Database resources of the National Center for Biotechnology Information

Genome-wide identification and characterization of DTX family genes highlighting their locations, functions, and regulatory factors in banana (Musa acuminata)

The detoxification efflux carriers (DTX) are a significant group of multidrug efflux transporter family members that play diverse functions in all kingdoms of living organisms. However, genome-wide identification and characterization of DTX family transporters have not yet been performed in banana, despite its importance as an economic fruit plant. Therefore, a detailed genome-wide analysis of DTX family transporters in banana (Musa acuminata) was conducted using integrated bioinformatics and systems biology approaches. In this study, a total of 37 DTX transporters were identified in the banana genome and divided into four groups (I, II, III, and IV) based on phylogenetic analysis. The gene structures, as well as their proteins' domains and motifs, were found to be significantly conserved. Gene ontology (GO) annotation revealed that the predicted DTX genes might play a vital role in protecting cells and membrane-bound organelles through detoxification mechanisms and the removal of drug molecules from banana cells. Gene regulatory analyses identified key transcription factors (TFs), cis-acting elements, and post-transcriptional regulators (miRNAs) of DTX genes, suggesting their potential roles in banana. Furthermore, the changes in gene expression levels due to pathogenic infections and non-living factor indicate that banana DTX genes play a role in responses to both biotic and abiotic stresses. The results of this study could serve as valuable tools to improve banana quality by protecting them from a range of environmental stresses.

Known phyla dominate the Tara Oceans RNA virome

A recent study proposed five new RNA virus phyla, two of which, 'Taraviricota' and 'Arctiviricota', were stated to be 'dominant in the oceans'. However, the study's assignments classify 28,353 putative RdRp-containing contigs to known phyla but only 886 (2.8%) to the five proposed new phyla combined. I re-mapped the reads to the contigs, finding that known phyla also account for a large majority (93.8%) of reads according to the study's classifications, and that contigs originally assigned to 'Arctiviricota' accounted for only a tiny fraction (0.01%) of reads from Arctic Ocean samples. Performing my own virus identification and classifications, I found that 99.95 per cent of reads could be assigned to known phyla. The most abundant species was Beihai picorna-like virus 34 (15% of reads), and the most abundant order-like cluster was classified as Picornavirales (45% of reads). Sequences in the claimed new phylum 'Pomiviricota' were placed inside a phylogenetic tree for established order Durnavirales with 100 per cent confidence. Moreover, two contigs assigned to the proposed phylum 'Taraviricota' were found to have high-identity alignments to dinoflagellate proteins, tentatively identifying this group of RdRp-like sequences as deriving from non-viral transcripts. Together, these results comprehensively contradict the claim that new phyla dominate the data.

Efficient strategy to isolate exosomes using anti-CD63 antibodies conjugated to gold nanoparticles

Exosomes, a subpopulation of Extracellular vesicles (EVs), are cell-secreted vesicles found in the majority of biological fluids, including breast milk, tears, sweat, blood and, urine. The density and size of these vesicles depend on a variety of factors, including age, gender and the biological condition of the individual. Researchers are now focusing on the selective extraction of exosomes from bodily fluids due to the unique biomolecule composition of exosomes, which is critical for diagnosis, disease, and regeneration. Furthermore, current approaches for exosome isolation have limitations, necessitating the development of a simpler and more effective technique to achieve this goal. In this study, we investigated a quick and effective strategy for isolating exosomes from serum using a bench-top centrifuge. This was accomplished by raising antibodies against exosome surface tetraspanins (CD9, CD63 & CD81) in Leghorn chickens due to their phylogenetic distance from humans and cost-effectiveness for commercial use. In order to separate exosomes from a complex biological fluid, the antibodies were further coupled with gold nanoparticles (AuNPs). The findings were validated using ELISA, spectrophotometry, and transmission electron microscopy (TEM). Using this technique, exosome isolation from serum was achieved rapidly and these were captured by using anti CD63 antibodies bound to AuNPs. To summarize, exosomes were purified from serum using anti-CD63 antibodies conjugated to gold nanoparticles (IgY@AuNPs). Consequently, the approach for exosome isolation from biological fluid could be useful for clinically monitoring the biological state of the patients.

Translation of Monoclonal Antibodies Pharmacokinetics from Animal to Human Using Physiologically Based Modeling in Open Systems Pharmacology (OSP) Suite: A Retrospective Analysis of Bevacizumab

Interspecies translation of monoclonal antibodies (mAbs) pharmacokinetics (PK) in presence of target-mediated drug disposition (TMDD) is particularly challenging. Incorporation of TMDD in physiologically based PK (PBPK) modeling is recent and needs to be consolidated and generalized to provide better prediction of TMDD regarding inter-species translation during preclinical and clinical development steps of mAbs. The objective of this study was to develop a generic PBPK translational approach for mAbs using the open-source software (PK-Sim® and Mobi®). The translation of bevacizumab based on data in non-human primates (NHP), healthy volunteers (HV), and cancer patients was used as a case example for model demonstration purpose. A PBPK model for bevacizumab concentration-time data was developed using data from literature and the Open Systems Pharmacology (OSP) Suite version 10. PK-sim® was used to build the linear part of bevacizumab PK (mainly FcRn-mediated), whereas MoBi® was used to develop the target-mediated part. The model was first developed for NHP and used for a priori PK prediction in HV. Then, the refined model obtained in HV was used for a priori prediction in cancer patients. A priori predictions were within 2-fold prediction error (predicted/observed) for both area under the concentration-time curve (AUC) and maximum concentration (Cmax) and all the predicted concentrations were within 2-fold average fold error (AFE) and average absolute fold error (AAFE). Sensitivity analysis showed that FcRn-mediated distribution and elimination processes must be accounted for at all mAb concentration levels, whereas the lower the mAb concentration, the more significant the target-mediated elimination. This project is the first step to generalize the full PBPK translational approach in Model-Informed Drug Development (MIDD) of mAbs using OSP Suite.

cazy_webscraper: local compilation and interrogation of comprehensive CAZyme datasets

Carbohydrate active enzymes (CAZymes) are pivotal in biological processes including energy metabolism, cell structure maintenance, signalling, and pathogen recognition. Bioinformatic prediction and mining of CAZymes improves our understanding of these activities and enables discovery of candidates of interest for industrial biotechnology, particularly the processing of organic waste for biofuel production. CAZy (www.cazy.org) is a high-quality, manually curated, and authoritative database of CAZymes that is often the starting point for these analyses. Automated querying and integration of CAZy data with other public datasets would constitute a powerful resource for mining and exploring CAZyme diversity. However, CAZy does not itself provide methods to automate queries, or integrate annotation data from other sources (except by following hyperlinks) to support further analysis. To overcome these limitations we developed cazy_webscraper, a command-line tool that retrieves data from CAZy and other online resources to build a local, shareable and reproducible database that augments and extends the authoritative CAZy database. cazy_webscraper's integration of curated CAZyme annotations with their corresponding protein sequences, up-to-date taxonomy assignments, and protein structure data facilitates automated large-scale and targeted bioinformatic CAZyme family analysis and candidate screening. This tool has found widespread uptake in the community, with over 35 000 downloads (from April 2021 to June 2023). We demonstrate the use and application of cazy_webscraper to: (i) augment, update and correct CAZy database accessions; (ii) explore the taxonomic distribution of CAZymes recorded in CAZy, identifying under-represented taxa and unusual CAZy class distributions; and (iii) investigate three CAZymes having potential biotechnological application for degradation of biomass, but lacking a representative structure in the PDB database. We describe in general how cazy_webscraper facilitates functional, structural and evolutionary studies to aid identification of candidate enzymes for further characterization, and specifically note that CAZy provides supporting evidence for recent expansion of the Auxiliary Activities (AA) CAZy family in eukaryotes, consistent with functions potentially specific to eukaryotic lifestyles.

Transcriptional Profiling Supports the Notochordal Origin of Chordoma and Its Dependence on a TGFΒ1-TBXT Network

Chordoma is a rare malignant tumor demonstrating notochordal differentiation. It is dependent on brachyury (TBXT), a hallmark notochordal gene and transcription factor, and shares histologic features and the same anatomic location as the notochord. In this study, we perform a molecular comparison of chordoma and notochord to identify dysregulated cellular pathways. The lack of a molecular reference from appropriate control tissue limits our understanding of chordoma and its relationship to notochord. Accordingly, we conducted an unbiased comparison of chordoma, human notochord, and an atlas of normal and cancerous tissue using gene expression profiling to clarify the chordoma/notochord relationship and potentially identify novel drug targets. We found striking consistency in gene expression profiles between chordoma and notochord, supporting the hypothesis that chordoma develops from notochordal remnants. We identified a 12-gene diagnostic chordoma signature and found that the TBXT/transforming growth factor (TGF)-β/SOX6/SOX9 pathway is hyperactivated in the tumor, suggesting that pathways associated with chondrogenesis are a central driver of chordoma development. Experimental validation in chordoma cells confirms these findings and emphasizes the dependence of chordoma proliferation and survival on TGF-β. Our computational and experimental evidence provides the first molecular connection between notochord and chordoma and identifies core members of a chordoma regulatory pathway involving TBXT. This pathway provides new therapeutic targets for this unique malignant neoplasm and highlights TGF-β as a prime druggable candidate.

Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter

The majority of microbial genomes have yet to be cultured, and most proteins identified in microbial genomes or environmental sequences cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. Here we present LookingGlass, a deep learning model encoding contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, that distinguishes reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned via transfer learning to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.

Computational methods to analyse microbial systems rely on reference databases which do not capture their full functional diversity. Here the authors develop a deep learning model and apply it using transfer learning, creating biologically useful models for multiple different tasks.

A functional Wood-Ljungdahl pathway devoid of a formate dehydrogenase in the gut acetogens Blautia wexlerae, Blautia luti and beyond

Species of the genus Blautia are typical inhabitants of the human gut and considered as beneficial gut microbes. However, their role in the gut microbiome and their metabolic features are poorly understood. Blautia schinkii was described as an acetogenic bacterium, characterized by a functional Wood-Ljungdahl pathway (WLP) of acetogenesis from H₂ + CO₂ . Here we report that two relatives, Blautia luti and Blautia wexlerae do not grow on H₂ + CO₂ . Inspection of the genome sequence revealed all genes of the WLP except genes encoding a formate dehydrogenase and an electron-bifurcating hydrogenase. Enzyme assays confirmed this prediction. Accordingly, resting cells neither converted H₂ + CO₂ nor H₂ + HCOOH + CO₂ to acetate. Carbon monoxide is an intermediate of the WLP and substrate for many acetogens. B. luti and B. wexlerae had an active CO dehydrogenase and resting cells performed acetogenesis from HCOOH + CO₂ + CO, demonstrating a functional WLP. Bioinformatic analyses revealed that many Blautia strains as well as other gut acetogens lack formate dehydrogenases and hydrogenases. Thus, the use of formate instead of H₂ + CO₂ as an interspecies hydrogen and electron carrier seems to be more common in the gut microbiome. This article is protected by copyright. All rights reserved.

Validation of scientific topic models using graph analysis and corpus metadata

Probabilistic topic modeling algorithms like Latent Dirichlet Allocation (LDA) have become powerful tools for the analysis of large collections of documents (such as papers, projects, or funding applications) in science, technology an innovation (STI) policy design and monitoring. However, selecting an appropriate and stable topic model for a specific application (by adjusting the hyperparameters of the algorithm) is not a trivial problem. Common validation metrics like coherence or perplexity, which are focused on the quality of topics, are not a good fit in applications where the quality of the document similarity relations inferred from the topic model is especially relevant. Relying on graph analysis techniques, the aim of our work is to state a new methodology for the selection of hyperparameters which is specifically oriented to optimize the similarity metrics emanating from the topic model. In order to do this, we propose two graph metrics: the first measures the variability of the similarity graphs that result from different runs of the algorithm for a fixed value of the hyperparameters, while the second metric measures the alignment between the graph derived from the LDA model and another obtained using metadata available for the corresponding corpus. Through experiments on various corpora related to STI, it is shown that the proposed metrics provide relevant indicators to select the number of topics and build persistent topic models that are consistent with the metadata. Their use, which can be extended to other topic models beyond LDA, could facilitate the systematic adoption of this kind of techniques in STI policy analysis and design.

Mouse models of immune dysfunction: their neuroanatomical differences reflect their anxiety-behavioural phenotype

Extensive evidence supports the role of the immune system in modulating brain function and behaviour. However, past studies have revealed striking heterogeneity in behavioural phenotypes produced from immune system dysfunction. Using magnetic resonance imaging, we studied the neuroanatomical differences among 11 distinct genetically modified mouse lines (n = 371), each deficient in a different element of the immune system. We found a significant and heterogeneous effect of immune dysfunction on the brains of both male and female mice. However, by imaging the whole brain and using Bayesian hierarchical modelling, we were able to identify patterns within the heterogeneous phenotype. Certain structures-such as the corpus callosum, midbrain, and thalamus-were more likely to be affected by immune dysfunction. A notable brain-behaviour relationship was identified with neuroanatomy endophenotypes across mouse models clustering according to anxiety-like behaviour phenotypes reported in literature, such as altered volume in brains regions associated with promoting fear response (e.g., the lateral septum and cerebellum). Interestingly, genes with preferential spatial expression in the most commonly affected regions are also associated with multiple sclerosis and other immune-mediated diseases. In total, our data suggest that the immune system modulates anxiety behaviour through well-established brain networks.

Quantitative model suggests both intrinsic and contextual features contribute to the transcript coding ability determination in cells

Gene transcription and protein translation are two key steps of the 'central dogma.' It is still a major challenge to quantitatively deconvolute factors contributing to the coding ability of transcripts in mammals. Here, we propose ribosome calculator (RiboCalc) for quantitatively modeling the coding ability of RNAs in human genome. In addition to effectively predicting the experimentally confirmed coding abundance via sequence and transcription features with high accuracy, RiboCalc provides interpretable parameters with biological information. Large-scale analysis further revealed a number of transcripts with a variety of coding ability for distinct types of cells (i.e. context-dependent coding transcripts), suggesting that, contrary to conventional wisdom, a transcript's coding ability should be modeled as a continuous spectrum with a context-dependent nature.

Biochemistry of methanol-dependent acetogenesis in Eubacterium callanderi KIST612

Methanol is the simplest of all alcohols, is universally distributed in anoxic sediments as a result of plant material decomposition and is constantly attracting attention as an interesting substrate for anaerobes like acetogens that can convert bio-renewable methanol into value-added chemicals. A major drawback in the development of environmentally friendly but economically attractive biotechnological processes is the present lack of information on biochemistry and bioenergetics during methanol conversion in these bacteria. The mesophilic acetogen Eubacterium callanderi KIST612 is naturally able to consume methanol and produce acetate as well as butyrate. To grasp the full potential of methanol-based production of chemicals, we analysed the genes and enzymes involved in methanol conversion to acetate and identified the redox carriers involved. We will display a complete model for methanol-derived acetogenesis and butyrogenesis in Eubacterium callanderi KIST612, tracing the electron transfer routes and shed light on the bioenergetics during the process.

Learning an expandable EMR-based medical knowledge network to enhance clinical diagnosis

Electronic medical records (EMRs) contain a wealth of knowledge that can be used to assist doctors in making clinical decisions like disease diagnosis. Constructing a medical knowledge network (MKN) to link medical concepts in EMRs is an effective way to manage this knowledge. The quality of the diagnostic result made by MKN-based clinical decision support system depends on the accuracy of medical knowledge and the completeness of the network. However, collecting knowledge is a long-lasting and cumulative process, which means it's hard to construct a complete MKN with limited data. This study was conducted with the objective of developing an expandable EMR-based MKN to enhance capabilities in making an initial clinical diagnosis. A network of symptom-indicate-disease knowledge in 992 Chinese EMRs (CEMRs) was manually constructed as Original-MKN, and an incremental expansion framework was applied to it to obtain an expandable MKN based on new CEMRs. The framework was composed by: (1) integrating external knowledge extracted from the medical information websites and (2) mining potential knowledge with new EMRs. The framework also adopts a diagnosis-driven learning method to estimate the effectiveness of each knowledge in clinical practice. Experimental results indicate that our expanded MKN achieves a precision of 0.837 for a recall of 0.719 in clinical diagnosis, which outperforms Original-MKN and four classical machine learning methods. Furthermore, both external medical knowledge and potential medical knowledge benefit MKN expansion and disease diagnosis. The proposed incremental expansion framework sustains the MKN learning new knowledge.

Assessing the genetic association between vitamin B6 metabolism and genetic generalized epilepsy

Altered vitamin B6 metabolism due to pathogenic variants in the gene PNPO causes early onset epileptic encephalopathy, which can be treated with high doses of vitamin B6. We recently reported that single nucleotide polymorphisms (SNPs) that influence PNPO expression in the brain are associated with genetic generalized epilepsy (GGE). However, it is not known whether any of these GGE-associated SNPs influence vitamin B6 metabolite levels. Such an influence would suggest that vitamin B6 could play a role in GGE therapy. Here, we performed genome-wide association studies (GWAS) to assess the influence of GGE associated genetic variants on measures of vitamin B6 metabolism in blood plasma in 2232 healthy individuals. We also asked if SNPs that influence vitamin B6 were associated with GGE in 3122 affected individuals and 20,244 controls. Our GWAS of vitamin B6 metabolites reproduced a previous association and found a novel genome-wide significant locus. The SNPs in these loci were not associated with GGE. We found that 84 GGE-associated SNPs influence expression levels of PNPO in the brain as well as in blood. However, these SNPs were not associated with vitamin B6 metabolism in plasma. By leveraging polygenic risk scoring (PRS), we found suggestive evidence of higher catabolism and lower levels of the active and transport forms of vitamin B6 in GGE, although these findings require further replication.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: is aromatic N-oxide a structural alert for predicting DNA-reactive mutagenicity?

(Quantitative) structure-activity relationship or (Q)SAR predictions of DNA-reactive mutagenicity are important to support both the design of new chemicals and the assessment of impurities, degradants, metabolites, extractables and leachables, as well as existing chemicals. Aromatic N-oxides represent a class of compounds that are often considered alerting for mutagenicity yet the scientific rationale of this structural alert is not clear and has been questioned. Because aromatic N-oxide-containing compounds may be encountered as impurities, degradants and metabolites, it is important to accurately predict mutagenicity of this chemical class. This article analysed a series of publicly available aromatic N-oxide data in search of supporting information. The article also used a previously developed structure-activity relationship (SAR) fingerprint methodology where a series of aromatic N-oxide substructures was generated and matched against public and proprietary databases, including pharmaceutical data. An assessment of the number of mutagenic and non-mutagenic compounds matching each substructure across all sources was used to understand whether the general class or any specific subclasses appear to lead to mutagenicity. This analysis resulted in a downgrade of the general aromatic N-oxide alert. However, it was determined there were enough public and proprietary data to assign the quindioxin and related chemicals as well as benzo[c][1,2,5]oxadiazole 1-oxide subclasses as alerts. The overall results of this analysis were incorporated into Leadscope's expert-rule-based model to enhance its predictive accuracy.

PARP1 rs1805407 Increases Sensitivity to PARP1 Inhibitors in Cancer Cells Suggesting an Improved Therapeutic Strategy

Personalized cancer therapy relies on identifying patient subsets that benefit from a therapeutic intervention and suggest alternative regimens for those who don't. A new data integrative approach, based on graphical models, was applied on our multi-modal -omics, and clinical data cohort of metastatic melanoma patients. We found that response to chemotherapy is directly linked to ten gene expression, four methylation variables and PARP1 SNP rs1805407. PARP1 is a DNA repair gene critical for chemotherapy response and for which FDA-approved inhibitors are clinically available (olaparib). We demonstrated that two PARP inhibitors (ABT-888 and olaparib) make SNP carrier cancer cells of various histologic subtypes more sensitive to alkylating agents, but they have no effect in wild-type cells. Furthermore, PARP1 inhibitors act synergistically with chemotherapy in SNP carrier cells (especially in ovarian cancer for which olaparib is FDA-approved), but they are additive at best in wild-type cancer cells. Taken together, our results suggest that the combination of chemotherapy and PARP1 inhibition may benefit the carriers of rs1805407 in the future and may be used in personalized therapy strategies to select patients that are more likely to respond to PARP inhibitors.

PIPEBAR and OverlapPER: tools for a fast and accurate DNA barcoding analysis and paired-end assembly

Background

Taxonomic identification of plants and insects is a hard process that demands expert taxonomists and time, and it’s often difficult to distinguish on morphology only. DNA barcodes allow a rapid species discovery and identification and have been widely used for taxonomic identification by targeting known gene regions that permit to discriminate these species. DNA barcode sequence analysis is usually carried out with processes and tools that still demand a high interaction with the user or researcher. To reduce at most such interaction, we proposed PIPEBAR, a pipeline for DNA chromatograms analysis of Sanger platform sequencing, ensuring high quality consensus sequences along with efficient running time. We also proposed a paired-end reads assembly tool, OverlapPER, which is used in sequence or independently of PIPEBAR.

Results

PIPEBAR is a command line tool to automatize the processing of large number of trace files. It is accurate as the proprietary Geneious tool and faster than most popular software for barcoding analysis. It is 7 times faster than Geneious and 14 times faster than SeqTrace for processing hundreds of barcoding sequences. OverlapPER is a novel tool for overlapping paired-end reads accurately that accepts both substitution and indel errors and returns both overlapped and non-overlapped regions between a pair of reads. OverlapPER obtained the best results compared to currently used tools when merging 1,000,000 simulated paired-end reads.

Conclusions

PIPEBAR and OverlapPER run on most operating systems and are freely available, along with supporting code and documentation, at https://sourceforge.net/projects/PIPEBAR/ and https://sourceforge.net/projects/overlapper-reads/.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2307-y) contains supplementary material, which is available to authorized users.

Analysis of viral diversity for vaccine target discovery

BACKGROUND

Viral vaccine target discovery requires understanding the diversity of both the virus and the human immune system. The readily available and rapidly growing pool of viral sequence data in the public domain enable the identification and characterization of immune targets relevant to adaptive immunity. A systematic bioinformatics approach is necessary to facilitate the analysis of such large datasets for selection of potential candidate vaccine targets.

RESULTS

This work describes a computational methodology to achieve this analysis, with data of dengue, West Nile, hepatitis A, HIV-1, and influenza A viruses as examples. Our methodology has been implemented as an analytical pipeline that brings significant advancement to the field of reverse vaccinology, enabling systematic screening of known sequence data in nature for identification of vaccine targets. This includes key steps (i) comprehensive and extensive collection of sequence data of viral proteomes (the virome), (ii) data cleaning, (iii) large-scale sequence alignments, (iv) peptide entropy analysis, (v) intra- and inter-species variation analysis of conserved sequences, including human homology analysis, and (vi) functional and immunological relevance analysis.

CONCLUSION

These steps are combined into the pipeline ensuring that a more refined process, as compared to a simple evolutionary conservation analysis, will facilitate a better selection of vaccine targets and their prioritization for subsequent experimental validation.

Identification of novel mazEF/pemIK family toxin-antitoxin loci and their distribution in the Staphylococcus genus

The versatile roles of toxin-antitoxin (TA) systems in bacterial physiology and pathogenesis have been investigated for more than three decades. Diverse TA loci in Bacteria and Archaea have been identified in genome-wide studies. The advent of massive parallel sequencing has substantially expanded the number of known bacterial genomic sequences over the last 5 years. In staphylococci, this has translated into an impressive increase from a few tens to a several thousands of available genomes, which has allowed us for the re-evalution of prior conclusions. In this study, we analysed the distribution of mazEF/pemIK family TA system operons in available staphylococcal genomes and their prevalence in mobile genetic elements. 10 novel mazEF/pemIK homologues were identified, each with a corresponding toxin that plays a potentially different and undetermined physiological role. A detailed characterisation of these TA systems would be exceptionally useful. Of particular interest are those associated with an SCCmec mobile genetic element (responsible for multidrug resistance transmission) or representing the joint horizontal transfer of TA systems and determinants of vancomycin resistance from enterococci. The involvement of TA systems in maintaining mobile genetic elements and the associations between novel mazEF/pemIK loci and those which carry drug resistance genes highlight their potential medical importance.

Application of a New Probabilistic Model for Mining Implicit Associated Cancer Genes from OMIM and Medline

An important issue in current medical science research is to find the genes that are strongly related to an inherited disease. A particular focus is placed on cancer-gene relations, since some types of cancers are inherited. As bio-medical databases have grown speedily in recent years, an informatics approach to predict such relations from currently available databases should be developed. Our objective is to find implicit associated cancer-genes from biomedical databases including the literature database. Co-occurrence of biological entities has been shown to be a popular and efficient technique in biomedical text mining. We have applied a new probabilistic model, called mixture aspect model (MAM) [ 48 ], to combine different types of co-occurrences of genes and cancer derived from Medline and OMIM (Online Mendelian Inheritance in Man). We trained the probability parameters of MAM using a learning method based on an EM (Expectation and Maximization) algorithm. We examined the performance of MAM by predicting associated cancer gene pairs. Through cross-validation, prediction accuracy was shown to be improved by adding gene-gene co-occurrences from Medline to cancer-gene co-occurrences in OMIM. Further experiments showed that MAM found new cancer-gene relations which are unknown in the literature. Supplementary information can be found at http://www.bic.kyotou.ac.jp/pathway/zhusf/CancerInformatics/Supplemental2006.html

Misincorporation by RNA polymerase is a major source of transcription pausing in vivo

The transcription error rate estimated from mistakes in end product RNAs is 10−3–10−5. We analyzed the fidelity of nascent RNAs from all actively transcribing elongation complexes (ECs) in Escherichia coli and Saccharomyces cerevisiae and found that 1–3% of all ECs in wild-type cells, and 5–7% of all ECs in cells lacking proofreading factors are, in fact, misincorporated complexes. With the exception of a number of sequence-dependent hotspots, most misincorporations are distributed relatively randomly. Misincorporation at hotspots does not appear to be stimulated by pausing. Since misincorporation leads to a strong pause of transcription due to backtracking, our findings indicate that misincorporation could be a major source of transcriptional pausing and lead to conflicts with other RNA polymerases and replication in bacteria and eukaryotes. This observation implies that physical resolution of misincorporated complexes may be the main function of the proofreading factors Gre and TFIIS. Although misincorporation mechanisms between bacteria and eukaryotes appear to be conserved, the results suggest the existence of a bacteria-specific mechanism(s) for reducing misincorporation in protein-coding regions. The links between transcription fidelity, human disease, and phenotypic variability in genetically-identical cells can be explained by the accumulation of misincorporated complexes, rather than mistakes in mature RNA.

The complete mitochondrial genome of the grooved carpet shell, Ruditapes decussatus (Bivalvia, Veneridae)

Despite the large number of animal complete mitochondrial genomes currently available in public databases, knowledge about mitochondrial genomics in invertebrates is uneven. This paper reports, for the first time, the complete mitochondrial genome of the grooved carpet shell, Ruditapes decussatus, also known as the European clam. Ruditapes decussatus is morphologically and ecologically similar to the Manila clam Ruditapes philippinarum, which has been recently introduced for aquaculture in the very same habitats of Ruditapes decussatus, and that is replacing the native species. Currently the production of the European clam is almost insignificant, nonetheless it is considered a high value product, and therefore it is an economically important species, especially in Portugal, Spain and Italy. In this work we: (i) assembled Ruditapes decussatus mitochondrial genome from RNA-Seq data, and validated it by Sanger sequencing; (ii) analyzed and characterized the Ruditapes decussatus mitochondrial genome, comparing its features with those of other venerid bivalves; (iii) assessed mitochondrial sequence polymorphism (SP) and copy number variation (CNV) of tandem repeats across 26 samples. Despite using high-throughput approaches we did not find evidence for the presence of two sex-linked mitochondrial genomes, typical of the doubly uniparental inheritance of mitochondria, a phenomenon known in ∼100 bivalve species. According to our analyses, Ruditapes decussatus is more genetically similar to species of the Genus Paphia than to the congeneric Ruditapes philippinarum, a finding that bolsters the already-proposed need of a taxonomic revision. We also found a quite low genetic variability across the examined samples, with few SPs and little variability of the sequences flanking the control region (Largest Unassigned Regions (LURs). Strikingly, although we found low nucleotide variability along the entire mitochondrial genome, we observed high levels of length polymorphism in the LUR due to CNV of tandem repeats, and even a LUR length heteroplasmy in two samples. It is not clear if the lack of genetic variability in the mitochondrial genome of Ruditapes decussatus is a cause or an effect of the ongoing replacement of Ruditapes decussatus with the invasive Ruditapes philippinarum, and more analyses, especially on nuclear sequences, are required to assess this point.

Solving Blue Light Riddles: New Lessons from Flavin-binding LOV Photoreceptors

Detection of blue light (BL) via flavin-binding photoreceptors (Fl-Blues) has evolved throughout all three domains of life. Although the main BL players, that is light, oxygen and voltage (LOV), blue light sensing using flavins (BLUF) and Cry (cryptochrome) proteins, have been characterized in great detail with respect to structure and function, still several unresolved issues at different levels of complexity remain and novel unexpected findings were reported. Here, we review the most prevailing riddles of LOV-based photoreceptors, for example: the relevance of water and/or small metabolites for the dynamics of the photocycle; molecular details of light-to-signal transduction events; the interplay of BL sensing by LOV domains with other environmental stimuli, such as BL plus oxygen-mediating photodamage and its impact on microbial lifestyles; the importance of the cell or chromophore redox state in determining the fate of BL-driven reactions; the evolutionary pathways of LOV-based BL sensing and associated functions through the diverse phyla. We will discuss major novelties emerged during the last few years on these intriguing aspects of LOV proteins by presenting paradigmatic examples from prokaryotic photosensors that exhibit the largest complexity and richness in associated functions.

Pyviko: an automated Python tool to design gene knockouts in complex viruses with overlapping genes

BACKGROUND

Gene knockouts are a common tool used to study gene function in various organisms. However, designing gene knockouts is complicated in viruses, which frequently contain sequences that code for multiple overlapping genes. Designing mutants that can be traced by the creation of new or elimination of existing restriction sites further compounds the difficulty in experimental design of knockouts of overlapping genes. While software is available to rapidly identify restriction sites in a given nucleotide sequence, no existing software addresses experimental design of mutations involving multiple overlapping amino acid sequences in generating gene knockouts.

RESULTS

Pyviko performed well on a test set of over 240,000 gene pairs collected from viral genomes deposited in the National Center for Biotechnology Information Nucleotide database, identifying a point mutation which added a premature stop codon within the first 20 codons of the target gene in 93.2% of all tested gene-overprinted gene pairs. This shows that Pyviko can be used successfully in a wide variety of contexts to facilitate the molecular cloning and study of viral overprinted genes.

CONCLUSIONS

Pyviko is an extensible and intuitive Python tool for designing knockouts of overlapping genes. Freely available as both a Python package and a web-based interface ( http://louiejtaylor.github.io/pyViKO/ ), Pyviko simplifies the experimental design of gene knockouts in complex viruses with overlapping genes.

Deciphering the evolutionary affiliations among bacterial strains (Pseudomonas and Frankia sp.) inhabiting same ecological niche using virtual RFLP and simulation-based approaches

To decipher an evolutionary lineage between two different but important bacterial groups, i.e., Pseudomonas strain (γ-Proteobacteria) and Frankia strain (actinobacteria) growing in the same ecological niche in and around of an actinorhizal plant Hippophae salicifolia D. Don, genetic diversity and comparative molecular phylogeny have been investigated using 16S rRNA gene sequences and computer-simulated and virtually directed restriction fragment length polymorphism (RFLP) through 10 restriction enzymes. Bayesian and coalescent analyses on the basis of 16S rRNA gene sequences suggested three major groups with close proximity between Pseudomonas and Frankia isolates. This result has been further validated based on the data observed through similarity coefficient value and computational RFLP. Principal component analysis and Mandel h and k statistical analysis also confirmed and strengthen the findings. Approximately 458 aligned sequence of all the taxa were used to decipher nucleotide diversity, polymorphism and gene flow between these taxa. Thus, our results suggest for a possible co-evolution or a heterologous gene transfer of distantly related microbial forms. Further, our study also advocate for the use of computer aided, virtual RFLP analysis as a cost effective and rapid identification tool.

Map-based cloning, identification and characterization of the w gene controlling white immature fruit color in cucumber (Cucumis sativus L.)

A single-nucleotide insertion resulted in a premature stop codon that is responsible for white immature fruit color in cucumber. Despite our previous progress in the mapping of the gene controlling white color in immature cucumber fruit and the identification of candidate genes, the specific gene that governs chlorophyll metabolism and its regulatory mechanism remains unknown. Here, we generated a mapping population consisting of 9497 F2 plants to delimit the controlling gene to an 8.2-kb physical interval that defines a sole candidate gene, APRR2. Sequencing the full-length DNA and cDNA of APRR2 allowed for identification of an allele, aprr2, encoding a truncated 101-amino acid protein due to a frameshift mutation and a premature stop codon. Gene structure prediction indicated that these 101 residues are located in a domain necessary for the function of the protein. The expression patterns of APRR2 were entirely consistent with the visual changes in green color intensity during fruit development. A microscopic observation of the fruit pericarp revealed fewer chloroplasts and a lower chloroplast chlorophyll storage capacity in Q24 (white) than in Q30 (green). A single-base insertion in the white color gene w, which leads to a premature stop codon, is hypothesized to have disabled the function of this gene in chlorophyll accumulation and chloroplast development. These findings contribute to basic research and the genetic improvement of fruit color.

ChlamyNET: a Chlamydomonas gene co-expression network reveals global properties of the transcriptome and the early setup of key co-expression patterns in the green lineage

BACKGROUND

Chlamydomonas reinhardtii is the model organism that serves as a reference for studies in algal genomics and physiology. It is of special interest in the study of the evolution of regulatory pathways from algae to higher plants. Additionally, it has recently gained attention as a potential source for bio-fuel and bio-hydrogen production. The genome of Chlamydomonas is available, facilitating the analysis of its transcriptome by RNA-seq data. This has produced a massive amount of data that remains fragmented making necessary the application of integrative approaches based on molecular systems biology.

RESULTS

We constructed a gene co-expression network based on RNA-seq data and developed a web-based tool, ChlamyNET, for the exploration of the Chlamydomonas transcriptome. ChlamyNET exhibits a scale-free and small world topology. Applying clustering techniques, we identified nine gene clusters that capture the structure of the transcriptome under the analyzed conditions. One of the most central clusters was shown to be involved in carbon/nitrogen metabolism and signalling, whereas one of the most peripheral clusters was involved in DNA replication and cell cycle regulation. The transcription factors and regulators in the Chlamydomonas genome have been identified in ChlamyNET. The biological processes potentially regulated by them as well as their putative transcription factor binding sites were determined. The putative light regulated transcription factors and regulators in the Chlamydomonas genome were analyzed in order to provide a case study on the use of ChlamyNET. Finally, we used an independent data set to cross-validate the predictive power of ChlamyNET.

CONCLUSIONS

The topological properties of ChlamyNET suggest that the Chlamydomonas transcriptome posseses important characteristics related to error tolerance, vulnerability and information propagation. The central part of ChlamyNET constitutes the core of the transcriptome where most authoritative hub genes are located interconnecting key biological processes such as light response with carbon and nitrogen metabolism. Our study reveals that key elements in the regulation of carbon and nitrogen metabolism, light response and cell cycle identified in higher plants were already established in Chlamydomonas. These conserved elements are not only limited to transcription factors, regulators and their targets, but also include the cis-regulatory elements recognized by them.

Energy Conservation Model Based on Genomic and Experimental Analyses of a Carbon Monoxide-Utilizing, Butyrate-Forming Acetogen, Eubacterium limosum KIST612

Eubacterium limosum KIST612 is one of the few acetogens that can produce butyrate from carbon monoxide. We have used a genome-guided analysis to delineate the path of butyrate formation, the enzymes involved, and the potential coupling to ATP synthesis. Oxidation of CO is catalyzed by the acetyl-coenzyme A (CoA) synthase/CO dehydrogenase and coupled to the reduction of ferredoxin. Oxidation of reduced ferredoxin is catalyzed by the Rnf complex and Na(+) dependent. Consistent with the finding of a Na(+)-dependent Rnf complex is the presence of a conserved Na(+)-binding motif in the c subunit of the ATP synthase. Butyrate formation is from acetyl-CoA via acetoacetyl-CoA, hydroxybutyryl-CoA, crotonyl-CoA, and butyryl-CoA and is consistent with the finding of a gene cluster that encodes the enzymes for this pathway. The activity of the butyryl-CoA dehydrogenase was demonstrated. Reduction of crotonyl-CoA to butyryl-CoA with NADH as the reductant was coupled to reduction of ferredoxin. We postulate that the butyryl-CoA dehydrogenase uses flavin-based electron bifurcation to reduce ferredoxin, which is consistent with the finding of etfA and etfB genes next to it. The overall ATP yield was calculated and is significantly higher than the one obtained with H2 + CO2. The energetic benefit may be one reason that butyrate is formed only from CO but not from H2 + CO2.

Integrative view of α2,3-sialyltransferases (ST3Gal) molecular and functional evolution in deuterostomes: significance of lineage-specific losses

Sialyltransferases are responsible for the synthesis of a diverse range of sialoglycoconjugates predicted to be pivotal to deuterostomes’ evolution. In this work, we reconstructed the evolutionary history of the metazoan α2,3-sialyltransferases family (ST3Gal), a subset of sialyltransferases encompassing six subfamilies (ST3Gal I–ST3Gal VI) functionally characterized in mammals. Exploration of genomic and expressed sequence tag databases and search of conserved sialylmotifs led to the identification of a large data set of st3gal-related gene sequences. Molecular phylogeny and large scale sequence similarity network analysis identified four new vertebrate subfamilies called ST3Gal III-r, ST3Gal VII, ST3Gal VIII, and ST3Gal IX. To address the issue of the origin and evolutionary relationships of the st3gal-related genes, we performed comparative syntenic mapping of st3gal gene loci combined to ancestral genome reconstruction. The ten vertebrate ST3Gal subfamilies originated from genome duplication events at the base of vertebrates and are organized in three distinct and ancient groups of genes predating the early deuterostomes. Inferring st3gal gene family history identified also several lineage-specific gene losses, the significance of which was explored in a functional context. Toward this aim, spatiotemporal distribution of st3gal genes was analyzed in zebrafish and bovine tissues. In addition, molecular evolutionary analyses using specificity determining position and coevolved amino acid predictions led to the identification of amino acid residues with potential implication in functional divergence of vertebrate ST3Gal. We propose a detailed scenario of the evolutionary relationships of st3gal genes coupled to a conceptual framework of the evolution of ST3Gal functions.

Finding gene regulatory network candidates using the gene expression knowledge base

Background

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of ‘omics’ data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis.

Results

We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions.

Conclusions

Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0386-y) contains supplementary material, which is available to authorized users.

The tau tubulin kinases TTBK1/2 promote accumulation of pathological TDP-43

Pathological aggregates of phosphorylated TDP-43 characterize amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD-TDP), two devastating groups of neurodegenerative disease. Kinase hyperactivity may be a consistent feature of ALS and FTLD-TDP, as phosphorylated TDP-43 is not observed in the absence of neurodegeneration. By examining changes in TDP-43 phosphorylation state, we have identified kinases controlling TDP-43 phosphorylation in a C. elegans model of ALS. In this kinome-wide survey, we identified homologs of the tau tubulin kinases 1 and 2 (TTBK1 and TTBK2), which were also identified in a prior screen for kinase modifiers of TDP-43 behavioral phenotypes. Using refined methodology, we demonstrate TTBK1 and TTBK2 directly phosphorylate TDP-43 in vitro and promote TDP-43 phosphorylation in mammalian cultured cells. TTBK1/2 overexpression drives phosphorylation and relocalization of TDP-43 from the nucleus to cytoplasmic inclusions reminiscent of neuropathologic changes in disease states. Furthermore, protein levels of TTBK1 and TTBK2 are increased in frontal cortex of FTLD-TDP patients, and TTBK1 and TTBK2 co-localize with TDP-43 inclusions in ALS spinal cord. These kinases may represent attractive targets for therapeutic intervention for TDP-43 proteinopathies such as ALS and FTLD-TDP.

FCDD: A Database for Fruit Crops Diseases

Fruit Crops Diseases Database (FCDD) requires a number of biotechnology and bioinformatics tools. The FCDD is a unique bioinformatics resource that compiles information about 162 details on fruit crops diseases, diseases type, its causal organism, images, symptoms and their control. The FCDD contains 171 phytochemicals from 25 fruits, their 2D images and their 20 possible sequences. This information has been manually extracted and manually verified from numerous sources, including other electronic databases, textbooks and scientific journals. FCDD is fully searchable and supports extensive text search. The main focus of the FCDD is on providing possible information of fruit crops diseases, which will help in discovery of potential drugs from one of the common bioresource-fruits. The database was developed using MySQL. The database interface is developed in PHP, HTML and JAVA. FCDD is freely available.

PIGD: a database for intronless genes in the Poaceae

Background

Intronless genes are a feature of prokaryotes; however, they are widespread and unequally distributed among eukaryotes and represent an important resource to study the evolution of gene architecture. Although many databases on exons and introns exist, there is currently no cohesive database that collects intronless genes in plants into a single database.

Description

In this study, we present the Poaceae Intronless Genes Database (PIGD), a user-friendly web interface to explore information on intronless genes from different plants. Five Poaceae species, Sorghum bicolor, Zea mays, Setaria italica, Panicum virgatum and Brachypodium distachyon, are included in the current release of PIGD. Gene annotations and sequence data were collected and integrated from different databases. The primary focus of this study was to provide gene descriptions and gene product records. In addition, functional annotations, subcellular localization prediction and taxonomic distribution are reported. PIGD allows users to readily browse, search and download data. BLAST and comparative analyses are also provided through this online database, which is available at http://pigd.ahau.edu.cn/.

Conclusion

PIGD provides a solid platform for the collection, integration and analysis of intronless genes in the Poaceae. As such, this database will be useful for subsequent bio-computational analysis in comparative genomics and evolutionary studies.

morFeus: a web-based program to detect remotely conserved orthologs using symmetrical best hits and orthology network scoring

Background

Searching the orthologs of a given protein or DNA sequence is one of the most important and most commonly used Bioinformatics methods in Biology. Programs like BLAST or the orthology search engine Inparanoid can be used to find orthologs when the similarity between two sequences is sufficiently high. They however fail when the level of conservation is low. The detection of remotely conserved proteins oftentimes involves sophisticated manual intervention that is difficult to automate.

Results

Here, we introduce morFeus, a search program to find remotely conserved orthologs. Based on relaxed sequence similarity searches, morFeus selects sequences based on the similarity of their alignments to the query, tests for orthology by iterative reciprocal BLAST searches and calculates a network score for the resulting network of orthologs that is a measure of orthology independent of the E-value. Detecting remotely conserved orthologs of a protein using morFeus thus requires no manual intervention. We demonstrate the performance of morFeus by comparing it to state-of-the-art orthology resources and methods. We provide an example of remotely conserved orthologs, which were experimentally shown to be functionally equivalent in the respective organisms and therefore meet the criteria of the orthology-function conjecture.

Conclusions

Based on our results, we conclude that morFeus is a powerful and specific search method for detecting remotely conserved orthologs. morFeus is freely available at http://bio.biochem.mpg.de/morfeus/. Its source code is available from Sourceforge.net (https://sourceforge.net/p/morfeus/).

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-263) contains supplementary material, which is available to authorized users.

Long-term tumor necrosis factor treatment induces NFκB activation and proliferation, but not osteoblastic differentiation of adipose tissue-derived mesenchymal stem cells in vitro

The pro-inflammatory cytokine tumor necrosis factor (TNF) is well known to induce differentiation of bone matrix-resorbing osteoclasts from hematopoietic stem cells. However, the impact of TNF on differentiation of bone matrix-forming osteoblasts from mesenchymal stem cells (MSC) was only fragmentarily studied so far. Therefore, we investigated what impact long-term TNF treatment has on osteoblastic differentiation of MSC isolated from the adipose tissue (ASC) in vitro. In summary, we found continuous TNF exposure to induce the nuclear factor of kappa B pathway in ASC as well as secretion of the pro-inflammatory chemokine interleukin 8, but not the mitogen-activated protein kinase and the apoptosis pathway in ASC. Moreover, TNF neither induced nor inhibited osteoblastic differentiation of ASC, but strongly increased their proliferation rate. In that manner, pro-inflammatory conditions in vivo may generate significantly increased numbers of progenitor cells, and ASC especially, in conjunction with external stimuli, may contribute to the events of ectopic ossification observed in chronic inflammatory diseases. The substantiation of the translation of our in vitro findings to the disease context encourages further in vivo studies.

DBSecSys: a database of Burkholderia mallei secretion systems

Background

Bacterial pathogenicity represents a major public health concern worldwide. Secretion systems are a key component of bacterial pathogenicity, as they provide the means for bacterial proteins to penetrate host-cell membranes and insert themselves directly into the host cells’ cytosol. Burkholderia mallei is a Gram-negative bacterium that uses multiple secretion systems during its host infection life cycle. To date, the identities of secretion system proteins for B. mallei are not well known, and their pathogenic mechanisms of action and host factors are largely uncharacterized.

Description

We present the Database of Burkholderia malleiSecretion Systems (DBSecSys), a compilation of manually curated and computationally predicted bacterial secretion system proteins and their host factors. Currently, DBSecSys contains comprehensive experimentally and computationally derived information about B. mallei strain ATCC 23344. The database includes 143 B. mallei proteins associated with five secretion systems, their 1,635 human and murine interacting targets, and the corresponding 2,400 host-B. mallei interactions. The database also includes information about 10 pathogenic mechanisms of action for B. mallei secretion system proteins inferred from the available literature. Additionally, DBSecSys provides details about 42 virulence attenuation experiments for 27 B. mallei secretion system proteins. Users interact with DBSecSys through a Web interface that allows for data browsing, querying, visualizing, and downloading.

Conclusions

DBSecSys provides a comprehensive, systematically organized resource of experimental and computational data associated with B. mallei secretion systems. It provides the unique ability to study secretion systems not only through characterization of their corresponding pathogen proteins, but also through characterization of their host-interacting partners.

The database is available at https://applications.bhsai.org/dbsecsys.

A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms

BACKGROUND

Understanding the molecular mechanisms involved in disease is critical for the development of more effective and individualized strategies for prevention and treatment. The amount of disease-related literature, including new genetic information on the molecular mechanisms of disease, is rapidly increasing. Extracting beneficial information from literature can be facilitated by computational methods such as the knowledge-discovery approach. Several methods for mining gene-disease relationships using computational methods have been developed, however, there has been a lack of research evaluating specific disease candidate genes.

RESULTS

We present a novel method for gathering and prioritizing specific disease candidate genes. Our approach involved the construction of a set of Medical Subject Headings (MeSH) terms for the effective retrieval of publications related to a disease candidate gene. Information regarding the relationships between genes and publications was obtained from the gene2pubmed database. The set of genes was prioritized using a "weighted literature score" based on the number of publications and weighted by the number of genes occurring in a publication. Using our method for the disease states of pain and Alzheimer's disease, a total of 1101 pain candidate genes and 2810 Alzheimer's disease candidate genes were gathered and prioritized. The precision was 0.30 and the recall was 0.89 in the case study of pain. The precision was 0.04 and the recall was 0.6 in the case study of Alzheimer's disease. The precision-recall curve indicated that the performance of our method was superior to that of other publicly available tools.

CONCLUSIONS

Our method, which involved the use of a set of MeSH terms related to disease candidate genes and a novel weighted literature score, improved the accuracy of gathering and prioritizing candidate genes by focusing on a specific disease.

Breast cancer prognosis risk estimation using integrated gene expression and clinical data

BACKGROUND

Novel prognostic markers are needed so newly diagnosed breast cancer patients do not undergo any unnecessary therapy. Various microarray gene expression datasets based studies have generated gene signatures to predict the prognosis outcomes, while ignoring the large amount of information contained in established clinical markers. Nevertheless, small sample sizes in individual microarray datasets remain a bottleneck in generating robust gene signatures that show limited predictive power. The aim of this study is to achieve high classification accuracy for the good prognosis group and then achieve high classification accuracy for the poor prognosis group.

METHODS

We propose a novel algorithm called the IPRE (integrated prognosis risk estimation) algorithm. We used integrated microarray datasets from multiple studies to increase the sample sizes (∼ 2,700 samples). The IPRE algorithm consists of a virtual chromosome for the extraction of the prognostic gene signature that has 79 genes, and a multivariate logistic regression model that incorporates clinical data along with expression data to generate the risk score formula that accurately categorizes breast cancer patients into two prognosis groups.

RESULTS

The evaluation on two testing datasets showed that the IPRE algorithm achieved high classification accuracies of 82% and 87%, which was far greater than any existing algorithms.

Novel Burkholderia mallei virulence factors linked to specific host-pathogen protein interactions

Burkholderia mallei is an infectious intracellular pathogen whose virulence and resistance to antibiotics makes it a potential bioterrorism agent. Given its genetic origin as a commensal soil organism, it is equipped with an extensive and varied set of adapted mechanisms to cope with and modulate host-cell environments. One essential virulence mechanism constitutes the specialized secretion systems that are designed to penetrate host-cell membranes and insert pathogen proteins directly into the host cell's cytosol. However, the secretion systems' proteins and, in particular, their host targets are largely uncharacterized. Here, we used a combined in silico, in vitro, and in vivo approach to identify B. mallei proteins required for pathogenicity. We used bioinformatics tools, including orthology detection and ab initio predictions of secretion system proteins, as well as published experimental Burkholderia data to initially select a small number of proteins as putative virulence factors. We then used yeast two-hybrid assays against normalized whole human and whole murine proteome libraries to detect and identify interactions among each of these bacterial proteins and host proteins. Analysis of such interactions provided both verification of known virulence factors and identification of three new putative virulence proteins. We successfully created insertion mutants for each of these three proteins using the virulent B. mallei ATCC 23344 strain. We exposed BALB/c mice to mutant strains and the wild-type strain in an aerosol challenge model using lethal B. mallei doses. In each set of experiments, mice exposed to mutant strains survived for the 21-day duration of the experiment, whereas mice exposed to the wild-type strain rapidly died. Given their in vivo role in pathogenicity, and based on the yeast two-hybrid interaction data, these results point to the importance of these pathogen proteins in modulating host ubiquitination pathways, phagosomal escape, and actin-cytoskeleton rearrangement processes.

Candidate gene association studies: a comprehensive guide to useful in silico tools

The candidate gene approach has been a pioneer in the field of genetic epidemiology, identifying risk alleles and their association with clinical traits. With the advent of rapidly changing technology, there has been an explosion of in silico tools available to researchers, giving them fast, efficient resources and reliable strategies important to find casual gene variants for candidate or genome wide association studies (GWAS). In this review, following a description of candidate gene prioritisation, we summarise the approaches to single nucleotide polymorphism (SNP) prioritisation and discuss the tools available to assess functional relevance of the risk variant with consideration to its genomic location. The strategy and the tools discussed are applicable to any study investigating genetic risk factors associated with a particular disease. Some of the tools are also applicable for the functional validation of variants relevant to the era of GWAS and next generation sequencing (NGS).

Dissecting the dynamics of HIV-1 protein sequence diversity

The rapid mutation of human immunodeficiency virus-type 1 (HIV-1) and the limited characterization of the composition and incidence of the variant population are major obstacles to the development of an effective HIV-1 vaccine. This issue was addressed by a comprehensive analysis of over 58,000 clade B HIV-1 protein sequences reported over at least 26 years. The sequences were aligned and the 2,874 overlapping nonamer amino acid positions of the viral proteome, each a possible core binding domain for human leukocyte antigen molecules and T-cell receptors, were quantitatively analyzed for four patterns of sequence motifs: (1) "index", the most prevalent sequence; (2) "major" variant, the most common variant sequence; (3) "minor" variants, multiple different sequences, each with an incidence less than that of the major variant; and (4) "unique" variants, each observed only once in the alignment. The collective incidence of the major, minor, and unique variants at each nonamer position represented the total variant population for the position. Positions with more than 50% total variants contained correspondingly reduced incidences of index and major variant sequences and increased minor and unique variants. Highly diverse positions, with 80 to 98% variant nonamer sequences, were present in each protein, including 5% of Gag, and 27% of Env and Nef, each. The multitude of different variant nonamer sequences (i.e. nonatypes; up to 68%) at the highly diverse positions, represented by the major, multiple minor, and multiple unique variants likely supported variants function both in immune escape and as altered peptide ligands with deleterious T-cell responses. The patterns of mutational change were consistent with the sequences of individual HXB2 and C1P viruses and can be considered applicable to all HIV-1 viruses. This characterization of HIV-1 protein mutation provides a foundation for the design of peptide-based vaccines and therapeutics.

RegTransBase--a database of regulatory sequences and interactions based on literature: a resource for investigating transcriptional regulation in prokaryotes

Background

Due to the constantly growing number of sequenced microbial genomes, comparative genomics has been playing a major role in the investigation of regulatory interactions in bacteria. Regulon inference mostly remains a field of semi-manual examination since absence of a knowledgebase and informatics platform for automated and systematic investigation restricts opportunities for computational prediction. Additionally, confirming computationally inferred regulons by experimental data is critically important.

Description

RegTransBase is an open-access platform with a user-friendly web interface publicly available at http://regtransbase.lbl.gov. It consists of two databases – a manually collected hierarchical regulatory interactions database based on more than 7000 scientific papers which can serve as a knowledgebase for verification of predictions, and a large set of curated by experts transcription factor binding sites used in regulon inference by a variety of tools. RegTransBase captures the knowledge from published scientific literature using controlled vocabularies and contains various types of experimental data, such as: the activation or repression of transcription by an identified direct regulator; determination of the transcriptional regulatory function of a protein (or RNA) directly binding to DNA or RNA; mapping of binding sites for a regulatory protein; characterization of regulatory mutations. Analysis of the data collected from literature resulted in the creation of Putative Regulons from Experimental Data that are also available in RegTransBase.

Conclusions

RegTransBase is a powerful user-friendly platform for the investigation of regulation in prokaryotes. It uses a collection of validated regulatory sequences that can be easily extracted and used to infer regulatory interactions by comparative genomics techniques thus assisting researchers in the interpretation of transcriptional regulation data.

Integration of mouse and human genome-wide association data identifies KCNIP4 as an asthma gene

Asthma is a common chronic respiratory disease characterized by airway hyperresponsiveness (AHR). The genetics of asthma have been widely studied in mouse and human, and homologous genomic regions have been associated with mouse AHR and human asthma-related phenotypes. Our goal was to identify asthma-related genes by integrating AHR associations in mouse with human genome-wide association study (GWAS) data. We used Efficient Mixed Model Association (EMMA) analysis to conduct a GWAS of baseline AHR measures from males and females of 31 mouse strains. Genes near or containing SNPs with EMMA p-values <0.001 were selected for further study in human GWAS. The results of the previously reported EVE consortium asthma GWAS meta-analysis consisting of 12,958 diverse North American subjects from 9 study centers were used to select a subset of homologous genes with evidence of association with asthma in humans. Following validation attempts in three human asthma GWAS (i.e., Sepracor/LOCCS/LODO/Illumina, GABRIEL, DAG) and two human AHR GWAS (i.e., SHARP, DAG), the Kv channel interacting protein 4 (KCNIP4) gene was identified as nominally associated with both asthma and AHR at a gene- and SNP-level. In EVE, the smallest KCNIP4 association was at rs6833065 (P-value 2.9e-04), while the strongest associations for Sepracor/LOCCS/LODO/Illumina, GABRIEL, DAG were 1.5e-03, 1.0e-03, 3.1e-03 at rs7664617, rs4697177, rs4696975, respectively. At a SNP level, the strongest association across all asthma GWAS was at rs4697177 (P-value 1.1e-04). The smallest P-values for association with AHR were 2.3e-03 at rs11947661 in SHARP and 2.1e-03 at rs402802 in DAG. Functional studies are required to validate the potential involvement of KCNIP4 in modulating asthma susceptibility and/or AHR. Our results suggest that a useful approach to identify genes associated with human asthma is to leverage mouse AHR association data.

SLC22A2 is associated with tubular creatinine secretion and bias of estimated GFR in renal transplantation

Genome-wide association studies reported SLC22A2 variants to be associated with serum creatinine. As SLC22A2 encodes the organic cation transporter 2 (OCT2), the association might be due to an effect on tubular creatinine handling. To test this hypothesis we studied the association of SLC22A2 polymorphisms with phenotypes of net tubular creatinine secretion: fractional creatinine excretion (FEcreat) and bias of estimated glomerular filtration rate (eGFR). We also studied the association with end-stage renal disease (ESRD) and graft failure (GF) in renal transplant recipients. SLC22A2 single nucleotide polymorphisms (SNPs), rs3127573 and rs316009, were genotyped in 1,142 ESRD patients receiving renal transplantation and 1,186 kidney donors as controls. GFR was measured with (125)I-iothalamate clearance. Creatinine clearance was also assessed. FEcreat was calculated from the simultaneous clearances of creatinine and (125)I-iothalamate. Donor rs316009 was associated with FEcreat (beta -0.053, P = 0.024) and with estimated [modification of diet in renal disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)] but not measured GFR. In line with this, donor rs316009 was associated with bias of the MDRD and CKD-EPI but not the Cockroft-Gault equation. Both SNPs were associated with ESRD: odds ratios [95% CI] 1.39 [1.16-1.67], P = 0.00065, and 1.23 [1.02-1.48], P = 0.042, for rs3127573 and rs316009, respectively. Neither SNP was associated with GF. Thus, SLC22A2 is associated with phenotypes of net tubular creatinine secretion and ESRD.

A contribution to the study of plant development evolution based on gene co-expression networks

Phototrophic eukaryotes are among the most successful organisms on Earth due to their unparalleled efficiency at capturing light energy and fixing carbon dioxide to produce organic molecules. A conserved and efficient network of light-dependent regulatory modules could be at the bases of this success. This regulatory system conferred early advantages to phototrophic eukaryotes that allowed for specialization, complex developmental processes and modern plant characteristics. We have studied light-dependent gene regulatory modules from algae to plants employing integrative-omics approaches based on gene co-expression networks. Our study reveals some remarkably conserved ways in which eukaryotic phototrophs deal with day length and light signaling. Here we describe how a family of Arabidopsis transcription factors involved in photoperiod response has evolved from a single algal gene according to the innovation, amplification and divergence theory of gene evolution by duplication. These modifications of the gene co-expression networks from the ancient unicellular green algae Chlamydomonas reinhardtii to the modern brassica Arabidopsis thaliana may hint on the evolution and specialization of plants and other organisms.

An insight into the sialotranscriptome of the cat flea, Ctenocephalides felis

BACKGROUND

Saliva of hematophagous arthropods contains a diverse mixture of compounds that counteracts host hemostasis. Immunomodulatory and antiinflammatory components are also found in these organisms' saliva. Blood feeding evolved at least ten times within arthropods, providing a scenario of convergent evolution for the solution of the salivary potion. Perhaps because of immune pressure from hosts, the salivary proteins of related organisms have considerable divergence, and new protein families are often found within different genera of the same family or even among subgenera. Fleas radiated with their vertebrate hosts, including within the mammal expansion initiated 65 million years ago. Currently, only one flea species-the rat flea Xenopsylla cheopis-has been investigated by means of salivary transcriptome analysis to reveal salivary constituents, or sialome. We present the analysis of the sialome of cat flea Ctenocephaides felis.

METHODOLOGY AND CRITICAL FINDINGS

A salivary gland cDNA library from adult fleas was randomly sequenced, assembled, and annotated. Sialomes of cat and rat fleas have in common the enzyme families of phosphatases (inactive), CD-39-type apyrase, adenosine deaminases, and esterases. Antigen-5 members are also common to both sialomes, as are defensins. FS-I/Cys7 and the 8-Cys families of peptides are also shared by both fleas and are unique to these organisms. The Gly-His-rich peptide similar to holotricin was found only in the cat flea, as were the abundantly expressed Cys-less peptide and a novel short peptide family.

CONCLUSIONS/SIGNIFICANCE

Fleas, in contrast to bloodsucking Nematocera (mosquitoes, sand flies, and black flies), appear to concentrate a good portion of their sialome in small polypeptides, none of which have a known function but could act as inhibitors of hemostasis or inflammation. They are also unique in expansion of a phosphatase family that appears to be deficient of enzyme activity and has an unknown function.

Impact of Pharmacogenomics on Clinical Practice in Oncology

Multiple drug strategies for many cancer types are now readily available and there is a clear need for tools to inform decision making on therapy selection. Although there is still a long way to go before pharmacogenomics achieves the goal of individualized selection of cancer treatment, promising progress is being made. Genetic testing for thiopurine methyltransferase (TPMT) variant alleles in patients prior to mercaptopurine administration, and for UGT1A1*28 in patients prior to administration of irinotecan therapy, along with the instigation of genotype-guided clinical trials (e.g. TYMS) are important advances in cancer pharmacogenomics. Markers for the toxicity and efficacy of many oncology drugs remain unknown; however, the examples highlighted here suggest progress is being made towards the incorporation of pharmacogenomics into clinical practice in oncology.