Rose Walbrugh and I are proud to announce one click sending of data from BiomeSight.com based in the UK to MicrobiomePrescription.com. After you get your BiomeSight data processed, you can send the data across without needing to download and upload. You will be sent an email with an automatic login link (no more making up and remembering passwords!).

How to Do It and get Expert System Suggestion

Log in to Biomesight then on left Menu:

• Click Third Part App
• Click Microbiome Prescription

Find the sample that you wish to send:

If you need to RESEND sample, see the bottom — there is an alternative way. A rotating circle will appear

When Complete, you will see this shown

Check your email. You should get one like below (it is sent to the email used by biomesight)

Click Link. You should see a page like below

Click on my Profile and then click “Just give me suggestion”

After a few minutes of calculation (over 2 million pieces of information is evaluated), a page like this will appear with usually 1-2000 items listed. Click on what type of item interests you most, for example [Probiotics] and they will be shown…. By default, best items are at the top and worse at the bottom but by clicking table titles you can change the sort order.

If you click the “books” on the right, you will see the precise logic used with links to clinical studies.

Other Options

Once the [Send] button is clicked, you will get an email like shown below that allows you to login and explore your data deeper.

Clicking the link will log you in automatically, and you will see your sample and it’s identified as originating with BiomeSight.

BiomeSight specific distributions of bacteria will automatically become available once there is a large enough sample.

DISCOUNT CODE

As part of this celebration, a discount code “MICRO” is offered on BiomeSight services. This results in £60 off, which brings the price down to £89 per kit ($110). Local USA fulfillment is now setup. Expedited 2 day delivery at £4.95.

Resend Sample

From Biomesight Support:

To be clear, there’s 2 ways to send it – on the row itself, the button will not be available it was sent already. But you can also do it by selecting the rows and using an alternative button that will send it again regardless. The screenshot below shows both.

New Feature

After transferring the data you will get two emails. One to log into the site. The second is a PDF analysis with suggestions and literature supporting the suggestions.

Bottom Line

Microbiome Prescription is dedicated to working with labs to enrich user experience and knowledge. BiomeSight has stepped up to the plate for cooperation and win-win attitude.

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]
•  Bacillus subtilis and Enterococcus faecium (A Retrospective Study of Probiotics for the Treatment of Children With Antibiotic-Associated Diarrhea [2020])
•  Lactobacillus casei DN 11400 (Commercially Available Probiotic Drinks Containing Lactobacillus Casei DN-114001 Reduce Antibiotic-Associated Diarrhea, 2014) i.e. Yakult drink
• Lactobacillus GG (culturelle), Bacillus coagulans, and S. boulardii appeared to be most effective [2008]

If you find any other studies that is explicit on the probiotic strains used with good results, please email me at Ken/at\lassesen.com

To help find which probiotics contain the above see this page.

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.”
• Pycnogenol Ameliorates Asthmatic Airway Inflammation and Inhibits the Function of Goblet Cells [2016]
  • “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”
• Allergic Rhinitis: A Proprietary Extract of Pinus Pinaster Aiton (Pycnogenol) Is Found to Improve the Symptoms Associated With Allergic Rhinitis [2016]
• Inhibitory Effects of Pycnogenol® (French Maritime Pine Bark Extract) on Airway Inflammation in Ovalbumin-Induced Allergic Asthma [2013]
• Pycnogenol® Improvements in Asthma Management [2011]
• A Randomized, Double-Blind, Placebo-Controlled Exploratory Study to Evaluate the Potential of Pycnogenol for Improving Allergic Rhinitis Symptoms[2010 ] “With the limited number of 39 patients statistical predications were unattainable. In conclusion, Pycnogenol improved allergic rhinitis symptoms when supplementation was started at least 5 weeks before the onset of the allergy season.”
• Pycnogenol as an Adjunct in the Management of Childhood Asthma [2004]
• Pycnogenol Inhibits Immunoglobulin E-mediated Allergic Response in Mast Cells [2009]
• Pycnogenol Inhibits the Release of Histamine From Mast Cells [2003]

Bottom Line

The research to date suggests that it may take 2 or more months before significant benefits may be seen in some cases.

SOURCES: https://www.paulsmarteurope.com/ and https://www.naturitas.us/

“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.

Source: https://www.paulsmarteurope.com/symbioflor-1-tropfen-drops-50ml/

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]

Research

Hafnia alvei HA4597 Strain Reduces Food Intake and Body Weight Gain and Improves Body Composition, Glucose, and Lipid Metabolism in a Mouse Model of Hyperphagic Obesity 2019

• “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. “

Commensal Hafnia Alvei Strain Reduces Food Intake and Fat Mass in Obese Mice-A New Potential Probiotic for Appetite and Body Weight Management 2020

• “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. “

Role of the Gut Microbiota in Host Appetite Control: Bacterial Growth to Animal Feeding Behaviour, 2017

Bottom Line

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).

It is available as a specialized cheese starter.

This 2019 review, Is a vegan or a vegetarian diet associated with the microbiota composition in the gut? Results of a new cross-sectional study and systematic review, concluded:

” 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.

What is in a FastQ File

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:

CCGGACTACTAGGGTTTCTAATCCTGTTTGCTCCCCACGCTTTCGAGCCTCACCGTCAGTTACCGTCCAGTAAGCCGCCTTCGCCACCGGTGTTCTACCCAATATCTACGCATTTCACCGCTACACTGGGTATTCCGCGATCCTCTCCAGA

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)
• Bugs in software

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

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)

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.

An upload from a 3rd party site may be done by posting json to http://microbiomeprescription.azurewebsites.net/api/upload

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).

{ 
“key”:”3rdpartyKey”,
“logon”:”3rdpartyId”,
“Password”:”3rdpartyPassword”,
“taxonomy”:[ 
      { 
“taxon”:2321,
“percent”:0.000304
      },
      { 
“taxon”:2841,
“percent”:0.000983
      }
   ]
}

The taxonomy uses the official taxon numbers and the percentage.

Logon

On your site, create a page that does a post to /email/logon3rd with two elements:

<form method=”post”
action=” http://microbiomeprescription.azurewebsites.net/email/logon3rd“><input type=”hidden” name=”logon” value=”whatever” />
<input type=”hidden” name=”password” value=”whatever” />
<input type=”submit” value=”Logon to MicrobiomePrescription” />
</form>