A 39 kb structural variant causing Lynch Syndrome detected by optical genome mapping and nanopore sequencing.

Lynch Syndrome (LS) is a hereditary cancer syndrome caused by pathogenic germline variants in one of the four mismatch repair (MMR) genes MLH1, MSH2, MSH6 and PMS2. It is characterized by a significantly increased risk of multiple cancer types, particularly colorectal and endometrial cancer, with autosomal dominant inheritance. Access to precise and sensitive methods for genetic testing is important, as early detection and prevention of cancer is possible when the variant is known. We present here two unrelated Norwegian families with family histories strongly suggestive of LS, where immunohistochemical and microsatellite instability analyses indicated presence of a pathogenic variant in MSH2, but targeted exon sequencing and multiplex ligation-dependent probe amplification (MLPA) were negative. Using Bionano optical genome mapping, we detected a 39 kb insertion in the MSH2 gene. Precise mapping of the insertion breakpoints and inserted sequence was performed by low-coverage whole-genome sequencing with an Oxford Nanopore MinION. The same variant was present in both families, and later found in other families from the same region of Norway, indicative of a founder event. To our knowledge, this is the first diagnosis of LS caused by a structural variant using these technologies. We suggest that structural variant detection be performed when LS is suspected but not confirmed with first-tier standard genetic testing.

The microbiota of uterine biopsies, cytobrush and vaginal swabs at artificial insemination in Norwegian red cows.

The individual resistance or tolerance against uterine disease in dairy cattle might be related to variations in the uterine tract microbiota. The uterine tract microbiota in dairy cattle is a field of increasing interest. However, its specific taxonomy and functional aspects is under-explored, and information about the microbiota in the endometrium at artificial insemination (AI) is still missing. Although uterine bacteria are likely to be introduced via the vaginal route, it has also been suggested that pathogens can be transferred to the uterus via a hematogenous route. Thus, the microbiota in different layers of the uterine wall may differ. Norwegian Red (NR) is a high fertility breed that also has a high prevalence of subclinical endometritis (SCE), an inflammation of the uterus that has a negative effect on dairy cattle fertility. However, in this breed the negative effect is only moderate, raising the question of whether this may be due to a favorable microbiota. In the present study we investigated the endometrial microbiota in NR at AI by biopsy and cytobrush samples, and comparing this to the vaginal microflora. The second objective was to describe potential differences at both distinct depths of the endometrium, in healthy vs SCE positive NR cows. We sampled 24 lactating and clinically healthy Norwegian red cows in their second heat or more after calving, presented for first AI. First, we obtained a vaginal swab and a cytobrush sample, in addition to a cytotape to investigate the animal's uterine health status with respect to SCE. Secondly, we acquired a biopsy sample from the uterine endometrium. Bacterial DNA from the 16S rRNA gene was extracted and sequenced with Illumina sequencing of the V3-V4 region. Alpha and beta diversity and taxonomic composition was investigated. Our results showed that the microbiota of endometrial biopsies was qualitatively different and more even than that of cytobrush and vaginal swab samples. The cytobrush samples and the vaginal swabs shared a similar taxonomic composition, suggesting that vaginal swabs may suffice to sample the surface-layer uterine microbiota at estrus. The current study gave a description of the microbiota in the healthy and SCE positive NR cows at AI. Our results are valuable as we continue to explore the mechanisms for high fertility in NR, and possible further improvements.

An extension to: Systematic assessment of commercially available low-input miRNA library preparation kits.

High-throughput sequencing has emerged as the favoured method to study microRNA (miRNA) expression, but biases introduced during library preparation have been reported. We recently compared the performance (sensitivity, reliability, titration response and differential expression) of six commercially-available kits on synthetic miRNAs and human RNA, where library preparation was performed by the vendors. We hereby supplement this study with data from two further commonly used kits (NEBNext, NEXTflex) whose manufacturers initially declined to participate. NEXTflex demonstrated the highest sensitivity, which may reflect its use of partially-randomized adapter sequences, but overall performance was lower than the QIAseq and TailorMix kits. NEBNext showed intermediate performance. We reaffirm that biases are kit specific, complicating the comparison of miRNA datasets generated using different kits.

Systematic assessment of commercially available low-input miRNA library preparation kits.

High-throughput sequencing is increasingly favoured to assay the presence and abundance of microRNAs (miRNAs) in biological samples, even from low RNA amounts, and a number of commercial vendors now offer kits that allow miRNA sequencing from sub-nanogram (ng) inputs. Although biases introduced during library preparation have been documented, the relative performance of current reagent kits has not been investigated in detail. Here, six commercial kits capable of handling <100ng total RNA input were used for library preparation, performed by kit manufactures, on synthetic miRNAs of known quantities and human total RNA samples. We compared the performance of miRNA detection sensitivity, reliability, titration response and the ability to detect differentially expressed miRNAs. In addition, we assessed the use of unique molecular identifiers (UMI) sequence tags in one kit. We observed differences in detection sensitivity and ability to identify differentially expressed miRNAs between the kits, but none were able to detect the full repertoire of synthetic miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the relative levels of the majority of miRNAs. UMI tags, at least within the input ranges tested, offered little advantage to improve data utility. In conclusion, biases in miRNA abundance are heavily influenced by the kit used for library preparation, suggesting that comparisons of datasets prepared by different procedures should be made with caution. This article is intended to assist researchers select the most appropriate kit for their experimental conditions.