Analysis of the Human Serum Proteome
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Gene Symbol Gene Description ACVR1B Activin a Receptor, Type IB
Table S1. Kinase clones included in human kinase cDNA library for yeast two-hybrid screening Gene Symbol Gene Description ACVR1B activin A receptor, type IB ADCK2 aarF domain containing kinase 2 ADCK4 aarF domain containing kinase 4 AGK multiple substrate lipid kinase;MULK AK1 adenylate kinase 1 AK3 adenylate kinase 3 like 1 AK3L1 adenylate kinase 3 ALDH18A1 aldehyde dehydrogenase 18 family, member A1;ALDH18A1 ALK anaplastic lymphoma kinase (Ki-1) ALPK1 alpha-kinase 1 ALPK2 alpha-kinase 2 AMHR2 anti-Mullerian hormone receptor, type II ARAF v-raf murine sarcoma 3611 viral oncogene homolog 1 ARSG arylsulfatase G;ARSG AURKB aurora kinase B AURKC aurora kinase C BCKDK branched chain alpha-ketoacid dehydrogenase kinase BMPR1A bone morphogenetic protein receptor, type IA BMPR2 bone morphogenetic protein receptor, type II (serine/threonine kinase) BRAF v-raf murine sarcoma viral oncogene homolog B1 BRD3 bromodomain containing 3 BRD4 bromodomain containing 4 BTK Bruton agammaglobulinemia tyrosine kinase BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) BUB1B BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast) C9orf98 chromosome 9 open reading frame 98;C9orf98 CABC1 chaperone, ABC1 activity of bc1 complex like (S. pombe) CALM1 calmodulin 1 (phosphorylase kinase, delta) CALM2 calmodulin 2 (phosphorylase kinase, delta) CALM3 calmodulin 3 (phosphorylase kinase, delta) CAMK1 calcium/calmodulin-dependent protein kinase I CAMK2A calcium/calmodulin-dependent protein kinase (CaM kinase) II alpha CAMK2B calcium/calmodulin-dependent -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Profiling Data
Compound Name DiscoveRx Gene Symbol Entrez Gene Percent Compound Symbol Control Concentration (nM) JNK-IN-8 AAK1 AAK1 69 1000 JNK-IN-8 ABL1(E255K)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317I)-nonphosphorylated ABL1 87 1000 JNK-IN-8 ABL1(F317I)-phosphorylated ABL1 100 1000 JNK-IN-8 ABL1(F317L)-nonphosphorylated ABL1 65 1000 JNK-IN-8 ABL1(F317L)-phosphorylated ABL1 61 1000 JNK-IN-8 ABL1(H396P)-nonphosphorylated ABL1 42 1000 JNK-IN-8 ABL1(H396P)-phosphorylated ABL1 60 1000 JNK-IN-8 ABL1(M351T)-phosphorylated ABL1 81 1000 JNK-IN-8 ABL1(Q252H)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(Q252H)-phosphorylated ABL1 56 1000 JNK-IN-8 ABL1(T315I)-nonphosphorylated ABL1 100 1000 JNK-IN-8 ABL1(T315I)-phosphorylated ABL1 92 1000 JNK-IN-8 ABL1(Y253F)-phosphorylated ABL1 71 1000 JNK-IN-8 ABL1-nonphosphorylated ABL1 97 1000 JNK-IN-8 ABL1-phosphorylated ABL1 100 1000 JNK-IN-8 ABL2 ABL2 97 1000 JNK-IN-8 ACVR1 ACVR1 100 1000 JNK-IN-8 ACVR1B ACVR1B 88 1000 JNK-IN-8 ACVR2A ACVR2A 100 1000 JNK-IN-8 ACVR2B ACVR2B 100 1000 JNK-IN-8 ACVRL1 ACVRL1 96 1000 JNK-IN-8 ADCK3 CABC1 100 1000 JNK-IN-8 ADCK4 ADCK4 93 1000 JNK-IN-8 AKT1 AKT1 100 1000 JNK-IN-8 AKT2 AKT2 100 1000 JNK-IN-8 AKT3 AKT3 100 1000 JNK-IN-8 ALK ALK 85 1000 JNK-IN-8 AMPK-alpha1 PRKAA1 100 1000 JNK-IN-8 AMPK-alpha2 PRKAA2 84 1000 JNK-IN-8 ANKK1 ANKK1 75 1000 JNK-IN-8 ARK5 NUAK1 100 1000 JNK-IN-8 ASK1 MAP3K5 100 1000 JNK-IN-8 ASK2 MAP3K6 93 1000 JNK-IN-8 AURKA AURKA 100 1000 JNK-IN-8 AURKA AURKA 84 1000 JNK-IN-8 AURKB AURKB 83 1000 JNK-IN-8 AURKB AURKB 96 1000 JNK-IN-8 AURKC AURKC 95 1000 JNK-IN-8 -
PHKG2, Active (SRP5062)
PHKG2, active, GST tagged, human PRECISIOÒ Kinase recombinant, expressed in Sf9 cells Catalog Number SRP5062 Storage Temperature –70°C Synonyms: GSD9C Figure 1. SDS-PAGE Gel of Typical Lot Product Description 70–95% (densitometry) PHKG2 is the hepatic and testis isoform of the gamma subunit of phosphorylase kinase. PHKG2 gene contains 10 exons and spans 9.5 kb and maps to chromosome 16p12.1-p11.2.1 Deficiency of PHK, a regulatory enzyme of glycogen metabolism, is responsible for 25% of all cases of glycogen storage disease and is genetically and clinically heterogeneous. Mutations in the PHKG2 gene lead to autosomal liver-specific PHK deficiency (glycogen storage disease IXc) and an increased risk of cirrhosis, and at least 11 PHKG2 mutations have been identified to date.2 Figure 2. Specific Activity of Typical Lot Recombinant, full-length, human PHKG2 was 59–81 nmole/min/mg expressed by baculovirus in Sf9 insect cells using an N-terminal GST tag. The gene accession number is NM_000294. Recombinant protein stored in 50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 0.25 mM DTT, 0.1 mM EDTA, 0.1 mM PMSF, and 25% glycerol. Molecular mass: ~70 kDa Purity: 70–95% (SDS-PAGE, see Figure 1) Specific Activity: 59–81 nmole/min/mg (see Figure 2) Precautions and Disclaimer Procedure This product is for R&D use only, not for drug, Preparation Instructions household, or other uses. Please consult the Material Kinase Assay Buffer – 25 mM MOPS, pH 7.2, 12.5 mM Safety Data Sheet for information regarding hazards glycerol 2-phosphate, 25 mM MgCl2, 5 mM EGTA, and and safe handling practices. -
Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. -
Supplementary Table 1. in Vitro Side Effect Profiling Study for LDN/OSU-0212320. Neurotransmitter Related Steroids
Supplementary Table 1. In vitro side effect profiling study for LDN/OSU-0212320. Percent Inhibition Receptor 10 µM Neurotransmitter Related Adenosine, Non-selective 7.29% Adrenergic, Alpha 1, Non-selective 24.98% Adrenergic, Alpha 2, Non-selective 27.18% Adrenergic, Beta, Non-selective -20.94% Dopamine Transporter 8.69% Dopamine, D1 (h) 8.48% Dopamine, D2s (h) 4.06% GABA A, Agonist Site -16.15% GABA A, BDZ, alpha 1 site 12.73% GABA-B 13.60% Glutamate, AMPA Site (Ionotropic) 12.06% Glutamate, Kainate Site (Ionotropic) -1.03% Glutamate, NMDA Agonist Site (Ionotropic) 0.12% Glutamate, NMDA, Glycine (Stry-insens Site) 9.84% (Ionotropic) Glycine, Strychnine-sensitive 0.99% Histamine, H1 -5.54% Histamine, H2 16.54% Histamine, H3 4.80% Melatonin, Non-selective -5.54% Muscarinic, M1 (hr) -1.88% Muscarinic, M2 (h) 0.82% Muscarinic, Non-selective, Central 29.04% Muscarinic, Non-selective, Peripheral 0.29% Nicotinic, Neuronal (-BnTx insensitive) 7.85% Norepinephrine Transporter 2.87% Opioid, Non-selective -0.09% Opioid, Orphanin, ORL1 (h) 11.55% Serotonin Transporter -3.02% Serotonin, Non-selective 26.33% Sigma, Non-Selective 10.19% Steroids Estrogen 11.16% 1 Percent Inhibition Receptor 10 µM Testosterone (cytosolic) (h) 12.50% Ion Channels Calcium Channel, Type L (Dihydropyridine Site) 43.18% Calcium Channel, Type N 4.15% Potassium Channel, ATP-Sensitive -4.05% Potassium Channel, Ca2+ Act., VI 17.80% Potassium Channel, I(Kr) (hERG) (h) -6.44% Sodium, Site 2 -0.39% Second Messengers Nitric Oxide, NOS (Neuronal-Binding) -17.09% Prostaglandins Leukotriene, -
Prognostic and Immune Implications of a Novel Ferroptosis-Related Ten-Gene Signature in Lung Adenocarcinoma
1058 Original Article Page 1 of 26 Prognostic and immune implications of a novel ferroptosis-related ten-gene signature in lung adenocarcinoma Chao Ma1,2^, Feng Li1,3, Huan Luo1 1Charité – Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany; 2Berlin Institute of Health Center for Regenerative Therapies and Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité – Universitätsmedizin Berlin, Berlin, Germany; 3Department of Surgery, Competence Center of Thoracic Surgery, Charité University Hospital Berlin, Berlin, Germany Contributions: (I) Conception and design: C Ma; (II) Administrative support: C Ma; (III) Provision of study materials or patients: C Ma, F Li; (IV) Collection and assembly of data: All authors; (V) Data analysis and interpretation: C Ma; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. Correspondence to: Chao Ma. Berlin Institute of Health Center for Regenerative Therapies and Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité – Universitätsmedizin Berlin, Berlin, Germany. Email: [email protected]. Background: Lung cancer has been the focus of attention for many researchers in recent years due to its leading contribution to cancer-related death worldwide, with lung adenocarcinoma (LUAD) being the most common histological type. Ferroptosis, a novel iron-dependent form of regulated cell death, can be induced by sorafenib. Emerging evidence shows that triggering ferroptosis has potential as a cancer therapy. This work aimed to build a ferroptosis-related gene signature for predicting the outcome of LUAD. Methods: The TCGA-LUAD dataset was set as the training cohort, and the GSE72094 and GSE68465 datasets were set as the validation cohorts. -
Full Disclosure Forms
Expanding genotype/phenotype of neuromuscular diseases by comprehensive target capture/NGS Xia Tian, PhD* ABSTRACT * Wen-Chen Liang, MD Objective: To establish and evaluate the effectiveness of a comprehensive next-generation * Yanming Feng, PhD sequencing (NGS) approach to simultaneously analyze all genes known to be responsible for Jing Wang, MD the most clinically and genetically heterogeneous neuromuscular diseases (NMDs) involving spi- Victor Wei Zhang, PhD nal motoneurons, neuromuscular junctions, nerves, and muscles. Chih-Hung Chou, MS Methods: All coding exons and at least 20 bp of flanking intronic sequences of 236 genes causing Hsien-Da Huang, PhD NMDs were enriched by using SeqCap EZ solution-based capture and enrichment method fol- Ching Wan Lam, PhD lowed by massively parallel sequencing on Illumina HiSeq2000. Ya-Yun Hsu, PhD ; 3 Thy-Sheng Lin, MD Results: The target gene capture/deep sequencing provides an average coverage of 1,000 per Wan-Tzu Chen, MS nucleotide. Thirty-five unrelated NMD families (38 patients) with clinical and/or muscle pathologic Lee-Jun Wong, PhD diagnoses but without identified causative genetic defects were analyzed. Deleterious mutations Yuh-Jyh Jong, MD were found in 29 families (83%). Definitive causative mutations were identified in 21 families (60%) and likely diagnoses were established in 8 families (23%). Six families were left without diagnosis due to uncertainty in phenotype/genotype correlation and/or unidentified causative Correspondence to genes. Using this comprehensive panel, we not only identified mutations in expected genes but Dr. Wong: also expanded phenotype/genotype among different subcategories of NMDs. [email protected] or Dr. Jong: Conclusions: Target gene capture/deep sequencing approach can greatly improve the genetic [email protected] diagnosis of NMDs. -
The Kinesin-2 Family Member KIF3C Regulates Microtubule Dynamics and Is Required for Axon Growth and Regeneration
The Journal of Neuroscience, July 10, 2013 • 33(28):11329–11345 • 11329 Development/Plasticity/Repair The Kinesin-2 Family Member KIF3C Regulates Microtubule Dynamics and Is Required for Axon Growth and Regeneration Laura F. Gumy,1,2 Daniel J. Chew,1 Elena Tortosa,2 Eugene A. Katrukha,2 Lukas C. Kapitein,2 Aviva M. Tolkovsky,1 Casper C. Hoogenraad,2 and James W. Fawcett1 1Centre for Brain Repair, University of Cambridge, Cambridge CB2 0PY, United Kingdom, and 2Division of Cell Biology, Faculty of Science, University of Utrecht, 3584CH Utrecht, The Netherlands Axonregenerationafterinjuryrequirestheextensivereconstruction,reorganization,andstabilizationofthemicrotubulecytoskeletonin the growth cones. Here, we identify KIF3C as a key regulator of axonal growth and regeneration by controlling microtubule dynamics and organization in the growth cone. KIF3C is developmentally regulated. Rat embryonic sensory axons and growth cones contain undetect- able levels of KIF3C protein that is locally translated immediately after injury. In adult neurons, KIF3C is axonally transported from the cell body and is enriched at the growth cone where it preferentially binds to tyrosinated microtubules. Functionally, the interaction of KIF3CwithEB3isnecessaryforitslocalizationatthemicrotubuleplus-endsinthegrowthcone.DepletionofKIF3Cinadultneuronsleads to an increase in stable, overgrown and looped microtubules because of a strong decrease in the microtubule frequency of catastrophes, suggesting that KIF3C functions as a microtubule-destabilizing factor. Adult axons lacking KIF3C, by RNA interference or KIF3C gene knock-out, display an impaired axonal outgrowth in vitro and a delayed regeneration after injury both in vitro and in vivo. Murine KIF3C knock-out embryonic axons grow normally but do not regenerate after injury because they are unable to locally translate KIF3C. These data show that KIF3C is an injury-specific kinesin that contributes to axon growth and regeneration by regulating and organizing the microtubule cytoskeleton in the growth cone. -
Comprehensive Analysis of Expression, Prognosis and Immune Infltrates for Kinesin Superfamily Members in Human Pancreatic Adenocarcinoma
Comprehensive Analysis of Expression, Prognosis and Immune Inltrates for Kinesin Superfamily Members in Human Pancreatic Adenocarcinoma Zehao Chen ( [email protected] ) Wuhan University https://orcid.org/0000-0001-9440-0782 Jie Wang Wuhan University Jian Xu Wuhan University Wenjie Zhu Wuhan University Jianxin Jiang Wuhan University Research Keywords: Kinesin superfamily, Pancreatic Adenocarcinoma, Expression, Prognostic value, Immune Inltrates Posted Date: June 16th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-550619/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/26 Abstract Background Kinesin superfamily (KIFs) has a long-reported signicant inuence on the initiation, development, and progress of pancreatic adenocarcinoma (PAAD). However, the expression level of different KIFs in PAAD and its relationship with the prognosis and immune inltration in patients with PAAD have not been fully elucidated. Methods Comprehensive bioinformatics analyses were done using data from UCSC XENA data hubs, TCGA databaseOncomine databases, GEO datasets, GEPIA, GTEx, The human protein atlas, Kaplan Meier plotter, cBioPortal, STRING and KEGG database. Then, the relationship between KIFs expression and tumor immune inltrates was studied by using the TIMER database. Results A total of 24 differentially expressed KIFs at transcriptional levels were identied between tumor tissues and normal tissues with 1 (KIF1A) downregulated and 23 (KIF2A, KIF2C, KIF3A, KIF3B, KIF3C, KIF4A, KIF5B, KIF7, KIF9, KIF10, KIF11, KIF13A, KIF13B, KIF15, KIF16B, KIF18B, KIF20A, KIF20B, KIF21B, KIF22, KIF23, KIF26B, KIFC1) overexpressed by GEPIA. The protein expression level of KIF3A, KIF3B, KIF4A, KIF5B, KIF7, KIF9, CENPE, KIF11, KIF13A, KIF13B, KIF15, KIF16B, KIF18B, KIF20A, KIF20B, KIF21B, KIF22, KIF23, KIF26B, KIFC1 are higher in PAAD tissues than in the adjacent tissues, which is almost the same as the results of KIFs at transcriptional levels in PAAD. -
Gene Networks Activated by Specific Patterns of Action Potentials in Dorsal Root Ganglia Neurons Received: 10 August 2016 Philip R
www.nature.com/scientificreports OPEN Gene networks activated by specific patterns of action potentials in dorsal root ganglia neurons Received: 10 August 2016 Philip R. Lee1,*, Jonathan E. Cohen1,*, Dumitru A. Iacobas2,3, Sanda Iacobas2 & Accepted: 23 January 2017 R. Douglas Fields1 Published: 03 March 2017 Gene regulatory networks underlie the long-term changes in cell specification, growth of synaptic connections, and adaptation that occur throughout neonatal and postnatal life. Here we show that the transcriptional response in neurons is exquisitely sensitive to the temporal nature of action potential firing patterns. Neurons were electrically stimulated with the same number of action potentials, but with different inter-burst intervals. We found that these subtle alterations in the timing of action potential firing differentially regulates hundreds of genes, across many functional categories, through the activation or repression of distinct transcriptional networks. Our results demonstrate that the transcriptional response in neurons to environmental stimuli, coded in the pattern of action potential firing, can be very sensitive to the temporal nature of action potential delivery rather than the intensity of stimulation or the total number of action potentials delivered. These data identify temporal kinetics of action potential firing as critical components regulating intracellular signalling pathways and gene expression in neurons to extracellular cues during early development and throughout life. Adaptation in the nervous system in response to external stimuli requires synthesis of new gene products in order to elicit long lasting changes in processes such as development, response to injury, learning, and memory1. Information in the environment is coded in the pattern of action-potential firing, therefore gene transcription must be regulated by the pattern of neuronal firing. -
Cyclin F-Chk1 Synthetic Lethality Mediated by E2F1 Degradation
bioRxiv preprint doi: https://doi.org/10.1101/509810; this version posted January 2, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Cyclin F-Chk1 synthetic lethality mediated by E2F1 degradation Kamila Burdova1, Hongbin Yang1, Roberta Faedda1, Samuel Hume1, Daniel Ebner2, Benedikt M Kessler2, Iolanda Vendrell1,2, David H Drewry3, Carrow I Wells3, Stephanie B Hatch2, Vincenzo D’Angiolella1* 1Medical Research Council Institute for Radiation Oncology, Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, UK 2Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford OX3 7FZ, UK 3Structural Genomics Consortium, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America * Corresponding Author: [email protected] Summary Cyclins are central engines of cell cycle progression when partnered with Cyclin Dependent Kinases (CDKs). Among the different cyclins controlling cell cycle progression, cyclin F does not partner with a CDK, but forms an E3 ubiquitin ligase, assembling through the F-box domain, an Skp1-Cul1-F-box (SCF) module. Although multiple substrates of cyclin F have been identified the vulnerabilities of cells lacking cyclin F are not known. Thus, we assessed viability of cells lacking cyclin F upon challenging cells with more than 200 kinase inhibitors. The screen revealed a striking synthetic lethality between Chk1 inhibition and cyclin F loss.