A computational interactome and functional annotation for the human proteome

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function

Analyzing Non-Alcoholic Fatty Liver Disease Risk Using Time-Series Model

Non-alcoholic fatty liver disease (NAFLD) is the most global frequent liver disease, with a prevalence of almost 20% in the overall population. NAFLD may progress to fibrosis and later into cirrhosis in addition to other diseases. Our objective is to stratify patients\u27 risks for NAFLD and advanced fibrosis over time and suggest preventive medical decisions. We used a cohort of individuals from the Tel-Aviv medical center. Time-series clustering machine learning model (Hidden Markov Models (HMM)) was used to profile fibrosis risk by modeling patients’ latent medical status and trajectories over time. The best-fitting model had three latent HMM states. Initial results show that tracking individuals over time and their relative risk for fibrosis at each point of time provides significant clinical insights regarding each state (and its group of individuals). Thus, longitudinal risk stratification can enable the early identification of specific individual groups following distinct medical trajectories based on their routine visits

A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection

available in PMC 2010 June 28.During the course of a viral infection, viral proteins interact with an array of host proteins and pathways. Here, we present a systematic strategy to elucidate the dynamic interactions between H1N1 influenza and its human host. A combination of yeast two-hybrid analysis and genome-wide expression profiling implicated hundreds of human factors in mediating viral-host interactions. These factors were then examined functionally through depletion analyses in primary lung cells. The resulting data point to potential roles for some unanticipated host and viral proteins in viral infection and the host response, including a network of RNA-binding proteins, components of WNT signaling, and viral polymerase subunits. This multilayered approach provides a comprehensive and unbiased physical and regulatory model of influenza-host interactions and demonstrates a general strategy for uncovering complex host-pathogen relationships.National Institutes of Health (U.S.) (grant U01 AI074575)National Institutes of Health (U.S.) (grant U54 AI057159)National Institutes of Health (U.S.) (NIH New Innovator Award)Ford Foundation (Predoctoral Fellowship)Ellison Medical FoundationNational Institutes of Health (U.S.) (NIH grant R01 HG001715)National Institutes of Health (U.S.) (grant P50 HG004233)National Institutes of Health (U.S.) (PIONEER award)Howard Hughes Medical InstituteBurroughs Wellcome Fund (Career Award at the Scientific Interface)Alfred P. Sloan Foundatio

Seroprevalence of Toxoplasma gondii infection in arthritis patients in eastern China

Background: There is accumulating evidence for an increased susceptibility to infection in patients with arthritis. We sought to understand the epidemiology of Toxoplasma gondii infection in arthritis patients in eastern China, given the paucity of data on the magnitude of T. gondii infection in these patients. Methods: Seroprevalence of T. gondii infection was assessed by enzyme-linked immunosorbent assay using a crude antigen of the parasite in 820 arthritic patients, and an equal number of healthy controls, from Qingdao and Weihai cities, eastern China. Sociodemographic, clinical and lifestyle information on the study participants were also obtained. Results: The prevalence of anti-T. gondii IgG was significantly higher in arthritic patients (18.8%) compared with 12% in healthy controls (P < 0.001). Twelve patients with arthritis had anti-T. gondii IgM antibodies comparable with 10 control patients (1.5% vs 1.2%). Demographic factors did not significantly influence these seroprevalence frequencies. The highest T. gondii infection seropositivity rate was detected in patients with rheumatoid arthritis (24.8%), followed by reactive arthritis (23.8%), osteoarthritis (19%), infectious arthritis (18.4%) and gouty arthritis (14.8%). Seroprevalence rates of rheumatoid arthritis and reactive arthritis were significantly higher when compared with controls (P < 0.001 and P = 0.002, respectively). A significant association was detected between T. gondii infection and cats being present in the home in arthritic patients (odds ratio [OR], 1.68; 95% confidence interval [CI]: 1.24 – 2.28; P = 0.001). Conclusions: These findings are consistent with and extend previous results, providing further evidence to support a link between contact with cats and an increased risk of T. gondii infection. Our study is also the first to confirm an association between T. gondii infection and arthritis patients in China. Implications for better prevention and control of T. gondii infection in arthritis patients are discussed. Trial registration: This is an epidemiological survey, therefore trial registration was not required

The Promoter of the pri-miR-375 Gene Directs Expression Selectively to the Endocrine Pancreas

microRNAs (miRNAs) are known to play an essential role in controlling a broad range of biological processes including animal development. Accordingly, many miRNAs are expressed preferentially in one or a small number of cell types. Yet the mechanisms responsible for this selectivity are not well understood. The aim of this study was to elucidate the molecular basis of cell-specific expression of the pri-miR-375 gene, which is selectively expressed in pancreatic islets, and has been implicated both in the development of islets, and the function of mature pancreatic beta cells. An evolutionarily conserved 768 bp region of DNA upstream of the pri-miR-375 gene was linked to GFP and luciferase reporter genes, and expression monitored in transgenic mice and transfected cultured cells. Deletion and targeted mutagenesis analysis was used to evaluate the functional significance of sequence blocks within the upstream fragment. 5′-RACE analysis was used for mapping the pri-miR-375 gene transcription start site. The conserved 768 bp region was able to direct preferential expression of a GFP reporter gene to pancreatic islets in transgenic mice. Deletion analysis using a luciferase reporter gene in transfected cultured cell lines confirmed the cell specificity of the putative promoter region, and identified several key cis-elements essential for optimal activity, including E-boxes and a TATA sequence. Consistent with this, 5′-RACE analysis identified a transcription start site within this DNA region, 24 bp downstream of the TATA sequence. These studies define the promoter of the pri-miR-375 gene, and show that islet-specific expression of the pri-miR-375 gene is controlled at the transcriptional level. Detailed analysis of the transcriptional mechanisms controlling expression of miRNA genes will be essential to permit a comprehensive understanding of the complex role of miRNAs such as miR-375 in developmental processes

System-wide transcriptome damage and tissue identity loss in COVID-19 patients

The molecular mechanisms underlying the clinical manifestations of coronavirus disease 2019 (COVID-19), and what distinguishes them from common seasonal influenza virus and other lung injury states such as acute respiratory distress syndrome, remain poorly understood. To address these challenges, we combine transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues to define body-wide transcriptome changes in response to COVID-19. We then match these data with spatial protein and expression profiling across 357 tissue sections from 16 representative patient lung samples and identify tissue-compartment-specific damage wrought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, evident as a function of varying viral loads during the clinical course of infection and tissue-type-specific expression states. Overall, our findings reveal a systemic disruption of canonical cellular and transcriptional pathways across all tissues, which can inform subsequent studies to combat the mortality of COVID-19 and to better understand the molecular dynamics of lethal SARS-CoV-2 and other respiratory infections., • Across all organs, fibroblast, and immune cell populations increase in COVID-19 patients • Organ-specific cell types and functional markers are lost in all COVID-19 tissue types • Lung compartment identity loss correlates with SARS-CoV-2 viral loads • COVID-19 uniquely disrupts co-occurrence cell type clusters (different from IAV/ARDS) , Park et al. report system-wide transcriptome damage and tissue identity loss wrought by SARS-CoV-2, influenza, and bacterial infection across multiple organs (heart, liver, lung, kidney, and lymph nodes) and provide a spatiotemporal landscape of COVID-19 in the lung

The NF-κB signaling pathway: Immunoregulation and immune evasion during toxoplasmosis

The contribution of NF-κB signaling to the regulation of protective immunity in Drosophila and humans indicates a role for this pathway as an evolutionarily conserved element in an organism\u27s response to infection (Hoffmann et al., 1999). Thus, NF-κB signaling exerts a strong selective pressure on infectious agents, providing a reproductive advantage to organisms that are able to modulate this pathway. The studies presented here address two aspects of the role of NF-κB in immunity to the intracellular parasite Toxoplasma gondii. First, they highlighted the activation of NF-κB 1 following infection as well as its requirement for resistance to this parasite; second, they revealed an ability of T. gondii to suppress NF-κB signaling in the cells that it parasitizes. These observations led to experiments that provided novel insights into the differential role of NF-κB1 in cells of differing lineages, and identified the phosphorylation of p65 as a novel target that can be manipulated by T. gondii to promote its survival. Through these studies we have gained an appreciation for the complex roles that NF-κB family members play in the regulation of immunity to T. gondii and the intimate relationship between this parasite and the NF-κB signaling pathway in infected cells. Understanding the mechanisms utilized by toxoplasma to regulate NF-κB signaling will aid in our appreciation of the host-parasite relationship as well as provide insight into the development of the immune response during infection

A High-Throughput Strategy for Dissecting Mammalian Genetic Interactions.

Comprehensive delineation of complex cellular networks requires high-throughput interrogation of genetic interactions. To address this challenge, we describe the development of a multiplex combinatorial strategy to assess pairwise genetic interactions using CRISPR-Cas9 genome editing and next-generation sequencing. We characterize the performance of combinatorial genome editing and analysis using different promoter and gRNA designs and identified regions of the chimeric RNA that are compatible with next-generation sequencing preparation and quantification. This approach is an important step towards elucidating genetic networks relevant to human diseases and the development of more efficient Cas9-based therapeutics

Stochastic State Transitions Give Rise to Phenotypic Equilibrium in Populations of Cancer Cells

Cancer cells within individual tumors often exist in distinct phenotypic states that differ in functional attributes. While cancer cell populations typically display distinctive equilibria in the proportion of cells in various states, the mechanisms by which this occurs are poorly understood. Here, we study the dynamics of phenotypic proportions in human breast cancer cell lines. We show that subpopulations of cells purified for a given phenotypic state return towards equilibrium proportions over time. These observations can be explained by a Markov model in which cells transition stochastically between states. A prediction of this model is that, given certain conditions, any subpopulation of cells will return to equilibrium phenotypic proportions over time. A second prediction is that breast cancer stem-like cells arise de novo from non-stem-like cells. These findings contribute to our understanding of cancer heterogeneity and reveal how stochasticity in single-cell behaviors promotes phenotypic equilibrium in populations of cancer cells.Broad Institute of MIT and HarvardBreast Cancer Research FoundationRaymond and Beverley Sackler Foundatio