ReviewCurrent opinion in HIV and AIDS2012
Dissecting the HIV-specific immune response: a systems biology approach.
Review in Current opinion in HIV and AIDS, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Integrative single-cell transcriptomic analysis reveals immunomodulatory hub genes and candidate compounds for HIV-associated chronic inflammation.Virology journal · 2025Article
- Leucocyte count and C-reactive protein cannot be relied upon in the diagnosis of acute appendicitis in HIV-infected patients.BJS open · 2021Article
- Transcriptomics and Targeted Proteomics Analysis to Gain Insights Into the Immune-control Mechanisms of HIV-1 Infected Elite Controllers.EBioMedicine · 2018Article
- HSP70 binding protein 1 (HspBP1) suppresses HIV-1 replication by inhibiting NF-κB mediated activation of viral gene expression.Nucleic acids research · 2016Article
- Putative roles of purinergic signaling in human immunodeficiency virus-1 infection.Biology direct · 2014Article
- Systems-level analysis of innate immunity.Annual review of immunology · 2014Review
- PD-1 coinhibitory signals: the link between pathogenesis and protection.Seminars in immunology · 2013Review
- Immunopathogenesis of simian immunodeficiency virus infection in nonhuman primates.Current opinion in HIV and AIDS · 2013Review
- Innate and adaptive anti-HIV immune responses in the female reproductive tract.Journal of reproductive immunology · 2013Review
- Systems biology: personalized medicine for the future?Current opinion in pharmacology · 2012Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
purpose of reviewSeveral unique HIV-infected or HIV-resistant cohorts have been studied over the years to try and delineate the correlates of protection. Although several mechanisms have been put forward, studies aiming to integrate the different mechanisms into a comprehensive model are still lacking. Current systems biology approaches emphasize the importance of unifying independent datasets, provide tools that facilitate hypothesis formulation and testing, and direct us toward uncovering novel therapeutic targets by defining molecular networks perturbed during disease. This review will focus on the current findings that utilized systems biology techniques in order to identify correlates of protection from HIV disease progression and resistance to infection in unique cohorts of individuals as well as in nonhuman primate models of SIV infection. RECENT
findingsUsing systems biology technologies and data analysis tools, the studies described herein have found that pathways implicated in survival, cell cycling, inflammation, and oxidative stress work in unison to limit pathology caused by chronic immune activation. This situation favors the survival of effector lymphocytes and limits the dissemination of viral particles in HIV elite controllers, exposed-uninfected individuals, and natural hosts of SIV infection. SUMMARY: Systems and computational biology tools have clearly expanded our understanding of HIV pathogenesis by unifying independent observations and by giving us novel molecular targets to pursue. These molecular signatures have the potential to uncover correlates of protection in HIV disease and, in the era of personalized medicine, to determine predictive signatures of treatment efficacy and/or failure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.