Gene Expression Meta-Analysis Reveals Interferon-Induced Genes Associated With SARS Infection in Lungs.

BACKGROUND: Severe Acute Respiratory Syndrome (SARS) corona virus (CoV) infections are a serious public health threat because of their pandemic-causing potential. This work is the first to analyze mRNA expression data from SARS infections through meta-analysis of gene signatures, possibly identifying therapeutic targets associated with major SARS infections. METHODS: This work defines 37 gene signatures representing SARS-CoV, Middle East Respiratory Syndrome (MERS)-CoV, and SARS-CoV2 infections in human lung cultures and/or mouse lung cultures or samples and compares them through Gene Set Enrichment Analysis (GSEA). To do this, positive and negative infectious clone SARS (icSARS) gene panels are defined from GSEA-identified leading-edge genes between two icSARS-CoV derived signatures, both from human cultures. GSEA then is used to assess enrichment and identify leading-edge icSARS panel genes between icSARS gene panels and 27 other SARS-CoV gene signatures. The meta-analysis is expanded to include five MERS-CoV and three SARS-CoV2 gene signatures. Genes associated with SARS infection are predicted by examining the intersecting membership of GSEA-identified leading-edges across gene signatures. RESULTS: Significant enrichment (GSEA p<0.001) is observed between two icSARS-CoV derived signatures, and those leading-edge genes defined the positive (233 genes) and negative (114 genes) icSARS panels. Non-random significant enrichment (null distribution p<0.001) is observed between icSARS panels and all verification icSARSvsmock signatures derived from human cultures, from which 51 over- and 22 under-expressed genes are shared across leading-edges with 10 over-expressed genes already associated with icSARS infection. For the icSARSvsmock mouse signature, significant, non-random significant enrichment held for only the positive icSARS panel, from which nine genes are shared with icSARS infection in human cultures. Considering other SARS strains, significant, non-random enrichment (p<0.05) is observed across signatures derived from other SARS strains for the positive icSARS panel. Five positive icSARS panel genes, CXCL10, OAS3, OASL, IFIT3, and XAF1, are found across mice and human signatures regardless of SARS strains. CONCLUSION: The GSEA-based meta-analysis approach used here identifies genes with and without reported associations with SARS-CoV infections, highlighting this approach's predictability and usefulness in identifying genes that have potential as therapeutic targets to preclude or overcome SARS infections.

An Educational Bioinformatics Project to Improve Genome Annotation.

Scientific advancement is hindered without proper genome annotation because biologists lack a complete understanding of cellular protein functions. In bacterial cells, hypothetical proteins (HPs) are open reading frames with unknown functions. HPs result from either an outdated database or insufficient experimental evidence (i.e., indeterminate annotation). While automated annotation reviews help keep genome annotation up to date, often manual reviews are needed to verify proper annotation. Students can provide the manual review necessary to improve genome annotation. This paper outlines an innovative classroom project that determines if HPs have outdated or indeterminate annotation. The Hypothetical Protein Characterization Project uses multiple well-documented, freely available, web-based, bioinformatics resources that analyze an amino acid sequence to (1) detect sequence similarities to other proteins, (2) identify domains, (3) predict tertiary structure including active site characterization and potential binding ligands, and (4) determine cellular location. Enough evidence can be generated from these analyses to support re-annotation of HPs or prioritize HPs for experimental examinations such as structural determination via X-ray crystallography. Additionally, this paper details several approaches for selecting HPs to characterize using the Hypothetical Protein Characterization Project. These approaches include student- and instructor-directed random selection, selection using differential gene expression from mRNA expression data, and selection based on phylogenetic relations. This paper also provides additional resources to support instructional use of the Hypothetical Protein Characterization Project, such as example assignment instructions with grading rubrics, links to training videos in YouTube, and several step-by-step example projects to demonstrate and interpret the range of achievable results that students might encounter. Educational use of the Hypothetical Protein Characterization Project provides students with an opportunity to learn and apply knowledge of bioinformatic programs to address scientific questions. The project is highly customizable in that HP selection and analysis can be specifically formulated based on the scope and purpose of each student's investigations. Programs used for HP analysis can be easily adapted to course learning objectives. The project can be used in both online and in-seat instruction for a wide variety of undergraduate and graduate classes as well as undergraduate capstone, honor's, and experiential learning projects.

Identification and prioritization of macrolideresistance genes with hypothetical annotation inStreptococcus pneumoniae.

Macrolide resistant Streptococcus pneumoniae infections have limited treatment options. While some resistance mechanisms are well established, ample understanding is limited by incomplete genome annotation (hypothetical genes). Some hypothetical genes encode a domain of unknown function (DUF), a conserved protein domain with uncharacterized function. Here, we identify and confirm macrolide resistance genes. We further explore DUFs from macrolide resistance hypothetical genes to prioritize them for experimental characterization. We found gene similarities between two macrolide resistance gene signatures from untreated and either erythromycin- or spiramycin-treated resistant Streptococcus pneumoniae. We confirmed the association of these gene sets with macrolide resistance through comparison to gene signatures from (i) second erythromycin resistant Streptococcus pneumoniae strain, and (ii) erythromycin-treated sensitive Streptococcus pneumoniae strain, both from non-overlapping datasets. Examination into which cellular processes these macrolide resistance genes belong found connections to known resistance mechanisms such as increased amino acid biosynthesis and efflux genes, and decreased ribonucleotide biosynthesis genes, highlighting the predictive ability of the method used. 22 genes had hypothetical annotation with 10 DUFs associated with macrolide resistance. DUF characterization could uncover novel co-therapies that restore macrolide efficacy across multiple macrolide resistant species. Application of the methods to other antibiotic resistances could revolutionize treatment of resistant infections.

Improved Annotations of 23 Differentially Expressed Hypothetical Proteins in Methicillin Resistant S.aureus.

Antibiotic resistant Staphylococcus aureus is a major public health concern effecting millions of people annually. Medical science has documented completely untreatable S. aureus infections. These strains are appearing in the community with increasing frequency. New diagnostic and therapeutic options are needed to combat this deadly infection. Interestingly, around 50% of the proteins in S. aureus are annotated as hypothetical. Methods to select hypothetical proteins related to antibiotic resistance have been inadequate. This study uses differential gene expression to identify hypothetical proteins related to antibiotic resistant phenotype strain variations. We apply computational tools to predict physiochemical properties, cellular location, sequence-based homologs, domains, 3D modeling, active site features, and binding partners. Nine of 23 hypothetical proteins were <100 residues, unlikely to be functional proteins based on size. Of the 14 differentially expressed hypothetical proteins examined, confident predictions on function could not be made. Most identified domains had unknown functions. Six hypothetical protein models had >50% confidence over >20% residues. These findings indicate the method of hypothetical protein identification is sufficient; however, current scientific knowledge is inadequate to properly annotate these proteins. This process should be repeated regularly until entire genomes are clearly and accurately annotated.

Characterization of TPP-binding proteins in Methanococci archaeal species.

Acetolactate synthase (ALS) is a highly conserved protein family responsible for producing branched chain amino acids. In Methanocaldococcus jannaschii, two ALS proteins, MJ0277 and MJ0663 exist though variations in features between them are noted. Researchers are quick to examine MJ0277 homologs due to their increased function and close relationship, but few have characterized MJ0663 homologs. This study identified homologs for both MJ0277 and MJ0663 in all 15 Methanococci species with fully sequenced genomes. EggNOG database does not define four of the MJ0663 homologs, JH146_1236, WP_004591614, WP_018154400, and EHP89635. BLASTP comparisons suggest these four proteins had around 30% identity to MJ0277 homologs, close to the identity similarities between other MJ0663 homologs to the MJ0277 homologous group. ExPASY physiochemical characterization shows a statistically significant difference in molecular weight and grand average hydropathy between homologous groups. CDD-BLAST showed distinct domains between homologous groups. MJ0277 homologs had TPP_AHAS and PRL06276 while MJ0663 homologs had TPP_enzymes super family and IlvB domains instead. Multiple sequence alignment using PROMALS3D showed the MJ0277 homologs a tighter group than MJ0663 and its homologs. PHYLIP showed these homologous groups as evolutionarily distinct yet equal distance from bacterial ALS proteins of established structure. The four proteins EggNOG did not define had the same features as other MJ0663 homologs. This indicates that JH146_1236, WP_004591614, WP_018154400, and EHP89635, should be included in EggNOG database cluster arCOG02000 with the other MJ0663 homologs.

Prediction driven functional annotation of hypothetical proteins in the major facilitator superfamily of S. aureus NCTC 8325.

Antibiotic resistance Staphylococcus aureus strains cause several life threatening infections. New drug treatment options are needed, but are slow to develop because 50% of the S. aureus genome is hypothetical. The goal of this is to aid in the annotation of the S. aureus NCTC 8325 genome by identifying hypothetical proteins related to the Major Facilitator Superfamily (MFS). The MFS is a broad protein group with members involved in drug efflux mechanisms causing resistance. To do this, sequences for three MFS proteins with x-ray crystal structures in E. coli were PSI-BLASTed against the S. aureus NCTC 8325 genome to identify homologs. Eleven identified hypothetical protein homologs underwent BLASTP against the non-redundant NCBI database to fit homologs specific to each hypothetical protein. ExPASy characterized the physiochemical features, CDD-BLAST and Pfam identified domains, and the SOSUI server defined transmembrane helices of each hypothetical protein. Based on size (300 - 700 amino acids), number of transmembrane helices (>7), CD06174 and MFS domains in CDD-BLAST and Pfam, respectively, and close relation to well-defined homologs, SAOUHSC_00058, SAOUHSC_00078, SAOUHSC_00952, SAOUHSC_02435, SAOUHSC_02752, and ABD31642.1 are members of the MFS. Further multiple-alignment and phylogeny analyses show SAOUHSC_00058 to be a quinolone resistance protein (NorB), SAOUHSC_00058 a siderophore biosynthesis protein (SbnD), SAOUHSC_00952 a glycolipid permease (LtaA), SAOUHSC_02435 a macrolide MFS transporter, SAOUHSC_02752 a chloramphenicol resistance (DHA1), and ABD31642.1 is a Bcr/CflA family drug resistance efflux transporter. These findings provide better annotation for the existing genome, and identify proteins related to antibiotic resistance in S. aureus NCTC 8325.