Biology subjects

Harland, C.

Publications and source records attributed to Harland, C..

3 recordsLinked to original sources

bioRxiv2023

Absolute Quantification of Microbiota in Shotgun Sequencing Using Host Cells or Spike-Ins

BackgroundAn ongoing challenge for DNA sequencing of samples containing microorganisms is the ability to meaningfully compare different samples and to connect the results back to clinically relevant disease states. The reads of DNA sequence from each sample do not, in and of themselves, give sufficient information to calculate the absolute abundances of each observed organism. Using relative abundances alone is insufficient to determine whether absolute abundances have increased or decreased in the organisms of interest from one sample to the next. This is a well-studied problem in 16S sequencing, but solutions in shotgun sequencing are lacking. Here we show how spike-ins can be used in shotgun sequencing to calculate absolute abundances of organisms present. We also propose the use of the host cells already in the sample as an alternative calculation method. Mammalian host cells are typically of sufficient size that they can be easily and cheaply counted prior to sequencing by a variety of methods and combining this with sequencing data provides sufficient information to calculate the absolute abundances of microbial organisms. ResultsMicrobial abundances in the samples calculated via this method were consistent with manufacturer-stated values of microbial communities, with qPCR, and with our method tested against itself with regard the spike-in and host-cell based options. R2 values on the log10 scale in these tests ranged from 0.85 to 0.98, and the log10-RMSE ranged from 0.1 to 0.7. ConclusionsThe proposed method can consistently calculate absolute microbial abundances to within an order of magnitude. Both versions of the method, where spike-ins are added to the samples, or where host cells in the sample are counted, are viable. Calculating absolute abundances allows for direct comparisons to be made between different samples. If disease-thresholds have been identified, absolute abundances can quantify disease states.

microbiology↗

bioRxiv2021

Non-additive QTL mapping of lactation traits in 124,000 sequence-imputed cattle reveals novel recessive loci

Deleterious recessive conditions have primarily been studied in a Mendelian disease context. Recently, several large effect, deleterious recessive mutations were discovered via non-additive GWAS of quantitative growth and developmental traits in cattle. This showed quantitative traits can be used as proxies of genetic disorders if they are indicative of whole animal health status and susceptible to underlying genetic conditions. Lactation traits might also reflect genetic disorders in cattle, given the increased energy demands of lactation and the substantial stresses imposed on the animal. Here, we report a screen of over 124,000 cows for recessive effects based on lactation traits. We discovered novel loci associated with five large recessive impacts on milk yield traits represented by missense variants (DOCK8, IL4R, KIAA0556, and SLC25A4) or premature stop variants (ITGAL, LRCH4, and RBM34) as candidate causal mutations. On milk composition traits, we identified several small effect dominance contributions to previously reported additive QTL. In contrasting analyses of milk yield and milk composition phenotypes, we note differing genetic architectures. Milk yield phenotypes presented lower heritabilities and fewer additive QTL, but higher non-additive genetic variance and a higher proportion of loci exhibiting dominance compared to milk composition phenotypes. Large-effect recessive QTL are segregating at surprisingly high frequencies in cattle. We speculate that the differences in genetic architecture between milk yield and milk composition phenotypes derive from underlying dissimilarities in the cellular and molecular representation of these traits. Lactation yields may act as a better proxy than milk composition traits for a wide range of underlying biological disorders affecting animal fitness

genetics↗

bioRxiv2021

The genomes of precision edited cloned calves show no evidence for off-target events or increased de novo mutagenesis

Animal health and welfare are at the forefront of public concern and the agricultural sector is responding by prioritising the selection of welfare-relevant traits in their breeding schemes. In some cases, welfare-enhancing traits such as horn-status (i.e., polled) or diluted coat colour, which could enhance heat tolerance, may not segregate in breeds of primary interest, highlighting gene-editing tools such as the CRISPR-Cas9 technology as an approach to rapidly introduce variation into these populations. A major limitation preventing the acceptance of CRISPR-Cas9 mediated gene-editing, however, is the potential for off-target mutagenesis, which has raised concerns about the safety and ultimate applicability of this technology. Here, we present a clone-based study design that has allowed a detailed investigation of off-target and de novo mutagenesis in a cattle line bearing edits in the PMEL gene for diluted coat-colour. No off-target events were detected from high depth whole genome sequencing performed in precursor cell-lines and resultant calves cloned from those edited and non-edited cell lines. Long molecule sequencing at the edited site and plasmid-specific PCRs did not reveal structural variations and/or plasmid integration events in edited samples. Furthermore, an in-depth analysis of de novo mutations across samples revealed that the mutation frequency and spectra were unaffected by editing status. Cells in culture, however, had a distinct mutation signature where de novo mutations were predominantly C>A mutations, and in cloned calves they were predominantly T>G mutations, deviating from the expected excess of C>T mutations. We conclude that the gene-edited cells and calves in this study did not present a higher mutation load than unedited controls. Cell culture and somatic cell nuclear transfer cloning processes contributed the major source of contrast in mutational profile between samples.

genomics↗