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Ross, E. M.

Publications and source records attributed to Ross, E. M..

9 recordsLinked to original sources

Metagenomic prediction of methane emissions in sheep using single- and multi-matrix BLUP models with taxonomic and functional microbial features

BackgroundEnteric methane emissions from ruminant livestock represent a major greenhouse gas contributor, yet identification of high- and low-emitting ruminants remains expensive and logistically challenging for agricultural methane mitigation strategies. Ruminal microbial profiles derived from long-read sequencing technology provide a potential proxy to predict methane production. The optimal bioinformatic pipelines for processing long-read metagenomic data to perform methane predictions have yet to be determined. Here we evaluated how different metagenomic analysis pipelines affect methane predictive model accuracy in grazing sheep. ResultsWe applied three bioinformatic pipelines to characterize the taxonomic and functional features of rumen microbiomes from 396 sheep. Functional abundance features were annotated from Clusters of Orthologous Genes (COG) or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The single-matrix model using COG features achieved the highest microbiability (m2 = 0.942: proportion of variance component explained by microbial features) and predictive accuracy (5-fold cross validation r = 0.609: Pearsons correlation between predicted and observed values). Both functional features outperformed all taxonomic features across all three pipelines in predictive accuracy. The multi-matrix models combined functional and taxonomic features slightly improved methane predictive accuracy across both 5-fold cross-validation and leave-one-day-out validation compared to the models using functional features alone. ConclusionsThese findings demonstrate the potential advantages of using long-read metagenomic data to predict enteric methane emissions in ruminants. COG-based functional features achieved the highest predictive accuracy among all feature types, suggesting that functional annotation of existing long-read sequences is sufficient for accurate methane prediction without requiring complementary taxonomic data.

genomics↗

Epigenetic Patterns of Xylanibacter ruminicola in Bovine Rumen Across Seasons and Pregnancy

Ruminants obtain nutrients through the microbial fermentation of plant material in the rumen. Xylanibacter ruminicola is a highly abundant bacterial species in the rumen. During fermentation, X. ruminicola utilizes diverse carbohydrates from plant materials to synthesize propionate, a volatile fatty acid providing energy to ruminants. However, variation in pasture quality (e.g, nutrient and fibre content) across seasons and host pregnancy status can alter the rumen microenvironment, potentially affecting microbial activity. Bacteria in culture display distinct methylation (a reversible epigenetic modification capable of gene regulation) changes in response to the growth environment. We hypothesized that the changes to the rumen environment would affect the DNA methylation patterns within the X. ruminicola genome. Rumen fluid from 37 female Brahman cattle (17 pregnant) were sampled across four seasons. DNA methylation profiles (N6-methyladenine, N4-methylcytosine, and 5-methylcytosine) of X. ruminicola across seasons (varying pasture quality) and pregnancy statuses characterized using Oxford Nanopore sequencing. After correcting for relative abundance, DNA methylation levels within the coding DNA sequences of several X. ruminicola genes differed between seasons and pregnancy status. Most of these genes were classified as ExbD/TolR family proteins and related to the protein transport process. Our study demonstrates that the DNA methylation profiles of rumen X. ruminicola genes vary with host environment factors. These results provide insight into the role of bacterial DNA methylation in mediating interactions between bacteria and their environments. Lay SummaryRuminants rely on rumen microbes to convert plants into nutrients. Xylanibacter ruminicola is a bacterial species in the rumen that produces nutrients for ruminants during plant fermentation. Changes in pasture quality and host pregnancy status can influence the activity of X. ruminicola, as reflected in the DNA methylation profile across its genome. DNA methylation is a reversible DNA modification that can affect gene activity and help bacterial adaptation to changing environments. Rumen fluid from 37 female Brahman cattle (17 pregnant) across four seasons were used to evaluate the pasture quality and host pregnancy effects on the DNA methylation profile of X. ruminicola. The relative abundances of X. ruminicola were influenced by pasture quality, but not by host pregnancy. However, host pregnancy status and changes in pasture quality influenced the DNA methylation signatures of X. ruminicola. Genes with DNA methylation changes were associated with the protein transport process. These findings suggest that the DNA methylation profiles of rumen X. ruminicola vary with host environment factors. Teaser TextThis study demonstrates that the DNA methylation profiles of rumen Xylanibacter ruminicola vary with host environment factors. These findings provide insight into the role of bacterial DNA methylation in mediating interactions between rumen bacteria and their environment.

microbiology↗

Short communication: Oral microbiome as a potential proxy for grazing livestock methane emissions

Enteric methane emissions from ruminant livestock contribute to global warming, creating an urgent need for effective mitigation strategies that do not compromise animal productivity and welfare. Methanogenic archaea within the rumen microbiome drive enteric methane emissions. However, large-scale rumen-fluid sampling in commercial production systems is impractical, due to its invasive nature and the associated logistical challenges. This study hypothesised that rumination enables the capture of rumen microbial signals within the oral cavity and using oral microbiome profiles to provide a practical, non-invasive alternative method for proxy methane phenotyping in commercial production systems. To test the hypothesis, we estimated the oral microbiability, defined as the proportion of phenotypic variance in methane emissions explained by oral microbiome variation. Samples were collected from 209 animals across two trials in Queensland, Australia. Oral microbiome samples were obtained from all animals, with paired rumen samples in one trial, and methane emissions were measured using either the sulphur hexafluoride (SF6) tracer technique or the GreenFeed system. Microbial features were characterised using taxonomic and functional annotations, and microbiability was estimated using mixed linear models incorporating microbiome-based relationship matrices. Although the small sample size limited strong conclusions, the oral microbiability estimates reported in this study were comparable to those derived from rumen samples. Functional microbial profiles generally explained a greater proportion of methane variation than taxonomic profiles, suggesting that microbial function is more closely linked to methane production than community composition alone. However, these differences were not statistically significant due to large standard errors. These findings suggest that oral microbiome sampling potentially provides a practical, minimally invasive, scalable proxy method for methane emissions of individual cattle in grazing systems, where direct methane gas measurements are labour-intensive and difficult to implement. Integrating oral microbiome profiles in the existing breeding model with the host genetics, weight and environmental factors could provide a promising pathway for enabling selection for low emissions and advancing reduced emissions livestock farming under real-world production conditions. Lay summaryCattle produce methane as part of their normal digestion and this contributes to climate change. Reducing methane emission in grazing livestock systems is therefore important. However, measuring methane from individual grazing animals is difficult, costly, and often impractical under commercial conditions. The rumen microbiome has been used as a proxy for estimating methane emissions, but collecting rumen samples is invasive and impractical for large-scale use. Because rumination transfers material from the rumen to the mouth, we investigated whether microbes found in cattle mouths could also be used to estimate how much methane an individual animal produced. We suggest that mouth-swab sampling method can be an alternative to rumen fluid sampling because it was less invasive, relatively quick and practically applicable in commercial conditions. Importantly, the microbiome explained a meaningful proportion of the between-animal variation for methane emission. This suggests that collection of mouth swabs is a potentially scalable alternative proxy method to identify cattle that naturally produce less methane. Overall, our findings support the potential use of oral ruminant microbial information to improve breeding and management strategies aimed at reducing methane emissions while maintaining productive livestock systems. Teaser TextThis study demonstrates that collecting oral swabs from the mouths of grazing beef cattle could provide a scalable method to estimate individual methane emissions in commercial production systems, offering a practical alternative to invasive rumen sampling and complex gas measurement systems. These findings support the development of scalable breeding and management strategies for methane mitigation in large-scale livestock production systems.

microbiology↗

Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane

Mate-allocation in breeding programs can improve progeny performance by exploiting non-additive effects. Breeding decisions in the mate-allocation approach are based on predicted progeny merit rather than parental breeding value. This is particularly attractive when non-additive effects are significant, and the best-predicted progeny can be clonally propagated, for example sugarcane. We compared mate-allocation strategies that leverage non-additive and heterozygosity effects to maximise sugarcane clonal performance to schemes that use only additive effects to maximise breeding value. We used phenotypes and genotypes from a population of 2,909 clones phenotyped in Australias sugarcane breeding programs final assessment trials for three traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fibre. The clones from the last generation of this data set were used as parents to simulate families from all possible crosses (1,225), each with 50 progenies. The breeding and clonal values of progeny were predicted using GBLUP (considering only additive effects) and the e-GBLUP model (incorporating additive, non-additive and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among the selected parents. Compared to the breeding value, the predicted progeny value of allocated crossing pairs based on clonal performance (e-GBLUP) increased by 57%, 12%, and 16% for TCH, CCS, and fibre, respectively. In our study, the mate-allocation strategy exploiting non-additive and heterozygosity effects resulted in better clonal performance. However, there was a noticeable decline in additive gain, particularly for TCH, which might have been caused by the presence of large epistatic effects. When crosses were chosen based on clonal performance for TCH, progenies inbreeding coefficients were found significantly lower than for random mating, indicating that utilising non-additive and heterozygosity effects has advantages for controlling inbreeding depression. Therefore, mate-allocation is recommended in clonal crops to improve clonal performance and reduce inbreeding.

genomics↗

The genome of tropically adapted Brahman cattle (Bos taurus indicus) reveals novel genome variation in production animals

Recent advances in sequencing technology have revolutionised access to large scale genomic data that can be assembled into a platinum quality genome. Here we present a high quality genome assembly with less than 300 gaps of a Brahman cow (B. taurus indicus). The assembly was generated using 195GB of PacBio and 169GB of Oxford Nanopore Technologies sequence data. The high quality genome assembly allows us to identify substantial GC content variation that is positively associated with gene rich islands, and negatively associated with genetic variation in the form of structural variants. In addition, 92371 structural variants that are segregating in the brahman population were identified. Gene ontology analysis revealed that genes with varying copy numbers were enriched for gene ontology terms related to immune function. This analysis has revealed the complex structure of the mammalian genome of an outbred species, and identifies the ability of long read data from diploid species can be used to not only assemble a high quality genome, but also discover novel genetic variation within that genome.

genomics↗

Prowler: A novel trimming algorithm for Oxford Nanopore sequence data

MotivationQuality control (QC) tools are critical in DNA sequencing analysis because they increase the accuracy of sequence alignments and thus the reliability of results. Oxford Nanopore Technologies (ONT) QC is currently rudimentary, generally based on whole read average quality. This results in discarding reads that contain regions of high quality sequence. Here we propose Prowler, a multi-window approach inspired by algorithms used to QC short read data. Importantly, we retain the phase and read length information by optionally replacing trimmed sections with Ns. ResultsProwler was applied to mammalian and bacterial datasets, to assess effects on alignment and assembly respectively. Compared to Nanofilt, alignments of data QCed with Prowler had lower error rates and more mapped reads. Assemblies of Prowler QCed data had a lower error rate than Nanofilt QCed data however this came at some cost to assembly contiguity. Availability and implementationProwler is implemented in Python and is available at: https://github.com/ProwlerForNanopore/ProwlerTrimmer Contacte.ross@uq.edu.au Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

QuAdTrim: Overcoming computational bottlenecks insequence quality control

With the recent torrent of high throughput sequencing (HTS) data the necessity for highly efficient algorithms for common tasks is paramount. One task for which the basis for all further analysis of HTS data is initial data quality control, that is, the removal or trimming of poor quality reads from the dataset. Here we present QuAdTrim, a quality control and adapter trimming algorithm for HTS data that is up to 57 times faster and uses less than 0.06% of the memory of other commonly used HTS quality control programs. QuAdTrim will reduce the time and memory required for quality control of HTS data, and in doing, will reduce the computational demands of a fundamental step in HTS data analysis. Additionally, QuAdTrim impliments the removal of homopolymer Gs from the 3 end of sequence reads, a common error generated on the NovaSeq, NextSeq and iSeq100 platforms. Availability and ImplementationThe source code is freely available on bitbucket under a BSD licence, see COPYING file for details: https://bitbucket.org/arobinson/quadtrim ContactAndrew Robinson andrewjrobinson at gmail dot com

bioinformatics↗

The small molecule KHS101 induces bioenergetic dysfunction in glioblastoma cells through inhibition of mitochondrial HSPD1

Pharmacological inhibition of uncontrolled cell growth with small molecule inhibitors is a potential strategy against glioblastoma multiforme (GBM), the most malignant primary brain cancer. Phenotypic profiling of the neurogenic small molecule KHS101 revealed the chemical induction of lethal cellular degradation in molecularly-diverse GBM cells, independent of their tumor subtype, whereas non-cancerous brain cells remained viable. Mechanism-of-action (MOA) studies showed that KHS101 specifically bound and inhibited the mitochondrial chaperone HSPD1. In GBM but not non-cancerous brain cells, KHS101 elicited the aggregation of an enzymatic network that regulates energy metabolism. Compromised glycolysis and oxidative phosphorylation (OXPHOS) resulted in the metabolic energy depletion in KHS101-treated GBM cells. Consistently, KHS101 induced key mitochondrial unfolded protein response factor DDIT3 in vitro and in vivo, and significantly reduced intracranial GBM xenograft tumor growth upon systemic administration, without discernible side effects. These findings suggest targeting of HSPD1-dependent oncometabolic pathways as an anti-GBM therapy.

cancer biology↗

Allele-specific multi-sample copy number segmentation

MotivationAllele-specific copy number alterations are commonly used to trace the evolution of tumours. A key step of the analysis is to segment genomic data into regions of constant copy number. For precise phylogenetic inference, breakpoints shared between samples need to be aligned to each other.\n\nResultsHere we present asmultipcf, an algorithm for allele-specific segmentation of multiple samples that infers private and shared segment boundaries of phylogenetically related samples. The output of this algorithm can directly be used for allele-specific copy number calling using ASCAT.\n\nAvailabilityasmultipcf is available as part of the ASCAT R package (version 2.5) from github.com/Crick-CancerGenomics/ascat

bioinformatics↗