Project description:Amplicon-based fungal metagenomic sequencing for the identification of fungal species in brain tissue from Alzheimer's disease. The study consists in 14 samples, sequenced using Illumina's paired-end technology.
Project description:Gaining new knowledge through fungal monoculture responses to lignocellulose is a widely used approach that can lead to better cocktails for lignocellulose saccharification (the enzymatic release of sugars which are subsequently used to make biofuels). However, responses in lignocellulose mixed cultures are rarely studied in the same detail even though in nature fungi often degrade lignocellulose as mixed communities. Using a dual RNA-seq approach, we describe the first study of the transcriptional responses of wild-type strains of Aspergillus niger, Trichoderma reesei and Penicillium chrysogenum in two and three mixed species shake-flask cultures with wheat straw. Based on quantification of species-specific rRNA, a set of conditions was identified where mixed cultures could be sampled so as to obtain sufficient RNA-seq reads for analysis from each species. The number of differentially-expressed genes varied from a couple of thousand to fewer than one hundred. The proportion of carbohydrate active enzyme (CAZy) encoding transcripts was lower in the majority of the mixed cultures compared to the respective straw monocultures. A small subset of P. chrysogenum CAZy genes showed five to ten-fold significantly increased transcript abundance in a two-species mixed culture with T. reesei. However, a substantial number of T. reesei CAZy transcripts showed reduced abundance in mixed cultures. The highly induced genes in mixed cultures indicated that fungal antagonism was a major part of the mixed cultures. In line with this, secondary metabolite producing gene clusters showed increased transcript abundance in mixed cultures and also mixed cultures with T. reesei led to a decrease in the mycelial biomass of A. niger. Significantly higher monomeric sugar release from straw was only measured using a minority of the mixed culture filtrates and there was no overall improvement. This study demonstrates fungal interaction with changes in transcripts, enzyme activities and biomass in the mixed cultures and whilst there were minor beneficial effects for CAZy transcripts and activities, the competitive interaction between T. reesei and the other fungi was the most prominent feature of this study.
Project description:Chronic acid suppression by proton pump inhibitor (PPI) has been hypothesized to alter the gut microbiota via a change in intestinal pH. To evaluate the changes in gut microbiota composition by long-term PPI treatment. Twenty-four week old F344 rats were fed with (n = 5) or without (n = 6) lansoprazole (PPI) for 50 weeks. Then, profiles of luminal microbiota in the terminal ileum were analyzed. Pyrosequencing for 16S rRNA gene was performed by genome sequencer FLX (454 Life Sciences/Roche) and analyzed by metagenomic bioinformatics.
Project description:To explain enhanced biofilm formation and increased dissemination of S. epidermidis in mixed-species biofilms, microarrays were used to explore differential gene expression of S. epidermidis in mixed-species biofilms. One sample from single species biofilm (S1) and mixed-species biofilm (SC2) were excluded from analyses for outliers. We observed upregulation (2.7%) and down regulation (6%) of S. epidermidis genes in mixed-species biofilms. Autolysis repressors lrgA and lrgB were down regulated 36-fold and 27-fold respectively and was associated with increased eDNA possibly due to enhanced autolysis in mixed-species biofilms. These data suggest that bacterial autolysis and release of eDNA in the biofilm matrix may be responsible for enhancement and dissemination of mixed-species biofilms of S. epidermidis and C. albicans.
Project description:Evaluation of short-read-only, long-read-only, and hybrid assembly approaches on metagenomic samples demonstrating how they affect gene and protein prediction which is relevant for downstream functional analyses. For a human gut microbiome sample, we use complementary metatranscriptomic, and metaproteomic data to evaluate the metagenomic-based protein predictions.