Collateral Lethal Gene Targeting via Metabolic Flux Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methodologies fail to systematically uncover collateral lethal genes in cancer cells, which are vulnerable due to the collateral deletion of essential genes, making them reliant on paralogues for metabolic compensation, thus requiring improved methods to target these vulnerabilities while sparing normal cells.
Innovation Solution
A system and method using metabolic flux analysis, machine learning, and genome-scale metabolic flux analysis to identify collateral lethal genes like MTHFD2, which provides a non-canonical oxidative function to compensate for mitochondrial dysfunction in cancer cells, leveraging computational modeling and stable-isotope tracing to validate therapeutic targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methodologies are used to identify gene associations, then general gene essentiality can be determined, but collateral lethal genes specific to deleted passenger metabolic genes cannot be systematically uncovered
Solution Approach 1:
The methodology segments the identification process into distinct computational stages: (1) genome-scale metabolic model reconstruction from genomic data, (2) metabolic flux analysis to identify compensatory pathways, (3) machine learning-based prediction of collateral lethal genes, and (4) experimental validation. This segmentation enables systematic identification of collateral lethal genes that cannot be achieved by conventional single-step methods.
Solution Approach 2:
The patent introduces computational metabolic models as an intermediary between genomic data and phenotypic outcomes. These models serve as a bridge that translates genetic deletion information into predictions of metabolic vulnerability and compensatory pathway activity, enabling the identification of collateral lethal genes without direct experimental screening of all possible gene combinations.
2Adaptability or versatility
If genetic loss-of-function screens are used to reveal paralogues, then gene essentiality can be identified, but improved methods are needed to exploit vulnerabilities while sparing normal cells
Solution Approach 1:
The methodology applies local quality by identifying tissue-specific and context-dependent metabolic vulnerabilities. Rather than treating all cells uniformly, the computational models incorporate cell-type-specific metabolic characteristics and genomic alterations, enabling selective targeting of cancer cell vulnerabilities (e.g., collateral lethal genes) while predicting sparing of normal cells that lack the same genetic context.
Solution Approach 2:
The approach captures the dynamic nature of metabolic adaptation by modeling how cancer cells rewire their metabolism in response to gene deletions. The metabolic flux analysis reveals time-dependent compensatory pathway activation and identifies critical nodes in these adaptive networks, enabling intervention at points where the cancer cells are most vulnerable before full compensation occurs.
3Reliability
If MTHFD2 is targeted in UQCR11-deleted ovarian tumors, then complete remission is achieved, but the mechanism involves non-canonical oxidative function providing mitochondrial NAD+
Solution Approach 1:
The patent applies inversion by discovering that MTHFD2 performs a non-canonical oxidative function (producing NAD+) opposite to its traditionally understood reductive function. This functional inversion occurs specifically in the context of UQCR11 deletion and mitochondrial dysfunction, revealing that the enzyme's role flips from NADH production to NAD+ production under these pathological conditions, creating a context-specific therapeutic vulnerability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively targets MTHFD2 in UQCR11-deleted ovarian tumors, leading to complete remission and revealing a novel therapeutic target for cancers and other diseases with mitochondrial dysfunction, demonstrating broad efficacy across varied genetic profiles and stromal compositions.
Implementation Method 1
MTHFD2 has a non-canonical oxidative function to provide mitochondrial NAD+
Implementation Method 2
The electron transport chain (ETC) is a series of redox reactions (oxidative phosphorylation or OXPHOS) that occur in the inter membrane space of the mitochondria
Implementation Method 3
use metabolic fluxes to map genetic information with phenotypic biological functions
Implementation Method 4
leveraging computational modeling and stable-isotope tracing to validate therapeutic targets
Data Source
AI summary
Provided herein are systems and methods for identifying collateral lethal genes via metabolic fluxes and the use of such genes for the identification of, selections of, and use of therapeutic agents. Further provided herein are identified targets, such as MTHFD2, that may be regulated to treat or prevent diseases and conditions.