Multiplexed functional metagenomic analysis of the infant microbiome identifies effectors of NF-κB, autophagy, and cellular redox state.

The human microbiota plays a critical role in host health. Proper development of the infant microbiome is particularly important. Its dysbiosis leads to both short-term health issues and long-term disorders lasting into adulthood. A central way in which the microbiome interacts with the host is through the production of effector molecules, such as proteins and small molecules. Here, a metagenomic library constructed from 14 infant stool microbiomes is analyzed for the production of effectors that modulate three distinct host pathways: immune response (nuclear factor κB [NF-κB] activation), autophagy (LC3-B puncta formation), and redox potential (NADH:NAD ratio). We identify microbiome-encoded bioactive metabolites, including commendamide and hydrogen sulfide and their associated biosynthetic genes, as well as a previously uncharacterized autophagy-inducing operon from Klebsiella spp. This work extends our understanding of microbial effector molecules that are known to influence host pathways. Parallel functional screening of metagenomic libraries can be easily expanded to investigate additional host processes.

MicroRNA-1205 Regulation of FRYL in Prostate Cancer.

High mortality rates of prostate cancer (PCa) are associated with metastatic castration-resistant prostate cancer (CRPC) due to the maintenance of androgen receptor (AR) signaling despite androgen deprivation therapies (ADTs). The 8q24 chromosomal locus is a region of very high PCa susceptibility that carries genetic variants associated with high risk of PCa incidence. This region also carries frequent amplifications of the PVT1 gene, a non-protein coding gene that encodes a cluster of microRNAs including, microRNA-1205 (miR-1205), which are largely understudied. Herein, we demonstrate that miR-1205 is underexpressed in PCa cells and tissues and suppresses CRPC tumors in vivo. To characterize the molecular pathway, we identified and validated fry-like (FRYL) as a direct molecular target of miR-1205 and observed its overexpression in PCa cells and tissues. FRYL is predicted to regulate dendritic branching, which led to the investigation of FRYL in neuroendocrine PCa (NEPC). Resistance toward ADT leads to the progression of treatment related NEPC often characterized by PCa neuroendocrine differentiation (NED), however, this mechanism is poorly understood. Underexpression of miR-1205 is observed when NED is induced in vitro and inhibition of miR-1205 leads to increased expression of NED markers. However, while FRYL is overexpressed during NED, FRYL knockdown did not reduce NED, therefore revealing that miR-1205 induces NED independently of FRYL.

Biosynthetic Interrogation of Soil Metagenomes Reveals Metamarin, an Uncommon Cyclomarin Congener with Activity against Mycobacterium tuberculosis.

Tuberculosis (TB) remains one of the deadliest infectious diseases. Unfortunately, the development of antibiotic resistance threatens our current therapeutic arsenal, which has necessitated the discovery and development of novel antibiotics against drug-resistant Mycobacterium tuberculosis (Mtb). Cyclomarin A and rufomycin I are structurally related cyclic heptapeptides assembled by nonribosomal peptide synthetases (NRPSs), which show potent anti-Mtb activity with a new cellular target, the caseinolytic protein ClpC1. An NRPS adenylation domain survey using DNA extracted from ∼2000 ecologically diverse soils found low cyclomarin/rufomycin biosynthetic diversity. In this survey, a family of cyclomarin/rufomycin-like biosynthetic gene clusters (BGC) that encode metamarin, an uncommon cyclomarin congener with potent activity against both Mtb H37Rv and multidrug-resistant Mtb clinical isolates was identified. Metamarin effectively inhibits Mtb growth in murine macrophages and increases the activities of ClpC1 ATPase and the associated ClpC1/P1/P2 protease complex, thus causing cell death by uncontrolled protein degradation.

Synthetic-Bioinformatic Natural Product Antibiotics with Diverse Modes of Action.

Bacterial natural products have inspired the development of numerous antibiotics in use today. As resistance to existing antibiotics has become more prevalent, new antibiotic lead structures and activities are desperately needed. An increasing number of natural product biosynthetic gene clusters, to which no known molecules can be assigned, are found in genome and metagenome sequencing data. Here we access structural information encoded in this underexploited resource using a synthetic-bioinformatic natural product (syn-BNP) approach, which relies on bioinformatic algorithms followed by chemical synthesis to predict and then produce small molecules inspired by biosynthetic gene clusters. In total, 157 syn-BNP cyclic peptides inspired by 96 nonribosomal peptide synthetase gene clusters were synthesized and screened for antibacterial activity. This yielded nine antibiotics with activities against ESKAPE pathogens as well as Mycobacterium tuberculosis. Not only are antibiotic-resistant pathogens susceptible to many of these syn-BNP antibiotics, but they were also unable to develop resistance to these antibiotics in laboratory experiments. Characterized modes of action for these antibiotics include cell lysis, membrane depolarization, inhibition of cell wall biosynthesis, and ClpP protease dysregulation. Increasingly refined syn-BNP-based explorations of biosynthetic gene clusters should allow for more rapid identification of evolutionarily inspired bioactive small molecules, in particular antibiotics with diverse mechanism of actions that could help confront the imminent crisis of antimicrobial resistance.

MGS-Fast: Metagenomic shotgun data fast annotation using microbial gene catalogs.

BACKGROUND: Current methods used for annotating metagenomics shotgun sequencing (MGS) data rely on a computationally intensive and low-stringency approach of mapping each read to a generic database of proteins or reference microbial genomes. RESULTS: We developed MGS-Fast, an analysis approach for shotgun whole-genome metagenomic data utilizing Bowtie2 DNA-DNA alignment of reads that is an alternative to using the integrated catalog of reference genes database of well-annotated genes compiled from human microbiome data. This method is rapid and provides high-stringency matches (>90% DNA sequence identity) of the metagenomics reads to genes with annotated functions. We demonstrate the use of this method with data from a study of liver disease and synthetic reads, and Human Microbiome Project shotgun data, to detect differentially abundant Kyoto Encyclopedia of Genes and Genomes gene functions in these experiments. This rapid annotation method is freely available as a Galaxy workflow within a Docker image. CONCLUSIONS: MGS-Fast can confidently transfer functional annotations from gene databases to metagenomic reads, with speed and accuracy.

miCloud: A Plug-n-Play, Extensible, On-Premises Bioinformatics Cloud for Seamless Execution of Complex Next-Generation Sequencing Data Analysis Pipelines.

The availability of low-cost small-factor sequencers, such as the Illumina MiSeq, MiniSeq, or iSeq, have paved the way for democratizing genomics sequencing, providing researchers in minority universities with access to the technology that was previously only affordable by institutions with large core facilities. However, these instruments are not bundled with software for performing bioinformatics data analysis, and the data analysis can be the main bottleneck for independent laboratories or even small clinical facilities that consider adopting genomic sequencing for medical applications. To address this issue, we have developed miCloud, a bioinformatics platform that enables genomic data analysis through a fully featured data analysis cloud, which seamlessly integrates with genome sequencers over the local network. The miCloud can be easily deployed without any prior bioinformatics expertise on any computing environment, from a laboratory computer workstation to a university computer cluster. Our platform not only provides access to a set of preconfigured RNA-Seq and CHIP-Seq bioinformatics pipelines, but also enables users to develop or install new preconfigured tools from the large selection available on open-source online Docker container repositories. The miCloud built-in analysis pipelines are also integrated with the Visual Omics Explorer framework (Kim et al., 2016), which provides rich interactive visualizations and publication-ready graphics from the next-generation sequencing data. Ultimately, the miCloud demonstrates a bioinformatics approach that can be adopted in the field for standardizing genomic data analysis, similarly to the way molecular biology sample preparation kits have standardized laboratory operations.

Bio-Docklets: virtualization containers for single-step execution of NGS pipelines.

Processing of next-generation sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized postanalysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers toward seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform. We present an approach for abstracting the complex data operations of multistep, bioinformatics pipelines for NGS data analysis. As examples, we have deployed 2 pipelines for RNA sequencing and chromatin immunoprecipitation sequencing, preconfigured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines as simply as running a single bioinformatics tool. This is achieved using a "meta-script" that automatically starts the Bio-Docklets and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface. The pipeline output is postprocessed by integration with the Visual Omics Explorer framework, providing interactive data visualizations that users can access through a web browser. Our goal is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts on any computing environment, whether a laboratory workstation, university computer cluster, or a cloud service provider. Beyond end users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.

Fibronectin and androgen receptor expression data in prostate cancer obtained from a RNA-sequencing bioinformatics analysis.

Prostate cancer is the second most commonly diagnosed male cancer in the world. The molecular mechanisms underlying its development and progression are still unclear. Here we show analysis of a prostate cancer RNA-sequencing dataset that was originally generated by Ren et al. [3] from the prostate tumor and adjacent normal tissues of 14 patients. The data presented here was analyzed using our RNA-sequencing bioinformatics analysis pipeline implemented on the bioinformatics web platform, Galaxy. The relative expression of fibronectin (FN1) and the androgen receptor (AR) were calculated in fragments per kilobase of transcript per million mapped reads, and represented in FPKM unit. A subanalysis is also shown for data from three patients, that includes the relative expression of FN1 and AR and their fold change. For interpretation and discussion, please refer to the article, "miR-1207-3p regulates the androgen receptor in prostate cancer via FNDC1/fibronectin" [1] by Das et al.

miR-1207-3p regulates the androgen receptor in prostate cancer via FNDC1/fibronectin.

Prostate cancer (PCa) is frequently diagnosed in men, and dysregulation of microRNAs is characteristic of many cancers. MicroRNA-1207-3p is encoded at the non-protein coding gene locus PVT1 on the 8q24 human chromosomal region, an established PCa susceptibility locus. However, the role of microRNA-1207-3p in PCa is unclear. We discovered that microRNA-1207-3p is significantly underexpressed in PCa cell lines in comparison to normal prostate epithelial cells. Increased expression of microRNA-1207-3p in PCa cells significantly inhibits proliferation, migration, and induces apoptosis via direct molecular targeting of FNDC1, a protein which contains a conserved protein domain of fibronectin (FN1). FNDC1, FN1, and the androgen receptor (AR) are significantly overexpressed in PCa cell lines and human PCa, and positively correlate with aggressive PCa. Prostate tumor FN1 expression in patients that experienced PCa-specific death is significantly higher than in patients that remained alive. Furthermore, FNDC1, FN1 and AR are concomitantly overexpressed in metastatic PCa. Consequently, these studies have revealed a novel microRNA-1207-3p/FNDC1/FN1/AR regulatory pathway in PCa.

Visual Omics Explorer (VOE): a cross-platform portal for interactive data visualization.

MOTIVATION: Given the abundance of genome sequencing and omics data, an opprtunity and challenge in bioinformatics relates to data mining and visualization. The majority of current bioinformatics visualizations are implemented either as multi-tier web server applications that require significant maintenance effort, or as client software that presumes technical expertise for installation. Here we present the Visual Omics Explorer (VOE), a cross-platform data visualization portal that is implemented using only HTML and Javascript code. VOE is a standalone software that can be loaded offline on the web browser from a local copy of the code, or over the internet without any dependency other than distributing the code through a file sharing service. VOE can interactively display genomics, transcriptomics, epigenomics and metagenomics data stored either locally or retrieved from cloud storage services, and runs on both desktop computers and mobile devices. AVAILABILITY AND IMPLEMENTATION: VOE is accessible at http://bcil.github.io/VOE/ CONTACT: agbiotec@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multiscale modeling of the causal functional roles of nsSNPs in a genome-wide association study: application to hypoxia.

BACKGROUND: It is a great challenge of modern biology to determine the functional roles of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on complex phenotypes. Statistical and machine learning techniques establish correlations between genotype and phenotype, but may fail to infer the biologically relevant mechanisms. The emerging paradigm of Network-based Association Studies aims to address this problem of statistical analysis. However, a mechanistic understanding of how individual molecular components work together in a system requires knowledge of molecular structures, and their interactions. RESULTS: To address the challenge of understanding the genetic, molecular, and cellular basis of complex phenotypes, we have, for the first time, developed a structural systems biology approach for genome-wide multiscale modeling of nsSNPs--from the atomic details of molecular interactions to the emergent properties of biological networks. We apply our approach to determine the functional roles of nsSNPs associated with hypoxia tolerance in Drosophila melanogaster. The integrated view of the functional roles of nsSNP at both molecular and network levels allows us to identify driver mutations and their interactions (epistasis) in H, Rad51D, Ulp1, Wnt5, HDAC4, Sol, Dys, GalNAc-T2, and CG33714 genes, all of which are involved in the up-regulation of Notch and Gurken/EGFR signaling pathways. Moreover, we find that a large fraction of the driver mutations are neither located in conserved functional sites, nor responsible for structural stability, but rather regulate protein activity through allosteric transitions, protein-protein interactions, or protein-nucleic acid interactions. This finding should impact future Genome-Wide Association Studies. CONCLUSIONS: Our studies demonstrate that the consolidation of statistical, structural, and network views of biomolecules and their interactions can provide new insight into the functional role of nsSNPs in Genome-Wide Association Studies, in a way that neither the knowledge of molecular structures nor biological networks alone could achieve. Thus, multiscale modeling of nsSNPs may prove to be a powerful tool for establishing the functional roles of sequence variants in a wide array of applications.