Back to Home

GLA v2.4 — Unified Systems Synthesis of DecodeME/PrecisionLife Genetics With the Gut–Liver–Autonomic Framework

Author: Michael Daniels · December 2025

Purpose

To integrate the largest combinatorial genetics dataset in ME/CFS (DecodeME/PrecisionLife) with the Gut–Liver–Autonomic (GLA) systems model, forming a cohesive mechanistic architecture that explains genetic vulnerability, clinical heterogeneity, post-exertional malaise (PEM), subtype structure, and therapeutic sequencing.

0. Executive Summary

DecodeME/PrecisionLife demonstrates that ME/CFS is not genetically silent. Instead, it is a polygenic, mechanistically heterogeneous, nonlinear condition in which disease risk emerges from interacting biological modules, rather than from single variants.

These interacting genetic modules map closely onto the GLA root architecture, amplifier system (M1/M2/M3), and SMPDL3B phenotypes. Together, they form a unified explanatory framework in which:

  • Genetics explains inherited vulnerability patterns
  • GLA explains symptom expression, PEM timing, subtype drift, and treatment sequencing

This synthesis integrates, within a single systems architecture:

  • combinatorial genetics
  • innate immunity and membrane biology
  • ER stress and EV glycome rigidity
  • hepatic metabolic load
  • vascular and endothelial instability
  • Ca²⁺–ROS ischemic loops
  • autonomic dysfunction
  • neuroimmune and synaptic fragility

The result is a coherent, biologically grounded model of ME/CFS as a systems disease of failed adaptive homeostasis.

Unified GLA–DecodeME Architecture A layered systems model showing DecodeME genetics expressed through root stress biology, SMPDL3B phenotype context, amplifier dominance, and downstream clinical expression. DecodeME / PrecisionLife + GLA v2.4 — Unified Systems Architecture DecodeME / PrecisionLife Genetics Polygenic, combinatorial risk signatures (nonlinear interaction modules) Root Stress Layer ER stress • EV glycome rigidity • hepatic metabolic load (GLA) SMPDL3B Phenotype Layer Deficient ↔ Shedding (membrane / microcirculation stability context) Amplifiers M1 metabolic • M2 vascular/endothelial • M3 autonomic/low-volume Clinical Expression PEM timing • orthostatic intolerance • cognitive/sensory load • metabolic crashes • vascular fragility
Diagram 1. DecodeME genetics and GLA physiology align as a single layered system: inherited risk → root stress biology → phenotype context → amplifier dominance → clinical expression.

1. Genetic Overlap: The Inherited Architecture Aligns With GLA’s Mechanistic Axes

Key finding

DecodeME identifies 259 candidate ME genes whose pathway enrichment converges on five dominant mechanistic domains:

  1. Innate immune signalling (TLR3, cytokine regulators, PDE4B)
  2. Cellular stress / ER / proteostasis pathways (MAPK9/JNK2)
  3. Calcium signalling and mitochondrial adaptation
  4. Neural and synaptic regulation (NLGN1, PTPRD)
  5. Autoimmune and MHC-associated regions (6p22.1)

These domains correspond closely to GLA v2.1–v2.3, despite the two frameworks having been developed independently using different data sources.

Genetic Clusters ↔ GLA Modules A left-to-right mapping showing DecodeME cluster themes aligning with GLA phenotype layers and amplifiers. DecodeME Cluster Themes GLA Mapping Immune / Cytokine Amplification TLR3 (context-dependent sensor) • PDE4B (amplifier) Calcium / Mitochondrial Adaptation Ca²⁺ handling • metabolic recovery vulnerability ER Stress / Proteostasis MAPK9 (JNK2) • stress response / remodeling limits Neural / Synaptic Regulation NLGN1 • PTPRD • cognition / sensory / sleep fragility SMPDL3B Shedding / Innate Amplifier Context Membrane reactivity • cytokine surges • inflammatory PEM M1 Metabolic Amplifier Ischemia → Ca²⁺ → ROS cascade • delayed PEM pattern Root Layer: EV–ER–GLA Load ER stress • EV glycome rigidity • hepatic load constraints Neuroimmune / M2–M3 Interaction Fog, sensory load, sleep disruption, autonomic drift One-to-one convergence supports unified architecture
Diagram 2. DecodeME cluster themes map directly onto GLA layers and amplifiers, suggesting independent convergence on the same disease architecture.

1.1 ME–Long COVID genetic overlap validates cross-condition mapping

DecodeME further demonstrates substantial genetic overlap between ME/CFS and long COVID:

  • 76 of 180 long COVID genes also appear in ME
  • Severe and Fatigue-Dominant long COVID pathways are most predictive of ME
  • Immune, metabolic, and cellular stress pathways dominate both conditions

This supports the use of long COVID EV, ER, vascular, and innate-immune research as upstream mechanistic evidence for the GLA framework.

2. Mechanistic Similarities: Genetic Pathways and GLA Amplifiers Are Structurally Aligned

DecodeME identifies four dominant mechanistic clusters.

GLA independently identified the same four amplifier systems.

2.1 Immune cluster ↔ SMPDL3B-shedding phenotype / innate amplifier

This cluster is characterized by TLR3-mediated innate sensing (context-dependent) together with PDE4B-driven cytokine amplification.

These genetic findings align with core features of the innate amplifier in GLA:

  • TLR4 hyperreactivity
  • SMPDL3B loss
  • PI-PLC–driven membrane instability

Importantly, TLR3 functions as a sensor, whose downstream effects depend on membrane and signaling stability, whereas PDE4B functions as an amplifier, directly disinhibiting TNF-α and IL-6 signaling. Together, these mechanisms explain inflammatory PEM, flu-like crashes, and cytokine surges when SMPDL3B-shedding physiology is present.

2.2 Calcium-metabolic cluster ↔ M1 metabolic amplifier

Genes involved in calcium signaling and mitochondrial adaptation are strongly enriched in DecodeME.

This aligns precisely with the GLA M1 metabolic amplifier, centered on the ischemia → Ca²⁺ → ROS cascade. Genetic vulnerability in calcium handling explains:

  • delayed PEM (24–48 hours)
  • muscle burning and “poisoned” sensations
  • impaired metabolic recovery
  • exertion-induced mitochondrial shutdown

This cluster provides inherited justification for the universality of metabolic PEM across ME subtypes.

2.3 Cellular stress / ER cluster ↔ GLA v2.3 EV–ER–GLA root layer

MAPK9 (JNK2) and related stress-response genes form one of the strongest genetic clusters.

This directly reinforces the GLA v2.3 root-layer model involving:

  • ER stress
  • EV glycome rigidity
  • hepatic metabolic overload
  • impaired post-exertional cellular remodeling

This cluster explains whole-system oversensitivity to exertion, food intake, sensory load, and stress, producing multi-axis PEM rather than isolated symptoms.

2.4 Neural-synaptic cluster ↔ neuroimmune amplifier (M2/M3-influenced)

Genes regulating synaptic structure, circadian stability, and neural signaling (including NLGN1 and PTPRD) are prominently represented.

These genetic findings connect directly to:

  • cognitive fog
  • sensory hypersensitivity
  • sleep disturbance
  • autonomic drift

Taken together, these findings suggest that neural and synaptic vulnerability may be intrinsic contributors, rather than purely secondary consequences of vascular or metabolic failure.

3. Clinical Phenotype Stratification: Genetics Explains Subtypes; GLA Explains Symptoms

PrecisionLife identifies mechanistic subgroups based on which pathways dominate an individual’s genetic signature. GLA explains how those genetic vulnerabilities are expressed through physiology.

3.1 Five mechanistic genetic subtypes

These genetic subtypes align closely with GLA’s amplifier and phenotype model:

Genetic subtype GLA phenotype Core symptoms
Immune-dominant SMPDL3B shedding Inflammatory PEM, flu-like crashes
Calcium-metabolic M1 Next-day PEM, metabolic shutdown
ER / stress-dominant BA–GLA high load Multi-axis crashes, food and exertion intolerance
Synaptic-dominant Neuroimmune Cognitive fog, sensory overload
Autoimmune / vascular M2/M3 Orthostatic intolerance, CBF reduction, presyncope

3.2 Subtype drift during recovery

DecodeME shows that some patients carry many distributed signatures, while others carry fewer but more concentrated signatures.

GLA explains that as:

  • ER stress decreases
  • BA–GLA load normalizes
  • endothelial tone stabilizes

patients may shift from M2 → M1 → low-amplifier states.

This concept of subtype drift is genetically plausible and clinically observed.

A left-to-right representation showing how dominant crash patterns can shift from vascular-dominant (M2) to metabolic-dominant (M1) and toward lower-amplifier stabilized states as physiological stability improves. Subtype Drift During Recovery M2 / Vascular Instability Perfusion-sensitive symptoms Orthostatic intolerance (OI) High fragility under load M1 / Metabolic-Dominant Delayed PEM (24–48h) Ischemia → Ca²⁺ → ROS bias Metabolic recovery limits Lower-Amplifier / Stabilized More resilient homeostasis Reduced crash amplification Better tolerance band ↓ ER stress ↓ BA–GLA load ↑ endothelial tone stability Interpretation: as root stress and vascular fragility improve, the dominant crash pattern can shift (drift) toward less amplified states.
Diagram 3. Subtype drift: improving stability can shift dominant patterns from perfusion-fragile (M2) → metabolic-dominant (M1) → lower-amplifier states.

4. Drug Repurposing Insights: Genetics Identifies Targets; GLA Identifies When They Are Safe

DecodeME highlights druggable pathways. GLA provides the sequencing and safety constraints.

4.1 PDE4B inhibition (e.g., apremilast)

  • Strong genetic support
  • Likely unsafe in unstable M2/M3 states (may worsen OI and autonomic instability)
  • Appropriate only in stabilized, innate-dominant phenotypes

GLA placement: midstream, late-phase intervention.

4.2 TLR3 agonism (rintatolimod)

  • Mechanistically appropriate for innate-deficient patients
  • Likely unsafe in active SMPDL3B-shedding phenotypes
  • Requires stabilized autonomic tone, low vascular fragility, and controlled ER stress

GLA placement: root-adjacent, post-stabilization.

4.3 MAPK9 / ER stress modulation

  • TUDCA directly reduces ER stress and JNK2 activation
  • Provides strong validation for GLA v2.3 root-layer positioning

GLA placement: root layer; safest broad upstream modulator.

4.4 Calcium handling

  • Confirms Phase 2.5 mitochondrial insulation strategy
  • Validates use of CoQ10, riboflavin, niacinamide, and TUDCA

GLA placement: Phase 2.5.

4.5 Neuroimmune tools

Agents such as guanfacine, clonidine (micro-dose), and low-dose naltrexone may be useful only after:

  • volume stability
  • endothelial stability
  • BA–GLA stabilization

GLA placement: downstream amplifier management.

4.6 SMPDL3B phenotype-specific interventions

Although SMPDL3B itself is not genetically encoded in DecodeME, its phenotypes are genetically contextualized.

Shedding phenotype

  • PI-PLC inhibition remains experimental and potentially dangerous
  • Requires extreme caution, especially in M2/M3 states

Deficient phenotype

  • Metformin (via AMPK activation) may improve membrane and metabolic stability
  • Genetics supports metabolic vulnerability in this subgroup

5. Methodology: Why Combinatorial Analytics Strengthens GLA

DecodeME’s combinatorial and machine-learning approach is uniquely suited to ME/CFS.

5.1 Captures nonlinear interactions

GLA amplifiers interact nonlinearly; combinatorial genetics captures the same structure.

5.2 Identifies mechanistic heterogeneity

PrecisionLife independently identifies immune-dominant, metabolic, ER-stress, synaptic, and autoimmune/vascular clusters that match GLA one-to-one.

5.3 Validates cross-condition architecture

Machine-learning importance shows:

  • severe long COVID ↔ mixed amplifiers
  • fatigue-dominant long COVID ↔ M1
  • autonomic/cognitive long COVID ↔ synaptic/autonomic amplifiers

5.4 Genetics provides blueprint; GLA provides expression

Genetics defines inherited vulnerability.

GLA explains lived physiology, PEM, subtype drift, and treatment response.

Genetics as Blueprint vs Physiology as Expression A two-panel comparison showing genetics as an inherited blueprint and GLA physiology as dynamic expression over time, linked by systems state. GENETICS Inherited blueprint • Largely static across time • Combinatorial / polygenic risk • Probabilistic vulnerability • Clustered pathway biases • Explains “why this person” GLA PHYSIOLOGY Dynamic expression • State-dependent & changeable • Amplifier dominance can shift • Explains PEM timing + phenotype • Guides treatment sequencing • Explains “what happens over time” Systems State Root stress, phenotype, amplifiers Key takeaway: genetics defines vulnerability; GLA explains expression, timing, reversibility, and safe sequencing.
Diagram 4. Genetics is the blueprint; GLA is the dynamic expression of that blueprint through systems state (root stress → phenotype → amplifiers).

Final Conclusion

DecodeME/PrecisionLife and GLA describe the same disease architecture from different angles:

  • Genetics reveals the inherited systems-level blueprint
  • GLA reveals the dynamic physiological architecture that produces PEM, OI, metabolic crashes, cognitive dysfunction, and vascular fragility

Together, they define ME/CFS as a multi-layered systems disease:

  • Root layer: genetic load → ER/EV/BA–GLA instability → SMPDL3B loops
  • Midstream layer: M1/M2/M3 amplifiers
  • Downstream layer: metabolic, autonomic, and neurocognitive expression

This unified framework provides:

  • a mechanistically precise model of ME/CFS
  • a blueprint for stratified clinical trials
  • a basis for precision treatment sequencing
  • a foundation for future biomarker development

References

DecodeME / PrecisionLife Combinatorial Genetics Preprint

Sardell, J. M., Das, S., Pearson, M., Kolobkov, D., Malinowski, A. R., Fullwood, L. M., Sanna, M., Baxter, H., McLellan, K., Natt, M., Lamirel, D., Chowdhury, S., Strivens, M. A., & Gardner, S. (2025). Identification of novel reproducible combinatorial genetic risk factors for myalgic encephalomyelitis in the DecodeME patient cohort and commonalities with long COVID . medRxiv preprint.

https://www.medrxiv.org/content/10.1101/2025.08.06.25333109v1

Note: This article is a preprint and has not been certified by peer review. It is made available under a CC-BY-ND 4.0 International license and should not be used to guide clinical practice.

Framework documents