Conventional medical science tend to think of one bacteria to one condition. These are known as Single-Bacterium Diseases.

Single-Bacterium Diseases

• Tuberculosis — caused by Mycobacterium tuberculosis​
• Diphtheria — caused by Corynebacterium diphtheriae​
• Cholera — caused by Vibrio cholerae​
• Leprosy (Hansen’s disease) — caused by Mycobacterium leprae​
• Whooping cough (Pertussis) — caused by Bordetella pertussis​
• Tetanus — caused by Clostridium tetani​
• Typhoid fever — caused by Salmonella typhi​
• Syphilis — caused by Treponema pallidum​
• Gonorrhea — caused by Neisseria gonorrhoeae​
• Lyme disease — caused by Borrelia burgdorferi​
• Gastric ulcer — caused by Helicobacter pylori​
• Strep throat — caused by Streptococcus pyogenes​
• Urinary tract infection — most commonly caused by Escherichia coli​
• Pneumonia — can be caused by Streptococcus pneumoniae​
• Meningitis — can be caused by Neisseria meningitidis (meningococcus), or Streptococcus pneumoniae​
• Bacterial vaginosis — often caused by Gardnerella vaginalis​

There are other conditions that could be cause by any one of several bacterium, but not bacterium cooperating with each other.

When we enter the world of microbiome dysbiosis, this simplicity disappears.

Case Study of number of bacterium associated with many symptoms

We return to our collection of 4,290 unique samples with 327 symptoms having statistical significant bacterium discussed in Significant Bacteria and Their Thresholds – Part 1. Restricting our data to associations with P < 0.01, the graph below shows the number of bacteria associated with each symptom.

My view is that symptoms arise from the metabolites produced by a specific combination of bacteria. Examining the data from KEGG: Kyoto Encyclopedia of Genes and Genomes, we see that some metabolites can be produced by hundreds of different bacterium. Some bacteria associated with a symptom may actually be due to secondary effects—reflecting shifts caused by other species—so distinguishing causal bacteria from merely correlated ones remains difficult.

A practical working hypothesis for reducing or eliminating a symptom is therefore to normalize the bacteria associated with it. A rational approach is to start with those that have the strongest association.

---

The Naïve Approach

A well-educated medical professional typically follows this reasoning:

1. Identify which bacteria are outside the normal range and linked to the patient’s symptoms.
2. Determine whether each bacterium is elevated or reduced.
3. Review substances known to influence these bacteria.
4. Recommend lifestyle or dietary changes based on those substances.

In practice, certain substances may be counter-indicated for other bacteria that are also out of range. This is often overlooked, as many professionals adopt a “find the first substances that address the bacterium shift” approach. This sometimes makes the patient worse.

Microbiome Prescription uses a manually curated database containing over 7.4 million relationships between substances and specific bacteria. Because of this depth, these potential conflicts are often identified and the risk of adverse effects is reduced.

A professional in this situation would reasonably expect to see a chart such as the one below for each of the bacterium associated with a symptom. The chart, table or other items giving a desired range of values.

With dozens of bacteria out of range, there is no clear objective ability to rank these bacteria by importance. There are a variety of speculative punts that could be tried:

• Rank them by the volume of bacteria
• Rank them by the deviation from the reference range, i.e.(value – mean)/standard deviation
• Rank them by any of many possible algorithms that could be tossed at this issue.

Turning the issue upside down

Let us take the concrete example promised in the earlier post: Long COVID. We have 538 samples with Long COVID in our population of 4,290 contributed samples. This is about 12% of the samples.

Filtering the associations to those bacterium with P < 0.0001; our highest priority or weight. We obtain the table below. While there are many bacteria, some are tightly related according to lineage:

Mycoplasmatota; Mollicutes; Anaeroplasmatales; Anaeroplasmataceae; Anaeroplasma

Taxon	tax_name	tax_rank
1737405	Tissierellales	order
626933	Odoribacter laneus	species
186332	Anaeroplasmatales	order
186333	Anaeroplasmataceae	family
85630	16SrX (Apple proliferation group)	species group
102260	16SrXV (Hibiscus witches’-broom group)	species group
47565	Candidatus Phytoplasma prunorum	species
2086	Anaeroplasma	genus

Filtering the associations to those bacterium with P < 0.001; we get a second table shown below. One of the bacteria is Lactobacillus jensenii, which is available as a probiotic (I have some in my fridge) — but we do not know if we want to increase or decrease this bacteria.

Taxon	tax_name	tax_rank
2330	Halanaerobium	genus
1381	Atopobium minutum	species
972	Halanaerobiaceae	family
724	Haemophilus	genus
194	Campylobacter	genus
28128	Prevotella corporis	species
72294	Campylobacteraceae	family
33037	Anaerococcus vaginalis	species
38288	Corynebacterium genitalium	species
42857	Moorella group	norank
45254	Dysgonomonas capnocytophagoides	species
45404	Beijerinckiaceae	family
47420	Hydrogenophaga	genus
102261	Candidatus Phytoplasma brasiliense	species
109790	Lactobacillus jensenii	species
89061	Weissella thailandensis	species
215579	Schlegelella	genus
382673	Syntrophomonas cellicola	species
386414	Hoylesella timonensis	species
1963360	Parachlamydiales	order
1853231	Odoribacteraceae	family

We could continue onwards and look at the 40 bacterium associated with P < 0..01 and 48 bacterium with P < .05. While potentially important, because of the lesser degree of association, we will ignore them here. My preference is always to favor highest probability and thus would only look at those in the above two tables.

Question: Is Lactobacillus Jensenii too high or too low

Some medical practitioners would hear the word “Lactobacillus” and immediately say “Take it” because they have a (questionable) belief that Lactobacillus will help everything! Is this the case here?

The Data for Lactobacillus Jensenii

The table below shows the data. The percentages have been transformed to percentiles for better presentation with a count of the occurrences at each. One of the first items some people will note is that this bacterium is not reported often; but there is enough data to get a P < 0.001 using Chi² . I disagree with the approach seen in some papers, to only examine very commonly reported bacterium.

%-ile Range	HasTotal	HasNotTotal
Not Present	344	3852
0.00	1	3
4.21	0	15
20.00	2	22
45.26	2	13
61.05	1	3
65.26	0	3
68.42	0	3
71.58	0	5
76.84	0	2
78.95	1	1
81.05	0	1
82.11	1	0
83.16	1	0
84.21	0	1
85.26	0	2
87.37	0	1
88.42	0	1
89.47	1	0
90.53	0	1
91.58	1	0
92.63	1	0
93.68	0	1
94.74	0	1
95.79	0	1
96.84	0	1
97.89	0	1
98.95	0	1
100.00	0	1

Doing a little more aggregation we get the table below. If a person has no L. Jensenii they have a 8% chance of having Long COVID, If there is any present, the odds increases 13% chance, a higher amount pushes it up to 17% (double the odds).

Conclusion: L. Jensenii probiotics are a definite to be avoided probiotic for Long COVID

%ile Range	HasTotal	HasNotTotal	Ratio
Not Present	344	3852	0.08
0.00	1	3	0.25
4.21	0	15	0
20.00	2	22	0.08
Over 20	9	44	0.17
Over All	12	84	0.13

Danger Will Robinson: Do not over simplify

Looking at another bacteria with P < 0.0001, we see charted below. The bacteria is commonly reported.

What is evident is that the association is range sensitive, and thus reference ranges:

• Below 17%ile is out of range
• Between 28%ile and 34%ile is out of range
• Over 60% is out of range

Many microbiologists would say that this does not make sense. At this point I should remind people of quorum sensing with bacteria.

Quorum sensing is a communication process that allows bacteria to sense and respond to their population density using chemical signaling molecules called autoinducers. Each bacterium produces and releases autoinducers into its environment. As the population grows, the concentration of these molecules increases. When a threshold level is reached, autoinducers bind to specific receptors, triggering changes in gene expression that coordinate group behaviors such as biofilm formation, virulence factor secretion, sporulation, and bioluminescence.

At this point, many minds may be going into ‘statistical culture shock‘. For me, it makes complete sense and is often seen across nature. They are sometimes termed “islands of stability” in some sciences. In our case “islands of symptoms” would be a more accurate name.

We may end up committed blasphemy against conventional linear mechanic thinking!

Examples:

Alloys With Maximum Strength at Specific Composition

• Iron-Carbon Steel: In carbon steel, maximum strength is usually achieved at a carbon content near 0.8% (known as “eutectoid steel”), where the steel forms a very fine pearlite structure on cooling. Both lower and higher carbon percentages reduce ductility or create brittleness, decreasing usable strength for many applications.​
• Aluminum-Copper Alloy (Al-Cu): The precipitation strengthening of aluminum alloys, such as in the Al-Cu system, reaches maximum effectiveness at about 4–5% copper by weight. Below or above this optimal range, strengthening diminishes because either not enough or too much second phase is created.​
• Nickel-Iron Alloys: For instance, “Permalloy” (a nickel-iron alloy) typically reaches its desired magnetic properties with about 80% nickel and 20% iron. Changes in this ratio result in reduced magnetic strength, which can be considered a parallel with mechanical strength in many alloys.​

Wildlife Systems with Optimal Mixtures

• Animal Diversity in Food Webs: Studies show that increasing the number of animal species generally increases total animal biomass and plant consumption rates, up to a point. Beyond this, higher diversity leads to increased intraguild predation (animals eating each other), which can reduce overall community efficiency and stability.​
• Keystone Species: Some ecosystems depend on a particular balance among keystone species and others. Removing or adding too many can severely weaken the system, in analogy to alloys with optimal composition.​
• Biodiversity-Function Relationship: The stability and strength of ecosystems typically follow a nonlinear relationship with species richness: there is a “sweet spot” where ecosystem functions (like carbon sequestration or primary production) are maximized.

Nuclear Islands of Stability

• The best-known island is around atomic numbers (Z) 114 to 126 and neutron number (N) 184, where theoretical calculations suggest nuclei could have half-lives of minutes, days, or potentially even years, instead of the microseconds typical for super heavy elements nearby.

Other Physical Sciences

• The term may be used metaphorically to describe stable orbital configurations or regions where system dynamics are less chaotic.

Returning to Long COVID

With Tissierellales we have a complex behavior. With Lactobacillus Jensenii we have a simple “if present, reduce it” finding. I returned to the lists above and attempted to identify those with a simple finding with a P < 0.05 for the odds ratios. This is shown in the table below.

• Reduce Beijerinckiaceae, family, With odds being almost 2x for those with Long COVID, i.e. 0.059 vs 0.026
• Increase Parachlamydiales, order, With odds being more than 10x for not having Long COVID, i.e.
  0.067 vs 0.1

Other have the odds being close to each other. For example, Tissierellales (0.978 vs 0.973). In the next post, we will examine more of the complex behavior ones.

Correction	Tax_name	Tax_rank	Symptom Odds	No Symptom Odds
	Anaeroplasma	genus	0.146	0.135
	Candidatus Phytoplasma prunorum	species	0.722	0.738
	16SrX (Apple proliferation group)	species group	0.722	0.735
	Anaeroplasmatales	order	0.146	0.135
	Anaeroplasmataceae	family	0.146	0.135
	Odoribacter laneus	species	0.494	0.506
	Tissierellales	order	0.978	0.973
	16SrXV (Hibiscus witches’-broom group)	species group	0.02	0.013
	Candidatus Phytoplasma brasiliense	species	0.02	0.014
	Lactobacillus jensenii	species	0.034	0.021
	Odoribacteraceae	family	0.961	0.941
Increase	Parachlamydiales	order	0.067	0.1
	Schlegelella	genus	0.062	0.042
	Syntrophomonas cellicola	species	0.039	0.019
Reduce	Hoylesella timonensis	species	0.348	0.295
	Weissella thailandensis	species	0.028	0.016
	Campylobacteraceae	family	0.419	0.386
	Campylobacter	genus	0.388	0.354
	Haemophilus	genus	0.775	0.745
	Halanaerobiaceae	family	0.222	0.184
	Atopobium minutum	species	0.042	0.027
	Halanaerobium	genus	0.222	0.184
	Prevotella corporis	species	0.497	0.457
	Anaerococcus vaginalis	species	0.213	0.215
	Corynebacterium genitalium	species	0.031	0.019
Reduce	Moorella group	norank	0.534	0.447
	Dysgonomonas capnocytophagoides	species	0.042	0.053
Reduce	Beijerinckiaceae	family	0.059	0.026
Reduce	Hydrogenophaga	genus	0.034	0.014

Summary

This post explains my approach for ranking which bacteria should be targeted for change, primarily using the P value to guide priority. I compared two types of bacteria: one that is rare and one that is common. Rare bacteria are usually omitted from standard analyses because their scarcity makes conventional measures—such as calculating the mean and standard deviation—poor indicators of significance. Overlooking these bacteria is a methodological error, especially when the data skew exceeds 2, making such traditional metrics inappropriate.

It’s important to keep in mind that bacteria engage in quorum sensing, which influences the metabolites they produce and likely affects the symptoms observed. For some bacteria, the difference in their relative abundance between people with and without a particular symptom can be substantial and may be all that is needed for significance.

Anyone who regularly reads peer-reviewed medical studies on the microbiome will notice findings reported as bacteria being “too high” or “too low,” with phrases like “trending” when statistical significance isn’t reached. Frankly, my reaction to 95% of these papers is an eye-roll, as the statistical methods used are often inappropriate for the data at hand. With multiple degrees in statistics, professional memberships, and experience, I’m acutely aware of both best practices and common pitfalls.

Microbiome data distributions frequently display extreme skewness—often greater than 20. In such cases, computing mean and standard deviation is simply incorrect.  My friend “Perplexity” writes Mean and standard deviation become inappropriate measures for computing significance if the distribution’s skewness is substantial—specifically, when the absolute skewness exceeds ±2.

Despite this, using these metrics remains standard in high school statistics and unfortunately persists in many life science studies. This “comfort zone” approach does nothing but cloud true findings in microbiome science.

My alternative methodology uses a much larger, highly annotated dataset—over 4,290 unique samples generously donated to Microbiome Prescription., most transferred from Biomesight.com. Importantly, these samples are uniformly processed and richly annotated with symptoms rather than diagnoses, yielding superior analytical clarity.

My Natural Questions to ask

Natural for a statistician that is.

• For people with a symptom or diagnosis
  • What are the significant bacteria associating (and likely causing) the symptom
  • What is the threshold levels for these bacteria
    • I use levels and not level because I have observed the same symptom may occur with a bacteria outside of a specific range. That is, too high or too low. I have also encountered this reported in a few studies, often hidden under a term like “altered microbiome”.
    • There is a dangerous assumption in the literature that significant bacteria must be either too high or too low. I unfortunately know Kierkegaard’s “Either/Or” well.
    • There are no universal threshold for all symptoms, each has its own
• For people without a symptom with a statistical model but with dysbiosis
  • How do you determine which bacteria are significant?
  • What is the threshold levels for these bacteria

Over the last decade, these are important questions because they lead directly to treatment suggestions.

They are also significant in evaluating progress. At present I have a forecasting algorithm that has a high prediction rate for symptoms from a microbiome. The forecasting algorithm also is useful for evaluating progress, an example for a recent sample the donor asked me to review.

Prediction

The checks indicates that the donor agrees that they have this symptom.

Monitoring

The person above followed the suggestions and the subsequent test results are shown below.

What are the most common bacteria associated with symptoms?

This is a generic question that is useful for health practitioners to know. For example, Kristina Mitts, of Mind Mood Microbiome and who I frequently correspond with, or Dr. Jason Hawrelak.  

Using more appropriate statistical methods on our sample of 4,292 distinct different samples; we found significant bacteria identified over 327 symptoms resulting in the following statistical significances.

Significance: P <	Count
0.05	13,855
0.01	12,411
0.001	7,614
0.0001	5,532

So what are the top one for each of these significance?

Overall Significance

Taxon	name	rank	Instances
820	Bacteroides uniformis	species	165
35833	Bilophila wadsworthia	species	142
35832	Bilophila	genus	139
818	Bacteroides thetaiotaomicron	species	137
1426	Parageobacillus thermoglucosidasius	species	133
118884	Gammaproteobacteria incertae sedis	no rank	125
871324	Bacteroides stercorirosoris	species	124
120580	Symbiobacterium toebii	species	122
53244	Desulfonatronovibrio	genus	122
543349	Symbiobacteriaceae	family	122
2733	Symbiobacterium	genus	122
1498	Hathewaya histolytica	species	122
454155	Paraprevotella xylaniphila	species	120

P < 0.05

Taxon	name	rank	Instances
2950010	Salidesulfovibrio	genus	47
221711	Salidesulfovibrio brasiliensis	species	46
658623	Chelonobacter	genus	45
69224	Erwinia psidii	species	44
213462	Syntrophobacterales	order	44
3024408	Syntrophobacteria	class	44
31977	Veillonellaceae	family	44
1843489	Veillonellales	order	43
550	Enterobacter cloacae	species	43
35832	Bilophila	genus	42
841	Roseburia	genus	41
53244	Desulfonatronovibrio	genus	41
871324	Bacteroides stercorirosoris	species	41
1260	Finegoldia magna	species	41
1498	Hathewaya histolytica	species	40

P < 0.01

Taxon	name	rank	Instances
35833	Bilophila wadsworthia	species	51
78448	Bifidobacterium pullorum	species	50
820	Bacteroides uniformis	species	47
841	Roseburia	genus	46
818	Bacteroides thetaiotaomicron	species	44
118884	Gammaproteobacteria incertae sedis	no rank	41
1769729	Hathewaya	genus	41
1426	Parageobacillus thermoglucosidasius	species	41
112902	Propionispora	genus	40
36853	Desulfitobacterium	genus	40
386414	Hoylesella timonensis	species	40
119065	unclassified Burkholderiales	family	40
1853231	Odoribacteraceae	family	40
400091	Hymenobacter xinjiangensis	species	39
209080	Propionispora hippei	species	39
871324	Bacteroides stercorirosoris	species	39
69224	Erwinia psidii	species	39
35832	Bilophila	genus	39

P < 0.001

Taxon	name	rank	Instances
820	Bacteroides uniformis	species	50
35833	Bilophila wadsworthia	species	37
35832	Bilophila	genus	32
118884	Gammaproteobacteria incertae sedis	no rank	32
658623	Chelonobacter	genus	31
246787	Bacteroides cellulosilyticus	species	31
120580	Symbiobacterium toebii	species	31
543349	Symbiobacteriaceae	family	31
253238	Ethanoligenens	genus	31
2733	Symbiobacterium	genus	31
292833	Candidatus Rhabdochlamydia	genus	30
324707	Candidatus Rhabdochlamydia crassificans	species	30
1426	Parageobacillus thermoglucosidasius	species	30
689704	Candidatus Rhabdochlamydiaceae	family	30
70190	Chroococcus	genus	29
402401	Chroococcus minutus	species	29
1890464	Chroococcaceae	family	29
283169	Odoribacter denticanis	species	28

P < 0.0001

Taxon	name	rank	Instances
820	Bacteroides uniformis	species	40
246787	Bacteroides cellulosilyticus	species	34
818	Bacteroides thetaiotaomicron	species	30
1963360	Parachlamydiales	order	30
454155	Paraprevotella xylaniphila	species	30
1426	Parageobacillus thermoglucosidasius	species	30
2733	Symbiobacterium	genus	29
543349	Symbiobacteriaceae	family	29
120580	Symbiobacterium toebii	species	29
35832	Bilophila	genus	26
191412	Chlorobiaceae	family	25
256319	Chlorobaculum	genus	25
244127	Anaerotruncus	genus	25
189723	Prevotella micans	species	25
53244	Desulfonatronovibrio	genus	25
324707	Candidatus Rhabdochlamydia crassificans	species	24
191410	Chlorobiia	class	24
35833	Bilophila wadsworthia	species	24

Summary

This is a high level overview of Significant Bacteria. The patterns above are specific for tests done by Biomesight; a lack of standardization results in using these identifications for other tests is unsafe (legal sense). Background here. IMHO, it is a moral responsibility for labs to produce similar tables.

The key findings are:

• “Common suspects” such as bifidobacterium and lactobacillus are missing!
• Large sample sizes with the same processing is critical. The processing must be the same as used in a clinical setting.
• Appropriate statistical methods must be used

Stay tune for the next part as we drill deeper into appropriate handing of data with some specific issues like Long COVID.

The input data that I used is publicly available at: https://citizenscience.microbiomeprescription.com/

Post Script

Probiotics and the above gets interesting. Take Bacteroides uniformis which is at the top of many of these tables. If we go to my bacteria association site,

We can determine the probiotics (available or pending) that will increase this bacteria (none decreases)

Again, the “cure all” lactobacillus and bifidobacterium genus is absent (apart from Ligilactobacillus ruminis which is not currently available).

Related posts:

• Probiotics Fundamentals: Part 1 Specific Strains
• Probiotics Fundamentals: Part 2 DOA on Shelf
• Probiotics Fundamentals: Part 3 Are they working?

In the first post of this series, Probiotics Fundamentals: Part 1 Specific Strains I cited strains that are available retail that has been researched. The logical starting point is to search for your needs, read the studies and then rank the probiotics in prefer order for doing a personal trial. You want to do one probiotic at a time with rotation and described in the prior post (see prior post).

To searching for strain specific studies of probiotics available retail. Click here.

No Study found or issue not listed

The next step is to look at the conditions that I have abstracted/extracted studies for, listed at “U.S. Nat. Lib. Medical Conditions Studies with Microbiome Shifts“. We are shifting from strain to species level. This gives several paths, let us examine Autism. There are

Based on Publish Studies of Species

Clicking on [Can You Help Improve Suggestions] will take you to a page. At the bottom you will see “Treatment Substances” which lists things that have helped in studies. Scan it for probiotic names, for example: 

A synbiotic formulation of Lactobacillus reuteri and inulin alleviates ASD-like behaviors in a mouse model: the mediating role of the gut-brain axis. Food & function (Food Funct ) Vol: 15 Issue: 1 Pages: 387-400 Pub: 2024 Jan 2 ePub: 2024 Jan 2 Authors Wang C,Chen W,Jiang Y,Xiao X,Zou Q,Liang J,Zhao Y,Wang Q,Yuan T,Guo R,Liu X,Liu Z

Which suggests L. Reuteri with inulin may help. The source is linked. Make sure you read them.

Based on Deficiencies of Probiotic Bacteria

Clicking on 🦠 Taxons will take you to a page showing all of the bacteria shifts reported for the condition.

Look for Lactobacillus, Bifidobacterium,etc  with  ⬇️

These species are found at lower levels, suggesting their metabolites are also reduced. Supplementing with them as single-strain probiotics is logical. Stay at the species level (e.g., Bifidobacterium longum) rather than higher classifications such as the genus Bifidobacterium. In general, avoid probiotic mixtures, as they may include strains that are counter-indicated (e.g., Bifidobacterium catenulatum, Bifidobacterium breve) or strains for which we lack sufficient information.

Based on Modelled correction of Bacteria

Clicking on 🥣 Candidates, will send the huge bacteria list above through a fuzzy logic expert system to compute suggestions with weights given for each one.

Note that this also lists ones to avoid.

Disagreements!

We can see that levels of Lactobacillus plantarum are low, but the model is telling us to avoid Lactobacillus plantarum. So what’s going on here?

The issue comes from the fact that the model/studies is based on multiple subgroups of people with Autism (or other conditions). The data might be accurate within each subgroup, but when you merge them together, you can end up with contradictions. So it’s not really a problem with the approach—it’s a problem with the data mix.

The best rule of thumb is to start with the things that show up as agreements across the data. For example: Bifidobacterium longum and Limosilactobacillus reuteri. Once you’ve tried probiotics that have clear agreements, then you can carefully experiment with the ones where there’s disagreement and see how your body responds.

The next level up in Probiotic Suggestions

It is pretty simple, get a microbiome test. My preferred tests are:

• Biomesight for 16s (economic, low resolution)
• Thorne for shotgun (more expensive but much higher detail)

You want to ideally get a test that reports on all of the common probiotic bacteria. Many common tests do not report many of these. For example: Diagnostic Solution GI-Map reports only

• Bifidobacterium [genus] and no species
• Lactobacillus [genus] and no species

On the other hand both of the above tests report species.

When you select a test, you should check Microbiome Prescription to see what the detection rate is. For example for Bifidobacterium longum, we see how often this is detected in samples.

• For the shotgun tests (Xenogene and Thorne) we see 96% and 100% of the time, if it show low, you can have confidence in taking some
• For SequentiaBiotech we see it is seen 25% of the time. If you have none reported we are left being uncertain if you actually have none or is the none because of the test’s methodology
  • See this post for more on these issues: The taxonomy nightmare before Christmas…

Another example is L.Reuteri where the shotgun tests find in in over 50% of samples, while some 16s finds in only 2% of samples.

Bottom Line

We’re piecing things together from lots of scattered knowledge, and there’s no single standard method—either for testing microbiomes in labs or for the studies themselves. Nothing here is clear-cut; everything’s kind of fuzzy, sometimes super fuzzy. In this post, the focus was on picking probiotics for a condition using literature (an “a priori” approach). Basically, it means trusting the data at face value, even though we know it isn’t rock-solid.

Some additional readings:

• Navigating microbiome variability: implications for research, diagnostics, and direct-to-consumer testing [2025]
• The standardisation of the approach to metagenomic human gut analysis: from sample collection to microbiome profiling [2022]
• Standardization of laboratory practices for the study of the human gut microbiome [2022]

I also foreshadowed the next post: Using a detailed microbiome test to select probiotics based on the whole microbiome.

I believe it is the consumer interest to share this email thread and to promote discussion of this issue.

Blacklisting is the action of suggesting something to be avoided or distrusted.

Request

[Customer name withheld] has forwarded me the PDF and some CSV files associated. She wishes to see what the recommendations from a fuzzy logic expert system that uses over 7.4 million facts based on data from the US National Library of Medicine will suggest.

 I know that the following data is very much available and possible to provide. Other firms like Biomesight.com, Thorne, Vitract and Precision Biome has no trouble providing it:

For all taxonomical layers (From Clade to strains [when available]) just 3 numbers are sufficient.

• NCBI Taxon Number, 
• Percentage Amount, 
• Percentile Ranking across a reference set of healthy individuals

Additional data is welcome, but not required:

1. Names
2. Your reference ranges 
3. etc

Percentiles should be actual percentiles and NOT percentiles estimated using mean and standard deviation. Most bacteria has a SKEW exceeding 20. Using the mean and average requires a SKEW of zero.

Your customer would greatly appreciate a speed return of an appropriate file. With that file in hand, we will add your lab to our list of over 50 labs that our free site supports. (See https://microbiomeprescription.com/Upload/Index ).

If you are unable to provide such, please tell us so we may black list your site as not supportable to spare other consumers a waste of money.

Response

Hi Ken, Thank you for your detailed email and for sharing your perspective on the data formats and metrics you require. We’d like to clarify that Microba uses the Genome Taxonomy Database (GTDB) for microbial classification. GTDB and NCBI classify genomes differently, our species consist of multiple genomes which may have different NCBI classifications, species level classifications cannot always be mapped to each other through name matching alone. Due to this, providing a microbiome profile in NCBI taxonomy is not practical nor would it be a correct representation of the actual microbiome profile. Once GTDB is formally supported within your workflow, please reach out and we can discuss options for providing the data your service requires. We appreciate your understanding and encourage you to support the more accurate, resolved, and phylogenetically consistent GTDB taxonomy. Kind regards, The Microba Team

Reply To Response

Many thanks for your reply. Our purpose is to provide clinical suggestions for review by medical professionals to people suffering from a wide variety of conditions using hallucination-free AI.

A simple example may be seen here. DepressionExample.

Unfortunately, Genome Taxonomy Database(GTDB) appears to be a research tool and IMHO seems very inappropriate and misleading to sell to consumers. GTD was first proposed in academic papers in August 2018. We were active in the microbiome before that and the de facto standard in the industry as then, and still is today, the NCBI. The leading consumer microbiome testing company back then was uBiome which provided NCBI identifiers. In the 7 years since first release, we have seen what appears to be some 226 revisions given the release of   10-RS226, dated April 16, 2025.

Checking the US National Library of Medicine, there appears to be less than 200 studies done using GTDB that are of likely clinical use. With NCBI, we found over 20,000 suitable studies. Regardless, no study done prior to 2020, likely 2022, can, in a legal sense, be safely used for clinical purposes. 

I am aware of many tools to convert GTDB to NCBI, a few are:

• TaxonKit: Command-line toolkit that supports creating NCBI-style taxdump files from GTDB and also reformatting and mapping taxonomies.
• gtdb_to_taxdump: Python tool to convert GTDB taxonomy files into NCBI taxdump format, usable by downstream tools like Kraken2.
• GTDB-Tk: Assigns genomes to GTDB taxonomy but includes metadata fields mapping to NCBI taxonomy, enabling conversion between formats.
• NCBI-GTDB Map: Direct tool for mapping GTDB taxonomy to NCBI taxonomy, supporting both directions and handling rank prefixes.
• gtdb-taxdump: A specialized toolkit for generating stable, trackable NCBI taxdump files from GTDB releases with reproducible taxon IDs.
• NCBI-taxonomist: Python command-line utility that retrieves, handles, and allows mapping of taxonomic information, supporting cross-database operations.

I am aware that folks embracing the hottest new technology can have an attitude, especially when most recent studies decline to use it. Intransient on this issue is not beneficial to your customers; people with challenging health issues unless you are willing to provide a GTDB based suggestions engine working off only GTDB studies, 

hallucination-free AI that is equivalent or better than what NCBI can provide. Until such time, I would advocate that you stop making misleading sales to consumers.

Given your response, I feel that I have no option back to blacklist you for clinical use.

Related posts:

• Probiotics Fundamentals: Part 1 Specific Strains
• Probiotics Fundamentals: Part 2 DOA on Shelf

From years of using different probiotics, I have developed some simple rules of thumbs on the use of probiotics. These rules have worked very well with probiotics from my favorite source: Maple Life Science™. Their probiotics show both manufacture date and expiry date. Typically they arrive within a month of the manufacture date direct from the manufacturer. IMHO, high probability from being alive at arrival.

Do one Species at a time

Maple Life Science™ probiotics are usually single species with just FOS as an additive. My usual preference is taking probiotic powder dissolved in warm water at least one hour away from any meals. Bacteria in your gut has to enter somewhere — and that location is the mouth. You may want to also alter your mouth microbiome so it is less likely to repopulate your gut bacteria with undesirables.

My typical pattern is doing one probiotic for 2 weeks and then rotate to another. See below for the rationale.

How do I know that they are different probiotics?

I could send them off for testing, but what I have observed is this:

• They are often slightly different colors
• They tastes differently

I do not know definitively if they are as claimed, but I do see that they are different.

Are there any changes within a week?

I monitor myself after starting each probiotics. I expect at least one of the following to change:

• Smell of farts
• Number of farts
• Frequency of stools
• Characteristics of stools using the Bristol stool chart
• Changes on my smart watch monitoring
• Changes of sleep patterns
• Change of eating habits/food preferences
• Changes of mood or alertness

If there are no changes, then I label the bottle as “No effect” and put it at the back of the refrigerator shelf. To me, probiotics should change the microbiome is some observable way. The above are indicators of change. This does take some self-awareness of each.

Personal Example: My wife has Crohn’s disease. Whenever she starts to have a flare, she takes Mutaflor (E.Coli Nissle 1917) probiotics and within 1-2 hours the flare ends. Probiotics impact should be apparent in hours or a few days.

Dosages titration

When I try a new probiotic, I usually start with the standard dosage. If there are no apparent change happening in 3 days, I double the dosage (and keep doubling every 3 days for up to 14 days). In practical terms:

• Day 1: 1 capsule
• Day 4: 2 capsules
• Day 7: 4 capsules
• Day 10: 8 capulses
• Day 13: 16 capsules

The logic is simple: there may be less viable bacteria (for some reason) and thus more capsules are needed to get effective dosages to induce a change.

Probiotic Rotation

To me, the purpose of probiotics is to change the microbiome, typically, a dysbiosis. The metabolites and bacteriocins being produced by the probiotics will alter the population by either increasing metabolites that may feed (increase) other bacteria or decrease other bacteria by the bacteriocins. In other words, I view the probiotics as a course correction around a reef.

Bacteriocins Resistance

Bacteriocins are natural antibiotics. Many antibiotics are derived from bacteriocins. This means that bacteriocins resistance needs to be considered. Typically, most of the targeted bacteria has some bacteria that are resistant to some form of antibiotics. These resistors will prosper because their sibling competitors are no longer there. I have read several studies that found pulsed or rotated antibiotics were more effective than continuous antibiotics. My take away is simple: “a course of probiotics” followed by rotation. How long should the course be? I take the duration from the typical duration of prescribted antibiotics (10-14 days).

Some known bacteriocins are listed below

1. Nisin – produced by Lactococcus lactis.
2. Pediocin PA-1/AcH – produced by Pediococcus acidilactici.
3. Enterocin AS-48 – produced by Enterococcus faecalis.
4. Colicin A – produced by Escherichia coli.
5. Colicin E1 – produced by Escherichia coli.
6. Microcin J25 – produced by Escherichia coli.
7. Plantaricin E – produced by Lactobacillus plantarum.
8. Plantaricin F – produced by Lactobacillus plantarum.
9. Leucocin A – produced by Leuconostoc gelidum.
10. Helveticin I – produced by Lactobacillus helveticus.
11. Lactocin MXJ 32A – produced by Lactobacillus coryniformis.
12. Enterolysin A – produced by Enterococcus faecalis.
13. Salivaricin – produced by Lactobacillus salivarius.
14. Pyocin S2 – produced by Pseudomonas aeruginosa.
15. Microcin E492 – produced by Klebsiella pneumoniae.
16. Lactococcin G – produced by Lactococcus lactis.
17. Plantaricin JK – produced by Lactobacillus plantarum.
18. Plantaricin EF – produced by Lactobacillus plantarum.
19. Goadsporin – produced by Streptomyces sp..
20. Plantazolicin – produced by Bacillus amyloliquefaciens.

Some antibiotics obtained from bacteria:

1. Streptomycin – from Streptomyces griseus
2. Chloramphenicol – from Streptomyces venezuelae
3. Tetracycline – from Streptomyces rimosus and Streptomyces aureofaciens
4. Erythromycin – from Saccharopolyspora erythraea (formerly Streptomyces erythraeus)
5. Neomycin – from Streptomyces fradiae
6. Lincomycin – from Streptomyces lincolnensis
7. Rifamycin – from Amycolatopsis rifamycinica (previously Streptomyces rifamycinica)
8. Vancomycin – from Amycolatopsis orientalis
9. Bacitracin – from Bacillus subtilis and Bacillus licheniformis
10. Gramicidin – from Bacillus brevis
11. Polymyxin B – from Bacillus polymyxa
12. Teicoplanin – from Actinoplanes teichomyceticus
13. Fusidic acid – from Fusidium coccineum (a fungus, included due to bacterial-related antibiotic use)
14. Novobiocin – from Streptomyces niveus
15. Ristocetin – from Amycolatopsis lurida
16. Mupirocin – from Pseudomonas fluorescens
17. Tyrocidine – from Bacillus brevis
18. Clavulanic acid – from Streptomyces clavuligerus
19. Daptomycin – from Streptomyces roseosporus
20. Carbapenems (e.g., Imipenem) – from Streptomyces species and related bacteria

Bottom Line

The bottom line is simple: rotate and note changes. If there are no changes,

Related posts:

• Probiotics Fundamentals: Part 1 Specific Strains
• Probiotics Fundamentals: Part 3 Are they working?
• Probiotics Fundamentals: Part 4 Probiotic Selection?

Below you see some data information shown on a few probiotic products. Many products do not show either production date nor best by date. They are not legally required.

My preference has always favor probiotics that includes manufacturing date. Two of the products above cite a three year shelf life (assuming appropriate storage). My favorite source, Maple Life Science™, ship directly to me from their factory. Usually they arrive within one month of manufacture (occasionally, the same month!)

Some Studies of special interest

The chart below is from Study of Viability, Storage Stability, and Shelf Life of Probiotic Instant Coffee Lactiplantibacillus plantarum Subsp. plantarum Dad-13 in Vacuum and Nonvacuum Packaging at Different Storage Temperatures [2022]

From the moment that a probiotic leaves a factory, temperature control is usually non-existent. The trucks that transport them are likely not refrigerated nor are wholesale warehouse storing them. When they arrive at a health food store they are typically placed in a refrigerated cabinet for presentation to customers. In many cases, if you insist on seeing where the bottles are stored before, do not be surprise to see that it is not a refrigerated area. It is possible that the probiotics may be subject to 37C(98.6F) for months before the shop keeper places it in the refrigerated cabinet. Each number on the left scale indicate 1/10 of the number above it.

A shipment from a east coast producer to a west coast store direct, is 4-8 days. If the shipment goes to a wholesaler’s warehouse than expect a few days more. The result in summer can easily be as much as 97% of the viable bacteria that leaves the factory may be killed if it takes 15 summer days (per above chart).

From Perplexity.ai, this is clear:

The short version is that during summer in the US, the amount of viable bacteria may be 1/100 of what the probiotic had at the factory. There are stick-on labels that will change color if storage exceeds a threshold — unfortunately, no one is using that.

Microbiological Quality and Antimicrobial Resistance of Commercial Probiotic Products for Food-Producing Animals [2024] reports no viable Lactobacillus was found in some products 

In terms of probiotics sold by microbiome testing companies, the only one that I know of that “ships direct from probiotic manufacturer” is PrecisionBiome.Eu that has established a relationship with a German probiotic producer. Their client base is the EU, so transit time from “vat to customer” is short.

Absence of Regulations Problem

There are recommendations such as Best Practices Voluntary Guidelines for Probiotics[2017] provides some guidance (ignored by most producers). Producers can make claims of shelf life (best before) of 10 years without consequences. With no manufacture date, no one knows when it was made. Calls to their customer lines will usually give questionable answers. The probiotic industry have many active lobbyists to inhibit anything that may effect profits.

Figure Pointing for Probiotics being DAO

If you buy a probiotic and found it is effectively DAO and contact the manufacturer. The manufacturer will claim no responsibility once it left their premise. It is the responsibility of the trucking companies and wholesaler storage. Those folks will then point at the retail store mishandling things.

This harsh reality is why I try to buy direct from the manufacturer (no Amazon, no health food stores).

Bottom Line

“I didn’t get any benefit from probiotics, I got them refrigerated from my trusted health food store“ is a frequent complaint that I hear. IMHO, they do not work because they have been well cooked! Our habit is order our year supply of probiotics from Maple Life Science™ in the fall and winter. The colder it is outside, the better it is.

My “rules of thumbs” on taking probiotics will be the next topic.

Other post in this theme:

• Probiotics Fundamentals: Part 2 DOA on Shelf
• Probiotics Fundamentals: Part 3 Are they working?
• Probiotics Fundamentals: Part 4 Probiotic Selection?

What is the difference between a Species and a Strain? To understand this, view Species as “dogs” and strains as specific types. Is picking a Chihuahua as a police dog a good choice, or a St. Bernard suitable for someone with disability living in a one room apartment?

The chart below shows different aspects of different strains for Lactobacillus Reuteri. When you buy a probiotic names “Lactobacillus Reuteri”, it is unlikely which species if was obtained from is specified on the bottle. If it was not from a human, it is very unlikely that it will reproduce or take root in your body.

Probiotic manufacturers and packagers are focused on making money. They will ask for the cheapest source for a probiotic that they expect to be able to sell for the greatest profit. Human source is not a factor, cheapness is!

I have known people that are histamine sensitive that are fine with one brand of Lactobacillus Reuteri but get sick from another brand…. Looking at the chart below, the answer is obvious: One has a histamine producer and one does not.

From prediction to function using evolutionary genomics: human-specific ecotypes of Lactobacillusreuteri have diverse probiotic functions[2014].

Researched but not for sale

This morning I was asked about Bacteroides fragilis BF839 which is cited in several studies on the US National Library of Medicine. Most of the studies are from 2024 or 2025. At present, it is not for sale anywhere and I do not expect it to be for five(5) years at least because of approval processes. Given the authors’ location, I expect it will be first available in China.

Researched and Stain is for sale 🙂

Several years ago I set up a free page listing those available (somewhere in the world). I also automated a weekly automatic scan of the US National Library of Medicine for any new studies using these strains. The page is kept up to date.

Occasionally, someone emails me about a new strain that has one or more studies associated. I add those to the list. If you find one that I missed, please email me!

The page allows searching across the studies abstracts for key words. For example, if you are interested in Autism, just enter that and click search. The page will then show the retail brands with links to the studies.

The intent of the page is discourage random trial of probiotics which has no effect (except on bank accounts).

List of Strains with name of product or seller

At present we are at 156 different strains. These are listed below.

• Akkermansia muciniphila WB-STR-0001: Pendukum Glucose Control
• Aspergillus oryzae NK: strong wakamoto w
• Bacillus clausii MCC 0538: Biome Ultra
• Bacillus clausii N/R: Enterogermina
• Bacillus clausii O/C: Enterogermina
• Bacillus clausii SC-109: MegaSporebiotic
• Bacillus clausii SIN: Enterogermina
• Bacillus clausii T: Enterogermina
• Bacillus coagulans ATCC 5856: Nature’s Sunshine Bacillus Coagulans,
• Bacillus coagulans DSM 17654: Biome Ultra
• Bacillus coagulans GBI-30: Schiff® Digestive Advantage® Daily Probiotic
• Bacillus coagulans SC-208: MegaSporebiotic
• Bacillus coagulans SC208: Eded 3 in 1
• Bacillus Coagulans SNZ1969: Jetson FIT
• Bacillus coagulans Unique IS-2: Optibac Probiotics Adult Gummies Probiotics
• Bacillus indicus HU36: MegaSporebiotic
• Bacillus licheniformis DSM5749: PrecisionBiome
• Bacillus mesentericus (subtilis) TO-A: AOR, Probiotic 3
• Bacillus subtilis ATCC SD 7280: Biome Ultra
• Bacillus subtilis BG01-4TM: VERNX
• Bacillus subtilis DE111 : Probiotic w/ DE111
• Bacillus subtilis DSM17299: PrecisionBiome
• Bacillus Subtilis HU58: MegaSporebiotic
• Bacillus Subtilis MB40: Country Farms Super Flora Probiotics
• Bacteroides xylanisolvens DSM 23964: AVI-1 (pending on market)
• Bifidobacterium adolescentis IVS-1: Synbiotic Health ISV-1
• Bifidobacterium adolescentis PRL2019: Gabapral
• Bifidobacterium animalis ssp. lactis 420: UltraFlora® Control
• Bifidobacterium animalis ssp. lactis CUL-34 (NCIMB 30153): Genestra Brands – HMF Neuro Powder
• Bifidobacterium animalis subsp lactis HN019: Optibac Every Day Max
• Bifidobacterium animalis subsp. lactis CECT 8145: Culturelle Healthy Metabolism + Weight Management Supplement
• Bifidobacterium bifidum BB-06: Custom Probiotics
• Bifidobacterium bifidum CUL-20 (NCIMB 30172) : Genestra Brands – HMF Neuro Powder
• Bifidobacterium bifidum G9-1 (BBG9-1): Phillips’® Colon Health
• Bifidobacterium bifidum HA-132: Human Probiotics
• Bifidobacterium bifidum MIMBb75 (SYN-HI-001): IRRITABLE COLON PRO KIJIMEA
• Bifidobacterium bifidum W23: Ecologic® Barrier for Metabolic Health
• Bifidobacterium breve HA-129: Human Probiotics
• Bifidobacterium Breve LMG 11613: Bioflora (Mexico)
• Bifidobacterium breve M-16V: Optibac Probiotics Professional
• Bifidobacterium breve PRL2020: PharmExtracta / Brevicillin Stick
• Bifidobacterium lactis BB-12: Nestlé
• Bifidobacterium lactis Bi-07: UltraFlora® Balance
• Bifidobacterium lactis Bl-04: Optibac Probiotics Every Day MAX Probiotics
• Bifidobacterium lactis BL-4: Custom Probiotics
• Bifidobacterium lactis W51: Ecologic® Barrier for Metabolic Health
• Bifidobacterium lactis W52: Ecologic® Barrier for Metabolic Health
• Bifidobacterium longum 1714: Zenflore®
• Bifidobacterium longum 35624: Align
• Bifidobacterium longum BB536: Probiotic Pro Bb536
• Bifidobacterium longum BL-05: Custom Probiotics
• Bifidobacterium longum ES1: PharmExtracta / Gliadines buccal stickpacks
• Bifidobacterium longum MM-2: Phillips’® Colon Health
• Bifidobacterium longum R0175: Calm Biotic®
• Bifidobacterium longum ssp. infantis R0033: Human Probiotics
• Bifidobacterium longum ssp. longum HA-135: Human Probiotics
• Bifidobacterium longum subsp. infantis Bi-26: Custom Probiotics
• Bifidobacterium longum subsp. infantis NLS: Life Start 2
• Bifidobacterium longum Subspecies infantis Strain EVC001: Evivo® Infant Probiotic Powder
• Bifidobacterium longum W108: Ecologic® Relief
• Bifidobacterium longum W11: PharmExtracta / Bowell
• Clostridium Butyricum MIYAIRI: Miyarisan
• Clostridium butyricum TO-A: AOR, Probiotic 3
• Clostridium butyricum WB-STR-0006 : Pendukum Glucose Control
• Enterococcus faecalis T-110: AOR, Probiotic 3
• Enterococcus faecium L3: PharmExtracta / INatal Sachets
• Enterococcus faecium SBS 1: Shin Biofermin S
• Enterococcus faecium SF 68: Bioflorin
• Enterococcus faecium W54: Ecologic® AAD
• Escherichia coli , Laves strain 1931: Colibiogen
• Eubacterium hallii WB-STR-0008: Pendukum Glucose Control
• Hafnia alvei HA4597: SymbioLife ® Satylia
• Heat Killed Lactobacillus plantarum L-137: Swanson Immunobiotic Immuno-LP20
• Lacticaseibacillus paracasei subsp. paracasei CNCM I-1518: DanActive
• Lacticaseibacillus rhamnosus HN001: Optibac Probiotics Babies & Children Probiotics
• Lactobacillus acidophillus CUL-21 (NCIMB 30156): Genestra Brands – HMF Neuro Powder
• Lactobacillus acidophilus ATCC 4356: resB® Lung Support
• Lactobacillus acidophilus CL1285: Bio-K+
• Lactobacillus acidophilus CUL-60 (NCIMB 30157): Genestra Brands – HMF Neuro Powder
• Lactobacillus acidophilus HA-122: Human Probiotics
• Lactobacillus acidophilus LA-14: Custom Probiotics
• Lactobacillus Acidophilus LA1: Flora Pro Fem with Cranberry
• Lactobacillus acidophilus NCFM: UltraFlora® Balance
• Lactobacillus acidophilus W22: Ecologic Performance
• Lactobacillus acidophilus W37: Ecologic® Barrier for Metabolic Health
• Lactobacillus acidophilus W55: Ecologic® Allergycare
• Lactobacillus brevis W63: Ecologic® Barrier for Metabolic Health
• Lactobacillus casei LBC80R: Bio-K+
• Lactobacillus casei LC-11: Custom Probiotics
• Lactobacillus casei R0215: PharmExtracta / INatal Sachets
• Lactobacillus casei Shirota: Yakult
• Lactobacillus casei W56: Ecologic® Barrier for Metabolic Health
• Lactobacillus casei W79: Ecologic® Relief
• Lactobacillus crispatus LbV 88 (DSM 22566): AZO Complete Feminine Balance
• Lactobacillus fermentum ME-3: Reg’Activ Essential ME-3
• Lactobacillus gasseri KS-13: Phillips’® Colon Health
• Lactobacillus gasseri LbV 150N (DSM 22583): AZO Complete Feminine Balance
• Lactobacillus Gasseri LMG 266: Bioflora (Mexico)
• Lactobacillus gasseri SBT2055: gasseri SP strain capsule
• LACTOBACILLUS HELVETICUS L10: LACTOBACILLUS HELVETICUS LAFTI® L10
• Lactobacillus helveticus R0052: Calm Biotic®
• Lactobacillus jensenii LbV 116 (DSM 22567): AZO Complete Feminine Balance
• Lactobacillus Kefiri LKF01 (DSM 32079) LKEF : Kefibios
• Lactobacillus paracasei (case)i 431: For daily immunity
• Lactobacillus paracasei 8700:2: UltraFlora® Cold Support
• Lactobacillus paracasei F-19: Optibac Probiotics For Women Probiotics
• Lactobacillus paracasei Lpc-37: Custom Probiotics
• Lactobacillus paracasei W20: Ecologic® AAD
• Lactobacillus plantarum 299v: Jarrow Formulas Ideal Bowel Support
• Lactobacillus plantarum DR7: SymbioLact ® Pro Sleep
• Lactobacillus plantarum HEAL9: UltraFlora® Cold Support
• Lactobacillus plantarum Lp-115: Custom Probiotics
• lactobacillus plantarum LP45: Natealth
• lactobacillus plantarum ps128: Neurobiotique PS128
• Lactobacillus plantarum W1: Winclove Senior
• Lactobacillus plantarum W21: Ecologic Travel
• Lactobacillus plantarum W62: Ecologic® AAD
• Lactobacillus reuteri ATCC PTA 5289: BioGaia® ProDentis
• Lactobacillus reuteri DSM 17938: BioGaia®
• Lactobacillus reuteri RC-14: RepHresh™ Pro-B™ Probiotic
• Lactobacillus rhamnosus CLR2: Bio-K+
• Lactobacillus rhamnosus GG: Culturelle®
• Lactobacillus rhamnosus GR-1: RepHresh™ Pro-B™ Probiotic
• Lactobacillus rhamnosus HA-111: Human Probiotics
• Lactobacillus rhamnosus LbV96 (DSM 22560): AZO Complete Feminine Balance
• Lactobacillus rhamnosus Lcr35: Provacare®
• Lactobacillus rhamnosus Lr-32: Custom Probiotics
• Lactobacillus rhamnosus LR1: Flora Pro Fem with Cranberry
• Lactobacillus rhamnosus R0011: Flora Pregnancy Care Probiotic
• Lactobacillus rhamnosus W71: Ecologic® AAD
• Lactobacillus Sakei pro-Bio-65: Kimchi Power
• Lactobacillus salivarius HA-118: Human Probiotics
• Lactobacillus Salivarius Ls-33: Custom Probiotics
• Lactobacillus salivarius UCC118: UltraFlora® Integrity
• Lactobacillus salivarius W24: Ecologic® Barrier for Metabolic Health
• Lactocaseibacillus rhamnosus ATCC 53103: resB® Lung Support
• Lactococcus lactis JCM5805 (L. lactis plasma): IMMUNOFLORA
• Lactococcus lactis LL-23: Custom Probiotics
• Lactococcus lactis W19: Ecologic® Barrier for Metabolic Health
• Lactococcus lactis W58: Ecologic® Barrier for Metabolic Health
• Lactoplantibacillus plantarum ATCC BAA-793: resB® Lung Support
• Limosilactobacillus reuteri SD5865: Natures-Way-Primadophilus-Reuteri-Pearls
• Mutaflor (Escherichia coli strain Nissle 1917): Mutaflor (Canada, Australia, Findland, Germany)
• Oxalobacter formigenes DSM 4420: TrueMeds
• Pediococcus Acidilactici NRRL B-50517: Imagilin Nutri-5051
• Prescript Assist(multi species): Prescript Assist (dispute on formula change)
• Propionibacterium freudenreichii shermanii : Optim Propionibacter
• Saccharomyces boulardii (Klaire Labs): Klaire Labs Saccharomyces boulardii
• Saccharomyces boulardii CNCM I-745: Florastor®
• Saccharomyces Boulardii THT 500101: Bioflora (Mexico)
• Saccharomyces cerevisiae boulardii CNCM I-1079: Pure Encapsulations
• Streptococcus salivarius K12: BLIS ThroatHealth Oral Probiotics
• Streptococcus thermophilus ST-21: Custom Probiotics
• Symbioflor 1: German Apotheke by Paul’s Mart
• Symbioflor 2: German Apotheke by Paul’s Mart
• VSL3 / Visbiome / De Simone Formulation: Alfasigma USA, Inc.

Safest Product for Correct Identification

These strains are usually under legal protection and thus the manufacturer has a vested (financial) interest to make sure that “what is advertised is delivered”.

Why is this important, just look at some of the literature

• Microbiological Quality and Antimicrobial Resistance of Commercial Probiotic Products for Food-Producing Animals [2024]
  • 64.4% were incorrectly labeled in either number of viable cells or bacterial species
  • 51.6% exhibited resistance to at least one antimicrobial agent
  • 26.8% had a lower number of viable cells than their label claims, No viable Lactobacillus was found in some products 
  • 57.8% comprised other species rather than those claimed on the contents

Your first choice should be the probiotics strains that are most likely to be as advertised and has been studied for the symptom of condition that you are interested in.

Mast Cell Activation Syndrome (MCAS) has been a frustrating condition to deal with. It is likely that the microbiome is a factor but finding bacteria patterns has had poor results. Recently I did a post One Consistent Pattern Across Different Vendors Results where the agreement from different lab tests on the bacteria involved simply had not agreement (see this page to illustrate). Using  estimates on compounds and enzymes using KEGG: Kyoto Encyclopedia of Genes and Genomes went two ways:

• enzymes associations was worse than bacteria
• compound produced was far better than bacteria, 31x better

I decided to drill into these associations restricted it to associations seen in ALL of uBiome, Biomesight and Ombre dataset. The goal is to do a manual walkthrough of using this data to identify issues and potential solutions.

Logic Flow

1. Obtain a large collection of microbiome samples process through different labs (in our case 3)
2. Estimate the net amount of compound produce from each sample
3. Using the symptom annotation, determine which compound are significant using chi²
4. Identify the compounds that are significant for all three labs (concurrent)
5. Remove the compounds that only occur in a few labs. Assume they are probable noise
6. Take a shotgun sample from an individual and compute the compounds
7. Identify where we have compound matching 5 (same direction)
8. Ignore/discard compound that are not produced/substrate by the bacteria in sample
9. Create a list by reversing taxa->Compound to compound->Taxa that are present in sample
10. Pass the bacteria list with desired shift(increase/decrease) to the Fuzzy Logic Expert System to generate suggestions
11. List the suggestions

[Step 4 Above] The compounds identified are below with ALL OF THEM BEING LOW:

KEGG Identifier	Name (linked to KEGG site)
C04030	(2,3-Dihydroxybenzoyl)adenylate
C04236	(2S)-2-Isopropyl-3-oxosuccinate
C20844	(2Z,4S,5R)-2-Amino-4,5,6-trihydroxyhex-2-enoate
C01186	(3S,5S)-3,5-Diaminohexanoate
C20845	(4S,5R)-4,5,6-Trihydroxy-2-iminohexanoate
C22385	(L-Cysteinyl)adenylate
C01143	(R)-5-Diphosphomevalonate
C00810	(R)-Acetoin
C01769	(S)-Acetoin
C11826	[GlcNAc-(1->4)-Mur2Ac(oyl-L-Ala-g-D-Glu-L-Lys-D-Ala-D-Ala)]n-diphosphoundecaprenol
C01281	[L-Glutamate:ammonia ligase (ADP-forming)]
C21879	1-(5-O-Phospho-beta-D-ribofuranosyl)-5-(sulfanylcarbonyl)pyridin-1-ium-3-carbonyl adenylate
C01159	2,3-Bisphospho-D-glycerate
C17234	2-Aminobut-2-enoate
C02631	2-Isopropylmaleate
C05848	4-Hydroxy-3-polyprenylbenzoate
C21877	5-Carboxy-1-(5-O-phospho-beta-D-ribofuranosyl)pyridin-1-ium-3-carbonyl adenylate
C22267	5-Methylaminomethyl-2-(Se-phospho)selenouridine34 in tRNA
C22266	5-Methylaminomethyl-2-(S-geranyl)thiouridine34 in tRNA
C01299	Adenylyl-[L-glutamate:ammonia ligase (ADP-forming)]
C05414	all-trans-Phytofluene
C05430	all-trans-zeta-Carotene
C19875	alpha-Kdo-(2->8)-alpha-Kdo-(2->4)-alpha-Kdo-(2->6)-lipid IVA
C01563	Carbamate
C20969	Carboxyphosphate
C00417	cis-Aconitate
C19724	Cobyrinate c-monamide
C02218	Dehydroalanine
C15853	Dehydrospermidine
C06040	Diglucosyldiacylglycerol
C11930	dTDP-2,6-dideoxy-D-glycero-hex-2-enos-4-ulose
C05359	e- (electron)
C05730	Glutathionylspermidine
C22173	Harderoheme III
C02985	L-Fucose 1-phosphate
C16238	Lipoyl-AMP
C19611	Manganese(3+)
C15854	N-(4-Aminobutylidene)-[eIF5A-precursor]-lysine
C03406	N-(L-Arginino)succinate
C21009	N4-Aminopropylspermidine
C03794	N6-(1,2-Dicarboxyethyl)-AMP
C18060	N-Acetyl-alpha-D-galactosamine 1-phosphate
C05379	Oxalosuccinate
C01241	Phosphatidyl-N-methylethanolamine
C00404	Polyphosphate
C15527	Precorrin 1
C03427	Prephytoene diphosphate
C21177	Prokaryotic ubiquitin-like protein
C21878	Pyridinium-3-carboxy-5-thiocarboxylic acid mononucleotide
C03541	THF-polyglutamate
C19080	tRNA with a 3′ CC end
C22409	UDP-3-O-[(3R)-3-hydroxyacyl]-alpha-D-glucosamine

The numbers could be low due to either:

• Not enough being produced
• More being consumed than being produced
• Severe skewness combined with low occurance

Bacteria produces and consumes the above metabolites/compounds.

We are able to compute the impact of probiotics on the above, these are listed under Probiotics modeled on Compounds on this link. None makes it better, in fact all are predicted to make things worse. Checking PubMed (while some probiotics may help), the number of studies are surprisingly sparse. Sparse studies for something like this suggests that there may be a large number of unpublished studies showing no effect or a negative effect. These unfavorable studies are very rarely published, especially when funded by someone owning the patent of the probiotic being tested.

Trying this approach on a Shotgun Sample of someone with MCAS [Step 5]

There are 52 compounds identified above. I have a Thorne sample. 42 of the compounds were less than 1%ile, 6 were over 50%ile. The pattern was obtained using this trio of 16s-labs (ubiome, biomesight and ombre) and the pattern appears to hold true for a sample from a shotgun analysis. We have 80% agreement when randomness would suggest < 2% agreement.

Looking at the details, we see that most of the 42 had no bacteria producing these compounds. This suggests that we are dealing with sparse data and possible false identification. Restricting to those reported as > 0%ile with over 200 samples we ended up with just 11 compounds.

Compound	Samples
cis-Aconitate	3860
N-(L-Arginino)succinate	5806
Oxalosuccinate	3613
4-Hydroxy-3-polyprenylbenzoate	6042
UDP-3-O-[(3R)-3-hydroxyacyl]-alpha-D-glucosamine	5407
N6-(1,2-Dicarboxyethyl)-AMP	6216
N-Acetyl-alpha-D-galactosamine 1-phosphate	6118
Prokaryotic ubiquitin-like protein	5072
(R)-5-Diphosphomevalonate	5996
2,3-Bisphospho-D-glycerate	6115
(L-Cysteinyl)adenylate	5028

[Step 7 Above] Returning to our Shotgun sample above, we found the following are low.

• cis-Aconitate with 3868 producers and 3869 consumers, some found not enough. Increase producers, reduce consumers
• Oxalosuccinate with 3747 producers and 3747 consumers
• N-Acetyl-alpha-D-galactosamine 1-phosphate with 282 producers and 291 consumers, too many consumers
• (L-Cysteinyl)adenylate with 120 producers and 120 consumers

Working backwards from these 4, we end up with 3,953 taxa on our first pass. For many of these bacteria there were none reported hence the challenge of encouraging bacteria that are not there!!!

Working from what is there! [Step 9/10]

Using cis-Aconitate and N-Acetyl-alpha-D-galactosamine 1-phosphate , we want to reduce consumers and increase producers. The result is that we want to:

• Increase these bacteria (most significant is at top)
  • Corynebacterium diphtheriae
  • Streptococcus anginosus
  • Rhodopseudomonas palustris
  • Ralstonia solanacearum
  • Pseudomonas oryzihabitans
  • Sorangium cellulosum
  • Pseudomonas putida
• Decrease these bacteria ((most significant is at bottom)
  • Cupriavidus necator
  • Cupriavidus basilensis
  • Thermaerobacter sp. FW80

Note we are talking species here. KEGG is actually at the strain level and we extrapolated to species. Passing this data to the fuzzy logic expert system we get as suggestions [Step 11]:

• henopodium quinoa {Quinoa}
• hydromorphone – an addictive pain killer
• proton-pump inhibitors (prescription)
• synthetic disaccharide derivative of lactose {Lactulose}
• Moringa Oleifera {Moringa}
• Limosilactobacillus fermentum {L. fermentum}
• Heyndrickxia coagulans {B. coagulans}
• Latilactobacillus sakei {Lactobacillus sakei}

Bottom line, it is possible to get suggestions. The route is not direct. The suggestions above are typically viewed as safe (except the one crossed out), and thus I will pass it along to the person whose sample I used. At the end of multiple steps, we ended up with four (4) compounds that mapped to some 10 bacteria to shift. Those bacteria resulted in just 7 suggestions. We are travelling well outside of the mapped world.

From Perplexity AI

To increase or decrease the abundance of the listed bacteria, specific strategies should be considered, but many target species are not commonly sought for probiotic modulation due to their environmental, pathogenic, or niche-specific nature. Below, approaches to influence each are summarized when evidence is available.

Increase These Bacteria

• Corynebacterium diphtheriae: There is no safe or recommended method to purposely increase C. diphtheriae, as it is a human pathogen responsible for diphtheria, a serious disease. Laboratory studies using animal models suggest that general Actinobacteria (the phylum to which Corynebacterium belongs) can be modulated using certain yeast probiotics or lactic acid bacteria in animal models, but these findings are not intended or safe for human manipulation targeting pathogenic species. Increasing this bacterium is not medically advised.
• Streptococcus anginosus: This member of the Streptococcus anginosus group is a commensal found mainly in the mouth, throat, and gut but is also opportunistically pathogenic. There is currently no evidence-based supplement or intervention to selectively increase S. anginosus in humans; it can be affected by local tissue conditions, diet, and the existing microbiome, but any intervention to increase such bacteria outside of medical settings is not recommended.
• Rhodopseudomonas palustris: This bacterium is environmental and photosynthetic, rarely present in the human body. In agricultural or industrial settings, it is cultured using media containing carbon (like molasses), nitrogen (like corn steep liquor), and trace minerals, kept at slightly acidic to neutral pH and moderate temperatures (around 37°C). There is no role for increasing this bacteria in humans; it is used as a soil and water probiotic for plants.
• Ralstonia solanacearum, Pseudomonas oryzihabitans, Sorangium cellulosum, Pseudomonas putida: These are mainly environmental, soil, or water-dwelling microbes. There is no supplement or dietary approach to increase these in the human gut or on human mucosa, and such actions are not studied for human health.

Decrease These Bacteria

• Cupriavidus necator: This environmental bacterium can be suppressed in biotechnological fermentations by nutrient limitation (especially removing nitrogen or phosphorus), but there is no guidance for targeting this in the human body, as it is not a usual component of the human microbiome. In environmental settings, its growth is limited by removing accessible carbon, nitrogen, or phosphate sources.
• Cupriavidus basilensis: Similar to C. necator, mainly handled in environmental and fermentation settings; standard medical interventions if infection occurs would use specific antibiotics based on sensitivity.
• Thermaerobacter sp. FW80: This is a thermophilic (heat-loving) environmental bacterium not typically present in or relevant to humans. Methods to decrease it would involve lowering environmental temperatures, not relevant to dietary or supplement choices.

Practical Notes

• Most of the bacteria on your list are not normal human microbiome residents and manipulating them carries significant health risks or is not practically or ethically possible.
• For environmental and plant-associated bacteria (such as R. palustris, P. putida, Ralstonia, etc.), enrichment is done in laboratory or agricultural settings with nutrient and temperature controls but is not safe or meaningful for humans.
• For bacteria that are pathogens or opportunists (C. diphtheriae, S. anginosus), intentional enrichment is never recommended.
• For reducing rare environmental or opportunistic bacteria in humans, general good hygiene and avoiding immunosuppression help, while in clinical cases, antibiotics may be used based on sensitivity.

In my prior post, Patterns of Microbiome Distributions Across Different Vendors, we saw that the distribution patterns were not consistent. With annotated samples from several different vendors, it was logical to see if there was consistency of bacteria identified across vendors for specific symptoms. This question was directed to me by Chidozie Ojobor, Ph.D.   The results can be viewed on my Special Studies page.

• Definitions:
  • Singleton: only found in one of the labs
  • Pair: two labs reported the same
  • Triplet: all three labs reported the same

Given the difference of sample sizes and thus significance levels, some lack of consensus is expected.

• uBiome: 790
• Thryve: 1542
• Biomesight: 4604

Over P < 0.001 data, we had the following

• Looking at bacteria to symptom agreement
  • 19558 singleton relationships
  • 1914 pair relationships
  • 94 triplet relationship. or 0.436%
• Looking at enzymes to symptom agreement
  • 74159 singleton relationships
  • 1629 pair relationships
  • 3 triplet relationship or 0.004%
• Looking at compound to symptom agreement
  • 69147 singleton relationships
  • 9364 pair relationships
  • 6189 triplet relationships or 7.3%

Of special interest is when we went to P < 0.0001 for compound to symptom , we got significantly better results for compounds

• 5952 singleton
• 45439 pair relationships
• 8153 triplet relationships or 13.7%

Bottom Line

While I am doing a naive estimate for compounds using KEGG data, the results support the model:

It is not the bacteria that causes the symptoms, it is the net amount of compounds produced by the bacteria that causes the symptoms. Surplus or deficiency of compounds can come from a vast array of different collections of bacteria. The bacteria may just be noise!!

This shifts any model to generate suggestions for a symptom to one further and significant indirection.

All of the date used is available as CSV files at Citizen Science Data Share.

This week I had some discussion with several CEOs of various microbiome testing companies and the question came up:

Can we take the percentile threshold from the Kaltoft-Møldrup(KM) Range computation and apply it to other tests?

If this is likely true, then we reduce the impact of the Blue “Whale in the Room”

Fortunately, I have the data to reasonably answer that (for the common good) and show charts from some of the 60+ providers that have had samples uploaded. Most people in this area are very siloed into one lab, one way and cannot see the forest, only the leaf that they are on.

Lactobacillus

Biomesight

Ubiome

Ombre

PrecisionBiome

Vitract

Thorne

Bifidobacterium Longum

Biomesight

Ubiome

Ombre

Not sufficient data (3 samples had it)

PrecisionBiome

Vitract

Not sufficient data (3 samples had it)

Thorne

Faecalibacterium prausnitzii

Biomesight

Ubiome

Ombre

PrecisionBiome

Vitract

Thorne

Bottom Line

The first item of note: Two labs will typically (99% odds!!) report no Bifidobacterium Longum! In both cases, they do report it, but rarely detect any. Some medical practitioners (not knowing better about the test) will advocate this strongly as a probiotic with no safe evidence support that. This returns us to Scott’s quotation cited above, the blue whale!

While conceptually, the ability to transfer percentile ranges between labs is very appealing, it appears to be unsafe. Looking at the lower bounds for Faecalibacterium prausnitzii we see the following suggested (eye balling – make your own estimates):

• Biomesight: 18%ile
• uBiome: 30%ile
• Ombre: 12%ile
• PrecisionBiome: 30%ile
• Vitract: 12%ile
• Thorne: 20%ile

To me, this settles the issues. The KM percentile estimates for ranges cannot be transfered across labs. They are lab specific (and unlike the naive mean and standard deviation approach that ignores the high skews) require significant sample size.