Symbiosis in insects is accumulating significant amount of studies: the description of a wide array of mutualistic associations across the evolutionary history of insects suggests that resident microbiota acts as a driving force by affecting several aspects of hosts biology. Among arthropods, mosquito midgut microbiota has been largely investigated, providing crucial insights on the role and implications of host-symbiont relationships. However, limited amount of studies addressed their efforts on the investigation of microbiota colonizing salivary glands and reproductive tracts, crucial organs for pathogen invasion and vertical transmission of symbiotic microorganisms. Using 16S rRNA gene sequencing-based approach, we analysed the microbiota of gut, salivary glands and reproductive tracts of several mosquito species, representing some of the main vectors of diseases, aiming at describing the dynamics of bacterial communities within the individual. We identified a shared core microbiota between different mosquito species, although interesting inter- and intra-species differences were detected. Additionally, our results showed deep divergences between genera, underlining microbiota specificity and adaptation to their host. The comprehensive landscape of the bacterial microbiota components may ultimately provide crucial insights and novel targets for possible application of symbionts in innovative strategies for the control of vector borne diseases, globally named Symbiotic Control (SC), and suggesting that the holobiont of different mosquito species may significantly vary. Moreover, mosquito species are characterized by distinctive microbiota in different organs, likely reflecting different functions and/or adaptation processes.

Candida albicans is a commensal fungus of the vaginal mucosa and the principal etiological agent of vaginal candidiasis. Vaginal dysbiosis has been reported during vulvovaginal candidiasis (VVC), with a progressive decrease in Lactobacillus crispatus population and an increase in L. iners population. To date, the role of L. iners in VVC pathogenesis remains scarcely explored. Herein we investigated the in vitro effect of L. iners cell-free supernatant (CFS) on the ability of C. albicans to form biofilms. Biomass and metabolic activity were measured by crystal violet and XTT assays. Further, light microscopy was performed to determine the effect of L. iners CFS on biofilm cellular morphology. We found that L. iners CFS induced a significant increase in biofilm formation by C. albicans clinical isolates which were categorized as moderate or weak biofilm producers. This effect was associated with an enhancement of hyphal/pseudohyphal growth, and the expression levels of HWP1 and ECE1, which are typical hyphae-associated genes, were upregulated. Overall, these results suggest that L. iners contributes to the pathogenesis of VVC and highlight the complexity of the interaction between C. albicans and vaginal lactobacilli. Understanding these interactions could prove essential for the development of new strategies for treating VVC.

In pregnancy, human amniotic fluid extracellular vesicles (HAF-EVs) exert anti-inflammatory effects on T cells and on monocytes, supporting their immunoregulatory roles. The specific mechanisms are still not completely defined. The aim of this study was to investigate the ability of HAF-EVs, isolated from pregnant women who underwent amniocentesis and purified by gradient ultracentrifugation, to affect inflammasome activation in the human monocytes. Proteomic studies revealed that HAF-EV samples expressed several immunoregulatory molecules as well as small amounts of endotoxin. Surprisingly, metagenomic analysis shows the presence of specific bacterial strain variants associated with HAF-EVs as potential sources of the endotoxin. Remarkably, we showed that a single treatment of THP-1 cells with HAF-EVs triggered inflammasome activation, whereas the same treatment followed by LPS and ATP sensitization prevented inflammasome activation, a pathway resembling monocyte refractories. A bioinformatics analysis of microbiota-HAF-EVs functional pathways confirmed the presence of enzymes for endotoxin biosynthesis as well as others associated with immunoregulatory functions. Overall, these data suggest that HAF-EVs could serve as a source of the isolation of a specific microbiota during early pregnancy. Moreover, HAF-EVs could act as a novel system to balance immune training and tolerance by modulating the inflammasome in monocytes or other cells.

The microbiome, i.e., the communities of microbes that inhabit the surfaces exposed to the external environment, participates in the regulation of host physiology, including the immune response against pathogens. At the same time, the immune response shapes the microbiome to regulate its composition and function. How the crosstalk between the immune system and the microbiome regulates the response to fungal infection has remained relatively unexplored. We have previously shown that strict anaerobes protect from infection with the opportunistic fungus Aspergillus fumigatus by counteracting the expansion of pathogenic Proteobacteria. By resorting to immunodeficient mouse strains, we found that the lung microbiota could compensate for the lack of B and T lymphocytes in Rag1^–/– mice by skewing the composition towards an increased abundance of protective anaerobes such as Clostridia and Bacteroidota. Conversely, NSG mice, with major defects in both the innate and adaptive immune response, showed an increased susceptibility to infection associated with a low abundance of strict anaerobes and the expansion of Proteobacteria. Further exploration in a murine model of chronic granulomatous disease, a primary form of immunodeficiency characterized by defective phagocyte NADPH oxidase, confirms the association of lung unbalance between anaerobes and Proteobacteria and the susceptibility to aspergillosis. Consistent changes in the lung levels of short-chain fatty acids between the different strains support the conclusion that the immune system and the microbiota are functionally intertwined during Aspergillus infection and determine the outcome of the infection.

Inflammatory bowel diseases (IBD) have a complex, poorly understood pathogenesis and lack long-lasting effective treatments. Recent research suggests that intestinal fungal dysbiosis may play a role in IBD development. This study investigates the effects of the glucocorticoid-induced leucine zipper protein (GILZp)”, known for its protective role in gut mucosa, and a yeast extract (Py) with prebiotic properties, either alone or combined, in DSS-induced colitis. Both treatments alleviated symptoms via overlapping or distinct mechanisms. In particular, they reduced the transcription levels of pro-inflammatory cytokines IL-1β and TNF-α, as well as the expression of the tight junction protein Claudin-2. Additionally, GILZp increased MUC2 transcription, while Py reduced IL-12p40 and IL-6 levels. Notably, both treatments were effective in restoring the intestinal burden of clinically important Candida and related species. Intestinal mycobiome analysis revealed that they were able to reduce colitis-associated fungal dysbiosis, and this effect was mainly the result of a decreased abundance of the Meyerozima genus, which was dominant in colitic mice. Overall, our results suggest that combined treatment regimens with GILZp and Py could represent a new strategy for the treatment of IBD by targeting multiple mechanisms, including the fungal dysbiosis.

In compositional next-generation sequencing (NGS) analyses (including microbiome studies, RNA-seq and metagenomics) the arithmetic mean (AM) of relative proportions is the default operator for summarizing feature abundances. We show that this default produces unstable rankings in real compositional data. Across 102 prevalent genera in the dietswap dataset (n=38 baseline samples), 23 genera (22.5%), including members of Bacteroides , Eubacterium and Bilophila , yielded opposite group-level conclusions under AM and the geometric mean (GM). This pattern reflects two formal properties of compositional aggregation. First, AM-based rankings change with the within-sample normalization domain, whereas GM-based rankings are invariant under the multiplicative structure of compositional data. Second, the centered log-ratio (CLR) transformation absorbs geometric averaging into the data representation, so that arithmetic averaging on CLR-space recovers the GM ranking exactly. Both properties were verified numerically on the dietswap dataset, where the Spearman correlation between GM- and CLR-based rankings was 1.000 in both groups. The operator-choice problem propagates to between-group differential inference: under AM, log ₂ fold-changes vary across normalizations and the relative ranking of features by effect size is not preserved; under GM and CLR, the ranking is preserved. We recommend GM-based summaries for feature ranking and CLR-transformed abundances for cross-sample comparisons. This change requires no new computational tools and is fully compatible with existing differential-abundance pipelines, but eliminates an under-recognized source of irreproducibility in biomarker discovery across microbiome studies, transcriptomics, metagenomics, and mass-spectrometry-based metabolomics, in all settings where features are quantified relative to a sample total. IMPORTANCE: Studies of the gut microbiome routinely identify which bacterial groups are more or less abundant in patients versus healthy controls, in different diets, or before and after a treatment. The same kind of comparison underlies sequencing-based analyses across biology, from gene expression to metagenomics. To do this, researchers must average the abundance of each measured entity across many samples, and the standard choice is the simple arithmetic average. We show that this choice can be misleading for any data where each measurement is expressed relative to a sample total, as is typical of sequencing-based assays, and that in real data it can flip the answer to which group is more enriched. Analyzing a published dietary intervention study, we found that one in five gut bacteria (including Bacteroides and Eubacterium ) gave opposite results depending on which average was used. Switching to the geometric average resolves this inconsistency and makes biomarker discovery more reproducible. This change is immediate to implement (it does not require new software or specialized training) and applies not only to microbiome studies, but to any biological measurement where what is detected, whether a gene transcript, a microbial taxon, or a metabolite, is quantified relative to a sample total: gene-expression analysis, metagenomics, and metabolomics among others.