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To date several studies address the important role of gut microbiome and its interplay with the human host in the health and disease status. However, the selection of a universal sampling matrix representative of the microbial biodiversity associated with the gastrointestinal (GI) tract, is still challenging. Here we present a study in which, through a deep metabarcoding analysis of the 16S rRNA gene, we compared two sampling matrices, feces (F) and colon washing feces (CWF), in order to evaluate their relative effectiveness and accuracy in representing the complexity of the human gut microbiome. A cohort of 30 volunteers was recruited and paired F and CWF samples were collected from each subject. Alpha diversity analysis confirmed a slightly higher biodiversity of CWF compared to F matched samples. Likewise, beta diversity analysis proved that paired F and CWF microbiomes were quite similar in the same individual, but remarkable inter-individual variability occurred among the microbiomes of all participants. Taxonomic analysis in matched samples was carried out to investigate the intra and inter individual/s variability. Firmicutes, Bacteroidota, Proteobacteria and Actinobacteriota were the main phyla in both F and CWF samples. At genus level, Bacteirodetes was the most abundant in F and CWF samples, followed by Faecalibacterium, Blautia and Escherichia-Shigella. Our study highlights an inter-individual variability greater than intra-individual variability for paired F and CWF samples. Indeed, an overall higher similarity was observed across matched F and CWF samples, suggesting, as expected, a remarkable overlap between the microbiomes inferred using the matched F and CWF samples. Notably, absolute quantification of total 16S rDNA by droplet digital PCR (ddPCR) revealed comparable overall microbial load between paired F and CWF samples. We report here the first comparative study on fecal and colon washing fecal samples for investigating the human gut microbiome and show that both types of samples may be used equally for the study of the gut microbiome. The presented results suggest that the combined use of both types of sampling matrices could represent a suitable choice to obtain a more complete overview of the human gut microbiota for addressing different biological and clinical questions., (© 2022. The Author(s).)

The possible existence of yet undiscovered human tumorigenic viruses is still under scrutiny. The development of large-scale sequencing technologies, coupled with bioinformatics techniques for the characterization of metagenomic sequences, have provided an invaluable tool for the detection of unknown, infectious, tumorigenic agents, as demonstrated by several recent studies. However, discoveries of novel viruses possibly associated with tumorigenesis are scarce at best. Here, we apply a rigorous bioinformatics workflow to investigate in depth tumor metagenomes from a small but carefully selected cohort of immunosuppressed patients. While a variegated bacterial microbiome was associated with each tumor, no evidence of the presence of putative oncoviruses was found. These results are consistent with the major findings of several recent papers and suggest that new human tumorigenic viruses are not common even in immunosuppressed populations.

Alzheimer's Disease (AD) is the most common cause of dementia affecting the elderly population worldwide. We have performed a comprehensive transcriptome profiling of Late-Onset AD (LOAD) patients using second generation sequencing technologies, identifying 2,064 genes, 47 lncRNAs and 4 miRNAs whose expression is specifically deregulated in the hippocampal region of LOAD patients. Moreover, analyzing the hippocampal, temporal and frontal regions from the same LOAD patients, we identify specific sets of deregulated miRNAs for each region, and we confirm that the miR-132/212 cluster is deregulated in each of these regions in LOAD patients, consistent with these miRNAs playing a role in AD pathogenesis. Notably, a luciferase assay indicates that miR-184 is able to target the 3'UTR NR4A2 - which is known to be involved in cognitive functions and long-term memory and whose expression levels are inversely correlated with those of miR-184 in the hippocampus. Finally, RNA editing analysis  reveals a general RNA editing decrease in LOAD hippocampus, with 14 recoding sites significantly and differentially edited in 11 genes. Our data underline specific transcriptional changes in LOAD brain and provide an important source of information for understanding the molecular changes characterizing LOAD progression.