Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/046607

In Silico method for identification of MHC class I-like molecules in whole genomes

Peter Reinink·Ildiko van Rhijn
View original source ↗Export metadata ↓

Abstract

Mining of genomic data is a valuable tool for the discovery of orthologous genes of close or distant relatives of humans and mice. Here we describe a standardized search method for the MHC class I-like molecules CD1 and MR1 and apply it to 18 mammalian genomes.

Explore related subjects

ImmunologyPreprint

Keep this discovery

BibTeXRIS

Peter Reinink, Ildiko van Rhijn. 2016-03-31. In Silico method for identification of MHC class I-like molecules in whole genomes. https://doi.org/10.1101/046607

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2016

ImSig: A resource for the identification and quantification of immune signatures in blood and tissue transcriptomics data

The outcome of many diseases is commonly correlated with the immune response at the site of pathology. The ability to monitor the status of the immune system in situ provides a mechanistic understanding of disease progression, a prognostic assessment and a guide for therapeutic intervention. Global transcriptomic data can be deconvoluted to provide an indication of the cell types present and their activation state, but the gene signatures proposed to date are either disease-specific or have been derived from data generated from isolated cell populations. Here we describe an improved set of immune gene signatures, ImSig, derived based on their co-expression in blood and tissue datasets. ImSig includes validated lists of marker genes for the main immune cell types and a number of core pathways. When used in combination with network analysis, ImSig is an accurate and easy to use approach for monitoring immune phenotypes in transcriptomic data derived from clinical samples.

Immunology↗
bioRxiv2016

Biophysical Attributes of CpG Presentation Control TLR9 Signaling to Differentially Polarize Systemic Immune-Responses

It is currently unknown whether and how mammalian pathogen-recognition receptors (PRR) respond to biophysical patterns of pathogen-associated molecular danger-signals. Using synthetic pathogen-like particles (PLPs) that mimic physical properties of bacteria or large-viruses, we have discovered that the quality and quantity of Toll-like-receptor-9 (TLR9)-signaling by CpG in mouse dendritic cells (mDC) is uniquely dependent on biophysical attributes, specifically the surface-density of CpG and size of the presenting PLP. These physical patterns control DC-programming by regulating kinetics and magnitude of MyD88-IRAK4 signaling, NF{kappa}B-driven responses, and STAT3 phosphorylation, which in turn controls differential T cell responses and in vivo immune-polarization, especially T-helper 1 (Th1) versus T-helper 2 (Th2) antibody responses. Our findings suggest that innate immune cells can sense and respond not only to molecular, but also pathogen-associated physical patterns (PAPPs), broadening the tools for modulating immunity, helping to better understand innate response mechanisms to pathogens and develop new and improved vaccines.

Immunology↗
bioRxiv2016

Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immunity

The immune system of vertebrate species consists of many different cell types that have distinct functional roles and are subject to different evolutionary pressures. Here, we first analysed gene conservation of all major immune cell types in human and mouse. Our results revealed higher gene turnover and faster evolution of trans-membrane proteins in NK cells compared to other immune cell populations, and especially T cells, but similar conservation of nuclear and cytoplasmic protein coding genes. To validate these findings in a distant vertebrate species, we used single-cell RNA-Sequencing of lck:GFP cells in zebrafish to obtain the first transcriptome of specific immune cell types in a non-mammalian species. Unsupervised clustering and single-cell TCR locus reconstruction identified three cell populations, T-cells, a novel type of NK-like cells and a smaller population of myeloid-like cells. Differential expression analysis uncovered new immune cell specific genes, including novel immunoglobulin-like receptors, and neofunctionalization of recently duplicated paralogs. Evolutionary analyses confirmed a higher gene turnover and lower conservation of trans-membrane proteins in NK cells compared to T cells in fish species, suggesting that this is a general property of immune cell types across all vertebrates.

Immunology↗