This upload contains the supplementary materials for the manuscript "The Relationship between HERV, Interleukin, and Transcription Factor Expression in ZIKV Infected versus Uninfected Trophoblastic Cells" (MDPI: Cells).

Authors:

Anderson Luís da Costa 1,†, Paula Prieto-Oliveira 2,3,†, Márcia Duarte-Barbosa 1,†, Robert Andreata-Santos 4, Cristina M. Peter 4,5, Thamires Prolo de Brito 4, Fernando Antoneli 5, Ricardo Durães-Carvalho 4,6, Marcelo R. S. Briones 5, Juliana T. Maricato 4, Paolo M. A. Zanotto 7, Denis Jacob Machado 2,3,‡ 
and Luiz M. R. Janini 1,4,‡

†    These authors contributed equally to this work and shared the first authorship.
‡    D.J.M. and L.M.R.J. are jointly credited as last authors.

Institutions:

1. Laboratory of Retrovirology, Discipline of Infectology, Department of Medicine, Federal University of São Paulo (EPM-UNIFESP), São Paulo 04039-032, Brazil
2. Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte, 9331 Robert D. Snyder Rd., Charlotte, NC 28223, USA
3. Computational Intelligence to Predict Health and Environmental Risks Center, University of North Caroli-na at Charlotte, 9201 University City BLVD., Charlotte, NC 28223, USA
4. Laboratory of Retrovirology, Discipline of Microbiology, Department of Microbiology, Immunology and Para-sitology, Federal University of São Paulo, São Paulo 04039-032, Brazil
5. Center for Medical Bioinformatics, Federal University of São Paulo, São Paulo 04039-032, Brazil
6. Department of Morphology and Genetics, Federal University of São Paulo, São Paulo 04039-032, Brazil
7. Laboratory of Molecular Evolution and Bioinformatics, Department of Microbiology, Institute of 
Biosciences, University of São Paulo, São Paulo 05508-000, Brazil

Contents:

Note: In the supplementary tables, the authors employed the term "NA" to refer to "Not Available" data. The term is widelly used in statistics and most statistical software, such as R, will recognize the term in the supplementary files, facilitating reusage.

For Appendix A, see file Appendix_A.pdf.

• Appendix A-Table A1: Oligonucleotide primer sequences used for ERV detection for Taqman® assays.
  
• Appendix A-Table A2: List of analyzed ERVs.
• Appendix A-Table A3: List of transcriptions factors and cytokines analyzed during array assay.
• Appendix A-Table A4: Reference genes were used as controls for the experiment.
• Appendix A-Table A5: Classification and function of interleukins and transcription factors.
• Appendix A-Figure A1: Summarized methodology.

For Appendix B, see compressed directory Appendix_B.zip.

• Appendix B-Table_A6.xlsx: This file contains four tabs: 1) ∆CT and ∆∆CT: It contains the gene expression levels of each gene in each condition tested in our study, as well as the calculation results of the ∆CT and ∆∆CT values for each gene. 2) Complete ∆∆CT values: it has the ∆∆CT values of the genes that presented known expression levels for all the conditions tested in our study. 3) Avg. ∆CT: It contains the average ∆CTs of each gene in cells infected by IEC or MR766 and non-infected cells for the BeWo and HTR8 lineages. 4) Paired ∆∆CT for genes: it has the ∆∆CT values of the genes, comparing these values in four conditions: BeWo infected with MR766, BeWo infected with IEC, HTR8 infected with MR766, and HTR8 infected with IEC.
• Appendix B-Table_A7.csv: It presents the log2FC values of all the genes analyzed in our study in four conditions: BeWo infected with MR766, BeWo infected with IEC, HTR8 infected with MR766, and HTR8 infected with IEC.
• Appendix B-Table_A8.csv: It contains the expression levels of the genes evaluated in this research, as well as the ∆CT, fold change (FC), and log2FC results.
• Appendix B-Table_A9.csv: It contains the ∆∆CT values of the genes in four conditions: BeWo infected with MR766, BeWo infected with IEC, HTR8 infected with MR766, and HTR8 infected with IEC.
• Appendix B-Table_A10.csv: It is the result of the paired gene correlation analysis, which includes the R-squared and P-values.
• Appendix B-scatterplot.py: Programming language: Python3. Example of how to run: python3 scatterplot.py. Input: Table_B2.csv. Output: scatterplot.pdf.
• Appendix B-boxplot.R: Programming language: R. Example of how to run: Rscript boxplot.R. Input: Table_B3.csv. Output: boxplots.pdf.
• Appendix B-correlations.R: Programming language: R. Example of how to run: Rscript boxplot.R. Input: Table_B4.csv. Output: my_correlation_results.csv.
• Appendix B-heatmap.py: Programming language: Python3. Example of how to run: python3 heatmap.py. Input: None needed (data in the script). Output: Heatmap.pdf.
• Appendix B-File_A1.docx: Plots summarizing the results from viral kinetics experiments (includes Figures A2-A5).

Abstract:

Zika virus (ZIKV) is an arbovirus with maternal, sexual, and TORCH-related transmission capa-bilities. After 2015, Brazil had the highest number of ZIVK-infected pregnant women who lost their babies or delivered them with Congenital ZIKV Syndrome (CZS). ZIKV triggers an immune defense in the placenta. This immune response counts with the participation of interleukins and transcription factors. Additionally, it has the potential involvement of human endogenous retro-viruses (HERVS). Interleukins are immune response regulators that aid immune tolerance and support syncytial structure development in the placenta, where syncytin receptors facilitate vital cell-to-cell fusion events. HERVs are remnants of ancient viral infections that integrate into the genome and produce syncytin proteins crucial for placental development. Since ZIKV can infect trophoblast cells, we analyzed the relationship between ZIKV infection, HERV, interleukin, and transcription factor modulations in the placenta. To investigate the impact of ZIKV on trophoblast cells, we examined two cell types (BeWo and HTR8) infected with ZIKV-MR766 (African) and ZIKV-IEC-Paraíba (Asian–Brazilian) using Taqman and RT2 Profiler PCR Array assays. Our re-sults indicate that early ZIKV infection (24–72 h) does not induce differential interleukins, tran-scription factors, and HERV expression. However, we show that the expression of a few of these host defense genes appears to be linked independently of ZIKV infection. Future studies involving additional trophoblastic cell lineages and extended infection timelines will illuminate the dynamic interplay between ZIKV, HERVs, interleukins, and transcription factors in the placenta.

Funding:

Source: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP). Postdoctoral fellows scholarship number: FAPESP Proc. Nos. 2023/07287-5 (M.D.B.), 2023/10230-5 (C.M.P), and 2021/05661-1 (R.A.S.); Young Investigator Program FAPESP Proc. Nos. 2019/01255-9 and 2021/03684-4 (R.D.C.); FAPESP theme project number 2020/08943-5 (L.M.R.J.).