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

Author

Scanu, Matteo, primary, Toto, Francesca, additional, Petito, Valentina, additional, Masi, Letizia, additional, Fidaleo, Marco, additional, Puca, Pierluigi, additional, Baldelli, Valerio, additional, Reddel, Sofia, additional, Vernocchi, Pamela, additional, Pani, Giovambattista, additional, Putignani, Lorenza, additional, Scaldaferri, Franco, additional, and Del Chierico, Federica, additional

Published
2024
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5. Gut Microbiota Ecology and Inferred Functions in Children With ASD Compared to Neurotypical Subjects

Author

Vernocchi, Pamela, Ristori, Maria Vittoria, Guerrera, Silvia, Guarrasi, Valerio, Conte, Federica, Russo, Alessandra, Lupi, Elisabetta, Albitar-Nehme, Sami, Gardini, Simone, Paci, Paola, Ianiro, Gianluca, Vicari, Stefano, Gasbarrini, Antonio, Putignani, Lorenza, Ianiro, Gianluca (ORCID:0000-0002-8318-0515), Vicari, Stefano (ORCID:0000-0002-5395-2262), Gasbarrini, Antonio (ORCID:0000-0002-7278-4823), Vernocchi, Pamela, Ristori, Maria Vittoria, Guerrera, Silvia, Guarrasi, Valerio, Conte, Federica, Russo, Alessandra, Lupi, Elisabetta, Albitar-Nehme, Sami, Gardini, Simone, Paci, Paola, Ianiro, Gianluca, Vicari, Stefano, Gasbarrini, Antonio, Putignani, Lorenza, Ianiro, Gianluca (ORCID:0000-0002-8318-0515), Vicari, Stefano (ORCID:0000-0002-5395-2262), and Gasbarrini, Antonio (ORCID:0000-0002-7278-4823)

Abstract

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 clas

Published
2022

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

Author

Vernocchi, Pamela, primary, Ristori, Maria Vittoria, additional, Guerrera, Silvia, additional, Guarrasi, Valerio, additional, Conte, Federica, additional, Russo, Alessandra, additional, Lupi, Elisabetta, additional, Albitar-Nehme, Sami, additional, Gardini, Simone, additional, Paci, Paola, additional, Ianiro, Gianluca, additional, Vicari, Stefano, additional, Gasbarrini, Antonio, additional, and Putignani, Lorenza, additional

Published
2022
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7. A Shaving Proteomic Approach to Unveil Surface Proteins Modulation of Multi-Drug Resistant Pseudomonas aeruginosa Strains Isolated From Cystic Fibrosis Patients

Author

Montemari, Anna Lisa, primary, Marzano, Valeria, additional, Essa, Nour, additional, Levi Mortera, Stefano, additional, Rossitto, Martina, additional, Gardini, Simone, additional, Selan, Laura, additional, Vrenna, Gianluca, additional, Onetti Muda, Andrea, additional, Putignani, Lorenza, additional, and Fiscarelli, Ersilia Vita, additional

Published
2022
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8. Impact of Two Antibiotic Therapies on Clinical Outcome and Gut Microbiota Profile in Liver Transplant Paediatric Candidates Colonized by Carbapenem-Resistant Klebsiella pneumoniae CR-KP

Author

Cardile, Sabrina, primary, Del Chierico, Federica, additional, Candusso, Manila, additional, Reddel, Sofia, additional, Bernaschi, Paola, additional, Pietrobattista, Andrea, additional, Spada, Marco, additional, Torre, Giuliano, additional, and Putignani, Lorenza, additional

Published
2021
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9. Longitudinal Multi-Omics Study of a Mother-Infant Dyad from Breastfeeding to Weaning: An Individualized Approach to Understand the Interactions Among Diet, Fecal Metabolome and Microbiota Composition

Author

Conta, Giorgia, primary, Del Chierico, Federica, additional, Reddel, Sofia, additional, Marini, Federico, additional, Sciubba, Fabio, additional, Capuani, Giorgio, additional, Tomassini, Alberta, additional, Di Cocco, Maria Enrica, additional, Laforgia, Nicola, additional, Baldassarre, Maria Elisabetta, additional, Putignani, Lorenza, additional, and Miccheli, Alfredo, additional

Published
2021
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10. Dysbiosis, Host Metabolism, and Non-communicable Diseases: Trialogue in the Inborn Errors of Metabolism

Author

Montanari, Chiara, primary, Parolisi, Sara, additional, Borghi, Elisa, additional, Putignani, Lorenza, additional, Bassanini, Giulia, additional, Zuvadelli, Juri, additional, Bonfanti, Cristina, additional, Tummolo, Albina, additional, Dionisi Vici, Carlo, additional, Biasucci, Giacomo, additional, Burlina, Alberto, additional, Carbone, Maria Teresa, additional, and Verduci, Elvira, additional

Published
2021
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11. Gut Microbiota and Related Electronic Multisensorial System Changes in Subjects With Symptomatic Uncomplicated Diverticular Disease Undergoing Rifaximin Therapy

Author

De Vincentis, Antonio, primary, Santonico, Marco, additional, Del Chierico, Federica, additional, Altomare, Annamaria, additional, Marigliano, Benedetta, additional, Laudisio, Alice, additional, Reddel, Sofia, additional, Grasso, Simone, additional, Zompanti, Alessandro, additional, Pennazza, Giorgio, additional, Putignani, Lorenza, additional, Guarino, Michele Pier Luca, additional, Cicala, Michele, additional, and Antonelli Incalzi, Raffaele, additional

Published
2021
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12. 16S Metagenomics Reveals Dysbiosis of Nasal Core Microbiota in Children With Chronic Nasal Inflammation: Role of Adenoid Hypertrophy and Allergic Rhinitis

Author

Marazzato, Massimiliano, primary, Zicari, Anna Maria, additional, Aleandri, Marta, additional, Conte, Antonietta Lucia, additional, Longhi, Catia, additional, Vitanza, Luca, additional, Bolognino, Vanessa, additional, Zagaglia, Carlo, additional, De Castro, Giovanna, additional, Brindisi, Giulia, additional, Schiavi, Laura, additional, De Vittori, Valentina, additional, Reddel, Sofia, additional, Quagliariello, Andrea, additional, Del Chierico, Federica, additional, Putignani, Lorenza, additional, Duse, Marzia, additional, Palamara, Anna Teresa, additional, and Conte, Maria Pia, additional

Published
2020
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13. Gut Mucosal and Fecal Microbiota Profiling Combined to Intestinal Immune System in Neonates Affected by Intestinal Ischemic Injuries

Author

Romani, Lorenza, primary, Del Chierico, Federica, additional, Chiriaco, Maria, additional, Foligno, Silvia, additional, Reddel, Sofia, additional, Salvatori, Guglielmo, additional, Cifaldi, Cristina, additional, Faraci, Simona, additional, Finocchi, Andrea, additional, Rossi, Paolo, additional, Bagolan, Pietro, additional, D'Argenio, Patrizia, additional, Putignani, Lorenza, additional, and Fusaro, Fabio, additional

Published
2020
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15. Fecal and Mucosal Microbiota Profiling in Irritable Bowel Syndrome and Inflammatory Bowel Disease

Author

Lo Presti, Alessandra, primary, Zorzi, Francesca, additional, Del Chierico, Federica, additional, Altomare, Annamaria, additional, Cocca, Silvia, additional, Avola, Alessandra, additional, De Biasio, Fabiola, additional, Russo, Alessandra, additional, Cella, Eleonora, additional, Reddel, Sofia, additional, Calabrese, Emma, additional, Biancone, Livia, additional, Monteleone, Giovanni, additional, Cicala, Michele, additional, Angeletti, Silvia, additional, Ciccozzi, Massimo, additional, Putignani, Lorenza, additional, and Guarino, Michele Pier Luca, additional

Published
2019
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16. Gut Microbiota Markers in Obese Adolescent and Adult Patients: Age-Dependent Differential Patterns

Author

Del Chierico, Federica, primary, Abbatini, Francesca, additional, Russo, Alessandra, additional, Quagliariello, Andrea, additional, Reddel, Sofia, additional, Capoccia, Danila, additional, Caccamo, Romina, additional, Ginanni Corradini, Stefano, additional, Nobili, Valerio, additional, De Peppo, Francesco, additional, Dallapiccola, Bruno, additional, Leonetti, Frida, additional, Silecchia, Gianfranco, additional, and Putignani, Lorenza, additional

Published
2018
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17. Gut Microbiota Profiling and Gut–Brain Crosstalk in Children Affected by Pediatric Acute-Onset Neuropsychiatric Syndrome and Pediatric Autoimmune Neuropsychiatric Disorders Associated With Streptococcal Infections

Author

Quagliariello, Andrea, primary, Del Chierico, Federica, additional, Russo, Alessandra, additional, Reddel, Sofia, additional, Conte, Giulia, additional, Lopetuso, Loris R., additional, Ianiro, Gianluca, additional, Dallapiccola, Bruno, additional, Cardona, Francesco, additional, Gasbarrini, Antonio, additional, and Putignani, Lorenza, additional

Published
2018
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19. Monitoring Perinatal Gut Microbiota in Mouse Models by Mass Spectrometry Approaches: Parental Genetic Background and Breastfeeding Effects

Author

Mortera Stefano, Levi, Del Chierico, Federica, Vernocchi, Pamela, Rosado Maria, M., Cavola, Agnese, Chierici, Marco, Pieroni, Luisa, Urbani, Andrea, Carsetti, Rita, Lante, Isabella, Dallapiccola, Bruno, Putignani, Lorenza, Urbani, Andrea (ORCID:0000-0001-9168-3174), Mortera Stefano, Levi, Del Chierico, Federica, Vernocchi, Pamela, Rosado Maria, M., Cavola, Agnese, Chierici, Marco, Pieroni, Luisa, Urbani, Andrea, Carsetti, Rita, Lante, Isabella, Dallapiccola, Bruno, Putignani, Lorenza, and Urbani, Andrea (ORCID:0000-0001-9168-3174)

Abstract

At birth, contact with external stimuli, such as nutrients derived from food, is necessary to modulate the symbiotic balance between commensal and pathogenic bacteria, protect against bacterial dysbiosis, and initiate the development of the mucosal immune response. Among a variety of different feeding patterns, breastfeeding represents the best modality. In fact, the capacity of breast milk to modulate the composition of infants' gut microbiota leads to beneficial effects on their health. In this study, we used newborn mice as a model to evaluate the effect of parental genetic background (i.e., IgA-producing mice and IgA-deficient mice) and feeding modulation (i.e., maternal feeding and cross-feeding) on the onset and shaping of gut microbiota after birth. To investigate these topics, we used either a culturomic approach that employed Matrix Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MS), or bottom-up Liquid Chromatography, with subsequent MSMS shotgun metaproteomic analysis that compared and assembled results of the two techniques. We found that the microbial community was enriched by lactic acid bacteria when pups were breastfed by wild-type (WT) mothers, while IgA-deficient milk led to an increase in the opportunistic bacterial pathogen (OBP) population. Cross-feeding results suggested that IgA supplementation promoted the exclusion of some OBPs and the temporary appearance of beneficial species in pups fed by WT foster mothers. Our results show that both techniques yield a picture of microbiota from different angles and with varying depths. In particular, our metaproteomic pipeline was found to be a reliable tool in the description of microbiota. Data from these studies are available via ProteomeXchange, with identifier PXD004033.

Published
2016

20. Monitoring Perinatal Gut Microbiota in Mouse Models by Mass Spectrometry Approaches: Parental Genetic Background and Breastfeeding Effects

Author

Levi Mortera, Stefano, primary, Del Chierico, Federica, additional, Vernocchi, Pamela, additional, Rosado, Maria M., additional, Cavola, Agnese, additional, Chierici, Marco, additional, Pieroni, Luisa, additional, Urbani, Andrea, additional, Carsetti, Rita, additional, Lante, Isabella, additional, Dallapiccola, Bruno, additional, and Putignani, Lorenza, additional

Published
2016
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22. Effects of sub-lethal high-pressure homogenization treatment on the outermost cellular structures and the volatile-molecule profiles of two strains of probiotic lactobacilli

Author

Tabanelli, Giulia, primary, Vernocchi, Pamela, additional, Patrignani, Francesca, additional, Del Chierico, Federica, additional, Putignani, Lorenza, additional, Vinderola, Gabriel, additional, Reinheimer, Jorge A., additional, Gardini, Fausto, additional, and Lanciotti, Rosalba, additional

Published
2015
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24. Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health

Author

Pamela Vernocchi, Federica Del Chierico, Lorenza Putignani, Vernocchi, Pamela, Del Chierico, Federica, and Putignani, Lorenza

Subjects
0301 basic medicine, Microbiology (medical), 030106 microbiology, lcsh:QR1-502, Diseases, Review, Gut microbiota, Computational biology, Gut flora, Dietary habit, Bioinformatics, Microbiology, lcsh:Microbiology, 03 medical and health sciences, Human health, Metabolomics, Metabolome, Profiling (information science), Disease, dietary habits, biology, biology.organism_classification, State of health, Omic approach, Metabolic pathway, 030104 developmental biology, Metagenomics, Metaproteomics, Healthy status
Abstract

The gut microbiota is composed of a huge number of different bacteria, which produce a large amount of compounds playing a key role in microbe selection and in the construction of a metabolic signaling network. The microbial activity is affected by environmental stimuli leading to the generation of a wide number of compounds, which influence the host metabolome and human health. Indeed, metabolic profiles related to the gut microbiota can offer deep insights on the impact of lifestyle and dietary factors on chronic and acute diseases. Metagenomics, metaproteomics and metabolomics are some of the meta-omics approaches to study the modulation of the gut microbiota. Metabolomic research applied to biofluids allows to: define the metabolic profile; identify and quantify classes and compounds of interest; characterize small molecules produced by intestinal microbes; and define the biochemical pathways of metabolites. Mass spectrometry and nuclear magnetic resonance spectroscopy are the principal technologies applied to metabolomics in terms of coverage, sensitivity and quantification. Moreover, the use of biostatistics and mathematical approaches coupled with metabolomics play a key role in the extraction of biologically meaningful information from wide datasets. Metabolomic studies in gut microbiota-related research have increased, focusing on the generation of novel biomarkers, which could lead to the development of mechanistic hypotheses potentially applicable to the development of nutritional and personalized therapies.

Published
2016
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25. The metaproteome of the gut microbiota in pediatric patients affected by COVID-19.

Author

Marzano V, Mortera SL, Marangelo C, Piazzesi A, Rapisarda F, Pane S, Del Chierico F, Vernocchi P, Romani L, Campana A, Palma P, and Putignani L

Subjects
Adult, Humans, Child, Adjuvants, Immunologic, Algorithms, Gastrointestinal Microbiome, COVID-19, Microbiota
Abstract

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Marzano, Mortera, Marangelo, Piazzesi, Rapisarda, Pane, Del Chierico, Vernocchi, Romani, Campana, Palma, Putignani and the CACTUS Study Team.)

Published
2023
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26. The Relationship Between Pediatric Gut Microbiota and SARS-CoV-2 Infection.

Author

Romani L, Del Chierico F, Macari G, Pane S, Ristori MV, Guarrasi V, Gardini S, Pascucci GR, Cotugno N, Perno CF, Rossi P, Villani A, Bernardi S, Campana A, Palma P, and Putignani L

Subjects
Adult, Bacteroides genetics, Bifidobacterium genetics, Child, Clostridium genetics, Feces microbiology, Humans, RNA, Ribosomal, 16S genetics, SARS-CoV-2, Systemic Inflammatory Response Syndrome, COVID-19 complications, Gastrointestinal Microbiome physiology
Abstract

This is the first study on gut microbiota (GM) in children affected by coronavirus disease 2019 (COVID-19). Stool samples from 88 patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and 95 healthy subjects were collected (admission: 3-7 days, discharge) to study GM profile by 16S rRNA gene sequencing and relationship to disease severity. The study group was divided in COVID-19 (68), Non-COVID-19 (16), and MIS-C (multisystem inflammatory syndrome in children) (4). Correlations among GM ecology, predicted functions, multiple machine learning (ML) models, and inflammatory response were provided for COVID-19 and Non-COVID-19 cohorts. The GM of COVID-19 cohort resulted as dysbiotic, with the lowest α-diversity compared with Non-COVID-19 and CTRLs and by a specific β-diversity. Its profile appeared enriched in Faecalibacterium , Fusobacterium , and Neisseria and reduced in Bifidobacterium , Blautia , Ruminococcus , Collinsella , Coprococcus , Eggerthella , and Akkermansia , compared with CTRLs ( p < 0.05). All GM paired-comparisons disclosed comparable results through all time points. The comparison between COVID-19 and Non-COVID-19 cohorts highlighted a reduction of Abiotrophia in the COVID-19 cohort ( p < 0.05). The GM of MIS-C cohort was characterized by an increase of Veillonella , Clostridium , Dialister , Ruminococcus , and Streptococcus and a decrease of Bifidobacterium , Blautia , Granulicatella , and Prevotella , compared with CTRLs. Stratifying for disease severity, the GM associated to "moderate" COVID-19 was characterized by lower α-diversity compared with "mild" and "asymptomatic" and by a GM profile deprived in Neisseria , Lachnospira , Streptococcus , and Prevotella and enriched in Dialister , Acidaminococcus , Oscillospora , Ruminococcus , Clostridium , Alistipes , and Bacteroides. The ML models identified Staphylococcus , Anaerostipes , Faecalibacterium , Dorea , Dialister , Streptococcus , Roseburia , Haemophilus , Granulicatella , Gemmiger , Lachnospira , Corynebacterium , Prevotella , Bilophila , Phascolarctobacterium , Oscillospira , and Veillonella as microbial markers of COVID-19. The KEGG ortholog (KO)-based prediction of GM functional profile highlighted 28 and 39 KO-associated pathways to COVID-19 and CTRLs, respectively. Finally, Bacteroides and Sutterella correlated with proinflammatory cytokines regardless disease severity. Unlike adult GM profiles, Faecalibacterium was a specific marker of pediatric COVID-19 GM. The durable modification of patients' GM profile suggested a prompt GM quenching response to SARS-CoV-2 infection since the first symptoms. Faecalibacterium and reduced fatty acid and amino acid degradation were proposed as specific COVID-19 disease traits, possibly associated to restrained severity of SARS-CoV-2-infected children. Altogether, this evidence provides a characterization of the pediatric COVID-19-related GM., Competing Interests: Author GM, VG, and SG are employed by GenomeUp SRL, Viale Pasteur, 6, 00144, Rome, Italy. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Romani, Del Chierico, Macari, Pane, Ristori, Guarrasi, Gardini, Pascucci, Cotugno, Perno, Rossi, Villani, Bernardi, Campana, Palma, Putignani and the CACTUS Study Team.)

Published
2022
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