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Satellite cells from spastic CP donors exhibit altered gene expression profiles in culture Karyn Robinson1, Stephanie Yeager1, Erin Crowgey1, Adam Marsh2, Jeffrey Myers1, Mokun Fatukasi1, Robert Akins1 1Nemours – Alfred I. duPont Hospital for Children, Wilmington, DE, 2Genome Profiling LLC, Newark, DE INTRODUCTION SATELLITE CELL DIFFERENTIATION PARTICIPANT DEMOGRAPHICS Control CP The muscles of patients with cerebral palsy (CP) exhibit altered growth, SC SC Tissue Tissue SC SC Tissue Tissue Control CP Sample Age Sex Sample Age Sex development, biomechanical properties, and structure. Studies of muscles from RNAseq DNAm RNAseq DNAm RNAseq DNAm RNAseq DNAm individuals with CP have demonstrated profound defects, including alterations in CN1 16.6 M * * CP1 15.6 M * * CN2 12.6 M * * CP2 19.1 M * fiber type distribution, increased sarcomere length, increased adipose tissue CN3 12.1 F * * CP3 12.6 F * * * infiltration, disorganized neuromuscular junctions, extracellular matrix CN4 12.7 F * * CP4 13.8 F * * * * abnormalities, and differences in gene expression profiles. Importantly, patients CN5 15.1 M * * * * CP5 18.0 F * * * * with CP have demonstrated differences in their satellite cells (SC), which are stem CN6 14.3 F * * * * CP6 12.8 F * * * * CN7 14.2 M * CP7 15.1 M * cells responsible for skeletal muscle growth and repair. In particular, patients with CN8 17.4 M * CP8 9.8 M * 1 CP have a reduced satellite cell population which may account for their impaired CN9 13.5 M * CP9 14.9 M * muscle growth and decreased ability to strengthen muscle; however, the CN10 13.0 M * CP10 12.2 M * mechanisms responsible for these differences are unclear. We used RNA CN11 14.8 M * sequencing (RNAseq) and DNA methylation (DNAm) analyses to investigate Differentiated satellite cells isolated from participants with and without CP. differences in skeletal muscle and differentiated satellite cells derived from Differentiated satellite cells that were isolated from participants with CP were DNA METHYLATION participants with CP compared to controls. morphologically similar to those isolated from control participants. Scale bar = 200µm. Satellite Cells Skeletal Muscle METHODS RNA SEQUENCING Satellite Cells: Upregulated in CP Skeletal muscle Gene LogFC UniProt ID Protein Name obtained during GNAT1 3.26 P11488 Guanine nucleotide-binding protein G(t) subunit alpha-1 TMEM252 3.24 Q8N6L7 Transmembrane protein 252 orthopedic surgery DTX4 2.68 Q9Y2E6 E3 ubiquitin-protein ligase DTX4 SEMA3F 2.44 Q13275 Semaphorin-3F MTHFR 2.25 P42898 Methylenetetrahydrofolate reductase MPEG1 2.17 Macrophage-expressed gene 1 protein Satellite cells Q2M385 NPTX2 2.15 P47972 Neuronal pentraxin-2 isolated and IGF2-AS 1.99 Q6U949 Putative insulin-like growth factor 2 antisense gene protein differentiated SULF1 1.86 Q8IWU6 Extracellular sulfatase Sulf-1 LIPC 1.85 P11150 Hepatic triacylglycerol lipase RNAseq: muscle = 7 control, 8 CP DNA sequencing: muscle = 2 control, 4 CP Satellite Cells: Downregulated in CP satellite cells = 6 control, 4 CP satellite cells = 6 control, 6 CP Gene LogFC UniProt ID Protein name FAM216B -5.99 Q8N7L0 Protein FAM216B HGF -5.19 P14210 Hepatocyte growth factor GFM1 -3.57 Q96RP9 Elongation factor G, mitochondrial CD248 -3.50 Q9HCU0 Endosialin Differential expression Methylation at CpG sites LAMA4 -3.46 Q16363 Laminin subunit alpha-4 LTBP1 -3.26 Q14766 Latent-transforming growth factor beta-binding protein 1 TMTC3 -3.19 Q6ZXV5 Transmembrane and TPR repeat-containing protein 3 THAP10 -3.14 Q9P2Z0 THAP domain-containing protein 10 TNC -3.07 P24821 Tenascin CP Control CP Control TPR -2.96 P12270 Nucleoprotein TPR Heatmap clustering of CpG site methylation. In SCs, there was significant RNAseq in differentiated satellite cells. Of 7459 transcripts expressed, 28 were differential methylation (p<0.05, FDR) at 1,460 sites. In skeletal muscle, there was significantly upregulated and 62 were significantly downregulated in CP (p<0.05, significant differential methylation at 4,694 sites. Hierarchical clustering based on FDR). The 10 largest and smallest log fold changes (LogFC) are shown. Figure from Zammit et al., 20062 % methylation (yellow = hypomethylated, blue = hypermethylated) was employed using the CpG sites with the lowest p-values. Quantitative differences in CpG site Patient enrollment: Ten participants with a diagnosis of spastic CP and 11 control methylation by diagnosis were apparent. Skeletal Muscle: Upregulated in CP participants were enrolled in this IRB-approved study at the Nemours - Alfred I. Gene LogFCUniProt ID Protein name CONCLUSIONS duPont Hospital for Children after written informed parental consent and participant HOXA10 3.51 P31260 Homeobox protein Hox-A10 assent. RHOBTB1 2.28 O94844 Rho-related BTB domain-containing protein 1 ARRDC2 2.26 Q8TBH0 Arrestin domain-containing protein 2 • Skeletal muscle tissue and differentiated satellite cells isolated from participants Satellite cell isolation and differentiation: Satellite cells were CSNK1D 2.21 P48730 Casein kinase I isoform delta with CP exhibited fundamental differences in gene expression and DNA RNLS 2.10 Q5VYX0 Renalase immunomagnetically isolated from surgical explants of skeletal muscle, grown to HNRNPDL 2.09 O14979 Heterogeneous nuclear ribonucleoprotein D-like methylation patterns from controls. confluence, and differentiated for 24 hours in low serum. C1orf74 2.04 Q96LT6 UPF0739 protein C1orf74 ENPP4 1.96 Q9Y6X5 Bis(5'-adenosyl)-triphosphatase ENPP4 • This study demonstrates the utility of ex vivo methods and advanced RNA sequencing: RNA was collected and sequenced using an Illumina HiSeq CFD 1.96 P00746 Complement factor D bioinformatics tools to elucidate physiological differences in the muscle of system with paired end reads. Reads were trimmed and filtered, aligned to the FBLN2 1.95 P98095 Fibulin-2 individuals with CP. human genome (hg19), and target feature counts were determined. Differential expression analysis was performed using edgeR in R studio. Skeletal Muscle: Downregulated in CP Gene LogFC UniProt ID Protein name ACKNOWLEDGEMENTS DNA methylation analysis: DNA libraries were prepared from methyl-sensitive GPR39 -3.73 O43194 G-protein coupled receptor 39 AACPDM Delaware Bioscience Center for Advanced Technology NR5A1 -3.54 Q13285 Steroidogenic factor 1 restriction endonuclease fragmented genomic DNA. Next generation sequencing PTGFRN -3.23 Q9P2B2 Prostaglandin F2 receptor negative regulator Pedal-with-Pete Foundation Delaware CTR ACCEL Program (U54-GM104941) (NGS) was performed on an Illumina ×10 platform by Macrogen USA. FASTQ data PARP8 -3.17 Q8N3A8 Poly [ADP-ribose] polymerase 8 Swank Foundation US National Science Foundation files were processed to calculate the probability of methylation at individual CpG WNK2 -2.71 Q9Y3S1 Serine/threonine-protein kinase WNK2 C8orf88 -2.66 P0DMB2 Uncharacterized protein C8orf88 3 REFERENCES sites through a commercial bioinformatics pipeline and software platform. TULP2 -2.59 O00295 Tubby-related protein 2 THBS3 -2.57 P49746 Thrombospondin-3 1. Dayanidhi S et al. (2015) Reduced satellite cell number in situ in muscular contractures P2RY14 -2.38 Q15391 P2Y purinoceptor 14 J Orthop Res SPIB -2.22 Q01892 Transcription factor Spi-B from children with cerebral palsy. 33, 1039-1045. 2. Zammit PS et al. (2006) The Skeletal Muscle Satellite Cell: The Stem Cell That Came in Nemours is an internationally recognized children’s health system that owns and operates the Nemours/Alfred I. duPont Hospital for Children in Wilmington, DE, along RNAseq in skeletal muscle. Of 6308 total transcripts expressed, 54 were with major pediatric specialty clinics in Delaware, Florida, Pennsylvania, and New Jersey. In October, 2012, it opened the full-service Nemours Children’s Hospital in From the Cold. J Histochem Cytochem 54: 1177-1191. Orlando, Florida. To learn more about Nemours, visit www.Nemours.org. significantly upregulated and 82 were significantly downregulated in CP (p<0.05, 3. Crowgey EL et al. (2018) Epigenetic machine learning: utilizing DNA methylation FDR). The 10 largest and smallest log fold changes (LogFC) are shown. patterns to predict spastic cerebral palsy. BMC Bioinformatics 19: 225-234..
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  • Hypoxia As an Evolutionary Force

    Hypoxia As an Evolutionary Force

    “The genetic architecture of adaptations to high altitude in Ethiopia” Gorka Alkorta-Aranburu1, Cynthia M. Beall2*, David B. Witonsky1, Amha Gebremedhin3, Jonathan K. Pritchard1,4, Anna Di Rienzo1* 1 Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America, 2 Department of Anthropology, Case Western Research University, Cleveland, Ohio, United States of America, 3 Department of Internal Medicine, Faculty of Medicine, Addis Ababa University, Addis Ababa, Ethiopia, 4 Howard Hughes Medical Institute * E-mail: [email protected] and [email protected] Corresponding authors: Anna Di Rienzo Department of Human Genetics University of Chicago 920 E. 58th Street Chicago, IL 60637, USA. Cynthia M. Beall Anthropology Department Case Western Reserve University 238 Mather Memorial Building 11220 Bellflower Road Cleveland, OH 44106, USA. 1 ABSTRACT Although hypoxia is a major stress on physiological processes, several human populations have survived for millennia at high altitudes, suggesting that they have adapted to hypoxic conditions. This hypothesis was recently corroborated by studies of Tibetan highlanders, which showed that polymorphisms in candidate genes show signatures of natural selection as well as well-replicated association signals for variation in hemoglobin levels. We extended genomic analysis to two Ethiopian ethnic groups: Amhara and Oromo. For each ethnic group, we sampled low and high altitude residents, thus allowing genetic and phenotypic comparisons across altitudes and across ethnic groups. Genome- wide SNP genotype data were collected in these samples by using Illumina arrays. We find that variants associated with hemoglobin variation among Tibetans or other variants at the same loci do not influence the trait in Ethiopians.