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@Pdorrestein1
Pieter C. Dorrestein
Disclosures
Scientific co-founder Advisor
Commitments approved by UC-San Diego
Why do we care about microbial metabolites?
Microbial metabolites Influence Human Health
Production of
energy metabolites,
neurotransmitters,
and vitamins
Many more
unknown
molecules
with
unknown
function
4
Anti- and Pro-Inflammatory Microbial Metabolites
Indole-3-Lactate (ILA)1-3
Produced by Bifidobacterium species
Modulates CD4+ T cells in the intestine and
reduce inflammation
1Ehrlich, A.M., et al. Indole-3-lactic acid associated with Bifidobacterium-dominated microbiota
significantly decreases inflammation in intestinal epithelial cells. BMC Microbiology (2020).
2Meng, D., et al. Indole-3-lactic acid, a metabolite of tryptophan, secreted by Bifidobacterium
longum subspecies infantis is anti-inflammatory in the immature intestine. Pediatric Research (2020)
3Laursen, M.F., et al. Bifidobacterium species associated with breastfeeding produce aromatic lactic
acids in the infant gut. Nature Microbiology (2021)
Colibactin4-6
4Arthur JC, Perez-Chanona E, Mühlbauer M, et al. Intestinal inflammation targets cancer-inducing
activity of the microbiota. Science. (2012)
4Cougnoux A, Dalmasso G, Martinez R, et al. Bacterial genotoxin colibactin promotes colon
tumour growth by inducing a senescence-associated secretory phenotype. Gut. (2014)
6Dziubańska-Kusibab, P.J., Berger, H., Battistini, F. et al. Colibactin DNA-damage signature
indicates mutational impact in colorectal cancer. Nature Medicine. (2020)
Produced by E. coli and other
Enterobacteriaceae
Involved in Inflammatory Bowel Disease
(IBD) and Colorectal Cancer
5
Untargeted Metabolomics
Untargeted Metabolomics
2016
Annotated Metabolome
Untargeted Metabolomics
2024
Microbial
Annotated Metabolome
Untargeted Metabolomics
Microbial
Human Genome
Microbiome Annotated Metabolome
Call by NIH is to create a community to help enable mechanistic
interpretation of the microbiome
What was proposed?
Collaborative Microbial Metabolite Center (CMMC)
Microbial Metabolite data and
knowledge of NIH PAR-21-253
grantees.
Curation, storage, and
knowledge enrichment
(Dorrestein, Wang, Bandeira)
Enable data reuse and
analysis.
(Dorrestein, Wang, Knight)
CMMC microbial data portal
upload, knowledge capture,
search and analysis
(Wang, Dorrestein)
Organize annual CMMC
meeting
(Dorrestein)
Documentation and training
(Wang, Dorrestein)
(NIDDK, NIAID, NIDCR, NIEHS, NCI, NICHD)
MK
Collaborative Microbial Metabolite Center (CMMC)
Microbial Metabolite data and
knowledge of NIH PAR-21-253
grantees.
Curation, storage, and
knowledge enrichment
(Dorrestein, Wang, Bandeira)
Enable data reuse and
analysis.
(Dorrestein, Wang, Knight)
CMMC microbial data portal
upload, knowledge capture,
search and analysis
(Wang, Dorrestein)
Organize annual CMMC
meeting
(Dorrestein)
Documentation and training
(Wang, Dorrestein)
(NIDDK, NIAID, NIDCR, NIEHS, NCI, NICHD)
MK
Collaborative Microbial Metabolite Center (CMMC)
Microbial Metabolite data and
knowledge of NIH PAR-21-253
grantees.
Curation, storage, and
knowledge enrichment
(Dorrestein, Wang, Bandeira)
Enable data reuse and
analysis.
(Dorrestein, Wang, Knight)
CMMC microbial data portal
upload, knowledge capture,
search and analysis
(Wang, Dorrestein)
Organize annual CMMC
meeting
(Dorrestein)
Documentation and training
(Wang, Dorrestein)
(NIDDK, NIAID, NIDCR, NIEHS, NCI, NICHD)
MK
Collaborative Microbial Metabolite Center (CMMC)
Microbial Metabolite data and
knowledge of NIH PAR-21-253
grantees.
Curation, storage, and
knowledge enrichment
(Dorrestein, Wang, Bandeira)
Enable data reuse and
analysis.
(Dorrestein, Wang, Knight)
CMMC microbial data portal
upload, knowledge capture,
search and analysis
(Wang, Dorrestein)
Organize annual CMMC
meeting
(Dorrestein)
Documentation and training
(Wang, Dorrestein)
(NIDDK, NIAID, NIDCR, NIEHS, NCI, NICHD)
MK
Collaborative Microbial Metabolite Center (CMMC)
Microbial Metabolite data and
knowledge of NIH PAR-21-253
grantees.
Curation, storage, and
knowledge enrichment
(Dorrestein, Wang, Bandeira)
Enable data reuse and
analysis.
(Dorrestein, Wang, Knight)
CMMC microbial data portal
upload, knowledge capture,
search and analysis
(Wang, Dorrestein)
Organize annual CMMC
meeting
(Dorrestein)
Documentation and training
(Wang, Dorrestein)
(NIDDK, NIAID, NIDCR, NIEHS, NCI, NICHD)
MK
Collaborative Microbial Metabolite Center (CMMC)
Microbial Metabolite data and
knowledge of NIH PAR-21-253
grantees.
Curation, storage, and
knowledge enrichment
(Dorrestein, Wang, Bandeira)
Enable data reuse and
analysis.
(Dorrestein, Wang, Knight)
CMMC microbial data portal
upload, knowledge capture,
search and analysis
(Wang, Dorrestein)
Organize annual CMMC
meeting
(Dorrestein)
Documentation and training
(Wang, Dorrestein)
(NIDDK, NIAID, NIDCR, NIEHS, NCI, NICHD)
MK
Other NIH
Awardees
World-wide
Other data
around the
world.
MIME
NPAtlas
MiBIG
Metabolights
Metabolomics
WB
Metabolic
models
+ others
CMMC Microbial data upload, knowledge capture and data driven enrichment
Indexed
CMMC Microbial data upload, knowledge capture and data driven enrichment
Indexed
CMMC Microbial data upload, knowledge capture and data driven enrichment
Indexed
CMMC Microbial data upload, knowledge capture and data driven enrichment
Step 1
Is built by…
What is
?
It’s a bird. No, It’s a Plane. It’s superman.
Democratizing metabolomics data knowledge
>450,000
accessions/month
Wang, Nature Biotech 2016
>450,000
accessions/month
Data
Democratizing metabolomics data knowledge
Wang, Nature Biotech 2016
>450,000
accessions/month
Data
Knowledge
Democratizing metabolomics data knowledge
Wang, Nature Biotech 2016
>450,000
accessions/month
Data
Knowledge
Democratizing metabolomics data knowledge
~3,400 projects, ~800,000 LC-MS/MS files
Wang, Nature Biotech 2016
>450,000
accessions/month
Data
Knowledge
Democratizing metabolomics data knowledge
~3,400 projects, ~800,000 LC-MS/MS files
Wang, Nature Biotech 2016
>50+ analysis tools + made compatible with 3rd
party tools such as metaboanalyst, Cytoscape, Qiime.
>450,000
accessions/month
Data
Knowledge
Democratizing metabolomics data knowledge
~3,400 projects, ~800,000 LC-MS/MS files
(USI + Indexing)
1.2 billion MS/MS spectra
Wang, Nature Biotech 2016
A prototype of what indexing enables
>450,000
accessions/month
Data
Knowledge
Democratizing metabolomics data knowledge
~3,400 projects, ~800,000 LC-MS/MS files
(USI + Indexing)
1.2 billion MS/MS spectra
Wang, Nature Biotech 2016
>450,000
accessions/month
Data
Knowledge
Democratizing metabolomics data knowledge
~3,400 projects, ~800,000 LC-MS/MS files
(USI + Indexing) + 5,000 additional projects?
1.2 billion MS/MS spectra
Wang, Nature Biotech 2016
MetaboLights
Building search engines
Wang, Nature Biotech 2021 Jarmusch, bioRxiv 2022, in review
>450,000
accessions/month
Data
Knowledge
Democratizing metabolomics data knowledge
CMMC leverages the community curated data driven GNPS ecosystem
1.2 billion MS/MS spectra
Wang, Nature Biotech 2016
MetaboLights
CMMC Microbial data upload, knowledge capture and data driven enrichment
CMMC Microbial data upload, knowledge capture and data driven enrichment
Knowledgebase
Helena Russo Shipei Xing
Knowledgebase
Helena Russo Shipei Xing
Upload was created with 1-on-1 meetings with individual R01 grantees
and we expect it to continue to evolve as needs arise.
Knowledgebase
https://www.biorxiv.org/content/10.1101/2020.05.09.086066v2
Only one that is required-USI
Helena Russo Shipei Xing
Knowledgebase
Helena Russo Shipei Xing
Knowledgebase
Helena Russo Shipei Xing
Knowledgebase
Helena Russo Shipei Xing
Helena Russo Shipei Xing
Knowledgebase
Knowledgebase
Helena Russo Shipei Xing
Knowledgebase
Helena Russo Shipei Xing
Knowledgebase
Helena Russo Shipei Xing
Knowledgebase
Helena Russo Shipei Xing
Helena Russo Shipei Xing
Knowledgebase
Knowledgebase
Helena Russo Shipei Xing
Where will the data go?
How can one reuse the data that is captured?
Where will the data go?
Materials: tinyurl.com/CMMCWS
Metabolomics data (LC-MS/MS)
-Rel intensities
-and in 14% annotations
-Relationships of structural
similarity.
CMMC community curated
Grown to
~5,000
annotations
of ~600
compounds
Call to the community
To populate at the end of this
workshop
Molecular networking enrichment workflow using CMMCkb.
Watrous PNAS 2012, Wang Nat Biotech 2016
Watrous PNAS 2012, Wang Nat Biotech 2016, Aron, Gentry Nat Protocols 2020
Molecular
Networking
Helena Russo Shipei Xing
~6,000 MS/MS spectra curated.
Helena Russo Shipei Xing
~6,000 MS/MS spectra curated.
3rd mechanism for data use in CMMC currently available.
3rd mechanism for data use in CMMC currently available.
Building microbeMASST
> 100 MILLION
MS/MS spectra
- Bacteria
- Fungi
- Human cells
- QC/Blanks
1
2
1
Wang, Mingxun, et al. "Sharing and community curation of mass spectrometry data with Global
Natural Products Social Molecular Networking." Nature biotechnology (2016)
2Jarmusch, Alan K., et al. "ReDU: a framework to find and reanalyze public mass spectrometry
data." Nature methods (2020)
In 2 months, 25 different research groups
(> 70 scientists) from 15 different
countries have deposited and made
publicly available their data
+
59
Simone Ming Wang
Anelize
Robin
Taxonomic Tree
> 60,000 unique files
- Bacteria
- Fungi
- Human cells
- QC/Blanks
Extraction of unique
taxonomic names or
NCBI IDs (2,335)
CHALLENGES
• Most samples do not have
associated genomic data
• Controlled metadata not
always available
• Several strains and species
not present or deposited
• Multi-domain tree
60
Simone Ming Wang
Anelize
Robin
Taxonomic Tree
Extraction of unique
taxonomic names or
NCBI IDs (2,335)
Blasting – 1,858
unique lineages
Taxonomic tree –
2,849 nodes
Domain 3
Phylum 16
Class 41
Order 109
Family 264
Genus 539
Species 1336
Strains 541
Bacteria
Actinobacteria
Bacteroidetes
Cyanobacteria
Deinococcus-Thermus
Firmicutes
Fusobacteria
Proteobacteria
Spirochaetes
Verrucomicrobia
Archaea
Halobacteria
Methanobacteria
Eukaryota
Ascomycota
Basidiomycota
Mucoromycota
Zoopagomycota
Chordata 61
Simone Ming Wang
Anelize
Robin
microbeMASST
Fast search of
MS/MS spectra
against indexed
MassIVE repository
and GNPS library
Simone Ming Wang
+
Anelize
Robin
Searches >60,000 LC-MS/MS data sets of microbial monocultures.
Zuffa, Nat Micro 2024 Simone Ming Wang
Anelize
Robin
64
Discovery of 1000s of new microbial bile acids – an
example of the use of the ecosystem
and what is becoming possible with CMMC.
Helena Ipsita Yasin
Conjugation:
Glycine/Taurine
Liver
BILE ACIDS
66
Why are bile acids
important?
Different
conjugations
Phe
Tyr
Leu
Quinn R et al. Global chemical effects of the microbiomeinclude new bile-acid conjugations. Nature 579, 123–129 (2020).
How many more bile acids modifications can
occur? How can we find them?
Data filtering using Mass
Spectrometry patterns
1 2 3
4
5
MASS SPEC QUERY LANGUAGE
Jarmusch AK et al. A Universal Language for Finding Mass Spectrometry Data Patterns. bioRxiv (2022).
68
Mingxun Wang
Glycocholic acid
BILE ACIDS FRAGMENTATION RULES
69
- 3 H2O
- Gly
BILE ACIDS FRAGMENTATION RULES
Glycocholic acid
70
- Gly
- 4 H2O
BILE ACIDS FRAGMENTATION RULES
Glycocholic acid
71
BILE ACIDS FRAGMENTATION RULES
Glycocholic acid
72
QUERY scaninfo(MS2DATA) WHERE
MS2PROD=337.25:TOLERANCEMZ=0.01:INTENSITYPERCENT=5 AND
MS2PROD=319.24:TOLERANCEMZ=0.01:INTENSITYPERCENT=5 AND
MS2PREC=X AND MS2PROD=X-
390.277:TOLERANCEMZ=0.01:INTENSITYPERCENT=5
QUERY TRANSLATION
Returning the scan information on MS2.
The following conditions are applied to find scans
in the mass spec data.
Finding MS2 peak at m/z 337.25 with a 0.01 m/z
tolerance and a minimum percent intensity
relative to base peak of 5.0%.
Finding MS2 peak at m/z 319.24 with a 0.01 m/z
tolerance and a minimum percent intensity
relative to base peak of 5.0%.
Finding MS2 spectra with a precursor m/z X.
Finding MS2 peak at m/z X-390.277 with a 0.01 m/z
tolerance and a minimum percent intensity
relative to base peak of 5.0%.
BILE ACIDS: MASSQL QUERY DESIGN
73
O
OH
Nonhydroxy Monohydroxy Dihydroxy
Trihydroxy Tetrahydroxy Pentahydroxy
O
OH
OH
HO
OH
OH
OH
O
OH
OH
HO
OH
OH
O
OH
OH
HO
OH
O
OH
OH
HO
O
OH
OH
BILE ACIDS: MASSQL QUERY DESIGN
74
~1.2 billion MS/MS spectra in GNPS/MassIVE
594,365 total MS/MS spectra from MassQL
21,483 unique MS/MS spectra from MassQL
5,576 delta masses (candidate modifications)
BILE ACIDS: MASSQL QUERY RESULTS
75
Precursor ion
m/z 465.32
Delta mass
m/z 57.02
Gly
– Free bile acid
(m/z 408.29)
HO OH
OH
O
OH
BILE ACIDS REACH DISTANT ORGANS: RODENTS
+
(Controlled vocabulary metadata)
Mass Spectrometry Search Tool
Wang M et al. Mass spectrometry searches using MASST. Nat Biotechnol 38, 23–26 (2020).
Jarmusch AK et al. ReDU: a framework to find and reanalyze public mass spectrometry data. Nat Methods 17, 901–904 (2020).
76
+
(Controlled vocabulary metadata)
Mass Spectrometry Search Tool
BILE ACIDS REACH DISTANT ORGANS: HUMANS
Wang M et al. Mass spectrometry searches using MASST. Nat Biotechnol 38, 23–26 (2020).
Jarmusch AK et al. ReDU: a framework to find and reanalyze public mass spectrometry data. Nat Methods 17, 901–904 (2020).
77
Can any of these be made by microbes?
25
Mohanty, Cell 2024, accepted Helena Ipsita
Helena Ipsita
25
Mohanty, Cell 2024, accepted
Reverse Metabolomics Reveals Disease Associations
Mining the Knowledge Graph of Public data
Gentry Nature 2024
0 25 50 75 100
% of samples per condition
dihydroxy BA trihydroxy BA
Ala
Arg
Asn
Asp
Cit
Cys
Gln
Glu
Gly
His
Leu
Lys
Met
Orn
Phe
Ser
Tau
Thr
Trp
Tyr
Val
Ala
Arg
Asn
Asp
Cit
Cys
Gln
Glu
Gly
His
Leu
Lys
Met
Orn
Phe
Ser
Tau
Thr
Trp
Tyr
Val
no disease reported
Chagas disease
circadian disorders
sleep deprivation
CD
UC
IBD
obesity
disease NOS
Amino acid conjugation
Health
Condition
0.0
0.2
0.4
0.6
0.8
proportion of samples
Emily Gentry
0 25 50 75 100
% of samples per condition
dihydroxy BA trihydroxy BA
Ala
Arg
Asn
Asp
Cit
Cys
Gln
Glu
Gly
His
Leu
Lys
Met
Orn
Phe
Ser
Tau
Thr
Trp
Tyr
Val
Ala
Arg
Asn
Asp
Cit
Cys
Gln
Glu
Gly
His
Leu
Lys
Met
Orn
Phe
Ser
Tau
Thr
Trp
Tyr
Val
no disease reported
Chagas disease
circadian disorders
sleep deprivation
CD
UC
IBD
obesity
disease NOS
Amino acid conjugation
Health
Condition
0.0
0.2
0.4
0.6
0.8
proportion of samples
Emily Gentry
Reverse Metabolomics Reveals Disease Associations
Mining the Knowledge Graph of Public data.
Gentry Nature 2024
N=19
N=90
N=46
IBD 200
(fecal)
Emily Gentry
CD
n=90
UC
n=46
non-IBD
n=19
Reverse Metabolomics Reveals Disease Associations
Mining the Knowledge Graph of Public data.
Gentry Nature 2024
Emily Gentry
Reverse Metabolomics Reveals Disease Associations
Mining the Knowledge Graph of Public data.
Pediatric IBD cohort
Gentry Nature 2024
IBD Association is Validated in Independent Cohorts
(and now have retention time match as well)
CD
n=265
UC
n=146
non-IBD
n=135
iHMP2 cohort
Gentry Nature 2024
0e+00
1e+06
2e+06
3e+06
4e+06
5e+06
His−CA
Arg−CA
Lys−CA
Gln−UDCA
/
Gln−HDCA
Asn−CA
Citrulline−CA
Gln−CA
His−CDCA
Asp−CA
His−DCA
Lys−CDCA
Glu−CA
Arg−DCA
Thr−CA
Lys−DCA
Ala−CA
Tyr−CA
Citrulline−CDCA
Asn−CDCA
Tyr−UDCA
/
Tyr−HDCA
Asn−DCA
Citrulline−DCA
Gln−CDCA
Asp−CDCA
Glu−CDCA
Met−CA
Thr−CDCA
Asp−DCA
Val−CA
Glu−DCA
Thr−DCA
Trp−CA
Ala−CDCA
Tyr−CDCA
Ile−CA
/
Leu−CA
Phe−CA
Ala−DCA
Val−CDCA
Met−CDCA
Met−DCA
Trp−CDCA
Val−DCA
Trp−DCA
Phe−CDCA
Phe−DCA
Conjugated Bile Acid
Peak
Area
Diagnosis
nonIBD
CD
UC
IBD Association is Validated in Independent Cohorts
(and now have retention time match as well)
CD
n=265
UC
n=146
non-IBD
n=135
iHMP2 cohort
* * * * *
*
*
* *
* * *
*
*
* *
*
* *
Many of these bile acid conjugates
are elevated in fecal samples of
patients with Crohn's disease
Gentry Nature 2024
Xavier lab – one f the R01 grantees.
Do they have functions?
In collaboration with the Hui lab – UC-San Diego Emily Gentry
The community is beginning to show activities
for these bile acids.
Accepted to Nature
FXR Germination
FXR
TGR5
PXR
TGR5
Stem cell
Intestinal barrier
Brain Injury
FXR
AHR
PPARa
PPARy
PPARd
VDR
CAR
All this information and data is now in the CMMCkb
In a reusable format.
Summary and some additional take home messages.
-We have a community curation system in place.
-We encourage data deposition. The more data and knowledge the
easier it will become to mechanistically interpret the microbiome. If you want to
contribute or be beta-testers for tools let us know.
-CMMC website with all information. https://cmmc.gnps2.org/
-All the resources are open access and FAIR compliant.
-Give hands on CMMC data science workshops (the past three
are recorded and documentation available on the CMMC website).
-We need to rewrite textbooks associated with microbial bile acids and roles
in biology.
Thanks
Thanks
+ >100 scientists
that have already
contributed to
the CMMCkb
Patterson, Baker,
Xavier labs->bile acids

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