HIV and innate immunity ? a genomics perspective

Innate immunity is a theme of increasing interest for HIV research. However, the term is overstretched to cover biological barriers, cellular systems, soluble factors, signaling pathways, and effectors and is inconsistently applied. A clearer semantic classification of the components of innate immunity is needed, which will have direct relevance to the interpretation of human genome variation. Here, we discuss genomic approaches that can assist in re-defining the perimeter of innate immunity. We place particular emphasis on the characteristics of effectors of the intracellular defense against HIV and other pathogens

Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq

Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data can be formalized under a general framework composed of (i) a metric to assess cell-to-cell similarities (with or without a dimensionality reduction step) and (ii) a graph-building algorithm (optionally making use of a cell clustering step). The Sincell R package implements a methodological toolbox allowing flexible workflows under such a framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. The functionalities of Sincell are illustrated in a real case study, which demonstrates its ability to discriminate noisy from stable cell-state hierarchies. AVAILABILITY AND IMPLEMENTATION: Sincell is an open-source R/Bioconductor package available at http://bioconductor.org/packages/sincell. A detailed manual and a vignette are provided with the package. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

HIV and innate immunity ? a genomics perspective

Innate immunity is a theme of increasing interest for HIV research. However, the term is overstretched to cover biological barriers, cellular systems, soluble factors, signaling pathways, and effectors and is inconsistently applied. A clearer semantic classification of the components of innate immunity is needed, which will have direct relevance to the interpretation of human genome variation. Here, we discuss genomic approaches that can assist in re-defining the perimeter of innate immunity. We place particular emphasis on the characteristics of effectors of the intracellular defense against HIV and other pathogens

On genomics, kin, and privacy

The storage of greater numbers of exomes or genomes raises the question of loss of privacy for the individual and for families if genomic data are not properly protected. Access to genome data may result from a personal decision to disclose, or from gaps in protection. In either case, revealing genome data has consequences beyond the individual, as it compromises the privacy of family members. Increasing availability of genome data linked or linkable to metadata through online social networks and services adds one additional layer of complexity to the protection of genome privacy.  The field of computer science and information technology offers solutions to secure genomic data so that individuals, medical personnel or researchers can access only the subset of genomic information required for healthcare or dedicated studies

Exploring viral infection using single-cell sequencing.

Single-cell sequencing (SCS) has emerged as a valuable tool to study cellular heterogeneity in diverse fields, including virology. By studying the viral and cellular genome and/or transcriptome, the dynamics of viral infection can be investigated at single cell level. Most studies have explored the impact of cell-to-cell variation on the viral life cycle from the point of view of the virus, by analyzing viral sequences, and from the point of view of the cell, mainly by analyzing the cellular host transcriptome. In this review, we will focus on recent studies that use single-cell sequencing to explore viral diversity and cell variability in response to viral replication

Innate immune defects in HIV permissive cell lines.

Primary CD4+ T cells and cell lines differ in their permissiveness to HIV infection. Impaired innate immunity may contribute to this different phenotype. We used transcriptome profiling of 1503 innate immunity genes in primary CD4+ T cells and permissive cell lines. Two clusters of differentially expressed genes were identified: a set of 249 genes that were highly expressed in primary cells and minimally expressed in cell lines and a set of 110 genes with the opposite pattern. Specific to HIV, HEK293T, Jurkat, SupT1 and CEM cell lines displayed unique patterns of downregulation of genes involved in viral sensing and restriction. Activation of primary CD4+ T cells resulted in reversal of the pattern of expression of those sets of innate immunity genes. Functional analysis of prototypical innate immunity pathways of permissive cell lines confirmed impaired responses identified in transcriptome analyses. Integrity of innate immunity genes and pathways needs to be considered in designing gain/loss functional genomic screens of viral infection

Analysis of stop-gain and frameshift variants in human innate immunity genes.

Loss-of-function variants in innate immunity genes are associated with Mendelian disorders in the form of primary immunodeficiencies. Recent resequencing projects report that stop-gains and frameshifts are collectively prevalent in humans and could be responsible for some of the inter-individual variability in innate immune response. Current computational approaches evaluating loss-of-function in genes carrying these variants rely on gene-level characteristics such as evolutionary conservation and functional redundancy across the genome. However, innate immunity genes represent a particular case because they are more likely to be under positive selection and duplicated. To create a ranking of severity that would be applicable to innate immunity genes we evaluated 17,764 stop-gain and 13,915 frameshift variants from the NHLBI Exome Sequencing Project and 1,000 Genomes Project. Sequence-based features such as loss of functional domains, isoform-specific truncation and nonsense-mediated decay were found to correlate with variant allele frequency and validated with gene expression data. We integrated these features in a Bayesian classification scheme and benchmarked its use in predicting pathogenic variants against Online Mendelian Inheritance in Man (OMIM) disease stop-gains and frameshifts. The classification scheme was applied in the assessment of 335 stop-gains and 236 frameshifts affecting 227 interferon-stimulated genes. The sequence-based score ranks variants in innate immunity genes according to their potential to cause disease, and complements existing gene-based pathogenicity scores. Specifically, the sequence-based score improves measurement of functional gene impairment, discriminates across different variants in a given gene and appears particularly useful for analysis of less conserved genes