The objective of this study is to understand the functional and metabolic potential of the microbial communities along the Apatlaco River and highlight activities related to bioremediation and its relationship with the Apatlaco’s pollutants, to enhance future design of more accurate bioremediation processes. Water samples were collected at four sampling sites along the Apatlaco River (S1–S4) and a whole metagenome shotgun sequencing was performed to survey and understand the microbial metabolic functions with potential for bioremediation. A HMMER search was used to detect sequence homologs related to polyethylene terephthalate (PET) and polystyrene biodegradation, along with bacterial metal tolerance in Apatlaco River metagenomes. Our results suggest that pollution is a selective pressure which enriches microorganisms at polluted sites, displaying metabolic capacities to tolerate and transform the contamination. According to KEGG annotation, all sites along the river have bacteria with genes related to xenobiotic biodegradation. In particular, functions such as environmental processing, xenobiotic biodegradation and glycan biosynthesis are over-represented in polluted samples, in comparison to those in the clean water site. This suggests a functional specialization in the communities that inhabit each perturbated point. Our results can contribute to the determination of the partition in a metabolic niche among different Apatlaco River prokaryotic communities, that help to contend with and understand the effect of anthropogenic contamination.

The application of machine learning (ML) techniques stands as a reliable method for aiding in the diagnosis of complex diseases. Recent studies have related the composition of the gut microbiota to the presence of autism spectrum disorder (ASD), but until now, the results have been mostly contradictory. This work proposes using machine learning to study the gut microbiome composition and its role in the early diagnosis of ASD. We applied support vector machines (SVMs), artificial neural networks (ANNs), and random forest (RF) algorithms to classify subjects as neurotypical (NT) or having ASD, using published data on gut microbiome composition. Naive Bayes, k-nearest neighbors, ensemble learning, logistic regression, linear regression, and decision trees were also trained and validated; however, the ones presented showed the best performance and interpretability. All the ML methods were developed using the SAS Viya software platform. The microbiome’s composition was determined using 16S rRNA sequencing technology. The application of ML yielded a classification accuracy as high as 90%, with a sensitivity of 96.97% and specificity reaching 85.29%. In the case of the ANN model, no errors occurred when classifying NT subjects from the first dataset, indicating a significant classification outcome compared to traditional tests and data-based approaches. This approach was repeated with two datasets, one from the USA and the other from China, resulting in similar findings. The main predictors in the obtained models differ between the analyzed datasets. The most important predictors identified from the analyzed datasets are Bacteroides, Lachnospira, Anaerobutyricum, and Ruminococcus torques. Notably, among the predictors in each model, there is the presence of bacteria that are usually considered insignificant in the microbiome’s composition due to their low relative abundance. This outcome reinforces the conventional understanding of the microbiome’s influence on ASD development, where an imbalance in the composition of the microbiota can lead to disrupted host–microbiota homeostasis. Considering that several previous studies focused on the most abundant genera and neglected smaller (and frequently not statistically significant) microbial communities, the impact of such communities has been poorly analyzed. The ML-based models suggest that more research should focus on these less abundant microbes. A novel hypothesis explains the contradictory results in this field and advocates for more in-depth research to be conducted on variables that may not exhibit statistical significance. The obtained results seem to contribute to an explanation of the contradictory findings regarding ASD and its relation with gut microbiota composition. While some research correlates higher ratios of Bacillota/Bacteroidota, others find the opposite. These discrepancies are closely linked to the minority organisms in the microbiome’s composition, which may differ between populations but share similar metabolic functions. Therefore, the ratios of Bacillota/Bacteroidota regarding ASD may not be determinants in the manifestation of ASD.

This paper describes an organosolv pretreatment of corncob waste to improve its anaerobic digestion for biogas production. Through a thermochemical process based on the use of ethanol and acetic acid, it was possible to separate the fractions of lignin, considered to be a natural inhibitor of anaerobic digestion processes. In addition, with this organosolv pretreatment, the available sugars in the carbohydrates present as monosaccharides, or simple sugars, were depolymerised, facilitating the digestion process. The obtained results include the chemical characterisation of the corncob, the hydrolysate, and the mixture with cow manure, finding that these substrates have potential to be used in anaerobic digestion. The total reducing sugars consumed were 96.8%, and total sugars were 85.75%. It was clearly observed that with the use of pretreatment with organosolv, the production of biogas was superior, because 484 NmL/gVS was obtained compared to the other reported treatments. It was also observed that adding the hydrolysate organosolv increased the production because the values of the control without hydrolysate were 120 NmL/gVS in the bottle experiment. When the experiment was scaled to the 5L reactor, the total volumes of biogas that were accumulated in 15 days of production were 5050 NmL/gVS and 1212 NmL/gVS with and without hydrolysate, respectively. This indicates that the organosolv pretreatment of corncob waste is effective in improving biogas production.

Objectives: In order to characterize a river-associated, enriched microbiome capable of degrading an anthraquinone dye from the oil blue family, as well as assessing its functional potential, we performed a taxa-specific metagenomic deconvolution analysis based on contact probability maps at the chromosomal level. This study will allow associating the genomic content of “Candidatus Afipia apatlaquensis” strain IBT-C3 with its phenotypic potential in the context of bioremediation of textile dyes. We anticipate that this resource will be very useful in comparative genomic clinical studies, contributing to understanding the genomic basis of Afipia pathogenicity. Data description: Here, we report the first draft genome sequence of “Candidatus Afipia apatlaquensis” sp. nov., strain IBT-C3, obtained by deconvolution of a textile-dye degrader microbiome in Mexico. The genome composite was deconvoluted using a Hi-C proximity ligation method. Whole-genome-based comparisons and phylogenomics reconstruction indicate that strain IBT-C3 represents a new species of the genus Afipia. The assembly completeness was 92.5% with 5,604,749 bp in length and 60.72% G+C content. The genome complement of IBT-C3 suggests a functional potential for decolorization of textile dyes, contrasting with previous reports of Afipia genus focused on its pathogenic potential.