PredicTF: prediction of bacterial transcription factors in complex microbial communities using deep learning.

BACKGROUND: Transcription factors (TFs) are proteins controlling the flow of genetic information by regulating cellular gene expression. A better understanding of TFs in a bacterial community context may open novel revenues for exploring gene regulation in ecosystems where bacteria play a key role. Here we describe PredicTF, a platform supporting the prediction and classification of novel bacterial TF in single species and complex microbial communities. PredicTF is based on a deep learning algorithm. RESULTS: To train PredicTF, we created a TF database (BacTFDB) by manually curating a total of 11,961 TF distributed in 99 TF families. Five model organisms were used to test the performance and the accuracy of PredicTF. PredicTF was able to identify 24-62% of the known TFs with an average precision of 88% in our five model organisms. We demonstrated PredicTF using pure cultures and a complex microbial community. In these demonstrations, we used (meta)genomes for TF prediction and (meta)transcriptomes for determining the expression of putative TFs. CONCLUSION: PredicTF demonstrated high accuracy in predicting transcription factors in model organisms. We prepared the pipeline to be easily implemented in studies profiling TFs using (meta)genomes and (meta)transcriptomes. PredicTF is an open-source software available at https://github.com/mdsufz/PredicTF .

Increased Malbranchea pulchella ß-glucosidase production and its application in agroindustrial residue hydrolysis: A research based on experimental designs.

ß-Glucosidases are a limiting factor in the conversion of cellulose to glucose for the subsequent ethanol production. Here, ß-glucosidase production by Malbranchea pulchella was optimized using Composite Central Designs and Response Surface Methodologies from a medium designed. The coefficient of determination (R2 ) was 0.9960, F-value was very high, and the lack of fit was found to be non-significant. This indicates a statistic valid and predictive result. M. pulchella enzymatic extract was successfully tested as an enzymatic cocktail in a mixture design using sugarcane bagasse, soybean hull and barley bagasse. We proved that the optimization of the ß-glucosidase production and the application in hydrolysis using unexpansive biomass and agricultural wastes can be accomplished by means of statistical methodologies. The strategy presented here can be useful for the improvement of enzyme production and the hydrolysis process, arising as an alternative for bioeconomy.

Complementary Roles of Wood-Inhabiting Fungi and Bacteria Facilitate Deadwood Decomposition.

Forests accumulate and store large amounts of carbon (C), and a substantial fraction of this stock is contained in deadwood. This transient pool is subject to decomposition by deadwood-associated organisms, and in this process it contributes to CO2 emissions. Although fungi and bacteria are known to colonize deadwood, little is known about the microbial processes that mediate carbon and nitrogen (N) cycling in deadwood. In this study, using a combination of metagenomics, metatranscriptomics, and nutrient flux measurements, we demonstrate that the decomposition of deadwood reflects the complementary roles played by fungi and bacteria. Fungi were found to dominate the decomposition of deadwood and particularly its recalcitrant fractions, while several bacterial taxa participate in N accumulation in deadwood through N fixation, being dependent on fungal activity with respect to deadwood colonization and C supply. Conversely, bacterial N fixation helps to decrease the constraints of deadwood decomposition for fungi. Both the CO2 efflux and N accumulation that are a result of a joint action of deadwood bacteria and fungi may be significant for nutrient cycling at ecosystem levels. Especially in boreal forests with low N stocks, deadwood retention may help to improve the nutritional status and fertility of soils.IMPORTANCE Wood represents a globally important stock of C, and its mineralization importantly contributes to the global C cycle. Microorganisms play a key role in deadwood decomposition, since they possess enzymatic tools for the degradation of recalcitrant plant polymers. The present paradigm is that fungi accomplish degradation while commensalist bacteria exploit the products of fungal extracellular enzymatic cleavage, but this assumption was never backed by the analysis of microbial roles in deadwood. This study clearly identifies the roles of fungi and bacteria in the microbiome and demonstrates the importance of bacteria and their N fixation for the nutrient balance in deadwood as well as fluxes at the ecosystem level. Deadwood decomposition is shown as a process where fungi and bacteria play defined, complementary roles.