#microbiome-based
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dingdingpeng.the100.ci
If you are wondering about their identification strategy.
Seems like they are controling for data collection site and years of education (and maybe also BMI). Seems a bit bold to assume that these were all existing confounders between soft drink consumption and depression and...the microbiome.
To test whether the effect of soft drink consumption on MDD diagnosis and symptom severity, respectively, was mediated by the gut microbiome, we used a mediation analysis with soft drink consumption as the predictor, Eggethella and Hungatella, respectively, as the mediator, and MDD diagnosis and symptom severity, respectively, as the outcome using the bias-corrected, nonparametric, bootstrap-based mediation analysis implemented in the Python pingouin library. Significance of the indirect (mediation) effect was computed using the permutation test as outlined by MacKinnon30 based on 10 000 permutations. Unless otherwise specified, all models included site and years of education as covariates to account for their potential confounding effects. We reran all the analyses including body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) as an additional covariate. For all analyses including microbiome data, library size (the total count of sequencing reads per sample in 16S rRNA gene sequencing) was added as a covariate. Age and sex did not differ between groups and were not included as covariates. Observations in which the dependent and/or independent variables exceeded 4 standard deviations from the mean were excluded to prevent undue influence from extreme values. Note that all hypothesis tests were 2-sided, with an α level set at .05. We performed sensitivity analyses for the mediation model, which are detailed in Supplement 1 (eMethods 4 in Supplement 1).
November 9, 2025 at 7:22 AM
scelse.bsky.social
To know about #FMT is one thing — but to speak with experts pioneering it is another. SCELSE's Prof Cynthia Whitchurch @cwhitch.bsky.social visited Sydney's Microbiome Research Centre, to explore gut microbiome links to mental health and disease for future collaborations.
To know about Fecal Microbiota Transplantation #FMT is one thing — but to speak directly with the experts pioneering its application is another. Prof Cynthia Whitchurch, SCELSE Research Director, had the opportunity to meet experts on microbiome at the Microbiome Research Centre (MRC) in Sydney, as she explored potential collaboration opportunities in gut microbiome research. She was impressed by MRC’s laser focus on advancing microbiome science — from uncovering how microbial communities influence disease to translating these insights into evidence-based guidance for clinicians, patients, and the public, while training the next generation of microbiome researchers. Her discussions with Prof Georgina Hold, Dr Howard Yim, Dr Xiato Jiang, and Dr Michelle Fitzmaurice explored the connection between the gut microbiome, mental health, and FMT — a promising avenue for addressing chronic and complex diseases. “Understanding how we can harness the microbiome to prevent and treat disease is one of the most exciting challenges in modern science,” said Prof Whitchurch. “I look forward to continuing this conversation when MRC Director Prof Emad El-Omar visits SCELSE in Singapore.” 📍 Microbiome Research Centre, Sydney, Australia, 31 Oct 2025 Pix(L-R): Dr Howard Yim (MRC), Prof Cynthia Whitchurch (SCELSE), Prof Georgina Hold (MRC), Dr Xiaotao Jiang (MRC), Dr Michelle Fitzmaurice (MRC)
November 5, 2025 at 4:55 AM
idw-online.de
Protecting the gut after stem cell transplantation: New evidence for the potential of microbiome-based therapies
Patients receiving intensive cancer treatments - such as radiation or stem cell transplantation - often suffer from severe damage t...
weiterlesen
Modern organoid cultures - small “mini-intestines” - are made from human stem cells in the laboratory. - Copyright © LIT
October 28, 2025 at 1:53 PM
smokeystafford.bsky.social
Wow. Yeah, emulsifiers are ubiquitous. I have IBS and diverticulitis and I've pretty much given up on processed foods (mostly to assist with weight loss, muscle gain and gut microbiome health). These days I prepare most of my meals myself, comprising mostly whole foods and mostly plant-based.
October 22, 2025 at 10:43 AM
imetascience.bsky.social
💡 30 scientific recommendations from the #CHINAGUT Conference!
👩‍🔬 63 experts joined forces to shape the future of:
🦠 #Probiotics
💊 Live biotherapeutic products
💩 #FMT
The goal: standardization, regulation & precision in #microbiome-based therapies.
🔗 doi.org/10.1002/imt2...
October 22, 2025 at 3:26 PM
imetascience.bsky.social
🧬 New insights into the diversity of #CRISPR–Cas systems!
#Metagenomic analyses uncovered 4,112 Cas12 proteins and 6 novel subtypes.
🤖 #AI-based Cas12fam annotates 18 domains.
🌿 Cas12 subtypes show habitat-specific enrichment.
🔗 doi.org/10.1002/imo2...
#microbiome #metagenomics #bioinformatics
Metagenomic mining reveals novel Cas12 subtypes and their evolutionary diversification
We identified 4112 Cas12 Proteins and 6 new Cas12 subtypes, revealed their significant diversity in N-terminal regions, repeat sequences, and sequences motifs. We developed an AI-driven algorithm, Ca...
onlinelibrary.wiley.com
October 21, 2025 at 2:25 PM
statsepi.bsky.social
This is literal nonsense, and the numbers there have zero actual meaning. Utter drivel. A shameful waste of precious research funds.
To achieve the goals of the program we seek advances in 3 thrust areas. To test if the gut microbiome is a missing link and determine if we can meet the requirements of a solution with the potential to reduce the number of children experiencing severe autism-related difficulties by 75%, we need advances coordinated across three thrust areas. Thrust 1: Identify the proportion of children experiencing ASD attributable to gut microbiome dysfunction, with >90% balanced accuracy, and identify what combination(s) of early life pressures (and when) drive the specific microbiome dysfunction influencing neurodevelopment. Thrust 2: Develop objective and scalable methods to: 1) screen the functional contribution of the maternal and infant gut microbiome to infant neurodevelopment to predict cognitive, language, social and executive function challenges, aiming for >70% accuracy; and 2) diagnose a gut microbiome-based functional disorder or disorders predictive of severe autism-related difficulties with >90% accuracy. These performance metrics are comparable to existing early ASD screens like M-CHAT, and sufficient to trigger referral for formal diagnosis, with the clinical grade accuracies expected for a diagnostic test for ASD and other metabolic disorders that result in neurodevelopmental challenges. Thrust 3: Pair diagnostic tests with strategies to protect, preserve and restore gut microbiome functions and resilience — in pregnancy and infancy — aiming for >80% effectiveness to support healthy neurodevelopment. Test whether this can meet the requirements of a solution with the potential to reduce the number of new cases of severe autism-related difficulties by 75%.
October 15, 2025 at 5:42 PM
raeslab.org
Ethical partnerships matter 🌎
Congrats to our colleague @rytito.bsky.social for this inspiring work with the Matsés Peoples from the Peruvian Amazon 🇵🇪 showing how culturally informed, trust-based collaboration can strengthen microbiome research 🧫✨
www.sciencedirect.com/science/arti...
Navigating trust and science: microbiome research in the Amazon
Including Indigenous Peoples in microbiome research is both a scientific imperative and an ethical responsibility. Our long-standing partnership with …
www.sciencedirect.com
October 9, 2025 at 5:51 AM
laurentjacob.bsky.social
The decisions for LEGEND are out: legend2025.sciencesconf.org/data/book_le...

I'm really looking forward to hearing these 21 exciting presentations (and additional 30 posters) next December.

If you want to attend too, registration is open until October 17th through legend2025.sciencesconf.org
Exploring the space of self-reproducing RNA using generative models, Martin Weigt Exploring the archaic introgression landscape of admixed populations through joint ancestry inference, Jazeps Medina Tretmanis [et al.] Predicting natural variation in the yeast phenotypic landscape with machine learning, Sakshi Khaiwal [et al.] Phylodynamic modeling with unsupervised Bayesian neural networks, Marino Gabriele [et al.] Likelihood-free inference of phylogenetic tree posterior distributions, Luc Blas- sel [et al.] Generative continuous time model reveals epistatic signatures in protein evolu- tion, Barrat-Charlaix Pierre Neural posterior estimation for high-dimensional genomic data from complex pop- ulation genetic models, Jiseon Min [et al.] A differentiable model for detecting diversifying selection directly from alignments in large-scale bacterial datasets, Leonie Lorenz [et al.] Detecting interspecific positive selection using transformers, Charlotte West [et al.] Predicting Multiple Sequence Alignment Uncertainty via Machine Learning, Lucia Martin-Fernandez [et al.] Graph Neural Networks for Likelihood-Free Inference in Diversification Mod- els, Amélie Leroy [et al.] Popformer: learning general signatures of genetic variation and natural selection with a self-supervised transformer, Leon Zong [et al.] PRIVET: PRIVacy metric based on Extreme value Theory, Antoine Szatkownik [et al.] Generative models for inferring the evolutionary history of the malaria vector Anopheles gambiae, Amelia Eneli [et al.] Language Models Outperform Supervised-Only Approaches for Conserved Ele- ment Comprehension, Eyes Robson [et al.] Identification and Classification of Orphan Genes, Spurious Orphan Genes, and Conserved Genes from the human microbiome, Chen Chen Neural Simulation-based inference of demography and selection, Francisco De Borja Campuzano Jiménez [et al.] Species Identification and aDNA Read Mapping Using k-mer Embeddings, Filip Thor [et al.] Contrastive Learning for Population Structure and Trait Prediction, Filip Thor [et al.] Protein and genomic language models chart a vast landscape of antiphage de- fenses, Mordret Ernest The Phylogenomics and Sparse Learning of Trait Innovations, Gaurav Diwan [et al.]
October 8, 2025 at 11:04 AM
amcollegegastro.bsky.social
Oral Microbiome Dysbiosis Is Associated With Precancerous Lesions and Disorders of Upper Gastrointestinal Tract: A Population-Based Study
Sadeghi & Sohrabi, et al.

📕 doi.org/10.14309/ajg.0000000000003279
September 29, 2025 at 4:00 PM
smcgrath.phd
A machine-learning model trained on recent NIH grant cancellations was applied to grants from 2014. It found that a keyword-based approach would have risked defunding foundational research in cancer screening, genomics, and the human microbiome.
#MLSky #AcademicSky
What research might be lost after the NIH’s cuts? Nature trained a bot to find out
We used machine-learning tools in an attempt to recreate the method for cutting funding, and then applied it to past US National Institutes of Health grants to reveal the broad-reaching consequences…
www.nature.com
September 25, 2025 at 3:13 PM
plosbiology.org
#Fungi in the gut #microbiome ( #mycobiome) are somewhat neglected. This study of gut fungal profiles across natural populations of humans & non-human primates, by @symbionticism.bsky.social &co, reveals significant fungal cospeciation patterns in #hominids @plosbiology.org 🧪 plos.io/46LX4zD
Phylosymbiosis between primates and their gut mycobiomes. Top left: Sample-wise mycobiome composition for each host species. The most abundant fungal genera and classes (Sordariomycetes, Saccharomycetes, and Dothiomycetes) are shown in colored bars, where the lightest color in the gradient designates the sum of all other genera in that fungal class. Light gray shows all other less abundant fungal genera. Top right: Primate phylogeny (left; scale bar shows MYA) and fungal Bray–Curtis beta diversity relationships based on 99% OTUs and aggregated by host species (right; shown with bootstrap support for branch placement and scale bar for relative branch lengths). Bottom: Cophylogenetic fungi with time-calibrated sequence divergences match hominid divergence times. Nucleotide sequence divergence of cophylogenetic fungi was calibrated to the Homo-Pan split (6 Ma), which was then used to estimate the Homo/Pan-G. gorilla split (7.1–9.2 Ma), shown with fungal icons. Estimated speciation times fall within the expected 7.1–9.2 Ma (dark orange) or 6–10 Ma (light orange)
September 23, 2025 at 4:49 PM
plosbiology.org
#Fungi in the gut #microbiome ( #mycobiome) are somewhat neglected. This study of gut fungal profiles across natural populations of humans & non-human primates, by @symbionticism.bsky.social &co, reveals significant fungal cospeciation patterns in #hominids @plosbiology.org 🧪 plos.io/46LX4zD
Phylosymbiosis between primates and their gut mycobiomes. Top left: Sample-wise mycobiome composition for each host species. The most abundant fungal genera and classes (Sordariomycetes, Saccharomycetes, and Dothiomycetes) are shown in colored bars, where the lightest color in the gradient designates the sum of all other genera in that fungal class. Light gray shows all other less abundant fungal genera. Top right: Primate phylogeny (left; scale bar shows MYA) and fungal Bray–Curtis beta diversity relationships based on 99% OTUs and aggregated by host species (right; shown with bootstrap support for branch placement and scale bar for relative branch lengths). Bottom: Cophylogenetic fungi with time-calibrated sequence divergences match hominid divergence times. Nucleotide sequence divergence of cophylogenetic fungi was calibrated to the Homo-Pan split (6 Ma), which was then used to estimate the Homo/Pan-G. gorilla split (7.1–9.2 Ma), shown with fungal icons. Estimated speciation times fall within the expected 7.1–9.2 Ma (dark orange) or 6–10 Ma (light orange)
September 23, 2025 at 1:00 PM
plosbiology.org
#Fungi in the gut #microbiome ( #mycobiome) are somewhat neglected. This study of gut fungal profiles across natural populations of humans & non-human primates, by @symbionticism.bsky.social &co, reveals significant fungal cospeciation patterns in #hominids @plosbiology.org 🧪 plos.io/46LX4zD
Phylosymbiosis between primates and their gut mycobiomes. Top left: Sample-wise mycobiome composition for each host species. The most abundant fungal genera and classes (Sordariomycetes, Saccharomycetes, and Dothiomycetes) are shown in colored bars, where the lightest color in the gradient designates the sum of all other genera in that fungal class. Light gray shows all other less abundant fungal genera. Top right: Primate phylogeny (left; scale bar shows MYA) and fungal Bray–Curtis beta diversity relationships based on 99% OTUs and aggregated by host species (right; shown with bootstrap support for branch placement and scale bar for relative branch lengths). Bottom: Cophylogenetic fungi with time-calibrated sequence divergences match hominid divergence times. Nucleotide sequence divergence of cophylogenetic fungi was calibrated to the Homo-Pan split (6 Ma), which was then used to estimate the Homo/Pan-G. gorilla split (7.1–9.2 Ma), shown with fungal icons. Estimated speciation times fall within the expected 7.1–9.2 Ma (dark orange) or 6–10 Ma (light orange)
September 23, 2025 at 7:57 AM
bugsinyourguts.bsky.social
If you are UK-based and working on any aspect of microbiomes (human, plant, insect, soil, animal, ...), please do sign up to Microbiome-Net for details of networking, funding and training opportunities.

forms.office.com/pages/respon...
September 13, 2025 at 6:31 AM
colinaverill.bsky.social
We're hiring an early career data scientist at Funga @funga-pbc.bsky.social! Austin TX based. We use ML to integrate forestry observations and fungal microbiome datasets, to better understand how to restore forest fungal biodiversity.

job-boards.greenhouse.io/funga/jobs/4...
Ecological Data Scientist
US
job-boards.greenhouse.io
March 4, 2025 at 1:02 PM
juicybyte.substack.com
This week in The Juicy Byte:
From kelp-based SPF to microbiome-friendly blush, sweeteners with antibiotic potential, and Glossier’s valuation reset—consumer health and beauty are getting more layered.

Here’s what stood out:
April 11, 2025 at 2:33 PM
phocks.bne.social.ap.brid.gy
"Hey, meat eaters. Your gut health can rival a vegan's. Here's how"

[Saved you a click]:

Eat lots of different plant-based foods, the more the better.

LOL. Genius. 🌱 💚

https://www.npr.org/sections/shots-health-news/2025/01/10/nx-s1-5254349/gut-health-microbiome-vegan-vegetarian-omnivore
January 13, 2025 at 7:10 AM
michaelokun.bsky.social
One small study showed probiotics may help constipation in Parkinson's. We need to learn more about probiotics and in my view it may need to be more individualized based on the microbiome. Early days. @profsimonlewis thanks for the update! @carleyrusch @MatthewBeke
December 2, 2024 at 12:56 AM
amirarsalansoltani.bsky.social
I encourage you to ingest a gallon of glyphosate over a week or two and let us know if you're okay and your gut microbiome is intact. I wish "experts" like you who boldly claim things based on correlational studies with lots of confounds do some actual research
www.youtube.com/watch?v=CxVX...
Exposing Why Farmers Can't Legally Replant Their Own Seeds
YouTube video by Veritasium
www.youtube.com
October 2, 2025 at 6:06 PM
gracefuluniverse.bsky.social
.
Nurses need to be equipped with
preventive healthcare AI
that assesses and addresses
- healthy and safe housing,
- access to healthy meals,
- strengths-based approaches, employment,
- gut microbiome health (serum SCFAs, bile acids, toxic LPS),
- cortisol-melatonin rhythm,
- 24h HF-HRV, SDNN.
October 16, 2025 at 8:43 AM
untonuggan.myatproto.social
People's microbiomes can vary *a lot*

And different species in the microbiome make different "stuff" out of plant chemicals

(I suspect the same microbes may also make different "stuff" in different situations)

So this is part of why there's such inconsistent effects from plant based chemicals
June 19, 2024 at 7:00 PM
lorenzadlung.com
Hi there!

This is Lorenz, systems biologist by training (🧬🖥️), lab head, based in Hamburg, Germany.

I like models in biology and math. My favorite organ is fat, home of plenty of immune cells (🔬).

Metabolism and microbiome for the win!

Looking for peers to connect in the world of science 🧪.
Lorenz presenting a case study as introduction at a scientific talk Lorenz writing ordinary differential equations on a whiteboard Schematic picture of an immune cell with lipid droplets inside and receptor molecules with bound ligand on the cell surface Histology slice of adipose tissue showing large, round fat cells in white and surrounding immune cells in pink
August 22, 2023 at 6:38 PM
mbdlab.bsky.social
In a nutshell: In a large-scale study mining public data, we identified microbiome-signatures that can differenriate between NAFLD and other commonly co-ocurring metabolic diseases. This could help in the more precise use of microbiome-based therapies for NAFLD.
2/n
January 21, 2025 at 10:12 AM
pnas.org
A look at the guts of 45 patients with relapsing-remitting multiple sclerosis reveals that Blautia and Akkermansia are key bacterial drivers of the disease, a finding that could inform microbiome-based diagnosis, prognosis, and therapy. In PNAS: ow.ly/jVPY50VeQVf
Blautia and/or Akkermansia drive the heterogeneous gut microbiome in persons with multiple sclerosis (PwMS) but not healthy controls (HCs).
March 11, 2025 at 6:39 PM