Detection of influent virulence and resistance genes in microarray data through quasi likelihood modeling

Publicly available genomic data are a great source of biological knowledge that can be extracted when appropriate data analysis is used. Predicting the biological function of genes is of interest to understand molecular mechanisms of virulence and resistance in pathogens and hosts and is important for drug discovery and disease control. This is commonly done by searching for similar gene expression behavior. Here, we used publicly available Streptococcus pyogenes microarray data obtained during primate infection to identify genes that have a potential influence on virulence and Phytophtora infestance inoculated tomato microarray data to identify genes potentially implicated in resistance processes. This approach goes beyond co-expression analysis. We employed a quasi-likelihood model separated by primate gender/inoculation condition to model median gene expression of known virulence/resistance factors. Based on this model, an influence analysis considering time course measurement was performed to detect genes with atypical expression. This procedure allowed for the detection of genes potentially implicated in the infection process. Finally, we discuss the biological meaning of these results, showing that influence analysis is an efficient and useful alternative for functional gene prediction.     

<Emphasis Type="Italic">Arcanobacterium pyogenes</Emphasis>: molecular pathogenesis of an animal opportunist

Arcanobacterium pyogenes is a commensal and an opportunistic pathogen of economically important livestock, causing diseases as diverse as mastitis, liver abscessation and pneumonia. This organism possesses a number of virulence factors that contribute to its pathogenic potential. A. pyogenes expresses a cholesterol-dependent cytolysin, pyolysin, which is a haemolysin and is cytolytic for immune cells, including macrophages. Expression of pyolysin is required for virulence and this molecule is the most promising vaccine candidate identified to date. A. pyogenes also possesses a number of adherence mechanisms, including two neuraminidases, the action of which are required for full adhesion to epithelial cells, and several extracellular matrix-binding proteins, including a collagen-binding protein, which may be required for adhesion to collagen-rich tissue. A. pyogenes also expresses fimbriae, which are similar to the type 2 fimbriae of Actinomyces naeslundii, and forms biofilms. However, the role of these factors in the pathogenesis of A. pyogenes infections remains to be elucidated. A. pyogenes also invades and survives within epithelial cells and can survive within J774A.1 macrophages for up to 72 h, suggesting an important role for A. pyogenes interaction with host cells during pathogenesis. The two component regulatory system, PloSR, up-regulates pyolysin expression and biofilm formation but down-regulates expression of proteases, suggesting that it may act as a global regulator of A. pyogenes virulence. A. pyogenes is a versatile pathogen, with an arsenal of virulence determinants. However, most aspects of the pathogenesis of infection caused by this important opportunistic pathogen remain poorly characterized.     

Network-Based Data Integration for Selecting Candidate Virulence Associated Proteins in the Cereal Infecting Fungus Fusarium graminearum

The identification of virulence genes in plant pathogenic fungi is important for understanding the infection process, host range and for developing control strategies. The analysis of already verified virulence genes in phytopathogenic fungi in the context of integrated functional networks can give clues about the underlying mechanisms and pathways directly or indirectly linked to fungal pathogenicity and can suggest new candidates for further experimental investigation, using a ‘guilt by association’ approach. Here we study 133 genes in the globally important Ascomycete fungus Fusarium graminearum that have been experimentally tested for their involvement in virulence. An integrated network that combines information from gene co-expression, predicted protein-protein interactions and sequence similarity was employed and, using 100 genes known to be required for virulence, we found a total of 215 new proteins potentially associated with virulence of which 29 are annotated as hypothetical proteins. The majority of these potential virulence genes are located in chromosomal regions known to have a low recombination frequency. We have also explored the taxonomic diversity of these candidates and found 25 sequences, which are likely to be fungal specific. We discuss the biological relevance of a few of the potentially novel virulence associated genes in detail. The analysis of already verified virulence genes in phytopathogenic fungi in the context of integrated functional networks can give clues about the underlying mechanisms and pathways directly or indirectly linked to fungal pathogenicity and can suggest new candidates for further experimental investigation, using a ‘guilt by association’ approach.     

Characterization of Streptococcus pyogenes Isolated from Balanoposthitis Patients Presumably Transmitted by Penile-Oral Sexual Intercourse

Streptococcus pyogenes is indigenous to the human pharynx and causes acute pharyngitis. Balanoposthitis is an inflammatory disease of the glans and the foreskin. However, balanoposthitis caused by S. pyogenes is not widely recognized as a sexually transmitted disease. In addition, bacteriological features of the isolates causing balanoposthitis are unclear. The four S. pyogenes strains isolated from adult balanoposthitis were examined. We performed emm typing, T antigen typing, RAPD assay, PCR assay for the streptococcal pyrogenic exotoxin-related genes and antibiotic-resistant genes, and antibiotic susceptibility assay. All four strains were suspected to be transmitted by penile-oral sexual intercourse, were found to be different by genetic analysis, and also harbored some antibiotic-resistant factors. We propose that S. pyogenes should be considered as a causative agent of sexually transmitted disease. The drug resistant S. pyogenes must be taken into account when balanoposthitis patients are treated with antibiotic.     

In silico prediction of drug targets in <Emphasis Type="Italic">Vibrio cholerae</Emphasis>

Identification of potential drug targets is the first step in the process of modern drug discovery, subjected to their validation and drug development. Whole genome sequences of a number of organisms allow prediction of potential drug targets using sequence comparison approaches. Here, we present a subtractive approach exploiting the knowledge of global gene expression along with sequence comparisons to predict the potential drug targets more efficiently. Based on the knowledge of 155 known virulence and their coexpressed genes mined from microarray database in the public domain, 357 coexpressed probable virulence genes for Vibrio cholerae were predicted. Based on screening of Database of Essential Genes using blastn, a total of 102 genes out of these 357 were enlisted as vitally essential genes, and hence good putative drug targets. As the effective drug target is a protein which is only present in the pathogen, similarity search of these 102 essential genes against human genome sequence led to subtraction of 66 genes, thus leaving behind a subset of 36 genes whose products have been called as potential drug targets. The gene ontology analysis using Blast2GO of these 36 genes revealed their roles in important metabolic pathways of V. cholerae or on the surface of the pathogen. Thus, we propose that the products of these genes be evaluated as target sites of drugs against V. cholerae in future investigations.     

Detection of <Emphasis Type="Italic">Streptococcus pyogenes</Emphasis> virulence genes in <Emphasis Type="Italic">Streptococcus dysgalactiae</Emphasis> subsp. <Emphasis Type="Italic">equisimilis</Emphasis> from Vellore,India

Streptococcus dysgalactiae subsp. equisimilis (SDSE), belonging to the group C and G streptococci, are human pathogens reported to cause clinical manifestations similar to infections caused by Streptococcus pyogenes. To scrutinize the distribution of gene coding for S. pyogenes virulence factors in SDSE, 255 isolates were collected from humans infected with SDSE in Vellore, a region in southern India, with high incidence of SDSE infections. Initial evaluation indicated SDSE isolates comprising of 82.35% group G and 17.64% group C. A multiplex PCR system was used to detect 21 gene encoding virulence-associated factors of S. pyogenes, like superantigens, DNases, proteinases, and other immune modulatory toxins. As validated by DNA sequencing of the PCR products, sequences homologous to speC, speG, speH, speI, speL, ssa and smeZ of the family of superantigen coding genes and for DNases like sdaD and sdc were detected in the SDSE collection. Furthermore, there was high abundance (48.12% in group G and 86.6% in group C SDSE) of scpA, the gene coding for C5a peptidase in these isolates. Higher abundance of S. pyogenes virulence factor genes was observed in SDSE of Lancefield group C as compared to group G, even though the incidence rates in former were lower. This study not only substantiates detection of S. pyogenes virulence factor genes in whole genome sequenced SDSE but also makes significant contribution towards the understanding of SDSE and its increasing virulence potential.     

Gene set analysis exploiting the topology of a pathway

Background  

Recently, a great effort in microarray data analysis is directed towards the study of the so-called gene sets. A gene set is defined by genes that are, somehow, functionally related. For example, genes appearing in a known biological pathway naturally define a gene set. The gene sets are usually identified from a priori biological knowledge. Nowadays, many bioinformatics resources store such kind of knowledge (see, for example, the Kyoto Encyclopedia of Genes and Genomes, among others). Although pathways maps carry important information about the structure of correlation among genes that should not be neglected, the currently available multivariate methods for gene set analysis do not fully exploit it.     

GO‐At : in silico prediction of gene function in Arabidopsis thaliana by combining heterogeneous data

Despite recent advances, accurate gene function prediction remains an elusive goal, with very few methods directly applicable to the plant Arabidopsis thaliana. In this study, we present GO‐At (gene ontology prediction in A. thaliana), a method that combines five data types (co‐expression, sequence, phylogenetic profile, interaction and gene neighbourhood) to predict gene function in Arabidopsis. Using a simple, yet powerful two‐step approach, GO‐At first generates a list of genes ranked in descending order of probability of functional association with the query gene. Next, a prediction score is automatically assigned to each function in this list based on the assumption that functions appearing most frequently at the top of the list are most likely to represent the function of the query gene. In this way, the second step provides an effective alternative to simply taking the ‘best hit’ from the first list, and achieves success rates of up to 79%. GO‐At is applicable across all three GO categories: molecular function, biological process and cellular component, and can assign functions at multiple levels of annotation detail. Furthermore, we demonstrate GO‐At’s ability to predict functions of uncharacterized genes by identifying ten putative golgins/Golgi‐associated proteins amongst 8219 genes of previously unknown cellular component and present independent evidence to support our predictions. A web‐based implementation of GO‐At ( http://www.bioinformatics.leeds.ac.uk/goat ) is available, providing a unique resource for plant researchers to make predictions for uncharacterized genes and predict novel functions in Arabidopsis.     

The type III effectors of Xanthomonas

A review of type III effectors (T3 effectors) from strains of Xanthomonas reveals a growing list of candidate and known effectors based on functional assays and sequence and structural similarity searches of genomic data. We propose that the effectors and suspected effectors should be distributed into 39 so-called Xop groups reflecting sequence similarity. Some groups have structural motifs for putative enzymatic functions, and recent studies have provided considerable insight into the interaction with host factors in their function as mediators of virulence and elicitors of resistance for a few specific T3 effectors. Many groups are related to T3 effectors of plant and animal pathogenic bacteria, and several groups appear to have been exploited primarily by Xanthomonas species based on available data. At the same time, a relatively large number of candidate effectors remain to be examined in more detail with regard to their function within host cells.     

Incorporating prior knowledge of gene functional groups into regularized discriminant analysis of microarray data

MOTIVATION: Discriminant analysis for high-dimensional and low-sample-sized data has become a hot research topic in bioinformatics, mainly motivated by its importance and challenge in applications to tumor classifications for high-dimensional microarray data. Two of the popular methods are the nearest shrunken centroids, also called predictive analysis of microarray (PAM), and shrunken centroids regularized discriminant analysis (SCRDA). Both methods are modifications to the classic linear discriminant analysis (LDA) in two aspects tailored to high-dimensional and low-sample-sized data: one is the regularization of the covariance matrix, and the other is variable selection through shrinkage. In spite of their usefulness, there are potential limitations with each method. The main concern is that both PAM and SCRDA are possibly too extreme: the covariance matrix in the former is restricted to be diagonal while in the latter there is barely any restriction. Based on the biology of gene functions and given the feature of the data, it may be beneficial to estimate the covariance matrix as an intermediate between the two; furthermore, more effective shrinkage schemes may be possible. RESULTS: We propose modified LDA methods to integrate biological knowledge of gene functions (or variable groups) into classification of microarray data. Instead of simply treating all the genes independently or imposing no restriction on the correlations among the genes, we group the genes according to their biological functions extracted from existing biological knowledge or data, and propose regularized covariance estimators that encourages between-group gene independence and within-group gene correlations while maintaining the flexibility of any general covariance structure. Furthermore, we propose a shrinkage scheme on groups of genes that tends to retain or remove a whole group of the genes altogether, in contrast to the standard shrinkage on individual genes. We show that one of the proposed methods performed better than PAM and SCRDA in a simulation study and several real data examples.     

<Emphasis Type="Italic">Trueperella pyogenes</Emphasis> isolated from a brain abscess of an adult roebuck (<Emphasis Type="Italic">Capreolus capreolus</Emphasis>)

The present study was designed to characterize phenotypically and genotypically a Trueperella pyogenes strain isolated from a brain abscess of an adult roebuck (Capreolus capreolus). The species identity could be confirmed by phenotypical investigations, by MALDI-TOF MS analysis, and by sequencing the 16S ribosomal RNA (rRNA) gene, the 16S–23S rRNA intergenic spacer region (ISR); by sequencing the target genes rpoB, gap, and tuf; and by detection of T. pyogenes chaperonin-encoding gene cpn60 with a previously developed loop-mediated isothermal amplification (LAMP) assay. The T. pyogenes strain could additionally be characterized by PCR-mediated amplification of several known and putative virulence factor-encoding genes which revealed the presence of the genes plo encoding pyolysin and nanH and nanP encoding neuraminidases; the genes fimA, fimC, and fimE encoding the fimbrial subunits FimA, FimC, and FimE; and the gene cbpA encoding collagen-binding protein CbpA. The present data give a detailed characterization of a T. pyogenes strain isolated from a brain abscess of a roebuck. However, the route of infection of the roebuck remains unclear.     

A decade with nucleic acid‐based microbiological methods in safety control of foods

In the last decade, nucleic acid‐based methods gradually started to replace or complement the culture‐based methods and immunochemical assays in routine laboratories involved in food control. In particular, real‐time polymerase chain reaction (PCR) was technically developed to the stage of good speed, sensitivity and reproducibility, at minimized risk of carry‐over contamination. Basic advantages provided by nucleic acid‐based methods are higher speed and added information, such as subspecies identification, information on the presence of genes important for virulence or antibiotic resistance. Nucleic acid‐based methods are attractive also to detect important foodborne pathogens for which no classical counterparts are available, namely foodborne pathogenic viruses. This review briefly summarizes currently available or developing molecular technologies that may be candidates for involvement in microbiological molecular methods in the next decade. Potential of nonamplification as well as amplification methods is discussed, including fluorescent in situ hybridization, alternative PCR chemistries, alternative amplification technologies, digital PCR and nanotechnologies.     

Identification of Immunity-related Genes in Arabidopsis and Cassava Using Genomic Data

Recent advances in genomic and post-genomic technologies have provided the opportu- nity to generate a previously unimaginable amount of information. However, biological knowledge is still needed to improve the understanding of complex mechanisms such as plant immune responses. Better knowledge of this process could improve crop production and management. Here, we used holistic analysis to combine our own microarray and RNA-seq data with public genomic data from Arabidopsis and cassava in order to acquire biological knowledge about the relationships between proteins encoded by immunity-related genes （IRGs） and other genes. This approach was based on a kernel method adapted for the construction of gene networks. The obtained results allowed us to propose a list of new IRGs. A putative function in the immunity pathway was predicted for the new IRGs. The analysis of networks revealed that our predicted IRGs are either well documented or recognized in previous co-expression studies. In addition to robust relationships between IRGs, there is evidence suggesting that other cellular processes may be also strongly related to immunity.     

Pathway analysis using random forests classification and regression

MOTIVATION: Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers. RESULTS: In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data. AVAILABILITY: Source code written in R is available from http://bioinformatics.med.yale.edu/pathway-analysis/rf.htm.     

The dissemination of C10 cysteine protease genes in <Emphasis Type="Italic">Bacteroides fragilis</Emphasis> by mobile genetic elements

Background  

The C10 family of cysteine proteases includes enzymes that contribute to the virulence of bacterial pathogens, such as SpeB in Streptococcus pyogenes. The presence of homologues of cysteine protease genes in human commensal organisms has not been examined. Bacteroides fragilis is a member of the dominant Bacteroidetes phylum of the human intestinal microbiota, and is a significant opportunistic pathogen.     

Molecular genetic tools in Toxoplasma and Plasmodium: achievements and future needs

The recent awarding of the Nobel prize to Andrew Fire and Craig Mello for the discovery of RNA-interference (RNAi) in plants once more demonstrated the importance of basic science in understanding biological mechanisms. Importantly, this discovery led to the establishment of powerful approaches to study gene function in a wide array of organisms. While a robust RNAi-technology remains elusive in apicomplexan parasites, other molecular genetic technologies have been introduced in recent years. Now, in the post genomic era, the task is to apply these methods to validate and functionally dissect an ever-expanding list of putative vaccine and drug candidates. The ultimate aim of such studies is to transform our knowledge of the genome to the knowledge of the phenome and ultimately new intervention strategies in these important pathogenic organisms. However, substantial limitations remain to the current repertoire of available molecular tools, which limits a comprehensive analysis of these candidates, especially of essential genes. This review summarises the methodologies available for functional gene analysis in apicomplexan parasites and discusses further needs in tool development.     

Network Analysis of Differential Expression for the Identification of Disease-Causing Genes

Genetic studies (in particular linkage and association studies) identify chromosomal regions involved in a disease or phenotype of interest, but those regions often contain many candidate genes, only a few of which can be followed-up for biological validation. Recently, computational methods to identify (prioritize) the most promising candidates within a region have been proposed, but they are usually not applicable to cases where little is known about the phenotype (no or few confirmed disease genes, fragmentary understanding of the biological cascades involved). We seek to overcome this limitation by replacing knowledge about the biological process by experimental data on differential gene expression between affected and healthy individuals. Considering the problem from the perspective of a gene/protein network, we assess a candidate gene by considering the level of differential expression in its neighborhood under the assumption that strong candidates will tend to be surrounded by differentially expressed neighbors. We define a notion of soft neighborhood where each gene is given a contributing weight, which decreases with the distance from the candidate gene on the protein network. To account for multiple paths between genes, we define the distance using the Laplacian exponential diffusion kernel. We score candidates by aggregating the differential expression of neighbors weighted as a function of distance. Through a randomization procedure, we rank candidates by p-values. We illustrate our approach on four monogenic diseases and successfully prioritize the known disease causing genes.