HIV Drug Resistant Prediction and Featured Mutants Selection using Machine Learning Approaches

HIV/AIDS is widely spread and ranks as the sixth biggest killer all over the world. Moreover, due to the rapid replication rate and the lack of proofreading mechanism of HIV virus, drug resistance is commonly found and is one of the reasons causing the failure of the treatment. Even though the drug resistance tests are provided to the patients and help choose more efficient drugs, such experiments may take up to two weeks to finish and are expensive. Because of the fast development of the computer, drug resistance prediction using machine learning is feasible. In order to accurately predict the HIV drug resistance, two main tasks need to be solved: how to encode the protein structure, extracting the more useful information and feeding it into the machine learning tools; and which kinds of machine learning tools to choose. In our research, we first proposed a new protein encoding algorithm, which could convert various sizes of proteins into a fixed size vector. This algorithm enables feeding the protein structure information to most state of the art machine learning algorithms. In the next step, we also proposed a new classification algorithm based on sparse representation. Following that, mean shift and quantile regression were included to help extract the feature information from the data. Our results show that encoding protein structure using our newly proposed method is very efficient, and has consistently higher accuracy regardless of type of machine learning tools. Furthermore, our new classification algorithm based on sparse representation is the first application of sparse representation performed on biological data, and the result is comparable to other state of the art classification algorithms, for example ANN, SVM and multiple regression. Following that, the mean shift and quantile regression provided us with the potentially most important drug resistant mutants, and such results might help biologists/chemists to determine which mutants are the most representative candidates for further research

Phylogenetic surveillance of viral genetic diversity and the evolving molecular epidemiology of human immunodeficiency virus type 1

With ongoing generation of viral genetic diversity and increasing levels of migration, the global human immunodeficiency virus type 1 (HIV-1) epidemic is becoming increasingly heterogeneous. In this study, we investigate the epidemiological characteristics of 5,675 HIV-1 pol gene sequences sampled from distinct infections in the United Kingdom. These sequences were phylogenetically analyzed in conjunction with 976 complete-genome and 3,201 pol gene reference sequences sampled globally and representing the broad range of HIV-1 genetic diversity, allowing us to estimate the probable geographic origins of the various strains present in the United Kingdom. A statistical analysis of phylogenetic clustering in this data set identified several independent transmission chains within the United Kingdom involving recently introduced strains and indicated that strains more commonly associated with infections acquired heterosexually in East Africa are spreading among men who have sex with men. Coalescent approaches were also used and indicated that the transmission chains that we identify originated in the late 1980s to early 1990s. Similar changes in the epidemiological structuring of HIV epidemics are likely to be taking in place in other industrialized nations with large immigrant populations. The framework implemented here takes advantage of the vast amount of routinely generated HIV-1 sequence data and can provide epidemiological insights not readily obtainable through standard surveillance methods

In Vivo validation of a bioinformatics based tool to identify reduced replication capacity in HIV-1.

Although antiretroviral drug resistance is common in treated HIV infected individuals, it is not a consistent indicator of HIV morbidity and mortality. To the contrary, HIV resistance-associated mutations may lead to changes in viral fitness that are beneficial to infected individuals. Using a bioinformatics-based model to assess the effects of numerous drug resistance mutations, we determined that the D30N mutation in HIV-1 protease had the largest decrease in replication capacity among known protease resistance mutations. To test this in silico result in an in vivo environment, we constructed several drug-resistant mutant HIV-1 strains and compared their relative fitness utilizing the SCID-hu mouse model. We found HIV-1 containing the D30N mutation had a significant defect in vivo, showing impaired replication kinetics and a decreased ability to deplete CD4+ thymocytes, compared to the wild-type or virus without the D30N mutation. In comparison, virus containing the M184V mutation in reverse transcriptase, which shows decreased replication capacity in vitro, did not have an effect on viral fitness in vivo. Thus, in this study we have verified an in silico bioinformatics result with a biological assessment to identify a unique mutation in HIV-1 that has a significant fitness defect in vivo

A dynamic Bayesian nonlinear mixed-effects model of HIV response incorporating medication adherence, drug resistance and covariates

HIV dynamic studies have contributed significantly to the understanding of HIV pathogenesis and antiviral treatment strategies for AIDS patients. Establishing the relationship of virologic responses with clinical factors and covariates during long-term antiretroviral (ARV) therapy is important to the development of effective treatments. Medication adherence is an important predictor of the effectiveness of ARV treatment, but an appropriate determinant of adherence rate based on medication event monitoring system (MEMS) data is critical to predict virologic outcomes. The primary objective of this paper is to investigate the effects of a number of summary determinants of MEMS adherence rates on virologic response measured repeatedly over time in HIV-infected patients. We developed a mechanism-based differential equation model with consideration of drug adherence, interacted by virus susceptibility to drug and baseline characteristics, to characterize the long-term virologic responses after initiation of therapy. This model fully integrates viral load, MEMS adherence, drug resistance and baseline covariates into the data analysis. In this study we employed the proposed model and associated Bayesian nonlinear mixed-effects modeling approach to assess how to efficiently use the MEMS adherence data for prediction of virologic response, and to evaluate the predicting power of each summary metric of the MEMS adherence rates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS376 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Structured, sparse regression with application to HIV drug resistance

We introduce a new version of forward stepwise regression. Our modification finds solutions to regression problems where the selected predictors appear in a structured pattern, with respect to a predefined distance measure over the candidate predictors. Our method is motivated by the problem of predicting HIV-1 drug resistance from protein sequences. We find that our method improves the interpretability of drug resistance while producing comparable predictive accuracy to standard methods. We also demonstrate our method in a simulation study and present some theoretical results and connections.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS428 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Treatment outcomes of new tuberculosis patients hospitalized in Kampala, Uganda: a prospective cohort study.

BACKGROUND: In most resource limited settings, new tuberculosis (TB) patients are usually treated as outpatients. We sought to investigate the reasons for hospitalisation and the predictors of poor treatment outcomes and mortality in a cohort of hospitalized new TB patients in Kampala, Uganda. METHODS AND FINDINGS: Ninety-six new TB patients hospitalised between 2003 and 2006 were enrolled and followed for two years. Thirty two were HIV-uninfected and 64 were HIV-infected. Among the HIV-uninfected, the commonest reasons for hospitalization were low Karnofsky score (47%) and need for diagnostic evaluation (25%). HIV-infected patients were commonly hospitalized due to low Karnofsky score (72%), concurrent illness (16%) and diagnostic evaluation (14%). Eleven HIV uninfected patients died (mortality rate 19.7 per 100 person-years) while 41 deaths occurred among the HIV-infected patients (mortality rate 46.9 per 100 person years). In all patients an unsuccessful treatment outcome (treatment failure, death during the treatment period or an unknown outcome) was associated with duration of TB symptoms, with the odds of an unsuccessful outcome decreasing with increasing duration. Among HIV-infected patients, an unsuccessful treatment outcome was also associated with male sex (P = 0.004) and age (P = 0.034). Low Karnofsky score (aHR = 8.93, 95% CI 1.88 - 42.40, P = 0.001) was the only factor significantly associated with mortality among the HIV-uninfected. Mortality among the HIV-infected was associated with the composite variable of CD4 and ART use, with patients with baseline CD4 below 200 cells/µL who were not on ART at a greater risk of death than those who were on ART, and low Karnofsky score (aHR = 2.02, 95% CI 1.02 - 4.01, P = 0.045). CONCLUSION: Poor health status is a common cause of hospitalisation for new TB patients. Mortality in this study was very high and associated with advanced HIV Disease and no use of ART

Natural selection favoring more transmissible HIV detected in United States molecular transmission network.

HIV molecular epidemiology can identify clusters of individuals with elevated rates of HIV transmission. These variable transmission rates are primarily driven by host risk behavior; however, the effect of viral traits on variable transmission rates is poorly understood. Viral load, the concentration of HIV in blood, is a heritable viral trait that influences HIV infectiousness and disease progression. Here, we reconstruct HIV genetic transmission clusters using data from the United States National HIV Surveillance System and report that viruses in clusters, inferred to be frequently transmitted, have higher viral loads at diagnosis. Further, viral load is higher in people in larger clusters and with increased network connectivity, suggesting that HIV in the United States is experiencing natural selection to be more infectious and virulent. We also observe a concurrent increase in viral load at diagnosis over the last decade. This evolutionary trajectory may be slowed by prevention strategies prioritized toward rapidly growing transmission clusters

Anatomic Fat Depots and Coronary Plaque Among Human Immunodeficiency Virus-Infected and Uninfected Men in the Multicenter AIDS Cohort Study.

Methods.  In a cross-sectional substudy of the Multicenter AIDS Cohort Study, noncontrast cardiac computed tomography (CT) scanning for coronary artery calcium (CAC) scoring was performed on all men, and, for men with normal renal function, coronary CT angiography (CTA) was performed. Associations between fat depots (visceral adipose tissue [VAT], abdominal subcutaneous adipose tissue [aSAT], and thigh subcutaneous adipose tissue [tSAT]) with coronary plaque presence and extent were assessed with logistic and linear regression adjusted for age, race, cardiovascular disease (CVD) risk factors, body mass index (BMI), and human immunodeficiency virus (HIV) parameters. Results.  Among HIV-infected men (n = 597) but not HIV-uninfected men (n = 343), having greater VAT was positively associated with noncalcified plaque presence (odds ratio [OR] = 1.04, P < .05), with a significant interaction (P < .05) by HIV serostatus. Human immunodeficiency virus-infected men had lower median aSAT and tSAT and greater median VAT among men with BMI <25 and 25-29.9 kg/m(2). Among HIV-infected men, VAT was positively associated with presence of coronary plaque on CTA after adjustment for CVD risk factors (OR = 1.04, P < .05), but not after additional adjustment for BMI. There was an inverse association between aSAT and extent of total plaque among HIV-infected men, but not among HIV-uninfected men. Lower tSAT was associated with greater CAC and total plaque score extent regardless of HIV serostatus. Conclusions.  The presence of greater amounts of VAT and lower SAT may contribute to increased risk for coronary artery disease among HIV-infected persons

Longitudinal sequencing of HIV-1 infected patients with low-level viremia for years while on ART shows no indications for genetic evolution of the virus

HIV-infected patients on antiretroviral therapy (ART) may present low-level viremia (LLV) above the detection level of current viral load assays. In many cases LLV is persistent but does not result in overt treatment failure or selection of drug resistant viral variants. To elucidate whether LLV reflects active virus replication, we extensively sequenced pol and env genes of the viral populations present before and during LLV in 18 patients and searched for indications of genetic evolution. Maximum likelihood phylogenetic trees were inspected for temporal structure both visually and by linear regression analysis of root-to-tip and pairwise distances. Viral coreceptor tropism was assessed at different time points before and during LLV. In none of the patients consistent indications for genetic evolution were found over a median period of 4.8 years of LLV. As such these findings could not provide evidence that active virus replication is the main driver of LLV