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Widespread changes in transcriptome profile of PNAS PLUS human mesenchymal stem cells induced by two-dimensional nanosilicates James K. Carrowa,1, Lauren M. Crossa,1, Robert W. Reesea, Manish K. Jaiswala, Carl A. Gregoryb, Roland Kaunasa, Irtisha Singhc,d,2, and Akhilesh K. Gaharwara,e,f,2 aDepartment of Biomedical Engineering, Texas A&M University, College Station, TX 77843; bDepartment of Molecular and Cellular Medicine, Institute of Regenerative Medicine, Texas A&M Health Science Center, College Station, TX 77843; cComputational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065; dTri-I Program in Computational Biology and Medicine, Weill Cornell Graduate College, New York, NY 10065; eDepartment of Material Sciences, Texas A&M University, College Station, TX 77843; and fCenter for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843 Edited by Catherine J. Murphy, University of Illinois at Urbana–Champaign, Urbana, IL, and approved February 27, 2018 (received for review September 20, 2017) Two-dimensional nanomaterials, an ultrathin class of materials such view of the cellular activity with pivotal insights about the af- as graphene, nanoclays, transition metal dichalcogenides (TMDs), fected cellular pathways. Based on these results, a range of and transition metal oxides (TMOs), have emerged as a new nanotechnology-based platforms have been developed for mo- generation of materials due to their unique properties relative to lecular diagnostics and genome-wide analysis (10). We propose to macroscale counterparts. However, little is known about the tran- utilize transcriptomics, high-throughput sequencing of expressed scriptome dynamics following exposure to these nanomaterials. transcripts (RNA-seq), to provide a holistic view of nanomaterial Here, we investigate the interactions of 2D nanosilicates, a layered interactions with the cellular machinery. RNA-seq is a power- clay, with human mesenchymal stem cells (hMSCs) at the whole- ful tool for an accurate quantification of expressed transcripts transcriptome level by high-throughput sequencing (RNA-seq). that largely overcomes limitations and biases of microarrays Analysis of cell–nanosilicate interactions by monitoring changes in (11–13). In this study, we will evaluate the potential of bioactive transcriptome profile uncovered key biophysical and biochemical 2D nanomaterials for regenerative medicine by uncovering mo- cellular pathways triggered by nanosilicates. A widespread alter- ation of genes was observed due to nanosilicate exposure as more lecular targets and affected signaling pathways at the whole- than 4,000 genes were differentially expressed. The change in transcriptome level. mRNA expression levels revealed clathrin-mediated endocytosis Synthetic 2D nanoclays have been recently evaluated for re- of nanosilicates. Nanosilicate attachment to the cell membrane generative medicine applications, due to their biocompatible char- and subsequent cellular internalization activated stress-responsive acteristics, high surface-to-volume ratio, and uniform shape pathways such as mitogen-activated protein kinase (MAPK), which compared with other types of 2D nanomaterials (3, 14–16). Synthetic + − subsequently directed hMSC differentiation toward osteogenic and clays such as nanosilicates (Na 0.7[(Mg5.5Li0.3Si8O20(OH)4] 0.7, chondrogenic lineages. This study provides transcriptomic insight Laponite XLG) have disk-shaped morphology and exhibit a dual on the role of surface-mediated cellular signaling triggered by nanomaterials and enables development of nanomaterials-based Significance therapeutics for regenerative medicine. This approach in under- – standing nanomaterial cell interactions illustrates how change in We demonstrate the use of next-generation sequencing tech- transcriptomic profile can predict downstream effects following nology (RNA-seq) to understand the effect of a two-dimensional nanomaterial treatment. nanomaterial on human stem cells at the whole-transcriptome level. Our results identify more than 4,000 genes that are sig- 2D nanomaterials | nanosilicates | human mesenchymal stem cells | nificantly affected, and several biophysical and biochemical whole-transcriptome sequencing | RNA-seq pathways are triggered by nanoparticle treatment. We expect that this systematic approach to understand widespread changes wo-dimensional nanomaterials have gained unprecedented in gene expression due to nanomaterial exposure is key to de- Tattention due to their unique atomically thin, layered, and velop new bioactive materials for biomedical applications. well-defined structure that provides distinctive physical and ENGINEERING chemical properties compared with bulk 3D counterparts (1–3). Author contributions: J.K.C., L.M.C., I.S., and A.K.G. designed research; J.K.C., L.M.C., R.W.R., M.K.J., and I.S. performed research; J.K.C., L.M.C., R.W.R., C.A.G., R.K., I.S., and As the dimensions of 2D nanomaterials are only a few nanometers A.K.G. analyzed data; and J.K.C., L.M.C., I.S., and A.K.G. wrote the paper. thick, they interact with biological moieties in a unique way and Conflict of interest statement: J.K.C. and A.K.G. are coauthors on US Patent Application have raised exciting questions about their interactions with cellular No. WO2017112802 A1 published on June 29, 2017 (US Provisional Patent Application No. components. In addition, different physical (e.g., size, shape, and 62/270,403 filed on December 21, 2015). charge) and chemical characteristics of 2D nanoparticles have a This article is a PNAS Direct Submission. multitude of effects on cells including toxicity, bioactivity, or This open access article is distributed under Creative Commons Attribution-NonCommercial- therapeutic capabilities, which are not well understood (4, 5). NoDerivatives License 4.0 (CC BY-NC-ND). Understanding cellular responses following treatment with Data deposition: The data reported in this paper have been deposited in the Gene Ex- pression Omnibus (GEO) database, https://www.ncbi.nlm.nih.gov/geo (accession no. nanomaterials will aid in evaluating their application for a range GSE108638). of biomedical and biotechnology applications. Recent emer- 1 J.K.C. and L.M.C. contributed equally to this work. gence in “omics” techniques providing readouts of different 2To whom correspondence may be addressed. Email: [email protected] or gaharwar@ biological processes, have allowed us to understand complex tamu.edu. biological interactions of synthetic nanoparticles and their tox- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. icity (6–9). Specifically, transcriptomics and proteomics have laid 1073/pnas.1716164115/-/DCSupplemental. down the necessary foundation to provide an unbiased global Published online April 11, 2018. www.pnas.org/cgi/doi/10.1073/pnas.1716164115 PNAS | vol. 115 | no. 17 | E3905–E3913 Downloaded by guest on September 25, 2021 AB Plasma protein Classification Pixels % Unmapped 481664 99.43 Nanosilicates 50 nm (red) 2752 0.57 2D Nanosilicates Cellular Components C E F Focal adhesion (GO:0005925) hMSCs hMSCs_nSi Cytosolic ribosome (GO:0022626) (Control) (Nanosilicates) Nanosilicates Internalization Cell surface (GO:0009986) Extracellular matrix (GO:0031012) 1 10 100 Plasma membrane region (GO:0098590) g/mL g/mL g/mL Endosome (GO:0005768) hMSCs Mitochonrial membrane (GO:0031966) 0 5 10 15 -log10(p-value) Count (a.u) Molecular Function 10 4 10 5 10 6 10 7.2 Structural molecule activity (GO:0005198) Z score Actin binding (GO:0003779) FL2-A Cytoskeletal protien binding (GO:0008092) 1.5 Ion transmembrane transporter activity (GO:0015075) Collagen binding (GO:0005518) *** 1 GTPase regulator activity (GO:0030695) Basal transcription machinery binding (GO:0001098) ** 0.5 Signaling receptor activity (GO:0038023) 0510 0 -log10(p-value) Biological Processes Protein localization to endoplasmic reticulum (GO:0070972) Protein targeting to membrane (GO:0006612) Fluorescence (a.u.) Extracellular matrix organization (GO:0030198) Cellular response to growth factor stimulus (GO:0071363) Endocytosis (GO:0006897) Positive regulation of cell proliferation (GO:0008284) Cell morphogenesis involved in differentiation (GO:0000904) Cellular response to oxygen levels (GO:0071453) Nystatin hMSCs ortmannin Endochondral bone growth (GO:0003416) No InhibitorW Positive regulation of intracellular signal transduction (GO:1902533) Response to oxidative stress (GO:0006979) Chlorpromazine Positive regulation of MAPK cascade (GO:0043410) TGF- receptor signaling pathway (GO:0007179) Notch signaling pathway (GO:0007219) D Canonical Wnt signaling pathway(GO:0060070) Co-localization of nanosilicates and lysosome BMP signaling (GO:0030509) Clatherin-mediated endocytosis (GO:0072583) 051015 -log10(p-value) H TGFB1 Stemness/ ACAN COL1A1 Regenerative Endocytosis HAS1 AFAP1 Capacity CLTCL1 RED-Nanosilicates GREEN-Lysosome FCHO2 SMOC1 WNT5A CLTB WASL ANGPT4 INHBA CCND1IRS1 ANKFY1 AHR RAMP1 SOCS5 G COMP NRP1 TGM2 REVIGO Gene Ontology treemap EP300 CBL TGFBR2 NOTCH2 (GO related to Cellular Components) HMOX1 FN1 JUND WISP1 APOE PAK2 ECT2 MIB1 MET Cytosolic DMPK CIB1 FGF5 ID2 Focal Endosome HRAS ribosome HIPK2 COL11A1 ITGAV adhesion DDIT4 IL6ST PDGFRA PRRX1 CDK6 MDFI EGFR TXNIP Kinase Basic Cell Cell IGFBP3 EGF Processes Side of Extracellular surface Signaling Others membrane matrix RGS4 IGFBP2 MUC20 C1QTNF1 TAOK1 Fig. 1. Biophysical interaction of nanosilicates and hMSCs. (A) Two-dimensional nanosilicates electrostatically
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  • Integrating Protein Copy Numbers with Interaction Networks to Quantify Stoichiometry in Mammalian Endocytosis

    Integrating Protein Copy Numbers with Interaction Networks to Quantify Stoichiometry in Mammalian Endocytosis

    bioRxiv preprint doi: https://doi.org/10.1101/2020.10.29.361196; this version posted October 29, 2020. 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-ND 4.0 International license. Integrating protein copy numbers with interaction networks to quantify stoichiometry in mammalian endocytosis Daisy Duan1, Meretta Hanson1, David O. Holland2, Margaret E Johnson1* 1TC Jenkins Department of Biophysics, Johns Hopkins University, 3400 N Charles St, Baltimore, MD 21218. 2NIH, Bethesda, MD, 20892. *Corresponding Author: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.10.29.361196; this version posted October 29, 2020. 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-ND 4.0 International license. Abstract Proteins that drive processes like clathrin-mediated endocytosis (CME) are expressed at various copy numbers within a cell, from hundreds (e.g. auxilin) to millions (e.g. clathrin). Between cell types with identical genomes, copy numbers further vary significantly both in absolute and relative abundance. These variations contain essential information about each protein’s function, but how significant are these variations and how can they be quantified to infer useful functional behavior? Here, we address this by quantifying the stoichiometry of proteins involved in the CME network. We find robust trends across three cell types in proteins that are sub- vs super-stoichiometric in terms of protein function, network topology (e.g.