for caspase 8 recruitment and activation. As BaxΔ2 shares core and C-terminal sequences with most Bax isoforms, our results not only reveal a structural basis for BaxΔ2-induced cell death, but also imply an intrinsic potential for aggregate-mediated caspase 8-dependent cell death in other Bax family members.

In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have been limited by technical challenges such as tough cell walls and low RNA quantity that prevent transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is demonstrated on two yeast species, Saccharomyces cerevisiae, a popular model organism, and Candida albicans, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 S. cerevisiae cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat shock S. cerevisiae using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of S. cerevisiae under heat shock. We also applied mDrop-seq to C. albicans cells, a polymorphic and clinically relevant species of yeast with a thicker cell wall compared to S. cerevisiae. Single-cell transcriptomes in 39,705 C. albicans cells were characterized using mDrop-seq under different conditions, including exposure to fluconazole, a common anti-fungal drug. We noted differential regulation in stress response and drug target pathways between C. albicans cells, changes in cell cycle patterns and marked increases in histone activity when treated with fluconazole. We demonstrate mDrop-seq to be an affordable and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to a better understanding of genetic variation in pathogens in response to stimuli and find immediate applications in investigating drug resistance, infection outcome and developing new drugs and treatment strategies.

No abstract