Prev Chronic Dis

Improving population health is not simple. Many instruments are available for changing behavior and consequent outcomes. However, the following basic principles should guide development of any incentive arrangement: 1) identify the desired outcome, 2) identify the behavior change that will lead to this outcome, 3) determine the potential effectiveness of the incentive in achieving the behavior change, 4) link a financial incentive directly to this outcome or behavior, 5) identify the possible adverse effects of the incentive, and 6) evaluate and report changes in the behavior or outcome in response to the incentive. A wide range of financial and nonfinancial incentives is available to encourage efficient behaviors and discourage costly and unproductive ones. Evidence for the beneficial effects of incentive programs has been slow to emerge, partly because such evidence must show how behaviors have changed because of the incentive. Nevertheless, the potential for incentive programs in health care seems large, and research should support their design and assess their effect

a metaproteomic pipeline to identify newborn mouse gut phylotypes

Abstract In order to characterize newborn mouse gut microbiota phylotypes in very early-life stages, an original metaproteomic pipeline, based on LC–MS 2 -spectra and Mascot driven NCBI non-redundant repository database interrogation was developed. An original computational analysis assisted in the generation of a taxonomic gut architecture from protein hits to operational taxonomic units (OTUs) and related functional categories. Regardless of the mouse's genetic background, a prevalence of Firmicutes (Lactobacillaceae) and Proteobacteria (Enterobacteriaceae) was observed among the entire Eubacteria taxonomic node. However, a higher abundance of Firmicutes was retrieved for Balb/c gut microbiota compared to Rag2 ko mice, the latter was mainly characterized by a Proteobacteria enriched microbiota. The metaproteomic-obtained OTUs were supported, for the identification (ID) of the cultivable bacteria fraction, corroborated by axenic culture-based MALDI-TOF MS IDs. Particularly, functional analysis of Rag2 ko mice gut microbiota proteins revealed the presence of abundant glutathione, riboflavin metabolism and pentose phosphate pathway components, possibly related to genetic background. The metaproteomic pipeline herein presented may represent a useful tool to investigate the highly debated onset of the human gut microbiota in the first days of life, when the bacterial composition, despite its very low diversity (complexity), is still very far from an exhaustive description and other complex microbial consortia. Biological significance The manuscript deals with a "frontier" topic regarding the study of the gut microbiota and the application of a metaproteomic pipeline to unveil the complexity of this fascinating ecosystem at the very early stages of life. Indeed during these phases, its diversity is very low but the bacterial content is highly "instable", and the relative balance between mucosal and fecal bacteria starts its dynamics of "fight" to get homeostasis. However, in the neonatal period, especially immediately after birth, a comprehensive description of this microbial eco-organ is still lacking, while it should be mandatory to highlight its first mechanisms of homeostasis and perturbation, while it co-develops with and within the host species. In order to unravel its low but almost unknown microbial community multiplicity, the newborn mouse gut, characterized by a "very" low complexity, was herein selected as model to design a LC–MS 2 -based shotgun metaproteomic approach, potentially suitable to study onset and shaping in human newborns. A microbiological semi-automatic computational analysis was performed to infer gut phylotypes; such as proof of evidence, related OTUs were compared to axenic-culture-based MALDI-TOF MS IDs showing consistency at family and phyla levels for the bacterial cultivable fraction. This article is part of a Special Issue entitled: Trends in Microbial Proteomics

Gut microbiota profile in infants with milk and/or egg allergy and evaluation of intestinal colonization and persistence of a probiotic mixture

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Functional and Taxonomic Traits of the Gut Microbiota in Type 1 Diabetes Children at the Onset: A Metaproteomic Study

Type 1 diabetes (T1D) is a chronic autoimmune metabolic disorder with onset in pediatric/adolescent age, characterized by insufficient insulin production, due to a progressive destruction of pancreatic beta-cells. Evidence on the correlation between the human gut microbiota (GM) composition and T1D insurgence has been recently reported. In particular, 16S rRNA-based metagenomics has been intensively employed in the last decade in a number of investigations focused on GM representation in relation to a pre-disease state or to a response to clinical treatments. On the other hand, few works have been published using alternative functional omics, which is more suitable to provide a different interpretation of such a relationship. In this work, we pursued a comprehensive metaproteomic investigation on T1D children compared with a group of siblings (SIBL) and a reference control group (CTRL) composed of aged matched healthy subjects, with the aim of finding features in the T1D patients' GM to be related with the onset of the disease. Modulated metaproteins were found either by comparing T1D with CTRL and SIBL or by stratifying T1D by insulin need (IN), as a proxy of beta-cells damage, showing some functional and taxonomic traits of the GM, possibly related to the disease onset at different stages of severity

Duodenal and faecal microbiota of celiac children: molecular, phenotype and metabolome characterization

BACKGROUND: Epidemiology of celiac disease (CD) is increasing. CD mainly presents in early childhood with small intestinal villous atrophy and signs of malabsorption. Compared to healthy individuals, CD patients seemed to be characterized by higher numbers of Gram-negative bacteria and lower numbers Gram-positive bacteria. RESULTS: This study aimed at investigating the microbiota and metabolome of 19 celiac disease children under gluten-free diet (treated celiac disease, T-CD) and 15 non-celiac children (HC). PCR-denaturing gradient gel electrophoresis (DGGE) analyses by universal and group-specific primers were carried out in duodenal biopsies and faecal samples. Based on the number of PCR-DGGE bands, the diversity of Eubacteria was the higher in duodenal biopsies of T-CD than HC children. Bifidobacteria were only found in faecal samples. With a few exceptions, PCR-DGGE profiles of faecal samples for Lactobacillus and Bifidobacteria differed between T-CD and HC. As shown by culture-dependent methods, the levels of Lactobacillus, Enterococcus and Bifidobacteria were confirmed to be significantly higher (P = 0.028; P = 0.019; and P = 0.023, respectively) in fecal samples of HC than in T-CD children. On the contrary, cell counts (CFU/ml) of presumptive Bacteroides, Staphylococcus, Salmonella, Shighella and Klebsiella were significantly higher (P = 0.014) in T-CD compared to HC children. Enterococcus faecium and Lactobacillus plantarum were the species most diffusely identified. This latter species was also found in all duodenal biopsies of T-CD and HC children. Other bacterial species were identified only in T-CD or HC faecal samples. As shown by Randomly Amplified Polymorphic DNA-PCR analysis, the percentage of strains identified as lactobacilli significantly (P = 0.011) differed between T-CD (ca. 26.5%) and HC (ca. 34.6%) groups. The metabolome of T-CD and HC children was studied using faecal and urine samples which were analyzed by gas-chromatography mass spectrometry-solid-phase microextraction and 1H-Nuclear Magnetic Resonance. As shown by Canonical Discriminant Analysis of Principal Coordinates, the levels of volatile organic compounds and free amino acids in faecal and/or urine samples were markedly affected by CD. CONCLUSION: As shown by the parallel microbiology and metabolome approach, the gluten-free diet lasting at least two years did not completely restore the microbiota and, consequently, the metabolome of CD children. Some molecules (e.g., ethyl-acetate and octyl-acetate, some short chain fatty acids and free amino acids, and glutamine) seems to be metabolic signatures of CD

Soluble Immune checkpoints, gut metabolites and performance status as parameters of response to Nivolumab treatment in NSCLC patients

Patients with non-small cell lung cancer (NSCLC) have been shown to benefit from the introduction of anti-PD1 treatment. However, not all patients experience tumor regression and durable response. The identification of a string of markers that are direct or indirect indicators of the immune system fitness is needed to choose optimal therapeutic schedules in the management of NSCLC patients. We analyzed 34 immuno-related molecules (14 soluble immune checkpoints, 17 cytokines/chemokines, 3 adhesion molecules) released in the serum of 22 NSCLC patients under Nivolumab treatment and the gut metabolomic profile at baseline. These parameters were correlated with performance status (PS) and/or response to treatment. Nivolumab affected the release of soluble immune checkpoints (sICs). Patients with a better clinical outcome and with an optimal PS (PS = 0) showed a decreased level of PD1 and maintained low levels of several sICs at first clinical evaluation. Low levels of PDL1, PDL2, Tim3, CD137 and BTLA4 were also correlated with a long response to treatment. Moreover, responding patients showed a high proportion of eubiosis-associated gut metabolites. In this exploratory study, we propose a combination of immunological and clinical parameters (sICs, PS and gut metabolites) for the identification of patients more suitable for Nivolumab treatment. This string of parameters validated in a network analysis on a larger cohort of patients could help oncologists to improve their decision-making in an NSCLC setting

Gut Microbiota Functional Traits, Blood pH, and Anti-GAD Antibodies Concur in the Clinical Characterization of T1D at Onset

Alterations of gut microbiota have been identified before clinical manifestation of type 1 diabetes (T1D). To identify the associations amongst gut microbiome profile, metabolism and disease markers, the 16S rRNA-based microbiota profiling and H-1-NMR metabolomic analysis were performed on stool samples of 52 T1D patients at onset, 17 T1D siblings and 57 healthy subjects (CTRL). Univariate, multivariate analyses and classification models were applied to clinical and -omic integrated datasets. In T1D patients and their siblings, Clostridiales and Dorea were increased and Dialister and Akkermansia were decreased compared to CTRL, while in T1D, Lachnospiraceae were higher and Collinsella was lower, compared to siblings and CTRL. Higher levels of isobutyrate, malonate, Clostridium, Enterobacteriaceae, Clostridiales, Bacteroidales, were associated to T1D compared to CTRL. Patients with higher anti-GAD levels showed low abundances of Roseburia, Faecalibacterium and Alistipes and those with normal blood pH and low serum HbA(1c) levels showed high levels of purine and pyrimidine intermediates. We detected specific gut microbiota profiles linked to both T1D at the onset and to diabetes familiarity. The presence of specific microbial and metabolic profiles in gut linked to anti-GAD levels and to blood acidosis can be considered as predictive biomarker associated progression and severity of T1D

Characterization of extracellular vesicles in osteoporotic patients compared to osteopenic and healthy controls

Extracellular vesicles (EVs) are mediators of a range of pathological conditions. However, their role in bone loss disease has not been well understood. In this study we characterized plasma EVs of 54 osteoporotic (OP) postmenopausal women compared to 48 osteopenic (OPN) and 44 healthy controls (CN), and we investigated their effects on osteoclasts and osteoblasts. We found no differences between the three groups in terms of anthropometric measurements and biochemical evaluation of serum calcium, phosphate, creatinine, PTH, 25-hydroxy vitamin D and bone biomarkers, except for an increase of CTX level in OP group. FACS analysis revealed that OP patients presented a significantly increased number of EVs and RANKL(+) EVs compared with both CN and OPN subjects. Total EVs are negatively associated with the lumbar spine T-score and femoral neck T-score. Only in the OPN patients we observed a positive association between the total number of EVs and RANKL(+) EVs with the serum RANKL. In vitro studies revealed that OP EVs supported osteoclastogenesis of healthy donor peripheral blood mononuclear cells at the same level observed following RANKL and M-CSF treatment, reduced the ability of mesenchymal stem cells to differentiate into osteoblasts, while inducing an increase of OSTERIX and RANKL expression in mature osteoblasts. The analysis of miRNome revealed that miR-1246 and miR-1224-5p were the most upregulated and downregulated in OP EVs; the modulated EV-miRNAs in OP and OPN compared to CN are related to osteoclast differentiation, interleukin-13 production and regulation of canonical WNT pathway. A proteomic comparison between OPN and CN EVs evidenced a decrease in fibrinogen, vitronectin, and clusterin and an increase in coagulation factors and apolipoprotein, which was also upregulated in OP EVs. Interestingly, an increase in RANKL(+) EVs and exosomal miR-1246 was also observed in samples from patients affected by Gorham-Stout disease, suggesting that EVs could be good candidate as bone loss disease biomarkers. (c) 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)

Gut microbiota functional profiling in autism spectrum disorders: bacterial VOCs and related metabolic pathways acting as disease biomarkers and predictors

BackgroundAutism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder. Major interplays between the gastrointestinal (GI) tract and the central nervous system (CNS) seem to be driven by gut microbiota (GM). Herein, we provide a GM functional characterization, based on GM metabolomics, mapping of bacterial biochemical pathways, and anamnestic, clinical, and nutritional patient metadata.MethodsFecal samples collected from children with ASD and neurotypical children were analyzed by gas-chromatography mass spectrometry coupled with solid phase microextraction (GC–MS/SPME) to determine volatile organic compounds (VOCs) associated with the metataxonomic approach by 16S rRNA gene sequencing. Multivariate and univariate statistical analyses assessed differential VOC profiles and relationships with ASD anamnestic and clinical features for biomarker discovery. Multiple web-based and machine learning (ML) models identified metabolic predictors of disease and network analyses correlated GM ecological and metabolic patterns.ResultsThe GM core volatilome for all ASD patients was characterized by a high concentration of 1-pentanol, 1-butanol, phenyl ethyl alcohol; benzeneacetaldehyde, octadecanal, tetradecanal; methyl isobutyl ketone, 2-hexanone, acetone; acetic, propanoic, 3-methyl-butanoic and 2-methyl-propanoic acids; indole and skatole; and o-cymene. Patients were stratified based on age, GI symptoms, and ASD severity symptoms. Disease risk prediction allowed us to associate butanoic acid with subjects older than 5 years, indole with the absence of GI symptoms and low disease severity, propanoic acid with the ASD risk group, and p-cymene with ASD symptoms, all based on the predictive CBCL-EXT scale. The HistGradientBoostingClassifier model classified ASD patients vs. CTRLs by an accuracy of 89%, based on methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, ethanol, butanoic acid, octadecane, acetic acid, skatole, and tetradecanal features. LogisticRegression models corroborated methyl isobutyl ketone, benzeneacetaldehyde, phenyl ethyl alcohol, skatole, and acetic acid as ASD predictors.ConclusionOur results will aid the development of advanced clinical decision support systems (CDSSs), assisted by ML models, for advanced ASD-personalized medicine, based on omics data integrated into electronic health/medical records. Furthermore, new ASD screening strategies based on GM-related predictors could be used to improve ASD risk assessment by uncovering novel ASD onset and risk predictors

Phylogenetic and Metabolic Tracking of Gut Microbiota during Perinatal Development

The colonization and development of gut microbiota immediately after birth is highly variable and depends on several factors, such as delivery mode and modality of feeding during the first months of life. A cohort of 31 mother and neonate pairs, including 25 at-term caesarean (CS) and 6 vaginally (V) delivered neonates (DNs), were included in this study and 121 meconium/faecal samples were collected at days 1 through 30 following birth. Operational taxonomic units (OTUs) were assessed in 69 stool samples by phylogenetic microarray HITChip and inter- and intra-individual distributions were established by inter-OTUs correlation matrices and OTUs co-occurrence or co-exclusion networks. H-1-NMR metabolites were determined in 70 stool samples, PCA analysis was performed on 55 CS DNs samples, and metabolome/OTUs co-correlations were assessed in 45 CS samples, providing an integrated map of the early microbiota OTUs-metabolome. A microbiota "core" of OTUs was identified that was independent of delivery mode and lactation stage, suggesting highly specialized communities that act as seminal colonizers of microbial networks. Correlations among OTUs, metabolites, and OTUs-metabolites revealed metabolic profiles associated with early microbial ecological dynamics, maturation of milk components, and host physiology.Peer reviewe