An integrative multi-omic analysis defines gut microbiota, mycobiota, and metabolic fingerprints in ulcerative colitis patients.

Background: Ulcerative colitis (UC) is a multifactorial chronic inflammatory bowel disease (IBD) that affects the large intestine with superficial mucosal inflammation. A dysbiotic gut microbial profile has been associated with UC. Our study aimed to characterize the UC gut bacterial, fungal, and metabolic fingerprints by omic approaches. Methods: The 16S rRNA- and ITS2-based metataxonomics and gas chromatography-mass spectrometry/solid phase microextraction (GC-MS/SPME) metabolomic analysis were performed on stool samples of 53 UC patients and 37 healthy subjects (CTRL). Univariate and multivariate approaches were applied to separated and integrated omic data, to define microbiota, mycobiota, and metabolic signatures in UC. The interaction between gut bacteria and fungi was investigated by network analysis. Results: In the UC cohort, we reported the increase of Streptococcus, Bifidobacterium, Enterobacteriaceae, TM7-3, Granulicatella, Peptostreptococcus, Lactobacillus, Veillonella, Enterococcus, Peptoniphilus, Gemellaceae, and phenylethyl alcohol; and we also reported the decrease of Akkermansia; Ruminococcaceae; Ruminococcus; Gemmiger; Methanobrevibacter; Oscillospira; Coprococus; Christensenellaceae; Clavispora; Vishniacozyma; Quambalaria; hexadecane; cyclopentadecane; 5-hepten-2-ol, 6 methyl; 3-carene; caryophyllene; p-Cresol; 2-butenal; indole, 3-methyl-; 6-methyl-3,5-heptadiene-2-one; 5-octadecene; and 5-hepten-2-one, 6 methyl. The integration of the multi-omic data confirmed the presence of a distinctive bacterial, fungal, and metabolic fingerprint in UC gut microbiota. Moreover, the network analysis highlighted bacterial and fungal synergistic and/or divergent interkingdom interactions. Conclusion: In this study, we identified intestinal bacterial, fungal, and metabolic UC-associated biomarkers. Furthermore, evidence on the relationships between bacterial and fungal ecosystems provides a comprehensive perspective on intestinal dysbiosis and ecological interactions between microorganisms in the framework of UC.

Williams-Beuren syndrome shapes the gut microbiota metaproteome.

Williams-Beuren syndrome (WBS) is a rare genetic neurodevelopmental disorder with multi-systemic manifestations. The evidence that most subjects with WBS face gastrointestinal (GI) comorbidities, have prompted us to carry out a metaproteomic investigation of their gut microbiota (GM) profile compared to age-matched healthy subjects (CTRLs). Metaproteomic analysis was carried out on fecal samples collected from 41 individuals with WBS, and compared with samples from 45 CTRLs. Stool were extracted for high yield in bacterial protein group (PG) content, trypsin-digested and analysed by nanoLiquid Chromatography-Mass Spectrometry. Label free quantification, taxonomic assignment by the lowest common ancestor (LCA) algorithm and functional annotations by COG and KEGG databases were performed. Data were statistically interpreted by multivariate and univariate analyses. A WBS GM functional dissimilarity respect to CTRLs, regardless age distribution, was reported. The alterations in function of WBSs GM was primarily based on bacterial pathways linked to carbohydrate transport and metabolism and energy production. Influence of diet, obesity, and GI symptoms was assessed, highlighting changes in GM biochemical patterns, according to WBS subsets' stratification. The LCA-derived ecology unveiled WBS-related functionally active bacterial signatures: Bacteroidetes related to over-expressed PGs, and Firmicutes, specifically the specie Faecalibacterium prausnitzii, linked to under-expressed PGs, suggesting a depletion of beneficial bacteria. These new evidences on WBS gut dysbiosis may offer novel targets for tailored interventions.

Dietary Fatty Acids Contribute to Maintaining the Balance between Pro-Inflammatory and Anti-Inflammatory Responses during Pregnancy.

BACKGROUND: During pregnancy, the balance between pro-inflammatory and anti-inflammatory responses is essential for ensuring healthy outcomes. Dietary Fatty acids may modulate inflammation. METHODS: We investigated the association between dietary fatty acids as profiled on red blood cells membranes and a few pro- and anti-inflammatory cytokines, including the adipokines leptin and adiponectin at ~38 weeks in 250 healthy women. RESULTS: We found a number of associations, including, but not limited to those of adiponectin with C22:3/C22:4 (coeff -1.44; p = 0.008), C18:1 c13/c14 (coeff 1.4; p = 0.02); endotoxin with C20:1 (coeff -0.9; p = 0.03), C22:0 (coeff -0.4; p = 0.05); MCP-1 with C16:0 (coeff 0.8; p = 0.04); and ICAM-1 with C14:0 (coeff -86.8; p = 0.045). Several cytokines including leptin were associated with maternal body weight (coeff 0.9; p = 2.31 × 10-5), smoking habits (i.e., ICAM-1 coeff 133.3; p = 0.09), or gestational diabetes (i.e., ICAM-1 coeff 688; p = 0.06). CONCLUSIONS: In a general cohort of pregnant women, the intake of fatty acids influenced the balance between pro- and anti-inflammatory molecules together with weight gain, smoking habits, and gestational diabetes.

The metaproteome of the gut microbiota in pediatric patients affected by COVID-19.

Introduction: The gut microbiota (GM) play a significant role in the infectivity and severity of COVID-19 infection. However, the available literature primarily focuses on adult patients and it is known that the microbiota undergoes changes throughout the lifespan, with significant alterations occurring during infancy and subsequently stabilizing during adulthood. Moreover, children have exhibited milder symptoms of COVID-19 disease, which has been associated with the abundance of certain protective bacteria. Here, we examine the metaproteome of pediatric patients to uncover the biological mechanisms that underlie this protective effect of the GM. Methods: We performed nanoliquid chromatography coupled with tandem mass spectrometry on a high resolution analytical platform, resulting in label free quantification of bacterial protein groups (PGs), along with functional annotations via COG and KEGG databases by MetaLab-MAG. Additionally, taxonomic assignment was possible through the use of the lowest common ancestor algorithm provided by Unipept software. Results: A COVID-19 GM functional dissimilarity respect to healthy subjects was identified by univariate analysis. The alteration in COVID-19 GM function is primarily based on bacterial pathways that predominantly involve metabolic processes, such as those related to tryptophan, butanoate, fatty acid, and bile acid biosynthesis, as well as antibiotic resistance and virulence. Discussion: These findings highlight the mechanisms by which the pediatric GM could contribute to protection against the more severe manifestations of the disease in children. Uncovering these mechanisms can, therefore, have important implications in the discovery of novel adjuvant therapies for severe COVID-19.

Gut Microbiota Ecological and Functional Modulation in Post-Stroke Recovery Patients: An Italian Study.

Ischemic stroke (IS) can be caused by perturbations of the gut-brain axis. An imbalance in the gut microbiota (GM), or dysbiosis, may be linked to several IS risk factors and can influence the brain through the production of different metabolites, such as short-chain fatty acids (SCFAs), indole and derivatives. This study examines ecological changes in the GM and its metabolic activities after stroke. Fecal samples of 10 IS patients were compared to 21 healthy controls (CTRLs). GM ecological profiles were generated via 16S rRNA taxonomy as functional profiles using metabolomics analysis performed with a gas chromatograph coupled to a mass spectrometer (GC-MS). Additionally fecal zonulin, a marker of gut permeability, was measured using an enzyme-linked immuno assay (ELISA). Data were analyzed using univariate and multivariate statistical analyses and correlated with clinical features and biochemical variables using correlation and nonparametric tests. Metabolomic analyses, carried out on a subject subgroup, revealed a high concentration of fecal metabolites, such as SCFAs, in the GM of IS patients, which was corroborated by the enrichment of SCFA-producing bacterial genera such as Bacteroides, Christensellaceae, Alistipes and Akkermansia. Conversely, indole and 3-methyl indole (skatole) decreased compared to a subset of six CTRLs. This study illustrates how IS might affect the gut microbial milieu and may suggest potential microbial and metabolic biomarkers of IS. Expanded populations of Akkermansia and enrichment of acetic acid could be considered potential disease phenotype signatures.

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

Background: Autism 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. Methods: Fecal 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. Results: The 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. Conclusion: Our 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.

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 ß-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 ß-cells damage, showing some functional and taxonomic traits of the GM, possibly related to the disease onset at different stages of severity.

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. © 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 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 1H-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 HbA1c 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.

Gut Microbiota Ecology and Inferred Functions in Children With ASD Compared to Neurotypical Subjects.

Autism spectrum disorders (ASDs) is a multifactorial neurodevelopmental disorder. The communication between the gastrointestinal (GI) tract and the central nervous system seems driven by gut microbiota (GM). Herein, we provide GM profiling, considering GI functional symptoms, neurological impairment, and dietary habits. Forty-one and 35 fecal samples collected from ASD and neurotypical children (CTRLs), respectively, (age range, 3-15 years) were analyzed by 16S targeted-metagenomics (the V3-V4 region) and inflammation and permeability markers (i.e., sIgA, zonulin lysozyme), and then correlated with subjects' metadata. Our ASD cohort was characterized as follows: 30/41 (73%) with GI functional symptoms; 24/41 (58%) picky eaters (PEs), with one or more dietary needs, including 10/41 (24%) with food selectivity (FS); 36/41 (88%) presenting high and medium autism severity symptoms (HMASSs). Among the cohort with GI symptoms, 28/30 (93%) showed HMASSs, 17/30 (57%) were picky eaters and only 8/30 (27%) with food selectivity. The remaining 11/41 (27%) ASDs without GI symptoms that were characterized by HMASS for 8/11 (72%) and 7/11 (63%) were picky eaters. GM ecology was investigated for the overall ASD cohort versus CTRLs; ASDs with GI and without GI, respectively, versus CTRLs; ASD with GI versus ASD without GI; ASDs with HMASS versus low ASSs; PEs versus no-PEs; and FS versus absence of FS. In particular, the GM of ASDs, compared to CTRLs, was characterized by the increase of Proteobacteria, Bacteroidetes, Rikenellaceae, Pasteurellaceae, Klebsiella, Bacteroides, Roseburia, Lactobacillus, Prevotella, Sutterella, Staphylococcus, and Haemophilus. Moreover, Sutterella, Roseburia and Fusobacterium were associated to ASD with GI symptoms compared to CTRLs. Interestingly, ASD with GI symptoms showed higher value of zonulin and lower levels of lysozyme, which were also characterized by differentially expressed predicted functional pathways. Multiple machine learning models classified correctly 80% overall ASDs, compared with CTRLs, based on Bacteroides, Lactobacillus, Prevotella, Staphylococcus, Sutterella, and Haemophilus features. In conclusion, in our patient cohort, regardless of the evaluation of many factors potentially modulating the GM profile, the major phenotypic determinant affecting the GM was represented by GI hallmarks and patients' age.

A Parallel Tracking of Salivary and Gut Microbiota Profiles Can Reveal Maturation and Interplay of Early Life Microbial Communities in Healthy Infants.

In this study, the onset and shaping of the salivary and gut microbiota in healthy newborns during the first period of life has been followed, evaluating the impact of salivary microbiota on the development of early fecal microbial communities. The microbiota of 80 salivary and 82 fecal samples that were collected from healthy newborns in the first six months of life, was investigated by 16S rRNA amplicon profiling. The microbial relationship within and between the saliva and gut ecosystems was determined by correlation heatmaps and co-occurrence networks. Streptococcus and Staphylococcus appeared as early commensals in the salivary microbiota, dominating this ecosystem through the time, while Fusobacterium, Prevotella, Porphyromonas, Granulicatella, and Veillonella were late colonizers. Enterobacteriaceae, Staphylococcus and Streptococcus were gut pioneers, followed by the anaerobic Bifidobacterium, Veillonella, Eggerthella, and Bacteroides. Streptococcus, Staphylococcus, and Veillonella were shared by the gut and saliva ecosystems. The saliva and gut microbiota seem to evolve independently, driven by local adaptation strategies, except for the oral Streptococcus and Veillonella that are involved in gut microbiota development as seeding species. This study offers a piece of knowledge on how the oral microbiota may affect the gut microbiota in healthy newborns, shedding light onto new microbial targets for the development of therapies for early life intestinal dysbiosis.

A metaproteomic-based gut microbiota profiling in children affected by autism spectrum disorders.

During the last decade, the evidences on the relationship between neurodevelopmental disorders and the microbial communities of the intestinal tract have considerably grown. Particularly, the role of gut microbiota (GM) ecology and predicted functions in Autism Spectrum Disorders (ASD) has been especially investigated by 16S rRNA targeted and shotgun metagenomics, trying to assess disease signature and their correlation with cognitive impairment or gastrointestinal (GI) manifestations of the disease. Herein we present a metaproteomic approach to point out the microbial gene expression profiles, their functional annotations, and the taxonomic distribution of gut microbial communities in ASD children. We pursued a LC-MS/MS based investigation, to compare the GM profiles of patients with those of their respective relatives and aged-matched controls, providing a quantitative evaluation of bacterial metaproteins by SWATH analysis. All data were managed by a multiple step bioinformatic pipeline, including network analysis. In particular, comparing ASD subjects with CTRLs, up-regulation was found for some metaproteins associated with Clostridia and with carbohydrate metabolism (glyceraldehyde-3-phosphate and glutamate dehydrogenases), while down-regulation was observed for others associated with Bacteroidia (SusC and SusD family together with the TonB dependent receptor). Moreover, network analysis highlighted specific microbial correlations among ASD subgroups characterized by different functioning levels and GI symptoms. SIGNIFICANCE: To the best of our knowledge, this study represents the first metaproteomic investigation on the gut microbiota of ASD children compared with relatives and age-matched CTRLs. Remarkably, the applied SWATH methodology allowed the attribution of differentially regulated functions to specific microbial taxa, offering a novel and complementary point of view with respect to previous studies.

Threshold of Reactivity and Tolerance to Precautionary Allergen-Labelled Biscuits of Baked Milk- and Egg-Allergic Children.

Extremely sensitive food-allergic patients may react to very small amounts of allergenic foods. Precautionary allergen labelling (PAL) warns from possible allergenic contaminations. We evaluated by oral food challenge the reactivity to a brand of PAL-labelled milk- and egg-free biscuits of children with severe milk and egg allergy. We explored the ability of proteomic methods to identify minute amounts of milk/egg allergens in such biscuits. Traces of milk and/or egg allergens in biscuits were measured by two different liquid-chromatography-mass spectrometry methods. The binding of patient's serum with egg/milk proteins was assessed using immunoblotting. None of the patients reacted to biscuits. Egg and milk proteins were undetectable with a limit of detection of 0.6 µg/g for milk and egg (method A), and of 0.1 and 0.3 µg /g for milk and egg, respectively (method B). The immunoblots did not show milk/egg proteins in the studied biscuits. Milk/egg content of the biscuits is far lower than 4 µg of milk or egg protein per gram of product, the minimal doses considered theoretically capable of causing reactions. With high sensitivity, proteomic assessments predict the harmlessness of very small amount of allergens in foods, and can be used to help avoiding unnecessary PAL.

Characterization of the gut-liver-muscle axis in cirrhotic patients with sarcopenia.

BACKGROUND & AIM: Sarcopenia is frequent in cirrhosis and is associated with unfavourable outcomes. The role of the gut-liver-muscle axis in this setting has been poorly investigated. The aim of this study was to identify gut microbiota, metabolic and inflammatory signatures associated with sarcopenia in cirrhotic patients. METHODS: Fifty cirrhotic patients assessed for the presence of sarcopenia by the quantification of muscle mass and strength were compared with age- and sex-matched controls. A multiomic analysis, including gut microbiota composition and metabolomics, serum myokines and systemic and intestinal inflammatory mediators, was performed. RESULTS: The gut microbiota of sarcopenic cirrhotic patients was poor in bacteria associated with physical function (Methanobrevibacter, Prevotella and Akkermansia), and was enriched in Eggerthella, a gut microbial marker of frailty. The abundance of potentially pathogenic bacteria, such as Klebsiella, was also increased, to the detriment of autochthonous ones. Sarcopenia was associated with elevated serum levels of pro-inflammatory mediators and of fibroblast growth factor 21 (FGF21) in cirrhotic patients. Gut microbiota metabolic pathways involved in amino acid, protein and branched-chain amino acid metabolism were up-regulated, in addition to ethanol, trimethylamine and dimethylamine production. Correlation networks and clusters of variables associated with sarcopenia were identified, including one centred on Klebsiella/ethanol/FGF21/Eggerthella/Prevotella. CONCLUSIONS: Alterations in the gut-liver-muscle axis are associated with sarcopenia in patients with cirrhosis. Detrimental but also compensatory functions are involved in this complex network.

Gut Microbiota Profile in Children with IgE-Mediated Cow's Milk Allergy and Cow's Milk Sensitization and Probiotic Intestinal Persistence Evaluation.

Food allergy (FA) and, in particular, IgE-mediated cow's milk allergy is associated with compositional and functional changes of gut microbiota. In this study, we compared the gut microbiota of cow's milk allergic (CMA) infants with that of cow's milk sensitized (CMS) infants and Healthy controls. The effect of the intake of a mixture of Bifidobacterium longum subsp. longum BB536, Bifidobacterium breve M-16V and Bifidobacterium longum subsp. infantis M-63 on gut microbiota modulation of CMA infants and probiotic persistence was also investigated. Gut microbiota of CMA infants resulted to be characterized by a dysbiotic status with a prevalence of some bacteria as Haemophilus, Klebsiella, Prevotella, Actinobacillus and Streptococcus. Among the three strains administered, B.longum subsp. infantis colonized the gastrointestinal tract and persisted in the gut microbiota of infants with CMA for 60 days. This colonization was associated with perturbations of the gut microbiota, specifically with the increase of Akkermansia and Ruminococcus. Multi-strain probiotic formulations can be studied for their persistence in the intestine by monitoring specific bacterial probes persistence and exploiting microbiota profiling modulation before the evaluation of their therapeutic effects.

Network Analysis of Gut Microbiome and Metabolome to Discover Microbiota-Linked Biomarkers in Patients Affected by Non-Small Cell Lung Cancer.

Several studies in recent times have linked gut microbiome (GM) diversity to the pathogenesis of cancer and its role in disease progression through immune response, inflammation and metabolism modulation. This study focused on the use of network analysis and weighted gene co-expression network analysis (WGCNA) to identify the biological interaction between the gut ecosystem and its metabolites that could impact the immunotherapy response in non-small cell lung cancer (NSCLC) patients undergoing second-line treatment with anti-PD1. Metabolomic data were merged with operational taxonomic units (OTUs) from 16S RNA-targeted metagenomics and classified by chemometric models. The traits considered for the analyses were: (i) condition: disease or control (CTRLs), and (ii) treatment: responder (R) or non-responder (NR). Network analysis indicated that indole and its derivatives, aldehydes and alcohols could play a signaling role in GM functionality. WGCNA generated, instead, strong correlations between short-chain fatty acids (SCFAs) and a healthy GM. Furthermore, commensal bacteria such as Akkermansia muciniphila, Rikenellaceae, Bacteroides, Peptostreptococcaceae, Mogibacteriaceae and Clostridiaceae were found to be more abundant in CTRLs than in NSCLC patients. Our preliminary study demonstrates that the discovery of microbiota-linked biomarkers could provide an indication on the road towards personalized management of NSCLC patients.

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.

Fused Omics Data Models Reveal Gut Microbiome Signatures Specific of Inactive Stage of Juvenile Idiopathic Arthritis in Pediatric Patients.

Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease in children. Herein, we evaluated the relationship between the gut microbiome (GM) and disease phenotype by an integrated omics fused approach. In a multicenter, observational cohort study, stools from Italian JIA patients were collected at baseline, active, and inactive disease stages, and their GM compared to healthy controls (CTRLs). The microbiota metabolome was analyzed to detect volatile- and non-volatile organic compounds (VOCs); the data were fused with operational taxonomic units (OTUs) from 16S RNA targeted-metagenomics and classified by chemometric models. Non-VOCs did not characterize JIA patients nor JIA activity stages compared to CTRLs. The core of VOCs, (Ethanol, Methyl-isobutyl-ketone, 2,6-Dimethyl-4-heptanone and Phenol) characterized patients at baseline and inactive disease stages, while the OTUs represented by Ruminococcaceae, Lachnospiraceae and Clostridiacea discriminated between JIA inactive stage and CTRLs. No differences were highlighted amongst JIA activity stages. Finally, the fused data discriminated inactive and baseline stages versus CTRLs, based on the contribution of the invariant core of VOCs while Ruminococcaceae concurred for the inactive stage versus CTRLs comparison. In conclusion, the GM signatures enabled to distinguish the inactive disease stage from CTRLs.

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery.

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

Mass Spectrometry Based-Proteomic Analysis of Anisakis spp.: A Preliminary Study towards a New Diagnostic Tool.

Anisakiasis is nowadays a well-known infection, mainly caused by the accidental ingestion of Anisakis larvae, following the consumption of raw or undercooked fishes and cephalopods. Due to the similarity of symptoms with those of common gastrointestinal disorders, this infection is often underestimated, and the need for new specific diagnostic tools is becoming crucial. Given the remarkable impact that MALDI-TOF MS biotyping had in the last decade in clinical routine practice for the recognition of bacterial and fungi strains, a similar scenario could be foreseen for the identification of parasites, such as nematodes. In this work, a MALDI-TOF MS profiling of Anisakis proteome was pursued with a view to constructing a first spectral library for the diagnosis of Anisakis infections. At the same time, a shotgun proteomics approach by LC-ESI-MS/MS was performed on the two main fractions obtained from protein extraction, to evaluate the protein species enriched by the protocol. A set of MALDI-TOF MS signals associated with proteins originating in the ribosomal fraction of the nematode extract was selected as a potential diagnostic tool for the identification of Anisakis spp.