A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer.

BACKGROUND: The pathway-based strategy has been recently proposed for identifying biomarkers with the advantages of higher biological interpretability and cross-data robustness than the conventional gene-based strategy. However, its utility in clinical applications has been limited due to the high computational complexity and ill-defined performance. OBJECTIVE: The current study presents a machine learning-based computational framework using multi-omics data for identifying a new modal of biomarkers, called pathway-derived core biomarkers, which have the advantages of both gene-based and pathway-based biomarkers. METHODS: Machine-learning methods and gene-pathway network were integrated to select the pathway-derived core biomarkers. Multiple machine-learning algorithms were used to construct and validate the diagnostic models of the biomarkers based on more than 1400 multi-omics clinical samples of esophageal squamous cell carcinoma (ESCC). RESULTS: The results showed that the classifier models based on the new modal biomarkers achieved superior performance in the training datasets with an average AUC/accuracy of 0.98/0.95 and 0.89/0.81 for mRNAs and miRNA, respectively, higher than the currently known classifier models based on the conventional gene-based strategy and pathway-based strategy. In the testing cohorts, the AUC/accuracy increased by 6.1 %/7.3 % than the models based on the native gene-based biomarkers. The improved performance was further confirmed in independent validation cohorts. Specifically, the sensitivity/specificity increased by ∼3 % and the variance significantly decreased by ∼69 % compared with that of the native gene-based biomarkers. Importantly, the pathway-derived core biomarkers also recovered 45 % more previously reported biomarkers than the gene-based biomarkers and are more functionally relevant to the ESCC etiology (involved in 14 versus 7 pathways related with ESCC or other cancer), highlighting the cross-data robustness of this new modal of biomarkers via enhanced functional relevance. CONCLUSIONS: The results demonstrated that the new modal of biomarkers not only have improved predicting performance and robustness, but also exhibit higher functional interpretability thus leading to the potential application in cancer diagnosis.

Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine.

BACKGROUND: With the development of cancer precision medicine, a huge amount of high-dimensional cancer information has rapidly accumulated regarding gene alterations, diseases, therapeutic interventions and various annotations. The information is highly fragmented across multiple different sources, making it highly challenging to effectively utilize and exchange the information. Therefore, it is essential to create a resource platform containing well-aggregated, carefully mined, and easily accessible data for effective knowledge sharing. METHODS: In this study, we have developed "Consensus Cancer Core" (Tri©DB), a new integrative cancer precision medicine knowledgebase and reporting system by mining and harmonizing multifaceted cancer data sources, and presenting them in a centralized platform with enhanced functionalities for accessibility, annotation and analysis. RESULTS: The knowledgebase provides the currently most comprehensive information on cancer precision medicine covering more than 40 annotation entities, many of which are novel and have never been explored previously. Tri©DB offers several unique features: (i) harmonizing the cancer-related information from more than 30 data sources into one integrative platform for easy access; (ii) utilizing a variety of data analysis and graphical tools for enhanced user interaction with the high-dimensional data; (iii) containing a newly developed reporting system for automated annotation and therapy matching for external patient genomic data. Benchmark test indicated that Tri©DB is able to annotate 46% more treatments than two officially recognized resources, oncoKB and MCG. Tri©DB was further shown to have achieved 94.9% concordance with administered treatments in a real clinical trial. CONCLUSIONS: The novel features and rich functionalities of the new platform will facilitate full access to cancer precision medicine data in one single platform and accommodate the needs of a broad range of researchers not only in translational medicine, but also in basic biomedical research. We believe that it will help to promote knowledge sharing in cancer precision medicine. Tri©DB is freely available at www.biomeddb.org , and is hosted on a cutting-edge technology architecture supporting all major browsers and mobile handsets.

Analysis and Characterization of Glutathione Peroxidases in an Environmental Microbiome and Isolated Bacterial Microorganisms.

Glutathione peroxidases (Gpx) are a group of antioxidant enzymes that protect cells or tissues against damage from reactive oxygen species (ROS). The Gpx proteins identified in mammals exhibit high catalytic activity toward glutathione (GSH). In contrast, a variety of non-mammalian Gpx proteins from diverse organisms, including fungi, plants, insects, and rodent parasites, show specificity for thioredoxin (TRX) rather than GSH and are designated as TRX-dependent peroxiredoxins. However, the study of the properties of Gpx in the environmental microbiome or isolated bacteria is limited. In this study, we analyzed the Gpx sequences, identified the characteristics of sequences and structures, and found that the environmental microbiome Gpx proteins should be classified as TRX-dependent, Gpx-like peroxiredoxins. This classification is based on the following three items of evidence: i) the conservation of the peroxidatic Cys residue; ii) the existence and conservation of the resolving Cys residue that forms the disulfide bond with the peroxidatic cysteine; and iii) the absence of dimeric and tetrameric interface domains. The conservation/divergence pattern of all known bacterial Gpx-like proteins in public databases shows that they share common characteristics with that from the environmental microbiome and are also TRX-dependent. Moreover, phylogenetic analysis shows that the bacterial Gpx-like proteins exhibit a star-like radiating phylogenetic structure forming a highly diverse genetic pool of TRX-dependent, Gpx-like peroxidases.

An Integrative Analysis Framework for Identifying the Prognostic Markers from Multidimensional RNA Data of Clear Cell Renal Cell Carcinoma.

The altered regulatory status of long noncoding RNA (lncRNA), miRNA, and mRNA and their interactions play critical roles in tumor proliferation, metastasis, and progression, which ultimately influence cancer prognosis. However, there are limited studies of comprehensive identification of prognostic biomarkers from combined data sets of the three RNA types in the highly metastatic clear cell renal cell carcinoma (ccRCC). The current study employed an integrative analysis framework of functional genomics approaches and machine learning methods to the lncRNA, miRNA, and mRNA data and identified 16 RNAs (3 lncRNAs, 6 miRNAs, and 7 mRNAs) of prognostic value, with 9 of them novel. A 16 RNA-based score was established for prognosis prediction of ccRCC with significance (P < 0.0001). The area under the curve for the score model was 0.868 to 0.870 in the training cohort and 0.714 to 0.778 in the validation cohort. Construction of the lncRNA-miRNA-mRNA interaction network showed that the downstream mRNAs and upstream lncRNAs in the network initiated from the miRNA or lncRNA markers exhibit significant enrichment in functional classifications associated with cancer metastasis, proliferation, progression, or prognosis. The functional analysis provided clear support for the role of the RNA biomarkers in predicting cancer prognosis. This study provides promising biomarkers for predicting prognosis of ccRCC using multidimensional RNA data, and these findings are expected to facilitate potential clinical applications of the biomarkers.

Genotyping and Molecular Characterization of Classical Swine Fever Virus Isolated in China during 2016-2018.

Classical swine fever (CSF) is a highly contagious disease of swine caused by classical swine fever virus (CSFV). For decades the disease has been controlled in China by a modified live vaccine (C-strain) of genotype 1. The emergent genotype 2 strains have become predominant in China in the past years that are genetically distant from the vaccine strain. Here, we aimed to evaluate the current infectious status of CSF, and for this purpose 24 isolates of CSFV were identified from different areas of China during 2016-2018. Phylogenetic analysis of NS5B, E2 and full genome revealed that the new isolates were clustered into subgenotype 2.1d and 2.1b, while subgenotype 2.1d was predominant. Moreover, E2 and Erns displayed multiple variations in neutralizing epitope regions. Furthermore, the new isolates exhibited capacity to escape C-strain-derived antibody neutralization compared with the Shimen strain (genotype 1). Potential positive selection sites were identified in antigenic regions of E2 and Erns, which are related with antibody binding affinity. Recombination events were predicted in the new isolates with vaccine strains in the E2 gene region. In conclusion, the new isolates showed molecular variations and antigenic alterations, which provide evidence for the emergence of vaccine-escaping mutants and emphasize the need of updated strategies for CSF control.

Whole transcriptome analysis reveals differential gene expression profile reflecting macrophage polarization in response to influenza A H5N1 virus infection.

BACKGROUND: Avian influenza A H5N1 virus can cause lethal disease in humans. The virus can trigger severe pneumonia and lead to acute respiratory distress syndrome. Data from clinical, in vitro and in vivo suggest that virus-induced cytokine dysregulation could be a contributory factor to the pathogenesis of human H5N1 disease. However, the precise mechanism of H5N1 infection eliciting the unique host response are still not well understood. METHODS: To obtain a better understanding of the molecular events at the earliest time points, we used RNA-Seq to quantify and compare the host mRNA and miRNA transcriptomes induced by the highly pathogenic influenza A H5N1 (A/Vietnam/3212/04) or low virulent H1N1 (A/Hong Kong/54/98) viruses in human monocyte-derived macrophages at 1-, 3-, and 6-h post infection. RESULTS: Our data reveals that two macrophage populations corresponding to M1 (classically activated) and M2 (alternatively activated) macrophage subtypes respond distinctly to H5N1 virus infection when compared to H1N1 virus or mock infection, a distinction that could not be made from previous microarray studies. When this confounding variable is considered in our statistical model, a clear set of dysregulated genes and pathways emerges specifically in H5N1 virus-infected macrophages at 6-h post infection, whilst was not found with H1N1 virus infection. Furthermore, altered expression of genes in these pathways, which have been previously implicated in viral host response, occurs specifically in the M1 subtype. We observe a significant up-regulation of genes in the RIG-I-like receptor signaling pathway. In particular, interferons, and interferon-stimulated genes are broadly affected. The negative regulators of interferon signaling, the suppressors of cytokine signaling, SOCS-1 and SOCS-3, were found to be markedly up-regulated in the initial round of H5N1 virus replication. Elevated levels of these suppressors could lead to the eventual suppression of cellular antiviral genes, contributing to pathophysiology of H5N1 virus infection. CONCLUSIONS: Our study provides important mechanistic insights into the understanding of H5N1 viral pathogenesis and the multi-faceted host immune responses. The dysregulated genes could be potential candidates as therapeutic targets for treating H5N1 disease.

Genomic Characterization of a Pattern D Streptococcus pyogenes emm53 Isolate Reveals a Genetic Rationale for Invasive Skin Tropicity.

UNLABELLED: The genome of an invasive skin-tropic strain (AP53) of serotype M53 group A Streptococcus pyogenes (GAS) is composed of a circular chromosome of 1,860,554 bp and carries genetic markers for infection at skin locales, viz, emm gene family pattern D and FCT type 3. Through genome-scale comparisons of AP53 with other GAS genomes, we identified 596 candidate single-nucleotide polymorphisms (SNPs) that reveal a potential genetic basis for skin tropism. The genome of AP53 differed by ∼30 point mutations from a noninvasive pattern D serotype M53 strain (Alab49), 4 of which are located in virulence genes. One pseudogene, yielding an inactive sensor kinase (CovS(-)) of the two-component transcriptional regulator CovRS, a major determinant for invasiveness, severely attenuated the expression of the secreted cysteine protease SpeB and enhanced the expression of the hyaluronic acid capsule compared to the isogenic noninvasive AP53/CovS(+) strain. The collagen-binding protein transcript sclB differed in the number of 5'-pentanucleotide repeats in the signal peptides of AP53 and Alab49 (9 versus 15), translating into different lengths of their signal peptides, which nonetheless maintained a full-length translatable coding frame. Furthermore, GAS strain AP53 acquired two phages that are absent in Alab49. One such phage (ΦAP53.2) contains the known virulence factor superantigen exotoxin gene tandem speK-slaA Overall, we conclude that this bacterium has evolved in multiple ways, including mutational variations of regulatory genes, short-tandem-repeat polymorphisms, large-scale genomic alterations, and acquisition of phages, all of which may be involved in shaping the adaptation of GAS in specific infectious environments and contribute to its enhanced virulence. IMPORTANCE: Infectious strains of S. pyogenes (GAS) are classified by their serotypes, relating to the surface M protein, the emm-like subfamily pattern, and their tropicity toward the nasopharynx and/or skin. It is generally agreed that M proteins from pattern D strains, which also directly bind human host plasminogen, are skin tropic. We have sequenced and characterized the genome of an invasive pattern D GAS strain (AP53) in comparison to a very similar strain (Alab49) that is noninvasive and developed a genomic rationale as to possible reasons for the skin tropicity of these two strains and the greater invasiveness of AP53.

Unique genomic arrangements in an invasive serotype M23 strain of Streptococcus pyogenes identify genes that induce hypervirulence.

The first genome sequence of a group A Streptococcus pyogenes serotype M23 (emm23) strain (M23ND), isolated from an invasive human infection, has been completed. The genome of this opacity factor-negative (SOF(-)) strain is composed of a circular chromosome of 1,846,477 bp. Gene profiling showed that this strain contained six phage-encoded and 24 chromosomally inherited well-known virulence factors, as well as 11 pseudogenes. The bacterium has acquired four large prophage elements, ΦM23ND.1 to ΦM23ND.4, harboring genes encoding streptococcal superantigen (ssa), streptococcal pyrogenic exotoxins (speC, speH, and speI), and DNases (spd1 and spd3), with phage integrase genes being present at one flank of each phage insertion, suggesting that the phages were integrated by horizontal gene transfer. Comparative analyses revealed unique large-scale genomic rearrangements that result in genomic rearrangements that differ from those of previously sequenced GAS strains. These rearrangements resulted in an imbalanced genomic architecture and translocations of chromosomal virulence genes. The covS sensor in M23ND was identified as a pseudogene, resulting in the attenuation of speB function and increased expression of the genes for the chromosomal virulence factors multiple-gene activator (mga), M protein (emm23), C5a peptidase (scpA), fibronectin-binding proteins (sfbI and fbp54), streptolysin O (slo), hyaluronic acid capsule (hasA), streptokinase (ska), and DNases (spd and spd3), which were verified by PCR. These genes are responsible for facilitating host epithelial cell binding and and/or immune evasion, thus further contributing to the virulence of M23ND. In conclusion, strain M23ND has become highly pathogenic as the result of a combination of multiple genetic factors, particularly gene composition and mutations, prophage integrations, unique genomic rearrangements, and regulated expression of critical virulence factors.