scholarly journals Computational Analysis of Molecular Interaction Networks Underlying Change of HIV-1 Resistance to Selected Reverse Transcriptase Inhibitors

2010 ◽  
Vol 4 ◽  
pp. BBI.S6247 ◽  
Author(s):  
Marcin Kierczak ◽  
Michał Dramiński ◽  
Jacek Koronacki ◽  
Jan Komorowski
Keyword(s):  
Drug Resistance ◽  
Reverse Transcriptase ◽  
Molecular Interaction ◽  
3D Structure ◽  
Resistance Mechanisms ◽  
Interaction Networks ◽  
Resistance Level ◽  
Molecular Interaction Networks ◽  
Monte Carlo Feature Selection ◽  
Hiv 1

Motivation Despite more than two decades of research, HIV resistance to drugs remains a serious obstacle in developing efficient AIDS treatments. Several computational methods have been developed to predict resistance level from the sequence of viral proteins such as reverse transcriptase (RT) or protease. These methods, while powerful and accurate, give very little insight into the molecular interactions that underly acquisition of drug resistance/hypersusceptibility. Here, we attempt at filling this gap by using our Monte Carlo feature selection and interdependency discovery method (MCFS-ID) to elucidate molecular interaction networks that characterize viral strains with altered drug resistance levels. Results We analyzed a number of HIV-1 RT sequences annotated with drug resistance level using the MCFS-ID method. This let us expound interdependency networks that characterize change of drug resistance to six selected RT inhibitors: Abacavir, Lamivudine, Stavudine, Zidovudine, Tenofovir and Nevirapine. The networks consider interdependencies at the level of physicochemical properties of mutating amino acids, eg,: polarity. We mapped each network on the 3D structure of RT in attempt to understand the molecular meaning of interacting pairs. The discovered interactions describe several known drug resistance mechanisms and, importantly, some previously unidentified ones. Our approach can be easily applied to a whole range of problems from the domain of protein engineering. Availability A portable Java implementation of our MCFS-ID method is freely available for academic users and can be obtained at: http://www.ipipan.eu/staff/m.draminski/software.htm .

scholarly journals A Rough Set-Based Model of HIV-1 Reverse Transcriptase Resistome

2009 ◽  
Vol 3 ◽  
pp. BBI.S3382 ◽  
Author(s):  
Marcin Kierczak ◽  
Krzysztof Ginalski ◽  
Michał Dramiñski ◽  
Jacek Koronacki ◽  
Witold Rudnicki ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Reverse Transcriptase ◽  
Rough Set ◽  
Molecular Mechanisms ◽  
3D Structure ◽  
Feature Selection Method ◽  
Resistance Mutations ◽  
Monte Carlo Feature Selection ◽  
Rt Inhibitors ◽  
Hiv 1

Reverse transcriptase (RT) is a viral enzyme crucial for HIV-1 replication. Currently, 12 drugs are targeted against the RT. The low fidelity of the RT-mediated transcription leads to the quick accumulation of drug-resistance mutations. The sequence-resistance relationship remains only partially understood. Using publicly available data collected from over 15 years of HIV proteome research, we have created a general and predictive rule-based model of HIV-1 resistance to eight RT inhibitors. Our rough set-based model considers changes in the physicochemical properties of a mutated sequence as compared to the wild-type strain. Thanks to the application of the Monte Carlo feature selection method, the model takes into account only the properties that significantly contribute to the resistance phenomenon. The obtained results show that drug-resistance is determined in more complex way than believed. We confirmed the importance of many resistance-associated sites, found some sites to be less relevant than formerly postulated and—more importantly—identified several previously neglected sites as potentially relevant. By mapping some of the newly discovered sites on the 3D structure of the RT, we were able to suggest possible molecular-mechanisms of drug-resistance. Importantly, our model has the ability to generalize predictions to the previously unseen cases. The study is an example of how computational biology methods can increase our understanding of the HIV-1 resistome.

Genetic Diversity and Drug Resistance of HIV-1 CRF55_01B in Guangdong, China

2020 ◽  
Vol 18 (3) ◽  
pp. 210-218
Author(s):  
Guolong Yu ◽  
Yan Li ◽  
Xuhe Huang ◽  
Pingping Zhou ◽  
Jin Yan ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Drug Resistance ◽  
Reverse Transcriptase ◽  
Phylogenetic Analyses ◽  
Resistance Mutations ◽  
Protease Inhibition ◽  
Subtype B ◽  
Primary Drug Resistance ◽  
Hiv 1 ◽  
Primary Drug

Background: HIV-1 CRF55_01B was first reported in 2013. At present, no report is available regarding this new clade’s polymorphisms in its functionally critical regions protease and reverse transcriptase. Objective: To identify the diversity difference in protease and reverse transcriptase between CRF55_01B and its parental clades CRF01_AE and subtype B; and to investigate CRF55_01B’s drug resistance mutations associated with the protease inhibition and reverse transcriptase inhibition. Methods: HIV-1 RNA was extracted from plasma derived from a MSM population. The reverse transcription and nested PCR amplification were performed following our in-house PCR procedure. Genotyping and drug resistant-associated mutations and polymorphisms were identified based on polygenetic analyses and the usage of the HIV Drug Resistance Database, respectively. Results: A total of 9.24 % of the identified CRF55_01B sequences bear the primary drug resistance. CRF55_01B contains polymorphisms I13I/V, G16E and E35D that differ from those in CRF01_AE. Among the 11 polymorphisms in the RT region, seven were statistically different from CRF01_AE’s. Another three polymorphisms, R211K (98.3%), F214L (98.3%), and V245A/E (98.3 %.), were identified in the RT region and they all were statistically different with that of the subtype B. The V179E/D mutation, responsible for 100% potential low-level drug resistance, was found in all CRF55_01B sequences. Lastly, the phylogenetic analyses demonstrated 18 distinct clusters that account for 35% of the samples. Conclusions: CRF55_01B’s pol has different genetic diversity comparing to its counterpart in CRF55_01B’s parental clades. CRF55_01B has a high primary drug resistance presence and the V179E/D mutation may confer more vulnerability to drug resistance.

scholarly journals HIV-1 Phenotypic Reverse Transcriptase Inhibitor Drug Resistance Test Interpretation Is Not Dependent on the Subtype of the Virus Backbone

PLoS ONE ◽  
2012 ◽  
Vol 7 (4) ◽  
pp. e34708 ◽  
Author(s):  
Michelle Bronze ◽  
Kim Steegen ◽  
Carole L. Wallis ◽  
Hans De Wolf ◽  
Maria A. Papathanasopoulos ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Reverse Transcriptase ◽  
Transcriptase Inhibitor ◽  
Resistance Test ◽  
Test Interpretation ◽  
Inhibitor Drug ◽  
Reverse Transcriptase Inhibitor ◽  
Hiv 1

Diversity of HIV-1 subtypes and transmitted drug-resistance mutations among minority HIV-1 variants in a Turkish cohort

2021 ◽  
Vol 19 ◽  
Author(s):  
Rabia Can Sarinoglu ◽  
Uluhan Sili ◽  
Ufuk Hasdemir ◽  
Burak Aksu ◽  
Guner Soyletir ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Reverse Transcriptase ◽  
Transcriptase Inhibitor ◽  
Transmitted Drug Resistance ◽  
Resistance Mutations ◽  
Drug Resistance Mutations ◽  
Subtype B ◽  
Treatment Naïve ◽  
Reverse Transcriptase Inhibitor ◽  
Hiv 1

Background: The World Health Organization (WHO) recommends the surveillance of transmitted drug resistance mutations (TDRMs) to ensure the effectiveness and sustainability of HIV treatment programs. Objective: Our aim was to determine the TDRMs and evaluate the distribution of HIV-1 subtypes using and compared next-generation sequencing (NGS) and Sanger-based sequencing (SBS) in a cohort of 44 antiretroviral treatment-naïve patients. Methods: All samples that were referred to the microbiology laboratory for HIV drug resistance analysis between December 2016 and February 2018 were included in the study. After exclusions, 44 treatment-naive adult patients with a viral load of >1000 copies/mL were analyzed. DNA sequencing for reverse transcriptase and protease regions was performed using both DeepChek ABL single round kit and Sanger-based ViroSeq HIV-1 Genotyping System. The mutations and HIV-1 subtypes were analyzed using the Stanford HIVdb version 8.6.1 Genotypic Resistance software, and TDRMs were assessed using the WHO surveillance drug-resistance mutation database. HIV-1 subtypes were confirmed by constructing a maximum-likelihood phylogenetic tree using Los Alamos IQ-Tree software. Results: NGS identified nucleos(t)ide reverse transcriptase inhibitor (NRTI)-TDRMs in 9.1% of the patients, non-nucleos(t)ide reverse transcriptase inhibitor (NNRTI)-TDRMs in 6.8% of the patients, and protease inhibitor (PI)-TDRMs in 18.2% of the patients at a detection threshold of ≥1%. Using SBS, 2.3% and 6.8% of the patients were found to have NRTI- and NNRTI-TDRMs, respectively, but no major PI mutations were detected. M41L, L74I, K65R, M184V, and M184I related to NRTI, K103N to NNRTI, and N83D, M46I, I84V, V82A, L24I, L90M, I54V to the PI sites were identified using NGS. Most mutations were found in low-abundance (frequency range: 1.0% - 4.7%) HIV-1 variants, except M41L and K103N. The subtypes of the isolates were found as follows; 61.4% subtype B, 18.2% subtype B/CRF02_AG recombinant, 13.6% subtype A, 4.5% CRF43_02G, and 2.3% CRF02_AG. All TDRMs, except K65R, were detected in HIV-1 subtype B isolates.. Conclusion: The high diversity of protease site TDRMs in the minority HIV-1 variants and prevalence of CRFs were remarkable in this study. All minority HIV-1 variants were missed by conventional sequencing. TDRM prevalence among minority variants appears to be decreasing over time at our center.

Development and evaluation of an assay for HIV-1 protease and reverse transcriptase drug resistance genotyping of all major group-M subtypes

2012 ◽  
Vol 54 (1) ◽  
pp. 21-25 ◽  
Author(s):  
Susan C. Aitken ◽  
Aletta Kliphuis ◽  
Carole L. Wallis ◽  
Mei Ling Chu ◽  
Quirine Fillekes ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Reverse Transcriptase ◽  
Major Group ◽  
Drug Resistance Genotyping ◽  
Hiv 1

scholarly journals Primary HIV drug resistance among newly HIV type-1 diagnosed patients in St. Petersburg

2021 ◽  
Vol 13 (1) ◽  
pp. 70-79
Author(s):  
Thierry Ingabire ◽  
A. V. Semenov ◽  
E. V. Esaulenko ◽  
E. B. Zueva ◽  
A. N. Schemelev ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Reverse Transcriptase ◽  
Direct Sequencing ◽  
Population Level ◽  
Multidrug Resistant ◽  
Antiretroviral Drugs ◽  
Drug Resistant ◽  
Resistant Virus ◽  
Primary Drug Resistance ◽  
Hiv 1

There is concern that the widespread use of antiretroviral drugs (ARV) to treat human immunodeficiency virus 1 (HIV-1) infection may result in the emergence of transmission of drug-resistant virus among persons newly infected with HIV-1. Russia is one of a growing number of countries in the world where drug-resistant HIV is becoming a serious health problem because it has the potential to compromise the efficacy of antiretroviral therapy (ART) at the population level.Materials and methods. We performed a genetic analysis of the HIV-1 plasma derived pol gene among the newly diagnosed ART-naïve HIV-1 infected patients during the period from November 2018 to October 2019 in the St. Petersburg Clinical Infectious Diseases Hospital named after S.P. Botkin. We used reverse transcriptase polymerase chain reaction (RT-PCR) followed by direct sequencing of PCR products to determine the prevalence of primary drug resistance (PDR) conferring mutations. HIV-1 genotypes were determined by phylogenetic analysis.Results. The predominant HIV-1 subtype was A1 (87.2%), followed by B (11.8%) and CRF06_cpx (1%). The overall prevalence of PDR was 11%. Virus with known resistance-conferring mutations to any nucleoside reverse transcriptase inhibitors (NRTIs) was found in 8 individuals, to any non NRTIs in 5 subjects, and to any protease inhibitors in 1 case. Multidrug-resistant virus was identified in 2 individuals (2%).Conclusion. The distribution of HIV-1 genotypes in St. Petersburg, Russia is diverse. The emerging prevalence of PDR in ART-naïve patients demonstrates the significance of constant monitoring due to the challenges it presents towards treatment.

scholarly journals A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ramin Hasibi ◽  
Tom Michoel
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Gene Expression Data ◽  
Biological Networks ◽  
Molecular Interaction ◽  
Interaction Networks ◽  
Omics Data ◽  
Expression Data ◽  
Molecular Interaction Networks ◽  
Graph Neural Networks

Abstract Background Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features. Results We studied the representation of transcriptional, protein–protein and genetic interaction networks in E. coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end Graph Feature Auto-Encoder framework for the prediction of node features utilizing the structure of the gene networks, which is trained on the feature prediction task, and showed that it performs better at predicting unobserved node features than regular MultiLayer Perceptrons. When applied to the problem of imputing missing data in single-cell RNAseq data, the Graph Feature Auto-Encoder utilizing our new graph convolution layer called FeatGraphConv outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data with our proposed approach. Conclusion Our proposed Graph Feature Auto-Encoder framework is a powerful approach for integrating and exploiting the close relation between molecular interaction networks and functional genomics data.

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