Viral Reactivation and the Microbiome

I have written about antivirals in the past, and thought that it was time to do an update.

Viral re-activation is common with ME/CFS [2017]. It’s a chicken and the egg scenario. Did the viral re-activation cause ME/CFS or did ME/CFS cause viral re-activation. Most people do not ask about the third leg of this stool: Was there something else that contributed to both viral re-activation and ME/CFS (or the contemporary “Long Haul Covid” syndrome).

My model for ME/CFS is a microbiome dysfunction. So the question becomes, can a microbiome dysfunction also account for viral re-activation? Latest research says yes and identifies some bacteria involved!

Correlation analyses between the microbiome and viral titers revealed a positive correlation with Gracilibacteria, Absconditabacteria, and Abiotrophia and a negative correlation between Oribacterium, Veillonella, and Haemophilus. There was also a significant positive correlation between microbiome richness and EBV viral titers.

The influence of spaceflight on the astronaut salivary microbiome and the search for a microbiome biomarker for viral reactivation [2020]

It appears to be a two way street: “Our study is the first to report the impact of long-term subclinical CMV infection on host immunity and gut microbiota” [2018]

It also extends to the food that is consumed and microbiome interaction (i.e. production of short- and medium-chain fatty acids by some bacteria consuming the food).

Our studies of the differential activities of SCFAs and MCFAs as inducers or inhibitors of viral reactivation have implications for oncolytic strategies. The HDAC inhibitors butyrate, phenylbutyrate, and VPA have been investigated as lytic activators in cells, mice, and patients (3185,89). One risk of applying lytic induction therapy is that incomplete inhibition of viral replication by antiviral drugs could allow secondary infection and disease progression

Activation and repression of Epstein-Barr Virus and Kaposi’s sarcoma-associated herpesvirus lytic cycles by short- and medium-chain fatty acids [2014]

Vitamin D levels are usually low, very low with ME/CFS patients. “vitamin D deficiency can be considered as a risk factor for CMV reactivation”[2019] “For EBV, viral load was significantly higher when 25(OH)D levels were low, demonstrating an inverse correlation between 25(OH)D levels and EBV load. ” [2018]. Anna Dorothea Hoeck,MD, has had success in putting some ME/CFS patients in remission by using high dosages of Vitamin D (likely those that also has viral reactivation).

Bottom Line

While there is not an abundance of literature, we see that the metabolites produced by the microbiome can activate or deactivate existing latent viral infections. We also know a shift in bacteria is associated with increase viral titers. Last, we know that virus can alter the microbiome.

We end up with three legs on the stool with the microbiome being significant. It is very significant because it is the easiest to change.

Virus reactivation alters the microbiome which then produces metabolites causing fatigue, etc. The altered microbiome then feeds the virus…. I have often used the expression “Viro-forming the microbiome”. It’s a feedback loop that can be hard to break.

For the Brain Fogged: Suggestions

A reader from Europe has ME/CFS with severe brain fog. One of the challenges with Microbiome manipulations is that there are many, many approaches that may be taken. We do not have clear evidence on which has better results. Rather than prescribing a magic bullet path, I have made most of these paths available on Microbiome Prescription.

For the brain fog, this presents a dilemma. I have Dr. Jason Hawrelak’s recommendations for quick, ultra-generalized suggestions. In corresponding with this reader, I realized that the loss of executive decision making and brain fog — very common with ME/CFS (and I have experienced it) — left the person as the typical “deer in headlights’ seen with ME/CFS.

This week, I came up with a elegant solution. Using the microbiome sample and the symptoms that have been entered, I crafted some AI to generate suggestions based on the strong statistical relationships we have discovered via citizen science. Preliminary results are looking good.

To use this, you must enter your symptoms when the sample was taken. It uses both the sample and the declared symptoms.

This feature is under Symptoms / Causing Bacteria

If you do not have any symptoms entered (or the symptoms lacks strong associations with the current data), you will see this display

Check the symptoms for the sample — do you have all of them?

Your results may look like this:

Ken’s result during a ME/CFS relapse. Negative means too few. Positive means too many

This reader’s list is much shorted, but with several things in common

You have two choices on getting suggestions…. Clicking the “use this profile for suggestions” OR build out a custom profile. If you are brain-fogged… do the first.

This takes you to the usual suggestion page where you can scope suggestions.

If you are brain-fog, leave as is, or uncheck some items

This then takes you to the suggestions page.

For this reader, all of his avoids had less weight than most of the items above… so it is mainly a take this result.

The values in this case are so low, that you can reasonably ignore them

And the list goes on with Apples, magnesium, selenium, oregano being on the positive list by inference.

Feed Back from the EU Reader

“Thank you very much for your help you can use in the future my data to write blog posts if you want no problem. You made me cry of joy in a dark hour.
Interesting return of result so the Jadin Model fits me:”

I used the Jadin antibiotics (following her rotation protocol) with great success in one of mine earlier relapses. He has a cooperative MD that is willing to prescribe them. Rifaximin became available after that protocol became available – it has been cited on several ME/CFS sites [1] [2]. Personally, I would keep to the Jadin protocol and only include if after a couple of cycles there has not been sufficient progress.

As a FYI: I am pleasantly delighted that the antibiotics predictions came out matching Jadin’s protocol and isolated a small number of high value antibiotics to consider.

Inline image

Any role for Lithium?

A reader asked me about this element today. I know that the abundance or absence of some minerals/elements have health impact. A simple introduction is here. A quick summary of interesting studies is shown below

Problems

Lithium increases hypothyroidism [2020] [2020], unfortunately hypothyroidism is common with ME/CFS [2019]. This makes it a high risk experiment if thyroid levels are low. Zinc supplements may reduce the risk [2017]

It’s known impact on the microbiome is below (and has been added to the analysis site)

” Lithium, valproate and aripiprazole administration significantly increased microbial species richness and diversity, while the other treatments were not significantly different from controls. At the genus level, several species belonging to Clostridium, Peptoclostridium, Intestinibacter and Christenellaceae were increased following treatment with lithium, valproate and aripiprazole when compared to the control group. “

Differential effects of psychotropic drugs on microbiome composition and gastrointestinal function [2018]

“Bacterial richness was increased in both treatments compared to vehicle-treated animals; moreover, at the genus level, lithium increased the relative abundance of Ruminococcaceae and decreased Bacteroides

Psychotropics and the Microbiome: a Chamber of Secrets [2019]

Bottom Line

It appears that Lithium was tried on ME/CFS patients a few decades ago. We can infer that the results were not significant on the patient population as a whole. On the flip side, it does appear to have positive neurological impact for select conditions, with some risk. For Autism where it is known that the SHANK3 defect is present, we need a well constructed study to determine th benefits to risk factors.

Fecal Matter Transplants and Phages

FMTs have been tried for Chronic Fatigue Syndrome/ME with mixed success. The why of failures has been an ongoing interest of mine. We may now have a significant factor that has been ignored in these attempts.

 Fecal microbiota transplantation (FMT) as a special organ transplant therapy, which can rebuild the intestinal flora, has raised the clinical concerns. It has been used in the refractory Clostridium difficile, inflammatory bowel disease, irritable bowel syndrome, chronic fatigue syndrome, and some non-intestinal diseases related to the metabolic disorders. But this method of treatment has not become a normal treatment, and many clinicians and patients can not accept it. 

[Research progress of fecal microbiota transplantation] 2015

This week’s Economist had an extended essay on Viruses and the like: The aliens among us/The Outsider within, this provides good background.

In addition to this, there was a podcast reporting success with FMT was associated with higher Phage Diversity in the donor. Phages are the police of the microbiome.

In this retrospective analysis, FMTs with increased bacteriophage α-diversity were more likely to successfully treat rCDI. In addition, the relative number of bacteriophage reads was lower in donations leading to a successful FMT. These results suggest that bacteriophage abundance may have some role in determining the relative success of FMT.

The success of fecal microbial transplantation in Clostridium difficile infection correlates with bacteriophage relative abundance in the donor: a retrospective cohort study (2019)

My earlier posts on FMT

Bottom Line

This implies that for a greater chance of success and less risk, than DYI fecal transfer, that a lab that tests for possible infections AND for phage state may yield the best results.

Microbiome Symptom Cascade

I just pushed a new page. The model is simple: as the microbiome changes, these changes may cause new symptoms to appear. I have personally seen this cascade in people around me. This page uses symptoms sets entered by users to give rough estimates of what may be occurring next. In some cases, seeing the future may encourage pre-emptive actions today.

https://www.microbiomeprescription.com/Explorer/SymptomCascade

The process is simple, pick an initial symptom in the drop down.

For the symptoms below, just click the one that you have also. This will build up a list symptoms and a qualified list of predictions.

You can change any selected items… in some cases, it may result in nothing being shown.

You can reset to the top one only, or modify what is shown (more or less)

Click Reset to return to just one symptom.

Digging down to the bacteria pattern

As you build up a collection of symptoms, you can quickly determine if there is a bacteria pattern associated. There is nothing gained from adding a 100% item to it…. it means that everyone is the subset has those symptoms so it is redundant. You want to add items that are less then 100%, ideally a symptom you have with the lowest probability.

This transfers your selection across to the page that does statistical analysis on the associated bacteria, as shown below

Accuracy of Prediction depends on YOUR Symptoms being added!

I know there is a “bias” in the data because we do not have an ideal population of samples and symptoms to work from. This page uses what we have, I believe the prediction is better than what a typical medical professional would suggest.

It can be made better by people adding more microbiomes and associated symptoms to the database. This is Citizen Science.