Experimental and Human Evidence for Lipocalin-2 (Neutrophil Gelatinase-Associated Lipocalin [NGAL]) in the Development of Cardiac Hypertrophy and heart failure

Experimental and Human Evidence for Lipocalin-2 (Neutrophil Gelatinase-Associated Lipocalin [NGAL]) in the Development of Cardiac Hypertrophy and Heart Failure Francine Z. Marques, MSc, PhD; Priscilla R. Prestes, MSc; Sean G. Byars, PhD; Scott C. Ritchie, MSc; Peter W€ urtz, PhD; Sheila K. Patel, PhD; Scott A. Booth, BSc; Indrajeetsinh Rana, PhD; Yosuke Minoda, BSc; Stuart P. Berzins, PhD; Claire L. Curl, PhD; James R. Bell, PhD; Bryan Wai, MBBS; Piyush M. Srivastava, MBBS; Antti J. Kangas, MSc; Pasi Soininen, PhD; Saku Ruohonen, PhD; Mika K€ ah€ onen, MD, PhD; Terho Lehtim€ aki, MD, PhD; Emma Raitoharju, PhD; Aki Havulinna, MSc; Markus Perola, MD, PhD; Olli Raitakari, MD, PhD; Veikko Salomaa, MD, PhD; Mika Ala-Korpela, PhD; Johannes Kettunen, PhD; Maree McGlynn, BSc; Jason Kelly, BSc; Mary E. Wlodek, PhD; Paul A. Lewandowski, PhD; Lea M. Delbridge, PhD; Louise M. Burrell, MBChB, PhD; Michael Inouye, PhD;* Stephen B. Harrap, MBBS, PhD;* Fadi J. Charchar, PhD* Background-—Cardiac hypertrophy increases the risk of developing heart failure and cardiovascular death. The neutrophil inflammatory protein, lipocalin-2 (LCN2/NGAL), is elevated in certain forms of cardiac hypertrophy and acute heart failure. However, a specific role for LCN2 in predisposition and etiology of hypertrophy and the relevant genetic determinants are unclear. Here, we defined the role of LCN2 in concentric cardiac hypertrophy in terms of pathophysiology, inflammatory expression networks, and genomic determinants. Methods and Results-—We used 3 experimental models: a polygenic model of cardiac hypertrophy and heart failure, a model of intrauterine growth restriction and Lcn2-knockout mouse; cultured cardiomyocytes; and 2 human cohorts: 114 type 2 diabetes mellitus patients and 2064 healthy subjects of the YFS (Young Finns Study). In hypertrophic heart rats, cardiac and circulating Lcn2 was significantly overexpressed before, during, and after development of cardiac hypertrophy and heart failure. Lcn2 expression was increased in hypertrophic hearts in a model of intrauterine growth restriction, whereas Lcn2-knockout mice had smaller hearts. In cultured cardiomyocytes, Lcn2 activated molecular hypertrophic pathways and increased cell size, but reduced proliferation and cell numbers. Increased LCN2 was associated with cardiac hypertrophy and diastolic dysfunction in diabetes mellitus. In the YFS, LCN2 expression was associated with body mass index and cardiac mass and with levels of inflammatory markers. The single-nucleotide polymorphism, rs13297295, located near LCN2 defined a significant cis-eQTL for LCN2 expression. Conclusions-—Direct effects of LCN2 on cardiomyocyte size and number and the consistent associations in experimental and human analyses reveal a central role for LCN2 in the ontogeny of cardiac hypertrophy and heart failure. (J Am Heart Assoc. 2017;6:e005971. DOI: 10.1161/JAHA.117.005971.) Key Words: concentric hypertrophy •C-reactive protein •gene coexpression networks •GlycA •hypertrophy •lipocalin-2 •NGAL •systems biology From the School of Applied and Biomedical Sciences, Faculty of Science and Technology, Federation University Australia, Ballarat, Victoria, Australia (F.Z.M., P.R.P., S.A.B., I.R., Y.M., S.P.B., J. Kelly, F.J.C.); Heart Failure Research Group, Baker Heart and Diabetes Research Institute, Melbourne, Victoria, Australia (F.Z.M., M.I.); Centre for Systems Genomics (S.G.B., S.C.R., M.I.), School of BioSciences (S.G.B., M.I.), Department of Pathology (S.G.B., S.C.R., M.I.), Department of Microbiology and Immunology, Peter Doherty Institute (S.P.B.), and Department of Physiology (C.L.C., J.R.B., L.M.D., M.I., S.B.H., F.J.C.), The University of Melbourne, Victoria, Australia; Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland (P.W., A.J.K., P.S., M.A.-K., J. Kettunen); Department of Medicine, The University of Melbourne (S.K.P., B.W., P.M.S., M.E.W., L.M.B.) and Department of Cardiology (B.W., P.M.S., L.M.B.), Austin Health, Heidelberg, Victoria, Australia; NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (P.S., M.A.-K., J. Kettunen); Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Finland (S.R., O.R.); Department of Clinical Physiology, University of Tampere and Tampere University Hospital, Tampere, Finland (M.K.); Fimlab Laboratories, Department of Clinical Chemistry, Pirkanmaa Hospital District, Schoolof Medicine, University of Tampere, Finland (T.L., E.R.); National Institute for Health and Welfare, Helsinki, Finland (A.H., M.P., V.S., J. Kettunen); Institute for Molecular Medicine Finland, University of Helsinki, Finland (M.P.); Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (O.R.); Medical Research Council Integrative Epidemiology Unit (M.A.-K.) and School of Social and Community Medicine (M.A.-K.), University of Bristol, United Kingdom; School of Medicine, Deakin University, Waurn Ponds, Victoria, Australia (M.M., P.A.L.); Department of Cardiovascular Sciences, University of Leicester, United Kingdom (F.J.C.). Accompanying Data S1, Tables S1 through S12, and Figures S1 through S4 are available at http://jaha.ahajournals.org/content/6/6/e005971/DC1/embed/ inline-supplementary-material-1.pdf *Dr Inouye, Dr Harrap, and Dr Charchar contributed equally to this work as co-senior authors. Correspondence to: Fadi J. Charchar, PhD, University of Ballarat, Room 228, F Building, Oppy Drive, Mt Helen, Ballarat, Victoria 3350, Australia. E-mail: [email protected] Received March 7, 2017; accepted May 2, 2017. ª2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. DOI: 10.1161/JAHA.117.005971 Journal of the American Heart Association 1 ORIGINAL RESEARCH Downloaded from http://ahajournals.org by on October 28, 2019 Cardiac hypertrophy is, after age, the single most important risk factor for cardiovascular death, 1 often as a result of heart failure. Hypertrophic remodeling of the heart is usually in response to increased workload, and the response to such stress has been shown to involve inflammatory pathways. 2–4 Indeed, chronic inflammatory processes have been implicated not only in response to stress, but also more generally as primary etiological factors in cardiovascular disease (CVD). 5 Cardiovascular remodeling depends on refashioning the interstitium, and inflammation stimulates molecules, such as matrix metalloproteinase-9 (MMP9), that degrade the interstitial matrix. 6 MMP9 levels have been associated with cardiovascular disease prognosis, 7 and MMP9 is stimulated by the protein lipocalin-2 (LCN2), also known as neutrophil gelatinase-associated lipocalin (NGAL). 8,9 LCN2 levels have been used to reflect tissue damage, particularly of the kidney, but more recently also for CVD manifestations, 10 including hypertensive cardiac hypertrophy, 11 coronary artery disease 12 and acute heart failure. 13 LCN2 has also been associated with long-term mortality following acute heart failure, independent of renal function. 14 However, it is unclear whether LCN2 is simply a marker of an inflammatory process or capable of direct effects on the heart that might contribute to cardiac hypertrophy and failure. In this study, we investigated the association of LCN2 with concentric cardiac hypertrophy in genetic and environmental experimental models and in relation to the normal variation of human heart size and cardiac hypertrophy in diabetes mellitus. We examined transcriptional associations with LCN2 and identified genetic polymorphisms influencing LCN2 expression. We determined the direct cellular effects of LCN2 in cultured cardiomyocytes. Our findings reveal increased LCN2 levels as a consistent association with cardiac hypertrophy in a variety of models and human cohorts, and our in vitro studies support a direct role for LCN2 in the origins of cardiomyocyte hypertrophy and reduced cardiomyocyte proliferation. Methods Detailed methods are available in the Data S1. Genetic Model of Cardiac Hypertrophy and Heart Failure The hypertrophic heart rat (HHR) is a normotensive inbred polygenic model of adult cardiac hypertrophy, heart failure, and premature death generated by us (Prof Stephen Harrap and Prof Lea Delbridge, University of Melbourne, Melbourne, Parkville, Australia). 15 HHRs have a reduced endowment of cardiomyocytes from very early life, a situation predisposing to hypertrophy and failure in later life. 15,16 Aged-matched male animals were sampled during the following periods: neonatal (postnatal day 2, n=11 HHR, n=10 Normal Heart Rat [NHR]), adolescent (4 weeks old, n=4 HHR and n=4 NHR for cardiomyocyte isolation), young adult (13 weeks old, n=7 HHR, n=7 NHR; 35 weeks old, n=8 NHR, n=11 HHR), and old adult (50 weeks old, n=11 HHR, n=10 NHR). Animals were euthanized by decapitation (neonatal) or with an overdose of pentobarbitone (Lethobarb; adult animals). The heart was immediately removed, and ventricles were dissected from the atria. Cardiac weight index (mg/g) was calculated from the total heart weight (mg) relative to total body weight (g) of the animal. The studies involving animals were approved by the Animal Ethics Committee of Deakin University and the University of Melbourne and ratified at Federation University Australia. They were performed according to the “Code of Practice for the Care and Use of Animals for Scientific Purposes”from the National Health & Medical Research Council of Australia. Rat Microarray Experiments RNA was extracted from the left ventricle of 2-day-old HHRs and NHRs (n=8/group, no pooling), and Affymetrix GeneChip Rat Gene 1.0 ST Arrays (Affymetrix, Santa Clara, CA) was used to assess genes differentially expressed with the assistance of the Ramaciotti Centre for Gene Function Analysis. The data set obtained has been deposited in the National Center for Biotechnology Information Gene Expression Omnibus Clinical Perspective What is New? •Using several animal models, in vitro and human studies, we identified LCN2 as a central gene in the developmental origins of cardiac hypertrophy leading to heart failure. •Increased LCN2 expression has defined effects of cardiomyocyte proliferation and hypertrophy that might explain cardiac hypertrophy and is likely to reflect chronic activation of inflammatory pathways. What are the Clinical Implications? •The experimental effects of LCN2 on cultured cardiomyocytes and in hearts of neonatal animals need to be corroborated in clinical studies of relationships between LCN2, heart size, and, if possible, cardiomyocyte numbers. •LCN2 could be targeted as a therapeutic target and also developed as an early marker for cardiac hypertrophy and heart failure. DOI: 10.1161/JAHA.117.005971 Journal of the American Heart Association 2 Lcn2 in Cardiac Hypertrophy Marques et al ORIGINAL RESEARCH Downloaded from http://ahajournals.org by on October 28, 2019 database according to Minimum Information About a Microarray Experiment guidelines with series accession number GSE38607. Differentially expressed genes were identified using a 2-sample ttest in the Partek Genomics Suite (version 6.6; Partek Inc, Chesterfield, MO), with Bonferroni-adjusted P<0.05 and fold difference higher than 2. Lcn2 mRNA and Protein Levels in Models of Cardiac Hypertrophy and Heart Failure Primers and conditions used for all real-time quantitative PCR (qPCR) are shown in Table S1. Amplification reactions used the SensiFast SYBR Low-ROX Kit qPCR reagent system (Bioline Reagents Ltd, London, UK) in a Viia7 qPCR instrument (Life Technologies, Life Technologies, Carlsbad, CA). Immunohistochemistry was performed using an anti-LCN2 Rabbit Polyclonal antibody (1:200 dilution, TA322583; OriGene Technologies, Rockville, MD), followed by the EnVision+System-HRP. Western blots were performed as previously described 17 using anti-LCN2 Rabbit Polyclonal antibody or b-actin (Cell Signaling Tecnology, Danvers, MA). Lcn2 plasma and left ventricle (LV) protein levels were measured by ELISA in duplicates in neonatal and adult HHR and NHR using the Lipocalin-2 Rat ELISA Kit (Abcam, Cambridge, UK) according to the supplier. Sanger sequencing was used to sequence 10 000 base pairs (bp) before and 2000 bp after the Lcn2 gene in the HHR and NHR (Table S1). Lcn2-Knockout Whole body and heart size of adult (12to 13-week-old) Lcn2KO (n=6) and age-matched wild-type mice C57BL/6 (n=4), generously donated by Prof Alan Aderem (Institute for Systems Biology, University of Washington, Seattle, WA), were measured upon death, and cardiac weight index was calculated as described above. Intrauterine Growth Restriction Rat Model An environmental model of cardiac hypertrophy was developed using Wistar Kyoto rats by intrauterine growth restriction, induced by uteroplacental insufficiency on day 18 of pregnancy (term being 22 days), was also investigated. 18,19 Six-month-old operated female and male rats (n=9) were compared to Wistar Kyoto female and male sham rats (n=16). In Vitro Experiments The pExpress vector containing the cDNA for the rat Lcn2 (2 ng/mL, MRN1768-98079404; Thermo Fisher Scientific, Waltham, MA) or empty vector (pExpress) were transfected into rat embryonic ventricular myocardial cells (H9c2) using Lipofectamine 2000 (Life Technologies). We counted the number of cells by hemocytometry with the use of the Countess Automated Cell Counter (Life Technologies). Wheat germ agglutinin and Hoechst staining was used to measure cell size, 20 and phospho-histone H3 staining was used to determine cell proliferation. 20 Apoptosis was investigated by flow cytometry using an Annexin-V: FITC Apoptosis Detection Kit I. All in vitro experiments were independently repeated 3 times, each time in triplicates. RNA-Sequencing and Molecular Pathways RNA was extracted from Lcn2-KO mice and cells transfected with Lcn2 plasmid for 48 hours (and respective controls). RNA from 3 samples of each group was sent to RNA-sequencing at the Australian Genome Research Facility using the Illumina HiSeq platform (v3 chemistry 100 bp paired-end sequencing). Each sample was considered an individual sample and no pooling was performed. Analysis of differential expression was performed in the R statistical programming environment (version 3.1.0) using Rsubread (version 1.14.2) and edgeR (version 3.6.8) Bioconductor packages (Table S2). 21 Pvalues were adjusted for multiple testing using the BenjaminiHochberg correction with a false discovery rate <0.05. Gene ontology enrichment analysis was performed on filtered lists of differentially expressed genes to ask which pathways were enriched in genes differentially expressed. Human Echocardiography Measurements Briefly, 114 individuals with echocardiographic measures were selected from a prospective cohort of type 2 diabetic subjects 22 whose basic characteristics are shown in Table S3. In addition, subjects with echocardiographic measures from the Young Finns Study (YFS) analyzed, shown in Table S4. The YFS is a longitudinal population-based study of 3596 individuals recruited during childhood in 1980. 23 Genome-wide genotype data, transcriptome-wide microarray profiling, C-reactive protein (CRP), glycoprotein acetylation (GlycA), and echocardiographic measurements were available for different subsets of 2064 individuals aged 34 to 48 years, participating in the 2011 follow-up study. 24–27 The cohort studies complied with the Declaration of Helsinki and were approved by the human ethics committee at each institution. All subjects gave informed consent. In both cohorts, echocardiographic examinations were performed using transthoracic echocardiography by an Acuson Sequoia 512 (Acuson, Mountain View, CA) with a 3.5-MHz scanning frequency phased-array transducer. From the ultrasound images, LV structure, systolic, and diastolic function were measured following the guidelines of the American Society of Echocardiography, as previously described. 28,29 DOI: 10.1161/JAHA.117.005971 Journal of the American Heart Association 3 Lcn2 in Cardiac Hypertrophy Marques et al ORIGINAL RESEARCH Downloaded from http://ahajournals.org by on October 28, 2019 Cardiac hypertrophy was defined as LV mass indexed to the body surface of >95 g/m 2 in women and >115 g/m 2 in men. 30 E/E0-ratio was calculated using the average values of lateral and septal e0velocity. 29 Human Plasma LCN2 Measurement Human plasma was used to measure LCN2 levels in duplicates in 121 subjects with type 2 diabetes mellitus using the Quantikine ELISA Human Lipocalin-2 Immunoassay (R&D Systems, Minneapolis, MN), according to the supplier. LCN2 mRNA Levels in Human Heart We used data in the repository Gene Expression Omnibus series GSE1145 to investigate the levels of LCN2 in human idiopathic dilated hearts (n=11 control hearts and n=15 idiopathic dilated hearts). We performed a whole-genome analysis using the Gene Expression Omnibus tools, including false discovery rate <0.05, to determine whether LCN2 was overexpressed in human idiopathic dilated hearts. GlycA Measurement GlycA reflects the integrated concentrations and glycosylation states of several of the most abundant inflammatory acutephase glycoproteins 31,32 measured with a proton nuclear magnetic resonance metabolomics platform. 33 CRP Measurement High-sensitivity CRP was quantified from serum samples using an automated analyzer with a latex turbidimetric immunoassay kit. Coexpression Networks and Quantitative Trait Loci Transcriptome-wide microarray profiling was performed on whole blood for 1650 individuals in the YFS as previously described. 24 Briefly, stabilized total RNA was obtained from whole blood for individuals in the YFS. RNA was hybridized to Illumina HT-12 (version 4; Illumina, San Diego, CA) BeadChip arrays, and raw probe data were exported with the Illumina BeadStudio software. Both positive and negative control probes were used to quantile normalize using the limma R package. 34 Probe intensities were reported on a log 2 scale. Identification and characterization of the gene coexpression network analyzed in this study is described in Ritchie et al. 32 Here, we defined the neutrophil module’s coexpression as the Spearman’s correlation coefficient between its 27 genes. 32 The average expression was used for genes with multiple microarray probes. Edges in the coexpression network were defined as the magnitude of the correlation exponentiated to the power of 4. A vector summarizing module expression was calculated for association testing as the first eigenvector of a principal components analysis on module expression. This summary expression profile captured 57% of the total variation in module gene expression. Association analyses are described in the Statistical Analyses section below. Genome-wide genotyping was carried out on whole-blood samples for 2442 individuals participating in the 2001 followup study of the YFS as previously described. 25 Sample and genotype quality control was performed for these 2442 individuals (Data S1). A combined total of 6 721 082 directly genotyped and imputed single-nucleotide polymorphisms (SNPs) passed quality control. Module quantitative trait loci (QTLs) were identified for 1386 individuals with matched genotype and gene expression data in the YFS through a genome-wide scan for SNPs associated with the summary expression profile using PLINK 1.90 beta (version 3.32). Individual associations were tested using a linear model of minor allele dosage on neutrophil module summary expression. An SNP was considered a module QTL where P<5910 8 (genome-wide significance). Models were adjusted for age, sex, and the first 2 principal components of the genotype data. The module QTL, rs13297295, on chromosome 9 was further tested for an association with LCN2 expression levels using the same model. Rs13297295 was also tested for association with GlycA and CRP in the 1712 individuals with matched genotype and GlycA or CRP data. Statistical Analyses R software (version 3.13; R Foundation for Statistical Computing, Vienna, Austria) was used for the analyses of the YFS data. The NetRep package (version 0.54) was used for network analyses. 35 Measurements of GlycA, routine lipids, CRP, body mass index (BMI), heart function (measured as early filling [E] to early diastolic mitral annular velocity [E0]—E/E0ratio, and E to late [A] diastolic filling—E/A ratio) were normalized using a natural logarithm transformation, and all continuous measurements were standardized to SD units in both cohorts. Module associations with the inflammatory biomarkers were assessed by linear regression of: neutrophil module expression on GlycA and CRP; linear regression of LCN2 expression on GlycA and CRP. To assess whether LCN2 was a mediator of the relationship between these biomarkers and the neutrophil module, we used linear regression of: GlycA and CRP on LCN2 expression and neutrophil module expression; CRP on LCN2 expression and neutrophil module expression; and GlycA on LCN2 expression and neutrophil module expression. All terms in the models were additive, and all models were adjusted for age and sex. Matched gene expression, GlycA, and CRP data DOI: 10.1161/JAHA.117.005971 Journal of the American Heart Association 4 Lcn2 in Cardiac Hypertrophy Marques et al ORIGINAL RESEARCH Downloaded from http://ahajournals.org by on October 28, 2019 were available for 1650 individuals. Associations between LCN2 expression and echocardiographic measurements in the YFS (Table S5) were tested by linear regression of each echocardiographic measurement on LCN2 expression, adjusting for age and sex. Each association was considered significant where P<0.05. Matched gene expression and echocardiographic data were available for between 1482 and 1573 individuals depending on the LV phenotype. Interand intraassay coefficients of variability were calculated for ELISAs, and only less than 15% variability was accepted (hence 7 human samples from the type 2 diabetes mellitus cohort were excluded from further analyses). Human plasma LCN2 levels were not normally distributed; therefore, LCN2 was log transformed for the association analyses presented in Table S3. An independent ttest was used to assess differences in continuous variables between those with and without cardiac hypertrophy or chi-square analyses for dichotomous variables. A general linear model analysis was performed to test for associations between presence of cardiac hypertrophy and plasma LCN2 levels after adjusting for variables from the univariable analysis with a Pvalue of <1.0 (age, sex, BMI, estimated glomerular filtration rate, and systolic blood pressure). We used untransformed LCN2 levels to perform Spearman Rho correlations between human plasma LCN2 and LV left ventricle mass and function in the type 2 diabetes mellitus cohort (Table S6). Significance was set at P<0.05. Results from the animal groups were tested for normal distribution using the Skewness and Kurtosis tests. Independent sample ttests (with Welch’s correction in the case of different variance) and ANOVA were used to compare the data between the animal groups. A 2-way ANOVA was used to compare between Lcn2 expression in the different cell types in HHRs and NHRs. Results Lcn2 Is Associated With Cardiac Size in Experimental Genetic and Environmental Models HHR and NHR A transcriptome analysis of neonatal P2 LV tissue identified 21 genes with significant differential expression between HHRs and NHRs (Table S7 and Figure S1) involving pathways for cardiovascular system development and function, and cell growth and proliferation (Tables S8 and S9), with Lcn2 showing the greatest differential expression (q=7910 11 ; Table S7). Elevated cardiac Lcn2 expression was validated by qPCR at postnatal day 2 (Figure 1A) and was found to persist with established hypertrophy at 13 weeks of age and with the emergence of heart failure at 35 or 50 weeks of age (Figure 1B) and further confirmed by 25-kDa Lcn2 monomer protein analyses (Figure 1C and 1D). Compared with its control strain, the NHR, we found significantly higher circulating Lcn2 in adult HHRs with established hypertrophy at 35 weeks of age (Figure 1E), but also soon after birth (Figure 1F) before hypertrophy is evident but cardiomyocyte numbers are already reduced. RNA and immunohistochemical studies (Figure 1G and 1H) showed Lcn2 expression in cardiomyocytes and noncardiomyocyte (fibroendothelial) cells. Correlation between cardiac Lcn2 mRNA and plasma Lcn2 in the HHRs and NHRs was r=0.996 (P<0.001). Sequencing the HHR and NHR Lcn2 genes for comparison with the published sequence for spontaneously hypertensive rats and Fisher 344 (original progenitors of HHR and NHR), we found 3 unique SNPs in the HHR, all inherited from the spontaneously hypertensive rats, with 1 being intronic and 2 being upstream of the coding sequence (Figure 2A through 2D). In the HHR heart, both Lcn2 mRNA and pre-mRNA levels were increased (Figure 2E), suggesting a transcriptional dysregulation of Lcn2 in the HHR. Transcription Factor Affinity Prediction (sTRAP) 36,37 analysis suggested that one of these SNPs (rs196968512) created a binding site for the enhancer, RAR-related orphan receptor A (Figure 2B and 2F). 38 Lcn2 knockout mice Hearts from adult mice with double knockout of the Lcn2 gene (Lcn2-KO) 39 were significantly smaller than age-matched wildtype mice (cardiac weight index 5.4 versus 5.9 mg/g; P=0.03; Figure 3A). RNA-sequencing of murine heart tissue from Lcn2-KO versus wild-type identified 16 significantly differentially expressed genes (Table S10) that are relevant to pathways related to hypertrophic cardiomyopathies (Table S11; Figure 3B and 3C). Intrauterine growth restricted rats Last, in an environmental model of intrauterine growth restriction, 18,19 we demonstrated that subsequent adult cardiac hypertrophy was associated with significantly higher levels of cardiac Lcn2 mRNA (P=0.0214; Figure 4). Lcn2 Overexpression Induces Hypertrophy in Cardiomyocytes and reduces proliferation We next sought to determine whether increased Lcn2 transcription in cardiomyocytes results in a hypertrophic phenotype. We transfected rat embryonic ventricular myocardial cells with a plasmid containing the Lcn2 mRNA sequence and performed imaging and RNA-sequencing analyses. Significant increase in the expression of Lcn2 in transfected cells (log fold change=4.44; q=0.0005; Figure S2) resulted in a significant increase in the size of transfected cells (Figure 5A and Figure S3A). In these hypertrophic cells, we found significantly increased expression of 2 genes previously linked DOI: 10.1161/JAHA.117.005971 Journal of the American Heart Association 5 Lcn2 in Cardiac Hypertrophy Marques et al ORIGINAL RESEARCH Downloaded from http://ahajournals.org by on October 28, 2019 with hypertrophic cardiomyopathy—thrombospondin 2 (log fold change=0.36; q=0.0005) and dynamin 1 (log fold change=0.48; q=0.044). 40,41 In addition to hypertrophy, overexpression of Lcn2 resulted in a significant decrease in cell numbers (Figure 5B) with a concomitant reduction in cells positive for phosphorylated histone H3, reflecting reduced cell mitosis (Figure 5C and Figure S3B). No change in apoptosis was observed (Figure S4). Pathway enrichment analysis of the 529 genes with nominally significant evidence of differential expression (unadjusted P<0.05) between transfected and control myocytes suggested dysregulation of genes related to cell cycle (Kyoto Encyclopedia of Genes and Genomes rno04110; P=0.006). There was suggestive evidence for genes related to hypertrophic cardiomyopathy (Kyoto Encyclopedia of Genes and Genomes rno05410; P=0.07) and dilated cardiomyopathy (Kyoto Encyclopedia of Genes and Genomes rno05414; P=0.09; Figure 5D and 5E; Table S12). Human LCN2 Is Associated With LV Hypertrophy in Diabetes Mellitus Obesity and type 2 diabetes mellitus have been associated with elevated levels of plasma LCN2. 42,43 Independently from those findings, cardiac hypertrophy has been associated with diastolic dysfunction and is recognized as a diabetic complication. 44 However, whether cardiac hypertrophy and diastolic dysfunction in type 2 diabetic patients is associated with high Figure 1. Overexpression of lipocalin-2 (Lcn2) in a polygenic model of cardiac hypertrophy. A, Relative expression levels of Lcn2 mRNA measured by real-time PCR in the heart of 2-day-old hypertrophic heart rat (HHR; n=10) compared to normal heart rat (NHR; n=8; P<0.0001), (B) 13-week-old (P=0.016; n=9/strain), 35-week-old (P<0.001; n=8 NHR and n=11 HHR), and 50-week old (P=0.0015; n=8 NHR and n=11 HHR) HHR compared to NHR. Heart Lcn2 (25-kDa monomer) protein is significantly higher in the HHR compared with NHR, measured by both (C) western blot (P=0.039; n=3/strain) and (D) ELISA (P=0.029; n=4/strain). E, Rat plasma Lcn2 in 35-week-old (P=0.0009; n=6/strain) and (F) 2-day-old (P<0.0001; n=5/strain) HHR compared to NHR. G, Lcn2 mRNA in cardiomyocytes (P=0.013; n=4/strain) and noncardiomyocytes (P=0.03) in the NHR and HHR. The interaction explained 5.135% of total variation (P=0.138), the cell type 6.91% of variation (P=0.0899), and the strain explained the majority of variation (63.58%; P=0.0001). H, Lcn2 staining in NHR and HHR hearts (9400 magnification; scale bar=200lm). *P<0.05; **P<0.01; ***P<0.001. Data shown as mean and error bars represent SEM. DOI: 10.1161/JAHA.117.005971 Journal of the American Heart Association 6 Lcn2 in Cardiac Hypertrophy Marques et al ORIGINAL RESEARCH Downloaded from http://ahajournals.org by on October 28, 2019 LCN2 levels is not known. Echocardiographic assessment of 114 patients with type 2 diabetes mellitus and normal renal function revealed significantly higher levels of mean plasma LCN2 in the 30 diabetic subjects with LV hypertrophy than those 84 without 44.0 ng/mL [95% CI, 38.350.6] versus 36.0 ng/mL [33.139.2] P=0.017) that remained after adjustment for age, sex, BMI, estimated glomerular filtration rate, and systolic blood pressure (P=0.034; Figure 6A). There Figure 2. Variants in the lipocalin-2 (Lcn2) gene, showing regions with variants in the HHR, according to the Rat Genome Database (RGD; version 5). A, Genotype analysis of the region of 10 000 bp around the Lcn2 gene, showing the origin of the variants observed in the HHR. Highlighted in gray are variants that differ from the reference genome, showing that the HHR carries 3 unique variants which were inherited from the SHR. B, Single-nucleotide polymorphism (SNP) on chr3: 16 767 791 (rs196968512 C/T) 1401 bp upstream of Lcn2 gene. C, SNP on chr3: 16 767 398 (G/A) in a highly conserved region 1001 bp upstream the Lcn2 gene. D, Nonfunctional intronic SNP originally from SHR on position chr3: 16 763 494 (rs198262931 C/T). E, Lcn2 pre-mRNA is also upregulated in the HHR compared to the NHR (n=5/strain), suggesting that it is dysregulated at the transcriptional level. F, The SNP, rs196968512, creates a new binding site for the transcription factor, Rora (region underlined in B), which acts as an enhancer for expression of Lcn2 and is exclusive of the HHR. The figure shows the binding site score and the Pvalues for the binding of Rora to that region. chr3 indicates rat chromosome 3; F344, Fisher 344 rat; HHR, hypertrophic heart rat; N/A, nonannotated SNP; NHR, normal heart rat; RGD, reference sequence from the Rat Genome Database v5; SHR, spontaneously hypertensive rat. DOI: 10.1161/JAHA.117.005971 Journal of the American Heart Association 7 Lcn2 in Cardiac Hypertrophy Marques et al ORIGINAL RESEARCH Downloaded from http://ahajournals.org by on October 28, 2019 was a positive correlation between LCN2 levels and LV mass (n=114; Spearman’sr=0.22; P=0.018; Figure 6B and Table S6). In diabetic subjects with cardiac hypertrophy, there was evidence of diastolic dysfunction (Table S3) with a significantly increased E/E0ratio (meanSD 15.24.5 versus 11.43.5; P<0.0001), but there was no association between these measurements and LCN2 (Table S6). LCN2 mRNA Is the Human Heart From subjects with idiopathic dilated cardiomyopathy, cardiac RNA expression data in a public repository (GSE1145) revealed overexpression of cardiac LCN2 after adjustment for multiple comparisons (Figure 6C; false discovery rate, q=0.008). LCN2 Expression, Cardiac Size and Function, and BMI in the YFS In the 1590 YFS individuals (mean age, 42 years) with matched echocardiographic and whole-blood gene expression data, linear regression analysis adjusted for age and sex showed that LCN2 expression was associated with various structural and functional LV phenotypes (Table S5). Higher LCN2 expression was associated with increased heart rate (P=6910 6 ), LV enddiastolic volume (P=0.02), and cardiac output (P=3910 6 ). LV mass (P=5910 5 ) and thickness of the interventricular septum (P=8910 4 ) were also positively correlated with LCN2 expression. Although the negative correlation between LCN2 expression and E/A ratio (P=5910 4 ) suggested diastolic impairment, this might have been confounded by the increased heart rate, 45 given that other measures of diastolic function (E/E0ratio, mitral E-wave declaration time, and isovolumic relaxation time) did not show significant A c B Figure 3. Heart size and associated pathways in lipocalin-2 (Lcn2)-knockout (KO) mice. A, Adult Lcn2-knockout mice have smaller hearts (*P=0.033; n=4 wild-type and n=6 Lcn2 KO). Data shown as mean and error bars represent standard error of mean. B, Genes and pathways differentially regulated in the heart of Lcn2-knockout. Hypertrophic cardiomyopathy (KEGG mmu05410, P=0.0007) is shown in red, dilated cardiomyopathy in blue (Kyoto Encyclopedia of Genes and Genomes [KEGG] mmu05412; P=0.008) and arrhythmogenic right ventricular cardiomyopathy in yellow (KEGG mmu05412; P=0.0003). The genes of dilated cardiomyopathy and hypertrophic cardiomyopathy pathways were the same, and therefore lines are overlapped. Each edge point indicates the chromosomal location for genes identified in specific pathways from the differentially expressed genes. Bar plots are the differentially expressed genes in each pathway. Red depicts genes upregulated, and blue those downregulated, represented as log2 fold change. C, Gene ontology analysis, showing log Pvalue. ABC indicates ATP-binding cassette; ECM extracellular matrix. Figure 4. Environmental model of cardiac hypertrophy overexpresses lipocalin-2 (Lcn2) mRNA (*P=0.0214; n=16 sham and 9 restricted mice). Data shown as mean and error bars represent SEM. DOI: 10.1161/JAHA.117.005971 Journal of the American Heart Association 8 Lcn2 in Cardiac Hypertrophy Marques et al ORIGINAL RESEARCH Downloaded from http://ahajournals.org by on October 28, 2019 correlations with LCN2 expression (Table S5). There was also no significant correlation between LCN2 expression and indices of systolic function, including ejection fraction and systolic wall velocities (Table S5). In addition, LCN2 expression correlated significantly with BMI (r=0.33; 95% CI, 0.28– 0.37; P=8910 42 ). In terms of LV phenotypes, BMI was associated with increased LV size, heart rate, end-diastolic volume, and cardiac output (data not shown). There was also more consistent evidence of reduced diastolic function with increasing BMI, although systolic function was normal. Regression models that included LCN2 expression and BMI revealed that the correlation between BMI and LCN2 expression could account for the associations observed for LCN2 expression alone (data not shown). This has been reported previously and interpreted as part of the low-grade inflammatory activation that accompanies obesity and predisposes to insulin resistance and type 2 diabetes mellitus. 42 LCN2 Is Central to a Neutrophil Gene Coexpression Network and Is Under Genetic Control Previous analysis of whole-blood gene expression data identified a reproducible tightly coexpressed gene module A E B D C Figure 5. Role of lipocalin-2 (Lcn2) in cardiac cells.A, Representation of wheat-germ agglutinin (red) and DAPI (blue) staining, used to estimate cell size (9400 magnification; scale bar=60 lm). Overexpression of Lcn2 increased the size of the cells when compared with cells transfected with the empty plasmid (P<0.0001). B, Overexpression of Lcn2 reduced the number of cells measured by hemocytometer (P=0.0052). C, Overexpression of Lcn2 resulted in cell-cycle arrest, observed by reduced phosphorylation of histone H3 (pH3; 9200 magnification; scale bar=100 lm; P<0.0001). D, Genes and pathways differentially regulated with overexpression of Lcn2. Cell cycle (Kyoto Encyclopedia of Genes and Genomes [KEGG] rno04110; P=0.006) is shown in green, hypertrophic cardiomyopathy (KEGG rno05410, P=0.07) in red, and dilated cardiomyopathy (KEGG rno05414; P=0.09) in blue. Genes of dilated cardiomyopathy and hypertrophic cardiomyopathy pathways were the same, and therefore lines overlapped. Each edge point indicates the chromosomal location for genes identified in specific pathways from the differentially expressed genes. Bar plots are the differentially expressed genes in each pathway. Red depicts genes upregulated, and blue those downregulated, represented as log2 fold change. E, Gene ontology analysis, showing log Pvalue. All experiments were run in 3 independent experiments, with at least 3 replicates each (total, 9 replicates). For experiments involving confocal microscopy, 10 different fields were analyzed per replicate. Positive controls were added to all experiments. **P<0.01; ***P<0.001. Data shown as mean and error bars represent SEM. DOI: 10.1161/JAHA.117.005971 Journal of the American Heart Association 9 Lcn2 in Cardiac Hypertrophy Marques et al ORIGINAL RESEARCH Downloaded from http://ahajournals.org by on October 28, 2019 Supplemental Material Downloaded from http://ahajournals.org by on October 28, 2019 Data S1. Supplemental Methods Animal Samples We then studied the hypertrophic heart rat (HHR) as a normotensive inbred polygenetic model of adult cardiac hypertrophy and failure that begins life with a smaller heart and fewer cells1, 2. Neonatal male (day 2, n=11 HHR, n=10 NHR), adolescent (4-week old, n=4 HHR and n=4 NHR for cardiomyocyte isolation), young adult (13 weeks-old, n=7 HHR, n=7 NHR) and old adult (50 weeks-old, n=11 HHR, n=10 NHR) age-matched animals were euthanized by decapitation (neonatal) or with an overdose of pentobarbitone (Lethobarb) (adult animals). The heart was immediately removed, and the ventricles were dissected from the atriums. Cardiac weight index (CWI, mg/g) was calculated from the total heart weight (mg) relative to total body weight (g) of the animal. HHR samples presented hypertrophy since 4 weeks of age (data not shown). Plasma was also collected. An environmental model of left ventricular hypertrophy developed using Wistar Kyoto (WKY) rats by intra-uterine growth restriction induced by uteroplacental insufficiency on day 18 of pregnancy (term=22 days) was also investigated 3, 4. Six month old male operated animals (n=3) were compared to WKY sham rats (n=7). For all samples, the tissues used were first preserved in liquid nitrogen and later transferred to a –80oC freezer, and RNA was extracted using miRNeasy kit (Qiagen). Based on the means and standard deviation reported in the manuscript, the number of samples used resulted in power >95% for both t-test and analysis of variance (ANOVA), as calculated using the software G*Power version 3.0.10 (http://www.gpower.hhu.de). The study was approved by the Animal Ethics Committee of the University of Melbourne and Deakin University, and ratified at Federation University Australia. Microarray Experiments and Analyses Total RNA was extracted from left ventricle of neonatal HHR (4 male, 4 female) and NHR (4 male, 4 female) using the miRNeasy Mini kit (Qiagen). Affymetrix GeneChip® Rat Gene 1.0 ST Arrays were used for transcriptome-wide gene expression analysis. Each animal was considered an individual sample and no pooling was performed. Briefly, mRNA was converted to ssDNA, labelled and hybridized to GeneChip® Rat Gene 1.0 ST Arrays, which analyse 27,342 gene transcripts using 722,254 probe sets (on average 26 probes per gene), according to the manufacturer’s instructions, and with the assistance of the Ramaciotti Centre for Gene Function Analysis (University of New South Wales, Sydney, Australia). The data set obtained has been deposited in the NCBI Gene Expression Omnibus (GEO) database according to MIAMI guidelines with series accession number GSE38607 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=bjsxbiiaowmwozy&acc=GSE38607). Results from arrays were normalized using robust-multi-array analysis (RMA). Differentially expressed genes were identified using a two-sample t-test in the Partek® Genomics Suite™ (version 6.6). Such genes were selected based on their Bonferroni-adjusted P-value <0.05 and fold difference higher than 2. Hierarchical clustering using Euclidean distance with all genes with Bonferroni P-value<0.05 was performed with Partek® Genomics Suite™. Gene Ontology (GO), used to further interpret the differentially expressed gene data set and to identify over-represented functional groups of genes, and Gene Set Enrichment Analysis (GSEA), used to highlight pathways overor under-represented as a set, were also performed in Partek. Molecular networks related to differentially expressed genes were built via the Ingenuity Pathway Analysis (IPA, Ingenuity® Systems, www.ingenuity.com) application using the “Core Analysis” function. Briefly, a data set Downloaded from http://ahajournals.org by on October 28, 2019 containing differentially expressed genes and respective fold differences were first uploaded into the application. The genes were then correlated based on previous association between genes or proteins and known functional roles of genes. The biological relationship between two genes, represented as nodes, is shown as a line. Nodes with different shapes indicate different functional class. Isolation of cardiomyocytes and other cells Cardiomyocytes were isolated from whole NHR and HHR hearts as we previously described 2. After 10 min incubation, cardiomyocytes were deposited in the bottom of tubes. The supernatant was considered to contain other cells than cardiomyocytes. Cardiomyocytes or other cells were pelleted by centrifugation at 4,000 RCF for 5 min in a refrigerated bench top centrifuge. Total RNA was extracted from both cardiomyocytes and other cells for each sample using the miRNeasy kit (Qiagen) as described above. cDNA and qPCR were performed as described above. Real-time Quantitative PCR (qPCR) First-strand complementary synthesis reaction was performed using the High Capacity cDNA Reverse Transcription Kit (Life Technologies). Primers were designed to flank an exon-exon junction using Primer3 5 and NCBI tool Primer Blast. Primers and conditions used for all qPCR are shown in Online Table 1. Amplification reactions used the SensiFastTM SYBR Low-ROX Kit qPCR reagent system (Bioline) in a Viia7 qPCR instrument (Life Technologies). Samples were run in duplicates. The specificity of the qPCR was ensured by melting curve analysis and electrophoresis in agarose gels (data not shown). The glyceraldehyde 3-phosphate dehydrogenase (Gapdh) was used as reference transcript. Significance was assessed by 2-ΔΔCT method 6. Plasma measurement of LCN2 In animal samples, after euthanasia, blood was immediately collected in EDTA tubes and centrifuged. Plasma was stored separately. Lcn2 plasma and left ventricle levels were measured by enzyme-linked immunosorbent assay (ELISA) in duplicates in neonatal and adult HHR and NHR using the Lipocalin-2 Rat ELISA Kit (Abcam) according to the supplier. Human plasma collected in heparin tubes was used to measure LCN2 levels using the Quantikine ELISA Human Lipocalin-2 Immunoassay (R&D Systems) according to the supplier. Interand intra-assay coefficients of variability were calculated and only less than 15% variability was accepted (hence 7 human samples were excluded from further analyses). The overall intra-assay coefficient was 4.3% and the inter-coefficient was 4.5%. Immunohistochemistry Immunohistochemistry was performed using an anti-LCN2 Rabbit Polyclonal antibody (1:200 dilution, OriGene Technologies, TA322583), followed by the EnVision+System-HRP (Dako). In immunohistochemistry images, the positive staining (Lcn2) was the standard brown while the counterstain was haematoxylin (stains blue). Images were obtained in an EVOS® XL Cell Imaging System (Life Technologies) microscope using a 400x magnification. Protein measurement of Lcn2 by Western Blots Protein was extracted from left ventricle of 13 week old HHR and NHR by the use of RIPA buffer (Sigma-Aldrich) and 1% Halt Protease & Phosphatase Inhibitor Cocktail (Thermo Scientific). Fifty micrograms of extracted rat proteins were resolved by 4–15% MiniPROTEAN® TGX™ Precast Gel (BioRad). Proteins were electroblotted on to a Nitrocellulose Membrane (Thermo Scientific). Membranes were blocked for two hours in 5% Downloaded from http://ahajournals.org by on October 28, 2019 skim milk, then incubated overnight at 4oC with Anti-LCN2 Rabbit Polyclonal antibody (1:2500 dilution, OriGene Technologies, TA322583) or β-actin (1:5000 dilution, Cell Signalling, 3700) in blocking solution, followed by several washing steps in PBS-Tween 20, 1 h at RT with secondary antibody (Lcn2: Anti-rabbit HRP-linked Antibody, 1:5000 dilution, Cell Signalling, 7074S; and β-actin: Anti-mouse HRP-linked Antibody, 1:5000 dilution, Cell Signalling, 7076S) in blocking solution, and several washing steps in PBSTween 20, before detection by enhanced chemiluminescence SuperSignal West Pico Substrate (Thermo Scientific) according to the manufacturer’s instructions. Images were captured with a UVITEC Alliance digital imaging system (Thermo Scientific). Bands were quantified using Image J software, and Lcn2 protein levels were calculated as a ratio relative to β-actin. Both 25 kDa monomer Lcn2 and Lcn2 complexed with matrix metallopeptidase 9 (Mmp9) were measured. DNA sequencing Sanger sequencing was used to sequence 10,000 base pairs (bp) before and 2,000 bp after the Lcn2 gene (primers and conditions in Online Table 1). Briefly we extracted DNA using PureLink® Genomic Extraction kit (Life Technologies), and then amplified it with IMMOLASE DNA Polymerase (Bioline) in a themocycler (BioRad). PCR fragments were purified using the Wizard® SV Gel and PCR Clean-Up System (Promega) and send out for sequencing at AGRF. We sequenced the DNA of both HHR and NHR and align them to the sequence of both SHR and Fisher 344 (the two strains which originated the HHR and NHR), and the reference genome from the Rat Genome Database (version 5). Lcn2-knockout Whole body and heart size of adult (12-13 week-old) whole-body Lcn2 knockout (Lcn2-KO, n=6) and age-matched wild-type mice C57BL/6 (n=4) were measured upon death, and CWI was calculated as described above. Both Lcn2-KO and wild-type mice were sourced from Prof Alan Aderem (University of Washington) 7. Vector containing Lcn2 The pExpress vector containing the cDNA for the rat Lcn2 (Thermo Scientific, catalogue number ID MRN1768-98079404) was transformed into JM109 competent cells (Promega). Constructs were cultivated in plates of Luria-Bertani (LB) medium containing agar and ampicillin (50 μg/ml). Individual colonies were selected and the presence of the vector was confirmed by restriction enzyme and Sanger sequencing. The colonies containing the vector were cultured in LB medium overnight, and plasmids were extracted using a PureLink™ HiPure Plasmid Filter Kit (Life Technologies). To create an empty pExpress vector to be used as a control, Lcn2 sequence was cut out of the plasmid using the restriction enzymes Not1 and SmaI (Promega). DNA Polymerase I Large (Klenow) Fragment (Promega) was used to generate blunted ends. The product was run in a 1% agarose gel, and the band of the correct size was purified from the gel using the Wizard SV Gel and PCR Clean-Up system (Promega). 163 ng of this purification was ligated using T4 DNA Ligase (Life Technologies), and this was transformed into competent cells as described above. The sequence of both plasmids has been confirmed by Sanger DNA sequencing using T7 and sp6 universal primers as a service at the Australian Genome Research Facility (AGRF) as a service. H9c2 Cell Transfections The rat embryonic ventricular myocardial cells (H9c2: ATCC® CRL1446™) were grown in Dulbecco’s modified Eagle’s medium (DMEM), containing 10% fetal bovine serum (FBS) (all from Life Technologies) at 37oC with 5% CO2. Cell culture medium was replaced every 48–72 hours. This cell line was specifically chosen because it has similar hypertrophic Downloaded from http://ahajournals.org by on October 28, 2019 properties as rat primary neonatal cardiomyocytes.8 The cells were purchased from ATCC, and short tandem repeats (STR) profiling was not available for this rat cell line. Moreover, cells were regularly tested for mycoplasma contamination using Hoechst staining (Life Technologies) and a Nikon C2 confocal microscope, with negative results during experiments. H9c2 cells were cultured in 6to 24-well plates and transfected with 2ng/ml of Lcn2 or empty (pExpress only) plasmid using Lipofectamine 2000TM (Life Technologies) in cells at 50% confluence. Untransfected and mock controls were also included. Preliminary experiments were used to determine the best concentration of cells, reagents and vector to have similar levels of Lcn2 as in HHR, so copying physiological levels (Fig. S6). The minimum concentration of vector and transfection reagents, according to the manufacturer’s recommendations, were used. All in vitro experiments were independently repeated 3 times, each time in triplicates (i.e., total of 9 biological replicates). Experiments always included Lcn2 vector, empty plasmid, untransfected cells and a positive control. RNA-sequencing and molecular pathways Total RNA was extracted from whole snap-frozen hearts of Lcn2-KO mice and cells transfected with Lcn2 plasmid for 48h (and respective controls) using the miRNeasy Mini kit (Qiagen). The RNA from 3 samples of each group was sent to RNA-sequencing at AGRF according to the manufacturer’s instructions using the Illumina HiSeq platform (v3 chemistry 100bp paired-end sequencing). Each sample was considered an individual sample and no pooling was performed. Read quality was assessed using the FastQC software version 0.10.1 (www.bioinformatics.babraham.ac.uk/projects/fastqc/), revealing high quality sequence and base scores (mean PHRED scores: overall=35.8, 1bp start=32.4, 100bp end=31.5), and thus read trimming was not applied. Analysis of differential expression was performed in the R statistical programming environment (version 3.1.0) using Rsubread (version 1.14.2) and edgeR (version 3.6.8) Bioconductor packages 9. Paired-end 100bp Illumina reads were aligned to mouse (UCSC mm10 assembly) and rat genomes (UCSC rn5 assembly) at the gene-level using Rsubread aligner with 82.4% and 92.1% of reads successfully mapped overall for mouse and rat samples, respectively. For individual sample and alignment characteristics (i.e. number of paired reads, % mapped, indels, etc) see Online Table 2. Differential expression (DE) of genes was assessed with edgeR. Genes with 1-countper-million mapped reads or less in at least one of the samples were filtered out due to unreliable data in any sample. Count data was then normalized by finding a set of scaling factors for the library sizes that adjust for compositional differences between samples and also help account for any biases due to changes in highly expressed genes 10. Scaling factors were calculated using the trimmed mean of M values (TMM) method 9, 11. Biological coefficient of variation (BCV) was estimated using the common dispersion method (negative binomial dispersion by conditional maximum likelihood) for both mouse (BCV=0.250) and rat (BCV=0.023) samples. Both values were above the threshold (BCV=0.01) suggested for technical replicates in the edgeR User’s Guide indicating sufficient biological variability for analysis. BCV is derived from total CV, which is the sum of estimated true biological (BCV) and technical variation (i.e. measurement error) across libraries. Gene-specific dispersions were then estimated using the empirical Bayes method (tagwise negative binomial) where expression differences that are consistent between replicates are more highly weighted than those that are not, which is necessary so that DE is not driven by outliers. DE was calculated by computing genewise exact tests for differences in the means between both groups (wildtype and knockout in mouse, control and transfected in rat) of negative-binomially distributed counts in edgeR. P-values were adjusted for multiple testing using the Benjamini-Hochberg Downloaded from http://ahajournals.org by on October 28, 2019 correction 12 with a FDR<0.05. To check experimental data quality with sample clustering and heatmaps, variance stabilized expression values (i.e. normalized for variation library size/sequencing depth) were generated using the R package DESeq (version 1.16.0) 13. This data was entered for the top 100 (ranked by unadjusted P-value, all P<0.01) differentially expressed genes into principal component analysis (PCA), hierarchical cluster analysis (HCA), gene-gene heatmap and gene-sample heatmap analysis in R. PCA and HCA confirmed control and experimental group differences (data not shown) and also provided further insight into genes that are associated with the main experimental manipulations in both the rat and mouse studies. GO enrichment analysis was performed on filtered lists (all genes with unadjusted edgeR P-value<0.05, including 529 and 721 rat and mouse genes, respectively) of differentially expressed genes using Database for Annotation, Visualization and Integrated Discovery (DAVID) 14, 15 to ask which Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched in genes differentially expressed in both mouse and rat samples. Circular plots were produced with the R package Rcircos (version 1.1.2) 16 and show the top 100 differentially expressed genes ranked by edgeR P-value and enriched pathways identified in KEGG. Number of cells and cell size We counted the number of cells after 48 hours of transfection by haemocytometry with the use of a Countess® Automated Cell Counter (Life Technologies). Wheat germ agglutinin (WGA) and Hoechst staining were performed as previously described to measure cell size 17. Cell size was measured in 10 different fields per slide obtained in a Nikon C2 confocal microscope using a 400x magnification. 823 cells in average were measured for each condition. Angiotensin II (Sigma-Aldrich) added to the cells for 48h at final concentration of 0.1 µM was used as a positive control. Cell cycle arrest To estimate cell proliferation, we counted phospho-histone H3 (pH3) stained cells after transfection with Lcn2 vector as described above. 500 nM of colcemid (Merck Biosciences), added 2 hours before fixing cells, was used as positive control. Briefly, cells on 12mm polyD-lysine/laminin coated coverslips were stained with pH3 and DAPI for fluorescence imaging as described somewhere else 18. We used the primary pH3 anti-rabbit antibody (Millipore, Cat# 06-570, 1:100 dilution) and secondary goat anti-rabbit AlexaFluor-488 antibody (Invitrogen, 1:500 dilution). Ten images per slide were obtained in a Nikon C2 confocal microscope using a 200x magnification (Fig. S7). Apoptosis Cell death and apoptosis were measured by flow cytometry using an Annexin-V: FITC Apoptosis Detection Kit I (BD Pharmingen, Cat No. 556547). Cells were detached from plate by mild trypsinization (Life Technologies) and were washed twice with cold phosphate buffer saline (PBS). Cells were then resuspended in binding buffer and stained with annexinV-FITC and propidium iodide as per manufacturer’s protocol. The stained cells were analysed using FACS Aria II (BD Biosciences). The data analysis was performed using FCS express (version 4) research edition analytical software. 100 nM staurosporine was used as a positive control. Human Samples Two independent cohorts were used (Online Tables 3 and 4). One hundred and twenty one subjects were selected from a prospective cohort of type-2 diabetes (Online Table 3) 19. Left Downloaded from http://ahajournals.org by on October 28, 2019 ventricle (LV) structure and function was studied using transthoracic echocardiography – Acuson Sequoia 512 (Acuson, Mountain View, CA, USA) with 3.5 MHz scanning frequency phased-array transducer. From the ultrasound images, LV structure, systolic and diastolic function were measured following the guidelines of American Society of Echocardiography, as previously described 20, 21. Cardiac hypertrophy was defined as LV mass indexed to the body surface of > 95 g/m2 in women and > 115 g/m2 in men 19. E/E'- ratio was calculated using the average values of lateral and septal e' velocity 21. Other phenotypes, including blood pressure, were also available. Subjects with chronic kidney disease were excluded due to previous association with LCN2 in the literature 22. Plasma samples were collected in heparin tubes, and stored at –80 ºC. All subjects gave informed consent and the study was approved by human ethics committee at the Austin Hospital, and ratified at Federation University Australia. The Cardiovascular Risk in Young Finns Study (YFS) is an ongoing population-based prospective study started in 1980 to study the emergence and progression of cardiovascular disease risk factors from childhood 23. 4,320 individuals from six age groups (3, 6, 9, 12, 15, and 18 years of age) were randomly recruited from the five major population centres of Finland (Helsinki, Kuopio, Turku, Oulu, and Tampere). A total of 3,596 individuals participated in the original study and 2,064 individuals responded to the 2011 follow-up study. Transcriptome-wide microarray profiling, genome-wide genotyping, GlycA, and echocardiography measurements were available for subsets of these 2,064 individuals 24-27. Baseline cohort characteristics are provided in Online Table 3. Ethics were approved by the Joint Commission on Ethics of the Turku University and the Turku University Central Hospital. Venous blood samples were collected after an overnight fast and serum samples were aliquoted and stored at –70ºC. Samples were collected after an overnight fast for the YFS cohort 23. GlycA measurement A proton nuclear magnetic resonance platform (Bruker AVANCE III, 500 MHz spectrometer) was used to quantify the concentrations of 106 circulating lipids, proteins, and metabolites from serum (including GlycA and total triglycerides) in the YFS cohort. Detailed experimental protocols are described in 28. CRP measurement High-sensitivity C-reactive protein (CRP) was quantified from YFS serum samples using an automated analyser with a latex turbidimetric immunoassay kit. Coexpression networks and quantitative trait locus Transcriptome wide microarray profiling was performed on whole blood for 1,650 individuals in the YFS as previously described 24. Briefly, stabilised total RNA was obtained from whole blood for individuals in the YFS using the PAXgene Blood RNA System. An Eppendorf BioPhotometer was used to evaluate RNA concentrations and purity and the isolation process validated using an Agilent RNA 6000 Nano Chip Kit. RNA was hybridized to Illumina HT-12 (version 4) BeadChip arrays and raw probe data was exported with the Illumina BeadStudio software. Negative control probes were used to background correct the microarrays. Both positive and negative control probes were used to quantile normalised using the limma R package 29. Probe intensities were reported on a log2 scale. Coexpression was calculated for genes composing the previously described neutrophil module 30 as the Spearman correlation coefficient between the gene expression microarray Downloaded from http://ahajournals.org by on October 28, 2019 data. The average probe intensity across samples was taken for genes with multiple probes (DEFA1B, OLFM4, COL17A1). Edges in the interaction network were defined as the absolute value of the coexpression exponentiated to the power 4 as in 30. The connectivity of each gene was calculated as the sum of edge weights to all other genes in the module then scaled by the connectivity of DEFA1B; the most connected gene. The scaled connectivity is a measure of biological importance to the network 31. A summary profile of module expression was calculated as the first eigenvector of a principal components analysis of the module’s gene expression data 32. The summary expression profile explained 57% of the variance the module’s gene expression matrix. Genome-wide genotyping was carried out on whole blood samples for 2,442 individuals participating in the 2001 follow-up study of the YFS as previously described 25. Sample and genotype quality control was performed for these 2,442 individuals. Briefly, DNA was extracted and genotyped on a custom 670K Illumina BeadChip array sharing 562,643 SNPs with the Illumina 610 BeadChip array. The custom array removed poorly performing SNPs from the 610 array and improved copy number variation coverage 25. Genotypes were called using the Illuminus genotype calling algorithm 33. 2 individuals were removed after initial clustering (call rate < 90%) and 54 samples were subsequently removed following Sanger genotyping pipeline quality control (low call rate: < 90%, failing heterozygosity tests, duplicate samples: >98% concordance on pairwise comparison, previously unknown relationship: >70% concordance on pairwise comparison, or failing Sequenom genotype fingerprinting: < 90% concordance for > 10 genotypes). 546,770 SNPs passed quality control. 3 individuals were removed due to low genotyping success (< 95%) and 1 individual was removed for failing sex checks. 51 individuals were excluded due to previous unknown close relation to another sample (pairwise identity by descent pi-hat > 0.2) and 2 individuals were removed due to cryptic relatedness. Missing genotypes and un-typed SNPs were imputed using the 1000 Genomes Phase 1 (v3) reference panel. Imputed and genotyped SNPs were subsequently excluded where the genotype calling probability for any allele < 90%, information score < 0.4, minor allele frequency < 1% or Hardy Weinberg equilibrium P-value < 5 x 10-6. A combined total of 6,721,082 directly genotyped and imputed SNPs passed quality control. Module quantitative trait loci (QTLs) were identified for 1,386 individuals with matched genotype and gene expression data in the YFS through a genome-wide scan for SNPs associated with the summary expression profile using the 1.90 beta (version 3.32) software. Individual associations were tested using a linear model of minor allele dosage on neutrophil module summary expression. A SNP was considered a module QTL where P < 5 x 10-8 (genome-wide significance). Models were adjusted for age, sex, and the first two principal components of the genotype data. The module QTL rs13297295 on chromosome 9 was further tested for an association with expression levels LCN2 using the same model. Rs13297295 was also tested for association with GlycA and CRP in the 1,712 individuals with matched genotype and GlycA or CRP levels. LCN2 mRNA levels in human heart We used data in the repository GEO series GSE1145 to investigate the levels of LCN2 in human idiopathic dilated hearts (n=11 control hearts and n=15 idiopathic dilated hearts). We performed a whole-genome analysis using the GEO tools, including false discovery rate (FDR) adjustment for multiple comparisons, to determine whether LCN2 was over-expressed in human idiopathic dilated hearts. Downloaded from http://ahajournals.org by on October 28, 2019 Table S1. Primers and conditions used to validate microarray gene expression results. Official gene symbol GenBank Accession # Primer Sequence (5’  3’) Concentration Product length Annealing temperature Rat Gapdh NM_017008.3 F: GGGGCTCTCTGCTCCTCCCTG 200 nM 108 bp 58ºC R: ACGGCCAAATCCGTTCACACCG Rat Lcn2 NM_130741.1 F: GGGCTGTCCGATGAACTGAA 200 nM 98 bp 58ºC R: CATTGGTCGGTGGGAACAGA Human ACTB NM_001101 F: CGCGAGAAGATGACCCAGAT R: GAGTCCATCACGATGCCAGT 200 nM 119 bp 66 ºC Human LCN2 NM_005564.3 F: CTACGGGAGAACCAAGGAGC R: CACTGGTCGATTGGGACAGG 200 nM 114 bp 66ºC Sequencing primers Fragment 1 NM_130741.1 F: GGGAGTTTGAGGCTACCTGG R: ATATTGTGCTTCCCTCCCCG 500 nM 1144 bp 55ºC Fragment 2 NM_130741.1 F: AGGTCAGGAGCAGCAAACAG R: TACTAGGTCCGATGTGTGCC 500 nM 1339 bp 55ºC Fragment 3 NM_130741.1 F: TCAACAGGATGTGCTGGCAA R: CAGGGTTTCACCTGACGAGG 200 nM 984 bp 58ºC Fragment 4 NM_130741.1 F: TGCGGTCCAGAAAGAAAGACA R: GACACCCTTCTCCCAGCAAC 200 nM 909 bp 58ºC Fragment 5 NM_130741.1 F: AATGGACAGGACTCGGGGAA R: TCGCTCCTTCAGTTCATCGG 200 nM 976 bp 58ºC Fragment 6 NM_130741.1 F: ACTTATGGAGTGTCCTGCGG R: AACACAGGTGGATGGGGAGA 200 nM 950 bp 58ºC Fragment 7 NM_130741.1 F: CAAGTCTGAGGGTTCGTTTGT R: ACACAGGTGGATGGGGAGA 200 nM 982 bp 58ºC Fragment 8 NM_130741.1 F: TGTCTGCCGCTCCATCTTTC R: ACCTTCCTGGCCCTAGACAT 200 nM 987 bp 58ºC *F: forward primer, R: reverse primer, bp: base pairs. Downloaded from http://ahajournals.org by on October 28, 2019 Table S2. RNA-sequencing alignment characteristics of individual sample. Sample Name Species Treatment Paired Reads Data Yield (bp) # Mapped % Mapped Correctly paired # Indels LCN2_KO_1 mouse Lcn2 knockout 14,884,071 2.98 Gb 11,954,145 80.3 9,773,674 46,999 LCN2_KO_2 mouse Lcn2 knockout 18,268,306 3.65 Gb 15,479,156 84.7 12,730,249 61,344 LCN2_KO_4 mouse Lcn2 knockout 18,081,296 3.62 Gb 15,286,738 84.5 12,666,558 60,985 WT_7 mouse Wild-type 18,478,701 3.70 Gb 14,802,202 80.1 12,280,683 62,887 WT_8 mouse Wild-type 20,477,226 4.10 Gb 17,082,133 83.4 13,926,290 65,430 WT_9 mouse Wild-type 19,197,759 3.84 Gb 15,633,393 81.4 12,863,795 59,517 P_1 rat Empty plasmid 18,079,037 3.62 Gb 16,643,663 92.1 13,676,516 120,877 P_2 rat Empty plasmid 18,570,379 3.71 Gb 17,095,842 92.1 13,966,919 127,350 P_3 rat Empty plasmid 16,296,670 3.26 Gb 14,986,199 92.0 12,217,148 110,047 L_1 rat Lcn2 plasmid 17,256,951 3.45 Gb 15,898,212 92.1 13,017,963 117,624 L_2 rat Lcn2 plasmid 16,652,741 3.33 Gb 15,343,993 92.1 12,576,273 113,638 L_3 rat Lcn2 plasmid 17,646,480 3.53 Gb 16,252,059 92.1 13,287,420 117,522 Total 213,889,617 42.79 Gb Downloaded from http://ahajournals.org by on October 28, 2019 positive thymic T cell selection biological process 5.16 0.00573 generation of precursor metabolites and energy biological process 5.16 0.00573 multicellular organismal response to stress biological process 5.16 0.00573 response to cold biological process 5.05 0.00640 MHC class II protein complex cellular component 4.99 0.00683 lysosome cellular component 4.86 0.00773 cell adhesion molecule binding molecular function 4.83 0.00801 chaperone mediated protein folding requiring cofactor biological process 4.83 0.00801 fascia adherens cellular component 4.83 0.00801 dorsal/ventral neural tube patterning biological process 4.54 0.01064 myoblast differentiation biological process 4.54 0.01064 water transport biological process 4.54 0.01064 laminin binding molecular function 4.42 0.01208 anterior/posterior axis specification biological process 4.42 0.01208 positive regulation of apoptotic process biological process 4.38 0.01251 apoptotic process biological process 4.36 0.01281 olfactory bulb development biological process 4.30 0.01359 fibroblast growth factor receptor binding molecular function 4.30 0.01359 SNAP receptor activity molecular function 4.30 0.01359 immune response biological process 4.28 0.01385 cytoplasmic membrane-bounded vesicle cellular component 4.26 0.01410 innate immune response biological process 4.19 0.01520 structural constituent of muscle molecular function 4.08 0.01686 response to drug biological process 4.03 0.01782 protein homotetramerization biological process 3.99 0.01844 protein homotrimerization biological process 3.98 0.01860 positive regulation of calcium-mediated signalling biological process 3.98 0.01860 positive regulation of cardiac muscle cell proliferation biological process 3.98 0.01860 Downloaded from http://ahajournals.org by on October 28, 2019 amino acid transmembrane transporter activity molecular function 3.98 0.01860 protein sumoylation biological process 3.98 0.01860 trans-Golgi network cellular component 3.97 0.01883 blood vessel development biological process 3.94 0.01937 oxidation-reduction process biological process 3.91 0.02001 cell-cell junction cellular component 3.87 0.02083 basolateral plasma membrane cellular component 3.86 0.02100 cytoplasm cellular component 3.83 0.02170 positive regulation of smooth muscle cell proliferation biological process 3.76 0.02338 integrin complex cellular component 3.72 0.02426 negative regulation of fibroblast proliferation biological process 3.72 0.02426 eukaryotic cell surface binding molecular function 3.56 0.02838 tubulin binding molecular function 3.56 0.02838 lysosome organization biological process 3.56 0.02838 RNA polymerase II transcription cofactor activity molecular function 3.49 0.03053 ATPase activity, coupled to transmembrane movement of substances molecular function 3.49 0.03053 positive regulation of BMP signalling pathway biological process 3.49 0.03053 positive regulation of interleukin-4 biosynthetic process biological process 3.46 0.03136 antigen processing and presentation, exogenous lipid antigen via MHC class Ib biological process 3.46 0.03136 medium-chain fatty acid transport biological process 3.46 0.03136 eye photoreceptor cell differentiation biological process 3.46 0.03136 NADP-retinol dehydrogenase activity molecular function 3.46 0.03136 thermoception biological process 3.46 0.03136 5-phosphoribose 1-diphosphate biosynthetic process biological process 3.46 0.03136 glycogen phosphorylase activity molecular function 3.46 0.03136 Downloaded from http://ahajournals.org by on October 28, 2019 mRNA cis splicing, via spliceosome biological process 3.46 0.03136 platelet activating factor biosynthetic process biological process 3.46 0.03136 myosin filament assembly biological process 3.46 0.03136 ferroxidase activity molecular function 3.46 0.03136 coated vesicle cellular component 3.46 0.03136 chemical homeostasis biological process 3.46 0.03136 protein localization to adherens junction biological process 3.46 0.03136 photoreceptor cell outer segment organization biological process 3.46 0.03136 death domain binding molecular function 3.46 0.03136 thiol oxidase activity molecular function 3.46 0.03136 cuticular plate cellular component 3.46 0.03136 pulmonary myocardium development biological process 3.46 0.03136 antigen processing and presentation of exogenous peptide antigen via MHC class I biological process 3.46 0.03136 optic cup morphogenesis involved in camera-type eye development biological process 3.46 0.03136 double-stranded RNA adenosine deaminase activity molecular function 3.46 0.03136 supraspliceosomal complex cellular component 3.46 0.03136 ubiquitin homeostasis biological process 3.46 0.03136 tangential migration from the subventricular zone to the olfactory bulb biological process 3.46 0.03136 positive regulation of dopamine receptor signalling pathway biological process 3.46 0.03136 specification of organ position biological process 3.46 0.03136 blood vessel endothelial cell proliferation involved in sprouting angiogenesis biological process 3.46 0.03136 BMP signalling pathway involved in heart induction biological process 3.46 0.03136 deltoid tuberosity development biological process 3.46 0.03136 mesodermal cell differentiation biological process 3.46 0.03136 Downloaded from http://ahajournals.org by on October 28, 2019 cloacal septation biological process 3.46 0.03136 regulation of pathway-restricted SMAD protein phosphorylation biological process 3.46 0.03136 trachea formation biological process 3.46 0.03136 bud elongation involved in lung branching biological process 3.46 0.03136 regulation of cartilage development biological process 3.46 0.03136 mesenchymal cell proliferation involved in ureteric bud development biological process 3.46 0.03136 ureter smooth muscle cell differentiation biological process 3.46 0.03136 plasma cell differentiation biological process 3.46 0.03136 glycosphingolipid biosynthetic process biological process 3.46 0.03136 connexin binding molecular function 3.46 0.03136 nucleolar ribonuclease P complex cellular component 3.46 0.03136 homocysteine S-methyltransferase activity molecular function 3.46 0.03136 inorganic diphosphate transport biological process 3.46 0.03136 sequestering of triglyceride biological process 3.46 0.03136 NADH pyrophosphatase activity molecular function 3.46 0.03136 response to iron(III) ion biological process 3.46 0.03136 response to anoxia biological process 3.46 0.03136 virus-infected cell apoptotic process biological process 3.46 0.03136 folic acid transporter activity molecular function 3.46 0.03136 creatine kinase activity molecular function 3.46 0.03136 regulation of mast cell activation biological process 3.46 0.03136 regulation of Fc receptor mediated stimulatory signalling pathway biological process 3.46 0.03136 ganglioside catabolic process biological process 3.46 0.03136 oligosaccharide catabolic process biological process 3.46 0.03136 glycerol channel activity molecular function 3.46 0.03136 forebrain cell migration biological process 3.46 0.03136 negative regulation of lymphocyte activation biological process 3.46 0.03136 Downloaded from http://ahajournals.org by on October 28, 2019 axon midline choice point recognition biological process 3.46 0.03136 olfactory bulb interneuron development biological process 3.46 0.03136 Roundabout signalling pathway biological process 3.46 0.03136 regulation of epidermal growth factor receptor signalling pathway biological process 3.46 0.03136 localization within membrane biological process 3.46 0.03136 low-density lipoprotein particle mediated signalling biological process 3.46 0.03136 alanine-tRNA ligase activity molecular function 3.46 0.03136 alanyl-tRNA aminoacylation biological process 3.46 0.03136 transferrin transport biological process 3.46 0.03136 endocardial cushion to mesenchymal transition involved in heart valve formation biological process 3.46 0.03136 ascending aorta morphogenesis biological process 3.46 0.03136 microsatellite binding molecular function 3.46 0.03136 arterial endothelial cell differentiation biological process 3.46 0.03136 cardiac vascular smooth muscle cell development biological process 3.46 0.03136 protease binding molecular function 3.46 0.03138 activation of MAPKK activity biological process 3.42 0.03275 neuromuscular junction development biological process 3.35 0.03503 response to retinoic acid biological process 3.35 0.03520 regulation of cell shape biological process 3.24 0.03925 cellular response to hydrogen peroxide biological process 3.22 0.03977 protein kinase B signalling cascade biological process 3.22 0.03977 cell adhesion biological process 3.20 0.04081 positive regulation of cell projection organization biological process 3.18 0.04159 T cell receptor binding molecular function 3.18 0.04159 positive regulation of NK T cell activation biological process 3.18 0.04159 neurotransmitter metabolic process biological process 3.18 0.04159 smooth muscle cell migration biological process 3.18 0.04159 positive regulation of ovulation biological process 3.18 0.04159 Downloaded from http://ahajournals.org by on October 28, 2019 peroxisome membrane biogenesis biological process 3.18 0.04159 vitamin binding molecular function 3.18 0.04159 biotin binding molecular function 3.18 0.04159 rhythmic synaptic transmission biological process 3.18 0.04159 sphingosine metabolic process biological process 3.18 0.04159 regulation of striated muscle contraction biological process 3.18 0.04159 glycosaminoglycan catabolic process biological process 3.18 0.04159 morphogenesis of a polarized epithelium biological process 3.18 0.04159 phospholipid dephosphorylation biological process 3.18 0.04159 AP-1 adaptor complex cellular component 3.18 0.04159 protein thiol-disulfide exchange biological process 3.18 0.04159 dichotomous subdivision of terminal units involved in salivary gland branching biological process 3.18 0.04159 epsilon DNA polymerase complex cellular component 3.18 0.04159 phospholipid transporter activity molecular function 3.18 0.04159 telomerase activity molecular function 3.18 0.04159 telomere maintenance via recombination biological process 3.18 0.04159 ferric-chelate reductase activity molecular function 3.18 0.04159 fat pad development biological process 3.18 0.04159 female meiosis I biological process 3.18 0.04159 exploration behaviour biological process 3.18 0.04159 intracellular distribution of mitochondria biological process 3.18 0.04159 negative regulation of dendritic spine morphogenesis biological process 3.18 0.04159 negative regulation of inclusion body assembly biological process 3.18 0.04159 telencephalon regionalization biological process 3.18 0.04159 negative regulation of T cell differentiation in thymus biological process 3.18 0.04159 negative regulation of immature T cell proliferation in thymus biological process 3.18 0.04159 Downloaded from http://ahajournals.org by on October 28, 2019 tendon cell differentiation biological process 3.18 0.04159 trachea development biological process 3.18 0.04159 prostate gland morphogenesis biological process 3.18 0.04159 negative regulation of prostatic bud formation biological process 3.18 0.04159 regulation of morphogenesis of a branching structure biological process 3.18 0.04159 negative regulation of thymocyte apoptotic process biological process 3.18 0.04159 negative regulation of branching involved in ureteric bud morphogenesis biological process 3.18 0.04159 epithelial structure maintenance biological process 3.18 0.04159 cardiac muscle fibre development biological process 3.18 0.04159 transepithelial transport biological process 3.18 0.04159 nucleotide diphosphatase activity molecular function 3.18 0.04159 nucleoside-triphosphate diphosphatase activity molecular function 3.18 0.04159 nucleoside triphosphate catabolic process biological process 3.18 0.04159 folic acid transport biological process 3.18 0.04159 water transmembrane transporter activity molecular function 3.18 0.04159 response to interferon-beta biological process 3.18 0.04159 phosphatidylserine biosynthetic process biological process 3.18 0.04159 beta-N-acetylhexosaminidase activity molecular function 3.18 0.04159 selenocysteine incorporation biological process 3.18 0.04159 negative regulation of chemokine-mediated signalling pathway biological process 3.18 0.04159 regulation of translational fidelity biological process 3.18 0.04159 negative regulation of vasodilation biological process 3.18 0.04159 negative regulation of wound healing biological process 3.18 0.04159 negative regulation of insulin secretion involved in cellular response to glucose stimulus biological process 3.18 0.04159 muscular septum morphogenesis biological process 3.18 0.04159 dorsal aorta morphogenesis biological process 3.18 0.04159 Downloaded from http://ahajournals.org by on October 28, 2019 pulmonary artery morphogenesis biological process 3.18 0.04159 vascular smooth muscle cell development biological process 3.18 0.04159 serine-type carboxypeptidase activity molecular function 3.18 0.04159 centrosome cellular component 3.17 0.04214 mediator complex cellular component 3.16 0.04222 ATP hydrolysis coupled proton transport biological process 3.16 0.04222 peptidyl-tyrosine dephosphorylation biological process 3.16 0.04222 intercalated disc cellular component 3.16 0.04222 embryonic hindlimb morphogenesis biological process 3.16 0.04222 response to bacterium biological process 3.11 0.04473 positive regulation of proteasomal ubiquitindependent protein catabolic process biological process 3.11 0.04473 odontogenesis biological process 3.11 0.04473 negative regulation of MAP kinase activity biological process 3.11 0.04473 adult walking behaviour biological process 3.05 0.04729 actin cytoskeleton reorganization biological process 3.05 0.04729 metal ion binding molecular function 3.04 0.04770 receptor binding molecular function 3.03 0.04816 sarcolemma cellular component 3.00 0.04956 cation binding molecular function 3.00 0.04991 blood vessel remodeling biological process 3.00 0.04991 Downloaded from http://ahajournals.org by on October 28, 2019 Table S9. Gene set enrichment analysis, based on gene ontology, of the gene list for differentially expressed genes between HHR and NHR (P<0.05). Gene Set Gene Set Description Number of Markers ES NES P-value HHR vs NHR 60048 cardiac muscle contraction 30 –0.57 –1.99 <0.001 Down 35335 peptidyl-tyrosine dephosphorylation 38 –0.56 –1.96 <0.001 Down 97110 scaffold protein binding 25 –0.55 –1.93 <0.001 Down 16459 myosin complex 42 –0.60 –1.90 <0.001 Down 30017 sarcomere 26 –0.59 –1.99 <0.001 Down 3774 motor activity 51 –0.53 –1.87 <0.001 Down 55010 ventricular cardiac muscle tissue morphogenesis 26 –0.58 –1.87 <0.001 Down 5089 Rho guanyl-nucleotide exchange factor activity 61 –0.53 –1.84 <0.001 Down 45859 regulation of protein kinase activity 18 –0.67 –1.82 <0.001 Down 35108 limb morphogenesis 24 –0.54 –1.78 <0.001 Down 3777 microtubule motor activity 68 –0.48 –1.78 <0.001 Down 45747 positive regulation of Notch signalling pathway 17 –0.66 –1.77 <0.001 Down 32880 regulation of protein localization 40 0.49 1.81 <0.001 Up 5216 ion channel activity 51 –0.51 –1.79 <0.001 Down 45087 innate immune response 87 0.43 1.80 <0.001 Up 9635 response to herbicide 17 0.59 1.82 <0.001 Up 36094 small molecule binding 17 0.79 1.79 <0.001 Up 42098 T cell proliferation 18 –0.66 –1.75 <0.001 Down 16592 mediator complex 30 0.53 1.78 <0.001 Up 30016 myofibril 46 –0.60 –1.79 <0.001 Down 30506 ankyrin binding 21 –0.70 –1.75 <0.001 Down 6730 one-carbon metabolic process 30 0.55 1.70 <0.001 Up 17134 fibroblast growth factor binding 28 0.61 1.83 <0.001 Up 8146 sulfotransferase activity 30 0.49 1.71 <0.001 Up 10765 positive regulation of sodium ion transport 21 0.54 1.71 <0.001 Up 45471 response to ethanol 139 0.43 1.71 <0.001 Up Downloaded from http://ahajournals.org by on October 28, 2019 15171 amino acid transmembrane transporter activity 20 0.64 1.87 <0.001 Up 5758 mitochondrial intermembrane space 43 0.56 1.72 <0.001 Up 1965 G-protein alpha-subunit binding 22 0.54 1.67 <0.001 Up 16925 protein sumoylation 23 0.52 1.65 <0.001 Up 15807 L-amino acid transport 20 0.58 1.66 <0.001 Up 9636 response to toxin 95 0.43 1.75 <0.001 Up 19233 sensory perception of pain 68 0.42 1.66 <0.001 Up 6164 purine nucleotide biosynthetic process 15 0.68 1.73 <0.001 Up 10862 positive regulation of pathway-restricted SMAD protein phosphorylation 23 0.71 1.73 <0.001 Up 9615 response to virus 100 0.44 1.83 <0.001 Up 6979 response to oxidative stress 126 0.39 1.62 <0.001 Up 5771 multivesicular body 20 0.51 1.61 <0.001 Up 71356 cellular response to tumor necrosis factor 42 0.46 1.61 <0.001 Up 30838 positive regulation of actin filament polymerization 32 0.45 1.57 <0.001 Up 9725 response to hormone stimulus 100 0.38 1.54 <0.001 Up 6103 2-oxoglutarate metabolic process 16 0.71 1.57 <0.001 Up 34446 substrate adhesion-dependent cell spreading 23 0.54 1.63 <0.001 Up 118 histone deacetylase complex 24 –0.47 –1.71 <0.001 Down 70207 protein homotrimerization 21 0.48 1.55 <0.001 Up 35265 organ growth 16 –0.56 –1.69 <0.001 Down 10165 response to X-ray 20 0.49 1.55 <0.001 Up 15908 fatty acid transport 18 0.56 1.57 <0.001 Up 7031 peroxisome organization 21 0.57 1.59 <0.001 Up 9966 regulation of signal transduction 26 –0.45 –1.68 <0.001 Down 7266 Rho protein signal transduction 31 0.42 1.63 <0.001 Up 5516 calmodulin binding 119 –0.39 –1.66 <0.001 Down 1968 fibronectin binding 19 0.66 1.83 <0.001 Up 30182 neuron differentiation 109 0.34 1.50 <0.001 Up Downloaded from http://ahajournals.org by on October 28, 2019 Table S11. Gene set enrichment analysis with differentially expressed genes between Lcn2-knockout compared to wild-type mice. Category Term Count Total % Involved Genes Fold Enrichment P-Value KEGG_PATHWAY mmu04510: Focal adhesion 31 198 15.7 MYL7, XIAP, ACTG1, ARHGAP5, COL6A6, COMP, ITGB6, PPP1R12A, PDGFD, EGF, PIK3R1, FN1, COL4A4, COL4A2, COL4A1, FLT1, ROCK2, ITGA1, ITGA2, HGF, FLNB, COL5A1, KDR, LAMA2, LAMA3, LAMA5, ITGA8, PDGFRA, LAMC1, MYLK, ITGA2B 4.36 7.98E-12 KEGG_PATHWAY mmu04512: ECM-receptor interaction 17 83 20.5 COL4A4, COL4A2, COL4A1, ITGA1, ITGA2, COL5A1, HMMR, LAMA2, LAMA3, COL6A6, LAMA5, COMP, ITGA8, ITGB6, LAMC1, ITGA2B, FN1 5.70 2.45E-08 KEGG_PATHWAY mmu05222: Small cell lung cancer 14 85 16.5 COL4A4, COL4A2, COL4A1, XIAP, ITGA2, CCNE2, LAMA2, LAMA3, CDKN2B, LAMA5, LAMC1, PIK3R1, FN1, ITGA2B 4.59 8.06E-06 KEGG_PATHWAY mmu05200: Pathways in cancer 29 323 9.0 XIAP, FOXO1, FGF12, ZBTB16, CCNE2, CDKN2B, TPR, EGF, PIK3R1, FN1, APC, COL4A4, COL4A2, COL4A1, EPAS1, RUNX1T1, ITGA2, BRCA2, HGF, STAT1, CTNNA3, WNT2B, LAMA2, CBLC, LAMA3, LAMA5, PDGFRA, LAMC1, ITGA2B 2.50 9.08E-06 KEGG_PATHWAY mmu04810: Regulation of actin cytoskeleton 20 217 9.2 MYL7, ROCK2, ITGAE, SSH2, ITGA1, ITGA2, FGF12, ACTG1, ITGA8, ITGB6, PPP1R12A, PDGFRA, 2.57 2.44E-04 Downloaded from http://ahajournals.org by on October 28, 2019 PDGFD, EGF, DIAP2, PIK3R1, MYLK, ITGA2B, APC, FN1 KEGG_PATHWAY mmu05412: Arrhythmogenic right ventricular cardiomyopathy (ARVC) 11 75 14.7 LAMA2, ACTG1, DMD, ITGA8, ITGB6, CACNG6, ITGA1, ITGA2, RYR2, CTNNA3, ITGA2B 4.08 2.96E-04 KEGG_PATHWAY mmu05410: Hypertrophic cardiomyopathy (HCM) 11 84 13.1 LAMA2, ACTG1, DMD, ITGA8, ITGB6, CACNG6, ITGA1, ITGA2, RYR2, TTN, ITGA2B 3.65 7.46E-04 KEGG_PATHWAY mmu05414: Dilated cardiomyopathy 11 92 12.0 LAMA2, ACTG1, DMD, ITGA8, ITGB6, CACNG6, ITGA1, ITGA2, RYR2, TTN, ITGA2B 3.33 0.001 KEGG_PATHWAY mmu04540: Gap junction 9 86 10.5 PLCB4, MAP3K2, GUCY1A2, PDGFRA, PDGFD, PRKG1, EGF, ITPR1, ITPR2 2.91 0.011 KEGG_PATHWAY mmu04144: Endocytosis 15 202 7.4 FLT1, ERBB4, PSD3, ASAP1, HSPA1A, EEA1, HSPA1B, KDR, CBLC, RAB11FIP2, TFRC, HSPA2, PDGFRA, H2-T24, EGF, AGAP2 2.07 0.013 KEGG_PATHWAY mmu04270: Vascular smooth muscle contraction 10 120 8.3 PLCB4, ROCK2, MYH11, PPP1R12A, GUCY1A2, ADRA1A, PRKG1, ITPR1, MYLK, ITPR2 2.32 0.027 Footnote: Count: number of genes from 721 differentially expressed genes (P<0.05) involved in that pathway; %: genes involved/total number of genes in pathway; P-value: modified Fisher exact P-value. Downloaded from http://ahajournals.org by on October 28, 2019 Table S12. Gene set enrichment analysis with the differentially expressed genes between cells transfected with Lcn2 or empty plasmid. Category Term Count Total % Involved genes Fold Enrichment P-Value KEGG_PATHWAY rno05222: Small cell lung cancer 10 83 12.0 E2F3, PTGS2, ITGA6, BCL2, BCL2L1, RB1, BIRC3, BIRC2, MYC, TRAF4 4.46 3.43E-04 KEGG_PATHWAY rno04510: Focal adhesion 13 195 6.7 CAV1, ROCK2, COL3A1, ITGA11, ITGB5, BIRC3, BIRC2, ITGA6, RASGRF1, BCL2, GSK3B, THBS2, MYLK 2.47 0.006 KEGG_PATHWAY rno04110: Cell cycle 10 126 7.9 E2F3, CCNH, PLK1, GSK3B, BUB1, CDC20, RB1, CDC25C, ABL1, MYC 2.94 0.006 KEGG_PATHWAY rno04142: Lysosome 9 117 7.7 TCIRG1, AP1S2, GUSB, HEXA, LGMN, CTSD, PPT1, FUCA1, MANBA 2.85 0.013 KEGG_PATHWAY rno05200: Pathways in cancer 15 317 4.7 E2F3, FGF7, PTGS2, BCL2L1, RB1, BIRC3, STAT1, BIRC2, FZD6, ITGA6, BCL2, GSK3B, ABL1, MYC, TRAF4 1.75 0.04 KEGG_PATHWAY rno00511: Other glycan degradation 3 16 18.8 HEXA, FUCA1, MANBA 6.94 0.067 KEGG_PATHWAY rno05410: Hypertrophic cardiomyopathy (HCM) 6 84 7.1 ACTC1, ITGA6, ITGA11, ITGB5, CACNG1, TNNI3 2.64 0.074 KEGG_PATHWAY rno04114: Oocyte meiosis 7 111 6.3 REC8, SLK, PLK1, BUB1, CDC20, AURKA, CDC25C 2.33 0.077 KEGG_PATHWAY rno05414: Dilated cardiomyopathy 6 90 6.7 ACTC1, ITGA6, ITGA11, ITGB5, CACNG1, TNNI3 2.47 0.09 Footnote: Count: number of genes from 529 differentially expressed genes (P<0.05) involved in that pathway; %: genes involved/total number of genes in pathway; P-value: modified Fisher exact P-value. Downloaded from http://ahajournals.org by on October 28, 2019 Figure S1. Hierarchical clustering showing that neonatal hypertrophic heart rat (HHR) (blue) and normal heart rat (NHR) (orange) form distinct groups based on gene expression (genes with Bonferroni adjustment <0.05). Numbers on the right are probeset numbers. Red depicts genes upregulated and green those down-regulated. Downloaded from http://ahajournals.org by on October 28, 2019 U n t r ansf e c t e d L c n 2 1 n g Lcn2 10 n g L c n 2 100 n g 0 5 1 0 100 200 300 2000 2500 3000 L c n 2 m R N A f o l d c h a n g e r e l a t i v e t o u n s t r a f e c t e d c e l l s * * * * * * * * * U n t r a n sfec t e d E m p t y p lasm i d Lcn2 1 n g 0 . 0 0 . 1 0 . 2 5 1 0 1 5 2 0 L c n 2 m R N A r e l a t i v e a b u n d a n c e * * * A B C E m p t y p l a s m i d L c n 2 Co n t r o l L c n 2 0 2 0 4 0 6 0 8 0 100 A n n e x i n V + c e l l s ( % o f t o t a l n u m b e r ) C o n t r o l L c n 2 D Figure S2. Over-expression of Lcn2 in vitro. Lcn2 was over-expressed in H9c2 cells with the use of a plasmid. A, Optimisation of the concentration of Lcn2 plasmid in vitro. B, There were higher Lcn2 mRNA in cells transfected with LCN2-plasmid. C, Over-expression of in cells Lcn2 visualised by immunofluorescence. D, There was no difference in apoptotic cells measured by flow cytometry in cells transfected with Lcn2 compared to those transfected with empty plasmid. *** indicates P<0.001. Error bars represent standard error of mean. Downloaded from http://ahajournals.org by on October 28, 2019 Figure S3. Representative of confocal microscopy images after transfection with Lcn2 plasmid, which provided results for Figure 4 in the main paper. A, Wheat-germ agglutinin (WGA, red) and DAPI (blue) staining, used to estimate cell size (400x magnification, scale bar = 60µm). B, Phospho-histone H3 (pH3, green) and DAPI (blue), used to estimate cell proliferation and arrest (200x magnification, scale bar = 100µm). Downloaded from http://ahajournals.org by on October 28, 2019 L i ve c e l l s I n t e r m e d i a t e cells Dead cells 0 5 1 0 1 5 2 0 5 0 6 0 7 0 8 0 9 0 1 0 0 A n n e x i n V + c e l l s ( % o f t o t a l n u m b e r ) C o n t r o l L c n 2 Figure S4. There was no difference in apoptotic cells measured by flow cytometry in cells transfected with Lcn2 compared to those transfected with empty plasmid, demonstrated by annexin V bound to apoptotic cells determined by flow cytometry. Data shown as number of cells as a percentage of the total number of cells. Error bars represent standard error of mean. Downloaded from http://ahajournals.org by on October 28, 2019 Supplemental References: 1. Porrello ER, Bell JR, Schertzer JD, Curl CL, McMullen JR, Mellor KM, Ritchie RH, Lynch GS, Harrap SB, Thomas WG and Delbridge LM. 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