BackgroundBiological interpretation of gene/protein lists resulting from -omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns. Unfortunately, there is a gap between machine-readable output of GO software and its human-interpretable form. This gap can be bridged by allowing users to simultaneously visualize and interact with term-term and gene-term relationships.ResultsWe created the open-source GOnet web-application (available at http://tools.dice-database.org/GOnet/), which takes a list of gene or protein entries from human or mouse data and performs GO term annotation analysis (mapping of provided entries to GO subsets) or GO term enrichment analysis (scanning for GO categories overrepresented in the input list). The application is capable of producing parsable data formats and importantly, interactive visualizations of the GO analysis results. The interactive results allow exploration of genes and GO terms as a graph that depicts the natural hierarchy of the terms and retains relationships between terms and genes/proteins. As a result, GOnet provides insight into the functional interconnection of the submitted entries.ConclusionsThe application can be used for GO analysis of any biological data sources resulting in gene/protein lists. It can be helpful for experimentalists as well as computational biologists working on biological interpretation of -omics data resulting in such lists.

BackgroundThe three-dimensional organization of the genome is tightly connected to its biological function. The Hi-C approach was recently introduced as a method that can be used to identify higher-order chromatin interactions genome-wide. The aim of this study was to determine genome-wide chromatin interaction frequencies using the Hi-C approach in mouse sperm cells and embryonic fibroblasts.ResultsThe obtained data demonstrate that the three-dimensional genome organizations of sperm and fibroblast cells show a high degree of similarity both with each other and with the previously described mouse embryonic stem cells. Both A- and B-compartments and topologically associated domains are present in spermatozoa and fibroblasts. Nevertheless, sperm cells and fibroblasts exhibit statistically significant differences between each other in the contact probabilities of defined loci. Tight packaging of the sperm genome results in an enrichment of long-range contacts compared with the fibroblasts. However, only 30% of the differences in the number of contacts are based on differences in the densities of their genome packages; the main source of the differences is the gain or loss of contacts that are specific for defined genome regions. We find that the dependence of the contact probability on genomic distance for sperm is close to the dependence predicted for the fractal globular folding of chromatin.ConclusionsOverall, we can conclude that the three-dimensional structure of the genome is passed through generations without being dramatically changed in sperm cells.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0642-0) contains supplementary material, which is available to authorized users.

Our results highlight for the first time that a significant proportion of cell doublets in flow cytometry, previously believed to be the result of technical artifacts and thus ignored in data acquisition and analysis, are the result of biological interaction between immune cells. In particular, we show that cell:cell doublets pairing a T cell and a monocyte can be directly isolated from human blood, and high resolution microscopy shows polarized distribution of LFA1/ICAM1 in many doublets, suggesting in vivo formation. Intriguingly, T cell-monocyte complex frequency and phenotype fluctuate with the onset of immune perturbations such as infection or immunization, reflecting expected polarization of immune responses. Overall these data suggest that cell doublets reflecting T cell-monocyte in vivo immune interactions can be detected in human blood and that the common approach in flow cytometry to avoid studying cell:cell complexes should be re-visited.

CD8 T cells are considered important contributors to the immune response against Mycobacterium tuberculosis, yet limited information is currently known regarding their specific immune signature and phenotype. In this study, we applied a cell population transcriptomics strategy to define immune signatures of human latent tuberculosis infection (LTBI) in memory CD8 T cells. We found a 41-gene signature that discriminates between memory CD8 T cells from healthy LTBI subjects and uninfected controls. The gene signature was dominated by genes associated with mucosal-associated invariant T cells (MAITs) and reflected the lower frequency of MAITs observed in individuals with LTBI. There was no evidence for a conventional CD8 T cell-specific signature between the two cohorts. We, therefore, investigated MAITs in more detail based on Va7.2 and CD161 expression and staining with an MHC-related protein 1 (MR1) tetramer. This revealed two distinct populations of CD8 + Va7.2 + CD161 + MAITs: MR1 tetramer + and MR1 tetramer 2 , which both had distinct gene expression compared with memory CD8 T cells. Transcriptomic analysis of LTBI versus noninfected individuals did not reveal significant differences for MR1 tetramer + MAITs. However, gene expression of MR1 tetramer 2 MAITs showed large interindividual diversity and a tuberculosis-specific signature. This was further strengthened by a more diverse TCR-a and-b repertoire of MR1 tetramer 2 cells as compared with MR1 tetramer +. Thus, circulating memory CD8 T cells in subjects with latent tuberculosis have a reduced number of conventional MR1 tetramer + MAITs as well as a difference in phenotype in the rare population of MR1 tetramer 2 MAITs compared with uninfected controls. ImmunoHorizons, 2020, 4: 292-307.