Distinguishable topology of the task-evoked functional genome networks in HIV-1 reservoirs
Janusz Wiśniewski,
Kamil Więcek,
Haider Ali,
Krzysztof Pyrc,
Anna Kula-Păcurar,
Marek Wagner,
Heng-Chang Chen
Affiliations
Janusz Wiśniewski
Quantitative Virology Research Group, Population Diagnostics Center, Łukasiewicz Research Network – PORT Polish Center for Technology Development, Stabłowicka 147, 54-066 Wrocław, Poland
Kamil Więcek
Quantitative Virology Research Group, Population Diagnostics Center, Łukasiewicz Research Network – PORT Polish Center for Technology Development, Stabłowicka 147, 54-066 Wrocław, Poland
Haider Ali
Molecular Virology Group, Małopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7A str, 30-387 Kraków, Poland; Doctoral School of Exact and Natural Sciences, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland
Krzysztof Pyrc
Virogenetics Laboratory of Virology, Małopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7A str, 30-387 Kraków, Poland
Anna Kula-Păcurar
Molecular Virology Group, Małopolska Centre of Biotechnology, Jagiellonian University, Gronostajowa 7A str, 30-387 Kraków, Poland
Marek Wagner
Innate Immunity Research Group, Life Sciences and Biotechnology Center, Łukasiewicz Research Network – PORT Polish Center for Technology Development, Stabłowicka 147, 54-066 Wrocław, Poland
Heng-Chang Chen
Quantitative Virology Research Group, Population Diagnostics Center, Łukasiewicz Research Network – PORT Polish Center for Technology Development, Stabłowicka 147, 54-066 Wrocław, Poland; Corresponding author
Summary: HIV-1 reservoirs display a heterogeneous nature, lodging both intact and defective proviruses. To deepen our understanding of such heterogeneous HIV-1 reservoirs and their functional implications, we integrated basic concepts of graph theory to characterize the composition of HIV-1 reservoirs. Our analysis revealed noticeable topological properties in networks, featuring immunologic signatures enriched by genes harboring intact and defective proviruses, when comparing antiretroviral therapy (ART)-treated HIV-1-infected individuals and elite controllers. The key variable, the rich factor, played a pivotal role in classifying distinct topological properties in networks. The host gene expression strengthened the accuracy of classification between elite controllers and ART-treated patients. Markov chain modeling for the simulation of different graph networks demonstrated the presence of an intrinsic barrier between elite controllers and non-elite controllers. Overall, our work provides a prime example of leveraging genomic approaches alongside mathematical tools to unravel the complexities of HIV-1 reservoirs.
