Tracing Associations with Symptoms/Diagnosis

Microbiome Prescription has a rich collection of annotated samples from different labs (uBiome, Ombre, Biomesight). The samples are annotated with self declared symptoms from a list of 548 different symptoms/diagnosis. 328 symptoms had statistically significant associations.

Biomesight: 4169 samples
Ombre: 1514 samples
uBiome: 795 samples

There are several possibility of associations to these symptoms, including:

Bacteria Association
Enzyme Association
Metabolite Association which we can decompose into
- Production
- Substrate (Consumers)
- Net Metabolite (Production – Consumer)

For each of these 5 vectors, we use these three statistical methods and set out criteria to p < 0.005:

Fisher’s exact test on prevalence of bacteria
Mann Whitney Wilcoxon Test
t-Test on Means

We used KEGG.JP data as a poor man method of compute metabolites.

To go directly to the page reporting data: Symptom Association

This processing took a few days hammering a PC at 90% CPU.

https://www.youtube.com/watch?v=sM4V5hesb1s

High Level Overview

Below we have counts of the associations found. It is clear that bacteria associations are weaker(fewer) than Enzymes by a factor of 4-10. With metabolites, the net metabolite appears a poorer estimator than either producers or substrates.

As would be expected, large population, we find more associations as the population increases.

Bacteria

Source	Associations
BiomeSight	33432
Thryve	28668
uBiome	9283

Enzymes

Source	Associations
BiomeSight	325170
Thryve	78485
uBiome	41707

Metabolite Production

Source	Associations
BiomeSight	226447
Thryve	49122
uBiome	26756

Metabolite Substrate

Source	Associations
BiomeSight	227053
Thryve	50164
uBiome	28162

Metabolite Net

Source	Associations
BiomeSight	123631
Thryve	25493
uBiome	14771

Data Accessibility

The findings are available on microbiome prescription

The key thing to be aware of is that the results are different using data from each lab. For background see Nightmare.

The page is simple:

Pick the Symptoms
Pick the number of TOP items you want from each lab.

This is then followed by 5 sortable and filterable tables.

Examples of agreement

For ME/CFS we have all three labs reporting these bacteria are significant:

Porphyromonas genus
Bifidobacterium genus
Bifidobacteriaceae family
Bifidobacteriales order
Hungateiclostridiaceae family

For Enzymes:

1.1.1.65 pyridoxine 4-dehydrogenase
1.8.98.6 formate:CoB-CoM heterodisulfide,ferredoxin reductase
2.1.1.172 16S rRNA (guanine¹²⁰⁷–N²)-methyltransferase
2.3.2.5 glutaminyl-peptide cyclotransferase
2.4.1.336 monoglucosyldiacylglycerol synthase
2.6.1.59 dTDP-4-amino-4,6-dideoxygalactose transaminase
2.7.7.15 choline-phosphate cytidylyltransferase
2.7.8.36 undecaprenyl phosphate N,N′-diacetylbacillosamine 1-phosphate transferase
3.4.21.83 oligopeptidase B
4.1.1.31 phosphoenolpyruvate carboxylase

For Net Metabolite we have a much longer list:

C01142 (3S)-3,6-Diaminohexanoate
C20748 (E)-4-(Trimethylammonio)but-2-enoyl-CoA
C02612 (R)-2-Methylmalate
C06010 (S)-2-Acetolactate
C00424 (S)-Lactaldehyde
C07281 [eIF5A-precursor]-lysine
C15811 [Enzyme]-cysteine
C15812 [Enzyme]-S-sulfanylcysteine
C16236 [Protein]-N6-(octanoyl)-L-lysine
C16832 [Protein]-N6-[(R)-dihydrolipoyl]-L-lysine
C21440 [Protein]-S-sulfanyl-L-cysteine
C01302 1-(2-Carboxyphenylamino)-1-deoxy-D-ribulose 5-phosphate
C04751 1-(5-Phospho-D-ribosyl)-5-amino-4-imidazolecarboxylate
C04677 1-(5′-Phosphoribosyl)-5-amino-4-imidazolecarboxamide
C06364 1,2-Diacyl-3-alpha-D-glucosyl-sn-glycerol
C00641 1,2-Diacyl-sn-glycerol
C15606 1,2-Dihydroxy-5-(methylthio)pent-1-en-3-one
C03657 1,4-Dihydroxy-2-naphthoate
C11437 1-Deoxy-D-xylulose 5-phosphate
C04006 1D-myo-Inositol 3-phosphate
C11811 1-Hydroxy-2-methyl-2-butenyl 4-diphosphate
C03972 2,3,4,5-Tetrahydrodipicolinate
C00691 2,4,6/3,5-Pentahydroxycyclohexanone
C21607 2,6-Di-O-alpha-D-mannosyl-1-phosphatidyl-1D-myo-inositol
C04691 2-Dehydro-3-deoxy-D-arabino-heptonate 7-phosphate
C20905 2-Iminobutanoate
C20904 2-Iminopropanoate
C21609 2-O-(6-O-Acyl-alpha-D-mannosyl)-1-phosphatidyl-1D-myo-inositol
C05807 2-Polyprenylphenol
C16463 3′,5′-Cyclic diGMP
C20934 3-Deoxy-D-glycero-D-galacto-non-2-ulopyranosonate
C04478 3-Deoxy-D-manno-octulosonate 8-phosphate
C00587 3-Hydroxybenzoate
C00141 3-Methyl-2-oxobutanoic acid
C00053 3′-Phosphoadenylyl sulfate
C00197 3-Phospho-D-glycerate
C11435 4-(Cytidine 5′-diphospho)-2-C-methyl-D-erythritol
C11355 4-Amino-4-deoxychorismate
C04752 4-Amino-5-hydroxymethyl-2-methylpyrimidine diphosphate
C00568 4-Aminobenzoate
C00334 4-Aminobutanoate
C04327 4-Methyl-5-(2-phosphooxyethyl)thiazole
C01180 4-Methylthio-2-oxobutanoic acid
C22411 4-O-{Poly[(2R)-glycerophospho]-(2R)-glycerophospho}-N-acetyl-beta-D-mannosaminyl-(1->4)-N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol
C21502 4-O-Di[(2R)-1-glycerophospho]-N-acetyl-beta-D-mannosaminyl-(1->4)-N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol
C03393 4-Phospho-D-erythronate
C00445 5,10-Methenyltetrahydrofolate
C00143 5,10-Methylenetetrahydrofolate
C03089 5-Methylthio-D-ribose
C00119 5-Phospho-alpha-D-ribose 1-diphosphate
C01300 6-(Hydroxymethyl)-7,8-dihydropterin
C01019 6-Deoxy-L-galactose
C04807 6-Hydroxymethyl-7,8-dihydropterin diphosphate
C20176 8-Oxo-dGDP
C22235 8-Oxo-GDP
C22382 Aceneuramate
C00033 Acetate
C00227 Acetyl phosphate
C00024 Acetyl-CoA
C00147 Adenine
C00212 Adenosine
C06508 Adenosyl cobinamide
C06506 Adenosyl cobyrinate a,c diamide
C00008 ADP
C00179 Agmatine
C00069 Alcohol
C00267 alpha-D-Glucose
C20237 alpha-Maltose 1-phosphate
C00014 Ammonia
C00020 AMP
C00002 ATP
C05345 beta-D-Fructose 6-phosphate
C00663 beta-D-Glucose 1-phosphate
C00576 Betaine aldehyde
C20568 beta-L-Arabinofuranosyl-(1->2)-beta-L-arabinofuranose
C00120 Biotin
C00323 Caffeoyl-CoA
C00169 Carbamoyl phosphate
C00090 Catechol
C00307 CDP-choline
C00269 CDP-diacylglycerol
C00513 CDP-glycerol
C00114 Choline
C00588 Choline phosphate
C00011 CO2
C00010 CoA
C06504 Cob(II)yrinate a,c diamide
C11545 Cobalt-precorrin 8
C05773 Cobyrinate
C00876 Coenzyme F420
C00063 CTP
C03492 D-4′-Phosphopantothenate
C00405 D-Amino acid
C00239 dCMP
C17010 Dehypoxanthine futalosine
C02269 Deoxynucleoside
C00677 Deoxynucleoside triphosphate
C04666 D-erythro-1-(Imidazol-4-yl)glycerol 3-phosphate
C00095 D-Fructose
C00354 D-Fructose 1,6-bisphosphate
C00333 D-Galacturonate
C00257 D-Gluconic acid
C00198 D-Glucono-1,5-lactone
C00329 D-Glucosamine
C00031 D-Glucose
C00103 D-Glucose 1-phosphate
C00258 D-Glycerate
C00235 Dimethylallyl diphosphate
C00013 Diphosphate
C17556 di-trans,poly-cis-Undecaprenyl phosphate
C00159 D-Mannose
C00636 D-Mannose 1-phosphate
C00039 DNA
C00110 Dolichyl phosphate
C03862 Dolichyl phosphate D-mannose
C00121 D-Ribose
C01151 D-Ribose 1,5-bisphosphate
C03319 dTDP-L-rhamnose
C00365 dUMP
C00460 dUTP
C00231 D-Xylulose 5-phosphate
C00125 Ferricytochrome c
C00126 Ferrocytochrome c
C00406 Feruloyl-CoA
C00061 FMN
C00122 Fumarate
C16999 Futalosine
C02686 Galactosylceramide
C00096 GDP-mannose
C00353 Geranylgeranyl diphosphate
C00051 Glutathione
C00116 Glycerol
C00184 Glycerone
C00111 Glycerone phosphate
C00037 Glycine
C00266 Glycolaldehyde
C00160 Glycolate
C02412 Glycyl-tRNA(Gly)
C00048 Glyoxylate
C00044 GTP
C00080 H+
C00288 HCO3-
C06250 Holo-[carboxylase]
C04298 Holo-[citrate (pro-3S)-lyase]
C00283 Hydrogen sulfide
C06399 Hydrogenobyrinate
C00530 Hydroquinone
C15603 Hydroquinone
C15809 Iminoglycine
C00130 IMP
C00463 Indole
C03506 Indoleglycerol phosphate
C00885 Isochorismate
C00129 Isopentenyl diphosphate
C03508 L-2-Amino-3-oxobutanoic acid
C15556 L-3,4-Dihydroxybutan-2-one 4-phosphate
C00041 L-Alanine
C00886 L-Alanyl-tRNA
C00049 L-Aspartate
C00441 L-Aspartate 4-semialdehyde
C20750 L-Carnitinyl-CoA
C00327 L-Citrulline
C02291 L-Cystathionine
C00097 L-Cysteine
C02700 L-Formylkynurenine
C00025 L-Glutamate
C01165 L-Glutamate 5-semialdehyde
C00064 L-Glutamine
C02047 L-Leucyl-tRNA
C00073 L-Methionine
C00077 L-Ornithine
C00508 L-Ribulose
C00065 L-Serine
C00188 L-Threonine
C00083 Malonyl-CoA
C05819 Menaquinol
C00409 Methanethiol
C06717 Mycothiol
C00137 myo-Inositol
C04916 N-(5′-Phospho-D-1′-ribulosylformimino)-5-amino-1-(5”-phospho-D-ribosyl)-4-imidazolecarboxamide
C20390 N-Acetyl-alpha-D-glucosaminyl-diphospho-trans,octacis-decaprenol
C00140 N-Acetyl-D-glucosamine
C00003 NAD+
C00004 NADH
C00006 NADP+
C00005 NADPH
C00436 N-Carbamoylputrescine
C00153 Nicotinamide
C00201 Nucleoside triphosphate
C00979 O-Acetyl-L-serine
C00009 Orthophosphate
C22151 Oxidized [2Fe-2S] ferredoxin
C01134 Pantetheine 4′-phosphate
C00864 Pantothenate
C00472 p-Benzoquinone
C00416 Phosphatidate
C03167 Phosphonoacetaldehyde
C19692 Polysulfide
C06408 Precorrin 8X
C00585 Protein tyrosine
C01167 Protein tyrosine phosphate
C02880 Protochlorophyllide
C00134 Putrescine
C00250 Pyridoxal
C00022 Pyruvate
C15602 Quinone
C22150 Reduced [2Fe-2S] ferredoxin
C01080 Reduced coenzyme F420
C00473 Retinol
C00255 Riboflavin
C00046 RNA
C00021 S-Adenosyl-L-homocysteine
C00019 S-Adenosyl-L-methionine
C04188 S-Methyl-5-thio-D-ribose 1-phosphate
C04582 S-Methyl-5-thio-D-ribulose 1-phosphate
C00623 sn-Glycerol 1-phosphate
C00315 Spermidine
C03539 S-Ribosyl-L-homocysteine
C00059 Sulfate
C00704 Superoxide
C00378 Thiamine
C00422 Triacylglycerol
C00066 tRNA
C01642 tRNA(Gly)
C01645 tRNA(Leu)
C00015 UDP
C19725 UDP-2,3-diacetamido-2,3-dideoxy-alpha-D-glucuronate
C00043 UDP-N-acetyl-alpha-D-glucosamine
C00203 UDP-N-acetyl-D-galactosamine
C01050 UDP-N-acetylmuramate
C05892 UDP-N-acetylmuramoyl-L-alanyl-gamma-D-glutamyl-L-lysine
C00105 UMP
C00106 Uracil
C01051 Uroporphyrinogen III
C00001 Water
C00385 Xanthine
C00655 Xanthosine 5′-phosphate

For Metabolite Producers:

C22302 (+)-6-Hydroxypinoresinol
C19831 (1R,6S)-6-Amino-5-oxocyclohex-2-ene-1-carboxylate
C05161 (2R,5S)-2,5-Diaminohexanoate
C04236 (2S)-2-Isopropyl-3-oxosuccinate
C20258 (2S,4S)-4-Hydroxy-2,3,4,5-tetrahydrodipicolinate
C01142 (3S)-3,6-Diaminohexanoate
C00566 (3S)-Citryl-CoA
C01186 (3S,5S)-3,5-Diaminohexanoate
C00712 (9Z)-Octadecenoic acid
C20748 (E)-4-(Trimethylammonio)but-2-enoyl-CoA
C22385 (L-Cysteinyl)adenylate
C02489 (R)-2-Hydroxyacid
C02612 (R)-2-Methylmalate
C04352 (R)-4′-Phosphopantothenoyl-L-cysteine
C00810 (R)-Acetoin
C00937 (R)-Lactaldehyde
C03912 (S)-1-Pyrroline-5-carboxylate
C06010 (S)-2-Acetolactate
C11838 (S)-4,5-Dihydroxypentane-2,3-dione
C03656 (S)-5-Amino-3-oxohexanoic acid
C00424 (S)-Lactaldehyde
C21068 [5-(Aminomethyl)furan-3-yl]methyl phosphate
C15977 [Dihydrolipoyllysine-residue (2-methylpropanoyl)transferase] S-(2-methylpropanoyl)dihydrolipoyllysine
C16254 [Dihydrolipoyllysine-residue succinyltransferase] S-succinyldihydrolipoyllysine
C07282 [eIF5A-precursor]-deoxyhypusine
C07281 [eIF5A-precursor]-lysine
C15811 [Enzyme]-cysteine
C15812 [Enzyme]-S-sulfanylcysteine
C22155 [Fe-S] cluster scaffold protein
C01281 [L-Glutamate:ammonia ligase (ADP-forming)]
C20730 [Protein]-FMN-L-Threonine
C16236 [Protein]-N6-(octanoyl)-L-lysine
C16832 [Protein]-N6-[(R)-dihydrolipoyl]-L-lysine
C01242 [Protein]-S8-aminomethyldihydrolipoyllysine
C21440 [Protein]-S-sulfanyl-L-cysteine
C21879 1-(5-O-Phospho-beta-D-ribofuranosyl)-5-(sulfanylcarbonyl)pyridin-1-ium-3-carbonyl adenylate
C04751 1-(5-Phospho-D-ribosyl)-5-amino-4-imidazolecarboxylate
C04823 1-(5′-Phosphoribosyl)-5-amino-4-(N-succinocarboxamide)-imidazole
C04677 1-(5′-Phosphoribosyl)-5-amino-4-imidazolecarboxamide
C04734 1-(5′-Phosphoribosyl)-5-formamido-4-imidazolecarboxamide
C02490 1,2-beta-D-Glucan
C00641 1,2-Diacyl-sn-glycerol
C00986 1,3-Diaminopropane
C06485 1,5-Anhydro-D-fructose
C00234 10-Formyltetrahydrofolate
C21835 12,18-Didecarboxysiroheme
C04230 1-Acyl-sn-glycero-3-phosphocholine
C11437 1-Deoxy-D-xylulose 5-phosphate
C04006 1D-myo-Inositol 3-phosphate
C11557 1-Phosphatidyl-1D-myo-inositol 5-phosphate
C19771 2′-(5-Triphosphoribosyl)-3′-dephospho-CoA
C04640 2-(Formamido)-N1-(5′-phosphoribosyl)acetamidine
C01159 2,3-Bisphospho-D-glycerate
C00691 2,4,6/3,5-Pentahydroxycyclohexanone
C20424 2,4-Diacetamido-2,4,6-trideoxy-D-mannopyranose
C21896 2,4-Diacetylphloroglucinol
C02780 2,5-Didehydro-D-gluconate
C21607 2,6-Di-O-alpha-D-mannosyl-1-phosphatidyl-1D-myo-inositol
C00900 2-Acetolactate
C17234 2-Aminobut-2-enoate
C11453 2-C-Methyl-D-erythritol 2,4-cyclodiphosphate
C04442 2-Dehydro-3-deoxy-6-phospho-D-gluconate
C04691 2-Dehydro-3-deoxy-D-arabino-heptonate 7-phosphate
C03827 2-Dehydro-3-deoxy-L-fuconate
C06892 2-Deoxy-5-keto-D-gluconic acid
C00672 2-Deoxy-D-ribose 1-phosphate
C06196 2′-Deoxyinosine 5′-phosphate
C19970 2-Hydroxy-dAMP
C20905 2-Iminobutanoate
C02631 2-Isopropylmaleate
C02222 2-Maleylacetate
C02226 2-Methylmaleate
C21608 2-O-(6-O-Acyl-alpha-D-mannosyl)-6-O-alpha-D-mannosyl-1-phosphatidyl-1D-myo-inositol
C19792 2-O-(alpha-D-Glucopyranosyl)-D-glycerate
C03586 2-Oxo-2,3-dihydrofuran-5-acetate
C06054 2-Oxo-3-hydroxy-4-phosphobutanoate
C00109 2-Oxobutanoate
C00026 2-Oxoglutarate
C11436 2-Phospho-4-(cytidine 5′-diphospho)-2-C-methyl-D-erythritol
C00631 2-Phospho-D-glycerate
C13309 2-Phytyl-1,4-naphthoquinone
C05807 2-Polyprenylphenol
C01267 3-(Imidazol-4-yl)-2-oxopropyl phosphate
C00575 3′,5′-Cyclic AMP
C16463 3′,5′-Cyclic diGMP
C20772 3-[(1-Carboxyvinyl)oxy]benzoate
C00944 3-Dehydroquinate
C02637 3-Dehydroshikimate
C20934 3-Deoxy-D-glycero-D-galacto-non-2-ulopyranosonate
C21383 3-Deoxy-D-glycero-D-galacto-non-2-ulopyranosonate 9-phosphate
C04046 3-D-Glucosyl-1,2-diacylglycerol
C20960 3-Hydroxy-5-phosphooxypentane-2,4-dione
C00141 3-Methyl-2-oxobutanoic acid
C03069 3-Methylcrotonyl-CoA
C00685 3-Oxoacyl-[acyl-carrier protein]
C02941 3-Oxo-Delta1-steroid
C00619 3-Oxo-Delta4-steroid
C00053 3′-Phosphoadenylyl sulfate
C00197 3-Phospho-D-glycerate
C00236 3-Phospho-D-glyceroyl phosphate
C03232 3-Phosphonooxypyruvate
C02798 3-Phosphonopyruvate
C22313 3-Sulfinopropanoate
C11435 4-(Cytidine 5′-diphospho)-2-C-methyl-D-erythritol
C16144 4,4′-Diapophytoene
C04556 4-Amino-2-methyl-5-(phosphooxymethyl)pyrimidine
C01279 4-Amino-5-hydroxymethyl-2-methylpyrimidine
C04752 4-Amino-5-hydroxymethyl-2-methylpyrimidine diphosphate
C00334 4-Aminobutanoate
C05848 4-Hydroxy-3-polyprenylbenzoate
C00156 4-Hydroxybenzoate
C00233 4-Methyl-2-oxopentanoate
C04327 4-Methyl-5-(2-phosphooxyethyl)thiazole
C22411 4-O-{Poly[(2R)-glycerophospho]-(2R)-glycerophospho}-N-acetyl-beta-D-mannosaminyl-(1->4)-N-acetyl-alpha-D-glucosaminyl-diphospho-ditrans,octacis-undecaprenol
C02964 4-O-beta-D-Glucopyranosyl-D-mannose
C19877 4-O-Phospho-alpha-Kdo-(2->6)-lipid IVA
C03082 4-Phospho-L-aspartate
C04294 5-(2-Hydroxyethyl)-4-methylthiazole
C04896 5-(5-Phospho-D-ribosylaminoformimino)-1-(5-phosphoribosyl)-imidazole-4-carboxamide
C00445 5,10-Methenyltetrahydrofolate
C00431 5-Aminopentanoate
C21877 5-Carboxy-1-(5-O-phospho-beta-D-ribofuranosyl)pyridin-1-ium-3-carbonyl adenylate
C15667 5-Carboxyamino-1-(5-phospho-D-ribosyl)imidazole
C16737 5-Deoxy-D-glucuronate
C22288 5-Deoxy-D-ribose
C03089 5-Methylthio-D-ribose
C01269 5-O-(1-Carboxyvinyl)-3-phosphoshikimate
C02805 5-Oxoprolyl-peptide
C00119 5-Phospho-alpha-D-ribose 1-diphosphate
C03838 5′-Phosphoribosylglycinamide
C19787 5′-S-Methyl-5′-thioinosine
C03773 6-Acetyl-beta-D-galactoside
C20773 6-Amino-6-deoxyfutalosine
C02954 6-Aminopenicillanate
C20830 6-Deoxy-6-sulfo-D-fructose
C19859 6-Methoxy-3-methyl-2-all-trans-polyprenyl-1,4-benzoquinol
C00345 6-Phospho-D-gluconate
C01037 7,8-Diaminononanoate
C15858 7,9,7′,9′-tetracis-Lycopene
C19759 7,9,9′-tricis-Neurosporene
C04643 7-Oxodeoxycholate
C20176 8-Oxo-dGDP
C19968 8-Oxo-dGMP
C22235 8-Oxo-GDP
C14909 9alpha-Hydroxyandrosta-1,4-diene-3,17-dione
C00028 Acceptor
C00033 Acetate
C05744 Acetoacetyl-[acp]
C00207 Acetone
C00227 Acetyl phosphate
C00024 Acetyl-CoA
C00147 Adenine
C00212 Adenosine
C06507 Adenosyl cobyrinate hexaamide
C01299 Adenylyl-[L-glutamate:ammonia ligase (ADP-forming)]
C00008 ADP
C20784 ADP-5-ethyl-4-methylthiazole-2-carboxylate
C00498 ADP-glucose
C00499 Allantoate
C04145 all-trans-Nonaprenyl diphosphate
C04146 all-trans-Octaprenyl diphosphate
C04465 alpha,alpha’-Trehalose 6,6′-bismycolate
C00446 alpha-D-Galactose 1-phosphate
C00936 alpha-D-Mannose
C02504 alpha-Isopropylmalate
C06365 alpha-Kojibiosyldiacylglycerol
C01888 Aminoacetone
C03373 Aminoimidazole ribotide
C00014 Ammonia
C00020 AMP
C06697 Arsenite
C00002 ATP
C21336 beta-1,2-Mannobiose
C05345 beta-D-Fructose 6-phosphate
C00221 beta-D-Glucose
C00663 beta-D-Glucose 1-phosphate
C01172 beta-D-Glucose 6-phosphate
C20569 beta-L-Arabinofuranose
C01563 Carbamate
C00169 Carbamoyl phosphate
C04419 Carboxybiotin-carboxyl-carrier protein
C20969 Carboxyphosphate
C05980 Cardiolipin
C00307 CDP-choline
C00269 CDP-diacylglycerol
C00789 CDP-ribitol
C05306 Chlorophyll a
C00114 Choline
C01794 Choloyl-CoA
C00251 Chorismate
C00417 cis-Aconitate
C00158 Citrate
C20419 CMP-N,N’-diacetyllegionaminate
C00011 CO2
C00010 CoA
C00175 Cobalt ion
C16242 Cobalt-precorrin 5A
C11545 Cobalt-precorrin 8
C05773 Cobyrinate
C19724 Cobyrinate c-monamide
C04832 Coenzyme M 7-mercaptoheptanoylthreonine-phosphate heterodisulfide
C03263 Coproporphyrinogen III
C00063 CTP
C00475 Cytidine
C22216 Cytidine 5′-{[hydroxy(2-hydroxyethyl)phosphonoyl]phosphate}
C04122 D-1-Aminopropan-2-ol O-phosphate
C03492 D-4′-Phosphopantothenate
C00993 D-Alanyl-D-alanine
C00405 D-Amino acid
C00360 dAMP
C00705 dCDP
C00239 dCMP
C03112 Deacetylcephalosporin C
C00857 Deamino-NAD+
C15495 Decylubiquinol
C15853 Dehydrospermidine
C17010 Dehypoxanthine futalosine
C21084 Demethylphylloquinol
C00330 Deoxyguanosine
C00677 Deoxynucleoside triphosphate
C00882 Dephospho-CoA
C04666 D-erythro-1-(Imidazol-4-yl)glycerol 3-phosphate
C00279 D-Erythrose 4-phosphate
C00354 D-Fructose 1,6-bisphosphate
C01094 D-Fructose 1-phosphate
C00085 D-Fructose 6-phosphate
C00905 D-Fructuronate
C01113 D-Galactose 6-phosphate
C00333 D-Galacturonate
C00257 D-Gluconic acid
C01236 D-Glucono-1,5-lactone 6-phosphate
C00329 D-Glucosamine
C00352 D-Glucosamine 6-phosphate
C00103 D-Glucose 1-phosphate
C00092 D-Glucose 6-phosphate
C00217 D-Glutamate
C00118 D-Glyceraldehyde 3-phosphate
C00258 D-Glycerate
C19879 D-glycero-alpha-D-manno-Heptose 1,7-bisphosphate
C19882 D-glycero-D-manno-Heptose 7-phosphate
C02965 D-Hexose 6-phosphate
C06419 D-Histidine
C05925 Dihydroneopterin phosphate
C20300 Dimethylarsinous acid
C00013 Diphosphate
C04574 di-trans,poly-cis-Undecaprenyl diphosphate
C17556 di-trans,poly-cis-Undecaprenyl phosphate
C00159 D-Mannose
C00636 D-Mannose 1-phosphate
C00275 D-Mannose 6-phosphate
C04299 D-myo-Inositol 1,2-cyclic phosphate
C00039 DNA
C03391 DNA 6-methylaminopurine
C01246 Dolichyl beta-D-glucosyl phosphate
C00621 Dolichyl diphosphate
C00515 D-Ornithine
C01151 D-Ribose 1,5-bisphosphate
C00117 D-Ribose 5-phosphate
C00199 D-Ribulose 5-phosphate
C01097 D-Tagatose 6-phosphate
C11930 dTDP-2,6-dideoxy-D-glycero-hex-2-enos-4-ulose
C06620 dTDP-3,4-dioxo-2,6-dideoxy-D-glucose
C00688 dTDP-4-dehydro-beta-L-rhamnose
C00842 dTDP-glucose
C16581 D-threo-Aldono-1,5-lactone
C00364 dTMP
C00365 dUMP
C00310 D-Xylulose
C00231 D-Xylulose 5-phosphate
C05359 e-
C00016 FAD
C00923 Ferricytochrome
C00125 Ferricytochrome c
C00126 Ferrocytochrome c
C00406 Feruloyl-CoA
C05199 Flavodoxin semiquinone
C06108 Fluoroacetate
C00061 FMN
C00798 Formyl-CoA
C00122 Fumarate
C16999 Futalosine
C02686 Galactosylceramide
C00035 GDP
C01222 GDP-4-dehydro-6-deoxy-D-mannose
C19881 GDP-D-glycero-alpha-D-manno-heptose
C00096 GDP-mannose
C00341 Geranyl diphosphate
C21217 Geranylgeranyl bacteriochlorophyllide a
C00353 Geranylgeranyl diphosphate
C02282 Glutaminyl-tRNA
C00051 Glutathione
C00127 Glutathione disulfide
C05730 Glutathionylspermidine
C00116 Glycerol
C00111 Glycerone phosphate
C00037 Glycine
C00160 Glycolate
C02412 Glycyl-tRNA(Gly)
C00144 GMP
C00581 Guanidinoacetate
C00242 Guanine
C01228 Guanosine 3′,5′-bis(diphosphate)
C04494 Guanosine 3′-diphosphate 5′-triphosphate
C19871 Guanylyl molybdenum cofactor
C00080 H+
C00462 Halide
C04649 Heparan sulfate N-acetyl-alpha-D-glucosaminide
C06250 Holo-[carboxylase]
C00282 Hydrogen
C01528 Hydrogen selenide
C00283 Hydrogen sulfide
C06399 Hydrogenobyrinate
C06503 Hydrogenobyrinate a,c diamide
C15603 Hydroquinone
C00530 Hydroquinone
C00262 Hypoxanthine
C00130 IMP
C00463 Indole
C00311 Isocitrate
C03281 Kanamycin A 3′-phosphate
C06026 KDO2-lipid A
C03508 L-2-Amino-3-oxobutanoic acid
C05231 L-3-Aminobutyryl-CoA
C00041 L-Alanine
C20958 L-Alanyl-L-glutamate
C00886 L-Alanyl-tRNA
C00062 L-Arginine
C02163 L-Arginyl-tRNA(Arg)
C00152 L-Asparagine
C03402 L-Asparaginyl-tRNA(Asn)
C00049 L-Aspartate
C00441 L-Aspartate 4-semialdehyde
C20750 L-Carnitinyl-CoA
C02291 L-Cystathionine
C00506 L-Cysteate
C00097 L-Cysteine
C02882 L-Cysteine-S-conjugate
C03125 L-Cysteinyl-tRNA(Cys)
C02045 L-Erythrulose
C00025 L-Glutamate
C00064 L-Glutamine
C03287 L-Glutamyl 5-phosphate
C02987 L-Glutamyl-tRNA(Glu)
C00135 L-Histidine
C02988 L-Histidyl-tRNA(His)
C00155 L-Homocysteine
C16238 Lipoyl-AMP
C00666 LL-2,6-Diaminoheptanedioate
C21386 L-Leucyl-L-arginyl-protein
C21457 L-Leucyl-L-aspartyl-protein
C21455 L-Leucyl-L-glutamyl-protein
C21387 L-Leucyl-L-lysyl-protein
C02047 L-Leucyl-tRNA
C00047 L-Lysine
C01931 L-Lysyl-tRNA
C00073 L-Methionine
C15999 L-Methionine (S)-S-oxide
C02430 L-Methionyl-tRNA
C01826 L-Norvaline
C02434 Long-chain ester
C00077 L-Ornithine
C03511 L-Phenylalanyl-tRNA(Phe)
C02702 L-Prolyl-tRNA(Pro)
C00508 L-Ribulose
C01101 L-Ribulose 5-phosphate
C02553 L-Seryl-tRNA(Ser)
C00188 L-Threonine
C20641 L-Threonylcarbamoyladenylate
C02992 L-Threonyl-tRNA(Thr)
C02839 L-Tyrosyl-tRNA(Tyr)
C02554 L-Valyl-tRNA(Val)
C04536 Magnesium protoporphyrin monomethyl ester
C01209 Malonyl-[acyl-carrier protein]
C00083 Malonyl-CoA
C01935 Maltodextrin
C02995 Maltose 6′-phosphate
C04809 Membrane-derived-oligosaccharide 6-(glycerophospho)-D-glucose
C05819 Menaquinol
C01732 Mesaconate
C00680 meso-2,6-Diaminoheptanedioate
C11440 Methionyl peptide
C00546 Methylglyoxal
C05924 Molybdopterin
C06717 Mycothiol
C00137 myo-Inositol
C15854 N-(4-Aminobutylidene)-[eIF5A-precursor]-lysine
C04916 N-(5′-Phospho-D-1′-ribulosylformimino)-5-amino-1-(5”-phospho-D-ribosyl)-4-imidazolecarboxamide
C03406 N-(L-Arginino)succinate
C20562 N-[(7,8-Dihydropterin-6-yl)methyl]-4-(beta-D-ribofuranosyl)aniline 5′-phosphate
C21009 N4-Aminopropylspermidine
C21010 N4-Bis(aminopropyl)spermidine
C22033 N5-(Cytidine 5′-diphosphoramidyl)-L-glutamine
C03794 N6-(1,2-Dicarboxyethyl)-AMP
C20751 N6-L-Threonylcarbamoyladenine in tRNA
C04501 N-Acetyl-alpha-D-glucosamine 1-phosphate
C20390 N-Acetyl-alpha-D-glucosaminyl-diphospho-trans,octacis-decaprenol
C00140 N-Acetyl-D-glucosamine
C00357 N-Acetyl-D-glucosamine 6-phosphate
C01289 N-Acetyl-D-glucosaminyldiphosphoundecaprenol
C02297 N-Acetyldiamine
C00645 N-Acetyl-D-mannosamine
C05539 N-Acetyl-L-2-amino-6-oxopimelate
C00624 N-Acetyl-L-glutamate
C04133 N-Acetyl-L-glutamate 5-phosphate
C01250 N-Acetyl-L-glutamate 5-semialdehyde
C21027 N-Acetylmuramic acid alpha-1-phosphate
C00625 N-Acyl-D-mannosamine
C00003 NAD+
C00004 NADH
C00006 NADP+
C00005 NADPH
C00438 N-Carbamoyl-L-aspartate
C00454 NDP
C19609 Nickel(2+)
C00153 Nicotinamide
C00455 Nicotinamide D-ribonucleotide
C00253 Nicotinate
C00533 Nitric oxide
C02565 N-Methylhydantoin
C21113 NMNH
C03523 N-Substituted amino acid
C04462 N-Succinyl-2-L-amino-6-oxoheptanedioate
C03419 Nucleoside 3′-phosphate
C01117 Nucleoside 5′-phosphate
C01077 O-Acetyl-L-homoserine
C00979 O-Acetyl-L-serine
C04260 O-D-Alanyl-poly(ribitol phosphate)
C01102 O-Phospho-L-homoserine
C00295 Orotate
C00009 Orthophosphate
C01118 O-Succinyl-L-homoserine
C00313 Oxalyl-CoA
C00139 Oxidized ferredoxin
C02869 Oxidized flavodoxin
C00007 Oxygen
C01134 Pantetheine 4′-phosphate
C00864 Pantothenate
C00472 p-Benzoquinone
C06564 Penicillin N
C15653 Peptide-L-methionine (R)-S-oxide
C03895 Peptide-L-methionine (S)-S-oxide
C03633 Peptidylproline (omega=0)
C15584 Phenol
C00582 Phenylacetyl-CoA
C00166 Phenylpyruvate
C00416 Phosphatidate
C00344 Phosphatidylglycerol
C04308 Phosphatidyl-N-dimethylethanolamine
C01241 Phosphatidyl-N-methylethanolamine
C00074 Phosphoenolpyruvate
C03167 Phosphonoacetaldehyde
C00562 Phosphoprotein
C02741 Phosphoribosyl-AMP
C03313 Phylloquinol
C16695 Plastoquinol-9
C00404 Polyphosphate
C19692 Polysulfide
C06407 Precorrin 4
C06416 Precorrin 5
C06408 Precorrin 8X
C00254 Prephenate
C21177 Prokaryotic ubiquitin-like protein
C00615 Protein histidine
C02188 Protein lysine
C04261 Protein N(pi)-phospho-L-histidine
C16237 Protein N6-(lipoyl)lysine
C20743 Protein N6-acetyl-L-lysine
C00585 Protein tyrosine
C02880 Protochlorophyllide
C20082 Pseudaminic acid
C01168 Pseudouridine 5′-phosphate
C21178 Pupylated protein
C15587 Purine
C21851 Pyridinium-3,5-biscarboxylic acid mononucleotide
C21767 Pyridinium-3,5-bisthiocarboxylate mononucleotide
C21878 Pyridinium-3-carboxy-5-thiocarboxylic acid mononucleotide
C00250 Pyridoxal
C00627 Pyridoxine phosphate
C00022 Pyruvate
C22150 Reduced [2Fe-2S] ferredoxin
C01080 Reduced coenzyme F420
C02745 Reduced flavodoxin
C22336 Reduced hydrogenase
C00376 Retinal
C00046 RNA
C04425 S-Adenosyl-4-methylthio-2-oxobutanoate
C00021 S-Adenosyl-L-homocysteine
C00019 S-Adenosyl-L-methionine
C00805 Salicylate
C05382 Sedoheptulose 7-phosphate
C06718 S-Formylmycothiol
C03431 S-Inosyl-L-homocysteine
C05778 Sirohydrochlorin
C22360 S-methyl-1-thio-D-ribulose 5-phosphate
C22359 S-Methyl-1-thio-D-xylulose 5-phosphate
C00623 sn-Glycerol 1-phosphate
C00093 sn-Glycerol 3-phosphate
C08250 Sophorose
C03539 S-Ribosyl-L-homocysteine
C05824 S-Sulfo-L-cysteine
C03641 Sterol 3-beta-D-glucoside
C01138 Streptomycin 6-phosphate
C00059 Sulfate
C02084 Tetrathionate
C03541 THF-polyglutamate
C01081 Thiamin monophosphate
C00378 Thiamine
C15814 Thiocarboxy-[sulfur-carrier protein]
C01755 Thiocyanate
C00145 Thiol
C00343 Thioredoxin disulfide
C20369 trans,octacis-Decaprenylphospho-beta-D-erythro-pentofuranosid-2-ulose
C00448 trans,trans-Farnesyl diphosphate
C00422 Triacylglycerol
C00066 tRNA
C04728 tRNA containing 5-[(methylamino)methyl]-2-thiouridylate
C19080 tRNA with a 3′ CC end
C19085 tRNA with a 3′ CCA end
C01635 tRNA(Ala)
C01642 tRNA(Gly)
C01645 tRNA(Leu)
C00390 Ubiquinol
C00015 UDP
C19725 UDP-2,3-diacetamido-2,3-dideoxy-alpha-D-glucuronate
C19745 UDP-2,3-diacetamido-2,3-dideoxy-alpha-D-mannuronate
C19823 UDP-2-acetamido-2,6-dideoxy-beta-L-arabino-hexos-4-ulose
C20395 UDP-2-acetamido-2-deoxy-alpha-D-ribo-hex-3-uluronate
C20849 UDP-2-acetamido-3-dehydro-2-deoxy-alpha-D-glucopyranose
C04613 UDP-2-acetamido-4-dehydro-2,6-dideoxyglucose
C03733 UDP-alpha-D-galactofuranose
C00052 UDP-alpha-D-galactose
C00190 UDP-D-xylose
C00167 UDP-glucuronate
C04702 UDPMurNAc(oyl-L-Ala-D-gamma-Glu-L-Lys-D-Ala-D-Ala)
C20357 UDP-N,N’-diacetylbacillosamine
C04631 UDP-N-acetyl-3-(1-carboxyvinyl)-D-glucosamine
C00043 UDP-N-acetyl-alpha-D-glucosamine
C22053 UDP-N-acetyl-alpha-D-muramoyl-L-alanyl-L-glutamate
C00203 UDP-N-acetyl-D-galactosamine
C01212 UDP-N-acetylmuramoyl-L-alanine
C00692 UDP-N-acetylmuramoyl-L-alanyl-D-glutamate
C00105 UMP
C00106 Uracil
C05326 Uridylyl-[protein-PII]
C00001 Water
C00385 Xanthine
C00655 Xanthosine 5′-phosphate

For Metabolite Substrate/Consumers

C05366 (+)-Pinoresinol
C04411 (2R,3S)-3-Isopropylmalate
C06326 (2S)-2-{[1-(R)-Carboxyethyl]amino}pentanoate
C04236 (2S)-2-Isopropyl-3-oxosuccinate
C04593 (2S,3R)-3-Hydroxybutane-1,2,3-tricarboxylate
C01142 (3S)-3,6-Diaminohexanoate
C01186 (3S,5S)-3,5-Diaminohexanoate
C05786 (3Z)-Phycocyanobilin
C20485 (4S)-4-Hydroxy-2-oxoglutarate
C20954 (5-Formylfuran-3-yl)methyl phosphate
C04856 (6S)-6beta-Hydroxy-1,4,5,6-tetrahydronicotinamide-adenine dinucleotide
C04899 (6S)-6beta-Hydroxy-1,4,5,6-tetrahydronicotinamide-adenine dinucleotide phosphate
C02214 (E)-Glutaconate
C22385 (L-Cysteinyl)adenylate
C03195 (R)-10-Hydroxystearate
C04272 (R)-2,3-Dihydroxy-3-methylbutanoate
C01087 (R)-2-Hydroxyglutarate
C02612 (R)-2-Methylmalate
C04352 (R)-4′-Phosphopantothenoyl-L-cysteine
C00810 (R)-Acetoin
C16241 (R)-Lipoate
C02912 (R)-Propane-1,2-diol
C03044 (R,R)-Butane-2,3-diol
C00898 (R,R)-Tartaric acid
C02103 (S)-2-Haloacid
C15565 (S)-2-Hydroxyacid
C11499 (S)-3-Sulfolactate
C00337 (S)-Dihydroorotate
C02917 (S)-Propane-1,2-diol
C07281 [eIF5A-precursor]-lysine
C15811 [Enzyme]-cysteine
C15812 [Enzyme]-S-sulfanylcysteine
C22154 [Fe-S] cluster scaffold protein carrying a [4Fe-4S]2+ cluster
C01281 [L-Glutamate:ammonia ligase (ADP-forming)]
C16240 [Lipoyl-carrier protein]-L-lysine
C21179 [Prokaryotic ubiquitin-like protein]-L-glutamine
C02743 [Protein]-L-cysteine
C19803 [Protein]-L-threonine
C16236 [Protein]-N6-(octanoyl)-L-lysine
C01242 [Protein]-S8-aminomethyldihydrolipoyllysine
C21440 [Protein]-S-sulfanyl-L-cysteine
C05250 [Protein-PII]
C21879 1-(5-O-Phospho-beta-D-ribofuranosyl)-5-(sulfanylcarbonyl)pyridin-1-ium-3-carbonyl adenylate
C04751 1-(5-Phospho-D-ribosyl)-5-amino-4-imidazolecarboxylate
C02739 1-(5-Phospho-D-ribosyl)-ATP
C04823 1-(5′-Phosphoribosyl)-5-amino-4-(N-succinocarboxamide)-imidazole
C04677 1-(5′-Phosphoribosyl)-5-amino-4-imidazolecarboxamide
C02490 1,2-beta-D-Glucan
C21335 1,2-beta-Oligomannan
C06364 1,2-Diacyl-3-alpha-D-glucosyl-sn-glycerol
C00641 1,2-Diacyl-sn-glycerol
C19776 1,2-Diacyl-sn-glycerol 3-diphosphate
C17207 1,4-beta-D-Mannooligosaccharide
C16538 1,5-Anhydro-D-mannitol
C19769 1,6-Anhydro-N-acetyl-beta-muramate
C00234 10-Formyltetrahydrofolate
C00681 1-Acyl-sn-glycerol 3-phosphate
C01234 1-Aminocyclopropane-1-carboxylate
C11811 1-Hydroxy-2-methyl-2-butenyl 4-diphosphate
C19703 1-O-[2-(L-Cysteinamido)-2-deoxy-alpha-D-glucopyranosyl]-1D-myo-inositol
C01194 1-Phosphatidyl-D-myo-inositol
C04637 1-Phosphatidyl-D-myo-inositol 4,5-bisphosphate
C20247 2-(2-Carboxy-4-methylthiazol-5-yl)ethyl phosphate
C19771 2′-(5-Triphosphoribosyl)-3′-dephospho-CoA
C04640 2-(Formamido)-N1-(5′-phosphoribosyl)acetamidine
C03972 2,3,4,5-Tetrahydrodipicolinate
C01159 2,3-Bisphospho-D-glycerate
C01240 2′,3′-Cyclic nucleotide
C04039 2,3-Dihydroxy-3-methylbutanoate
C20246 2-[(2R,5Z)-2-Carboxy-4-methylthiazol-5(2H)-ylidene]ethyl phosphate
C21895 2-Acetylphloroglucinol
C17234 2-Aminobut-2-enoate
C22039 2-Carboxy-1,4-naphthoquinone
C11434 2-C-Methyl-D-erythritol 4-phosphate
C04442 2-Dehydro-3-deoxy-6-phospho-D-gluconate
C04691 2-Dehydro-3-deoxy-D-arabino-heptonate 7-phosphate
C03826 2-Dehydro-3-deoxy-D-xylonate
C19969 2-Hydroxy-dATP
C05123 2-Hydroxyethanesulfonate
C06451 2-Hydroxyethylphosphonate
C20905 2-Iminobutanoate
C20904 2-Iminopropanoate
C02631 2-Isopropylmaleate
C19858 2-Methoxy-6-all-trans-polyprenyl-1,4-benzoquinol
C15882 2-Methyl-6-phytylquinol
C17570 2-Methyl-6-solanyl-1,4-benzoquinol
C21609 2-O-(6-O-Acyl-alpha-D-mannosyl)-1-phosphatidyl-1D-myo-inositol
C19791 2-O-(alpha-D-Glucopyranosyl)-3-phospho-D-glycerate
C00026 2-Oxoglutarate
C11436 2-Phospho-4-(cytidine 5′-diphospho)-2-C-methyl-D-erythritol
C00631 2-Phospho-D-glycerate
C00988 2-Phosphoglycolate
C13309 2-Phytyl-1,4-naphthoquinone
C20772 3-[(1-Carboxyvinyl)oxy]benzoate
C11638 3-Amino-2-oxopropyl phosphate
C04287 3D-3,5/4-Trihydroxycyclohexane-1,2-dione
C00944 3-Dehydroquinate
C21383 3-Deoxy-D-glycero-D-galacto-non-2-ulopyranosonate 9-phosphate
C02514 3-Fumarylpyruvate
C00141 3-Methyl-2-oxobutanoic acid
C02939 3-Methylbutanoyl-CoA
C22131 3”-O-Acetyl-ADP-D-ribose
C02940 3-Oxo-5alpha-steroid
C01876 3-Oxosteroid
C00197 3-Phospho-D-glycerate
C02798 3-Phosphonopyruvate
C02508 3′-Ribonucleotide
C22312 3-Sulfanylpropanoate
C06118 4-(4-Deoxy-alpha-D-gluc-4-enuronosyl)-D-galacturonate
C20559 4-(beta-D-Ribofuranosyl)aniline 5′-phosphate
C11435 4-(Cytidine 5′-diphospho)-2-C-methyl-D-erythritol
C04556 4-Amino-2-methyl-5-(phosphooxymethyl)pyrimidine
C20267 4-Amino-5-aminomethyl-2-methylpyrimidine
C01279 4-Amino-5-hydroxymethyl-2-methylpyrimidine
C04752 4-Amino-5-hydroxymethyl-2-methylpyrimidine diphosphate
C05848 4-Hydroxy-3-polyprenylbenzoate
C00989 4-Hydroxybutanoic acid
C13635 4-Hydroxyphenacyl alcohol
C04327 4-Methyl-5-(2-phosphooxyethyl)thiazole
C20236 4-O-beta-D-Mannopyranosyl-D-glucopyranose
C20749 4-Trimethylammoniobutanoyl-CoA
C04294 5-(2-Hydroxyethyl)-4-methylthiazole
C04896 5-(5-Phospho-D-ribosylaminoformimino)-1-(5-phosphoribosyl)-imidazole-4-carboxamide
C00445 5,10-Methenyltetrahydrofolate
C00143 5,10-Methylenetetrahydrofolate
C04454 5-Amino-6-(5′-phospho-D-ribitylamino)uracil
C00990 5-Aminopentanamide
C21877 5-Carboxy-1-(5-O-phospho-beta-D-ribofuranosyl)pyridin-1-ium-3-carbonyl adenylate
C15667 5-Carboxyamino-1-(5-phospho-D-ribosyl)imidazole
C05198 5′-Deoxyadenosine
C16737 5-Deoxy-D-glucuronate
C00664 5-Formiminotetrahydrofolate
C00170 5′-Methylthioadenosine
C01269 5-O-(1-Carboxyvinyl)-3-phosphoshikimate
C01879 5-Oxoproline
C03090 5-Phosphoribosylamine
C04376 5′-Phosphoribosyl-N-formylglycinamide
C02520 5′-Ribonucleotide
C20773 6-Amino-6-deoxyfutalosine
C20737 6-Geranylgeranyl-2-methylbenzene-1,4-diol
C03847 6-Phospho-beta-D-galactoside
C15858 7,9,7′,9′-tetracis-Lycopene
C19759 7,9,9′-tricis-Neurosporene
C01092 8-Amino-7-oxononanoate
C19967 8-Oxo-dGTP
C22234 8-Oxo-GTP
C15857 9,9′-dicis-zeta-Carotene
C00033 Acetate
C00024 Acetyl-CoA
C00173 Acyl-[acyl-carrier protein]
C00040 Acyl-CoA
C06506 Adenosyl cobyrinate a,c diamide
C01299 Adenylyl-[L-glutamate:ammonia ligase (ADP-forming)]
C15813 Adenylyl-[sulfur-carrier protein]
C00008 ADP
C00498 ADP-glucose
C00301 ADP-ribose
C03687 Alkane-alpha,omega-diamine
C05847 all-trans-Polyprenyl diphosphate
C04218 alpha,alpha’-Trehalose 6-mycolate
C00984 alpha-D-Galactose
C00446 alpha-D-Galactose 1-phosphate
C06156 alpha-D-Glucosamine 1-phosphate
C00267 alpha-D-Glucose
C00668 alpha-D-Glucose 6-phosphate
C20440 alpha-D-Ribose 1,2-cyclic phosphate 5-phosphate
C00620 alpha-D-Ribose 1-phosphate
C20237 alpha-Maltose 1-phosphate
C03373 Aminoimidazole ribotide
C00014 Ammonia
C00020 AMP
C20144 Androsta-1,4-diene-3,17-dione
C06249 Apo-[carboxylase]
C11215 Arsenate ion
C00002 ATP
C11242 Bacteriochlorophyll a
C21336 beta-1,2-Mannobiose
C21454 beta-1,2-Mannotriose
C00099 beta-Alanine
C00602 beta-D-Galactoside
C00663 beta-D-Glucose 1-phosphate
C20887 beta-D-Mannosyl-1,4-N-acetyl-D-glucosamine
C20568 beta-L-Arabinofuranosyl-(1->2)-beta-L-arabinofuranose
C00486 Bilirubin
C00120 Biotin
C00323 Caffeoyl-CoA
C01563 Carbamate
C00169 Carbamoyl phosphate
C01353 Carbonic acid
C04419 Carboxybiotin-carboxyl-carrier protein
C20969 Carboxyphosphate
C00513 CDP-glycerol
C00185 Cellobiose
C00916 Cephalosporin C
C00695 Cholic acid
C00588 Choline phosphate
C00919 Choline sulfate
C04431 cis-4-Carboxymethylenebut-2-en-4-olide
C00417 cis-Aconitate
C00158 Citrate
C00128 CMP-N-acetylneuraminate
C00011 CO2
C17401 Cobalt-factor III
C11540 Cobalt-precorrin 4
C11545 Cobalt-precorrin 8
C11538 Cobalt-sirohydrochlorin
C05773 Cobyrinate
C19724 Cobyrinate c-monamide
C04628 Coenzyme B
C00876 Coenzyme F420
C03576 Coenzyme M
C00791 Creatinine
C00063 CTP
C00177 Cyanide ion
C00973 Cyclomaltodextrin
C00475 Cytidine
C00380 Cytosine
C03492 D-4′-Phosphopantothenate
C00239 dCMP
C00857 Deamino-NAD+
C02970 Decaprenol phosphate
C15494 Decylubiquinone
C15853 Dehydrospermidine
C17010 Dehypoxanthine futalosine
C19847 Demethylmenaquinol
C21084 Demethylphylloquinol
C00677 Deoxynucleoside triphosphate
C00882 Dephospho-CoA
C04666 D-erythro-1-(Imidazol-4-yl)glycerol 3-phosphate
C00279 D-Erythrose 4-phosphate
C00095 D-Fructose
C00354 D-Fructose 1,6-bisphosphate
C00085 D-Fructose 6-phosphate
C01113 D-Galactose 6-phosphate
C00257 D-Gluconic acid
C00198 D-Glucono-1,5-lactone
C01236 D-Glucono-1,5-lactone 6-phosphate
C00031 D-Glucose
C00103 D-Glucose 1-phosphate
C00217 D-Glutamate
C00118 D-Glyceraldehyde 3-phosphate
C00258 D-Glycerate
C19880 D-glycero-alpha-D-manno-Heptose 1-phosphate
C19878 D-glycero-alpha-D-manno-Heptose 7-phosphate
C00286 dGTP
C00738 D-Hexose
C05196 Dihydroflavodoxin
C00921 Dihydropteroate
C20522 Dihydrourocanate
C00235 Dimethylallyl diphosphate
C00013 Diphosphate
C01345 dITP
C04574 di-trans,poly-cis-Undecaprenyl diphosphate
C17556 di-trans,poly-cis-Undecaprenyl phosphate
C03189 DL-Glycerol 1-phosphate
C00476 D-Lyxose
C00159 D-Mannose
C00636 D-Mannose 1-phosphate
C00039 DNA
C00821 DNA adenine
C04213 Dolichyl diphosphooligosaccharide
C00515 D-Ornithine
C01068 D-Ribitol 5-phosphate
C00121 D-Ribose
C00117 D-Ribose 5-phosphate
C00309 D-Ribulose
C00199 D-Ribulose 5-phosphate
C11930 dTDP-2,6-dideoxy-D-glycero-hex-2-enos-4-ulose
C04346 dTDP-4-amino-4,6-dideoxy-D-galactose
C11922 dTDP-4-oxo-2,6-dideoxy-D-glucose
C11907 dTDP-4-oxo-6-deoxy-D-glucose
C00842 dTDP-glucose
C03319 dTDP-L-rhamnose
C02143 D-threo-Aldose
C00459 dTTP
C00365 dUMP
C00460 dUTP
C00231 D-Xylulose 5-phosphate
C05359 e-
C15973 Enzyme N6-(dihydrolipoyl)lysine
C02391 Ester
C00189 Ethanolamine
C00016 FAD
C00125 Ferricytochrome c
C00924 Ferrocytochrome
C00126 Ferrocytochrome c
C19775 Fluoroacetyl-CoA
C00061 FMN
C03479 Folinic acid
C00067 Formaldehyde
C00488 Formamide
C00058 Formate
C00798 Formyl-CoA
C11439 Formyl-L-methionyl peptide
C16999 Futalosine
C00325 GDP-L-fucose
C00096 GDP-mannose
C00341 Geranyl diphosphate
C07292 Glutaredoxin
C00051 Glutathione
C05730 Glutathionylspermidine
C00116 Glycerol
C00184 Glycerone
C03120 Glycerophosphodiester
C02412 Glycyl-tRNA(Gly)
C00144 GMP
C00044 GTP
C01228 Guanosine 3′,5′-bis(diphosphate)
C00080 H+
C00288 HCO3-
C04384 Heparan sulfate alpha-D-glucosaminide
C00205 hn
C06250 Holo-[carboxylase]
C06399 Hydrogenobyrinate
C00530 Hydroquinone
C00168 Hydroxypyruvate
C22226 Icosanoyl-[(phenol)carboxyphthiodiolenone synthase]
C00130 IMP
C00331 Indolepyruvate
C00885 Isochorismate
C00311 Isocitrate
C17658 Isolithocholate
C05557 Isopenicillin N
C00129 Isopentenyl diphosphate
C00081 ITP
C00304 Kanamycin
C03508 L-2-Amino-3-oxobutanoic acid
C00041 L-Alanine
C20957 L-Alanyl-D-glutamate
C20925 L-Alanyl-gamma-D-glutamyl-meso-2,6-diaminoheptanedioate
C05519 L-Allothreonine
C00259 L-Arabinose
C16739 L-Arginyl-protein
C00152 L-Asparagine
C00049 L-Aspartate
C00441 L-Aspartate 4-semialdehyde
C21458 L-Aspartyl-protein
C06251 Lauroyl-KDO2-lipid IV(A)
C00318 L-Carnitine
C00327 L-Citrulline
C00097 L-Cysteine
C02882 L-Cysteine-S-conjugate
C01720 L-Fuconate
C00025 L-Glutamate
C01165 L-Glutamate 5-semialdehyde
C00064 L-Glutamine
C02986 L-Glutaminyl-peptide
C21456 L-Glutamyl-protein
C00860 L-Histidinol
C01100 L-Histidinol phosphate
C00263 L-Homoserine
C00770 L-Idonate
C16238 Lipoyl-AMP
C02051 Lipoylprotein
C00666 LL-2,6-Diaminoheptanedioate
C00123 L-Leucine
C02047 L-Leucyl-tRNA
C00047 L-Lysine
C21388 L-Lysyl-protein
C00073 L-Methionine
C00638 Long-chain fatty acid
C00077 L-Ornithine
C00079 L-Phenylalanine
C00148 L-Proline
C00508 L-Ribulose
C05688 L-Selenocysteine
C00065 L-Serine
C00188 L-Threonine
C12147 L-Threonine O-3-phosphate
C20641 L-Threonylcarbamoyladenylate
C00183 L-Valine
C03516 Magnesium protoporphyrin
C00083 Malonyl-CoA
C04566 Membrane-derived-oligosaccharide D-glucose
C00409 Methanethiol
C17530 Methyl acetate
C03620 Monocarboxylic acid amide
C15585 myo-Inositol phosphate
C15854 N-(4-Aminobutylidene)-[eIF5A-precursor]-lysine
C04916 N-(5′-Phospho-D-1′-ribulosylformimino)-5-amino-1-(5”-phospho-D-ribosyl)-4-imidazolecarboxamide
C03406 N-(L-Arginino)succinate
C20418 N,N’-Diacetyllegionaminate
C22293 N4-Acetylcytidine
C21009 N4-Aminopropylspermidine
C21785 N5-Phospho-L-glutamine
C03794 N6-(1,2-Dicarboxyethyl)-AMP
C04501 N-Acetyl-alpha-D-glucosamine 1-phosphate
C00140 N-Acetyl-D-glucosamine
C00357 N-Acetyl-D-glucosamine 6-phosphate
C00645 N-Acetyl-D-mannosamine
C00624 N-Acetyl-L-glutamate
C01250 N-Acetyl-L-glutamate 5-semialdehyde
C03708 N-Acetyl-O-acetylneuraminate
C00437 N-Acetylornithine
C03000 N-Acyl-D-glucosamine
C00625 N-Acyl-D-mannosamine
C00003 NAD+
C00004 NADH
C00006 NADP+
C00005 NADPH
C15586 N-D-Ribosylpurine
C21769 Ni(II)-pyridinium-3,5-bisthiocarboxylate mononucleotide
C00455 Nicotinamide D-ribonucleotide
C00244 Nitrate
C00533 Nitric oxide
C00697 Nitrogen
C00887 Nitrous oxide
C03880 N-Substituted aminoacyl-tRNA
C04421 N-Succinyl-LL-2,6-diaminoheptanedioate
C01075 N-Sulfo-D-glucosamine
C21158 Nucleoside 3′,5′-bisphosphate
C00201 Nucleoside triphosphate
C01077 O-Acetyl-L-homoserine
C00979 O-Acetyl-L-serine
C05752 Octanoyl-[acp]
C06055 O-Phospho-4-hydroxy-L-threonine
C01102 O-Phospho-L-homoserine
C01005 O-Phospho-L-serine
C01103 Orotidine 5′-phosphate
C00009 Orthophosphate
C01118 O-Succinyl-L-homoserine
C00209 Oxalate
C00036 Oxaloacetate
C00313 Oxalyl-CoA
C22151 Oxidized [2Fe-2S] ferredoxin
C02869 Oxidized flavodoxin
C22335 Oxidized hydrogenase
C01134 Pantetheine 4′-phosphate
C00864 Pantothenate
C00472 p-Benzoquinone
C00395 Penicillin
C03798 Peptidylproline (omega=180)
C07086 Phenylacetic acid
C00416 Phosphatidate
C00350 Phosphatidylethanolamine
C00344 Phosphatidylglycerol
C03892 Phosphatidylglycerophosphate
C01241 Phosphatidyl-N-methylethanolamine
C00074 Phosphoenolpyruvate
C02741 Phosphoribosyl-AMP
C05427 Phytyl diphosphate
C00653 Poly(ribitol phosphate)
C00404 Polyphosphate
C19692 Polysulfide
C02463 Precorrin 2
C06406 Precorrin 3B
C06407 Precorrin 4
C06319 Precorrin 6Y
C06408 Precorrin 8X
C18239 Precursor Z
C00254 Prephenate
C03428 Presqualene diphosphate
C21177 Prokaryotic ubiquitin-like protein
C01845 Propan-2-ol
C00017 Protein
C00615 Protein histidine
C02188 Protein lysine
C04261 Protein N(pi)-phospho-L-histidine
C20743 Protein N6-acetyl-L-lysine
C01167 Protein tyrosine phosphate
C02880 Protochlorophyllide
C02067 Pseudouridine
C21851 Pyridinium-3,5-biscarboxylic acid mononucleotide
C21878 Pyridinium-3-carboxy-5-thiocarboxylic acid mononucleotide
C00250 Pyridoxal
C00647 Pyridoxamine phosphate
C00314 Pyridoxine
C00627 Pyridoxine phosphate
C00022 Pyruvate
C15602 Quinone
C00030 Reduced acceptor
C01080 Reduced coenzyme F420
C00138 Reduced ferredoxin
C01847 Reduced FMN
C00473 Retinol
C00255 Riboflavin
C03802 Ribonucleoside triphosphate
C00046 RNA
C05729 R-S-Cysteinylglycine
C18235 S-(Hydroxymethyl)mycothiol
C00019 S-Adenosyl-L-methionine
C06153 scyllo-Inositol
C00447 Sedoheptulose 1,7-bisphosphate
C05382 Sedoheptulose 7-phosphate
C00493 Shikimate
C00315 Spermidine
C03539 S-Ribosyl-L-homocysteine
C00370 Sterol
C00232 Succinate semialdehyde
C00089 Sucrose
C06125 Sulfatide
C20829 Sulfoquinovose
C05761 Tetradecanoyl-[acp]
C03541 THF-polyglutamate
C00378 Thiamine
C00342 Thioredoxin
C00214 Thymidine
C20370 trans,octacis-Decaprenylphospho-beta-D-arabinofuranose
C20368 trans,octacis-Decaprenylphospho-beta-D-ribofuranose
C20420 tritrans,heptacis-Undecaprenyl phosphate
C17324 tRNA adenine
C11478 tRNA containing 5-(aminomethyl)-2-thiouridine
C02211 tRNA precursor
C00868 tRNA uridine
C19080 tRNA with a 3′ CC end
C19078 tRNA with a 3′ cytidine
C01635 tRNA(Ala)
C01636 tRNA(Arg)
C01639 tRNA(Cys)
C01641 tRNA(Glu)
C01642 tRNA(Gly)
C01643 tRNA(His)
C01645 tRNA(Leu)
C01646 tRNA(Lys)
C01647 tRNA(Met)
C01648 tRNA(Phe)
C01649 tRNA(Pro)
C01650 tRNA(Ser)
C01651 tRNA(Thr)
C01652 tRNA(Trp)
C00787 tRNA(Tyr)
C01653 tRNA(Val)
C00399 Ubiquinone
C19725 UDP-2,3-diacetamido-2,3-dideoxy-alpha-D-glucuronate
C20359 UDP-2-acetamido-3-amino-2,3-dideoxy-alpha-D-glucuronate
C04630 UDP-2-acetamido-4-amino-2,4,6-trideoxy-alpha-D-glucose
C19961 UDP-4-amino-4,6-dideoxy-N-acetyl-beta-L-altrosamine
C00052 UDP-alpha-D-galactose
C00029 UDP-glucose
C00167 UDP-glucuronate
C20357 UDP-N,N’-diacetylbacillosamine
C04573 UDP-N-acetyl-2-amino-2-deoxy-D-glucuronate
C00043 UDP-N-acetyl-alpha-D-glucosamine
C22053 UDP-N-acetyl-alpha-D-muramoyl-L-alanyl-L-glutamate
C00203 UDP-N-acetyl-D-galactosamine
C01050 UDP-N-acetylmuramate
C01212 UDP-N-acetylmuramoyl-L-alanine
C00692 UDP-N-acetylmuramoyl-L-alanyl-D-glutamate
C05892 UDP-N-acetylmuramoyl-L-alanyl-gamma-D-glutamyl-L-lysine
C00105 UMP
C01968 Undecaprenol
C05893 Undecaprenyl-diphospho-N-acetylmuramoyl-(N-acetylglucosamine)-L-alanyl-gamma-D-glutamyl-L-lysyl-D-alanyl-D-alanine
C00086 Urea
C01051 Uroporphyrinogen III
C00075 UTP
C00001 Water
C00655 Xanthosine 5′-phosphate
C00700 XTP
C00379 Xylitol

Transcribing Lab Results 2025 Update

This post deals with lab results that can not be uploaded for anyone of dozens of reasons. the current list is shown below. I am going to do a walkthru in 3 manners for the latest addition SynLab (EU):

Written Description
Video using a PC

Test Name	Bacteria Reported
All Bacteria [Family] Reported	128 Bacteria
All Bacteria [Genus] Reported	720 Bacteria
All Taxonomies from tests	178 Bacteria
Biomefx	79 Bacteria
Bioscreen (cfu/gm)	30 Bacteria
Biovis Microbiome Plus (cfu/g)	40 Bacteria
Chuckling Goat	40 Bacteria
DayTwo	76 Bacteria
Diagnostic Solution GI-Map (cfu/gm)	72 Bacteria
Estudio de Disbiosis: Intestinal + Parasitos	13 Bacteria
GanzImmun Diagnostic A6 (cfu/gm)	76 Bacteria
GanzImmun Diagnostics AG Befundbericht	25 Bacteria
Genova Gi Effects (cfu/g)	69 Bacteria
Genova Parasitology (cfu/g)	7 Bacteria
GI EcologiX (Invivo)	55 Bacteria
GI360 Stool (UK)	66 Bacteria
Gut Zoomer (vibrant-wellness)	152 Bacteria
HealthPath	60 Bacteria
InVitaLab (cfu/gm)	23 Bacteria
Kyber Kompakt (cfu/g)	11 Bacteria
Laboratorio Teletest	202 Bacteria
Medivere Mikrobiom Plus Stuhlanalyse	55 Bacteria
Medivere: Darm Mikrobiom Stuhltest (16s limited)	16 Bacteria
Medivere: Darn Magen Diagnostik (16s Limited)	16 Bacteria
Medivere: Gesundsheitscheck Darm (16s Limited)	17 Bacteria
Metagenomics Stool (De Meirleir) (16s Limited)	53 Bacteria
Microbiome Healthpath Maxi	73 Bacteria
MyBiota (Austria)	133 Bacteria
Nordic Laboratories	56 Bacteria
NutriPATH	51 Bacteria
Randox Health	33 Bacteria
Smart Gut (16s – Limited Taxonomy)	23 Bacteria
SynLab (EU)	111 Bacteria
Tarmkollen Mega	50 Bacteria
TinyHealth	140 Bacteria
Verisana (cfu/ml) aka (kbe/ml)	11 Bacteria
Viome (Latest Reports Fail to provide ANY measurements)	30 Bacteria

Written Description

The usual best practice is to PRINT the report from the lab and have a pen handy.

First step is to go thru and circle the high and low.

For High, if you are more than DOUBLE the high of the range, put 2 up arrow
For Low, if you are less than HALF the high of the range, put 2 up arrow

The next step can become a little confusing because the same bacteria may have multiple names – your lab uses one, Microbiome Prescription uses another name. We use the standardized names from the NCBI Taxonomy Browser because those names are used by most labs.

To help resolve this issue, we often list the bacteria in the same sequence as on the report.

Some Reports will list one bacteria at many places which can add to confusion
Some bacteria do not have matches…
- CAG names are produced by an alternative naming schema that do not map to any NCBI Ids
- Often strains are given, since they do not precisely match, we ignore them and go with the species or genus instead (“closest match”)
- For some genus, the alternative schema breaks things down into _A, _B, _C, _D subgroups. We ignore those
- Since we are entering ONLY abnormal, then use an that are abnormal when there are many to choose from!

We also give some of the alternative names to the right side. If it is a 2 part name, the second part is usually key to making a match

Next we indicate whether the lab says too high or too low. If normal, do nothing. This is made easier if you have a printed copy that has been marked up.

Once you finished entering the data, fill in the bottom. and then clock Do Analysis. You do not need to enter any emails if you wish, but if you want to explore options later, it saves having to re-enter the data.

The Do Analysis will take you to a page to select what type of modifiers you want, etc.

Video using a PC

This is a long video (40 minutes) that does the entire long test results.

Bacteria Shifts that are Statistically Significant for Mast Cell Issues

We have two self reported symptoms with sufficient samples to explore associations:

Comorbid: Histamine or Mast Cell issues
Official Diagnosis: Mast Cell Dysfunction

I have done simplified tables below. One item that was very interesting is that some Bifidobacterium was too high and others too low. Of the four low bacteria, only Bifidobacterium breve is available commercially. Low Lactobacillus was not reported anywhere and high Lactobacillales is reported

Too High
- Bifidobacteriaceae
- Bifidobacteriales
- Bifidobacterium
- Bifidobacterium adolescentis
- Bifidobacterium adolescentis JCM 15918
- Bifidobacterium angulatum
- Bifidobacterium gallicum
Too Low
- Bifidobacterium breve
- Bifidobacterium catenulatum PV20-2
- Bifidobacterium catenulatum subsp. kashiwanohense
- Bifidobacterium cuniculi

Everything below is P < 0.005 (or 1 in 200 of happening at random).

Official Diagnosis: Mast Cell Dysfunction

Biomesight

Bacteria	Rank	Shift
Anaerofustis	genus	Too High
Anaerofustis stercorihominis	species	Too High
Luteibacter	genus	Too Low
Luteibacter anthropi	species	Too Low

Ombre

Bacteria	Rank	Shift
Actinomycetes incertae sedis	no rank	Too High
Comamonadaceae	family	Too High
Deinococci	class	Too High
Deinococcota	phylum	Too High
Desulfocella	genus	Too High
Desulfocella halophila	species	Too High
Emticicia	genus	Too High
Hungateiclostridiaceae	family	Too High
Hungateiclostridium	genus	Too High
Limosilactobacillus	genus	Too High
Limosilactobacillus fermentum	species	Too High
Listeria	genus	Too High
Listeriaceae	family	Too High
Methylococcaceae	family	Too High
Methylococcales	order	Too High
Microbacter	genus	Too High
Neisseriaceae	family	Too High
Neisseriales	order	Too High
Oscillatoriales incertae sedis	no rank	Too High
Paracoccaceae	family	Too High
Pseudoscillatoria	genus	Too High
Pseudoscillatoria coralii	species	Too High
Rickettsia	genus	Too High
Slackia heliotrinireducens	species	Too High
Sphingobacterium	genus	Too High
Staphylococcus	genus	Too High
unclassified Burkholderiales	family	Too High
unclassified Clostridiales	family	Too High
Varibaculum	genus	Too High

Comorbid: Histamine or Mast Cell issues

We have a lot more annotated samples on this self-reported symptoms. There is fuzziness between a pure histamine issue and a mast cell issue

Ombre

Bacteria	Rank	Shift
Absiella tortuosum	species	Too High
Actinomycetes incertae sedis	no rank	Too High
Actinopolysporales	order	Too High
Agaribacter	genus	Too High
Agaribacter marinus	species	Too High
Anaeromicropila	genus	Too High
Anaeromicropila populeti	species	Too High
Blastocatellia	class	Too High
Cerasicoccus frondis	species	Too High
Clostridium grantii	species	Too High
Comamonadaceae	family	Too High
Cryomorphaceae	family	Too High
Deinococci	class	Too High
Deinococcota	phylum	Too High
Desulfitobacteriaceae	family	Too High
Desulfitobacterium	genus	Too High
Desulfobacteriaceae	family	Too High
Desulfocella	genus	Too High
Desulfocella halophila	species	Too High
Desulfofarcimen acetoxidans	species	Too High
Desulfosporosinus	genus	Too High
Desulfuromonadaceae	family	Too High
Desulfuromonadia	class	Too High
Emticicia	genus	Too High
Fusibacter	genus	Too High
Gammaproteobacteria incertae sedis	no rank	Too High
Halopolyspora	genus	Too High
Halopolyspora alba	species	Too High
Holdemania massiliensis	species	Too High
Hydrogenibacillus	genus	Too High
Hydrogenibacillus schlegelii	species	Too High
Limosilactobacillus	genus	Too High
Limosilactobacillus fermentum	species	Too High
Listeria	genus	Too High
Listeriaceae	family	Too High
Mesomycoplasma conjunctivae	species	Too High
Methylococcaceae	family	Too High
Microbacter	genus	Too High
Microbacter margulisiae	species	Too High
Mzabimycetaceae	family	Too High
Neisseriaceae	family	Too High
Neisseriales	order	Too High
Nostocales	order	Too High
Odoribacter laneus	species	Too High
Oscillatoriales incertae sedis	no rank	Too High
Oscillibacter valericigenes	species	Too High
Paracoccaceae	family	Too High
Parasporobacterium	genus	Too High
Pedobacter	genus	Too High
Planctomycetales	order	Too High
Planctomycetia	class	Too High
Planctomycetota	phylum	Too High
Pontibacillus	genus	Too High
Pontibacillus halophilus	species	Too High
Porphyromonas somerae	species	Too High
Propioniferax	genus	Too High
Propioniferax innocua	species	Too High
Proteinivorax tanatarense	species	Too High
Pseudoramibacter	genus	Too High
Pseudoramibacter alactolyticus	species	Too High
Pseudorhodobacter	genus	Too High
Pseudoscillatoria	genus	Too High
Pseudoscillatoria coralii	species	Too High
Rhodocyclaceae	family	Too High
Rhodocyclales	order	Too High
Rickettsia	genus	Too High
Rickettsiaceae	family	Too High
Rickettsiales	order	Too High
Rickettsieae	tribe	Too High
Saccharofermentans	genus	Too High
Saccharofermentans acetigenes	species	Too High
Sedimentibacter	genus	Too High
Sphingobacterium	genus	Too High
spotted fever group	species group	Too High
Stackebrandtia nassauensis	species	Too High
Stomatobaculum	genus	Too High
Texcoconibacillus	genus	Too High
Texcoconibacillus texcoconensis	species	Too High
Thiohalobacter	genus	Too High
Thiohalobacter thiocyanaticus	species	Too High
Thiohalobacteraceae	family	Too High
Thiohalobacterales	order	Too High
Thiohalorhabdaceae	family	Too High
Thiohalorhabdales	order	Too High
Verrucomicrobiaceae	family	Too High
Weeksellaceae	family	Too High

Biomesight

Bacteria	Rank	Shift
Acidaminococcus	genus	Too Low
Acidaminococcus fermentans	species	Too Low
Actinomycetes	class	Too High
Actinomycetota	phylum	Too High
Amedibacillus	genus	Too High
Amedibacillus dolichus	species	Too High
Anaerobranca	genus	Too High
Anaerobranca zavarzinii	species	Too High
Anaerolinea	genus	Too High
Anaerolinea thermolimosa	species	Too High
Anaerolineaceae	family	Too High
Anaerolineales	order	Too High
Anaerotruncus	genus	Too Low
Anaerotruncus colihominis	species	Too Low
Archaea	superkingdom	Too Low
Atopobium fossor	species	Too Low
Azoarcus	genus	Too High
Bacteroidaceae	family	Too Low
Bacteroides	genus	Too Low
Bacteroides acidifaciens	species	Too Low
Bacteroides cellulosilyticus	species	Too Low
Bacteroides fluxus	species	Too Low
Bacteroides uniformis	species	Too Low
Bifidobacteriaceae	family	Too High
Bifidobacteriales	order	Too High
Bifidobacterium	genus	Too High
Bifidobacterium adolescentis	species	Too High
Bifidobacterium adolescentis JCM 15918	strain	Too High
Bifidobacterium angulatum	species	Too High
Bifidobacterium breve	species	Too Low
Bifidobacterium catenulatum PV20-2	strain	Too Low
Bifidobacterium catenulatum subsp. kashiwanohense	subspecies	Too Low
Bifidobacterium cuniculi	species	Too Low
Bifidobacterium gallicum	species	Too High
Bilophila	genus	Too Low
Bilophila wadsworthia	species	Too Low
Blautia	genus	Too Low
Caloramator mitchellensis	species	Too High
Candidatus Tammella caduceiae	species	Too High
Catenibacterium	genus	Too High
Catenibacterium mitsuokai	species	Too High
Cetobacterium	genus	Too High
Chloroflexota	phylum	Too High
Coprococcus	genus	Too High
Coprococcus eutactus	species	Too High
Coraliomargarita	genus	Too High
Coraliomargarita	genus	Too Low
Coraliomargarita akajimensis	species	Too High
Coraliomargarita akajimensis	species	Too Low
Coraliomargaritaceae	family	Too High
Coraliomargaritaceae	family	Too Low
Deferribacter	genus	Too High
Deferribacter autotrophicus	species	Too High
Deferribacteraceae	family	Too High
Deferribacterales	order	Too High
Deferribacteres	class	Too High
Deferribacterota	phylum	Too High
Desulfitobacterium	genus	Too Low
Desulfomonilaceae	family	Too High
Desulfomonilales	order	Too High
Desulfomonilia	class	Too High
Desulforamulus	genus	Too High
Ectothiorhodospira imhoffii	species	Too High
Entomoplasmataceae	family	Too Low
Entomoplasmatales	order	Too Low
Eubacterium limosum	species	Too High
Euryarchaeota	phylum	Too Low
Faecalibacterium	genus	Too High
Fusobacterium nucleatum	species	Too High
Hathewaya	genus	Too Low
Hathewaya histolytica	species	Too Low
Helicobacter	genus	Too High
Helicobacter	genus	Too Low
Helicobacteraceae	family	Too High
Helicobacteraceae	family	Too Low
Holdemanella	genus	Too High
Holdemanella biformis	species	Too High
Holdemania	genus	Too Low
Hoylesella loescheii	species	Too High
Hyphomicrobiales	order	Too High
Hyphomicrobiales	order	Too Low
Johnsonella	genus	Too Low
Johnsonella ignava	species	Too Low
Lachnobacterium	genus	Too High
Lactobacillales	order	Too High
Lactococcus	genus	Too High
Limosilactobacillus	genus	Too Low
Luteibacter	genus	Too High
Luteibacter anthropi	species	Too High
Lysobacter deserti	species	Too High
Mesoplasma	genus	Too Low
Mesoplasma entomophilum	species	Too Low
Methanobacteria	class	Too Low
Methanobacteriaceae	family	Too Low
Methanobacteriales	order	Too Low
Methanobrevibacter	genus	Too Low
Methanobrevibacter smithii	species	Too Low
Methanomada group	clade	Too Low
Mogibacterium vescum	species	Too High
Mollicutes	class	Too High
Mycobacteriaceae	family	Too High
Mycobacterium	genus	Too High
Mycoplasmatota	phylum	Too High
Myxococcales	order	Too High
Myxococcia	class	Too High
Myxococcota	phylum	Too High
Natranaerobiales	order	Too High
Pedobacter	genus	Too Low
Phascolarctobacterium faecium	species	Too Low
Phocaeicola	genus	Too Low
Phocaeicola massiliensis	species	Too High
Phocaeicola paurosaccharolyticus	species	Too Low
Polyangia	subclass	Too High
Prevotella dentasini	species	Too High
Prevotellaceae	family	Too High
Prosthecobacter	genus	Too High
Proteinivoraceae	family	Too High
Ruminococcus callidus	species	Too High
Schaalia naturae	species	Too High
Segatella	genus	Too High
Segatella copri	species	Too High
Segatella paludivivens	species	Too High
Shewanella upenei	species	Too High
Slackia	genus	Too High
Slackia isoflavoniconvertens	species	Too Low
Sphingobium	genus	Too High
Sutterella stercoricanis	species	Too High
Syntrophales	order	Too High
Syntrophia	class	Too High
Syntrophomonadaceae	family	Too High
Thermus	genus	Too High
Thiothrix ramosa	species	Too High

Bottom Line

The above data will eventually be incorporated into the expert system suggestions on Microbiome Prescription.

Bacteria Shifts that are Statistically Significant for Long COVID

The process is very simple, for a condition like ME/CFS, we compute the expected number of samples reporting this bacteria (based on people without Long COVID) and compare it to the actual number seen. This can be used to compute a statistical value called Chi-Square (χ²), This is then used to compute the chance of it happening at random. This is possible because we have over 3600 samples from some labs and thus able to detect things better.

Actual example:

Tetragenococcus halophilus – Species reported by Biomesight
- Expected to see 15
- Actually seen 59
In other words almost 4x more common than expected. The probability is
- 1.68054690853052E-30
- or 1 chance in 600,000,000,000,000,000,000,000,000,000 of happening at random.
This suggests that we should reduce it to remedy Long COVID [with the other 92 bacteria involved]
- Bacteria Shifts that are Statistically Significant for ME/CFS

Biomesight and Ombre identifies bacteria using different methodologies so often give different names and amounts. For background on this lack of standardization, see The taxonomy nightmare before Christmas…

The data below is for samples marked with “Official Diagnosis: COVID19 (Long Hauler)”. Only Biomesight had sufficient data to get patterns.

Long COVID appears similar to ME/CFS, so comparing results below to those in this post: Bacteria Shifts that are Statistically Significant for ME/CFS, may provide further insight.

Unlike some conditions shown below, it is not just one bacteria involved but combinations.

Peptic ulcer disease: Helicobacter pylori
Tetanus: Clostridium tetani
Typhoid fever: Salmonella typhi
Diphtheria: Corynebacterium diphtheriae
Syphilis: Treponema pallidum
Cholera: Vibrio cholerae
Leprosy: Mycobacterium leprae
Tuberculosis: Mycobacterium tuberculosis
Sinusitis: Corynebacterium tuberculostearicum

Biomesight Data

We have more data from Biomesight which means better (more) detection of significant bacteria. The data is very different from ME/CFS. We have 16 bacteria too high and 61 bacteria too low. With ME/CFS and the same lab, we have 12 bacteria that are too low and 116 bacteria that are too high.

We have some commonalities

Bifidobacterium adolescentis is too low for both Long COVID and ME/CFS
Lactobacillus crispatus is too high
Another probiotic genus, Lactococcus, is also too high

Tax_Name	Tax_Rank	Expected	Observed	Shift	Probability
50 kb inversion clade	clade	77.3	54	Too Low	0.008002
Acinetobacter antiviralis	species	13.7	24	Too High	0.00524
Acinetobacter johnsonii	species	18.1	30	Too High	0.004944
Actinopolyspora	genus	62.3	35	Too Low	0.001477
Actinopolysporaceae	family	62.3	35	Too Low	0.001477
Actinopolysporales	order	62.3	35	Too Low	0.001477
Aeromonadaceae	family	81.8	57	Too Low	0.006169
Alkalibacterium	genus	112.5	81	Too Low	0.005041
Anaerococcus lactolyticus	species	23.2	38	Too High	0.002205
Anaerococcus prevotii	species	20.1	33	Too High	0.003987
ant, tsetse, mealybug, aphid, etc. endosymbionts	clade	82.7	58	Too Low	0.006624
Bifidobacterium adolescentis	strain	103.5	65	Too Low	0.002509
Chromatium	genus	61.3	34	Too Low	0.00355
Chromatium weissei	species	61.2	34	Too Low	0.00355
Chromobacterium group	no rank	15.3	26	Too High	0.006127
Citrobacter	genus	64.1	41	Too Low	0.003939
Clostridium neonatale	species	13.7	25	Too High	0.002196
Cohnella	genus	108.6	78	Too Low	0.005067
Coraliomargarita	genus	96.4	70	Too Low	0.00718
Coraliomargarita akajimensis	species	96.3	70	Too Low	0.007357
core genistoids	clade	77.3	54	Too Low	0.008002
Corynebacterium striatum	species	16.9	28	Too High	0.006887
Crotalarieae	tribe	77.3	54	Too Low	0.008002
Deferribacteraceae	family	98.2	71	Too Low	0.006129
Deferribacterales	order	98.2	71	Too Low	0.006129
Deferribacteres	class	98.2	71	Too Low	0.006129
Deferribacterota	phylum	98.2	71	Too Low	0.006129
Desulfallaceae	family	148.6	108	Too Low	0.001472
Enterobacter cloacae complex	species group	86.4	60	Too Low	0.004516
Enterobacter hormaechei	species	85.4	57	Too Low	0.002134
Enterobacteriaceae incertae sedis	no rank	82.7	58	Too Low	0.006624
Erysipelothrix inopinata	species	54.2	21	Too Low	4.45E-05
Fabaceae	family	77.3	54	Too Low	0.008002
Fabales	order	77.3	54	Too Low	0.008002
fabids	clade	77.3	54	Too Low	0.008002
genistoids sensu lato	clade	77.3	54	Too Low	0.008002
Granulicella	genus	16.4	29	Too High	0.001841
Granulicella tundricola	species	16.2	29	Too High	0.00148
Hallella bergensis	species	20.1	33	Too High	0.003987
Lactobacillus crispatus	species	26.5	43	Too High	0.001406
Lactococcus	genus	161.5	201	Too High	0.001877
Leptospira	genus	89.5	61	Too Low	0.002559
Leptospira licerasiae	species	89.4	61	Too Low	0.002701
Leptospiraceae	family	89.5	61	Too Low	0.002559
Leptospirales	order	89.5	61	Too Low	0.002559
Lysinibacillus	genus	51.5	32	Too Low	0.006618
Lysinibacillus parviboronicapiens	species	50.4	29	Too Low	0.002564
Macrococcus	genus	118.9	89	Too Low	0.006111
Microbacteriaceae	family	99.5	72	Too Low	0.005912
Moorella group	norank	152.6	188	Too High	0.004132
Oxalobacter	genus	130.9	99	Too Low	0.005356
Oxalobacter vibrioformis	species	94.9	65	Too Low	0.007793
Papilionoideae	subfamily	77.3	54	Too Low	0.008002
Peptoniphilus lacrimalis	species	51.8	72	Too High	0.004884
Piscirickettsiaceae	family	51.5	29	Too Low	0.007262
Psychrobacter	genus	138.9	99	Too Low	0.001332
Psychrobacter glacialis	species	75.1	51	Too Low	0.00545
rosids	clade	77.3	54	Too Low	0.008002
Rothia	genus	77.3	54	Too Low	0.008002
Rothia mucilaginosa	species	64.1	40	Too Low	0.002631
Sporotomaculum	genus	148.6	108	Too Low	0.001472
Sporotomaculum syntrophicum	species	146.7	107	Too Low	0.001751
Streptococcus massiliensis	species	53.6	34	Too Low	0.007353
Syntrophobacteraceae	family	118.3	83	Too Low	0.00291
Tetragenococcus halophilus	species	18.0	59	Too High	3.63E-22
Thiomicrospira	genus	43.7	26	Too Low	0.007396
Tolumonas	genus	80.7	55	Too Low	0.004169
Tolumonas auensis	species	79.9	54	Too Low	0.003748
Trabulsiella	genus	59.1	37	Too Low	0.004074
Vagococcus	genus	99.2	72	Too Low	0.00718
Varibaculum cambriense	species	17.3	30	Too High	0.002302

Bottom Line

My personal view is that this pattern is not unexpected. ME/CFS microbiome is typically after years of the dysbiosis microbiome evolving. With Long COVID, we have the microbiome still trying to stabilize.

Bif. Adolescentis

And all Lactobacillus and Lactococcus probiotics should be avoided.

The above information will be eventually integrated into Microbiome Prescription suggestions expert system. The purpose is to first identify the bacteria of concern.

The following bacteria were reported by 2 or 3 of the ME/CFS analysis and the same shift seen with Long COVID.

Anaerococcus murdochii	species — sibling high in ME/CFS
Peptoniphilus lacrimalis	species – HIGH EVERYWHERE
Varibaculum	genus – HIGH EVERYWHERE

Varibaculum, particularly Varibaculum cambriense, has been identified as a potential pathogen associated with various human infections, especially in skin and soft tissues² ⁶. This anaerobic, gram-positive bacterium was first described in 2003 and has since been isolated from several clinical cases².

A new species, Varibaculum timonense, has been isolated from human stool samples, indicating that the genus Varibaculum may have a broader presence in the human microbiome than previously recognized³.

While Varibaculum species are not yet widely known pathogens, their isolation from various infection sites suggests they may play a more significant role in human health than currently understood. Further research is needed to fully elucidate the pathogenic potential and clinical importance of these bacteria.

A new set of Associations to Symptoms coming

The process is very simple, for a condition like Long COVID, we compute the expected number of samples reporting this bacteria (based on people without Long COVID) and compare it to the actual number seen. This can be used to compute a statistical value called Chi-Square (χ²), This is then used to compute the chance of it happening at random. This is possible because we have over 3600 samples from some labs and thus able to detect things better.

Actual example:

Tetragenococcus halophilus – Species reported by Biomesight
- Expected to see 15
- Actually seen 59
In other words almost 4x more common than expected. The probability is
- 1.68054690853052E-30
- or 1 chance in 600,000,000,000,000,000,000,000,000,000 of happening at random.
This suggests that we should reduce it to remedy Long COVID [with the other 92 bacteria involved]
- Bacteria Shifts that are Statistically Significant for ME/CFS
- Bacteria Shifts that are Statistically Significant for Long COVID

Not all symptoms have many bacteria associated

A few examples (using Biomesight data). All samples are P < 0.01 (1 in 1000)

Myalgia (pain)
- Mobiluncus — too high
- Peptoniphilus asaccharolyticus — too high
- Campylobacter ureolyticus — too high
Headaches
- Microbacterium — too high
- Anaerococcus hydrogenalis — too high
- Eubacterium limosum — too high
- Peptoniphilus asaccharolyticus — too high
Recurrent flu-like symptoms
- Sphingomonas — too high
- Chromatium — too high
- Chromatium weissei — too high
Excessive adrenaline
- unclassified Bacteroidetes Order II — too low
- Bifidobacterium adolescentis — too low. Implies that Bifidobacterium adolescentis probiotics may help
Difficulty reading
- Bifidobacterium indicum — too low
Upset stomach
- Streptococcus anginosus – too high
- Viridiplantae (kingdom) – green plants! – too high (how this shows up in results, I will leave to Biomesight to explain)
Tingling feeling
- Bifidobacterium indicum – too high
- Prevotella bivia – too low
Need to nap during each day
- Kushneria – too low
- Prevotella bivia – too low
Difficulty falling asleep
- Alkalithermobacter thermoalcaliphilus – too low
- Paraprevotella xylaniphila – too high
Absent-mindedness
- Corynebacterium aurimucosum — too low
- Streptococcus gordonii — too low
- Catenibacterium mitsuokai – too low
Mood swings
- Glaciecola – too high
Acne
- Mogibacterium vescum — too low
- Listeria — too low
- Listeria innocua — too low
- Mogibacterium vescum — too low
Dry Mouth
- Prevotella bivia — too low
- Prevotella disiens — too low
- Clostridium malenominatum — too low

The reasons that there may be few bacteria associated may originate in symptoms being self-declared and there is a wide variety of actual shifts.

Longevity and the microbiome

Just a quick note on some bacteria are associated with long life,

Bacteria	Shift	Sources
Sutterella	Low	1
Megamonas	Low	1,9
Oscillospira	High	2
Collinsella	High	2
Odoribacter	High	2
Turicibacter	High	2
Campylobacter	High	2
Anaerofustis	High	2
Faecalibacterium	Low	2
Burkholderiales	Low	2
Akkermansia muciniphila	High	3,4,6,9,11
Eggerthella lenta	High	3
B. uniformis	Low	3
Eubacterium rectale	High	3, Low 8,12
Methanobrevibacter smithii,	High	3,8,12
Escherichia coli	High	3,6,12
Faecalibacterium prausnitzii	Low	3,6,8,12
C. comes	Low	3
Bifidobacterium	High	4,6,12
Christensenellaceae	High	4,6, 12
Bifidobacterium adolescentis	High	8
Lactobacillus	High	9
Ruminococceae	High	12
Proteobacteria	High	12
Anaerotruncus colihominus	High	12
Porphyromonaceae	High	12
Rikenellaceae	High	12
Roseburia	High	12
Bacteroidetes	High	12

Sources

Other studies of interest

The Power of Environment: A Comprehensive Review of the Exposome’s Role in Healthy Aging, Longevity, and Preventive Medicine—Lessons from Blue Zones and Cilento 2025

Clinical Microbiome Issues: Questions from a Reader

A reader sent an email with some good questions. He is pleased with the results. Looking at repeat users of Microbiome Prescription (tried suggestions and came back within a year for more suggestion) is almost 69%. This suggests happy users.

That system works great!

Could you explain the main differences between the OLD UI and NEW UI? Sometimes the data doesn’t seem to match up well, and I’m unsure which one I should use.

I’d like to understand the symptoms sections better, as they look very different in both UIs. The old UI symptoms make much more sense for me.

I have a Biomesight test for a friend with many gut symptoms, but when I analyze the data, I’m not seeing much in terms of actionable recommendations for things to add or remove. I do see a little more in terms of statistical significance in the OLD UI. What would be the most accurate way to read the data in a case like this?

I primarily use the full consensus reporting feature in the database. Are there any advanced features or sections you think I should familiarize myself with?

What is the main difference between symptoms and medical conditions in the database? My understanding is that medical conditions are more supported by proven data, while symptoms are based more on self-reporting. Is that correct?

If possible, I’d love to see your workflow for analyzing a test.

Could you explain the main differences between the OLD UI and NEW UI? Sometimes the data doesn’t seem to match up well, and I’m unsure which one I should use.

The site evolves as I keep getting better insight on data and different ways of getting statistically significant data. In general, when I get a new insight it is added as a new feature while keeping the older approaches. The older approaches appear to work well, but I want to keep pressing forward finding “better ways”. Actually, the way may not be better for everyone — rather better for some cases. It is the classic “no algorithm works for every one”.

Using Monte Carlo Model that builds consensus suggestions, my hope is that these various approaches will net better suggestions.

I avoid dropping methods. It upsets some people. Also these older methods work well for some.

Suggestion: Use both and work with the Consensus

I’d like to understand the symptoms sections better, as they look very different in both UIs. The old UI symptoms make much more sense for me.

At present there are at least three different ways of forecasting symptoms. Most of the methods pick slightly different sets of bacteria with different weights. Forecasting symptoms depends on which regression / modelling is used. Some examples:

Over and Under Represented for Symptoms by Lab
Symptom Association Studies which can be based on many approaches
- Difference of Average or Difference of Median
- Chi-square values between top and bottom X%iles
- etc

I have in my backlog to test each of these methods to evaluate their ability forecast symptoms. This also require tuning each of these to try to get the best accuracy in forecasting. That is likely at least a month of work (once I get the cycles).

In short, different methods were tried to detect statistical significance using both parametric and non-parametric methods. When there were a sufficient number of bacteria found significant, then a forecaster is built.

When I get time to do comparison before forecasting accuracy, the number of choices will likely be reduced.

I have a Biomesight test for a friend with many gut symptoms, but when I analyze the data, I’m not seeing much in terms of actionable recommendations for things to add or remove. I do see a little more in terms of statistical significance in the OLD UI. What would be the most accurate way to read the data in a case like this?

IMHO, the most accurate is checking the symptoms they have and use that for suggestions. It is the most likely way to pick the significant bacteria to focus on.

I usually use the metabolites and enzymes approach to select probiotics. Typically this will be the same probiotics in suggestions but in a different order. I give the probiotics suggested based on KEGG data a higher value because the suggestions above are based on what has been studied (which tends to be erratic). The KEGG data is based on the DNA/RNA of the microbiome and far less sensitive to what has been studied in clinical studies.

I primarily use the full consensus reporting feature in the database. Are there any advanced features or sections you think I should familiarize myself with?

Nothing at the moment, On the [Changing Your Microbiome] under “Suggestions for building general consensus”. These are the four most promising methods. New methods will likely be added at the bottom of this list as they are added.

What is the main difference between symptoms and medical conditions in the database? My understanding is that medical conditions are more supported by proven data, while symptoms are based more on self-reporting. Is that correct?

Medical conditions are those reported in the literature — unfortunately every study uses different processing. If the same study samples processed through a different process, different bacteria will be found significant (See The taxonomy nightmare before Christmas…). These are “best efforts” selection when we do not have sufficient data for a condition or symptoms for the specific processing lab that you are using.

The “inhouse” associations are always done using data from the same processing lab, so the identification of the “lab named bacteria” are consistent. This is the most likely to pick the right bacteria (according to the lab). One major difference is that Medical conditions are often based on 30-60 samples alone. For Biomesight data, we often have 600 samples and thus better ability to identify.

If possible, I’d love to see your workflow for analyzing a test.

Detecting Dysbiosis using Chi-Square

A reader wrote:

After uploading my sample, it gave a chi-square score of 1116 (image attached). Does this warrant any change in treatment approach (just asking as most of the scores I’ve seen posted on your blog are below 100)?

The short answer is no. This indicate that dysbiosis is likely happening. It is likely that is already known (hence getting a test).

The Simple Logic

We look at different bacteria at the genus level. Naively, this should be the equivalent of having independent variables. For each bacteria, we get the percentile ranking (in terms of a reference population). The odds of any bacteria being in the 1-10%ile range is 1 in 10. The same applies to every other range and bacteria.

This becomes a simple statistics problems. We would expect every range to have about 10% of the genus in it. We can then calculate whether the actual distribution conforms to this expectation using Chi-Square. If there is no dysbiosis, we would expect the significance to be 0.95 or less. Many users have significance being 0.9999 or higher; that is, very strong indicator of dysbiosis.

In the above example, we have definite dysbiosis. We have a large number of bacteria that are too high percentile. We do not know the precise ones that are problematic, we have a list of possible bacteria that we would want to reduce.

Since we do not know the explicit bacteria to focus on (only a collection of candidates), we cannot generate suggestions explicitly from this information.

Technical Note: The Percentile is computed from those reporting some of each genus. The percentile could be done across all tests (i.e. not found included); that approach results in a much more complicated computation.

I view Chi-Square as a better alternative to Diversity Indices. Most diversity indices apply to only certain condition. IMHO, it is a more robust measure because it is based purely on statistics and uses a reference set.

The Shannon index has been studied in relation to:

Septic shock: A study found that low bacterial diversity (Shannon index <3.0) was associated with higher 28-day mortality rates in septic shock patients¹.
General health status: The Gut Microbiome Health Index (GMHI), which incorporates the Shannon index, was used to distinguish between healthy and non-healthy individuals across various conditions².
Parkinson’s disease: However, a study found that the Shannon index was not significantly associated with Parkinson’s disease or other neurological disorders⁶.

In microbiome studies, several diversity indices are frequently used to analyze the composition and structure of microbial communities. These indices can be broadly categorized into two types: alpha diversity (within-sample diversity) and beta diversity (between-sample diversity).

Alpha Diversity Indices

Shannon index: Measures both richness and evenness of species in a community.
Simpson’s index: Reflects the probability that two randomly selected individuals belong to different species.
Chao1 index: Estimates species richness, particularly useful for data sets skewed toward low-abundance classes.
Observed number of Amplicon Sequence Variants (ASVs): Counts the number of unique sequences in a sample.
Phylogenetic Diversity (PD): Considers the evolutionary relationships between species.
ACE (Abundance-based Coverage Estimator) index: Estimates species richness, accounting for rare species.

Beta Diversity Indices

Bray-Curtis dissimilarity: Considers both the presence/absence and abundance of species.
UniFrac:
- Unweighted UniFrac: Considers presence/absence of species and their phylogenetic relationships.
- Weighted UniFrac: Incorporates abundance information along with phylogenetic relationship.
Jaccard index: Measures the similarity between sample sets based on presence/absence of species.

These diversity indices provide different perspectives on microbial community structure and are often used in combination to gain a comprehensive understanding of microbiome diversity3 6.

Possible Paths to healing the Gut

A reader posted on Facebook:

Ken Lassesen Hi Ken, maybe you can explain this: based on my latest biomesight test, one suggestions recurs in most of the suggestions on MicrobiomePerscripitons.com: Sucralose.
Sucralose is not regarded as particularly beneficial for the gut or overall health, actually it is associated with leaky gut and can decrease the diversity of bacteria. But I guess it comes up as it can modulate certain bacteria short term in a way that can potentially be beneficial for me?

Common Paths Starting Points

I have seen the following being very common:

[A] You complain about symptoms and a friend speculate that you have X, for example “Acid Stomach”
[B] You see a medical professional, often a naturopath, and the say “You appear to have T” example, “Gluten Issues”
[C] You see a medical professional, who perform an extensive list of tests. These tests results precisely match a known condition. example: Heliobacter pylori causing peptic ulcer.
[D] You go the “self-serve” approach using microbiome tests and ‘heal thyself’. Borrowing from Hippocrates: ‘First do no harm‘, ‘Let food be thy medicine and medicine be thy food’, ‘Walking is the best medicine’ and ‘All diseases begin in the gut‘.
- Often this is the result of disappointment or non-availability of [A],[B] and [C].

Typical Treatment Path for [A]

This is usually done by following friends suggestions or random searching of the internet for solutions. In short, it is an influencer treatment plan. Sometimes these treatment will work; the majority of people will get short term relief at best, if any,

Typical Treatment Path for [B]

This is usually done by the medical professionals working off their clinical experience and suggesting what they perceived to work. This is rarely objective, rather subjective. Their decisions are based on their view through rose-color glasses.

A simple example: “Jill Muller came to see me and said she would follow my advice. She did not come for a follow-up appointment –hence my treatment advice worked!” Reality, Jill followed the advice and became much worse, she concluded that this medical practitioner does not know what they are talking about and went elsewhere. To the practitioner, the lack of more appointments is proof that their treatment plan worked very well.

Sometimes these treatment will work; the majority of people will get short term relief at best, if any,

Typical Treatment Path for [C]

This is usually done by the medical professionals working off their clinical experience influenced by clinical studies and pharmaceutical sales representatives. For many conditions, these treatment will work to either cure or slow progression. These practitioner knows exactly what their target is. There can often be failure or less than desired progress because the current body of approved treatments is insufficient.

Two examples that I am personally familiar with are Mast Cell Activation Syndrome and Crohn’s Disease. Many other conditions like Autism, Depression, Anxiety, Alzheimer’s Disease, etc.

As with all of the above, when the treatment fails or is insufficient, path [D] is often taken

Typical Treatment Path for [D]

Following Hippocrates, All diseases begin in the gut. The problem is that despite having microbiome test results, we do not have clarity on what the target is.

From personal experience, I took a uBiome test and downloaded their FASTQ file and then processed it through:

Thryve/Ombre
BiomeSight
Sequentia Biotech

I got 4 sets of interpretations of this digital microbiome sample. This was not taking 4 samples and sending it to these labs (hence differences could be ascribed to where the sample was taken in the stool), but one sample, processed into a single digital description and then processed.

There was less than 5% agreement on which bacteria were out of range. In one case, a bacteria was too high according to one lab and too log according to another lab. For more information see: The taxonomy nightmare before Christmas…

Influencer versus Single Study of Single Bacteria versus Holistic

I view an influencer as a person who looks at a single microbiome test (from random lab) and say “It looks like …. and you should take ….”. The basis of the recommendation is often internet legend. When an influencer is asked to provide studies supporting their recommendation, there is typically no answer. IMHO, quod erat demonstrandum on this being internet legend.

A medical professional, often a naturopath, or a well-educated self-learner may provide a study supporting their suggestions. Often this study will show that one and only one bacteria is changed by their suggestions. If you ask these people for a study showing that the same suggestions will help a different bacteria that is out of reference ranges, it is very unlikely that they can provide one.

I term this as the “bacteria tunnel vision” version.

The Holistic or Microbiome Prescription approach is to accept two things:

Reference Ranges are usually suspect from labs (see Using Ranges based on Averages is BOGUS) and compute ranges in a more valid way.
Decisions should be based on all available studies (at present 98,841direct citations generating 2,301,058 expert system rules)
- This volume of data to consider require software and not brain-ware

The suggestions generated should always be reviewed by a knowledgeable and experienced medical professional before doing. Dosages are often a critical factor (more is not better always). There can be significant medical conditions that would exclude some suggestions. The suggestions are not “this is a protocol”, rather this is a list of things that are more likley to help than hurt — do whatever you feel comfortable with.

Returning to the question

To speed an accurate response, I used Perplexity and ask for studies.

Perplexity on sucralose consumption and leaky gut

From perplexity:

Recent studies have shown a potential association between sucralose consumption and leaky gut syndrome, although the evidence is not yet conclusive from clinical studies in humans.

Laboratory and Animal Studies

Several laboratory and animal studies have demonstrated concerning effects of sucralose on gut health:

A study led by North Carolina State University found that sucralose and its metabolite sucralose-6-acetate damaged the junctions between intestinal cells, potentially causing leaky gut1 7.
In mice, sucralose administration for 6 months altered gut microbiome composition and increased pro-inflammatory gene expression in the liver, which could contribute to inflammation and gut permeability4.
Another mouse study showed that even low doses of sucralose altered gut microbiome composition, affecting intestinal barrier function8.

Human Studies

While direct clinical evidence in humans is limited, some studies suggest potential effects:

A randomized, placebo-controlled study found that acute intake of sucrose, but not sucralose, resulted in post-prandial endotoxemia (increased blood endotoxin levels). However, this study did not directly measure gut permeability5.
Researchers have called for revisiting the safety and regulatory status of sucralose based on its potential genotoxicity and effects on gut health7.

It’s important to note that more comprehensive clinical studies in humans are needed to definitively establish the link between sucralose consumption and leaky gut syndrome. The current evidence, while concerning, is primarily based on laboratory and animal studies, which may not directly translate to human health outcomes.

Look at “acute intake of sucrose, but not sucralose” above. Microbiome Prescription does not give dosages except for a few items, and those are references to clinical studies only. My take would be moderate intake, not excessive dosages. The appropriate dosage should be coming from your knowledgeable and experienced medical professional

Can sucralose decrease the diversity of bacteria, with studies

From perplexity:

Yes, studies have shown that sucralose can decrease the diversity of gut bacteria:

A 2022 study on mice found that sucralose administration altered gut microbiome diversity. The number of bacterial features and Shannon index (a measure of diversity) showed a downward trend in groups given higher doses of sucralose compared to the control group1.
Research on rats demonstrated that a 1.5 mg/mL concentration of sucralose led to the lowest α-diversity in gut microbiota, indicating a reduction in bacterial diversity1.
A human study conducted over 10 weeks showed that sucralose consumption altered the abundance of certain bacterial phyla, specifically increasing Firmicutes while not affecting Actinobacteria or Bacteroidetes. It also led to a 3-fold increase in Blautia coccoides and a 0.66-fold decrease in Lactobacillus acidophilus2.
A 6-month study on mice revealed that sucralose altered the gut microbiome composition, affecting 14 different bacterial genera over time compared to the control group5.

While these studies provide evidence for sucralose’s impact on gut bacterial diversity, it’s important to note that more comprehensive clinical studies in humans are needed to fully understand the long-term effects of sucralose on gut microbiota diversity.

I agree that it changes bacteria — that is what the goal is. Again note “higher doses of sucralose“. The bacteria changed are listed here.

What is the appropriate dosage

Again from perplexity with reference. If you keep to these limits, you should be fine.

The healthy daily amount of Splenda (sucralose) is determined by regulatory bodies based on extensive safety studies. Here are the key points regarding the recommended daily intake:

The U.S. Food and Drug Administration (FDA) has established an Acceptable Daily Intake (ADI) for sucralose of 5 mg per kilogram of body weight per day3 7.
The European Food Safety Authority (EFSA) and the Joint FAO/WHO Expert Committee on Food Additives (JECFA) have set a higher ADI of 15 mg per kilogram of body weight per day1 2.
These ADI levels are considered conservative, representing an amount 100 times less than the quantity found to have no observed adverse effects in toxicology studies3.

To put this into perspective:

For a 150-pound (68 kg) person, the FDA’s ADI would be equivalent to consuming about 340 mg of sucralose daily3.
This translates to approximately 23 individual packets of Splenda per day, well above typical consumption levels3 7.

It’s important to note that:

Current estimated intake levels are well below these ADIs. A conservative mean estimate of sucralose intake from beverages among adults in the U.S. is about 1.6 mg/kg of body weight per day3.
The ADI applies to all population groups, including children, pregnant women, and individuals with medical conditions1.

While these guidelines suggest that Splenda is safe when consumed within these limits, recent recommendations from the World Health Organization (WHO) advise against the use of non-sugar sweeteners for weight control4. As with any dietary component, moderation is key, and individuals should consider their overall diet and health goals when consuming artificial sweeteners.

Bottom Line

A lot of the answers came from https://www.perplexity.ai/. I use this resource heavily to get summaries with references to data sources and then always check the source to make sure that this AI did not misread the study.

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Using Ranges based on Averages is BOGUS

To illustrate this, we use our collection of distinct microbiome samples processed through BiomeSight (N: 3656).

Species: Phocaeicola massiliensis

Basic Statistics;

Minimum: 0.001 %
Maximum: 89.1%
Median: 0.254%
Mean / Average: 7.6%
Mode: 12.4%
Standard Deviation: 14.6%
5 Percentile: 0.009%
95 Percentile: 43.7%
Harmonic Mean: 0.035%
Geometric Mean: 0.445%
Skew: 1.5
Kurtosis: 0.035

When we apply Stats Class 101 methods, we get:

Mean +/- 1.95 SD ==> (-21% to 36.2%)
Box-Plot-Whiskers ==> (-9.4%, 15.8%)

WAIT: Having negative amount of bacteria!!! That is absurd!

What we should see if data was normal

Wait, Mean, Median and Mode should be next door to each other!!!

What do we see when we chart this data. The charts are identical — NOT!

What should be used to compute range?

There are many better suited statistical methods. A few are:

Kolmogorov-Smirnov test
Kruskal-Wallis test
Wilcoxon signed-rank test
Mann-Whitney U test
Bothe/Z-scores
Median Absolute Deviation

My Preference: Patent Pending Kaltoft Moldrup Algorithm

The basis of it is doing a data transformation, then taking derivates to get an almost straight line. When the data leaves the line is where it is deemed to be abnormal. The following diagrams illustrates the process.

Example: Original Data

2nd derivative line

3rd derivate line

4th derivative line (where we see the desired straight line in purple)

An example with real data. Most of the abnormal data is at the bottom in this example

Another more complex example indicating more complexity in the bacteria behavior in situ of the microbiome.

Another example showing both high and low abnormal areas

Bottom Line

Many suggested ranges are based on mean and never tests if methods that apply to a normal distribution/ bell curve applies. A small number of ranges are based on percentiles, i.e. over 95%ile or below 5%ile. Using percentiles is better but as suggested by the last curves above, this does not suggest evidence of being abnormal.

The patent pending Kaltoft Moldrup Algorithm appears to identify abnormal values in the classic sense of abnormal. It does require significant mathematical and statistical skills.

High Level Overview

Bacteria

Enzymes

Metabolite Production

Metabolite Substrate

Metabolite Net

Data Accessibility

Examples of agreement

Written Description

Video using a PC

Official Diagnosis: Mast Cell Dysfunction

Biomesight

Ombre

Comorbid: Histamine or Mast Cell issues

Ombre

Biomesight

Bottom Line

Biomesight Data

Bottom Line

Not all symptoms have many bacteria associated

Sources

Could you explain the main differences between the OLD UI and NEW UI? Sometimes the data doesn’t seem to match up well, and I’m unsure which one I should use.

I’d like to understand the symptoms sections better, as they look very different in both UIs. The old UI symptoms make much more sense for me.

I primarily use the full consensus reporting feature in the database. Are there any advanced features or sections you think I should familiarize myself with?

What is the main difference between symptoms and medical conditions in the database? My understanding is that medical conditions are more supported by proven data, while symptoms are based more on self-reporting. Is that correct?

If possible, I’d love to see your workflow for analyzing a test.

The Simple Logic

Alpha Diversity Indices

Beta Diversity Indices

Common Paths Starting Points

Typical Treatment Path for [A]

Typical Treatment Path for [B]

Typical Treatment Path for [C]

Typical Treatment Path for [D]

Influencer versus Single Study of Single Bacteria versus Holistic

Returning to the question

Perplexity on sucralose consumption and leaky gut

Laboratory and Animal Studies

Human Studies

Can sucralose decrease the diversity of bacteria, with studies

What is the appropriate dosage

Bottom Line

Species: Phocaeicola massiliensis

What we should see if data was normal

What should be used to compute range?

My Preference: Patent Pending Kaltoft Moldrup Algorithm

Bottom Line

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