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Статті в журналах з теми "Gene dependency"

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Автор: Grafiati

Опубліковано: 7 липня 2024

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1

Marcireau, Christophe, Fréderic Lacroix, Dietmar Hoffmann, May Cindhuchao, Loreley Calvet, Yvette Ruffin, and Laurent Debussche. "Abstract 1673: KRAS dependency, a gene editing approach." Cancer Research 84, no. 6_Supplement (March 22, 2024): 1673. http://dx.doi.org/10.1158/1538-7445.am2024-1673.

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Abstract The RAS family genes encode small GTP-binding cytoplasmic proteins that are important signaling molecules. They regulate cell growth, survival and differentiation by coupling receptor activation to downstream effector pathways. Activating mutations of oncogenic RAS pathway genes are frequently detected in human cancers. The role of KRAS in tumor formation is not questionable. It’s implication in tumor maintenance is less well validated. The KRAS dependency is a key question to answer before to develop KRAS inhibitor. Targeted genome editing using CRISPR/Cas9 is a relatively new, revolutionary technology allowing for efficient and directed alterations of the genome. CRISPR is a genome-editing platform that makes use of the bacterially-derived endonuclease Cas9 to introduce DNA double-strand breaks at precise locations in the genome using complementary guide RNAs. These double strand breaks can be repaired by homologous recombination DNA repair mechanisms thank to a donor DNA template. By this way mutations can be introduced at desired loci, DNA fragment can be deleted or introduce at the desired location. We aimed at investigating the KRAS dependence and we applied the CRISPR-Cas9 technology to engineer isogenic cancer cells harboring a “conditional KRAS gene knockout" by exchanging the KRAS endogenous promoter. This promoter replacement approach can be used also to validate new targets. Citation Format: Christophe Marcireau, Fréderic Lacroix, Dietmar Hoffmann, May Cindhuchao, Loreley Calvet, Yvette Ruffin, Laurent Debussche. KRAS dependency, a gene editing approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1673.

2

Tsai, Kun-Che, Shin-Yu Fang, Shu-Jyuan Yang, Ming-Jium Shieh, Win-Li Lin, and Wen-Shiang Chen. "Time dependency of ultrasound-facilitated gene transfection." Journal of Gene Medicine 11, no. 8 (August 2009): 729–36. http://dx.doi.org/10.1002/jgm.1347.

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3

Croce, Carlo M. "miRNAs in the spotlight: Understanding cancer gene dependency." Nature Medicine 17, no. 8 (August 2011): 935–36. http://dx.doi.org/10.1038/nm0811-935.

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4

Zhang, Qing, Xiaodan Fan, Yejun Wang, Mingan Sun, Samuel S. M. Sun, and Dianjing Guo. "A Model-Based Method for Gene Dependency Measurement." PLoS ONE 7, no. 7 (July 19, 2012): e40918. http://dx.doi.org/10.1371/journal.pone.0040918.

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5

Gao, Xin, Daniel Q. Pu, and Peter X. K. Song. "Transition Dependency: A Gene-Gene Interaction Measure for Times Series Microarray Data." EURASIP Journal on Bioinformatics and Systems Biology 2009 (2009): 1–12. http://dx.doi.org/10.1155/2009/535869.

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6

Li, Zhong, and Zhou. "Prediction of Bone Metastasis in Breast Cancer Based on Minimal Driver Gene Set in Gene Dependency Network." Genes 10, no. 6 (June 17, 2019): 466. http://dx.doi.org/10.3390/genes10060466.

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Bone is the most frequent organ for breast cancer metastasis, and thus it is essential to predict the bone metastasis of breast cancer. In our work, we constructed a gene dependency network based on the hypothesis that the relation between one gene and the risk of bone metastasis might be affected by another gene. Then, based on the structure controllability theory, we mined the driver gene set which can control the whole network in the gene dependency network, and the signature genes were selected from them. Survival analysis showed that the signature could distinguish the bone metastasis risks of cancer patients in the test data set and independent data set. Besides, we used the signature genes to construct a centroid classifier. The results showed that our method is effective and performed better than published methods.

7

Zhou, Xiangdong, Keith C. C. Chan, Zhihua Huang, and Jingbin Wang. "Determining dependency and redundancy for identifying gene–gene interaction associated with complex disease." Journal of Bioinformatics and Computational Biology 18, no. 05 (October 2020): 2050035. http://dx.doi.org/10.1142/s0219720020500353.

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As interactions among genetic variants in different genes can be an important factor for predicting complex diseases, many computational methods have been proposed to detect if a particular set of genes has interaction with a particular complex disease. However, even though many such methods have been shown to be useful, they can be made more effective if the properties of gene–gene interactions can be better understood. Towards this goal, we have attempted to uncover patterns in gene–gene interactions and the patterns reveal an interesting property that can be reflected in an inequality that describes the relationship between two genotype variables and a disease-status variable. We show, in this paper, that this inequality can be generalized to [Formula: see text] genotype variables. Based on this inequality, we establish a conditional independence and redundancy (CIR)-based definition of gene–gene interaction and the concept of an interaction group. From these new definitions, a novel measure of gene–gene interaction is then derived. We discuss the properties of these concepts and explain how they can be used in a novel algorithm to detect high-order gene–gene interactions. Experimental results using both simulated and real datasets show that the proposed method can be very promising.

8

Ding, Yunfeng, Luis E. Contreras-Llano, Eliza Morris, Michelle Mao, and Cheemeng Tan. "Minimizing Context Dependency of Gene Networks Using Artificial Cells." ACS Applied Materials & Interfaces 10, no. 36 (August 16, 2018): 30137–46. http://dx.doi.org/10.1021/acsami.8b10029.

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9

Pons, Guillem, Gabriel Gallo-Oller, Natalia Navarro, Patricia Zarzosa, Júlia Sansa-Girona, Lia García-Gilabert, Ainara Magdaleno, et al. "Analysis of Cancer Genomic Amplifications Identifies Druggable Collateral Dependencies within the Amplicon." Cancers 15, no. 6 (March 7, 2023): 1636. http://dx.doi.org/10.3390/cancers15061636.

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The identification of novel therapeutic targets for specific cancer molecular subtypes is crucial for the development of precision oncology. In the last few years, CRISPR/Cas9 screens have accelerated the discovery and validation of new targets associated with different tumor types, mutations, and fusions. However, there are still many cancer vulnerabilities associated with specific molecular features that remain to be explored. Here, we used data from CRISPR/Cas9 screens in 954 cancer cell lines to identify gene dependencies associated with 16 common cancer genomic amplifications. We found that high-copy-number genomic amplifications generate multiple collateral dependencies within the amplified region in most cases. Further, to prioritize candidate targets for each chromosomal region amplified, we integrated gene dependency parameters with both druggability data and subcellular location. Finally, analysis of the relationship between gene expression and gene dependency led to the identification of genes, the expression of which may constitute predictive biomarkers of dependency. In conclusion, our study provides a set of druggable targets specific for each amplification, opening the possibility to specifically target amplified tumors on this basis.

10

Grzywacz, Anna, Wojciech Barczak, Jolanta Chmielowiec, Krzysztof Chmielowiec, Aleksandra Suchanecka, Grzegorz Trybek, Jolanta Masiak, Paweł Jagielski, Katarzyna Grocholewicz, and Blazej Rubiś. "Contribution of Dopamine Transporter Gene Methylation Status to Cannabis Dependency." Brain Sciences 10, no. 6 (June 23, 2020): 400. http://dx.doi.org/10.3390/brainsci10060400.

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The susceptibility to cannabis dependency results from the influence of numerous factors such as social, genetic, as well as epigenetic factors. Many studies have attempted to discover a molecular basis for this disease. However, our study aimed at evaluating the connection between altered methylation of the dopamine transporter gene (DAT1) promoter CpG sites and cannabis dependency. In the cases of some DNA sequences, including the DAT1 gene region, their methylation status in blood cells may reflect a systemic modulation in the whole organism. Consequently, we isolated the DNA from the peripheral blood cells from a group of 201 cannabis-dependent patients and 285 controls who were healthy volunteers and who were matched for age and sex. The DNA was subjected to bisulfite conversion and sequencing. Our analysis revealed no statistical differences in the general methylation status of the DAT1 gene promoter CpG island between the patients and controls. Yet, the analysis of individual CpG sites where methylation occurred indicated significant differences. These sites are known to be bound by transcription factors (e.g., SP1, p53, PAX5, or GR), which, apart from other functions, were shown to play a role in the development of the nervous system. Therefore, DAT1 gene promoter methylation studies may provide important insight into the mechanism of cannabis dependency.

11

Wang, Ke, Lei Shi, Xiaona Liang, Pei Zhao, Wanxin Wang, Junxian Liu, Yanan Chang, et al. "The gene TaWOX5 overcomes genotype dependency in wheat genetic transformation." Nature Plants 8, no. 2 (January 13, 2022): 110–17. http://dx.doi.org/10.1038/s41477-021-01085-8.

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12

Ikeda, Hiroki, Qing Yong, Takeshi Kurose, Takeshi Todo, Wataru Mizunoya, Tohru Fushiki, Yutaka Seino, and Yuichiro Yamada. "Clock gene defect disrupts light-dependency of autonomic nerve activity." Biochemical and Biophysical Research Communications 364, no. 3 (December 2007): 457–63. http://dx.doi.org/10.1016/j.bbrc.2007.10.058.

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13

Pei, Xin-Hai, Feng Bai, Tateki Tsutsui, Hiroaki Kiyokawa, and Yue Xiong. "Genetic Evidence for Functional Dependency of p18Ink4c on Cdk4." Molecular and Cellular Biology 24, no. 15 (August 1, 2004): 6653–64. http://dx.doi.org/10.1128/mcb.24.15.6653-6664.2004.

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ABSTRACT The INK4 family of cyclin-dependent kinase (CDK) inhibitors negatively regulates cyclin D-dependent CDK4 and CDK6 and induces the growth-suppressive function of Rb family proteins. Mutations in the Cdk4 gene conferring INK4 resistance are associated with familial and sporadic melanoma in humans and result in a wide spectrum of tumors in mice, suggesting that INK4 is a major regulator of CDK4. Mice lacking the Cdk4 gene exhibit various defects in many organs associated with hypocellularity, whereas loss of the p18 Ink4c gene results in widespread hyperplasia and organomegaly. To genetically test the notion that the function of INK4 is dependent on CDK4, we generated p18; Cdk4 double-mutant mice and examined the organs and tissues which developed abnormalities when either gene is deleted. We show here that, in all organs we have examined, including pituitary, testis, pancreas, kidney, and adrenal gland, hyperproliferative phenotypes associated with p18 loss were canceled. The double-mutant mice exhibited phenotypes very close to or indistinguishable from that of Cdk4 single-mutant mice. Mice lacking p27 Kip1 develop widespread hyperplasia and organomegaly similar to those developed by p18-deficient mice. The p27; Cdk4 double-mutant mice, however, displayed phenotypes intermediate between those of p27 and Cdk4 single-mutant mice. These results provide genetic evidence that in mice p18 Ink4c and p27 Kip1 mediate the transduction of different cell growth and proliferation signals to CDK4 and that p18 Ink4c is functionally dependent on CDK4.

14

Zhou, Yujia, Gregory P. Takacs, Jatinder K. Lamba, Christopher Vulpe, and Christopher R. Cogle. "Functional Dependency Analysis Identifies Potential Druggable Targets in Acute Myeloid Leukemia." Cancers 12, no. 12 (December 10, 2020): 3710. http://dx.doi.org/10.3390/cancers12123710.

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Refractory disease is a major challenge in treating patients with acute myeloid leukemia (AML). Whereas the armamentarium has expanded in the past few years for treating AML, long-term survival outcomes have yet to be proven. To further expand the arsenal for treating AML, we searched for druggable gene targets in AML by analyzing screening data from a lentiviral-based genome-wide pooled CRISPR-Cas9 library and gene knockout (KO) dependency scores in 15 AML cell lines (HEL, MV411, OCIAML2, THP1, NOMO1, EOL1, KASUMI1, NB4, OCIAML3, MOLM13, TF1, U937, F36P, AML193, P31FUJ). Ninety-four gene KOs met the criteria of (A) specifically essential to AML cell survival, (B) non-essential in non-AML cells, and (C) druggable according to three-dimensional (3D) modeling or ligand-based druggability scoring. Forty-four of 94 gene-KOs (47%) had an already-approved drug match and comprised a drug development list termed “deKO.” Fifty of 94 gene-KOs (53%) had no drug in development and comprised a drug discovery list termed “disKO.” STRING analysis and gene ontology categorization of the disKO targets preferentially cluster in the metabolic processes of UMP biosynthesis, IMP biosynthesis, dihydrofolate metabolism, pyrimidine nucleobase biosynthesis, vitellogenesis, and regulation of T cell differentiation and hematopoiesis. Results from this study serve as a testable compendium of AML drug targets that, after validation, may be translated into new therapeutics.

15

Chen, Xiao, Yinglu Li, and Chao Lu. "Abstract A015: Cancer co-dependency mapping identifies a functional interplay between PRC2 and MLL-MEN1 complex in lymphoma." Cancer Research 82, no. 23_Supplement_2 (December 1, 2022): A015. http://dx.doi.org/10.1158/1538-7445.cancepi22-a015.

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Abstract Interplays between chromatin-associated complexes and modifications critically contribute to the partitioning of epigenome into stable and functionally distinct domains. Yet there is a lack of systematic identification of chromatin crosstalk mechanisms, limiting our understanding of the dynamic transition between chromatin states during development and disease. Here we perform co-dependency mapping of genes encoding nuclear proteins using CRISPR-Cas9-mediated fitness screens in pan-cancer cell lines to quantify gene-gene functional relationships. We identify 84 co-dependency modules and further define the molecular context underlying the essentiality of these modules by incorporating mutational, epigenome, gene expression and drug sensitivity profiles of cell lines. These analyses assign new protein complex functions and biochemical interactions, and predict new chromatin crosstalk, including an unexpected co-dependency between two transcriptionally counteracting chromatin complexes - polycomb repressive complex 2 (PRC2) and MLL-MEN1 complex. We show that PRC2-mediated H3K27 tri-methylation regulates the genome-wide distribution of MLL1 and MEN1. In lymphoma cells with EZH2 gain-of-function mutations, the re-localization of MLL-MEN1 complex drives oncogenic gene expression and results in a hypersensitivity to pharmacologic inhibition of MEN1. Together, our findings provide a resource for discovery of trans-regulatory interactions as mechanisms of chromatin regulation and potential targets of synthetic lethality. Citation Format: Xiao Chen, Yinglu Li, Chao Lu. Cancer co-dependency mapping identifies a functional interplay between PRC2 and MLL-MEN1 complex in lymphoma. [abstract]. In: Proceedings of the AACR Special Conference: Cancer Epigenomics; 2022 Oct 6-8; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_2):Abstract nr A015.

16

Das, Sunanda, and Asit Kumar Das. "Probability Based Most Informative Gene Selection From Microarray Data." International Journal of Rough Sets and Data Analysis 5, no. 1 (January 2018): 1–12. http://dx.doi.org/10.4018/ijrsda.2018010101.

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Microarray datasets have a wide application in bioinformatics research. Analysis to measure the expression level of thousands of genes of this kind of high-throughput data can help for finding the cause and subsequent treatment of any disease. There are many techniques in gene analysis to extract biologically relevant information from inconsistent and ambiguous data. In this paper, the concepts of functional dependency and closure of an attribute of database technology are used for finding the most important set of genes for cancer detection. Firstly, the method computes similarity factor between each pair of genes. Based on the similarity factors a set of gene dependency is formed from which closure set is obtained. Subsequently, conditional probability based interestingness measurements are used to determine the most informative gene for disease classification. The proposed method is applied on some publicly available cancerous gene expression dataset. The result shows the effectiveness and robustness of the algorithm.

17

Ellegast, Jana M., Gabriela Alexe, Subha Baniya, Amanda Hamze, Audrey Taillon, Biniam Adane, Amy Saur Conway, et al. "Functional Dissection of Cellular Programs to Uncover Novel Gene Dependencies in AML." Blood 142, Supplement 1 (November 28, 2023): 1393. http://dx.doi.org/10.1182/blood-2023-189304.

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Genome-scale CRISPR-Cas9 screens have the power to unveil the Achilles' heel of neoplastic cells. Typically, analyses of such large-scale data sets focus on single gene dependencies. An alternative strategy is to evaluate functional networks enriched in a disease or molecular subset of interest. We applied this strategy to the Broad Institute's Cancer Dependency Map (DepMap) data set consisting of genome-scale CRISPR-Cas9 screens in over 1000 cancer cell line models. We hypothesized that interrogating enriched pathways with context-specific dependencies leads to the discovery of functionally informed gene dependencies. Single sample gene set enrichment analysis (ssGSEA) of over 1000 cancer cell lines in the DepMap identified hematologic malignancies to be highly enriched for signatures associated with elevated transcriptional activity. Notably, within hematologic malignancies, acute myeloid leukemia (AML) with a rearrangement in the KMT2A gene ( KMT2Ar-AML) was the most significantly enriched for dependency on transcriptional activity-associated gene signatures. We next interrogated KMT2Ar-AML gene dependencies (excluding common essential genes) with the human kinase database KinMap (http://www.kinhub.org/kinmap/) to identify readily druggable genes involved in transcriptional activity. Cyclin-dependent kinase 13 ( CDK13) was the only gene meeting these criteria. Further DepMap data analysis confirmed that KMT2Ar-AML cell lines were indeed more strongly dependent on CDK13 than all other cancer cell lines screened. Moreover, when we performed GSEA on the gene dependencies observed in CDK13-dependent versus non-dependent cell lines, we found transcriptional activity and KMT2A-target gene sets as top hits. We next validated AML cell dependency on CDK13 with orthogonal genetic approaches (CRISPR-Cas9 knock-out and shRNA) in vitro in human AML cell lines and cells from patient-derived xenograft (PDX) models of KMT2Ar-AML. Perturbation of CDK13 induced cell death with hallmarks of apoptosis. Doxycycline-inducible shRNA directed against CDK13 also impaired AML progression in the peripheral blood and bone marrow in an MV4-11- KMT2Ar xenograft. Moreover, in an orthotopic PDX model, doxycycline inducible knock-out of CDK13 in mice with established disease significantly reduced leukemia burden in the peripheral blood and bone marrow. CDK13 is reported to regulate transcriptional elongation and the clearance of prematurely terminated RNAs. To decipher why AML cells, particularly KMT2Ar, need CDK13 to survive, we identified the CDK13 binding sites and histone marks involved in transcriptional regulation in AML using CUT and RUN and ChIP-seq: We determined that CDK13 co-localizes with H3K4me3 and H3K27ac at promoter sites. Global gene expression studies following the knock-out of CDK13 revealed that the majority of CDK13 bound genes were also differentially expressed, with significantly more genes decreased than increased in expression after CDK13 knock-out. GSEA of CDK13-bound genes with decreased expression upon CDK13 knock-out identified gene signatures associated with transcriptional activation and KMT2A-targets. We next studied the characteristics of CDK13 target genes and observed a predominance of long genes (total length of gene exons > 10 kb, GRCh38.p12). GSEA of genes ranked by size, confirmed that longer genes were enriched for CDK13 target genes, CDK13 differential AML dependencies, and for KMT2A-target transcriptional signatures. Additionally, following CDK13 perturbation in KMT2Ar-AML we observed a decrease of Pol2/pPol2Ser2 occupancy at the transcription end site of long genes. In summary, using functional pathway analyses, we identified CDK13 as a candidate gene dependency enriched in KMT2Ar-AML, a target not immediately prioritized with single gene analysis. Mechanistically, our data support that CDK13 controls Pol2 processivity, with CDK13 perturbation leading to a decreased occupancy at the 3' end of long genes. KMT2Ar, CDK13 dependent cells are also enriched for dependency on long genes, thereby suggesting a molecular basis for the CDK13 dependency in KMT2Ar-AML.

18

Weiskittel, Taylor M., Andrew Cao, Kevin Meng-Lin, Zachary Lehmann, Benjamin Feng, Cristina Correia, Cheng Zhang, et al. "Network Biology-Inspired Machine Learning Features Predict Cancer Gene Targets and Reveal Target Coordinating Mechanisms." Pharmaceuticals 16, no. 5 (May 16, 2023): 752. http://dx.doi.org/10.3390/ph16050752.

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Anticipating and understanding cancers’ need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors’ dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

19

Liu, Jianxiao, Zonglin Tian, Yingjie Xiao, Haijun Liu, Songlin Hao, Xiaolong Zhang, Chaoyang Wang, Jianchao Sun, Huan Yu, and Jianbing Yan. "Gene Regulatory Relationship Mining Using Improved Three-Phase Dependency Analysis Approach." IEEE/ACM Transactions on Computational Biology and Bioinformatics 17, no. 1 (January 1, 2020): 339–46. http://dx.doi.org/10.1109/tcbb.2018.2872993.

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20

CHANG, JEONG-HO, KYU-BAEK HWANG, S. JUNE OH, and BYOUNG-TAK ZHANG. "BAYESIAN NETWORK LEARNING WITH FEATURE ABSTRACTION FOR GENE-DRUG DEPENDENCY ANALYSIS." Journal of Bioinformatics and Computational Biology 03, no. 01 (February 2005): 61–77. http://dx.doi.org/10.1142/s0219720005000874.

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Combined analysis of the microarray and drug-activity datasets has the potential of revealing valuable knowledge about various relations among gene expressions and drug activities in the malignant cell. In this paper, we apply Bayesian networks, a tool for compact representation of the joint probability distribution, to such analysis. For the alleviation of data dimensionality problem, the huge datasets were condensed using a feature abstraction technique. The proposed analysis method was applied to the NCI60 dataset () consisting of gene expression profiles and drug activity patterns on human cancer cell lines. The Bayesian networks, learned from the condensed dataset, identified most of the salient pairwise correlations and some known relationships among several features in the original dataset, confirming the effectiveness of the proposed feature abstraction method. Also, a survey of the recent literature confirms the several relationships appearing in the learned Bayesian network to be biologically meaningful.

21

Prabha, Swayam, Wen-Zhong Zhou, Jayanth Panyam, and Vinod Labhasetwar. "Size-dependency of nanoparticle-mediated gene transfection: studies with fractionated nanoparticles." International Journal of Pharmaceutics 244, no. 1-2 (September 2002): 105–15. http://dx.doi.org/10.1016/s0378-5173(02)00315-0.

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22

Pavličev, Mihaela, and James M. Cheverud. "Constraints Evolve: Context Dependency of Gene Effects Allows Evolution of Pleiotropy." Annual Review of Ecology, Evolution, and Systematics 46, no. 1 (December 4, 2015): 413–34. http://dx.doi.org/10.1146/annurev-ecolsys-120213-091721.

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23

Wang, Wenyu, Alina Malyutina, Alberto Pessia, Jani Saarela, Caroline A. Heckman, and Jing Tang. "Combined gene essentiality scoring improves the prediction of cancer dependency maps." EBioMedicine 50 (December 2019): 67–80. http://dx.doi.org/10.1016/j.ebiom.2019.10.051.

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24

TOHSATO, YUKAKO, TOMOYA BABA, YUSAKU MAZAKI, MASAHIRO ITO, BARRY L. WANNER, and HIROTADA MORI. "ENVIRONMENTAL DEPENDENCY OF GENE KNOCKOUTS ON PHENOTYPE MICROARRAY ANALYSIS IN ESCHERICHIA COLI." Journal of Bioinformatics and Computational Biology 08, supp01 (December 2010): 83–99. http://dx.doi.org/10.1142/s021972001000521x.

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Systematic studies have revealed that single gene deletions often display little phenotypic effects under laboratory conditions and that in many cases gene dispensability depends on the experimental conditions. To elucidate the environmental dependency of genes, we analyzed the effects of gene deletions by Phenotype MicroArray™ (PM), a system for quantitative screening of thousands of phenotypes in a high-throughput manner. Here, we proposed a new statistical approach to minimize error inherent in measurements of low respiration rates and find which mutants showed significant phenotypic changes in comparison to the wild-type. We show analyzing results from comprehensive PM assays of 298 single-gene knockout mutants in the Keio collection and two additional mutants under 1,920 different conditions. We focused on isozymes of these genes as simple duplications and analyzed correlations between phenotype changes and protein expression levels. Our results revealed divergence of the environmental dependency of the gene among the knockout genes and have also given some insights into possibilities of alternative pathways and availabilities of information on protein synthesis patterns to classify or predict functions of target genes from systematic phenotype screening.

25

Chen, Mei-Ju May, Jun Li, Gordon B. Mills, and Han Liang. "Predicting Cancer Cell Line Dependencies From the Protein Expression Data of Reverse-Phase Protein Arrays." JCO Clinical Cancer Informatics, no. 4 (September 2020): 357–66. http://dx.doi.org/10.1200/cci.19.00144.

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PURPOSE Predicting cancer dependencies from molecular data can help stratify patients and identify novel therapeutic targets. Recently available data on large-scale cancer cell line dependency allow a systematic assessment of the predictive power of diverse molecular features; however, the protein expression data have not been rigorously evaluated. By using the protein expression data generated by reverse-phase protein arrays, we aimed to assess their predictive power in identifying cancer dependencies and to develop a related analytic tool for community use. MATERIALS AND METHODS By using a machine learning schema, we conducted an analysis of feature importance based on cancer dependency and multiomic data from the DepMap and Cancer Cell Line Encyclopedia projects. We assessed the consistency of cancer dependency data between CRISPR/Cas9 and short hairpin RNA–mediated perturbation platforms. For a fair comparison, we focused on a set of genes with robust dependency data and four available expression-related features (copy number alteration, DNA methylation, messenger RNA expression, and protein expression) and performed the same-gene predictions of the cancer dependency using different molecular features. RESULTS For the genes surveyed, we observed that the protein expression data contained substantial predictive power for cancer dependencies, and they were the best predictive feature for the CRISPR/Cas9-based dependency data. We also developed a user-friendly protein-dependency analytic module and integrated it with The Cancer Proteome Atlas; this module allows researchers to explore and analyze our results intuitively. CONCLUSION This study provides a systematic assessment for predicting cancer dependencies of cell lines from different expression-related features of a gene. Our results suggest that protein expression data are a highly valuable information resource for understanding tumor vulnerabilities and identifying therapeutic opportunities.

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Kaiser, Sandra, Luise Henrich, Iva Kiessling, Benedikt Loy, and Nils Schallner. "Neuroprotection via Carbon Monoxide Depends on the Circadian Regulation of CD36-Mediated Microglial Erythrophagocytosis in Hemorrhagic Stroke." International Journal of Molecular Sciences 25, no. 3 (January 30, 2024): 1680. http://dx.doi.org/10.3390/ijms25031680.

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The molecular basis for circadian dependency in stroke due to subarachnoid hemorrhagic stroke (SAH) remains unclear. We reasoned that microglial erythrophagocytosis, crucial for SAH response, follows a circadian pattern involving carbon monoxide (CO) and CD36 surface expression. The microglial BV-2 cell line and primary microglia (PMG) under a clocked medium change were exposed to blood ± CO (250 ppm, 1 h) in vitro. Circadian dependency and the involvement of CD36 were analyzed in PMG isolated from control mice and CD36−/− mice and by RNA interference targeting Per-2. In vivo investigations, including phagocytosis, vasospasm, microglia activation and spatial memory, were conducted in an SAH model using control and CD36−/− mice at different zeitgeber times (ZT). In vitro, the surface expression of CD36 and its dependency on CO and phagocytosis occurred with changed circadian gene expression. CD36−/− PMG exhibited altered circadian gene expression, phagocytosis and impaired responsiveness to CO. In vivo, control mice with SAH demonstrated circadian dependency in microglia activation, erythrophagocytosis and CO-mediated protection at ZT2, in contrast to CD36−/− mice. Our study indicates that circadian rhythmicity modulates microglial activation and subsequent CD36-dependent phagocytosis. CO altered circadian-dependent neuroprotection and CD36 induction, determining the functional outcome in a hemorrhagic stroke model. This study emphasizes how circadian rhythmicity influences neuronal damage after neurovascular events.

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Ellegast, Jana M., Philipp J. Rauch, Larisa V. Kovtonyuk, Rouven Müller, Ulrich Wagner, Yasuyuki Saito, Nicole Wildner-Verhey van Wijk, et al. "inv(16) and NPM1mut AMLs engraft human cytokine knock-in mice." Blood 128, no. 17 (October 27, 2016): 2130–34. http://dx.doi.org/10.1182/blood-2015-12-689356.

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Yeo, Shen Yong, Wai Yee Wong, Jesslyn, Tan Boon Toh, Valerie Shiwen Yang, and Xing Yi Woo. "Abstract P58: Utilizing Cancer Vulnerabilities and Dependencies to Explore Cancer Biomarkers by Triangulating Large-Scale Gene Knockout and Drug Response Data." Cancer Research 84, no. 8_Supplement (April 15, 2024): P58. http://dx.doi.org/10.1158/1538-7445.fcs2023-p58.

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Abstract The identification of biomarkers of therapeutic response for different cancers presents significant challenges owing to the heterogeneity of these diseases. Whole-genome CRISPR gene knockout screening (DepMap) and large-scale drug dosing (e.g. PRISM, and Sanger GDSC) performed across large panels of cancer cell lines reveal a whole spectrum of cancer vulnerabilities and dependencies that can be used to identify potential biomarkers and perturbed pathways in subtypes of cancers. In this study, we identified tumor type-specific dependent genes with CRISPR screening data. Functional relationships among genes were systematically examined by clustering genes with pronounced dependencies based on established biological pathways and conducting a co-dependency analysis. We found that both tumor type and subtype-specific analysis allowed the classification of cell lines based on gene dependency. Next, we correlated gene dependency of the selected dependent genes with normalised drug sensitivity scores (AUC) to triangulate potential biomarkers of drug response. We first applied this method to several tumor types which are well-characterised by known cancer drivers, and successfully identified known biomarkers and targeted pathways. However, we also identified previously unknown gene-drug relationships which will be further explored. Subsequent analysis of rarer cancers, including soft tissue sarcoma, yielded promising gene targets. This analysis pipeline can be used extensively to mine DepMap data using any subset of cell lines of interest for biomarker discovery. Citation Format: Shen Yong Yeo, Wai Yee Wong, Jesslyn, Tan Boon Toh, Valerie Shiwen Yang, Xing Yi Woo. Utilizing Cancer Vulnerabilities and Dependencies to Explore Cancer Biomarkers by Triangulating Large-Scale Gene Knockout and Drug Response Data [abstract]. In: Proceedings of Frontiers in Cancer Science; 2023 Nov 6-8; Singapore. Philadelphia (PA): AACR; Cancer Res 2024;84(8_Suppl):Abstract nr P58.

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Corbel, Stephane, Karl Hodel, Kathleen Tran, Maci Meyers, Jing Tong, Michele Baltay, Peter Kilfeather, et al. "Abstract 1062: A comprehensive CRISPR-enabled functional genomics profiling platform in acute myeloid leukemia (AML): Pilot study and validation of Fx Heme." Cancer Research 83, no. 7_Supplement (April 4, 2023): 1062. http://dx.doi.org/10.1158/1538-7445.am2023-1062.

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Abstract AML is the most common acute leukemia in adults over 50 years of age. Current therapeutic approaches are focused on high or low dose chemotherapy in the front line setting, with use of molecularly targeted agents in unfit patients or the relapsed/refractory setting. However, routine genomic profiling for common molecular lesions (e.g. FLT3 mutation) incompletely stratifies the target patient population, and more personalized and comprehensive approaches could be of significant clinical benefit. To this end, we have developed an in vitro functional genomics platform for the systematic characterization of gene dependency on drug targets using CRISPR/Cas9 technology. Methods: Retrospective pre-therapy samples were obtained for a cohort of AML patients who subsequently went onto therapy with a combination of the tyrosine kinase inhibitor sorafenib plus chemotherapy (e.g. GCLAM). Unsorted primary tumor cells from these samples were transduced with lentivirus harboring Cas9 enzyme and an sgRNA library. This customized sgRNA library (Fx Heme) was designed to inhibit expression of genes encoding the targets of all FDA-approved oncology drugs, as well as internal positive and negative references. Transduced cells were harvested at multiple timepoints, and sgRNA distribution was assessed by amplicon sequencing of DNA barcodes. Changes in barcode abundance were quantitated and aggregated to calculate gene-level phenotype scores, enabling identification of gene dependencies. Dependency on targets of sorafenib was compared to clinical outcome (complete response (CR) without minimal residual disease (MRD), incomplete response, CR with MRD, or refractory) following treatment with sorafenib plus chemotherapy. Results: Among the 22 AML patients analyzed, 14 reached CR and 8 were classified as incomplete responders (refractory or MRD). Gene dependency on sorafenib targets including FLT3, KIT, PDGFR, and all RAF isoforms was frequently observed (12/22 cases). Among cases indicating any sorafenib target gene dependency, 5/12 (42%) demonstrated dependence on 2 or more sorafenib target genes. Overall, Fx Heme achieved 78.6% sensitivity, 87.5% specificity, and 91.7% PPV for prediction of observed clinical outcome. In contrast, stratification based on FLT3 mutation (internal tandem duplication or tyrosine kinase domain mutation) demonstrated 64.3% sensitivity, 50% specificity, 69.2% PPV. In summary, our results establish feasibility of CRISPR-based Fx Heme comprehensive functional genomic profiling for AML precision medicine, and validate this approach in a retrospective study cohort. Functional genomics may contribute to more effective personalization of AML treatment by uncovering drug target dependencies not readily identified by conventional genomic profiling, in an unbiased, comprehensive and patient-specific fashion. Citation Format: Stephane Corbel, Karl Hodel, Kathleen Tran, Maci Meyers, Jing Tong, Michele Baltay, Peter Kilfeather, Yali Li, Christian Schmedt, Srinath Sampath, Srihari Sampath. A comprehensive CRISPR-enabled functional genomics profiling platform in acute myeloid leukemia (AML): Pilot study and validation of Fx Heme [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1062.

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Kanai, Ryan, Emily Norton, Patrick Stern, Richard O. Hynes, and John M. Lamar. "Identification of a Gene Signature That Predicts Dependence upon YAP/TAZ-TEAD." Cancers 16, no. 5 (February 20, 2024): 852. http://dx.doi.org/10.3390/cancers16050852.

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Targeted therapies are effective cancer treatments when accompanied by accurate diagnostic tests that can help identify patients that will respond to those therapies. The YAP/TAZ-TEAD axis is activated and plays a causal role in several cancer types, and TEAD inhibitors are currently in early-phase clinical trials in cancer patients. However, a lack of a reliable way to identify tumors with YAP/TAZ-TEAD activation for most cancer types makes it difficult to determine which tumors will be susceptible to TEAD inhibitors. Here, we used a combination of RNA-seq and bioinformatic analysis of metastatic melanoma cells to develop a YAP/TAZ gene signature. We found that the genes in this signature are TEAD-dependent in several melanoma cell lines, and that their expression strongly correlates with YAP/TAZ activation in human melanomas. Using DepMap dependency data, we found that this YAP/TAZ signature was predictive of melanoma cell dependence upon YAP/TAZ or TEADs. Importantly, this was not limited to melanoma because this signature was also predictive when tested on a panel of over 1000 cancer cell lines representing numerous distinct cancer types. Our results suggest that YAP/TAZ gene signatures like ours may be effective tools to predict tumor cell dependence upon YAP/TAZ-TEAD, and thus potentially provide a means to identify patients likely to benefit from TEAD inhibitors.

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Bernthaler, Andreas, Irmgard Mühlberger, Raul Fechete, Paul Perco, Arno Lukas, and Bernd Mayer. "A dependency graph approach for the analysis of differential gene expression profiles." Molecular BioSystems 5, no. 12 (2009): 1720. http://dx.doi.org/10.1039/b903109j.

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Ma, P. C. H., and K. C. C. Chan. "Inferring Gene Regulatory Networks From Expression Data by Discovering Fuzzy Dependency Relationships." IEEE Transactions on Fuzzy Systems 16, no. 2 (April 2008): 455–65. http://dx.doi.org/10.1109/tfuzz.2007.894969.

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Rapoport, Rachel, Avraham Greenberg, Zohar Yakhini, and Itamar Simon. "A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms." Biology 13, no. 3 (March 8, 2024): 175. http://dx.doi.org/10.3390/biology13030175.

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Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes’ spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomic intervals are large, including replication timing domains, large H3K9me3 and H3K27me3 domains, HiC compartments and lamina-associated domains (LADs). Intriguingly, applying SAGO to prostate cancer samples with large copy number alteration (CNA) domains eliminated most of the enriched GO terms, thus helping to accurately identify biologically relevant gene sets linked to oncogenic processes, free from spatial bias.

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Obst, Reinhard, Hannah Rabenstein, Anne Behrendt, Joachim Ellwart, Marion Horsch, and Johannes Beckers. "Differential antigen-dependency of CD4+ and CD8+ T cells (P1338)." Journal of Immunology 190, no. 1_Supplement (May 1, 2013): 208.12. http://dx.doi.org/10.4049/jimmunol.190.supp.208.12.

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Abstract Though T cell expansion and effector differentiation are triggered and, perhaps, maintained by antigen, the proliferative behaviors of CD4+ and CD8+ T cells responding to timed antigen presentation have rarely been compared side by side. Proliferation and effector differentiation of TCR transgenic and polyclonal T cells of different backgrounds were analyzed following transient and continuous TCR signals. We found CD4+ T cell proliferation to be dependent on prolonged antigen presence, whereas CD8+ T cells were able to divide and differentiate into effector cells in the absence of it. This proliferation of CD8+ T cells is truly autonomous and independent of any self-MHC derived signals. The discontinued proliferation of CD4+ T cells was not caused by coinhibitory signals like or the lack of inflammatory stimuli and also found in memory cells. Gene expression analyses illustrated differences in global gene transcription between the two subsets following stimulation periods of different lenghts. These T cell data reflect the MHC class difference in that class II molecules, unlike class I, were stabilized on activated DCs in vivo.

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Kettyle, Laura M., Charles-Étienne Lebert-Ghali, Ivan V. Grishagin, Glenda J. Dickson, Paul G. O’Reilly, David A. Simpson, Janet J. Bijl, Ken I. Mills, Guy Sauvageau, and Alexander Thompson. "Pathways, Processes, and Candidate Drugs Associated with a Hoxa Cluster-Dependency Model of Leukemia." Cancers 11, no. 12 (December 17, 2019): 2036. http://dx.doi.org/10.3390/cancers11122036.

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High expression of the HOXA cluster correlates with poor clinical outcome in acute myeloid leukemias, particularly those harboring rearrangements of the mixed-lineage-leukemia gene (MLLr). Whilst decreased HOXA expression acts as a readout for candidate experimental therapies, the necessity of the HOXA cluster for leukemia maintenance has not been fully explored. Primary leukemias were generated in hematopoietic stem/progenitor cells from Cre responsive transgenic mice for conditional deletion of the Hoxa locus. Hoxa deletion resulted in reduced proliferation and colony formation in which surviving leukemic cells retained at least one copy of the Hoxa cluster, indicating dependency. Comparative transcriptome analysis of Hoxa wild type and deleted leukemic cells identified a unique gene signature associated with key pathways including transcriptional mis-regulation in cancer, the Fanconi anemia pathway and cell cycle progression. Further bioinformatics analysis of the gene signature identified a number of candidate FDA-approved drugs for potential repurposing in high HOXA expressing cancers including MLLr leukemias. Together these findings support dependency for an MLLr leukemia on Hoxa expression and identified candidate drugs for further therapeutic evaluation.

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Sahin, Ilyas, Yawara Kawano, Romanos Sklavenitis-Pistofidis, Michele Moschetta, Yuji Mishima, Salomon Manier, Antonio Sacco, et al. "Citron Rho-interacting kinase silencing causes cytokinesis failure and reduces tumor growth in multiple myeloma." Blood Advances 3, no. 7 (April 2, 2019): 995–1002. http://dx.doi.org/10.1182/bloodadvances.2018028456.

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Abstract Citron Rho-interacting serine/threonine kinase (CIT) is a serine/threonine kinase that acts as a key component of the midbody and is essential for cytokinesis. CIT has been reported to be highly expressed in some tumor tissues and to play a role in cancer proliferation; however, the significance of CIT has not been investigated in multiple myeloma (MM). Here, we identified, by protein microarray and immunohistochemistry, that CIT is 1 of the upregulated proteins in the plasma cells of MM patients compared with healthy controls. Analysis of a gene expression profile data set showed that MM patients with high CIT gene expression had significantly worse overall survival compared with MM patients with low CIT gene expression. CIT silencing in MM cell lines induced cytokinesis failure and resulted in decreased MM cell proliferation in vitro and in vivo. TP53 expression was found to be an independent predictor of CIT dependency, with low-TP53 cell lines exhibiting a strong dependency on CIT. This study provides the rationale for CIT being a potential therapeutic target in MM in future trials.

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Maria, Elisa C. J., Isabel Salazar, Luis Sanz, and Miguel A. Gómez-Villegas. "Using Copula to Model Dependence When Testing Multiple Hypotheses in DNA Microarray Experiments: A Bayesian Approximation." Mathematics 8, no. 9 (September 4, 2020): 1514. http://dx.doi.org/10.3390/math8091514.

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Many experiments require simultaneously testing many hypotheses. This is particularly relevant in the context of DNA microarray experiments, where it is common to analyze many genes to determine which of them are differentially expressed under two conditions. Another important problem in this context is how to model the dependence at the level of gene expression. In this paper, we propose a Bayesian procedure for simultaneously testing multiple hypotheses, modeling the dependence through copula functions, where all available information, both objective and subjective, can be used. The approach has the advantage that it can be used with different dependency structures. Simulated data analysis was performed to examine the performance of the proposed approach. The results show that our procedure captures the dependence appropriately classifying adequately a high percentage of true and false null hypotheses when choosing a prior distribution beta skewed to the right for the initial probability of each null hypothesis, resulting in a very powerful procedure. The procedure is also illustrated with real data.

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Khaliq and Fallahi-Sichani. "Epigenetic Mechanisms of Escape from BRAF Oncogene Dependency." Cancers 11, no. 10 (October 1, 2019): 1480. http://dx.doi.org/10.3390/cancers11101480.

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About eight percent of all human tumors (including 50% of melanomas) carry gain-of-function mutations in the BRAF oncogene. Mutated BRAF and subsequent hyperactivation of the MAPK signaling pathway has motivated the use of MAPK-targeted therapies for these tumors. Despite great promise, however, MAPK-targeted therapies in BRAF-mutant tumors are limited by the emergence of drug resistance. Mechanisms of resistance include genetic, non-genetic and epigenetic alterations. Epigenetic plasticity, often modulated by histone-modifying enzymes and gene regulation, can influence a tumor cell’s BRAF dependency and therefore, response to therapy. In this review, focusing primarily on class 1 BRAF-mutant cells, we will highlight recent work on the contribution of epigenetic mechanisms to inter- and intratumor cell heterogeneity in MAPK-targeted therapy response.

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Awofala, Awoyemi A., Susan Jones, and Jane A. Davies. "The Heat Shock Protein 26 Gene is Required for Ethanol Tolerance in Drosophila." Journal of Experimental Neuroscience 5 (January 2011): JEN.S6280. http://dx.doi.org/10.4137/jen.s6280.

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Stress plays an important role in drug- and addiction-related behaviours. However, the mechanisms underlying these behavioural responses are still poorly understood. In the light of recent reports that show consistent regulation of many genes encoding stress proteins including heat shock proteins following ethanol exposure in Drosophila, it was hypothesised that transition to alcohol dependence may involve the dysregulation of the circuits that mediate behavioural responses to stressors. Thus, behavioural genetic methodologies were used to investigate the role of the Drosophila hsp26 gene, a small heat shock protein coding gene which is induced in response to various stresses, in the development of rapid tolerance to ethanol sedation. Rapid tolerance was quantified as the percentage difference in the mean sedation times between the second and first ethanol exposure. Two independently isolated P-element mutations near the hsp26 gene eliminated the capacity for tolerance. In addition, RNAi-mediated functional knockdown of hsp26 expression in the glial cells and the whole nervous system also caused a defect in tolerance development. The rapid tolerance phenotype of the hsp26 mutants was rescued by the expression of the wild-type hsp26 gene in the nervous system. None of these manipulations of the hsp26 gene caused changes in the rate of ethanol absorption. Hsp26 genes are evolutionary conserved, thus the role of hsp26 in ethanol tolerance may present a new direction for research into alcohol dependency.

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Hawkins, Hayley J., Betelehem W. Yacob, Monica E. Brown, Brandon R. Goldstein, John J. Arcaroli, Stacey M. Bagby, Sarah J. Hartman, et al. "Examination of Wnt signaling as a therapeutic target for pancreatic ductal adenocarcinoma (PDAC) using a pancreatic tumor organoid library (PTOL)." PLOS ONE 19, no. 4 (April 10, 2024): e0298808. http://dx.doi.org/10.1371/journal.pone.0298808.

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Pancreatic ductal adenocarcinoma (PDAC) presents at advanced stages and is refractory to most treatment modalities. Wnt signaling activation plays a critical role in proliferation and chemotherapeutic resistance. Minimal media conditions, growth factor dependency, and Wnt dependency were determined via Wnt inhibition for seven patient derived organoids (PDOs) derived from pancreatic tumor organoid libraries (PTOL). Organoids demonstrating response in vitro were assessed in vivo using patient-derived xenografts. Wnt (in)dependent gene signatures were identified for each organoid. Panc269 demonstrated a trend of reduced organoid growth when treated with ETC-159 in combination with paclitaxel or gemcitabine as compared with chemotherapy or ETC-159 alone. Panc320 demonstrated a more pronounced anti-proliferative effect in the combination of ETC-159 and paclitaxel but not with gemcitabine. Panc269 and Panc320 were implanted into nude mice and treated with ETC-159, paclitaxel, and gemcitabine as single agents and in combination. The combination of ETC-159 and paclitaxel demonstrated an anti-tumor effect greater than ETC-159 alone. Extent of combinatory treatment effect were observed to a lesser extent in the Panc320 xenograft. Wnt (in)dependent gene signatures of Panc269 and 320 were consistent with the phenotypes displayed. Gene expression of several key Wnt genes assessed via RT-PCR demonstrated notable fold change following treatment in vivo. Each pancreatic organoid demonstrated varied niche factor dependencies, providing an avenue for targeted therapy, supported through growth analysis following combinatory treatment of Wnt inhibitor and standard chemotherapy in vitro. The clinical utilization of this combinatory treatment modality in pancreatic cancer PDOs has thus far been supported in our patient-derived xenograft models treated with Wnt inhibitor plus paclitaxel or gemcitabine. Gene expression analysis suggests there are key Wnt genes that contribute to the Wnt (in)dependent phenotypes of pancreatic tumors, providing plausible mechanistic explanation for Wnt (in)dependency and susceptibility or resistance to treatment on the genotypic level.

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Wang, Jie-Huei, and Yi-Hau Chen. "Network-adjusted Kendall’s Tau Measure for Feature Screening with Application to High-dimensional Survival Genomic Data." Bioinformatics 37, no. 15 (January 29, 2021): 2150–56. http://dx.doi.org/10.1093/bioinformatics/btab064.

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Abstract Motivation In high-dimensional genetic/genomic data, the identification of genes related to clinical survival trait is a challenging and important issue. In particular, right-censored survival outcomes and contaminated biomarker data make the relevant feature screening difficult. Several independence screening methods have been developed, but they fail to account for gene–gene dependency information, and may be sensitive to outlying feature data. Results We improve the inverse probability-of-censoring weighted (IPCW) Kendall’s tau statistic by using Google’s PageRank Markov matrix to incorporate feature dependency network information. Also, to tackle outlying feature data, the nonparanormal approach transforming the feature data to multivariate normal variates are utilized in the graphical lasso procedure to estimate the network structure in feature data. Simulation studies under various scenarios show that the proposed network-adjusted weighted Kendall’s tau approach leads to more accurate feature selection and survival prediction than the methods without accounting for feature dependency network information and outlying feature data. The applications on the clinical survival outcome data of diffuse large B-cell lymphoma and of The Cancer Genome Atlas lung adenocarcinoma patients demonstrate clearly the advantages of the new proposal over the alternative methods. Supplementary information Supplementary data are available at Bioinformatics online.

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Lall, Snehalika, Sumanta Ray, and Sanghamitra Bandyopadhyay. "RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data." PLOS Computational Biology 17, no. 10 (October 19, 2021): e1009464. http://dx.doi.org/10.1371/journal.pcbi.1009464.

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Gene selection in unannotated large single cell RNA sequencing (scRNA-seq) data is important and crucial step in the preliminary step of downstream analysis. The existing approaches are primarily based on high variation (highly variable genes) or significant high expression (highly expressed genes) failed to provide stable and predictive feature set due to technical noise present in the data. Here, we propose RgCop, a novel regularized copula based method for gene selection from large single cell RNA-seq data. RgCop utilizes copula correlation (Ccor), a robust equitable dependence measure that captures multivariate dependency among a set of genes in single cell expression data. We formulate an objective function by adding l1 regularization term with Ccor to penalizes the redundant co-efficient of features/genes, resulting non-redundant effective features/genes set. Results show a significant improvement in the clustering/classification performance of real life scRNA-seq data over the other state-of-the-art. RgCop performs extremely well in capturing dependence among the features of noisy data due to the scale invariant property of copula, thereby improving the stability of the method. Moreover, the differentially expressed (DE) genes identified from the clusters of scRNA-seq data are found to provide an accurate annotation of cells. Finally, the features/genes obtained from RgCop is able to annotate the unknown cells with high accuracy.

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Ma, Xiaomin, Xuelian Li, and Uwe Ludewig. "Arbuscular mycorrhizal colonization outcompetes root hairs in maize under low phosphorus availability." Annals of Botany 127, no. 1 (September 2, 2020): 155–66. http://dx.doi.org/10.1093/aob/mcaa159.

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Abstract Background and Aims An increase in root hair length and density and the development of arbuscular mycorrhiza symbiosis are two alternative strategies of most plants to increase the root–soil surface area under phosphorus (P) deficiency. Across many plant species, root hair length and mycorrhization density are inversely correlated. Root architecture, rooting density and physiology also differ between species. This study aims to understand the relationship among root hairs, arbuscular mycorrhizal fungi (AMF) colonization, plant growth, P acquisition and mycorrhizal-specific Pi transporter gene expression in maize. Methods Using nearly isogenic maize lines, the B73 wild type and the rth3 root hairless mutant, we quantified the effect of root hairs and AMF infection in a calcareous soil under P deficiency through a combined analysis of morphological, physiological and molecular factors. Key Results Wild-type root hairs extended the rhizosphere for acid phosphatase activity by 0.5 mm compared with the rth3 hairless mutant, as measured by in situ zymography. Total root length of the wild type was longer than that of rth3 under P deficiency. Higher AMF colonization and mycorrhiza-induced phosphate transporter gene expression were identified in the mutant under P deficiency, but plant growth and P acquisition were similar between mutant and the wild type. The mycorrhizal dependency of maize was 33 % higher than the root hair dependency. Conclusions The results identified larger mycorrhizal dependency than root hair dependency under P deficiency in maize. Root hairs and AMF inoculation are two alternative ways to increase Pi acquisition under P deficiency, but these two strategies compete with each other.

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Foroughmand-Araabi, Mohammad-Hadi, Bahram Goliaei, Kasra Alishahi, Mehdi Sadeghi, and Sama Goliaei. "Codon usage and protein sequence pattern dependency in different organisms: A Bioinformatics approach." Journal of Bioinformatics and Computational Biology 13, no. 02 (April 2015): 1550002. http://dx.doi.org/10.1142/s021972001550002x.

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Although it is known that synonymous codons are not chosen randomly, the role of the codon usage in gene regulation is not clearly understood, yet. Researchers have investigated the relation between the codon usage and various properties, such as gene regulation, translation rate, translation efficiency, mRNA stability, splicing, and protein domains. Recently, a universal codon usage based mechanism for gene regulation is proposed. We studied the role of protein sequence patterns on the codons usage by related genes. Considering a subsequence of a protein that matches to a pattern or motif, we showed that, parts of the genes, which are translated to this subsequence, use specific ratios of synonymous codons. Also, we built a multinomial logistic regression statistical model for codon usage, which considers the effect of patterns on codon usage. This model justifies the observed codon usage preference better than the classic organism dependent codon usage. Our results showed that the codon usage plays a role in controlling protein levels, for genes that participate in a specific biological function. This is the first time that this phenomenon is reported.

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Chan, A. M., T. P. Fleming, E. S. McGovern, M. Chedid, T. Miki, and S. A. Aaronson. "Expression cDNA cloning of a transforming gene encoding the wild-type G alpha 12 gene product." Molecular and Cellular Biology 13, no. 2 (February 1993): 762–68. http://dx.doi.org/10.1128/mcb.13.2.762-768.1993.

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Using an expression cDNA cloning approach, we examined human tumor cell lines for novel oncogenes that might evade detection by conventional techniques. We isolated a transforming sequence that was highly efficient in transforming NIH 3T3 mouse fibroblasts. DNA sequence analysis identified the gene as the human homolog of a recently cloned alpha subunit of mouse GTP-binding protein G alpha 12. NIH 3T3 cells transfected with G alpha 12 cDNA grew in soft agar and were tumorigenic in nude mice. There were no apparent mutations in the cloned cDNA in comparison with a G alpha 12 cDNA clone isolated from a normal human epithelial cell library, implying that overexpression alone was sufficient to cause NIH 3T3 cell transformation. The observed altered growth properties mediated by G alpha 12 showed a certain degree of dependency on serum factors, and its mitogenic potential was also potently inhibited by suramin treatment.

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Chan, A. M., T. P. Fleming, E. S. McGovern, M. Chedid, T. Miki, and S. A. Aaronson. "Expression cDNA cloning of a transforming gene encoding the wild-type G alpha 12 gene product." Molecular and Cellular Biology 13, no. 2 (February 1993): 762–68. http://dx.doi.org/10.1128/mcb.13.2.762.

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Using an expression cDNA cloning approach, we examined human tumor cell lines for novel oncogenes that might evade detection by conventional techniques. We isolated a transforming sequence that was highly efficient in transforming NIH 3T3 mouse fibroblasts. DNA sequence analysis identified the gene as the human homolog of a recently cloned alpha subunit of mouse GTP-binding protein G alpha 12. NIH 3T3 cells transfected with G alpha 12 cDNA grew in soft agar and were tumorigenic in nude mice. There were no apparent mutations in the cloned cDNA in comparison with a G alpha 12 cDNA clone isolated from a normal human epithelial cell library, implying that overexpression alone was sufficient to cause NIH 3T3 cell transformation. The observed altered growth properties mediated by G alpha 12 showed a certain degree of dependency on serum factors, and its mitogenic potential was also potently inhibited by suramin treatment.

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Banerjee, Samiran, and Steven D. Siciliano. "Factors Driving Potential Ammonia Oxidation in Canadian Arctic Ecosystems: Does Spatial Scale Matter?" Applied and Environmental Microbiology 78, no. 2 (November 11, 2011): 346–53. http://dx.doi.org/10.1128/aem.06132-11.

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ABSTRACTAmmonia oxidation is a major process in nitrogen cycling, and it plays a key role in nitrogen limited soil ecosystems such as those in the arctic. Although mm-scale spatial dependency of ammonia oxidizers has been investigated, little is known about the field-scale spatial dependency of aerobic ammonia oxidation processes and ammonia-oxidizing archaeal and bacterial communities, particularly in arctic soils. The purpose of this study was to explore the drivers of ammonia oxidation at the field scale in cryosols (soils with permafrost within 1 m of the surface). We measured aerobic ammonia oxidation potential (both autotrophic and heterotrophic) and functional gene abundance (bacterialamoAand archaealamoA) in 279 soil samples collected from three arctic ecosystems. The variability associated with quantifying genes was substantially less than the spatial variability observed in these soils, suggesting that molecular methods can be used reliably evaluate spatial dependency in arctic ecosystems. Ammonia-oxidizing archaeal and bacterial communities and aerobic ammonia oxidation were spatially autocorrelated. Gene abundances were spatially structured within 4 m, whereas biochemical processes were structured within 40 m. Ammonia oxidation was driven at small scales (<1m) by moisture and total organic carbon, whereas gene abundance and other edaphic factors drove ammonia oxidation at medium (1 to 10 m) and large (10 to 100 m) scales. In these arctic soils heterotrophs contributed between 29 and 47% of total ammonia oxidation potential. The spatial scale for aerobic ammonia oxidation genes differed from potential ammonia oxidation, suggesting that in arctic ecosystems edaphic, rather than genetic, factors are an important control on ammonia oxidation.

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Richter, Camden, David Mayhew, Jonathan P. Rennhack, Jonathan So, Elizabeth H. Stover, Justin H. Hwang, and Danuta Szczesna-Cordary. "Genomic Amplification and Functional Dependency of the Gamma Actin Gene ACTG1 in Uterine Cancer." International Journal of Molecular Sciences 21, no. 22 (November 18, 2020): 8690. http://dx.doi.org/10.3390/ijms21228690.

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Sarcomere and cytoskeleton genes, or actomyosin genes, regulate cell biology including mechanical stress, cell motility, and cell division. While actomyosin genes are recurrently dysregulated in cancers, their oncogenic roles have not been examined in a lineage-specific fashion. In this report, we investigated dysregulation of nine sarcomeric and cytoskeletal genes across 20 cancer lineages. We found that uterine cancers harbored the highest frequencies of amplification and overexpression of the gamma actin gene, ACTG1. Each of the four subtypes of uterine cancers, mixed endometrial carcinomas, serous carcinomas, endometroid carcinomas, and carcinosarcomas harbored between 5~20% of ACTG1 gene amplification or overexpression. Clinically, patients with ACTG1 gains had a poor prognosis. ACTG1 gains showed transcriptional patterns that reflect activation of oncogenic signals, repressed response to innate immunity, or immunotherapy. Functionally, the CRISPR-CAS9 gene deletion of ACTG1 had the most robust and consistent effects in uterine cancer cells relative to 20 other lineages. Overall, we propose that ACTG1 regulates the fitness of uterine cancer cells by modulating cell-intrinsic properties and the tumor microenvironment. In summary, the ACTG1 functions relative to other actomyosin genes support the notion that it is a potential biomarker and a target gene in uterine cancer precision therapies.

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Kuznetsova, T., J. A. Staessen, T. Reineke, A. Olszanecka, A. Ryabikov, V. Tikhonoff, E. Casiglia, R. Fagard, K. Kawecka-Jaszcz, and E. Brand. "CONTEXT-DEPENDENCY OF THE RELATION BETWEEN LEFT VENTRICULAR MASS AND AGT GENE VARIANTS." Journal of Hypertension 22, Suppl. 2 (June 2004): S346—S347. http://dx.doi.org/10.1097/00004872-200406002-01210.

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Jin, Liyuan, Said Nawab, Mengli Xia, Xiaoyan Ma, and Yi‐Xin Huo. "Context‐dependency of synthetic minimal promoters in driving gene expression: a case study." Microbial Biotechnology 12, no. 6 (October 2, 2019): 1476–86. http://dx.doi.org/10.1111/1751-7915.13489.

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