Widespread Rewiring of Genetic Networks upon Cancer Signaling Pathway Activation

Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

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“…[34] Pearson correlation coefficients (PCC) Internal Measure between-state functional similarity. [23,110] Silhouette width Internal…”

Section: Measurements Categories Usage Referencesmentioning

confidence: 99%

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

et al. 2019

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“…The relative abundance of gRNAs targeting each of ∼18,000 genes in WT cells provides an estimate of single mutant fitness, whereas the relative abundance of gRNAs in a query mutant cell line provides an estimate of double mutant fitness. Since mutant phenotypes can strongly depend on culture conditions (Billmann et al , 2018) and most standard cell culture media contains supra-physiological nutrient levels that could mask phenotypic effects of perturbing certain metabolic pathways, we performed our screens utilizing media conditions containing the minimum amounts of glucose and glutamine required to sustain proliferation of HAP1 cells; termed limiting media (Figure S1c, see Methods).…”

Section: Resultsmentioning

confidence: 99%

Systematic mapping of genetic interactions for de novo fatty acid synthesis

Aregger

Billmann

et al. 2019

Preprint

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28The de novo synthesis of fatty acids has emerged as a therapeutic target for various 29 diseases including cancer. While several translational efforts have focused on direct 30 perturbation of de novo fatty acid synthesis, only modest responses have been associated 31 with mono-therapies. Since cancer cells are intrinsically buffered to combat metabolic 32 stress, cells may adapt to loss of de novo fatty acid biosynthesis. To explore cellular 33 response to defects in fatty acid synthesis, we used pooled genome-wide CRISPR 34 screens to systematically map genetic interactions (GIs) in human HAP1 cells carrying a 35 loss-of-function mutation in FASN, which catalyzes the formation of long-chain fatty acids. 36FASN mutant cells showed a strong dependence on lipid uptake that was reflected by 37 negative GIs with genes involved in the LDL receptor pathway, vesicle trafficking, and 38 protein glycosylation. Further support for these functional relationships was derived from 39 additional GI screens in query cell lines deficient for other genes involved in lipid 40 metabolism, including LDLR, SREBF1, SREBF2, ACACA. Our GI profiles identified a 41 potential role for a previously uncharacterized gene LUR1 (C12orf49) in exogenous lipid 42 uptake regulation. Overall, our data highlights the genetic determinants underlying the 43 cellular adaptation associated with loss of de novo fatty acid synthesis and demonstrate 44 the power of systematic GI mapping for uncovering metabolic buffering mechanisms in 45 human cells. 65cells adapt to perturbation of de novo fatty acid synthesis could help identify new 66 targetable vulnerabilities that may inform novel therapeutic strategies or biomarker 67 approaches. 69Mapping genetic interaction (GI) networks provides a powerful approach for identifying the 70 functional relationships between genes and their corresponding pathways. The systematic 71 exploration of pairwise GIs in model organisms revealed that GIs often occur among 72 4 functionally related genes and that GI profiles organize a hierarchy of functional modules 73 (Costanzo et al, 2016; Fischer et al, 2015). Thus, GI mapping has become an effective 74 strategy for identifying functional modules and annotating the roles of previously 75 uncharacterized genes. Model organism GI mapping has also provided insight into the 76 mechanistic basis of cellular plasticity or phenotypic switching that occurs as cells evolve 77 within their environments (Harrison et al, 2007; Szappanos et al, 2011). Accordingly, the 78 insights gained through systematic interrogation of GIs have fuelled significant interest to 79 leverage these approaches towards functionally annotating the human genome. 81Recent technological advances using CRISPR-Cas enable the systematic mapping of GIs 82 in human cells (Wright et al, 2016; Doench, 2018). Here, we explore genome-wide GI 83 screens within the context of human query mutant cells defective for de novo fatty acid 84 synthesis. We systematically mapped genome-wide GI profiles for six genes involved in ...

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“…Similarly, we identified 11 modules with more than 10 genes in the immune cells of tumor condition (Supplemental Figure S8). However, the two sets of module assignments only have an adjusted Rand index of 0.10 and a normalized mutual information of 0.49, implying widespread rewiring of gene networks in the breast cancer condition [73].…”

Section: Sclinks Identifies Gene Network Changes In Breast Cancermentioning

confidence: 99%

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

2020

Preprint

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A system-level understanding of the regulation and coordination mechanisms of gene expression is essential to understanding the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell-type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The source code used in this article is available at https://github.com/Vivianstats/scLink.

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Widespread Rewiring of Genetic Networks upon Cancer Signaling Pathway Activation

Cited by 38 publications

References 53 publications

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

Systematic mapping of genetic interactions for de novo fatty acid synthesis

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

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