Proteo-Transcriptomic Dynamics of Cellular Response to HIV-1 Infection

Golumbeanu, Monica; Desfarges, Sébastien; Hernandez, Céline; Quadroni, Manfredo; Rato, Sylvie; Mohammadi, Pejman; Telenti, Amalio; Beerenwinkel, Niko; Ciuffi, Angela

doi:10.1038/s41598-018-36135-3

Cited by 26 publications

(26 citation statements)

References 65 publications

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“…A new genome-wide method is presented to identify genes with bimodal patterns of expression using GMM analysis [14] for the stratification of samples. GMM has been previously used in the analysis of gene expression data [23][24][25] but to our knowledge this is the first application of such a method for the identification of genes with bimodal expression patterns.…”

Section: Discussionmentioning

confidence: 99%

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A new genome-wide method to identify genes with bimodal gene expression

Justino

Reis²,

Fonseca³

et al. 2020

Preprint

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A new method is presented to detect bimodality in gene expression data using the Gaussian Mixture Models to cluster samples in each mode. We have used the method to search for bimodal genes in data from 25 tumor types available from The Cancer Genome Atlas. The method identified 554 genes with bimodal gene expression, of which 46 were identified in more than one cancer type. To further illustrate the impact of the method, we show that 96 out of the 554 genes with bimodal expression patterns presented different prognosis when patients belonging to the two expression peaks are compared. The software to execute the method and the corresponding documentation are available at https://github.com/LabBiosystemUFRN/Bimodality_Genes.

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Section: Discussionmentioning

confidence: 99%

“…The computational pipeline to execute the method is freely available at chromosome microchimerism in the endometrium during endometriosis and in cases of infertility. Reprod Biol Endocrinol 17, (2019). doi.org/10.1186/s12958-019-0465-z…”

Section: Discussionmentioning

confidence: 99%

A new genome-wide method to identify genes with bimodal gene expression

Justino

Reis²,

Fonseca³

et al. 2020

Preprint

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“…Most of these studies are focused on identifying differentially expressed proteins (DEPs) between the same conditions as those in the transcriptomics studies (see above); however, some of the studies have features unique to proteomics experiments. For example, Golumbeanu M. and colleagues measured both the proteomics and transcriptomics responses to HIV-1 infection in SupT1 CD4+ T cells at five time points (Golumbeanu et al, 2019). The simultaneous assessment of changes in the transcriptome and proteome under the same cells and other experimental conditions is extremely important for the building of in silico integrative models that provide the most accurate results.…”

Section: Proteomics Datamentioning

confidence: 99%

Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction

Ivanov

Lagunin

Filimonov

et al. 2020

Preprint

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The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV-1-host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV-host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV-human protein-protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies may represent a basis for developing new anti-HIV therapeutic strategies such as new small-molecule drugs preventing infection of CD4+ cells and viral replication, effective vaccines, "shock and kill" and "block and lock" approaches to cure latent infection.

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“…Recently, Golumbeanu et al utilized a proteo-transcriptomic approach, in combination with a Gaussian mixed-effects model, to investigate time-series patterns of gene expression. They analysed both PPIs directions, virus and host, proposing a more detailed view of host response to HIV infection, as well as the first step to understand regulatory mechanisms involved in it [77] (Table A1). Understanding the mechanisms underlying sepsis and/or resistance will enable us to predict severity and outcome of the infection [78,79].…”

Section: Modeling the Immune Systemmentioning

confidence: 99%

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

2019

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Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host’s immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.

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Proteo-Transcriptomic Dynamics of Cellular Response to HIV-1 Infection

Cited by 26 publications

References 65 publications

A new genome-wide method to identify genes with bimodal gene expression

A new genome-wide method to identify genes with bimodal gene expression

Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction

Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

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