Hemodynamic and transcriptomic studies suggest early left ventricular dysfunction in a preclinical model of severe mitral regurgitation

Daniella Corporan; Daisuke Onohara; Alan Amedi; Maher Saadeh; Robert A Guyton; Sandeep Kumar; Muralidhar Padala

doi:10.1016/j.jtcvs.2020.08.119

. Author manuscript; available in PMC: 2022 Mar 1.

Published in final edited form as: J Thorac Cardiovasc Surg. 2020 Oct 8;161(3):961–976.e22. doi: 10.1016/j.jtcvs.2020.08.119

Hemodynamic and transcriptomic studies suggest early left ventricular dysfunction in a preclinical model of severe mitral regurgitation

Daniella Corporan ^a, Daisuke Onohara ^a, Alan Amedi ^a, Maher Saadeh ^a, Robert A Guyton ^a,^b, Sandeep Kumar ^c, Muralidhar Padala ^a,^b

PMCID: PMC7889661 NIHMSID: NIHMS1637588 PMID: 33277035

Abstract

Objective:

Primary mitral regurgitation is a valvular lesion in which the left ventricular ejection fraction remains preserved for long periods, delaying a clinical trigger for mitral valve intervention. In this study, we sought to investigate whether adverse left ventricular remodeling occurs before a significant fall in ejection fraction and characterize these changes.

Methods:

Sixty-five rats were induced with severe mitral regurgitation by puncturing the mitral valve leaflet with a 23-G needle using ultrasound guidance. Rats underwent longitudinal cardiac echocardiography at biweekly intervals and hearts explanted at 2 weeks (n = 15), 10 weeks (n = 15), 20 weeks (n = 15), and 40 weeks (n = 15). Sixty age- and weight-matched healthy rats were used as controls. Unbiased RNA-sequencing was performed at each terminal point.

Results:

Regurgitant fraction was 40.99 9.40%, with pulmonary flow reversal in the experimental group, and none in the control group. Significant fall in ejection fraction occurred at 14 weeks after mitral regurgitation induction. However, before 14 weeks, end-diastolic volume increased by 93.69 52.38% (P < .0001 compared with baseline), end-systolic volume increased by 118.33 47.54% (P < .0001 compared with baseline), and several load-independent pump function indices were reduced. Transcriptomic data at 2 and 10 weeks before fall in ejection fraction indicated up-regulation of myocyte remodeling and oxidative stress pathways, whereas those at 20 and 40 weeks indicated extracellular matrix remodeling.

Conclusions:

In this rodent model of mitral regurgitation, left ventricular ejection fraction was preserved for a long duration, yet rapid and severe left ventricular dilatation, and biological remodeling occurred before a clinically significant fall in ejection fraction.

Keywords: mitral valve prolapse, mitral regurgitation, ventricular remodeling, primary mitral regurgitation, neo-chordoplasty, ejection fraction, heart murmur

Graphical Abstract

graphic file with name nihms-1637588-f0012.jpg

Rapid LV remodeling occurs despite preserved ejection fraction in mitral regurgitation.

Primary mitral regurgitation (MR) resulting from myxomatous degenerative valve disease occurs in 1.7% of the American populace and is the second most common form of valvular heart disease.^1–3 The regurgitation from the highpressure left ventricle into the low impedance left atrium reduces forward stroke volume, elevates left ventricular (LV) filling pressures, and imposes a chronic, low-pressure, volume overload on the LV.⁴ The LVadapts to this hemodynamic stress with acute hypercontractility but progressive eccentric remodeling, manifesting primarily as LV dilatation and increased sphericity. Ejection fraction(EF) is largely preserved for prolonged periods, and thus referral to mitral valve diagnosed,^5,6 Although this strategy is currently recommended, patients who undergo mitral valve repair or replacement present with paradoxical new-onset LV dysfunction after correction of MR.⁷ This could indicate that the functional deficit in the LV before the mitral valve surgery is masked by the low impedance flow into the left atrium, keeping the EF high despite poor function. As the risk associated with mitral valve repair falls, a push toward earlier intervention before a fall in EF is proposed.⁸ However, identifying that earlier time point would require an understanding of the natural history of remodeling after the onset of MR, which is currently lacking. In this study, we sought to develop a animal model of severe MR and test the hypothesis that significant LV chamber dilatation and reduction in load independent contractile indices occurs before a significant fall in EF. Toward this end, in this study, we used a controlled rodent model of MR^9,10 to investigate hereto unknown longitudinal changes in LV chamber volume and shape, contractile function, and the myocardial transcriptome at 2, 10, 20, and 40 weeks (Figure 1).

FIGURE 1. — *Top row*, Schematic depicting the study design and sample sizes used in each group. *Bottom row*, Salient findings from the study describing the effect of mitral regurgitation on the function, structure and transcriptome of the left ventricular myocardium.

METHODS

Ethical Statement

Use of animals was approved by the Institutional Animal Care and Use Committee at Emory University and performed per the guidelines of the National Institutes of Health. Animals were purchased from a single vendor (Envigo, Indianapolis, Ind), housed in a temperature- and humidity-controlled environment with a 12:12-hour light cycle, at an Association for Assessment and Accreditation of Laboratory Animal Care–accredited facility.

Animal Selection

Adult, male, Sprague–Dawley rats (n = 125, 350–400 g) were used and socially housed prior to surgery and singly housed after surgery, with continuous access to drinking water and rat chow. Rats were randomized to be induced with severe MR (n = 65) or act as sham controls (n = 60). Sample sizes were estimated to test the hypothesis that severe MR will cause a 25% greater end-diastolic volume (EDV) compared with sham rats at each time point, with 80% power and an alpha of 0.05.

Surgical Procedures

Procedures to induce severe MR were described earlier.^10,11 Rats were sedated, intubated, and mechanically ventilated. Two percent to 2.5% isoflurane mixed in 100% oxygen was used for anesthesia, body temperature was maintained at 37°C, and an electrocardiogram was acquired (Mouse Monitor; Indus Instruments, Webster, Tex). Carprofen (2.5 mg/kg, subcutaneously [SQ]), gentamicin (6 mg/kg, SQ), and sterile saline (1 mL, SQ) were then administered. With the rat in a right decubitus position, a left thoracotomy was performed in the fifth intercostal space to access the heart. The apex was stabilized with a purse-string suture (6–0 PROLENE), and an 8-Fr intracardiac echo probe was inserted into the esophagus to achieve a 2-chamber view of the heart (Figure 2, A1). A 23-G needle was inserted through the apical purse-string into the anterior mitral leaflet (Figure 2, A2–3), creating a hole and severe MR (Figure 2, B1). The purse-string was gently tightened, the thoracotomy was closed, and a temporary chest tube was used. Rats were extubated, carprofen (2.5 mg/kg, SQ) was immediately administered, and Buprenex (0.02 mg/kg, SQ) was administered within 3 hours. Postoperative pain was managed with carprofen (SQ, 5 mg/kg, postoperative days 1–3) and gentamicin (SQ, 6 mg/kg, postoperative days 1–3), and Buprenex (SQ, 0.05 mg/kg, as needed). At 2 weeks after the surgery, severity of MR was assessed by transesophageal echocardiography (TEE; Figure 2, B1–3), and the rats were followed to 2, 10, 20, or 40 weeks (n = 15 per group). In human years, these time points scale to 1, 5, 10, and 20 human years.¹² In the sham group, 15 age- and weight-matched rats that underwent surgery were followed and terminated at the same time points. The entire procedure is depicted in Video 1.

FIGURE 2. — Rodent model of severe MR using an image-guided needle technique to puncture the mitral valve leaflet. A1, The animal is placed in right decubitus position, with an intracardiac ultrasound probe inserted into the esophagus to enable transesophageal imaging. A2, Ultrasound image depicting a needle inserted into the beating heart via the ventricular apex. A3,With ultrasound guidance, the needle tip is advanced into the mitral valve leaflet to perforate it. B1-B3, Induction of MR is validated in real time using color Doppler imaging of the regurgitant jet, pulsed-wave Doppler of flow reversal, and pulmonary vein flow reversal. B4, In a representative rat heart explanted at the end of the experiment, the perforation in the anterior leaflet is depicted. The hole is defined and matches the size of the needle. C1, Severe MR was confirmed in all the ratsat 2 weeks after thesurgery. The severity of regurgitation is quantified hereby measuring the areaofthe MRjet and normalizing it to the left atrial area. C2, Regurgitant volume was also calculated using the Doppler data obtained from the rodents and using the area of the regurgitant orifice, confirming with another technique that the mitral regurgitation is severe. C3, An indirect approach to confirming the severity of regurgitation is depicted, wherein reversal of the pulmonary vein flow due to acute elevation of the left atrial pressure from regurgitation is noted. The S/D ratio is positive in the sham animals, whereas it is reversed in the rats with mitral regurgitation. C4, Survivalcurves are presented for the rats that completed 40weeks of follow-up in both the MR and sham groups. The *color-shaded area* represents the 95% confidence limits. MR, Mitral regurgitation; *S/D*, systolic-diastolic.

Cardiac Echocardiography

Transthoracic echocardiography and TEE were performed just before surgery (baseline) and at biweekly intervals after surgery, until termination. Transthoracic echocardiography was performed with the rats under mild sedation, and parasternal long-axis and short-axis B-mode and M-mode images were obtained with a 21-MHz rat probe on a Visualsonics 2100 system (FIJUFILM Visualsonics, Inc, Tokyo, Japan). Endocardial and epicardial borders were traced to calculate LV volumes, EF, LV mass, wall thickness, and fractional wall thickening by 2 blinded users and interobserver variability is quantified (Tables E1 and E2). TEE was performed with the rats intubated and anesthetized, and using an 8-Fr intracardiac echo probe (8 MHz, Acunav; Biosense Webster, Inc, Irvine, Calif). High esophageal views of the chambers and mitral valve and color Doppler images were obtained. Pulsed-wave Doppler imaging was performed at the mitral valve and the pulmonary vein to assess flows. MR severity was evaluated using 3 quantification methods: MR jet area (%), regurgitant volume, and systolic pulmonary systolic-diastolic ratio. MR jet area (%) was measured by tracing the regurgitant jet on color Doppler and normalized to left atrial area. Severe MR jet area (%) was defined as ≥30%, as per the guidelines of the American Society of Echocardiography.¹³ Regurgitant volume was calculated by multiplying the MR velocity time integral measured on pulsed wave Doppler by the area of the circular regurgitant orifice created by the 23-G needle (0.64 mm outer diameter). Regurgitant volume 80 mL was considered severe MR. Pulmonary flow reversal was assessed as the ratio of the systolic (Swave) and diastolic (D-wave) velocities. Severe MR was defined by a systolic blunting or total reversal of the systolic-diastolic wave ratio.

Invasive Hemodynamic Measurements

Pressure volume (PV) loops were obtained in all rats at termination, with a 1.9-Fr transapical admittance catheter (ADV500; Transonic Systems, London, Ontario, Canada) under 10 to 15 seconds of apnea. Preload reduction was achieved by compressing the inferior vena cava. End-systolic and end-diastolic pressure volume relationships and other pump function indices, such as EDV versus stroke work (preload recruitable stroke work), maximum dP/dt versus EDV, and pressure volume loop area (PVA) versus EDV were computed.

Tissue Collection and RNA Seq

Rats were heparinized and terminated under deep anesthesia, with 100 mg/kg Euthasol (390 mg/mL pentobarbital sodium mixed with 50 mg/mL phenytoin sodium). The heart and lungs were immediately excised, photographed, and weighed. The LV free wall was separated, and the mid-ventricular region was snap frozen. Total RNA was extracted from 5 samples in each group using the miRNeasy Mini Kit (Yerkes NHP Genomics Core, Emory University, Atlanta, Ga). Postquality check, RNA-sequencing was performed using the Illumina platform to generate unaligned reads (Novogene, Sacramento, Calif). RNAseq data analysis was performed using Partek Flow (Partek Inc, St Louis, Mo; build 4.0.15). Version (rn6) rat reference genome were used to map reads. The post-alignment QC module of Partek Flow was used to visualize the average base quality score per position as well as the mapping quality per alignment. The mapped reads were quantified using the RefSeq transcripts-2019–11-01 annotation for quantification using the Partek E/M method to determine the differential gene expression using gene-specific analysis comparing each experimental group against sham. Significant differentially expressed genes were shortlisted using a cutoff of P <.05 and 2 ≤ fold change ≤ −2. Functional enrichment analysis was performed using Protein Analysis Through Evolutionary Relationships (version 15), Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes databases, to identify molecular and biochemical pathways. All RNA-seq data have been deposited in the NCBI Gene Expression Omnibus database with experiment series accession number GSE148559.

Statistical Analysis

Prism 7.0 (GraphPad Software, Inc, La Jolla, Calif) was used for data analysis. Data following a gaussian distribution are depicted as mean 1 standard deviation, and non-normal data are represented as median with interquartile range. MR severity was compared using a paired t test for normally distributed data, and a Wilcoxon matched-pairs test for nonparametric data, comparing baseline and 2-week values. To compare longitudinal echocardiography data (EDV, end-systolic volume [ESV], stroke volume [SV], EF) from the sham and MR group, a repeated-measure 2-way analysis of variance with Tukey’s multiple comparisons was performed, when all data points were available. For all other echo and PV loop data, a one-way analysis of variance with Tukey’s multiple comparisons test was used to compare groups with normally distributed data. Mann-Whitney U test with Dunn’s correction and multiple comparisons was used for nonparametric data.

RESULTS

Validation of Severe MR and Overall Mortality

There were no procedural complications or mortality associated with the MR surgery. Two weeks after surgery, MR jet area normalized to left atrial area was 40.99 ± 9.40%, compared with 0.25 ± 1.34% at baseline (P < .0001) (Figure 2, C1). Mitral regurgitant volume was 119.50 ± 32.43 μL compared with 0 μL at baseline (P < .0001) (Figure 2, C2). Pulmonary venous flow reversal was observed, with systolic to diastolic wave ratio reversal from 0.76 ± 0.18 at baseline to −0.84 ± 1.02 with MR (P < .0001) (Figure 2, C3). MR severity persisted through the entire follow-up (Figure E1). Overall survival in MR group was 92.31% (Figure 2, C4), with 5 rats dying in the first 2 weeks. There were no deaths in the sham group.

Longitudinal LV Chamber Remodeling

Representative images of hearts explanted after 40 weeks are shown in Figure E2, A1-B1, with corresponding B-mode and M-mode frames shown in Figure E2, A2-B3. In the rats with MR, pronounced dilation of the LV chamber is evident, compared with sham (Videos 2 and 3). With MR, EDV increased by 28.36% at 2 weeks (P = .008), 65.01% at 10 weeks (P <.0001), 87.21% at 20 weeks (P <.0001), and 100.17% at 40 weeks (P <.0001), compared with baseline. LV EDV did not change in the sham group (Figure 3, A1). The rate of change in EDV was highest immediately after MR was induced and remained greater than 20 μL/week for the first eight weeks after MR onset (Figure 3, A2). Change in ESV was more gradual—unchanged at 2 weeks, increased by 11.50% at 10 weeks (P < .0001), 126.58% at 20 weeks (P < .0001), and 172.77% at 40 weeks (P<.0001) (Figure 3, B1). The greatest rate of change in ESV occurred after 8 weeks with MR, plateaued by 22 weeks and then increased through 34 weeks (Figure 3, B2). Stroke volume was significantly elevated compared with baseline values at all time points throughout the 40-week study (P <.0001) (Figure 3, C1). After MR onset, EF was elevated compared with baseline values for the first 6 weeks (P <.05) but was otherwise unchanged through 12 weeks compared with baseline (Figure 3, D1). Significant reduction in EF occurred at 14 weeks post-MR (P = .0023) and remained significantly decreased thereafter. By 40 weeks, EF decreased to 56.36 ± 3.06% compared with 66.85 ± 5.15% at baseline (P <.0001). The greatest rate of change in EF occurred at 8 weeks after MR onset and remained negative thereafter (Figure 3, D2).

FIGURE 3. — Longitudinal changes in cardiac function in the sham-operated rats and those induced with severe MR. A1, EDV was significantly elevated in the MR group as early as 4 weeks after inducing the valve lesion and remained significantly elevated throughout the follow-on duration. A2, The rate of change of EDV was greatest in the earliest time period after inducing MR, and plateauing around 8 weeks after inducing the valve lesion. From weeks 8 to 40, the rate of change of EDV was minimal. B1, Similar trends were observed in the changes in ESV, with a rise in ESV occurring at 8 weeks after induction of mitral regurgitation. C1-2, Absolute changes and rate of change of stroke volume. D1-2, Longitudinal changes in ejection fraction, with a significant fall in ejection fraction occurring at 12 weeks after inducing MR. The largest rate of change was also observed within the first 10 weeks and plateaued thereafter. In the graphs, the *marker* indicates the mean and the *shaded region* represents the standard deviation. *Blue stars* below the horizontal axes represent statistical significance when the group means were compared at the same time point (P < .05). *Red stars* below the horizontal axes indicates statistical significance when comparing the mean value at that time point, to the mean value at baseline in the same group (P < .05). *EDV*, End-diastolic volume; *ESV*, end-systolic volume; MR, mitral regurgitation.

LV internal diameter in diastole increased after 2 weeks of MR compared with baseline (P = .022) and remained increased thereafter (Figure E3, A1). LV internal diameter in systole was significantly increased after 8 weeks of MR compared with baseline and remained significantly elevated thereafter (Figure E3, A2). LV anterior wall thickness in diastole slightly increased after MR at 2 weeks compared with sham (P = .032) but remained unchanged at all other time points over 40 weeks (Figure E3, B1). LV posterior wall thickness in diastole and systole was not significantly different compared with sham after onset of MR (Figure E3, C1-C2).

There were no changes in heart rate after MR onset compared with age- and weight-matched shams controls (Table 1). Heart weight was decreased after 2 weeks of MR compared with sham (P < .0001), and then significantly increased at 10, 20, and 40 weeks after MR compared with sham (P = .0011, P = .0068, P < .0001, respectively). When normalized to body weight, heart weight was increased after 2 weeks of MR compared with sham (P < .0001). However, when normalized to tibia length, heart weight was decreased (P = .0001). LV mass and LV mass index followed a similar trend of increasing after onset of MR. LV mass index was significantly greater at 2, 10, and 40 weeks after MR onset, with a slight decrease at 20 weeks, although not significant. Lung weight was significantly higher in the MR groups at 2, 10, and 20 weeks after MR onset compared to age- and weight-matched sham controls (P = .002, P < .0001, P = .0038, respectively).

TABLE 1.

Heart weight, LV mass, and lung weight measured at termination in the MR and sham groups at 2, 10, 20, and 40 weeks

	2 wk			10 wk			20 wk			40 wk
	Sham	MR	P value	Sham	MR	P value	Sham	MR	P value	Sham	MR	P value
Heart rate, bpm	333.50 ± 34.90	334.20 ± 25.02	n.s.	330.40 ± 26.27	314.00 ± 32.23	n.s.	319.40 ± 24.40	286.70 ± 85.96	n.s.	325.90 ± 24.38	311.70 ± 44.56	n.s.
Heart weight, g	1.55 ± 0.099	1.35 ± 0.10	.00010	1.45 ± 0.10	1.65 ± 0.19	.0011	1.52 ± 0.13	1.78 ± 0.29	.0068	1.60 ± 0.11	2.00 ± 0.36	.00010
Body weight, g	408.00 ± 22.42	404.00 ± 18.82	n.s.	445.30 ± 27.74	477.30 ± 39.90	.032	450.00 ± 23.20	510.70 ± 53.91	.0006	522.70 ± 14.86	528.80 ± 51.62	n.s.
Tibia length, cm	4.50 ± 0.23	4.41 ± 0.29	n.s.	4.62 ± 0.19	4.97 ± 0.58	n.s.	4.82 ± 0.08	4.54 ± 0.40	.016	5.10 ± 0.085	4.94 ± 0.25	.016
Heart weight/body weight, %	0.33 ± 0.027	0.38 ± 0.025	.00010	0.33 ± 0.020	0.35 ± 0.047	n.s.	0.34 ± 0.036	0.35 ± 0.048	n.s.	0.31 ± 0.019	0.38 ± 0.063	.00030
Heart weight/tibia length, g/cm	0.35 ± 0.036	0.30 ± 0.027	.00010	0.32 ± 0.025	0.34 ± 0.049	n.s.	0.32 ± 0.024	0.39 ± 0.076	.00040	0.31 ± 0.022	0.40 ± 0.066	.00010
LV mass, mg	1295.00 ± 144.80	1566.00 ± 292.70	.0033	1335.00 ± 187.90	1869.00 ± 364.70	.00010	1361.00 ± 147.40	1382.00 ± 453.50	n.s.	1540.00 ± 150.50	1868.00 ± 411.80	.0070
LV mass index	3.18 ± 0.35	3.90 ± 0.84	.0047	2.99 ± 0.31	3.94 ± 0.81	.00020	3.00 ± 0.34	2.77 ± 0.97	n.s.	2.95 ± 0.27	3.54 ± 0.72	.0057
Lung weight, g	2.12 ± 0.61	2.95 ± 0.66	.0020	1.81 ± 0.40	2.66 ± 0.49	.00010	2.02 ± 0.45	2.84 ± 0.54	.0038	2.07 ± 0.54	2.45 ± 0.93	n.s.

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Data are represented as mean ± SD. MR, Mitral regurgitation; n.s., not significant; LV, left ventricle.

LV PV Loops and Myocardial Energetics

Representative PV loops are shown in Figure 4, A. After the onset of MR, we observed widening of the PV loop at 2 weeks (red curve), and a rightward shift by 10 weeks (green curve), 20 weeks (yellow curve), and 40 weeks (cyan curve). PVA and stroke work were significantly increased by 20 and 40 weeks compared with sham (P <.05) (Figure 4, B and C). Potential energy was slightly decreased after 2 weeks of MR compared with sham and significantly increased at 10, 20, and 40 weeks compared with 2 weeks (P = .0259, P = .0108, P < .0001, respectively). By 40 weeks, potential energy was significantly increased compared with sham (P = .0001) (Figure 4, D). The overall efficiency of the LV, the ratio of stroke work and PVA, slowly decreased by 10 weeks after MR and was significantly decreased at 40 weeks compared to sham (P = .0411) (Figure 4, E).

LV systolic indices derived from pressure volume loops are represented in Figure 5. End-systolic pressure remained unchanged after 2 weeks of MR compared with sham and was significantly decreased at 10 weeks (P = .041) and at 40 weeks (P < .0001) compared with sham (Figure 5, A). dP/dt_max, which represents the maximum rate of pressure rise during isovolumetric contraction, was decreased after 10 weeks of MR compared with sham (P = .0046) but remained unchanged at all other time points (Figure 5, B). End-systolic elastance, which is a more load insensitive measure of LV contractility, was decreased at all time points after the onset of severe MR compared with sham with significance at 10, 20, and 40 weeks (P < .05) (Figure 5, C). Similarly, the relationship between dP/dt_max and EDV, which also indicates a more load insensitive measure of LV contractility compared to dP/dt_max alone, was decreased at all time points after onset of MR compared with sham with significance at 10 and 40 weeks (P<.05) (Figure 5, D). Preload recruitable stroke work was significantly decreased at 40 weeks compared with sham (P = .027) (Figure 5, E). The relationship between PVA and EDV, which represents preload recruitable PVA, also showed a decreasing trend at 10 and 40 weeks after MR onset compared with sham, although not significant (Figure 5, F).

LV diastolic parameters derived from pressure volume loops are represented in Figure 6. End-diastolic pressure (EDP) showed a biphasic increase from 2 to 40 weeks. After 2 weeks of MR, EDP increased from 8.68 ± 2.10 mm Hg to 10.91 ± 3.83 mm Hg compared with sham. By 10 weeks, EDP was decreased and then subsequently increased to 8.99 ± 1.66 mm Hg by 20 weeks compared with sham (P = .021). By 40 weeks, EDP was unchanged compared with sham (Figure 6, A). The EDP volume relationship did not reveal any significant changes compared with sham (Figure 6, B). dP/dt_min, which represents the maximum pressure decay during isovolumetric relaxation, was significantly decreased after 10 and 40 weeks of MR compared with sham (P = .028, P < .0001, respectively) (Figure 6, C). Tau G was significantly increased after 40 weeks of MR compared with sham (P = .014) (Figure 6, D). Arterial elastance was decreased after onset of MR compared with sham with significance at 40 weeks (P = .0008) (Figure 6, E). Ventriculo-arterial coupling (end-systolic elastance/arterial elastance), which determines the efficiency of energy transfer from the arterial system to systemic circulation, showed a trend of decreasing after 10 weeks of MR onset compared with sham, although none of the values showed significance (Figure 6, F).

Longitudinal Transcriptomic Alterations in the LV

RNA-sequencing analysis provided an important glimpse at the key gene signaling events occurring in the LV tissue post-MR. Principle component analysis revealed that the sham and each MR group (2, 10, 20, and 40 weeks) significantly differed (Figure 7). Figure 7 also shows the distribution of the number of significantly altered genes after onset of MR compared with sham. After 2 weeks of MR, 108 genes were up-regulated and 31 genes were down-regulated. After 10 weeks of MR, 73 genes were up-regulated and 47 genes were down-regulated. By 20 weeks, 51 genes were up-regulated and 83 genes were down-regulated. By 40 weeks after MR, 690 genes were up-regulated and 732 genes were down-regulated. Tables E3-E6 represent the top 20 genes that were significantly up-regulated and down-regulated after 2, 10, 20, and 40 weeks of MR compared with sham. Figure 7 and Table E7 shows the top 25 Gene Ontology–enriched terms at each time point. After 2 weeks, there was a significant alteration of the “cell cycle process” and “cytoskeletal part and organization,” suggesting an increase in cell proliferation and changes in the cellular cytoskeletal network. At 10 weeks, there was a significant alteration of “response to stimulus” and “developmental process,” indicating cellular adaptation and activation of fetal gene programming. At 20 weeks, there was a significant alteration of genes belonging to “extracellular region, extracellular matrix structure and constituents” and “response to stimulus,” indicating cellular adaptation response and pathologic extracellular matrix remodeling. By 40 weeks, there was a significant alteration of genes involved in “multicellular organismal process” and “plasma membrane,” suggesting an intricate role of plasma membrane proteins and an organized LV tissue response to MR. Table E8 represents the top 3 Protein Analysis Through Evolutionary Relationships–identified pathways in the up- and down-regulated genes after MR onset.

FIGURE 7. — Volcano plots showing the distribution of significantly altered genes (*left*), summary of gene expression after MR surgery compared with sham displayed in heat maps (*left middle*), principle component analysis (*right middle*), and top 25 Gene Ontology terms (*right*) in the (A) MR 2-week, (B) MR 10-week, (C) MR 20-week, and (D) MR 40-week groups. MR, Mitral regurgitation.

Kyoto Encyclopedia of Genes and Genomes analysis revealed several biological pathways characterized by the up-regulated and down-regulated genes at each time-point after MR onset, which is summarized in Table E9. After 2 weeks of MR, pathways involving cell cycle, cellular senescence, p53, and renin–angiotensin system signaling were activated. By 10 weeks, the most enriched pathway was the aldosterone synthesis and secretion signaling along with activation of amino-acid metabolism. By 20 weeks, pathways involving regulation of extracellular matrix genes, collagens, elastin and thrombospondins and proinflammatory genes and cytokines (intercellular adhesion molecule 1, vascular cell adhesion molecule 1, and interleukin-17 signaling were activated). By 40 weeks, a plethora of pathways belonging to oxidative phosphorylation, thermogenesis, longevity, AMPK signaling, cardiac muscle contraction, and calcium signaling were activated.

DISCUSSION

Our study is one of the first to map the longitudinal geometric, functional, and transcriptional remodeling in the LV with uncorrected hemodynamic stress from primary MR up to 40 weeks. This duration in the rodent life is equivalent to 20 years in human life, making this study of relevance to the clinical scenario where patients survive with uncorrected primary MR for long periods of time. After the onset of MR, rapid and significant changes occurred in the diastolic geometry and function of the heart, in response to the elevated end-diastolic wall stress from the volume overload. An immediate adaptive response was observed, resulting in an increase in LV EDV by 28% within 2 weeks, 65% within 10 weeks, 87% by 20 weeks, and 100% by 40 weeks. This rise in volumes paralleled reduction in the filling pressures that were acutely elevated after MR onset. LV mass was significantly greater in the MR group at all the time points, indicating a hypertrophic response in the myocardium that occurs early and persists alongside the remodeling process. Mass increased by 18.65% by 2 weeks, 51.5% by 10 weeks, 48.77% by 20 weeks, and 46.68% by 40 weeks, compared with baseline. In the initial 10 weeks, both chamber volume and mass increased, but in the subsequent period only a rise in chamber volume was observed. Change in ventricular geometry and mass would require structural changes in the myocyte or nonmyocyte compartments of the heart. In the early stages after an injury, tissue edema could play a role, but often such edema is resolved within a short period. In the myocyte compartment, Liu and colleagues¹⁴ previously demonstrated that cardiomyocytes are deformed in response to volume overload from an arteriovenous fistula, with a rise in both cell length and cross-sectional area. Contrastingly, volume overload in the dog demonstrated elongation but no change in cardiomyocyte cross-sectional area, which may be explained by the lower severity of volume overload in this study.¹⁵ Echocardiographic data from this study indicate a rise in ventricular circumference, but not thickness, indicating that cardiomyocyte elongation may have occurred in this model. MR from mitral valve puncture is likely to result in a lower volume overload compared with the arteriovenous fistula, explaining these findings. The nonmyocyte fraction, consisting of vasculature and capillaries, extracellular matrix, fibroblasts, and variety of immune cells, could change with myocardial stress. In a previous investigation in this model, we reported degradation of the extracellular matrix rather than its accumulation, which was corroborated by others in other volume overload models as well.^10,16–18 Further investigations in this area are warranted to overcome the current dearth of data.

Systolic function paralleled changes in EDV, with some exceptions. EF was significantly elevated after the onset of MR for 4 weeks, which may be attributed to an acute hyperkinetic response of the LV to increased filling and reduced afterload from the regurgitation. Such a response of the ventricle is observed clinically as well, in patients with acute-onset primary MR from chordal rupture or other lesions. EF was significantly lower at 18 weeks after MR onset when compared with the sham group at the same time point and was lower by 14 weeks when compared with the baseline measurements in the MR group. However, ESV was significantly elevated compared with sham by 10 weeks after MR, and by 8 weeks when compared with the baseline value of the MR group. This is a significant observation that indicates that ESV may be a better and earlier indicator of LV dysfunction. In the 2017 update to the clinical guidelines, mitral valve repair is recommended in patients with severe MR with an EF<60% and a LV ESV greater than 40 mm, as LV dysfunction may have already developed in these patients.⁶ Our data confirm these findings in the rat, providing the opportunity to study the effect of MR repair in reversing the ventricular remodeling. Despite an early significant rise in ESV, a delayed change in EF may be attributed to the load dependence of this measurement. The afterload reducing effect of MR, uncouples end-systole from end ejection, resulting in greater EFs that are not indicative of cardiac dysfunction. This hypothesis is confirmed from invasive hemodynamic information obtained in this study, where preload adjusted contractile indices were significantly depressed in the regurgitation group by 10 weeks. Such an earlier fall in load-independent contractile indices in the setting of MR have also been reported by Kim and colleagues¹⁹ and Rodriguez and colleagues.²⁰

Transcriptomic data from our study provide a critical snapshot of molecular signaling events that take place in the LV upon onset of primary MR in a chronic setting at 4 different time points. Our findings demonstrate that an acute onset of MR (2 weeks) causes differential regulation of genes associated with cellular proliferation, cytoskeletal remodeling, and genes responsible for cellular adaptation to stress signaling. These include genes from nuclear as well as cytoplasmic compartments. Important cell survival and proliferation pathways such as the p53 pathway was activated, which is essential for cellular response to intrinsic and extrinsic stressors; paralleled by up-regulation of several genes required for DNA replication. Several genes in the cardiac thyrotropin-release hormone pathway were upregulated as well, which is a process implicated in cardiac hypertrophy.²¹ Similar analysis of downregulated genes at this time point revealed involvement of p53 signaling, transcription regulation by bZip transcription factor signaling, endothelin signaling pathway, and Gi alpha– and Gs alpha–mediated pathways, to name a few. p53 signaling is a master regulator of the cardiac transcriptome, which is essential to maintain physiological cardiac tissue homeostasis.²² Elevated wall stress perturbs the native homeostasis in the heart tissue, whose restoration is likely orchestrated by p53 signaling through up-regulation and down-regulation of specific genes. Nomura and colleagues²³ recently implicated the role of p53 in early-stage hypertrophy and later stages of heart failure as well, hypothesizing that p53 activation occurs when sustained external stimulus induces accumulative oxidative DNA damage. Interestingly, several genes from Iroquois Homeobox family (Irx1, 3, and 5) were downregulated at 2 weeks post-MR. Irx genes have early regulatory functions in embryonic patterning and specification and play a later role in post development function such as EC coupling, and down-regulation of Irx genes post early onset MR may have many clinical implications.²⁴ These findings warrant better understanding of the mechanisms underlying abnormal conduction in the setting of advanced MR.

By 10 weeks of MR when systolic function was depressed in this model, the most up-regulated signaling cascades were oxidative stress response and angiotensin II stimulated signaling through G proteins and beta-arrestin; and the most downregulated were integrin signaling and Gonadotropin-releasing hormone receptor pathways. Oxidative stress occurs when reactive oxygen species (ROS) are generated by cells and are not adequately countered by the intrinsic antioxidant systems that are intrinsic to most tissues. Such pathologic stress has been implicated in adverse cardiac remodeling and dysfunction in other forms of cardiac stress, including volume overload from different lesions.^25,26 Robison and colleages²⁷ recently demonstrated that ROS accumulation in the cardiomyocytes may be governed by intracellular microtubule deformation, which is dependent upon cardiomyocyte resting length. In these dilated hearts with MR, where cardiomyocytes may be elongated, it is possible that such mechano-regulatory accumulation of ROS occurs and warrants further investigation. Previous studies of myocardial tissue from heart failure also demonstrated elevated AngII signaling, which was associated with a rise in ROS generation and oxidative stress.²⁸ Downstream signaling in the AngII pathway is mediated by ROS, likely mediating gene expression that is involved in pathological cardiac remodeling and mitochondrial dysfunction. Gladden and colleagues²⁹ reported in patients with chronic MR, disruption of the mitochondria with parallel rise in ROS. In examining the down-regulated pathways, suppression of integrin signaling is of interest, as chronic hemodynamic stress has been implicated to stimulate this signaling cascade in the heart. Down-regulation of integrin signaling in these hearts may indicate a feedback loop wherein excessive accumulation of associated proteins may have mediated reduced transcription of relevant genes.³⁰ Interestingly, DNA Topoisomerase II alpha (Top2a), which was upregulated at the 2-week time point, was noted to be down-regulated at 10-week time point. The role of Top2a has been well established in the settings of cancer, where it has been shown to play a role in cell proliferation and chemo- and radio-sensitization of tumors.³¹ Importantly, Top2a knockout mice demonstrate prenatal lethality prior to heart atrial septation, suggesting a critical role of Top2a in MR-based LV pathology.

At 20 weeks, elevated transcription of oxidative stress response genes also paralleled a rise in transforming growth factor-beta signaling, and dopamine receptor–mediated signaling pathways. Transforming growth factor-beta signaling is associated with a variety of signaling cascades, including cardiac hypertrophy and cardiac fibrosis, both of which could occur in these later stages of remodeling.³² The role of dopamine receptor mediated signaling is unclear but may be relevant as distribution of all types of dopamine receptors were detected in the heart in previous studies.³³ At this time point, several mechanosensitive pathways, such as integrin, Wnt, and Cadherin signaling, were significantly down-regulated, along with other pathways such as p53, and inflammation mediated by chemokine and cytokine signaling. The dynamics of such suppression must be combined with estimation of proteins in the muscle, which was not within the scope of this work.

By 40 weeks, a new-onset burst of several new genes and pathways were observed, indicating an accelerated momentum toward heart failure. Activation of PI3-kinase and p53 signaling via glucose deprivation signaling indicates endoplasmic reticulum stress, which may indicate cellular metabolic imbalance and apoptosis.³⁴ Significant up-regulation of pathways relevant to adrenaline and noradrenaline biosynthesis, alpha adrenergic receptor signaling, and CCKR signaling at this time point also indicate an adaptation to increased sympathetic activity in the heart. Kaye and colleagues³⁵ previously reported evidence of such elevated cardiac sympathetic activity in heart failure.

We also identified novel long–noncoding RNAs from our study. These include LOC102550325 (down-regulated at 2 weeks post-MR), H19 imprinted maternally expressed transcript (down-regulated at 2 weeks), LOC100910189 (up-regulated at 20 weeks post-MR), and RGD1566401 and LOC102546391 (both down-regulated at 40 weeks post-MR). Although not much is known about the lnc-RNAs identified from our study, H19 has been previously reported to negatively regulate cardiomyocyte remodeling and hypertrophy.³⁶ Collectively, it would be interesting to further elucidate the role of these newly identified noncoding RNAs in the pathophysiology of LV remodeling post-MR.

Advancing these transcriptional changes to clinical application requires further efforts that link gene expression to proteome, proteome to metabolome, and then identifying biomarkers that can be measured in the bloodstream that relate to these changes in the myocardium. In addition, knowledge of the longitudinal transcriptional changes can aid in therapeutic discovery. At 2 weeks, several cytoskeletal remodeling pathways are implicated. Chen and colleagues³⁷ recently demonstrated the role of cytoskeletal remodeling in heart failure progression and demonstrate the benefit of drugs such as parthenolide and colchicine, which modify the detyrosination of the cytoskeletal elements. Transcriptomic data also indicate that the shift from early structural changes to chronic pathologic remodeling is mediated by oxidative stress signaling, which can be targeted with antioxidant drugs.³⁸ In the late stage of remodeling, involvement of several fibrosis and mechano-sensitive genes indicates processes that are similar to other forms of heart failure, unifying later stages of remodeling in MR to other forms of cardiac dysfunction.

Limitations

As with any preclinical study, application of these data to clinical scenarios should be considered within the scope of limitations of a preclinical study. This rodent model of MR has acute onset of severe regurgitation, which represents a subset of patients with degenerative MR. Nonetheless, acute chordal rupture leading to MR is often the most common etiology of patients presenting to surgery. Selection of interrogation windows to assess cardiac dysfunction and transcriptome were arbitrary in this model but were adequately spaced to capture the temporal changes in remodeling in this model. The novel genes identified in this study need to be further examined to determine their functional importance in the context of MR, as well as other cardiac diseases in animal models and patient datasets in the future. Careful examination and subsequent validation of these genes may provide important therapeutic and early diagnostic targets of MR related pathological remodeling of the LV. Finally, although this study reveals a high-resolution, longitudinal map of cardiac remodeling at the functional, geometric and biological levels in the setting of MR, further studies are required to understand the effects of correcting MR at these varied time points to assess the potential for reverse remodeling.

Supplementary Material

NIHMS1637588-supplement-1.docx^{(13.1MB, docx)}

VIDEO 1. Video showing the operative procedure of creating mitral regurgitation on the beating heart in the rat, using echocardiographic guidance. Video available at: https://www.jtcvs.org/article/S0022-5223(20)32784-7/fulltext.

Download video file^{(80MB, mp4)}

VIDEO 2. Video depicting the beating heart in a rat from the sham group, depicting a smaller left atrium and left ventricle. Video available at: https://www.jtcvs.org/article/S0022-5223(20)32784-7/fulltext.

Download video file^{(11.9MB, mp4)}

VIDEO 3. Video depicting the beating heart in a rat after 40 weeks of mitral regurgitation, depicting a severely enlarged left atrium and left ventricle. Video available at: https://www.jtcvs.org/article/S0022-5223(20)32784-7/fulltext.

Download video file^{(9.8MB, mp4)}

CENTRAL MESSAGE.

Left ventricular dysfunction occurs before fall in ejection fraction in severe mitral regurgitation in an experimental rodent model.

PERSPECTIVE.

Patients with primary mitral regurgitation have preserved ejection fraction, which is currently used as a clinical trigger for intervention. In a rodent model, we demonstrate left ventricular dysfunction and adverse biological remodeling occur before a clinically significant reduction in ejection fraction. These data can be valuable in defining better clinical indices to capture dysfunction.

Acknowledgments

The authors acknowledge Laura Susan Schmarkey for project management support and Robert Hernandez-Merlo, DVM, for veterinary technician support.

This work was funded by American Heart Association grants 14SDG20380081 and 19PRE34380625; National Heart, Lung, and Blood Institute grants R01HL133667, R01HL135145, and R01HL140325; and infrastructure support from the Carlyle Fraser Heart Center at Emory University Hospital Midtown.

Abbreviations and Acronyms

EDP: end-diastolic pressure
EDV: end-diastolic volume
EF: ejection fraction
ESV: end-systolic volume
LV: left ventricle/ventricular
MR: mitral regurgitation
PV: pressure volume
PVA: pressure volume loop area
ROS: reactive oxygen species
SQ: subcutaneous
TEE: transesophageal echocardiography

Discussion

Presenter: Dr Daniella Corporan

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Dr Amy Hackmann (Dallas, Tex). Thank you, Dr Kane and Dr Ugalde, for inviting me and congratulations on this excellent work, Ms Corporan and Dr Padala. I have a few questions for you. I think the most burning for the audience is: How do we translate this work to clinical care? If we see a patient who maybe is graded only as moderate mitral regurgitation (MR) but has evidence of a dilated left ventricle (LV), should they be referred for early surgery, or we should we still continue to wait for just that severe MR threshold?

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Dr Daniella Corporan (Atlanta, Ga). Based on our studies, we see that there’s early and progressive remodeling that occurs very soon after inducing MR. In our case, we see end-diastolic volume increased by 2 weeks, which is equivalent to 1 year in humans. If we look at the current guidelines, which are based off of ejection fraction, we see that the decline in ejection fraction occurs beyond the point at which there’s already active and progressive remodeling. Our data indicate that early surgery could be beneficial, but this needs to be confirmed in larger animals and patients as well.

Dr Hackmann. Thank you. What time frame do we have to intervene on patients? Is our window of opportunity for preventing long-term LV damage a few months, a few years, a decade? What should we recommend for patients?

Dr Corporan. That’s a very interesting question. In our rodent model, we’ve characterized it out to 40 weeks, which is equivalent to about 20 human years. It depends on the indices that we look at. If we look at ejection fraction, it takes a very long time for that parameter to decrease.

But if we look at end-diastolic volume or the rate of change of end-diastolic volume, then those parameters change a lot sooner and so it looks like the 2-week time point, which is equivalent to about 1 year, could be a possible time point to intervene.

Dr Hackmann. Great. So if we start to see LV changes, we should intervene within a year. And since our goal here is to identify relatively asymptomatic patients but who have mitral regurgitation and the potential for LV damage, is there any kind of a screening biomarker or any type of other signal that we can look for in patients other than identifying depressed LV function and severe MR?

Dr Corporan. For the asymptomatic patients, there’s currently no specific biomarker that’s used in the setting of primary MR. But from some of the transcriptomic studies that we’ve shown, we’ve identified some very distinct pathways that are activated at that 2-week time point. In future studies, we want to investigate whether these can be detected as potential biomarkers to be used. In addition to the biomarkers that could be of value, also longitudinal tracing of these patients, possibly with biomarkers, but also with imaging-based LV parameters could be of value as well.

Dr Hackmann. You also showed that there was a very big jump in the end-systolic volume between the 10 and 20 weeks, which would be I guess about a 5-year period in a human patient. Do you know what can account for that? And do we see the same thing if we do longitudinal studies of patients with MR?

Dr Corporan. For that later increase in end-systolic volume, if we think about the LV structurally and what accounts for end-diastolic volume to increase rapidly, we know that cardiomyocytes can elongate to account for some increase in end-diastolic volume. But once that occurs, then that is when possibly the end-systolic volume and the contractile properties of the cardiomyocytes could be altered, which may explain why that occurs a little bit later. With longitudinal tracing, in the future it could be interesting to do some more experiments, potentially on the isolated cardiomyocytes themselves, to look at when exactly the function decreases.

FIGURE E1. — A1, Quantification of regurgitant jet area measured every 2 weeks in the MR (*red*) and sham (*blue*) group. A2, Regurgitant jet area normalized to left atrial area. A3, Regurgitant volume. B1, Longitudinal pulmonary venous flow systolic wave S-wave velocity. B2, Pulmonary venous flow systolic wave D-wave velocity. B3, Pulmonary venous flow systolic wave S/D wave ratio. Data are represented as mean ± standard deviation. *Red stars* represent a statistical significance in the MR group compared with MR baseline values (P < .05). MR, Mitral regurgitation.

FIGURE E2. — A1, and B1, Gross morphology of hearts that had a sham surgery (A1) or MR surgery (B1) after 40 weeks. A2 and B2, Long-axis B-mode images of the left ventricle at 40 weeks. A3 and B3, Long-axis M-mode images of the left ventricle at 40 weeks. MR, Mitral regurgitation.

FIGURE E3. — A, Left ventricular internal diameter at diastole (A1) and systole (A2) measured every 2 weeks in the MR and sham groups. B, Anterior wall thickness at diastole (B1) and systole (B2). C, Posterior wall thickness at diastole (C1) and systole (C2). Data are represented as median and interquartile range. *Blue stars* represent statistical significance between the MR and sham group at the same time point (P <.05). *Red stars* represent statistical significance between the MR group and the MR baseline value (P < .05). LV, Left ventricle; MR, mitral regurgitation.

TABLE E1.

Variability among 2 independent users for analysis of TTE LAX B-mode measurements

	TTE LAX B-mode
	End-systolic volume	End-diastolic volume	Stroke volume	Ejection fraction	Fractional shortening	Cardiac output
Echo 1
Reviewer 1	166.161205	524.786137	358.624932	68.383124	17.535455	110.368976
Reviewer 2	110.496628	472.261729	361.765101	76.602672	19.424852	111.514877
% difference	33.500345	10.0087263	−0.8756137	−12.019849	−10.774725	−1.0382456
Echo 2
Reviewer 1	169.746356	626.68867	456.942314	72.912136	22.011786	146.168639
Reviewer 2	175.280125	626.888541	451.608416	72.039667	30.438836	142.067967
% difference	−3.2600223	−0.0318932	1.16730227	1.19660327	−38.284263	2.805439
Echo 3
Reviewer 1	183.335865	548.589935	365.25407	66.59486	15.740376	118.718939
Reviewer 2	185.941383	504.093275	318.151891	63.113695	15.292601	103.2658
% difference	−1.421172	8.11109668	12.8957301	5.22737791	2.84475415	13.0165744
Echo 4
Reviewer 1	650.802977	1392.80793	742.004948	53.284405	21.484639	203.906984
Reviewer 2	525.542983	1288.61826	863.075272	66.9768	22.572024	239.212471
% difference	19.2469916	7.48054833	−16.316646	−25.696815	−5.0612207	−17.314506
Echo 5
Reviewer 1	165.476298	561.793798	396.3175	70.537761	21.87421	136.632206
Reviewer 2	174.219272	561.496593	387.277321	68.972337	13.93366	134.607613
% difference	−5.2835204	0.05290286	2.28104462	2.2192709	36.3009681	1.48178315
Echo 6
Reviewer 1	177.717098	559.953433	382.236335	68.261732	18.860589	131.099819
Reviewer 2	161.593402	513.725218	352.131816	68.544779	18.379092	119.591938
% difference	9.07267572	8.2557249	7.8758915	−0.4146496	2.55292663	8.77795339
Echo 7
Reviewer 1	153.665831	486.460525	332.794694	68.372822	18.481752	103.840034
Reviewer 2	144.241142	417.719898	273.478756	64.424866	15.764034	85.445515
% difference	6.13323661	14.1307719	17.8235828	5.7741598	14.7048721	17.7142845
Echo 8
Reviewer 1	488.203068	1141.73393	653.530863	57.23981	12.382606	207.475659
Reviewer 2	318.241589	975.843651	657.602063	67.388056	15.790809	206.599099
% difference	34.8136852	14.5296794	−0.6229545	−17.72935	−27.524117	0.42248811
Echo 9
Reviewer 1	184.172088	550.120803	365.948715	66.528037	15.987639	119.394747
Reviewer 2	217.060877	554.904252	337.843374	60.883184	13.068891	109.657266
% difference	−17.85764	−0.869527	7.68013108	8.48492343	18.2562791	8.15570303
Echo 10
Reviewer 1	302.808831	925.395796	622.586965	67.290292	20.15892	179.362154
Reviewer 2	224.655493	971.371345	746.715851	76.872337	23.078151	211.480151
% difference	25.8094646	−4.9682038	−19.937598	−14.239862	−14.481088	−17.906786
Average % difference	10.0754044	5.66998263	1.19708698	−4.719819	−2.1465614	1.6114688

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TTE, Transthoracic echocardiography.

TABLE E2.

Variability among 2 independent users for analysis of TTE LAX M-mode measurements

	TTE LAX M-mode
	IVS; d	IVS; s	LVID; d	LVID; s	LVPW; d	LVPW; s	LV mass	LV mass (corrected)
Echo 1
Reviewer 1	1.952557	3.299148	8.281534	5.184375	2.221875	3.052273	1436.90019	1149.52015
Reviewer 2	1.975	2.928448	8.581034	5.380172	1.975	3.064655	1406.65135	1125.32108
% difference	−1.1494159	11.2362343	−3.6164797	−3.7766751	11.1111111	−0.4056649	2.10514504	2.10514501
Echo 2
Reviewer 1	2.088506	3.246264	8.603736	5.198563	2.752514	3.851899	1888.45912	1510.7673
Reviewer 2	1.702586	2.860345	8.717241	5.312069	2.656034	3.95	1656.64355	1325.31484
% difference	18.4782806	11.8880966	−1.3192525	−2.1834111	3.50515928	−2.5468217	12.2753823	12.2753823
Echo 3
Reviewer 1	1.885227	3.142045	8.124432	5.049716	2.04233	3.097159	1278.64971	1022.91977
Reviewer 2	1.906897	2.928448	8.172414	5.039655	2.451724	3.74569	1497.24675	1197.7974
% difference	−1.1494637	6.79802485	−0.590589	0.19923893	−20.045438	−20.939545	−17.095929	−17.095928
Echo 4
Reviewer 1	2.173295	3.068182	12.34375	9.034091	1.775568	2.698864	2573.60275	2058.8822
Reviewer 2	1.896552	2.758621	13.017241	9.396552	1.982759	3.103448	2756.8619	2205.48952
% difference	12.7337982	10.089395	−5.4561296	−4.0121469	−11.668998	−14.9909	−7.1207241	−7.1207241
Echo 5
Reviewer 1	2.009052	2.894397	8.308621	5.192888	2.309437	3.536515	1516.05169	1212.84136
Reviewer 2	1.975	3.064655	8.581034	5.175862	2.315517	3.473276	1580.19562	1264.1565
% difference	1.69492875	−5.8823306	−3.2786789	0.3278715	−0.2632676	1.78817282	−4.2309853	−4.2309853
Echo 6
Reviewer 1	2.334091	3.837784	8.012216	4.690625	2.603409	3.366477	1745.08998	1396.07198
Reviewer 2	2.587931	3.813793	8.444828	4.699138	2.519828	3.541379	1987.00591	1589.60473
% difference	−10.875326	0.62512638	−5.3994051	−0.1814897	3.21044446	−5.1954016	−13.862662	−13.862662
Echo 7
Reviewer 1	2.045455	2.769886	6.988636	4.666193	1.768466	2.791193	967.996317	774.397054
Reviewer 2	1.875	2.909483	7.306034	4.655172	2.198276	3.103448	1140.93557	912.748454
% difference	8.3333537	−5.0398103	−4.5416302	0.23618826	−24.304114	−11.187152	−17.865693	−17.865693
Echo 8
Reviewer 1	2.8125	3.853125	9.3375	6.24375	2.5875	3.628125	2513.26753	2010.61402
Reviewer 2	2.475	3.584483	9.643966	5.803448	2.731034	4.011207	2503.83567	2003.06853
% difference	12	6.97205515	−3.2820991	7.05188388	−5.5472077	−10.558677	0.37528277	0.37528277
Echo 9
Reviewer 1	1.952557	2.850284	8.057102	5.633239	1.885227	2.423864	1221.42243	977.137944
Reviewer 2	1.634483	2.656034	8.376724	5.516379	1.77069	2.656034	1103.21866	882.574927
% difference	16.2901262	6.81511035	−3.9669598	2.07447261	6.07550178	−9.5785077	9.67755038	9.6775504
Echo 10
Reviewer 1	2.435701	3.910606	10.105208	5.705777	2.505533	4.106859	2500.40103	2000.32082
Reviewer 2	1.994828	3.14569	10.127586	6.291379	1.918103	2.838793	1820.77051	1456.61641
% difference	18.1004565	19.5600375	−0.2214502	−10.263317	23.4453108	30.8767844	27.1808606	27.1808607
Average % difference	7.44567387	6.30619392	−3.1672674	−1.0527385	−1.4481499	−4.2737712	−0.8561772	−0.8561772

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TTE, Transthoracic echocardiography; IVS, interventricular septum; d, end diastole; s, end systole; LVID, left ventricular internal dimension; LVPW, left ventricular posterior wall; LV, left ventricular.

TABLE E3.

The top 20 up-regulated and down-regulated genes in the MR 2-week group compared with sham

Up-regulated genes at 2 wk				Down-regulated genes at 2 wk
Gene	Description	Fold change	P value	Gene	Description	Fold change	P value
Polq	DNA Polymerase Theta	4.67	.0079	LOC102550325	Uncharacterized gene	−4.33	.023
Cmahp	Cytidine Monophospho-N-Acetylneuraminic Acid Hydroxylase, Pseudogene	4.58	.043	Prkar1b	Protein Kinase CAMP-Dependent Type I Regulatory Subunit Beta	−4.21	.0025
Ska1	Spindle And Kinetochore Associated Complex Subunit 1	4.55	.0020	Tnnt1	Troponin T1, Slow Skeletal Type	−3.64	.028
Trh	Thyrotropin Releasing Hormone	4.52	.0081	Irx5	Iroquois Homeobox 5	−3.24	.025
Cdk1	Cyclin Dependent Kinase 1	4.17	.0018	Irx3	Iroquois Homeobox 3	−2.90	.0079
Cdc6	Cell Division Cycle 6	4.14	.0052	Cxcl14	C-X-C Motif Chemokine Ligand 14	−2.83	.0049
Cenpf	Centromere Protein F	4.04	.00021	Irx1	Iroquois Homeobox 1	−2.82	.032
Ccna2	Cyclin A2	3.92	.0034	Nfe2	Nuclear Factor, Erythroid 2	−2.71	.034
Nek2	NIMA Related Kinase 2	3.88	.00063	Ube3d	Ubiquitin Protein Ligase E3D	−2.67	.027
Cdkn3	Cyclin Dependent Kinase Inhibitor 3	3.80	.0035	Penk	Proenkephalin	−2.66	.0031
Melk	Maternal Embryonic Leucine Zipper Kinase	3.70	.00099	Hmgcll1	3-Hydroxymethyl-3-Methylglutaryl-CoA Lyase Like 1	−2.60	.013
Ptx3	Pentraxin 3	3.65	.021	Epor	Erythropoietin Receptor	−2.56	.015
Pbk	PDZ Binding Kinase	3.64	.0022	Nsg1	Neuronal Vesicle Trafficking Associated 1	−2.55	.011
Iqgap3	IQ Motif Containing GTPase Activating Protein 3	3.53	.00080	Rec114	REC114 Meiotic Recombination Protein	−2.51	.0074
Tph1	Tryptophan Hydroxylase 1	3.49	.022	Fosb	FosB Proto-Oncogene, AP-1 Transcription Factor Subunit	−2.51	.014
Ccnf	Cyclin F	3.44	.00091	Igfbp3	Insulin Like Growth Factor Binding Protein 3	−2.40	.00051
Prc1	Protein Regulator Of Cytokinesis 1	3.40	.0014	Cytl1	Cytokine Like 1	−2.39	.046
Tpx2	TPX2 Microtubule Nucleation Factor	3.38	.00053	Msx2	Msh Homeobox 2	−2.36	.020
Top2a	DNA Topoisomerase II Alpha	3.31	.00036	Rtn4rl2	Reticulon 4 Receptor Like 2	−2.33	.025
Racgap1	Rac GTPase Activating Protein 1	3.30	.00063	Cd99l2	CD99 Molecule Like 2	−2.31	.0044

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TABLE E4.

The top 20 up-regulated and down-regulated genes in the MR 10-week group compared with sham

Up-regulated genes at 10 wk				Down-regulated genes at 10 wk
Gene	Description	Fold change	P value	Gene	Description	Fold change	P value
Ugt1a1	UDP Glucuronosyltransferase Family 1 Member A1	10.91	.036	RT1-CE11	MHC class I family related	−7.63	.000031
Nppa	Natriuretic Peptide A	5.44	.029	Msln	Mesothelin	−5.72	.0087
Dupd1	Dual Specificity Phosphatase And Pro Isomerase Domain Containing 1	5.26	.0070	Upk1b	Uroplakin 1B	−5.57	.037
Dusp15	Dual Specificity Phosphatase 15	4.21	.00077	Egr3	Early Growth Response 3	−4.38	.0053
Pfkfb1	6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 1	3.82	.000019	C4a	Complement C4A (Rodgers Blood Group)	−4.00	.014
Sult1a1	Sulfotransferase Family 1A Member 1	3.33	.0000072	Lyc2	Lysozyme Like 1	−3.71	.012
Csrp2	Cysteine And Glycine Rich Protein 2	3.10	.0000044	Kcna4	Potassium Voltage-Gated Channel Subfamily A Member 4	−3.36	.0014
Lrtm2	Leucine Rich Repeats And Transmembrane Domains 2	3.08	.048	Cdca3	Cell Division Cycle Associated 3	−3.06	.020
Il22ra2	Interleukin 22 Receptor Subunit Alpha 2	3.06	.0074	Nipsnap2	Nipsnap Homolog 2	−3.05	.028
Tlcd1	TLC Domain Containing 1	2.80	.035	Baalc	BAALC Binder Of MAP3K1 And KLF4	−2.95	.0041
Chrna1	Cholinergic Receptor Nicotinic Alpha 1 Subunit	2.79	.036	B3gat1	Beta-1,3-Glucuronyltransferase 1	−2.76	.017
Oas1k	Oligoadenylate synthetase 1K	2.74	.00057	Top2a	DNA Topoisomerase II Alpha	−2.76	.0022
B3galt5	Beta-1,3-Galactosyltransferase 5	2.71	.044	Myo16	Myosin XVI	−2.69	.00054
Per2	Period Circadian Regulator 2	2.60	.0000050	H19	H19 Imprinted Maternally Expressed Transcript	−2.67	.046
Hpd	4-Hydroxyphenylpyruvate Dioxygenase	2.60	.00018	Hmgcll1	3-Hydroxymethyl-3-Methylglutaryl-CoA Lyase Like 1	−2.55	.0064
Bex1	Brain Expressed X-Linked 1	2.59	.0020	Col1a1	Collagen Type I Alpha 1 Chain	−2.51	.011
Sdcbp2	Syndecan Binding Protein 2	2.59	.0073	Basp1	Brain Abundant Membrane Attached Signal Protein 1	−2.46	.0047
Cplx1	Complexin 1	2.56	.010	Fosb	FosB Proto-Oncogene, AP-1 Transcription Factor Subunit	−2.44	.011
Agt	Angiotensinogen	2.55	.0070	Ldlr	Low Density Lipoprotein Receptor	−2.44	.0016
Tnfrsf25	TNF Receptor Superfamily Member 25	2.51	.000029	Cyr61	Cellular Communication Network Factor 1	−2.43	.00073

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TABLE E5.

The top 20 up-regulated and down-regulated genes in the MR 20-week group compared with sham

Up-regulated genes at 20 wk				Down-regulated genes at 20 wk
Gene	Description	Fold change	P value	Gene	Description	Fold change	P value
Dupd1	Dual Specificity Phosphatase And Pro Isomerase Domain Containing 1	10.09	.0000063	Ptx3	Pentraxin 3	−7.48	.040
Adra2c	Adrenoceptor Alpha 2C	6.62	.0061	Upk1b	Uroplakin 1B	−5.13	.040
Ephx4	Epoxide Hydrolase 4	5.50	.023	Msln	Mesothelin	−4.80	.015
Dusp15	Dual Specificity Phosphatase 15	4.70	.0000016	Cdca3	Cell Division Cycle Associated 3	−4.08	.010
Atp1a3	ATPase Na+/K+ Transporting Subunit Alpha 3	4.58	.0012	Ccl2	C-C Motif Chemokine Ligand 2	−3.70	.019
LOC100910189	Long noncoding RNA related gene	4.23	.012	Wnt9b	Wnt Family Member 9B	−3.67	.016
Camk2b	Calcium/Calmodulin Dependent Protein Kinase II Beta	4.17	.000090	Robo3	Roundabout Guidance Receptor 3	−3.66	.042
Sox8	SRY-Box Transcription Factor 8	3.96	.0099	Krt5	Keratin 5	−3.63	.046
Sypl2	Synaptophysin Like 2	3.84	.0014	Egr2	Early Growth Response 2	−3.45	.015
Chrna1	Cholinergic Receptor Nicotinic Alpha 1 Subunit	3.82	.00091	Col1a1	Collagen Type I Alpha 1 Chain	−3.41	.0010
Cds1	CDP-Diacylglycerol Synthase 1	3.30	.00028	Egr3	Early Growth Response 3	−3.40	.0057
Maoa	Monoamine Oxidase A	3.28	.00025	Scara3	Scavenger Receptor Class A Member 3	−3.38	.036
Fa2h	Fatty Acid 2-Hydroxylase	3.19	.0025	C4a	Complement C4A (Rodgers Blood Group)	−3.34	.041
Pfkfb1	6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 1	3.18	.00026	Has1	Hyaluronan Synthase 1	−3.20	.033
Oas1k	Oligoadenylate synthetase 1K	3.17	.00042	Slc1a5	Solute Carrier Family 1 Member 5	−3.13	.00072
Epn3	Epsin 3	3.16	.00019	Pcdh17	Protocadherin 17	−3.09	.017
Hpd	4-Hydroxyphenylpyruvate Dioxygenase	3.10	.0011	Pimreg	PICALM Interacting Mitotic Regulator	−3.09	.015
Habp2	Hyaluronan Binding Protein 2	3.00	.0035	Adamts3	ADAM Metallopeptidase With Thrombospondin Type 1 Motif 3	−3.07	.022
Gdf15	Growth Differentiation Factor 15	2.99	.00077	Fosb	FosB Proto-Oncogene, AP-1 Transcription Factor Subunit	−3.03	.0031
Ankrd23	Ankyrin Repeat Domain 23	2.97	.000027	Thbs1	Thrombospondin 1	−3.01	.00048

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TABLE E6.

The top 20 up-regulated and down-regulated genes in the MR 40-week group compared with sham

Up-regulated genes at 40 wk				Down-regulated genes at 40 wk
Gene	Description	Fold change	P value	Gene	Description	Fold change	P value
Adcy1	Adenylate Cyclase 1	17.88	.00000028	Cldn11	Claudin 11	−11.15	.027
Syt13	Synaptotagmin 13	17.19	.00035	Cdca3	Cell Division Cycle Associated 3	−7.47	.00096
Nos1	Nitric Oxide Synthase 1	14.39	.000026	Postn	Periostin	−6.55	.036
Tgfa	Transforming Growth Factor Alpha	13.94	.0000032	Zg16	Zymogen Granule Protein 16	−5.75	.040
Fbxo32	F-Box Protein 32	13.41	.0000000076	RGD1566401	Maternally Expressed 3 like	−5.41	.00011
Adra2c	Adrenoceptor Alpha 2C	12.74	.00018	Cblc	Cbl Proto-Oncogene C	−4.99	.000038
Atp1a3	ATPase Na+/K+ Transporting Subunit Alpha 3	11.78	.000038	Tnnt1	Troponin T1, Slow Skeletal Type	−4.90	.0042
Hcn4	Hyperpolarization Activated Cyclic Nucleotide Gated Potassium Channel 4	10.48	.00016	LOC102546391	Long noncoding RNA like gene	−4.84	.000010
Tbx20	T-Box Transcription Factor 20	9.80	.0000016	Lpal2	Lipoprotein(A) Like 2, Pseudogene	−4.59	.040
Ccnd2	Cyclin D2	9.69	.00000099	Fscn3	Fascin Actin-Bundling Protein 3	−4.33	.00040
Maoa	Monoamine Oxidase A	9.62	.0000036	Myl6b	Myosin Light Chain 6B	−4.33	.00011
Prkaa2	Protein Kinase AMP-Activated Catalytic Subunit Alpha 2	8.23	.0000057	Snrpg	Small Nuclear Ribonucleoprotein Polypeptide G	−4.20	.00018
Dgkg	Diacylglycerol Kinase Gamma	7.86	.00018	Kif22	Kinesin Family Member 22	−4.13	.00017
Cavin4	Caveolae Associated Protein 4	7.56	.0000039	Col11a2	Collagen Type XI Alpha 2 Chain	−4.12	.0000043
Cds1	CDP-Diacylglycerol Synthase 1	7.22	.00000073	Psme2	Proteasome Activator Subunit 2	−4.11	.0000014
Gfod1	Glucose-Fructose Oxidoreductase Domain Containing 1	7.06	.0000015	Zmynd10	Zinc Finger MYND-Type Containing 10	−4.03	.0000031
Ncam1	Neural Cell Adhesion Molecule 1	6.92	.0000017	RT1-CE14	MHC class I family related	−4.01	.00000076
Dupd1	Dual Specificity Phosphatase And Pro Isomerase Domain Containing 1	6.76	.00020	Cdca8	Cell Division Cycle Associated 8	−3.97	.00061
Pkia	CAMP-Dependent Protein Kinase Inhibitor Alpha	6.73	.0000080	Pimreg	PICALM Interacting Mitotic Regulator	−3.95	.0079
Prkn	Parkin RBR E3 Ubiquitin Protein Ligase	6.68	.000024	Syndig1	Synapse Differentiation Inducing 1	−3.88	.00058

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TABLE E7.

GO analysis of top 25 significantly altered categories, comparing MR versus sham differentially expressed genes

Category	GO-BP term	Enrichment score	Gene count	Gene symbol	P value
MR 2 wk
GO:0022402	Cell cycle process	54.41	44	NEK2, NUF2, KNSTRN, INCENP, CKAP2, BUB1B, TOP2A, RACGAP1, KIF18B, KIF20A, NDC80, KIF11, KIF22, KIF23, KIF2C, SGO2, KIFC1, TPX2, ESPL1, E2F1, E2F7, CIT, IQGAP3, TACC3, ASPM, CDC6, CCNF, CDK1, KNTC1, CKS2, ECT2, FOXM1, NUSAP1, BIRC5, SPC25, MYBL2, CDC20, CCNA2, CCNB1, AURKB, CDKN3, CEP55, UBE2C, CENPW	2.34E-24
GO:1903047	Mitotic cell cycle process	62.82	40	NEK2, NUF2, KNSTRN, INCENP, CKAP2, BUB1B, TOP2A, RACGAP1, KIF18B, KIF20A, NDC80, KIF11, KIF22, KIF23, KIF2C, KIFC1, TPX2, ESPL1, E2F1, E2F7, CIT, IQGAP3, TACC3, CDC6, CCNF, CDK1, KNTC1, CKS2, ECT2, FOXM1, NUSAP1, BIRC5, SPC25, MYBL2, CDC20, CCNA2, CCNB1, AURKB, CEP55, UBE2C	5.21E-28
GO:0044430	Cytoskeletal part	24.22	41	ACKR2, NEK2, KNSTRN, INCENP, CKAP2, TNNT1, BUB1B, TOP2A, RACGAP1, KIF18B, KIF20A, KIF20B, SARM1, PRC1, NDC80, KIF11, KIF22, KIF23, KIF2C, KIFC1, SKA1, TPX2, ESPL1, E2F1, TACC3, ASPM, CDC6, CCNF, CDK1, KNTC1, ECT2, NUSAP1, BIRC5, CDC20, HMMR, CCNB1, CCNB2, AURKB, LMNB1, CEP55, CENPF	3.04E-11
GO:0051726	Regulation of cell cycle	34.15	39	NEK2, MSX2, KNSTRN, INCENP, MKI67, BUB1B, TOP2A, RACGAP1, KIF20A, KIF20B, PRC1, NDC80, KIF11, KIF23, SGO2, TGM1, TPX2, E2F1, E2F7, CIT, TACC3, ASPM, CDC6, CCNF, CDK1, KNTC1, CKS2, ECT2, FOXM1, NUSAP1, BIRC5, CDC20, CCNA2, CCNB1, CCNB2, AURKB, CDKN3, UBE2C, CENPF	1.48E-15
GO:0010564	Regulation of cell cycle process	36.06	32	NEK2, MSX2, KNSTRN, INCENP, MKI67, BUB1B, RACGAP1, KIF20A, KIF20B, PRC1, NDC80, KIF11, KIF23, SGO2, TPX2, E2F1, E2F7, CIT, TACC3, CDC6, CCNF, CDK1, ECT2, FOXM1, NUSAP1, BIRC5, CDC20, CCNB1, CCNB2, AURKB, UBE2C, CENPF	2.18E-16
GO:0007010	Cytoskeleton organization	24.06	30	NEK2, DIAPH3, NUF2, KNSTRN, TNNT1, RACGAP1, KIF18B, KIF20A, PRC1, NDC80, KIF11, KIF23, KIF2C, KIFC1, TPX2, ESPL1, CIT, TACC3, ASPM, CCL7, CDK1, NUSAP1, BIRC5, SPC25, MYBL2, CDC20, HMMR, CCNB1, AURKB, CENPW	3.56E-11
GO:0007017	Microtubule-based process	27.45	27	NEK2, NUF2, KNSTRN, RACGAP1, KIF18B, KIF20A, KIF20B, PRC1, NDC80, KIF11, KIF22, KIF23, KIF2C, KIFC1, TPX2, ESPL1, TACC3, ASPM, CDK1, NUSAP1, BIRC5, SPC25, MYBL2, CDC20, CCNB1, AURKB, CENPW	1.20E-12
GO:0000226	Microtubule cytoskeleton Organization	29.75	23	NEK2, NUF2, KNSTRN, KIF18B, KIF20A, PRC1, NDC80, KIF11, KIF2C, KIFC1, TPX2, ESPL1, TACC3, ASPM, CDK1, NUSAP1, BIRC5, SPC25, MYBL2, CDC20, CCNB1, AURKB, CENPW	1.20E-13
GO:0005819	Spindle	38.86	21	KNSTRN, INCENP, BUB1B, RACGAP1, KIF20A, PRC1, KIF11, KIF22, KIF23, KIFC1, SKA1, TPX2, ESPL1, TACC3, ASPM, CDC6, CDK1, ECT2, NUSAP1, AURKB, CENPF	1.32E-17
GO:0051301	Cell division	29.4	20	NUF2, KNSTRN, TOP2A, KIF18B, KIF20A, KIF2C, KIFC1, SKA1, ASPM, CDC6, CCNF, CDK1, CKS2, BIRC5, SPC25, CDC20, CDCA2, CCNB1, CENPT, CENPW	1.71E-13
GO:0007051	Spindle organization	37.77	18	NEK2, NUF2, KNSTRN, KIF20A, NDC80, KIF11, KIFC1, TPX2, ESPL1, TACC3, ASPM, CDK1, SPC25, MYBL2, CDC20, CCNB1, AURKB, CENPW	3.97E-17
GO:0007059	Chromosome Segregation	40.86	18	NEK2, NUF2, KNSTRN, INCENP, T0P2A, KIF18B, HJURP, NDC80, SKA1, ESPL1, CIT, NUSAP1, BIRC5, SPC25, CDCA2, CENPF, CENPT, CENPW	1.80E-18
GO:0051276	Chromosome organization	22.63	18	NEK2, NUF2, KNSTRN, INCENP, BUB1B, T0P2A, KIF18B, NDC80, KIF22, SG02, ESPL1, CIT, CDK1, NUSAP1, CDC20, CENPI, CENPT, CENPW	1.49E-10
GO:0090068	Positive Regulation of cell cycle process	22.72	17	MSX2, RACGAP1, KIF20B, NDC80, KIF23, SG02, E2F7, CIT, CDC6, CDK1, ECT2, NUSAP1, BIRC5, CDC20, CCNB1, AURKB, UBE2C	1.36E-10
GO:0000776	Kinetochore	36.17	17	NEK2, NUF2, KNSTRN, INCENP, BUB1B, HJURP, NDC80, KIF2C, SG02, MIS18BP1, BIRC5, SPC25, AURKB, CENPF, CENPI, CENPT, CENPW	1.96E-16
GO:0051783	Regulation of Nuclear division	26.71	17	NEK2, MSX2, MKI67, BUB1B, KIF20B, NDC80, KIF11, SG02, CIT, TACC3, CDC6, NUSAP1, BIRC5, CDC20, CCNB1, AURKB, UBE2C	2.52E-12
GO:0051983	Regulation of chromosome segregation	30.92	15	NEK2, KNSTRN, MKI67, BUB1B, RACGAP1, NDC80, KIF2C, SG02, CIT, TACC3, CDC6, ECT2, BIRC5, CCNB1, AURKB	3.74E-14
GO:0030496	Midbody	25.8	15	NEK2, INCENP, RACGAP1, KIF20A, KIF20B, PRC1, KIF23, CIT, ASPM, CDK1, ECT2, BIRC5, AURKB, CEP55, CENPF	6.27E-12
GO:0072686	Mitotic spindle	28.32	13	KNSTRN, RACGAP1, KIF11, KIF22, KIF23, KIFC1, SKA1, TPX2, ESPL1, TACC3, CDK1, ECT2, NUSAP1	5.03E-13
GO:1902850	Microtubule cytoskeleton organization involved in mitosis	25.48	13	NEK2, NUF2, NDC80, KIF11, KIFC1, TPX2, TACC3, NUSAP1, SPC25, MYBL2, CDC20, CCNB1, AURKB	8.62E-12
GO:0007052	Mitotic spindle organization	27	12	NEK2, NUF2, NDC80, KIF11, KIFC1, TPX2, TACC3, SPC25, MYBL2, CDC20, CCNB1, AURKB	1.88E-12
GO:0000775	Chromosome, centromeric region	26.49	11	INCENP, MKI67, KIF20A, HJURP, KIF2C, SG02, BIRC5, AURKB, CENPF, CENPT, CENPW	3.13E-12
GO:0000777	Condensed chromosome kinetochore	27.01	11	NUF2, KNSTRN, BUB1B, HJURP, NDC80, KIF2C, MIS18BP1, BIRC5, SPC25, CENPT, CENPW	1.86E-12
GO:0032465	Regulation of cytokinesis	22.55	11	INCENP, RACGAP1, KIF20A, KIF20B, PRC1, KIF23, E2F7, CIT, CDC6, ECT2, AURKB	1.61E-10
GO:0000281	Mitotic cytokinesis	22.93	10	INCENP, CKAP2, RACGAP1, KIF20A, KIF23, CIT, ECT2, NUSAP1, BIRC5, CEP55	1.10E-10
MR 10 wk
GO:0032502	Developmental Process	7.54	52	ARHGAP5, C0L1A1, KCNK2, UGT1A1, MSLN, RH0BTB1, MSX2, NPFF, NPPA, NRP2, PER2, MKI67, T0P2A, PNMT, PLA2R1, SYPL2, TCAP, S0X4, HIVEP3, BASP1, AGT, H19, HSD17B1, PLAGL1, PFKFB1, FANCD2, CSRP2, UPK1B, SLC12A5, FA2H, DMP1, EGR3, CYS1, TDRD1, LIMS1, EYA2, CAMK2B, GLI1, GPC3, CHRNA1, MY016, CCNB2, CCND2, AURKB, WDR62, MEGF9, JPH2, DPYSL3, LDLR, LIM2. SSTR3. ATP1A3	5.29E-04
GO:0050896	Response to stimulus	6.39	52	COL1A1, ACKR2, LYC2, KCNK2, UGT1A1, MSX2, NPFF, NPPA, NRP2, PER2, OPN4, MKI67, TOP2A, PNMT, PLA2R1, ANKRD23, TCAP, SOX4, C4A, CPEB1, AGT, H19, CFD, B3GAT1, HSD17B1, PFKFB1, OAS1K, TAOK3, FANCD2, UPK1B, CNR2, SLC12A5, SULT1A1, EGR3, FCNA, CASTOR1, LIMS1, CAMK2B, FOSB, GLI1, CARD9, GPC3, B3GALT5, CHRNA1, CCND2, AURKB, DPYSL3, LDLR, MC4R, PANX2, SSTR3, ATP1A3	1.68E-03
GO:0048856	Anatomical structure development	7.06	39	ARHGAP5, COL1A1, KCNK2, UGT1A1, MSLN, MSX2, NPFF, NPPA, NRP2, MKI67, TOP2A, PNMT, SYPL2, TCAP, SOX4, AGT, H19, HSD17B1, PFKFB1, CSRP2, SLC12A5, DMP1, EGR3, CYS1, TDRD1, LIMS1, EYA2, GLI1, GPC3, MYO16, CCNB2, CCND2, WDR62, MEGF9, JPH2, DPYSL3, LIM2, SSTR3, ATP1A3	8.59E-04
GO:0006950	Response to stress	6.93	35	COL1A1, ACKR2, LYC2, KCNK2, UGT1A1, MSX2, NPFF, NPPA, PER2, MKI67, TOP2A, PNMT, PLA2R1, TCAP, SOX4, C4A, CPEB1, AGT, H19, CFD, B3GAT1, PFKFB1, OAS1K, TAOK3, FANCD2, CNR2, SLC12A5, FCNA, CAMK2B, GLI1, CARD9, DPYSL3, LDLR, PANX2, SSTR3	9.76E-04
GO:0009605	Response to external stimulus	7.39	26	COL1A1, LYC2, KCNK2, UGT1A1, NPPA, PER2, OPN4, ANKRD23, TCAP, C4A, CPEB1, AGT, H19, CFD, PFKFB1, OAS1K, UPK1B, CNR2, FCNA, CASTOR1, FOSB, CARD9, GPC3, B3GALT5, LDLR, SSTR3	6.19E-04
GO:0048513	Animal organ development	6.7	24	ARHGAP5, COL1A1, UGT1A1, MSLN, MSX2, NPFF, NPPA, NRP2, MKI67, TOP2A, PNMT, SYPL2, TCAP, SOX4, AGT, H19, HSD17B1, PFKFB1, CYS1, GLI1, GPC3, CCNB2, CCND2, LIM2	1.24E-03
GO:0009719	Response to endogenous stimulus	5.82	21	COL1A1, UGT1A1, MSX2, NPPA, TOP2A, PNMT, CPEB1, AGT, H19, PFKFB1, SULT1A1, EGR3, CASTOR1, LIMS1, CAMK2B, FOSB, CCND2, LDLR, MC4R, SSTR3, ATP1A3	2.96E-03
GO:0009628	Response to abiotic stimulus	6.82	21	COL1A1, KCNK2, NPPA, PER2, OPN4, MKI67, TOP2A, PNMT, ANKRD23, TCAP, SOX4, CPEB1, AGT, B3GAT1, FANCD2, SLC12A5, FOSB, CCND2, AURKB, LDLR, ATP1A3	1.10E-03
GO:0009725	Response to hormone	6.01	17	COL1A1, UGT1A1, MSX2, NPPA, TOP2A, PNMT, CPEB1, AGT, H19, PFKFB1, SULT1A1, FOSB, CCND2, LDLR, MC4R, SSTR3, ATP1A3	2.47E-03
GO:1901652	Response to peptide	6.74	12	COL1A1, NPPA, PNMT, CPEB1, AGT, H19, PFKFB1, CARD9, CCND2, LDLR, MC4R, ATP1A3	1.18E-03
GO:0043434	Response to peptide hormone	5.87	10	COL1A1, NPPA, PNMT, CPEB1, AGT, H19, PFKFB1, CCND2, LDLR, MC4R	2.83E-03
GO:0031960	Response to corticosteroid	5.99	8	COL1A1, UGT1A1, PNMT, H19, PFKFB1, SULT1A1, FOSB, SSTR3	2.49E-03
GO:0022836	Gated channel activity	5.94	7	KCNA4, KCNK2, KCNN3, TMEM63C, KCNJ14, CHRNA1, JPH2	2.64E-03
GO:0022839	Ion-gated channel activity	6.08	7	KCNA4, KCNK2, KCNN3, TMEM63C, KCNJ14, CHRNA1, JPH2	2.28E-03
GO:0006813	Potassium ion transport	7.2	6	KCNA4, KCNK2, KCNN3, KCNJ14, SLC12A5, ATP1A3	7.48E-04
GO:0015079	Potassium ion transmembrane transporter activity	7.53	6	KCNA4, KCNK2, KCNN3, KCNJ14, SLC12A5, ATP1A3	5.35E-04
GO:0071805	Potassium ion transmembrane transport	7.79	6	KCNA4, KCNK2, KCNN3, KCNJ14, SLC12A5, ATP1A3	4.14E-04
GO:0006956	Complement activation	6.29	3	C4A, CFD, FCNA	1.85E-03
GO:0044305	Calyx of Held	6.79	3	KCNK2, CPLX1, ATP1A3	1.13E-03
GO:0006584	Catecholamine metabolic process	5.97	3	PNMT, SULT1A1, MAOA	2.55E-03
GO:0009712	Catechol-containing compound metabolic Process	5.97	3	PNMT, SULT1A1, MAOA	2.55E-03
GO:0042923	Neuropeptide binding	6.31	2	MC4R, SSTR3	1.82E-03
GO:0008499	UDP-Galactose:beta-N-acetylglucosamine beta-1,3-Galactosyltransferase activity	6.31	2	B3GAT1, B3GALT5	1.82E-03
GO:0005184	Neuropeptide hormone activity	6.03	2	NPFF, NPPA	2.41E-03
GO:0090427	Activation of meiosis	7.54	2	MSX2, CAMK2B	5.30E-04
MR 20 wk
GO:0050896	Response to stimulus	9.02	62	COL1A1, COL1A2, LOXL2, COL3A1, LYC2, KCNE1, NPPA, PDK4, OPN4, MKI67, PLLP, TOP2A, PNMT, ICAM1, NCAM1, PRCP, PTX3, KIF22, ANKRD23, SQLE, UCP3, WNT9B, C4A, MPEG1, VCAM1, H19, ELN, FN1, LCK, FBXO32, NES, NPY, IQGAP3, PFKFB1, ATF3, ASS1, DISC1, CCL2, OAS1K, FANCD2, UPK1B, FOSL1, SULT1A1, NR4A1, FOXC2, EGR2, EGR3, RASGRP4, HABP2, PRR5L, CAMK2B, FOSB, HAS1, CARD9, CHRNA1, THBS1, HDAC9, PTPRT, CCNB1, REEP6, LDLR, ATP1A3	1.21E-04
GO:0009605	Response to external stimulus	13.57	35	COL1A1, COL3A1, LYC2, NPPA, PDK4, OPN4, ICAM1, NCAM1, PTX3, ANKRD23, UCP3, WNT9B, C4A, MPEG1, VCAM1, H19, ELN, FN1, LCK, NES, NPY, PFKFB1, ATF3, ASS1, CCL2, OAS1K, UPK1B, FOSL1, RASGRP4, FOSB, CARD9, THBS1, CCNB1, REEP6, LDLR	1.28E-06
GO:0009719	Response to endogenous stimulus	12.2	30	COL1A1, COL1A2, COL3A1, KCNE1, NPPA, TOP2A, PNMT, ICAM1, UCP3, VCAM1, H19, ELN, FN1, FBXO32, PFKFB1, ASS1, CCL2, FOSL1, SULT1A1, NR4A1, FOXC2, EGR2, EGR3, CAMK2B, FOSB, HAS1, THBS1, HDAC9, LDLR, ATP1A3	5.03E-06
GO:0009628	Response to abiotic stimulus	11.67	28	COL1A1, LOXL2, COL3A1, KCNE1, NPPA, OPN4, MKI67, TOP2A, PNMT, ICAM1, ANKRD23, UCP3, VCAM1, ELN, LCK, FBXO32, NES, NPY, DISC1, CCL2, FANCD2, FOSL1, FOSB, THBS1, CCNB1, REEP6, LDLR, ATP1A3	8.52E-06
GO:0044421	Extracellular region part	11.25	26	COL1A1, COL1A2, LOXL2, COL3A1, MSLN, COL8A1, NPPA, ADAMTS3, LRRC17, ICAM1, PTX3, GPRC5A, WNT9B, TPX2, C4A, VCAM1, ELN, FN1, GDF15, NPY, CCL2, HABP2, SCARA3, THBS1, CDCA8, LDLR	1.29E-05
GO:1901698	Response to nitrogen compound	10.9	26	COL1A1, COL1A2, COL3A1, KCNE1, NPPA, PNMT, ICAM1, NCAM1, UCP3, VCAM1, H19, FN1, PFKFB1, ASS1, CCL2, FOSL1, NR4A1, EGR2, HABP2, CAMK2B, FOSB, CARD9, THBS1, HDAC9, LDLR, ATP1A3	1.85E-05
GO:0010243	Response to organonitrogen compound	10.58	25	COL1A1, COL1A2, COL3A1, KCNE1, NPPA, PNMT, ICAM1, NCAM1, UCP3, VCAM1, H19, FN1, PFKFB1, ASS1, CCL2, FOSL1, NR4A1, EGR2, HABP2, CAMK2B, FOSB, CARD9, HDAC9, LDLR, ATP1A3	2.54E-05
GO:0009725	Response to hormone	11.51	24	COL1A1, COL1A2, NPPA, TOP2A, PNMT, ICAM1, UCP3, H19, ELN, FN1, FBXO32, PFKFB1, ASS1, CCL2, FOSL1, SULT1A1, NR4A1, FOXC2, EGR2, FOSB, THBS1, HDAC9, LDLR, ATP1A3	1.01E-05
GO:0005615	Extracellular space	10.7	22	COL1A1, COL1A2, LOXL2, COL3A1, MSLN, COL8A1, NPPA, ADAMTS3, LRRC17, ICAM1, PTX3, WNT9B, C4A, VCAM1, ELN, FN1, GDF15, NPY, CCL2, HABP2, SCARA3, THBS1	2.25E-05
GO:1901652	Response to peptide	13.38	18	COL1A1, NPPA, PNMT, ICAM1, UCP3, VCAM1, H19, FN1, PFKFB1, ASS1, CCL2, NR4A1, EGR2, HABP2, CARD9, HDAC9, LDLR, ATP1A3	1.55E-06
GO:0031960	Response to corticosteroid	14.68	14	COL1A1, PNMT, ICAM1, UCP3, H19, ELN, FN1, FBXO32, PFKFB1, ASS1, CCL2, FOSL1, SULT1A1, FOSB	4.20E-07
GO:0043434	Response to peptide hormone	9.96	14	COL1A1, NPPA, PNMT, ICAM1, UCP3, H19, FN1, PFKFB1, ASS1, CCL2, NR4A1, EGR2, HDAC9, LDLR	4.73E-05
GO:0048545	Response to steroid hormone	11.61	14	COL1A1, PNMT, ICAM1, UCP3, H19l, ELN, FN1, FBXO32, PFKFB1, ASS1, CCL2, FOSL1, SULT1A1, FOSB	9.04E-06
GO:0051384	Response to glucocorticoid	13.78	13	PNMT, ICAM1, UCP3, H19, ELN, FN1, FBXO32, PFKFB1, ASS1, CCL2, FOSL1, SULT1A1, FOSB	1.03E-06
GO:0071229	Cellular response to acid chemical	12.49	12	COL1A1, COL1A2, COL3A1, PDK4, ELN, FN1, ASS1, CCL2, CAMK2B, CCNB1, LDLR, ATP1A3	3.76E-06
GO:0030198	Extracellular matrix organization	12.63	11	COL1A1, COL1A2, LOXL2, COL3A1, COL8A1, PTX3, ELN, FN1, FOXC2, SCARA3, HAS1	3.28E-06
GO:0043062	Extracellular Structure organization	11.26	11	COL1A1, COL1A2, LOXL2, COL3A1, COL8A1, PTX3, ELN, FN1, FOXC2, SCARA3, HAS1	1.29E-05
GO:0031012	Extracellular matrix	9.42	11	COL1A1, COL1A2, LOXL2, COL3A1, COL8A1, LRRC17, PTX3, ELN, FN1, SCARA3, THBS1	8.10E-05
GO:0005201	Extracellular matrix structural constituent	15.37	7	COL1A1, COL1A2, COL3A1, COL8A1, ELN, FN1, SCARA3	2.11E-07
GO:0071549	Cellular response to dexamethasone stimulus	10.19	6	PNMT, ICAM1, ELN, FBXO32, ASS1, CCL2	3.75E-05
GO:0071398	Cellular response to fatty acid	9.9	6	PDK4, FN1, ASS1, CCL2, CCNB1, LDLR	5.01E-05
GO:0044344	Cellular response to fibroblast growth factor stimulus	9.98	5	COL1A1, ELN, CCL2, NR4A1, EGR3	4.64E-05
GO:0051310	Metaphase plate congression	9.59	5	KIF22, GEM, CDCA8, CCNB1, CENPF	6.82E-05
GO:0071774	Response to fibroblast growth factor	9.24	5	COL1A1, ELN, CCL2, NR4A1, EGR3	9.72E-05
GO:0005584	Collagen type I Trimer	9.05	2	COL1A1, COL1A2	1.17E-04
MR 40 wk
GO:0016020	Membrane	11.3	120	LPIN1, ZMYND10, GPR22, PDE3A, PDE4D, HSPA13, ADCY1, KCND2, KCNK3, MPP5, MGRN1, ACOX1, PIP4K2B, CHRM2, OGDH, NPR3, MYOT, BMPR1A, ADRB1, PDPK1, SLC4A4, GSK3B, PHKB, CAVIN4, NCAM1, NCEH1, CLIC4, SC5D, SYNPO, CREB3L2, KIF1B, PI4K2A, MFAP3L, SEC23A, PRKAA2, PRKAB2, PRKACB, ZBTB16, BACE1, FAM210B, TGFA, SPTB, AKAP6, SLCO3A1, CORIN, CPD, MVB12B, CPNE3, HK2, ADIPOR2, MCU, NIPSNAP2, ANKH, YAP1, ANGPT1, ATF6, CAP2, RASL10B, ASS1, CBLC, XPR1, CDS1, CDS2, CDV3, MAN1A1, PRKAR2A, MTFR1, ZHX2, EXTL3, NAPEPLD, KPNA4, ZMPSTE24, SLC16A1, FOXO3, SLMAP, ENAH, LIN7C, ERC1, DNM1L, PRKCE, LIMS1, SLC38A1, MAGT1, CAMK2B, GJA3, GLG1, HCN2, HCN4, PDLIM5, PTGFR, PARD3B, GPAM, FZD1, B3GALT2, CHRNA1, PTPRN, PTPRT, CDH22, LMAN1, CSNK2A1, GNA13, CCND2, INSR, GNAO1, IRS2, ADRA1A, JPH1, JPH2, NDUFA1, LOC500331, ABCB7, GPD1L, PCYT1A, ABHD2, PCYOX1, MAOA, SLC25A30, LOC108348108, PCDH7, ATP1A3	1.24E-05
GO:0048513	Animal organ development	13.28	53	LPIN1, ARHGAP5, PDE4D, COL11A2, ADCY1, NFIA, NFIB, KCNK3, QK, TGFBR3, BMPR1A, NTN1, ARID5B, CAVIN4, AMIGO1, NCAM1, CREB3L2, KIF5B, NCOA2, RPA1, ZBTB16, TBX5, SPTB, ESRRB, AK4, ADIPOR2, LBH, WFS1, ALPK3, RRM2B, YAP1, MICAL2, ANGPT1, ATF6, ASS1, PPARA, ZMPSTE24, TEAD1, FGF1, PROX1, HCN2, PDLIM5, HYAL1, TGFB2, SOX12, CDH22, CSNK2A1, CCND2, INSR, IRS2, ADRA1A, JPH1, LIFR	1.71E-06
GO:0005886	Plasma membrane	10.85	76	GPR22, PDE4D, HSPA13, ADCY1, KCND2, KCNK3, MPP5, MGRN1, ACOX1, PIP4K2B, CHRM2, MYOT, BMPR1A, ADRB1, PDPK1, GSK3B, PHKB, CAVIN4, NCAM1, CLIC4, PI4K2A, MFAP3L, PRKACB, ZBTB16, BACE1, TGFA, SPTB, AKAP6, SLCO3A1, CORIN, CPD, MVB12B, CPNE3, HK2, ADIPOR2, ANKH, ANGPT1, CAP2, CBLC, XPR1, CDV3, PRKAR2A, MTFR1, ZHX2, SLC16A1, SLMAP, ENAH, LIN7C, DNM1L, PRKCE, LIMS1, SLC38A1, MAGT1, GJA3, HCN2, HCN4, PTGFR, GPAM, FZD1, CHRNA1, PTPRN, PTPRT, CDH22, CSNK2A1, INSR, IRS2, ADRA1A, JPH1, JPH2, LOC500331, GPD1L, PCYT1A, ABHD2, PCYOX1, PCDH7, ATP1A3	1.93E-05
GO:0032501	Multicellular organismal process	10.63	71	PDE3A, PDE4D, COL11A2, ADCY1, NFIB, NFIC, KCND2, TGFBR3, NPR3, BMPR1A, ADRB1, GSK3B, ARID5B, HIPK2, OPA3, BTBD9, NCAM1, CLIC4, REST, DDAH1, SYNPO, NCOA2, RPA1, PRKAA2, ZBTB16, TBX5, SOBP, ESRRB, CORIN, FTO, ADIPOR2, IGFBP5, WFS1, IF4EBP2, MN1, FBXO32, RRM2B, YAP1, ANGPT1, ATF6, RASL10B, PPARA, ZHX2, GFPT1, NAPEPLD, ZMPSTE24, TBX20, FOXO3, SLMAP, MTURN, XPNPEP3, TENT5C, DNM1L, LIMS1, SLC38A1, PROX1, MAGT1, CAMK2B, MAP1A, GJA3, HCN4, TGFB2, CHRNA1, MBNL1, GNA13, CCND2, INSR, ADRA1A, ADRA2C, JPH2, ATP1A3	2.42E-05
GO:0003008	System process	11.74	39	PDE3A, PDE4D, COL11A2, ADCY1, NFIB, NFIC, KCND2, NPR3, ADRB1, OPA3, BTBD9, NCAM1, REST, DDAH1, SYNPO, SOBP, CORIN, WFS1, EIF4EBP2, FBXO32, RRM2B, YAP1, ANGPT1, ATF6, RASL10B, ZMPSTE24, TBX20, SLMAP, XPNPEP3, DNM1L, MAGT1, CAMK2B, MAP1A, GJA3, CHRNA1, CCND2, ADRA1A, ADRA2C, ATP1A3	7.94E-06
GO:0071495	Cellular response to endogenous stimulus	11.46	35	LPIN1, PDE3A, PDE4D, BMPR1A, PDPK1, GSK3B, AMIGO1, REST, KIF1B, NCOA2, PRKAA2, BACE1, FAM210B, AKAP6, SESN3, CPEB4, IGFBP5, FBXO32, ASS1, GFPT1, ZMPSTE24, FOXO3, PRKCE, LIMS1, CAMK2B, HCN2, HCN4, HYAL1, PTGFR, GPAM, FZD1, CCND2, INSR, IRS2, ATP1A3	1.06E-05
GO:0045927	Positive regulation of growth	12.39	18	TGFBR3, BMPR1A, ADRB1, NTN1, GSK3B, NCAM1, TBX5, AKAP6, WFS1, YAP1, EXTL3, TBX20, PROX1, HYAL1, GPAM, TGFB2, CSNK2A1, INSR	4.15E-06
GO:0055024	Regulation of cardiac muscle tissue development	13.31	11	TGFBR3, BMPR1A, ADRB1, GSK3B, NCAM1. TBX5, AKAP6, YAP1, PARA, TBX20, JPH2	1.65E-06
GO:0030165	PDZ domain binding	12.45	11	ACOX1, TGFBR3, ADRB1, NCOA2, CRIM1, EXOC4, LIN7C, ERC1, HCN2, SNTB1, FZD1	3.91E-06
GO:0060420	Regulation of heart growth	12.92	10	TGFBR3, BMPR1, AADRB1, NCAM1, TBX5, AKAP6, YAP1, PPARA, TBX20, PROX1	2.45E-06
GO:0019935	Cyclic-nucleotide-mediated signaling	10.8	11	PDE3A, PDE4D, PDE7B, ADCY1, ADRB1, EIF4EBP2, PRKAR2A, PTGFR, GNA13, ADRA1A, ADRA2C	2.03E-05
GO:0030018	Z disc	10.56	11	NEBL, MYOT, SYNPO2, CAVIN4, SPHKAP, SYNPO, FBXO32, PDLIM5, ADRA1A, JPH1, JPH2	2.59E-05
GO:0019933	cAMP-mediated signaling	10.96	10	PDE3A, PDE4D, PDE7B, ADCY1, ADRB1, EIF4EBP2, PTGFR, GNA13, ADRA1A, ADRA2C	1.74E-05
GO:0046620	Regulation of organ growth	10.41	10	TGFBR3, BMPR1A, ADRB1, NCAM1, TBX5, AKAP6, YAP1, PPARA, TBX20, PROX1	3.02E-05
GO:0060421	Positive regulation of heart growth	15.51	9	TGFBR3, BMPR1A, ADRB1, NCAM1, TBX5, AKAP6, YAP1, TBX20, PROX1	1.84E-07
GO:0055025	Positive regulation of cardiac muscle tissue development	14.17	9	TGFBR3, BMPR1A, ADRB1, GSK3B, NCAM1, TBX5, AKAP6, YAP1, TBX20	6.98E-07
GO:0046622	Positive regulation of organ growth	14	9	TGFBR3, BMPR1A, ADRB1, NCAM1, TBX5, AKAP6, YAP1, TBX20, PROX1	8.30E-07
GO:0055021	Regulation of cardiac muscle tissue growth	11.45	9	TGFBR3, BMPR1A, ADRB1, NCAM1, TBX5, AKAP6, YAP1, PPARA, TBX20	1.07E-05
GO:0045844	Positive regulation of striated muscle tissue development	11.21	9	TGFBR3, BMPR1A, ADRB1, GSK3B, NCAM1, TBX5, AKAP6, YAP1, TBX20	1.35E-05
GO:0048636	Positive regulation of muscle organ development	11.21	9	TGFBR3, BMPR1A, ADRB1, GSK3B, NCAM1, TBX5, AKAP6, YAP1, TBX20	1.35E-05
GO:1901863	Positive regulation of muscle tissue development	11.1	9	TGFBR3, BMPR1A, ADRB1, GSK3B, NCAM1, TBX5, AKAP6, YAP1, TBX20	1.51E-05
GO:2000736	Regulation of stem cell differentiation	11.52	8	BMPR1A, GSK3B, REST, TBX5, ESRRB, LBH, YAP1, TGFB2	9.95E-06
GO:0055023	Positive regulation of cardiac muscle tissue growth	13.54	8	TGFBR3, BMPR1A, ADRB1, NCAM1, TBX5, AKAP6, YAP1, TBX20	1.32E-06
GO:0060045	Positive regulation of cardiac muscle cell proliferation	11.47	6	TGFBR3, BMPR1A, NCAM1, TBX5, YAP1, TBX20	1.04E-05
GO:1902459	Positive regulation of stem cell population maintenance	15.58	5	REST, ESRRB, LBH, YAP1, TEAD1	1.71E-07

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GO, Gene Ontology; MR, mitral regurgitation, BP, Biological Process.

TABLE E8.

Three PANTHER-classified pathways of the up- and down-regulated genes after MR onset

MR 2-wk up-regulated genes	MR 2-wk down-regulated genes
p53 pathway	p53 pathway
Thyrotropin-releasing hormone receptor signaling pathway	Endothelin signaling pathway
DNA replication	Heterotrimeric G-protein signaling pathway-Gi alpha– and Gs alpha–mediated pathway
MR 10-wk up-regulated genes	MR 10-wk down-regulated genes

Angiotensin II-stimulated signaling through G proteins and beta-arrestin	Integrin signaling pathway
Oxidative stress response	DNA replication
Serine glycine biosynthesis	Nicotinic acetylcholine receptor signaling pathway
MR 20-wk up-regulated genes	MR 20-wk down-regulated genes

TGF-beta signaling pathway	p53 pathway
Oxidative stress response	Inflammation mediated by cytokine and chemokine signaling pathway
Adrenaline and noradrenaline biosynthesis	Integrin signaling pathway
MR 40-wk up-regulated genes	MR 40-wk down-regulated genes

Heterotrimeric G-protein signaling pathway–Gi alpha and Gs alpha–mediated pathway	EGF receptor signaling pathway
Dopamine receptor mediated signaling pathway	Integrin signaling pathway
Adrenaline and noradrenaline biosynthesis	Cell cycle

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MR, Mitral regurgitation; TGF, transforming growth factor; EGF, epidermal growth factor.

TABLE E9.

KEGG analysis of the significantly altered pathways, comparing MR versus sham differentially expressed genes

Category	KEGG pathway	Enrichment score	Gene count	Gene symbol	P value
MR 2 wk
path:rno04110	Cell cycle	13.17	9	BUB1B, CCNA2, CCNB1, CCNB2, CDC20, CDC6, CDK1, E2F1, ESPL1	1.91E-06
path:rno04218	Cellular senescence	10.22	9	CCNA2, CCNB1, CCNB2, CDK1, E2F1, FOXM1, IGFBP3, IL6, MYBL2	3.65E-05
path:rno04115	p53 signaling pathway	7.49	5	CCNB1, CCNB2, CDK1, IGFBP3, RRM2	5.61E-04
path:rno04914	Progesterone-mediated oocyte maturation	6.78	5	CCNA2, CCNB1, CCNB2, CDK1, KIF22	1.14E-03
path:rno05166	Human T-cell leukemia virus 1 infection	6.59	8	BUB1B, CCNA2, CCNB2, CDC20, E2F1, ESPL1, IL6, MSX2	1.37E-03
path:rno04614	Renin-angiotensin system	6.55	3	CMA1, CPA3, MCPT1L1	1.43E-03
path:rno04061	Viral protein interaction with cytokine and cytokine receptor	5.71	4	CCL7, CXCL14, IL6, PF4	3.33E-03
path:rno04114	Oocyte meiosis	5.62	5	CCNB1, CCNB2, CDC20, CDK1, ESPL1	3.61E-03
path:rno04060	Cytokine-cytokine receptor interaction	4.97	6	CCL7, CXCL14, EPOR, IL6, LTB, PF4	6.92E-03
path:rno04657	IL-17 signaling pathway	3.48	3	CCL7, FOSB, IL6	.03
path:rno05020	Prion diseases	3.26	2	C6, IL6	.04
MR 10 wk
path:rno04925	Aldosterone synthesis and secretion	6.54	5	AGT, ATP1A3, CAMK2B, LDLR, NPPA	1.45E-03
path:rno00350	Tyrosine metabolism	6.37	3	HPD, MAOA, PNMT	1.70E-03
path:rno04927	Cortisol synthesis and secretion	6.25	4	AGT, KCNA4, KCNK2, LDLR	1.93E-03
path:rno00360	Phenylalanine metabolism	5.17	2	HPD, MAOA	5.67E-03
path:rno04080	Neuroactive ligand-receptor interaction	5.17	6	AGT, CHRNA1, CNR2, MC4R, NPFF, SSTR3	5.70E-03
path:rno04934	Cushing syndrome	4.65	5	AGT, CAMK2B, KCNA4, KCNK2, LDLR	9.53E-03
path:rno00982	Drug metabolism - cytochrome P450	4.52	3	FMO2, MAOA, UGT1A1	.01
path:rno00340	Histidine metabolism	4.43	2	ASPA, MAOA	.01
path:rno05031	Amphetamine addiction	4.02	3	CAMK2B, FOSB, MAOA	.02
path:rno04971	Gastric acid secretion	3.82	3	ATP1A3, CAMK2B, KCNK2	.02
path:rno04974	Protein digestion and absorption	3.65	3	ATP1A3, COL17A1, COL1A1	.03
path:rno04911	Insulin secretion	3.44	3	ATP1A3, CAMK2B, KCNN3	.03
path:rno00250	Alanine, aspartate, and glutamate metabolism	3.36	2	AGT, ASPA	.03
path:rno00140	Steroid hormone biosynthesis	3.06	2	HSD17B1, UGT1A1	.05
path:rno00260	Glycine, serine, and threonine metabolism	3.06	2	AGT, MAOA	.05
path:rno04913	Ovarian steroidogenesis	3.06	2	HSD17B1, LDLR	.05
path:rno05030	Cocaine addiction	2.9	2	FOSB, MAOA	.05
MR 20 wk
path:rno04974	Protein digestion and absorption	12.4	7	ATP1A3, COL1A1, COL1A2, COL3A1, ELN, PRCP, SLC1A5	4.11E-06
path:rno04933	AGE-RAGE signaling pathway in diabetic complications	9.29	7	CCL2, COL1A1, COL1A2, COL3A1, FN1, ICAM1, VCAM1	9.22E-05
path:rno05144	Malaria	6.37	4	CCL2, ICAM1, THBS1, VCAM1	1.71E-03
path:rno04925	Aldosterone synthesis and secretion	6.22	5	ATP1A3, CAMK2B, LDLR, NPPA, NR4A1	1.99E-03
path:rno00350	Tyrosine metabolism	6.17	3	HPD, MAOA, PNMT	2.09E-03
path:rno05143	African trypanosomiasis	5.4	3	ICAM1, NPPA, VCAM1	4.54E-03
path:rno04512	ECM-receptor interaction	5.11	4	COL1A1, COL1A2, FN1, THBS1	6.06E-03
path:rno00360	Phenylalanine metabolism	5.03	2	HPD, MAOA	6.52E-03
path:rno05146	Amoebiasis	4.56	4	COL1A1, COL1A2, COL3A1, FN1	.01
path:rno04934	Cushing syndrome	4.39	5	CAMK2B, LDLR, NR4A1, RASD1, WNT9B	.01
path:rno05031	Amphetamine addiction	3.83	3	CAMK2B, FOSB, MAOA	.02
path:rno05034	Alcoholism	3.64	4	FOSB, HDAC9, MAOA, NPY	.03
path:rno04657	IL-17 signaling pathway	3.3	3	CCL2, FOSB, FOSL1	.04
path:rno04115	p53 signaling pathway	3.26	3	CCNB1, CCNB2, THBS1	.04
path:rno05418	Fluid shear stress and atherosclerosis	2.92	4	ASS1, CCL2, ICAM1, VCAM1	.05
MR 40 wk
path:rno04932	Nonalcoholic fatty liver disease (NAFLD)	14.21	36	ADIPOR2, AKT2, BAX, COX4I1, COX4I2, COX6B1, COX6C, COX7A2, COX7C, COX8B, ERN1, GSK3B, INSR, IRS1, IRS2, ITCH, NDUFA1, NDUFA10L1, NDUFA11, NDUFA6, NDUFA7, NDUFB2, NDUFB3, NDUFB7, NDUFB9, NDUFS1, NDUFS5, NDUFS6, PIK3R1, PPARA, PRKAA2, PRKAB2, RXRA, UQCR10, UQCR11, UQCRB	6.76E-07
path:rno03010	Ribosome	13.9	35	FAU, MRPL14, MRPL27, RPL11, RPL13A, RPL18, RPL21, RPL22, RPL23A, RPL27, RPL27A, RPL30, RPL31, RPL32, RPL35A, RPL36, RPL36AL, RPL37A, RPL38, RPL39, RPL41, RPLP1, RPLP2, RPS10, RPS12, RPS15, RPS15A, RPS17, RPS18, RPS19, RPS24, RPS27A, RPS28, RPS8, RPS9	9.17E-07
path:rno04714	Thermogenesis	13.04	44	ADCY1, ADCY5, ATF2, ATP5E, ATP5G3, ATP5H, ATP5I, ATP5J2, ATP5L, COA5, COX17, COX4I1, COX4I2, COX6B1, COX6C, COX7A2, COX7C, COX8B, CREB3L2, MAPK11, NDUFA1, NDUFA10L1, NDUFA11, NDUFA6, NDUFA7, NDUFB2,. NDUFB3, NDUFB7, NDUFB9, NDUFS1, NDUFS5, NDUFS6, PPARGC1A, PRKAA2, PRKAB2, PRKACB, PRKG1, RPS6KB2, SMARCC1, TSC1, UQCR10, UQCR11, UQCRB, ZFP516	2.18E-06
path:rno00190	Oxidative phosphorylation	13.01	30	ATP5E, ATP5G3, ATP5H, ATP5I, ATP5J2, ATP5L, ATP6V0B, COX17, COX4I1, COX4I2, COX6B1, COX6C, COX7A2, COX7C, COX8B, NDUFA1, NDUFA10L1, NDUFA11, NDUFA6, NDUFA7, NDUFB2, NDUFB3, NDUFB7, NDUFB9, NDUFS1, NDUFS5, NDUFS6, UQCR10, UQCR11, UQCRB	2.23E-06
path:rno05016	Huntington disease	11.36	36	AP2S1, ATP5E, ATP5G3, ATP5H, BAX, BBC3, COX4I1, COX4I2, COX6B1, COX6C, COX7A2, COX7C, COX8B, CREB3L2, DCTN4, GNAQ, NDUFA1, NDUFA10L1, NDUFA11, NDUFA6, NDUFA7, NDUFB2, NDUFB3, NDUFB7, NDUFB9, NDUFS1, NDUFS5, NDUFS6, POLR2H, POLR2I, POLR2L, PPARGC1A, REST, UQCR10, UQCR11, UQCRB	1.16E-05
path:rno05010	Alzheimer disease	10.43	35	APH1B, ATF6, ATP5E, ATP5G3, ATP5H, BACE1, COX4I1, E60COX4I2, COX6B1, COX6C, COX7A2, COX7C, COX8B, ERN1, GNAQ, GSK3B, IDE, LPL, NDUFA1, NDUFA10L1, NDUFA11, NDUFA6, NDUFA7, NDUFB2, NDUFB3, NDUFB7, NDUFB9, NDUFS1, NDUFS5, NDUFS6, NOS1, PPP3CB, UQCR10, UQCR11, UQCRB	2.94E-05
path:rno05012	Parkinson disease	9.07	28	ADCY5, ATP5E, ATP5G3, ATP5H, COX4I1, COX4I2, COX6B1, COX6C, COX7A2, COX7C, COX8B, NDUFA1, NDUFA10L1, NDUFA11, NDUFA6, NDUFA7, NDUFB2, NDUFB3, NDUFB7, NDUFB9, NDUFS1, NDUFS5, NDUFS6, PRKACB, PRKN, UQCR10, UQCR11, UQCRB	1.15E-04
path:rno04211	Longevity regulating pathway	7.98	20	ADCY1, ADCY5, ADIPOR2, AKT2, ATF2, BAX, CREB3L2, FOXO1, FOXO3, INSR, IRS1, IRS2, PIK3R1, PPARGC1A, PRKAA2, PRKAB2, PRKACB, RPS6KB2, SESN3, TSC1	3.43E-04
path:rno00564	Glycerophospholipid metabolism	7.98	20	AGPAT4, CDS1, CDS2, CHPT1, DGKG, DGKQ. DGKZ, GPAM, GPAT3, GPD1, GPD1L, GPD2, LCAT, LPCAT2, LPGAT1, LPIN1, PCYT1A, PLA2G2A, PLA2G4B, PLD4	3.43E-04
path:rno04723	Retrograde endocannabinoid signaling	7.54	25	ADCY1, ADCY5, GNAO1, GNAQ, GNG11, GNG7, GNGT2, KCNJ3, MAPK11, NAPEPLD, NDUFA1, NDUFA10L1, NDUFA11, NDUFA6, NDUFA7, NDUFB2, NDUFB3, NDUFB7, NDUFB9, NDUFS1, NDUFS5, NDUFS6, PRKACB, PRKCG, SLC17A7	5.30E-04
path:rno04922	Glucagon signaling pathway	7.24	21	ACACA, AKT2, ATF2, CDK1, CAMK2B, CAMK2G, CREB3L2, FBP2, FOXO1, GCGR, GNAQ, PCK1, PCK2, PHKA1, PHKB, PPARA, PPARGC1A, PPP3CB, PRKAA2, PRKAB2, PRKACB	7.16E-04
path:rno04910	Insulin signaling pathway	6.88	26	ACACA, AKT2, CRK, CRKL, FBP2, FOXO1, GSK3B, HK2, INSR, IRS1, IRS2, PCK1, PCK2, PDPK1, PHKA1, PHKB, PIK3R1, PPARGC1A, PPP1R3A, PRKAA2, PRKAB2, PRKACB, PRKAR2A, RPS6KB2, SH2B2, TSC1	1.03E-03
path:rno05032	Morphine addiction	6.87	17	ADCY1, ADCY5, ADORA1, ARRB1, GNAO1, GNG11, GNG7, GNGT2, KCNJ3, PDE1C, PDE3A, PDE4C, PDE4D, PDE7A, PDE7B, PRKACB, PRKCG	1.04E-03
path:rno04213	Longevity regulating pathway—multiple species	6.63	15	ADCY1, ADCY5, AKT2, EIF4EBP2, FOXO1, FOXO3, HSPA1B, INSR, IRS1, IRS2, PIK3R1, PRKAA2, PRKAB2, PRKACB, RPS6KB2	1.32E-03
path:rno04152	AMPK signaling pathway	6.54	24	ACACA, ADIPOR2, ADRA1A, AKT2, CREB3L2, FBP2, FOXO1, FOXO3, INSR, IRS1, IRS2, PCK1, PCK2, PDPK1, PFKFB4, PIK3R1, PPARGC1A, PPP2R3A, PPP2R5C, PRKAA2, PRKAB2, RAB14, RPS6KB2, TSC1	1.44E-03
path:rno04923	Regulation of lipolysis in adipocytes	6.43	13	ABHD5, ADCY1, ADCY5, ADORA1, ADRB1, AKT2, FABP4, INSR, IRS1, IRS2, PIK3R1, PRKACB, PRKG1	1.61E-03
path:rno04713	Circadian entrainment	5.42	18	ADCY1, ADCY5, CAMK2B, CAMK2G, GNAO1, GNAQ, GNG11, GNG7, GNGT2, KCNJ3, NOS1, NOS1AP, PER3, PRKACB, PRKCG, PRKG1, RASD1, RYR2	4.44E-03
path:rno04725	Cholinergic synapse	5.19	19	ADCY1, ADCY5, AKT2, BCL2, CAMK2B, CAMK2G, CHRM2, CREB3L2, GNAO1, GNAQ, GNG11, GNG7, GNGT2, KCNJ12, KCNJ2, KCNJ3, PIK3R1, PRKACB, PRKCG	5.59E-03
path:rno00230	Purine metabolism	5.04	21	ADCY1, ADCY5, AK4, AK6, AMPD1, AMPD3, ENTPD5, GDA, NME1, NME2, NME3, NT5C, NT5C1A, PDE1C, PDE3A, PDE4C, PDE4D, PDE7A, PDE7B, RRM2, RRM2B	6.45E-03
path:rno04260	Cardiac muscle contraction	4.64	15	ATP1A3, CACNA2D1, COX4I1, COX4I2, COX6B1, COX6C, COX7A2, COX7C, COX8B, MYL4, RYR2, TNNI3, UQCR10, UQCR11, UQCRB	9.65E-03
path:rno04931	Insulin resistance	4.53	20	AKT2, CREB3L2, FOXO1, GFPT1, GSK3B, INSR, IRS1, IRS2, MGEA5, PCK1, PCK2, PDPK1, PIK3R1, PPARA, PPARGC1A, PPP1R3A, PRKAA2, PRKAB2, PRKCE, RPS6KB2	.01
path:rno04911	Insulin secretion	4.47	14	ADCY1, ADCY5, ATF2, ATP1A3, CAMK2B, CAMK2G, CREB3L2, GNAQ, KCNMB4, KCNN4, PRKACB, PRKCG, RYR2, STX1A	.01
path:rno04022	cGMP-PKG signaling pathway	4.4	26	ADCY1, ADCY5, ADORA1, ADRA1A, ADRA2B, ADRA2C, ADRB1, AKT2, ATF2, ATP1A3, CREB3L2, GATA4, GNA13, GNAQ, INSR, IRS1, IRS2, KCNMB4, MEF2A, MEF2D, MYL9, MYLK3, PDE3A, PPP3CB, PRKCE, PRKG1	.01
path:rno04068	FoxO signaling pathway	4.36	22	AKT2, CCNB2, CCND2, EGF, FBXO32, FOXO1, FOXO3, HOMER1, INSR, IRS1, IRS2, MAPK11, PCK1, PCK2, PDPK1, PIK3R1, PLK1, PRKAA2, PRKAB2, SETD7, TGFB2, TGFBR2	.01
path:rno00240	Pyrimidine metabolism	4.28	11	DCTD, ENTPD5, NME1, NME2, NME3, NT5C, NT5C1A, RRM2, RRM2B, TK1, UCK2	.01
path:rno04928	Parathyroid hormone synthesis, secretion, and action	4.23	17	ADCY1, ADCY5, ARRB1, ATF2, BCL2, CREB3L2, GNA13, GNAQ, LRP6, MEF2A, MEF2D, PDE4C, PDE4D, PRKACB, PRKCG, RXRA, VDR	.01
path:rno04914	Progesterone-mediated oocyte maturation	4.17	16	ADCY1, ADCY5, AKT2, ANAPC11, CCNB2, CDC27, CDK1, CPEB1, CPEB4, KIF22, MAD2L2, MAPK11, PIK3R1, PKMYT1, PLK1, PRKACB	.02
path:rno04728	Dopaminergic synapse	4.1	21	ADCY5, AKT2, ATF2, CAMK2B, CAMK2G, CREB3L2, GNAO1, GNAQ, GNG11, GNG7, GNGT2, GSK3B, KCNJ3, KIF5B, MAOA, MAPK11, PPP2R3A, PPP2R5C, PPP3CB, PRKACB, PRKCG	.02
path:rno04724	Glutamatergic synapse	4.01	17	ADCY1, ADCY5, GNAO1, GNAQ, GNG11, GNG7, GNGT2, GRIK4, HOMER1, KCNJ3, PLA2G4B, PPP3CB, PRKACB, PRKCG, SHANK1, SLC17A7, SLC38A1	.02
path:rno04114	Oocyte meiosis	3.94	19	ADCY1, ADCY5, ANAPC11, CAMK2B, CAMK2G, CCNB2, CDC20, CDC27, CDK1, CPEB1, CPEB4, MAD2L2, MAPK11, PKMYT1, PLK1, PPP2R5C, PPP3CB, PRKACB, SLK	.02
path:rno04926	Relaxin signaling pathway	3.92	20	ADCY1, ADCY5, AKT2, ARRB1, ATF2, COL1A1, COL3A1, CREB3L2, GNA15, GNAO1, GNG11, GNG7, GNGT2, MAP2K4, MAPK11, NOS1, PIK3R1, PRKACB, RXFP1, TGFBR2	.02
path:rno00512	Mucin type O-glycan biosynthesis	3.82	6	C1GALT1, GALNT1, GALNT10, GALNT17, GALNT6, ST3GAL1	.02
path:rno04978	Mineral absorption	3.8	9	ATOX1, ATP1A3, ATP7A, MT2A, SLC26A6, SLC26A9, SLC9A3, STEAP2, VDR	.02
path:rno05231	Choline metabolism in cancer	3.8	17	AKT2, CHPT1, DGKG, DGKQ, DGKZ, EGF, PCYT1A, PDGFA, PDGFD, PDPK1, PIK3R1, PLA2G4B, PLCG1, PRKCG, RPS6KB2, SLC22A1, TSC1	.02
path:rno04916	Melanogenesis	3.77	15	ADCY1, ADCY5, CAMK2B, CAMK2G, CREB3L2, FZD1, FZD5, FZD8, GNAO1, GNAQ, GSK3B, POMC, PRKACB, PRKCG, WNT9B	.02
path:rno04012	ErbB signaling pathway	3.55	15	AKT2, CAMK2B, CAMK2G, CRK, CRKL, EGF, GSK3B, MAP2K4, PAK2, PAK6, PIK3R1, PLCG1, PRKCG, RPS6KB2, TGFA	.03
path:rno00100	Steroid biosynthesis	3.5	5	DHCR24, EBP, HSD17B7, SC5D, SOAT1	.03
path:rno04920	Adipocytokine signaling pathway	3.41	13	ADIPOR2, AKT2, IRS1, IRS2, PCK1, PCK2, POMC, PPARA, PPARGC1A, PRKAA2, PRKAB2, RXRA, TRADD	.03
path:rno04371	Apelin signaling pathway	3.39	21	ADCY1, ADCY5, AKT2, APLNR, GNA13, GNAQ, GNG11, GNG7, GNGT2, MEF2A, MEF2D, MYL4, MYLK3, NOS1, PPARGC1A, PRKAA2, PRKAB2, PRKACB, PRKCE, RPS6KB2, RYR2	.03
path:rno04020	Calcium signaling pathway	3.06	24	ADCY1, ADRA1A, ADRB1, CAMK2B, CAMK2G, CHRM2, GNA15, GNAQ, MCU, MYLK3, NOS1, NTSR1, PDE1C, PHKA1, PHKB, PLCE1, PLCG1, PPP3CB, PRKACB, PRKCG, PTGFR, RYR2, TBXA2R, TPCN2	.05
path:rno04971	Gastric acid secretion	3.04	11	ADCY1, ADCY5, ATP1A3, CAMK2B, CAMK2G, GNAQ, KCNE2, KCNJ2, MYLK3, PRKACB, PRKCG	.05
path:rno00531	Glycosaminoglycan degradation	2.91	4	ARSB, GNS, HYAL1, IDUA	.05

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KEGG, Kyoto Encyclopedia of Genes and Genomes; ECM, extracellular matrix; IL, interleukin.

Footnotes

Webcast

You can watch a Webcast of this AATS meeting presentation by going to: https://aats.blob.core.windows.net/media/20AM/Presentations/Invasive%20Hemodynamics%20and%20Transcript.mp4.

graphic file with name nihms-1637588-f0013.jpg

Conflict of Interest Statement

M.P. received consulting fees from Heart Repair Technologies, Inc, which did not have any role in this study. All other authors reported no conflicts of interest.

The Journal policy requires editors and reviewers to disclose conflicts of interest and to decline handling or reviewing manuscripts for which they may have a conflict of interest. The editors and reviewers of this article have no conflicts of interest.

References

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Supplementary Materials

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[R1] 1.Nkomo VT, Gardin JM, Skelton TN, Gottdiener JS, Scott CG, Enriquez-Sarano M. Burden of valvular heart diseases: a population-based study. Lancet. 2006;368:1005–11. [DOI] [PubMed] [Google Scholar]

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PERMALINK

Hemodynamic and transcriptomic studies suggest early left ventricular dysfunction in a preclinical model of severe mitral regurgitation

Daniella Corporan, BS

Daisuke Onohara, MD, PhD

Alan Amedi, BS

Maher Saadeh, BS

Robert A Guyton, MD

Sandeep Kumar, PhD

Muralidhar Padala, PhD

Abstract

Objective:

Methods:

Results:

Conclusions:

Graphical Abstract

FIGURE 1.

METHODS

Ethical Statement

Animal Selection

Surgical Procedures

FIGURE 2.

Cardiac Echocardiography

Invasive Hemodynamic Measurements

Tissue Collection and RNA Seq

Statistical Analysis

RESULTS

Validation of Severe MR and Overall Mortality

Longitudinal LV Chamber Remodeling

FIGURE 3.

TABLE 1.

LV PV Loops and Myocardial Energetics

FIGURE 4.

FIGURE 5.

FIGURE 6.

Longitudinal Transcriptomic Alterations in the LV

FIGURE 7.

DISCUSSION

Limitations

Supplementary Material

CENTRAL MESSAGE.

PERSPECTIVE.

Acknowledgments

Abbreviations and Acronyms

Discussion

FIGURE E1.

FIGURE E2.

FIGURE E3.

TABLE E1.

TABLE E2.

TABLE E3.

TABLE E4.

TABLE E5.

TABLE E6.

TABLE E7.

TABLE E8.

TABLE E9.

Footnotes

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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