Technical note:The persistence of microbial-specific DNA sequences through gastric digestion in lambs and its potential use as microbial markers

Two groups of 5 lambs were euthanized at the weaning (T45) and fattening stages (T90) to evaluate the use of microbial ribosomal DNA (rDNA) sequences as potential microbial markers in relation to purine bases (PB) as a conventional marker. Both microbial markers originated similar microbial N concentrations (mg/g of DM), although T45 showed decreased values compared with the T90 group when either PB or rDNA were considered (P = 0.02). The survival of microbial rDNA was determined in 3 digestive sites (omasum, abomasum, and duodenum), but no substantial differences were observed, indicating that rDNA maintains the molecular stability along the sampling sites analyzed. Contrarily PB concentration increased successively along the digestive tract (P < 0.05), likely as a consequence of the endogenous PB secretion. Undegraded milk PB may also explain the overestimation of the microbial N concentration (2.8 times greater) using PB than rDNA sequences. Abomasum was the sampling site where the best agreement between PB and rDNA estimations was observed. Protozoal N concentration was irrelevant in T45 animals, although substantial in T90 lambs (18% of microbial N). In conclusion, bacterial 16S and protozoal 18S rDNA sequences may persist through the gastric digestive tract and their utilization as a highly specific microbial marker should not be neglected.This study was supported by a FPU grant from the Education and Science Spanish Ministry (project: AGL 2004-02910/GAN) and by a University of Zaragoza project (UZ2008-BIO-04)

Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques

Facioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder that shows a preference for the facial, shoulder and upper arm muscles. FSHD affects about one in 20-400,000 people, and no effective therapeutic strategies are known to halt disease progression or reverse muscle weakness or atrophy. Many genes may be incorrectly regulated in affected muscle tissue, but the mechanisms responsible for the progressive muscle weakness remain largely unknown. Although machine learning (ML) has made significant inroads in biomedical disciplines such as cancer research, no reports have yet addressed FSHD analysis using ML techniques. This study explores a specific FSHD data set from a ML perspective. We report results showing a very promising small group of genes that clearly separates FSHD samples from healthy samples. In addition to numerical prediction figures, we show data visualizations and biological evidence illustrating the potential usefulness of these results.Peer ReviewedPostprint (published version

Peripheral blood mononuclear cells (PBMC) microbiome is not affected by colon microbiota in healthy goats

This work was supported by grants from the Spanish Ministry of Science and Innovation (MCI) co-financed with FEDER funds [AGL2017-86757 to LA, AGL2017-86938-R to DRY]. Other contributions were SAF2015-65327-R to JA and SAF2015-73549-JIN to HR. LA is a Ramón y Cajal fellow [RYC-2013-13666] from the Spanish Ministry of Science and Innovation. APC is a recipient of a fellowship from the University of the Basque Country. We thank the MCI for the Severo Ochoa Excellence accreditation (SEV-2016-0644) and the Basque Department of Industry, Tourism and Trade (Etortek and Elkartek programs

Addressing global ruminant agricultural challenges through understanding the rumen microbiome::Past, present and future

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in “omic” data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent “omics” approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges

Temporal stability of the rumen microbiota in beef cattle, and response to diet and supplements

Acknowledgements Sampling of ruminal digesta was carried out at Scotland’s Rural College (SRUC) by Laura Nicoll, Lesley Deans and Claire Broadbent. Sequencing using Illumina MiSeq was carried out by Edinburgh Genomics, The University of Edinburgh. Edinburgh Genomics is partly supported through core grants from NERC (R8/H10/56), MRC (MR/K001744/1) and BBSRC (BB/J004243/1). Data were processed using the Maxwell High Performance Computing Cluster of the University of Aberdeen IT Service (www.abdn.ac.uk/staffnet/research/hpc.php), provided by Dell Inc. and supported by Alces Software. Funding This work was funded by the Rural and Environment Science and Analytical Services Division (RESAS) of the Scottish Government as a collaborative HEI project between The University of Aberdeen, The Roslin Institute, and Scotland’s Rural College (SRUC). The funding body had no role in the design of the study or collection, analysis, or interpretation of data or in writing the manuscript.Peer reviewedPublisher PD