This post extends the analysis of microbial involvement in ME/CFS pathophysiology by focusing on metabolites produced or consumed by bacteria, rather than on individual bacterial species seen in the earlier post Microbial involvement in myalgic encephalomyelitis/chronic fatigue syndrome pathophysiology. . This shift in perspective is valuable because:

Metabolite-Centric Analysis

Bacterial Metabolic Activity: Bacteria produce and consume various metabolites, which can significantly impact the host’s metabolic environment¹³.Metabolic Imbalances: Different bacterial compositions can lead to similar metabolite imbalances, making metabolite profiles potentially more informative than bacterial species profiles alone^{7 8}.

Advantages of This Approach

1. Net Effect: By examining metabolites, we can assess the overall impact of the microbiome on the host, regardless of the specific bacterial species present⁵.
2. Consistency: Metabolite imbalances may be more consistent across patients than bacterial species composition, which can vary widely⁷.
3. Functional Insight: This approach provides insight into the functional consequences of microbiome dysbiosis in ME/CFS^{3 8}.

KEGG Application

Using the KEGG: Kyoto Encyclopedia of Genes and Genomes,(KEGG) allows for:

• Mapping of metabolites to specific pathways
• Identification of key metabolic alterations in ME/CFS patients
• Potential discovery of new biomarkers or therapeutic targets⁷

Metabolite Profiling in ME/CFS

Recent studies have identified several metabolic alterations in ME/CFS patients:

• Disruptions in energy metabolism and mitochondrial function^{2 5}
• Alterations in lipid metabolism, including changes in ceramides and complex lipids⁴
• Disturbances in amino acid metabolism⁸

Clinical Implications

Understanding metabolite profiles in ME/CFS could lead to:

• Improved diagnostic tools
• Identification of potential therapeutic targets
• Personalized treatment approaches based on individual metabolic profiles58

I am showing the numbers for Biomesight sample below. Conclusions across Ombre, uBiome and Biomesight are at the bottom.

Warning: These are the chemical names — a few are available as supplements with more common name.

BiomeSight Results

I did three slice-and-dice

• Producers
• Consumers
• Net metabolites (Producers – Consumers) – this is like the most important

Remember: results may be different for different labs. Also, these are estimates of the metabolites

Metabolite Producers

• DNA N4-methylcytosine <= 31.1
• Cytidine 5′-diphosphoramidate <= 28
• Pyridoxal <= 24.3
• Allantoate <= 23.6
• [L-Glutamate:ammonia ligase (ADP-forming)] <= 22.9
• Adenylyl-[L-glutamate:ammonia ligase (ADP-forming)] <= 22.9
• Uridylyl-[protein-PII] <= 22.6
• 4-Hydroxybenzoate <= 19.6
• 5-Phospho-D-xylonate <= 18.5
• 5-Phospho-L-arabinate <= 18.5
• Formyl-CoA <= 18
• Aminoacrylate <= 17.3
• Methylaminoacrylate <= 17.3
• Acetoacetate <= 17.3
• 5-Carboxyamino-1-(5-phospho-D-ribosyl)imidazole <= 17.3
• UDP-N-acetyl-alpha-D-muramoyl-L-alanyl-L-glutamate <= 17
• N-Acyl-L-homoserine <= 16.7
• (R)-Piperazine-2-carboxylate <= 16.1
• 2-[(2-Aminoethylcarbamoyl)methyl]-2-hydroxybutanedioate <= 16
• D-Mannitol 1-phosphate <= 16
• D-Erythritol 1-phosphate <= 15.6
• 2-Acetylphloroglucinol <= 15.5
• 3-Dehydrocarnitine <= 15.2
• Deoxynucleoside <= 14.9
• Formaldehyde <= 14.5
• (S)-3-Acetyloctanal <= 14.4
• Pyrrole-2-carbonyl-[pcp] <= 14.4
• (L-Prolyl)adenylate <= 14.4
• (L-Arginyl)adenylate <= 14.4
• 2”-Nucleotidylgentamicin <= 13.9
• 4-O-(beta-L-Arabinofuranosyl)-(2S,4S)-4-hydroxyproline <= 13.3
• beta-L-Arabinofuranosyl-(1->2)-beta-L-arabinofuranose <= 13.3
• Polysulfide <= 13.2
• 6-Deoxy-6-sulfo-D-fructose <= 13
• 1-Phosphatidyl-1D-myo-inositol 5-phosphate <= 12.9
• 2-Dehydro-3-deoxy-D-galactonate <= 12.8
• beta-L-Arabinofuranose <= 12.8
• Maltose 6′-phosphate <= 12.7
• Cytidine <= 12.5
• [beta-GlcNAc-(1->4)-Mur2Ac(oyl-L-Ala-gamma-D-Glu-6-N-(beta-D-Asp)-L-Lys-D-Ala-D-Ala)]n <= 12.5
• O-Phospho-L-homoserine <= 12.3
• 2,5-Diamino-6-(5-phospho-D-ribosylamino)pyrimidin-4(3H)-one <= 12.3
• Butanoyl-CoA <= 12.2
• 4-Amino-5-hydroxymethyl-2-methylpyrimidine <= 12.1
• Oxalyl-CoA <= 12.1
• 5-(2-Hydroxyethyl)-4-methylthiazole <= 11.8
• 2-Hydroxyornithine lipid <= 11.5
• N3-Acetyl-2-deoxystreptamine antibiotic <= 11.3
• Protein histidine <= 11.3
• Protein N6-acetyl-L-lysine <= 11.2
• Protoporphyrinogen IX <= 11
• Divinylprotochlorophyllide <= 10.9

Metabolite Consumers (Substrates)

We have a shorter list with 5 metabolites bubbling to the surface as excessive metabolites.

• Linalool >= 96.4
• 6-Oxocyclohex-1-ene-1-carbonyl-CoA >= 96.4
• 2-epi-5-epi-Valiolone >= 96.2
• Lupanine >= 95.2
• 4′-Hydroxyacetophenone >= 95.2
• DNA cytosine <= 31.1
• Xylitol <= 29.4
• N5-(Cytidine 5′-diphosphoramidyl)-L-glutamine <= 28
• 6-Hydroxynicotinate <= 27.6
• Pyridoxine <= 24.1
• [L-Glutamate:ammonia ligase (ADP-forming)] <= 22.9
• Adenylyl-[L-glutamate:ammonia ligase (ADP-forming)] <= 22.9
• 2-Amino-2-deoxyisochorismate <= 22.7
• [Protein-PII] <= 22.6
• Formyl-CoA <= 20.4
• D-Erythrulose 4-phosphate <= 20 alpha-Maltose 1-phosphate <= 18.8
• L-Arabino-1,4-lactone 5-phosphate <= 18.5
• D-Xylono-1,4-lactone 5-phosphate <= 18.5
• N-Acyl-L-homoserine lactone <= 18.1
• Oxalyl-CoA <= 18
• Methylureidoacrylate <= 17.3
• Ureidoacrylate <= 17.3
• UDP-N-acetyl-alpha-D-muramoyl-L-alanyl-L-glutamate <= 17
• D-arabino-Hex-3-ulose 6-phosphate <= 17
• beta-Alaninamide <= 16.1
• (R)-Piperazine-2-carboxamide <= 16.1
• 2-[(L-Alanin-3-ylcarbamoyl)methyl]-2-hydroxybutanedioate <= 16
• Erythritol <= 15.6
• alpha-L-Rhamnopyranosyl-(1->3)-N-acetyl-alpha-D-glucosaminyl-diphospho-trans,octacis-decaprenol <= 15.5 2,4-Diacetylphloroglucinol <= 15.5
• (S)-Allantoin <= 15.1
• L-Prolyl-[pcp] <= 14.4
• trans-2-Octenal <= 14.4
• (L-Prolyl)adenylate <= 14.4
• (L-Arginyl)adenylate <= 14.4
• (5-L-Glutamyl)-L-amino acid <= 13.6
• 4-O-(beta-L-Arabinofuranosyl-(1->2)-beta-L-arabinofuranosyl-(1->2)-beta-L-arabinofuranosyl)-(2S,4S)-4-hydroxyproline <= 13.3
• Sulfoquinovose <= 13
• 1-Phosphatidyl-D-myo-inositol 4,5-bisphosphate <= 12.9
• D-Galactonate <= 12.8
• beta-L-Arabinofuranosyl-(1->2)-beta-L-arabinofuranose <= 12.8
• Oxalate <= 12.7
• N4-Acetylcytidine <= 12.5
• D-Aspartate <= 12.5
• [beta-GlcNAc-(1->4)-Mur2Ac(oyl-L-Ala-gamma-D-Glu-L-Lys-D-Ala-D-Ala)]n <= 12.5
• 3′-Phosphoadenylyl sulfate <= 12.4
• 2,5-Diamino-6-(5-phospho-D-ribitylamino)pyrimidin-4(3H)-one <= 12.3
• D-Xylulose 5-phosphate <= 12.2
• 4-Amino-5-aminomethyl-2-methylpyrimidine <= 12.1
• Deoxynucleoside 5′-phosphate <= 11.9
• Thiosulfate <= 11.6
• Ornithine lipid <= 11.5
• 2-Deoxystreptamine antibiotic <= 11.3
• UDP-alpha-D-galactofuranose <= 11.2
• 3′,5′-Cyclic AMP <= 11.1
• [Sulfatase]-L-serine <= 11
• trans-2,3-Dehydroacyl-CoA <= 10.9
• D-Serine <= 10.6
• 5,6-Dihydrothymine <= 10.4
• Electron-transferring flavoprotein <= 10.3
• D-Mannose <= 10.1
• Ethanol <= 10.1

Net Modifiers

Here we have shorter list with

• (R)-3-(4-Hydroxyphenyl)lactoyl-CoA >= 98.9
• 14alpha-Formylsteroid >= 98.7
• (E)-2-Methylgeranyl diphosphate >= 98.3
• Harderoheme III >= 97
• D-Erythritol 1-phosphate >= 83.4
• 1-(5-O-Phospho-beta-D-ribofuranosyl)-5-(sulfanylcarbonyl)pyridin-1-ium-3-carbonyl adenylate >= 56.8
• 8-Oxo-GDP <= 32.5 8-Oxo-dGDP <= 32.5
• 2,4-Diketo-3-deoxy-L-fuconate <= 27.2
• S-(Hercyn-2-yl)-L-cysteine S-oxide <= 27
• L-Formylkynurenine <= 24.6
• 2-[(2-Aminoethylcarbamoyl)methyl]-2-hydroxybutanedioate <= 23.5
• alpha-Oxo-benzeneacetic acid <= 23.4
• trans-o-Hydroxybenzylidenepyruvate >= 22.8
• 2-(alpha-D-Mannosyl)-3-phosphoglycerate <= 22
• Reduced FMN <= 20.6
• Deamino-NAD+ <= 19.7
• 5-(5-Phospho-D-ribosylaminoformimino)-1-(5-phosphoribosyl)-imidazole-4-carboxamide <= 18.5
• cis-2,3-Dihydroxy-2,3-dihydro-p-cumate >= 18.2
• Phthalate <= 17.9
• alpha-Ribazole <= 17.7
• beta-D-Fructose 6-phosphate <= 17.5
• Reduced electron-transferring flavoprotein <= 16.5
• GDP-L-fucose <= 16.4
• 3-Hydroxy-5,9,17-trioxo-4,5:9,10-disecoandrosta-1(10),2-dien-4-oate <= 15.4
• 2-Keto-D-gluconic acid <= 14.6
• 6-(Hydroxymethyl)-7,8-dihydropterin <= 14.4
• Formaldehyde <= 13.9
• Adenosyl cobyrinate hexaamide <= 13.6
• 3-Deoxy-D-manno-octulosonate <= 12.9
• L-Fuculose 1-phosphate <= 12.8
• D-Glutamate <= 12.5
• L-Tyrosyl-tRNA(Tyr) <= 12.1
• Maltose 6′-phosphate <= 11.8
• O-Phospho-L-serine <= 11.8
• 4-Guanidinobutanal <= 11.7
• 7-Carboxy-7-carbaguanine <= 11.7
• CDP-diacylglycerol <= 11.1
• Protoporphyrinogen IX <= 11.1
• 5-Guanidino-2-oxopentanoate <= 11
• N5-Phospho-L-glutamine <= 10.9
• D-1-Aminopropan-2-ol O-phosphate <= 10.5
• Thymine <= 10.5
• (2-Amino-1-hydroxyethyl)phosphonate <= 10.5
• Hydrogenobyrinate a,c diamide <= 10.2
• 2-Amino-3-carboxymuconate semialdehyde <= 10

Across Labs Consolidation

The analysis of metabolites across multiple microbiome testing platforms (Ombre, Biomesight, and uBiome) reveals a more consistent pattern of metabolite imbalances compared to bacterial species identification.

Potential Consequences of Low GDP-L-fucose (the top one)

The deficiency in GDP-L-fucose could have several implications:

• Altered Immune Response: It may affect the proper functioning of the immune system, potentially impacting inflammatory processes¹².
• Cancer-Related Changes: Low levels might influence tumor progression or immune evasion mechanisms, as fucosylation is often altered in cancer²⁴.
  • First-degree relatives: A clinic-based study reported that first-degree relatives of ME/CFS patients had a significantly higher(four times) prevalence of any cancer compared to controls (OR 4.06) [2022]
• Cellular Communication: It could disrupt normal cell-cell interactions and signaling pathways dependent on fucosylated glycans³.

Metabolite	Threshold	Low	High
GDP-L-fucose	16.5	12.1	21.2
Holo-[citrate (pro-3S)-lyase]	12.7	1.8	23.4
N,N’-Diacetyllegionaminate	12.1	5.4	22
alpha-Oxo-benzeneacetic acid	11.8	1.9	23.4
Oxalyl-CoA	11.3	2	22.4
S-Methyl-5-thio-D-ribose 1-phosphate	11.2	1.3	30.6
Malonyl-CoA	10.6	2.5	26.3
1,2-Diacyl-3-alpha-D-glucosyl-sn-glycerol	10.5	5.4	20

Translate Low Metabolites to Probiotics

Many of these metabolites are produced by probiotics, so in terms of highest importance and reasonably available probiotics, I produced the list below.

The top one is one is one that had very dramatic positive effect for me when I relapsed into ME/CFS (after the worse herxheimer reaction that I have ever experienced): Mutaflor (E.Coli Nissle 1917). I took it as a result of a 1999 study in Australia reporting very low levels of E.Coli in CFS patients [As a FYI, 16s tests do a very poor reporting on E.Coli].

The retail product microbiome labs/ megasporebiotic has several of the next on the list.

1. Escherichia coli (Mutaflor, SymbioFlor-2) : 100%
2. Bacillus thuringiensis: 70%
3. Bacillus licheniformis: 67%
4. Bacillus subtilis: 66%
5. Bacillus subtilis subsp. natto: 67%
6. Clostridium butyricum: 57% of the top
7. Heyndrickxia coagulans (a.k.a. Bacillus coagulans): 65%
8. Lactiplantibacillus plantarum: 59%
9. Enterococcus faecalis: 57%
10. Akkermansia muciniphila: 54%

I asked perplexity, which foods may increase any of the above metabolites. The following were reported:

• Spinach
• Rhubarb
• Beets
• Nuts
• Chocolate
• Tea
• Wheat bran
• Strawberries

Bottom Line

While the metabolite-focused approach provides valuable insights into the biochemical imbalances associated with ME/CFS, its immediate clinical applications are somewhat limited. The probiotics and the food suggestions are reasonable and I see several of the items appearing on suggestions from the expert system for ME/CFS patients.