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Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses.

ABSTRACT: Next-generation sequencing (NGS) provides unrestricted access to the genome, but it produces 'big data' exceeding in amount and complexity the classical analytical approaches. We introduce a bioinformatics-based classifying biomarker that uses emergent properties in genetics to separate pain patients requiring extremely high opioid doses from controls. Following precisely calculated selection of the 34 most informative markers in the OPRM1, OPRK1, OPRD1 and SIGMAR1 genes, pattern of genotypes belonging to either patient group could be derived using a k-nearest neighbor (kNN) classifier that provided a diagnostic accuracy of 80.6±4%. This outperformed alternative classifiers such as reportedly functional opioid receptor gene variants or complex biomarkers obtained via multiple regression or decision tree analysis. The accumulation of several genetic variants with only minor functional influences may result in a qualitative consequence affecting complex phenotypes, pointing at emergent properties in genetics.

SUBMITTER: Kringel D

PROVIDER: S-EPMC5637232 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses.

Kringel D D Ultsch A A Zimmermann M M Jansen J-P JP Ilias W W Freynhagen R R Griessinger N N Kopf A A Stein C C Doehring A A Resch E E Lötsch J J

The pharmacogenomics journal 20160503 5

Next-generation sequencing (NGS) provides unrestricted access to the genome, but it produces 'big data' exceeding in amount and complexity the classical analytical approaches. We introduce a bioinformatics-based classifying biomarker that uses emergent properties in genetics to separate pain patients requiring extremely high opioid doses from controls. Following precisely calculated selection of the 34 most informative markers in the OPRM1, OPRK1, OPRD1 and SIGMAR1 genes, pattern of genotypes be ...[more]

PMID: 27139154

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Project description:IntroductionCurrent clinical detection of Human immunodeficiency virus 1 (HIV-1) is used to target viral genes and proteins. However, the immunoassay, such as viral culture or Polymerase Chain Reaction (PCR), lacks accuracy in the diagnosis, as these conventional assays rely on the stable genome and HIV-1 is a highly-mutated virus. Next generation sequencing (NGS) promises to be transformative for the practice of infectious disease, and the rapidly reducing cost and processing time mean that this will become a feasible technology in diagnostic and research laboratories in the near future. The technology offers the superior sensitivity to detect the pathogenic viruses, including unknown and unexpected strains.AimTo leverage the NGS technology in order to improve current HIV-1 diagnosis and genotyping methods.Materials and methodsTen blood samples were collected from HIV-1 infected patients which were diagnosed by RT PCR at Singapore Communicable Disease Centre, Tan Tock Seng Hospital from October 2014 to March 2015. Viral RNAs were extracted from blood plasma and reversed into cDNA. The HIV-1 cDNA samples were cleaned up using a PCR purification kit and the sequencing library was prepared and identified through MiSeq.ResultsTwo common mutations were observed in all ten samples. The common mutations were identified at genome locations 1908 and 2104 as missense and silent mutations respectively, conferring S37N and S3S found on aspartic protease and reverse transcriptase subunits.ConclusionThe common mutations identified in this study were not previously reported, therefore suggesting the potential for them to be used for identification of viral infection, disease transmission and drug resistance. This was especially the case for, missense mutation S37N which could cause an amino acid change in viral proteases thus reducing the binding affinity of some protease inhibitors. Thus, the unique common mutations identified in this study could be used as diagnostic biomarkers to indicate the origin of infection as being from Singapore.

Dataset Information

Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses.

Publications

Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses.

Similar Datasets

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