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Urinary Sediment Transcriptomic and Longitudinal Data to Investigate Renal Function Decline in Type 1 Diabetes

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Urinary Sediment Transcriptomic and Longitudinal Data to Investigate Renal Function Decline in Type 1 Diabetes ORIGINAL RESEARCH published: 30 April 2020 doi: 10.3389/fendo.2020.00238 Urinary Sediment Transcriptomic and Longitudinal Data to Investigate Renal Function Decline in Type 1 Diabetes Maria Beatriz Monteiro 1, Tatiana S. Pelaes 1, Daniele P. Santos-Bezerra 1, Karina Thieme 2, Antonio M. Lerario 3, Sueli M. Oba-Shinjo 4, Ubiratan F. Machado 2, Marisa Passarelli 5,6, Suely K. N. Marie 4 and Maria Lúcia Corrêa-Giannella 1,6* 1 Laboratório de Carboidratos e Radioimunoensaio (LIM-18), Faculdade de Medicina, Hospital das Clinicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil, 2 Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil, 3 Division of Metabolism, Endocrinology, and Diabetes, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States, 4 Laboratory of Molecular and Cellular Biology (LIM-15), Faculdade de Medicina, Hospital das Clinicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil, 5 Laboratório de Lípides (LIM-10), Faculdade de Medicina, Hospital das Clinicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil, 6 Programa de Pós-graduação em Medicina, Universidade Nove de Julho (UNINOVE), São Paulo, Brazil Edited by: Ondrejˇ Šeda, Introduction: Using a discovery/validation approach we investigated associations Charles University, Czechia between a panel of genes selected from a transcriptomic study and the estimated Reviewed by: glomerular filtration rate (eGFR) decline across time in a cohort of type 1 diabetes Ilse Sofia Daehn, Icahn School of Medicine at Mount (T1D) patients. Sinai, United States Tomislav Bulum, Experimental: Urinary sediment transcriptomic was performed to select highly University of Zagreb, Croatia modulated genes in T1D patients with rapid eGFR decline (decliners) vs. patients with *Correspondence: stable eGFR (non-decliners). The selected genes were validated in samples from a T1D Maria Lúcia Corrêa-Giannella cohort (n = 54, mean diabetes duration of 21 years, 61% women) followed longitudinally [email protected] for a median of 12 years in a Diabetes Outpatient Clinic. Specialty section: Results: In the discovery phase, the transcriptomic study revealed 158 genes This article was submitted to Clinical Diabetes, significantly different between decliners and non-decliners. Ten genes increasingly up a section of the journal or down-regulated according to renal function worsening were selected for validation by Frontiers in Endocrinology qRT-PCR; the genes CYP4F22, and PMP22 were confirmed as differentially expressed Received: 13 September 2019 comparing decliners vs. non-decliners after adjustment for potential confounders. Accepted: 01 April 2020 Published: 30 April 2020 CYP4F22, LYPD3, PMP22, MAP1LC3C, HS3ST2, GPNMB, CDH6, and PKD2L1 Citation: significantly modified the slope of eGFR in T1D patients across time. Monteiro MB, Pelaes TS, Santos-Bezerra DP, Thieme K, Conclusions: Eight genes identified as differentially expressed in the urinary sediment Lerario AM, Oba-Shinjo SM, of T1D patients presenting different eGFR decline rates significantly increased the Machado UF, Passarelli M, Marie SKN accuracy of predicted renal function across time in the studied cohort. These genes and Corrêa-Giannella ML (2020) Urinary Sediment Transcriptomic and may be a promising way of unveiling novel mechanisms associated with diabetic kidney Longitudinal Data to Investigate Renal disease progression. Function Decline in Type 1 Diabetes. Front. Endocrinol. 11:238. Keywords: diabetic kidney disease, transcriptomics, renal function decline, longitudinal data, type 1 diabetes, doi: 10.3389/fendo.2020.00238 urine Frontiers in Endocrinology | www.frontiersin.org 1 April 2020 | Volume 11 | Article 238 Monteiro et al. Transcriptomics and Prediction of Renal Function Decline INTRODUCTION for leucocyturia (>10,000 leucocytes per cubic millimeter of urine) were discarded. Total RNA was isolated using Trizol Diabetic kidney disease (DKD) is a major cause of end-stage renal reagent (ThermoFisher Scientific, Carlsbad, CA) and RNeasy disease (ESRD) worldwide. The prevalence of ESRD in patients Minikit (Qiagen, Germantown, MD) followed by reverse- with diabetes (DM) is up to 10 times higher than those without transcription to cDNA using High Capacity cDNA Reverse DM (1). The search for both prognostic and surrogate endpoint Transcription Kit (ThermoFisher Scientific, Carlsbad, CA). biomarkers for DKD has received more attention in the recent Clinical and demographic data were collected starting from years. However, at present no novel biomarkers are routinely the date each patient was admitted to the Diabetes Clinic used in the clinic or in trials (2). and ending in 2018 or until reaching end-stage renal disease Recently, the use of urine in the study of DKD has (median follow-up of 12 years; mean±SD follow-up of 11 ± 2.9 been increasing with the use of quantitative polymerase years). Data collection included sex, age, T1D duration, use of chain reaction (PCR) or ELISA (3). In the last decade, next angiotensin converting enzyme inhibitors (ACEi) or angiotensin generation sequencing (NGS) methods made it possible to receptor blocker (ARB), and glycated hemoglobin (HbA1c) generate inexpensive, reproducible, and high throughput nucleic values. To evaluate renal function across time, we collected acid sequence data providing new opportunities for unbiased all measurements for serum creatinine taken during the entire discovery of novel pathophysiologic pathways of disease, as well follow-up period (at least one per year of follow-up, median of as for the identification of novel disease biomarkers (4). 3 measurements per year). Serum creatinine was measured by Besides being an organ-specific sample, urine is an easily the Jaffé reaction, standardized to IDMS traceable creatinine and obtainable material, without the need of invasive procedures. used to calculate the estimated glomerular filtration rate (eGFR) We previously described the expression of markers of proximal using the Chronic Kidney Disease Epidemiology Collaboration tubule epithelial cells and podocytes in the human urine sediment (CKD-EPI) equation (7). while attempting to correlate clinical markers of kidney disease with expression of target genes in this biological material (5). The Library Preparation and RNA Sequencing same approach was taken by other research groups (3) showing Four T1D patients with an eGFR decline ≥3.5 mL/min/1.73 the use of urine as a translatable strategy to study markers of m2 per year of follow-up (decliners) eGFR, four T1D patients kidney injury without the need of invasive interventions. with an eGFR decline <3.5 mL/min/1.73 m2 per year of In an attempt to identify new pathways associated with DKD follow-up (non-decliners), and two non-diabetic controls were progression, we performed a transcriptomic analysis using total selected for the transcriptomic study. Messenger RNA was RNA isolated from urine sediment cells collected from patients checked for quality (RIN value >7.0) and quantity using Agilent with type 1 DM (T1D), presenting or not rapid renal function 2200 TapeStation (Agilent Technologies) employing the High decline. Candidate genes identified in the transcriptomic study Sensitivity D1000 ScreenTape assay. RNA-seq libraries were were validated in a cohort of T1D patients followed from 2006 to prepared using Illumina TruSeq Stranded Total RNA Library 2018. Eight of the 10 validated genes significantly increased the Prep Kit with Ribo-Zero GoldR (San Diego, CA) from 100 ng accuracy of predicted renal function across time in the studied of purified total RNA according to the manufacturer’s protocol. cohort. These genes may be a promising way of unveiling novel Paired-end reads were generated in the Illumina HiSeq 2000 mechanisms associated with DKD progression. platform. The dataset is available with National Center for Biotechnology Information’s Gene Expression Omnibus database MATERIALS AND METHODS under the accession number GSE140627. Participants Bioinformatic Analysis This study was conducted in compliance with the Institutional Reads were aligned to the hg38 version of the human Ethics Committee and the Declaration of Helsinki of 1975, reference genome (downloaded from ftp://ftp.ensembl.org/pub/ revised in 1983, with informed consent being given to all release-94/fasta/homo_sapiens/dna/ using STAR (8). Metrics participants and it was approved by the Ethics Committee of such as number of reads, duplicate levels, total number the University of São Paulo Medical School (Cappesq, approval of expressed genes and ribosomal RNA contamination were #1,536,656). Fifty-four T1D patients were recruited from 2012 generated with RNASeqC (9). Count data was generated with to 2016 at the Diabetes Outpatient Clinic, Hospital das Clinicas featureCounts (10). Non-expressed genes were flagged and da Faculdade de Medicina da Universidade de São Paulo, Brazil. removed from downstream analysis using DAFS (11). Data T1D was diagnosed in patients presenting hyperglycaemia, normalization and logCPM transformation was performed using positive autoantibodies [glutamic acid decarboxylase [GAD], the voom function from the R/Bioconductor tool limma (11). islet cell antibodies or tyrosine phosphatase-like insulinoma Differential gene expression analysis was performed using limma. antigen 2 [IA-2]], undetectable C-peptide or ketoacidosis and Pairwise comparisons between control samples and diabetic insulin requirement within 3 months after diagnosis. All samples
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