A dimensional reduction approach to modulate the core ruminal microbiome associated with methane emissions via selective breeding

The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations.     

Nucleotide sequence and analysis of the centromeric region of yeast chromosome IX

We have determined the nucleotide sequence of a cosmid (pIX338) containing the centromere region of yeast (Saccharomyces cerevisiae) chromosome IX. The complete nucleotide sequence of 33·8 kb was obtained by using an efficient directed sequencing strategy in combination with automated DNA sequencing on the A.L.F. DNA sequencer. Sequence analysis revealed the presence of 17 open reading frames (ORFs), four of them previously known yeast genes (sly12, pan1, sts1 and prl1), a tRNA gene and the centromere motif. Exhaustive database searches detected sequence homologues of known function for as many as 14 of the 17 ORFs. These include a mammalian tyrosine kinase substrate; the Escherichia coli cell cycle protein MinD; the human inositol polyphosphate-5-phosphatase (gene OCRL) involved in Lowe's syndrome, a developmental disorder; and helicases, for which the new yeast member defines a distinct DEAD/H-box subfamily. A surprisingly large fraction of the ORFs (at least six out of 17) in the centromeric region are apparently involved in RNA or DNA binding. The nucleotide sequence reported here has been submitted to the EMBL data library under the accession number X79743.     

Expert system for clustering prokaryotic species by their metabolic features

Studying the communities of microbial species is highly important since many natural and artificial processes are mediated by groups of microbes rather than by single entities. One way of studying them is the search of common metabolic characteristics among microbial species, which is not only a potential measure for the differentiation and classification of closely-related organisms but also their study allows the finding of common functional properties that may describe the way of life of entire organisms or species. In this work we propose an expert system (ES), making the main contribution, to cluster a complex data set of 365 prokaryotic species by 114 metabolic features, information which may be incomplete for some species. Inspired on the human expert reasoning and based on hierarchical clustering strategies, our proposed ES estimates the optimal number of clusters adequate to divide the dataset and afterwards it starts an iterative process of clustering, based on the Self-organizing Maps (SOM) approach, where it finds relevant clusters at different steps by means of a new validity index inspired on the well-known Davies Bouldin (DB) index. In order to monitor the process and assess the behavior of the ES the partition obtained at each step is validated with the DB validity index. The resulting clusters prove that the use of metabolic features combined with the ES is able to handle a complex dataset that can help in the extraction of underlying information, gaining advantage over other existing approaches, that may relate metabolism with phenotypic, environmental or evolutionary characteristics in prokaryotic species.     

Mapping tumour heterogeneity with pulsed 3D CEST MRI in non-enhancing glioma at 3 T

Objective

Amide proton transfer (APT) weighted chemical exchange saturation transfer (CEST) imaging is increasingly used to investigate high-grade, enhancing brain tumours. Non-enhancing glioma is currently less studied, but shows heterogeneous pathophysiology with subtypes having equally poor prognosis as enhancing glioma. Here, we investigate the use of CEST MRI to best differentiate non-enhancing glioma from healthy tissue and image tumour heterogeneity.

Materials & Methods

A 3D pulsed CEST sequence was applied at 3 Tesla with whole tumour coverage and 31 off-resonance frequencies (+6 to -6 ppm) in 18 patients with non-enhancing glioma. Magnetisation transfer ratio asymmetry (MTRasym) and Lorentzian difference (LD) maps at 3.5 ppm were compared for differentiation of tumour versus normal appearing white matter. Heterogeneity was mapped by calculating volume percentages of the tumour showing hyperintense APT-weighted signal.

Results

LDamide gave greater effect sizes than MTRasym to differentiate non-enhancing glioma from normal appearing white matter. On average, 17.9 % ± 13.3 % (min–max: 2.4 %–54.5 %) of the tumour volume showed hyperintense LDamide in non-enhancing glioma.

Conclusion

This works illustrates the need for whole tumour coverage to investigate heterogeneity in increased APT-weighted CEST signal in non-enhancing glioma. Future work should investigate whether targeting hyperintense LDamide regions for biopsies improves diagnosis of non-enhancing glioma.