Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Impact of process temperature and organic loading rate on cellulolytic / hydrolytic biofilm microbiomes during biomethanation of ryegrass silage revealed by genome-centered metagenomics and metatranscriptomics

ABSTRACT: Two-stage two-phase biogas reactor systems consisting each of one batch downflow hydrolysis reactor (HR, vol. 10 L), one process fluid storage tank (vol. 10 L), and one downstream upflow anaerobic filter reactor (AF, vol. 10 L), were operated at mesophilic (M, 37 °C) and thermophilic (T, 55 °C) temperatures and over a period of > 750 d (Figure 1, Additional file 1). For each reactor system and for each process temperature, two replicates were conducted in parallel, denominated further as biological replicates. Further process details were as previously published. Start-up of all fermenters were performed using liquid fermenter material from a biogas plant converting cattle manure in co-digestion with grass and maize silage and other biomass at varying concentrations and at mesophilic temperatures. Silage of perennial ryegrass (Lolium perenne L.) was digested as sole substrate in batches of varying amounts with retention times of 28 d (storage of bale silage at -20 °C, cutting length 3 cm, volatile substances (VS) 32 % of fresh mass (FM), total Kjeldahl nitrogen 7.6 g kgFM-1, NH4+-N 0.7 g kgFM-1, acetic acid 2.6 g kgFM-1, propionic acid < 0.04 g kgFM-1, lactic acid 2.6 g kgFM-1, ethanol 2.2 g kgFM-1, C/N ratio 19.3, chemical oxygen demand (COD) 357.7 g kgFM-1, analysis of chemical properties according to [6]. No spoilage was observed in the silage. Biogas yields were calculated as liters normalized to 0 °C and 1013 hPa (LN) per kilogram volatile substances (kgVS). For chemical analysis, samples were taken from the effluents of HR and AF. For sequencing of 16S rRNA gene amplicon libraries, microbial metagenomes, and microbial metatranscriptomes, samples were taken from the silage digestate in the HR digested for 2 d. At this time point, high AD rates were detected as indicated by the fast increase of volatile fatty acids (VFA), e.g., acetic acid. Sampling was performed at two different organic loading rates (OLR), i.e., batch-fermentation of 500 g (denominated as “low OLR”, samples MOLR500 and TOLR500) and 1,500 g silage (denominated as “increased OLR”, samples MOLR1500 and TOLR1500).

INSTRUMENT(S): Illumina HiSeq 1500

ORGANISM(S): soil metagenome

SUBMITTER: Daniel Wibberg

PROVIDER: E-MTAB-7533 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:The objective of this study was to examine changes in muscle gene expression of growing steers during a period of dietary energy restriction followed by a period of realimentation. Crossbred Aberdeen Angus x Holstein Friesian (n = 24) steers were assigned to one of two feeding treatments. Over a 99 d period, 1 group (n=12) was offered a high energy control diet consisting of concentrates ad libitum and 7 kg of grass silage per head daily. The second group (n=12) was offered an energy restricted diet consisting of grass silage ad libitum plus 0.5 kg of concentrate per head per day. From the end of the differential feeding period (99 d), both groups of animals were offered a total mixed ration (grass silage:concentrate ratio of 80:20). This period, which lasted 200 d, was known as the realimentation period. All animals were slaughtered on d 299 of the study. Muscle biopsies were collected at 2 time points (end of the differential feeding period (d 99) and during the realimentation period (d131). RNA was extracted and muscle gene expression was examined using RNA-seq technology and bioinformatic analysis. During the differential feeding period, 17 over-represented pathways were identified, including the peroxisome proliferator activated receptor signalling, glycolysis/ gluconeogenesis and metabolic pathways controlling the metabolism of lipids and lipoproteins which indicate reduced energy intake and fat tissue accumulation occurring in muscle tissue during the restriction phase. During the realimentation period, 164 differentially expressed genes were annotated to 9 over-represented pathways including starch and sucrose metabolism, carbohydrate digestion and absorption and TGF-β signalling pathway. It is hypothesised that the signalling effects of the TGF-β pathway were reduced thereby promoting accelerated cell growth and proliferation in muscle tissue of animals experiencing compensatory growth. This information can be exploited in genomic breeding programmes to assist selection of cattle with a greater ability to compensate following a period dietary restriction. 24 muscle RNA samples were analysed in total. 6 samples were from muscle biopsies collected at the end of a period of dietary restriction (d99) and 6 samples were from muscle biopsies collected at the peak of compensatory growth (d131). In addition, RNA was also analysed from 6 samples collected from animals fed ad libitum at each of these two timepoints.

Project description:The objective of this study was to examine changes in muscle gene expression of growing bulls during a period of dietary energy restriction followed by a period of subsequent realimentation and compensatory growth. Purebred Holstein Friesian bulls (n=20) were assigned to one of two feeding treatments (i) restricted feed allowance for 125 days (n=10) followed by ad libitum access to feed for a further 55 days or (ii) a control group with ad libitum access to feed through out the 180 days trial (n=10). The first 125 days of the trial were denoted as Peirod 1, during which treatment groups were fed differentially. The subsequent 55 days, denoted as Period 2 during which all bulls were fed ad libitum. All bulls received the same diet of 70% concentrate 30% grass silage through out the experimental trial,with the amount of feed provided different dependnet on each treatment group. Muscle biopsies were collected at 2 time points (end of the differential feeding in Period 1 (d 120) and during the realimentation phase in Period 2 (d 15 of re-alimentation). RNA was extracted and muscle gene expression was examined using RNAseq technology and bioinformatic analysis. During the differential feeding period, over-represented pathways including fatty acid beta oxidaiton, oxidative phosphorylation and TCA cyclewere identified, which indicate utilisation of lipid sotes for energy utilisaiton and also alterations in energy produciton during dietary restriciton in muscle. During the realimentation period, pathways involved in energy produciton were over-represented, with the direction of fold changes opposite to that of these pathways in Period 1. additionally, a number of genes involved in cell division and cellular proliferation were up-regulated during compensatory growth in re-alimentation, thereby promoting accelerated cell growth and proliferation in muscle tissue of animals experiencing compensatory growth. This information can be exploited in genomic breeding programmes to assist selection of cattle with a greater ability to compensate following a period dietary restriction. 40 muscle RNA samples were analysed in total. 10 samples were from muscle biopsies collected at the end of a period of dietary restriction (d 120) and 10 samples were from muscle biopsis collected during the initial stages of compensatory growth (d 15 of re-alimentation). In addition, RNA was also anlaysed from 10 samples collected from animals fed a libitum at each of these two timepoints.

Project description:The structure and function of the microbiome inhabiting the rumen are, amongst other factors, mainly shaped by the animal’s feed intake. Describing the influence of different diets on the inherent community arrangement and associated metabolic activities of the most active ruminal fractions (bacteria and archaea) is of great interest for animal nutrition, biotechnology and climatology. Samples were obtained from three fistulated Jersey cows rotationally fed with corn silage, grass silage or hay, each supplemented with a concentrate mixture. Samples were fractionated into ruminal fluid, squeezed solid and solid matter. DNA, proteins and metabolites were analyzed subsequently. DNA extracts were used for Illumina sequencing of the 16S rRNA gene and the metabolomes of rumen fluids were determined by 500MHz-NMR spectroscopy. Tryptic peptides derived from protein extracts were measured by LC-ESI-MS/MS and spectra were processed by a two-step database search for quantitative metaproteome characterization. Protein- and DNA-based datasets revealed significant differences between sample fractions and diets and affirmed similar trends concerning shifts in phylogenetic composition. Ribosomal genes and proteins belonging to the phylum of Proteobacteria, particularly Succinivibrionaceae, exhibited a higher abundance in corn silage-based samples while fiber-degraders of the Lachnospiraceae family emerged in great quantities throughout the solid phase fractions. The analysis of 8163 quantified bacterial proteins revealed the presence of 166 carbohydrate active enzymes in varying abundance. Cellulosome affiliated proteins were less expressed in the grass silage, glycoside hydrolases appeared in slightest numbers in the corn silage. Most expressed glycoside hydrolases belonged to families 57 and 2. Enzymes analogous to ABC transporters for amino acids and monosaccharides were more abundant in the corn silage whereas oligosaccharide transporters showed a higher abundance in the fiber-rich diets. Proteins involved in carbon metabolism were detected in high numbers and identification of metabolites like short-chain fatty acids, methylamines and phenylpropionate by NMR enabled linkage between producers and products. This study forms a solid basis to retrieve deeper insight into the complex network of gut microbial adaptation.

Dataset Information

Impact of process temperature and organic loading rate on cellulolytic / hydrolytic biofilm microbiomes during biomethanation of ryegrass silage revealed by genome-centered metagenomics and metatranscriptomics

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets