1,721,167 research outputs found
A genomic and transcriptomic approach to characterize oenological Saccharomyces cerevisiae strains.
Full text linkGenus Saccharomyces includes a large number of microorganisms that are important for industrial applications such as the production of fermented beverages, biofuel and baking. Natural selection combined with domestication applied selective pressures to the genome of this yeast producing large numbers of different strains with specialized phenotypes.
During the last decades thousand of strains have been phenotypically characterized but correlation between phenotype and genotype is not yet completely unveiled. Genome sequence analysis is a crucial step to obtain a general description of gene content and highlight differences between strains. In this study the homozygous derivatives of four ecotypical Saccharomyces cerevisiae strains isolated from Raboso and Prosecco fermented grape bunch have been successfully sequenced using next generation sequencing, and a variety of tools have been used and developed to solve the complex task of genome finishing.
A detailed overview of gene expression in different winemaking and laboratory strains has also been performed using SOLiD RNA-seq. Samples growth in synthetic wine media on controlled bioreactors have been collected during fermentation process. Our results revealed a transcriptional fingerprint characterizing oenological strains adaptation to stressful environment. A comparison between differences in promoter sequences between strains and their downstream effect on gene expression have been performed and the results show a higher influence of tandem repeat variability respect to mutations on transcription factor binding sites. Finally using statistical analysis we correlate the genetic traits of strains with their metabolic properties and we obtained a global overview of fermentation performances in the different genetic groups.Il genere Saccharomyces comprende un gran numero di microrganismi di interesse tecnologico, utilizzati ad esempio per la produzione di bevande fermentate, biocarburanti e per la panificazione. La selezione naturale unita alla domesticazione ha determinato una pressione selettiva che ha modificato il genoma di questi lieviti producendo un ampio numero di ceppi diversi con fenotipi specializzati.
Negli ultimi anni centinaia di ceppi sono stati caratterizzati dal punto di vista fenotipico ma una correlazione tra il fenotipo e il genotipo non è stata ancora completamente chiarita. L’analisi del sequenziamento genomico è un passo cruciale per ottenere una descrizione globale del contenuto genico e per evidenziare le differenze tra i ceppi. In questo studio sono stati sequenziati i genomi degli omozigoti derivati da quattro ceppi ecotipici di S. cerevisiae isolati da grappoli fermentati di Prosecco e Raboso Piave utilizzando sequenziatori di Nuova Generazione. Numerosi strumenti informatici sono stati utilizzati e sviluppati per adempire al complesso compito del finishing.
Inoltre una dettagliata panoramica dell’espressione genica in 5 ceppi di vinificazione e 1 di laboratorio è stata effettuata utilizzando la tecnica RNA-seq con la metodologia SOLiD. I lieviti sono stati cresciuti in mosto sintetico in bioreattori controllati e dei campioni sono stati prelevati durante il processo fermentativo. I risultati hanno rivelato un profilo trascrizionale caratteristico dell’adattamento dei ceppi enologici allo stress dell’ambiente di vinificazione. Un confronto tra le differenze nelle sequenze promotoriali tra i ceppi e il successivo effetto a catena sull’espressione genica è stato considerato e i risultati evidenziano una maggior influenza della variabilità delle tandem repeat rispetto alle mutazioni sui siti di binding dei fattori di trascrizione. Infine utilizzando dei modelli statistici siamo riusciti a correlare le caratteristiche genetiche dei ceppi con le loro proprietà metaboliche e ad avere una visione globale dell’abilità di fermentazione dei diversi ceppi
The microbiome of biogas reactors treating lignocellulosic substrates revealed different mechanisms for carbohydrates utilization.
No full textThe present study dissected the microbiome of biogas reactors treating lignocellulosic substrate and swine manure by means of high throughput Illumina sequencing. A comparative metagenomic analysis allowed to identify the microbial species firmly attached to the digested lignocellulosic particles and to distinguish them from the planktonic microbes floating in the liquid medium. Proteobacteria and Firmicutes were the most abundant phyla identified respectively in the liquid samples and firmly attached to the grass, and accounted approximately 17 and 22% of the total microbial counts. Additionally, Actinobacteria were also presented in both samples but in lower relative abundance. Assembly of the shotgun reads followed by a binning process led to the extraction of 151 genome bins, out of which 80 microbial species were completely new and not previously deposited in any database. Moreover, it was shown that 25 microbial genomes were more enriched (>2 fold) in the firmly attached grass samples compared to the liquid phase. A bioinformatic approach based on multiple databases for functional annotation (KEGG, COG, SEED and dbCAN) demonstrated that these microbial species encode enzymes related to carbohydrate utilisation and present numerous carbohydrate binding modules. Finally, it was found that apart from the cellulosome multi-enzyme complex, specific microbes, such as Bacteroidetes, present different mechanisms for binding and degrading the lignocellulose due to the presence of multiple CBM6 modules in beta-xylosidase and endoglucanase proteins or SLH modules in unknown proteins
Impact of genomic differences on phenothypic variability in four vineyard Saccharomyces cerevisiae strains
No full textLa genomica dei batteri lattici: quali benefici per il settore caseario? Un caso studio: Streptococcus thermophilus
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Analyzing dominance potential and formic acid resistance in Saccharomyces cerevisiae strains: unveiling phenotypic and genomic characteristics
No full textDeciphering phenotypic and genomic features in Saccharomyces cerevisiae strains with high dominance potential and formic acid resistance.
No full textLignocellulosic biomass is one of the most promising substrates for the production of bioethanol. However, the fermentation of this feedstock is still nonprofitable. In fact, lignocellulosic substrates require a pretreatment step, which releases inhibitors detrimental to the growth of S. cerevisiae and, thus, to the fermentation itself. Nowadays, in sugarcane-to-ethanol industrial plants, there is usually a rapid succession of yeast strains, and the dominant one(s) can overcome the starter. Therefore, both dominant potential and inhibitor tolerance are crucial traits for the selection of superior yeast strains.
In this study, thanks to a hybrid approach combinings biotechnology and bioinformatics, a wide collection of S. cerevisiae strains composed of laboratory, industrial and oenological strains was investigated using a comparative genomic analysis. A cluster of 20 strains was then selected on the basis of their promising robustness and tested for their ability to survive when mixed together. During the selection process, different stresses typical of lignocellulosic ethanol production were applied, including i) low readily assimilable nitrogen (RAN) (30 mg/L); ii) high concentrations of acetic acid (3 g/L); iii) formic acid (1.0 and 1.2 g/L). Few strains showed outstanding fitness and were selected for genomic insights. Gene copy numbers and SNPs were analyzed in order to prioritize variants and better assess their linkage with phenotypes. A novel cluster of variants impacting key genes involved in formic acid and yeast dominance was found and deeply investigated. These findings can support the development of superior S. cerevisiae strains for lignocellulosic bioethanol production
Transcriptome structure variability in Saccharomyces cerevisiae strains determined with a newly developed assembly software
Full text linkRNA-seq studies have an important role for both large-scale analysis of gene expression and for transcriptome reconstruction. However, the lack of software specifically developed for the analysis of the transcriptome structure in lower eukaryotes, has so far limited the comparative studies among different species and strains.Results: In order to fill this gap, an innovative software called ORA (Overlapped Reads Assembler) was developed. This software allows a simple and reliable analysis of the transcriptome structure in organisms with a low number of introns. It can also determine the size and the position of the untranslated regions (UTR) and of polycistronic transcripts. As a case study, we analyzed the transcriptional landscape of six S. cerevisiae strains in two different key steps of the fermentation process. This comparative analysis revealed differences in the UTR regions of transcripts. By extending the transcriptome analysis to yeast species belonging to the Saccharomyces genus, it was possible to examine the conservation level of unknown non-coding RNAs and their putative functional role.Conclusions: By comparing the results obtained using ORA with previous studies and with the transcriptome structure determined with other software, it was proven that ORA has a remarkable reliability. The results obtained from the training set made it possible to detect the presence of transcripts with variable UTRs between S. cerevisiae strains. Finally, we propose a regulatory role for some non-coding transcripts conserved within the Saccharomyces genus and localized in the antisense strand to genes involved in meiosis and cell wall biosynthesis
Protein evolution in deep sea bacteria: an analysis of amino acids substitution rates.
Full text linkBACKGROUND:
Abyssal microorganisms have evolved particular features that enable them to grow in their extreme habitat. Genes belonging to specific functional categories are known to be particularly susceptible to high-pressure; therefore, they should show some evidence of positive selection. To verify this hypothesis we computed the amino acid substitution rates between two deep-sea microorganisms, Photobacterium profundum SS9 and Shewanella benthica KT99, and their respective shallow water relatives.
RESULTS:
A statistical analysis of all the orthologs, led to the identification of positive selected (PS) genes, which were then used to evaluate adaptation strategies. We were able to establish "Motility" and "Transport" as two classes significantly enriched with PS genes. The prevalence of transporters led us to analyze variable amino acids (PS sites) by mapping them according to their membrane topology, the results showed a higher frequency of substitutions in the extra-cellular compartment. A similar analysis was performed on soluble proteins, mapping the PS sites on the 3D structure, revealing a prevalence of substitutions on the protein surface. Finally, the presence of some flagellar proteins in the Vibrionaceae PS list confirms the importance of bacterial motility as a SS9 specific adaptation strategy.
CONCLUSION:
The approach presented in this paper is suitable for identifying molecular adaptations to particular environmental conditions. The statistical method takes into account differences in the ratio between non-synonymous to synonymous substitutions, thus allowing the detection of the genes that underwent positive selection. We found that positive selection in deep-sea adapted bacteria targets a wide range of functions, for example solute transport, protein translocation, DNA synthesis and motility. From these data clearly emerges an involvement of the transport and metabolism processes in the deep-sea adaptation strategy of both bathytypes considered, whereas the adaptation of other biological processes seems to be specific to either one or the other. An important role is hypothesized for five PS genes belonging to the transport category that had been previously identified as differentially expressed in microarray experiments. Strikingly, structural mapping of PS sites performed independently on membrane and soluble proteins revealed that residues under positive selection tend to occur in specific protein regions
Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities
Full text linkMulti-omics data integration via mechanistic models of metabolism is a scalable and flexible framework for exploring biological hypotheses in microbial systems. However, although most microorganisms are unculturable, such multi-omics modeling is limited to isolate microbes or simple synthetic communities. Here, we developed an approach for modeling microbial activity and interactions that leverages the reconstruction of metagenome-assembled genomes and associated genome-centric metatranscriptomes. At its core, we designed a method for condition-specific metabolic modeling of microbial communities through the integration of metatranscriptomic data. Using this approach, we explored the behavior of anaerobic digestion consortia driven by hydrogen availability and human gut microbiota dysbiosis associated with Crohn’s disease, identifying condition-dependent amino acid requirements in archaeal species and a reduced short-chain fatty acid exchange network associated with disease, respectively. Our approach can be applied to complex microbial communities, allowing a mechanistic contextualization of multi-omics data on a metagenome scale.<br/
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