HIV-1 viral load outcomes and the evolution of drug-resistance in low-income settings without virological monitoring

WHO guidelines recommend viral load monitoring for all HIV-1 positive patients on antiretroviral therapy (ART). However, few low-income countries have virological monitoring widely available, and patients may remain on virologically failing regimens. This could compromise future ART through the accumulation of drug resistance mutations and result in worse long-term clinical outcomes. The DART trial was conducted in Uganda and Zimbabwe and compared clinically driven monitoring with or without routine CD4 measurement in ART-naïve adult patients. Annual plasma viral load was retrospectively measured for 1,762 patients. This thesis investigates how no laboratory monitoring impacts virological failure and the development of drug resistance. Time to persistent virological failure was analysed, and analytical weights were calculated to correct for non-random sampling. The long-term durability of first-line ART was remarkable; 21% of patients on an NRTI-NNRTI regimen and 40% on a triple-NRTI regimen experienced persistent virological failure by 240 weeks. Routine CD4 monitoring did not reduce virological failure. Deaths after 48 weeks of ART are widely assumed to be due to virological failure or non-adherence. Analyses revealed that a surprisingly high number of these deaths (40%) occurred without virological criteria for treatment switch being met. Routine CD4 monitoring reduced the rate of death with virological failure but did not impact deaths with virological suppression. Cross-sectional analyses quantified HIV-1 drug resistance at the end of first-line ART. On NRTI-NNRTI regimens, 88% had NRTI resistance, and 66% had NNRTI resistance. Routine CD4 monitoring did not reduce the prevalence or extent of drug resistance. The order and rate of HIV-1 drug resistance mutations were explored using repeated genotypes within patients. On NRTI-NNRTI regimens, NRTI and NNRTI mutations developed at a rate of 0.96 and 0.21 per year respectively. Mutagenic tree models demonstrated that ART regimen influenced the order and rate in which mutations occurred

The Structural Basis for the Interdependence of Drug Resistance in the HIV-1 Protease

The human immunodeficiency virus type 1 (HIV-1) protease (PR) is a critical drug target as it is responsible for virion maturation. Mutations within the active site (1°) of the PR directly interfere with inhibitor binding while mutations distal to the active site (2°) to restore enzymatic fitness. Increasing mutation number is not directly proportional to the severity of resistance, suggesting that resistance is not simply additive but that it is interdependent. The interdependency of both primary and secondary mutations to drive protease inhibitor (PI) resistance is grossly understudied. To structurally and dynamically characterize the direct role of secondary mutations in drug resistance, I selected a panel of single-site mutant protease crystal structures complexed with the PI darunavir (DRV). From these studies, I developed a network hypothesis that explains how mutations outside the active site are able to perpetuate changes to the active site of the protease to disrupt inhibitor binding. I then expanded the panel to include highly mutated multi-drug resistant variants. To elucidate the interdependency between primary and secondary mutations I used statistical and machine-learning techniques to determine which specific mutations underlie the perturbations of key inter-molecular interactions. From these studies, I have determined that mutations distal to the active site are able to perturb the global PR hydrogen bonding patterns, while primary and secondary mutations cooperatively perturb hydrophobic contacts between the PR and DRV. Discerning and exploiting the mechanisms that underlie drug resistance in viral targets could proactively ameliorate both current treatment and inhibitor design for HIV-1 targets

A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. The review shows that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exists in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption of BNs; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review empowers researchers and clinicians with an analytical framework and findings that will enable understanding of the need to address the problems of restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice

HIV-1 evolution, disease progression and molecular epidemiology of HIV-1 single and HIV-1 and HIV-2 dual-infected individuals in Guinea-Bissau

The two genetically related human lentiviruses known today, HIV-1 (which is pandemic) and HIV-2 (which mainly is confined to West Africa), are the causative agents of AIDS. Progressive immune dysfunction and AIDS develop in most cases of untreated HIV-1 infection, but only in approximately 25-30% of HIV-2 infected individuals. The V1-V3 region of the HIV-1 env gp120 is important for HIV-1 coreceptor use, and represents an informative region for both molecular epidemiology and intrapatient phylogenetic analyses due to high level of genetic variation. In this doctoral dissertation, HIV-1 V1-V3 sequences in combination with clinical disease markers were used to investigate HIV-1 evolution, disease progression, coreceptor tropism and molecular epidemiology of HIV-1. All sequences were derived from single (HIV-1 only) or dual-infected (HIV-1 and HIV-2) individuals from Guinea-Bissau, West Africa. The main findings was that CRF02_AG represents the most common form of HIV-1 in Guinea-Bissau, and that HIV-1 was introduced into the country on at least six different occasions between 1976 and 1981. Dual-infected individuals had a 46% lower mortality rate and a 53% longer progression-time to AIDS compared to single-infected individuals. CD4+ T cell counts were higher at corresponding time-points after infection among dual-infected individuals, reflecting the slower disease progression rate at the cellular immune level. In addition, CD8+ T cell counts were increasing at a faster rate in single than in dual-infected individuals. Stratified analyses showed that these observations were most prominent among the subgroup of dual-infected individuals that became HIV-1 infected after an established HIV-2 infection. Moreover, the HIV-1 genetic diversity was significantly lower in dual than in single-infected individuals at comparable time-points after infection. HIV-1 coreceptor tropism was investigated in late-stage disease by the use of a recombinant virus phenotypic assay that were confirmed to accurately predict the coreceptor tropism of HIV-1 subtype A and CRF02_AG. CXCR4 tropism has been coupled to an increased HIV-1 disease progression rate in late-stage disease. We found that HIV-1 CRF02_AG CXCR4 tropism was frequent (86%) and increased over time on the population level, indicating an evolving epidemic. In addition, a literature analysis showed a similar evolving epidemic for HIV-1 subtype C. Genotypic analysis suggested that the total number of charged amino acids could be important in predicting HIV-1 CRF02_AG coreceptor tropism. Finally, HIV-1 CXCR4-tropism was more common in single (79%) than in dual-infected individuals (35%). Understanding the underlying mechanisms responsible for the inhibitory effects exerted by HIV-2 against HIV-1 could be important for the development of future HIV-1 vaccines and therapeutics

Modelling timing in blood cancers

Dysregulation of biological processes in normal cells can lead to the abnormal growth of tumours. Oncogenesis requires the acquisition of advantageous mutations to expand in a fluctuating environment. Cancer cells gain these genetic and epigenetic alterations at different timing in their development, resulting in the formation of heterogeneous cell populations which interact and compete with each others inside tumours. At later stages, by escaping the immune system and acquiring malignant properties, some cancer cells manage to evade the primary tumour and spread in different organs to form metastases. Hence, tumour development in healthy tissues endure several biological changes whilst progressing and the order between these molecular and cellular events may modify prognosis. This thesis addresses the influence of biological event timing on blood cancer progression and clinical outcomes. It first investigates the therapeutic efficacy of p53 restoration in a lymphoma mouse model. While several therapy schedules are tested, all fail due to resistance emergence. Computational modelling establishes the cell dynamics in these tumours and how to use it to propose alternative treatment strategies. Data availability leads this work to explore the impact of molecular evolution in myeloid malignancies. Notably, one study has found that Myeloproliferative Neoplasms patients with both JAK2 and TET2 mutations have different disease characteristics with distinct mutation order. My analyses identify HOXA9 as a potential prognosis marker and biological switch responsible for patient stratification in these patients and in Acute Myeloid Leukemia. Additionally, a molecular network identifies the hematopoietic regulators involved in the branching evolution of Myeloproliferative Neoplasms. Further investigations of the Acute Myeloid Leukemia data show the possible involvement of APP, a gene associated to Alzheimer disease, in early cell fate commitment in hematopoiesis and in poor survival prognosis in undifferentiated leukemia when lowly expressed. Finally, this thesis examines the regulatory dynamics behind three clusters of Acute Myeloid Leukemia patients with distinct levels of HOXA9 and APP expression. By building a program inferring molecular motifs from biological observations, genes which may interact with HOXA9 and APP are identified.Microsoft Research and the MRC Cancer Unit

Going viral : an integrated view on virological data analysis from basic research to clinical applications

Viruses are of considerable interest for several fields of life science research. The genomic richness of these entities, their environmen- tal abundance, as well as their high adaptability and, potentially, pathogenicity make treatment of viral diseases challenging. This thesis proposes three novel contributions to antiviral research that each concern analysis procedures of high-throughput experimen- tal genomics data. First, a sensitive approach for detecting viral genomes and transcripts in sequencing data of human cancers is presented that improves upon prior approaches by allowing de- tection of viral nucleotide sequences that consist of human-viral homologs or are diverged from known reference sequences. Sec- ond, a computational method for inferring physical protein contacts from experimental protein complex purification assays is put for- ward that allows statistically meaningful integration of multiple data sets and is able to infer protein contacts of transiently binding protein classes such as kinases and molecular chaperones. Third, an investigation of minute changes in viral genomic populations upon treatment of patients with the mutagen ribavirin is presented that first characterizes the mutagenic effect of this drug on the hepatitis C virus based on deep sequencing data.Viren sind von beträchtlichem Interesse für die biowissenschaftliche Forschung. Der genetische Reichtum, die hohe Vielfalt, wie auch die Anpassungsfähigkeit und mögliche Pathogenität dieser Organismen erschwert die Behandlung von viralen Erkrankungen. Diese Promotionsschrift enthält drei neuartige Beiträge zur antiviralen Forschung welche die Analyse von experimentellen Hochdurchsatzdaten der Genomik betreffen: erstens, ein sensitiver Ansatz zur Entdeckung viraler Genome und Transkripte in Sequenzdaten humaner Karzinome, der die Identifikation von viralen Nukleotidsequenzen ermöglicht, die von Referenzgenomen ab- weichen oder homolog zu humanen Faktoren sind. Zweitens, eine computergestützte Methode um physische Proteinkontakte von experimentellen Proteinkomplex-Purifikationsdaten abzuleiten welche die statistische Integration von mehreren Datensätzen erlaubt um insbesondere Proteinkontakte von flüchtig interagierenden Proteinklassen wie etwa Kinasen und Chaperonen aus den Daten ableiten zu können. Drittens, eine Untersuchung von kleinsten Änderungen viraler Genompopulationen während der Behandlung von Patienten mit dem Mutagen ribavirin die zum ersten Mal die mutagene Wirkung dieses Medikaments auf das Hepatitis C Virus mittels Tiefensequenzdaten nachweist

Machine-learning-based identification of factors that influence molecular virus-host interactions

Viruses are the cause of many infectious diseases such as the pandemic viruses: acquired immune deficiency syndrome (AIDS) and coronavirus disease 2019 (COVID-19). During the infection cycle, viruses invade host cells and trigger a series of virus-host interactions with different directionality. Some of these interactions disrupt host immune responses or promote the expression of viral proteins and exploitation of the host system thus are considered ‘pro-viral’. Some interactions display ‘pro-host’ traits, principally the immune response, to control or inhibit viral replication. Concomitant pro-viral and pro-host molecular interactions on the same host molecule suggests more complex virus-host conflicts and genetic signatures that are crucial to host immunity. In this work, machinelearning-based prediction of virus-host interaction directionality was examined by using data from Human immunodeficiency virus type 1 (HIV-1) infection. Host immune responses to viral infections are mediated by interferons(IFNs) in the initial stage of the immune response to infection. IFNs induce the expression of many IFN-stimulated genes (ISGs), which make the host cell refractory to further infection. We propose that there are many features associated with the up-regulation of human genes in the context of IFN-α stimulation. They make ISGs predictable using machine-learning models. In order to overcome the interference of host immune responses for successful replication, viruses adopt multiple strategies to avoid being detected by cellular sensors in order to hijack the machinery of host transcription or translation. Here, the strategy of mimicry of host-like short linear motifs (SLiMs) by the virus was investigated by using the example of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The integration of in silico experiments and analyses in this thesis demonstrates an interactive and intimate relationship between viruses and their hosts. Findings here contribute to the identification of host dependency and antiviral factors. They are of great importance not only to the ongoing COVID-19 pandemic but also to the understanding of future disease outbreaks