Will Dampier

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

BackgroundHost 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.MethodsWe 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.ResultsOur 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.ConclusionA 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.

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.

Attenuation of Forkhead signaling by the retinal determination factor DACH1

The Drosophila Dachshund (Dac) gene, cloned as a dominant inhibitor of the hyperactive growth factor mutant ellipse, encodes a key component of the retinal determination gene network that governs cell fate. Herein, cyclic amplification and selection of targets identified a DACH1 DNA-binding sequence that resembles the FOX (Forkhead box–containing protein) binding site. Genome-wide in silico promoter analysis of DACH1 binding sites identified gene clusters populating cellular pathways associated with the cell cycle and growth factor signaling. ChIP coupled with high-throughput sequencing mapped DACH1 binding sites to corresponding gene clusters predicted in silico and identified as weight matrix resembling the cyclic amplification and selection of targets–defined sequence. DACH1 antagonized FOXM1 target gene expression, promoter occupancy in the context of local chromatin, and contact-independent growth. Attenuation of FOX function by the cell fate determination pathway has broad implications given the diverse role of FOX proteins in cellular biology and tumorigenesis.

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 Parkinsons 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.

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 Crohns 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. (Inflamm Bowel Dis 2012;)

Large-scale integration of microarray data reveals genes and pathways common to multiple cancer types

The global gene expression analysis of cancer and healthy tissues typically results in large numbers of genes that are significantly altered in cancer. Such data, however, has been difficult to interpret due to the high level of variation of gene lists across laboratories and the small sample sizes used in individual studies. In this investigation, we compiled microarray data obtained from the same platform family from 84 laboratories, resulting in a database containing 1,043 healthy tissue samples and 4,900 cancer samples for 13 different tissue types. The primary cancers considered included adrenal gland, brain, breast, cervix, colon, kidney, liver, lung, ovary, pancreas, prostate and skin tissues. We normalized the data together and analyzed subsets for the discovery of genes involved in normal to cancer transformation. Our integrated significance analysis of microarrays approach produced top 400 gene lists for each of the 13 cancer types. These lists were highly statistically enriched with genes already associated with cancer in research publications excluding microarray studies (p < 1.31 E ‐ 12). The genes MTIM and RRM2 appeared in nine and TOP2A in eight lists of significantly altered genes in cancer. In total, there were 132 genes present in at least four gene lists, 11 of which were not previously associated with cancer. The list contains 17 metal ions and 15 adenyl ribonucleotide binding proteins, six kinases and six transcription factors. Our results point to the value of integrating microarray data in the study of combination drug therapies targeting metastasis.

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

BackgroundIndependently 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.ResultsHighly 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.ConclusionOur 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.

CCAAT Enhancer Binding Protein and Nuclear Factor of Activated T Cells Regulate HIV-1 LTR via a Novel Conserved Downstream Site in Cells of the Monocyte-Macrophage Lineage

Transcriptional control of the human immunodeficiency virus type 1 (HIV-1) promoter, the long terminal repeat (LTR), is achieved by interactions with cis-acting elements present both upstream and downstream of the start site. In silico transcription factor binding analysis of the HIV-1 subtype B LTR sequences revealed a potential downstream CCAAT enhancer binding protein (C/EBP) binding site. This binding site (+158 to+172), designated DS3, was found to be conserved in 67% of 3,858 unique subtype B LTR sequences analyzed in terms of nucleotide sequence as well as physical location in the LTR. DS3 was found to be well represented in other subtypes as well. Interestingly, DS3 overlaps with a previously identified region that bind members of the nuclear factor of activated T cells (NFAT) family of proteins. NFATc2 exhibited a higher relative affinity for DS3 as compared with members of the C/EBP family (C/EBP α and β). DS3 was able to compete efficiently with the low-affinity upstream C/EBP binding site I with respect to C/EBP binding, suggesting utilization of both NFAT and C/EBP. Moreover, cyclosporine A treatment, which has been shown to prevent dephosphorylation and nuclear translocation of NFAT isoforms, resulted in enhanced C/EBPα binding. The interactions at DS3 were also validated in an integrated HIV-1 LTR in chronically infected U1 cells. A binding knockout of DS3 demonstrated reduced HIV-1 LTR-directed transcription under both basal and interleukin-6-stimulated conditions only in cells of the monocyte-macrophage lineage cells and not in cells of T-cell origin. Thus, the events at DS3 positively regulate the HIV-1 promoter in cells of the monocyte-macrophage lineage.

Bioinformatic Analysis of HIV-1 Entry and Pathogenesis

The evolution of human immunodeficiency virus type 1 (HIV-1) with respect to co-receptor utilization has been shown to be relevant to HIV-1 pathogenesis and disease. The CCR5-utilizing (R5) virus has been shown to be important in the very early stages of transmission and highly prevalent during asymptomatic infection and chronic disease. In addition, the R5 virus has been proposed to be involved in neuroinvasion and central nervous system (CNS) disease. In contrast, the CXCR4-utilizing (X4) virus is more prevalent during the course of disease progression and concurrent with the loss of CD4(+) T cells. The dual-tropic virus is able to utilize both co-receptors (CXCR4 and CCR5) and has been thought to represent an intermediate transitional virus that possesses properties of both X4 and R5 viruses that can be encountered at many stages of disease. The use of computational tools and bioinformatic approaches in the prediction of HIV-1 co-receptor usage has been growing in importance with respect to understanding HIV-1 pathogenesis and disease, developing diagnostic tools, and improving the efficacy of therapeutic strategies focused on blocking viral entry. Current strategies have enhanced the sensitivity, specificity, and reproducibility relative to the prediction of co-receptor use; however, these technologies need to be improved with respect to their efficient and accurate use across the HIV-1 subtypes. The most effective approach may center on the combined use of different algorithms involving sequences within and outside of the env-V3 loop. This review focuses on the HIV-1 entry process and on co-receptor utilization, including bioinformatic tools utilized in the prediction of co-receptor usage. It also provides novel preliminary analyses for enabling identification of linkages between amino acids in V3 with other components of the HIV-1 genome and demonstrates that these linkages are different between X4 and R5 viruses.

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.