Identification and prioritization of macrolideresistance genes with hypothetical annotation inStreptococcus pneumoniae

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.

A novel electrochemical biosensor based on peptidoglycan and platinum-nickel-copper nano-cube for rapid detection of Gram-positive bacteria

A novel non-enzyme electrochemical biosensor for the rapid detection of Gram-positive bacteria has been constructed that relys on a stable and efficient combination between the peptidoglycan layer and platinum-nickel-copper nanocubes (Pt-Ni-Cu NCs). Briefly, bacteria were first captured by a specific antibody. Then, the electrochemical signal materials (Pt-Ni-Cu NCs) were bound to the bacteria peptidoglycan layer using specific structural and surface features. The rapid and sensitive bacterial detection was then achieved using intrinsic electrochemical characteristics and superoxidase-like activity of the Pt-Ni-Cu NCs. Moreover, the nature of peptidoglycan covering the whole bacteria provided the premise for signal amplification. Under optimal conditions, the electrochemical signal variation was proportional to the concentration of bacteria ranging from 1.5 × 10² to 1.5 × 10⁸ CFU/mL with a detection limit of 42 CFU/mL using a working potential of - 0.4 V. This electrochemical biosensor has been successfully applied to detect bacteria concentrations in urine samples, and the recoveries range from 90.4 to 107%. The proposed biosensor could be applied for broad-spectrum detection of Gram-positive bacteria since most Gram-positive bacteria possess a thick peptidoglycan layer. The developed electrochemical biosensing strategy might be used as a potential tool for clinical pathogenic bacteria detection and point-of-care testing (POCT).