Unraveling potential enzymes and their functional role in fine cocoa beans fermentation using temporal shotgun metagenomics.

Cocoa beans fermentation is a spontaneous process, essential for the generation of quality starting material for fine chocolate production. The understanding of this process has been studied by the application of high-throughput sequencing technologies, which grants a better assessment of the different microbial taxa and their genes involved in this microbial succession. The present study used shotgun metagenomics to determine the enzyme-coding genes of the microbiota found in two different groups of cocoa beans varieties during the fermentation process. The statistical evaluation of the most abundant genes in each group and time studied allowed us to identify the potential metabolic pathways involved in the success of the different microorganisms. The results showed that, albeit the distinction between the initial (0 h) microbiota of each varietal group was clear, throughout fermentation (24-144 h) this difference disappeared, indicating the existence of selection pressures. Changes in the microbiota enzyme-coding genes over time pointed to the distinct ordering of fermentation at 24-48 h (T1), 72-96 h (T2), and 120-144 h (T3). At T1, the significantly more abundant enzyme-coding genes were related to threonine metabolism and those genes related to the glycolytic pathway, explained by the abundance of sugars in the medium. At T2, the genes linked to the metabolism of ceramides and hopanoids lipids were clearly dominant, which are associated with the resistance of microbial species to extreme temperatures and pH values. In T3, genes linked to trehalose metabolism, related to the response to heat stress, dominated. The results obtained in this study provided insights into the potential functionality of microbial community succession correlated to gene function, which could improve cocoa processing practices to ensure the production of more stable quality end products.

Integrating microbial metagenomics and physicochemical parameters and a new perspective on starter culture for fine cocoa fermentation.

Cocoa beans used for chocolate production are fermented seeds of Theobroma cacao obtained by a natural fermentation process. The flavors and chemical compounds produced during the fermentation process make this step one of the most important in fine chocolate production. Herein, an integrative analysis of the variation of microbial community structure, using a shotgun metagenomics approach and associated physicochemical features, was performed during fermentation of fine cocoa beans. Samples of Forastero variety (FOR) and a mixture of two hybrids (PS1319 and CCN51) (MIX) from Bahia, Brazil, were analyzed at 7 different times. In the beginning (0 h), the structures of microbial communities were very different between FOR and MIX, reflecting the original plant-associated microbiomes. The highest change in microbial community structures occurred at the first 24 h of fermentation, with a marked increase in temperature and acetic acid concentration, and pH decrease. At 24-48 h both microbial community structures were quite homogenous regarding temperature, acetic acid, succinic acid, pH, soluble proteins and total phenols. During 72-96 h, the community structure resembles an acidic and warmer environment, prevailing few acetic acid bacteria. Taxonomic richness and abundance at 72-144 h exhibited significant correlation with temperature, reducing sugars, succinic, and acetic acids. Finally, we recommend that dominant microbial species of spontaneous fine cocoa fermentations should be considered as inoculum in accordance with the farm/region and GMP to maintain a differential organoleptic feature for production of fine chocolate. In our study, a starter inoculum composed of Acetobacter pausterianus and Hanseniaspora opuntiae strains is indicated.

POTION: an end-to-end pipeline for positive Darwinian selection detection in genome-scale data through phylogenetic comparison of protein-coding genes.

BACKGROUND: Detection of genes evolving under positive Darwinian evolution in genome-scale data is nowadays a prevailing strategy in comparative genomics studies to identify genes potentially involved in adaptation processes. Despite the large number of studies aiming to detect and contextualize such gene sets, there is virtually no software available to perform this task in a general, automatic, large-scale and reliable manner. This certainly occurs due to the computational challenges involved in this task, such as the appropriate modeling of data under analysis, the computation time to perform several of the required steps when dealing with genome-scale data and the highly error-prone nature of the sequence and alignment data structures needed for genome-wide positive selection detection. RESULTS: We present POTION, an open source, modular and end-to-end software for genome-scale detection of positive Darwinian selection in groups of homologous coding sequences. Our software represents a key step towards genome-scale, automated detection of positive selection, from predicted coding sequences and their homology relationships to high-quality groups of positively selected genes. POTION reduces false positives through several sophisticated sequence and group filters based on numeric, phylogenetic, quality and conservation criteria to remove spurious data and through multiple hypothesis corrections, and considerably reduces computation time thanks to a parallelized design. Our software achieved a high classification performance when used to evaluate a curated dataset of Trypanosoma brucei paralogs previously surveyed for positive selection. When used to analyze predicted groups of homologous genes of 19 strains of Mycobacterium tuberculosis as a case study we demonstrated the filters implemented in POTION to remove sources of errors that commonly inflate errors in positive selection detection. A thorough literature review found no other software similar to POTION in terms of customization, scale and automation. CONCLUSION: To the best of our knowledge, POTION is the first tool to allow users to construct and check hypotheses regarding the occurrence of site-based evidence of positive selection in non-curated, genome-scale data within a feasible time frame and with no human intervention after initial configuration. POTION is available at http://www.lmb.cnptia.embrapa.br/share/POTION/.