On Facebook, in response to my post ME/CFS: The Evils of Lactobacillus Probiotics?, a reader raised a question about MCAS

I responded with two studies from PubMed

• “mast cell degranulation is impaired following exposure to bacteria such as escherichia coli” Escherichia coli exposure inhibits exocytic SNARE-mediated membrane fusion in mast cells [2014]
• “non-pathogenic commensal escherichia coli bacteria can inhibit degranulation of mast cells.” Non-pathogenic commensal Escherichia coli bacteria can inhibit degranulation of mast cells [2008]

I forwarded these on to a friend suffer from MCAS. She responded that she was mentally lost in the studies. She decided to ask Perplexity. to explain. She was delighted with the response and I thought that Perplexity should have a guest post!

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This abstract describes how mast cells, which are key players in allergic reactions, release inflammatory substances through a process called degranulation. Here’s a simplified explanation:

Mast Cell Degranulation Process

1. Mast cells contain small packages (granules) filled with inflammatory chemicals.
2. When triggered, these granules fuse with the cell’s outer membrane to release their contents.
3. This fusion is controlled by a set of proteins called SNAREs, including Syntaxin4, SNAP23, and VAMP8.

Normal Degranulation Mechanism

1. A protein called IKKβ attaches to and modifies (phosphorylates) SNAP23.
2. The modified SNAP23 then joins with Syntaxin4 and VAMP8.
3. This three-protein complex drives the fusion of granules with the cell membrane, releasing the inflammatory substances.

Effect of E. coli on Degranulation

The researchers discovered that exposing mast cells to E. coli bacteria interferes with this process:

1. E. coli prevents IKKβ from interacting with and modifying SNAP23.
2. As a result, SNAP23 doesn’t join with Syntaxin4 and VAMP8 as effectively.
3. This disrupts the formation of the protein complex needed for granule fusion.
4. Consequently, the release of inflammatory substances from the granules is reduced.

In essence, the study shows that E. coli can suppress the allergic response of mast cells by interfering with the molecular machinery responsible for releasing inflammatory chemicals.

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Similarly, I was ping by a reader who said “You can’t buy these in the US” — That is correct in that you will not find them in your local health food store. I live in the US and have no trouble getting them by internet order shipped to my door:

• Mutaflor: Feel Good Natural Health Store.
• Symbioflor-2: German Apotheke

All of these strains have many studies published.

This is a preview of the next generation of analysis. I described a mathematical model in Microbiome Guilds, Metabolites and Enzymes. I mentioned a concept in it and over the weekend tried the concept out. It worked and is very sweet.

To explain it, look at the chart below. The blue line is for those that have a symptom and the orange line is what is expected. If you divide observed by expected for different percentiles, you get an odds ratio. Most people know odds ratio (OR) from things like:

For current male smokers consuming >30 cigarettes daily:

• Squamous Cell Carcinoma (SqCC): OR = 103.5
• Small Cell Lung Cancer (SCLC): OR = 111.3
• Adenocarcinoma (AdCa): OR = 21.91

This pattern does not determine that you will absolutely get it. It means that your are more likely — odds. (My native environment as a statistican)

Biomesight Bacteria

The genus bacteria listed below, each have at least an odds ratio of 1.5 for general fatigue using Biomesight data if your percentile is below the amount show. I stopped listing at 10%ile items

• Bifidobacterium <= 48.7
• Collinsella <= 41
• Coprococcus <= 39.3
• Desulfosporosinus <= 38.7
• Lachnobacterium <= 37.6
• Oribacterium <= 35.8
• Lactobacillus <= 30.5
• Pseudobutyrivibrio <= 29.5
• Legionella <= 28.9
• Roseburia <= 28.3
• Faecalibacterium <= 27.8
• Lachnospira <= 27.3
• Turicibacter <= 27.1
• Mycoplasma <= 25.8
• Peptococcus <= 24.3
• Coraliomargarita <= 23.8
• Sedimentibacter <= 23.7
• Rhodothermus <= 23
• Tindallia <= 22.4
• Thiothrix <= 21.8
• Eubacterium <= 21.7
• Thermicanus <= 21.6
• Sutterella <= 21.5
• Alkaliphilus <= 21.5
• Luteibacter <= 21.1
• Sphingobacterium <= 21.1
• Candidatus Phytoplasma <= 20.5
• Anaerostipes <= 20.4
• Haemophilus <= 19.9
• Moorella <= 19.1
• Catenibacterium <= 18.7
• Olivibacter <= 18.5
• Novispirillum <= 18.4
• Butyricimonas <= 18.3
• Natronincola <= 17.9
• Macrococcus <= 17.3
• Runella <= 16.6
• Tepidanaerobacter <= 16.1
• Caldicellulosiruptor <= 15.7
• Enterococcus <= 15.4
• Serratia <= 15.3
• Salinicoccus <= 15.2
• Gemella <= 14.9
• Odoribacter <= 14.7
• Thiohalorhabdus <= 14.6
• Dorea <= 14.2
• Escherichia <= 14.1
• Chlorobaculum <= 14
• Parabacteroides <= 14
• Calothrix <= 13.8
• Megasphaera <= 13.8
• Selenomonas <= 13.6
• Acetobacterium <= 13.6
• Slackia <= 13.4
• Pseudoclostridium <= 13.4
• Peptoniphilus <= 12.4
• Tetragenococcus <= 12.2
• Johnsonella <= 12
• Akkermansia <= 11.8
• Veillonella <= 11.5
• Holdemanella <= 11.5
• Streptococcus <= 11.2
• Pectinatus <= 11.2
• Pedobacter <= 11.1
• Klebsiella <= 11
• Dysgonomonas <= 11
• Erysipelothrix <= 10.9
• Desulfurispora <= 10.7
• Dolichospermum <= 10.5
• Mogibacterium <= 10.4
• Bilophila <= 10.2
• Ruminiclostridium <= 10.2
• Finegoldia <= 10.1

If you have 10 of them then 1.5 ^ 10 = 57x greater odds of having general fatigue. It is NOT one bacteria causing it, or even a specific group of bacteria, but different combinations of possible bacteria.

I should mention that these numbers only applies to Biomesight data. “results from one pipeline cannot be safely applied to another“. For background see: The taxonomy nightmare before Christmas.

Ombre Equivalent Bacteria

If you have Ombre’s microbiome results, these are the critical bacteria:

• Collinsella <= 44.2
• Erysipelatoclostridium <= 41.8
• Bifidobacterium <= 41.1
• Thomasclavelia <= 39.2
• Lactobacillus <= 30.1
• Dorea <= 27.4
• Fusicatenibacter <= 24.1
• Gemmiger <= 23
• Terrisporobacter <= 17.9
• Sutterella <= 16.1
• Coprobacter <= 15
• Coprococcus <= 13.7
• Haemophilus <= 13.6
• Flavonifractor <= 13
• Casaltella <= 13
• Ruminiclostridium <= 12.5
• Faecalicatena <= 12.3
• Mediterraneibacter <= 11.9
• Slackia <= 11.8
• Paraprevotella <= 11.5
• Eubacterium <= 11.3
• Subdoligranulum <= 11.2
• Lachnospira <= 10.3
• Phocaeicola <= 10.2

uBiome Equivalent Bacteria

This illustrates well the fact that ranges will differ a lot between tests.

• Subdoligranulum <= 43
• Faecalibacterium <= 38.9
• Pseudobutyrivibrio <= 26.2
• Veillonella <= 22.4
• Dorea <= 21.4
• Fusicatenibacter <= 21.1
• Hespellia <= 19
• Oscillibacter <= 18.6
• Roseburia <= 18.2
• Odoribacter <= 17.9
• Collinsella <= 15.9
• Intestinibacter <= 15.8
• Bifidobacterium <= 15.1
• Clostridioides <= 14.6
• Papillibacter <= 14.5
• Actinomyces <= 14.2
• Sutterella <= 12.9
• Blautia <= 12.8
• Parabacteroides <= 11.9
• Marvinbryantia <= 11.6
• Anaerotruncus <= 10.8

Bottom Line

This transforms the ability to determine if a bacteria is too high or low. Given a symptom or condition, we can determine the bacteria likely involved and if the level is likely (odds) to contribute to the symptom.

We can thus focus on exactly the bacteria of concern and ifnore the noise elsewhere.

Stay tune, there is a lot of coding to do to implement this.

Trial of algorithm

I was helping someone transfer data from biomesight.com and decided to run their latest sample thru the odds ratio I had derived this weekend. She marked their symptoms, see below. The numbers are the odds ratio.

The evidence that hypersensitivity to noise is likely microbiome dysfunction based is sweet — since there is no conventional treatment for it.