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We also ran a proof-of-concept identifying foods and nutrients that were associated with the onset of metabolic disease in an adult cohort.

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Food genomic material was detected only in about half of infant stool samples but increased at the onset of solid food consumption and was ubiquitous in adult stool samples.

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Using a decoy-aware mapping approach with additional consistency filtering we could show good sensitivity and specificity in simulated sequencing samples with a false positive rate around 1-10 reads per million.

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Building a comprehensive genomic database for as many foods in FOODB as possible we could connect individual genomes to nutrient content. For now we can match 77% of all foods in FOODB with taxonomic information and the next version of the database will push this to 90%.

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We also ran a proof-of-concept identifying foods and nutrients that were associated with the onset of metabolic disease in an adult cohort.

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Food genomic material was detected only in about half of infant stool samples but increased at the onset of solid food consumption and was ubiquitous in adult stool samples.

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Christian Diener @cdiener.com · 13/10/2023

Building a comprehensive genomic database for as many foods in FOODB as possible we could connect individual genomes to nutrient content. For now we can match 77% of all foods in FOODB with taxonomic information. The next version of the database will push this to 90%.

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The final day of the 2023 ISB Microbiome series will start in 30m. Today we have a symposium packed with amazing speakers, so be there or be ▣. 🦠+💻=💕 Session 1 will feature @ceciliaNoecker and @lab_maier . #ISBMicro23 #microbiome @isbsci

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Christian Diener @cdiener.com · 13/10/2023

The final day of the 2023 ISB Microbiome series will start in 30m. Today we have a symposium packed with amazing speakers, so be there or be ▣. 🦠+💻=💕 Session 1 will feature @cecilianoecker.bsky.social and Lisa Maier. #ISBMicro23 #microbiome

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Christian Diener @cdiener.com · 12/10/2023

We will start day 2 of the Virtual ISB Microbiome course 2023 in 1h. Today, we will learn how to predict strain engraftment using metabolic modeling. isbscience.org/microbiome2023 The course will be based on Alex's preprint: doi.org/10.1101/2023.... 🦠+💻=💕 #microbiome #ISBMicro23

Microbe plushies in front of an anerobic chamber.

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Christian Diener @cdiener.com · 12/10/2023

We will start day 2 of the ISB Microbiome course 2023 in 1h. Today, let's learn how to predict strain engraftment using metabolic modeling. 🦠=💕 isbscience.org/microbiome2023 The course will be based on Alex's preprint:...

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Christian Diener @cdiener.com · 11/10/2023

Only 45 minutes left until we start our Virtual ISB Microbiome course 2023. Can't wait to meet all the participants and chat with you about the course. 🦠+💻=💕. isbscience.org/microbiome2023 #microbiome #microbiomesky

Screen in front of ISB banner with microbe plushies.

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Christian Diener @cdiener.com · 11/10/2023

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Christian Diener @cdiener.com · 31/07/2023

Back in Europe for a bit. Climate sure is something else here 🥵

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Alex also showed that his strategy can reproduce the success of a recently published probiotic cocktail to prevent C. diff infection by @VedantaBio and predicts that its mechanism of action is blockage of the C. diff niche. #noxp

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Looking at this a bit more, one surprising thing is that C. diff uptake fluxes clustered into 3-4 distinct metabolic niches that were correlated with C. diff. growth and were highly reproducible across thousands of samples. #noxp

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This also worked when modeling individuals with recurrent C. diff infection receiving FMTs, where he could correctly predict the susceptible state before treatment and the return to normal levels after. #noxp

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Christian Diener @cdiener.com · 22/03/2023

The cool thing about this is that you can predict pathogen invasion potential even for a microbiome that does not contain the pathogen at all 🤯 #noxp

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How do you get around that? Use a steady state method that predicts growth rates that look like they could have come from a dynamic model, for instance by enriching for growth rates that can be reached easily from inoculation. #noxp

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Christian Diener @cdiener.com · 22/03/2023

One issue is the objective itself. One can formulate a community-wide growth rate, but that is rarely maximized in the real world, creating uncertainty in the growth rate and flux predictions. #noxp

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Christian Diener @cdiener.com · 02/03/2023

Simulating a high fiber diet switch in individualized community models for >2.5k individuals we show that one can rescue non-responders but the best strategy is different for each individual.

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Christian Diener @cdiener.com · 02/03/2023

One of the cool things you can do with mechanistic models is that you can predict interventions without training data so we tested the ability of individualized models to predict the effect of fiber interventions in 3 ex vivo studies.

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Christian Diener @cdiener.com · 02/03/2023

New preprint out in the Gibbons lab and a monumental effort by @BioBohmann. We evaluated the ability of metabolic community models to predict short-chain fatty acid (SCFA) production. doi.org/10.1101/2023.02.28.530516

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Christian Diener @cdiener.com · 09/12/2022

Hello Boston. Apart from taking bad photos I'm looking forward to chat metabolic modeling of microbial consortia with some real(tm) humans at @ChEnected #ICME22. 🦠+💩=🔥

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Christian Diener @cdiener.com · 06/12/2022

Pack your bags folks, it's decided. /s #chatgpt3 #16S #metagenomics

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Christian Diener @cdiener.com · 14/10/2022

Gut and microbes are ready for the symposium, are you? We'll be starting in 35 minutes with @mathildpoyet and @goyoiraola 🦠+🌎=❤️ #isbmicro22 @isbsci

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Christian Diener @cdiener.com · 13/10/2022

Day 2 starting in 40 minutes at 9 PT. Looking forward to an awesome course on modeling dietary interventions to the microbiome by @BioBohmann #isbmicro22 @isbsci

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Christian Diener @cdiener.com · 12/10/2022

Microbes and gut are ready. We start in less than one hour! 🦠=❤️ #isbmicro22 @isbsci

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Christian Diener @cdiener.com · 07/02/2022

A similar pattern could be observed for sphingolipids where many precursors were mostly explained by the microbiome but some sphingolipids showed hybrid patterns with a lot of heterogeneity across different metabolites.

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Christian Diener @cdiener.com · 07/02/2022

But what happens to microbial metabolites as they get metabolized by our body? We looked at secondary bile acids and were surprised to see that hepatically modified forms had large genetic variance components, even if the unmodified forms were all solely microbiome-associated 🤯

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Christian Diener @cdiener.com · 07/02/2022

Well, for microbiome/genetics the answer is both. Most metabolites only associate with the microbiome *or* genetics, but about ⅓ of all affected metabolites will associate with both. That includes ¾ of all metabolites that have a genetic component.

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Christian Diener @cdiener.com · 14/10/2021

Half an hour until the start of day 2. Nobody identified the faculty-tive anaerobes. It was hard for sure because they were E. coli and Shigella dysenteriae. The one on the left is a chestnut 😬 🦠=❤️ #ISBMicrobiome21 @isbsci

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Christian Diener @cdiener.com · 13/10/2021

One more hour to go. Got some last minute TAs, do you know their names? #ISBMicrobiome21 @isbsci

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Christian Diener @cdiener.com · 09/03/2021

Happy that our non-responder paper is finally out. We show that single-housed 🐁 can spontaneously tolerate antibiotic assault by a 🦠-wide transcriptional response. Get the scoop in our blog post...

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Christian Diener @cdiener.com · 08/01/2021

During the reviews, we also quantified some immune markers (IL-6 and CRP). Both associated with the microbiome, but whereas the associated taxa did return to near-normal levels with treatment, the immune markers remained altered 🤔

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Christian Diener @cdiener.com · 15/10/2020

Only half an hour before we start the @isbsci Microbiome course 2020. So excited we got participants from 7 continents. 🤯

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Christian Diener @cdiener.com · 29/05/2020

@ZENODO_ORG Hi Zenodo, I'm getting internal server errors for all datasets.

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Christian Diener @cdiener.com · 31/01/2020

Me: It doesn't rain *that* much in Seattle: Seattle: *rains every day for 2 months straight* Me:

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Christian Diener @cdiener.com · 21/01/2020

Want to try it out? Get the Python package micom-dev.github.io/micom based on the amazing @open_cobrapy or use the Qiime 2 plugin github.com/micom-dev/q2-micom/blob/…

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All T2D associations were, but obesity and cardiovascular associations were explained completely by T2D. Ergo bonus take-home: If you study microbiome <-> obesity links don't forget to correct for glucose levels and insulin efficiency.

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Risk was assessed by counting known T2D risk factors. Accumulation of those had a pretty good signal in 🦠. Treatment itself returned 🦠 closer to healthy levels (though we were a bit underpowered). But how do we know that this is a T2D effect and obesity for instance?

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There were several 🦠 related with glucose levels and insulin, each one associating with different measures. Which ones are more important? 🤔 Well, there were 4 genera that did not only change gradually with disease progression but also with disease risk itself.

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