Gene Expression Panel for Universal Stress State Assessment
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Solution Overview
Problem
Existing biomarkers for stress detection are species and stressor-specific, making them non-transferrable and unsatisfactory for universal stress state identification in subjects.
Innovation Solution
Detection and quantification of the expression of at least four genes selected from a list of twenty-six genes, including Ankrd33b, Anxa1, Anxa2, Chac1, Cidea, Col1a1, Col12a1, Col14a1, Efemp1, G0s2, Gfpt2, Hmox1, Kctd12, Kera, Lgals1, Mgp, Mrc1, Nes, Panx1, Postn, Runx1, Serpinh1, Sh2b2, Slit3, Thbs1, and Tnc, to identify and quantify stress states in subjects independently of species and stressors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional stress biomarkers (circulating hormones, catabolism products, inflammation biomarkers) are used for stress detection, then stress state can be identified in specific conditions, but the biomarkers are transient and species-specific, making them unsuitable for universal application across different stress conditions and species
Solution Approach 1:
The patent applies universality by developing a gene expression panel comprising 26 specific genes that can detect stress states across multiple species (bovines, ovines, caprines, equines, porcines, and humans) and various stress conditions (transport, heat stress, inflammation, nutritional stress). This multi-functional gene panel replaces species-specific biomarkers with a universal diagnostic tool that functions across diverse biological systems and stressor types.
Solution Approach 2:
The patent applies parameter changes by shifting the detection parameter from transient circulating biomarkers (hormones, catabolism products) to stable gene expression levels. Gene expression provides a more persistent and measurable parameter that reflects stress state without the transient nature of hormonal fluctuations, enabling more reliable and timely intervention.
2Ease of manufacture
If in vitro stress models are used to identify stress biomarkers, then cellular stress responses can be studied, but the results are rarely translatable in vivo
Solution Approach 1:
The patent applies preliminary action by conducting comprehensive in vivo validation across multiple species and stress conditions before deploying the gene expression panel. The panel was developed and validated using actual in vivo stress models (transport stress in bovines, heat stress in ovines, inflammation in caprines, nutritional stress in porcines) rather than relying solely on in vitro models, ensuring translatability to real-world applications.
Solution Approach 2:
The patent uses gene expression as an intermediary that bridges in vitro findings and in vivo reality. By measuring gene expression in accessible tissues (blood, muscle, adipose) that reflect systemic stress responses, the panel translates cellular stress mechanisms into clinically relevant in vivo diagnostics, overcoming the translation gap between in vitro models and living organisms.
3Productivity
If traditional animal performance parameters (weight gain, feed intake, mortality) are used to assess animal welfare, then productivity can be measured, but these parameters do not specifically reflect animal stress state
Solution Approach 1:
The patent applies segmentation by separating stress detection from general performance measurement. Instead of using aggregate performance parameters that mix productivity and welfare indicators, the gene expression panel segments stress detection into a specific, independent diagnostic tool. This allows simultaneous measurement of both productivity (weight gain, feed intake) and stress state (gene expression profile) without one confounding the other.
Solution Approach 2:
The patent replaces mechanical/physiological measurement systems (weight scales, feed bins, mortality records) with a molecular detection system (gene expression analysis). This substitution provides direct molecular evidence of stress state rather than inferring stress from indirect performance changes, enabling specific and timely stress detection independent of productivity metrics.
Data Source
AI summary
A process for identifying a stress state in a subject, for assessing the stress response level in a subject, for predicting the efficacy of an intervention solution in a subject, for monitoring the efficacy of an intervention solution in a subject and/or for identifying an intervention solution for a subject, having a step of detecting the expression and/or quantifying the expression level, in a sample of the subject, of at least four genes selected from a group made of twenty-six genes, as well as a kit having a way for amplifying and/or detecting the expression of the genes, and uses thereof.