Long COVID: microbiome scents – we smell a skunk!

This is using data from the study being done with BiomeSight. We will only use their samples. After the first review, a z-score of 6.4 or higher (or a lots of items) was set as a cutoff point. The following ignore False Detection Rate.

  • Conclusion: the ENZYME production of the microbiome is by far the strongest indicator.
  • The reference set consists of 1037 heterogenous samples (i.e. no Long COVID, but a variety of medical conditions) and 154 samples with Long COVID

Taxon Patterns

Care needs to be taken with these numbers because the frequency of reporting on a bacteria is a factor that impacts the z-score. The data for this table is available at Citizen Science site and independent analysis is strongly recommended. This table is a simplified view of very complex data.

tax_nametax_rankNo Symptom MeanSymptom MeanZ-ScoreChange
Terrabacteria groupclade71504052088510.473%
Firmicutesphylum6524525028309.077%
Tenericutesphylum25626362-7.9248%
Eubacterialesorder6098884824687.979%
Mollicutesclass25626362-7.9248%
Clostridiaclass6137434877197.879%
Emticicia oligotrophicaspecies7692553-6.8332%
Faecalibacterium prausnitziispecies100292142415-6.7142%

End Product Patterns

End products only had a single item above our 6.3 z-score threshold with a very small shift.

EndProductNo Symptom MeanSymptom MeanNo Symptom StdDevChange
H2132913076.698%

KEGG Enzyme Patterns

This is where we see a massive number of patterns(182!!) with very high z-scores (i.e. 6.4 or higher). This hints that the bacteria associated with these enzymes may be a good target to modify.

EnzymeNameNo Symptom MeanSymptom MeanNo Symptom StdDevChange
dihydrourocanate:acceptor oxidoreductase58562147222-18.2251%
(S)-3-hydroxy-3-methylglutaryl-CoA acetoacetate-lyase (acetyl-CoA-forming)55210142006-18257%
(1->4)-alpha-D-galacturonan reducing-end-disaccharide-lyase54601139740-17.7256%
acetyl-CoA:kanamycin-B N6′-acetyltransferase55382140425-17.7254%
acetyl-CoA:2-deoxystreptamine-antibiotic N3-acetyltransferase56590141511-17.6250%
poly(deoxyribonucleotide)-3′-hydroxyl:5′-phospho-poly(deoxyribonucleotide) ligase (ATP or NAD+)55562141080-17.6254%
D-serine ammonia-lyase (pyruvate-forming)55931140065-17.6250%
poly(deoxyribonucleotide)-3′-hydroxyl:5′-phospho-poly(deoxyribonucleotide) ligase (ATP, ADP or GTP)55562141080-17.6254%
alpha-maltose-6′-phosphate 6-phosphoglucohydrolase57944142024-17.5245%
ATP phosphohydrolase (ABC-type, iron(III) enterobactin-importing)57953141331-17.4244%
protein-Npi-phospho-L-histidine:D-mannose Npi-phosphotransferase66964152717-17.4228%
ATP phosphohydrolase (ABC-type, Fe3+-transporting)68676154113-17.4224%
D-psicose 3-epimerase70754155871-17.2220%
D-tagatose 3-epimerase70754155871-17.2220%
2′-(5-triphosphoribosyl)-3′-dephospho-CoA:apo-[citrate (pro-3S)-lyase] 2′-(5-phosphoribosyl)-3′-dephospho-CoA-transferase77143161549-17.1209%
ATP:3′-dephospho-CoA 5-triphospho-alpha-D-ribosyltransferase78363162298-17207%
2,4,6/3,5-pentahydroxycyclohexanone 2-isomerase75196158863-16.9211%
ATP:[protein]-L-tyrosine O-phosphotransferase (non-specific)60964143510-16.9235%
acetyl-CoA:citrate CoA-transferase79352162680-16.7205%
L-aspartate:tRNAAsx ligase (AMP-forming)63596144560-16.7227%
poly(deoxyribonucleotide)-3′-hydroxyl:5′-phospho-poly(deoxyribonucleotide) ligase (ATP)69642156282-16.7224%
penicillin amidohydrolase69734151011-16.6217%
protein-Npi-phospho-L-histidine:D-mannitol Npi-phosphotransferase57950140690-16.5243%
ATP:D-erythronate 4-phosphotransferase65433145262-16.4222%
acetate:holo-[citrate-(pro-3S)-lyase] ligase (AMP-forming)90668176404-16.4195%
ATP:D-threonate 4-phosphotransferase65433145262-16.4222%
D-aspartate:[beta-GlcNAc-(1->4)-Mur2Ac(oyl-L-Ala-gamma-D-Glu-L-Lys-D-Ala-D-Ala)]n ligase (ADP-forming)73487157884-16.4215%
4-phospho-D-erythronate:NAD+ 3-oxidoreductase65773145502-16.3221%
4-phospho-D-threonate:NAD+ 3-oxidoreductase65773145502-16.3221%
nucleoside-triphosphate diphosphohydrolase69217153915-16.2222%
4-amino-5-aminomethyl-2-methylpyrimidine aminohydrolase75806165018-15.7218%
ATP:D-glycero-alpha-D-manno-heptose 7-phosphate 1-phosphotransferase81281169414-15.7208%
aryl-ester hydrolase77314159122-15.6206%
palmitoyl-CoA hydrolase76772157265-15.4205%
UDP-alpha-D-glucose:1,2-diacyl-sn-glycerol 3-alpha-D-glucosyltransferase91112172382-15.4189%
D-tagatose 1,6-bisphosphate D-glyceraldehyde-3-phosphate-lyase (glycerone-phosphate-forming)75959152459-15.2201%
ADP-alpha-D-glucose:alpha-D-glucose-1-phosphate 4-alpha-D-glucosyltransferase (configuration-retaining)63077146386-15.1232%
L-glutamate:tRNAGlx ligase (AMP-forming)97313177576-14.5182%
oligosaccharide 6-alpha-glucohydrolase96720174292-14.3180%
S-adenosyl-L-methionine:tRNA (adenine22-N1)-methyltransferase96117168859-13.9176%
alkylated-DNA glycohydrolase (releasing methyladenine and methylguanine)93342182716-13.7196%
sn-glycerol 3-phosphate:quinone oxidoreductase113940189562-13.6166%
L-iditol:NAD+ 2-oxidoreductase113731190510-13.4168%
(3S)-citryl-CoA oxaloacetate-lyase (acetyl-CoA-forming)108775197009-13.3181%
N-succinyl-LL-2,6-diaminoheptanedioate amidohydrolase88237163157-13185%

KEGG Product

Products are the output of enzymes. Various enzymes may produce the same product. Our starting assumption was that products would have stronger association than enzymes. That was not shown in the data.

CompoundNameNo Symptom MeanSymptom MeanNo Symptom StdDevChange
Acetoacetate3787855442-8.1146%
Reduced electron-transferring flavoprotein106971149551-6.9140%
Dialkyl phosphate7732553-6.8330%
Indole-3-acetate7732553-6.8330%
Pseudouridine 5′-phosphate109418150579-6.7138%
3-Hydroxy-3-(methylthio)propanoyl-CoA7582494-6.7329%
3-Oxopropionyl-CoA7582494-6.7329%
N-Acetyl-beta-D-glucosaminylamine7602473-6.7325%
(2E,4Z)-2,4-Dienoyl-CoA6880995899-6.6139%
Short-chain trans-2,3-dehydroacyl-CoA103627144711-6.6140%
(2E,4E)-2,4-Dienoyl-CoA6880995899-6.6139%
4-(4-Deoxy-alpha-D-gluc-4-enuronosyl)-D-galacturonate3396147705-6.6140%
4-Hydroxyphenylglyoxylate113250152269-6.5134%
Oleoyl-[acyl-carrier protein]7352376-6.5323%
(4Z)-Hexadec-4-enoyl-[acyl-carrier protein]7352376-6.5323%
N6′-Acetylkanamycin-B3476248298-6.5139%
(6Z)-Hexadec-6-enoyl-[acyl-carrier protein]7352376-6.5323%
Pyocyanine7512381-6.5317%
(1E,3E)-4-Hydroxybuta-1,3-diene-1,2,4-tricarboxylate14304549-6.5318%
Aldose7642429-6.4318%
Molybdoenzyme molybdenum cofactor119172159672-6.4134%
N3-Acetyl-2-deoxystreptamine antibiotic3593949415-6.4137%

KEGG Substrate

Subtrate are the fuel for enzymes reaction. Various enzymes may consume the same compound. Our starting assumption was that substrate would have stronger association than enzymes. That was not shown in the data.

CompoundNameNo Symptom MeanSymptom MeanNo Symptom StdDevChange
Dihydrourocanate3802655395-7.8146%
(S)-3-Hydroxy-3-methylglutaryl-CoA3482050269-7.4144%
Electron-transferring flavoprotein106880149551-6.9140%
threo-3-Hydroxy-D-aspartate7602532-6.9333%
3-(Methylthio)acryloyl-CoA7572494-6.8329%
3-Hydroxy-3-(methylthio)propanoyl-CoA7572494-6.8329%
3-Oxopropionyl-CoA7572494-6.8329%
ADP-sugar7722553-6.8331%
Aryl dialkyl phosphate7722553-6.8331%
beta-D-Mannose7722553-6.8331%
D-erythro-3-Hydroxyaspartate7612532-6.8333%
Pseudouridine105195147050-6.8140%
N4-(Acetyl-beta-D-glucosaminyl)asparagine7592473-6.7326%
Short-chain acyl-CoA103536144711-6.7140%
(2-Amino-1-hydroxyethyl)phosphonate7522431-6.6323%
trans-2,3-Dehydroacyl-CoA6874595899-6.6140%
(S)-4-Hydroxymandelate113176152269-6.5135%
5-Methylphenazine-1-carboxylate7502381-6.5317%
Hexadecanoyl-[acp]14684753-6.5324%
Kanamycin B3472948298-6.5139%
Octadecanoyl-[acyl-carrier protein]7342376-6.5324%
(1E)-4-Oxobut-1-ene-1,2,4-tricarboxylate7392338-6.4316%
2-Deoxystreptamine antibiotic3591649415-6.4138%
Adenylated molybdopterin119083159672-6.4134%
Alditol7632429-6.4318%
beta-Carotene7202314-6.4321%
Molybdate119083159672-6.4134%

Bottom Line

Several years ago, I hypothesized that a symptom or condition is the result of a coming together of many small deviations in individual bacteria representation. There may be 10 different combination of bacteria with none overlapping causing a symptom. The inspiration for this was observing the literature and experience of people with Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) — a sibling condition to Long COVID. This model is contrary to the common belief that there is a single or small number of items that is the cause. My looking at Brain fog (using same technique as above Brain Fog: Microbiome scents…) came up with nothing. That was not desired, but almost expected because that population is very heterogenous for cause with a long time since the triggering event for the microbiome to diverge from each other (often treatment attempts would be a factor). With long COVID, we have a short time since the triggering event and the people tend to be treatment naïve, This makes finding patterns a lot easier (when you look under the right rocks!).

Almost everything is overproduction. This may be caused by the immune system ramping up to provide fuel to fight COVID. The microbiome is stuck in an on-state, likely with cross talk between enzymes keeping it stuck on. The term of the Pasteur Institute for Tropical Medicine, “an occult infection” describes the behavior seen nicely.

Addressing the few microbiome shifts is one approach — but the enzymes dominate in both statistical significance and number of items, It is likely the best path to address the enzymes instead of individual bacteria.

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Brain Fog: Microbiome scents…

At present in our citizen science database we have samples reporting brain fog for:

  • Biomesight: 124 samples
  • Ombre/Thryve: 151 samples
  • UBiome/Thryve: 170 samples

Results from different labs cannot be safely aggregated, so we will investigate on a lab by lab basis. One lab will read data as bacteria A and a different lab as bacteria B.

For very different and strong results using the same process see: Long COVID: microbiome scents – we smell a skunk!

Taxon Patterns

By bacteria found nothing common across labs.

BacteriaubiomeOmbreBiomeSight
FaecalibacteriumLow
SubdoligranulumLow
Hespellia High
PorphyromonasHigh
OscillibacterHigh
AnaerovibrioHigh
StreptococcusHigh
Only Genus were inspected that were frequently seen

End Product Patterns

Ubiome was nothing significant. As above, nothing was in common between the labs. End Products have been weak to predict in prior analysis.

End ProductuBiomeOmbreBiome Sight
Bacteriocin: (several)Less
DaidzeinLess
L-TryptophanLess
Gamma-Amino butyric acid (GABA)Less
UrolithinsLess
Pyruvic acidLess
MethanolLess
PentanolLess

KEGG Enzyme Patterns

ubiome gave 280 candidates, biomesight just 2, ombre had 40 candidates. There was nothing in common.

EnzymeuBiomeOmbreBiomeSight
(1->4)-alpha-D-galacturonan lyaseHigh
15-cis-phytoene:acceptor oxidoreductase (lycopene-forming)High
nitrous oxide:ferricytochrome-c oxidoreductaseHigh
CDP-choline phosphohydrolaseHigh

KEGG Product

Biomesight returned nothing, Ombre just 7 candidates and ubiome 23. There was nothing in common.

KEGG Substrate

Biomesight returned 2 candidates, Ombre returned 42 candidates and ubiome 86. There were a few things in common between Ombre and uBiome. False Detection rate is a risk.

Bottom Line

I am disappointed in not finding many associations. I will pass the torch to others to see if there is literature connecting these to coagulation or vascular constriction/dilatation .

A comment about Gluten Issues MISINFORMATION

Saying “gluten is bad for you” is the same as saying the “bacteria are bad for you” (or “vitamins are good for you”. In some cases bacteria can be good for you, i.e. probiotics. Some vitamins can be bad, for example, “Vitamin D Toxicity“[2022]. These are over simplification and sweeping generalizations. To me, they are akin to saying “Blacks are criminals”, “Irish are drunkards”, and “Italians are part of the Mafia”.

”Gluten is a complex mixture of hundreds of related but distinct proteins, mainly [in wheat] gliadin and glutenin. Similar storage proteins exist as secalin in rye, hordein in barley, and avenins in oats and are collectively referred to as “gluten.” ” What is gluten? (US National Library of Medicine)
Barley is free of glutenins and gliadins, the troublesome glutens. You may be using “All black men are criminals” reasoning. You really need to be tested for which types of gluten proteins you reactive to and not go for internet-legend that all glutens are bad.

YES – you may feel better eating gluten free, but the why is more likely to be a wheat allergy than gluten issue!

Looking at how the microbiome is influenced by barley, oats, rye, and wheat we see major differences –– which I ascribed to the chemical difference of the type of gluten in each. In most western diet, many items described as “Rye Bread” contain wheat, an example is below. People react to it and thus associate rye (the labelling) to problems.

An example, Barley increases Ruminococcus according to 3 studies while wheat decreases it. For Clostridium botulinum: Barley and wheat increases while rye decreases. While a gluten free diet is reported to decrease both of these bacteria.

Bottom Line

You really should be tested for each type of gluten (even if your diagnosis is celiac disease). Going completely gluten free may make correcting a microbiome dysfunction a lot harder. Less than 1% of the population has a medical need to go gluten free [2018]. It is well sold by influencers on the internet.

“Gluten-free diets have soared in popularity in recent years. But, shunning gluten has no heart benefits for people without celiac disease, and it may mean consuming a diet lacking heart-healthy whole grains, according to the quarter-century study.”

Eating Gluten-Free Without a Medical Reason? WebMd,

A 2018 study lists the following risks of doing it without a proven medical need:

Potential Harms of a GFD
Deficiencies of micronutrients and fiber
Increases in fat content of foods
Hyperlipidemia
Hyperglycemia
Coronary artery disease
Increased financial costs
Social impairment or restrictions

GF has a higher frequency of osteopenia and osteoporosis than in controls has been reported [2014]

A 2021 study reports “the currently available gluten-free products in the market are generally known to be lower in proteins, vitamins, and minerals and to contain higher lipids, sugar, and salt compared to their gluten-containing counterparts….  Some studies have shown that commercialized gluten-free food products are often not gluten free. “

in Efficacy of Popular Diets Applied by Endurance Athletes on Sports Performance: Beneficial or Detrimental? A Narrative Review [2021] “when applied to non-celiac athletes, [Gluten Free] can create a large energy deficit and low energy availability, impairing both metabolic health and performance.” This is of especial concern when a symptom prior to going GF is tiredness.

“Beware of influencers!” Often they get big bucks for selling a concept to you!

New Feature: Over and Under Representation

No, I am not talking about voting politics in the US!

While doing an analysis, I went to the raw data to try to understand the sample. The result is the addition of a new section on the [Research Features] tab. Unlike most items, this is not directly actionable. An analogy:

You have gotten 100 used coins from the bank and proceeded to toss each one once. You would expect to get 50 heads and 50 tails. You got 20 heads and 80 tails. This means that these 100 coins have bias that is statistically significant. You do not know which are the problem (unfair) coins.

The same issue applied to vectors of the microbiome.

A reader had just emailed me that they have done another sample and it occur to me to view a time series of this person over time to see what this new report offers. The person reports some improvements following Dr. Artificial Intelligence suggestions. I included Dr. Jason Hawrelak rating on each for reference

Nov 21, 2021, Jason: 56%ile
March 15,2022, Jason: 95.6%
May 16, 2022, Jason: 89%ile
June 15, 2022, Jason: 89%ile

The biggest improvement with Dr. Jason Hawrelak was between the first two. KEGG Compounds went from being under produced for both high and low, to over on all subsequent ones. The pattern of over and under kept consistent until the very last one where bacteria edged into significance. I do have concerns with single digit Z-Scores, because of the false discovery rate.

What does Over Representation of Low Bacteria mean exactly? It means that the number of different bacteria types sitting below 10% was much higher than expected. It may imply a more diverse population with a lot of token representation.

What does Under Representation of High Bacteria mean exactly? It’s the flip side of above. The number of different bacteria types sitting above 90% was much lower than expected. It may imply a population without full representation.

WARNING: Do not assign undue significance to a change of z-score with the same sign.

On a personal note, seeing bacteria shift into significance from insignificance, looks like a good thing. It means that the prior microbiome has become disrupted. Our goal is to disrupt the stable dysfunctional microbiome causing symptoms.

Again, this is both an experimental feature AND it’s interpretation is not easy.

Deep Dive into Antibiotics Suggestions

The Antibiotics List for MD is done with the 15%ile criteria. The page just lists the antibiotics. It is intended as a quick reasonable estimator.

Some people are able to get multiple antibiotics. They wish to make sure that what the antibiotics work against are different bacteria.

To find out the bacteria that each impacts is not difficult.

  • First, set display level to Advance
  • Second, we need to click on Research Features.
  • Third select Advance Suggestions.
  • Fourth, set criteria to 15% (or whatever you wish to use instead)
  • Fifth, Check Show links to studies
  • Sixth, Check Antibiotics, uncheck everything else

Then get suggestions. A new Column appears

Clicking on that will show the studies used and the impact on various bacteria.

What the Kefir is in Kefir?

A reader message me about Kefir. My usual response is “You do not know what you are getting”. While for a person with near normal health, it likely does some good (keeping with the concept of hygiene hypothesis), this is not so clear for more severe dysbiosis of the gut.

“Kefir grains consist of complex symbiotic mixtures of bacteria and yeasts, and are reported to impart numerous health-boosting properties to milk and water kefir beverages. ” [2022]

Which bacteria could be in Kefir

Typical Kefir Label – What are these cultures?

There is no legal requirement to report the name of the bacteria in the kefir, nor the genus, species or strains. Each batch may have a significantly different mixture of bacteria. You want live and active cultures? Go to the forest and eat a spoonful of dirt!!

What has been seen in Kefir?

From this study: A Big World in Small Grain: A Review of Natural Milk Kefir Starters, 2020 we see the following reported:

  • Acetobacter
    • pasteurianus
    • not classified species
  • Dekkera
    • anomalus
    • bruxellensis [2022]
  • Enterobacter
    • not classified species
  • Kazachstania
    • exigua
    • not classified species
  • Kluyveromyces
    • marxianus
  • Lactobacillus
    • amilovorus
    • buchneri
    • crispatus
    • helveticus
    • kefiranofaciens
    • kefirgranum
    • kefiri
    • mesenteroides
    • mali [2022]
    • nagelii [2022]
    • otakiensis
    • parabuchneri
    • paracasei [2022]
    • parakefiri
    • rhamnosus [2022]
    • sunkii
  • Lactococcus
    • lactis
  • Lentilactobacillus
  • Leuconostoc 
    • pseudomesenteroides [2022]
    • not classified species
  • Liquorilactobacillus
  • Nauvomozyma
    • not classified species
  • Oenococcus
  • Saccharomyces
    • cerevisiae
    • not classified species

And the list grows every year (look at the number of 2022 citations above).

Water vs Milk Kefir?

” in this study, the variety of WK grain microbial consortia was wider than that of MK grains, and this significantly affected the resultant WK products.” Water kefir grains vs. milk kefir grains: Physical, microbial and chemical comparison [2022]

And from A comparison of milk kefir and water kefir: Physical, chemical, microbiological and functional properties [2021] “The two different fermented beverages produced from these grains have different physical and chemical characteristics and different microbiological composition.”

From Milk Kefir to Water Kefir: Assessment of Fermentation Processes, Microbial Changes and Evaluation of the Produced Beverages [2022] ” It is indeed reported that kefir grains may adapt to new available carbon sources affecting their granulation and the microbial growth on them, as well as the microbial characteristic of the final beverage.”

Bottom Line

Kefir is not a precise product, even in the vaguest terms. I am bias to juggling as few balls at a time as possible… Kefir feels like working with all of the balls in an IKEA kids ball room. It’s spinning the barrel of a bacteria roulette — especially since the same product from the same manufacturer may be different in the next batch. For grown at home kefir, the variability will be a lot higher.

Follow up Analysis for ME/CFS (After COVID)

Foreword – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The Next Episode of the Story

This person’s early analysis is at IBS + BioNTech COVID Vaccine -> ME/CFS? He forwarded these notes:

  • I have done everything as planned since your first review
  • I maybe went up from 15% to 20%. I was able to reintroduce some new activities, but still am lying in bed most of the time. Also taking piracetam seems to help.
  • I still won’t be able to do the analysis myself.
    • COMMENT: ME/CFS patients are a priority for me because I personally understand their brain fog and cognitive impairments from past experiences.
  • I have had COVID in the meantime in case that matters for your analysis, but I did not notice any changes afterwards.

Analysis

Given the recent post for another ME/CFS person who had COVID too, with the result that their microbiome became a good match for long COVID and a poor match for ME/CFS, this was my first question. Fortunately, the sample was done via Biomesight, he did not needed with FASTQ files and transferring them. To keep the story short, I looked at his shifts compared to annotated sampled and compare to literature from the US National Library of Medicine nothing shifted between the samples. There is no shift towards Long COVID from ME/CFS in this case.

Comparing Samples

I do not know the answers. I have a model. Models often need adjustments so comparing samples (for better or worst) in a consistent manner is part of my learning process.

First thing we see a dramatic change with rare bacteria being seen much more often and common bacteria less often. There are more genus seen (184 vs 141) and more Species (230 vs 161) but this may be due the better sample reads in the latest sample (82,102 reads versus 55,117 reads).

PercentileLatest
Genus		Latest
Species		Earlier
Genus		Earlier
Species
0 – 9476524
10 – 1923271016
20 – 2919161611
30 – 3913171213
40 – 4915181314
50 – 5916172332
60 – 6915221419
70 – 7917221314
80 – 8913162422
90 – 996101416
Average18.423.014.116.1
Std Dev11.015.46.27.4
  • Kegg Probiotics
    • The maximum value went down from 18.45 to 4.61 (indicating less compound are an extremely low level).
      • There were 6 compound listed before and it dropped to just 1 (Aromatic aldehyde) when using 1% filtering level.
      • There were 18 compound listed before and it dropped to just 1 when using 5% filtering level.
      • There were 19 compound listed before and it dropped to just 3 when using 10% filtering level.

Was there improvement? Despite the potential confusion because of sample quality we had 3 indicators of improvement:

  • Significantly less matches to known medical conditions profiles
  • Significantly less compound that the person appears to be low in (using data derived from Kyoto Encyclopedia of Genes and Genomes )
  • The person feeling subjectively better and doing more activities

Most of the other measures are the same or difficult to interpret. There is one possible concern, the high levels of Prevotella copri is an indicator of mycotoxin, typically from moulds and fungi. Considering that the time between the samples was winter with close windows and heating — there could be an environment issues here – so lots of fresh air may be good.

Over to Suggestions

There are various algorithms to suggesting probiotics, the strongest results are for:

Doing my usual consensus building

Among the top items are ones that are supported for ME/CFS in studies, including:

Unfortunately, some of the items have no studies. Given that the suggestions are based solely on bacteria with no knowledge of the diagnosis, the convergence with the literature suggests that the suggestions are very appropriate. Two different roads came to the same conclusion. In data science this is sometimes called “cross validation”. In Scotland, “O ye’ll tak’ the high road, and I’ll tak’ the low road,
And I’ll be in Scotland a’fore ye,”

I looked at the antibiotic list for the latest sample and the top two are typically used for ME/CFS:

And interesting that several others often used are NOT recommended: azithromycin (which is a macrolide ?!?), minocycline [2021], fluoroquinolone, doxycycline.

ME/CFS is a heterogeneous condition with a wide variety of microbiome dysfunctions. I believe that using the microbiome to target the best candidate antibiotics is the rational way to proceed.

ME2_ConsensusDownload

Questions and Answers

  • Question: Sadly I do not tolerate chocolate, but I will try it out again.
    • Answer: These are suggestions, do only what you are comfortable with. Nothing is required. The chocolate issue is interesting, my daughter does not tolerate most chocolates, she discovered that it was the type of sugar (i.e. made with liquid sugar / liquid glucose — adverse reaction) made with solid sugar — happiness. See Health effects of glucose syrup
    • If you try again, you may wish to determine the type of sugar actually being used first.
  • Question: Is there no avoid list?
    • Answer: Yes, in the download, any item with a NEGATIVE value in the priority is an avoid
Avoid Cnt is the number of time that suggestions placed it into avoid
  • Question: Is 1 capsule of Equilibrium per day really enough?
    • Answer: I honestly do not know. There is no literature to work from. If you take more, than separate them (i.e. 12 hrs apart)
  • Question: It seemed whenever I took turmeric that I was getting more nervous and anxious. Still take it now and then?
    • Answer: As above, do only what you are comfortable with — there are hundreds of items listed. Anxiety is contrary to the effects of turmeric / curcumin reported in the literature [2021] [2019] [2018] [2017]. If turmeric is causing die-off of bacteria that causes vascular constriction, that would result in anxiety. If you tolerate aspirin or niacin (flushing type), then try taking those with the turmeric.

ME/CFS x COVID :- Long COVID instead

Foreword – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships.

Backstory of Latest Sample

In light of your recent few blog posts about uploads without many microbiome shifts to work with, I was thinking this could be a beneficial walkthrough video for what seems to be the opposite.

I was doing pretty well on my antibiotic rotations (mainly tetracycline two weeks on, two weeks off since Aug of 2021) until Feb or so when I had a major crash / flare that I’m still suffering from.

I did have a very mild case of Covid in mid January that felt no worse than a regular cold.

But from what little I can parse from this sample, it seems I may be struggling with long Covid. I say little, because my brain fog is extremely dense. 

And all of the results I’m getting for this sample via your site seem so drastically different from what has been going on over the last 7  years (my oldest sample is from 2015).

Comparison of samples

This person has samples going back to 2015 using uBiome. Unfortunately for comparison we need to keep to the same lab (why? read The taxonomy nightmare before Christmas…).

Jason Hawrelak Criteria etc

We finally see an improvement with Jason’s criteria. We also may be seeing more diversity with the increase of Genus and Species found. I say may because this could be a side-effect of a low raw count in some samples.

DatePercentileUnhealthy BacteriaGenusSpecies
2022-04-1198.8 %ile8220303
2022-01-1189 %ile1189141
2021-03-0989 %ile8108153
2020-05-2789% ile7153223
Finally, we have a significant improvement
Expected values ar 10% for each line

I decided to look at the raw reads (which are captured from Thryve and Biomesights)

Sample DateRaw Reads
5/27/202043311
3/9/202129247
1/11/202217630
4/11/2022153194
The cause of the jumps above may be the number of reads from the sample

This lead me to look at what typical raw counts are from Ombre/Thryve

To find the raw counts for your sample, open the csv and look for this line

taxon_id,rank,name,parent,count,...
2,kingdom,Bacteria,,45341,...

What is the consequences? It means that rarer bacteria may be ghost-like, appearing or disappearing from sample to sample. This adds let one more layer of fuzziness to doing analysis and generating suggestions.

First Question: ME/CFS or Long COVID microbiome or both?

This person uploaded the Ombre FASTQ files to BiomeSight so I may used data from the Long COVID study there. Both condition present similarly, I am curious to see if we have sufficient reference data to decide which condition is a better match.

RankName ( 👍 match National Library of Medicine Citations for Long COVID)Your valuePercentile
clade FCB group250605.9
class Bacteroidia 👍227004.1
class Betaproteobacteria 👍151013.3
class Spirochaetia14086.1
family Bacteroidaceae 👍206905.4
family Eubacteriaceae 👍65030.9
genus Caloramator 👍 [family]152068.5
genus Nostoc2030.6
genus Roseburia 👍1223034.6
norank Eubacteriales incertae sedis 👍 [family]6011.9
order Bacteroidales 👍227004.1
order Burkholderiales147012.9
phylum Spirochaetes14086
species Butyrivibrio proteoclasticus 👍[genus]103.6
species Faecalibacterium prausnitzii 👍30195098.5
species Roseburia faecis 👍 [family]62024.3
Long Covid matches against Biomesight 154 Samples


RankName
(👍 matches National Library of Medicine Citations for Chronic Fatigue Syndrome		Your valuePercentile
family Halanaerobiaceae2037
genus Anaerovibrio57065.1
genus Finegoldia207
genus Halanaerobium2031.8
genus Leuconostoc103.2
genus Pediococcus103.9
order Syntrophobacterales103.9
species Anaerotruncus colihominis85060.2
species Anaerovibrio lipolyticus57065.4
species Bacteroides acidifaciens 👎[sibling]100.9
species Bacteroides fluxus 👎[sibling]209.8
species Clostridium akagii 👎[sibling]105.5
species Clostridium cadaveris 👎[sibling]103.8
species Finegoldia magna101.8
species Odoribacter denticanis 👍[sibling]102.5
species Prevotella copri 👍[sibling]100.6
ME/CFS matches against Biomesight 62 Samples

We have concurrent matches for both both conditions

  • Finegoldia magna, which is not reported in the literature
  • The table above hints that he is at present much closer to Long COVID than ME/CFS.

I am not sure about the political correctness of saying “Congrads! You no longer have ME/CFS, you have Long COVID!” is what the microbiome reads like.

What is interesting is that the microbiome constantly shifts/evolves, with Long COVID the infection is constant and the duration since the infection is short — hence less evolution of the microbiome over all patients. With ME/CFS the triggering infection possibilities are huge with 20, 30, 40 years of evolution of the microbiome — hence patterns are diffused by time and original infection.

Looking at deficiency of compounds produced, we see a dramatic drop from the previous sample suggesting that bacteria are getting the needed inputs for correct functioning.

Sample Date1%ile5%ile10%ile
5/27/202041460
3/9/202121416
1/11/2022197233244
4/11/202262852
Kegg Compounds below %ile shown

Where do we go from here

I am going to do consensus, but do only 3 items:

  • Hand Picked Bacteria using the study in progress data using BiomeSight (16 bacteria)
  • Using US National Library of medicine filter to Long COVID using BiomeSight and Box-Whiskers (14 bacteria)
  • Using US National Library of medicine filter to Long COVID using Ombre and Box-Whiskers (14 bacteria)

The consensus is below as a download. Since antibiotics are being prescribed at present, I included that in the suggestions criteria.

MECOVID_ConsensusDownload

Some highlights

Why did I focus on the ME/CFS ones? Path of least resistance for the prescribing MD – the MD accepts ME/CFS and thus will have low resistance to prescriptions often used for ME/CFS. Asking for them for Long COVID could get rolling of eyes…. As always, we are using these off-label for their computed microbiome effect. For the prescription items, I would suggest rotation (one item for 10 days, then a 0-10 day break, then another item (or repeat if limited to one item).

Long Covid Study – VERY early data

This post is intended for researchers by pointing to bacteria whose genetics are likely significant for long COVID. The raw data is below. Preliminary z-scores indicated that they are significant (Pr < 0.01) and no filtering has occurred for False Detection Rate. Users are advised to perform their own statistics.

Note: These results are lab-specific, using the data provided by BiomeSight.

The reference site for the study is: http://longcovid.microbiomeprescription.com/, the raw data is available at: http://citizenscience.microbiomeprescription.com/

Symptom ObsNo Symptom ObsLabSymptomName
152998biomesightOfficial Diagnosis: COVID19 (Long Hauler
The sample population
Bacteriatax_rankNo Symptom CountSymptom CountNo Symptom Frequency %Symptom Frequency %
Lactococcusgenus62013662.189.5
Negativicoccusgenus4618646.256.6
Pedobacter kwangyangensisspecies3547635.550
Hydrogenophilaceaefamily4809048.159.2
Nostocgenus3446834.544.7
Veillonella montpellierensisspecies5179451.861.8
Rhodothermalesorder24362.423.7
Gillisia limnaeaspecies58611458.775
Tetragenococcusgenus60110760.270.4
Gillisiagenus59211459.375
Paenibacillaceaefamily77113377.387.5
Tetragenococcus halophilusspecies1015510.136.2
Rhodothermiaclass24362.423.7
Hydrogenophilaliaclass479904859.2
Bifidobacterium brevespecies4318243.253.9
Thermosediminibacteralesorder72557.236.2
Bifidobacterium choerinumspecies65411765.577
Bifidobacterium longumspecies71512971.684.9
Hydrogenophilalesorder4809048.159.2
Hydrogenophilusgenus4308143.153.3
Rhodothermaeotaphylum24362.423.7
Items deem significant based on Bernoulli distribribution
Bacteriatax_rankNo Symptom MeanSymptom MeanNo Symptom StdDevSymptom Std DevSymptom ObsNo Symptom Obs
Porphyromonas bennonisspecies3177131435.92361.143288
Clostridiaclass618307487672199278.7150542.1152995
Insolitispirillum peregrinumspecies82591219815534.521217.596593
Sphingobacteriumgenus112515901432.31764.9150938
Sphingobacteriiaclass296344221929577.839150.6152995
Lelliottia amnigenaspecies9222052676.6592.238301
Roseburia faecisspecies13441712219243.98796.5152978
Leptolyngbyaceaefamily67266152.61051.535232
cellular organismsnorank9940549883959012.55355.3152996
Opitutaeclass142315447.2759.379604
Caloramator mitchellensisspecies81621358019886.927582.2145925
Leptolyngbyagenus67266152.21051.535230
Sphingobacteriaceaefamily259593736226540.835832.1152994
Cytophagiaclass9482232289911229.4150960
Aphanizomenonaceaefamily222124374.4152.8108653
Betaproteobacteriaclass282712148026518.817465.6151993
Pseudanabaenalesorder110230274973.341297
Pseudanabaenaceaefamily68230141.9973.441297
Spirosomaceaefamily73022823089.712178.6127791
Tenericutesphylum250763955922.116841.5147957
Puniceicoccaceaefamily139301443741.779599
Sutterella wadsworthensisspecies64181049110990.413485.8108636
Eubacterialesorder614586482370199230.3150329.8152995
Dolichospermumgenus217125371.1153.2107646
Dialister invisusspecies479680359328.110909.7105622
Mollicutesclass250763955922.116841.5147957
Coprococcus catusspecies12568681371850136804
Treponemataceaefamily1604544813832.332843.837235
Dorea formicigeneransspecies143491415911112.9142902
Bacteroides eggerthiispecies85451538521533.530777.757394
Cytophagalesorder9482232289911229.4150960
Spirochaetalesorder1551544813579.132843.837244
Terrabacteria groupclade721047521084231350.3165314.8152996
Cytophagaceaefamily78521892917.911452.3144907
Caloramatorgenus88591412620084.927641.6151972
Spirochaetesphylum90337431012027201.354441
Emticiciagenus78425533286.612937.6112693
Leptolyngbya laminosaspecies66266152.81051.535228
Burkholderialesorder279632130526363.517380.9151993
Pedobactergenus8642130411010617703.6151980
Burkholderiaceaefamily361197598.7409.8135886
Eubacteriales incertae sedisnorank375161837.1256.6139895
Butyrivibrio proteoclasticusspecies4962031108.1350.589657
Dolichospermum curvumspecies20197393.5137.787505
Desulfovibrio fairfieldensisspecies7202631789.350843284
Bacteroidaceaefamily300654238983181167141573.4152995
Bacteroidiaclass426327369089188681.8168273.3152995
Acidaminococcusgenus98423044664.69773.8105707
Bacteroidalesorder426327369089188681.8168273.3152995
Spirochaetaceaefamily1598544813805.732843.837236
Porphyromonas asaccharolyticaspecies3141553970.48744.843238
Erysipelotrichaceaefamily6385347510831.84414.9152993
Roseburiagenus300331920134050.417905.1152991
FCB groupclade448905391426199421.4186334152996
Dialistergenus466879129234.310868.1107646
Lachnospiraceaefamily219269176420109155.381234.8152995
Cerasicoccusgenus249650650.71037.434250
Sphingobacterialesorder296344221929577.839150.6152995
Eubacteriaceaefamily354315557588.14328.8152989
Synechococcaceaefamily68230141.6973.441299
Acidaminococcus intestinispecies3007991051.32292.137222
Acholeplasma hippikonspecies4268121052.52058.935260
Treponemagenus1604544813832.332843.837235
Firmicutesphylum657764502820205065.5155846.7152997
Caloramator indicusspecies37310652091.53540.544373
Faecalibacterium prausnitziispecies10010914176677192.187778152986
Spirochaetiaclass903374310119.827201.354441
Prevotella stercoreaspecies50771001018648.525185.554406
Insolitispirillumgenus82591219815534.521217.596593
Emticicia oligotrophicaspecies78525533291.112937.7112691
Lelliottiagenus9222052676.6592.238301
Sphingobacterium bambusaespecies316506452.11018.8140825
Items deemed significant based on mean and standard deviation