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In recent years, the involvement of the gut microbiota in disease and health has been investigated by sequencing the 16S gene from fecal samples. Dysbiotic gut microbiota was also observed in Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by gastrointestinal symptoms. However, despite the relevant number of studies, it is still difficult to identify a typical dysbiotic profile in ASD patients. The discrepancies among these studies are due to technical factors (i.e., experimental procedures) and external parameters (i.e., dietary habits). In this paper, we collected 959 samples from eight available projects (540 ASD and 419 Healthy Controls, HC) and reduced the observed bias among studies. Then, we applied a Machine Learning (ML) approach to create a predictor able to discriminate between ASD and HC. We tested and optimized three algorithms: Random Forest, Support Vector Machine and Gradient Boosting Machine. All three algorithms confirmed the importance of five different genera, including Parasutterella and Alloprevotella . Furthermore, our results show that ML algorithms could identify common taxonomic features by comparing datasets obtained from countries characterized by latent confounding variables.

Seahorses are considered a flagship species for conservation efforts and due to their conservation status, improving knowledge on their dietary composition while applying a non-invasive approach, could be useful. Using Hippocampus guttulatus as a case study, the present study represents pioneering research into investigating the diet of seahorses by NGS-based DNA metabarcoding of fecal samples. The study developed and tested the protocol for fecal DNA metabarcoding during the feeding trials where captive seahorses were fed on a diet of known composition; the process was subsequently applied on fecal samples collected from wild individuals. The analysis of samples collected during the feeding trials indicated the reliability of the applied molecular approach by allowing the characterization of the effectively ingested prey. In the field study, among detected prey species, results revealed that the majority of the seahorse samples contained taxa such as Amphipoda, Decapoda, Isopoda, and Calanoida, while less common prey taxa were Gastropoda and Polyplacophora. As only a small amount of starting fecal material is needed and the sampling procedure is neither invasive nor lethal. The present study indicates DNA metabarcoding as useful for investigating seahorse diet and could help define management and conservation actions.

Microorganisms inhabiting saline environments are an interesting ecological model for the study of the adaptation of organisms to extreme living conditions and constitute a precious resource of enzymes and bioproducts for biotechnological applications. We analyzed the microbial communities in nine ponds with increasing salt concentrations (salinity range 4.9-36.0%) of the Saltern of Margherita di Savoia (Italy), the largest thalassohaline saltern in Europe. A deep-metabarcoding NGS procedure addressing separately the V5-V6 and V3-V4 hypervariable regions of the 16S rRNA gene of Bacteria and Archaea , respectively, and a CARD-FISH (catalyzed reporter deposition fluorescence in situ hybridization) analysis allowed us to profile the dynamics of microbial populations at the different salt concentrations. Both the domains were detected throughout the saltern, even if the low relative abundance of Archaea in the three ponds with the lowest salinities prevented the construction of the relative amplicon libraries. The highest cell counts were recorded at 14.5% salinity for Bacteria and at 24.1% salinity for Archaea . While Bacteria showed the greatest number of genera in the first ponds (salinity range 4.9-14.5%), archaeal genera were more numerous in the last ponds of the saltern (salinity 24.1-36.0%). Among prokaryotes, Salinibacter was the genus with the maximum abundance (~49% at 34.6% salinity). Other genera detected at high abundance were the archaeal Haloquadratum (~43% at 36.0% salinity) and Natronomonas (~18% at 13.1% salinity) and the bacterial " Candidatus Aquiluna" (~19% at 14.5% salinity). Interestingly, " Candidatus Aquiluna" had not been identified before in thalassohaline waters.