Lecture 20 Chronic Diseases PDF

FQ2024 MIC111 L20: linking dysbioses and chronic disease What are some challenges to microbiome engineering? Goal: to re-engineer dybosis microbiomes as a therapy or engineer to prevent dysbiosis —> remember microbiome ecosystem stability is defined by: What are some challenges to microbiome en...

FQ2024 MIC111 L20: linking dysbioses and chronic disease What are some challenges to microbiome engineering? Goal: to re-engineer dybosis microbiomes as a therapy or engineer to prevent dysbiosis —> remember microbiome ecosystem stability is defined by: What are some challenges to microbiome engineering? Goal: to re-engineer dybosis microbiomes as a therapy or engineer to prevent dysbiosis —> remember microbiome ecosystem stability is defined by: Functional redundance Colonization resistance Ecosystem resilience What are some challenges to microbiome engineering? Limits to the introduction of microbes to GI What are some challenges to microbiome engineering? Stomach: gastric enzymes and low pH Limits to the introduction of microbes to GI What are some challenges to microbiome engineering? Stomach: gastric enzymes and low pH Small intestine – bile acids and digestive enzymes limit probiotic bacteria viability through cell membrane disruption and DNA damage. Limits to the introduction of microbes to GI What are some challenges to microbiome engineering? Stomach: gastric enzymes and low pH Small intestine – bile acids and digestive enzymes limit probiotic bacteria viability through cell membrane disruption and DNA damage. Colon – probiotics compete with host microbiota for nutrients and adhesion sites to mucosa. Limits to the introduction of microbes to GI What are some challenges to microbiome engineering? Stomach: gastric enzymes and low pH Small intestine – bile acids and digestive enzymes limit probiotic bacteria viability through cell membrane disruption and DNA damage. Colon – probiotics compete with host microbiota for nutrients and adhesion sites to mucosa. Limits to the introduction of microbes to GI What are some challenges to microbiome engineering? Stomach: gastric enzymes and low pH Small intestine – bile acids and digestive enzymes limit probiotic bacteria viability through cell membrane disruption and DNA damage. Colon – probiotics compete with host microbiota for nutrients and adhesion sites to mucosa. Colonization resistance: probiotic microbiome limited in ability to colonize and thus lost Limits to the introduction of (excreted); unable to change overall GI microbes to GI microbiota community structure or diversity. What are some challenges to microbiome engineering? Stomach: gastric enzymes and low pH Small intestine – bile acids and digestive enzymes limit probiotic bacteria viability through cell membrane disruption and DNA damage. Colon – probiotics compete with host microbiota for nutrients and adhesion sites to mucosa. Colonization resistance: probiotic microbiome limited in ability to colonize and thus lost Limits to the introduction of (excreted); unable to change overall GI microbes to GI microbiota community structure or diversity. Evaluating health claims (vs. marketing) efficacy = how effective? dosage = what is the e f f e c t i ve amount? safety = what are the costs/ benefits? Some challenges to probiotic/synbiotic strategies Some challenges to probiotic/synbiotic strategies successful colonization of host (colonization resistance) Some challenges to probiotic/synbiotic strategies successful colonization of host (colonization resistance) cultivation of strains (anaerobes, fermenters, most bacteria in microbiome remain uncultivated) Some challenges to probiotic/synbiotic strategies successful colonization of host (colonization resistance) cultivation of strains (anaerobes, fermenters, most bacteria in microbiome remain uncultivated) host microbiome variation/host genetic variation Some challenges to probiotic/synbiotic strategies successful colonization of host (colonization resistance) cultivation of strains (anaerobes, fermenters, most bacteria in microbiome remain uncultivated) host microbiome variation/host genetic variation delivery (ingestion?) Some challenges to probiotic/synbiotic strategies successful colonization of host (colonization resistance) cultivation of strains (anaerobes, fermenters, most bacteria in microbiome remain uncultivated) host microbiome variation/host genetic variation delivery (ingestion?) dosage (too little growth, too much growth?) What is the “effective” dose of probiotics? What is the “effective” dose of probiotics? How long are they retained? What is the “effective” dose of probiotics? How long are they retained? Is there toxicity when overabundant? Microbiomes and diets variation in microbiome taxa and metabolism between individuals infant feeding diet exposure to animals age geographic location host genetics medication (antibiotics)  Is microbiome diversity more drivenARTICLE by RESEARCH diet or host genetics? cy with which human phenotypes can be predicted, consistent Ashkenazi Yemenite Middle Eastern previous smaller-scale study15. North African Sephardi Other a b ly, we successfully replicate our results in 836 Dutch individu- Genetics (P < 10–32) Microbiome (NS) Principal component 2 (0.24%) h genotypes and metagenomic data, from the LifeLines DEEP 0.1 0.3 cohort8. Taken together, our results demonstrate that the gut 0.2 PCO2 (7.56%) iome is predominantly shaped by environmental factors, and 0.1 gly correlated with many human phenotypes after accounting 0 0 genetics. –0.1 –0.2 –0.1 s –0.3 died a cohort of 1,046 healthy Israeli adults from whom we col- –0.4 blood for genotyping and phenotyping, stools for metagenome –0.04 0 0.04 –0.2 –0.1 0 0.1 0.2 0.3 0.4 0.5 cing and 16S rRNA gene sequencing, anthropometric measure- Principal component 1 (0.50%) PCO1 (11.14%) and answers to food frequency and lifestyle questionnaires12 c d e NS ded Data Table 1 and Supplementary Table 1). We performed NS NS Microbiome dissimilarity (BC) 1.0 ping at 712,540 SNPs and imputed them to 5,567,647 SNPs Firmicutes Phylum distribution 0.8 ods). Stool samples were profiled using both metagenome (log, normalized) Bacteroidetes Actinobacteria cing and 16S rRNA gene sequencing, and then analysed at Proteobacteria 0.6 e taxonomic levels; the results presented here are based on Verrucomicrobia 0.4 Euryarchaeota nome species analysis (results at metagenome phylum, class, Viruses amily, genus or bacterial gene levels, and for 16S genus and 0.2 onal taxonomic unit levels, are provided in Supplementary 0 where appropriate). We included covariates for age, gender, stool 0 25 50 75 100 Y e fric i M S me an D d d l e p h n ite en s i rig n s st i Y e fric i M S me n d l p h te O n er A z e r E a rd Ea rd A az in t o te r th n a a er on method, and self-reported daily median caloric, fat, protein id e n i th iff e a e a Shared ancestry (%) th e n or e N shk or hk As bohydrate consumption (Methods). A N i Figure 1 | Genetic ancestry is not significantly associated with ed evidence of microbiome–genetic associations microbiome composition. a, Genetic principal components are strongly mple consists of self-reported Ashkenazi (n = 508), North associated with self-reported ancestry, with Ashkenazi (n = 345), North  Is microbiome diversity more drivenARTICLE by RESEARCH diet or host genetics? cy with which human phenotypes can be predicted, consistent Ashkenazi Yemenite Middle Eastern previous smaller-scale study15. North African Sephardi Other a b ly, we successfully replicate our results in 836 Dutch individu- Genetics (P < 10–32) Microbiome (NS) Principal component 2 (0.24%) h genotypes and metagenomic data, from the LifeLines DEEP 0.1 0.3 cohort8. Taken together, our results demonstrate that the gut 0.2 PCO2 (7.56%) iome is predominantly shaped by environmental factors, and whatwith gly correlated aremanyrelative humancontributions of phenotypes after accounting 0 0.1 0 genetics. genetics vs. diet in shaping –0.1 –0.2 –0.1 s microbiomes? –0.3 died a cohort of 1,046 healthy Israeli adults from whom we col- –0.4 blood for genotyping and phenotyping, stools for metagenome –0.04 0 0.04 –0.2 –0.1 0 0.1 0.2 0.3 0.4 0.5 cing and 16S rRNA gene sequencing, anthropometric measure- Principal component 1 (0.50%) PCO1 (11.14%) and answers to food frequency and lifestyle questionnaires12 c d e NS ded Data Table 1 and Supplementary Table 1). We performed NS NS Microbiome dissimilarity (BC) 1.0 ping at 712,540 SNPs and imputed them to 5,567,647 SNPs Firmicutes Phylum distribution 0.8 ods). Stool samples were profiled using both metagenome (log, normalized) Bacteroidetes Actinobacteria cing and 16S rRNA gene sequencing, and then analysed at Proteobacteria 0.6 e taxonomic levels; the results presented here are based on Verrucomicrobia 0.4 Euryarchaeota nome species analysis (results at metagenome phylum, class, Viruses amily, genus or bacterial gene levels, and for 16S genus and 0.2 onal taxonomic unit levels, are provided in Supplementary 0 where appropriate). We included covariates for age, gender, stool 0 25 50 75 100 Y e fric i M S me an D d d l e p h n ite en s i rig n s st i Y e fric i M S me n d l p h te O n er A z e r E a rd Ea rd A az in t o te r th n a a er on method, and self-reported daily median caloric, fat, protein id e n i th iff e a e a Shared ancestry (%) th e n or e N shk or hk As bohydrate consumption (Methods). A N i Figure 1 | Genetic ancestry is not significantly associated with ed evidence of microbiome–genetic associations microbiome composition. a, Genetic principal components are strongly mple consists of self-reported Ashkenazi (n = 508), North associated with self-reported ancestry, with Ashkenazi (n = 345), North  Is microbiome diversity more drivenARTICLE by RESEARCH diet or host genetics? cy with which human phenotypes can be predicted, consistent Ashkenazi Yemenite Middle Eastern previous smaller-scale study15. North African Sephardi Other a b ly, we successfully replicate our results in 836 Dutch individu- Genetics (P < 10–32) Microbiome (NS) Principal component 2 (0.24%) h genotypes and metagenomic data, from the LifeLines DEEP 0.1 0.3 cohort8. Taken together, our results demonstrate that the gut 0.2 PCO2 (7.56%) iome is predominantly shaped by environmental factors, and whatwith gly correlated aremanyrelative humancontributions of phenotypes after accounting 0 0.1 0 genetics. genetics vs. diet in shaping –0.1 –0.2 –0.1 s microbiomes? –0.3 died a cohort of 1,046 healthy Israeli adults from whom we col- –0.4 genetic blood for genotypingorigins are distinct and phenotyping, (A)metagenome stools for but –0.04 0 0.04 –0.2 –0.1 0 0.1 0.2 0.3 0.4 0.5 cing and 16S rRNA gene sequencing, anthropometric measure- Principal component 1 (0.50%) PCO1 (11.14%) microbiome composition can remain and answers to food frequency and lifestyle questionnaires12 c d e NS ded Datasimilar regardless Table 1 and (B, Table Supplementary C, D) 1). We performed NS NS Microbiome dissimilarity (BC) 1.0 ping at 712,540 SNPs and imputed them to 5,567,647 SNPs Firmicutes Phylum distribution 0.8 ods). Stool samples were profiled using both metagenome (log, normalized) Bacteroidetes Actinobacteria cing and 16S rRNA gene sequencing, and then analysed at Proteobacteria 0.6 e taxonomic levels; the results presented here are based on Verrucomicrobia 0.4 Euryarchaeota nome species analysis (results at metagenome phylum, class, Viruses amily, genus or bacterial gene levels, and for 16S genus and 0.2 onal taxonomic unit levels, are provided in Supplementary 0 where appropriate). We included covariates for age, gender, stool 0 25 50 75 100 Y e fric i M S me an D d d l e p h n ite en s i rig n s st i Y e fric i M S me n d l p h te O n er A z e r E a rd Ea rd A az in t o te r th n a a er on method, and self-reported daily median caloric, fat, protein id e n i th iff e a e a Shared ancestry (%) th e n or e N shk or hk As bohydrate consumption (Methods). A N i Figure 1 | Genetic ancestry is not significantly associated with ed evidence of microbiome–genetic associations microbiome composition. a, Genetic principal components are strongly mple consists of self-reported Ashkenazi (n = 508), North associated with self-reported ancestry, with Ashkenazi (n = 345), North  Is microbiome diversity more drivenARTICLE by RESEARCH diet or host genetics? cy with which human phenotypes can be predicted, consistent Ashkenazi Yemenite Middle Eastern previous smaller-scale study15. North African Sephardi Other a b ly, we successfully replicate our results in 836 Dutch individu- Genetics (P < 10–32) Microbiome (NS) Principal component 2 (0.24%) h genotypes and metagenomic data, from the LifeLines DEEP 0.1 0.3 cohort8. Taken together, our results demonstrate that the gut 0.2 PCO2 (7.56%) iome is predominantly shaped by environmental factors, and whatwith gly correlated aremanyrelative humancontributions of phenotypes after accounting 0 0.1 0 genetics. genetics vs. diet in shaping –0.1 –0.2 –0.1 s microbiomes? –0.3 died a cohort of 1,046 healthy Israeli adults from whom we col- –0.4 genetic blood for genotypingorigins are distinct and phenotyping, (A)metagenome stools for but –0.04 0 0.04 –0.2 –0.1 0 0.1 0.2 0.3 0.4 0.5 cing and 16S rRNA gene sequencing, anthropometric measure- Principal component 1 (0.50%) PCO1 (11.14%) microbiome composition can remain and answers to food frequency and lifestyle questionnaires12 c d e NS ded Datasimilar regardless Table 1 and Supplementary(B, Table C, D) 1). We performed NS NS Microbiome dissimilarity (BC) 1.0 ping at 712,540 SNPs and imputed them to 5,567,647 SNPs Firmicutes Phylum distribution estimated ods). Stool samples were 20% of inter-person profiled using both metagenome (log, normalized) Bacteroidetes 0.8 Actinobacteria cing and 16S rRNA gene sequencing, and then analysed at 0.6 variation e taxonomic in diversity levels; the is associated results presented with here are based on Proteobacteria Verrucomicrobia Euryarchaeota 0.4 nome species analysis diet or drugs(results at metagenome phylum, class, Viruses amily, genus or bacterial gene levels, and for 16S genus and 0.2 onal taxonomic unit levels, are provided in Supplementary 0 where appropriate). We included covariates for age, gender, stool 0 25 50 75 100 Y e fric i M S me an D d d l e p h n ite en s i rig n s st i Y e fric i M S me n d l p h te O n er A z e r E a rd Ea rd A az in t o te r th n a a er on method, and self-reported daily median caloric, fat, protein id e n i th iff e a e a Shared ancestry (%) th e n or e N shk or hk As bohydrate consumption (Methods). A N i Figure 1 | Genetic ancestry is not significantly associated with ed evidence of microbiome–genetic associations microbiome composition. a, Genetic principal components are strongly mple consists of self-reported Ashkenazi (n = 508), North associated with self-reported ancestry, with Ashkenazi (n = 345), North  Is microbiome diversity more drivenARTICLE by RESEARCH diet or host genetics? cy with which human phenotypes can be predicted, consistent Ashkenazi Yemenite Middle Eastern previous smaller-scale study15. North African Sephardi Other a b ly, we successfully replicate our results in 836 Dutch individu- Genetics (P < 10–32) Microbiome (NS) Principal component 2 (0.24%) h genotypes and metagenomic data, from the LifeLines DEEP 0.1 0.3 cohort8. Taken together, our results demonstrate that the gut 0.2 PCO2 (7.56%) iome is predominantly shaped by environmental factors, and whatwith gly correlated aremanyrelative humancontributions of phenotypes after accounting 0 0.1 0 genetics. genetics vs. diet in shaping –0.1 –0.2 –0.1 s microbiomes? –0.3 died a cohort of 1,046 healthy Israeli adults from whom we col- –0.4 genetic blood for genotypingorigins are distinct and phenotyping, (A)metagenome stools for but –0.04 0 0.04 –0.2 –0.1 0 0.1 0.2 0.3 0.4 0.5 cing and 16S rRNA gene sequencing, anthropometric measure- Principal component 1 (0.50%) PCO1 (11.14%) microbiome composition can remain and answers to food frequency and lifestyle questionnaires12 c d e NS ded Datasimilar regardless Table 1 and Supplementary(B, Table C, D) 1). We performed NS NS Microbiome dissimilarity (BC) 1.0 ping at 712,540 SNPs and imputed them to 5,567,647 SNPs Firmicutes Phylum distribution estimated ods). Stool samples were 20% of inter-person profiled using both metagenome (log, normalized) Bacteroidetes 0.8 Actinobacteria cing and 16S rRNA gene sequencing, and then analysed at 0.6 variation e taxonomic in diversity levels; the is associated results presented here are based with on Proteobacteria Verrucomicrobia Euryarchaeota 0.4 nome species analysis diet or drugs(results at metagenome phylum, class, Viruses amily, genus or bacterial gene levels, and for 16S genus and 0.2 microbiome onal taxonomic composition unit levels, are provided isin more Supplementary 0 where appropriate). We included covariates for age, gender, stool 0 25 50 75 100 associated with daily chronic medianconditions Y e fric i M S me an D d d l e p h n ite en s i rig n s st i Y e fric i M S me n d l p h te O n er A z e r E a rd Ea rd A az in t o te r th n a a er on method, and self-reported caloric, fat, protein id e n i th iff e a e a Shared ancestry (%) th e n or e N shk or hk As bohydrate consumption (Methods). A than host genetics N i Figure 1 | Genetic ancestry is not significantly associated with ed evidence of microbiome–genetic associations microbiome composition. a, Genetic principal components are strongly mple consists of self-reported Ashkenazi (n = 508), North associated with self-reported ancestry, with Ashkenazi (n = 345), North  Is microbiome diversity more drivenARTICLE by RESEARCH diet or host genetics? ARTICLE RESEARCH cy with which human phenotypes can be predicted, consistent Ashkenazi Yemenite Middle Eastern previous smaller-scale study15. North African Sephardi Other a b ly, we successfully y with which human replicate our results phenotypes can be inpredicted, 836 Dutchconsistent individu- Ashkenazi Genetics (P < 10–32) Yemenite Middle Eastern Microbiome (NS) 2 (0.24%) h genotypes previous and metagenomic smaller-scale study15. data, from the LifeLines DEEP 0.1 North African Sephardi Other 8 a b 0.3 cohort. Taken together, ly, we successfully replicate ourour results demonstrate results in 836 Dutch that the gut individu- Genetics (P < 10–32) 0.2 Microbiome (NS) PCO2 (7.56%) 2 (0.24%) hiome is predominantly genotypes and metagenomicshapeddata, by environmental from the LifeLinesfactors, and DEEP what are relative contributions of 0.1 0.1 component 0.3 gly correlated cohort 8. Takenwith manyour together, human phenotypes results demonstrate afterthat accounting the gut 0 0.20 PCO2 (7.56%) genetics. iome genetics vs.shaped is predominantly diet in shaping factors, and by environmental –0.1 0.1 component gly correlated with many human phenotypes after accounting 0 –0.1 –0.2 0 sgenetics.microbiomes? Principal –0.3 –0.1 died a cohort of 1,046 healthy Israeli adults from whom we col- –0.4 –0.2 s blood genetic for genotypingorigins are distinct and phenotyping, (A)metagenome stools for but –0.1 –0.04 0 0.04 –0.3 –0.2 –0.1 0 0.1 0.2 0.3 0.4 0.5 Principal cing aand 16S rRNA gene sequencing, anthropometric measure- Principal component 1 (0.50%) PCO1 (11.14%) died cohort of 1,046 microbiome healthy Israeli adults composition from canwhom remain we col- –0.4 12 and loodanswers to foodand for genotyping frequency and lifestyle phenotyping, stools questionnaires for metagenome c –0.04 0 0.04 d –0.2 –0.1 0 0.1 e 0.2 0.3 0.4 0.5 NS and similar ded Data Table regardless 1 and Supplementary (B,anthropometric C, D) Table 1). We performed NS PCO1 (11.14%)NS (BC) (BC) cing 16S rRNA gene sequencing, measure- Principal component 1 (0.50%) 1.0 ping at 712,540 and answers SNPsfrequency to food and imputed them toquestionnaires and lifestyle 5,567,647 SNPs 12 c d e dissimilarity Firmicutes distribution ods). ded Data estimated Stool samples Table 1 andwere20% of inter-person profiled Supplementary using Tableboth1). Wemetagenome performed (log, normalized) NS Bacteroidetes 0.8 1.0 NS NS Actinobacteria cing at ping and 16S rRNA 712,540 SNPsgeneand sequencing, imputed them andtothen analysed 5,567,647 SNPsat 0.6 variation in diversity is associated with dissimilarity Proteobacteria Firmicutes distribution 0.8 e taxonomic levels; were the results presented heremetagenome are based on (log, normalized) ods). Stool samples profiled using both Verrucomicrobia Bacteroidetes 0.4 Euryarchaeota Actinobacteria PhylumPhylum nomeand species analysis (results at metagenome phylum, class, Microbiome cing 16S rRNA diet or gene drugs sequencing, and then analysed at Viruses Proteobacteria 0.6 eamily, genus or taxonomic bacterial levels; gene levels, the results and here presented for 16Saregenus basedandon Verrucomicrobia 0.2 microbiome composition is in more Euryarchaeota 0.4 onal taxonomic nome unit levels, species analysis are (results at provided metagenome Supplementary phylum, class, Microbiome Viruses 0 where amily,appropriate). We included genus or bacterial covariates gene levels, andfor forage, 16Sgender, genusstool and 0.2 0 25 50 75 100 associated withare chronic medianconditions Y e f r i c i Y f r i zi id e n n id S e e n n D dl ph iDte d p ite en s i re as d i or rn s as i Y e fric i m an d l p h te d p te rn er Ea rd e E ard A az in em ca A a on method, and self-reported daily caloric, fat, protein e r E a rd e E a r n t te onal taxonomic unit levels, provided in Supplementary id e n i id S e e n i th te r th n ig Shared ancestry (%) th e n iff e a iff le h or hke No hke l h O or k N h 0 s As bohydrate where consumption appropriate). (Methods). Wegenetics included covariates for age, gender, stool A than host 0 25 50 75 100 M S me aM M S me M rig n s i i an O n er A z Af az in t o te r th n a er on method, and self-reported daily median caloric, fat, protein th Y e ric e a Shared ancestry (%) th e n st Figure 1 | Genetic ancestry is not significantly associated with or hk ed evidence of microbiome–genetic associations As As bohydrate consumption (Methods).