Mslar: Microbial synthetic lethal and rescue database.

Synthetic lethality (SL) occurs when mutations in two genes together lead to cell or organism death, while a single mutation in either gene does not have a significant impact. This concept can also be extended to three or more genes for SL. Computational and experimental methods have been developed to predict and verify SL gene pairs, especially for yeast and Escherichia coli. However, there is currently a lack of a specialized platform to collect microbial SL gene pairs. Therefore, we designed a synthetic interaction database for microbial genetics that collects 13,313 SL and 2,994 Synthetic Rescue (SR) gene pairs that are reported in the literature, as well as 86,981 putative SL pairs got through homologous transfer method in 281 bacterial genomes. Our database website provides multiple functions such as search, browse, visualization, and Blast. Based on the SL interaction data in the S. cerevisiae, we review the issue of duplications' essentiality and observed that the duplicated genes and singletons have a similar ratio of being essential when we consider both individual and SL. The Microbial Synthetic Lethal and Rescue Database (Mslar) is expected to be a useful reference resource for researchers interested in the SL and SR genes of microorganisms. Mslar is open freely to everyone and available on the web at http://guolab.whu.edu.cn/Mslar/.

Prediction of protein-coding small ORFs in multi-species using integrated sequence-derived features and the random forest model.

Proteins encoded by small open reading frames (sORFs) can serve as functional elements playing important roles in vivo. Such sORFs also constitute the potential pool for facilitating the de novo gene birth, driving evolutionary innovation and species diversity. Therefore, their theoretical and experimental identification has become a critical issue. Herein, we proposed a protein-coding sORFs prediction method merely based on integrative sequence-derived features. Our prediction performance is better or comparable compared with other nine prevalent methods, which shows that our method can provide a relatively reliable research tool for the prediction of protein-coding sORFs. Our method allows users to estimate the potential expression of a queried sORF, which has been demonstrated by the correlation analysis between our possibility estimation and codon adaption index (CAI). Based on the features that we used, we demonstrated that the sequence features of the protein-coding sORFs in the two domains have significant differences implying that it might be a relatively hard task in terms of cross-domain prediction, hence domain-specific models were developed, which allowed users to predict protein-coding sORFs both in eukaryotes and prokaryotes. Finally, a web-server was developed and provided to boost and facilitate the study of the related field, which is freely available at http://guolab.whu.edu.cn/codingCapacity/index.html.

GMMAD: a comprehensive database of human gut microbial metabolite associations with diseases.

BACKGROUND: The natural products, metabolites, of gut microbes are crucial effect factors on diseases. Comprehensive identification and annotation of relationships among disease, metabolites, and microbes can provide efficient and targeted solutions towards understanding the mechanism of complex disease and development of new markers and drugs. RESULTS: We developed Gut Microbial Metabolite Association with Disease (GMMAD), a manually curated database of associations among human diseases, gut microbes, and metabolites of gut microbes. Here, this initial release (i) contains 3,836 disease-microbe associations and 879,263 microbe-metabolite associations, which were extracted from literatures and available resources and then experienced our manual curation; (ii) defines an association strength score and a confidence score. With these two scores, GMMAD predicted 220,690 disease-metabolite associations, where the metabolites all belong to the gut microbes. We think that the positive effective (with both scores higher than suggested thresholds) associations will help identify disease marker and understand the pathogenic mechanism from the sense of gut microbes. The negative effective associations would be taken as biomarkers and have the potential as drug candidates. Literature proofs supported our proposal with experimental consistence; (iii) provides a user-friendly web interface that allows users to browse, search, and download information on associations among diseases, metabolites, and microbes. The resource is freely available at http://guolab.whu.edu.cn/GMMAD . CONCLUSIONS: As the online-available unique resource for gut microbial metabolite-disease associations, GMMAD is helpful for researchers to explore mechanisms of disease- metabolite-microbe and screen the drug and marker candidates for different diseases.

Comprehensive review of the identification of essential genes using computational methods: focusing on feature implementation and assessment.

Essential genes have attracted increasing attention in recent years due to the important functions of these genes in organisms. Among the methods used to identify the essential genes, accurate and efficient computational methods can make up for the deficiencies of expensive and time-consuming experimental technologies. In this review, we have collected researches on essential gene predictions in prokaryotes and eukaryotes and summarized the five predominant types of features used in these studies. The five types of features include evolutionary conservation, domain information, network topology, sequence component and expression level. We have described how to implement the useful forms of these features and evaluated their performance based on the data of Escherichia coli MG1655, Bacillus subtilis 168 and human. The prerequisite and applicable range of these features is described. In addition, we have investigated the techniques used to weight features in various models. To facilitate researchers in the field, two available online tools, which are accessible for free and can be directly used to predict gene essentiality in prokaryotes and humans, were referred. This article provides a simple guide for the identification of essential genes in prokaryotes and eukaryotes.

Altered frequency of peripheral B-cell subsets and their correlation with disease activity in patients with systemic lupus erythematosus: A comprehensive analysis.

Alternations of peripheral B-cell subsets are closely related to disease activity in systemic lupus erythematosus (SLE) and may also predict the relapse of SLE. In this study, we aimed to comprehensively analyse the frequency of peripheral B-cell subsets, and their correlation with disease activity in patients with SLE. The results showed that for B-cell subsets in the antigen-independent differentiation stage, the frequency of the peripheral hematopoietic stem cell (HSC) subset in all patients with SLE was significantly higher than that of control patients. Surprisingly, several significant correlations were noted in newly diagnosed patients with SLE including a positive correlation in the frequency of the common lymphoid progenitor cell (CLP) with cholesterol serum levels. For B-cell subsets in the antigen-dependent differentiation stage, the frequency of naïve B-cell (N-B) subsets in all patients with SLE was significantly higher than that in the control patients. Moreover, the frequency of plasmablasts positively correlated with the SLEDAI score in the newly diagnosed patients. For memory B-cell (M-B) subtypes in the antigen-dependent differentiation stage, the frequency of the class-switched memory B-cell (CSM-B) subsets was positively correlated with the serum levels of complement C3. Notably, the frequency of the CSM-B subset also negatively correlated with the SLEDAI score, whereas the non-class-switched memory B-cell (NSM-B) subset was positively correlated with the serum levels of haemoglobin. Collectively, these findings may contribute to a better understanding of the role played by different B-cell subsets in the pathogenesis of SLE.

RIscoper: a tool for RNA-RNA interaction extraction from the literature.

MOTIVATION: Numerous experimental and computational studies in the biomedical literature have provided considerable amounts of data on diverse RNA-RNA interactions (RRIs). However, few text mining systems for RRIs information extraction are available. RESULTS: RNA Interactome Scoper (RIscoper) represents the first tool for full-scale RNA interactome scanning and was developed for extracting RRIs from the literature based on the N-gram model. Notably, a reliable RRI corpus was integrated in RIscoper, and more than 13 300 manually curated sentences with RRI information were recruited. RIscoper allows users to upload full texts or abstracts, and provides an online search tool that is connected with PubMed (PMID and keyword input), and these capabilities are useful for biologists. RIscoper has a strong performance (90.4% precision and 93.9% recall), integrates natural language processing techniques and has a reliable RRI corpus. AVAILABILITY AND IMPLEMENTATION: The standalone software and web server of RIscoper are freely available at www.rna-society.org/riscoper/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HE4 overexpression decreases pancreatic cancer Capan-1 cell sensitivity to paclitaxel via cell cycle regulation.

BACKGROUND: Paclitaxel is a first-line chemotherapy drug for pancreatic, ovarian, endometrial cancers and other malignancies. However, its efficacy is often compromised by decreased cell sensitivity or the development of resistance. Human epididymis protein 4 (HE4) is highly expressed in gynecologic and pancreatic cancer tissues, and its serum levels are used for patient triage and assistant diagnosis of gynecologic cancers. Previous studies have shown that HE4 overexpression could promote cancer cell proliferation and the growth of tumor xenografts, which suggests its potential involvement in cancer chemosensitivity. METHODS: Two pancreatic cancer cell lines, Capan-1 and Suit-2, were transiently transfected with an HE4 overexpression plasmid, and transfected cells were treated with paclitaxel. S-phase cells were labeled using BrdU, and cell positivity rates were determined by counting BrdU-positive cells. Following HE4 overexpression and/or drug treatment, a western blotting analysis was performed to determine the protein alterations of PCNA and p21, two important cell cycle regulators. RESULTS: HE4 overexpression not only promoted the proliferation of the Capan-1 pancreatic cells, but also significantly decreased cell sensitivity to paclitaxel. Results from western blotting showed that paclitaxel inhibited cell proliferation by decreasing the expression of PCNA and increasing the expression of p21. Data analysis indicated interactive actions between HE4 function and paclitaxel effects, both converging to cell cycle regulation. CONCLUSION: These findings suggest that HE4 could be a potential therapeutic target for the sensitization of pancreatic cancer cells to paclitaxel treatment. HE4 expression levels may be used to predict the sensitivity of pancreatic cancer patients to paclitaxel.

Anti-CRISPRdb: a comprehensive online resource for anti-CRISPR proteins.

CRISPR-Cas is a tool that is widely used for gene editing. However, unexpected off-target effects may occur as a result of long-term nuclease activity. Anti-CRISPR proteins, which are powerful molecules that inhibit the CRISPR-Cas system, may have the potential to promote better utilization of the CRISPR-Cas system in gene editing, especially for gene therapy. Additionally, more in-depth research on these proteins would help researchers to better understand the co-evolution of bacteria and phages. Therefore, it is necessary to collect and integrate data on various types of anti-CRISPRs. Herein, data on these proteins were manually gathered through data screening of the literatures. Then, the first online resource, anti-CRISPRdb, was constructed for effectively organizing these proteins. It contains the available protein sequences, DNA sequences, coding regions, source organisms, taxonomy, virulence, protein interactors and their corresponding three-dimensional structures. Users can access our database at http://cefg.uestc.edu.cn/anti-CRISPRdb/ without registration. We believe that the anti-CRISPRdb can be used as a resource to facilitate research on anti-CRISPR proteins and in related fields.

Zisland Explorer: detect genomic islands by combining homogeneity and heterogeneity properties.

Genomic islands are genomic fragments of alien origin in bacterial and archaeal genomes, usually involved in symbiosis or pathogenesis. In this work, we described Zisland Explorer, a novel tool to predict genomic islands based on the segmental cumulative GC profile. Zisland Explorer was designed with a novel strategy, as well as a combination of the homogeneity and heterogeneity of genomic sequences. While the sequence homogeneity reflects the composition consistence within each island, the heterogeneity measures the composition bias between an island and the core genome. The performance of Zisland Explorer was evaluated on the data sets of 11 different organisms. Our results suggested that the true-positive rate (TPR) of Zisland Explorer was at least 10.3% higher than that of four other widely used tools. On the other hand, the new tool did not lose overall accuracy with the improvement in the TPR and showed better equilibrium among various evaluation indexes. Also, Zisland Explorer showed better accuracy in the prediction of experimental island data. Overall, the tool provides an alternative solution over other tools, which expands the field of island prediction and offers a supplement to increase the performance of the distinct predicting strategy. We have provided a web service as well as a graphical user interface and open-source code across multiple platforms for Zisland Explorer, which is available at http://cefg.uestc.edu.cn/Zisland_Explorer/ or http://tubic.tju.edu.cn/Zisland_Explorer/.

Comprehensive exploration of the enzymes catalysing oxygen-involved reactions and COGs relevant to bacterial oxygen utilization.

To better understand the mechanisms of bacterial adaptation in oxygen environments, we explored the aerobic living-associated genes in bacteria by comparing Clusters of Orthologous Groups of proteins' (COGs) frequencies and gene expression analyses and 38 COGs were detected at significantly higher frequencies (p-value less than 1e-6) in aerobes than in anaerobes. Differential expression analyses between two conditions further narrowed the prediction to 27 aerobe-specific COGs. Then, we annotated the enzymes associated with these COGs. Literature review revealed that 14 COGs contained enzymes catalysing oxygen-involved reactions or products involved in aerobic pathways, suggesting their important roles for survival in aerobic environments. Additionally, protein-protein interaction analyses and step length comparisons of metabolic networks suggested that the other 13 COGs may function relevantly with the 14 enzymes-corresponding COGs, indicating that these genes may be highly associated with oxygen utilization. Phylogenetic and evolutionary analyses showed that the 27 COGs did not have similar trees, and all suffered purifying selection pressures. The divergent times of species containing or lacking aerobic COGs validated that the appearing time of oxygen-utilizing gene was approximately 2.80 Gyr ago. In addition to help better understand oxygen adaption, our method may be extended to identify genes relevant to other living environments.

Accurate prediction of human essential genes using only nucleotide composition and association information.

MOTIVATION: Previously constructed classifiers in predicting eukaryotic essential genes integrated a variety of features including experimental ones. If we can obtain satisfactory prediction using only nucleotide (sequence) information, it would be more promising. Three groups recently identified essential genes in human cancer cell lines using wet experiments and it provided wonderful opportunity to accomplish our idea. Here we improved the Z curve method into the λ-interval form to denote nucleotide composition and association information and used it to construct the SVM classifying model. RESULTS: Our model accurately predicted human gene essentiality with an AUC higher than 0.88 both for 5-fold cross-validation and jackknife tests. These results demonstrated that the essentiality of human genes could be reliably reflected by only sequence information. We re-predicted the negative dataset by our Pheg server and 118 genes were additionally predicted as essential. Among them, 20 were found to be homologues in mouse essential genes, indicating that some of the 118 genes were indeed essential, however previous experiments overlooked them. As the first available server, Pheg could predict essentiality for anonymous gene sequences of human. It is also hoped the λ-interval Z curve method could be effectively extended to classification issues of other DNA elements. AVAILABILITY AND IMPLEMENTATION: http://cefg.uestc.edu.cn/Pheg. CONTACT: fbguo@uestc.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ACFIS: a web server for fragment-based drug discovery.

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/.

BDB: biopanning data bank.

The BDB database (http://immunet.cn/bdb) is an update of the MimoDB database, which was previously described in the 2012 Nucleic Acids Research Database issue. The rebranded name BDB is short for Biopanning Data Bank, which aims to be a portal for biopanning results of the combinatorial peptide library. Last updated in July 2015, BDB contains 2904 sets of biopanning data collected from 1322 peer-reviewed papers. It contains 25,786 peptide sequences, 1704 targets, 492 known templates, 447 peptide libraries and 310 crystal structures of target-template or target-peptide complexes. All data stored in BDB were revisited, and information on peptide affinity, measurement method and procedures was added for 2298 peptides from 411 sets of biopanning data from 246 published papers. In addition, a more professional and user-friendly web interface was implemented, a more detailed help system was designed, and a new on-the-fly data visualization tool and a series of tools for data analysis were integrated. With these new data and tools made available, we expect that the BDB database would become a major resource for scholars using phage display, with improved utility for biopanning and related scientific communities.

Laribacter hongkongensis anaerobic adaptation mediated by arginine metabolism is controlled by the cooperation of FNR and ArgR.

Laribacter hongkongensis is a fish-borne pathogen associated with invasive infections and gastroenteritis. Its adaptive mechanisms to oxygen-limiting conditions in various environmental niches remain unclear. In this study, we compared the transcriptional profiles of L. hongkongensis under aerobic and anaerobic conditions using RNA-sequencing. Expression of genes involved in arginine metabolism significantly increased under anoxic conditions. Arginine was exploited as the sole energy source in L. hongkongensis for anaerobic respiration via the arginine catabolism pathway: specifically via the arginine deiminase (ADI) pathway. A transcriptional regulator FNR was identified to coordinate anaerobic metabolism by tightly regulating the expression of arginine metabolism genes. FNR executed its regulatory function by binding to FNR boxes in arc operons promoters. Survival of isogenic fnr mutant in macrophages decreased significantly when compared with wild-type; and expression level of fnr increased 8 h post-infection. Remarkably, FNR directly interacted with ArgR, another regulator that influences the biological fitness and intracellular survival of L. hongkongensis by regulating arginine metabolism genes. Our results demonstrated that FNR and ArgR work in coordination to respond to oxygen changes in both extracellular and intracellular environments, by finely regulating the ADI pathway and arginine anabolism pathways, thereby optimizing bacterial fitness in various environmental niches.

Identification and analysis of genomic islands in Burkholderia cenocepacia AU 1054 with emphasis on pathogenicity islands.

BACKGROUND: Genomic islands (GIs) are genomic regions that reveal evidence of horizontal DNA transfer. They can code for many functions and may augment a bacterium's adaptation to its host or environment. GIs have been identified in strain J2315 of Burkholderia cenocepacia, whereas in strain AU 1054 there has been no published works on such regions according to our text mining and keyword search in Medline. RESULTS: In this study, we identified 21 GIs in AU 1054 by combining two computational tools. Feature analyses suggested that the predictions are highly reliable and hence illustrated the advantage of joint predictions by two independent methods. Based on putative virulence factors, four GIs were further identified as pathogenicity islands (PAIs). Through experiments of gene deletion mutants in live bacteria, two putative PAIs were confirmed, and the virulence factors involved were identified as lipA and copR. The importance of the genes lipA (from PAI 1) and copR (from PAI 2) for bacterial invasion and replication indicates that they are required for the invasive properties of B. cenocepacia and may function as virulence determinants for bacterial pathogenesis and host infection. CONCLUSIONS: This approach of in silico prediction of GIs and subsequent identification of potential virulence factors in the putative island regions with final validation using wet experiments could be used as an effective strategy to rapidly discover novel virulence factors in other bacterial species and strains.

ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes.

In 2003, we developed an ab initio program, ZCURVE 1.0, to find genes in bacterial and archaeal genomes. In this work, we present the updated version (i.e. ZCURVE 3.0). Using 422 prokaryotic genomes, the average accuracy was 93.7% with the updated version, compared with 88.7% with the original version. Such results also demonstrate that ZCURVE 3.0 is comparable with Glimmer 3.02 and may provide complementary predictions to it. In fact, the joint application of the two programs generated better results by correctly finding more annotated genes while also containing fewer false-positive predictions. As the exclusive function, ZCURVE 3.0 contains one post-processing program that can identify essential genes with high accuracy (generally >90%). We hope ZCURVE 3.0 will receive wide use with the web-based running mode. The updated ZCURVE can be freely accessed from http://cefg.uestc.edu.cn/zcurve/ or http://tubic.tju.edu.cn/zcurveb/ without any restrictions.

Zinc Finger and X-Linked Factor (ZFX) Binds to Human SET Transcript 2 Promoter and Transactivates SET Expression.

SET (SE Translocation) protein carries out multiple functions including those for protein phosphatase 2A (PP2A) inhibition, histone modification, DNA repair, and gene regulation. SET overexpression has been detected in brain neurons of patients suffering Alzheimer's disease, follicle theca cells of Polycystic Ovary Syndrome (PCOS) patients, and ovarian cancer cells, indicating that SET may play a pathological role for these disorders. SET transcript 2, produced by a specific promoter, represents a major transcript variant in different cell types. In this study, we characterized the transcriptional activation of human SET transcript 2 promoter in HeLa cells. Promoter deletion experiments and co-transfection assays indicated that ZFX, the Zinc finger and X-linked transcription factor, was able to transactivate the SET promoter. A proximal promoter region containing four ZFX-binding sites was found to be critical for the ZFX-mediated transactivation. Mutagenesis study indicated that the ZFX-binding site located the closest to the transcription start site accounted for most of the ZFX-mediated transactivity. Manipulation of ZFX levels by overexpression or siRNA knockdown confirmed the significance and specificity of the ZFX-mediated SET promoter activation. Chromatin immunoprecipitation results verified the binding of ZFX to its cognate sites in the SET promoter. These findings have led to identification of ZFX as an upstream factor regulating SET gene expression. More studies are required to define the in vivo significance of this mechanism, and specifically, its implication for several benign and malignant diseases related to SET dysregulation.

SMAL: A Resource of Spontaneous Mutation Accumulation Lines.

Mutation is the ultimate source of genetic variation and evolution. Mutation accumulation (MA) experiments are an alternative approach to study de novo mutation events directly. We have constructed a resource of Spontaneous Mutation Accumulation Lines (SMAL; http://cefg.uestc.edu.cn/smal), which contains all the current publicly available MA lines identified by high-throughput sequencing. We have relocated and mapped the mutations based on the most recent genome annotations. A total of 5,608 single base mutations and 540 other mutations were obtained and are recorded in the current version of the SMAL database. The integrated data in SMAL provide detailed information that can be used in new theoretical analyses. We believe that the SMAL resource will help researchers better understand the processes of genetic variation and the incidence of disease.

Multiple Factors Drive Replicating Strand Composition Bias in Bacterial Genomes.

Composition bias from Chargaff's second parity rule (PR2) has long been found in sequenced genomes, and is believed to relate strongly with the replication process in microbial genomes. However, some disagreement on the underlying reason for strand composition bias remains. We performed an integrative analysis of various genomic features that might influence composition bias using a large-scale dataset of 1111 genomes. Our results indicate (1) the bias was stronger in obligate intracellular bacteria than in other free-living species (p-value=0.0305); (2) Fusobacteria and Firmicutes had the highest average bias among the 24 microbial phyla analyzed; (3) the strength of selected codon usage bias and generation times were not observably related to strand composition bias (p-value=0.3247); (4) significant negative relationships were found between GC content, genome size, rearrangement frequency, Clusters of Orthologous Groups (COG) functional subcategories A, C, I, Q, and composition bias (p-values<1.0×10(-8)); (5) gene density and COG functional subcategories D, F, J, L, and V were positively related with composition bias (p-value<2.2×10(-16)); and (6) gene density made the most important contribution to composition bias, indicating transcriptional bias was associated strongly with strand composition bias. Therefore, strand composition bias was found to be influenced by multiple factors with varying weights.

MimoDB 2.0: a mimotope database and beyond.

Mimotopes are peptides with affinities to given targets. They are readily obtained through biopanning against combinatorial peptide libraries constructed by phage display and other display technologies such as mRNA display, ribosome display, bacterial display and yeast display. Mimotopes have been used to infer the protein interaction sites and networks; they are also ideal candidates for developing new diagnostics, therapeutics and vaccines. However, such valuable peptides are not collected in the central data resources such as UniProt and NCBI GenPept due to their 'unnatural' short sequences. The MimoDB database is an information portal to biopanning results of random libraries. In version 2.0, it has 15,633 peptides collected from 849 papers and grouped into 1818 sets. Besides the core data on panning experiments and their results, broad background information on target, template, library and structure is included. An accompanied benchmark has also been compiled for bioinformaticians to develop and evaluate their new models, algorithms and programs. In addition, the MimoDB database provides tools for simple and advanced searches, structure visualization, BLAST and alignment view on the fly. The experimental biologists can easily use the database as a virtual control to exclude possible target-unrelated peptides. The MimoDB database is freely available at http://immunet.cn/mimodb.