Hufeng Zhou

Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation

Traditional Chinese Medicine (TCM) is widely practised and is viewed as an attractive alternative to conventional medicine. Quantitative information about TCM prescriptions, constituent herbs and herbal ingredients is necessary for studying and exploring TCM.

The NF-κB Genomic Landscape in Lymphoblastoid B Cells

The nuclear factor κB (NF-κΒ) subunits RelA, RelB, cRel, p50, and p52 are each critical for B cell development and function. To systematically characterize their responses to canonical and noncanonical NF-κB pathway activity, we performed chromatin immunoprecipitation followed by high-throughput DNA sequencing (ChIP-seq) analysis in lymphoblastoid B cell lines (LCLs). We found a complex NF-κB-binding landscape, which did not readily reflect the two NF-κB pathway paradigms. Instead, 10 subunit-binding patterns were observed at promoters and 11 at enhancers. Nearly one-third of NF-κB-binding sites lacked κB motifs and were instead enriched for alternative motifs. The oncogenic forkhead box protein FOXM1 co-occupied nearly half of NF-κB-binding sites and was identified in protein complexes with NF-κB on DNA. FOXM1 knockdown decreased NF-κB target gene expression and ultimately induced apoptosis, highlighting FOXM1 as a synthetic lethal target in B cell malignancy. These studies provide a resource for understanding mechanisms that underlie NF-κB nuclear activity and highlight opportunities for selective NF-κB blockade.

Epstein–Barr Virus Nuclear Antigen 3C binds to BATF/IRF4 or SPI1/IRF4 composite sites and recruits Sin3A to repress CDKN2A

Significance Epstein–Barr virus (EBV) is an important causative agent of B-cell lymphomas and Hodgkin disease in immune-deficient people, including HIV-infected people. The experiments described here were undertaken to determine the mechanisms through which the EBV-encoded nuclear protein EBNA3C blocks the cell p14ARF and p16INK4A tumor suppressor-mediated inhibition of EBV-infected B-cell growth, thereby unfettering EBV-driven B-cell proliferation. The experiments also identify the molecular basis for diverse EBNA3C enhancer interactions with cell DNA-binding proteins and cell DNA to regulate MYC, pRB, BCL2, and BIM expression. Surprisingly, EBNA3C’s role in enhancer-mediated cell gene transcription up-regulation is primarily mediated by combinatorial effects with cell transcription factors, most notably AICEs, EICEs, and RUNX3. Epstein–Barr virus nuclear antigen 3C (EBNA3C) repression of CDKN2A p14ARF and p16INK4A is essential for immortal human B-lymphoblastoid cell line (LCL) growth. EBNA3C ChIP-sequencing identified >13,000 EBNA3C sites in LCL DNA. Most EBNA3C sites were associated with active transcription; 64% were strong H3K4me1- and H3K27ac-marked enhancers and 16% were active promoters marked by H3K4me3 and H3K9ac. Using ENCODE LCL transcription factor ChIP-sequencing data, EBNA3C sites coincided (±250 bp) with RUNX3 (64%), BATF (55%), ATF2 (51%), IRF4 (41%), MEF2A (35%), PAX5 (34%), SPI1 (29%), BCL11a (28%), SP1 (26%), TCF12 (23%), NF-κB (23%), POU2F2 (23%), and RBPJ (16%). EBNA3C sites separated into five distinct clusters: (i) Sin3A, (ii) EBNA2/RBPJ, (iii) SPI1, and (iv) strong or (v) weak BATF/IRF4. EBNA3C signals were positively affected by RUNX3, BATF/IRF4 (AICE) and SPI1/IRF4 (EICE) cooccupancy. Gene set enrichment analyses correlated EBNA3C/Sin3A promoter sites with transcription down-regulation (P < 1.6 × 10−4). EBNA3C signals were strongest at BATF/IRF4 and SPI1/IRF4 composite sites. EBNA3C bound strongly to the p14ARF promoter through SPI1/IRF4/BATF/RUNX3, establishing RBPJ-, Sin3A-, and REST-mediated repression. EBNA3C immune precipitated with Sin3A and conditional EBNA3C inactivation significantly decreased Sin3A binding at the p14ARF promoter (P < 0.05). These data support a model in which EBNA3C binds strongly to BATF/IRF4/SPI1/RUNX3 sites to enhance transcription and recruits RBPJ/Sin3A- and REST/NRSF-repressive complexes to repress p14ARF and p16INK4A expression.

Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions

BackgroundH. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs.ResultsWe develop a stringent homology-based prediction approach by taking into account (i) differences between eukaryotic and prokaryotic proteins and (ii) differences between inter-species and intra-species PPI interfaces. We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis. These analyses support the validity of our prediction result, and clearly show that our approach has better performance in predicting H. sapiens-M. tuberculosis H37Rv PPIs. Using our stringent homology-based approach, we have predicted a set of highly plausible H. sapiens-M. tuberculosis H37Rv PPIs which might be useful for many of related studies. Based on our analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent homology-based approach, we have discovered several interesting properties which are reported here for the first time. We find that both host proteins and pathogen proteins involved in the host-pathogen PPIs tend to be hubs in their own intra-species PPI network. Also, both host and pathogen proteins involved in host-pathogen PPIs tend to have longer primary sequence, tend to have more domains, tend to be more hydrophilic, etc. And the protein domains from both host and pathogen proteins involved in host-pathogen PPIs tend to have lower charge, and tend to be more hydrophilic.ConclusionsOur stringent homology-based prediction approach provides a better strategy in predicting PPIs between eukaryotic hosts and prokaryotic pathogens than a conventional homology-based approach. The properties we have observed from the predicted H. sapiens-M. tuberculosis H37Rv PPI network are useful for understanding inter-species host-pathogen PPI networks and provide novel insights for host-pathogen interaction studies.ReviewersThis article was reviewed by Michael Gromiha, Narayanaswamy Srinivasan and Thomas Dandekar.

Epstein–Barr virus nuclear antigen leader protein localizes to promoters and enhancers with cell transcription factors and EBNA2

Significance Epstein–Barr virus nuclear antigen (EBNA) leader protein (LP) and EBNA2 (E2) up-regulation of virus and cell gene expression is important for human B-lymphocyte conversion to continuous, potentially malignant, lymphoblast cell lines. Although the molecular mechanism(s) underlying LP and E2 regulation of cell gene expression have been partially elucidated, LP ChIP-sequencing studies have now revealed that LP and LP/E2 interact, genome-wide, with human B-cell transcription factors, mostly at or near prepatterned promoter sites, to increase cell transcription factor occupancies, increase activation-associated histone marks, and positively affect cell gene transcription. Epstein–Barr virus (EBV) nuclear antigens EBNALP (LP) and EBNA2 (E2) are coexpressed in EBV-infected B lymphocytes and are critical for lymphoblastoid cell line outgrowth. LP removes NCOR and RBPJ repressive complexes from promoters, enhancers, and matrix-associated deacetylase bodies, whereas E2 activates transcription from distal enhancers. LP ChIP-seq analyses identified 19,224 LP sites of which ∼50% were ±2 kb of a transcriptional start site. LP sites were enriched for B-cell transcription factors (TFs), YY1, SP1, PAX5, BATF, IRF4, ETS1, RAD21, PU.1, CTCF, RBPJ, ZNF143, SMC3, NFκB, TBLR, and EBF. E2 sites were also highly enriched for LP-associated cell TFs and were more highly occupied by RBPJ and EBF. LP sites were highly marked by H3K4me3, H3K27ac, H2Az, H3K9ac, RNAPII, and P300, indicative of activated transcription. LP sites were 29% colocalized with E2 (LP/E2). LP/E2 sites were more similar to LP than to E2 sites in associated cell TFs, RNAPII, P300, and histone H3K4me3, H3K9ac, H3K27ac, and H2Az occupancy, and were more highly transcribed than LP or E2 sites. Gene affected by CTCF and LP cooccupancy were more highly expressed than genes affected by CTCF alone. LP was at myc enhancers and promoters and of MYC regulated ccnd2, 23 med complex components, and MYC regulated cell survival genes, igf2r and bcl2. These data implicate LP and associated TFs and DNA looping factors CTCF, RAD21, SMC3, and YY1/INO80 chromatin-remodeling complexes in repressor depletion and gene activation necessary for lymphoblastoid cell line growth and survival.

Traditional Chinese medicine information database

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PROGRESS IN COMPUTATIONAL STUDIES OF HOST{PATHOGEN INTERACTIONS

Host-pathogen interactions are important for understanding infection mechanism and developing better treatment and prevention of infectious diseases. Many computational studies on host-pathogen interactions have been published. Here, we review recent progress and results in this field and provide a systematic summary, comparison and discussion of computational studies on host-pathogen interactions, including prediction and analysis of host-pathogen protein-protein interactions; basic principles revealed from host-pathogen interactions; and database and software tools for host-pathogen interaction data collection, integration and analysis.

Comparative analysis and assessment of M. tuberculosis H37Rv protein-protein interaction datasets

BackgroundM. tuberculosis is a formidable bacterial pathogen. There is thus an increasing demand on understanding the function and relationship of proteins in various strains of M. tuberculosis. Protein-protein interactions (PPIs) data are crucial for this kind of knowledge. However, the quality of the main available M. tuberculosis PPI datasets is unclear. This hampers the effectiveness of research works that rely on these PPI datasets. Here, we analyze the two main available M. tuberculosis H37Rv PPI datasets. The first dataset is the high-throughput B2H PPI dataset from Wang et al’s recent paper in Journal of Proteome Research. The second dataset is from STRING database, version 8.3, comprising entirely of H37Rv PPIs predicted using various methods. We find that these two datasets have a surprisingly low level of agreement. We postulate the following causes for this low level of agreement: (i) the H37Rv B2H PPI dataset is of low quality; (ii) the H37Rv STRING PPI dataset is of low quality; and/or (iii) the H37Rv STRING PPIs are predictions of other forms of functional associations rather than direct physical interactions.ResultsTo test the quality of these two datasets, we evaluate them based on correlated gene expression profiles, coherent informative GO term annotations, and conservation in other organisms. We observe a significantly greater portion of PPIs in the H37Rv STRING PPI dataset (with score ≥ 770) having correlated gene expression profiles and coherent informative GO term annotations in both interaction partners than that in the H37Rv B2H PPI dataset. Predicted H37Rv interologs derived from non-M. tuberculosis experimental PPIs are much more similar to the H37Rv STRING functional associations dataset (with score ≥ 770) than the H37Rv B2H PPI dataset. H37Rv predicted physical interologs from IntAct also show extremely low similarity with the H37Rv B2H PPI dataset; and this similarity level is much lower than that between the S. aureus MRSA252 predicted physical interologs from IntAct and S. aureus MRSA252 pull-down PPIs. Comparative analysis with several representative two-hybrid PPI datasets in other species further confirms that the H37Rv B2H PPI dataset is of low quality. Next, to test the possibility that the H37Rv STRING PPIs are not purely direct physical interactions, we compare M. tuberculosis H37Rv protein pairs that catalyze adjacent steps in enzymatic reactions to B2H PPIs and predicted PPIs in STRING, which shows it has much lower similarities with the B2H PPIs than with STRING PPIs. This result strongly suggests that the H37Rv STRING PPIs more likely correspond to indirect relationships between protein pairs than to B2H PPIs. For more precise support, we turn to S. cerevisiae for its comprehensively studied interactome. We compare S. cerevisiae predicted PPIs in STRING to three independent protein relationship datasets which respectively comprise PPIs reported in Y2H assays, protein pairs reported to be in the same protein complexes, and protein pairs that catalyze successive reaction steps in enzymatic reactions. Our analysis reveals that S. cerevisiae predicted STRING PPIs have much higher similarity to the latter two types of protein pairs than to two-hybrid PPIs. As H37Rv STRING PPIs are predicted using similar methods as S. cerevisiae predicted STRING PPIs, this suggests that these H37Rv STRING PPIs are more likely to correspond to the latter two types of protein pairs rather than to two-hybrid PPIs as well.ConclusionsThe H37Rv B2H PPI dataset has low quality. It should not be used as the gold standard to assess the quality of other (possibly predicted) H37Rv PPI datasets. The H37Rv STRING PPI dataset also has low quality; nevertheless, a subset consisting of STRING PPIs with score ≥770 has satisfactory quality. However, these STRING “PPIs” should be interpreted as functional associations, which include a substantial portion of indirect protein interactions, rather than direct physical interactions. These two factors cause the strikingly low similarity between these two main H37Rv PPI datasets. The results and conclusions from this comparative analysis provide valuable guidance in using these M. tuberculosis H37Rv PPI datasets in subsequent studies for a wide range of purposes.

Epstein–Barr virus nuclear antigen 3A partially coincides with EBNA3C genome-wide and is tethered to DNA through BATF complexes

Significance Epstein–Barr Virus (EBV)-infected lymphoblasts can give rise to non-Hodgkin’s lymphomas, Hodgkin’s disease, and lymphoproliferative disorders, especially in immunosuppressed and HIV-infected individuals. EBV-driven lymphoblast growth requires EBV nuclear antigen 3A (EBNA3A) for suppression of CDKN2A-mediated cell senescence responses. We have described the EBNA3A genome-wide landscape in EBV-infected human lymphoblasts. EBNA3A was found mostly at strong enhancers, colocalized with BATF, ETS, IRF4, and RUNX3. EBNA3A was tethered to DNA through BATF protein complexes. Epstein–Barr Virus (EBV) conversion of B-lymphocytes to Lymphoblastoid Cell Lines (LCLs) requires four EBV nuclear antigen (EBNA) oncoproteins: EBNA2, EBNALP, EBNA3A, and EBNA3C. EBNA2 and EBNALP associate with EBV and cell enhancers, up-regulate the EBNA promoter, MYC, and EBV Latent infection Membrane Proteins (LMPs), which up-regulate BCL2 to protect EBV-infected B-cells from MYC proliferation-induced cell death. LCL proliferation induces p16INK4A and p14ARF-mediated cell senescence. EBNA3A and EBNA3C jointly suppress p16INK4A and p14ARF, enabling continuous cell proliferation. Analyses of the EBNA3A human genome-wide ChIP-seq landscape revealed 37% of 10,000 EBNA3A sites to be at strong enhancers; 28% to be at weak enhancers; 4.4% to be at active promoters; and 6.9% to be at weak and poised promoters. EBNA3A colocalized with BATF-IRF4, ETS-IRF4, RUNX3, and other B-cell Transcription Factors (TFs). EBNA3A sites clustered into seven unique groups, with differing B-cell TFs and epigenetic marks. EBNA3A coincidence with BATF-IRF4 or RUNX3 was associated with stronger EBNA3A ChIP-Seq signals. EBNA3A was at MYC, CDKN2A/B, CCND2, CXCL9/10, and BCL2, together with RUNX3, BATF, IRF4, and SPI1. ChIP-re-ChIP revealed complexes of EBNA3A on DNA with BATF. These data strongly support a model in which EBNA3A is tethered to DNA through a BATF-containing protein complexes to enable continuous cell proliferation.

Stringent DDI-based Prediction of H. sapiens-M. tuberculosis H37Rv Protein-Protein Interactions

BackgroundH. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited.ResultsWe develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces.We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI.ConclusionsThe stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.