Author: Research

Updated Reading List of Long COVID and the Microbiome

Citations Found – Note: Subsidized 16s Test Kits may be available
Reversion of Gut Microbiota during the Recovery Phase in Patients with Asymptomatic or Mild COVID-19: Longitudinal Study.
Microorganisms (Microorganisms ) Vol: 9 Issue 6 Pages:
Pub: 2021 Jun 7 Epub: 2021 Jun 7 Authors Kim HN , Joo EJ , Lee CW , Ahn KS , Kim HL , Park DI , Park SK ,
Summary
The gut microbiome of COVID-19 recovered patients returns to uninfected status in a minority-dominated United States cohort.
Gut microbes (Gut Microbes ) Vol: 13 Issue 1 Pages: 1-15
Pub: 2021 Jan-Dec Epub: Authors Newsome RC , Gauthier J , Hernandez MC , Abraham GE , Robinson TO , Williams HB , Sloan M , Owings A , Laird H , Christian T , Pride Y , Wilson KJ , Hasan M , Parker A , Senitko M , Glover SC , Gharaibeh RZ , Jobin C ,
Summary
Gut Microbiota May Not Be Fully Restored in Recovered COVID-19 Patients After 3-Month Recovery.
Frontiers in nutrition (Front Nutr ) Vol: 8 Issue Pages: 638825
Pub: 2021 Epub: 2021 May 13 Authors Tian Y , Sun KY , Meng TQ , Ye Z , Guo SM , Li ZM , Xiong CL , Yin Y , Li HG , Zhou LQ ,
Summary
Temporal association between human upper respiratory and gut bacterial microbiomes during the course of COVID-19 in adults.
Communications biology (Commun Biol ) Vol: 4 Issue 1 Pages: 240
Pub: 2021 Feb 18 Epub: 2021 Feb 18 Authors Xu R , Lu R , Zhang T , Wu Q , Cai W , Han X , Wan Z , Jin X , Zhang Z , Zhang C ,
Summary
16S rRNA gene sequencing of rectal swab in patients affected by COVID-19.
PloS one (PLoS One ) Vol: 16 Issue 2 Pages: e0247041
Pub: 2021 Epub: 2021 Feb 17 Authors Mazzarelli A , Giancola ML , Farina A , Marchioni L , Rueca M , Gruber CEM , Bartolini B , Ascoli Bartoli T , Maffongelli G , Capobianchi MR , Ippolito G , Di Caro A , Nicastri E , Pazienza V , INMI COVID-19 study group. ,
Summary
Alterations of the Gut Microbiota in Patients with COVID-19 or H1N1 Influenza.
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America (Clin Infect Dis ) Vol: Issue Pages:
Pub: 2020 Jun 4 Epub: 2020 Jun 4 Authors Gu S , Chen Y , Wu Z , Chen Y , Gao H , Lv L , Guo F , Zhang X , Luo R , Huang C , Lu H , Zheng B , Zhang J , Yan R , Zhang H , Jiang H , Xu Q , Guo J , Gong Y , Tang L , Li L ,
Summary
Gut Microbiota Interplay With COVID-19 Reveals Links to Host Lipid Metabolism Among Middle Eastern Populations.
Frontiers in microbiology (Front Microbiol ) Vol: 12 Issue Pages: 761067
Pub: 2021 Epub: 2021 Nov 5 Authors Al Bataineh MT , Henschel A , Mousa M , Daou M , Waasia F , Kannout H , Khalili M , Kayasseh MA , Alkhajeh A , Uddin M , Alkaabi N , Tay GK , Feng SF , Yousef AF , Alsafar HS ,
Summary
Gut microbiota dynamics in a prospective cohort of patients with post-acute COVID-19 syndrome.
Gut (Gut ) Vol: Issue Pages:
Pub: 2022 Jan 26 Epub: 2022 Jan 26 Authors Liu Q , Mak JWY , Su Q , Yeoh YK , Lui GC , Ng SSS , Zhang F , Li AYL , Lu W , Hui DS , Chan PK , Chan FKL , Ng SC ,
Summary
Alterations in microbiota of patients with COVID-19: potential mechanisms and therapeutic interventions.
Signal transduction and targeted therapy (Signal Transduct Target Ther ) Vol: 7 Issue 1 Pages: 143
Pub: 2022 Apr 29 Epub: 2022 Apr 29 Authors Wang B , Zhang L , Wang Y , Dai T , Qin Z , Zhou F , Zhang L ,
Summary
Alterations in Gut Microbiota of Patients With COVID-19 During Time of Hospitalization.
Gastroenterology (Gastroenterology ) Vol: 159 Issue 3 Pages: 944-955.e8
Pub: 2020 Sep Epub: 2020 May 20 Authors Zuo T , Zhang F , Lui GCY , Yeoh YK , Li AYL , Zhan H , Wan Y , Chung ACK , Cheung CP , Chen N , Lai CKC , Chen Z , Tso EYK , Fung KSC , Chan V , Ling L , Joynt G , Hui DSC , Chan FKL , Chan PKS , Ng SC ,
Summary
Alterations in Fecal Fungal Microbiome of Patients With COVID-19 During Time of Hospitalization until Discharge.
Gastroenterology (Gastroenterology ) Vol: 159 Issue 4 Pages: 1302-1310.e5
Pub: 2020 Oct Epub: 2020 Jun 26 Authors Zuo T , Zhan H , Zhang F , Liu Q , Tso EYK , Lui GCY , Chen N , Li A , Lu W , Chan FKL , Chan PKS , Ng SC ,
Summary
Challenges in the Management of SARS-CoV2 Infection: The Role of Oral Bacteriotherapy as Complementary Therapeutic Strategy to Avoid the Progression of COVID-19.
Frontiers in medicine (Front Med (Lausanne) ) Vol: 7 Issue Pages: 389
Pub: 2020 Epub: 2020 Jul 7 Authors d`Ettorre G , Ceccarelli G , Marazzato M , Campagna G , Pinacchio C , Alessandri F , Ruberto F , Rossi G , Celani L , Scagnolari C , Mastropietro C , Trinchieri V , Recchia GE , Mauro V , Antonelli G , Pugliese F , Mastroianni CM ,
Summary
It Ain`t Over `Til It`s Over: SARS CoV-2 and Post-infectious Gastrointestinal Dysmotility.
Digestive diseases and sciences (Dig Dis Sci ) Vol: Issue Pages:
Pub: 2022 Mar 30 Epub: 2022 Mar 30 Authors Coles MJ , Masood M , Crowley MM , Hudgi A , Okereke C , Klein J ,
Summary
Gut Microbiota Disruption in COVID-19 or Post-COVID Illness Association with severity biomarkers: A Possible Role of Pre / Pro-biotics in manipulating microflora.
Chemico-biological interactions (Chem Biol Interact ) Vol: 358 Issue Pages: 109898
Pub: 2022 May 1 Epub: 2022 Mar 21 Authors Alharbi KS , Singh Y , Hassan Almalki W , Rawat S , Afzal O , Alfawaz Altamimi AS , Kazmi I , Al-Abbasi FA , Alzarea SI , Singh SK , Bhatt S , Chellappan DK , Dua K , Gupta G ,
Summary
The significance of the gut microbiome in post-COVID-19 gastrointestinal symptoms.
Clinical medicine (London, England) (Clin Med (Lond) ) Vol: 22 Issue 2 Pages: 189-190
Pub: 2022 Mar Epub: Authors Lampejo T ,
Summary
Integrated analysis of gut microbiome and host immune responses in COVID-19.
Frontiers of medicine (Front Med ) Vol: 16 Issue 2 Pages: 263-275
Pub: 2022 Apr Epub: 2022 Mar 8 Authors Xu X , Zhang W , Guo M , Xiao C , Fu Z , Yu S , Jiang L , Wang S , Ling Y , Liu F , Tan Y , Chen S ,
Summary
Respiratory dysfunction three months after severe COVID-19 is associated with gut microbiota alterations.
Journal of internal medicine (J Intern Med ) Vol: 291 Issue 6 Pages: 801-812
Pub: 2022 Jun Epub: 2022 Mar 17 Authors Vestad B , Ueland T , Lerum TV , Dahl TB , Holm K , Barratt-Due A , Kåsine T , Dyrhol-Riise AM , Stiksrud B , Tonby K , Hoel H , Olsen IC , Henriksen KN , Tveita A , Manotheepan R , Haugli M , Eiken R , Berg Å , Halvorsen B , Lekva T , Ranheim T , Michelsen AE , Kildal AB , Johannessen A , Thoresen L , Skudal H , Kittang BR , Olsen RB , Ystrøm CM , Skei NV , Hannula R , Aballi S , Kvåle R , Skjønsberg OH , Aukrust P , Hov JR , Trøseid M , NOR-Solidarity study group. ,
Summary
Gut Microbiome Alterations in COVID-19.
Genomics, proteomics & bioinformatics (Genomics Proteomics Bioinformatics ) Vol: Issue Pages:
Pub: 2021 Sep 21 Epub: 2021 Sep 21 Authors Zuo T , Wu X , Wen W , Lan P ,
Summary

1000 MDs later… they came to this site

This sample reminds me of an earlier sample that was similar frustratingly normal looking despite the person having significant issues. That person went with the suggestions that I could muster for 2 months and then did another sample. His symptoms improved slightly AND the microbiome because usefully abnormal. I am hoping that will be the case with this person.

In terms of mathematic models, the person’s microbiome seems to be on an infection point. The diagram below illustrates this concept with the blue line indicating recovery.

Back Story

  • I’m 33 years old and I have been sick for my last 12 years.
  • I suffer from:
    • Multiple chemical sensitivity,
    • Gut issues,
    • Severe insomnia,
    • Loss of collagen in joints and muscle
    • along many others.
  • The 1000 doctors that I have visited in Spain never had much idea.
  • The OAT test says bad methylation but i don’t know how to interpreted it or what to do, nor my doctors.
  • I’ve already doing all the basics, no gluten, cow dairy, processed foods etc.
  • I can’t eat much but I eat fish, meat and some very cooked tomato and cauliflower.
  • I was diagnosed with Giardia but it was not treated because I did not have diarrhea or constipation.
  • I feared the antibiotics causing candida issues. In other test more recent didnt show up but i think was another method of testing

Analysis

See the YouTube for more information and walk thru. Note: Giardia has a high incidence of evolving into chronic microbiome dysfunction (treated or untreated), see this post.

Using Health Analysis Page

  • Health Status – 1 Healthy, 7 Unhealthy
  • Jason Hawrelak – at 99%ile , no significant issues
  • Potential Medical Conditions showed only hypersomnia, the opposite of what is being experienced

Looking at Unhealthy Bacteria, one caught my eye: Rickettsia (i.e. any of the spotted fevers, including Mediterranean Spotted Fever – Boutonneuse Fever). I would suggest getting tested for spotted fevers.

First Probiotics

I am finding that this is a friendly start point because we have multiple logics available to determine them (which, of course, can result in disagreement). To my surprise, nothing showed up – it appears that the collection of bacteria where outside of expected/supported species! (I spent a couple of hours verifying that if was not a coding error). Similar for using symptoms with KEGG Enzymes. This was totally unexpected and a little concerning (both in terms of this person’s microbiome as well as what the site is able to handle). Fortunately, we have the other logic that produces results

The top probiotic suggestions in my view are just two:

Consensus Report

As has become my custom, I whipped thru all of the suggestions using expert criteria.

  •  Use JasonH (15 Criteria) – 5 matches
  •  Use Medivere (54 Criteria) – 5 matches
  •  Use Metagenomics (59 Criteria) – 5 matches
  •  Use Nirvana/CosmosId (36 Criteria) – 5 matches
  •  Use XenoGene (22 Criteria) – 5 matches
  •  Standard Lab Ranges (+/- 2 Std Dev) – 40 matches (5%_)
  •  Box Plot Whisker – 68 matches (9%)
  •  Kaltoft-Moltrup Normal Ranges – 70 matches (9%)
  •  Percentile in top or bottom 10%ile – 134 matches (18%)

Looking at the consensus number of suggestions for the above, the numbers were similar, suggesting that despite the differences number of bacteria selected, the suggestions were likely similar.

The lowest number was for Standard Lab Ranges (+/- 2 Std Dev)

Takes

My personal pick of the top suggestions are below (excluding probiotics cited above):

Avoids

The following are items that are highest concern in my opinion

Number of suggests from best take to worst avoid (139, 53, 40, 116, 25, 63)

The land of Supplements

We only have consensus report producing examples. There was very few items that occurred in more than 1 or 2 suggestions. The best of a poor situation were:

Definitely avoid the following

In general, the rest of vitamins and minerals were too close to come to a conclusion for. In that situation, I would suggest avoiding instead of rolling dice.

Supplemental Information

The reader also provided an OATS test done by Teletest Lab Analisis(laboratory@teletest.es) in Spain (and in Spanish). Abnormal values were (with links to Spanish Wikipedia)

Post Script

I’ve been prescribed rifaximin + boulardii based on symptoms only. Ive been told is.”” Good antibiotic”” 

Feedback
Rifaximin is on the top of suggested antibiotics!! A lovely agreement….

We were not as lucky with saccharomyces boulardii (probiotics) which has a 1:3 ratio between take and avoid. I would suggest doing Rifaximin without the boulardii, after the course is finished — give it a try in isolation. Doing both at the same time will generate confusion over the cause of improvement or deterioration. As always, discuss with your MD before doing.

Bad methylation

This was reported from the oats test. “The OAT assesses organic acid markers which indicate methylation status through the crucial cofactors ie B12, folate B9, B6. One of the most sensitive markers for vitamin B12 deficiency is methylmalonic acid and kynurenate is a reliable marker for B6 status.” [Src]

Negotiations with your Medical Professional

Few medical professionals are aware of the microbiome side-effects of the medications they prescribe. As a result of exchanges with a user about to be prescribed antibiotics based on a positive Lyme test, I created here 2 pages intended for you to share with your Medical Professional.

Where the new pages are located

The Antibiotic Page

What the Antibiotics Page looks like.

Drugs Page

The list of drugs for which we have available data.

Why one species may work for you and another not

Today I had a long Zoom call with someone that we hope to cooperate with for Long COVID. She mentioned that one species of probiotics works very well for some people with long COVID and it’s sibling do not. I explained that my working hypothesis is that a lot of interactions depends on the enzymes.

This resulted in us doing a quick lookup experiment using Microbiome Prescription databases.

I kept to only the probiotics that are retail available.

This hints that the reason that it works is due to the following enzymes (linked to Kyoto Encyclopedia of Genes and Genomes):

In theory, the 13 items above may be excellent candidates for novel pharmaceuticals to be trialed for treating long COVID.

This same approach may be done with other conditions and probiotics. By trials identify which probiotics have the most impact, use the same approach to identify possible enzymes causing the difference, then do a clinical trial.

Family’s Microbiome: Autism, Constipation et al

This blog is using reports from NirvanaBiome for three members of a family. NirvanaBiome use CosmosID for processing.

In this analysis we have people from the same family, implies DNA and inherited microbiome are similar.

  • A 10 y.o. son who has ASD/ADHD.
    • Eats various meats, dairy, grains, some beans, and a limited variety of vegetables and fruit. 
  • A 7 y.o. daughter who has been battling constipation for a little over a year now and had some sort of traveler’s diarrhea back in 2017 from which her GI system never seemed to fully recover IMO.
    • She’s a very picky and self-limited eater.  Refuses to eat any meat, but she does consume dairy in the form of yogurt and cheese and ice cream.  Most of her diet is some form of processed carb, and the only fruits and vegetables she consumes are in the form of vegetable powders – potato starch based vegetable chips, granola bars with vegetable powder in them, fruit and vegetable powder added to pancakes, etc.  She will eat fruit pouches that contain peach, apricot and banana, and she will sometimes eat fresh broccoli, cauliflower, some hummus and red lentil pasta..  She will drink a whey protein drink , and mother adds pea protein powder to things like pancakes as well. 
  • The mother diet is similar to the son’s diet

Analysis

Before getting to suggestions, I want to use the available information to understand better the three microbiome, especially since two are children. Children have a different microbiome than adults. Caution has to be taken with any data of children — we do not know what “normal” is because studies are rarely age specific.

From Aging progression of human gut microbiota [2019], the Group Number indicate age ranges.

I first look at clustering of bacteria, looking at bacteria in common to all three using percentiles. The number of bacteria that had readings very close to each other was shockingly high, far more than I had expected.

Bacteria NameRankSonMotherDaughter
Ruminococcus lactarisspecies92.493.793.5
Oscillibacter ruminantiumspecies81.583.683.3
Alistipes indistinctusspecies79.576.880.2
Faecalibacteriumgenus25.427.929
[Bacteroides] coagulansspecies41.444.741.1
Hungatellagenus0.244
Porphyromonadaceaefamily20.416.320.8
Adlercreutzia equolifaciensspecies959892.8
Bifidobacterium catenulatumspecies95.499.293.9
Ruminococcaceae bacterium CC59_002Dstrain88.994.494.4
Aminicellagenus90.685.491.2
Salinispirillum marinumspecies34.230.937.5
Eubacteriaceaefamily61.866.258.8
CCUG 54292species67.359.667.3
Eubacterium ruminantiumspecies57.854.462.1
Eubacteriumgenus58.963.555.8
Burkholderialesorder18.225.922.2
Fibrobacteriaclass18.326.122.5
Bifidobacterium pseudocatenulatumspecies87.293.886
Coprococcus catus GD/7strain37.533.329.2
Sutterellaceaefamily1916.324.7
Clostridium sp.species79.476.785.2

The amount of similarity caused me to want to cross check that this pattern is actually there and not the result of randomness.

Is this “seeing things” in the data or Seeing things?

I grab three other samples from different people who used CosmosId and plotted the difference between bacteria that were share in common. The number of bacteria in common with the family is 2.3x more than a random sample. Remember that many families will be found in almost all samples.

Filtering on species, results in a more dramatic change, with 2.68x more in common. Moving down to strains, it increased further to 3.64x more in common.

The shared diet, living space and DNA clearly results in a “family microbiome” pattern. It would be interesting to do an analysis of some couples that have lived together for at least 10 years (any volunteers?)

We will be using this shared pattern for a customized analysis. It may allow us to better isolated what may be contributing to the two issues cited above (and ignore items that may be high or low compared to the general population)

Identifying Probable Age related items

The microbiome changes with age, and for a number of bacteria we see what appear to be an age based signature.

Given that two are children and one is an adult, we can use how the microbiome changes with age to explain more of the results. The mother is either significantly higher to the levels seen by the kids, or significantly lower.

Bacteria NameRankSonMotherDaughter
Rhodocyclaceaefamily0.310.8⬆️3.5
Lactobacillaceaefamily6.412.5⬆️2.4
Fibrobacteresphylum5.116.3⬆️7.5
Varibaculum cambriensespecies3.516.6⬆️8.8
Clostridium clostridioformespecies011.3⬆️3.8
FCB groupclade8.719.2⬆️4.7
Bacteroidetes/Chlorobi groupclade8.219.1⬆️4.2
Bacteroidetesphylum7.218.3⬆️2.8
Tyzzerellagenus0.717.3⬆️3
Fusobacteriumgenus317.1⬆️0.5
Bacteroidetesclass7.320.2⬆️3
Bacteroidalesorder7.320.2⬆️3
Faecalibacterium prausnitzii (Hauduroy et al. 1937) Duncan et al. 2002species2240.1⬆️21.1
Corynebacteriaceaefamily9.221.3⬆️1.6
Caseobactergenus921.4⬆️1.6
Christensenellagenus69.490.3⬆️68.2
Christensenellaceaefamily66.488.9⬆️65.2
Fibrobacteriaclass18.326.1⬆️22.5
Bacteroides rodentiumspecies3.828.1⬆️5.4
Bryantella formatexigensspecies5.428.8⬆️0.7
unclassified Erysipelotrichaceae (miscellaneous)no rank28.85.8👇34.6
Lachnospiragenus3.230.3⬆️1
Parabacteroides distasonis CL09T03C24strain7040👇70
Ruminococcus gnavusspecies60.830.7👇60.7
Desulfovibrionaceaefamily27.558.1⬆️31.9
Desulfovibrionalesorder27.358⬆️31.8
Phocaeicolagenus57.625.6👇55.3
Eubacterium halliispecies58.490.5⬆️69.8
Butyrivibriogenus9.236.9⬆️4.2
Intestinimonasgenus26.36.4👇46.5
Bilophilagenus3171.⬆️137.5
Notice how small the difference is between the children in the above list

The kids have two different issues:

  • ASD/ADHD
  • Constipation

If there was a third normal child in the same age range, picking bacteria would likely be trivial. What we have are just the difference. We do not know if having higher or lower is the cause of the issue. What we do have are bacteria to research and the mother.

Bacteria NameRankSonMotherDaughter
Actinobaculumgenus84.429.94.5👇
Actinomaduragenus36.926.20.1👇
Agathobaculumgenus0.71.421.7⬆️
Alistipesgenus77.496.935.6👇
Clostridioidesgenus81.3⬆️24.435.6
Coprobacillusgenus81.3⬆️40.7
Discomycesgenus4.8👇93.942.5
Doreagenus75.398.920.1👇
Eggerthellagenus95.8⬆️4621.7
Erysipelatoclostridiumgenus0.8👇9.46.1
Escherichiagenus32.5👇70.861.8
Flavonifractorgenus0.1👇15.420.4
Holdemaniagenus12.1👇17.921.5
Intestinibactergenus26.8658.3⬆️
Lactobacillusgenus20.429.911👇
Oscillibactergenus44.743.580.3⬆️
Pseudoflavonifractorgenus0.3👇47.740.8
Roseburiagenus4.728.757.7⬆️
Ruminococcusgenus3.7👇95.437.9
Streptococcusgenus83.3⬆️69.12.6
Subdoligranulumgenus37.247.974.2⬆️
Veillonellagenus0.4👇14.99.4
We mark the outliers above — where the values are the same for two

Looking at US Library of Medicine studies on ADHD, we have the following matches for the son:

Looking at Autism

Nine shifts appear to match studies reported for the son.

Shifting to the daughter for constipation (using Functional constipation / chronic idiopathic constipation), we have just one

It is important to remember that the studies being referred to, were done on adults usually.

I was curious about Actinomadura because of the differences. It appears to occur in about 3% of samples, hence to get a reasonable sample to get relationships from, a clinical study would need to have 30/3% = 1000 participants and use a 16s processor that detects this bacteria (many do not). Worst yet, because of this rarity, we know of nothing that impacts it.

What to do when there is no information on changing a bacteria!!!

This actually opens the door to using bacteria-to-bacteria associations. We have 111 of them!

Seen when display level is increased.

Looking at one with the highest positive associations, Desulfofarcimen, we see some items that will increase this bacteria (and by increasing that bacteria, Actinomadura should increase): cranberry polyphenols, Goji (berry,juice), walnuts, saccharin. Using all of the 111 interactions, we found only one thing that shows up as increasing Actinomadura (and many things to decrease), choline deficiency (i.e. reduce choline in diet). Items to avoid would be:

  • jatropha curcas linn. (euphorbiaceae)
  • nigella sativa seed (black cumin)
  • oplopanax horridus(Devil’s Club)
  • hypericin(St. John’s Wort)
  • kefe cumin (laser trilobum l.)
  • foeniculum vulgare (Fennel)
  • lactobacillus bulgaricus (probiotics)

The only likely item would be yogurt containing lactobacillus bulgaricus).

Fortunately, most of the bacteria at the genus level has some studies on them.

Symptom Prediction

This is from binary logistic regression which I have been working on but have not released. I picked the symptoms that appear viable for the reported issues. This is problematic because the regression was trained on adult data and not children — this is a mere exploration

The higher the number, the more likely, the lower the number the less likely.

SYmptomNameSonMotherDaughter
Neurological: Short-term memory issues-1.8-4.0-2.5
Neurological: Disorientation-5.1-7.2-6.6
Immune Manifestations: Constipation0.5-0.21.2
Neurological: High degree of Empathy before onset-5.6-45.7-18.4
Comorbid: Small intestinal bacterial overgrowth (SIBO)-3.41.1-3.9
Asymptomatic: Live in house with person with probable microbiome dysfunction-7.21.5-11.2
Neurocognitive: Difficulty paying attention for a long period of time0.70.3-0.3
Neurocognitive: Can only focus on one thing at a time0.0-4.5-3.6
Autism: Official Diagnosis-1.8-3.8-2.5
Comorbid: Constipation and Explosions (not diarrohea)0.9– ∞1.7
Comorbid: Constipation and Diarrohea (not explosions)-2.40.3-0.5

The son is the most probable for:

  • Autism: Official Diagnosis
    • Neurological: Disorientation
  • Neurocognitive: Difficulty paying attention for a long period of time (i.e. ADHD)
    • Neurological: Short-term memory issues
    • Neurocognitive: Can only focus on one thing at a time

The daughter is the most probable for:

  • Immune Manifestations: Constipation
    • Comorbid: Constipation and Explosions (not diarrohea)

The mother is most probable for:

  • Asymptomatic: Live in house with person with probable microbiome dysfunction
  • Comorbid: Small intestinal bacterial overgrowth (SIBO)
  • Comorbid: Constipation and Diarrohea (not explosions)

While we had regression-training issues, using the “in the same microbiome family” comparison approach, the prediction largely modelled the actual symptoms reported.

Suggestions

For Daughter

The daughter is the most challenging — only one match to PubMed studies that matches for constipation. I went to the sample, and made sure these two symptoms were marked. Then I went to the new feature:

The goal is to provide probiotics that supplement enzymes that the person is low in

Next I flip over to data from Clinical Studies, The most studied was Lactobacillus rhamnosus GG (i.e. Culturelle®), followed by my old favorite Mutaflor (Escherichia coli strain Nissle 1917) which has limited availability Mutaflor (Canada, Australia, Finland, Germany) with Symbioflor-2 being a good alternative. Remember this is based on number of studies, not effectiveness.

Last, Ruminococcus is a bit of a challenge because both high and low values are associated in the literature — given the age, I deem it not safe to pursue changes in isolation.

Looking at the heath analysis, everything looks good except for high Escherichia coli and Bacteroides fragilis. I suspect bad E.Coli is a factor for the constipation which suggests that the strong good E.Coli in Symbioflor-2 (or Mutaflor) would be beneficial. Using the various expert opinions (remember those are for adults!) we see 8,25, 76, 141 and 172 bacteria being selected.

Going over to consensus, we see the following items outstanding (and likely path of least resistance to the daughter):

And the should-avoid including:

Bottom Line for Daughter

I would be inclined to bacillus subtilis alternating every 2 weeks with bacillus coagulans (probiotics) and bifidobacterium longum (probiotics) as the likely easiest to get. If not sufficient progress, then order Symboflor-2 from Germany (site that ships to the world)

For Son

Doing the same pattern, adding symptoms and then doing KEGG computations for probiotics, we end up with a short list (in order): Prescript-Assist®/SBO Probiotic, enviromedica terraflora sbo probiotic, microbiome labs/ megasporebiotic, o’donnell / flora-balance — not a single lactobacillus or bifidobacterium in the list.

We have a reasonable list of bacteria identified above for Autism and ADHD, so I will run two advance suggestions with 15% selection and US Library of Medicine findings. The results were interesting – there was nothing in common with both sets of suggestions. The top items were:

Now over to the canned expert suggestions, Using the various expert opinions (remember those are for adults!) we see 7,10, 66, 121 and 225 bacteria being selected. With the following being good suggestions:

The avoid list top items were

This is very similar to the daughter and likely reflect the commonality of the microbiome.

The Mother

The meals for the kids are likely good suggestions for the entire family. There are not any active issues with the mother and the commonality of bacteria in all of the microbiome will lead to similar diet. I should mention that alcohol appears on the avoid list (not called out for the kids).

REMINDER

All of these are suggestions coming from mathematical models and not clinical experience. Suggestions should be reviewed by a knowledgeable medical professional before starting.

I am a computer scientist and a statistician. I am not licensed to practice medicine, and where I live has strict laws about ‘appearing to practice medicine’. What I can do for readers is to write a public blog (anonymous) from your data and back story as an education post on using the software and the statistics it produces. I cannot consult. The content should be reviewed by a medical professional before implementing.

Follow up Microbiome Analysis from a prior post

This is a follow up to my blog post of Dec 30, 2021. Rosacea, Circulation and mild CFS. The person has tried the suggestions, and now we will attempt to see what the consequences are and the next set of suggestions.

Remember, the suggestions are based on mathematical modelling using clinical studies on study populations, so they may work or not work for individuals.

Reminder:
I am a computer scientist and a statistician. I am not licensed to practice medicine, and where I live has strict laws about ‘appearing to practice medicine’. What I can do for readers is to write a public blog (anonymous) from your data and back story as an education post on using the software and the statistics it produces. I cannot consult. The content should be reviewed by a medical professional before implementing.

High Level Measures

Bacteria Selected using Expert Criteria

MethodPriorLatest
Use JasonH (15 Criteria)56
Use Medivere (54 Criteria)56
Use Metagenomics (59 Criteria)56
Use Nirvana/CosmosId (36 Criteria)56
 Use XenoGene (22 Criteria)56
 Standard Lab Ranges (+/- 2 Std Dev)86
Box Plot Whisker3027
Kaltoft-Moltrup Normal Ranges7884
Percentile in top or bottom 10 %6399

My impression is that the microbiome has become more diverse, in one sense, unstable. The increase in the number of bacteria types reported (591) moved it just above the typical count for BiomeSight (578). My personal experience is that this is a good sign, the microbiome is changing, I experienced this spike is variety before my microbiome settled down into a new, healthier normal.

Symptoms Change

“This things has improved:

  • Less bloated
  • Seborrhoeic dermatitis is gone 
  • Better stool
  • Better libido“

Using the regression for all symptoms we had regressions for, we had 154 improved out of 209 items, or 74% had improvement in the prediction of symptoms.

Overall: Appears to be Improved

This person was a challenge originally because there was no dominate shifts or “smoking guns”. Being at the 99+% for Dr. Jason Hawrelak recommendations and the same items returned from other expert suggestions (many with more criteria) had no significant change. Supporting improvement: Increase in bacteria types closer to typical; significant decrease in number of Unhealthy Bacteria; improved symptoms; and last, prediction of symptoms had a major improvement.

Next Round of Suggestions

After the above sample, he actually started two more items:

  • 10 days with doxycycline
  • started to take rosemary “Feels pretty good taking it. ” 

Probiotics

There are many Ways of Choosing Probiotics, I will look at two below:

KEGG AI Computed Probiotics

The differences actually shocked me, a very very dramatic difference. On the current sample I see what is often on ME/CFS patients list appear at the top: miyarisan (jp) / miyarisan with also L. Plantarum Probiotic Powder. This suggests that he is moving towards/through a more typical ME/CFS microbiome. Given that he has issues but everything appeared normal or good, I take this as a good sign – we are exposing the issues.

Prior SampleCurrent Sample
CustomProbiotics.com / L. Brevis Probiotic Powder
2
CustomProbiotics.com / L. Rhamnosus Probiotic Powder
2
digestive care
2
Metabolics / Lactobacillus Rhamnosus Powder
2
miyarisan (jp) / miyarisan
2
PureGG
2
spain (es) / bivos
2
spain (es) / kaleidon
2
spain (es) / ns florabiotic instant
2
spain (es) / suerobivos
2
reg’activ / immune & vitality
1.83
CustomProbiotics.com / L. Salivarius Probiotic Powder
1.67
miyarisan (jp) / miyarisan
907
enviromedica terraflora sbo probiotic
828.67
bio-botanical research / proflora4r restorative probiotic
730.25
perfect pass / perfect pass probiotic bacillus spore
730.25
CustomProbiotics.com / L. Plantarum Probiotic Powder
722.2
goodbelly drink
722.2
HLH BIOPHARMA(DE) / LACTOBACT ® LDL-CONTROL
722.2
jarrow formula / ideal bowel support® lp299v®
722.2
Metabolics / Lactobacillus Plantarum Powder
722.2
Prescript-Assist®/SBO Probiotic
694.5
Sun Wave Pharma/Bio Sun Instant
690.92

We also have the option of probiotics based on symptoms (adjusted for the microbiome). See Using Samples and Symptoms to Suggest Probiotics post. The data is shown below in decreasing weight order. The nice thing to see is the decrease in the weight of everyone. One totally disappeared (the sole enterococcus faecalis one). It is interesting to note that while above using only the microbiome and resulted in major shifts between samples, when the symptoms are combined the suggestions are very similar and actually reflect improvement of the microbiome.

Prior SampleLatest Sample
Prescript-Assist®/SBO Probiotic (34)
enviromedica terraflora sbo probiotic (4.4)
symbiopharm / symbioflor 1 (3.8)
klaire labs / biospora (3.7)
microbiome labs/ megasporebiotic (3.6)		Prescript-Assist®/SBO Probiotic (22.6)
enviromedica terraflora sbo probiotic (3.5)
perfect pass / perfect pass probiotic bacillus spore (2.9)
klaire labs / biospora (2.8)
microbiome labs/ megasporebiotic (2.7)

Suggestions

I am going to do my current practice of relying on consensus reports because they are now quick to generate. I will be doing a consensus from:

I will be including everything, since the reader is able to persuade his medical professional to prescribe.

Consensus Results

The following are my picks from the options presented. I provided some links to where it helped ME/CFS –i.e. the suggestions are reasonable

Items to reduce or avoid

Remember: These are suggestions, items that improve odds.

Prescription Suggestions

This is done using advance suggestions and flipping the selections:

We do the same 4 methods of picking bacteria above

The top suggestions were:

Secondary positive suggestions are:

Dangers of Filtering

The person tried using the ME/CFS filter and got very different results. This person has mild ME/CFS; the studies on the US National Library of Medicine are for ME/CFS are typically severe and matches a yard of criteria for inclusion in the study. It is often not safe to use there filters when you self-diagnosis or are mild/controlled.

Bacteria Selected using Expert Criteria

MethodFiltering By ME/CFSLatest
Use JasonH (15 Criteria)46
Use Medivere (54 Criteria)46
Use Metagenomics (59 Criteria)46
Use Nirvana/CosmosId (36 Criteria)46
 Use XenoGene (22 Criteria)46
 Standard Lab Ranges (+/- 2 Std Dev)36
Box Plot Whisker527
Kaltoft-Moltrup Normal Ranges1284
Percentile in top or bottom 10 %899
Adding filtering dropped the bacteria consider by a large amount (up to 92%!!).

My usual criteria has been to have at least 1-2 dozen bacteria. With the new consensus report, having a large number of bacteria seems to produce clearer results.

Bottom Line

Suggestions to be discussed with their medical probiotics

In terms of prescription (doing rotation with breaks between):

Supplements to try:

We saw improvements between the sample when this reader implemented some of the suggestions. Remember, the suggestions improves the odds, they do not guarantee nor is there any requirement or protocol to follow.

Follow up Comments from the person

Watched the video you uploaded – it was great to get a video and watch how you did for my test.
Quercetin and resveratrol seems to be something to avoid when I did it your way – so I will cut that out.

High red meat and high beef diet seems to be something to avoid (felt it myself also).Been cutting out all red meat for some days now – feels pretty good.
One thing to avoid is also “vegetable/fruit juice-based diets”. I drink like 1-1,5 liter juice a day. I will try to reduce it but it’s really hard – do not feel good eating to much meat, fat or starch. So when cutting out juice I do not get enough calories. Calories are really important to me – which I also see now on my avoid-list – “low energy diet/ calorie restriction” is on it. 
One other thing that is interesting is that potatoes is on the “Highest Adverse Risk”-list. I eat potatoes every day.

Key Bacteria for Symptoms

I extracted out the items that are recurring as good predictors for various symptoms. These are listed below. This is intended for those interested in research and diving deep. See AI Computed Probiotics from Symptoms for background.

See also Key Enzymes for Symptoms and Key Compounds for Symptoms. What is surprising is that “non-of-the-usual suspects” were included with the exception of spotted fever groups (i.e. Rickettsia, Lyme) and Staphylococcus aureus (see Staphylococcus aureus – the CFS maintainer?)

tax_nametax_rankTaxon
Protostomiaclade33317
Calditrichiaclass1962850
Paraneopteracohort33342
Alteromonadaceaefamily72275
Coxiellaceaefamily118968
Euzebyaceaefamily908622
Holophagaceaefamily574976
Ignavibacteriaceaefamily795749
Nocardiopsaceaefamily83676
Thermoanaerobacterales Family III. Incertae Sedisfamily543371
unclassified Clostridialesfamily186813
Alkalibaculumgenus696745
Anaerobiumgenus1855714
Anaerocellagenus1634949
Anaerococcusgenus165779
Cellulosilyticumgenus698776
Cryomorphagenus246876
Desulfomonilegenus2357
Euzebyagenus908623
Haliangiumgenus162027
Hungatellagenus1649459
Lacibacteriumgenus1500420
Pectobacteriumgenus122277
Spiroplasmagenus2132
Sporomusagenus2375
Terrisporobactergenus1505652
Trichococcusgenus82802
Vagococcusgenus2737
Neopterainfraclass33340
unclassified Chloroflexinorank167963
Actinomycetalesorder2037
Chlorobialesorder191411
Euzebyalesorder908621
Hemipteraorder7524
Ignavibacterialesorder795748
Acetonema longumspecies2374
Anaerobranca zavarziniispecies436000
Anaeromyxobacter dehalogenansspecies161493
Bacteroides propionicifaciensspecies392838
Bifidobacterium thermacidophilumspecies246618
Brevibacterium paucivoransspecies170994
Brochothrix thermosphactaspecies2756
Caloramator mitchellensisspecies908809
Candidatus Tammella caduceiaespecies435141
Carboxylicivirga linearisspecies1628157
Casaltella massiliensisspecies938278
Corynebacterium canisspecies679663
Desemzia incertaspecies82801
Desulfarculus baarsiispecies453230
Desulfonispora thiosulfatigenesspecies83661
Desulfovibrio idahonensisspecies575978
Desulfovibrio psychrotoleransspecies415242
Desulfurispirillum alkaliphilumspecies393030
Dolosigranulum pigrumspecies29394
Haliscomenobacter hydrossisspecies2350
Lactobacillus japonicusspecies29399
Marvinbryantia formatexigensspecies168384
Mediterraneibacter faecisspecies592978
Megasphaera hominisspecies159836
Phascolarctobacterium succinatutensspecies626940
Proteiniborus ethanoligenesspecies415015
Pseudoflavonifractor capillosusspecies106588
Pseudoramibacter alactolyticusspecies113287
Pseudoscillatoria coraliispecies693994
Skermanella aerolataspecies393310
Slackia sp. NATTSspecies647703
Spiroplasma ixodetisspecies2141
Staphylococcus aureusspecies1280
Streptomonospora albaspecies183763
Thermoactinomyces vulgarisspecies2026
Thermophagus xiamenensisspecies385682
Thermovenabulum ferriorganovorumspecies159731
Uliginosibacterium gangwonensespecies392736
Veillonella cricetispecies103891
Verrucomicrobium spinosumspecies2736
spotted fever groupspecies group114277
Eukaryotasuperkingdom2759

Key Compounds for Symptoms

I extracted out the items that are recurring as predictors for various symptoms. These are listed below. This is intended for those interested in research and diving deep. See AI Computed Probiotics from Symptoms for background

See also: Key Enzymes for Symptoms and Key Compounds for Symptoms

CompoundNameFormula
(S)-LimoneneC10H16
10-FormyltetrahydrofolateC20H23N7O7
2-Methyl-3-oxopropanoateC4H6O3
3-Oxo acidC3H3O3R
4-Hydroxybutanoic acidC4H8O3
AlcoholHOR
Amino acidC2H4NO2R
AmmoniaNH3
CDP-ribitolC14H25N3O15P2
CoAC21H36N7O16P3S
D-Amino acidC2H4NO2R
D-AspartateC4H7NO4
D-GalactoseC6H12O6
EthylamineC2H7N
FormateCH2O2
Geranyl diphosphateC10H20O7P2
HaloacetateC2H3O2X
L-HomocysteineC4H9NO2S
L-IdonateC6H12O7
MannitolC6H14O6
MethanolCH4O
N-Acylneuraminate 9-phosphateC10H17NO12PR
NAD+C21H28N7O14P2
NADHC21H29N7O14P2
NADP+C21H29N7O17P3
NADPHC21H30N7O17P3
N-CarbamoylputrescineC5H13N3O
OrthophosphateH3PO4
OxygenO2
PeptideC2H4NO2R(C2H2NOR)n
PhosphatidylglycerolC8H13O10PR2
PyridoxalC8H9NO3
PyruvateC3H4O3
Thioredoxin disulfideC10H12N4O4S2R4
TrimethylamineC3H9N
Trimethylamine N-oxideC3H9NO
tRNA(Gln)
tRNA(Ile)
tRNA(Pro)
tRNA(Ser)
tRNA(Thr)
tRNA(Trp)
UDP-glucuronateC15H22N2O18P2

Key Enzymes for Symptoms

I extracted out the items that are recurring as predictors for various symptoms. These are listed below. This is intended for those interested in research and diving deep. See AI Computed Probiotics from Symptoms for background.

See also: Key Bacteria for Symptoms and Key Compounds for Symptoms

EcKeyOtherNameEnzymeName
1.15.1.1superoxide dismutase;superoxidase dismutase;copper-zinc superoxide dismutase;Cu-Zn superoxide dismutase;ferrisuperoxide dismutase;superoxide dismutase I;superoxide dismutase II;SOD;Cu,Zn-SOD;Mn-SOD;Fe-SOD;SODF;SODS;SOD-1;SOD-2;SOD-3;SOD-4;hemocuprein;erythrocuprein;cytocuprein;cuprein;hepatocupreinsuperoxide:superoxide oxidoreductase
1.17.1.1CDP-4-dehydro-6-deoxyglucose reductase;CDP-4-keto-6-deoxyglucose reductase;cytidine diphospho-4-keto-6-deoxy-D-glucose reductase;cytidine diphosphate 4-keto-6-deoxy-D-glucose-3-dehydrogenase;CDP-4-keto-deoxy-glucose reductase;CDP-4-keto-6-deoxy-D-glucose-3-dehydrogenase system;NAD(P)H:CDP-4-keto-6-deoxy-D-glucose oxidoreductaseCDP-4-dehydro-3,6-dideoxy-D-glucose:NAD(P)+ 3-oxidoreductase
1.19.1.1flavodoxin—NADP+ reductase;FPRflavodoxin:NADP+ oxidoreductase
1.2.1.10acetaldehyde dehydrogenase (acetylating);aldehyde dehydrogenase (acylating);ADA;acylating acetaldehyde dehyrogenase;DmpF;BphJacetaldehyde:NAD+ oxidoreductase (CoA-acetylating)
1.3.1.1dihydropyrimidine dehydrogenase (NAD+);dihydropyrimidine dehydrogenase;dihydrothymine dehydrogenase;pyrimidine reductase;thymine reductase;uracil reductase;dihydrouracil dehydrogenase (NAD+)5,6-dihydropyrimidine:NAD+ oxidoreductase
1.4.1.1alanine dehydrogenase;AlaDH;L-alanine dehydrogenase;NAD+-linked alanine dehydrogenase;alpha-alanine dehydrogenase;NAD+-dependent alanine dehydrogenase;alanine oxidoreductase;NADH-dependent alanine dehydrogenaseL-alanine:NAD+ oxidoreductase (deaminating)
1.7.1.13preQ1 synthase;YkvM;QueF;preQ0 reductase;preQ0 oxidoreductase;7-cyano-7-deazaguanine reductase;queuine synthase (incorrect as queuine is not the product);queuine:NADP+ oxidoreductase (incorrect as queuine is not the product)7-aminomethyl-7-carbaguanine:NADP+ oxidoreductase
1.97.1.4[formate-C-acetyltransferase]-activating enzyme;PFL activase;PFL-glycine:S-adenosyl-L-methionine H transferase (flavodoxin-oxidizing, S-adenosyl-L-methionine-cleaving);formate acetyltransferase activating enzyme;formate acetyltransferase-glycine dihydroflavodoxin:S-adenosyl-L-methionine oxidoreductase (S-adenosyl-L-methionine cleaving);pyruvate formate-lyase activating enzyme;pyruvate formate-lyase 1 activating enzyme[formate C-acetyltransferase]-glycine dihydroflavodoxin:S-adenosyl-L-methionine oxidoreductase (S-adenosyl-L-methionine cleaving)
2.10.1.1molybdopterin molybdotransferase;MoeA;Cnx1 (ambiguous)adenylyl-molybdopterin:molybdate molybdate transferase (AMP-forming)
2.2.1.1transketolase;glycolaldehydetransferasesedoheptulose-7-phosphate:D-glyceraldehyde-3-phosphate glycolaldehydetransferase
2.5.1.1dimethylallyltranstransferase;geranyl-diphosphate synthase;prenyltransferase;dimethylallyltransferase;DMAPP:IPP-dimethylallyltransferase;(2E,6E)-farnesyl diphosphate synthetase;diprenyltransferase;geranyl pyrophosphate synthase;geranyl pyrophosphate synthetase;trans-farnesyl pyrophosphate synthetase;dimethylallyl-diphosphate:isopentenyl-diphosphate dimethylallyltranstransferaseprenyl-diphosphate:3-methylbut-3-en-1-yl-diphosphate prenyltranstransferase
2.6.1.1aspartate transaminase;glutamic-oxaloacetic transaminase;glutamic-aspartic transaminase;transaminase A;AAT;AspT;2-oxoglutarate-glutamate aminotransferase;aspartate alpha-ketoglutarate transaminase;aspartate aminotransferase;aspartate-2-oxoglutarate transaminase;aspartic acid aminotransferase;aspartic aminotransferase;aspartyl aminotransferase;AST (ambiguous);glutamate-oxalacetate aminotransferase;glutamate-oxalate transaminase;glutamic-aspartic aminotransferase;glutamic-oxalacetic transaminase;glutamic oxalic transaminase;GOT (enzyme) [ambiguous];L-aspartate transaminase;L-aspartate-alpha-ketoglutarate transaminase;L-aspartate-2-ketoglutarate aminotransferase;L-aspartate-2-oxoglutarate aminotransferase;L-aspartate-2-oxoglutarate-transaminase;L-aspartic aminotransferase;oxaloacetate-aspartate aminotransferase;oxaloacetate transferase;aspartate:2-oxoglutarate aminotransferase;glutamate oxaloacetate transaminaseL-aspartate:2-oxoglutarate aminotransferase
2.8.1.1thiosulfate sulfurtransferase;thiosulfate cyanide transsulfurase;thiosulfate thiotransferase;rhodanese;rhodanasethiosulfate:cyanide sulfurtransferase
2.9.1.1L-seryl-tRNASec selenium transferase;L-selenocysteinyl-tRNASel synthase;L-selenocysteinyl-tRNASec synthase selenocysteine synthase;cysteinyl-tRNASec-selenium transferase;cysteinyl-tRNASec-selenium transferaseselenophosphate:L-seryl-tRNASec selenium transferase
3.10.1.1N-sulfoglucosamine sulfohydrolase;sulfoglucosamine sulfamidase;heparin sulfamidase;2-desoxy-D-glucoside-2-sulphamate sulphohydrolase (sulphamate sulphohydrolase)N-sulfo-D-glucosamine sulfohydrolase
3.11.1.1phosphonoacetaldehyde hydrolase;phosphonatase;2-phosphonoacetylaldehyde phosphonohydrolase2-oxoethylphosphonate phosphonohydrolase
3.2.1.1alpha-amylase;glycogenase;alpha amylase;endoamylase;Taka-amylase A;1,4-alpha-D-glucan glucanohydrolase4-alpha-D-glucan glucanohydrolase
3.3.1.1adenosylhomocysteinase;S-adenosylhomocysteine synthase;S-adenosylhomocysteine hydrolase (ambiguous);adenosylhomocysteine hydrolase;S-adenosylhomocysteinase;SAHase;AdoHcyaseS-adenosyl-L-homocysteine hydrolase
3.4.11.1leucyl aminopeptidase;leucine aminopeptidase;leucyl peptidase;peptidase S;cytosol aminopeptidase;cathepsin III;L-leucine aminopeptidase;leucinaminopeptidase;leucinamide aminopeptidase;FTBL proteins;proteinates FTBL;aminopeptidase II;aminopeptidase III;aminopeptidase INULL
3.6.1.1inorganic diphosphatasediphosphate phosphohydrolase
3.7.1.12cobalt-precorrin 5A hydrolase;CbiGcobalt-precorrin 5A acylhydrolase
3.8.1.2(S)-2-haloacid dehalogenase;2-haloacid dehalogenase[ambiguous];2-haloacid halidohydrolase [ambiguous][ambiguous];2-haloalkanoic acid dehalogenase;2-haloalkanoid acid halidohydrolase;2-halocarboxylic acid dehalogenase II;DL-2-haloacid dehalogenase[ambiguous];L-2-haloacid dehalogenase;L-DEX(S)-2-haloacid halidohydrolase
4.4.1.1cystathionine gamma-lyase;homoserine deaminase;homoserine dehydratase;cystine desulfhydrase;cysteine desulfhydrase;gamma-cystathionase;cystathionase;homoserine deaminase-cystathionase;gamma-CTL;cystalysin;cysteine lyase;L-cystathionine cysteine-lyase (deaminating);CGLL-cystathionine cysteine-lyase (deaminating; 2-oxobutanoate-forming)
4.6.1.1adenylate cyclase;adenylylcyclase;adenyl cyclase;3′,5′-cyclic AMP synthetase;ATP diphosphate-lyase (cyclizing)ATP diphosphate-lyase (cyclizing; 3′,5′-cyclic-AMP-forming)
4.7.1.1alpha-D-ribose 1-methylphosphonate 5-phosphate C-P-lyase;phnJ (gene name)alpha-D-ribose-1-methylphosphonate-5-phosphate C-P-lyase (methane-forming)
4.99.1.12pyridinium-3,5-bisthiocarboxylic acid mononucleotide nickel chelatase;LarC;P2TMN nickel chelataseNi(II)-pyridinium-3,5-bisthiocarboxylate mononucleotide nickel-lyase (pyridinium-3,5-bisthiocarboxylate-mononucleotide-forming)
5.1.1.1alanine racemase;L-alanine racemasealanine racemase
5.6.1.9(R)-2-hydroxyacyl-CoA dehydratase activating ATPase;archerase;(R)-2-hydroxyacyl-CoA dehydratase activator;(R)-2-hydroxyacyl-CoA dehydratase activase;fldI (gene name);hgdC (gene name);hadI (gene name);lcdC (gene name)reduced flavodoxin:(R)-2-hydroxyacyl-CoA dehydratase electron transferase (ATP-hydrolyzing)
6.1.1.1tyrosine—tRNA ligaseL-tyrosine:tRNATyr ligase (AMP-forming)
6.2.1.1acetate—CoA ligase;acetyl-CoA synthetase;acetyl activating enzyme;acetate thiokinase;acyl-activating enzyme;acetyl coenzyme A synthetase;acetic thiokinase;acetyl CoA ligase;acetyl CoA synthase;acetyl-coenzyme A synthase;short chain fatty acyl-CoA synthetase;short-chain acyl-coenzyme A synthetase;ACSacetate:CoA ligase (AMP-forming)
6.3.1.1aspartate—ammonia ligase;asparagine synthetase;L-asparagine synthetaseL-aspartate:ammonia ligase (AMP-forming)
6.4.1.1pyruvate carboxylase;pyruvic carboxylasepyruvate:carbon-dioxide ligase (ADP-forming)
6.5.1.1DNA ligase (ATP);polydeoxyribonucleotide synthase (ATP);polynucleotide ligase (ambiguous);sealase;DNA repair enzyme (ambiguous);DNA joinase (ambiguous);DNA ligase (ambiguous);deoxyribonucleic ligase (ambiguous);deoxyribonucleate ligase (ambiguous);DNA-joining enzyme (ambiguous);deoxyribonucleic-joining enzyme (ambiguous);deoxyribonucleic acid-joining enzyme (ambiguous);deoxyribonucleic repair enzyme (ambiguous);deoxyribonucleic joinase (ambiguous);deoxyribonucleic acid ligase (ambiguous);deoxyribonucleic acid joinase (ambiguous);deoxyribonucleic acid repair enzyme (ambiguous);poly(deoxyribonucleotide):poly(deoxyribonucleotide) ligase (AMP-forming)poly(deoxyribonucleotide)-3′-hydroxyl:5′-phospho-poly(deoxyribonucleotide) ligase (ATP)
6.6.1.1magnesium chelatase;protoporphyrin IX magnesium-chelatase;protoporphyrin IX Mg-chelatase;magnesium-protoporphyrin IX chelatase;magnesium-protoporphyrin chelatase;magnesium-chelatase;Mg-chelatase;Mg-protoporphyrin IX magnesio-lyaseMg-protoporphyrin IX magnesium-lyase
7.1.1.1proton-translocating NAD(P)+ transhydrogenase;pntA (gene name);pntB (gene name);NNT (gene name)NADPH:NAD+ oxidoreductase (H+-transporting)
7.2.1.1NADH:ubiquinone reductase (Na+-transporting);Na+-translocating NADH-quinone reductase;Na+-NQRNADH:ubiquinone oxidoreductase (Na+-translocating)
7.3.2.1ABC-type phosphate transporter;phosphate ABC transporter;phosphate-transporting ATPase (ambiguous)ATP phosphohydrolase (ABC-type, phosphate-importing)
7.4.2.1ABC-type polar-amino-acid transporter;histidine permease;polar-amino-acid-transporting ATPaseATP phosphohydrolase (ABC-type, polar-amino-acid-importing)
7.6.2.10ABC-type glycerol 3-phosphate transporter;glycerol-3-phosphate ABC transporter;glycerol-3-phosphate-transporting ATPaseATP phosphohydrolase (ABC-type, sn-glycerol 3-phosphate-importing)

Using Samples and Symptoms to Suggest Probiotics

In this post, AI Computed Probiotics from Symptoms, we could calculate probiotics that could help for one symptom at a time for the general population. This is nice if you have just one symptoms and no microbiome details. See Ways of Choosing Probiotics for an overview on picking probiotics.

We can do better, a new page is up that will allow us to calculate the probiotics based on multiple symptoms PLUS your microbiome sample! In other words using all available information. (I will not create a page to handle multiple symptoms with no sample — you need to get a sample).

Where the page is located on the menus

You must have entered symptoms for this to work. If not, you will see this appearing

After you enter symptoms, a page may appear like below

Example of suggestions

I should emphasis a few things:

  • This is by retail probiotic name.
    • The probiotic must be available somewhere in the world. It may not be available where you live
    • If you wish to know which species are in the probiotic, just click the name.
      • Probiotics with the same numbers are likely the same species (i.e. no difference)
  • The Weight is an estimate of how much of the missing enzyme it will provide (weight is based on odds)
  • Enzyme Means is the number of Enzymes that will be provided by it
  • Species is the number of different species in it.

Practical Example

Using the above example, the person founds that Prescript-Assist®/SBO Probiotic is either not available or too expensive (watch costs!) and proceeded down the list:

Cross Validation

We have other ways of suggesting probiotics

  • Looking them up by research from this page [Search for probiotics by studies] – unfortunately, this will not giving a ranking
  • Using the KEGG based calculation without using symptoms:
Where the suggestions without symptoms being taken into account is located here. REMEMBER to raise your display level to see this link
For the same sample as above, we have 2 of the three top suggestions being the same!
  • The third way is by suggestions — here, the choice of bacteria selection can result in a wide variation of probiotics suggested and contradictory results as shown below:
lactobacillus salivarius is on an avoid.

What to do with contrary results?

We potentially have 4 opinions

  • From Symptoms + KEGG
  • From KEGG alone
  • From the literature
  • From suggestions

To take or not to take should be done on consensus (i.e. ideally 3 says to take). Of the above methods, the one with the weakest quality of data is from suggestions (because it is so dependent on studies being done! ). For the one in conflict, lactobacillus salivarius (AKA Ligilactobacillus salivarius), there were studies found in the above link (strain specific for retail probiotics), NOTE: I missed them on the first pass because I did not enter the name in “Search for” and had left the default ‘constipation’ there

One of the symptoms was brain fog and depression. “sad mood” is a sufficient match.

To translate the methods into human “detective” terms

  • KEGG — DNA is a match
  • KEGG + microbiome Sample — DNA and video is a match
  • Researched Studies — Profiling by race, sex, age etc is a match (studies are done on populations, not individuals) – it is truly “bacteria profiling”
  • Suggestions based on bacteria picked — close to setting up police stops to detect drunk drivers. The number of people arrested depends on time, location etc of where the stops are done.