FibroChip, a Functional DNA Microarray to Monitor Cellulolytic and Hemicellulolytic Activities of Rumen Microbiota

Abstract

Ruminants fulfill their energy needs for growth primarily through microbial breakdown of plant biomass in the rumen. Several biotic and abiotic factors influence the efficiency of fiber degradation, which can ultimately impact animal productivity and health. To provide more insight into mechanisms involved in the modulation of fibrolytic activity, a functional DNA microarray targeting genes encoding key enzymes involved in cellulose and hemicellulose degradation by rumen microbiota was designed. Eight carbohydrate-active enzyme (CAZyme) families (GH5, GH9, GH10, GH11, GH43, GH48, CE1, and CE6) were selected which represented 392 genes from bacteria, protozoa, and fungi. The DNA microarray, designated as FibroChip, was validated using targets of increasing complexity and demonstrated sensitivity and specificity. In addition, FibroChip was evaluated for its explorative and semi-quantitative potential. Differential expression of CAZyme genes was evidenced in the rumen bacterium Fibrobacter succinogenes S85 grown on wheat straw or cellobiose. FibroChip was used to identify the expressed CAZyme genes from the targeted families in the rumen of a cow fed a mixed diet based on grass silage. Among expressed genes, those encoding GH43, GH5, and GH10 families were the most represented. Most of the F. succinogenes genes detected by the FibroChip were also detected following RNA-seq analysis of RNA transcripts obtained from the rumen fluid sample. Use of the FibroChip also indicated that transcripts of fiber degrading enzymes derived from eukaryotes (protozoa and anaerobic fungi) represented a significant proportion of the total microbial mRNA pool. FibroChip represents a reliable and high-throughput tool that enables researchers to monitor active members of fiber degradation in the rumen.

Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model

“…Random forest (rf) has been extensively applied as a classifier in chemoinformatics ( Zeng et al, 2019b , 2020a , b ; Song et al, 2020 ) and bioinformatics ( Zhang J. et al, 2016 ; Guo and Xu, 2018 ; Deng et al, 2019a ; Liu et al, 2019a ; Lv H. et al, 2019 ; Lv Z. et al, 2019 ; Lv et al, 2020 ; Ru et al, 2019 ; Wei et al, 2019 ; Xu et al, 2019 ; Tang et al, 2020 ; Yu et al, 2020 ). Rf was applied in this paper.…”

Addressing Global Ruminant Agricultural Challenges Through Understanding the Rumen Microbiome: Past, Present, and Future

“…Overall, COBRA approaches provide rational tools for metabolic engineering. The number of applications is broad and includes the development of tools for (i) studying interactions among different microbial groups, i.e., protozoa, fungi, archaea, bacteria and viruses or bacteriophages, (ii) developing selective cultivation strategies for as yet uncultured rumen microbes (Pope et al, 2011 ), (iii) designing methane mitigation strategies by exploiting the metabolic networks of genome-sequenced rumen archaea (Leahy et al, 2010 ; Pope et al, 2011 ), and (iv) developing prediction tools that exploit microbiome biomarkers for fiber hydrolysis (Dai et al, 2015 ; Comtet-Marre et al, 2017 , 2018 ) and methane production (Popova et al, 2013 ; Shi et al, 2014 ; Auffret et al, 2017b ).…”

A mechanistic overview of ruminal fibre digestion.

“…Coinciding with rapidly improving DNA-based sequencing and bioinformatics techniques that are enabling rumen microbiologists to create 1000's of microbial genomes from the rumen, functional RNA-and peptide-based expression studies have also experienced a transformation. Several chip-based microarrays have been developed over the years to quantify transcript levels from both bacterial as well as protozoan and fungal populations (Abot et al, 2016, Comtet-Marre et al, 2018. Overall, these methods have illustrated the high expression values of various cellulases and hemicellulases from both prokaryotic and eukaryotic origin, although the design of such chips on previously available CAZyme data, prevent the detection of specific activities from individual populations that have not yet been genomically sampled.…”