Prediktivno modeliranje retroviralnih integracija virusa HIV-1 u aktivirane CD4+ T stanice

master's thesis

• Title (english): Predictive modelling of retroviral HIV-1 integration in activated CD4+ T cells
• Author: Moreno Martinović
• Mentor: Gordan Lauc (mentor)
• Committee member: Gordan Lauc (predsjednik povjerenstva)
• Committee member: Gordana Maravić Vlahoviček (član povjerenstva)
• Committee member: Kristian Vlahoviček (član povjerenstva)
• Granter: University of Zagreb; Faculty of Pharmacy and Biochemistry; (Department of biochemistry and molecular biology); Zagreb
• Defense date and country: 2020-09-16, Croatia
• Scientific / art field, discipline and subdiscipline: BIOMEDICINE AND HEALTHCARE; Pharmacy; Medical Biochemistry

Abstract

Virus humane imunodeficijencije 1 (HIV-1) je uzročnik stečenog sindroma imunodeficijencije kod ljudi. Unatoč
uspjesima antiretroviralne terapije, perzistencija virusa uslijed uspostave transkripcijske latencije i dalje predstavlja
barijeru prema izlječenju. Integracija HIV-1 u ljudski genom je složena međuigra virusa, kromatina stanice domaćina i
nuklearne organizacije na koju utječe mnogo čimbenika. HIV-1 integrira aktivno prepisujuće gene bogate aktivnim
histonskim oznakama i prostorno povezane odjeljke genoma s više super-pojačivača. Metode strojnog učenja su dobar
izbor za analizu takvih problema jer omogućuju istraživanje velikog broja čimbenika, mogu pronaći složene veze i
uzorke u velikim biološkim skupovima podataka i pružiti uvid u relativnu značajnost čimbenika. Model razvijen u
ovom radu postiže dobar učinak diskriminacije gena koje HIV-1 rekurentno integrira, potvrđuje dosadašnje spoznaje i
kvantificira koliko prostorni podaci doprinose mogućnosti predviđanja integracija u odnosu na gensku ekspresiju,
epigenetske modifikacije i stanje kromatina. Analizom značajnosti varijabli permutacijskim testom, aktivne histonske
oznake (H3K27ac i H3K36me3 unutar gena i H3K4me3 oko mjesta početka transkripcije) i razina genske ekspresije
su određene kao glavni faktori diskriminacije takvih gena u odnosu na gene bez integracija. Analizom površina ispod
ROC krivulja, ustanovljeno je da prostorna podjela genoma u odjeljke i lokalizacija super-pojačivača unutar istih,
neovisno o histonskim oznakama, značajno doprinose diskriminaciji (p < 0.05). Također je primijećeno da isključenje
podataka o genskoj ekspresiji iz modela ne uzrokuje gubitak informacija kakav bi se očekivao na temelju relativne
značajnosti određene permutacijskim testom. Računalne metode i prediktivno modeliranje dovode do konzistentnih
zaključaka i koristan su alat za proučavanje retroviralne integracije. Slične metode bi se mogle primijeniti u različitim
fazama infekcije kako bi se potencijalno otkrile razlike u mehanizmu odabira integracijskih mjesta i objasnila
perzistencija HIV-1 i nastanak latentnog spremnika, što bi moglo biti primjenjivo u razvoju antiretroviralne terapije.
Također, ovdje razvijene i opisane računalne analitičke metode mogu biti korisne za daljnje istraživanje mnogih
aspekata retroviralne DNA integracije i drugih bioloških problema.

Abstract (english)

Human immunodeficiency virus (HIV-1) is responsible for acquired immune deficiency syndrome in humans. Despite
the advances of antiretroviral therapy, persistence of the virus due to establishment of transcriptional latency still
remains a barrier for the cure. HIV-1 integration presents as a complex interplay between the virus, cell chromatin and
nuclear organisation and is influenced by a myriad of factors. HIV-1 preferentially integrates actively transcribed
genes rich in active histone marks and spatially connected genomic compartments with more super-enhancers.
Machine learning methods are a good choice for analyses of such problems as they allow for an exploration of a
multitude of factors and they can discern complex patterns in big biological datasets, as well as provide insight into
relative importance of factors. Model developed in this paper achieves good discrimination of recurrently integrated
genes, confirms previous findings and quantifies the importance of spatial data in predicting HIV-1 integrations
compared to gene expression, histone modifications and chromatin state. Variable importance analysis, using a
permutation test, determines higher gene expression levels and active histone marks (H3K27ac and H3K36me3 in
gene bodies, and H3K4me3 in transcription start site neighbourhood) as main factors in discrimination of recurrently
integrated genes from genes without integrations. ROC curve analysis reveals that the spatial division of genome into
compartments and localisation of super-enhancers inside them significantly contribute to discrimination (p < 0.05),
independently of histone marks and gene expression. It is also found that removal of gene expression data from the
model does not lead to information loss that would be expected from relative importances determined by the
permutation test. Computational methods and predictive modeling lead to consistent conclusions and provide useful
tools for retroviral integration study. Similar methods could be used in order to infer differences in mechanisms of
integration site selection in different infection phases and clarify the formation of a latent HIV-1 reservoir, which
could subsequently be used to advance antiretroviral therapy development. Also, herein developed and described
computational analysis methods can be useful for further research of many aspects of retroviral DNA integrations, as
well as other biological problems.

• Keywords: retroviralna integracija; hiv-1 spremnik; latentna infekcija; genomski odjeljci; super-pojačivači; histonske modifikacije
• Keywords (english): retroviral integration; HIV-1 reservoir; latent infection; genomic compartments; super-enhancers; histone modifications
• Language: croatian
• URN:NBN: urn:nbn:hr:163:506704
• Study programme: Title: Pharmacy; Study programme type: university; Study level: integrated undergraduate and graduate; Academic / professional title: magistar/magistra farmacije (magistar/magistra farmacije)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2021-05-13 12:34:54