Cyclin D1 integrates G9a-mediated histone methylation.

Lysine methylation of histones and non-histone substrates by the SET domain containing protein lysine methyltransferase (KMT) G9a/EHMT2 governs transcription contributing to apoptosis, aberrant cell growth, and pluripotency. The positioning of chromosomes within the nuclear three-dimensional space involves interactions between nuclear lamina (NL) and the lamina-associated domains (LAD). Contact of individual LADs with the NL are dependent upon H3K9me2 introduced by G9a. The mechanisms governing the recruitment of G9a to distinct subcellular sites, into chromatin or to LAD, is not known. The cyclin D1 gene product encodes the regulatory subunit of the holoenzyme that phosphorylates pRB and NRF1 thereby governing cell-cycle progression and mitochondrial metabolism. Herein, we show that cyclin D1 enhanced H3K9 dimethylation though direct association with G9a. Endogenous cyclin D1 was required for the recruitment of G9a to target genes in chromatin, for G9a-induced H3K9me2 of histones, and for NL-LAD interaction. The finding that cyclin D1 is required for recruitment of G9a to target genes in chromatin and for H3K9 dimethylation, identifies a novel mechanism coordinating protein methylation.

Mitochondrial Haplogroup Influences Motor Function in Long-Term HIV-1-Infected Individuals.

Evolutionary divergence of the mitochondrial genome has given rise to distinct haplogroups. These haplogroups have arisen in specific geographical locations and are responsible for subtle functional changes in the mitochondria that may provide an evolutionary advantage in a given environment. Based on these functional differences, haplogroups could define disease susceptibility in chronic settings. In this study, we undertook a detailed neuropsychological analysis of a cohort of long-term HIV-1-infected individuals in conjunction with sequencing of their mitochondrial genomes. Stepwise regression analysis showed that the best model for predicting both working memory and declarative memory were age and years since diagnosis. In contrast, years since diagnosis and sub-haplogroup were significantly predictive of psychomotor speed. Consistent with this, patients with haplogroup L3e obtained better scores on psychomotor speed and dexterity tasks when compared to the remainder of the cohort, suggesting that this haplogroup provides a protective advantage when faced with the combined stress of HIV-1 infection and long-term antiretroviral therapies. Differential performance on declarative memory tasks was noted for individuals with other sub-L haplogroups, but these differences were not as robust as the association between L3e and psychomotor speed and dexterity tasks. This work provides evidence that mitochondrial haplogroup is related to neuropsychological test performance among patients in chronic disease settings such as HIV-1 infection.

Kinase-independent role of cyclin D1 in chromosomal instability and mammary tumorigenesis.

Cyclin D1 is an important molecular driver of human breast cancer but better understanding of its oncogenic mechanisms is needed, especially to enhance efforts in targeted therapeutics. Currently, pharmaceutical initiatives to inhibit cyclin D1 are focused on the catalytic component since the transforming capacity is thought to reside in the cyclin D1/CDK activity. We initiated the following study to directly test the oncogenic potential of catalytically inactive cyclin D1 in an in vivo mouse model that is relevant to breast cancer. Herein, transduction of cyclin D1(-/-) mouse embryonic fibroblasts (MEFs) with the kinase dead KE mutant of cyclin D1 led to aneuploidy, abnormalities in mitotic spindle formation, autosome amplification, and chromosomal instability (CIN) by gene expression profiling. Acute transgenic expression of either cyclin D1(WT) or cyclin D1(KE) in the mammary gland was sufficient to induce a high CIN score within 7 days. Sustained expression of cyclin D1(KE) induced mammary adenocarcinoma with similar kinetics to that of the wild-type cyclin D1. ChIP-Seq studies demonstrated recruitment of cyclin D1(WT) and cyclin D1(KE) to the genes governing CIN. We conclude that the CDK-activating function of cyclin D1 is not necessary to induce either chromosomal instability or mammary tumorigenesis.

Bioinformatics analysis reveals transcriptome and microRNA signatures and drug repositioning targets for IBD and other autoimmune diseases.

BACKGROUND: Inflammatory bowel disease (IBD) is a complex disorder involving pathogen infection, host immune response, and altered enterocyte physiology. Incidences of IBD are increasing at an alarming rate in developed countries, warranting a detailed molecular portrait of IBD. METHODS: We used large-scale data, bioinformatics tools, and high-throughput computations to obtain gene and microRNA signatures for Crohn's disease (CD) and ulcerative colitis (UC). These signatures were then integrated with systemic literature review to draw a comprehensive portrait of IBD in relation to autoimmune diseases. RESULTS: The top upregulated genes in IBD are associated with diabetogenesis (REG1A, REG1B), bacterial signals (TLRs, NLRs), innate immunity (DEFA6, IDO1, EXOSC1), inflammation (CXCLs), and matrix degradation (MMPs). The downregulated genes coded tight junction proteins (CLDN8), solute transporters (SLCs), and adhesion proteins. Genes highly expressed in UC compared to CD included antiinflammatory ANXA1, transporter ABCA12, T-cell activator HSH2D, and immunoglobulin IGHV4-34. Compromised metabolisms for processing of drugs, nitrogen, androgen and estrogen, and lipids in IBD correlated with an increase in specific microRNA. Highly expressed IBD genes constituted targets of drugs used in gastrointestinal cancers, viral infections, and autoimmunity disorders such as rheumatoid arthritis and asthma. CONCLUSIONS: This study presents a clinically relevant gene-level portrait of IBD subtypes and their connectivity to autoimmune diseases. The study identified candidates for repositioning of existing drugs to manage IBD. Integration of mice and human data point to an altered B-cell response as a cause for upregulation of genes in IBD involved in other aspects of immune defense such as interferon-inducible responses.

Identification of common biological pathways and drug targets across multiple respiratory viruses based on human host gene expression analysis.

BACKGROUND: Pandemic and seasonal respiratory viruses are a major global health concern. Given the genetic diversity of respiratory viruses and the emergence of drug resistant strains, the targeted disruption of human host-virus interactions is a potential therapeutic strategy for treating multi-viral infections. The availability of large-scale genomic datasets focused on host-pathogen interactions can be used to discover novel drug targets as well as potential opportunities for drug repositioning. METHODS/RESULTS: In this study, we performed a large-scale analysis of microarray datasets involving host response to infections by influenza A virus, respiratory syncytial virus, rhinovirus, SARS-coronavirus, metapneumonia virus, coxsackievirus and cytomegalovirus. Common genes and pathways were found through a rigorous, iterative analysis pipeline where relevant host mRNA expression datasets were identified, analyzed for quality and gene differential expression, then mapped to pathways for enrichment analysis. Possible repurposed drugs targets were found through database and literature searches. A total of 67 common biological pathways were identified among the seven different respiratory viruses analyzed, representing fifteen laboratories, nine different cell types, and seven different array platforms. A large overlap in the general immune response was observed among the top twenty of these 67 pathways, adding validation to our analysis strategy. Of the top five pathways, we found 53 differentially expressed genes affected by at least five of the seven viruses. We suggest five new therapeutic indications for existing small molecules or biological agents targeting proteins encoded by the genes F3, IL1B, TNF, CASP1 and MMP9. Pathway enrichment analysis also identified a potential novel host response, the Parkin-Ubiquitin Proteasomal System (Parkin-UPS) pathway, which is known to be involved in the progression of neurodegenerative Parkinson's disease. CONCLUSIONS: Our study suggests that multiple and diverse respiratory viruses invoke several common host response pathways. Further analysis of these pathways suggests potential opportunities for therapeutic intervention.

ChIP sequencing of cyclin D1 reveals a transcriptional role in chromosomal instability in mice.

Chromosomal instability (CIN) in tumors is characterized by chromosomal abnormalities and an altered gene expression signature; however, the mechanism of CIN is poorly understood. CCND1 (which encodes cyclin D1) is overexpressed in human malignancies and has been shown to play a direct role in transcriptional regulation. Here, we used genome-wide ChIP sequencing and found that the DNA-bound form of cyclin D1 occupied the regulatory region of genes governing chromosomal integrity and mitochondrial biogenesis. Adding cyclin D1 back to Ccnd1(-/-) mouse embryonic fibroblasts resulted in CIN gene regulatory region occupancy by the DNA-bound form of cyclin D1 and induction of CIN gene expression. Furthermore, increased chromosomal aberrations, aneuploidy, and centrosome abnormalities were observed in the cyclin D1-rescued cells by spectral karyotyping and immunofluorescence. To assess cyclin D1 effects in vivo, we generated transgenic mice with acute and continuous mammary gland-targeted cyclin D1 expression. These transgenic mice presented with increased tumor prevalence and signature CIN gene profiles. Additionally, interrogation of gene expression from 2,254 human breast tumors revealed that cyclin D1 expression correlated with CIN in luminal B breast cancer. These data suggest that cyclin D1 contributes to CIN and tumorigenesis by directly regulating a transcriptional program that governs chromosomal stability.

HIV protein sequence hotspots for crosstalk with host hub proteins.

HIV proteins target host hub proteins for transient binding interactions. The presence of viral proteins in the infected cell results in out-competition of host proteins in their interaction with hub proteins, drastically affecting cell physiology. Functional genomics and interactome datasets can be used to quantify the sequence hotspots on the HIV proteome mediating interactions with host hub proteins. In this study, we used the HIV and human interactome databases to identify HIV targeted host hub proteins and their host binding partners (H2). We developed a high throughput computational procedure utilizing motif discovery algorithms on sets of protein sequences, including sequences of HIV and H2 proteins. We identified as HIV sequence hotspots those linear motifs that are highly conserved on HIV sequences and at the same time have a statistically enriched presence on the sequences of H2 proteins. The HIV protein motifs discovered in this study are expressed by subsets of H2 host proteins potentially outcompeted by HIV proteins. A large subset of these motifs is involved in cleavage, nuclear localization, phosphorylation, and transcription factor binding events. Many such motifs are clustered on an HIV sequence in the form of hotspots. The sequential positions of these hotspots are consistent with the curated literature on phenotype altering residue mutations, as well as with existing binding site data. The hotspot map produced in this study is the first global portrayal of HIV motifs involved in altering the host protein network at highly connected hub nodes.

Sequence- and interactome-based prediction of viral protein hotspots targeting host proteins: a case study for HIV Nef.

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk.

Host sequence motifs shared by HIV predict response to antiretroviral therapy.

BACKGROUND: The HIV viral genome mutates at a high rate and poses a significant long term health risk even in the presence of combination antiretroviral therapy. Current methods for predicting a patient's response to therapy rely on site-directed mutagenesis experiments and in vitro resistance assays. In this bioinformatics study we treat response to antiretroviral therapy as a two-body problem: response to therapy is considered to be a function of both the host and pathogen proteomes. We set out to identify potential responders based on the presence or absence of host protein and DNA motifs on the HIV proteome. RESULTS: An alignment of thousands of HIV-1 sequences attested to extensive variation in nucleotide sequence but also showed conservation of eukaryotic short linear motifs on the protein coding regions. The reduction in viral load of patients in the Stanford HIV Drug Resistance Database exhibited a bimodal distribution after 24 weeks of antiretroviral therapy, with 2,000 copies/ml cutoff. Similarly, patients allocated into responder/non-responder categories based on consistent viral load reduction during a 24 week period showed clear separation. In both cases of phenotype identification, a set of features composed of short linear motifs in the reverse transcriptase region of HIV sequence accurately predicted a patient's response to therapy. Motifs that overlap resistance sites were highly predictive of responder identification in single drug regimens but these features lost importance in defining responders in multi-drug therapies. CONCLUSION: HIV sequence mutates in a way that preferentially preserves peptide sequence motifs that are also found in the human proteome. The presence and absence of such motifs at specific regions of the HIV sequence is highly predictive of response to therapy. Some of these predictive motifs overlap with known HIV-1 resistance sites. These motifs are well established in bioinformatics databases and hence do not require identification via in vitro mutation experiments.

Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs.

BACKGROUND: Host protein-protein interaction networks are altered by invading virus proteins, which create new interactions, and modify or destroy others. The resulting network topology favors excessive amounts of virus production in a stressed host cell network. Short linear peptide motifs common to both virus and host provide the basis for host network modification. METHODS: We focused our host-pathogen study on the binding and competing interactions of HIV-1 and human proteins. We showed that peptide motifs conserved across 70% of HIV-1 subtype B and C samples occurred in similar positions on HIV-1 proteins, and we documented protein domains that interact with these conserved motifs. We predicted which human proteins may be targeted by HIV-1 by taking pairs of human proteins that may interact via a motif conserved in HIV-1 and the corresponding interacting protein domain. RESULTS: Our predictions were enriched with host proteins known to interact with HIV-1 proteins ENV, NEF, and TAT (p-value < 4.26E-21). Cellular pathways statistically enriched for our predictions include the T cell receptor signaling, natural killer cell mediated cytotoxicity, cell cycle, and apoptosis pathways. Gene Ontology molecular function level 5 categories enriched with both predicted and confirmed HIV-1 targeted proteins included categories associated with phosphorylation events and adenyl ribonucleotide binding. CONCLUSION: A list of host proteins highly enriched with those targeted by HIV-1 proteins can be obtained by searching for host protein motifs along virus protein sequences. The resulting set of host proteins predicted to be targeted by virus proteins will become more accurate with better annotations of motifs and domains. Nevertheless, our study validates the role of linear binding motifs shared by virus and host proteins as an important part of the crosstalk between virus and host.

Collagen's triglycine repeat number and phylogeny suggest an interdomain transfer event from a Devonian or Silurian organism into Trichodesmium erythraeum.

Two competing effects at two vastly different scales may explain collagen's current translation length. The necessity to have long molecules for maintaining mechanical integrity at the organism and supraorganism scales may be limited by the need to have small molecules capable of robust self-assembly at the nanoscale. The triglycine repeat regions of all 556 currently cataloged organisms with collagen-like genes were ranked by length. This revealed a sharp boundary in the GXY transcript number at 1032 amino acids (344 GXY repeats). An anomalous exception, however, is the intron-free Trichodesmium erythraeum collagen gene. Immunogold atomic force microscopy reveals, for the first time, the presence of a collagen-like protein in T. erythraeum. A phylogenetic protein sequence analysis which includes vertebrates, nonvertebrates, shrimp white spot syndrome virus, Streptococcus equi, and Bacillus cereus predicts that the collagen-like sequence may have emerged shortly after the divergence of fibrillar and nonfibrillar collagens. The presence of this anomalously long collagen gene within a prokaryote may represent an interdomain transfer from eukaryotes into prokaryotes that gives T. erythraeum the ability to form blooms that cover hundreds of square kilometers of ocean. We propose that the collagen gene entered the prokaryote intron-free only after it had been molded by years of mechanical selective pressure in larger organisms and only after large, dense food sources such as marine vertebrates became available. This anomalously long collagen-like sequence may explain T. erythraeum's ability to aggregate and thus concentrate its toxin for food-source procurement.

Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets.

BACKGROUND: Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal) samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a) all genes on the microarray platform and b) a list of known disease-related genes (a priori selection). We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms. RESULTS: Highly discriminative expression profiles were produced on both simulated gene expression data and expression data from breast cancer and lymphoma datasets on the basis of ER and BCL-6 expression, respectively. Use of relapse-free status to identify profiles for prognosis prediction resulted in poorly discriminative decision rules. Supervised feature selection resulted in more accurate classifications than random or a priori selection, however, the difference in prediction error decreased as the number of features increased. These results held when decision rules were applied across-datasets to samples profiled on the same microarray platform. CONCLUSION: Our results show that many gene sets predict molecular phenotypes accurately. Given this, expression profiles identified using different training datasets should be expected to show little agreement. In addition, we demonstrate the difficulty in predicting relapse directly from microarray data using supervised machine learning approaches. These findings are relevant to the use of molecular profiling for the identification of candidate biomarker panels.

Signaling perturbations induced by invading H. pylori proteins in the host epithelial cells: a mathematical modeling approach.

Helicobacter pylori (H. pylori), a gram-negative bacterium, infects the stomach of approximately 50% of the world population. H. pylori infection is a risk factor for developing chronic gastric ulcers and gastric cancer. The bacteria produce two main cytotoxic proteins: Vacuolating cytotoxin A (VacA) and Cytotoxin-Associated gene A (CagA). When these proteins enter the host cell they interfere with the host MAP Kinase and Apoptosis signaling pathways leading to aberrant cell growth and premature apoptosis. The present study expanded existing quantitative models of the MAP Kinase and Apoptosis signaling pathways to take into account the protein interactions across species using the CellDesigner tool. The resulting network contained hundreds of differential equations in which the coefficients for the biochemical rate constants were estimated from previously published studies. The effect of VacA and CagA on the function of this network were simulated by increasing levels of bacterial load. Simulations showed that increasing bacterial load affected the MAP Kinase signaling in a dose dependant manner. The introduction of CagA decreased the activation time of mapK signaling and extended activation indefinitely despite normal cellular activity to deactivate the protein. Introduction of VacA produced a similar response in the apoptosis pathway. Bacterial load activated both pathways even in the absence of external stimulation. Time course of emergence of transcription factors associated with cell division and cell death predicted by our simulation showed close agreement with that determined from a publicly accessible microarray data set of H. pylori infected stomach epithelium. The quantitative model presented in this study lays the foundation for investigating the affects of single nucleotide polymorphisms (SNPs) on the efficiency of drug treatment.