OpenAi's new ChatGPT

Thanks @Braganca

What happens if you feed it the blood, metabolites and proteomics measures that were abnormal in the recent Beentjes et al. study? What would it then give as an explanation for these findings.

For example these were all increased in ME/CFS patients compared to controls:

1 Leptin

2 Cadherin-related family member 5

3 All-trans-retinol dehydrogenase [NAD(+)] ADH4

4 Cadherin-related family member 2

5 Retinol-binding protein 5

6 Lysosomal Pro-X carboxypeptidase

7 SPARC-related modular calcium-binding protein 1

8 Cholinesterase

9 Complement factor H

10 Receptor tyrosine-protein kinase erbB-2

11 Lysosome membrane protein 2

12 A disintegrin and metalloproteinase with thrombospondin motifs 15

13 Keratin, type I cytoskeletal 18

14 Ribonuclease 4

15 Phosphatidylcholine-sterol acyltransferase

16 Histidine-rich glycoprotein

17 Inactive serine protease PAMR1

18 Complement factor I

19 Carboxypeptidase M

20 Properdin

 

@ME/CFS Skeptic This is the response from the o1 model, I’m not sure if the model o1-pro that Derya is using is different or more advanced...

====

Below is a synthesis of how these findings might fit into broader ME/CFS pathophysiology. The upregulation of these diverse proteins—ranging from immune and inflammatory mediators (complement factors, proteases) to metabolic regulators (leptin, retinol pathway proteins) and structural/adhesion molecules (cadherins, keratins)—suggests a chronic, multi-system disturbance. Please note that the explanation below is not definitive, as ME/CFS research is still evolving, but it highlights plausible connections.

1. Chronic Immune Activation & Inflammation

Complement Factors (H, I, Properdin)

• These components regulate the complement cascade, a key part of innate immunity. Upregulation can indicate ongoing or excessive immune activation. In ME/CFS, immune dysregulation may leave patients in a chronic, low-grade inflammatory state—elevating complement proteins.


Proteolytic Enzymes and Protease-Related Proteins

• Several of the listed proteins are enzymes or enzyme regulators (e.g., Carboxypeptidase M, Lysosomal Pro-X carboxypeptidase, ADAMTS15). Such enzymes can modulate inflammation, tissue remodeling, and the breakdown of extracellular matrix or signaling molecules. Chronic immune activation can drive the expression of these proteases.

Histidine-Rich Glycoprotein (HRG) and SPARC-Related Proteins

• HRG plays roles in coagulation and immune complex clearance, while SPARC-related proteins modulate cell-matrix interactions. Their upregulation can reflect ongoing inflammation, attempts at tissue repair, or dysregulated immune complex handling.


2. Metabolic and Hormonal Dysregulation

Leptin

• Leptin is traditionally known for regulating energy balance and appetite, but it also has immunomodulatory functions. Elevated leptin has been described in some ME/CFS cohorts and may point to metabolic stress or persistent low-grade inflammation (leptin can promote Th1-type immune responses).


Retinol Pathway Components (ADH4, Retinol-Binding Protein 5)

• Vitamin A (retinol) is crucial for immune regulation and mucosal integrity. Abnormalities in vitamin A processing (e.g., elevated retinol-binding proteins and retinol dehydrogenase) could reflect a dysregulated immune response or impaired ability to handle oxidative stress and inflammation. Retinoid pathways are also involved in gene expression and may be tied to epigenetic or metabolic shifts in chronic diseases.

Phosphatidylcholine-sterol Acyltransferase (LCAT)

• LCAT is involved in lipid metabolism, especially in the formation of mature HDL particles. Elevated LCAT or changes in lipid pathways might indicate an altered or compensatory response to chronic inflammation, as lipid transport and inflammation are closely intertwined.


3. Neuroinflammation and Neurological Aspects

Cholinesterase

• An increase in cholinesterase (an enzyme that breaks down acetylcholine) could hint at altered cholinergic signaling. Acetylcholine plays anti-inflammatory roles via the vagus nerve’s “cholinergic anti-inflammatory pathway.” If the body is increasing cholinesterase, it may dampen some anti-inflammatory signaling—potentially contributing to ongoing inflammation.


Receptor Tyrosine-Protein Kinase erbB-2 (HER2)

• Although classically studied in oncology, erbB-2 (HER2) can also be involved in growth and survival signaling in other tissues, including neural cells. Dysregulation of growth factor receptors might reflect chronic tissue stress or attempts at repair.


4. Structural and Adhesion Molecules

Cadherin-Related Family Members (5 and 2)

• Cadherins mediate cell-to-cell adhesion, crucial in maintaining tissue integrity, including the blood-brain barrier (BBB) and vascular endothelium. Increased cadherin-related proteins may indicate either a reactive attempt to reinforce cell boundaries against ongoing inflammatory insults or a sign of endothelial/epithelial stress.


Keratin, Type I Cytoskeletal 18

• Keratins are part of the cytoskeleton in epithelial cells. Elevated circulating levels can occur due to cell turnover or stress (sometimes measured as markers of cell damage in liver or epithelial tissues). This could reflect higher baseline tissue stress or damage in ME/CFS.


5. Lysosomal and Extracellular Matrix Remodeling


Lysosome-Associated Proteins (LAMP2, Lysosomal Carboxypeptidase)

• Lysosomal enzymes and membrane proteins often go up when cells are undergoing stress or needing more autophagy/“clean-up” capacity. Chronic inflammation can boost lysosomal function, both in macrophages and other cells, as a response to clearing cell debris or immune complexes.


ADAMTS15 (A Disintegrin and Metalloproteinase with Thrombospondin motifs 15)

• ADAMTS enzymes participate in extracellular matrix (ECM) remodeling. Upregulation might relate to persistent tissue remodeling or micro-injury in muscle, vasculature, or connective tissues—common in chronic inflammatory states.


6. Putting It All Together: A Multi-System Disruption

1. Immune System: Elevated complement factors, proteases, and immunomodulatory proteins (HRG, leptin) suggest a system under chronic inflammatory strain.

2. Metabolic Pathways: Alterations in the retinol and lipid pathways could signify an adaptive (or maladaptive) response to persistent oxidative stress and immune activation.

3. Neuroimmune and Structural Components: Increases in cholinesterase, cadherins, and cytoskeletal proteins point to possible neuroinflammatory or endothelial involvement, consistent with the widespread symptoms seen in ME/CFS (e.g., “brain fog,” orthostatic intolerance, sensitivity to stimuli).

4. Cellular Stress & Repair: Upregulation of lysosomal and ECM-remodeling enzymes underscores an ongoing attempt to handle cellular damage or turnover.


Overall Explanation

The upregulation of these proteins likely reflects a persistent, low-grade inflammatory and metabolic stress state in ME/CFS. This “chronic activation” scenario can involve overlapping pathways: immune cells repeatedly triggered (complement activation, proteases), energy metabolism strained (leptin, retinol pathway shifts), and cellular or tissue-level remodeling efforts (cadherins, ECM proteases, lysosomal enzymes).

Such findings align with broader themes in ME/CFS research—namely, that patients often show evidence of immune dysregulation, altered neuroendocrine function, and metabolic/mitochondrial stress. While these results do not pinpoint a single cause, they reinforce the notion of a multi-system pathology requiring integrative research and treatment approaches.


Disclaimer

The above discussion is a scientific interpretation of possible biological relevance. It is not a definitive conclusion about diagnostic markers or therapeutic targets. Further replication and functional studies are needed to confirm how each protein specifically contributes to ME/CFS pathophysiology. If you have questions regarding diagnosis or treatment, please consult a qualified medical professional.

 

Here is the answer from anthropic Claude AI. Mentioning MECFS to the Prompt biases the results:

Prompt : The following have been found to be increased in a patient group. suggest causes :


1 Leptin
2 Cadherin-related family member 5
3 All-trans-retinol dehydrogenase [NAD(+)] ADH4
4 Cadherin-related family member 2
5 Retinol-binding protein 5
6 Lysosomal Pro-X carboxypeptidase
7 SPARC-related modular calcium-binding protein 1
8 Cholinesterase
9 Complement factor H
10 Receptor tyrosine-protein kinase erbB-2
11 Lysosome membrane protein 2
12 A disintegrin and metalloproteinase with thrombospondin motifs 15
13 Keratin, type I cytoskeletal 18
14 Ribonuclease 4
15 Phosphatidylcholine-sterol acyltransferase
16 Histidine-rich glycoprotein
17 Inactive serine protease PAMR1
18 Complement factor I
19 Carboxypeptidase M
20 Properdin

Answer :

Let me analyze these proteins and suggest potential underlying causes/conditions. Several patterns emerge from this list:

1. Metabolic/Adipose Tissue Related:
- Leptin (adipokine involved in energy homeostasis)
- Phosphatidylcholine-sterol acyltransferase (lipid metabolism)
- These suggest potential metabolic syndrome or obesity-related conditions

2. Retinol/Vitamin A Metabolism:

- ADH4 (retinol metabolism)
- Retinol-binding protein 5
- Could indicate altered vitamin A metabolism or liver dysfunction

3. Strong Complement/Immune System Component:
- Complement factor H
- Complement factor I
- Properdin
- Suggests inflammatory or immune-mediated process

4. Cell Adhesion/Structure:
- Cadherin-related family members (2 and 5)
- Keratin 18
- SPARC-related protein
- Points to potential tissue remodeling or epithelial changes

5. Proteolysis/Protein Processing:
- Lysosomal Pro-X carboxypeptidase
- Carboxypeptidase M
- ADAMTS15
- Suggests active tissue remodeling or protein turnover

6. Growth/Cancer Related:
- ErbB-2 (HER2) receptor
- Often associated with certain cancers

Given these patterns, some potential conditions to consider:

1. Cancer (particularly epithelial/breast cancer due to HER2, KRT18)
2. Metabolic syndrome/obesity
3. Chronic inflammatory condition
4. Liver disease
5. Autoimmune disorder

and now same Prompt for o1 chatGPT

Summary of Common Causes Across Proteins

Inflammation: Most of the listed proteins are linked to inflammation or immune response.

Cancer: Several are markers or mediators of cancer progression.

Liver Disease: Involvement of proteins related to metabolism suggests liver involvement.

Autoimmune Diseases: Dysregulation of the immune system could lead to elevated levels.

Metabolic Disorders: Dysregulation in metabolism, such as in obesity or diabetes, could elevate some proteins.

Interesting to note that 3 out of 5 conditions listed are in line with the original study.

 

I think, this is the future of medical research : I am sharing below a dialogue using a detailed prompt which integrates symptoms, findings and then asks for causal factors. During the end of the conversation I submit two hypotheses : One for "viral persistence" and one for n-linked glycosylation disruption. Needless to say that what is used for prompting could be biasing the results.

Check out the full dialogue here :https://chatgpt.com/share/676bc5c6-57f8-8006-b485-e3043d35f408

A part of the dialogue follows :

A researcher suggests that viral persistence is the cause. Comment on the relevance of this hypothesis

The hypothesis of viral persistence as the underlying cause of the syndrome is highly relevant and merits consideration. Here’s a detailed evaluation of this hypothesis in the context of the provided information:

1. Relevance of Viral Persistence
Viral persistence refers to the inability of the immune system to completely clear a virus, leading to chronic, low-level infection or reactivation of latent viruses. This hypothesis aligns well with the clinical and biochemical findings:

Triggers Linked to Viral Infections

• EBV (Epstein-Barr Virus): A well-documented example of viral latency and reactivation, EBV can lead to long-term immune dysregulation, mitochondrial stress, and metabolic shifts.
• COVID-19 (SARS-CoV-2): Persistent viral RNA and proteins have been identified in some patients with long COVID, contributing to prolonged inflammatory and metabolic responses.

2. Mechanisms of Viral Persistence and Syndrome Features
Immune Dysregulation:

• Chronic Inflammation: Persistent viral presence triggers prolonged activation of the innate immune system, including mast cells and NK cells, as seen in the syndrome.
• Complement System Activation: Elevated complement factors (e.g., factor H, properdin) in patients suggest ongoing immune responses, potentially driven by viral antigens.
• Low Morning Cortisol: Chronic infections may dysregulate the hypothalamic-pituitary-adrenal (HPA) axis, leading to adrenal insufficiency and an inability to regulate inflammation.

Mitochondrial Dysfunction:

• Many viruses, including EBV and SARS-CoV-2, reprogram host cell metabolism to support their replication, causing:
  • Reduced oxidative phosphorylation efficiency.
  • Increased glycolysis (Warburg-like effect), matching observed glycolysis dysregulation in the syndrome.

Endoplasmic Reticulum (ER) Stress:

• Persistent viral replication can overwhelm the ER’s protein-folding machinery, contributing to observed stress markers such as decreased phosphatidylcholines and elevated fibrinogen.

Neurotransmitter and BH4 Metabolism:

• Viral persistence may disrupt tetrahydrobiopterin (BH4) metabolism, affecting serotonin and dopamine production. For instance, EBV has been linked to neurotransmitter pathway disruptions.

Histamine and Mast Cell Activation:

• Chronic viral infections can stimulate mast cells directly or via inflammatory mediators, resulting in impaired histamine clearance and increased sensitivity.

Connective Tissue and Ehlers-Danlos Syndrome:

• Viral persistence might weaken connective tissues indirectly by promoting chronic inflammation, oxidative stress, and nutrient depletion, exacerbating EDS symptoms.

3. Supporting Evidence for Viral Persistence
Viral Metabolite Fingerprints:

• Elevated metabolites like xanthine, xanthosine, and kynurenine suggest chronic inflammation and immune activation, which are hallmarks of viral persistence.
• The presence of complement proteins, lysosomal enzymes, and histidine-rich glycoproteins indicates ongoing cellular and immune responses, potentially linked to viral activity.

Overlapping Syndromes:

• Conditions like long COVID and chronic EBV infection share striking similarities with this syndrome, further suggesting that persistent viral antigens could be a driver.

4. Limitations of the Viral Persistence Hypothesis
While viral persistence is a compelling hypothesis, it may not fully account for all aspects of the syndrome:

• Environmental Triggers: Non-viral factors like organophosphate and mold exposure could independently induce similar dysregulation through mechanisms unrelated to viral persistence.
• Metabolic Dysregulation: The extensive metabolic imbalances observed may not entirely stem from viral factors and might involve additional processes, such as genetic predisposition or mitochondrial primary disorders.

And now for a second hypothesis. Observe how there is conflicting information in the answer (related to mannose) :

Another researcher suggests that n-linked glycosylation disruption is the cause. Comment on the relevance of this hypothesis




The hypothesis of N-linked glycosylation disruption as the cause of the syndrome is highly relevant and compelling, particularly in light of the provided information. N-linked glycosylation is a critical cellular process in which oligosaccharides are attached to asparagine residues of proteins, affecting their folding, stability, localization, and function. Disruption of this pathway could account for many of the observed features of the syndrome.

1. Relevance of N-linked Glycosylation Disruption
Central Role of Glycosylation in Cellular Function
N-linked glycosylation impacts:

• Immune Response: Proper glycosylation is essential for the function of immunoglobulins, complement proteins, and cell-surface receptors, all of which are implicated in the syndrome.
• Protein Folding and ER Stress: Disruption of glycosylation can lead to the accumulation of misfolded proteins in the endoplasmic reticulum (ER), triggering ER stress. This aligns with the observed markers of ER stress.
• Metabolic Pathways: Glycosylation enzymes and precursors depend on proper flux through metabolic pathways, such as glycolysis and nucleotide sugar synthesis. Disruption could explain the syndrome’s glycolysis dysregulation and metabolic abnormalities.

Triggers Linked to Glycosylation Defects
Several of the syndrome's triggers—viral infections (e.g., EBV, COVID-19) and environmental exposures (e.g., organophosphates, mold)—can impair glycosylation:

• Viruses: Many viruses interfere with host glycosylation machinery to evade the immune system or promote their replication. EBV and SARS-CoV-2, for instance, can alter N-linked glycosylation of immune proteins.
• Environmental Toxins: Organophosphates and mold toxins can inhibit enzymes involved in glycosylation pathways, leading to systemic disruption.

2. Mechanisms of N-linked Glycosylation Disruption and Syndrome Features
Endoplasmic Reticulum (ER) Stress:

• Disrupted glycosylation results in misfolded glycoproteins accumulating in the ER, causing unfolded protein response (UPR) activation. This matches findings like reduced phosphatidylcholines and elevated markers of ER stress.

Immune Dysregulation:

• Improper glycosylation of immunoglobulins and complement proteins can lead to:
  • Overactive innate immunity (e.g., mast cell activation, NK cell dysregulation).
  • Chronic inflammation, as seen in the syndrome.
  • Impaired clearance of pathogens and persistent viral antigens.

Metabolic Disruptions:

• Decreased Glycolytic Intermediates: Glycosylation depends on metabolic precursors like glucose and nucleotide sugars. Observed glycolysis dysregulation and reduced levels of glycosylation-related metabolites (e.g., mannose) suggest impaired substrate availability.
• Nitrogen and Urea Cycle Dysregulation: Glycosylation is nitrogen-intensive, and disruption could exacerbate elevated ammonia levels and other nitrogen metabolism issues.

Connective Tissue and Ehlers-Danlos Syndrome:

• N-linked glycosylation is vital for structural proteins like collagen. Disrupted glycosylation could directly impair collagen stability, explaining the association with connective tissue disorders like Ehlers-Danlos syndrome.

Mast Cell Activation and Histamine Clearance:

• Glycosylation is critical for enzymes that degrade histamine and stabilize mast cells. Disruption could lead to the observed mast cell activation and histamine intolerance.

Neurotransmitter and BH4 Metabolism:

• Glycosylation affects the transport and enzymatic activity of neurotransmitter-related proteins, potentially explaining serotonin and dopamine metabolism disruptions.

3. Supporting Evidence for Glycosylation Disruption
Biochemical Markers:

• Decreased levels of phosphatidylcholines, sphingomyelins, and phosphoglycerides are consistent with impaired glycoprotein and glycolipid synthesis.
• Elevated mannose-related metabolites suggest glycosylation pathway perturbations.
• Reduced N-linked glycan precursors or intermediates could align with findings such as reduced levels of glycosylation-related lipids and amino acids.

Overlap with Known Disorders:

• Congenital disorders of glycosylation (CDGs) present with symptoms that overlap with this syndrome, including metabolic dysregulation, immune abnormalities, and connective tissue issues.

Triggers and Glycosylation Disruption:

• Viral Triggers: Viruses like EBV and SARS-CoV-2 hijack glycosylation machinery, modifying host immune glycoproteins and enzymes.
• Toxins: Mold and organophosphates are known to disrupt glycosylation through oxidative stress and direct inhibition of glycosylation enzymes.

4. Limitations of the Glycosylation Hypothesis
While the hypothesis is strong, some aspects require further exploration:

• Trigger Independence: Glycosylation disruption may not fully explain symptoms arising from purely metabolic or mitochondrial dysfunction (e.g., ammonia elevation from urea cycle defects).
• Non-glycosylation-related Metabolites: Some findings, like low morning cortisol or elevated fibrinogen, may have indirect rather than direct links to glycosylation defects.

5. Integrating Glycosylation Disruption into the Causal Model
A plausible scenario is that N-linked glycosylation disruption acts as a primary driver or critical amplifier of the syndrome:

1. Triggers (e.g., EBV, SARS-CoV-2, toxins) damage glycosylation pathways, leading to ER stress, immune dysregulation, and metabolic imbalances.
2. Secondary Effects: Impaired glycoprotein function perpetuates chronic inflammation, immune activation, and connective tissue dysfunction.
3. Downstream Consequences: Feedback loops between glycosylation defects, mitochondrial dysfunction, and metabolic dysregulation sustain and exacerbate the disease state.

 

The problem here is we have a sort of GIGO (garbage in garbage out) situation. Chatgpt is repeating a lot of false assumptions that researchers often make.

We don’t have evidence of that. Probably there is no chronic inflammation since this has been heavily studied.

Probably not true either, see Hutan’s recent review of the literature.

Anyways, not gonna go through the whole message, but chatgpt is trained on the current literature, a lot of which is problematic, as unfortunately the case in medicine, much research presents a possibility as a final finding, a correlation as a causation etc.

If we are to figure out ME, we need someone that can rethink many of our assumptions, because they often turn out to be at best oversimplified at worse false, and chatgpt can’t do that, it is by making a regurgitation of all our assumptions.

That doesn’t mean it isn’t a useful tool. In fact it’s incredibly useful. But the spark that leads to an advancement in ME research won’t come from an LLM (atleast one with capabilities not far ahead of todays) but an LLM might help analyse results, elaborate theories etc. It’s a tool.

 

Very interesting to see how these things are developing but sorry to say I don't think this "o1" model is particularly useful. Ironically it's repeating the same mistakes as a lot of third rate academics - using the same generalities, talking in the same speculative, broad-brush terms, muddling together disparate ideas in a rather confused way... all very Twitter-sciencey, really. I would never have had any use for this.

 

What patients think or what researchers think about the usefulness of these tools is irrelevant really. What matters is actionable results and knowing the strengths and weaknesses of these tools . If such tools can help us connect pieces of the puzzle together then this is what counts.

Unfortunately it was the decision of humans that similar methods have not been used even when they identified relevant research targets years ago (from 2015 to be precise) before human researchers have found them.

Luckily all of this is documented in this forum and elsewhere.

 

That is the primary problem. They're just vague regurgitations of generalities, literally less than you'd learn simply by reading the associated Wikipedia articles.

There is nothing compelling in the above responses to build a detailed hypothesis upon.

 

Perhaps this particular run appears to output generalities ( I believe it is not) but I have seen numerous cases where LLMs are able to analyze information taking into account different conceptual levels and identifying information which was not readily available.

I haven't seen a single case where LLMs are given differentially expressed metabolites, a group of symptoms, comorbidities related to MECFS with the purpose of analyzing this information and asking for causal factors. I have done it and I must say I am impressed.

Until then I find it counter intuitive to dismiss this technology so easily.