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Abstract Background Increasing evidence links the gut microbiota (GM) to Alzheimer’s disease (AD) but the mechanisms through which gut bacteria influence the brain are still unclear. This study tests the hypothesis that GM and mediators of the microbiota-gut-brain axis (MGBA) are associated with the amyloid cascade in sporadic AD. Methods We included 34 patients with cognitive impairment due to AD (CI-AD), 37 patients with cognitive impairment not due to AD (CI-NAD), and 13 cognitively unimpaired persons (CU). We studied the following systems: (1) fecal GM, with 16S rRNA sequencing; (2) a panel of putative MGBA mediators in the blood including immune and endothelial markers as bacterial products (i.e., lipopolysaccharide, LPS), cell adhesion molecules (CAMs) indicative of endothelial dysfunction (VCAM-1, PECAM-1), vascular changes (P-, E-Selectin), and upregulated after infections (NCAM, ICAM-1), as well as pro- (IL1β, IL6, TNFα, IL18) and anti- (IL10) inflammatory cytokines; (3) the amyloid cascade with amyloid PET, plasma phosphorylated tau (pTau-181, for tau pathology), neurofilament light chain (NfL, for neurodegeneration), and global cognition measured using MMSE and ADAScog. We performed 3-group comparisons of markers in the 3 systems and calculated correlation matrices for the pooled group of CI-AD and CU as well as CI-NAD and CU. Patterns of associations based on Spearman’s rho were used to validate the study hypothesis. Results CI-AD were characterized by (1) higher abundance of Clostridia_UCG-014 and decreased abundance of Moryella and Blautia (p

Abstract Background Bipolar Disorder (BD) is a complex mental disease characterized by recurrent episodes of mania and depression. Lithium (Li) represents the mainstay of BD pharmacotherapy, despite the narrow therapeutic index and the high variability in treatment response. However, although several studies have been conducted, the molecular mechanisms underlying Li therapeutic effects remain unclear. Methods In order to identify molecular signatures and biological pathways associated with Li treatment response, we conducted transcriptome and miRNome microarray analyses on lymphoblastoid cell lines (LCLs) from 20 patients diagnosed with BD classified as Li responders (n = 11) or non-responders (n = 9). Results We found 335 mRNAs and 77 microRNAs (miRNAs) significantly modulated in BD responders versus non-responders. Interestingly, pathway and network analyses on these differentially expressed molecules suggested a modulatory effect of Li on several immune-related functions. Indeed, among the functional molecular nodes, we found NF-κB and TNF. Moreover, networks related to these molecules resulted overall inhibited in BD responder patients, suggesting anti-inflammatory properties of Li. From the integrative analysis between transcriptomics and miRNomics data carried out using miRComb R package on the same samples from patients diagnosed with BD, we found 97 significantly and negatively correlated mRNA-miRNA pairs, mainly involved in inflammatory/immune response. Conclusions Our results highlight that Li exerts modulatory effects on immune-related functions and that epigenetic mechanisms, especially miRNAs, can influence the modulation of different genes and pathways involved in Li response. Moreover, our data suggest the potentiality to integrate data coming from different high-throughput approaches as a tool to prioritize genes and pathways.

Major depressive disorder (MDD) is a complex mental disorder where the neurochemical, neuroendocrine, immune, and metabolic systems are impaired. The microbiota-gut-brain axis is a bidirectional network where the central and enteric nervous systems are linked through the same endocrine, immune, neural, and metabolic routes dysregulated in MDD. Thus, gut-brain axis abnormalities in MDD patients may, at least in part, account for the symptomatic features associated with MDD. Recent investigations have suggested that the oral microbiome also plays a key role in this complex molecular picture of relationships. As on one hand there is a lot of what we know and on the other hand little of what we still need to know, we structured this review focusing, in the first place, on putting all pieces of this complex puzzle together, underlying the endocrine, immune, oxidative stress, neural, microbial neurotransmitters, and metabolites molecular interactions and systems lying at the base of gut microbiota (GM)–brain-depression interphase. Then, we focused on promising but still under-explored areas of research strictly linked to the GM and potentially involved in MDD development: (i) the interconnection of GM with oral microbiome that can influence the neuroinflammation-related processes and (ii) gut phageome (bacteria-infecting viruses). As conclusions and future directions, we discussed potentiality but also pitfalls, roadblocks, and the gaps to be bridged in this exciting field of research. By the development of a broader knowledge of the biology associated with MDD, with the inclusion of the gut/oral microbiome, we can accelerate the growth toward a better global health based on precision medicine.

Amplicon high-throughput sequencing of 16S ribosomal RNA (rRNA) gene is currently the most widely used technique to investigate complex gut microbial communities. Microbial identification might be influenced by several factors, including the choice of bioinformatic pipelines, making comparisons across studies difficult. Here, we compared four commonly used pipelines (QIIME2, Bioconductor, UPARSE and mothur) run on two operating systems (OS) (Linux and Mac), to evaluate the impact of bioinformatic pipeline and OS on the taxonomic classification of 40 human stool samples. We applied the SILVA 132 reference database for all the pipelines. We compared phyla and genera identification and relative abundances across the four pipelines using the Friedman rank sum test. QIIME2 and Bioconductor provided identical outputs on Linux and Mac OS, while UPARSE and mothur reported only minimal differences between OS. Taxa assignments were consistent at both phylum and genus level across all the pipelines. However, a difference in terms of relative abundance was identified for all phyla (p < 0.013) and for the majority of the most abundant genera (p < 0.028), such as Bacteroides (QIIME2: 24.5%, Bioconductor: 24.6%, UPARSE-linux: 23.6%, UPARSE-mac: 20.6%, mothur-linux: 22.2%, mothur-mac: 21.6%, p < 0.001). The use of different bioinformatic pipelines affects the estimation of the relative abundance of gut microbial community, indicating that studies using different pipelines cannot be directly compared. A harmonization procedure is needed to move the field forward.