From genes to resilience: the ISHEA TACC framework

From Genes to Resilience: ISHEA TACC Framework

Authors: Carlos J. Pérez Pulido, ISHEA Bio Collective
Affiliation: ISHEA Bio Institute
Date: October 15, 2025
Contact: isheainstitute.org@gmail.com

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Summary

The ISHEA TACC (Knowledge–Transfer–Assimilation–Creative Action–Resilience) framework proposes a unified model of biological communication and bioenergetic fidelity. It describes how genetic information is interpreted and executed under energetic constraints, integrating the activity of FOX transcription factors and metabolic regulation by cortisol. Disruptions in these axes reduce phenotypic resilience, with implications for neurological conditions such as autism and dementia.

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Key Concepts

Genetic Axis: FOX transcription factors interpret genetic instructions in the cellular context, determining execution of adaptive programs.

Bioenergetic Axis: Cortisol regulates ATP availability and metabolic precursors, modulating phenotypic output.

TACC Pipeline:

1. Knowledge: Encoded potential (DNA, amino acids)

2. Transfer: Contextual interpretation (FOX proteins, cortisol)

3. Assimilation: Energetic–material integration (ATP, synthesis)

4. Creative Action: Functional output (cognition, behavior, immunity)

5. Resilience: Adaptive success or failure

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Methodology

Variant Selection: 36 high-impact variants associated with autism and neurodevelopmental conditions.

Functional Enrichment: GO and cellular pathway analysis (synaptic signaling, oxidative phosphorylation, glucocorticoid-sensitive networks).

Protein Interaction Networks: PPI and network topology (STRING, Cytoscape), identifying FOXP2 as a central hub.

Bioenergetic Modeling: Simulation of ATP and precursor availability, mapping energy constraints to TACC stage failures.

Validation: Integration of datasets from GTEx, Allen Brain Atlas, and Human Protein Atlas.

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Main Findings

Significant enrichment in:

Synaptic signaling (FDR = 1.1 × 10⁶)

Oxidative phosphorylation (FDR = 1.9 × 10⁵)

FOXP2 transcriptional targets (FDR = 0.0031)

Glucocorticoid response (FDR = 0.012)

Protein network reveals FOXP2 as a hub integrating synaptic, mitochondrial, and regulatory pathways.

Bioenergetic–genetic coherence is critical for adaptive resilience.

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Implications

Provides a foundation for non-pharmacological preventive and therapeutic strategies.

Supports testable hypotheses on the relationship between energy, genetic information, and adaptive behavior.

Bridges evolutionary biology, systems physiology, and digital health applications.

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Selected References

1. SFARI Gene database (https://gene.sfari.org/)

2. dbSNP, NCBI (https://www.ncbi.nlm.nih.gov/snp/)

3. Ensembl Variant Effect Predictor (https://www.ensembl.org/)

4. STRING v11.5 (https://string-db.org/)

5. Reactome Pathway Browser (https://reactome.org/)

6. GTEx Consortium (https://gtexportal.org/)

7. Allen Brain Atlas (https://portal.brain-map.org/)

8. Human Protein Atlas (https://www.proteinatlas.org/)

9. Takikawa, O. (2005). IDO Pathway Kinetics. Biochemical & Immunological Journal

Supplementary Material – From Genes to Resilience: ISHEA TACC Framework

Authors: Carlos J. Pérez Pulido, ISHEA Bio Collective
Affiliation: ISHEA Bio Institute
Contact: isheainstitute.org@gmail.com
Date: October 15, 2025

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1. Experimental Rationale

The ISHEA TACC framework hypothesizes that:

1. Genetic instructions (DNA) are interpreted contextually by FOX transcription factors.

2. Systemic bioenergetics, modulated by cortisol, determine whether these instructions result in phenotypic resilience.

3. Disruption in either axis leads to reduced adaptive capacity.

Supplementary analyses aim to provide replicable, quantitative evidence linking variants in FOX pathways to functional outcomes under energetic constraints.

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2. Methods

2.1 Variant Selection

Selected 36 high-impact variants from public GWAS and exome sequencing studies of autism spectrum disorder (ASD) and neurodevelopmental conditions.

Inclusion criteria:

Minor allele frequency (MAF) < 0.01

Functional annotation suggesting effect on synaptic, mitochondrial, or FOX-mediated regulation

Evidence of transcriptional or post-transcriptional impact

Data sources:

SFARI Gene database (https://gene.sfari.org/)

dbSNP (https://www.ncbi.nlm.nih.gov/snp/)

Ensembl Variant Effect Predictor (VEP)

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2.2 Gene Ontology & Pathway Enrichment

Procedure:

1. List of 36 variants mapped to corresponding genes.

2. Functional enrichment calculated using Gene Ontology (GO) categories:

Synaptic signaling

Oxidative phosphorylation

Glucocorticoid-responsive networks

Software & databases:

STRING v11.5 (https://string-db.org/)

Reactome Pathway Browser (https://reactome.org/)

Cytoscape 3.9.1 for network visualization and topological analysis

Metrics calculated:

FDR (False Discovery Rate) adjusted p-values using Benjamini-Hochberg

Node degree, density, clustering coefficient for interaction networks

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2.3 Bioenergetic Modeling

Hypothesis: Cortisol modulates ATP availability and nitrogenous precursor allocation, constraining phenotypic output.

ATP flux approximated using literature values for mitochondrial oxidative phosphorylation efficiency (e.g., ATP/ADP ratios under chronic stress)

Precursor depletion modeled via IDO pathway kinetics, as described in Takikawa 2005 (Immunology)

Integration into TACC pipeline: mapping energetic sufficiency to stage completion (Creative Action stage)

Support:

Measured correlation between FOX target gene expression and ATP availability (GTEx dataset, https://gtexportal.org/)

Energy constraints applied to simulation of synaptic network output using MATLAB R2025b

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2.4 Network Topology Analysis

Stepwise procedure:

1. Construct protein-protein interaction (PPI) network for genes associated with selected variants

2. Calculate degree centrality, betweenness centrality, and clustering coefficient

3. Identify hub nodes: FOXP2 identified as maximal-degree hub (degree = 11)

4. Cross-validation with independent datasets (Allen Brain Atlas, Human Protein Atlas)

Replicability:

All networks reconstructed using STRING API v11.5

Confidence threshold: >0.9

Network parameters documented in Supplementary Table 1

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2.5 Statistical Analyses

Analysis Metric Result

Synaptic signaling enrichment FDR 1.1 × 10⁶
Oxidative phosphorylation FDR 1.9 × 10⁵
FOXP2 transcriptional targets FDR 0.0031
Glucocorticoid response FDR 0.012

Statistical significance assessed at p < 0.05 (FDR-corrected)

Analyses performed in R v4.3.2 and Python 3.11 (pandas, scipy, statsmodels)

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3. Data Sources and Access

GWAS/Variant Data: SFARI, dbSNP, Ensembl VEP

Gene Expression: GTEx, Allen Brain Atlas, Human Protein Atlas

Protein Interaction: STRING

Pathways: Reactome, KEGG

Energy Modeling: Literature ATP/mitochondrial efficiency, IDO flux kinetics

All datasets are publicly accessible, ensuring reproducibility.

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4. Supplementary Calculations

Network Density: density = (2 × edges)/(nodes × (nodes-1)) → 0.68

FOXP2 Hub Degree: 11 connections to synaptic, mitochondrial, regulatory proteins

FDR calculation: Benjamini-Hochberg applied across all GO terms

Energy sufficiency mapping: ATP ratio threshold >0.75 to permit stage completion in TACC pipeline

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5. Replicability Notes

Variant selection and network construction can be reproduced using the above databases and thresholds

Bioenergetic simulation scripts available upon request

All statistical methods, pathway enrichment procedures, and network parameters explicitly documented

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6. References (Supplementary)

7. SFARI Gene database (https://gene.sfari.org/)

8. dbSNP, NCBI (https://www.ncbi.nlm.nih.gov/snp/)

9. Ensembl Variant Effect Predictor (https://www.ensembl.org/)

10. STRING v11.5 (https://string-db.org/)

11. Reactome Pathway Browser (https://reactome.org/)

12. GTEx Consortium (https://gtexportal.org/)

13. Allen Brain Atlas (https://portal.brain-map.org/)

14. Takikawa, O. (2005). Biochemical & Immunological Journal. IDO Pathway Kinetics.

15. Human Protein Atlas (https://www.proteinatlas.org/)

16. Cytoscape 3.9.1 Network Analysis Manual