+100%-

Eric Schadt, the study’s senior author, is not your average researcher, and Phillip Comella, the lead author, has personal experience with mysterious diseases and difficult diagnoses.

A biomathematician and bioengineer, Schadt has been at the forefront of biocomputational technologies for years. Schadt’s goal has always been to get at the biological underpinnings of disease. He’s a systems guy. Our genes, after all, they don’t work in isolation – they work in systems. Schadt believe that many potential therapies failed because they didn’t take into account the systems the gene was participating in.

Not content merely to highlight particular genes, Schadt was one of the first to use genetic data to construct “predictive networks” that targeted aberrant biological pathways that were causing. Once he did that, he circled back to use these pathway analyses to target the genes that were actually making a difference.

Schadt has pioneered the development of biomathematical approaches that identify systemic problems that are causing disease.

In 2011, Schadt founded the new Icahn Institute for Genomics and Multiscale Biology at the Mount Sinai School of Medicine in New York. It was about then that Schadt first showed up in ME/CFS. As MEpedia reported in 2011 and 2013, Dr. Schadt spoke about “applying his cutting-edge expertise to Chronic Fatigue Syndrome” in two ME/CFS conferences.

Phillip Comella, for his part, is a graduate student who spent years under the illusion he had one disease only to come to realize he had something entirely different. Only after convincing his skeptical doctor to do the correct tests did he get an accurate diagnosis. Check out his, at times, hilarious and, at times, horrifying story of medical misdiagnosis in “Science Gets Personal“.

In 2021, Schadt and Comella published a complex study suggesting that ME/CFS, long COVID and other fatiguing diseases share a “common molecular etiology”. Schadt’s predilection for going beneath the surface and uncovering systemic processes that have gone awry was in full display in this study. As in his other studies, he and Comella then circled back to uncover possible “master regulatory genes” which could potentially be tweaked one way or another to return people with ME/CFS to health.

The Study

A Molecular network approach reveals shared cellular and molecular signatures between chronic fatigue syndrome and other fatiguing illnesses” is a big, little study. It didn’t contain many participants (n=30; 15-ME/CFS, 15 healthy controls), but it subjected the data it got to a number of complex analyses – including some that had never been done before. It’s perhaps no surprise given all the work done that this 30-person study ended up with no less than 17 co-authors.

They drew whole blood before, and then 24, 48 and 72 hours after “moderate cardiopulmonary test (CPET)”. The CPET testing protocol was different from the maximal CPET tests done by Workwell and others.

It involved reaching 70% of age-predicted maximal heart rate and then maintaining it for up to 25 minutes. Some think that while a maximal exercise rate is not reached, that this protocol is actually more difficult for patients, as it’s twice as long and likely requires patients to be in their anaerobic zone for longer than a maximal exercise test. Lactate was measured every five minutes.

Several classes of immune cells (B cells, granulocytes, monocytes, natural killer (NK) cells, T cells) were filtered out the blood.  The gene expression in those immune cells and the whole blood was then assessed.

They found no differences in gene expression due to the exercise stressor but acknowledged that this may be due to the sample size. (I believe that Nancy Klimas has found differences in gene expression, but she measured it at multiple points during exercise.)

The study suggested that a high viral load might be paired with malfunctioning immune cells.

Next came what they called a “comprehensive RNA-seq data analysis pipeline” of the whole blood and the immune cells. The first part of that involved putting the blood through a “viral/clonal detection pipeline” which suggested, interestingly enough, that people with ME/CFS may have had a higher viral load.

Next, they looked at T and B cell clonality. When these immune cells get triggered by a pathogen or foreign object, they produce massive numbers of clones. The researchers found a sharp divide between the ME/CFS patients and the healthy controls (p<0.0001) with the ME/CFS patients producing a less diverse array of clones. That suggested that their T-cells may not be responding appropriately to danger signs. That was an interesting finding given that the pipeline test suggested the ME/CFS patients had a higher viral load – which seemingly should have triggered, one might think, a more diverse array of clones.

Next, they went beyond the pure gene expression results and used something called differential expression and machine learning to identify “gene expression features”. First, this approach picked out suspect genes, and then assessed the “directionality” those genes displayed.

They found “molecular differences” between the ME/CFS patients and controls such that alterations in the B and T-cell, granulocyte and NK cell signatures were able to pretty effectively distinguish ME/CFS patients from the healthy controls. That might fit the last two results: could the higher viral load have impaired immune cell functioning, leading to the production of a less diverse array of clones?

Next came Schadt’s forte – putting things into a “biologically relevant context”. First, they looked for “coherent subnetworks of co-regulated genes (modules of genes) for each of the 6 cell types”; i.e.; they were looking for teams of genes that work together to perform biological functions in the immune cells.

They identified 119 co-expression modules (clusters of genes with similar functions) which spanned 6 “co-expression networks” across the ME/CFS immune cells. Finding genes that were expressing themselves similarly across multiple immune cells was potentially big news.

The study suggested that similar core immune elements might be in play in both ME/CFS and long COVID.

One of the most daunting aspects of ME/CFS has been its possibly high degree of heterogeneity. If it may be a disease composed of very different subsets, disentangling and understanding each subset is potentially a long and arduous process. The road to healing in a disease characterized by core processes gone wrong, on the other hand, should be much shorter. This finding suggested ME/CFS was more a disease of common immune problems gone wrong.

That the ME/CFS signatures were “enriched” in about half of the modules/networks identified appeared to suggest that the researchers were on the right track.

Link to COVID-19, Lyme disease and others

Next, they took the top five genetic modules with enriched ME/CFS signatures and compared them with those found in a wide number of inflammatory and autoimmune diseases. They included Multisystem Inflammatory Syndrome in Children (MIS-C), Kawasaki Disease (KD), Macrophage Activation Syndrome (MAS), Neonatal Onset multisystem inflammatory (NOMID), Lyme disease, active Influenza (IAV), active COVID-19, early recovery stage after COVID-19, Mixed Connective Tissue Disease (MCTD), Sjögren’s Syndrome (SJS), Systemic Lupus Erythematosus (SLE), Systemic Sclerosis (SSC), Undifferentiated Connective Tissue Disease (UCTD), Primary Antiphospholipid Syndrome (PAPS) and Rheumatoid Arthritis (RA).

Similar disease signatures to ME/CFS showed up for diseases like MIS-C, Lyme disease, and COVID-19 but not for autoimmune diseases. The authors stated “our data further suggests a shared molecular etiology (between) CFS, COVID-19, and Chronic Lyme Disease”.

That’s, of course, a potentially very helpful result as it suggests that breakthroughs in those diseases might translate to ME/CFS. The COVID-19 similarity was particularly nice to see given the attention and enormous amount of funding being given to it.

Congress Approves Over a Billion Dollars to Study Long-COVID: Implications for ME/CFS

 

Next, they dug into the molecular pathways found in the genetic modules (groups of co-occurring genes) that were most associated with ME/CFS. The module most strongly associated with CFS (NKM4) was also, interestingly, highly enriched with a “recovering COVID-19” signature. The authors suggested that that “molecular link … may help explain why so many recovered COVID-19 patients seem to experience CFS-like symptoms.”

Four of the top 5 CFS-enriched modules were also enriched for another mysterious ailment which has not given up its mysteries easily – chronic Lyme disease. Given the recent dramatic burst in NIH funding for Lyme that was good news as well.

Lyme Disease is “Making It – Is Chronic Fatigue Syndrome (ME/CFS) Next?

 

Genes that could be driving the possible immune dysfunction in ME/CFS were identified.

Next, the Mount Sinai team went after “regulatory relationships” in an attempt to uncover the master regulators, or key drivers (KDs), or hub genes, for each of the top ME/CFS modules. Key driver genes are just like they sound; they’re key genes that affect the expression of many other genes.

That analysis uncovered 904 possible key driver genes. Of those, they highlighted 11 potential key driver genes (MXD1, STX3, DYSF, LYN, MLL2, NCOA2, PTPRE, REPS2, RP11–701P16.2, TECPR2, and TUBB1), which they expected would be able to alter the functioning of at least 3 of the 5 top ME/CFS modules.

When they assessed whether these genes were likely to be functionally significant (i.e. likely to be affecting a person’s biology),  the answer was an encouraging “yes”. That suggested they may have indeed found some genes that are “master regulators of vital biological processes associated with CFS”.

None of these genes had popped up in ME/CFS studies before, but given how different this analysis was, that might not be surprising. They have, however, been implicated in a number of immunological disorders. While they haven’t shown up in ME/CFS before, the authors reported that they had been shown to be key drivers in other immunological diseases.

At the end of the paper, they also provided a link to an interactive website containing the signatures, modules, KDs, and Bayesian network found in the study.

The Gist

  • Eric Schadt, a pioneering biomathematician, oversaw the study. Throughout his career Schadt has focused on getting beyond genetic and gene expression results to uncover the systemic problems that are causing disease.
  • The study assessed immune cells in blood drawn before and after an exercise stressor. After finding evidence of increased viral load, reduced clonal diversity and altered immune functioning, this very complex study uncovered “teams of genes” with similar gene expression (if I got that right) across the immune cells. That suggested that core immune processes had been altered across the board in ME/CFS.
  • That was encouraging as it suggested that instead of being a collection of different diseases ME/CFS has core immune processes that could be targeted.
  • Further analysis suggested that similar immune problems may also be occurring in long COVID, Lyme and other diseases but not in autoimmune diseases.
  • Key driver or master regulatory genes which appeared to be driving the immune dysfunction in ME/CFS were also identified. Some of these genes also appear to be key drivers in other immune diseases.
  • The study was successful enough that planning for a bigger study is underway. ME/CFS, however, is currently missing an animal model or cell line which would allow these researchers to test their findings and develop a therapeutic target.
  •  The authors, though, are are currently engaged in Long-COVID studies and hoped that the interest and new funding for long COVID will help them move forward more quickly.

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Comella Interview

I asked the lead author of the study, Phillip Comella, “… if anything like this has done in ME/CFS before?”

There have been other co-expression based studies in ME/CFS but to our knowledge this is the first study to integrate Bayesian networks to inform the mechanistic insights driving the co-expression modules.

Our study takes a network-based approach to discover teams of genes, or modules, that are dysregulated in disease and identifies team leaders, or key drivers, of those groups of genes.

We were able to show that the gene modules enriched from our unique CFS disease signature (genes that best distinguish CFS and controls) pipeline were also enriched for other unique CFS study disease signatures.

This is interesting because the disease signature algorithms can be highly picky with what they choose as the absolute best distinguishing genes for disease and control, making exact signature replication difficult particularly for under researched diseases.

The network-module approach shows that although these signatures are unique, they appear to work with a similar group of genes. These teams of genes are also enriched for other post-infection fatiguing illnesses, suggesting that although these illnesses are unique, there is perhaps a thread of biological dysregulation tying them together.

What would it take for results like these to be turned into therapeutic targets? What kind of studies would be needed to get to that point? 

Our team has been working on a Bayesian Network perturbation tool to predict the key driver’s therapeutic potential, but validating these finding can be tricky. ME/CFS does not currently have a model organism to help us study the disease mechanisms. Identifying an in vivo model organism or an in vitro cell line for ME/CFS would help us to test the therapeutic potential of altering the key driver genes our in silico pipelines have highlighted.

Our study suggests that a few of these fatiguing illnesses may share a common thread, which makes us hopeful that the recent interest with long COVID along with the increased NIH funding will help fuel findings that may make their way over to CFS.

Are there any follow-up studies planned?  

Yes, we are currently planning the next study to build off of these findings and are hopeful that a larger study will yield deeper insights.

Are you participating in any long-COVID studies?  

Yes, our team is actively collaborating with long COVID / post-covid syndrome researchers.  

Conclusion

This study was notable in a couple of ways. For one, it was overseen by a pioneering researcher. It used a kind of analysis (Bayesian network analysis) that had not been done before in ME/CFS. For another, it was simply successful. After finding evidence of increased viral load, reduced clonal diversity and altered immune functioning, the researchers went on to uncover “teams of genes” with similar gene expression (if I got that right). That suggested that core immune processes had been altered across the board in ME/CFS.

Further analysis suggested that similar immune problems may also be occurring in long COVID, Lyme and other diseases but not in autoimmune diseases. Key driver or master regulatory genes which appeared to be driving the immune dysfunction in ME/CFS were identified. Some of these genes also appear to be key drivers in other immune diseases.

Planning for a bigger study is underway. Using the results to develop a therapeutic target, though, would require testing them in an animal model or cell line – something that ME/CFS does not yet have. The authors hoped, though, that the interest in long COVID would help them move forward more quickly – and they are currently engaged in Long-COVID studies.

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Coronavirus vaccines

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