On Genotyping Polymorphic HLA Genes — Ambiguities and Quality Measures Using NGS

The major histocompatibility complex (MHC) region of the human genome is the most polymorphic sequence part on chromosome 6; this roughly 4 Mbase long stretch contains many genes involved in immune response and disease association. The HLA genes have a crucial role in transplantation; patients receiving organs or bone marrow from matching donors have significantly higher chance for survival. NGS-based HLA typing brings the hope of accurate genomic consensus sequences by relatively cheap and simple laboratory workflow. Using either targeted or whole-genome sequencing data, there are a lot of possibilities to get ambiguous results (combinations of several alleles as a result instead of a single pair). These can be sample- or reference-related, or the results of artifacts generated during the targeting and amplifying step. NGS technology itself has additional artifacts leading to ambiguity listed in our paper. The final bioinformatics step will not be able to resolve all the ambiguities; we are also proposing quality control metrics to assess the final ambiguity and typing failure

An implanted device enables in vivo monitoring of extracellular vesicle-mediated spread of pro-inflammatory mast cell response in mice

Abstract Mast cells have been shown to release extracellular vesicles (EVs) in vitro. However, EV-mediated mast cell communication in vivo remains unexplored. Primary mast cells from GFP-transgenic and wild type mice, were grown in the presence or absence of lipopolysaccharide (LPS), and the secreted EVs were separated from the conditioned media. Mast cell-derived EVs were next cultured with LPS-naïve mast cells, and the induction of TNF-α expression was monitored. In addition, primary mast cells were seeded in diffusion chambers that were implanted into the peritoneal cavities of mice. Diffusion chambers enabled the release of GFP+ mast cell-derived EVs in vivo into the peritoneal cavity. Peritoneal lavage cells were assessed for the uptake of GFP+ EVs and for TNF-α production. In vitro, LPS-stimulated mast cell-derived EVs were efficiently taken up by non-stimulated mast cells, and induced TNF-α expression in a TLR4, JNK and P38 MAPK dependent manner. In vivo, using implanted diffusion chambers, we confirmed the release and transmission of mast cell-derived EVs to other mast cells with subsequent induction of TNF-α expression. These data show an EV-mediated spreading of pro-inflammatory response between mast cells, and provide the first in vivo evidence for the biological role of mast cell-derived EVs

Tantestületi klímavizsgálat

Dolgozatomban az ELTE Bárczi Gusztáv Gyógypedagógiai Kar gyakorló intézményének tantestületi klímáját vizsgáltam meg. A tantestület tagjainak válaszait többféle kategória alapján értékeltem. Ez a tény a klíma igen alapos feltárására adott lehetőséget, pontosan rávilágítva arra, hogy az egyes csoportok a „közös” eredménytől függetlenül mely területeken látják a legnagyobb problémákat

HLA typing from 1000 genomes whole genome and whole exome illumina data.

Specific HLA genotypes are known to be linked to either resistance or susceptibility to certain diseases or sensitivity to certain drugs. In addition, high accuracy HLA typing is crucial for organ and bone marrow transplantation. The most widespread high resolution HLA typing method used to date is Sanger sequencing based typing (SBT), and next generation sequencing (NGS) based HLA typing is just starting to be adopted as a higher throughput, lower cost alternative. By HLA typing the HapMap subset of the public 1000 Genomes paired Illumina data, we demonstrate that HLA-A, B and C typing is possible from exome sequencing samples with higher than 90% accuracy. The older 1000 Genomes whole genome sequencing read sets are less reliable and generally unsuitable for the purpose of HLA typing. We also propose using coverage % (the extent of exons covered) as a quality check (QC) measure to increase reliability

Read length distribution for different experiments.

<p>Read length was the first crucial quality check value. Only paired samples were considered having reads longer than 76 bps on both part of the pair. Reads shorter than 76 bps were practically useless: most of the mistyped samples had shorter read lengths.</p

Minimal average coverage % for exons 2 and 3.

<p>For the second QC filter based on we expected that the average for exons 2 and 3 has to reach 80%. The concordance is around 90% for this value and higher concordance could be reached only if the sample size decreases significantly.</p

Minimal coverage % for exons 2 and 3.

<p>To pass the first QC filter we expected for every sample that the for both exons 2 and 3 were higher than 70%. It was because when plotting concordance vs. the concordance is higher than 90% when the is at least 70% and there is no strong improvement using higher values.</p

Typing concordance versus read length.

<p>Read length can serve as the very first quality check measure; pairs with having reads shorter than 76 bps are showing very high ambiguity and mistyping. Even 76 bps is a lower bound for correct typing, samples with shorter read lengths are less concordant. Picture showing concordance and read length for all the 217 whole exome samples; there were 360 typings (HLA-A,B,C both alleles) with readlength 76 bps, 366 typings for samples with readlength 90 bps and 514 typings for 100 or 101 bps reads (one of the HLA-C typing for 100 bps samples was unsuccessful giving no typings at all.)</p

Filtering work-flow.

<p>There 1000 Genomes index file was first filtered for paired Illumina samples. There are 270 Coriell cell lines in the HapMap set, from the 1000 Genomes samples we had to select only those IDs which are among these cell lines. After separating the whole genome and whole exome sequencing experiments, these two types of samples were analyzed separately since the average coverage depth is very different for the two datasets. Those samples where the readlength was less than 76 base pairs for any part of the pair were thrown away and was not processed further. Finally, HLA typing was successful only for samples that were passing the coverage QC measures. The first such measure was that the coverage % for either exons 2 or 3 had to be at least 70% – if any of the exons was covered in less extent, the typing for that gene (for both alleles) was discarded. Furthermore, typing also failed if the average coverage % calculated for exons 2 and 3 was less than 80%.</p

Difference in HLA-A*03:01:01:01 and HLA-A*03:21N alignments.

<p>Both alleles show relatively good coverage, but reads covering HLA-A*03:21N exon 4 (the distinguishing part between the two alleles) are from other genes and pseudogenes like HLA-H, or HLA-B,C and E. Mistyping is mostly due to this phenomenon when analyzing whole-exome or whole-genome samples; reads from other regions are brought in as "alignment noise". This in most cases result in mistyping to a rare allele, though in some unfortunate cases to a different common one. Mistyping tends to be systematic: valid types are usually mistyped to the same rare allele.</p