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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.

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Bacterial degradation of crude oil is a promising strategy for reducing the concentration of hydrocarbons in contaminated environments. In the first part of this study, we report the enrichment of two bacterial consortia from deep sediments of the Gulf of Mexico with crude oil as the sole carbon and energy source. We conducted a comparative analysis of the bacterial community in the original sediment, assessing its diversity, and compared it to the enrichment observed after exposure to crude oil in defined cultures. The consortium exhibiting the highest hydrocarbon degradation was predominantly enriched with Rhodococcus (75%). Bacterial community analysis revealed the presence of other hydrocarbonoclastic members in both consortia. In the second part, we report the isolation of the strain Rhodococcus sp. GOMB7 with crude oil as a unique carbon source under microaerobic conditions and its characterization. This strain demonstrated the ability to degrade long-chain alkanes, including eicosane, tetracosane, and octacosane. We named this new strain Rhodococcus qingshengii GOMB7. Genome analysis revealed the presence of several genes related to aromatic compound degradation, such as benA, benB, benC, catA, catB, and catC; and five alkB genes related to alkane degradation. Although members of the genus Rhodococcus are well known for their great metabolic versatility, including the aerobic degradation of recalcitrant organic compounds such as petroleum hydrocarbons, this is the first report of a novel strain of Rhodococcus capable of degrading long-chain alkanes under microaerobic conditions. The potential of R. qingshengii GOMB7 for applications in bioreactors or controlled systems with low oxygen levels offers an energy-efficient approach for treating crude oil-contaminated water and sediments.

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As a result of mining activities, waste of different types is generated. One example is mine tailings that contain potentially toxic elements such as heavy metals that negatively impact the environment and human health. Hence, developing treatments to guarantee its efficient elimination from the environment is necessary. Among these treatments, phytoremediation takes advantage of the potential of different plant species, to remove heavy metals from polluted sites. Gliricidia sepium is a tree that grows up to 15 m high and distributed from southern Mexico to Central America. This study evaluates the heavy metal bioaccumulation capacity in roots and leaves, and the effect of such bioaccumulation on fifteen macro- and one micro-morphological characters of G. sepium growing during 360 days in control, and in mine tailing substrates. G. sepium individuals growing on the exposed substrate registered the following average heavy metal bioaccumulation pattern in the roots: Fe > Pb > Zn > Cu, while in the leaf tissue, the bioaccumulation pattern was Cu > Fe > Pb > Zn. Macro- and micro-morphological characters evaluated in G. sepium decreased in plants exposed to metals. The translocation factor showed that Cu and Pb registered average values greater than 1. In conclusion, G. sepium is a species with potential for the phytoremediation of soils contaminated with Fe, Cu, and Pb, and for phytostabilizing soils polluted with Fe, Pb, Zn, and Cu, along with its ability to establish itself and turn into an abundant plant species in polluted sites, its capacity to bioaccumulate heavy metals in roots and leaves, and its high rate of HM translocation.

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Methanobacterium paludis is a hydrogenotrophic archaea first described in 2014 and isolated from a peatland area. So far, there is only one sequenced genome of this taxon. Here, we report the draft genome sequence of M. paludis IBT-C12, a metagenome-assembled genome (MAG) from sediments in the Apatlaco River, Mexico.

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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.

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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.

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The purpose of this work was to produce iron nanoparticles (Fe-NP) by microbial pathway from anaerobic bacteria grown in anaerobic fluidized bed reactors (AnFBRs) that constitute a new stage of a waste-based biorefinery. Bioparticles from biological fluidized bed reactors from a biorefinery of organic fraction of municipal solid wastes (that produces hydrolysates rich in reducing sugars) were nanodecorated (embedded nanobioparticle or nanodecorated bioparticle, ENBP) by biological reduction of iron salts. Factors "origin of bioparticles" (either from hydrogenogenic or methanogenic fluidized bed reactor) and "type of iron precursor salt" (iron chloride or iron citrate) were explored. SEM and high-resolution transmission electron microscopy (HRTEM) showed amorphous distribution of nanoparticles (NP) on the bioparticles surface, although small structures that are nanoparticle-like could be seen in the SEM micrographs. Some agglomeration of NPs was confirmed by DLS. Average NP size was lower in general for NP in ENBP-M than ENBP-H according to HRTEM. The factors did not have a significant influence on the specific surface area of NPs, which was high and in the range 490 to 650 m2 g-1. Analysis by EDS displayed consistent iron concentration 60-65% iron in nanoparticles present in ENBP-M (bioparticles previously grown in methanogenic bioreactor), whereas the iron concentration in NPs present in ENBP-H (bioparticles previously grown in hydrogenogenic bioreactor) was more variable in a range from 8.5 to 62%, depending on the iron salt. X-ray diffraction patterns showed the typical peaks for magnetite at 35° (3 1 1), 43° (4 0 0), and 62° (4 0 0); moreover, siderite diffraction pattern was found at 26° (0 1 2), 38° (1 1 0), and 42° (1 1 3). Results of infrared analysis of ENBP in our work were congruent with presence of magnetite and occasionally siderite determined by XRD analysis as well as presence of both Fe+2 and F+3 (and selected satellite signal peaks) observed by XPS. Our results on the ENBPs hold promise for water treatment, since iron NPs are commonly used in wastewater technologies that treat a wide variety of pollutants. Finally, the biological production of ENBP coupled to a biorefinery could become an environmentally friendly platform for nanomaterial biosynthesis as well as an additional source of revenues for a waste-based biorefinery.

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The increasing demand for clean water resources for human consumption, is raising concerning about the sustainable worldwide provisioning. In Mexico, rivers near to high-density urbanizations are subject to irrational exploitation where polluted water is a risk for human health. Therefore, the aims of this study are to analyze water quality parameters and bacterial community dynamics to understand the relation between them, in the Apatlaco river, which presents a clear environmental perturbance. Parameters such as total coliforms, chemical oxygen demand, harness, ammonium, nitrite, nitrate, total Kjeldahl nitrogen, dissolved oxygen, total phosphorus, total dissolved solids, and temperature were analyzed in 17 sampling points along the river. The high pollution level was registered in the sampling point 10 with 480 mg/L chemical oxygen demand, 7 mg/L nitrite, 34 mg/L nitrate, 2 mg/L dissolved oxygen, and 299 mg/L of total dissolved solids. From these sites, we selected four samples for DNA extraction and performed a metagenomic analysis using a whole metagenome shotgun approach, to compare the microbial communities between polluted and non-polluted sites. In general, Proteobacteria was the most representative phylum in all sites. However, the clean water reference point was enriched with microorganism from the Limnohabitans genus, a planktonic bacterium widespread in freshwater ecosystems. Nevertheless, in the polluted sampled sites, we found a high abundance of potential opportunistic pathogen genera such as Acinetobacter, Arcobacter, and Myroides, among others. This suggests that in addition to water contamination, an imminent human health risk due to pathogenic bacteria can potentially affect a population of ∼1.6 million people dwelling nearby. These results will contribute to the knowledge regarding anthropogenic pollution on the microbial population dynamic and how they affect human health and life quality.

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Agave tequilana bagasse is the main solid waste of the tequila manufacturing and represents an environmental issue as well as a potential feedstock for biofuel production due to its lignocellulosic composition and abundance. In this contribution, this feedstock was subjected to pretreatments with HCl and H2SO4 for sugar recovery and methane was produced from the hydrolysates in batch and sequencing batch reactors (AnSBR). Sugar recovery was optimized by using central composite designs at different levels of temperature, acid concentration and hydrolysis time. Results showed that at optimal conditions, the HCl pretreatment induced higher sugar recoveries than the H2SO4 one, 0.39 vs. 0.26 g total sugars/g bagasse. Furthermore, the H2SO4 hydrolysate contained higher concentrations of potential inhibitory compounds (furans and acetic acid). Subsequent anaerobic batch assays demonstrated that the HCl hydrolysate is a more suitable substrate for methane production; a four-fold increase was found. A second optimization by using HCl as acid catalyst and methane production as the response variable demonstrated that softer hydrolysis conditions are required to optimize methane production as compared to sugar recovery (1.8% HCl, 119 °C and 103min vs. 1.9% HCl, 130 °C and 133min). This softer conditions were used to feed an AnSBR for 110 days and evaluate its stability at three different cycle times (5, 3 and 2 days). Results showed stable reactor performances at cycle times of 5 and 3 days, obtaining the highest methane yield and production at 3 days, 0.28 NL CH4/g-COD and 1.04 NL CH4/d respectively. Operation at shorter cycle times is not advised due to microbial imbalance.

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