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1. Learning Robust Multi-label Sample Specific Distances for Identifying HIV-1 Drug Resistance

   https://doi.org/10.1007/978-3-030-17083-7_4  

   Brand, Lodewijk ; Yang, Xue ; Liu, Kai ; Elbeleidy, Saad ; Wang, Hua ; Zhang, Hao ( April 2019 , The Proceedings of the 23rd Annual International Conference on Research in Computational Molecular Biology (RECOMB 2019))

   Acquired immunodeficiency syndrome (AIDS) is a syndrome caused by the human immunodeficiency virus (HIV). During the progression of AIDS, a patient’s the immune system is weakened, which increases the patient’s susceptibility to infections and diseases. Although antiretroviral drugs can effectively suppress HIV, the virus mutates very quickly and can become resistant to treatment. In addition, the virus can also become resistant to other treatments not currently being used through mutations, which is known in the clinical research community as cross-resistance. Since a single HIV strain can be resistant to multiple drugs, this problem is naturally represented as a multi-label classification problem. Given this multi-class relationship, traditional single-label classification methods usually fail to effectively identify the drug resistances that may develop after a particular virus mutation. In this paper, we propose a novel multi-label Robust Sample Specific Distance (RSSD) method to identify multi-class HIV drug resistance. Our method is novel in that it can illustrate the relative strength of the drug resistance of a reverse transcriptase sequence against a given drug nucleoside analogue and learn the distance metrics for all the drug resistances. To learn the proposed RSSDs, we formulate a learning objective that maximizes the ratio of the summations of a number of ℓ1-norm distances, which is difficult to solve in general. To solve this optimization problem, we derive an efficient, non-greedy, iterative algorithm with rigorously proved convergence. Our new method has been verified on a public HIV-1 drug resistance data set with over 600 RT sequences and five nucleoside analogues. We compared our method against other state-of-the-art multi-label classification methods and the experimental results have demonstrated the effectiveness of our proposed method. 

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2. A Bayesian nonparametric approach for inferring drug combination effects on mental health in people with HIV

   https://doi.org/10.1111/biom.13508  

   Jin, Wei ; Ni, Yang ; Rubin, Leah H. ; Spence, Amanda B. ; Xu, Yanxun ( July 2021 , Biometrics)

   Although combination antiretroviral therapy (ART) with three or more drugs is highly effective in suppressing viral load for people with HIV (human immunodeficiency virus), many ART agents may exacerbate mental health‐related adverse effects including depression. Therefore, understanding the effects of combination ART on mental health can help clinicians personalize medicine with less adverse effects to avoid undesirable health outcomes. The emergence of electronic health records offers researchers' unprecedented access to HIV data including individuals' mental health records, drug prescriptions, and clinical information over time. However, modeling such data is challenging due to high dimensionality of the drug combination space, the individual heterogeneity, and sparseness of the observed drug combinations. To address these challenges, we develop a Bayesian nonparametric approach to learn drug combination effect on mental health in people with HIV adjusting for sociodemographic, behavioral, and clinical factors. The proposed method is built upon the subset‐tree kernel that represents drug combinations in a way that synthesizes known regimen structure into a single mathematical representation. It also utilizes a distance‐dependent Chinese restaurant process to cluster heterogeneous populations while considering individuals' treatment histories. We evaluate the proposed approach through simulation studies, and apply the method to a dataset from the Women's Interagency HIV Study, showing the clinical utility of our model in guiding clinicians to prescribe informed and effective personalized treatment based on individuals' treatment histories and clinical characteristics.

    

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3. Elucidating molecular interactions of L-nucleotides with HIV-1 reverse transcriptase and mechanism of M184V-caused drug resistance

   https://doi.org/10.1038/s42003-019-0706-x  

   Hung, Magdeleine ; Tokarsky, E. John ; Lagpacan, Leanna ; Zhang, Lijun ; Suo, Zucai ; Lansdon, Eric B. ( December 2019 , Communications Biology)

   Emtricitabine (FTC) and lamivudine (3TC), containing an oxathiolane ring with unnatural (−)-stereochemistry, are widely used nucleoside reverse transcriptase inhibitors (NRTIs) in anti-HIV therapy. Treatment with FTC or 3TC primarily selects for the HIV-1 RT M184V/I resistance mutations. Here we provide a comprehensive kinetic and structural basis for inhibiting HIV-1 RT by (−)-FTC-TP and (−)-3TC-TP and drug resistance by M184V. (−)-FTC-TP and (−)-3TC-TP have higher binding affinities (1/K_d) for wild-type RT but slower incorporation rates than dCTP. HIV-1 RT ternary crystal structures with (−)-FTC-TP and (−)-3TC-TP corroborate kinetic results demonstrating that their oxathiolane sulfur orients toward the DNA primer 3′-terminus and their triphosphate exists in two different binding conformations. M184V RT displays greater (>200-fold)K_dfor theL-nucleotides and moderately higher (>9-fold)K_dfor theD-isomers compared to dCTP. The M184V RT structure illustrates how the mutation repositions the oxathiolane of (−)-FTC-TP and shifts its triphosphate into a non-productive conformation.

    

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4. Spatial-Temporal Networks for Antibiogram Pattern Prediction

   https://doi.org/10.1109/ICHI57859.2023.00039  

   Fu, Xingbo ; Chen, Chen ; Dong, Yushun ; Vullikanti, Anil ; Klein, Eili ; Madden, Gregory ; Li, Jundong ( June 2023 , IEEE)

   An antibiogram is a periodic summary of antibiotic resistance results of organisms from infected patients to selected antimicrobial drugs. Antibiograms help clinicians to understand regional resistance rates and select appropriate antibiotics in prescriptions. In practice, significant combinations of antibiotic resistance may appear in different antibiograms, forming antibiogram patterns. Such patterns may imply the prevalence of some infectious diseases in certain regions. Thus it is of crucial importance to monitor antibiotic resistance trends and track the spread of multi-drug resistant organisms. In this paper, we propose a novel problem of antibiogram pattern prediction that aims to predict which patterns will appear in the future. Despite its importance, tackling this problem encounters a series of challenges and has not yet been explored in the literature. First of all, antibiogram patterns are not i.i.d as they may have strong relations with each other due to genomic similarities of the underlying organisms. Second, antibiogram patterns are often temporally dependent on the ones that are previously detected. Furthermore, the spread of antibiotic resistance can be significantly influenced by nearby or similar regions. To address the above challenges, we propose a novel Spatial-Temporal Antibiogram Pattern Prediction framework, STAPP, that can effectively leverage the pattern correlations and exploit the temporal and spatial information. We conduct extensive experiments on a real-world dataset with antibiogram reports of patients from 1999 to 2012 for 203 cities in the United States. The experimental results show the superiority of STAPP against several competitive baselines. 

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5. Disrupting the ArcA Regulatory Network Amplifies the Fitness Cost of Tetracycline Resistance in Escherichia coli

   https://doi.org/10.1128/msystems.00904-22  

   Arrieta-Ortiz, Mario L. ; Pan, Min ; Kaur, Amardeep ; Pepper-Tunick, Evan ; Srinivas, Vivek ; Dash, Ananya ; Immanuel, Selva Rupa ; Brooks, Aaron N. ; Shepherd, Tyson R. ; Baliga, Nitin S. ( February 2023 , mSystems)

   Oliveira, Pedro H. (Ed.)

   ABSTRACT There is an urgent need for strategies to discover secondary drugs to prevent or disrupt antimicrobial resistance (AMR), which is causing >700,000 deaths annually. Here, we demonstrate that tetracycline-resistant (Tet R ) Escherichia coli undergoes global transcriptional and metabolic remodeling, including downregulation of tricarboxylic acid cycle and disruption of redox homeostasis, to support consumption of the proton motive force for tetracycline efflux. Using a pooled genome-wide library of single-gene deletion strains, at least 308 genes, including four transcriptional regulators identified by our network analysis, were confirmed as essential for restoring the fitness of Tet R E. coli during treatment with tetracycline. Targeted knockout of ArcA, identified by network analysis as a master regulator of this new compensatory physiological state, significantly compromised fitness of Tet R E. coli during tetracycline treatment. A drug, sertraline, which generated a similar metabolome profile as the arcA knockout strain, also resensitized Tet R E. coli to tetracycline. We discovered that the potentiating effect of sertraline was eliminated upon knocking out arcA , demonstrating that the mechanism of potential synergy was through action of sertraline on the tetracycline-induced ArcA network in the Tet R strain. Our findings demonstrate that therapies that target mechanistic drivers of compensatory physiological states could resensitize AMR pathogens to lost antibiotics. IMPORTANCE Antimicrobial resistance (AMR) is projected to be the cause of >10 million deaths annually by 2050. While efforts to find new potent antibiotics are effective, they are expensive and outpaced by the rate at which new resistant strains emerge. There is desperate need for a rational approach to accelerate the discovery of drugs and drug combinations that effectively clear AMR pathogens and even prevent the emergence of new resistant strains. Using tetracycline-resistant (Tet R ) Escherichia coli , we demonstrate that gaining resistance is accompanied by loss of fitness, which is restored by compensatory physiological changes. We demonstrate that transcriptional regulators of the compensatory physiologic state are promising drug targets because their disruption increases the susceptibility of Tet R E. coli to tetracycline. Thus, we describe a generalizable systems biology approach to identify new vulnerabilities within AMR strains to rationally accelerate the discovery of therapeutics that extend the life span of existing antibiotics. 

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