I am currently working with BiomeSight.com to add Taxon numbers to their downloadable reports.
At first sight, this should be easy, the sample of their complete taxonomy looks like this:
The problem is that their software have forced items into an unnatural structure to make presentation easy. An item that is under Class — when you go to NCBI Taxonomy Browser may be listed as:
Sub-Class
Super-Class
Order
Sub Order
Family
The result is that many many items have to be resolve by manual inspection of NCBI to find the apparent match and individually assigned. Example below, notice the “Group II” item which required working from existing matches for a line to identify probable candidates.
Update [MicrobiomeSight] Set OID=2731342 Where [Order]='Group II'
Update [MicrobiomeSight] Set OID=1643688 Where [Order]='Leptospirae'
Other issues concern differences of spelling and renaming, i.e. Cerasicoccales vs Cerasicoccus that was found….
While NCBI shows
We have a possible old/atypical name being used which obtuficates reports. This is one of the key reasons that I am pushing for taxon numbers in all uploads because without them, we would have massive inconsistencies.
After getting all of the Genus and Above resolved, I hit an issue with the species.. namely the list shown below remain unresolved. A few I did a google for and found no hits. Many had incomplete names.
Example:
Bacillus polyfermenticus in NCBI is Bacillus velezensis variant polyfermenticus
Acholeplasma ales
Burkholderia eae
Candidatus Methylacidiphilum infernorum
Cryocola poae
Dechloromonas fungiphilus
Desulfovibrio aceae
Enterobacter aceae
Enterobacter rottae
Erwinia dispersa
Haererehalobacter salaria
Haloterrigena gari
Herpetosiphon agaradhaerens
Megasphaera geminatus
Mycobacterium indicus
Oscillospira eae
Tessaracoccus terricola
Pasteurella eae
Stenotrophomonas retroflexus
Stenotrophomonas griseosporeus
Trabulsiella farmeri
Vibrio bacterium
Bottom Line
All Phylum, Orders, Classes, Families and Genus had matching taxon assigned. At the Species level, 6445 were identified and 21 were not. This means 99.7% of species were given taxon numbers. I expect BiomeSight.com to offer uploadable formats soon, ideally with automatic transfer from their web site.
I just got out of the hospital for cellulitis where I was treated with IV antibiotics. My discharges notes said “take antibiotics to prevent diarrhea”. I asked which ones… blank faces. No one seem to have a concrete idea. So this is a review of the literature:
“Antibiotic-associated diarrhea (AAD) occurs in approximately 25% of patients receiving antibiotics.” [2008]
” analyzing 25 randomized controlled trials of probiotics for the prevention of AAD … more than half of the trials demonstrated efficacy of the probiotic. ” So a random probiotic may have just a 50% chance of being effective. [2008]
“We identified no evidence that a multistrain preparation of lactobacilli and bifidobacteria was effective in prevention of AAD or CDD. ” [2013]
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).
” 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 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.
By uploading, you consent to allow your microbiome data and symptoms to be made available to citizen scientists for further discoveries.
Required consent is cited above. 3rd party is responsible to obtain consent.
Json Structure
The structure is simple:
The key is issued by us and identifies where the data is coming from (“source”)
logon and password are the authentication pair that you generate. These are used for logging on. Logon and Password should be the same for all samples from the same user (so we can display on a timeline).
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