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.

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.

SweepCluster: A SNP clustering tool for detecting gene-specific sweeps in prokaryotes.

BACKGROUND: The gene-specific sweep is a selection process where an advantageous mutation along with the nearby neutral sites in a gene region increases the frequency in the population. It has been demonstrated to play important roles in ecological differentiation or phenotypic divergence in microbial populations. Therefore, identifying gene-specific sweeps in microorganisms will not only provide insights into the evolutionary mechanisms, but also unravel potential genetic markers associated with biological phenotypes. However, current methods were mainly developed for detecting selective sweeps in eukaryotic data of sparse genotypes and are not readily applicable to prokaryotic data. Furthermore, some challenges have not been sufficiently addressed by the methods, such as the low spatial resolution of sweep regions and lack of consideration of the spatial distribution of mutations. RESULTS: We proposed a novel gene-centric and spatial-aware approach for identifying gene-specific sweeps in prokaryotes and implemented it in a python tool SweepCluster. Our method searches for gene regions with a high level of spatial clustering of pre-selected polymorphisms in genotype datasets assuming a null distribution model of neutral selection. The pre-selection of polymorphisms is based on their genetic signatures, such as elevated population subdivision, excessive linkage disequilibrium, or significant phenotype association. Performance evaluation using simulation data showed that the sensitivity and specificity of the clustering algorithm in SweepCluster is above 90%. The application of SweepCluster in two real datasets from the bacteria Streptococcus pyogenes and Streptococcus suis showed that the impact of pre-selection was dramatic and significantly reduced the uninformative signals. We validated our method using the genotype data from Vibrio cyclitrophicus, the only available dataset of gene-specific sweeps in bacteria, and obtained a concordance rate of 78%. We noted that the concordance rate could be underestimated due to distinct reference genomes and clustering strategies. The application to the human genotype datasets showed that SweepCluster is also applicable to eukaryotic data and is able to recover 80% of a catalog of known sweep regions. CONCLUSION: SweepCluster is applicable to a broad category of datasets. It will be valuable for detecting gene-specific sweeps in diverse genotypic data and provide novel insights on adaptive evolution.

Distinct Regioselectivity of Fungal P450 Enzymes for Steroidal Hydroxylation.

In this study, we identified two P450 enzymes (CYP5150AP3 and CYP5150AN1) from Thanatephorus cucumeris NBRC 6298 by combination of transcriptome sequencing and heterologous expression in Pichia pastoris The biotransformation of 11-deoxycortisol and testosterone by Pichia pastoris whole cells coexpressing the cyp5150ap3 and por genes demonstrated that the CYP5150AP3 enzyme possessed steroidal 7ß-hydroxylase activities toward these substrates, and the regioselectivity was dependent on the structures of steroidal compounds. CYP5150AN1 catalyzed the 2ß-hydroxylation of 11-deoxycortisol. It is interesting that they display different regioselectivity of hydroxylation from that of their isoenzyme, CYP5150AP2, which possesses 19- and 11ß-hydroxylase activities.IMPORTANCE The steroidal hydroxylases CYP5150AP3 and CYP5150AN1 together with the previously characterized CYP5150AP2 belong to the CYP5150A family of P450 enzymes with high amino acid sequence identity, but they showed completely different regioselectivities toward 11-deoxycortisol, suggesting the regioselectivity diversity of steroidal hydroxylases of CYP5150 family. They are also distinct from the known bacterial and fungal steroidal hydroxylases in substrate specificity and regioselectivity. Biocatalytic hydroxylation is one of the important transformations for the functionalization of steroid nucleus rings but remains a very challenging task in organic synthesis. These hydroxylases are useful additions to the toolbox of hydroxylase enzymes for the functionalization of steroids at various positions.

A Fungal P450 Enzyme from Thanatephorus cucumeris with Steroid Hydroxylation Capabilities.

In this study, we identified a P450 enzyme (STH10) and an oxidoreductase (POR) from Thanatephorus cucumeris NBRC 6298 by a combination of transcriptome sequencing and heterologous expression in Pichia pastoris The biotransformation of 11-deoxycortisol was performed by using Pichia pastoris whole cells coexpressing sth10 and por, and the product analysis indicated that the STH10 enzyme possessed steroidal 19- and 11ß-hydroxylase activities. This is a novel fungal P450 enzyme with 19-hydroxylase activity, which is different from the known steroidal aromatase cytochrome P450 19 (CYP19) and CYP11B families of enzymes.IMPORTANCE Hydroxylation is one of the most important reactions in steroid functionalization; in particular, C-19 hydroxylation produces a key intermediate for the synthesis of 19-nor-steroid drugs without a C-19 angular methyl group in three chemoenzymatic steps, in contrast to the current industrial process, which uses 10 chemical reactions. However, hydroxylation of the C-19 angular methyl group remains a very challenging task due to the high level of steric resistance to the C-19 methyl group between the A and B rings. The present report describes a novel fungal P450 enzyme with 19-hydroxylase activity. This opens a new venue for searching effective biocatalysts for the useful process of steroidal C-19 hydroxylation, although further studies for better understanding of the structural basis of the regioselectivity and substrate specificity of this fungal steroidal 19-hydroxylase are warranted to facilitate the engineering of this enzyme for industrial applications.

High-throughput metagenomic analysis of petroleum-contaminated soil microbiome reveals the versatility in xenobiotic aromatics metabolism.

The soil with petroleum contamination is one of the most studied soil ecosystems due to its rich microorganisms for hydrocarbon degradation and broad applications in bioremediation. However, our understanding of the genomic properties and functional traits of the soil microbiome is limited. In this study, we used high-throughput metagenomic sequencing to comprehensively study the microbial community from petroleum-contaminated soils near Tianjin Dagang oilfield in eastern China. The analysis reveals that the soil metagenome is characterized by high level of community diversity and metabolic versatility. The metageome community is predominated by γ-Proteobacteria and α-Proteobacteria, which are key players for petroleum hydrocarbon degradation. The functional study demonstrates over-represented enzyme groups and pathways involved in degradation of a broad set of xenobiotic aromatic compounds, including toluene, xylene, chlorobenzoate, aminobenzoate, DDT, methylnaphthalene, and bisphenol. A composite metabolic network is proposed for the identified pathways, thus consolidating our identification of the pathways. The overall data demonstrated the great potential of the studied soil microbiome in the xenobiotic aromatics degradation. The results not only establish a rich reservoir for novel enzyme discovery but also provide putative applications in bioremediation.

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.

Novel genomic rearrangements mediated by multiple genetic elements in Streptococcus pyogenes M23ND confer potential for evolutionary persistence.

Symmetric genomic rearrangements around replication axes in genomes are commonly observed in prokaryotic genomes, including Group A Streptococcus (GAS). However, asymmetric rearrangements are rare. Our previous studies showed that the hypervirulent invasive GAS strain, M23ND, containing an inactivated transcriptional regulator system, covRS, exhibits unique extensive asymmetric rearrangements, which reconstructed a genomic structure distinct from other GAS genomes. In the current investigation, we identified the rearrangement events and examined the genetic consequences and evolutionary implications underlying the rearrangements. By comparison with a close phylogenetic relative, M18-MGAS8232, we propose a molecular model wherein a series of asymmetric rearrangements have occurred in M23ND, involving translocations, inversions and integrations mediated by multiple factors, viz., rRNA-comX (factor for late competence), transposons and phage-encoded gene segments. Assessments of the cumulative gene orientations and GC skews reveal that the asymmetric genomic rearrangements did not affect the general genomic integrity of the organism. However, functional distributions reveal re-clustering of a broad set of CovRS-regulated actively transcribed genes, including virulence factors and metabolic genes, to the same leading strand, with high confidence (p-value ~10-10). The re-clustering of the genes suggests a potential selection advantage for the spatial proximity to the transcription complexes, which may contain the global transcriptional regulator, CovRS, and other RNA polymerases. Their proximities allow for efficient transcription of the genes required for growth, virulence and persistence. A new paradigm of survival strategies of GAS strains is provided through multiple genomic rearrangements, while, at the same time, maintaining genomic integrity.

CovRS-Regulated Transcriptome Analysis of a Hypervirulent M23 Strain of Group A Streptococcus pyogenes Provides New Insights into Virulence Determinants.

UNLABELLED: The two-component control of virulence (Cov) regulator (R)-sensor (S) (CovRS) regulates the virulence of Streptococcus pyogenes (group A Streptococcus [GAS]). Inactivation of CovS during infection switches the pathogenicity of GAS to a more invasive form by regulating transcription of diverse virulence genes via CovR. However, the manner in which CovRS controls virulence through expression of extended gene families has not been fully determined. In the current study, the CovS-regulated gene expression profiles of a hypervirulent emm23 GAS strain (M23ND/CovS negative [M23ND/CovS(-)]) and a noninvasive isogenic strain (M23ND/CovS(+)), under different growth conditions, were investigated. RNA sequencing identified altered expression of ∼ 349 genes (18% of the chromosome). The data demonstrated that M23ND/CovS(-) achieved hypervirulence by allowing enhanced expression of genes responsible for antiphagocytosis (e.g., hasABC), by abrogating expression of toxin genes (e.g., speB), and by compromising gene products with dispensable functions (e.g., sfb1). Among these genes, several (e.g., parE and parC) were not previously reported to be regulated by CovRS. Furthermore, the study revealed that CovS also modulated the expression of a broad spectrum of metabolic genes that maximized nutrient utilization and energy metabolism during growth and dissemination, where the bacteria encounter large variations in available nutrients, thus restructuring metabolism of GAS for adaption to diverse growth environments. From constructing a genome-scale metabolic model, we identified 16 nonredundant metabolic gene modules that constitute unique nutrient sources. These genes were proposed to be essential for pathogen growth and are likely associated with GAS virulence. The genome-wide prediction of genes associated with virulence identifies new candidate genes that potentially contribute to GAS virulence. IMPORTANCE: The CovRS system modulates transcription of ∼ 18% of the genes in the Streptococcus pyogenes genome. Mutations that inactivate CovR or CovS enhance the virulence of this bacterium. We determined complete transcriptomes of a naturally CovS-inactivated invasive deep tissue isolate of an emm23 strain of S. pyogenes (M23ND) and its complemented avirulent variant (CovS(+)). We identified diverse virulence genes whose altered expression revealed a genetic switching of a nonvirulent form of M23ND to a highly virulent strain. Furthermore, we also systematically uncovered for the first time the comparative levels of expression of a broad spectrum of metabolic genes, which reflected different metabolic needs of the bacterium as it invaded deeper tissue of the human host.

Direct cloning, expression of a thermostable xylanase gene from the metagenomic DNA of cow dung compost and enzymatic production of xylooligosaccharides from corncob.

OBJECTIVES: To acquire a thermostable xylanase, that is suitable for xylooligosaccharide production from pretreated corncobs, the metagenomic method was used to obtain the gene from an uncultured environmental microorganism. RESULTS: A thermostable xylanase-encoding gene (xyn10CD18) was cloned directly from the metagenomic DNA of cow dung compost. When xyn10CD18 was expressed in Bacillus megaterium MS941, extracellular xylansae activity at 106 IU/ml was achieved. The purified recombinant Xyn10CD18 was optimally active at pH 7 and 75 °C as measured over 10 min. It retained over 55% of its initial activity at 70 °C and pH 7 after 24 h. Its action on birchwood xylan for 18 h liberated xylooligosaccharides with 2°-4° of polymerization, with xylobiose and xylotetraose as the main products. When pretreated corncobs were hydrolyzed by Xyn10CD18 for 18 h, the xylooligosaccharides (DP 2-4) products increased to 80% and the xylose was just increased by 3%. CONCLUSION: Xyn10CD18 is a thermostable endoxylanase and is a promising candidate for biomass conversion and xylooligosaccharide production.

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.

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.

The genetic variation landscape of African swine fever virus reveals frequent positive selection and adaptive flexibility.

African swine fever virus (ASFV) is a lethal disease agent that causes high mortality in swine population and devastating loss in swine industries. The development of efficacious vaccines has been hindered by the gap in knowledge concerning genetic variation of ASFV and the genetic factors involved in host adaptation and virus-host interactions. In this study, we performed a meta-genetic study of ASFV aiming to profile the variation landscape and identify genetic factors with signatures of positive selection and relevance to host adaptation. Our data reveal a high level of genetic variability of ASFV shaped by both diversifying selection and selective sweep. The selection signatures are widely distributed across the genome with the diversifying selection falling within 29 genes and selection sweep within 25 genes, highlighting strong signals of adaptive evolution of ASFV. Further examination of the sequence properties reveals the link of the selection signatures with virus-host interactions and adaptive flexibility. Specifically, we discovered a site at 157th of the key antigen protein EP402R under diversifying selection, which is located in the cytotoxic T-cell epitope related to the low level of cross-reaction in T-cell response. Importantly, two multigene families MGF360 and MGF505, the host range factors of ASFV, exhibit divergent selection among the paralogous members, conferring sequence pools for genetic diversification and adaptive capability. By integrating the genes with selection signatures into a unified framework of interactions between ASFV and hosts, we showed that the genes are involved in multiple processes of host immune interaction and virus life cycles, and may play crucial roles in circumventing host defence systems and enhancing adaptive fitness. Our findings will allow enhanced understanding of genetic basis of rapid spreading and adaptation of ASFV among the hosts.

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.

Comparative pathogenomic characterization of a non-invasive serotype M71 strain Streptococcus pyogenes NS53 reveals incongruent phenotypic implications from distinct genotypic markers.

The strains serotyped as M71 from group A Streptococcus are common causes of pharyngeal and skin diseases worldwide. Here we characterize the genome of a unique non-invasive M71 human isolate, NS53. The genome does not contain structural rearrangements or large-scale gene gains/losses, but encodes a full set of non-truncated known virulence factors, thus providing an ideal reference for comparative studies. However, the NS53 genome showed incongruent phenotypic implications from distinct genotypic markers. NS53 is characterized as an emm pattern D and FCT (fibronectin-collagen-T antigen) type-3 strain, typical of skin tropic strains, but is phylogenetically close to emm pattern E strains with preference for both skin and pharyngeal infections. We propose that this incongruence could result from recombination within the emm gene locus, or, alternatively, selection has been against those genetic alterations. Combined with the inability to select for CovS switching, a process is indicated whereby NS53 has been pre-adapted to specific host niches selecting against variations in CovS and many other genes. This may allow the strain to attain successful colonization and long-term survival. A balance between genetic variations and fitness may exist for this bacterium to form a stabilized genome optimized for survival in specific host environments.

Phenotypic differentiation of Streptococcus pyogenes populations is induced by recombination-driven gene-specific sweeps.

Genomic recombination plays an important role in driving adaptive evolution and population differentiation in bacteria. However, controversy exists as to the effects of recombination on population diversity and differentiation, i.e., recombination is frequent enough to sweep through the population at selected gene loci (gene-specific sweeps), or the recombination rate is low without interfering genome-wide selective sweeps. Observations supporting either view are sparse. Pathogenic bacteria causing infectious diseases are promising candidates to provide observations of recombination. However, phenotype-associated differentiations are usually vague among them due to diverse disease manifestations. Here we report a population genomic study of the group A Streptococcus pyogenes (GAS), a human pathogen with highly recombining genomes. By employing a genome-wide association study on single nucleotide polymorphisms (SNPs), we demonstrate a phenotypic differentiation of GAS, represented by separate clustering of two sublineages associated with niche-specific infections, i.e., skin infection and pharyngitis-induced acute rheumatic fever. By quantifying SNPs associated with the differentiation in a statistical and phylogenetic context, we propose that the phenotype-associated differentiation arose through recombination-driven gene-specific sweeps, rather than genome-wide sweeps. Our work provides a novel paradigm of phenotype-associated differentiation induced by gene-specific sweeps in a human pathogen and has implications for understanding of driving forces of bacterial evolution.

Structure and sequence analysis-based engineering of pullulanase from Anoxybacillus sp. LM18-11 for improved thermostability.

Pullulanase (EC 3.2.1.41) is a well-known starch-debranching enzyme. Enhancing the thermostability of Pullulanase is required for industrial application. In this study, we used two methods to improve the thermostability of the pullulanase from Anoxybacillus sp. LM18-11; these methods were the modified amino acid consensus method combined with the analyses of the residue water-exposed surface (ACC) and the deletion of flexible domains. Four mutants (Y477A, Y175C, L215C and R473E) were obtained via the modified consensus method exhibited varying degrees of improvements in terms of thermostability. One deletion mutant termed D3 (residues(686-688)) was obtained and exhibited enhanced thermostability due to deletion of the flexible region at the C-terminus. The combination of the two strategies yielded the mutant M18 (Y477A/D3/Y175C/L215P/R473E). It retained 66% of its initial activity after incubation at 60 °C for 72 hrs, whereas that of the wild-type enzyme was only 35%. After incubation at 65 °C for 4 h, M18 retained 50.6% of its initial activity, whereas that of the wild-type was only 16.8%, respectively. Additionally, kinetic studies revealed that the Km of M17 (Y477A/D3/Y175C/L215P) was decreased by 33.9% and that the Kcat/Km value of M17 increased by 50%, while M18 exhibited Km and Kcat/Km values that were similar to those of the wild-type enzyme. The attractive improved thermostability and the high catalytic efficiency made M17 and M18 more suitable for industrial application.