Global analysis of host-pathogen interactions that regulate early-stage HIV-1 replication.

Human Immunodeficiency Viruses (HIV-1 and HIV-2) rely upon host-encoded proteins to facilitate their replication. Here, we combined genome-wide siRNA analyses with interrogation of human interactome databases to assemble a host-pathogen biochemical network containing 213 confirmed host cellular factors and 11 HIV-1-encoded proteins. Protein complexes that regulate ubiquitin conjugation, proteolysis, DNA-damage response, and RNA splicing were identified as important modulators of early-stage HIV-1 infection. Additionally, over 40 new factors were shown to specifically influence the initiation and/or kinetics of HIV-1 DNA synthesis, including cytoskeletal regulatory proteins, modulators of posttranslational modification, and nucleic acid-binding proteins. Finally, 15 proteins with diverse functional roles, including nuclear transport, prostaglandin synthesis, ubiquitination, and transcription, were found to influence nuclear import or viral DNA integration. Taken together, the multiscale approach described here has uncovered multiprotein virus-host interactions that likely act in concert to facilitate the early steps of HIV-1 infection.

A cell-level quality control workflow for high-throughput image analysis.

BACKGROUND: Image-based high throughput (HT) screening provides a rich source of information on dynamic cellular response to external perturbations. The large quantity of data generated necessitates computer-aided quality control (QC) methodologies to flag imaging and staining artifacts. Existing image- or patch-level QC methods require separate thresholds to be simultaneously tuned for each image quality metric used, and also struggle to distinguish between artifacts and valid cellular phenotypes. As a result, extensive time and effort must be spent on per-assay QC feature thresholding, and valid images and phenotypes may be discarded while image- and cell-level artifacts go undetected. RESULTS: We present a novel cell-level QC workflow built on machine learning approaches for classifying artifacts from HT image data. First, a phenotype sampler based on unlabeled clustering collects a comprehensive subset of cellular phenotypes, requiring only the inspection of a handful of images per phenotype for validity. A set of one-class support vector machines are then trained on each biologically valid image phenotype, and used to classify individual objects in each image as valid cells or artifacts. We apply this workflow to two real-world large-scale HT image datasets and observe that the ratio of artifact to total object area (ARcell) provides a single robust assessment of image quality regardless of the underlying causes of quality issues. Gating on this single intuitive metric, partially contaminated images can be salvaged and highly contaminated images can be excluded before image-level phenotype summary, enabling a more reliable characterization of cellular response dynamics. CONCLUSIONS: Our cell-level QC workflow enables identification of artificial cells created not only by staining or imaging artifacts but also by the limitations of image segmentation algorithms. The single readout ARcell that summaries the ratio of artifacts contained in each image can be used to reliably rank images by quality and more accurately determine QC cutoff thresholds. Machine learning-based cellular phenotype clustering and sampling reduces the amount of manual work required for training example collection. Our QC workflow automatically handles assay-specific phenotypic variations and generalizes to different HT image assays.

Human host factors required for influenza virus replication.

Influenza A virus is an RNA virus that encodes up to 11 proteins and this small coding capacity demands that the virus use the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only informs us of the molecular pathways exploited by the virus but also provides further targets that could be pursued for antiviral drug development. Here we use an integrative systems approach, based on genome-wide RNA interference screening, to identify 295 cellular cofactors required for early-stage influenza virus replication. Within this group, those involved in kinase-regulated signalling, ubiquitination and phosphatase activity are the most highly enriched, and 181 factors assemble into a highly significant host-pathogen interaction network. Moreover, 219 of the 295 factors were confirmed to be required for efficient wild-type influenza virus growth, and further analysis of a subset of genes showed 23 factors necessary for viral entry, including members of the vacuolar ATPase (vATPase) and COPI-protein families, fibroblast growth factor receptor (FGFR) proteins, and glycogen synthase kinase 3 (GSK3)-beta. Furthermore, 10 proteins were confirmed to be involved in post-entry steps of influenza virus replication. These include nuclear import components, proteases, and the calcium/calmodulin-dependent protein kinase (CaM kinase) IIbeta (CAMK2B). Notably, growth of swine-origin H1N1 influenza virus is also dependent on the identified host factors, and we show that small molecule inhibitors of several factors, including vATPase and CAMK2B, antagonize influenza virus replication.

Drug target prediction through deep learning functional representation of gene signatures.

Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institute's L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

Chemical and biological properties of frequent screening hits.

High-throughput screening (HTS) has become an important technology for the drug discovery process. It has been noted that certain compounds frequently appear as hits in many screening campaigns. By mining an HTS database covering large chemical space and diverse biological functions, we identified many novel chemical features, as well as several biological processes that were associated with a significant portion of frequent hits. However, we also noted that several marketed drugs also contained characteristics that commonly were associated with frequent hits. This observation suggested that current generally employed strategies for triaging compounds may result in the removal of compounds with desirable properties. Therefore, we developed a novel strategy that overlaid chemical scaffolds and biological processes, along with empirical hit frequency data, in order to provide a more functional frequent hit triage strategy; the risk of removing biologically relevant frequent hits was reduced compared to the typical empirical hit frequency-based filtering strategy.

In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen.

The growing resistance to current first-line antimalarial drugs represents a major health challenge. To facilitate the discovery of new antimalarials, we have implemented an efficient and robust high-throughput cell-based screen (1,536-well format) based on proliferation of Plasmodium falciparum (Pf) in erythrocytes. From a screen of approximately 1.7 million compounds, we identified a diverse collection of approximately 6,000 small molecules comprised of >530 distinct scaffolds, all of which show potent antimalarial activity (<1.25 microM). Most known antimalarials were identified in this screen, thus validating our approach. In addition, we identified many novel chemical scaffolds, which likely act through both known and novel pathways. We further show that in some cases the mechanism of action of these antimalarials can be determined by in silico compound activity profiling. This method uses large datasets from unrelated cellular and biochemical screens and the guilt-by-association principle to predict which cellular pathway and/or protein target is being inhibited by select compounds. In addition, the screening method has the potential to provide the malaria community with many new starting points for the development of biological probes and drugs with novel antiparasitic activities.

Predicting drug approvals: The Novartis data science and artificial intelligence challenge.

We describe a novel collaboration between academia and industry, an in-house data science and artificial intelligence challenge held by Novartis to develop machine-learning models for predicting drug-development outcomes, building upon research at MIT using data from Informa as the starting point. With over 50 cross-functional teams from 25 Novartis offices around the world participating in the challenge, the domain expertise of these Novartis researchers was leveraged to create predictive models with greater sophistication. Ultimately, two winning teams developed models that outperformed the baseline MIT model-areas under the curve of 0.88 and 0.84 versus 0.78, respectively-through state-of-the-art machine-learning algorithms and the use of newly incorporated features and data. In addition to validating the variables shown to be associated with drug approval in the earlier MIT study, the challenge also provided new insights into the drivers of drug-development success and failure.

Proteomic Analysis of Non-human Primate Peripheral Blood Mononuclear Cells During Burkholderia mallei Infection Reveals a Role of Ezrin in Glanders Pathogenesis.

Burkholderia mallei, the causative agent of glanders, is a gram-negative intracellular bacterium. Depending on different routes of infection, the disease is manifested by pneumonia, septicemia, and chronic infections of the skin. B. mallei poses a serious biological threat due to its ability to infect via aerosol route, resistance to multiple antibiotics and to date there are no US Food and Drug Administration (FDA) approved vaccines available. Induction of innate immunity, inflammatory cytokines and chemokines following B. mallei infection, have been observed in in vitro and small rodent models; however, a global characterization of host responses has never been systematically investigated using a non-human primate (NHP) model. Here, using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach, we identified alterations in expression levels of host proteins in peripheral blood mononuclear cells (PBMCs) originating from naïve rhesus macaques (Macaca mulatta), African green monkeys (Chlorocebus sabaeus), and cynomolgus macaques (Macaca fascicularis) exposed to aerosolized B. mallei. Gene ontology (GO) analysis identified several statistically significant overrepresented biological annotations including complement and coagulation cascade, nucleoside metabolic process, vesicle-mediated transport, intracellular signal transduction and cytoskeletal protein binding. By integrating an LC-MS/MS derived proteomics dataset with a previously published B. mallei host-pathogen interaction dataset, a statistically significant predictive protein-protein interaction (PPI) network was constructed. Pharmacological perturbation of one component of the PPI network, specifically ezrin, reduced B. mallei mediated interleukin-1ß (IL-1ß). On the contrary, the expression of IL-1ß receptor antagonist (IL-1Ra) was upregulated upon pretreatment with the ezrin inhibitor. Taken together, inflammasome activation as demonstrated by IL-1ß production and the homeostasis of inflammatory response is critical during the pathogenesis of glanders. Furthermore, the topology of the network reflects the underlying molecular mechanism of B. mallei infections in the NHP model.

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.

Gene expression signatures and small-molecule compounds link a protein kinase to Plasmodium falciparum motility.

Calcium-dependent protein kinases play a crucial role in intracellular calcium signaling in plants, some algae and protozoa. In Plasmodium falciparum, calcium-dependent protein kinase 1 (PfCDPK1) is expressed during schizogony in the erythrocytic stage as well as in the sporozoite stage. It is coexpressed with genes that encode the parasite motor complex, a cellular component required for parasite invasion of host cells, parasite motility and potentially cytokinesis. A targeted gene-disruption approach demonstrated that pfcdpk1 seems to be essential for parasite viability. An in vitro biochemical screen using recombinant PfCDPK1 against a library of 20,000 compounds resulted in the identification of a series of structurally related 2,6,9-trisubstituted purines. Compound treatment caused sudden developmental arrest at the late schizont stage in P. falciparum and a large reduction in intracellular parasites in Toxoplasma gondii, which suggests a possible role for PfCDPK1 in regulation of parasite motility during egress and invasion.

Chemical-text hybrid search engines.

As the amount of chemical literature increases, it is critical that researchers be enabled to accurately locate documents related to a particular aspect of a given compound. Existing solutions, based on text and chemical search engines alone, suffer from the inclusion of "false negative" and "false positive" results, and cannot accommodate diverse repertoire of formats currently available for chemical documents. To address these concerns, we developed an approach called Entity-Canonical Keyword Indexing (ECKI), which converts a chemical entity embedded in a data source into its canonical keyword representation prior to being indexed by text search engines. We implemented ECKI using Microsoft Office SharePoint Server Search, and the resultant hybrid search engine not only supported complex mixed chemical and keyword queries but also was applied to both intranet and Internet environments. We envision that the adoption of ECKI will empower researchers to pose more complex search questions that were not readily attainable previously and to obtain answers at much improved speed and accuracy.

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.

Snapshot PK: a rapid rodent in vivo preclinical screening approach.

Described in this article are strategies implemented to increase the throughput of in vivo rodent pharmacokinetic (PK) studies using the snapshot PK study design and automated methods for compound submission, sample processing, data analysis and reporting. Applying snapshot PK studies to categorize the oral exposure of >1300 discovery compounds as low, moderate or high resulted in an attrition rate of 86%. The follow up full PK studies on the remaining compounds found that 98% of the compounds were predicted in the correct (69%) or adjacent (29%) oral exposure category by the snapshot PK studies. These results demonstrate that the snapshot PK screen in rodents can serve as an effective and efficient in vivo tool in the compound selection process in drug discovery.

In silico discovery of transcription regulatory elements in Plasmodium falciparum.

BACKGROUND: With the sequence of the Plasmodium falciparum genome and several global mRNA and protein life cycle expression profiling projects now completed, elucidating the underlying networks of transcriptional control important for the progression of the parasite life cycle is highly pertinent to the development of new anti-malarials. To date, relatively little is known regarding the specific mechanisms the parasite employs to regulate gene expression at the mRNA level, with studies of the P. falciparum genome sequence having revealed few cis-regulatory elements and associated transcription factors. Although it is possible the parasite may evoke mechanisms of transcriptional control drastically different from those used by other eukaryotic organisms, the extreme AT-rich nature of P. falciparum intergenic regions (approximately 90% AT) presents significant challenges to in silico cis-regulatory element discovery. RESULTS: We have developed an algorithm called Gene Enrichment Motif Searching (GEMS) that uses a hypergeometric-based scoring function and a position-weight matrix optimization routine to identify with high-confidence regulatory elements in the nucleotide-biased and repeat sequence-rich P. falciparum genome. When applied to promoter regions of genes contained within 21 co-expression gene clusters generated from P. falciparum life cycle microarray data using the semi-supervised clustering algorithm Ontology-based Pattern Identification, GEMS identified 34 putative cis-regulatory elements associated with a variety of parasite processes including sexual development, cell invasion, antigenic variation and protein biosynthesis. Among these candidates were novel motifs, as well as many of the elements for which biological experimental evidence already exists in the Plasmodium literature. To provide evidence for the biological relevance of a cell invasion-related element predicted by GEMS, reporter gene and electrophoretic mobility shift assays were conducted. CONCLUSION: This GEMS analysis demonstrates that in silico regulatory element discovery can be successfully applied to challenging repeat-sequence-rich, base-biased genomes such as that of P. falciparum. The fact that regulatory elements were predicted from a diverse range of functional gene clusters supports the hypothesis that cis-regulatory elements play a role in the transcriptional control of many P. falciparum biological processes. The putative regulatory elements described represent promising candidates for future biological investigation into the underlying transcriptional control mechanisms of gene regulation in malaria parasites.

A systematic approach to understand the mechanism of action of the bisthiazolium compound T4 on the human malaria parasite, Plasmodium falciparum.

BACKGROUND: In recent years, a major increase in the occurrence of drug resistant falciparum malaria has been reported. Choline analogs, such as the bisthiazolium T4, represent a novel class of compounds with strong potency against drug sensitive and resistant P. falciparum clones. Although T4 and its analogs are presumed to target the parasite's lipid metabolism, their exact mechanism of action remains unknown. Here we have employed transcriptome and proteome profiling analyses to characterize the global response of P. falciparum to T4 during the intraerythrocytic cycle of this parasite. RESULTS: No significant transcriptional changes were detected immediately after addition of T4 despite the drug's effect on the parasite metabolism. Using the Ontology-based Pattern Identification (OPI) algorithm with an increased T4 incubation time, we demonstrated cell cycle arrest and a general induction of genes involved in gametocytogenesis. Proteomic analysis revealed a significant decrease in the level of the choline/ethanolamine-phosphotransferase (PfCEPT), a key enzyme involved in the final step of synthesis of phosphatidylcholine (PC). This effect was further supported by metabolic studies, which showed a major alteration in the synthesis of PC from choline and ethanolamine by the compound. CONCLUSION: Our studies demonstrate that the bisthiazolium compound T4 inhibits the pathways of synthesis of phosphatidylcholine from choline and ethanolamine in P. falciparum, and provide evidence for post-transcriptional regulations of parasite metabolism in response to external stimuli.

A systematic map of genetic variation in Plasmodium falciparum.

Discovering novel genes involved in immune evasion and drug resistance in the human malaria parasite, Plasmodium falciparum, is of critical importance to global health. Such knowledge may assist in the development of new effective vaccines and in the appropriate use of antimalarial drugs. By performing a full-genome scan of allelic variability in 14 field and laboratory strains of P. falciparum, we comprehensively identified approximately 500 genes evolving at higher than neutral rates. The majority of the most variable genes have paralogs within the P. falciparum genome and may be subject to a different evolutionary clock than those without. The group of 211 variable genes without paralogs contains most known immunogens and a few drug targets, consistent with the idea that the human immune system and drug use is driving parasite evolution. We also reveal gene-amplification events including one surrounding pfmdr1, the P. falciparum multidrug-resistance gene, and a previously uncharacterized amplification centered around the P. falciparum GTP cyclohydrolase gene, the first enzyme in the folate biosynthesis pathway. Although GTP cyclohydrolase is not the known target of any current drugs, downstream members of the pathway are targeted by several widely used antimalarials. We speculate that an amplification of the GTP cyclohydrolase enzyme in the folate biosynthesis pathway may increase flux through this pathway and facilitate parasite resistance to antifolate drugs.

An integrated clinical-genomics approach identifies a candidate multi-analyte blood test for serous ovarian carcinoma.

PURPOSE: Cancer of the ovary confers the worst prognosis among women with gynecologic malignancies, underscoring the need to develop new biomarkers for detection of early disease, particularly those that can be readily monitored in the blood. EXPERIMENTAL DESIGN: We developed an algorithm to identify secreted proteins encoded among approximately 22,500 genes on commercial oligonucleotide arrays and applied it to gene expression profiles of 67 stage I to IV serous papillary carcinomas and 9 crudely enriched normal ovarian tissues, to identify putative diagnostic markers. ELISAs were used to validate increased levels of secreted proteins in patient sera encoded by genes with differentially high expression. RESULTS: We identified 275 genes predicted to encode secreted proteins with increased/decreased expression in ovarian cancers (<0.5- or >2-fold, P < 0.001). The serum levels of four of these proteins (matrix metalloproteinase-7, osteopontin, secretory leukoprotease inhibitor, and kallikrein 10) were significantly elevated in a series of 67 independent patients with serous ovarian carcinomas compared with 67 healthy controls (P < 0.001, Wilcoxon rank sum test). Optimized support vector machine classifiers with as few as two of these markers (osteopontin or kallikrein 10/matrix metalloproteinase-7) in combination with CA-125 yielded sensitivity and specificity values ranging from 96% to 98.7% and 99.7% to 100%, respectively, with the ability to discern early-stage disease from normal, healthy controls. CONCLUSIONS: Our data suggest that this assay combination warrants further investigation as a multi-analyte diagnostic test for serous ovarian adenocarcinoma.

A Fully Automated High-Throughput Flow Cytometry Screening System Enabling Phenotypic Drug Discovery.

The goal of high-throughput screening is to enable screening of compound libraries in an automated manner to identify quality starting points for optimization. This often involves screening a large diversity of compounds in an assay that preserves a connection to the disease pathology. Phenotypic screening is a powerful tool for drug identification, in that assays can be run without prior understanding of the target and with primary cells that closely mimic the therapeutic setting. Advanced automation and high-content imaging have enabled many complex assays, but these are still relatively slow and low throughput. To address this limitation, we have developed an automated workflow that is dedicated to processing complex phenotypic assays for flow cytometry. The system can achieve a throughput of 50,000 wells per day, resulting in a fully automated platform that enables robust phenotypic drug discovery. Over the past 5 years, this screening system has been used for a variety of drug discovery programs, across many disease areas, with many molecules advancing quickly into preclinical development and into the clinic. This report will highlight a diversity of approaches that automated flow cytometry has enabled for phenotypic drug discovery.