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Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception
bioRxiv preprint doi: https://doi.org/10.1101/212431; this version posted November 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 4.0 International license. Genetic variation across the human olfactory receptor repertoire alters odor perception Casey Trimmer1,*, Andreas Keller2, Nicolle R. Murphy1, Lindsey L. Snyder1, Jason R. Willer3, Maira Nagai4,5, Nicholas Katsanis3, Leslie B. Vosshall2,6,7, Hiroaki Matsunami4,8, and Joel D. Mainland1,9 1Monell Chemical Senses Center, Philadelphia, Pennsylvania, USA 2Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, New York, USA 3Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, USA 4Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA 5Department of Biochemistry, University of Sao Paulo, Sao Paulo, Brazil 6Howard Hughes Medical Institute, New York, New York, USA 7Kavli Neural Systems Institute, New York, New York, USA 8Department of Neurobiology and Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA 9Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA *[email protected] ABSTRACT The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity. -
ZNF354C Is a Transcriptional Repressor That Inhibits Endothelial Angiogenic Sprouting James A
www.nature.com/scientificreports OPEN ZNF354C is a transcriptional repressor that inhibits endothelial angiogenic sprouting James A. Oo1,3, Barnabas Irmer1, Stefan Günther2, Timothy Warwick1, Katalin Pálf1, Judit Izquierdo Ponce1, Tom Teichmann1, Beatrice Pfüger‑Müller1,3, Ralf Gilsbach1,3, Ralf P. Brandes1,3 & Matthias S. Leisegang1,3* Zinc fnger proteins (ZNF) are a large group of transcription factors with diverse functions. We recently discovered that endothelial cells harbour a specifc mechanism to limit the action of ZNF354C, whose function in endothelial cells is unknown. Given that ZNF354C has so far only been studied in bone and tumour, its function was determined in endothelial cells. ZNF354C is expressed in vascular cells and localises to the nucleus and cytoplasm. Overexpression of ZNF354C in human endothelial cells results in a marked inhibition of endothelial sprouting. RNA‑sequencing of human microvascular endothelial cells with and without overexpression of ZNF354C revealed that the protein is a potent transcriptional repressor. ZNF354C contains an active KRAB domain which mediates this suppression as shown by mutagenesis analysis. ZNF354C interacts with dsDNA, TRIM28 and histones, as observed by proximity ligation and immunoprecipitation. Moreover, chromatin immunoprecipitation revealed that the ZNF binds to specifc endothelial‑relevant target‑gene promoters. ZNF354C suppresses these genes as shown by CRISPR/Cas knockout and RNAi. Inhibition of endothelial sprouting by ZNF354C is dependent on the amino acids DV and MLE of the KRAB domain. These results demonstrate that ZNF354C is a repressive transcription factor which acts through a KRAB domain to inhibit endothelial angiogenic sprouting. Te vascular system is controlled by numerous signaling pathways and growth factors which all contribute to the regulation of gene expression. -
Trastuzumab Modulates the Protein Cargo of Extracellular Vesicles Released by ERBB2+ Breast Can‐ Cer Cells
Supplementary Material: Trastuzumab Modulates the Protein Cargo of Extracellular Vesicles Released by ERBB2+ Breast Can‐ cer Cells Silvia Marconi, Sara Santamaria, Martina Bartolucci, Sara Stigliani, Cinzia Aiello, Maria Cristina Gagliani, Grazia Bellese, Andrea Petretto, Katia Cortese and Patrizio Castagnola Table S1. Antibodies used in the study. Antibody Catalog number Manufacturer Anti‐ALIX sc‐271975 Santa Cruz1 Anti‐CD9 PA5‐85955 Thermofisher Scientific2 Anti‐CD63 sc‐15363 Santa Cruz Anti‐ErbB2 (9G6) sc‐08 Santa Cruz Anti‐GAPDH 14C10 Cell signaling3 Anti‐HSP90 sc‐13119 Santa Cruz 1 Dallas, TX, USA; 2 Waltham, MA, USA; 3 Danvers, MA, USA. Table S2. Differentially regulated proteins by trastuzumab Tz treatment in extracellular vesicles EVs purified from SKBR‐ 3 cells with a statistically significant p‐value resulted from Studentʹs T‐test. The gene symbols coding for proteins downregulated by Tz (and hence upregulated in IgG treated cells) are highlighted in red while proteins upregulated by Tz are highlighted in blue. Official Gene Symbol 1 Gene product (Protein name) ACVR1B Activin A receptor type 1B ANO1 Anoctamin 1 ARFGEF2 ADP Ribosylation Factor Guanine Nucleotide Exchange Factor 2 BTN2A1 Butyrophilin subfamily 2 member A1 CIAPIN1 Cytokine Induced Apoptosis Inhibitor 1 CIT Citron Rho‐Interacting Serine/Threonine Kinase CPPED1 Calcineurin Like Phosphoesterase Domain Containing 1 DNAH7 Dynein Axonemal Heavy Chain 7 EIF3F Eukaryotic translation initiation factor 3 subunit F ESD Esterase D ESYT2 Extended Synaptotagmin 2 F2RL1 F2R Like Trypsin Receptor 1 RIPOR3 RIPOR Family Member 3 FZD6 Frizzled‐6 GAN Gigaxonin GTPBP2 GTP‐binding protein 2 GUCD1 Guanylyl Cyclase Domain Containing 1 HNRNPM Heterogeneous nuclear ribonucleoprotein M LMAN2 Lectin, Mannose Binding 2 LRRC8A Volume‐regulated anion channel subunit LRRC8A NOTCH4 Notch Receptor 4 NT5C2 5ʹ‐Nucleotidase, Cytosolic II PCID2 PCI domain‐containing 2 PDCD5 Programmed cell death 5 PHLDB3 Pleckstrin homology like domain family B member 3 PLAA Phospholipase A2 activating protein Membranes 2021, 11, 199. -
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. -
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Supplementary Figure S1. Results of flow cytometry analysis, performed to estimate CD34 positivity, after immunomagnetic separation in two different experiments. As monoclonal antibody for labeling the sample, the fluorescein isothiocyanate (FITC)- conjugated mouse anti-human CD34 MoAb (Mylteni) was used. Briefly, cell samples were incubated in the presence of the indicated MoAbs, at the proper dilution, in PBS containing 5% FCS and 1% Fc receptor (FcR) blocking reagent (Miltenyi) for 30 min at 4 C. Cells were then washed twice, resuspended with PBS and analyzed by a Coulter Epics XL (Coulter Electronics Inc., Hialeah, FL, USA) flow cytometer. only use Non-commercial 1 Supplementary Table S1. Complete list of the datasets used in this study and their sources. GEO Total samples Geo selected GEO accession of used Platform Reference series in series samples samples GSM142565 GSM142566 GSM142567 GSM142568 GSE6146 HG-U133A 14 8 - GSM142569 GSM142571 GSM142572 GSM142574 GSM51391 GSM51392 GSE2666 HG-U133A 36 4 1 GSM51393 GSM51394 only GSM321583 GSE12803 HG-U133A 20 3 GSM321584 2 GSM321585 use Promyelocytes_1 Promyelocytes_2 Promyelocytes_3 Promyelocytes_4 HG-U133A 8 8 3 GSE64282 Promyelocytes_5 Promyelocytes_6 Promyelocytes_7 Promyelocytes_8 Non-commercial 2 Supplementary Table S2. Chromosomal regions up-regulated in CD34+ samples as identified by the LAP procedure with the two-class statistics coded in the PREDA R package and an FDR threshold of 0.5. Functional enrichment analysis has been performed using DAVID (http://david.abcc.ncifcrf.gov/) -
Cellular and Molecular Signatures in the Disease Tissue of Early
Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of -
De Novo, Systemic, Deleterious Amino Acid Substitutions Are Common in Large Cytoskeleton‑Related Protein Coding Regions
BIOMEDICAL REPORTS 6: 211-216, 2017 De novo, systemic, deleterious amino acid substitutions are common in large cytoskeleton‑related protein coding regions REBECCA J. STOLL1, GRACE R. THOMPSON1, MOHAMMAD D. SAMY1 and GEORGE BLANCK1,2 1Department of Molecular Medicine, Morsani College of Medicine, University of South Florida; 2Immunology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA Received June 13, 2016; Accepted October 31, 2016 DOI: 10.3892/br.2016.826 Abstract. Human mutagenesis is largely random, thus large Introduction coding regions, simply on the basis of probability, represent relatively large mutagenesis targets. Thus, we considered Genetic damage is largely random and therefore tends to the possibility that large cytoskeletal-protein related coding affect the larger, functional regions of the human genome regions (CPCRs), including extra-cellular matrix (ECM) more frequently than the smaller regions (1). For example, coding regions, would have systemic nucleotide variants that a systematic study has revealed that cancer fusion genes, on are not present in common SNP databases. Presumably, such average, are statistically, significantly larger than other human variants arose recently in development or in recent, preceding genes (2,3). The large introns of potential cancer fusion genes generations. Using matched breast cancer and blood-derived presumably allow for many different productive recombina- normal datasets from the cancer genome atlas, CPCR single tion opportunities, i.e., many recombinations that would allow nucleotide variants (SNVs) not present in the All SNPs(142) for exon juxtaposition and the generation of hybrid proteins. or 1000 Genomes databases were identified. Using the Protein Smaller cancer fusion genes tend to be associated with the rare Variation Effect Analyzer internet-based tool, it was discov- types of cancer, for example EWS RNA binding protein 1 in ered that apparent, systemic mutations (not shared among Ewing's sarcoma. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Identification of Eqtls and Sqtls Associated with Meat Quality in Beef Joel D
Leal-Gutiérrez et al. BMC Genomics (2020) 21:104 https://doi.org/10.1186/s12864-020-6520-5 RESEARCH ARTICLE Open Access Identification of eQTLs and sQTLs associated with meat quality in beef Joel D. Leal-Gutiérrez*, Mauricio A. Elzo and Raluca G. Mateescu Abstract Background: Transcription has a substantial genetic control and genetic dissection of gene expression could help us understand the genetic architecture of complex phenotypes such as meat quality in cattle. The objectives of the present research were: 1) to perform eQTL and sQTL mapping analyses for meat quality traits in longissimus dorsi muscle; 2) to uncover genes whose expression is influenced by local or distant genetic variation; 3) to identify expression and splicing hot spots; and 4) to uncover genomic regions affecting the expression of multiple genes. Results: Eighty steers were selected for phenotyping, genotyping and RNA-seq evaluation. A panel of traits related to meat quality was recorded in longissimus dorsi muscle. Information on 112,042 SNPs and expression data on 8588 autosomal genes and 87,770 exons from 8467 genes were included in an expression and splicing quantitative trait loci (QTL) mapping (eQTL and sQTL, respectively). A gene, exon and isoform differential expression analysis previously carried out in this population identified 1352 genes, referred to as DEG, as explaining part of the variability associated with meat quality traits. The eQTL and sQTL mapping was performed using a linear regression model in the R package Matrix eQTL. Genotype and year of birth were included as fixed effects, and population structure was accounted for by including as a covariate the first PC from a PCA analysis on genotypic data. -
Identification of 42 Genes Linked to Stage II Colorectal Cancer Metastatic Relapse
Int. J. Mol. Sci. 2016, 17, 598; doi:10.3390/ijms17040598 S1 of S16 Supplementary Materials: Identification of 42 Genes Linked to Stage II Colorectal Cancer Metastatic Relapse Rabeah A. Al-Temaimi, Tuan Zea Tan, Makia J. Marafie, Jean Paul Thiery, Philip Quirke and Fahd Al-Mulla Figure S1. Cont. Int. J. Mol. Sci. 2016, 17, 598; doi:10.3390/ijms17040598 S2 of S16 Figure S1. Mean expression levels of fourteen genes of significant association with CRC DFS and OS that are differentially expressed in normal colon compared to CRC tissues. Each dot represents a sample. Table S1. Copy number aberrations associated with poor disease-free survival and metastasis in early stage II CRC as predicted by STAC and SPPS combined methodologies with resident gene symbols. CN stands for copy number, whereas CNV is copy number variation. Region Cytoband % of CNV Count of Region Event Gene Symbols Length Location Overlap Genes chr1:113,025,076–113,199,133 174,057 p13.2 CN Loss 0.0 2 AKR7A2P1, SLC16A1 chr1:141,465,960–141,822,265 356,305 q12–q21.1 CN Gain 95.9 1 SRGAP2B MIR5087, LOC10013000 0, FLJ39739, LOC10028679 3, PPIAL4G, PPIAL4A, NBPF14, chr1:144,911,564–146,242,907 1,331,343 q21.1 CN Gain 99.6 16 NBPF15, NBPF16, PPIAL4E, NBPF16, PPIAL4D, PPIAL4F, LOC645166, LOC388692, FCGR1C chr1:177,209,428–177,226,812 17,384 q25.3 CN Gain 0.0 0 chr1:197,652,888–197,676,831 23,943 q32.1 CN Gain 0.0 1 KIF21B chr1:201,015,278–201,033,308 18,030 q32.1 CN Gain 0.0 1 PLEKHA6 chr1:201,289,154–201,298,247 9093 q32.1 CN Gain 0.0 0 chr1:216,820,186–217,043,421 223,235 q41 CN -
Supplemental Table S1. Primers for Sybrgreen Quantitative RT-PCR Assays
Supplemental Table S1. Primers for SYBRGreen quantitative RT-PCR assays. Gene Accession Primer Sequence Length Start Stop Tm GC% GAPDH NM_002046.3 GAPDH F TCCTGTTCGACAGTCAGCCGCA 22 39 60 60.43 59.09 GAPDH R GCGCCCAATACGACCAAATCCGT 23 150 128 60.12 56.52 Exon junction 131/132 (reverse primer) on template NM_002046.3 DNAH6 NM_001370.1 DNAH6 F GGGCCTGGTGCTGCTTTGATGA 22 4690 4711 59.66 59.09% DNAH6 R TAGAGAGCTTTGCCGCTTTGGCG 23 4797 4775 60.06 56.52% Exon junction 4790/4791 (reverse primer) on template NM_001370.1 DNAH7 NM_018897.2 DNAH7 F TGCTGCATGAGCGGGCGATTA 21 9973 9993 59.25 57.14% DNAH7 R AGGAAGCCATGTACAAAGGTTGGCA 25 10073 10049 58.85 48.00% Exon junction 9989/9990 (forward primer) on template NM_018897.2 DNAI1 NM_012144.2 DNAI1 F AACAGATGTGCCTGCAGCTGGG 22 673 694 59.67 59.09 DNAI1 R TCTCGATCCCGGACAGGGTTGT 22 822 801 59.07 59.09 Exon junction 814/815 (reverse primer) on template NM_012144.2 RPGRIP1L NM_015272.2 RPGRIP1L F TCCCAAGGTTTCACAAGAAGGCAGT 25 3118 3142 58.5 48.00% RPGRIP1L R TGCCAAGCTTTGTTCTGCAAGCTGA 25 3238 3214 60.06 48.00% Exon junction 3124/3125 (forward primer) on template NM_015272.2 Supplemental Table S2. Transcripts that differentiate IPF/UIP from controls at 5%FDR Fold- p-value Change Transcript Gene p-value p-value p-value (IPF/UIP (IPF/UIP Cluster ID RefSeq Symbol gene_assignment (Age) (Gender) (Smoking) vs. C) vs. C) NM_001178008 // CBS // cystathionine-beta- 8070632 NM_001178008 CBS synthase // 21q22.3 // 875 /// NM_0000 0.456642 0.314761 0.418564 4.83E-36 -2.23 NM_003013 // SFRP2 // secreted frizzled- 8103254 NM_003013 -
WO 2019/068007 Al Figure 2
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/068007 Al 04 April 2019 (04.04.2019) W 1P O PCT (51) International Patent Classification: (72) Inventors; and C12N 15/10 (2006.01) C07K 16/28 (2006.01) (71) Applicants: GROSS, Gideon [EVIL]; IE-1-5 Address C12N 5/10 (2006.0 1) C12Q 1/6809 (20 18.0 1) M.P. Korazim, 1292200 Moshav Almagor (IL). GIBSON, C07K 14/705 (2006.01) A61P 35/00 (2006.01) Will [US/US]; c/o ImmPACT-Bio Ltd., 2 Ilian Ramon St., C07K 14/725 (2006.01) P.O. Box 4044, 7403635 Ness Ziona (TL). DAHARY, Dvir [EilL]; c/o ImmPACT-Bio Ltd., 2 Ilian Ramon St., P.O. (21) International Application Number: Box 4044, 7403635 Ness Ziona (IL). BEIMAN, Merav PCT/US2018/053583 [EilL]; c/o ImmPACT-Bio Ltd., 2 Ilian Ramon St., P.O. (22) International Filing Date: Box 4044, 7403635 Ness Ziona (E.). 28 September 2018 (28.09.2018) (74) Agent: MACDOUGALL, Christina, A. et al; Morgan, (25) Filing Language: English Lewis & Bockius LLP, One Market, Spear Tower, SanFran- cisco, CA 94105 (US). (26) Publication Language: English (81) Designated States (unless otherwise indicated, for every (30) Priority Data: kind of national protection available): AE, AG, AL, AM, 62/564,454 28 September 2017 (28.09.2017) US AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, 62/649,429 28 March 2018 (28.03.2018) US CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, (71) Applicant: IMMP ACT-BIO LTD.