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Metabolite Effect on Angiogenesis: Insights from Transcriptome Analysis

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Abstract

Metabolic status of the cells is important in the expression of the angiogenic phenotype in endothelial cells. Our earlier studies demonstrated the effects of metabolites such as lactate, citrate and lipoxygenase products, on VEGFA-VEGFR2 signaling pathway. Though this link between metabolite status and molecular mechanisms of angiogenesis is becoming evident, it is not clear how it affects genome-level expression in endothelial cells, critical to angiogenesis. In the present study, computational analysis was carried out on the transcriptome data of 4 different datasets where HUVECs were exposed to low and high glucose, both in vitro and in vivo, and the expression of a key enzyme involved in glucose metabolism is altered. The differentially expressed genes belonging to both VEGFA-VEGFR2 signaling pathway, as well as several VEGF signature genes as hub genes were also identified. These findings suggest the metabolite dependence, particularly glucose dependence, of angiogenesis, involving modulation of genome-level expression of angiogenesis- functional genome. This is important in tumor angiogenesis where reprogramming of metabolism is critical.

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Abbreviations

Acronyms :

Expansion

ADP:

Adenosine Di Phosphate

AMPK:

AMP-activated protein kinase

BAM:

Compressed Binary version of Sequence Alignment Map

BC:

Betweenness Centrality

BP:

Biological Processes

CAM:

Cell Adhesion Molecule

CC:

Closeness Centrality

CNSA:

Chinese Nucleotide Sequence Archive

DAVID:

Database for Annotation, Visualization and Integrated Discovery

DC:

Degree Centrality

DEG:

Differentially Expressed Gene

EC:

Endothelial Cell

ECM:

Extra Cellular Matrix

ENA:

European Nucleotide Archive

ER:

Endoplasmic Reticulum

ERK:

Extracellular signal-Regulated Kinase

ESCC:

Esophageal Squamous Cell Carcinoma

FC:

Fold change

FDR:

False Discovery Rate

FOXO:

Forkhead Box

FPKM:

Fragments Per Kilobase per Million Reads

FTP:

File Transfer Protocol

GEO:

Gene Expression Omnibus

GO:

Gene Ontology

HDF:

Human Dermal Fibroblast

HDMEC:

Human Dermal Microvascular Endothelial Cell

HIF:

Hypoxia Inducing Factor

HISAT:

Hierarchical Indexing for Spliced Alignment of Transcripts

HUVECs:

Human Umbilical Vein Endothelial Cells

IRE1:

Inositol-Requiring Enzyme 1

KEGG:

Kyoto Encyclopedia of Genes and Genomes

MAPK:

Mitogen- Activated Protein Kinase

MCODE:

Molecular Complex Detection

NOD:

Nucleotide-binding and Oligomerization Domain

PGC-1α:

Pparγ Coactivator 1 alpha

PI3K-Akt:

Phosphatidyl-Inositol 3-Kinase- Ak strain Transforming

PPARγ:

Peroxisome Proliferator- Activated Receptor gamma

PPI:

Protein-Protein Interaction

RIG-I:

Retinoic acid-Inducible Gene I

RNA-seq:

RNA-sequencing

SAM:

Sequence Alignment Map

STRING:

Search Tool for the Retrieval of Interacting Genes/Proteins

Wnt:

Wingless-related integration site

Gene Symbol :

Gene Name

ACAT2:

Acetyl-CoA Acetyltransferase 2

ANLN:

Anillin Actin Binding Protein

ASPM:

Assembly Factor for Spindle Microtubules

ATF6:

Activating Transcription Factor 6

C8orf33:

Chromosome 8 Open Reading Frame 33

CALR:

Calreticulin

CCL2:

C-C Motif Chemokine Ligand 2

CCNB1:

Cyclin B1

CCNB2:

Cyclin B2

CEP55:

Centrosomal Protein 55

CTSD:

Cathepsin D

CXCL1:

C-X-C Motif Chemokine Ligand 1

CXCL10:

C-X-C Motif Chemokine Ligand 10

CXCL2:

C-X-C Motif Chemokine Ligand 2

CXCL6:

C-X-C Motif Chemokine Ligand 6

CXCL8:

C-X-C Motif Chemokine Ligand 8

CXCR4:

C-X-C Motif Chemokine Receptor 4

DNAJB9:

Dnaj Heat Shock Protein Family (Hsp40) Member B9

FBXO5:

F-Box Protein 5

FGF2:

Fibroblast Growth Factor 2

HEK:

Human Epidermal Keratinocyte

HERPUD1:

Homocysteine Inducible ER Protein with Ubiquitin Like Domain 1

HIF:

Hypoxia Inducing Factor

HYOU1:

Hypoxia Up-Regulated 1

ICAM1:

Intercellular Adhesion Molecule 1

IFI6:

Interferon Alpha Inducible Protein 6

IFIH1:

Interferon Induced with Helicase C Domain 1

IFIT3:

Interferon Induced Protein with Tetratricopeptide Repeats 3

IFITM1:

Interferon Induced Transmembrane Protein 1

ISG15:

ISG15 Ubiquitin Like Modifier

LMNB1:

Lamin B1

MDA5:

Melanoma Differentiation-Associated protein 5

MRPS10:

Mitochondrial Ribosomal Protein S10

MX1:

MX Dynamin Like GTPase 1

MYCN:

MYCN Proto-Oncogene, bHLH Transcription Factor

OAS1:

2′-5′-Oligoadenylate Synthetase 1

OAS2:

2′-5′-Oligoadenylate Synthetase 2

OAS3:

2′-5′-Oligoadenylate Synthetase 3

P4HA2:

Prolyl 4-Hydroxylase Subunit Alpha 2

P4HB:

Prolyl 4-Hydroxylase Subunit Beta

PDIA6:

Protein Disulfide Isomerase Family A Member 6

PFKFB3:

6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 3

PTGS1:

Prostaglandin-Endoperoxide Synthase 1

RACGAP1:

Rac GTPase Activating Protein 1

SDF2L1:

Stromal Cell Derived Factor 2 Like 1

SELE:

Selectin E

SLIT2:

Slit Guidance Ligand 2

TGFB2:

Transforming Growth Factor Beta 2

TNFAIP2:

TNF Alpha Induced Protein 2

TPX2:

TPX2 Microtubule Nucleation Factor

TYMP:

Thymidine Phosphorylase

VCAM1:

Vascular Cell Adhesion Molecule 1

VEGFA:

Vascular Endothelial Growth Factor A

VEGFR2:

Vascular Endothelial Growth Factor Receptor 2

XAF1:

XIAP Associated Factor 1

References

  1. Carmeliet, P., & Jain, R. K. (2011). Molecular mechanisms and clinical applications of angiogenesis. Nature, 473(7347), 298–307. https://doi.org/10.1038/nature10144.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Chung, A. S., & Ferrara, N. (2011). Developmental and pathological angiogenesis. Annual Review of Cell and Developmental Biology, 27(1), 563–584. https://doi.org/10.1146/annurev-cellbio-092910-154002.

    Article  CAS  PubMed  Google Scholar 

  3. Sewduth, R., & Santoro, M. M. (2016). “Decoding” angiogenesis: New facets controlling endothelial cell behavior. Frontiers in Physiology, 7, 306 https://doi.org/10.3389/fphys.2016.00306.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Soumya, S. J., Athira, A. P., Binu, S., & Sudhakaran, P. R. (2016). mTOR as a Modulator of Metabolite Sensing Relevant to Angiogenesis, In Molecules to Medicine with mTOR: Translating Critical Pathways into Novel Therapeutic Strategies, 1st ed. (pp. 229–243). Kenneth Maiese: Academic Press. https://doi.org/10.1016/B978-0-12-802733-2.00014-1.

    Book  Google Scholar 

  5. Kumar, V. B. S., Viji, R. I., Kiran, M. S., & Sudhakaran, P. R. (2007). Endothelial cell response to lactate: Implication of PAR modification of VEGF. Journal of Cellular Physiology, 211(2), 477–485. https://doi.org/10.1002/JCP.20955.

    Article  CAS  PubMed  Google Scholar 

  6. Binu, S., Soumya, S. J., & Sudhakaran, P. R. (2013). Metabolite control of angiogenesis: Angiogenic effect of citrate. The Journal of Physiology and Biochemistry, 69(3), 383–395. https://doi.org/10.1007/s13105-012-0220-9.

    Article  CAS  PubMed  Google Scholar 

  7. Soumya, S. J., Binu, S., Helen, A., Anil Kumar, K., Reddanna, P., & Sudhakaran, P. R. (2012). Effect of 15-lipoxygenase metabolites on angiogenesis: 15(S)-HPETE is angiostatic and 15(S)-HETE is angiogenic. The Journal of Inflammation Research, 61(7), 707–718. https://doi.org/10.1007/S00011-012-0463-5/.

    Article  CAS  Google Scholar 

  8. Soumya, S. J., Binu, S., Helen, A., Reddanna, P., & Sudhakaran, P. R. (2013). 15 (S)-HETE-induced angiogenesis in adipose tissue is mediated through activation of PI3K/Akt/mTOR signaling pathway. Biochemistry and Cell Biology, 91(6), 498–505. https://doi.org/10.1139/BCB-2013-0037.

    Article  CAS  PubMed  Google Scholar 

  9. Soumya, S. J., Binu, S., Helen, A., Reddanna, P., & Sudhakaran, P. R. (2014). 15-LOX metabolites and angiogenesis: Angiostatic effect of 15(s)-hpete involves induction of apoptosis in adipose endothelial cells. PeerJ, 2, e635 https://doi.org/10.7717/peerj.635/.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Binu, S., Soumya, S. J., Kumar, V. B. S., & Sudhakaran, P. R. (2012). Poly-ADP-ribosylation of vascular endothelial growth factor and its implications on angiogenesis. Advances in Experimental Medicine and Biology, 749, 269–278. https://doi.org/10.1007/978-1-4614-3381-1_18.

    Article  CAS  PubMed  Google Scholar 

  11. Lau, A. N., & Vander Heiden, M. G. (2020). Metabolism in the tumor microenvironment. Annual Review of Cancer Biology, 4, 17–40. https://doi.org/10.1146/annurev-cancerbio-030419-033333.

    Article  Google Scholar 

  12. Lin, X., Xiao, Z., Chen, T., Liang, S. H., & Guo, H. (2020). Glucose metabolism on tumor plasticity, diagnosis, and treatment. Frontiers Oncology, 10, 317 https://doi.org/10.3389/fonc.2020.00317.

    Article  Google Scholar 

  13. Fadini, G. P., Albiero, M., Bonora, B. M., & Avogaro, A. (2019). Angiogenic abnormalities in diabetes mellitus: mechanistic and clinical aspects. The Journal of Clinical Endocrinology and Metabolism, 104(11), 5431–5444. https://doi.org/10.1210/JC.2019-00980.

    Article  PubMed  Google Scholar 

  14. Abhinand, C. S., Raju, R., Soumya, S. J., Arya, P. S., & Sudhakaran, P. R. (2016). VEGF-A/VEGFR2 signaling network in endothelial cells relevant to angiogenesis. The Journal of Cell Communication and Signaling, 10(4), 347–354. https://doi.org/10.1007/s12079-016-0352-8.

    Article  PubMed  Google Scholar 

  15. Sunitha, P., Raju, R., Sajil, C. K., Abhinand, C. S., Nair, A. S., Oommen, O. V., Sugunan, V. S., & Sudhakaran, P. R. (2019). Temporal VEGFA responsive genes in HUVECs: Gene signatures and potential ligands/receptors fine-tuning angiogenesis. Cell Communication and Signaling, 13(4), 561–571. https://doi.org/10.1007/S12079-019-00541-7/.

    Article  CAS  Google Scholar 

  16. Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Journal of Bioinformatics, 30(15), 2114–2120. https://doi.org/10.1093/bioinformatics/btu170.

    Article  CAS  Google Scholar 

  17. Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., & Salzberg, S. L. (2016). Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nature Protocols, 11(9), 1650–1667. https://doi.org/10.1038/nprot.2016.095.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., & Durbin, R. (2009). The sequence alignment/map format and SAMtools. Journal of Bioinformatics, 25(16), 2078–2079. https://doi.org/10.1093/bioinformatics/btp352.

    Article  CAS  Google Scholar 

  19. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods, 5(7), 621–628. https://doi.org/10.1038/nmeth.1226.

    Article  CAS  PubMed  Google Scholar 

  20. Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37(1), 1–13. https://doi.org/10.1093/nar/gkn923.

    Article  CAS  Google Scholar 

  21. Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4(1), 44–57. https://doi.org/10.1038/nprot.2008.211.

    Article  CAS  Google Scholar 

  22. Szklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., Doncheva, N. T., Legeay, M., Fang, T., Bork, P., Jensen, L. J., & von Mering, C. (2021). The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Research, 49(D1), D605–D612. https://doi.org/10.1093/nar/gkaa1074.

    Article  CAS  PubMed  Google Scholar 

  23. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504. https://doi.org/10.1101/GR.1239303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bader, G. D., & Hogue, C. W. V. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4(1), 1–27. https://doi.org/10.1186/1471-2105-4-2.

    Article  Google Scholar 

  25. Tang, Y., Li, M., Wang, J., Pan, Y., & Wu, F. X. (2015). CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems, 127, 67–72. https://doi.org/10.1016/J.biosystems.2014.11.005.

    Article  CAS  PubMed  Google Scholar 

  26. Jeong, H., Mason, S. P., Barabási, A. L., & Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature, 411(6833), 41–42. https://doi.org/10.1038/35075138.

    Article  CAS  PubMed  Google Scholar 

  27. Jin, G., Wang, Q., Pei, X., Li, X., Hu, X., Xu, E., & Li, M. (2019). mRNAs expression profiles of high glucose-induced memory in human umbilical vein endothelial cells. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 12, 1249–1261. https://doi.org/10.2147/DMSO.S206270.

    Article  CAS  Google Scholar 

  28. Xu, E., Hu, X., Li, X., Jin, G., Zhuang, L., Wang, Q., & Pei, X. (2020). Analysis of long non-coding RNA expression profiles in high-glucose treated vascular endothelial cells. BMC Endocrine Disorders, 20(1), 1–10. https://doi.org/10.1186/S12902-020-00593-6/.

    Article  Google Scholar 

  29. Zhang, S., Ke, Z., Yang, C., Zhou, P., Jiang, H., Chen, L., Li, Y., & Li, Q. (2021). High glucose causes distinct expression patterns of primary human skin cells by RNA sequencing. Frontiers in Endocrinology, 12, 152 https://doi.org/10.3389/fendo.2021.603645/.

    Article  Google Scholar 

  30. Ambra, R., Manca, S., Palumbo, M. C., Leoni, G., Natarelli, L., de Marco, A., Consoli, A., Pandolfi, A., & Virgili, F. (2014). Transcriptome analysis of human primary endothelial cells (HUVEC) from umbilical cords of gestational diabetic mothers reveals candidate sites for an epigenetic modulation of specific gene expression. Genomics, 103(5–6), 337–348. https://doi.org/10.1016/j.ygeno.2014.03.003.

    Article  CAS  PubMed  Google Scholar 

  31. De Bock, K., Georgiadou, M., Schoors, S., Kuchnio, A., Wong, B. W., & Cantelmo, A. R. et al. (2013). Role of PFKFB3-driven glycolysis in vessel sprouting. Cell, 154(3), 651–663. https://doi.org/10.1016/j.cell.2013.06.037.

    Article  CAS  PubMed  Google Scholar 

  32. Du, W., Ren, L., Hamblin, M. H., & Fan, Y. (2021). Endothelial cell glucose metabolism and angiogenesis. Biomedicines, 9(2), 147 https://doi.org/10.3390/biomedicines9020147.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kim, J. A., & Yeom, Y. I. (2018). Metabolic signaling to epigenetic alterations in cancer. Biomolecules & Therapeutics, 26(1), 69–80. https://doi.org/10.4062/biomolther.2017.185.

    Article  CAS  Google Scholar 

  34. Zhou, J.-W., Wang, H., Sun, W., Han, N.-N., & Chen, L. (2020). ASPM is a predictor of overall survival and has therapeutic potential in endometrial cancer. The American Journal of Translational Research, 12(5), 1942–1953. https://pubmed.ncbi.nlm.nih.gov/32509189/.

    CAS  PubMed  Google Scholar 

  35. Li, B., Zhu, H. B., Song, G. D., Cheng, J. H., Li, C. Z., Zhang, Y. Z., & Zhao, P. (2019). Regulating the CCNB1 gene can affect cell proliferation and apoptosis in pituitary adenomas and activate epithelial-to-mesenchymal transition. Oncology Letter, 18(5), 4651–4658. https://doi.org/10.3892/ol.2019.10847.

    Article  CAS  Google Scholar 

  36. Fernández, L. P., Gómez de Cedrón, M., & Ramírez de Molina, A. (2020). Alterations of lipid metabolism in cancer: implications in prognosis and treatment. Frontier Oncology, 10, 2144 https://doi.org/10.3389/fonc.2020.577420.

    Article  Google Scholar 

  37. Rojas, M. A., Santana, I., Lemtalsi, T., Caldwell, W., & Caldwell, R. B. (2020). Role of acyl-coenzyme A: cholesterol transferase (ACAT1) in pathological angiogenesis. Investigative Ophthalmology & Visual Science, 61(7), 5408

    Google Scholar 

  38. Pranjol, M. Z. I., Gutowski, N. J., Hannemann, M., & Whatmore, J. L. (2018). Cathepsin D non-proteolytically induces proliferation and migration in human omental microvascular endothelial cells via activation of the ERK1/2 and PI3K/AKT pathways. Biochimica et Biophysica Acta - Molecular and Cell Research, 1865(1), 25–33. https://doi.org/10.1016/j.bbamcr.2017.10.005.

    Article  CAS  Google Scholar 

  39. Popson, S. A., & Hughes, C. C. W. (2010). A role for IFITM proteins in angiogenesis. FASEB Journal, 24(S1), 750–1. https://doi.org/10.1096/fasebj.24.1_supplement.750.1.

    Article  Google Scholar 

  40. Popson, S. A., Ziegler, M. E., Chen, X., Holderfield, M. T., Shaaban, C. I., Fong, A. H., Welch-Reardon, K. M., Papkoff, J., & Hughes, C. C. W. (2014). Interferon-induced transmembrane protein 1 regulates endothelial lumen formation during angiogenesis. Arteriosclerosis, Thrombosis, and Vascular Biology, 34(5), 1011–1019. https://doi.org/10.1161/ATVBAHA.114.303352.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Cheriyath, V., Kaur, J., Davenport, A., Khalel, A., Chowdhury, N., & Gaddipati, L. (2018). G1P3 (IFI6), a mitochondrial localised antiapoptotic protein, promotes metastatic potential of breast cancer cells through mtROS. British Journal of Cancer, 119(1), 52–64. https://doi.org/10.1038/s41416-018-0137-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Liu, Z., Gu, S., Lu, T., Wu, K., Li, L., Dong, C., & Zhou, Y. (2020). IFI6 depletion inhibits esophageal squamous cell carcinoma progression through reactive oxygen species accumulation via mitochondrial dysfunction and endoplasmic reticulum stress. The Journal of Experimental & Clinical Cancer Research, 39(1), 1–28. https://doi.org/10.1186/s13046-020-01646-3.

    Article  CAS  Google Scholar 

  43. Chawla-Sarkar, M., Lindner, D. J., Liu, Y. F., Williams, B. R., Sen, G. C., Silverman, R. H., & Borden, E. C. (2003). Apoptosis and interferons: role of interferon-stimulated genes as mediators of apoptosis. Apoptosis, 8(3), 237–249. https://doi.org/10.1023/A:1023668705040.

    Article  CAS  PubMed  Google Scholar 

  44. Maia, C. J., Rocha, S. M., Socorro, S., Schmitt, F., & Santos, C. R. (2016). Oligoadenylate synthetase 1 (OAS1) expression in human breast and prostate cancer cases, and its regulation by sex steroid hormones. Advances in Modern Oncology Research, 2(2), 97–110. https://doi.org/10.18282/amor.v2.i1.70.

    Article  CAS  Google Scholar 

  45. Li, C., Wang, J., Zhang, H., Zhu, M., Chen, F., Hu, Y., Liu, H., Zhu, H., Li, C., Wang, J., Zhang, H., Zhu, M., Chen, F., Hu, Y., Liu, H., & Zhu, H. (2014). Interferon-stimulated gene 15 (ISG15) is a trigger for tumorigenesis and metastasis of hepatocellular carcinoma. Oncotarget, 5(18), 8429–8441. https://doi.org/10.18632/oncotarget.2316.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Zuo, C., Sheng, X., Ma, M., Xia, M., Ouyang, L., Zuo, C., Sheng, X., Ma, M., Xia, M., & Ouyang, L. (2016). ISG15 in the tumorigenesis and treatment of cancer: An emerging role in malignancies of the digestive system. Oncotarget, 7(45), 74393–74409. https://doi.org/10.18632/oncotarget.11911.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Aljohani, A. I., Joseph, C., Kurozumi, S., Mohammed, O. J., Miligy, I. M., Green, A. R., & Rakha, E. A. (2020). Myxovirus resistance 1 (MX1) is an independent predictor of poor outcome in invasive breast cancer. Breast Cancer Research and Treatment, 181(3), 541–551. https://doi.org/10.1007/S10549-020-05646-X.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Deng, C., Zhang, D., Shan, S., Wu, J., Yang, H., & Yu, Y. (2007). Angiogenic effect of intercellular adhesion molecule-1. Journal of Huazhong University of Science and Technology - Medical Science, 27(1), 9–12. https://doi.org/10.1007/S11596-007-0103-4.

    Article  Google Scholar 

  49. Imai, S. I., & Guarente, L. (2014). NAD+ and sirtuins in aging and disease. Trends in Cell Biology, 24, 464–471. https://doi.org/10.1016/j.tcb.2014.04.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hetz, C., & Papa, F. R. (2018). The unfolded protein response and cell fate control. Molecular Cell, 69, 169–181. https://doi.org/10.1016/j.molcel.2017.06.017.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the SIUCEB and DBT-BIF support to the Department of Computational Biology and Bioinformatics, University of Kerala, India for providing the necessary facilities and the Campus Computing Facility (CCF) at the Central Laboratory for Instrumentation and Facilitation (CLIF) at the University of Kerala for providing the HPC cluster facility to carry out this research work.

Author Contributions

Conceptualization, P.S. and P.R.S.; methodology, P.S. and P.R.S.; software, P.S.; validation, P.S. and K.R.A.; formal analysis, P.S. and P.R.S; data curation, P.S.; writing—original draft preparation, P.S. and P.R.S..; writing—review and editing, P.S., K.R.A., A.S.N., O.V.O. and P.R.S.; supervision, P.R.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Kerala State Council for Science, Technology and Environment (KSCSTE), Govt. of Kerala, by way of fellowship to K.R.A. P.R.S was supported by ISCA, Kolkata.

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Sunitha, P., Arya, K.R., Nair, A.S. et al. Metabolite Effect on Angiogenesis: Insights from Transcriptome Analysis. Cell Biochem Biophys 80, 519–536 (2022). https://doi.org/10.1007/s12013-022-01078-0

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