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The rumen wharves conglomerate of symbiotic fibrolytic microflora with the potential to produce repertoire of carbohydrate-active enzymes to elicit the degradation of recalcitrant plant lignocellulosic complex into simpler forms that can be utilized by the host. A metatranscriptomics approach was adopted to explore the metabolically active microbiota and the transcripts involved in the deconstruction of lignocellulose in Indian crossbred cattle rumen. The experimental animals were fed with mixed diet for a period of twenty-one days and the total RNA was isolated from the rumen digesta using a modified total RNA extraction protocol. After mRNA enrichment, the cDNA libraries were subjected to sequencing under Illumina HiSeq platform and generated about 2.89 GB of raw sequences. The contig assembly was accomplished using two different tools and the redundant sequences were removed before further processing. A total of 133,930 orfs were predicted from the assembled contigs using MetaGeneMark tool and the taxonomic affiliation of orfs were achieved using MEGAN 6.0 Community Edition. A total of 17 bacterial, 5 eukaryotic and 1 archaeal phylum were identified from rumen metatranscriptomic dataset. Phylum Bacteroidetes were acknowledged as the most abundant and metabolically active rumen bacterial populations in cattle rumen with 36,414 orfs corresponding to it, followed by Firmicutes with 6,349 orfs. Further, MEGAN annotation of metatranscriptomic data at species level revealed that Prevotella brevis and Prevotella ruminicola as the most metabolically active bacterial populations representing 4,022 and 2,725 orfs each. The functional annotation of metatranscriptomic data using COG database revealed that a large number of transcripts (~16%) were corresponding to translation, ribosomal structure and biogenesis and carbohydrate transport and metabolism (15%). dbCAN annotation of rumen metatranscriptomic data identified and classified 5,267 transcripts belonging to 168 CAZyme families (GH-52%, GT-26%, CBM-14%, CE-6%, PL-2% and AA-0%). The microbial community analysis of the CAZyme encoding transcripts using M5NR database revealed that a significant proportion of CAZymes were contributed by genera Prevotella (37%) and Bacteroides (21%). The cattle rumen microbiome metatranscriptome analysis presented in this study facilitated the detection of large number of transcripts encoding diverse CAZymes that actively take part in the hydrolysis of plant lignocellulose complex that may be useful for improving livestock and biofuel generation. [ABSTRACT FROM AUTHOR]

Aim: Severe mental illnesses (SMI), such as bipolar disorder and schizophrenia, are highly heritable, and have a complex pattern of inheritance. Genome‐wide association studies detect a part of the heritability, which can be attributed to common genetic variation. Examination of rare variants with next‐generation sequencing may add to the understanding of the genetic architecture of SMI. Methods: We analyzed 32 ill subjects from eight multiplex families and 33 healthy individuals using whole‐exome sequencing. Prioritized variants were selected by a three‐step filtering process, which included: deleteriousness by five in silico algorithms; sharing within families by affected individuals; rarity in South Asian sample estimated using the Exome Aggregation Consortium data; and complete absence of these variants in control individuals from the same gene pool. Results: We identified 42 rare, non‐synonymous deleterious variants (~5 per pedigree) in this study. None of the variants were shared across families, indicating a 'private' mutational profile. Twenty (47.6%) of the variant harboring genes were previously reported to contribute to the risk of diverse neuropsychiatric syndromes, nine (21.4%) of which were of Mendelian inheritance. These included genes carrying novel deleterious variants, such as the GRM1 gene implicated in spinocerebellar ataxia 44 and the NIPBL gene implicated in Cornelia de Lange syndrome. Conclusion: Next‐generation sequencing approaches in family‐based studies are useful to identify novel and rare variants in genes for complex disorders like SMI. The findings of the study suggest a potential phenotypic burden of rare variants in Mendelian disease genes, indicating pleiotropic effects in the etiology of SMI. [ABSTRACT FROM AUTHOR]