Dataset Information

Identification of a glioma functional network from gene fitness data using machine learning.

ABSTRACT: Glioblastoma multiforme (GBM) is an aggressive form of brain tumours that remains incurable despite recent advances in clinical treatments. Previous studies have focused on sub-categorizing patient samples based on clustering various transcriptomic data. While functional genomics data are rapidly accumulating, there exist opportunities to leverage these data to decipher glioma-associated biomarkers. We sought to implement a systematic approach to integrating data from high throughput CRISPR-Cas9 screening studies with machine learning algorithms to infer a glioma functional network. We demonstrated the network significantly enriched various biological pathways and may play roles in glioma tumorigenesis. From densely connected glioma functional modules, we further predicted 12 potential Wnt/β-catenin signalling pathway targeted genes, including AARSD1, HOXB5, ITGA6, LRRC71, MED19, MED24, METTL11B, SMARCB1, SMARCE1, TAF6L, TENT5A and ZNF281. Cox regression modelling with these targets was significantly associated with glioma overall survival prognosis. Additionally, TRIB2 was identified as a glioma neoplastic cell marker in single-cell RNA-seq of GBM samples. This work establishes novel strategies for constructing functional networks to identify glioma biomarkers for the development of diagnosis and treatment in clinical practice.

SUBMITTER: Xiang CX

PROVIDER: S-EPMC8831986 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Identification of a glioma functional network from gene fitness data using machine learning.

Xiang Chun-Xiang CX Liu Xi-Guo XG Zhou Da-Quan DQ Zhou Yi Y Wang Xu X Chen Feng F

Journal of cellular and molecular medicine 20220119 4

Glioblastoma multiforme (GBM) is an aggressive form of brain tumours that remains incurable despite recent advances in clinical treatments. Previous studies have focused on sub-categorizing patient samples based on clustering various transcriptomic data. While functional genomics data are rapidly accumulating, there exist opportunities to leverage these data to decipher glioma-associated biomarkers. We sought to implement a systematic approach to integrating data from high throughput CRISPR-Cas9 ...[more]

PMID: 35044082

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Project description:There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules ("functional modules"), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets.ImportanceThis study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

Project description:Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been hindered by a lack of consensus in diagnostic criteria and limited use of objective biomarkers. Previous research from our group utilized resting-state functional magnetic resonance imaging (fMRI) to measure functional network connectivity (FNC), which revealed several sex- and region-dependent alterations in FNC as a result of moderate PAE relative to controls. Considering that FNC is sensitive to moderate PAE, this study explored the use of FNC data and machine learning methods to detect PAE among a sample of rodents exposed to alcohol prenatally and controls. We utilized previously acquired resting state fMRI data collected from adult rats exposed to moderate levels of prenatal alcohol (PAE) or a saccharin control solution (SAC) to assess FNC of resting state networks extracted by spatial group independent component analysis (GICA). FNC data were subjected to binary classification using support vector machine (SVM) -based algorithms and leave-one-out-cross validation (LOOCV) in an aggregated sample of males and females (n = 48; 12 male PAE, 12 female PAE, 12 male SAC, 12 female SAC), a males-only sample (n = 24; 12 PAE, 12 SAC), and a females-only sample (n = 24; 12 PAE, 12 SAC). Results revealed that a quadratic SVM (QSVM) kernel was significantly effective for PAE detection in females. QSVM kernel-based classification resulted in accuracy rates of 62.5% for all animals, 58.3% for males, and 79.2% for females. Additionally, qualitative evaluation of QSVM weights implicates an overarching theme of several hippocampal and cortical networks in contributing to the formation of correct classification decisions by QSVM. Our results suggest that binary classification using QSVM and adult female FNC data is a potential candidate for the translational development of novel and non-invasive techniques for the identification of FASD.

Dataset Information

Identification of a glioma functional network from gene fitness data using machine learning.

Publications

Identification of a glioma functional network from gene fitness data using machine learning.

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