Antibiotic resistance genes prediction via whole genome sequence analysis of Stenotrophomonas maltophilia.

BACKGROUND: Stenotrophomonas maltophilia (S. maltophilia) is the first dominant ubiquitous bacterial species identified from the genus Stenotrophomonas in 1943 from a human source. S. maltophilia clinical strains are resistance to several therapies, this study is designed to investigate the whole genome sequence and antimicrobial resistance genes prediction in Stenotrophomonas maltophilia (S. maltophilia) SARC-5 and SARC-6 strains, isolated from the nasopharyngeal samples of an immunocompromised patient. METHODS: These bacterial strains were obtained from Pakistan Institute of Medical Sciences (PIMS) Hospital, Pakistan. The bacterial genome was sequenced using a whole-genome shotgun via a commercial service that used an NGS (Next Generation Sequencing) technology called as Illumina Hiseq 2000 system for genomic sequencing. Moreover, detailed in-silico analyses were done to predict the presence of antibiotic resistance genes in S. maltophilia. RESULTS: Results showed that S. maltophilia is a rare gram negative, rod-shaped, non sporulating bacteria. The genome assembly results in 24 contigs (>500 bp) having a size of 4668,850 bp with 65.8% GC contents. Phylogenetic analysis showed that SARC-5 and SARC-6 were closely related to S. maltophilia B111, S. maltophilia BAB-5317, S. maltophilia AHL, S. maltophilia BAB-5307, S. maltophilia RD-AZPVI_04, S. maltophilia JFZ2, S. maltophilia RD_MAAMIB_06 and lastly with S. maltophilia sp ROi7. Moreover, the whole genome sequence analysis of both SARC-5 and SARC-6 revealed the presence of four resistance genes adeF, qacG, adeF, and smeR. CONCLUSION: Our study confirmed that S. maltophilia SARC-5 and SARC-6 are one of the leading causes of nosocomial infection which carry multiple antibiotic resistance genes.

Characterization of HLA-A*33:03 epitopes via immunoprecipitation and LC-MS/MS.

Human leukocyte antigen (HLA) class I has more than 18,000 alleles, each of which binds to a set of unique peptides from the cellular degradome. Deciphering the interaction between antigenic peptides and HLA proteins is crucial for understanding immune responses in autoimmune diseases and cancer. In this study, we aimed to characterize the peptidome that binds to HLA-A*33:03, which is one of the most prevalent HLA-A alleles in the Northeast Asian population, but poorly studied. For this purpose, we analyzed the HLA-A*33:03 monoallelic B cell line using immunoprecipitation of HLA-A and peptide complexes, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). In this study, we identified 5731 unique peptides that were associated with HLA A*33:03, and experimentally validated the affinity of 40 peptides for HLA-A*33:03 and their stability in HLA A*33:03-peptides complexes. To our knowledge, this study represents the largest dataset of peptides associated with HLA-A*33:03. Also, this is the first study in which HLA A*33:03-associated peptides were experimentally validated.

A Nine-Gene Signature for Predicting the Response to Preoperative Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer.

Preoperative chemoradiotherapy (PCRT) and subsequent surgery is the standard multimodal treatment for locally advanced rectal cancer (LARC), albeit PCRT response varies among the individuals. This creates a dire necessity to identify a predictive model to forecast treatment response outcomes and identify patients who would benefit from PCRT. In this study, we performed a gene expression study using formalin-fixed paraffin-embedded (FFPE) tumor biopsy samples from 156 LARC patients (training cohort n = 60; validation cohort n = 96); we identified the nine-gene signature (FGFR3, GNA11, H3F3A, IL12A, IL1R1, IL2RB, NKD1, SGK2, and SPRY2) that distinctively differentiated responders from non-responders in the training cohort (accuracy = 86.9%, specificity = 84.8%, sensitivity = 81.5%) as well as in an independent validation cohort (accuracy = 81.0%, specificity = 79.4%, sensitivity = 82.3%). The signature was independent of all pathological and clinical features and was robust in predicting PCRT response. It is readily applicable to the clinical setting using FFPE samples and Food and Drug Administration (FDA) approved hardware and reagents. Predicting the response to PCRT may aid in tailored therapies for respective responders to PCRT and improve the oncologic outcomes for LARC patients.

IPCT: Integrated Pharmacogenomic Platform of Human Cancer Cell Lines and Tissues.

: (1) Motivation: The exponential increase in multilayered data, including omics, pathways, chemicals, and experimental models, requires innovative strategies to identify new linkages between drug response information and omics features. Despite the availability of databases such as the Cancer Cell Line Encyclopedia (CCLE), the Cancer Therapeutics Response Portal (CTRP), and The Cancer Genome Atlas (TCGA), it is still challenging for biologists to explore the relationship between drug response and underlying genomic features due to the heterogeneity of the data. In light of this, the Integrated Pharmacogenomic Database of Cancer Cell Lines and Tissues (IPCT) has been developed as a user-friendly way to identify new linkages between drug responses and genomic features, as these findings can lead not only to new biological discoveries but also to new clinical trials. (2) Results: The IPCT allows biologists to compare the genomic features of sensitive cell lines or small molecules with the genomic features of tumor tissues by integrating the CTRP and CCLE databases with the REACTOME, cBioPortal, and Expression Atlas databases. The input consists of a list of small molecules, cell lines, or genes, and the output is a graph containing data entities connected with the queried input. Users can apply filters to the databases, pathways, and genes as well as select computed sensitivity values and mutation frequency scores to generate a relevant graph. Different objects are differentiated based on the background color of the nodes. Moreover, when multiple small molecules, cell lines, or genes are input, users can see their shared connections to explore the data entities common between them. Finally, users can view the resulting graphs in the online interface or download them in multiple image or graph formats. (3) Availability and Implementation: The IPCT is available as a web application with an integrated MySQL database. The web application was developed using Java and deployed on the Tomcat server. The user interface was developed using HTML5, JQuery v.3.1.0 , and the Cytoscape Graph API v.1.0.4. The IPCT can be accessed at http://ipct.ewostech.net. The source code is available at https://github.com/muhammadshoaib/ipct.

Genomics of drug sensitivity in bladder cancer: an integrated resource for pharmacogenomic analysis in bladder cancer.

BACKGROUND: Bladder cancer has numerous genomic features that are potentially actionable by targeted agents. Nevertheless, both pre-clinical and clinical research using molecular targeted agents have been very limited in bladder cancer. RESULTS: We created the Genomics of Drug Sensitivity in Bladder Cancer (GDBC) database, an integrated database (DB) to facilitate the genomic understanding of bladder cancer in relation to drug sensitivity, in order to promote potential therapeutic applications of targeted agents in bladder cancer treatment. The GDBC database contains two separate datasets: 1) in-house drug sensitivity data, in which 13 targeted agents were tested against 10 bladder cancer cell lines; 2) data extracted and integrated from public databases, including the Cancer Therapeutics Research Portal, Cancer Cell Line Encyclopedia, Genomics of Drug Sensitivity in Cancer, Kyoto Encyclopedia of Genes and Genomes, and the Cancer Gene Census databases, as well as bladder cancer genomics data and synthetic lethality/synthetic dosage lethality connections. CONCLUSIONS: GDBC is an integrated DB of genomics and drug sensitivity data with a specific focus on bladder cancer. With a user-friendly web-interface, GDBC helps users generate genomics-based hypotheses that can be tested experimentally using drugs and cell lines included in GDBC.

cMapper: gene-centric connectivity mapper for EBI-RDF platform.

MOTIVATION: In this era of biological big data, data integration has become a common task and a challenge for biologists. The Resource Description Framework (RDF) was developed to enable interoperability of heterogeneous datasets. The EBI-RDF platform enables an efficient data integration of six independent biological databases using RDF technologies and shared ontologies. However, to take advantage of this platform, biologists need to be familiar with RDF technologies and SPARQL query language. To overcome this practical limitation of the EBI-RDF platform, we developed cMapper, a web-based tool that enables biologists to search the EBI-RDF databases in a gene-centric manner without a thorough knowledge of RDF and SPARQL. RESULTS: cMapper allows biologists to search data entities in the EBI-RDF platform that are connected to genes or small molecules of interest in multiple biological contexts. The input to cMapper consists of a set of genes or small molecules, and the output are data entities in six independent EBI-RDF databases connected with the given genes or small molecules in the user's query. cMapper provides output to users in the form of a graph in which nodes represent data entities and the edges represent connections between data entities and inputted set of genes or small molecules. Furthermore, users can apply filters based on database, taxonomy, organ and pathways in order to focus on a core connectivity graph of their interest. Data entities from multiple databases are differentiated based on background colors. cMapper also enables users to investigate shared connections between genes or small molecules of interest. Users can view the output graph on a web browser or download it in either GraphML or JSON formats. AVAILABILITY AND IMPLEMENTATION: cMapper is available as a web application with an integrated MySQL database. The web application was developed using Java and deployed on Tomcat server. We developed the user interface using HTML5, JQuery and the Cytoscape Graph API. cMapper can be accessed at http://cmapper.ewostech.net Readers can download the development manual from the website http://cmapper.ewostech.net/docs/cMapperDocumentation.pdf. Source Code is available at https://github.com/muhammadshoaib/cmapperContact:smahn@gachon.ac.krSupplementary information: Supplementary data are available at Bioinformatics online.

The somatic POLE P286R mutation defines a unique subclass of colorectal cancer featuring hypermutation, representing a potential genomic biomarker for immunotherapy.

Early-onset colorectal cancers (EOCRCs) may have biological or genomic features distinct from late-onset CRCs (LOCRCs). Previous studies have mostly focused on the germline predisposition conditions of EOCRCs, but we hypothesized that EOCRCs may have distinct somatic aberrations that accelerate cancer development. To identify the somatic aberrations that accelerate cancer development at an early age, we conducted whole exome sequencing for 28 polyposis-unrelated, microsatellite stable (MSS) EOCRCs with no known germline predisposition conditions. Surprisingly, we found two distinct groups in the context of mutational burden: 6 hypermutated cases with 2325 to 10973 mutations and 22 nonhypermutated cases with 47 to 154 mutations. Further analysis revealed that four of the six hypermutated cases had the same POLE P286R mutation. We validated this finding in 83 MSS EOCRCs and 27 MSS LOCRCs, which revealed that 7.2% of EOCRCs (6/83) had the POLE P286R mutation, which was not found in LOCRCs. Clinicopathologically, EOCRCs with POLE mutations occurred far more frequently in the right colon than in the left colon, affecting men more frequently than women. In summary, we have identified a unique subclass of colon cancer characterized by a hypermutation associated with the POLE mutation. The acquisition of the POLE mutation leading to hypermutation can accelerate cancer development. Clinically, this subset with hypermutation may be susceptible to immune checkpoint blockade.

Genomic portrait of resectable hepatocellular carcinomas: implications of RB1 and FGF19 aberrations for patient stratification.

UNLABELLED: Hepatic resection is the most curative treatment option for early-stage hepatocellular carcinoma, but is associated with a high recurrence rate, which exceeds 50% at 5 years after surgery. Understanding the genetic basis of hepatocellular carcinoma at surgically curable stages may enable the identification of new molecular biomarkers that accurately identify patients in need of additional early therapeutic interventions. Whole exome sequencing and copy number analysis was performed on 231 hepatocellular carcinomas (72% with hepatitis B viral infection) that were classified as early-stage hepatocellular carcinomas, candidates for surgical resection. Recurrent mutations were validated by Sanger sequencing. Unsupervised genomic analyses identified an association between specific genetic aberrations and postoperative clinical outcomes. Recurrent somatic mutations were identified in nine genes, including TP53, CTNNB1, AXIN1, RPS6KA3, and RB1. Recurrent homozygous deletions in FAM123A, RB1, and CDKN2A, and high-copy amplifications in MYC, RSPO2, CCND1, and FGF19 were detected. Pathway analyses of these genes revealed aberrations in the p53, Wnt, PIK3/Ras, cell cycle, and chromatin remodeling pathways. RB1 mutations were significantly associated with cancer-specific and recurrence-free survival after resection (multivariate P = 0.038 and P = 0.012, respectively). FGF19 amplifications, known to activate Wnt signaling, were mutually exclusive with CTNNB1 and AXIN1 mutations, and significantly associated with cirrhosis (P = 0.017). CONCLUSION: RB1 mutations can be used as a prognostic molecular biomarker for resectable hepatocellular carcinoma. Further study is required to investigate the potential role of FGF19 amplification in driving hepatocarcinogenesis in patients with liver cirrhosis and to investigate the potential of anti-FGF19 treatment in these patients.

Sequence Maneuverer: tool for sequence extraction from genomes.

UNLABELLED: The availability of genomic sequences of many organisms has opened new challenges in many aspects particularly in terms of genome analysis. Sequence extraction is a vital step and many tools have been developed to solve this issue. These tools are available publically but have limitations with reference to the sequence extraction, length of the sequence to be extracted, organism specificity and lack of user friendly interface. We have developed a java based software package having three modules which can be used independently or sequentially. The tool efficiently extracts sequences from large datasets with few simple steps. It can efficiently extract multiple sequences of any desired length from a genome of any organism. The results are crosschecked by published data. AVAILABILITY: URL 1: http://ww3.comsats.edu.pk/bio/ResearchProjects.aspx URL 2: http://ww3.comsats.edu.pk/bio/SequenceManeuverer.aspx.