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Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.IMPORTANCEUnderstanding bacterial interactions at the community level is critical for microbiology, and leveraging metabolic networks presents an efficient and effective approach. The introduction of this novel method for predicting interactions through machine learning classifiers has the potential to advance the field by reducing the number of experimental assays required and contributing to the development of more effective bacterial consortia., Competing Interests: The authors declare no conflict of interest.

Herein we report the use of an environmental multimetal(loid)-resistant strain, MF05, to biosynthesize single- or multi-element nanostructures under anaerobic conditions. Inorganic nanostructure synthesis typically requires methodologies and conditions that are harsh and environmentally hazardous. Thus, green/eco-friendly procedures are desirable, where the use of microorganisms and their extracts as bionanofactories is a reliable strategy. First, MF05 was entirely sequenced and identified as an Escherichia coli-related strain with some genetic differences from the traditional BW25113. Secondly, we compared the CdS nanostructure biosynthesis by whole-cell in a design defined minimal culture medium containing sulfite as the only sulfur source to obtain sulfide reduction from a low-cost chalcogen reactant. Under anaerobic conditions, this process was greatly favored, and irregular CdS (ex. 370 nm; em. 520-530 nm) was obtained. When other chalcogenites were tested (selenite and tellurite), only spherical Se0 and elongated Te0 nanostructures were observed by TEM and analyzed by SEM-EDX. In addition, enzymatic-mediated chalcogenite (sulfite, selenite, and tellurite) reduction was assessed by using MF05 crude extracts in anaerobiosis; similar results for nanostructures were obtained; however Se0 and Te0 formation were more regular in shape and cleaner (with less background). Finally, the in vitro nanostructure biosynthesis was assessed with salts of Ag, Au, Cd, and Li alone or in combination with chalcogenites. Several single or binary nanostructures were detected. Our results showed that MF05 is a versatile anaerobic bionanofactory for different types of inorganic NS. synthesis., Competing Interests: The authors have declared that no competing interests exist.

Clostridium baratii strains are rare opportunistic pathogens associated with botulism intoxication. They have been isolated from foods, soil and be carried asymptomatically or cause botulism outbreaks. Is not taxonomically related to Clostridium botulinum , but some strains are equipped with BoNT/F7 cluster. Despite their relationship with diseases, our knowledge regarding the genomic features and phylogenetic characteristics is limited. We analyzed the pangenome of C. baratii to understand the diversity and genomic features of this species. We compared existing genomes in public databases, metagenomes, and one newly sequenced strain isolated from an asymptomatic subject. The pangenome was open, indicating it comprises genetically diverse organisms. The core genome contained 28.49% of the total genes of the pangenome. Profiling virulence factors confirmed the presence of phospholipase C in some strains, a toxin capable of disrupting eukaryotic cell membranes. Furthermore, the genomic analysis indicated significant horizontal gene transfer (HGT) events as defined by the presence of prophage genomes. Seven strains were equipped with BoNT/F7 cluster. The active site was conserved in all strains, identifying a missing 7-aa region upstream of the active site in C. baratii genomes. This analysis could be important to advance our knowledge regarding opportunistic clostridia and better understand their contribution to disease.

Microbial communities perform emergent activities that are essentially different from those carried by their individual members. The gut microbiome and its metabolites have a significant impact on the host, contributing to homeostasis or disease. Food molecules shape this community, being fermented through cross-feeding interactions of metabolites such as lactate, acetate, and amino acids, or products derived from macromolecule degradation. Mathematical and experimental approaches have been applied to understand and predict the interactions between microorganisms in complex communities such as the gut microbiota. Rational and mechanistic understanding of microbial interactions is essential to exploit their metabolic activities and identify keystone taxa and metabolites. The latter could be used in turn to modulate or replicate the metabolic behavior of the community in different contexts. This review aims to highlight recent experimental and modeling approaches for studying cross-feeding interactions within the gut microbiome. We focus on short-chain fatty acid production and fiber fermentation, which are fundamental processes in human health and disease. Special attention is paid to modeling approaches, particularly kinetic and genome-scale stoichiometric models of metabolism, to integrate experimental data under different diet and health conditions. Finally, we discuss limitations and challenges for the broad application of these modeling approaches and their experimental verification for improving our understanding of the mechanisms of microbial interactions., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)

Consumption of fresh fruit is known to protect against non-communicable diseases due to the fruit's content in compounds with an antioxidant capacity, among them is polyphenols. Asian plums ( Prunus salicina L .) accumulate more than 40 phenolic compounds, with a remarkable diversity in their profiles, depending on the variety and environmental conditions. Although candidate genes have been indicated to control this trait, the loci controlling its phenotypic variation have not yet been defined in this species. The aim of this work was to identify the quantitative trait Loci (QTL) controlling the phenolic compounds content in the Asian plum skin and flesh. Using UHPLC-DAD-Orbitrap-MS, we determined that cyanidin-3-glucoside and cyanidin-3-rutinoside are the main anthocyanins in Asian plums. Other anthocyanins found to a lesser extent were tentatively identified as cyanidin bound to different sugar and procyanidin moieties. Then we phenotyped fruits of 92 and 80 F1 seedlings from the cross < "98.99" × "Angeleno"> (98 Ang) for two harvest seasons. We used HPLC-DAD to quantify single anthocyanin and spectrophotometric techniques to determine the total content of phenols, flavonoids, procyanidins, and antioxidant activity (DPPH and FRAP). To determine the phenotype-genotype association of phenolic compounds content, phenotypic values (adjusted by linear mixed-effects models), genotypic data and linkage maps were analyzed with the multiple QTL model (MQM) approach. We found a total of 21 significant trait-marker associations: 13 QTLs segregating from "98.99" and 8 QTLs from "Angeleno." From these associations, 8 corresponded to phenolic compound content in the flesh and 13 in the skin. Phenotype variance was explained by the detected loci , ranging from 12.4 to 27.1%. The identified loci are related to the content of cyanidin-3-glucoside (LG4), cyanidin-3-rutinoside (LG4), total flavonoids and procyanidins (LG5 and LG8), and minor anthocyanin compounds (LG3 and LG4). These results will help improve the efficiency of breeding programs for the generation of Asian plum varieties with high phenolic compound content., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Valderrama-Soto, Salazar, Sepúlveda-González, Silva-Andrade, Gardana, Morales, Battistoni, Jiménez-Muñoz, González, Peña-Neira, Infante and Pacheco.)