Core 54 Gene Set in ME/CFS

From PrecisionLife Candidate Genes to the Core 54

This page is derived from the PrecisionLife combinatorial ME/CFS analysis, which identifies a broad set of approximately 259 candidate genes associated with disease risk, phenotype variation, and shared biology across ME/CFS cohorts.

Rather than treating this gene set as a flat list, it has been systematically processed to create a structured working layer:

The resulting Core 54 should therefore be understood as:

Within this framework, genes are not interpreted as isolated drivers. Instead, they are positioned according to their influence on four key control variables:

Together, these variables determine cumulative unresolved duration:

D ∝ A(t) · (1 − p(t)) · τ_eff

Acquisition occurs when unresolved duration exceeds reset capacity:

D > R

In GLA terms, this page functions as a mechanistic bridge between polygenic findings and recovery-phase control failure. It shows where the strongest genetic signal concentrates across Gate 0 → Gate 5, and how those signals map onto the variables that govern persistence.

The purpose of this page is therefore not to catalogue genes, but to show:

Gate Definitions — Control-Layer Summary

The GLA gate architecture describes how polygenic signals distribute across a single recovery-control system. These gates do not represent separate mechanisms or stages of disease. They are layered modifiers of four control variables: A(t) (retrigger rate), p(t) (closure probability), τ_eff (signal duration), and R (reset capacity).

Persistence emerges only when cumulative unresolved activity exceeds reset capacity: D > R. This is the singular acquisition condition.

All gates represent layered modifiers of a single control system. Different perturbations may enter at different gates, but all converge on the same acquisition condition: D > R. No gate defines an independent mechanism of disease.

Gate 0 — Signal Termination Fidelity / Baseline Shutdown Precision

Count: 5 Primary variable: p(t)

Gate 0 represents the baseline quality of signal termination and recovery-phase shutdown prior to any acquisition or persistent instability.

At this level, signaling pathways can still activate and resolve normally, but the precision and reliability of signal termination are slightly reduced. This does not produce disease on its own, but it lowers the margin for clean recovery after stress.

Core biology

The genes in this block are primarily involved in turning signals off, including:

Genes in this block

Interpretation

Gate 0 defines the baseline fidelity of signal shutdown across cellular systems.

When functioning well:

When weakened:

This does not represent a pathological state. However, it creates a condition where:

In control terms:

Position in the model

Gate 0 is:

It is a predisposition layer, defining how cleanly the system can terminate signaling under normal conditions.

If this layer is weak, recovery begins from a noisier and less stable baseline, increasing the likelihood that higher gates—especially Gate 1—will be engaged under stress.

Gate 1 — Calcium Signaling Overlap and Recovery-Phase Instability

Count: 9 Primary variables: A(t), τ_eff

Gate 1 represents the first stage where instability becomes dynamically expressed, but remains fully reversible.

At this level, the system is still capable of normal activation and recovery, but the timing of signaling events begins to break down, particularly during the recovery phase following stress.

The central feature of Gate 1 is:

This leads to partial overlap of signaling activity, especially in calcium-dependent pathways.

Core biological processes

Gate 1 is primarily driven by interactions between:

Under normal conditions:

In Gate 1:

This creates a state of recovery-phase instability, not structural failure.

Genes in this block

These genes primarily regulate calcium entry, release, and recovery timing, along with upstream inputs that increase signaling frequency.

Calcium signaling and ER–MAM timing core

Upstream load and retrigger inputs

Interpretation

Gate 1 should be understood as a timing instability in signal termination, not a failure of signaling itself.

The key transition is:

This produces two linked effects:

1. Increased retrigger rate (A(t))

2. Prolonged effective signaling duration (τ_eff)

Net effect

Together, these changes create a system where:

This does not yet represent disease, because:

However, it creates the conditions for duration to accumulate over time, especially if stress is repeated.

Position in the model

Gate 1 is:

It does not define acquisition.

Instead, it provides the mechanical and temporal conditions that allow:

Gate 2 — ER Regulatory Field and Recovery Authorization (Hinge Layer)

Count: 11 Primary variables: p(t), R

Gate 2 corresponds to the acquisition hinge layer within the broader endoplasmic reticulum (ER) regulatory field. At this stage, recovery is no longer governed purely by timing, but depends on whether reset is actively reauthorized following stress.

The ER regulatory field is a distributed control environment governing recovery-phase completion, including:

These systems collectively determine whether signaling platforms are fully reset between cycles.

Core biological processes

Under normal conditions:

This allows each signaling cycle to terminate cleanly and prevents accumulation across cycles.

In Gate 2:

Control-level effect

Gate 2 produces two critical changes in system behavior:

1. Reduced closure reliability (p(t))

2. Reduced reset capacity (R)

Acquisition transition (hinge condition)

When unresolved activity accumulates beyond the system’s ability to reset:

D > R

this transition is executed at the level of the ER regulatory field.

Biologically, this corresponds to persistent recovery-phase non-closure, where signaling systems are no longer fully reset between cycles and instability becomes self-sustaining.

Genes in this block

These genes cluster around three tightly connected control functions:

1. Sterol sensing and lipid reset regulation

2. ER quality control and recovery capacity

3. Membrane organization and microdomain stability

Position in the model

Gate 2 is the singular hinge layer within the system.

All upstream instability (Gate 1), structural feedback (Gate 3), and routing variability (Gate 4) converge on this layer.

Final framing

Gate 2 is the point at which:

Gate 3 — Structural Embedding of Incomplete Reset

Count: 3 Primary variables: A(t) sensitivity, p(t)

Gate 3 represents the stage at which incomplete recovery becomes structurally embedded following failure of reset authorization within the ER regulatory field (Gate 2).

At this point, the system is no longer operating in a fully reversible state. Instead, persistent non-closure at the hinge level (Gate 2) begins to alter the physical organization of cellular systems.

In this context, Gate 3 should be understood as:

Core biological processes

Following hinge engagement (D > R):

These changes affect how cellular components are assembled, transported, and anchored, particularly within membrane microdomains and vesicular transport systems.

Genes in this block

These genes regulate membrane anchoring, vesicle trafficking, and cargo persistence:

Interpretation

Gate 3 represents a transition from:

The key effect is:

In practical terms:

Control-level effect

This produces a characteristic shift in system behavior:

1. Increased retrigger sensitivity (A(t) sensitivity)

2. Reduced closure reliability (p(t))

Net effect

Gate 3 lowers the threshold required to re-enter instability:

Position in the model

Gate 3 is a downstream consequence of hinge engagement.

Its primary role is to feed back into Gate 1:

Final framing

Gate 3 is the layer where:

Gate 4 — Routing, Clearance, and Re-Entry Variability

Count: 12 Primary variables: A(t) variance, p(t)

Gate 4 represents instability in how biological material is routed, processed, and cleared, introducing variability in how signals persist and re-enter the system.

Unlike Gate 3, which reflects structural consequences of incomplete reset, Gate 4 primarily influences how external and internal signals are reintroduced into signaling pathways.

In this context, Gate 4 should be understood as:

Core biological processes

Under normal conditions:

In Gate 4:

These changes do not create new signals, but alter when and how existing signals are reintroduced.

Genes in this block

These genes influence recognition, extracellular structure, and clearance dynamics:

Interpretation

Gate 4 modifies the timing and variability of signal persistence and re-entry:

This occurs through several mechanisms:

1. Extracellular vesicle (EV) persistence

2. Immune complex uptake

3. Endosomal routing variability

4. Mechanosensory and membrane-domain effects

5. Topology-dependent clearance

Net effect

Gate 4 converts persistence into:

This is experienced at the system level as:

Position in the model

Gate 4 operates as an input and amplification layer feeding into Gate 1.

External perturbations (e.g., infection, immune activation, vascular changes) can act through this layer:

Final framing

Gate 4 is the layer where:

Gate 5 — Cellular Maintenance Capacity and Recovery Bandwidth

Count: 14 Primary variable: R

Gate 5 represents the stage at which the system’s ability to repair, rebuild, and fully recover after stress becomes intrinsically limited.

At this level, instability is no longer driven primarily by excessive signaling or poor timing. Instead, it reflects a reduction in the system’s baseline capacity to restore normal function between cycles.

This includes processes such as:

Core biological processes

Under normal conditions:

These processes ensure that each recovery phase restores the system to a stable baseline, even after repeated stress.

In Gate 5:

As a result, the system accumulates unresolved damage or incomplete recovery across cycles.

Genes in this block

These genes reflect core components of cellular maintenance and recovery systems:

1. Mitochondrial and redox support

2. Proteostasis and intracellular repair

3. Genome stability and replication integrity

4. Transcriptional regulation and rebuilding programs

Interpretation

Gate 5 represents a shift from:

The defining feature is:

Control-level effect

This produces a distinct pattern:

1. Reduced reset capacity (R)

2. Increased sensitivity to normal load

Because recovery capacity is reduced:

Net effect

Gate 5 shifts the system into a state where:

This means:

Position in the model

Gate 5 is:

It is a severity and persistence layer.

It reflects the point where:

rather than the amount of stress or signaling activity.

Final framing

Gate 5 is the layer where:

Where the Strongest Genetic Signal Converges

When the full gene set is scored and organized within the GLA framework, a clear pattern emerges: the strongest genetic signals do not distribute randomly across biology. Instead, they converge on specific control points that govern recovery-phase completion.

The highest concentration sits at two key layers: Gate 1 (overlap timing) and Gate 2 (membrane reset authorization). These are the points where signaling duration is extended, and where recovery must be actively completed for the system to return to baseline.

These genes sit at the intersection of the processes that determine whether signaling is cleanly resolved or allowed to persist. In control terms, they influence the variables that govern cumulative unresolved duration:

A(t), p(t), τ_eff, and R

Together, these determine whether signaling activity is fully cleared, or whether it carries forward into subsequent cycles. Persistence emerges when unresolved duration accumulates beyond the system’s ability to reset:

D > R

Supporting structure beneath the leading genes

Just below this leading cluster, additional groups of genes reinforce the same biological pattern. These are not isolated findings, but coherent layers that support the same control points:

Together, these layers reinforce a consistent picture: instability does not arise from a single pathway, but from coordinated pressure on signal termination, overlap timing, membrane reset, and clearance processes.

Patterns in the broader 259-gene landscape

Looking beyond the retained Core 54, the wider candidate set shows three additional patterns that help contextualize the model:

First, a group of genes just outside the retained set cluster near the hinge, including ADPRH, CHCHD6, SLC25A24, TMTC1, and YWHAB. These suggest that additional mechanisms related to ER–mitochondrial timing and reset control may contribute at the boundary of the current working layer.

Second, a larger group of genes maps to visible execution layers, including neuroautonomic signaling, membrane anchoring, and endothelial biology. These appear to reflect how instability is expressed physiologically, rather than where it is acquired.

Third, a smaller set of genes associated with immune modulation and recovery buffering — including C3, LAIR1, IGF1R, and RB1CC1 — suggests that once persistence is established, additional systems may shape its maintenance and severity.

One additional bridge gene worth noting is ABCC6, which appears to connect sterol handling with extracellular and vascular interfaces. While not hinge-adjacent, it may influence how upstream signals are translated into systemic effects.

Core 54 Gene Table

Master reference table mapping the 54 retained core genes across the GLA gate architecture, including gate placement, face, primary control variable, biological function, and concise GLA role.

This table maps each gene across the GLA control architecture, showing where it acts (Gate), how it expresses within the system (Face), and which control variable it most plausibly influences. In GLA v3.0, the Face column does not refer to a biological compartment, but to how the same underlying control failure becomes visible at different levels:

• Visible → where dysfunction is physiologically expressed (e.g., endothelium, muscle)
• Stability → where signaling organization and membrane precision degrade
• Authorization → where recovery-phase reset becomes non-permissive (control layer)
• Cross-face → genes that influence multiple layers simultaneously

These are not separate mechanisms, but different expressions of the same recovery-phase control failure.

Interpretation, Limits, and Version State

This page presents the current Core 54 gene set as a structured, higher-confidence working layer within the GLA framework. It represents the subset of genes that most consistently align with the model’s control architecture across Gates 0–5, linking polygenic signals to mechanisms of recovery-phase instability.

Within this framework, genes are not treated as isolated findings. Instead, they are organized according to their most plausible roles in:

The resulting distribution is not random. The strongest concentration of retained genes sits in domains governing signaling overlap, membrane stability, and recovery control, consistent with the model’s central premise that ME/CFS reflects a failure of recovery-phase completion rather than a single upstream defect.

Interpretive scope and limits

This page provides a mechanistic organization of gene-level signals, not a definitive or final classification of disease biology.

It does not assume that:

Instead, the Core 54 should be understood as a working synthesis, where gene placement reflects the best current interpretation of how polygenic signals map onto control-layer functions within the GLA architecture.

Version state

The current map is intentionally maintained at the 54-gene level to preserve mechanistic coverage, structure, and interpretability at this stage of development.

Further refinement is expected, including:

At present, the emphasis is on maintaining a coherent working layer that captures the structure of the system, rather than prematurely compressing the model into a minimal set.

References

Sardell JM, Das S, Pearson M, Kolobkov D, Malinowski AR, Fullwood LM, Sanna M, Baxter H, McLellan K, Natt M, Lamirel D, Chowdhury S, Strivens MA, Gardner S.

Identification of Novel Reproducible Combinatorial Genetic Risk Factors for Myalgic Encephalomyelitis in the DecodeME Patient Cohort and Commonalities with Long COVID

medRxiv 2025.12.01.25341362.

doi: https://doi.org/10.64898/2025.12.01.25341362