KEGG OC: a large-scale automatic construction of taxonomy-based ortholog clusters

Distinct Changes in Metabolic Profile and Sensory Quality with Different Varieties of Chrysanthemum (Juhua) Tea Measured by LC-MS-Based Untargeted Metabolomics and Electronic Tongue

Chrysanthemum tea, a typical health tea with the same origin as medicine and food, is famous for its unique health benefits and flavor. The taste and sensory quality of chrysanthemum (Juhua) tea are mainly determined by secondary metabolites. Therefore, the present research adopted untargeted metabolomics combined with an electronic tongue system to analyze the correlation between the metabolite profiles and taste characteristics of different varieties of chrysanthemum tea. The results of sensory evaluation showed that there were significant differences in the sensory qualities of five different varieties of chrysanthemum tea, especially bitterness and astringency. The results of principal component analysis (PCA) indicated that there were significant metabolic differences among the five chrysanthemum teas. A total of 1775 metabolites were identified by using untargeted metabolomics based on UPLC-Q-TOF/MS analysis. According to the variable importance in projection (VIP) values of the orthogonal projections to latent structures discriminant analysis (OPLS-DA), 143 VIP metabolites were found to be responsible for metabolic changes between Huangju and Jinsi Huangju tea; among them, 13 metabolites were identified as the key metabolites of the differences in sensory quality between them. Kaempferol, luteolin, genistein, and some quinic acid derivatives were correlated with the "astringency" attributes. In contrast, l-(-)-3 phenyllactic acid and L-malic acid were found to be responsible for the "bitterness" and "umami" attributes in chrysanthemum tea. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that the flavonoid and flavonol biosynthesis pathways had important effects on the sensory quality of chrysanthemum tea. These findings provide the theoretical basis for understanding the characteristic metabolites that contribute to the distinctive sensory qualities of chrysanthemum tea.

Characterization of a GH10 extremely thermophilic xylanase from the metagenome of hot spring for prebiotic production

A xylanase gene (named xyngmqa) was identified from the metagenomic data of the Gumingquan hot spring (92.5 °C, pH 9.2) in Tengchong City, Yunnan Province, southwest China. It showed the highest amino acid sequence identity (82.70%) to endo-1,4-beta-xylanase from Thermotoga caldifontis. A constitutive expression plasmid (denominated pSHY211) and double-layer plate (DLP) method were constructed for cloning, expression, and identification of the XynGMQA gene. The XynGMQA gene was synthesized and successfully expressed in Escherichia coli DH5α. XynGMQA exhibited optimal activity at 90 °C and pH 4.6, being thermostable by maintaining 100% of its activity after 2 h incubated at 80 °C. Interestingly, its enzyme activity was enhanced by high temperatures (70 and 80 °C) and low pH (3.0-6.0). About 150% enzyme activity was detected after incubation at 70 °C for 20 to 60 min or 80 °C for 10 to 40 min, and more than 140% enzyme activity after incubation at pH 3.0 to 6.0 for 12 h. Hydrolytic products of beechwood xylan with XynGMQA were xylooligosaccharides, including xylobiose (X2), xylotriose (X3), and xylotetraose (X4). These properties suggest that XynGMQA as an extremely thermophilic xylanase, may be exploited for biofuel and prebiotic production from lignocellulosic biomass.

Comparative proteomics in captive giant pandas to identify proteins involved in age-related cataract formation

Approximately 20% of aged captive giant pandas (Ailuropoda melanoleuca) have cataracts that impair their quality of life. To identify potential biomarkers of cataract formation, we carried out a quantitative proteomics analysis of 10 giant pandas to find proteins differing in abundance between healthy and cataract-bearing animals. We identified almost 150 proteins exceeding our threshold for differential abundance, most of which were associated with GO categories related to extracellular localization. The most significant differential abundance was associated with components of the proteasome and other proteins with a role in proteolysis or its regulation, most of which were depleted in pandas with cataracts. Other modulated proteins included components of the extracellular matrix or cytoskeleton, as well as associated signaling proteins and regulators, but we did not find any differentially expressed transcription factors. These results indicate that the formation of cataracts involves a complex post-transcriptional network of signaling inside and outside lens cells to drive stress responses as a means to address the accumulation of protein aggregates triggered by oxidative damage. The modulated proteins also indicate that it should be possible to predict the onset of cataracts in captive pandas by taking blood samples and testing them for the presence or absence of specific protein markers.

OrthoDB v11: annotation of orthologs in the widest sampling of organismal diversity

OrthoDB provides evolutionary and functional annotations of genes in a diverse sampling of eukaryotes, prokaryotes, and viruses. Genomics continues to accelerate our exploration of gene diversity and orthology is the most precise way of bridging gene functional knowledge with the rapidly expanding universe of genomic sequences. OrthoDB samples the most diverse organisms with the best quality genomics data to provide the leading coverage of species diversity. This update of the underlying data to over 18 000 prokaryotes and almost 2000 eukaryotes with over 100 million genes propels the coverage to another level. This achievement also demonstrates the scalability of the underlying OrthoLoger software for delineation of orthologs, freely available from https://orthologer.ezlab.org. In addition to the ab-initio computations of gene orthology used for the OrthoDB release, the OrthoLoger software allows mapping of novel gene sets to precomputed orthologs and thereby links to their annotations. The LEMMI-style benchmarking of OrthoLoger ensures its state-of-the-art performance and is available from https://lemortho.ezlab.org. The OrthoDB web interface has been further developed to include a pairwise orthology view from any gene to any other sampled species. OrthoDB-computed evolutionary annotations as well as extensively collated functional annotations can be accessed via REST API or SPARQL/RDF, downloaded or browsed online from https://www.orthodb.org.

OncoboxPD: human 51 672 molecular pathways database with tools for activity calculating and visualization

OncoboxPD (Oncobox pathway databank) available at https://open.oncobox.com is the collection of 51 672 uniformly processed human molecular pathways. Superposition of all pathways formed interactome graph of protein–protein interactions and metabolic reactions containing 361 654 interactions and 64 095 molecular participants. Pathways are uniformly classified by biological processes, and each pathway node is algorithmically functionally annotated by specific activator/repressor role. This enables online calculation of statistically supported pathway activation levels (PALs) with the built-in bioinformatic tool using custom RNA/protein expression profiles. Each pathway can be visualized as static or dynamic graph, where vertices are molecules participating in a pathway and edges are interactions or reactions between them. Differentially expressed nodes in a pathway can be visualized in two-color mode with user-defined color scale. For every comparison, OncoboxPD also generates a graph summarizing top up- and downregulated pathways.

In silico Analysis of SARS-CoV-2 ORF8-Binding Proteins Reveals the Involvement of ORF8 in Acquired-Immune and Innate-Immune Systems

SARS-CoV-2 is the causative agent of a new type of coronavirus infection, COVID-19, which has rapidly spread worldwide. The overall genome sequence homology between SARS-CoV-2 and SARS-CoV is 79%. However, the homology of the ORF8 protein between these two coronaviruses is low, at ~26%. Previously, it has been suggested that infection by the ORF8-deleted variant of SARS-CoV-2 results in less severe symptoms than in the case of wild-type SARS-CoV-2. Although we found that ORF8 is involved in the proteasome autoimmunity system, the precise role of ORF8 in infection and pathology has not been fully clarified. In this study, we determined a new network of ORF8-interacting proteins by performing in silico analysis of the binding proteins against the previously described 47 ORF8-binding proteins. We used as a dataset 431 human protein candidates from Uniprot that physically interacted with 47 ORF8-binding proteins, as identified using STRING. Homology and phylogenetic profile analyses of the protein dataset were performed on 446 eukaryotic species whose genome sequences were available in KEGG OC. Based on the phylogenetic profile results, clustering analysis was performed using Ward's method. Our phylogenetic profiling showed that the interactors of the ORF8-interacting proteins were clustered into three classes that were conserved across chordates (Class 1: 152 proteins), metazoans (Class 2: 163 proteins), and eukaryotes (Class 3: 114 proteins). Following the KEGG pathway analysis, classification of cellular localization, tissue-specific expression analysis, and a literature study on each class of the phylogenetic profiling cluster tree, we predicted that the following: protein members in Class 1 could contribute to COVID-19 pathogenesis via complement and coagulation cascades and could promote sarcoidosis; the members of Class 1 and 2, together, may contribute to the downregulation of Interferon-β; and Class 3 proteins are associated with endoplasmic reticulum stress and the degradation of human leukocyte antigen.

Generalized vec trick for fast learning of pairwise kernel models

Pairwise learning corresponds to the supervised learning setting where the goal is to make predictions for pairs of objects. Prominent applications include predicting drug-target or protein-protein interactions, or customer-product preferences. In this work, we present a comprehensive review of pairwise kernels, that have been proposed for incorporating prior knowledge about the relationship between the objects. Specifically, we consider the standard, symmetric and anti-symmetric Kronecker product kernels, metric-learning, Cartesian, ranking, as well as linear, polynomial and Gaussian kernels. Recently, a $$O(nm+nq)$$ O ( n m + n q ) time generalized vec trick algorithm, where $$n$$ n , $$m$$ m , and $$q$$ q denote the number of pairs, drugs and targets, was introduced for training kernel methods with the Kronecker product kernel. This was a significant improvement over previous $$O(n^2)$$ O ( n 2 ) training methods, since in most real-world applications $$m,q<< n$$ m , q < < n . In this work we show how all the reviewed kernels can be expressed as sums of Kronecker products, allowing the use of generalized vec trick for speeding up their computation. In the experiments, we demonstrate how the introduced approach allows scaling pairwise kernels to much larger data sets than previously feasible, and provide an extensive comparison of the kernels on a number of biological interaction prediction tasks.

Hi-C deconvolution of a textile dye-related microbiome reveals novel taxonomic landscapes and links phenotypic potential to individual genomes

Microbial biodiversity is represented by a variety of genomic landscapes adapted to dissimilar environments on Earth. These genomic landscapes contain functional signatures connected with the community phenotypes. Here, we assess the genomic microbial diversity landscape at a high-resolution level of a polluted river-associated microbiome (Morelos, México), cultured in a medium enriched with anthraquinone Deep Blue 35 dye. We explore the resultant textile dye microbiome to infer links between predicted biodegradative functions, and metagenomic and metabolic potential, especially using the information obtained from individual reconstructed genomes. By using Hi-C proximity-ligation deconvolution method, we deconvoluted 97 genome composites (80% potentially novel species). The main taxonomic determinants were Methanobacterium, Clostridium, and Cupriavidus genera constituting 50, 22, and 11% of the total community profile. Also, we observed a rare biosphere of novel taxa without clear taxonomic standing. Removal of 50% chemical oxygen demand with 23% decolorization was observed after 30 days of dye enrichment. Genes related to catalase-peroxidase, polyphenol oxidase, and laccase enzymes were predicted as associated with textile dye biodegradation phenotype under our study conditions, highlighting the potential of metagenome-wide analysis to predict biodegradative determinants. This study prompts high-resolution screening of individual genomes within textile dye river sediment microbiomes or complex communities under environmental pressures.

Zinc finger protein 483 (ZNF483) regulates neuronal differentiation and methyl-CpG-binding protein 2 (MeCP2) intracellular localization

Rett syndrome (OMIM #312750) is a developmental neurological disorder that is caused by a mutation in methyl-CpG-binding protein 2 (MeCP2). MeCP2 localizes to the nucleus, binds to methylated DNA, and regulates gene expression during neuronal development. MeCP2 assembles multiple protein complexes and its functions are controlled by interactions with its binding partners. Therefore, functional analysis of MeCP2 binding proteins is important. Previously, we proposed nine MeCP2-binding candidates in the cerebral cortex. In this study, we characterized and examined the function of the MeCP2 binding protein zinc finger protein 483 (ZNF483) to determine the significance of the MeCP2-ZNF483 interaction in neuronal development. Phylogenetic profiling revealed that the ZNF483 protein is broadly conserved in metazoans. In contrast, MeCP2 was obtained during evolution to chordates. To investigate ZNF483 functions, ZNF483-knockout P19 cell lines were established using the CRISPR-Cas9 system. These cell lines showed decreased cell proliferation, altered aggregate formation, decreased neuronal marker NeuN expression, and altered MeCP2 phosphorylation patterns. Notably, cytosolic localization of MeCP2 was enhanced by ZNF483-overexpression. Taken together, we propose that ZNF483 might be involved in the promotion of neuronal differentiation by regulating the subcellular localization of MeCP2 in P19 cells.

Next-Generation Sequencing for Whole-Genome Characterization of Weissella cibaria UTNGt21O Strain Originated From Wild Solanum quitoense Lam. Fruits: An Atlas of Metabolites With Biotechnological Significance

The whole genome of Weissella cibaria strain UTNGt21O isolated from wild fruits of Solanum quitoense (naranjilla) shrub was sequenced and annotated. The similarity proportions based on the genus level, as a result of the best hits for the entire contig, were 54.84% with Weissella, 6.45% with Leuconostoc, 3.23% with Lactococcus, and 35.48% no match. The closest genome was W. cibaria SP7 (GCF_004521965.1) with 86.21% average nucleotide identity (ANI) and 3.2% alignment coverage. The genome contains 1,867 protein-coding genes, among which 1,620 were assigned with the EggNOG database. On the basis of the results, 438 proteins were classified with unknown function from which 247 new hypothetical proteins have no match in the nucleotide Basic Local Alignment Search Tool (BLASTN) database. It also contains 78 tRNAs, six copies of 5S rRNA, one copy of 16S rRNA, one copy of 23S rRNA, and one copy of tmRNA. The W. cibaria UTNGt21O strain harbors several genes responsible for carbohydrate metabolism, cellular process, general stress responses, cofactors, and vitamins, conferring probiotic features. A pangenome analysis indicated the presence of various strain-specific genes encoded for proteins responsible for the defense mechanisms as well as gene encoded for enzymes with biotechnological value, such as penicillin acylase and folates; thus, W. cibaria exhibited high genetic diversity. The genome characterization indicated the presence of a putative CRISPR-Cas array and five prophage regions and the absence of acquired antibiotic resistance genes, virulence, and pathogenic factors; thus, UTNGt21O might be considered a safe strain. Besides, the interaction between the peptide extracts from UTNGt21O and Staphylococcus aureus results in cell death caused by the target cell integrity loss and the release of aromatic molecules from the cytoplasm. The results indicated that W. cibaria UTNGt21O can be considered a beneficial strain to be further exploited for developing novel antimicrobials and probiotic products with improved technological characteristics.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.

The Functional Classification of ORF8 in SARS-CoV-2 Replication, Immune Evasion, and Viral Pathogenesis Inferred through Phylogenetic Profiling

ORF8 is a highly variable genomic region of SARS-CoV-2. Although non-essential and the precise functions are unknown, it has been suggested that this protein assists in SARS-CoV-2 replication in the early secretory pathway and in immune evasion. We utilized the binding partners of SARS-CoV-2 proteins in human HEK293T cells and performed genome-wide phylogenetic profiling and clustering analyses in 446 eukaryotic species to predict and discover ORF8 binding partners that share associated functional mechanisms based on co-evolution. Results classified 47 ORF8 binding partner proteins into 3 clusters (groups 1-3), which were conserved in vertebrates (group 1), metazoan (group 2), and eukaryotes (group 3). Gene ontology analysis indicated that group 1 had no significant associated biological processes, while groups 2 and 3 were associated with glycoprotein biosynthesis process and ubiquitin-dependent endoplasmic reticulum-associated degradation pathways, respectively. Collectively, our results classified potential genes that might be associated with SARS-CoV-2 viral pathogenesis, specifically related to acute respiratory distress syndrome, and the secretory pathway. Here, we discuss the possible role of ORF8 in viral pathogenesis and in assisting viral replication and immune evasion via secretory pathway, as well as the possible factors associated with the rapid evolution of ORF8.

OrthoDB in 2020: evolutionary and functional annotations of orthologs

OrthoDB provides evolutionary and functional annotations of orthologs, inferred for a vast number of available organisms. OrthoDB is leading in the coverage and genomic diversity sampling of Eukaryotes, Prokaryotes and Viruses, and the sampling of Bacteria is further set to increase three-fold. The user interface has been enhanced in response to the massive growth in data. OrthoDB provides three views on the data: (i) a list of orthologous groups related to a user query, which are now arranged to visualize their hierarchical relations, (ii) a detailed view of an orthologous group, now featuring a Sankey diagram to facilitate navigation between the levels of orthology, from more finely-resolved to more general groups of orthologs, as well as an arrangement of orthologs into an interactive organism taxonomy structure, and (iii) we added a gene-centric view, showing the gene functional annotations and the pair-wise orthologs in example species. The OrthoDB standalone software for delineation of orthologs, Orthologer, is freely available. Online BUSCO assessments and mapping to OrthoDB of user-uploaded data enable interactive exploration of related annotations and generation of comparative charts. OrthoDB strives to predict orthologs from the broadest coverage of species, as well as to extensively collate available functional annotations, and to compute evolutionary annotations such as evolutionary rate and phyletic profile. OrthoDB data can be assessed via SPARQL RDF, REST API, downloaded or browsed online from https://orthodb.org.

System, Method and Software for Calculation of a Cannabis Drug Efficiency Index for the Reduction of Inflammation

There are many varieties of Cannabis sativa that differ from each other by composition of cannabinoids, terpenes and other molecules. The medicinal properties of these cultivars are often very different, with some being more efficient than others. This report describes the development of a method and software for the analysis of the efficiency of various cannabis extracts to detect the anti-inflammatory properties of the various cannabis extracts. The method uses high-throughput gene expression profiling data but can potentially use other omics data as well. According to the signaling pathway topology, the gene expression profiles are convoluted into the signaling pathway activities using a signaling pathway impact analysis (SPIA) method. The method was tested by inducing inflammation in human 3D epithelial tissues, including intestine, oral and skin, and then exposing these tissues to various extracts and then performing transcriptome analysis. The analysis showed a different efficiency of the various extracts in restoring the transcriptome changes to the pre-inflammation state, thus allowing to calculate a different cannabis drug efficiency index (CDEI).

Comparative Analysis of the Gut Microbial Communities of the Eurasian Kestrel (Falco tinnunculus) at Different Developmental Stages

The gut microflora play a very important role in the life of animals. Although an increasing number of studies have investigated the gut microbiota of birds in recent years, there is a lack of research work on the gut microbiota of wild birds, especially carnivorous raptors, which are thought to be pathogen vectors. There are also a lack of studies focused on the dynamics of the gut microbiota during development in raptors. In this study, 16S rRNA gene amplicon high-throughput sequencing was used to analyze the gut microbiota community composition of a medium-sized raptor, the Eurasian Kestrel (Falco tinnunculus), and to reveal stage-specific signatures in the gut microbiota of nestlings during the pre-fledging period. Moreover, differences in the gut microbiota between adults and nestlings in the same habitat were explored. The results indicated that the Eurasian Kestrel hosts a diverse assemblage of gut microbiota. Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes were the primary phyla shared within the guts of adults and chicks. However, adults harbored higher abundances of Proteobacteria while nestlings exhibited higher abundances of Firmicutes and Actinobacteria, and consequently the majority of dominant genera observed in chicks differed from those in adults. Although no significant differences in diversity were observed across the age groups during nestling ontogeny, chicks from all growth stages harbored richer and more diverse bacterial communities than adults. In contrast, the differences in gut microbial communities between adults and younger nestlings were more pronounced. The gut microbes of the nestlings in the last growth stage were converged with those of the adults. This study provides basic reference data for investigations of the gut microbiota community structure of wild birds and deepens our understanding of the dynamics of the gut microflora during raptor development.

Molecular assessment and transcriptome profiling of wild fish populations of Oryzias mekongensis and O. songkhramensis (Adrianichthyidae: Beloniformes) from Thailand

Among the fish of the genus Oryzias, two species are frequently used as model animals in biological research. In Thailand, Oryzias mekongensis is usually found in natural freshwater near the Mekong Basin in the northeast region, while O. songkhramensis inhabits the Songkhram Basin. For differential morphological identification, the coloured bands on the dorsal and ventral margins of the caudal fin are used to distinguish O. mekongensis from O. songkhramensis. However, these characteristics are insufficient to justify species differentiation, and little molecular evidence is available to supplement them. This study aimed to investigate the molecular population and transcriptome profiles of adult O. mekongensis and O. songkhramensis. In the molecular tree based on cytochrome b sequences, O. mekongensis exhibited four clades that were clearly distinguished from O. songkhramensis. Clade 1 of the O. mekongensis population was close to the Mekong River and lived in the eastern portion of the upper northeast region. Clade 2 was far from the Mekong River and inhabited the middle region of the Songkhram River. Clade 3 was positioned to the west of the Songkhram River, and clade 4 was to the south of the Songkhram River Basin. After RNA sequencing using an Illumina HiSeq 2500 platform, the gene category annotations hardly differentiated the species and were discussed in the text. Based on the present findings, population dispersal of these Oryzias species might be associated with geographic variations of the upper northeast region. Molecular genetics and transcriptome profiling might advance our understanding of the evolution of teleost fish.

Molecular pathophysiology of diabetes mellitus during pregnancy with antenatal complications

Gestational diabetes mellitus is a daunting problem accompanied by severe fetal development complications and type 2 diabetes mellitus in postpartum. Diagnosis of diabetic conditions occurs only in the second trimester, while associated antenatal complications are typically revealed even later. We acquired an assay of peripheral and cord blood samples of patients with different types of diabetes mellitus who delivered either healthy newborns or associated with fetopathy complications. Obtained data were handled with qualitative and quantitative analysis. Pathways of molecular events involved in diabetes mellitus and fetopathy were reconstructed based on the discovered markers and their quantitative alteration. Plenty of pathways were integrated to differentiate the type of diabetes and to recognize the impact of the diabetic condition on fetal development. The impaired triglycerides transport, glucose uptake, and consequent insulin resistance are mostly affected by faulted lipid metabolism (APOM, APOD, APOH, APOC1) and encouraged by oxidative stress (CP, TF, ORM2) and inflammation (CFH, CFB, CLU) as a secondary response accompanied by changes in matrix architecture (AFM, FBLN1, AMBP). Alterations in proteomes of peripheral and cord blood were expectedly unequal. Both up- and downregulated markers were accommodated in the cast of molecular events interconnected with the lipid metabolism, RXR/PPAR-signaling pathway, and extracellular architecture modulation. The obtained results congregate numerous biological processes to molecular events that underline diabetes during gestation and uncover some critical aspects affecting fetal growth and development.

Dissecting molecular network structures using a network subgraph approach

Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs. Graph energy, reciprocity and cyclomatic complexity can optimally specify network subgraphs with some degree of degeneracy. Seventy-one molecular networks were analyzed from three network types: cancer networks, signal transduction networks, and cellular processes. Molecular networks are built from a finite number of subgraph patterns and subgraphs with large graph energies are not present, which implies a graph energy cutoff. In addition, certain subgraph patterns are absent from the three network types. Thus, the Shannon entropy of the subgraph frequency distribution is not maximal. Furthermore, frequently-observed subgraphs are irreducible graphs. These novel findings warrant further investigation and may lead to important applications. Finally, we observed that cancer-related cellular processes are enriched with subgraph-associated driver genes. Our study provides a systematic approach for dissecting biological networks and supports the conclusion that there are organizational principles underlying molecular networks.

Proteome of Glioblastoma-Derived Exosomes as a Source of Biomarkers

Extracellular vesicles (EV) are involved in important processes of glioblastoma multiforme (GBM), including malignancy and invasion. EV secreted by glioblastoma cells may cross the hematoencephalic barrier and carry molecular cargo derived from the tumor into the peripheral circulation. Therefore, the determination of the molecular composition of exosomes released by glioblastoma cells seems to be a promising approach for the development of non-invasive methods of the detection of the specific exosomal protein markers in the peripheral blood. The present study aimed to determine the common exosomal proteins presented in preparations from different cell lines and search potential glioblastoma biomarkers in exosomes. We have performed proteomics analysis of exosomes obtained from the conditioned culture medium of five glioblastoma cell lines. A list of 133 proteins common for all these samples was generated. Based on the data obtained, virtual two-dimensional electrophoresis (2DE) maps of proteins presented in exosomes of glioblastoma cells were constructed and the gene ontology (GO) analysis of exosome proteins was performed. A correlation between overexpressed in glial cell proteins and their presence in exosomes have been found. Thus, the existence of many potential glioblastoma biomarkers in exosomes was confirmed.

MBGD update 2018: microbial genome database based on hierarchical orthology relations covering closely related and distantly related comparisons

The Microbial Genome Database for Comparative Analysis (MBGD) is a database for comparative genomics based on comprehensive orthology analysis of bacteria, archaea and unicellular eukaryotes. MBGD now contains 6318 genomes. To utilize the database for both closely related and distantly related genomes, MBGD previously provided two types of ortholog tables: the standard ortholog table containing one representative genome from each genus covering the entire taxonomic range and the taxon specific ortholog tables for each taxon. However, this approach has a drawback in that the standard ortholog table contains only genes that are conserved in the representative genomes. To address this problem, we developed a stepwise procedure to construct ortholog tables hierarchically in a bottom-up manner. By using this approach, the new standard ortholog table now covers the entire gene repertoire stored in MBGD. In addition, we have enhanced several functionalities, including rapid and flexible keyword searching, profile-based sequence searching for orthology assignment to a user query sequence, and displaying a phylogenetic tree of each taxon based on the concatenated core gene sequences. For integrative database searching, the core data in MBGD are represented in Resource Description Framework (RDF) and a SPARQL interface is provided to search them. MBGD is available at http://mbgd.genome.ad.jp/.

OrthoDB v10: sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs

OrthoDB (https://www.orthodb.org) provides evolutionary and functional annotations of orthologs. This update features a major scaling up of the resource coverage, sampling the genomic diversity of 1271 eukaryotes, 6013 prokaryotes and 6488 viruses. These include putative orthologs among 448 metazoan, 117 plant, 549 fungal, 148 protist, 5609 bacterial, and 404 archaeal genomes, picking up the best sequenced and annotated representatives for each species or operational taxonomic unit. OrthoDB relies on a concept of hierarchy of levels-of-orthology to enable more finely resolved gene orthologies for more closely related species. Since orthologs are the most likely candidates to retain functions of their ancestor gene, OrthoDB is aimed at narrowing down hypotheses about gene functions and enabling comparative evolutionary studies. Optional registered-user sessions allow on-line BUSCO assessments of gene set completeness and mapping of the uploaded data to OrthoDB to enable further interactive exploration of related annotations and generation of comparative charts. The accelerating expansion of genomics data continues to add valuable information, and OrthoDB strives to provide orthologs from the broadest coverage of species, as well as to extensively collate available functional annotations and to compute evolutionary annotations. The data can be browsed online, downloaded or assessed via REST API or SPARQL RDF compatible with both UniProt and Ensembl.

Logicome Profiler: Exhaustive detection of statistically significant logic relationships from comparative omics data

Logic relationship analysis is a data mining method that comprehensively detects item triplets that satisfy logic relationships from a binary matrix dataset, such as an ortholog table in comparative genomics. Thanks to recent technological advancements, many binary matrix datasets are now being produced in genomics, transcriptomics, epigenomics, metagenomics, and many other fields for comparative purposes. However, regardless of presumed interpretability and importance of logic relationships, existing data mining methods are not based on the framework of statistical hypothesis testing. That means, the type-1 and type-2 error rates are neither controlled nor estimated. Here, we developed Logicome Profiler, which exhaustively detects statistically significant triplet logic relationships from a binary matrix dataset (Logicome means ome of logics). To test all item triplets in a dataset while avoiding false positives, Logicome Profiler adjusts a significance level by the Bonferroni or Benjamini-Yekutieli method for the multiple testing correction. Its application to an ocean metagenomic dataset showed that Logicome Profiler can effectively detect statistically significant triplet logic relationships among environmental microbes and genes, which include those among urea transporter, urease, and photosynthesis-related genes. Beyond omics data analysis, Logicome Profiler is applicable to various binary matrix datasets in general for finding significant triplet logic relationships. The source code is available at https://github.com/fukunagatsu/LogicomeProfiler.

Transcriptomic and Genomic Testing to Guide Individualized Treatment in Chemoresistant Gastric Cancer Case

Gastric cancer is globally the fifth leading cause of cancer death. We present a case report describing the unique genomic characteristics of an Epstein–Barr virus-negative gastric cancer with esophageal invasion and regional lymph node metastasis. Genomic tests were performed first with the stomach biopsy using platforms FoundationOne, OncoDNA, and Oncopanel at Dana Farber Institute. Following neoadjuvant chemotherapy, residual tumor was resected and the stomach and esophageal residual tumor samples were compared with the initial biopsy by whole exome sequencing and molecular pathway analysis platform Oncobox. Copy number variation profiling perfectly matched the whole exome sequencing results. A moderate agreement was seen between the diagnostic platforms in finding mutations in the initial biopsy. Final data indicate somatic activating mutation Q546K in PIK3CA gene, somatic frameshifts in PIH1D1 and FBXW7 genes, stop-gain in TP53BP1, and a few somatic mutations of unknown significance. RNA sequencing analysis revealed upregulated expressions of MMP7, MMP9, BIRC5, and PD-L1 genes and strongly differential regulation of several molecular pathways linked with the mutations identified. According to test results, the patient received immunotherapy with anti-PD1 therapy and is now free of disease for 2 years. Our data suggest that matched tumor and normal tissue analyses have a considerable advantage over tumor biopsy-only genomic tests in stomach cancer.

Diversity of Clinically Relevant Outcomes Resulting from Hypofractionated Radiation in Human Glioma Stem Cells Mirrors Distinct Patterns of Transcriptomic Changes

Hypofractionated radiotherapy is the mainstay of the current treatment for glioblastoma. However, the efficacy of radiotherapy is hindered by the high degree of radioresistance associated with glioma stem cells comprising a heterogeneous compartment of cell lineages differing in their phenotypic characteristics, molecular signatures, and biological responses to external signals. Reconstruction of radiation responses in glioma stem cells is necessary for understanding the biological and molecular determinants of glioblastoma radioresistance. To date, there is a paucity of information on the longitudinal outcomes of hypofractionated radiation in glioma stem cells. This study addresses long-term outcomes of hypofractionated radiation in human glioma stem cells by using a combinatorial approach integrating parallel assessments of the tumor-propagating capacity, stemness-associated properties, and array-based profiling of gene expression. The study reveals a broad spectrum of changes in the tumor-propagating capacity of glioma stem cells after radiation and finds association with proliferative changes at the onset of differentiation. Evidence is provided that parallel transcriptomic patterns and a cumulative impact of pathways involved in the regulation of apoptosis, neural differentiation, and cell proliferation underly similarities in tumorigenicity changes after radiation.

Intratumoral Heterogeneity and Longitudinal Changes in Gene Expression Predict Differential Drug Sensitivity in Newly Diagnosed and Recurrent Glioblastoma

Background: Inevitable recurrence after radiochemotherapy is the major problem in the treatment of glioblastoma, the most prevalent type of adult brain malignancy. Glioblastomas are notorious for a high degree of intratumor heterogeneity manifest through a diversity of cell types and molecular patterns. The current paradigm of understanding glioblastoma recurrence is that cytotoxic therapy fails to target effectively glioma stem cells. Recent advances indicate that therapy-driven molecular evolution is a fundamental trait associated with glioblastoma recurrence. There is a growing body of evidence indicating that intratumor heterogeneity, longitudinal changes in molecular biomarkers and specific impacts of glioma stem cells need to be taken into consideration in order to increase the accuracy of molecular diagnostics still relying on readouts obtained from a single tumor specimen. Methods: This study integrates a multisampling strategy, longitudinal approach and complementary transcriptomic investigations in order to identify transcriptomic traits of recurrent glioblastoma in whole-tissue specimens of glioblastoma or glioblastoma stem cells. In this study, 128 tissue samples of 44 tumors including 23 first diagnosed, 19 recurrent and 2 secondary recurrent glioblastomas were analyzed along with 27 primary cultures of glioblastoma stem cells by RNA sequencing. A novel algorithm was used to quantify longitudinal changes in pathway activities and model efficacy of anti-cancer drugs based on gene expression data. Results: Our study reveals that intratumor heterogeneity of gene expression patterns is a fundamental characteristic of not only newly diagnosed but also recurrent glioblastomas. Evidence is provided that glioblastoma stem cells recapitulate intratumor heterogeneity, longitudinal transcriptomic changes and drug sensitivity patterns associated with the state of recurrence. Conclusions: Our results provide a transcriptional rationale for the lack of significant therapeutic benefit from temozolomide in patients with recurrent glioblastoma. Our findings imply that the spectrum of potentially effective drugs is likely to differ between newly diagnosed and recurrent glioblastomas and underscore the merits of glioblastoma stem cells as prognostic models for identifying alternative drugs and predicting drug response in recurrent glioblastoma. With the majority of recurrent glioblastomas being inoperable, glioblastoma stem cell models provide the means of compensating for the limited availability of recurrent glioblastoma specimens.

BioHackathon 2015: Semantics of data for life sciences and reproducible research

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

Quantitation of Molecular Pathway Activation Using RNA Sequencing Data

Intracellular molecular pathways (IMPs) control all major events in the living cell. IMPs are considered hotspots in biomedical sciences and thousands of IMPs have been discovered for humans and model organisms. Knowledge of IMPs activation is essential for understanding biological functions and differences between the biological objects at the molecular level. Here we describe the Oncobox system for accurate quantitative scoring activities of up to several thousand molecular pathways based on high throughput molecular data. Although initially designed for gene expression and mainly RNA sequencing data, Oncobox is now also applicable for quantitative proteomics, microRNA and transcription factor binding sites mapping data. The Oncobox system includes modules of gene expression data harmonization, aggregation and comparison and a recursive algorithm for automatic annotation of molecular pathways. The universal rationale of Oncobox enables scoring of signaling, metabolic, cytoskeleton, immunity, DNA repair, and other pathways in a multitude of biological objects. The Oncobox system can be helpful to all those working in the fields of genetics, biochemistry, interactomics, and big data analytics in molecular biomedicine.

Targeted enzyme gene re-positioning: A computational approach for discovering alternative bacterial enzymes for the synthesis of plant-specific secondary metabolites

Plant-biosynthesised secondary metabolites are unique sources of pharmaceuticals, food additives, and flavourings, among other industrial uses. However, industrial production of these metabolites is difficult because of their structural complexity, dangerousness and unfriendliness to natural environment, so the development of new methods to synthesise them is required. In this study, we developed a novel approach to identifying alternative bacterial enzyme to produce plant-biosynthesised secondary metabolites. Based on the similarity of enzymatic reactions, we searched for candidate bacterial genes encoding enzymes that could potentially replace the enzymes in plant-specific secondary metabolism reactions that are contained in the KEGG database (enzyme re-positioning). As a result, we discovered candidate bacterial alternative enzyme genes for 447 plant-specific secondary metabolic reaction. To validate our approach, we focused on the ability of an enzyme from Streptomyces coelicolor strain A3(2) strain to convert valencene to the grapefruit metabolite nootkatone, and confirmed its enzymatic activity by gas chromatography-mass spectrometry. This enzyme re-positioning approach may offer an entirely new way of screening enzymes that cannot be achieved by most of other conventional methods, and it is applicable to various other metabolites and may enable microbial production of compounds that are currently difficult to produce industrially.

In Silico Study of Rett Syndrome Treatment-Related Genes, MECP2, CDKL5, and FOXG1, by Evolutionary Classification and Disordered Region Assessment

Rett syndrome (RTT), a neurodevelopmental disorder, is mainly caused by mutations in methyl CpG-binding protein 2 (MECP2), which has multiple functions such as binding to methylated DNA or interacting with a transcriptional co-repressor complex. It has been established that alterations in cyclin-dependent kinase-like 5 (CDKL5) or forkhead box protein G1 (FOXG1) correspond to distinct neurodevelopmental disorders, given that a series of studies have indicated that RTT is also caused by alterations in either one of these genes. We investigated the evolution and molecular features of MeCP2, CDKL5, and FOXG1 and their binding partners using phylogenetic profiling to gain a better understanding of their similarities. We also predicted the structural order-disorder propensity and assessed the evolutionary rates per site of MeCP2, CDKL5, and FOXG1 to investigate the relationships between disordered structure and other related properties with RTT. Here, we provide insight to the structural characteristics, evolution and interaction landscapes of those three proteins. We also uncovered the disordered structure properties and evolution of those proteins which may provide valuable information for the development of therapeutic strategies of RTT.

Differentially expressed genes between systemic sclerosis and rheumatoid arthritis

Background

Evidence is accumulating to characterise the key differences between systemic sclerosis (SSc) and rheumatoid arthritis (RA), which are similar but distinct systemic autoimmune diseases. However, the differences at the genetic level are not yet clear. Therefore, the aim of the present study was to identify key differential genes between patients with SSc and RA.

Methods

The Gene Expression Omnibus database was used to identify differentially expressed genes (DEGs) between SSc and RA biopsies. The DEGs were then functionally annotated using Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways with the Database for Annotation, Visualization and Integrated Discovery (DAVID) tools. A protein–protein interaction (PPI) network was constructed with Cytoscape software. The Molecular Complex Detection (MCODE) plugin was also used to evaluate the biological importance of the constructed gene modules.

Results

A total of 13,556 DEGs were identified between the five SSc patients and seven RA patients, including 13,465 up-regulated genes and 91 down-regulated genes. Interestingly, the most significantly enriched GO terms of up- and down-regulated genes were related to extracellular involvement and immune activity, respectively, and the top six highly enriched KEGG pathways were related to the same processes. In the PPI network, the top 10 hub nodes and top four modules harboured the most relevant genes contributing to the differences between SSc and RA, including key genes such as IL6, EGF, JUN, FGF2, BMP2, FOS, BMP4, LRRK2, CTNNB1, EP300, CD79, and CXCL13.

Conclusions

These genes such as IL6, EGF, JUN, FGF2, BMP2, FOS, BMP4, LRRK2, CTNNB1, EP300, CD79, and CXCL13 can serve as new targets for focused research on the distinct molecular pathogenesis of SSc and RA. Furthermore, these genes could serve as potential biomarkers for differential diagnoses or therapeutic targets for treatment.

Electronic supplementary material

The online version of this article (10.1186/s41065-019-0091-y) contains supplementary material, which is available to authorized users.

Plasma exosomes stimulate breast cancer metastasis through surface interactions and activation of FAK signaling

PURPOSE

The interaction between malignant cells and surrounding healthy tissues is a critical factor in the metastatic progression of breast cancer (BC). Extracellular vesicles, especially exosomes, are known to be involved in inter-cellular communication during cancer progression. In the study presented herein, we aimed to evaluate the role of circulating plasma exosomes in the metastatic dissemination of BC and to investigate the underlying molecular mechanisms of this phenomenon.

METHODS

Exosomes isolated from plasma of healthy female donors were applied in various concentrations into the medium of MDA-MB-231 and MCF-7 cell lines. Motility and invasive properties of BC cells were examined by random migration and Transwell invasion assays, and the effect of plasma exosomes on the metastatic dissemination of BC cells was demonstrated in an in vivo zebrafish model. To reveal the molecular mechanism of interaction between plasma exosomes and BC cells, a comparison between un-treated and enzymatically modified exosomes was performed, followed by mass spectrometry, gene ontology, and pathway analysis.

RESULTS

Plasma exosomes stimulated the adhesive properties, two-dimensional random migration, and transwell invasion of BC cells in vitro as well as their in vivo metastatic dissemination in a dose-dependent manner. This stimulatory effect was mediated by interactions of surface exosome proteins with BC cells and consequent activation of focal adhesion kinase (FAK) signaling in the tumor cells.

CONCLUSIONS

Plasma exosomes have a potency to stimulate the metastasis-promoting properties of BC cells. This pro-metastatic property of normal plasma exosomes may have impact on the course of the disease and on its prognosis.

Functional Properties of Circulating Exosomes Mediated by Surface-Attached Plasma Proteins

Background

Exosomes and other types of extracellular vesicles present an important component of circulating plasma. Exosomes released by endothelial and blood cells account for majority of plasma exosomal population; exosomes secreted by other cells might cross tissue-plasma barrier and reach circulating plasma as well. Definitely, exosomes of different cellular origins are different by content and function. However, exosomal surface membrane interacts with plasma components. This interaction may alter composition of exosomal surface and hence, provide these vesicles with new functional properties. This study was aimed to estimate composition and possible functional role of proteins attached on the surface of plasma exosomes.

Methods

Here, extracellular vesicles from human plasma were isolated by ultracentrifugation and treated by trypsin. Trypsinized and native exosomes were analyzed by nanoparticle tracking analysis, Western blotting and quantitative high-resolution mass spectrometry.

Results

Surface-attached proteins were removed from exosomes isolated from plasma of healthy donors by incubation with serine protease (trypsin). Treatment did not impact exosomes integrity while slightly reduced hydrodynamic radius. Mass spectrometry revealed 259 exosomal proteins; among them 79 proteins were completely removed and more than half of the proteins were partially removed by trypsinization. Gene ontology functional annotation revealed mostly extracellular locations of proteins cleaved from a surface of the plasma exosomes. Moreover, proteins cleaved from the exosome surface are supposed to be implicated into integrin-linked kinase (ILK), focal adhesion kinase (FAK) and other pathways connecting cell surface with intracellular signaling cascades.

Conclusion

Taken together, our results demonstrate that a surface of circulating exosomes is decorated by plasma proteins, and these proteins can mask tissue-specific characteristic of the exosomal surface membrane and provide exosomes with new and uniform properties.

Multiobjective optimization to reconstruct biological networks

Automated methods for reconstructing biological networks are becoming increasingly important in computational systems biology. Public databases containing information on biological processes for hundreds of organisms are assisting in the inference of such networks. This paper proposes a multiobjective genetic algorithm method to reconstruct networks related to metabolism and protein interaction. Such a method utilizes structural properties of scale-free networks and known biological information about individual genes and proteins to reconstruct metabolic networks represented as enzyme graph and protein interaction networks. We test our method on four commonly-used protein networks in yeast. Two are networks related to the metabolism of the yeast: KEGG and BioCyc. The other two datasets are networks from protein-protein interaction: Krogan and BioGrid. Experimental results show that the proposed method is capable of reconstructing biological networks by combining different omics data and structural characteristics of scale-free networks. However, the proposed method to reconstruct the network is time-consuming because several evaluations must be performed. We parallelized this method on GPU to overcome this limitation by parallelizing the objective functions of the presented method. The parallel method shows a significant reduction in the execution time over the GPU card which yields a 492-fold speedup.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel

Background

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers.

Results

In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far.

Conclusions

We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

Structure Based Virtual Screening Studies to Identify Novel Potential Compounds for GPR142 and Their Relative Dynamic Analysis for Study of Type 2 Diabetes

GPR142 (G protein receptor 142) is a novel orphan GPCR (G protein coupled receptor) belonging to "Class A" of GPCR family and expressed in β cells of pancreas. In this study, we reported the structure based virtual screening to identify the hit compounds which can be developed as leads for potential agonists. The results were validated through induced fit docking, pharmacophore modeling, and system biology approaches. Since, there is no solved crystal structure of GPR142, we attempted to predict the 3D structure followed by validation and then identification of active site using threading and ab initio methods. Also, structure based virtual screening was performed against a total of 1171519 compounds from different libraries and only top 20 best hit compounds were screened and analyzed. Moreover, the biochemical pathway of GPR142 complex with screened compound2 was also designed and compared with experimental data. Interestingly, compound2 showed an increase in insulin production via Gq mediated signaling pathway suggesting the possible role of novel GPR142 agonists in therapy against type 2 diabetes.

Insights into Microalga and Bacteria Interactions of Selected Phycosphere Biofilms Using Metagenomic, Transcriptomic, and Proteomic Approaches

Microalga are of high relevance for the global carbon cycling and it is well-known that they are associated with a microbiota. However, it remains unclear, if the associated microbiota, often found in phycosphere biofilms, is specific for the microalga strains and which role individual bacterial taxa play. Here we provide experimental evidence that Chlorella saccharophila, Scenedesmus quadricauda, and Micrasterias crux-melitensis, maintained in strain collections, are associated with unique and specific microbial populations. Deep metagenome sequencing, binning approaches, secretome analyses in combination with RNA-Seq data implied fundamental differences in the gene expression profiles of the microbiota associated with the different microalga. Our metatranscriptome analyses indicates that the transcriptionally most active bacteria with respect to key genes commonly involved in plant–microbe interactions in the Chlorella (Trebouxiophyceae) and Scenedesmus (Chlorophyceae) strains belong to the phylum of the α-Proteobacteria. In contrast, in the Micrasterias (Zygnematophyceae) phycosphere biofilm bacteria affiliated with the phylum of the Bacteroidetes showed the highest gene expression rates. We furthermore show that effector molecules known from plant–microbe interactions as inducers for the innate immunity are already of relevance at this evolutionary early plant-microbiome level.

Global Transcriptome Profiling of Xanthomonas oryzae pv. oryzae under in planta Growth and in vitro Culture Conditions

Xanthomonas oryzae pv. oryzae (Xoo), the causative agent of bacterial blight, is a major threat to rice productivity. Here, we performed RNA-Seq based transcriptomic analysis of Xoo transcripts isolated under in planta growth (on both susceptible and resistant hosts) and in vitro culture conditions. Our in planta extraction method resulted in successful enrichment of Xoo cells and provided RNA samples of high quality. A total of 4,619 differentially expressed genes were identified between in planta and in vitro growth conditions. The majority of the differentially expressed genes identified under in planta growth conditions were related to the nutrient transport, protease activity, stress tolerance, and pathogenicity. Among them, over 1,300 differentially expressed genes were determined to be secretory, including 184 putative type III effectors that may be involved in Xoo pathogenicity. Expression pattern of some of these identified genes were further validated by semi-quantitative RT-PCR. Taken together, these results provide a transcriptome overview of Xoo under in planta and in vitro growth conditions with a focus on its pathogenic processes, deepening our understanding of the behavior and pathogenicity of Xoo.

Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data

High throughput technologies opened a new era in biomedicine by enabling massive analysis of gene expression at both RNA and protein levels. Unfortunately, expression data obtained in different experiments are often poorly compatible, even for the same biologic samples. Here, using experimental and bioinformatic investigation of major experimental platforms, we show that aggregation of gene expression data at the level of molecular pathways helps to diminish cross- and intra-platform bias otherwise clearly seen at the level of individual genes. We created a mathematical model of cumulative suppression of data variation that predicts the ideal parameters and the optimal size of a molecular pathway. We compared the abilities to aggregate experimental molecular data for the 5 alternative methods, also evaluated by their capacity to retain meaningful features of biologic samples. The bioinformatic method OncoFinder showed optimal performance in both tests and should be very useful for future cross-platform data analyses.

Differentially expressed proteins in glioblastoma multiforme identified with a nanobody-based anti-proteome approach and confirmed by OncoFinder as possible tumor-class predictive biomarker candidates

Glioblastoma multiforme is the most frequent primary malignancy of the central nervous system. Despite remarkable progress towards an understanding of tumor biology, there is no efficient treatment and patient outcome remains poor. Here, we present a unique anti-proteomic approach for selection of nanobodies specific for overexpressed glioblastoma proteins. A phage-displayed nanobody library was enriched in protein extracts from NCH644 and NCH421K glioblastoma cell lines. Differential ELISA screenings revealed seven nanobodies that target the following antigens: the ACTB/NUCL complex, VIM, NAP1L1, TUFM, DPYSL2, CRMP1, and ALYREF. Western blots showed highest protein up-regulation for ALYREF, CRMP1, and VIM. Moreover, bioinformatic analysis with the OncoFinder software against the complete "Cancer Genome Atlas" brain tumor gene expression dataset suggests the involvement of different proteins in the WNT and ATM pathways, and in Aurora B, Sem3A, and E-cadherin signaling. We demonstrate the potential use of NAP1L1, NUCL, CRMP1, ACTB, and VIM for differentiation between glioblastoma and lower grade gliomas, with DPYSL2 as a promising "glioma versus reference" biomarker. A small scale validation study confirmed significant changes in mRNA expression levels of VIM, DPYSL2, ACTB and TRIM28. This work helps to fill the information gap in this field by defining novel differences in biochemical profiles between gliomas and reference samples. Thus, selected genes can be used to distinguish glioblastoma from lower grade gliomas, and from reference samples. These findings should be valuable for glioblastoma patients once they are validated on a larger sample size.

Characterising PvRBSA: an exclusive protein from Plasmodium species infecting reticulocytes

Background

Plasmodium vivax uses multiple ligand-receptor interactions for preferential invasion of human reticulocytes. Several of these ligands have been identified by in silico approaches based on the role displayed by their orthologs in other Plasmodium species during initial adhesion or invasion. However, the cell adhesion role of proteins that are exclusive to species that specifically invade reticulocytes (as P. vivax and P. cynomolgi) has not been evaluated to date. This study aimed to characterise an antigen shared between Plasmodium species that preferentially infect reticulocytes with a focus on assessing its binding activity to target cells.

Results

An in silico analysis was performed using P. vivax proteome data to identify and characterise one antigen shared between P. vivax and P. cynomolgi. This led to identification of the pvrbsa gene present in the P. vivax VCG-I strain genome. This gene is transcribed in mature schizonts and encodes a protein located on the parasite surface. rPvRBSA was antigenic and capable of binding to a population of reticulocytes with a different Duffy phenotype. Interestingly, the molecule showed a higher percentage of binding to immature human reticulocytes (CD71^hi).

Conclusions

This study describes for the first time, a molecule involved in host cell binding that is exclusive in reticulocyte-infecting Plasmodium species. This suggest that PvRBSA is an antigenic adhesin that plays a role in parasite binding to target cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-017-2185-6) contains supplementary material, which is available to authorized users.

Comparative genomic analysis of bacteriocin-producing Weissella cibaria 110

Weissella cibaria 110 was isolated from plaa-som, a Thai fermented fish product, and known to produce the weissellicin 110 bacteriocin. We carried out comprehensive comparative genomic analysis of W. cibaria 110 with four other non-bacteriocin-producing W. cibaria strains and identified potential antibiotic-resistant genes. We further identified a type III restriction-modification system, a TA system, and a bacteriocin gene cluster that are unique in W. cibaria 110. Genes related to bacteriocin biosynthesis are organized in clusters and are encoded with minimum genetic machinery consisting of structural cognate immunity genes, including ABC transporter and immunity protein. Finally, we predicted W. cibaria 110 to produce a class IId bacteriocin, weissellicin 110, which is 31 amino acids in length and contains a 21-amino-acid N-terminal leader peptide. This is the first bacteriocin-producing sequencing genome in W. cibaria, and we describe the difference between the bacteriocin-producing and non bacteriocin-producing strains from genome point of view.

HieranoiDB: a database of orthologs inferred by Hieranoid

HieranoiDB (http://hieranoiDB.sbc.su.se) is a freely available on-line database for hierarchical groups of orthologs inferred by the Hieranoid algorithm. It infers orthologs at each node in a species guide tree with the InParanoid algorithm as it progresses from the leaves to the root. Here we present a database HieranoiDB with a web interface that makes it easy to search and visualize the output of Hieranoid, and to download it in various formats. Searching can be performed using protein description, identifier or sequence. In this first version, orthologs are available for the 66 Quest for Orthologs reference proteomes. The ortholog trees are shown graphically and interactively with marked speciation and duplication nodes that show the inferred evolutionary scenario, and allow for correct extraction of predicted orthologs from the Hieranoid trees.

Bioinformatics Meets Biomedicine: OncoFinder, a Quantitative Approach for Interrogating Molecular Pathways Using Gene Expression Data

We propose a biomathematical approach termed OncoFinder (OF) that enables performing both quantitative and qualitative analyses of the intracellular molecular pathway activation. OF utilizes an algorithm that distinguishes the activator/repressor role of every gene product in a pathway. This method is applicable for the analysis of any physiological, stress, malignancy, and other conditions at the molecular level. OF showed a strong potential to neutralize background-caused differences between experimental gene expression data obtained using NGS, microarray and modern proteomics techniques. Importantly, in most cases, pathway activation signatures were better markers of cancer progression compared to the individual gene products. OF also enables correlating pathway activation with the success of anticancer therapy for individual patients. We further expanded this approach to analyze impact of micro RNAs (miRs) on the regulation of cellular interactome. Many alternative sources provide information about miRs and their targets. However, instruments elucidating higher level impact of the established total miR profiles are still largely missing. A variant of OncoFinder termed MiRImpact enables linking miR expression data with its estimated outcome on the regulation of molecular processes, such as signaling, metabolic, cytoskeleton, and DNA repair pathways. MiRImpact was used to establish cancer-specific and cytomegaloviral infection-linked interactomic signatures for hundreds of molecular pathways. Interestingly, the impact of miRs appeared orthogonal to pathway regulation at the mRNA level, which stresses the importance of combining all available levels of gene regulation to build a more objective molecular model of cell.

High-Quality Genome Assembly and Annotation for Plasmodium coatneyi, Generated Using Single-Molecule Real-Time PacBio Technology

Plasmodium coatneyi is a protozoan parasite species that causes simian malaria and is an excellent model for studying disease caused by the human malaria parasite, P. falciparum Here we report the complete (nontelomeric) genome sequence of P. coatneyi Hackeri generated by the application of only Pacific Biosciences RS II (PacBio RS II) single-molecule real-time (SMRT) high-resolution sequence technology and assembly using the Hierarchical Genome Assembly Process (HGAP). This is the first Plasmodium genome sequence reported to use only PacBio technology. This approach has proven to be superior to short-read only approaches for this species.

Identification of Enzyme Genes Using Chemical Structure Alignments of Substrate-Product Pairs

Although there are several databases that contain data on many metabolites and reactions in biochemical pathways, there is still a big gap in the numbers between experimentally identified enzymes and metabolites. It is supposed that many catalytic enzyme genes are still unknown. Although there are previous studies that estimate the number of candidate enzyme genes, these studies required some additional information aside from the structures of metabolites such as gene expression and order in the genome. In this study, we developed a novel method to identify a candidate enzyme gene of a reaction using the chemical structures of the substrate-product pair (reactant pair). The proposed method is based on a search for similar reactant pairs in a reference database and offers ortholog groups that possibly mediate the given reaction. We applied the proposed method to two experimentally validated reactions. As a result, we confirmed that the histidine transaminase was correctly identified. Although our method could not directly identify the asparagine oxo-acid transaminase, we successfully found the paralog gene most similar to the correct enzyme gene. We also applied our method to infer candidate enzyme genes in the mesaconate pathway. The advantage of our method lies in the prediction of possible genes for orphan enzyme reactions where any associated gene sequences are not determined yet. We believe that this approach will facilitate experimental identification of genes for orphan enzymes.

BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences

BlastKOALA and GhostKOALA are automatic annotation servers for genome and metagenome sequences, which perform KO (KEGG Orthology) assignments to characterize individual gene functions and reconstruct KEGG pathways, BRITE hierarchies and KEGG modules to infer high-level functions of the organism or the ecosystem. Both servers are made freely available at the KEGG Web site (http://www.kegg.jp/blastkoala/). In BlastKOALA, the KO assignment is performed by a modified version of the internally used KOALA algorithm after the BLAST search against a non-redundant dataset of pangenome sequences at the species, genus or family level, which is generated from the KEGG GENES database by retaining the KO content of each taxonomic category. In GhostKOALA, which utilizes more rapid GHOSTX for database search and is suitable for metagenome annotation, the pangenome dataset is supplemented with Cd-hit clusters including those for viral genes. The result files may be downloaded and manipulated for further KEGG Mapper analysis, such as comparative pathway analysis using multiple BlastKOALA results.