Additional file 1: of VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology

Installation Guide. Description: Detailed user guide for installation and usage of VacSol. (PDF 1178 kb

Identification of Circulating Biomarker Candidates for Hepatocellular Carcinoma (HCC): An Integrated Prioritization Approach

<div><p>Hepatocellular carcinoma (HCC) is the world’s third most widespread cancer. Currently available circulating biomarkers for this silently progressing malignancy are not sufficiently specific and sensitive to meet all clinical needs. There is an imminent and pressing need for the identification of novel circulating biomarkers to increase disease-free survival rate. In order to facilitate the selection of the most promising circulating protein biomarkers, we attempted to define an objective method likely to have a significant impact on the analysis of vast data generated from cutting-edge technologies. Current study exploits data available in seven publicly accessible gene and protein databases, unveiling 731 liver-specific proteins through initial enrichment analysis. Verification of expression profiles followed by integration of proteomic datasets, enriched for the cancer secretome, filtered out 20 proteins including 6 previously characterized circulating HCC biomarkers. Finally, interactome analysis of these proteins with midkine (MDK), dickkopf-1 (DKK-1), current standard HCC biomarker alpha-fetoprotein (AFP), its interacting partners in conjunction with HCC-specific circulating and liver deregulated miRNAs target filtration highlighted seven novel statistically significant putative biomarkers including complement component 8, alpha (C8A), mannose binding lectin (MBL2), antithrombin III (SERPINC1), 11β-hydroxysteroid dehydrogenase type 1 (HSD11B1), alcohol dehydrogenase 6 (ADH6), beta-ureidopropionase (UPB1) and cytochrome P450, family 2, subfamily A, polypeptide 6 (CYP2A6). Our proposed methodology provides a swift assortment process for biomarker prioritization that eventually reduces the economic burden of experimental evaluation. Further dedicated validation studies of potential putative biomarkers on HCC patient blood samples are warranted. We hope that the use of such integrative secretome, interactome and miRNAs target filtration approach will accelerate the selection of high-priority biomarkers for other diseases as well, that are more amenable to downstream clinical validation experiments.</p></div

Schematic outline of multi-step HCC circulating biomarkers prioritization process.

<p>Liver-specific proteins extracted from various databases were screened using SignalP 4.1, SecretomeP 2.0, ExoCarta, TargetP 1.1 and TMHMM v. 2.0 servers to assess their secretory nature. Liver-specific secreted proteins once verified for their expression in liver (HPA and BioGPS) and blood (Plasma Proteome Database) were further prioritized depending upon their presence in secretome proteome of HCC patients, HCC cell lines and primary human hepatocytes. To infer possible involvement of prioritized proteins in HCC pathogenesis, their interactome analysis was done with AFP (as a standard biomarker for the diagnosis of HCC). Interacting proteins were then analysed for their interaction with HCC specific liver deregulated and circulating miRNA. Results were then statistically verified using SurvExpress validation tool to finally prioritize putative circulating biomarkers for HCC.</p

Additional file 1: of Pangenome and immuno-proteomics analysis of Acinetobacter baumannii strains revealed the core peptide vaccine targets

Virulent protein in core proteome: Virulent proteins (295) present in core conserved genome (2445) of A. baumannii are identified, and found out to facilitate the bacteria in its pathogenesis. (XLSX 123 kb

Performance and accuracy evaluation (%) of databases.

<p><b>3A</b>. Graphical representation of databases performance has been shown in percentages. BioGPS database revealed 97%, VeryGene database 92%, TiSGeD database 76%, TiGER database 66%, UniGene database 37%, C-It database 5% and the HPA unveiling 45% performance for the identification of liver-specific protein biomarkers. Performance % was calculated by dividing number of proteins identified by each database to total number of proteins that passed the filtering criteria. <b>3B</b>. Graphical representation of accuracy of the initial protein identifications with HPA database showing the highest accuracy of 25%, VeryGene database showing 8% accuracy, TiSGeD database showing 15%, TiGER database showing 8%, UniGene database showing 19%, C-It showing 2% and BioGPS database showing 20% accuracy. The accuracy was calculated by dividing number of proteins that had passed the filtering criteria by each database to the total number of proteins each database initially identified.</p

Interactome network analysis (miRNA-gene).

<p>Interactome network analysis of HCC-specific circulating miRNAs and genes encoding candidate protein biomarkers (retrieved using miRWalk, miRTarBase, TargetScan and microRNA.org) were visualized using Cytoscape software. The red colored circles represent seven final prioritized candidate marker proteins in our study.</p

Total number of liver-specific proteins identified in gene and protein databases.

<p>Total number of liver-specific proteins identified in gene and protein databases.</p

Liver-specific secreted/shed proteins identified by each database utilized in this study.

<p>Liver-specific secreted/shed proteins identified by each database utilized in this study.</p

Seven statistically significant putative HCC specific biomarkers prioritized through integrated <i>in-silico</i> approach.

<p>Seven statistically significant putative HCC specific biomarkers prioritized through integrated <i>in-silico</i> approach.</p

Additional file 6: of Pangenome and immuno-proteomics analysis of Acinetobacter baumannii strains revealed the core peptide vaccine targets

Eptiope analysis of 13 prioritized proteins. B-cell epitope predictions, T-cell epitope location, antigenicity, NetsurfP exposure of epitopes. (XLSX 63 kb