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 tissues²⁶. 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.