This is using data from the study being done with BiomeSight. We will only use their samples. After the first review, a z-score of 6.4 or higher (or a lots of items) was set as a cutoff point. The following ignore False Detection Rate.
- Conclusion: the ENZYME production of the microbiome is by far the strongest indicator.
- The reference set consists of 1037 heterogenous samples (i.e. no Long COVID, but a variety of medical conditions) and 154 samples with Long COVID
Taxon Patterns
Care needs to be taken with these numbers because the frequency of reporting on a bacteria is a factor that impacts the z-score. The data for this table is available at Citizen Science site and independent analysis is strongly recommended. This table is a simplified view of very complex data.
| tax_name | tax_rank | No Symptom Mean | Symptom Mean | Z-Score | Change |
| Terrabacteria group | clade | 715040 | 520885 | 10.4 | 73% |
| Firmicutes | phylum | 652452 | 502830 | 9.0 | 77% |
| Tenericutes | phylum | 2562 | 6362 | -7.9 | 248% |
| Eubacteriales | order | 609888 | 482468 | 7.9 | 79% |
| Mollicutes | class | 2562 | 6362 | -7.9 | 248% |
| Clostridia | class | 613743 | 487719 | 7.8 | 79% |
| Emticicia oligotrophica | species | 769 | 2553 | -6.8 | 332% |
| Faecalibacterium prausnitzii | species | 100292 | 142415 | -6.7 | 142% |
End Product Patterns
End products only had a single item above our 6.3 z-score threshold with a very small shift.
| EndProduct | No Symptom Mean | Symptom Mean | No Symptom StdDev | Change |
| H2 | 1329 | 1307 | 6.6 | 98% |
KEGG Enzyme Patterns
This is where we see a massive number of patterns(182!!) with very high z-scores (i.e. 6.4 or higher). This hints that the bacteria associated with these enzymes may be a good target to modify.
| EnzymeName | No Symptom Mean | Symptom Mean | No Symptom StdDev | Change |
| dihydrourocanate:acceptor oxidoreductase | 58562 | 147222 | -18.2 | 251% |
| (S)-3-hydroxy-3-methylglutaryl-CoA acetoacetate-lyase (acetyl-CoA-forming) | 55210 | 142006 | -18 | 257% |
| (1->4)-alpha-D-galacturonan reducing-end-disaccharide-lyase | 54601 | 139740 | -17.7 | 256% |
| acetyl-CoA:kanamycin-B N6′-acetyltransferase | 55382 | 140425 | -17.7 | 254% |
| acetyl-CoA:2-deoxystreptamine-antibiotic N3-acetyltransferase | 56590 | 141511 | -17.6 | 250% |
| poly(deoxyribonucleotide)-3′-hydroxyl:5′-phospho-poly(deoxyribonucleotide) ligase (ATP or NAD+) | 55562 | 141080 | -17.6 | 254% |
| D-serine ammonia-lyase (pyruvate-forming) | 55931 | 140065 | -17.6 | 250% |
| poly(deoxyribonucleotide)-3′-hydroxyl:5′-phospho-poly(deoxyribonucleotide) ligase (ATP, ADP or GTP) | 55562 | 141080 | -17.6 | 254% |
| alpha-maltose-6′-phosphate 6-phosphoglucohydrolase | 57944 | 142024 | -17.5 | 245% |
| ATP phosphohydrolase (ABC-type, iron(III) enterobactin-importing) | 57953 | 141331 | -17.4 | 244% |
| protein-Npi-phospho-L-histidine:D-mannose Npi-phosphotransferase | 66964 | 152717 | -17.4 | 228% |
| ATP phosphohydrolase (ABC-type, Fe3+-transporting) | 68676 | 154113 | -17.4 | 224% |
| D-psicose 3-epimerase | 70754 | 155871 | -17.2 | 220% |
| D-tagatose 3-epimerase | 70754 | 155871 | -17.2 | 220% |
| 2′-(5-triphosphoribosyl)-3′-dephospho-CoA:apo-[citrate (pro-3S)-lyase] 2′-(5-phosphoribosyl)-3′-dephospho-CoA-transferase | 77143 | 161549 | -17.1 | 209% |
| ATP:3′-dephospho-CoA 5-triphospho-alpha-D-ribosyltransferase | 78363 | 162298 | -17 | 207% |
| 2,4,6/3,5-pentahydroxycyclohexanone 2-isomerase | 75196 | 158863 | -16.9 | 211% |
| ATP:[protein]-L-tyrosine O-phosphotransferase (non-specific) | 60964 | 143510 | -16.9 | 235% |
| acetyl-CoA:citrate CoA-transferase | 79352 | 162680 | -16.7 | 205% |
| L-aspartate:tRNAAsx ligase (AMP-forming) | 63596 | 144560 | -16.7 | 227% |
| poly(deoxyribonucleotide)-3′-hydroxyl:5′-phospho-poly(deoxyribonucleotide) ligase (ATP) | 69642 | 156282 | -16.7 | 224% |
| penicillin amidohydrolase | 69734 | 151011 | -16.6 | 217% |
| protein-Npi-phospho-L-histidine:D-mannitol Npi-phosphotransferase | 57950 | 140690 | -16.5 | 243% |
| ATP:D-erythronate 4-phosphotransferase | 65433 | 145262 | -16.4 | 222% |
| acetate:holo-[citrate-(pro-3S)-lyase] ligase (AMP-forming) | 90668 | 176404 | -16.4 | 195% |
| ATP:D-threonate 4-phosphotransferase | 65433 | 145262 | -16.4 | 222% |
| D-aspartate:[beta-GlcNAc-(1->4)-Mur2Ac(oyl-L-Ala-gamma-D-Glu-L-Lys-D-Ala-D-Ala)]n ligase (ADP-forming) | 73487 | 157884 | -16.4 | 215% |
| 4-phospho-D-erythronate:NAD+ 3-oxidoreductase | 65773 | 145502 | -16.3 | 221% |
| 4-phospho-D-threonate:NAD+ 3-oxidoreductase | 65773 | 145502 | -16.3 | 221% |
| nucleoside-triphosphate diphosphohydrolase | 69217 | 153915 | -16.2 | 222% |
| 4-amino-5-aminomethyl-2-methylpyrimidine aminohydrolase | 75806 | 165018 | -15.7 | 218% |
| ATP:D-glycero-alpha-D-manno-heptose 7-phosphate 1-phosphotransferase | 81281 | 169414 | -15.7 | 208% |
| aryl-ester hydrolase | 77314 | 159122 | -15.6 | 206% |
| palmitoyl-CoA hydrolase | 76772 | 157265 | -15.4 | 205% |
| UDP-alpha-D-glucose:1,2-diacyl-sn-glycerol 3-alpha-D-glucosyltransferase | 91112 | 172382 | -15.4 | 189% |
| D-tagatose 1,6-bisphosphate D-glyceraldehyde-3-phosphate-lyase (glycerone-phosphate-forming) | 75959 | 152459 | -15.2 | 201% |
| ADP-alpha-D-glucose:alpha-D-glucose-1-phosphate 4-alpha-D-glucosyltransferase (configuration-retaining) | 63077 | 146386 | -15.1 | 232% |
| L-glutamate:tRNAGlx ligase (AMP-forming) | 97313 | 177576 | -14.5 | 182% |
| oligosaccharide 6-alpha-glucohydrolase | 96720 | 174292 | -14.3 | 180% |
| S-adenosyl-L-methionine:tRNA (adenine22-N1)-methyltransferase | 96117 | 168859 | -13.9 | 176% |
| alkylated-DNA glycohydrolase (releasing methyladenine and methylguanine) | 93342 | 182716 | -13.7 | 196% |
| sn-glycerol 3-phosphate:quinone oxidoreductase | 113940 | 189562 | -13.6 | 166% |
| L-iditol:NAD+ 2-oxidoreductase | 113731 | 190510 | -13.4 | 168% |
| (3S)-citryl-CoA oxaloacetate-lyase (acetyl-CoA-forming) | 108775 | 197009 | -13.3 | 181% |
| N-succinyl-LL-2,6-diaminoheptanedioate amidohydrolase | 88237 | 163157 | -13 | 185% |
KEGG Product
Products are the output of enzymes. Various enzymes may produce the same product. Our starting assumption was that products would have stronger association than enzymes. That was not shown in the data.
| CompoundName | No Symptom Mean | Symptom Mean | No Symptom StdDev | Change |
| Acetoacetate | 37878 | 55442 | -8.1 | 146% |
| Reduced electron-transferring flavoprotein | 106971 | 149551 | -6.9 | 140% |
| Dialkyl phosphate | 773 | 2553 | -6.8 | 330% |
| Indole-3-acetate | 773 | 2553 | -6.8 | 330% |
| Pseudouridine 5′-phosphate | 109418 | 150579 | -6.7 | 138% |
| 3-Hydroxy-3-(methylthio)propanoyl-CoA | 758 | 2494 | -6.7 | 329% |
| 3-Oxopropionyl-CoA | 758 | 2494 | -6.7 | 329% |
| N-Acetyl-beta-D-glucosaminylamine | 760 | 2473 | -6.7 | 325% |
| (2E,4Z)-2,4-Dienoyl-CoA | 68809 | 95899 | -6.6 | 139% |
| Short-chain trans-2,3-dehydroacyl-CoA | 103627 | 144711 | -6.6 | 140% |
| (2E,4E)-2,4-Dienoyl-CoA | 68809 | 95899 | -6.6 | 139% |
| 4-(4-Deoxy-alpha-D-gluc-4-enuronosyl)-D-galacturonate | 33961 | 47705 | -6.6 | 140% |
| 4-Hydroxyphenylglyoxylate | 113250 | 152269 | -6.5 | 134% |
| Oleoyl-[acyl-carrier protein] | 735 | 2376 | -6.5 | 323% |
| (4Z)-Hexadec-4-enoyl-[acyl-carrier protein] | 735 | 2376 | -6.5 | 323% |
| N6′-Acetylkanamycin-B | 34762 | 48298 | -6.5 | 139% |
| (6Z)-Hexadec-6-enoyl-[acyl-carrier protein] | 735 | 2376 | -6.5 | 323% |
| Pyocyanine | 751 | 2381 | -6.5 | 317% |
| (1E,3E)-4-Hydroxybuta-1,3-diene-1,2,4-tricarboxylate | 1430 | 4549 | -6.5 | 318% |
| Aldose | 764 | 2429 | -6.4 | 318% |
| Molybdoenzyme molybdenum cofactor | 119172 | 159672 | -6.4 | 134% |
| N3-Acetyl-2-deoxystreptamine antibiotic | 35939 | 49415 | -6.4 | 137% |
KEGG Substrate
Subtrate are the fuel for enzymes reaction. Various enzymes may consume the same compound. Our starting assumption was that substrate would have stronger association than enzymes. That was not shown in the data.
| CompoundName | No Symptom Mean | Symptom Mean | No Symptom StdDev | Change |
| Dihydrourocanate | 38026 | 55395 | -7.8 | 146% |
| (S)-3-Hydroxy-3-methylglutaryl-CoA | 34820 | 50269 | -7.4 | 144% |
| Electron-transferring flavoprotein | 106880 | 149551 | -6.9 | 140% |
| threo-3-Hydroxy-D-aspartate | 760 | 2532 | -6.9 | 333% |
| 3-(Methylthio)acryloyl-CoA | 757 | 2494 | -6.8 | 329% |
| 3-Hydroxy-3-(methylthio)propanoyl-CoA | 757 | 2494 | -6.8 | 329% |
| 3-Oxopropionyl-CoA | 757 | 2494 | -6.8 | 329% |
| ADP-sugar | 772 | 2553 | -6.8 | 331% |
| Aryl dialkyl phosphate | 772 | 2553 | -6.8 | 331% |
| beta-D-Mannose | 772 | 2553 | -6.8 | 331% |
| D-erythro-3-Hydroxyaspartate | 761 | 2532 | -6.8 | 333% |
| Pseudouridine | 105195 | 147050 | -6.8 | 140% |
| N4-(Acetyl-beta-D-glucosaminyl)asparagine | 759 | 2473 | -6.7 | 326% |
| Short-chain acyl-CoA | 103536 | 144711 | -6.7 | 140% |
| (2-Amino-1-hydroxyethyl)phosphonate | 752 | 2431 | -6.6 | 323% |
| trans-2,3-Dehydroacyl-CoA | 68745 | 95899 | -6.6 | 140% |
| (S)-4-Hydroxymandelate | 113176 | 152269 | -6.5 | 135% |
| 5-Methylphenazine-1-carboxylate | 750 | 2381 | -6.5 | 317% |
| Hexadecanoyl-[acp] | 1468 | 4753 | -6.5 | 324% |
| Kanamycin B | 34729 | 48298 | -6.5 | 139% |
| Octadecanoyl-[acyl-carrier protein] | 734 | 2376 | -6.5 | 324% |
| (1E)-4-Oxobut-1-ene-1,2,4-tricarboxylate | 739 | 2338 | -6.4 | 316% |
| 2-Deoxystreptamine antibiotic | 35916 | 49415 | -6.4 | 138% |
| Adenylated molybdopterin | 119083 | 159672 | -6.4 | 134% |
| Alditol | 763 | 2429 | -6.4 | 318% |
| beta-Carotene | 720 | 2314 | -6.4 | 321% |
| Molybdate | 119083 | 159672 | -6.4 | 134% |
Bottom Line
Several years ago, I hypothesized that a symptom or condition is the result of a coming together of many small deviations in individual bacteria representation. There may be 10 different combination of bacteria with none overlapping causing a symptom. The inspiration for this was observing the literature and experience of people with Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) — a sibling condition to Long COVID. This model is contrary to the common belief that there is a single or small number of items that is the cause. My looking at Brain fog (using same technique as above Brain Fog: Microbiome scents…) came up with nothing. That was not desired, but almost expected because that population is very heterogenous for cause with a long time since the triggering event for the microbiome to diverge from each other (often treatment attempts would be a factor). With long COVID, we have a short time since the triggering event and the people tend to be treatment naïve, This makes finding patterns a lot easier (when you look under the right rocks!).
Almost everything is overproduction. This may be caused by the immune system ramping up to provide fuel to fight COVID. The microbiome is stuck in an on-state, likely with cross talk between enzymes keeping it stuck on. The term of the Pasteur Institute for Tropical Medicine, “an occult infection” describes the behavior seen nicely.
Addressing the few microbiome shifts is one approach — but the enzymes dominate in both statistical significance and number of items, It is likely the best path to address the enzymes instead of individual bacteria.
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