Multi omics analysis of fibrotic kidneys in two mouse models

Abstract

Kidney fibrosis represents an urgent unmet clinical need due to the lack of effective therapies and an inadequate understanding of the molecular pathogenesis. We have generated a comprehensive and combined multi-omics dataset (proteomics, mRNA and small RNA transcriptomics) of fibrotic kidneys that is searchable through a user-friendly web application: http://hbcreports.med.harvard.edu/fmm/ . Two commonly used mouse models were utilized: a reversible chemical-induced injury model (folic acid (FA) induced nephropathy) and an irreversible surgically-induced fibrosis model (unilateral ureteral obstruction (UUO)). mRNA and small RNA sequencing, as well as 10-plex tandem mass tag (TMT) proteomics were performed with kidney samples from different time points over the course of fibrosis development. The bioinformatics workflow used to process, technically validate, and combine the single omics data will be described. In summary, we present temporal multi-omics data from fibrotic mouse kidneys that are accessible through an interrogation tool (Mouse Kidney Fibromics browser) to provide a searchable transcriptome and proteome for kidney fibrosis researchers.

Fig. 1

Schematic of study design, data generation and processing. Overview of how the kidney fibrosis models were set up including flow charts for mRNA-seq, proteomics and small RNA-seq profiling in the kidneys.

Fig. 2

Principal component analysis (PCA) of all UUO datasets and FA proteins. The normalized expression abundance of mRNAs, proteins and miRNA was used. Each color represents a time point in the dataset. (a) miRNA expression in kidneys from the UUO model shows day 3 and 7 being in the same cluster, while the normal and the latest time points are distinct. (b) The greatest variation in gene expression in the UUO model is observed along the first principal component (PC) between normal and injured samples, with the second PC separating injury times. (c) A similar pattern is observed using UUO protein expression, with higher consistency within sample groups allowing for better discrimination between time points. (d) Protein expression in the FA model shows different clusters for each time point, PC1 separating normal from the injured samples, and PC2 separating early injury from later time points.

Fig. 3

Expression profiles of fibrosis and injury markers and housekeeping genes. UUO mRNA (dotted line), UUO protein (dashed line), FA protein (standard line), and UUO miRNA (standard line). (a) The fibrosis markers α-smooth muscle actin (Acta2), collagen (Col1a1) and fibronectin (Fn1) show increasing expression over time for UUO mRNA, UUO protein and FA protein datasets. (b) Kidneyinjury markers clusterin (Clu), kidney injury molecule 1 (Kim-1 alias Havcr) and lipocalin-2 (Ngal alias Lcn2) show increased expression early on without further significant increases over time. (c) Nine commonly used housekeeping genes show no change of expression in all the datasets. (d) miRNAs miR-192 and -21 are involved in kidney pathogenesis and show expression changes over time for the UUO model.