Phosphoproteomics of Retinoblastoma: a Pilot Study Identifies Aberrant Kinases
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Supplemental Information to Mammadova-Bach Et Al., “Laminin Α1 Orchestrates VEGFA Functions in the Ecosystem of Colorectal Carcinogenesis”
Supplemental information to Mammadova-Bach et al., “Laminin α1 orchestrates VEGFA functions in the ecosystem of colorectal carcinogenesis” Supplemental material and methods Cloning of the villin-LMα1 vector The plasmid pBS-villin-promoter containing the 3.5 Kb of the murine villin promoter, the first non coding exon, 5.5 kb of the first intron and 15 nucleotides of the second villin exon, was generated by S. Robine (Institut Curie, Paris, France). The EcoRI site in the multi cloning site was destroyed by fill in ligation with T4 polymerase according to the manufacturer`s instructions (New England Biolabs, Ozyme, Saint Quentin en Yvelines, France). Site directed mutagenesis (GeneEditor in vitro Site-Directed Mutagenesis system, Promega, Charbonnières-les-Bains, France) was then used to introduce a BsiWI site before the start codon of the villin coding sequence using the 5’ phosphorylated primer: 5’CCTTCTCCTCTAGGCTCGCGTACGATGACGTCGGACTTGCGG3’. A double strand annealed oligonucleotide, 5’GGCCGGACGCGTGAATTCGTCGACGC3’ and 5’GGCCGCGTCGACGAATTCACGC GTCC3’ containing restriction site for MluI, EcoRI and SalI were inserted in the NotI site (present in the multi cloning site), generating the plasmid pBS-villin-promoter-MES. The SV40 polyA region of the pEGFP plasmid (Clontech, Ozyme, Saint Quentin Yvelines, France) was amplified by PCR using primers 5’GGCGCCTCTAGATCATAATCAGCCATA3’ and 5’GGCGCCCTTAAGATACATTGATGAGTT3’ before subcloning into the pGEMTeasy vector (Promega, Charbonnières-les-Bains, France). After EcoRI digestion, the SV40 polyA fragment was purified with the NucleoSpin Extract II kit (Machery-Nagel, Hoerdt, France) and then subcloned into the EcoRI site of the plasmid pBS-villin-promoter-MES. Site directed mutagenesis was used to introduce a BsiWI site (5’ phosphorylated AGCGCAGGGAGCGGCGGCCGTACGATGCGCGGCAGCGGCACG3’) before the initiation codon and a MluI site (5’ phosphorylated 1 CCCGGGCCTGAGCCCTAAACGCGTGCCAGCCTCTGCCCTTGG3’) after the stop codon in the full length cDNA coding for the mouse LMα1 in the pCIS vector (kindly provided by P. -
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. -
Supplementary Table 1
Table S1. List of Genes Differentially Expressed in YB-1 siRNA-Transfected MCF-7 Cells Unigene Accession Symbol Description Mean fold change Hs.17466 NM_004585 RARRES3 retinoic acid receptor responder (tazarotene induced) 3 3.57 Hs.64746 NM_004669 CLIC3 chloride intracellular channel 3 3.33 Hs.516155 NM_001747 CAPG capping protein (actin filament), gelsolin-like 2.88 Hs.926 NM_002463 MX2 myxovirus (influenza virus) resistance 2 (mouse) 2.72 Hs.643494 NM_005410 SEPP1 selenoprotein P, plasma, 1 2.70 Hs.267038 BC017500 POF1B premature ovarian failure 1B 2.67 Hs.20961 U63917 GPR30 G protein- coupled receptor 30 2532.53 Hs.199877 BE645967 CPNE4 copine IV 2.49 Hs.632177 NM_017458 MVP major vault protein 2.48 Hs.528836 AA005023 NOD27 nucleotide-binding oligomerization domains 27 2.43 Hs.360940 AL589866 dJ222E13.1 kraken-like 2.40 Hs.515575 AI743780 PLAC2 placenta-specific 2 2.37 Hs.25674 AI827820 MBD2 methyl-CpG binding domain protein 2 2.36 Hs.12229 AA149594 TIEG2 TGFB inducible early growth response 2 2.33 Hs.482730 AA053711 EDIL3 EGF-like repeats and discoidin I-like domains 3 2.32 Hs. 370771 NM_000389 CDKN1A cyclin- depen dent kinase in hibitor 1A (p 21, Cip 1) 2282.28 Hs.469175 AI341537 JFC1 NADPH oxidase-related, C2 domain-containing protein 2.28 Hs.514821 AF043341 CCL5 chemokine (C-C motif) ligand 5 2.27 Hs.591292 NM_023915 GPR87 G protein-coupled receptor 87 2.26 Hs.500483 NM_001613 ACTA2 actin, alpha 2, smooth muscle, aorta 2.24 Hs.632824 NM_006729 DIAPH2 diaphanous homolog 2 (Drosophila) 2.22 Hs.369430 NM_000919 PAM peptidylglycine -
32-3099: PRKAB1 Recombinant Protein Description
9853 Pacific Heights Blvd. Suite D. San Diego, CA 92121, USA Tel: 858-263-4982 Email: [email protected] 32-3099: PRKAB1 Recombinant Protein Alternative Name : AMPK,HAMPKb,5'-AMP-activated protein kinase subunit beta-1,AMPK subunit beta-1,AMPKb,PRKAB1. Description Source : E.coli. PRKAB1 Human Recombinant produced in E.Coli is a single, non-glycosylated, polypeptide chain containing 293 amino acids (1-270 a.a.) and having a molecular mass of 32.8 kDa. The PRKAB1 is fused to a 23 amino acid His Tag at N- Terminus and purified by proprietary chromatographic techniques. 5'-AMP-activated protein kinase subunit beta-1 (PRKAB1) hinders protein, carbohydrate and lipid biosynthesis, in addition to cell growth and proliferation. AMPK is a heterotrimer comprised of an alpha catalytic subunit, and non-catalytic beta and gamma subunits. AMPK acts via direct phosphorylation of metabolic enzymes, and longer-term effects by phosphorylation of transcription regulators. PRKAB1 is a regulator of cellular polarity by remodeling the actin cytoskeleton; most likely by indirectly activating myosin. Beta non-catalytic subunit acts as a scaffold on which the AMPK complex compiles, through its C-terminus that joins alpha (PRKAA1 or PRKAA2) and gamma subunits (PRKAG1, PRKAG2 or PRKAG3). Product Info Amount : 5 µg Purification : Greater than 85% as determined by SDS-PAGE. The PRKAB1 protein solution (0.5mg/ml) contains 20mM Tris-HCl buffer (pH 8.0), 0.15M NaCl, Content : 10% glycerol and 1mM DTT. Store at 4°C if entire vial will be used within 2-4 weeks. Store, frozen at -20°C for longer periods of Storage condition : time. -
NFKBIZ Mutation Prevalence in the Arthur/Schmitz/Chapuy Cohorts
Supplemental Tables/Figures: Table S1: NFKBIZ mutation prevalence in the Arthur/Schmitz/Chapuy cohorts. Subtype Cohort All ABC DLC Schmitz Chapuy # patients 1006 511 330 466 210 UTR 101 (10) 66 (13) 39 (12) 49 (11) 13 (6) Amplification 86 (8.5) 66 (12.9) 23 (7) 35 (8) 28 (13) NFKBIZ Mutation TOTAL 174 (17) 120 (23.5) 57 (17) 79 (17) 38 (18) Table S2: NFKBIZ 3′ UTR mutations in other WGS cohorts. Cohort # Patients NFKBIZ UTR mutations (%) Publication BL_Adult 81 1 (1.2) Grande_et_al,2019 BL_Pediatric 124 1 (0.8) unpublished DLBCL_cell_lines 15 2 (13.3) Morin_et_al,2013 DLBCL_BC 117 11 (9.4) Arthur_et_al,2018 DLBCL_ICGC 87 11 (12.6) Hübschmann_et_al,2021 FL_ICGC 100 4 (4) Hübschmann_et_al,2021 FL_Kridel 48 1 (2.1) Kridel_et_al,2016 Table S3: COO and LymphGen classifications of DLBCLs and FLs from other cohorts. NFKBIZ UTR mutations (%) DLBCL (%) FL (%) ABC 13 (41.9) 13 (54.2) 0 (0) GCB 4 (12.9) 4 (16.7) 0 (0) COO UNCLASS 2 (6.5) 2 (8.3) 0 (0) NA 12 (38.7) 5 (20.8) 5 (100) TOTAL 31 24 5 BN2 13 (41.9) 9 (37.5) 3 (60) EZB 4 (12.9) 2 (8.3) 2 (40) ST2 1 (3.2) 1 (4.2) 0 (0) LymphGen Other 9 (29) 8 (33.3) 0 (0) Composite 2 (6.5) 2 (8.3) 0 (0) NA 2 (6.5) 2 (8.3) 0 (0) TOTAL 31 24 5 Table S4: LymphGen classifications of all patients and those with NFKBIZ mutations in Arthur/Schmitz/Chapuy cohorts. -
NICU Gene List Generator.Xlsx
Neonatal Crisis Sequencing Panel Gene List Genes: A2ML1 - B3GLCT A2ML1 ADAMTS9 ALG1 ARHGEF15 AAAS ADAMTSL2 ALG11 ARHGEF9 AARS1 ADAR ALG12 ARID1A AARS2 ADARB1 ALG13 ARID1B ABAT ADCY6 ALG14 ARID2 ABCA12 ADD3 ALG2 ARL13B ABCA3 ADGRG1 ALG3 ARL6 ABCA4 ADGRV1 ALG6 ARMC9 ABCB11 ADK ALG8 ARPC1B ABCB4 ADNP ALG9 ARSA ABCC6 ADPRS ALK ARSL ABCC8 ADSL ALMS1 ARX ABCC9 AEBP1 ALOX12B ASAH1 ABCD1 AFF3 ALOXE3 ASCC1 ABCD3 AFF4 ALPK3 ASH1L ABCD4 AFG3L2 ALPL ASL ABHD5 AGA ALS2 ASNS ACAD8 AGK ALX3 ASPA ACAD9 AGL ALX4 ASPM ACADM AGPS AMELX ASS1 ACADS AGRN AMER1 ASXL1 ACADSB AGT AMH ASXL3 ACADVL AGTPBP1 AMHR2 ATAD1 ACAN AGTR1 AMN ATL1 ACAT1 AGXT AMPD2 ATM ACE AHCY AMT ATP1A1 ACO2 AHDC1 ANK1 ATP1A2 ACOX1 AHI1 ANK2 ATP1A3 ACP5 AIFM1 ANKH ATP2A1 ACSF3 AIMP1 ANKLE2 ATP5F1A ACTA1 AIMP2 ANKRD11 ATP5F1D ACTA2 AIRE ANKRD26 ATP5F1E ACTB AKAP9 ANTXR2 ATP6V0A2 ACTC1 AKR1D1 AP1S2 ATP6V1B1 ACTG1 AKT2 AP2S1 ATP7A ACTG2 AKT3 AP3B1 ATP8A2 ACTL6B ALAS2 AP3B2 ATP8B1 ACTN1 ALB AP4B1 ATPAF2 ACTN2 ALDH18A1 AP4M1 ATR ACTN4 ALDH1A3 AP4S1 ATRX ACVR1 ALDH3A2 APC AUH ACVRL1 ALDH4A1 APTX AVPR2 ACY1 ALDH5A1 AR B3GALNT2 ADA ALDH6A1 ARFGEF2 B3GALT6 ADAMTS13 ALDH7A1 ARG1 B3GAT3 ADAMTS2 ALDOB ARHGAP31 B3GLCT Updated: 03/15/2021; v.3.6 1 Neonatal Crisis Sequencing Panel Gene List Genes: B4GALT1 - COL11A2 B4GALT1 C1QBP CD3G CHKB B4GALT7 C3 CD40LG CHMP1A B4GAT1 CA2 CD59 CHRNA1 B9D1 CA5A CD70 CHRNB1 B9D2 CACNA1A CD96 CHRND BAAT CACNA1C CDAN1 CHRNE BBIP1 CACNA1D CDC42 CHRNG BBS1 CACNA1E CDH1 CHST14 BBS10 CACNA1F CDH2 CHST3 BBS12 CACNA1G CDK10 CHUK BBS2 CACNA2D2 CDK13 CILK1 BBS4 CACNB2 CDK5RAP2 -
Download Author Version (PDF)
Molecular BioSystems Accepted Manuscript This is an Accepted Manuscript, which has been through the Royal Society of Chemistry peer review process and has been accepted for publication. Accepted Manuscripts are published online shortly after acceptance, before technical editing, formatting and proof reading. Using this free service, authors can make their results available to the community, in citable form, before we publish the edited article. We will replace this Accepted Manuscript with the edited and formatted Advance Article as soon as it is available. You can find more information about Accepted Manuscripts in the Information for Authors. Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the Ethical guidelines still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains. www.rsc.org/molecularbiosystems Page 1 of 29 Molecular BioSystems Mutated Genes and Driver Pathways Involved in Myelodysplastic Syndromes—A Transcriptome Sequencing Based Approach Liang Liu1*, Hongyan Wang1*, Jianguo Wen2*, Chih-En Tseng2,3*, Youli Zu2, Chung-che Chang4§, Xiaobo Zhou1§ 1 Center for Bioinformatics and Systems Biology, Division of Radiologic Sciences, Wake Forest University Baptist Medical Center, Winston-Salem, NC 27157, USA. 2 Department of Pathology, the Methodist Hospital Research Institute, -
Original Article a Database and Functional Annotation of NF-Κb Target Genes
Int J Clin Exp Med 2016;9(5):7986-7995 www.ijcem.com /ISSN:1940-5901/IJCEM0019172 Original Article A database and functional annotation of NF-κB target genes Yang Yang, Jian Wu, Jinke Wang The State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, People’s Republic of China Received November 4, 2015; Accepted February 10, 2016; Epub May 15, 2016; Published May 30, 2016 Abstract: Backgrounds: The previous studies show that the transcription factor NF-κB always be induced by many inducers, and can regulate the expressions of many genes. The aim of the present study is to explore the database and functional annotation of NF-κB target genes. Methods: In this study, we manually collected the most complete listing of all NF-κB target genes identified to date, including the NF-κB microRNA target genes and built the database of NF-κB target genes with the detailed information of each target gene and annotated it by DAVID tools. Results: The NF-κB target genes database was established (http://tfdb.seu.edu.cn/nfkb/). The collected data confirmed that NF-κB maintains multitudinous biological functions and possesses the considerable complexity and diversity in regulation the expression of corresponding target genes set. The data showed that the NF-κB was a central regula- tor of the stress response, immune response and cellular metabolic processes. NF-κB involved in bone disease, immunological disease and cardiovascular disease, various cancers and nervous disease. NF-κB can modulate the expression activity of other transcriptional factors. Inhibition of IKK and IκBα phosphorylation, the decrease of nuclear translocation of p65 and the reduction of intracellular glutathione level determined the up-regulation or down-regulation of expression of NF-κB target genes. -
Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients with Stable Coronary Heart Disease
Supplementary Online Content Ganz P, Heidecker B, Hveem K, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. doi: 10.1001/jama.2016.5951 eTable 1. List of 1130 Proteins Measured by Somalogic’s Modified Aptamer-Based Proteomic Assay eTable 2. Coefficients for Weibull Recalibration Model Applied to 9-Protein Model eFigure 1. Median Protein Levels in Derivation and Validation Cohort eTable 3. Coefficients for the Recalibration Model Applied to Refit Framingham eFigure 2. Calibration Plots for the Refit Framingham Model eTable 4. List of 200 Proteins Associated With the Risk of MI, Stroke, Heart Failure, and Death eFigure 3. Hazard Ratios of Lasso Selected Proteins for Primary End Point of MI, Stroke, Heart Failure, and Death eFigure 4. 9-Protein Prognostic Model Hazard Ratios Adjusted for Framingham Variables eFigure 5. 9-Protein Risk Scores by Event Type This supplementary material has been provided by the authors to give readers additional information about their work. Downloaded From: https://jamanetwork.com/ on 10/02/2021 Supplemental Material Table of Contents 1 Study Design and Data Processing ......................................................................................................... 3 2 Table of 1130 Proteins Measured .......................................................................................................... 4 3 Variable Selection and Statistical Modeling ........................................................................................ -
Application of Microrna Database Mining in Biomarker Discovery and Identification of Therapeutic Targets for Complex Disease
Article Application of microRNA Database Mining in Biomarker Discovery and Identification of Therapeutic Targets for Complex Disease Jennifer L. Major, Rushita A. Bagchi * and Julie Pires da Silva * Department of Medicine, Division of Cardiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; [email protected] * Correspondence: [email protected] (R.A.B.); [email protected] (J.P.d.S.) Supplementary Tables Methods Protoc. 2021, 4, 5. https://doi.org/10.3390/mps4010005 www.mdpi.com/journal/mps Methods Protoc. 2021, 4, 5. https://doi.org/10.3390/mps4010005 2 of 25 Table 1. List of all hsa-miRs identified by Human microRNA Disease Database (HMDD; v3.2) analysis. hsa-miRs were identified using the term “genetics” and “circulating” as input in HMDD. Targets CAD hsa-miR-1 Targets IR injury hsa-miR-423 Targets Obesity hsa-miR-499 hsa-miR-146a Circulating Obesity Genetics CAD hsa-miR-423 hsa-miR-146a Circulating CAD hsa-miR-149 hsa-miR-499 Circulating IR Injury hsa-miR-146a Circulating Obesity hsa-miR-122 Genetics Stroke Circulating CAD hsa-miR-122 Circulating Stroke hsa-miR-122 Genetics Obesity Circulating Stroke hsa-miR-26b hsa-miR-17 hsa-miR-223 Targets CAD hsa-miR-340 hsa-miR-34a hsa-miR-92a hsa-miR-126 Circulating Obesity Targets IR injury hsa-miR-21 hsa-miR-423 hsa-miR-126 hsa-miR-143 Targets Obesity hsa-miR-21 hsa-miR-223 hsa-miR-34a hsa-miR-17 Targets CAD hsa-miR-223 hsa-miR-92a hsa-miR-126 Targets IR injury hsa-miR-155 hsa-miR-21 Circulating CAD hsa-miR-126 hsa-miR-145 hsa-miR-21 Targets Obesity hsa-mir-223 hsa-mir-499 hsa-mir-574 Targets IR injury hsa-mir-21 Circulating IR injury Targets Obesity hsa-mir-21 Targets CAD hsa-mir-22 hsa-mir-133a Targets IR injury hsa-mir-155 hsa-mir-21 Circulating Stroke hsa-mir-145 hsa-mir-146b Targets Obesity hsa-mir-21 hsa-mir-29b Methods Protoc. -
Human Prefoldin Modulates Co-Transcriptional Pre-Mrna Splicing
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.14.150466; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. BIOLOGICAL SCIENCES: Biochemistry Human prefoldin modulates co-transcriptional pre-mRNA splicing Payán-Bravo L 1,2, Peñate X 1,2 *, Cases I 3, Pareja-Sánchez Y 1, Fontalva S 1,2, Odriozola Y 1,2, Lara E 1, Jimeno-González S 2,5, Suñé C 4, Reyes JC 5, Chávez S 1,2. 1 Instituto de Biomedicina de Sevilla, Universidad de Sevilla-CSIC-Hospital Universitario V. del Rocío, Seville, Spain. 2 Departamento de Genética, Facultad de Biología, Universidad de Sevilla, Seville, Spain. 3 Centro Andaluz de Biología del Desarrollo, CSIC-Universidad Pablo de Olavide, Seville, Spain. 4 Department of Molecular Biology, Institute of Parasitology and Biomedicine "López Neyra" IPBLN-CSIC, PTS, Granada, Spain. 5 Andalusian Center of Molecular Biology and Regenerative Medicine-CABIMER, Junta de Andalucia-University of Pablo de Olavide-University of Seville-CSIC, Seville, Spain. Correspondence: Sebastián Chávez, IBiS, campus HUVR, Avda. Manuel Siurot s/n, Sevilla, 41013, Spain. Phone: +34-955923127: e-mail: [email protected]. * Co- corresponding; [email protected]. bioRxiv preprint doi: https://doi.org/10.1101/2020.06.14.150466; this version posted July 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Prefoldin is a heterohexameric complex conserved from archaea to humans that plays a cochaperone role during the cotranslational folding of actin and tubulin monomers. -
Mutations in Splicing Factor Genes in Myeloid Malignancies: Significance and Impact on Clinical Features
cancers Review Mutations in Splicing Factor Genes in Myeloid Malignancies: Significance and Impact on Clinical Features Valeria Visconte 1, Megan O. Nakashima 2 and Heesun J. Rogers 2,* 1 Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USA; [email protected] 2 Department of Laboratory Medicine, Cleveland Clinic, Cleveland, OH 44195, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-216-445-2719 Received: 15 October 2019; Accepted: 19 November 2019; Published: 22 November 2019 Abstract: Components of the pre-messenger RNA splicing machinery are frequently mutated in myeloid malignancies. Mutations in LUC7L2, PRPF8, SF3B1, SRSF2, U2AF1, and ZRSR2 genes occur at various frequencies ranging between 40% and 85% in different subtypes of myelodysplastic syndrome (MDS) and 5% and 10% of acute myeloid leukemia (AML) and myeloproliferative neoplasms (MPNs). In some instances, splicing factor (SF) mutations have provided diagnostic utility and information on clinical outcomes as exemplified by SF3B1 mutations associated with increased ring sideroblasts (RS) in MDS-RS or MDS/MPN-RS with thrombocytosis. SF3B1 mutations are associated with better survival outcomes, while SRSF2 mutations are associated with a shorter survival time and increased AML progression, and U2AF1 mutations with a lower remission rate and shorter survival time. Beside the presence of mutations, transcriptomics technologies have shown that one third of genes in AML patients are differentially expressed, leading to altered transcript stability, interruption of protein function, and improper translation compared to those of healthy individuals. The detection of SF mutations demonstrates the importance of splicing abnormalities in the hematopoiesis of MDS and AML patients given the fact that abnormal splicing regulates the function of several transcriptional factors (PU.1, RUNX1, etc.) crucial in hematopoietic function.