Taxonomic and functional assessment using metatranscriptomics reveals the effect of Angus cattle on rumen microbial signatures.

A greater understanding of the rumen microbiota and its function may help find new strategies to improve feed efficiency in cattle. This study aimed to investigate whether the cattle breed affects specific ruminal taxonomic microbial groups and functions associated with feed conversion ratio (FCR), using two genetically related Angus breeds as a model. Total RNA was extracted from 24 rumen content samples collected from purebred Black and Red Angus bulls fed the same forage diet and then subjected to metatranscriptomic analysis. Multivariate discriminant analysis (sparse partial least square discriminant analysis (sPLS-DA)) and analysis of composition of microbiomes were conducted to identify microbial signatures characterizing Black and Red Angus cattle. Our analyses revealed relationships among bacterial signatures, host breeds and FCR. Although Black and Red Angus are genetically similar, sPLS-DA detected 25 bacterial species and 10 functions that differentiated the rumen microbial signatures between those two breeds. In Black Angus, we identified bacterial taxa Chitinophaga pinensis, Clostridium stercorarium and microbial functions with large and small subunits ribosomal proteins L16 and S7 exhibiting a higher abundance in the rumen microbiome. In Red Angus, nonetheless, we identified the poorly characterized bacterial taxon Oscillibacter valericigenes with a higher abundance and pathways related to carbohydrate metabolism. Analysis of composition of microbiomes revealed that C. pinensis and C. stercorarium exhibited a higher abundance in Black Angus compared to Red Angus associated with FCR, suggesting that these bacterial species may play a key role in the feed conversion efficiency of forage-fed bulls. This study highlights how the discovery of signatures of bacterial taxa and their functions can be used to harness the full potential of the rumen microbiome in Angus cattle.

Temporal development of the oral microbiome and prediction of early childhood caries.

Human microbiomes are predicted to assemble in a reproducible and ordered manner yet there is limited knowledge on the development of the complex bacterial communities that constitute the oral microbiome. The oral microbiome plays major roles in many oral diseases including early childhood caries (ECC), which afflicts up to 70% of children in some countries. Saliva contains oral bacteria that are indicative of the whole oral microbiome and may have the ability to reflect the dysbiosis in supragingival plaque communities that initiates the clinical manifestations of ECC. The aim of this study was to determine the assembly of the oral microbiome during the first four years of life and compare it with the clinical development of ECC. The oral microbiomes of 134 children enrolled in a birth cohort study were determined at six ages between two months and four years-of-age and their mother's oral microbiome was determined at a single time point. We identified and quantified 356 operational taxonomic units (OTUs) of bacteria in saliva by sequencing the V4 region of the bacterial 16S RNA genes. Bacterial alpha diversity increased from a mean of 31 OTUs in the saliva of infants at 1.9 months-of-age to 84 OTUs at 39 months-of-age. The oral microbiome showed a distinct shift in composition as the children matured. The microbiome data were compared with the clinical development of ECC in the cohort at 39, 48, and 60 months-of-age as determined by ICDAS-II assessment. Streptococcus mutans was the most discriminatory oral bacterial species between health and current disease, with an increased abundance in disease. Overall our study demonstrates an ordered temporal development of the oral microbiome, describes a limited core oral microbiome and indicates that saliva testing of infants may help predict ECC risk.

PTRF/cavin-1 neutralizes non-caveolar caveolin-1 microdomains in prostate cancer.

Caveolin-1 has a complex role in prostate cancer and has been suggested to be a potential biomarker and therapeutic target. As mature caveolin-1 resides in caveolae, invaginated lipid raft domains at the plasma membrane, caveolae have been suggested as a tumor-promoting signaling platform in prostate cancer. However, caveola formation requires both caveolin-1 and cavin-1 (also known as PTRF; polymerase I and transcript release factor). Here, we examined the expression of cavin-1 in prostate epithelia and stroma using tissue microarray including normal, non-malignant and malignant prostate tissues. We found that caveolin-1 was induced without the presence of cavin-1 in advanced prostate carcinoma, an expression pattern mirrored in the PC-3 cell line. In contrast, normal prostate epithelia expressed neither caveolin-1 nor cavin-1, while prostate stroma highly expressed both caveolin-1 and cavin-1. Utilizing PC-3 cells as a suitable model for caveolin-1-positive advanced prostate cancer, we found that cavin-1 expression in PC-3 cells inhibits anchorage-independent growth, and reduces in vivo tumor growth and metastasis in an orthotopic prostate cancer xenograft mouse model. The expression of α-smooth muscle actin in stroma along with interleukin-6 (IL-6) in cancer cells was also decreased in tumors of mice bearing PC-3-cavin-1 tumor cells. To determine whether cavin-1 acts by neutralizing caveolin-1, we expressed cavin-1 in caveolin-1-negative prostate cancer LNCaP and 22Rv1 cells. Caveolin-1 but not cavin-1 expression increased anchorage-independent growth in LNCaP and 22Rv1 cells. Cavin-1 co-expression reversed caveolin-1 effects in caveolin-1-positive LNCaP cells. Taken together, these results suggest that caveolin-1 in advanced prostate cancer is present outside of caveolae, because of the lack of cavin-1 expression. Cavin-1 expression attenuates the effects of non-caveolar caveolin-1 microdomains partly via reduced IL-6 microenvironmental function. With circulating caveolin-1 as a potential biomarker for advanced prostate cancer, identification of the molecular pathways affected by cavin-1 could provide novel therapeutic targets.

A pilot study on transcriptome data analysis of folliculogenesis in pigs.

Three different stages of pig antral follicles have been studied in a granulosa-cell transcriptome analysis on nylon microarrays (1152 clones). The data have been generated from seven RNA follicle pools and several technical replicates were made. The objective of this paper was to state the feasibility of a transcriptomic protocol for the study of folliculogenesis in the pig. A statistical analysis was chosen, relying on the linear mixed model (LMM) paradigm. Low variability within technical replicates was hence checked with a LMM. Relevant genes that might be involved in the studied process were then selected. For the most significant genes, statistical methods such as principal component analysis and unsupervised hierarchical clustering were applied to assess their relevance, and a random forest analysis proved their predictive value. The selection of genes was consistent with previous studies and also allowed the identification of new genes whose role in pig folliculogenesis will be further investigated.

In vivo gene expression in granulosa cells during pig terminal follicular development.

Ovarian antral follicular development is clearly dependent on pituitary gonadotrophins FSH and LH. Although the endocrine mechanism that controls ovarian folliculogenesis leading to ovulation is quite well understood, the detailed mechanisms and molecular determinants in the different follicular compartments remain to be clarified. The aim of this study was to identify the genes differentially expressed in pig granulosa cells along the terminal ovarian follicle growth, to gain a comprehensive view of these molecular mechanisms. First, we developed a specific micro-array using cDNAs from suppression subtractive hybridization libraries (345 contigs) obtained by comparison of three follicle size classes: small, medium and large antral healthy follicles. In a second step, a transcriptomic analysis using cDNA probes from these three follicle classes identified 79 differentially expressed transcripts along the terminal follicular growth and 26 predictive genes of size classes. The differential expression of 18 genes has been controlled using real-time PCR experiments validating the micro-array analysis. Finally, the integration of the data using Ingenuity Pathways Analysis identified five gene networks providing descriptive elements of the terminal follicular development. Specifically, we observed: (1) the down-expression of ribosomal protein genes, (2) the genes involved in lipid metabolism and (3) the down-expression of cell morphology and ion-binding genes. In conclusion, this study gives new insight into the gene expression during pig terminal follicular growth in vivo and suggested, in particular, a morphological change in pig granulosa cells accompanying terminal follicular growth.