Phylogeny-based classification of microbial communities.

MOTIVATION: Next-generation sequencing coupled with metagenomics has led to the rapid growth of sequence databases and enabled a new branch of microbiology called comparative metagenomics. Comparative metagenomic analysis studies compositional patterns within and between different environments providing a deep insight into the structure and function of complex microbial communities. It is a fast growing field that requires the development of novel supervised learning techniques for addressing challenges associated with metagenomic data, e.g. sensitivity to the choice of sequence similarity cutoff used to define operational taxonomic units (OTUs), high dimensionality and sparsity of the data and so forth. On the other hand, the natural properties of microbial community data may provide useful information about the structure of the data. For example, similarity between species encoded by a phylogenetic tree captures the relationship between OTUs and may be useful for the analysis of complex microbial datasets where the diversity patterns comprise features at multiple taxonomic levels. Even though some of the challenges have been addressed by learning algorithms in the literature, none of the available methods take advantage of the inherent properties of metagenomic data. RESULTS: We proposed a novel supervised classification method for microbial community samples, where each sample is represented as a set of OTU frequencies, which takes advantage of the natural structure in microbial community data encoded by a phylogenetic tree. This model allows us to take advantage of environment-specific compositional patterns that may contain features at multiple granularity levels. Our method is based on the multinomial logistic regression model with a tree-guided penalty function. Additionally, we proposed a new simulation framework for generating 16S ribosomal RNA gene read counts that may be useful in comparative metagenomics research. Our experimental results on simulated and real data show that the phylogenetic information used in our method improves the classification accuracy. AVAILABILITY AND IMPLEMENTATION: http://www.cs.ucr.edu/~tanaseio/metaphyl.htm.

Targeting p130Cas- and microtubule-dependent MYC regulation sensitizes pancreatic cancer to ERK MAPK inhibition.

To identify therapeutic targets for KRAS mutant pancreatic cancer, we conduct a druggable genome small interfering RNA (siRNA) screen and determine that suppression of BCAR1 sensitizes pancreatic cancer cells to ERK inhibition. Integrative analysis of genome-scale CRISPR-Cas9 screens also identify BCAR1 as a top synthetic lethal interactor with mutant KRAS. BCAR1 encodes the SRC substrate p130Cas. We determine that SRC-inhibitor-mediated suppression of p130Cas phosphorylation impairs MYC transcription through a DOCK1-RAC1-ß-catenin-dependent mechanism. Additionally, genetic suppression of TUBB3, encoding the ßIII-tubulin subunit of microtubules, or pharmacological inhibition of microtubule function decreases levels of MYC protein in a calpain-dependent manner and potently sensitizes pancreatic cancer cells to ERK inhibition. Accordingly, the combination of a dual SRC/tubulin inhibitor with an ERK inhibitor cooperates to reduce MYC protein and synergistically suppress the growth of KRAS mutant pancreatic cancer. Thus, we demonstrate that mechanistically diverse combinations with ERK inhibition suppress MYC to impair pancreatic cancer proliferation.

Dexamethasone inhibits respiratory syncytial virus-driven mucus production while increasing viral replication without altering antiviral interferon signaling.

Respiratory syncytial virus (RSV) infection can cause mucus overproduction and bronchiolitis in infants leading to severe disease and hospitalization. As a therapeutic strategy, immune modulatory agents may help prevent RSV-driven immune responses that cause severe airway disease. We developed a high throughput screen to identify compounds that reduced RSV-driven mucin 5AC (Muc5AC) expression and identified dexamethasone. Despite leading to a pronounced reduction in RSV-driven Muc5AC, dexamethasone increased RSV infection in vitro and delayed viral clearance in mice. This correlated with reduced expression of a subset of immune response genes and reduced lymphocyte infiltration in vivo. Interestingly, dexamethasone increased RSV infection levels without altering antiviral interferon signaling. In summary, the immunosuppressive activities of dexamethasone had favorable inhibitory effects on RSV-driven mucus production yet prevented immune defense activities that limit RSV infection in vitro and in vivo. These findings offer an explanation for the lack of efficacy of glucocorticoids in RSV-infected patients.

Identification of a Xist silencing domain by Tiling CRISPR.

Despite essential roles played by long noncoding RNAs (lncRNAs) in development and disease, methods to determine lncRNA cis-elements are lacking. Here, we developed a screening method named "Tiling CRISPR" to identify lncRNA functional domains. Using this approach, we identified Xist A-Repeats as the silencing domain, an observation in agreement with published work, suggesting Tiling CRISPR feasibility. Mechanistic analysis suggested a novel function for Xist A-repeats in promoting Xist transcription. Overall, our method allows mapping of lncRNA functional domains in an unbiased and potentially high-throughput manner to facilitate the understanding of lncRNA functions.

Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.

A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.

Mapping the malaria parasite druggable genome by using in vitro evolution and chemogenomics.

Chemogenetic characterization through in vitro evolution combined with whole-genome analysis can identify antimalarial drug targets and drug-resistance genes. We performed a genome analysis of 262 Plasmodium falciparum parasites resistant to 37 diverse compounds. We found 159 gene amplifications and 148 nonsynonymous changes in 83 genes associated with drug-resistance acquisition, where gene amplifications contributed to one-third of resistance acquisition events. Beyond confirming previously identified multidrug-resistance mechanisms, we discovered hitherto unrecognized drug target-inhibitor pairs, including thymidylate synthase and a benzoquinazolinone, farnesyltransferase and a pyrimidinedione, and a dipeptidylpeptidase and an arylurea. This exploration of the P. falciparum resistome and druggable genome will likely guide drug discovery and structural biology efforts, while also advancing our understanding of resistance mechanisms available to the malaria parasite.

High-Throughput Assay and Discovery of Small Molecules that Interrupt Malaria Transmission.

Preventing transmission is an important element of malaria control. However, most of the current available methods to assay for malaria transmission blocking are relatively low throughput and cannot be applied to large chemical libraries. We have developed a high-throughput and cost-effective assay, the Saponin-lysis Sexual Stage Assay (SaLSSA), for identifying small molecules with transmission-blocking capacity. SaLSSA analysis of 13,983 unique compounds uncovered that >90% of well-characterized antimalarials, including endoperoxides and 4-aminoquinolines, as well as compounds active against asexual blood stages, lost most of their killing activity when parasites developed into metabolically quiescent stage V gametocytes. On the other hand, we identified compounds with consistent low nanomolar transmission-blocking activity, some of which showed cross-reactivity against asexual blood and liver stages. The data clearly emphasize substantial physiological differences between sexual and asexual parasites and provide a tool and starting points for the discovery and development of transmission-blocking drugs.

A broad analysis of resistance development in the malaria parasite.

Microbial resistance to chemotherapy has caused countless deaths where malaria is endemic. Chemotherapy may fail either due to pre-existing resistance or evolution of drug-resistant parasites. Here we use a diverse set of antimalarial compounds to investigate the acquisition of drug resistance and the degree of cross-resistance against common resistance alleles. We assess cross-resistance using a set of 15 parasite lines carrying resistance-conferring alleles in pfatp4, cytochrome bc1, pfcarl, pfdhod, pfcrt, pfmdr, pfdhfr, cytoplasmic prolyl t-RNA synthetase or hsp90. Subsequently, we assess whether resistant parasites can be obtained after several rounds of drug selection. Twenty-three of the 48 in vitro selections result in resistant parasites, with time to resistance onset ranging from 15 to 300 days. Our data indicate that pre-existing resistance may not be a major hurdle for novel-target antimalarial candidates, and focusing our attention on fast-killing compounds may result in a slower onset of clinical resistance.

Separating metagenomic short reads into genomes via clustering.

BACKGROUND: The metagenomics approach allows the simultaneous sequencing of all genomes in an environmental sample. This results in high complexity datasets, where in addition to repeats and sequencing errors, the number of genomes and their abundance ratios are unknown. Recently developed next-generation sequencing (NGS) technologies significantly improve the sequencing efficiency and cost. On the other hand, they result in shorter reads, which makes the separation of reads from different species harder. Among the existing computational tools for metagenomic analysis, there are similarity-based methods that use reference databases to align reads and composition-based methods that use composition patterns (i.e., frequencies of short words or l-mers) to cluster reads. Similarity-based methods are unable to classify reads from unknown species without close references (which constitute the majority of reads). Since composition patterns are preserved only in significantly large fragments, composition-based tools cannot be used for very short reads, which becomes a significant limitation with the development of NGS. A recently proposed algorithm, AbundanceBin, introduced another method that bins reads based on predicted abundances of the genomes sequenced. However, it does not separate reads from genomes of similar abundance levels. RESULTS: In this work, we present a two-phase heuristic algorithm for separating short paired-end reads from different genomes in a metagenomic dataset. We use the observation that most of the l-mers belong to unique genomes when l is sufficiently large. The first phase of the algorithm results in clusters of l-mers each of which belongs to one genome. During the second phase, clusters are merged based on l-mer repeat information. These final clusters are used to assign reads. The algorithm could handle very short reads and sequencing errors. It is initially designed for genomes with similar abundance levels and then extended to handle arbitrary abundance ratios. The software can be download for free at http://www.cs.ucr.edu/∼tanaseio/toss.htm. CONCLUSIONS: Our tests on a large number of simulated metagenomic datasets concerning species at various phylogenetic distances demonstrate that genomes can be separated if the number of common repeats is smaller than the number of genome-specific repeats. For such genomes, our method can separate NGS reads with a high precision and sensitivity.