ERA-NET NEURON: The Gut Microbiome in Neuroinflammation and Neurodevelopmental Disorders
Lead Research Organisation:
Imperial College London
Department Name: Surgery and Cancer
Abstract
The brain is an extraordinarily complex organ that develops early on in embryonic life.
Neurodevelopment results from the execution of epigenetic and genetic programs, and is strongly influenced by environmental factors. A precise sequence of events is initiated, hich, while disrupted by genetic defects or environmental insults such as infections, provokes non-recoverable developmental alterations leading to mental diseases. In addition to the synaptic and/or connectivity dysfunctions associated to neurodevelopmental disorders, inflammation in the brain (otherwise called neuroinflammation) is one of the hallmarks shared by different neurodevelopmental disorders, such as Down syndrome, Fragile X syndrome and Autism Spectrum Disorders. In fact, neuroinflammation
drastically affects brain activity and behaviour. Recent studies demonstrate the existence of a gutbrain axis through which the intestinal microbiota is able to modulate inflammation and impact
behaviour. The µNeuroINF project will explore the hypothesis that our gut bacteria may trigger
neuroinflammation, which in turn impacts metabolism and behaviour, and ultimately contributes to
neurodevelopmental disease progression. Using mouse models of Down syndrome, Fragile X
syndrome and autism spectrum disorders, we will study the gut bacteria, their genes, proteins and
metabolites to identify which microbial metabolites common to these diseases are absorbed in the gut and diffuse into the bloodstream to reach the brain. The potential pro-inflammatory role of these metabolites will be investigated in vitro by screening their pharmacological targets in the host, and in vivo in the animal models of the disease. This will ultimately lead to novel therapeutic strategies for NDD driven by the gut microbiome. The µNeuroINF initiative is an innovative multidisciplinary project
targeting the role of the gut bacteria in neuroinflammation. Our unique research strategy uses cuttingedge technologies to explore how gut microbes modulate brain inflammation in neurodevelopmental
disorders. Not only the project will demonstrate one of the most fundamental mechanisms by which
our gut bacteria influence our behaviour, but it will also identify the microbial metabolites that can be used to better monitor brain inflammation ("biomarkers") or to lead to new drugs ("lead compounds") for the treatment of neuroinflammation
Neurodevelopment results from the execution of epigenetic and genetic programs, and is strongly influenced by environmental factors. A precise sequence of events is initiated, hich, while disrupted by genetic defects or environmental insults such as infections, provokes non-recoverable developmental alterations leading to mental diseases. In addition to the synaptic and/or connectivity dysfunctions associated to neurodevelopmental disorders, inflammation in the brain (otherwise called neuroinflammation) is one of the hallmarks shared by different neurodevelopmental disorders, such as Down syndrome, Fragile X syndrome and Autism Spectrum Disorders. In fact, neuroinflammation
drastically affects brain activity and behaviour. Recent studies demonstrate the existence of a gutbrain axis through which the intestinal microbiota is able to modulate inflammation and impact
behaviour. The µNeuroINF project will explore the hypothesis that our gut bacteria may trigger
neuroinflammation, which in turn impacts metabolism and behaviour, and ultimately contributes to
neurodevelopmental disease progression. Using mouse models of Down syndrome, Fragile X
syndrome and autism spectrum disorders, we will study the gut bacteria, their genes, proteins and
metabolites to identify which microbial metabolites common to these diseases are absorbed in the gut and diffuse into the bloodstream to reach the brain. The potential pro-inflammatory role of these metabolites will be investigated in vitro by screening their pharmacological targets in the host, and in vivo in the animal models of the disease. This will ultimately lead to novel therapeutic strategies for NDD driven by the gut microbiome. The µNeuroINF initiative is an innovative multidisciplinary project
targeting the role of the gut bacteria in neuroinflammation. Our unique research strategy uses cuttingedge technologies to explore how gut microbes modulate brain inflammation in neurodevelopmental
disorders. Not only the project will demonstrate one of the most fundamental mechanisms by which
our gut bacteria influence our behaviour, but it will also identify the microbial metabolites that can be used to better monitor brain inflammation ("biomarkers") or to lead to new drugs ("lead compounds") for the treatment of neuroinflammation
Technical Summary
Neurodevelopmental disorders (NDD) appear during nervous system development and maturation and originate from a variety of genetic and environmental causes or a combination of both. NDDs
result in lifelong intellectual disability and behavioural alterations (anxiety, hyperactivity, social
impairment, communication deficits) and have therefore extremely heavy socio-economical
consequences. Neuroinflammation is one of the hallmarks shared by NDD, including Down syndrome
(DS, 1:800), Fragile X syndrome (FXS, 1:6000) and Autism Spectrum Disorders (ASD, 1:100). Recent
studies demonstrate the existence of a gut-brain axis through which the intestinal microbiota is able to
modulate inflammation. The µNeuroINF project is driven by our hypothesis that the gut microbiome
modulates neuroinflammation impacting metabolism and behaviour and contributing to NDD
pathophysiology. By studying common effects across three mouse models of NDD: i) the Fmr1-KO
mouse model of FXS, ii) the Ts65Dn trisomic mouse model of DS, and iii) the Maternal Immune
Activation (MIA) ASD mouse model, we aim to unravel mechanisms by which gut microbial
metabolites modulate neuroinflammation and contribute to NDD-related behavioural phenotypes. Our
three specific objectives are: 1) to acquire complete metabolomic, metagenomic and metaproteomic
neuroinflammatory signatures in three NDD mouse models, 2) to identify microbial metabolites
modulating neuroinflammation in NDD, 3) to screen their pharmacological targets and test in vivo their effect on neuroinflammation and behaviour. Meta-"omics" will reveal microbial genes, proteins and
metabolites associated with variations in brain cytokines, immune cell populations and behavioural phenotypes in NDD. Microbial metabolites will be screened for pharmacological targets such as receptors and kinases, and mechanisms will be validated in vivo to explore the modulatory role of the gut microbiome on immunity and behaviour. This will ultimately lead to novel personalised medicine
and therapeutic strategies for NDD and neuroinflammation, driven by the gut microbiome.
result in lifelong intellectual disability and behavioural alterations (anxiety, hyperactivity, social
impairment, communication deficits) and have therefore extremely heavy socio-economical
consequences. Neuroinflammation is one of the hallmarks shared by NDD, including Down syndrome
(DS, 1:800), Fragile X syndrome (FXS, 1:6000) and Autism Spectrum Disorders (ASD, 1:100). Recent
studies demonstrate the existence of a gut-brain axis through which the intestinal microbiota is able to
modulate inflammation. The µNeuroINF project is driven by our hypothesis that the gut microbiome
modulates neuroinflammation impacting metabolism and behaviour and contributing to NDD
pathophysiology. By studying common effects across three mouse models of NDD: i) the Fmr1-KO
mouse model of FXS, ii) the Ts65Dn trisomic mouse model of DS, and iii) the Maternal Immune
Activation (MIA) ASD mouse model, we aim to unravel mechanisms by which gut microbial
metabolites modulate neuroinflammation and contribute to NDD-related behavioural phenotypes. Our
three specific objectives are: 1) to acquire complete metabolomic, metagenomic and metaproteomic
neuroinflammatory signatures in three NDD mouse models, 2) to identify microbial metabolites
modulating neuroinflammation in NDD, 3) to screen their pharmacological targets and test in vivo their effect on neuroinflammation and behaviour. Meta-"omics" will reveal microbial genes, proteins and
metabolites associated with variations in brain cytokines, immune cell populations and behavioural phenotypes in NDD. Microbial metabolites will be screened for pharmacological targets such as receptors and kinases, and mechanisms will be validated in vivo to explore the modulatory role of the gut microbiome on immunity and behaviour. This will ultimately lead to novel personalised medicine
and therapeutic strategies for NDD and neuroinflammation, driven by the gut microbiome.
Publications
Abdul Rahim MBH
(2019)
Diet-induced metabolic changes of the human gut microbiome: importance of short-chain fatty acids, methylamines and indoles.
in Acta diabetologica
Bermudez-Martin P
(2021)
The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota.
in Microbiome
Boulangé CL
(2016)
Impact of the gut microbiota on inflammation, obesity, and metabolic disease.
in Genome medicine
Brial F
(2020)
The Natural Metabolite 4-Cresol Improves Glucose Homeostasis and Enhances ß-Cell Function.
in Cell reports
Chilloux J
(2016)
The microbial-mammalian metabolic axis: a critical symbiotic relationship.
in Current opinion in clinical nutrition and metabolic care
Dumas M
(2015)
Metabolic Profiling and Phenotyping of Central Nervous System Diseases: Metabolites Bring Insights into Brain Dysfunctions
in Journal of Neuroimmune Pharmacology
Dumas ME
(2016)
Is the way we're dieting wrong?
in Genome medicine
| Title | Additional file 10 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 9: Figure S4. Associations of bacterial families with iron-related genes (discovery cohort, n = 35). Only significant correlations are coloured. Genes were measured by real time-PCR. Bacterial families with a significant positive association with serum ferritin concentrations are highlighted in dark red, whereas those with a significant negative association are highlighted in dark blue. ChREBP, carbohydrate response element binding protein; LCN2, Lipocailin 2; MitoNEET, Mitochondrial Inner NEET Protein; TFRC, Transferrin Receptor; SLC40A1, Solute Carrier Family 40 Member 1 (Ferroportin); TF, Transferrin; FTH1, Ferritin Heavy Chain 1; HAMP, Hepcidin Antimicrobial Peptide; FTL, Ferritin Light Chain; IRP1, Iron Regulatory Protein 1. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_10_of_Iron_status_influences_non... |
| Title | Additional file 10 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 9: Figure S4. Associations of bacterial families with iron-related genes (discovery cohort, n = 35). Only significant correlations are coloured. Genes were measured by real time-PCR. Bacterial families with a significant positive association with serum ferritin concentrations are highlighted in dark red, whereas those with a significant negative association are highlighted in dark blue. ChREBP, carbohydrate response element binding protein; LCN2, Lipocailin 2; MitoNEET, Mitochondrial Inner NEET Protein; TFRC, Transferrin Receptor; SLC40A1, Solute Carrier Family 40 Member 1 (Ferroportin); TF, Transferrin; FTH1, Ferritin Heavy Chain 1; HAMP, Hepcidin Antimicrobial Peptide; FTL, Ferritin Light Chain; IRP1, Iron Regulatory Protein 1. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_10_of_Iron_status_influences_non... |
| Title | Additional file 11 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 10: Figure S5. Associations the gut microbiome composition with hs-CRP and the gut microbiome functionality with serum ferritin. a) Volcano plot of differential bacterial genera and b) taxa associated with hs-CRP as calculated by DESeq2 from shotgun metagenomic sequencing in the independent cohort of obese and non-obese patients, adjusting for age, BMI, and sex. Fold change associated with a unit change in hs-CRP and adjusted p-values are plotted for each genus or taxon, respectively. Significantly different taxa are coloured according to phylum. c) Permutation test for the goodness-of-fit (R2Y) and goodness of prediction (Q2Y) obtained from the O-PLS model between serum ferritin and metagenome functions in the independent cohort (n = 130 obese and non-obese patients). d) Significant metagenome functions based on EggNOG functional annotations associated with serum ferritin in the independent cohort (n = 130 obese and non-obese patients). Initially, a significant O-PLS model between serum ferritin and metagenome functions was obtained for the independent cohort of obese and non-obese patients (R2Y=0.69, Q2Y=0.36, p<0.001). Then, significant O-PLS variables were further validated by pSC adjusting for age, sex, and BMI. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_11_of_Iron_status_influences_non... |
| Title | Additional file 11 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 10: Figure S5. Associations the gut microbiome composition with hs-CRP and the gut microbiome functionality with serum ferritin. a) Volcano plot of differential bacterial genera and b) taxa associated with hs-CRP as calculated by DESeq2 from shotgun metagenomic sequencing in the independent cohort of obese and non-obese patients, adjusting for age, BMI, and sex. Fold change associated with a unit change in hs-CRP and adjusted p-values are plotted for each genus or taxon, respectively. Significantly different taxa are coloured according to phylum. c) Permutation test for the goodness-of-fit (R2Y) and goodness of prediction (Q2Y) obtained from the O-PLS model between serum ferritin and metagenome functions in the independent cohort (n = 130 obese and non-obese patients). d) Significant metagenome functions based on EggNOG functional annotations associated with serum ferritin in the independent cohort (n = 130 obese and non-obese patients). Initially, a significant O-PLS model between serum ferritin and metagenome functions was obtained for the independent cohort of obese and non-obese patients (R2Y=0.69, Q2Y=0.36, p<0.001). Then, significant O-PLS variables were further validated by pSC adjusting for age, sex, and BMI. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_11_of_Iron_status_influences_non... |
| Title | Additional file 12 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 11: Figure S6. Associations of ferritin-related transcripts with liver fat accumulation. a) Permutation tests for the goodness-of-fit (R2Y) and goodness of prediction (Q2Y) obtained from the O-PLS model between the liver fat accumulation degree and transcripts that were significantly associated with serum ferritin. b) Significant transcripts associated with liver fat accumulation from O-PLS regression loadings. Hepatic genes belonging to the transcriptomic signature associated with serum ferritin and the gut microbiome are highlighted in dark red and blue. c) Further validation of O-PLS identified transcripts by pSC adjusting for age, BMI, sex, and country. d-g) Boxplots showing four hepatic genes identified in the transcriptomic signature associated with serum ferritin and the microbiome according to the serum ferritin quartiles (Q1-Q4). |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_12_of_Iron_status_influences_non... |
| Title | Additional file 12 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 11: Figure S6. Associations of ferritin-related transcripts with liver fat accumulation. a) Permutation tests for the goodness-of-fit (R2Y) and goodness of prediction (Q2Y) obtained from the O-PLS model between the liver fat accumulation degree and transcripts that were significantly associated with serum ferritin. b) Significant transcripts associated with liver fat accumulation from O-PLS regression loadings. Hepatic genes belonging to the transcriptomic signature associated with serum ferritin and the gut microbiome are highlighted in dark red and blue. c) Further validation of O-PLS identified transcripts by pSC adjusting for age, BMI, sex, and country. d-g) Boxplots showing four hepatic genes identified in the transcriptomic signature associated with serum ferritin and the microbiome according to the serum ferritin quartiles (Q1-Q4). |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_12_of_Iron_status_influences_non... |
| Title | Additional file 13 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 12: Figure S7. Iron supplementation leads to triglyceride accumulation and induces the expression of lipid and iron metabolism genes in primary human hepatocytes. a) Micrographs of primary human hepatocytes stained with Oil Red-O (representative images are from n = 4 independent batches). b) Quantification of lipid accumulation. O.D., Optical Density. c-h) FABP4, FABP5, FATP5, CD36, FTH, and FTL expression in hepatocytes. Data are mean ± SEM. Comparisons by one-way ANOVA. *p<0.05, **p<0.01, ***p<0.001 compared to control group based on t-test. #p<0.05, ##p<0.01, ###p<0.001 compared to PA group based on t-test. Ctrl, control group; PA, palmitic acid; Fe48h, pre-treatment iron 50µM for 48h; Fe72h, pre-treatment iron 50µM for 72h; Fe48h + PA, pre-treatment iron 50µM for 48h + palmitic acid 200µM for 24h; Fe72h + PA, pre-treatment iron 50µM for 72h + palmitic acid 200µM for 24h. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_13_of_Iron_status_influences_non... |
| Title | Additional file 13 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 12: Figure S7. Iron supplementation leads to triglyceride accumulation and induces the expression of lipid and iron metabolism genes in primary human hepatocytes. a) Micrographs of primary human hepatocytes stained with Oil Red-O (representative images are from n = 4 independent batches). b) Quantification of lipid accumulation. O.D., Optical Density. c-h) FABP4, FABP5, FATP5, CD36, FTH, and FTL expression in hepatocytes. Data are mean ± SEM. Comparisons by one-way ANOVA. *p<0.05, **p<0.01, ***p<0.001 compared to control group based on t-test. #p<0.05, ##p<0.01, ###p<0.001 compared to PA group based on t-test. Ctrl, control group; PA, palmitic acid; Fe48h, pre-treatment iron 50µM for 48h; Fe72h, pre-treatment iron 50µM for 72h; Fe48h + PA, pre-treatment iron 50µM for 48h + palmitic acid 200µM for 24h; Fe72h + PA, pre-treatment iron 50µM for 72h + palmitic acid 200µM for 24h. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_13_of_Iron_status_influences_non... |
| Title | Additional file 14 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 13: Figure S8. PcoA based on Canberra beta diversity comparing high fat diet (HFD) and non-high fat diet (No-HFD) for different iron doses. a) low-iron (LI) fed mice, b) low-normal-iron (LNI) fed mice, c) the high-normal iron (HNI) fed mice, d) moderately-high (MHI) iron fed mice. Differences in microbial composition were assessed by PERMANOVA analyses using the Adonis function in vegan R package with 999 permutations. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_14_of_Iron_status_influences_non... |
| Title | Additional file 14 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 13: Figure S8. PcoA based on Canberra beta diversity comparing high fat diet (HFD) and non-high fat diet (No-HFD) for different iron doses. a) low-iron (LI) fed mice, b) low-normal-iron (LNI) fed mice, c) the high-normal iron (HNI) fed mice, d) moderately-high (MHI) iron fed mice. Differences in microbial composition were assessed by PERMANOVA analyses using the Adonis function in vegan R package with 999 permutations. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_14_of_Iron_status_influences_non... |
| Title | Additional file 7 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 6: Figure S1. Flow chart of the study human cohorts and omics analyses pipeline. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_7_of_Iron_status_influences_non-... |
| Title | Additional file 7 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 6: Figure S1. Flow chart of the study human cohorts and omics analyses pipeline. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_7_of_Iron_status_influences_non-... |
| Title | Additional file 8 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 7: Figure S2. Associations of serum ferritin with hs-CRP. a) Association of hs-CRP with serum ferritin quartiles in the discovery cohort and b) an independent cohort of obese and non-obese patients (Mann-Kendall trend test and Wilcoxon tests). |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_8_of_Iron_status_influences_non-... |
| Title | Additional file 8 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 7: Figure S2. Associations of serum ferritin with hs-CRP. a) Association of hs-CRP with serum ferritin quartiles in the discovery cohort and b) an independent cohort of obese and non-obese patients (Mann-Kendall trend test and Wilcoxon tests). |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_8_of_Iron_status_influences_non-... |
| Title | Additional file 9 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 8: Figure S3. Associations of serum ferritin with the gut microbiome in the human cohorts. Permutation tests for the goodness-of-fit (R2Y) and goodness of prediction (Q2Y) obtained from the O-PLS model between serum ferritin and a) bacterial families or b) bacterial genera in a subsample of obese women from the discovery and replication cohorts from Italy and Spain (n = 56). c) Significant families and d) genera associated with serum ferritin from O-PLS regression loadings. Families and genera associated positively and negatively associated with serum ferritin from Mnet regression models are highlighted in dark red and blue, respectively. e) Significant families and f) genera associated with serum ferritin after further validation of the O-PLS significant variables by pSC adjusting for age, BMI, country, and hs-CRP. g) Associations of bacterial families and h) genera associated with serum ferritin by DESeq2 analysis from shotgun metagenomic sequencing data in the independent cohort of obese and non-obese patients (n = 130), adjusting for age, BMI, sex, and hs-CRP. Families and genera also associated with serum ferritin in the discovery and replication cohorts based on Mnet regression models are highlighted in dark red, whereas those also identified from O-PLS modelling are highlighted in dark pink. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_9_of_Iron_status_influences_non-... |
| Title | Additional file 9 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 8: Figure S3. Associations of serum ferritin with the gut microbiome in the human cohorts. Permutation tests for the goodness-of-fit (R2Y) and goodness of prediction (Q2Y) obtained from the O-PLS model between serum ferritin and a) bacterial families or b) bacterial genera in a subsample of obese women from the discovery and replication cohorts from Italy and Spain (n = 56). c) Significant families and d) genera associated with serum ferritin from O-PLS regression loadings. Families and genera associated positively and negatively associated with serum ferritin from Mnet regression models are highlighted in dark red and blue, respectively. e) Significant families and f) genera associated with serum ferritin after further validation of the O-PLS significant variables by pSC adjusting for age, BMI, country, and hs-CRP. g) Associations of bacterial families and h) genera associated with serum ferritin by DESeq2 analysis from shotgun metagenomic sequencing data in the independent cohort of obese and non-obese patients (n = 130), adjusting for age, BMI, sex, and hs-CRP. Families and genera also associated with serum ferritin in the discovery and replication cohorts based on Mnet regression models are highlighted in dark red, whereas those also identified from O-PLS modelling are highlighted in dark pink. |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2021 |
| URL | https://springernature.figshare.com/articles/figure/Additional_file_9_of_Iron_status_influences_non-... |
| Description | I was invited to join the Knowledge Transfer Network's Microbiome innovation network Advisory Board to help it develop its strategic roadmap, which will be used to approach policy makers and high-level stakeholders, including funding bodies. |
| Geographic Reach | National |
| Policy Influence Type | Membership of a guideline committee |
| URL | https://ktn-uk.org/news/ktns-microbiome-innovation-network-launches-the-microbiome-strategy-roadmap/ |
| Description | I was invited to participate in an event organised by the Microbiology Society to develop the society's Microbiome Policy, so that to inform the public and various stakeholders and to influence government policy on the subject. |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| URL | http://www.microbiologysociety.org/policy/microbiome-policy-project.cfm |
| Description | IMMEDIATE |
| Amount | £860,000 (GBP) |
| Funding ID | 10058101 |
| Organisation | Innovate UK |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2023 |
| End | 12/2026 |
| Title | Additional file 2 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 1:. Baseline characteristics of the independent cohort. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_2_of_Iron_status_influences_non... |
| Title | Additional file 2 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 1:. Baseline characteristics of the independent cohort. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_2_of_Iron_status_influences_non... |
| Title | Additional file 3 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 2:. Gut microbiome bacterial species associated with the serum ferritin. Results of differential bacterial abundance associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_3_of_Iron_status_influences_non... |
| Title | Additional file 3 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 2:. Gut microbiome bacterial species associated with the serum ferritin. Results of differential bacterial abundance associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_3_of_Iron_status_influences_non... |
| Title | Additional file 3 of The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota |
| Description | Additional file 2: Table S1. ANCOM analysis: significant microbial features (from ASV to phylum) discriminating p-Cresol from Control microbiota (relative to Fig. 3). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_3_of_The_microbial_metabolite_p... |
| Title | Additional file 3 of The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota |
| Description | Additional file 2: Table S1. ANCOM analysis: significant microbial features (from ASV to phylum) discriminating p-Cresol from Control microbiota (relative to Fig. 3). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_3_of_The_microbial_metabolite_p... |
| Title | Additional file 3: of Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series |
| Description | Supplementary Tables 4-6. (XLSX 28452 kb) |
| Type Of Material | Database/Collection of data |
| Year Produced | 2016 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_3_of_Topological_analysis_of_me... |
| Title | Additional file 3: of Topological analysis of metabolic networks integrating co-segregating transcriptomes and metabolomes in type 2 diabetic rat congenic series |
| Description | Supplementary Tables 4-6. (XLSX 28452 kb) |
| Type Of Material | Database/Collection of data |
| Year Produced | 2016 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_3_of_Topological_analysis_of_me... |
| Title | Additional file 4 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 3:. Metagenome functions based on KEGG annotation associated with the serum ferritin. Results of metagenome KEGG functions associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_4_of_Iron_status_influences_non... |
| Title | Additional file 4 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 3:. Metagenome functions based on KEGG annotation associated with the serum ferritin. Results of metagenome KEGG functions associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_4_of_Iron_status_influences_non... |
| Title | Additional file 4 of The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota |
| Description | Additional file 3: Table S2. ANCOM analysis: significant microbial features (from ASV to phylum) discriminating FMTControl from FMTp-Cresol mice, 3 weeks post-FMT (relative to Fig. 5). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_4_of_The_microbial_metabolite_p... |
| Title | Additional file 4 of The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota |
| Description | Additional file 3: Table S2. ANCOM analysis: significant microbial features (from ASV to phylum) discriminating FMTControl from FMTp-Cresol mice, 3 weeks post-FMT (relative to Fig. 5). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_4_of_The_microbial_metabolite_p... |
| Title | Additional file 5 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 4:. Metagenome functions based on EggNOG annotation associated with the serum ferritin. Results of metagenome EggNOG functions associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_5_of_Iron_status_influences_non... |
| Title | Additional file 5 of Iron status influences non-alcoholic fatty liver disease in obesity through the gut microbiome |
| Description | Additional file 4:. Metagenome functions based on EggNOG annotation associated with the serum ferritin. Results of metagenome EggNOG functions associated with ferritin as calculated from shotgun metagenomic sequencing in an independent cohort of obese and non-obese subjects, adjusting for age, BMI, sex, and hs-CRP. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_5_of_Iron_status_influences_non... |
| Title | Additional file 5 of The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota |
| Description | Additional file 4: Table S3. Output of blast sequence analysis for ThiH, Tyr and HpdA/B/C enzymes involved in p-Cresol synthesis (relative to Table 1). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_5_of_The_microbial_metabolite_p... |
| Title | Additional file 5 of The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota |
| Description | Additional file 4: Table S3. Output of blast sequence analysis for ThiH, Tyr and HpdA/B/C enzymes involved in p-Cresol synthesis (relative to Table 1). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_5_of_The_microbial_metabolite_p... |
| Title | Additional file 6 of The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota |
| Description | Additional file 5: Table S4. Details on ASV sequences and taxonomic affiliation. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_6_of_The_microbial_metabolite_p... |
| Title | Additional file 6 of The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota |
| Description | Additional file 5: Table S4. Details on ASV sequences and taxonomic affiliation. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_6_of_The_microbial_metabolite_p... |
| Title | Additional file 7 of The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota |
| Description | Additional file 6: Table S6. Compilation of ANOVA statistics for behavioral data analyses and PERMANOVA statistics for 16S rRNA gene sequencing-based richness and diversity analyses. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_7_of_The_microbial_metabolite_p... |
| Title | Additional file 7 of The microbial metabolite p-Cresol induces autistic-like behaviors in mice by remodeling the gut microbiota |
| Description | Additional file 6: Table S6. Compilation of ANOVA statistics for behavioral data analyses and PERMANOVA statistics for 16S rRNA gene sequencing-based richness and diversity analyses. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://springernature.figshare.com/articles/dataset/Additional_file_7_of_The_microbial_metabolite_p... |
| Description | Joint-funding from MRC/ANR/BMBF/MINECO as part of ERANET Neuron II award |
| Organisation | Bellvitge Biomedical Research Institute |
| Country | Spain |
| Sector | Academic/University |
| PI Contribution | The current MRC award was made as part of an ERANET joint funding initiative for the µNeuroInf collaborative project, investigating the role of the gut microbiome in neuroinflammation and neurodevelopment disorders. As part of this award our team at Imperial has profiled the metabolome of animal models of neurodevelopmental disorders and has implemented methods for metagenomic sample preparation and data analysis. Our group has also screened a selection of 10 microbial metabolites against a panel of >500 pharmacological targets that are clinically relevant. We have followed up and tested whether these microbial metabolites are pro-inflammatory or anti-inflammatory by cell-based assays using brain immune cells (microglia) and cells in charge of feeding neutrons (astrocytes) in order to reduce the number of animals used for validations. |
| Collaborator Contribution | Partners involved in the consortium have generated animal models for Down Syndrome (IDIBELL, Barcelona, Spain), Fragile X Syndrome and Autism (CNRS, Nice, France) which have been studied by proteomics (University of Tuebingen, Germany). |
| Impact | This collaboration has resulted in the publication of 3 reviews (listed in the publication output) during the first year of activity: - one review on metabolomics in neuroscience - one review on the pharmacology of microbial metabolites - one review on the use of microbiome data in personal nutrition |
| Start Year | 2015 |
| Description | Joint-funding from MRC/ANR/BMBF/MINECO as part of ERANET Neuron II award |
| Organisation | Eberhard Karls University of Tübingen |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | The current MRC award was made as part of an ERANET joint funding initiative for the µNeuroInf collaborative project, investigating the role of the gut microbiome in neuroinflammation and neurodevelopment disorders. As part of this award our team at Imperial has profiled the metabolome of animal models of neurodevelopmental disorders and has implemented methods for metagenomic sample preparation and data analysis. Our group has also screened a selection of 10 microbial metabolites against a panel of >500 pharmacological targets that are clinically relevant. We have followed up and tested whether these microbial metabolites are pro-inflammatory or anti-inflammatory by cell-based assays using brain immune cells (microglia) and cells in charge of feeding neutrons (astrocytes) in order to reduce the number of animals used for validations. |
| Collaborator Contribution | Partners involved in the consortium have generated animal models for Down Syndrome (IDIBELL, Barcelona, Spain), Fragile X Syndrome and Autism (CNRS, Nice, France) which have been studied by proteomics (University of Tuebingen, Germany). |
| Impact | This collaboration has resulted in the publication of 3 reviews (listed in the publication output) during the first year of activity: - one review on metabolomics in neuroscience - one review on the pharmacology of microbial metabolites - one review on the use of microbiome data in personal nutrition |
| Start Year | 2015 |
| Description | Joint-funding from MRC/ANR/BMBF/MINECO as part of ERANET Neuron II award |
| Organisation | National Center for Scientific Research (Centre National de la Recherche Scientifique CNRS) |
| Department | Institute of Molecular and Cellular Pharmacology |
| Country | France |
| Sector | Academic/University |
| PI Contribution | The current MRC award was made as part of an ERANET joint funding initiative for the µNeuroInf collaborative project, investigating the role of the gut microbiome in neuroinflammation and neurodevelopment disorders. As part of this award our team at Imperial has profiled the metabolome of animal models of neurodevelopmental disorders and has implemented methods for metagenomic sample preparation and data analysis. Our group has also screened a selection of 10 microbial metabolites against a panel of >500 pharmacological targets that are clinically relevant. We have followed up and tested whether these microbial metabolites are pro-inflammatory or anti-inflammatory by cell-based assays using brain immune cells (microglia) and cells in charge of feeding neutrons (astrocytes) in order to reduce the number of animals used for validations. |
| Collaborator Contribution | Partners involved in the consortium have generated animal models for Down Syndrome (IDIBELL, Barcelona, Spain), Fragile X Syndrome and Autism (CNRS, Nice, France) which have been studied by proteomics (University of Tuebingen, Germany). |
| Impact | This collaboration has resulted in the publication of 3 reviews (listed in the publication output) during the first year of activity: - one review on metabolomics in neuroscience - one review on the pharmacology of microbial metabolites - one review on the use of microbiome data in personal nutrition |
| Start Year | 2015 |
| Title | MWASTools: an integrated pipeline to perform metabolome-wide association studies |
| Description | MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results. |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Open Source License? | Yes |
| Impact | The MWASTools R package provides an integrated pipeline with efficient analysis and visualization tools for: (i) performing QC analysis; (ii) conducting robust MWAS analysis with efficient handling of epidemiological confounders; (iii) structural assignment of metabolic features of interest; (iv) biological interpretation of MWAS results. The MWASTools package can be applied to both targeted and untargeted metabonomic datasets, acquired with different analytical platforms. |
| URL | http://www.bioconductor.org/packages/release/bioc/html/MWASTools.html |
| Title | MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways |
| Description | MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes. |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Open Source License? | Yes |
| Impact | 'MetaboSignal' is a versatile package integrating metabolic and signaling transduction pathways to build parsimonious visualizations of gene-metabolite associations based on the analysis of network topology. MetaboSignal is a biomolecular navigation system allowing the exploration of organism-specific, and even tissue-specific, relationships between any given gene and any given metabolite retrieved from KEGG. This approach is ideally suited to identify candidate genes in metabotype-QTL studies (e.g. trans-acting associations), or to identify biological pathways affected in transgenic models (e.g. knock-out, CRISPR-Cas9). |
| URL | http://bioconductor.org/packages/release/bioc/html/MetaboSignal.html |
| Description | Imperial Fringe (science festival): Food of Tomorrow |
| Form Of Engagement Activity | Participation in an open day or visit at my research institution |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | Animation of a stand on the microbiome entitled Meet your micro-lodgers. This was part of the wider Food of Tomorrow event. Over 1,200 people signed up for the event, and we estimate at least 800 attended over the course of the evening. Those who attended came from the sectors represented in the registration, which included representatives from over 30 food companies (including Sainsbury's, Unilever, Youngs, Innocent drinks, GÜ Puds, United Biscuits), 61 alumni, stakeholders from government and funding agencies, and students from 12 London schools. |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www3.imperial.ac.uk/newsandeventspggrp/imperialcollege/eventssummary/event_8-12-2015-11-54-47 |
| Description | Participation in "Big Biology Week" public engagement event, Hills Road College, Cambridge, 15th October 2016. |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | In this event, I chaired a session sponsored by the Microbiology Society to raise pupils, parents and general public's awareness of the gut microbiome. The event was held on Saturday 15th of October 2016 at Hills Road Sixth Form College in Cambridge to ensure maximum outreach. The event was designed to be fun, engaging and informative for children and adults. This was one of more than a 100 events organised by the Royal Society of Biology: https://www.rsb.org.uk/get-involved/biologyweek |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www.hillsroad.ac.uk/college-life/events/2016/10/15/default-calendar/big-biology-day |