An illness to a single bacteria is a standard medical practice. Helicobacter pylori for ulcer, or one of several E. Coli (E. coli O157:H7 and other STECs like E. coli O145 and E. coli O121:H19) for food poisoning.

If we expand our eyes and ask “What if the condition is caused by over or under production of some metabolite?”

For CFS over production of d-lactic acid has been proposed, see this post

• Approaches to D-Lactic Acidosis
• D-lactic Acidosis -Sauerkraut is not good for you if you have CFS!

Let us assume that this is part of it. So which bacteria produces it?

Our old friend DataPunk lists:

PRODUCED AS ENDPRODUCT BY: 

1. Aerococcus
2. Allobaculum
3. Anaerostipes
4. Atopobium
5. Bacillus coagulans
6. Bifidobacterium
7. Cardiobacteriales
8. Cardiobacterium
9. Carnobacterium
10. Catenibacterium
11. Enterococcus
12. Faecalibaculum
13. Faecalibaculum rodentium
14. Gemella
15. Haemophilus
16. Holdemania
17. Lachnobacterium
18. Lactobacillales
19. Lactobacillus
20. Lactobacillus acidophilus
21. Lactobacillus casei
22. Lactobacillus delbrueckii
23. Lactobacillus helveticus
24. Lactobacillus rhamnosus
25. Lactococcus
26. Leptotrichia
27. Leuconostoc
28. Microbacterium
29. Moryella
30. Oenococcus
31. Pediococcus
32. Rothia
33. Ruminococcus faecis
34. Scardovia
35. Serratia marcescens
36. Streptococcus
37. Tetragenococcus
38. Vagococcus

Let us suppose that we also have low or no butyrate – again datapunk lists:

PRODUCED AS ENDPRODUCT BY:

1. Agathobacter rectalis
2. Allobaculum
3. Anaerostipes
4. Anaerostipes hadrus
5. Anaerotruncus
6. Butyricicoccus
7. Butyricicoccus pullicaecorum
8. Butyricicoccus sp. ORNL_6EZ5-Gt_4_Pl1-35
9. Butyricicoccus sp. ORNL_6EZ5-Gt_6_Pl2-147
10. Butyricicoccus sp. ORNL_V42_E05
11. Butyricicoccus sp. ORNL_W42_C10
12. Butyricimonas
13. Catenibacterium
14. Christensenella
15. Cloacibacillus
16. Cloacibacillus porcorum
17. Clostridia
18. Clostridiales
19. Clostridium
20. Clostridium butyricum
21. Coprococcus
22. Defluviitalea
23. Eubacteriaceae
24. Eubacterium limosum
25. Eubacterium oxidoreducens
26. Faecalibacterium
27. Faecalibacterium prausnitzii
28. Flavonifractor
29. Flavonifractor plautii
30. Fusibacter
31. Fusobacterium
32. Lachnobacterium
33. Lachnospiraceae
34. Moryella
35. Moryella indoligenes
36. Oscillospira
37. Peptoniphilus
38. Roseburia
39. Roseburia faecis
40. Roseburia hominis
41. Roseburia intestinalis
42. Roseburia inulinivorans
43. Ruminococcus torques
44. Subdoligranulum

So how many possible “too much and not enough” pairs are there if we just pick just one from each list…  38 x 44 = 1,672!!! 

My current gut feeling is that taking the % of each taxonomy in each group –> producing a fuzzy measure of a 0.23% Lactic acid and 0.01% butyrate, may reveal clearer patterns for symptoms than the individual bacteria taxonomy. This is a fuzzy measure because the production of lactic acid and butyrate vary greatly from genus to genus.

Other options for stepping away from pine needles of individual bacteria to looking at tree as a whole include KEGG pathways,

KEGG pathways produce some tentative correlation for D-Arginine and D-ornithine metabolism in CFS/IBS in my earlier post.

Bottom Line

The same change of metabolites in the body can happen in many many different ways. Looking at the metabolites (even with fuzzy ‘punted’ data base on what is available) may produce better predictive results than just looking at individual bacteria.