A reader asked about this, which I have no covered yet. Increased allergies and mast cell issues often occur with microbiome dysfunction and chronic fatigue syndrome.
Pycnogenol® (Extract of French Maritime Pine Bark) for the Treatment of Chronic Disorders [2012] “Due to small sample size, limited numbers of trials per condition, variation in outcomes evaluated and outcome measures used, as well as the risk of bias in the included studies, no definitive conclusions regarding the efficacy or safety of Pycnogenol(®) are possible.” Note dates of later studies below….
Effect of Pycnogenol® on an Experimental Rat Model of Allergic Conjunctivitis[AC] 2018 “The animal model of AC was successfully developed by using aforementioned way. PYC is a safe herbal product and it has alleviated the findings of ovalbumin-induced AC-similar to dexamethasone-histologically in this experimental model.”
“PYC inhibited the number of total inflammatory cells and levels of interleukin (IL)-4, IL-5, IL-9, and IL-13 in bronchoalveolar lavage fluid of OVA-induced mice”
“The probiotic Symbioflor 1 is a historical concoction of 10 isolates of Enterococcus faecalis. Pulsed-field gel electrophoresis revealed two groups: one comprising eight identical clones (DSM16430, DSM16432, DSM16433, DSM16435 to DSM16439) and a further two isolates (DSM16431, DSM16434) with marginally different profile” [2016]
“A double-blind, placebo-controlled multicenter study in 157 patients with chronic recurrent sinusitis investigated the occurrence of acute relapses during treatment of patients with a bacterial immunostimulant (3 x 30 drops/day), comprised of cells and autolysate of human Enterococcus faecalis bacteria (Symbioflor 1, n = 78) in comparison to placebo (n = 79)…. the occurrence of relapses (50 incidents) was about half (56%) the number observed under placebo (90 incidents)” [2002]
“the time span until occurrence of the first relapse was clearly longer under verum[Symbioflor-1] (699 days) than under placebo (334 days) and after the end of the observation period 91% of patients under verum experienced only one relapse compared to 62% in the placebo group (p = 0.01). ” [2001]
From PubMed
“Compared with the controls, probiotic intervention significantly upregulated the level of IL-10 and TGF-β, downregulated levels of IFN-γ, and increased progesterone level that reversed the trend of being Th1 predominance state ” [2020]
“1. E. faecalis stimulates the liberation of interleukin 1 (IL-1 beta) and interleukin-6 (IL-6) in a dose-dependent manner; the E. faecalis induced liberation of IL-1 beta and IL-6 is inhibited by dexamethasone (Dm) but not by cyclosporin A (CsA).” ” 2. E. faecalis stimulates the liberation of gamma-interferon (IFN-gamma) in a dose-dependent manner, which is inhibited by both Dm and CsA.” “3. Phytohemagglutinin (PHA)-induced liberation of gamma-IFN and interleukin-2 (IL-2) is inhibited by E. faecalis in a dose-dependent manner. ” [1994]
“For instance, Escherichia coli Nissle 1917 was a poor inducer of iNOS gene expression compared to the other E. coli strains, while Enterococcus faecalis Symbioflor-1 was more potent in this respect compared to all the eleven Gram-positive strains tested. ” [2014]
Personal Experience
I have used this for sinus issues in the past and it has been effective in clearing them.
A reader forwarded this to me with the following comment..
My mother is a health 58 years old women a little bit over weight she started to have after 2 weeks of use: headaches and feeling very fatigued so I think she has die-off, The interesting thing that happen she has swollen lymph nodes under her arm pits for about 25 plus years, the lumps started to regress, I cannot find the microbiome condition associated with this and this strain of probiotic she started to use.
Entero Satys
This is available from France. Link here. International availability is unknown. “Hafnia alvei is a psychrotrophic bacterium, it originates in raw milk and continues to grow in cheeses such as Camembert. abundant levels of Hafnia alvei can be found in raw milk cheese ” [Wikipedia]
“In conclusion, the present study showed that a daily provision of the H. alvei HA4597™ strain in genetically obese and hyperphagic ob/ob mice with HFD-exacerbated obesity decreased body weight gain, improved body composition, decreased food intake, and ameliorated several metabolic parameters, including plasma glucose and total cholesterol levels. “
“Finally, the low abundance of ClpB gene expressing Enterobacterales species found in the microbiota of obese subjects in the present in silico analysis may indicate insufficient anorexigenic signaling from the gut microbiota to the host, further providing the rationale for supplementation of commensal bacteria expressing the ClpB protein with an α-MSH-like motif. “
There was no detail microbiome information cited in studies above. The mechanism of operation was increase production of a metabolite from this bacteria that alters the number of meals.
The daily dosage is 50 million CFU ** / 100 billion cells, i.e. 5 x 10^7. Some (made in France) Camembert are reported to exceed this level in 1 gram (especially the surface).
People have asked why going different suggestion choices give different results – sometimes contradictory ones! The suggestions are determined by the bacteria included to alter. There is no magic way to select the bacteria. The site gives you a variety of choices/methods reflecting various requests expressed. This post attempts to explain these choices. Remember a typical microbiome result may be 600 bacteria – picking a dozen bacteria at random will give different suggestions every time. Many of the bacteria are ‘noise’ with no health impact in most cases
Quick Suggestions
This looks at only the bacteria in Dr. Jason Hawrelak criteria for a healthy gut. If you are outside of his ranges then low values are attempted to be increased and high values are decreased.
Number of Bacteria Considered: 15
If any other published author care to provide their criteria and grant permission to use, it can be added.
Medical Conditions
When you click on one of the items on the “Adjust Condition A Priori” link there is no microbiome to refer to so one is synthetically created. This is done by looking at the reported shifts and computing one.
From the Autism Profile
We apply some fuzzy logic here.
If just one report, we run with that value
If equal number of high and low we ignore.
If different number of high and low, we compute the difference, and deem the winner to be included
We then create a profile using the 12%ile value for low and 87% for high value.
The resulting synthetic microbiome is then processed using the 50%ile as our reference, scaling. The data to be processed may look like this:
Example for Autism
Number of Bacteria Considered: depending on condition: 5- 300 bacteria, typically 30
Advance Suggestions
This is the workhorse which gives many options to both increase and decrease bacteria included. It takes all bacteria (regardless of possible medical significance) as a starting point and adjusts them.
Add in all those I am missing that are seen in % of other samples
No bacteria is seen in every samples. Some people have none of some bacteria and they are concerned about this. This allows you to include very common bacteria that you are missing with a zero value. It is questionable if this philosophical belief have significance. The most common bacteria is listed below.
Limit to Taxonomy Rank of ….
It appears that often the real health significant items are at the lowest level of the bacteria hierarchy. There are good Lactobacillus strains and there are bad Lactobacillus strains (which have been reported to be fatal). This allows you to focus only the bottom levels. The more levels, the more bacteria are targeted – and the greater that ‘noise’ may hide what is significant.
A simple analogy. A kid at a school did some vandalism, you have a vague idea of who (the Species). Do you proceed to punish him with all of his friends (i.e. the Genus)? Do you punish those in classes that he is in (the Family) – keeping all of the classes in for detention. Do you push the entire grade in that school (the Order)? The entire school (the Class). Morale and school performance will change for those impacted.
Number of Taxa at different ranks seen in at least 10% of uploads
Bacteria Selection Choices
This attempted to filter to outliers before the [My Taxa View] was created which allowed hand selection. The philosophical reasoning is that very high and very low are the most probable cause of health issues. This discards bacteria that are in the middle range. You specify if you want to focus only on:
top/bottom 6% – Example Count: 6
top/bottom 12% – Example Count: 35
top/bottom 18% – Example Count: 66
Filter by High Lactic Acid/Lactate Producers
This was a special early request from a reader. It will filters to those bacteria that are lactic acid producers where the values are above the 50%ile. Everything else is excluded. This functionality has been improved using EndProducts Explorer and hand picking the taxa (thus you can do it for any end product in our system). Values are scaled from the difference to the median value.
This was retained because lactic acid issues often result in cognitive impairment, hence a simple route for those people.
Deprecated: Filtering by….
Filtering by medical conditions, symptoms have been deprecated and replaced by hand picked taxa. This allows unlimited combinations of conditions and symptoms to be handled.
Where to go to pick shifts matching end products, symptoms, medical conditions
My Biome View
This is for people that wishes to ‘eye-ball’ the choice of bacteria. This shows the relative ranking/percentile and how many samples have it. For a bacteria that is seen in only a dozen sample (like Legionellaceae below) or with a count of 100 or less, is unlikely to have any significance.
How are Hand Pick Taxa Handled?
We maintain the same pattern: What is the difference from the median/50%ile (NEVER the average) and we then scale it and feed those values into the suggestion engine.
How are Lab Results handled
The original approach of giving every bacteria equal weight has been updated recently. Like with Medical Conditions above, we create a synthetic microbiome using
1 down arrow for 18%ile value
2 down arrow for 12%ile value
2 down arrow for 6%ile value
1 up arrow for 82%ile value
2 up arrow for 88%ile value
3 down arrow for 94%ile value
Bottom Line
We always use a provided range (Jason Hawrelak) or the difference from the 50%ile/median. We never use Average — and feel that any lab that reference averages do not really understand the data and lack adequate statistical staff. Once upon a time, in the early days we used average but as we got familiar with the data we realized how wrong that approach was — the data is not a bell curve/normal distribution. A simple example is below.
While the medical condition is autism, the same approach may be applied to other conditions.
Technical Study on Autism Microbiome – comparing citizen science to published science. There is little agreement between published studies, but citizen science agrees with some published studies.
Child Autism microbiome over time – Part 2 – We look at the predicted symptoms for each of these 11 samples and how certain bacteria cluster that are associated with autism
End Products and Autism, etc – We look at citizen science identification of end product shifts associated with autism. Often the pattern is not too high Or too low BUT too high and too low — that is, out of balance
Child Autism microbiome over time – Part 3 – we examine the end products over the two years and saw that Camel Milk with L.Reuteri made a significant change in the microbiome. A side effect was that Eubacteriaceae started to climb and kept climbing until it was very extreme. This bacteria produces formic acid which alters the pH of the gut and is hostile to many bacteria, including Bifidobacterium.
” No consistent association between a vegan diet or vegetarian diet and microbiota composition compared to omnivores could be identified. Moreover, some studies revealed contradictory results. This result could be due to high microbial individuality, and/or differences in the applied approaches. Standardized methods with high taxonomical and functional resolutions are needed to clarify this issue. “
I have seen that also in extracting facts to the database. While diet (based on these studies) is still on the suggestions list, it is not recommended to use. Specific food is a very different question. Diets tend to be nebulous collections of foods making things very undefined.
I recall reading reviews of difference of reports by bloggers who took two samples from the same stool and sent them to different analysis labs. There are a dozen possible explanation for those differences.
Due to the demise of uBiome, a number of former users downloaded their FASTQ data files and processed that data through different providers that will determine the bacteria taxonomy from FastQ files. Most of us naively believed that the reports would be similar – after all it is digital data in and thus similar taxonomy would be delivered… It appears that things are a lot more complex than that.
A taxonomy download may be 20-30,000 bytes. This contains the bacteria name and hopefully the taxa number with the percentage or count out of a million. The FastQ file is the result of a machine reading the DNA bits of bacteria in your microbiome. It is a lot bigger. DNA bits are represented by 4 characters (A,T,C,G) The typical data would be 170,000,000 bytes (170 Megs).
If you examine the text, yes text, you will see line after line with:
These strings have been matched to certain bacteria, just like your DNA would match to you (and other people closely related). If you go over the US National Library of Medicine, you will find information on these sequences, like this for Bacillus subtilis , a common probiotic.
So, the process is matching up to a reference set. At this point of time we walk into the time trap!
A firm like uBiome may have gotten the latest values when it was started. I suspect a business decision was made not to constantly update them. Why you ask? The answer is simple, to maintain consistency and comparability from sample to sample over time. If they use newer ones, then they should reprocess the old ones to be consistent, but then reports will change in minor or major ways — resulting in support emails and phone calls. Support can be a major expense. So keep to what we started with. I suspected that with uBiome Plus, they were working on using new reference values, after all it was a different test!!
Each provider has a different set of reference sequences. Their sequences may be proprietary (not in the publish site above). This means that to compare results, you need to use the same reference sequences to match with your FastQ microbiome data. If not, it may result in a “bible” by taking page 1 from King James Bible, page 2 from the Vulgate, page 3 from Tynsdale’s translation, etc. Things become a hash.
Another issue also arises, bacteria get renamed or refined. The names used in an older reference library may not match the names in a latter reference library.
For myself, I have the FastQ for all of my uBiome tests and my Thryve Inside tests. I will continue on requiring these FastQ files from testing firms so I can keep the ability to compare samples to each other overtime by running them through the same provider.
I have created a page to allow comparison between FastQ files processed to taxonomy by different provider. The button to get to it, is at the top of the Samples Page – “FastQ Results Comparison”
This takes you to a list of all of your samples. Note that I have 4 samples with the same date below. It is actually just 1 FastQ file interpreted by four different providers. There are additional providers.
This produces a report showing the normalize count (scaled to be per million). I also have the raw count on the page as tool tips over each numbers.
Who has the right numbers?
Without full disclosure by all of the providers, it is difficult to tell.
With all things equal, the current provider that you are getting samples processed through would be the first choice. Why? it allows you to do immediate comparisons. This is not that critical because both https://www.biomesight.com/and https://metagenomics.sequentiabiotech.com/ will convert a FastQ file to a taxonomy in less than a hour.
What about Research Findings?
Fortunately, researchers use the same process for each study. That means that the results are relatively independent of the process used. It does mean that Study A may find some bacteria are high or low and this is NOT reported in Study B. The why may be very simple, that bacteria was never looked for. Things get fuzzy. With the distribution of bacteria known for a particular method, then we can determine if it is high or low… but that means sufficient samples with that method. With uBiome, we had a large number of samples from this one provider and that allow us to make some good citizen science progress.
Bottom Line on why the difference
Different reference libraries
Change in bacteria classifications (same sequence, different name)
I recall reading reviews of difference of reports by bloggers who took two samples from the same stool and sent them to different analysis labs. There are a dozen possible explanation for those differences.
Due to the demise of uBiome, a number of former users downloaded their FASTQ data files and processed that data through different providers that will determine the bacteria taxonomy from FastQ files. Most of us naively believed that the reports would be similar – after all it is digital data in and thus similar taxonomy would be delivered… It appears that things are a lot more complex than that.
A taxonomy download may be 20-30,000 bytes. This contains the bacteria name and hopefully the taxa number with the percentage or count out of a million. The FastQ file is the result of a machine reading the DNA bits of bacteria in your microbiome. It is a lot bigger. DNA bits are represented by 4 characters (A,T,C,G) The typical data would be 170,000,000 bytes (170 Megs).
If you examine the text, yes text, you will see line after line with:
These strings have been matched to certain bacteria, just like your DNA would match to you (and other people closely related). If you go over the US National Library of Medicine, you will find information on these sequences, like this for Bacillus subtilis , a common probiotic.
So, the process is matching up to a reference set. At this point of time we walk into the time trap!
A firm like uBiome may have gotten the latest values when it was started. I suspect a business decision was made not to constantly update them. Why you ask? The answer is simple, to maintain consistency and comparability from sample to sample over time. If they use newer ones, then they should reprocess the old ones to be consistent, but then reports will change in minor or major ways — resulting in support emails and phone calls. Support can be a major expense. So keep to what we started with. I suspected that with uBiome Plus, they were working on using new reference values, after all it was a different test!!
Each provider has a different set of reference sequences. Their sequences may be proprietary (not in the publish site above). This means that to compare results, you need to use the same reference sequences to match with your FastQ microbiome data. If not, it may result in a “bible” by taking page 1 from King James Bible, page 2 from the Vulgate, page 3 from Tynsdale’s translation, etc. Things become a hash.
Another issue also arises, bacteria get renamed or refined. The names used in an older reference library may not match the names in a latter reference library.
For myself, I have the FastQ for all of my uBiome tests and my Thryve Inside tests. I will continue on requiring these FastQ files from testing firms so I can keep the ability to compare samples to each other overtime by running them through the same provider.
I have created a page to allow comparison between FastQ files processed to taxonomy by different provider. The button to get to it, is at the top of the Samples Page – “FastQ Results Comparison”
This takes you to a list of all of your samples. Note that I have 4 samples with the same date below. It is actually just 1 FastQ file interpreted by four different providers. There are additional providers.
This produces a report showing the normalize count (scaled to be per million). I also have the raw count on the page as tool tips over each numbers.
Who has the right numbers?
Without full disclosure by all of the providers, it is difficult to tell.
With all things equal, the current provider that you are getting samples processed through would be the first choice. Why? it allows you to do immediate comparisons. This is not that critical because both https://www.biomesight.com/and https://metagenomics.sequentiabiotech.com/ will convert a FastQ file to a taxonomy in less than a hour.
What about Research Findings?
Fortunately, researchers use the same process for each study. That means that the results are relatively independent of the process used. It does mean that Study A may find some bacteria are high or low and this is NOT reported in Study B. The why may be very simple, that bacteria was never looked for. Things get fuzzy. With the distribution of bacteria known for a particular method, then we can determine if it is high or low… but that means sufficient samples with that method. With uBiome, we had a large number of samples from this one provider and that allow us to make some good citizen science progress.
Bottom Line on why the difference
Different reference libraries
Change in bacteria classifications (same sequence, different name)
I just pushed out an update on http://microbiomeprescription.azurewebsites.net/ that may help you understand what various prescription, over the counter and some supplements may be doing to your microbiome.
Select any of the links highlighted below
The next page will show some choices at the top:
Compare Impact
This is intended to allow you to better choice between alternatives – for example Aspirin versus Paracetamol (acetaminophen). I am sure people will find more uses for it.
The process is simple, search for each item, and put a check beside it. Select the Compare Impact radio button and then click the submit button below it.
This will take you to a page listing the impact side by side. In this case we seel that their impacts are similar, but different on a few items. At the family level there are a few differences
If a family that is important to you is shifted the wrong way, you may wish to consider the better one
Compensate
This is intended when you are prescribed drugs to treat some conditions and wish to reduce the impact on the microbiome by counteracting the drug or drugs impact on the microbiome.
For this example, we pick lovastatin (a statin), Famotidine (Pepcid AC).
We may wish to first see how much impact they have together (do they reinforce or counteract each other)
Bad news — they reinforce each other in decreasing many families
Just pressing back, and changing radio buttons, and submit produces suggestions.
The suggestions are done by creating a virtual microbiome report based on the above shifts and running that through our AI engine.
The suggestion page is the new format with the long lists hidden until you ask to see them.
The Take or Avoid list is defaulted to 100 items (which is one reason that I toggle visibility). Remember – none of these items are guaranteed to work, nor do you need to take all of them. Each item increases your odds…
The avoid list values are a lot higher, and thus you may wish by reducing any of these items that you are taking.
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