Child with Autism, etc Microbiome Tool

This technically can be used for any microbiome dysfunction when there are multiple people living in the same house that are related.


You will see in my earlier post There is no normal or reference microbiome! illustrates that a “typical” microbiome varies greatly by many factors. This makes it extremely difficult to know if there are atypical values in a microbiome.

Known factors that can shift the microbiome includes:

  • Diet
  • Age
  • Gender
  • Longitude (distance from the equator)
  • DNA (thus race and ethnic group)
    • Epigenetic changes should be included here
    • For example FUT2, see this post.

When dealing with conditions known to be microbiome responsive, that is a change of microbiome can improve or deteriorate symptoms, we prefer not to be chasing a bacteria/taxa that is actually irrelevant to the condition because the shift was due to other factors described above.

About a year ago I worked with a mother with an autistic son, and put up my first attempt to doing a family analysis. Over the last year, the site and my understanding has evolved. I have just updated this feature to be closer to what a useful tool may be. I have a personal interest because I am a high function autism spectrum individual. Some prior posts.

The purpose of the tool is attempt to control for as many known variations of the microbiome as possible. The ideal solution would be a child with some condition, say autism, that have two same gender siblings — one older and one younger. With this combination we have restricted DNA impact, diet impact, longitude impact, gender impact, and age impact. Any aspect of the microbiome that is shifted away from the siblings are suspect.

Unfortunately, such families have become rare. With a single child family, we end up needing mom’s and dad’s as the best available control for DNA.

The Tool

At the top of the sample page there is a compare between family members button which will be there if you have two or more samples.

On the next page you will see the samples available. You can only compare samples that have been processed in the same manner. uBiome and Thryve are not compatible (see this post for why). You can select the source at the top (and only those from that source are listed).

In this case, we know that we are dealing with autism, so we include information about what published studies have reported. Our page before we view the taxonomy data may look like this:

You can only pick one Unhealthy and as many healthy as you wish.

Now we are ready to click the button at the bottom of the page.

On the next page you will useful information. Under Direction are letters for a few taxa. This come from published studies.

We have Alphaproteobacteria being reported as “High”. We see that the middle 50% are between 277 and 20493, Dick is way above this range with 31826. This looks like a good match. On the left, by the bacteria name is a checkbox. Check it.

Continuing down the page you may see further agreements. Put checkboxes on all of them.

This one should not be checked, because it looks like the household diet results in low for everyone. Thus Dick’s values (none) are not unusual.
This should be checked, because everyone has some and Dick has none AND low values have been reported in studies.
Rest of family is in the normal range, Dick is high and studies report high

When you are done, click the button at the top of the page.

Why are there duplicates with different values?

Each published study is a line. Some found high and some found low. There are many reasons why studies may differ. It is up to you to include or exclude.


This will return you to the sample page and a golden row of buttons will appear.

Clicking “View Hand Picked Taxa” will show the bacteria picked

Getting suggestions that may improve

Clicking the gold suggestion button, takes you to a screen that shows groups of microbiome modifiers that you wish to consider. I have covered these in previous videos.

In this case, we see the following suggestions:

Looks like all of the sugar replacements are bad.
And a list of recommended probiotics

Bottom Line

This tool can be used in many ways — for example a mother has a condition and eats close to what the family does. She would be the unhealthy one and the husband and kids for be the healthy one.

The goal is to create a ‘familial microbiome’ reference to compare to. We also use information from published studies to try focusing on the most probable bacteria involved.

Neuroinflammation Bacteria

A reader asked me if we knew which bacteria causes neuroinflammation. This is what I could find documented (often in association with specific medical conditions that have neuroinflammation)

Nutrients 11 02714 g003 550

Undigested Food and Gut Microbiota May Cooperate in the Pathogenesis of Neuroinflammatory Diseases: A Matter of Barriers and a Proposal on the Origin of Organ Specificity. [2019]
  • ” increased in abundance in mice with PD microbiomes include Proteus sp., Bilophila sp., and Roseburia sp., with a concomitant loss of members of families Lachnospiraceae, Rikenellaceae, and Peptostreptococcaceae, as well as Butyricicoccus sp. (Figure 6E). Interestingly, some taxa are altered only in ASO (Different DNA) animals (e.g. Proteus sp., Bilophila sp., and Lachnospiraceae), while others display significant changes independent of mouse genotype (e.g. Rosburia sp., Rikenellaceae, and Enterococcus sp.) … depletions in members of family Lachnospiraceae and Ruminococceae in recipient mice, a notable finding as these same genera are significantly reduced in fecal samples directly from PD patients  ” [2017]
  • ”  the relative abundance of 89 taxa differed significantly between groups with the majority of these changes accounted for by the Clostridiales order and within that, Lachnospiraceae and Ruminococcaceae. These taxa showed a range of macronutrient specific correlations with place memory. In addition, Distance based Linear Models found relationships between memory, inflammation-related hippocampal genes and the gut microbiota. ” The effect of short-term exposure to energy-matched diets enriched in fat or sugar on memory, gut microbiota and markers of brain inflammation and plasticity. [2016]
  •  Finally, alteration of gut microbiota (i.e., an increase in Bacteroidetes phylum and in Prevotellaceae family at the end of the treatment and a decrease in Ruminococcaceae family, Oscillospira and Lachnospira genus at the end of the withdrawal period) was detected. In conclusion, finasteride treatment in male rats has long term effects on depressive-like behavior, hippocampal neurogenesis and neuroinflammation and gut microbiota composition. [2019]
  • the inflammation related family Erysipelotrichaceae was more abundant  [2018]

Bottom Line

Low levels of Ruminococcaceae and Lachnospiraceae is the most common feature.

These two are also associated with:

Misc Notes:

  • Sesame seed decreases [2019]

The taxonomy nightmare before Christmas…

This post is intended to educate people more on the technical aspects of the microbiome. I am not talking about taking 4 samples from one stool and sending it to 4 different testing company. I am talking about one sample sent to one testing company which then provided their analysis and a FASTQ file. The raw data.

What is a FASTQ file (besides being megabytes big)? It is the DNA (technically the RNA) of the bacteria in the stool. It looks like this (using the 4 letters that DNA has):


The file that I am using as text would be around 16 megabytes. This data comes from a lab machine. The company then processes it through their software to match up sequences to bacteria.

In this post, I am using the FASTQ from uBiome and getting reports on the bacteria from:

  • ubiome
  • thryve inside
  • biomesight
  • sequentia biotech.

Naively, one would expect almost identical results. What I got is shown in detail below. At a high level we had the following taxa counts reported

  • ubiome – 253
  • thryve inside – 632
  • biomesight – 558
  • sequentia biotech 366

I did a more technical post on my other blog. From some providers, a taxonomy may be 40% on another 2% or even none… ugly!

Standards seekers put the human microbiome in their sights, 2019

The headaches!

Number One Issue: You cannot, repeat cannot, compare a taxonomy report from one lab with another. EVER!

  • I have 8 uBiome reports and 2 Thryve reports. I can compare the uBiome to each other and the Thryve to each other. I can never mix their direct taxonomy reports !

Number Two Issue: If I wish to compare different lab reports, I MUST obtain the FastQ files from each lab and process them thru the same provider. The FastQ files are the raw data! For me, I prefer to push them through multiple providers which means that the 10 reports suddenly become 40 or 50 different reports in my site.

My Headaches

I need to revise my site to show data by specific provider (while keeping the across all provider data still available). A lot of pages to revise and test.

The CCI thing and ME/CFS

A reader has asked me about CCI because of this post on Facebook

CCI – Craniocervical instability – Read article on it too

Back to strict scientific method…

If I said, “ME/CFS – has blood diagnose, have not found a single patient whose does not have blood, not a single one” people would look at me a little strange. Why? Because it is known that everyone has blood.

What is needed is simple and needs some numbers done in a proper study:

  • Is this specific for CFS or common across many conditions?
    • What percentage of people without CFS has CCI
    • What percentage of people with IBS but without CFS has CCI
    • What percentage of people with FM but without CFS has CCI
    • What percentage of people with Alzheimer’s have CCI
    • What percentage of people with Physical Brain Trauma have CCI
  • What percentage of people is recommended treatment 100% effective for?
    • Spontaneous remission is well know in ME/CFS with people assigning their last attempt to be the cause. It’s typical human association

Reading the up to date, with extensive documentation on ME-Pedia article on CCI I am left with the impression that it will be found in many patients and that this would be implied by the name of ME which means myalgic encephalomyelitis. Encephalomyelitis is inflammation of the brain and spinal cord. This was the original name and accurately describes typical findings of MRI and SPECT scans (see this post). ”

Inflammation causes compression…

My take is that the inflammation is primarily caused by metabolites from the microbiome. I say primarily, because parts of the brain may react to those metabolites and proceed to dump other signalling chemicals/metabolites into the body. A feedback loop. A feedback loop with a viable simple point of interruption — the gut microbiome.

Bottom Line

Remission from CCI is theoretically possible and has probably happens. Some chiropractors will likely claim that appropriate adjustment may be sufficient. A surgery impacts the spinal cord and may easily interrupt some feedback mechanisms. Below is the results of people who have had CCI
Published 2019,

The problem is that there is not a single multi-patients study on ME/CFS patients have been published on it. Looking at the multi-patient study with five years of follow up, show above- yes! It can result in improvement: totally expected because of its impact on the inflammation of the spinal cord. As a universal (or even common) method to full remission — the evidence does not support it.

Some taxa now have Modifiers

These were determined by the “Friend-or-Foe” analysis. This was done by examining which pairs of bacteria increase or decrease with a strong relationship (correlation). If something like Walnuts causes my friends to increase, then it will likely cause me to increase as a side effect. If Melatonin causes my friends to decrease, then it will likely cause me to decrease.

A video showing how we get this data

These were generated only for taxa that had:

  • No direct citations
  • No direct citations for the parents (only done when there are no direct citations)
  • No direct citations for the descendents (only done when there are no direct citations)

In short, no information. The logic being used is simple, we infer only when we have no data. Only the top 10 influences are used.

In terms of displays:

Inferred will usually be the only item listed for those below

Items added

class Epsilonproteobacteria
class Lentisphaeria
class Methanobacteria
class Synergistia
family Eggerthellaceae
family Peptoniphilaceae
genus Finegoldia
genus Gelria
genus Gemella
genus Murdochiella
genus Peptoniphilus
no_rank Bacillales Family XI. Incertae Sedis
no_rank Bacillales incertae sedis
no_rank Chlamydiae/Verrucomicrobia group
order Acidaminococcales
order Bifidobacteriales
order Coriobacteriales
order Corynebacteriales
order Eggerthellales
order Fibrobacterales
order Flavobacteriales
order Fusobacteriales
order Propionibacteriales
order Synergistales
order Tissierellales
order Veillonellales
order Victivallales
species Actinomyces odontolyticus
species Finegoldia magna
species Murdochiella sp. S9 PR-10
species Peptoniphilus lacrimalis
species Peptoniphilus sp. 1-14
species Peptoniphilus sp. 2002-38328
species Peptoniphilus sp. gpac018A
species Varibaculum sp. CCUG 45114
subclass Actinobacteridae
suborder Actinomycineae
suborder Coriobacterineae