scholarly journals Prevalence of Non-B HIV-1 Subtypes in North Italy and Analysis of Transmission Clusters Based on Sequence Data Analysis

2019 ◽  
Vol 8 (1) ◽  
pp. 36 ◽  
Author(s):  
Giovanni Lorenzin ◽  
Franco Gargiulo ◽  
Arnaldo Caruso ◽  
Francesca Caccuri ◽  
Emanuele Focà ◽  
...  
Keyword(s):  
Public Health ◽  
Sequence Data ◽  
Human Mobility ◽  
Heterosexual Couples ◽  
Molecular Surveillance ◽  
Coalescence Model ◽  
Coding Regions ◽  
Hiv Epidemic ◽  
Viral Subtypes ◽  
Hiv 1

HIV-1 diversity is increasing in European countries due to immigration flows, as well as travels and human mobility, leading to the circulation of both new viral subtypes and new recombinant forms, with important implications for public health. We analyzed 710 HIV-1 sequences comprising protease and reverse-transcriptase (PR/RT) coding regions, sampled from 2011 to 2017, from naive patients in Spedali Civili Hospital, Brescia, Italy. Subtyping was performed by using a combination of different tools; the phylogenetic analysis with a structured coalescence model and Makarov Chain Monte Carlo was used on the datasets, to determine clusters and evolution. We detected 304 (43%) patients infected with HIV-1 non-B variants, of which only 293 sequences were available, with four pure subtypes and five recombinant forms; subtype F1 (17%) and CRF02_AG (51.1%) were most common. Twenty-five transmission clusters were identified, three of which included >10 patients, belonging to subtype CRF02_AG and subtype F. Most cases of alleged transmission were between heterosexual couples. Probably due to strong migratory flows, we have identified different subtypes with particular patterns of recombination or, as in the case of the subtype G (18/293, 6.1%), to a complete lack of relationship between the sequenced strains, revealing that they are all singletons. Continued HIV molecular surveillance is most important to analyze the dynamics of the boost of transmission clusters in order to implement public health interventions aimed at controlling the HIV epidemic.

scholarly journals Challenges in evaluating the use of viral sequence data to identify HIV transmission networks for public health

2020 ◽  
Vol 12 (s1) ◽  
Author(s):  
Rami Kantor ◽  
John P. Fulton ◽  
Jon Steingrimsson ◽  
Vladimir Novitsky ◽  
Mark Howison ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Hiv Transmission ◽  
Sequence Data ◽  
The United States ◽  
Viral Sequence ◽  
Transmission Networks ◽  
New Methods ◽  
Hiv Epidemic ◽  
The World

AbstractGreat efforts are devoted to end the HIV epidemic as it continues to have profound public health consequences in the United States and throughout the world, and new interventions and strategies are continuously needed. The use of HIV sequence data to infer transmission networks holds much promise to direct public heath interventions where they are most needed. As these new methods are being implemented, evaluating their benefits is essential. In this paper, we recognize challenges associated with such evaluation, and make the case that overcoming these challenges is key to the use of HIV sequence data in routine public health actions to disrupt HIV transmission networks.

scholarly journals Empirical comparison of analytical approaches for identifying molecular HIV-1 clusters

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vlad Novitsky ◽  
Jon A. Steingrimsson ◽  
Mark Howison ◽  
Fizza S. Gillani ◽  
Yuanning Li ◽  
...  
Keyword(s):  
Public Health ◽  
Rhode Island ◽  
Health Interventions ◽  
Empirical Comparison ◽  
Public Health Interventions ◽  
Molecular Sequences ◽  
Cluster Identification ◽  
Hiv Epidemic ◽  
Analytical Approaches ◽  
Hiv 1

Abstract Public health interventions guided by clustering of HIV-1 molecular sequences may be impacted by choices of analytical approaches. We identified commonly-used clustering analytical approaches, applied them to 1886 HIV-1 Rhode Island sequences from 2004–2018, and compared concordance in identifying molecular HIV-1 clusters within and between approaches. We used strict (topological support ≥ 0.95; distance 0.015 substitutions/site) and relaxed (topological support 0.80–0.95; distance 0.030–0.045 substitutions/site) thresholds to reflect different epidemiological scenarios. We found that clustering differed by method and threshold and depended more on distance than topological support thresholds. Clustering concordance analyses demonstrated some differences across analytical approaches, with RAxML having the highest (91%) mean summary percent concordance when strict thresholds were applied, and three (RAxML-, FastTree regular bootstrap- and IQ-Tree regular bootstrap-based) analytical approaches having the highest (86%) mean summary percent concordance when relaxed thresholds were applied. We conclude that different analytical approaches can yield diverse HIV-1 clustering outcomes and may need to be differentially used in diverse public health scenarios. Recognizing the variability and limitations of commonly-used methods in cluster identification is important for guiding clustering-triggered interventions to disrupt new transmissions and end the HIV epidemic.

scholarly journals What Should Health Departments Do with HIV Sequence Data?

Viruses ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1018
Author(s):  
Ethan Romero-Severson ◽  
Arshan Nasir ◽  
Thomas Leitner
Keyword(s):  
Public Health ◽  
Sequence Data ◽  
Public Health Practice ◽  
Health Benefit ◽  
Routine Care ◽  
Health Departments ◽  
Genetic Sequencing ◽  
Hiv Surveillance ◽  
Hiv Epidemic ◽  
Health Authorities

Many countries and US states have mandatory statues that require reporting of HIV clinical data including genetic sequencing results to the public health departments. Because genetic sequencing is a part of routine care for HIV infected persons, health departments have extensive sequence collections spanning years and even decades of the HIV epidemic. How should these data be used (or not) in public health practice? This is a complex, multi-faceted question that weighs personal risks against public health benefit. The answer is neither straightforward nor universal. However, to make that judgement—of how genetic sequence data should be used in describing and combating the HIV epidemic—we need a clear image of what a phylogenetically enhanced HIV surveillance system can do and what benefit it might provide. In this paper, we present a positive case for how up-to-date analysis of HIV sequence databases managed by health departments can provide unique and actionable information of how HIV is spreading in local communities. We discuss this question broadly, with examples from the US, as it is globally relevant for all health authorities that collect HIV genetic data.

Substance Abuse and Drug-Related Death, Suicidal Ideation, and Suicide: A Review

Crisis ◽  
1999 ◽  
Vol 20 (1) ◽  
pp. 28-35 ◽  
Author(s):  
Annie Mino ◽  
Arnaud Bousquet ◽  
Barbara Broers
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Substance Abuse ◽  
Suicidal Ideation ◽  
Family Support ◽  
Drug Users ◽  
Suicide Attempts ◽  
Public Health Problem ◽  
Major Public Health Problem ◽  
Hiv Epidemic

The high mortality rate among drug users, which is partly due to the HIV epidemic and partly due to drug-related accidental deaths and suicides, presents a major public health problem. Knowing more about prevalence, incidence, and risk factors is important for the development of rational preventive and therapeutic programs. This article attempts to give an overview of studies of the relations between substance abuse, suicidal ideation, suicide, and drug-related death. Research in this field is hampered by the absence of clear definitions, and results of studies are rarely comparable. There is, however, consensus about suicidal ideation being a risk factor for suicide attempts and suicide. Suicidal ideation is also a predictor of suicide, especially among drug users. It is correlated with an absence of family support, with the severity of the psychosocial dysfunctioning, and with multi-drug abuse, but also with requests for treatment. Every clinical examination of a drug user, not only of those who are depressed, should address the possible presence of suicidal ideation, as well as its intensity and duration.

Genotypic Methods for HIV Drug Resistance Monitoring: The Opportunities and Challenges Faced by China

2019 ◽  
Vol 17 (4) ◽  
pp. 225-239 ◽  
Author(s):  
Lulu Zuo ◽  
Ke Peng ◽  
Yihong Hu ◽  
Qinggang Xu
Keyword(s):  
Drug Resistance ◽  
Virological Suppression ◽  
Testing Methods ◽  
Hiv Drug Resistance ◽  
Advantages And Disadvantages ◽  
Hiv Epidemic ◽  
Routine Surveillance ◽  
The World ◽  
Global Project ◽  
Hiv 1

AIDS is a globalized infectious disease. In 2014, UNAIDS launched a global project of “90-90-90” to end the HIV epidemic by 2030. The second and third 90 require 90% of HIV-1 infected individuals receiving antiretroviral therapy (ART) and durable virological suppression. However, wide use of ART will greatly increase the emergence and spreading of HIV drug resistance and current HIV drug resistance test (DRT) assays in China are seriously lagging behind, hindering to achieve virological suppression. Therefore, recommending an appropriate HIV DRT method is critical for HIV routine surveillance and prevention in China. In this review, we summarized the current existing HIV drug resistance genotypic testing methods around the world and discussed the advantages and disadvantages of these methods.

scholarly journals Increasing proportions of HIV-1 non-B subtypes and of NNRTI resistance between 2013 and 2016 in Germany: Results from the national molecular surveillance of new HIV-diagnoses

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0206234 ◽  
Author(s):  
Andrea Hauser ◽  
Alexandra Hofmann ◽  
Karolin Meixenberger ◽  
Britta Altmann ◽  
Kirsten Hanke ◽  
...  
Keyword(s):  
Molecular Surveillance ◽  
Nnrti Resistance ◽  
Hiv 1

scholarly journals Recognition of Variant HIV-1 Epitopes from Diverse Viral Subtypes by Vaccine-Induced CTL

2004 ◽  
Vol 173 (3) ◽  
pp. 1941-1950 ◽  
Author(s):  
Denise M. McKinney ◽  
Rhonda Skvoretz ◽  
Brian D. Livingston ◽  
Cara C. Wilson ◽  
Michelle Anders ◽  
...  
Keyword(s):  
Viral Subtypes ◽  
Hiv 1

scholarly journals Nextstrain: real-time tracking of pathogen evolution

2017 ◽  
Author(s):  
James Hadfield ◽  
Colin Megill ◽  
Sidney M. Bell ◽  
John Huddleston ◽  
Barney Potter ◽  
...  
Keyword(s):  
Public Health ◽  
Real Time ◽  
Web Application ◽  
Sequence Data ◽  
Data Types ◽  
Bioinformatics Pipeline ◽  
Public Health Importance ◽  
Viral Genomes ◽  
Effective Public Health ◽  
Interactive Visualisation

AbstractSummaryUnderstanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualisation platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, publicly available for use by health professionals, epidemiologists, virologists and the public alike.Availability and implementationAll code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.

scholarly journals Detection of novel HIV-1 drug resistance mutations by support vector analysis of deep sequence data and experimental validation

2019 ◽  
Author(s):  
Mariano Avino ◽  
Emmanuel Ndashimye ◽  
Daniel J. Lizotte ◽  
Abayomi S. Olabode ◽  
Richard M. Gibson ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Drug Treatment ◽  
Treatment Outcomes ◽  
Sequence Data ◽  
Wild Type Virus ◽  
Support Vector ◽  
Wild Type ◽  
Subtype B ◽  
The World ◽  
Hiv 1

AbstractThe global HIV-1 pandemic comprises many genetically divergent subtypes. Most of our understanding of drug resistance in HIV-1 derives from subtype B, which predominates in North America and western Europe. However, about 90% of the pandemic represents non-subtype B infections. Here, we use deep sequencing to analyze HIV-1 from infected individuals in Uganda who were either treatment-naïve or who experienced virologic failure on ART without the expected patterns of drug resistance. Our objective was to detect potentially novel associations between mutations in HIV-1 integrase and treatment outcomes in Uganda, where most infections are subtypes A or D. We retrieved a total of 380 archived plasma samples from patients at the Joint Clinical Research Centre (Kampala), of which 328 were integrase inhibitor-naïve and 52 were raltegravir (RAL)-based treatment failures. Next, we developed a bioinformatic pipeline for alignment and variant calling of the deep sequence data obtained from these samples from a MiSeq platform (Illumina). To detect associations between within-patient polymorphisms and treatment outcomes, we used a support vector machine (SVM) for feature selection with multiple imputation to account for partial reads and low quality base calls. Candidate point mutations of interest were experimentally introduced into the HIV-1 subtype B NL4-3 backbone to determine susceptibility to RAL in U87.CD4.CXCR4 cells. Finally, we carried out replication capacity experiments with wild-type and mutant viruses in TZM-bl cells in the presence and absence of RAL. Our analyses not only identified the known major mutation N155H and accessory mutations G163R and V151I, but also novel mutations I203M and I208L as most highly associated with RAL failure. The I203M and I208L mutations resulted in significantly decreased susceptibility to RAL (44.0-fold and 54.9-fold, respectively) compared to wild-type virus (EC50=0.32 nM), and may represent novel pathways of HIV-1 resistance to modern treatments.Author summaryThere are many different types of HIV-1 around the world. Most of the research on how HIV-1 can become resistant to drug treatment has focused on the type (B) that is the most common in high-income countries. However, about 90% of infections around the world are caused by a type other than B. We used next-generation sequencing to analyze samples of HIV-1 from patients in Uganda (mostly infected by types A and D) for whom drug treatment failed to work, and whose infections did not fit the classic pattern of adaptation based on B. Next, we used machine learning to detect mutations in these virus populations that could explain the treatment outcomes. Finally, we experimentally added two candidate mutations identified by our analysis to a laboratory strain of HIV-1 and confirmed that they conferred drug resistance to the virus. Our study reveals new pathways that other types of HIV-1 may use to evolve resistance to drugs that make up the current recommended treatment for newly diagnosed individuals.

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