Analysis of differentially expressed genes related to the development of diabetic cardiomyopathy in the myocardium of type-2 diabetic rats

doi:10.21203/rs.3.rs-941874/v1

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Analysis of differentially expressed genes related to the development of diabetic cardiomyopathy in the myocardium of type-2 diabetic rats

https://doi.org/10.21203/rs.3.rs-941874/v1

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Background

Diabetic cardiomyopathy (DCM) contributes to type-II diabetics (T2DM) patient mortality. However, the molecular mechanism of DCM remains unclarified.

Results

The present study analyzed the differentially expressed genes (DEGs) related to the occurrence and development of DCM. Furthermore, T2DM rat animal models with different courses of DCM were established, and these were confirmed by the results of the oral glucose tolerance test (OGTT), the blood glucose and lipid profile examination, and M-echocardiographic results. Then, transcriptome sequencing was utilized to screen the DCM-related genes. A total 18,410 genes and 19,184 unigenes in each group obtained the Gene Ontology (GO) annotation. The bioinformatics analysis results revealed that there were 360 DEGs in the 8w-T2DM group, including 191 upregulated genes and 169 downregulated genes, and 381 DEGs in the 16w-T2DM group, including 258 upregulated genes and 123 downregulated genes. The DEGs were enriched in 19 cellular components, 21 molecular functions, and 22 biological processes. The KEGG pathway analysis results revealed the in the 8w-T2DM group, the DEGs were enriched in the pathways of unsaturated fatty acids biosynthesis, PPAR signaling, and fatty acid elongation, degradation and metabolism. In the 16w-T2DM groups, the pathways that enriched the DEGs involved were antigen processing and presentation, complement and coagulation cascades, cell adhesion molecules (CAMs), and viral myocarditis. The upregulated genes, were as follows: Cers1, Acot1, Hmgcs2, NTSR-1, PDK4 and Cxcl13. The downregulated genes were, as follows: Abcd2 and Fcrl-2. These were identified as correlated to the occurrence and development of DCM. The differential expression was validated by quantitative polymerase chain (Q-PCR).

Conclusions

Cers1, the gene involved in lipid metabolism, had the highest Log2FC value and might be considered the DCM-related gene for further investigation. This might help screen for the potential pharmaceutical target for DCM.

Epigenetics & Genomics

Diabetic cardiomyocyte

Transcriptome sequencing

Differentially expressed genes

GO annotation

KEGG pathway

Q-PCR validation

In recent years, diabetes has been rapidly emerging worldwide. By 2045, the number of people with diabetes may reach 700 million [1]. In China, 90% of adult diabetic patients will have type-2 Diabetes (T2DM), which is characterized by hyperglycemia and metabolism disorder caused by insulin resistance[2]. T2DM has multiple complications, including macrovascular dysfunction, microvascular dysfunction, and nerve system damage. The risk of having cardiovascular disease in T2DM patients is much higher than in non-diabetic people. Diabetic cardiomyopathy (DCM) is one of the macrovascular dysfunction complications of T2DM, and is the major cause of T2DM patient mortality[3]. DCM was first discovered in 1972, when Rubler et al [4]. classified this as an independent disease directly caused by diabetes with no other cardiovascular risk factors. Subsequently, studies have revealed that diabetes can directly cause unique cardiac structural and functional changes, and coronary atherosclerosis[5]. DCM can cause left ventricular hypertrophy and diastolic dysfunction in diabetics patients, leading to systolic dysfunction, and even congestive heart failure, eventually causing death[6].

A large number of epidemiological studies and basic clinical researches have shown that the occurrence and development of DCM might be correlated to several factors, such as the myocardial metabolic disorder caused by insulin resistance, oxidative stress, and mitochondrial dysfunction due to myocardial intracellular calcium homeostasis disorders, as well as myocardial systolic and diastolic dysfunction, and coronary microvascular endothelial dysfunction and inflammation caused by cardiac remodeling, such as myocyte hypertrophy, apoptosis and fibrosis[7][8].

Although there are many explanations on the occurrence and development of DCM, there is still a lack of comprehensive understanding on the molecular mechanism of DCM. Furthermore, the genes involved have not been clearly described. The present study investigated the molecular mechanism of DCM by screening the differentially expressed gene (DEG) profiles of T2DM rats at different disease courses. It is hoped that this would help provide a clear insight into DCM at the molecular level, and reveal novel therapeutic targets for DCM.

Oral glucose tolerance test (OGTT)

The OGTT results revealed that fasting glucose levels in both T2DM groups increased, and were significantly higher than those in the control groups (P<0.01, Supplementary Fig.S1A and S1C). The AUC for the T2DM groups significantly increased (Both, P<0.01; Supplementary Fig.S1B and S1D). This indicates that the oral glucose tolerance of T2DM rats was lower, and that the sensitivity against insulin became weaker. This means that the T2DM animal model was successfully established.

The blood glucose level and lipid profile

The fasting blood glucose(FBG) and plasma insulin concentrations significantly increased in 8w-T2DM and 16w-T2DM rats, when compared to those in the control group, while the calculated insulin sensitivity index (ISI) decreased (Fig.1A, P<0.01). The serum TC, TG and LDL levels of the 8w- and 16w-T2DM rats were also higher than those in the control group, while the HDL concentrations were lower (Fig.1B, P<0.05). This suggests that these 8w-16w T2DM rats had hyperinsulinemia, decreased insulin sensitivity index, and disordered glucose and lipid metabolism, further proving the T2DM rat model.

Cardiac functions and structure assessment

The M-visual supersonic results revealed that for the rats in each group, the heart rate (Fig.2A) remain unchanged, for rats in 16w T2DM group, the cardiac output (Fig.2B) showed lower that those in control group, for rats in the 8w T2DM group, the LVEF (%, Fig.2C) and LVFS (%, Fig.2D) were significantly lower than those in control groups (Both, P<0.01). Along with the ESV (Fig.2H) and LVIDs (Fig.2J), these significantly increased (Both, P<0.01). This means that the LV systolic function decreased, and that the morphology changed at the end of the systole, when these T2DM rats were at eight weeks. In addition, the IVRT (Fig.2E) was significantly prolonged (P<0.01), and the E/A ratio significantly decreased (P<0.01, Fig.2F). Furthermore, the EDV (Fig.2G) and LVIDd (Fig.2I) obviously increased (Both, P<0.01) in this group. This evidence shows that in 8w T2DM rats, the left ventricular diastole function decreases, and the morphology changes at the end of diastole. However, the LVPWd, LVPWs, IVSd and IVSs (Fig.2K, 2L, 2M and 2N, respectively) in this group exhibited no significant change (All, P>0.05). That is, the cardiac structure in T2DM rats did not change when these rats are at eight weeks disease duration. In the 16w-T2DM group rats, the impaired cardiac contractile and diastolic function results were similar to those in the 8w-T2DM group, but the LVPWs and LVPWd increased (All, P<0.05). However, the IVSs (Fig.2M) and IVSd (Fig.2N) did not change in this group (Both, P>0.05). This means that when the T2DM disease duration extended to 16 weeks, the cardiac dysfunction and morphology change did not improved, and the cardiac structure changed, which eventually led to DCM.

DEG distribution analysis

The DEG data was obtained using pairwise comparison, based on the clean data collected from the transcriptome sequencing. A total of 360 DEGs were found in the 8w-T2DM group, which included 191 upregulated genes and 169 downregulated genes, while 381 DEGs were found in the 16w-T2DM group, which included 258 upregulated genes and 123 downregulated genes (Fig.3). The most enriched upregulated and downregulated DEGs in each group are shown.(Table 1, 2, 3).

Table 1 The most enriched DEGs analysis in 8w-Con. vs. 8w-T2DM

Genes	Description	8w-Con.	8w-T2DM	FDR	Log2FC	Regulated
Cers1	Ceramide synthase 1	0.00166	2.90620	0	6.65384	up
Angptl4	Angiopoietin-Like 4	0.79732	38.88240	0	5.73014	up
Psme1-ps1	Proteasome Activator Subunit 1 %2C Pseudogene 1	0.26867	18.11600	0	5.74576	up
Acot4	Acyl-coenzyme A thioesterase 4	0.03262	1.51484	4.46e-09	5.15689	up
Klk12	Kallikrein related-peptidase 12	0.09411	3.45200	3.39e-07	4.88791	up
Ampd1	Adenosine monophosphate deaminase 1	0.03886	1.23081	1.49e-05	4.60919	up
Acot1	Acyl-CoA thioesterase 1	0.21021	4.70500	0	4.57048	up
Pdk4	Pyruvate dehydrogenase kinase 4	7.73082	151.93100	0	4.48028	up
Pik3c2g	Phosphatidylinositol-4-phosphate 3-kinase%2C catalytic subunit type 2 gamma	0.14132	2.11851	0	3.85912	up
Ntsr1	Neurotensin receptor 1	0.10610	1.23265	1.16e-10	3.64634	up
Ms4a8	Subfamily A Member 8	183.263	0.14595	0	-9.39531	down
Fcrl2	Fc receptor-like 2	6.30646	0.16346	0	-4.84251	down
Cd5l	Cd5 molecule-like	1.85299	0.05354	1.72e-07	-4.21018	down
Sez6l	Seizure related 6 homologs like	0.62317	0.01804	3.09e-07	-4.1738	down
Cxcl13	C-X-C motif chemokine ligand 13	92.80720	5.07440	0	-4.11961	down
Myo5c	Myosin VC	0.55780	0.01767	1.00e-06	-4.09818	down
Fam111a	Family with sequence similarity 111 member A	16.9195	1.11637	0	-3.80469	down
Siglec8	Sialic acid binding Ig-like lectin 8	3.51559	0.22520	8.39e-09	-3.66710	down
Nox4	NADPH oxidase 4	3.70935	0.36859	4.74e-12	-3.20692	down

Data are described as FDR, the Benjamini-Hochberg adjusted P value and Log2 FC: the log 2-fold-change. When the FDR is <0.01 and Log2 FC value is less or more than 2, the gene is considered as the significantly differential expression. 8w-Con: 8-week control group; 8w-T2DM: 8-week type 2 diabetic group. In this table, the genes with the least value of FDR and the most or least value of Log2 FC are listed，up：up-regulated, down：down-regulated.

Table 2 The most enriched DEGs analysis in 16w-Con. vs. 16w-T2DM

Genes	Description	16w-Con.	16w-T2DM	FDR	Log2FC	Regulated
Alb	Serum albumin	0	13.15950	0	7.83603	up
Kng2	Low molecular weight growth-promoting factor	0	5.80065	0	6.04716	up
Serpina1	Short peptide from AAT	0	3.30124	6.68e-09	5.16163	up
Hk3	Hexokinase-3	0.24670	8.52415	0	5.04975	up
Ntsr1	Neurotensin receptor type 1	0.02961	1.21478	2.97e-12	4.77885	up
Gbp3	Guanylate-binding protein 7	0.68416	17.36620	0	4.57709	up
Acot1	Acyl-Coa Thioesterase 1	0.13867	3.79557	9.44e-15	4.55370	up
Fgb	Fibrinogen beta chain	0	1.80872	4.53e-05	4.54248	up
Naa11	N-alpha-acetyltransferase 11	0	0.84529	8.62e-05	4.48616	up
Pik3c2g	Phosphatidylinositol 4-phosphate 3-kinase C2 domain-containing subunit gamma	0.14687	3.14149	0	4.34361	up
Nefh	Neurofilament heavy polypeptide	0.56062	0	1.69e-07	-4.96439	down
Tcp11x2	T-complex protein 11 X-linked protein 2	4.88193	0	1.69e-07	-4.96439	down
Fcrl2	Fc receptor-like protein 2	1.49494	0	0.000646	-4.29757	down
Fam64a	Protein FAM64A	1.27215	0	0.001233	-4.23085	down
Lrrc51	Leucine-rich repeat-containing protein 51	4.31443	0	0.002358	-4.16089	down
Sez6l	Seizure 6-like protein	1.32443	0.06721	5.77e-15	-4.01543	down
Mycn	N-myc	3.04910	0.27550	5.55e-16	-3.39850	down
Prox2	Prospero homeobox protein 2	1.05145	0.05132	0.006091	-3.37755	down
Myh6	Myosin-6	1897.4	186.871	0	-3.30293	down
Abcd2	ATP binding cassette subfamily D member 2	0.91571	0.07727	7.17e-06	-3.27809	down

Data are described as FDR, the Benjamini-Hochberg adjusted P value and Log2 FC: the log 2-fold-change. When the FDR is <0.01 and Log2 FC value is less or more than 2, the gene is considered as the significantly differential expression. 16w-Con: 16-week Control group; 16w-T2DM: 16-week type 2 diabetic group. In this table, the genes with the least value of FDR and the most or least value of Log2 FC are listed，up：up-regulated, down：down-regulated.

Table 3 The most enriched DEGs analysis in 8w-T2DM vs. 16w-T2DM

Genes	Description	8w-T2DM		16w-T2DM		FDR	Log2FC		Regulated
Alb	Albumin		0	13.15950	0			8.02210		up
Kng2	Kininogen 2		0	5.80065	0			6.23135		up
Fgb	Fibrinogen beta chain		0	1.80872	7.36e-05			4.72178		up
Fga	Fibrinogen alpha chain		0	0.72933	0.00201			4.41346		up
Apoc3	Apolipoprotein C3		0	4.42796	0.00390			4.34302		up
Serpina1	Serpin family a member 1		0.09946	3.30124	1.57e-07			4.33062		up
Fgg	Fibrinogen gamma chain		0.09160	2.95033	2.92e-07			4.29408		up
Hpx	Hemopexin		0.07613	1.61040	0.00175			3.65222		up
Itih4	Inter-Alpha-Trypsin inhibitor heavy chain family%2C member 4		0.03869	0.70125	0.00605			3.53184		up
Itih3	Inter-Alpha trypsin inhibitor%2C heavy chain 3		0.07999	0.92159	0.00032			3.37347		up
Reg3b	Regenerating islet-derived 3 beta		-5.61021	8.22114	0			-5.61021		down
RGD1565566	Similar to 60S ribosomal protein L18a		-4.44796	15.1144	0.53585			-4.44796		down
Myl7	Myosin Light Chain 7		-3.71014	105.227	8.00468			-3.71014		down
Reg3g	Regenerating family member 3 gamma		-3.50938	11.2475	0.95851			-3.50938		down
Ifit1	Interferon-induced protein with tetratricopeptide repeats 1		-3.14062	12.8878	1.52538			-3.14062		down
Lyc2	Lysozyme C type 2		-2.63764	3.80713	0.65223			-2.63764		down
Ido1	Indoleamine 2%2C3-Dioxygenase 1		-1.75435	113.40200	36.2809			-1.75435		down
Angptl4	Angiopoietin-like 4		-1.68426	38.88240	13.0127			-1.68423		down
Pdk4	Pyruvate Dehydrogenase Kinase 4		151.931	64.77420	0			-1.35808		down
Cxcl11	C-X-C motif chemokine ligand 11		9.24500	3.90342	0.00017			-1.3377		down

Data are described as FDR, the Benjamini-Hochberg adjusted P value and Log2 FC: the log 2 fold-change. When the FDR is <0.01 and Log2 FC value is less or more than 2, the gene is considered as the significantly differential expression. 8w-T2DM: 8-week type 2 diabetic group; 16w-T2DM: 16-week type 2 diabetic group. In this table, the genes with the least value of FDR and the most or least value of Log2 FC are listed，up：up-regulated, down：down-regulated.

GO annotation

A total of 18,410 genes and 19,184 Unigenes have obtained the GO annotation in each group. These DEGs were enriched in 19 cellular components, 21 molecular functions, and 22 biological processes (Fig.4).

The enriched KEGG pathways

The DEGs were then classified into KEGG functional pathway according to the GO annotation results (Fig.5). The top 10 most DEGs enriched pathways in the different groups were analyzed using the KEGG database (Table 4). In the 8w-T2DM group, the DEGs are enriched mostly in the fatty acid degradation, metabolism, elongation, and unsaturated fatty acid biosynthesis pathways. In the 16w-T2DM group, the DEGs enriched in pathways involved infection, antigen-presenting, and viral myocarditis. The enriched DEGs between two different DCM durations mainly focused on unsaturated fatty acid synthesis, fatty acid elongation, and complement and coagulation cascades.

Table 4 The top 10 enriched KEGG pathways

Pathways	KO	Enrichment Factor	Q-value
8w-Con.vs 8w-T2DM
Fatty acid elongation	ko00062	14.41	6.09826e-06
Fatty acid degradation	ko00071	12.15	1.20475e-07
Fatty acid metabolism	ko01212	10.28	8.23118e-07
Biosynthesis of unsaturated fatty acids	ko01040	11.22	0.00160
Butanoate metabolism	ko00650	9.72	0.01871
PPAR signaling pathway	ko03320	8.62	7.69155e-08
Malaria	ko5144	7.34	0.00154
Herpes simplex infection	ko05168	3.37	0.00944
Peroxisome	ko04146	4.86	0.03157
Valine, leucine and isoleucine degradation	ko00280		0.01193
16w-Con.vs 16w-T2DM
Staphylococcus aureus infection	ko05150	12.06	6.55486e-12
Viral myocarditis	ko05416	8.56	2.26084e-10
Antigen processing and presentation	ko04612	7.99	3.24590e-09
Complement and coagulation cascades	ko04610	7.55	4.53476e-07
Leishmaniasis	ko05140	7.51	1.81095e-06
Allograft rejection	ko05330	7.41	2.87191e-05
Systemic lupus erythematosus	ko05322	6.07	2.80060e-06
Cell adhesion molecules	ko04514	4.77	2.00421e-06
Tuberculosis	ko05152	4.49	2.31785e-06
Phagosome	ko04145	4.37	3.71036e-06
8w-T2DM vs 16w-T2DM
Biosynthesis of unsaturated fatty acids	ko01040	18.47	0.14375
Fatty acid elongation	ko00062	17.79	0.15484
Complement and coagulation cascades	ko04610	16.01	0.00035
Ovarian steroidogenesis	ko04913	14.13	0.03347
Linoleic acid metabolism	ko00591	12.98	0.28629
African trypanosomiasis	ko05143	12.01	0.33270
Tryptophan metabolism	ko00380	10.44	0.43467
Toll-like receptor signaling pathway	ko04620	7.58	0.19650
Platelet activation	ko04611	5.38	0.49881
Chemokine signaling pathway	ko04062	4.07	1

KO: KEGG pathways ID; Enrichment factor: the ratio between the number of DEGs and the number of whole genes annotated in a certain pathway; the more significant the enrichment factor, the more the DEGs were enriched in this pathway. Q-value: multi-adjusted p-value for the False Discovery Rate (FDR), the smaller Q-value, and the higher reliability in DEGs enrichment analysis. 8w-Con.: 8-week control group; 16w-Con.: 16-week control group; 8w-T2DM: 8-week type-2 diabetic group; 16w-T2DM: 16-week type 2 diabetic group.

The candidate DCM-related genes based on the bioinformatics analysis

The candidate DCM-related genes were screened out based on the GO annotation and KEGG pathway enrichment results (Table 5) and further verification.

Table 5 The candidate DCM-related genes

Genes	Description	Predicted pathways	FDR	Log2FC	Regulated
Cers1	Ceramide synthase 1	Lipid transport and metabolism	0	6.653841	up
Acot1	acyl-CoA thioesterase1	Lipid transport and metabolism	4.46e-09	5.156896	up
NTSR-1	Neurotensin receptor-1	Post-transitional modification	3.39e-07	4.887915	up
PDK4	Pyruvate Dehydrogenase Kinase 4	Signal transduction mechanisms	0	4.480288	up
Fcrl 2	Fc receptor-like 2	Signal Transduction mechanisms	0	-4.84251	down
Cxcl13	C-X-C motif chemokine ligand 13	Signal Transduction mechanisms	0	-4.11961	down
Hmgcs 2	3-hydroxy-3-methylglutaryl-Coenzyme A synthase 2	Signal Transduction mechanisms	1.00e-06	-4.09818	down
Abcd2	ATP-binding cassette sub-family D member 2	Lipid transport and metabolism	7.18e-06	-3.27809	down

Data are described as FDR, the Benjamini-Hochberg adjusted P value and Log2 FC: the log 2-fold-change. When the FDR is <0.01 and Log2 FC value is less or more than 2, the gene is considered as the significantly differential expression. In this table, the genes with the least value of FDR and the most or least value of Log2 FC are listed.

Q-PCR validation

Cers1, Acot1, NTSR-1, PDK4, Abcd2, Fcrl-2, CXCL13, and Hmgcs2 were validated by Q-PCR, and exhibited statistically significant up-regulation or down-regulation in the T2DM groups. The mRNA expression level of Cers1 in the 8w-T2DM group was significantly higher than that in the 8w-Control group (Log2 FC=3.6, P<0.01; Fig.6A), and this was still higher in the 16w-T2DM group (Log2 FC=1.7, P<0.05; Fig.6A). Acot1 is significantly upregulated in the 8w-T2DM group (Log2 FC=3.6, P<0.01; Fig.6B), but was not differentially expressed in the 16w-T2DM group (Log2 FC=1.1, P>0.05; Fig.6B). Both Acot1 and Cers1 were involved in lipid metabolism and related signaling pathways. NTSR1 was significantly upregulated in both T2DM groups (Both Log2 FC =2, P<0.01; Fig.6C). PDK4 exhibited a significant upregulation in the 8w- and 16w-T2DM groups, especially in the 16w-T2DM group (Log2 FC=37.68, P<0.01; Fig.6D), which might have affected the fatty acid absorption, and led to cardiac lipid toxicity during the AMPK pathway activation. Abcd2 was significantly downregulated in the 16w-T2DM group (Log2 FC=-3.6, P<0.01; Fig.6E), but was not significantly and differentially expressed in 8w-T2DM group (Log2 FC=0.05, P>0.05; Fig.6E). This may be significantly correlated with the VLCFA accumulation in the myocardial tissue of 8w- and 16w-T2DM rats during DCM. Fcrl 2 was downregulated in the 8w- and 16w-T2DM groups (Log2 FC=-4.6, P<0.01; Fig.6F). CXCL13 was slightly downregulated in the 8w-T2DM group (Log2 FC=-0.6, P>0.05; Fig.6G), but was upregulated in the 16w-T2DM groups (Log2 FC=1.8, P<0.05; Fig.6G). Hmgcs2 was significantly upregulated in the 8w- and 16w-T2DM groups (Log2FC=6.28, P<0.01; Log2 FC=2.0, P<0.05, respectively; Fig.6H).

DCM represents a significant complication of type-2 diabetes mellitus. This can be classified into four stages, according to its clinical characteristics and echocardiography features, as follows: Most patients at stage-I DCM have impaired glucose tolerance and metabolic syndrome, with LV diastolic dysfunction and hypertrophy, but present as asymptomatic, and echocardiography reveals the increase in LV mass, diastolic dysfunction, and decrease in tissue velocities, but with normal ejection fraction. At stage-II DCM, patients are in the status of chronic hyperglycemia with the cardiac function of NYHA II (New York Heart Association Functional Classification). Echocardiography can identify the increased LV mass and wall thickness, and the diastolic and systolic dysfunction (EF<50%) mild cavity dilatation. At stage-III DCM, patients will have significant abnormalities of metabolic status with insulin resistance. Diabetes mellitus with microangiopathic complications will present with a continuous decline in cardiac function (NYHA II-NYHA III), accompanied by the aggravation of LV systolic dysfunction and dilatation. This can be easily detected by echocardiography, associated with arterial hypertension. DCM patients at stage IV are under DM with micro- and macro-angiopathic complications. The imaging examination can identify the moderate-severe systolic dysfunction cavity dilatation associated with coronary artery disease (NYHA II-NYHA IV), and the apparent systemic pathological damage, including coronary atherosclerosis, ischemic myocardial infarction, and persistent heart failure. Mátyás C et al.[9] and Jia G et al.[10] recently concluded that the most common complication of DCM was LV diastolic dysfunction at the early stages, LV systolic dysfunction and hypertrophy are undoubtedly the characteristic pathological phenomena with DCM progression.

DCM animal models

The present study revealed that in T2DM rats with 8w and 16w disease durations, the oral glucose tolerance decreased, and the fasting glucose significantly increased. After the intragastric administration of glucose (2g/kg), the blood glucose values at all time points significantly increased, leading to AUC enhancement. The significant elevation of sera TC, TG and LDL levels, and the reduction of HDL concentration indicate that rats in the T2DM groups developed hyperlipidemia and insulin resistance, which are consistent with the essential characteristics of T2DM patients or animal models, as evaluated by various studies[11][12].This additionally proves that the T2DM rat model was successfully established in present study. The M-echocardiography results revealed that in 8w-T2DM rats, the ejection fraction, short-axis shortening rate, and LV filling E/A peak ratio significantly decreased, indicating the decrease in LV systolic/diastolic function and the morphologic change. In 16w-T2DM rats, the decrease in LV systolic function, impaired diastolic function, and pathological changes of LV morphology were similar to those for the 8w rats, with the addition of increase in end-systolic and diastolic LV posterior wall thickness. These results were consistent with the pathological evaluation results for DCM from other researchers[13], proving the DCM formation in the present T2DM animal models.

When the DCM rat models were successfully established, myocardial cells of each group were sent to the laboratory for transcriptome sequencing. The DCM-related DEGs and involved pathways were screened based on the bioinformatics analysis results, excluding age, gender, organ specificity, and other factors[14]. These DEGs are mainly concentrated in the following aspects: lipid transportation and metabolism, such as the biological processes related to the synthesis, degradation, extension and metabolism of unsaturated fatty acids; relevant signal conduction mechanisms: peroxidase-proliferator-activated-receptor (PPAR), and adenosine 5‘-monophosphate (AMP)-activated protein kinase (AMPK) pathways; other factors, such as inflammatory factors.

DCM-related DEGs in lipids metabolism disorder

The investigators selected eight DCM-related DEGs based on the bioinformatics analysis results, and validated by Q-PCR in the myocardium of DCM rats. The Q-PCR validation results were consistent with the transcriptome sequencing results. Among these eight DEGs, the gene that directly involved lipid transportation and metabolisms, such as Cers1, Acot1 and Hmgcs2, were upregulated, while Abcd2 was downregulated.

Cers1, which is one of the six isoforms in the Cers gene family (Cers1~6), synthesizes the C18 ceramide only through the de novo synthesis pathway. In 2018, Holland W.L. et al.[15]reported that the serum ceramide level can be considered as a potential biological indicator to predict diabetes mellitus, insulin resistance, and heart disease. A large cohort study revealed that serum ceramide with a specific length of the carbon chain (C16, C18, C20, C22, etc.) in non-diabetes North Americans is associated with high risk of insulin resistance (HOMA-IR study) and diabetes [16]. The present finding on the upregulated Cers1 in the DCM groups suggest that ceramide might be accumulated, and lead to myocardial cell apoptosis, eventually causing DCM[17]. Cers1, which had the highest Log2FC value (Log2FC=6.89), might be considered the DCM-related gene for further investigation. Acot1, which belongs to the Acot enzyme family, can enhance the acyl acid hydrolysis rate[18]. The Hmgcs2 gene encodes a regulatory enzyme for ketogenic action in the liver mitochondria, and this was upregulated in the liver and kidneys of obese rats. The promoter region of the Hmgcs2 gene contains the peroxisome proliferation regulatory element (PPRE) regulated by the transcriptional regulation of PPAR, which can enhance liver fatty acid oxidation [19]. Both genes were upregulated in the present DCM group, which might have resulted in free fatty acid accumulation, eventually leading to lipotoxicity. This is one of the main reasons that cause DCM. Acot1 and Cers1 were both involved in lipid metabolism and related signaling pathways

Abcd2 can specifically utilize a shorter chain VLCFA and unsaturated fatty acids, as substrates, and mediate the VLCFA substance transportation into peroxisomes. In the Abcd 1/2 knocked out mouse models, VLCFA was significantly accumulated in mice with a deleted Abcd 2, when compared to those with Abcd 1 knockout [20]. It was found that Abcd2 was downregulated in the myocardium of 16w DCM rats, which might hint that the weak VLCFA transportation into the peroxisome and the VLCFA accumulation in cardiomyocytes would eventually lead to lipid accumulation.

The DEGs involved in the free fatty acid metabolism was upregulated in the present research, which might hint that one of the leading causes of DCM could be fatty acid metabolism disorder, especially the accumulation of ceramide, free fatty acid, and ketone body in myocardial cells. This would cause myocardial lipotoxicity, and lead to the occurrence of DCM. The DEGs that were upregulated and downregulated in the different DCM groups of rats in the present study matched the DCM course development trend.

DCM-related DEGs involved in the PPAR and AMPK pathways

The PPAR signaling pathway and AMPK signaling pathway were involved in the DCM occurrence, based on the KEGG pathway enrichment analysis results. The DCM-related DEGs in the present study involved in these pathways included PDK4, NTSR, Acot1 and Hmgcs2. PPARs (PPAR α, β and γ) play an important role in lipid cell differentiation and lipid metabolic regulation. When the PPAR forms with RXR as a dimer, and the activated PPAR combines with PPRE, this can regulate absorbance, utilization, aerobic oxidation, and fatty acid storage[21]. Increased PPAR activity leads to the over-expression of related enzymes involved in fatty acid absorbance and metabolism. Acot1 regulates the PPARα reporter activity by producing fatty acid ligand, and PDK4 is a critical node in the cellular glucose metabolism network, which directly responses to the whole energy metabolism network[22]. When the PPAR pathway is activated, the enhanced PPARα activity can lead to the excessive expression of PDK4. Thus, this increases the fatty acid β-oxidation, enhance the myocardial oxygen consumption, and release oxyradical, which in turn leads to mitochondria uncoupling and calcium imbalance, and the inhibition of the pyruvate dehydrogenase complex expression and glucose aerobic oxidation, and result in diastolic cardiac dysfunction[23]. It was found that the PDK4 expression in the DCM group of rats was significantly upregulated. In addition, the PDK4 expression was significantly upregulated in the 16w-DCM group, when compared to the 8w-DCM group, indicating that the PPAR pathway plays different roles at different stages of DCM. This may be ultimately correlated to the pathophysiological process of DCM. The evidence in mouse and cell experiments have shown that NT/NTSR1 can reduce the activation degree of AMPK, thereby stimulating fatty acid absorption. The drosophila model demonstrated that the NT expression in intestinal endocrine cells lead to lipid aggregation, obesity, and decreased AMPK signaling activity[24]. In the present study, NTSR1 was significantly upregulated in both DCM groups, which may reduce AMPK pathway activation, thereby affecting fatty acid absorption, and leading to lipid accumulation and cell apoptosis in cardiomyocyte.

DCM-related DEGs involved in inflammatory

Multiple factors might be involved in the pathogenesis mechanism of DCM. In addition to metabolic disorders, inflammatory factors also play an important role in DCM development. It was found that inflammatory factors genes, Fcrl 2 and Cxcl13, were significantly and differentially expressed in the DCM groups. Fcrl 2 was significantly downregulated in 8w/16w DCM rats, and Cxcl13 was slightly downregulated in 8w DCM rats (P>0.05), but was significantly upregulated in the myocardium of 16w DCM rats. Fcrl 2 is the only FC receptor that contains intracellular signals, and encodes transmembrane proteins expressed on the surface of B memory cells. This can independently initiate cellular effects, and trigger various immune responses[25]. The Fcrl expression is downregulated when B cells are activated to form germinal centers. Cxcl13, which is a chemokine for T- and B-lymphocytes, can induce the pro-inflammatory phenotype of diabetic rats accompanied by its receptors CXC5[26], which may trigger fibrosis through the activation of immune cells. The present finding on the Fcrl2 downregulation and Cxcl13 upregulation explains that one of the DCM pathogenesis mechanisms is the inflammatory environment related to the T2DM, which stimulates fibroblast proliferation and myofibroblast transformation, increases collagen deposition, and leads to myocardial fibrosis, thereby causing DCM [27][28].

The present study shows that the genes, which were directly involved or participated as signaling pathways in the synthesis and metabolism of fatty acid, were most differentially and significantly expressed in the myocardium of DCM rats. This means that fatty acid metabolism disorder, especially the ceramide, free fatty acid, and ketone accumulation, is the critical mechanism of DCM occurrence and development. Cers1, which is the gene involved in lipid metabolism, synthesizes C18 Ceramide, has the highest Log2FC value according to the bioinformatic analysis(Log2 FC=6.653, FDR=0; Table 1) and Q-PCR validation results of animal models (Log2 FC=3.6; P<0.01, Fig.6A). This might be considered as a DCM-related gene for further investigation. The present research provides the molecular evidence of DCM progression, helps screen for potential drug targets, and facilitate DCM treatment strategies.

Animal models

The Zucker rats (SPF, male, 180-200 g/per) were provided by Guangdong Medical Lab Animal Centre (Guangdong Province, China), with licenses number: SYXK(Yue)2016-0168. These were randomly divided into four groups: two T2DM groups with eight weeks and 16 weeks of disease duration, named as 8w-T2DM, and 16w-T2DM, respectively; relevant control groups, named as 8w-Control and 16w-Control, respectively. There were five rats in each group.

The T2DM animal models were established by following the standard procedure in our lab[29]. Briefly, rats were housed in standard mouse cages subject to a 12-hour light/12-hour dark cycle for one week. Then, these rats were fed with high-fat diet (HFD) for four weeks in advance, and subsequently underwent fasting for 12 hours. Afterwards, those in the 8w/16w T2DM groups were administered with streptozocin (1% STZ in citrate buffer, 35 mg/kg, i.p.), and fed with HFD for eight weeks and 16 weeks, respectively. The HFD comprised of regular chow (79%), egg yolk (10%), pork fat (10%), cholesterol (1%), and bile salt (0.1%). Then, the urine sugar was determined by urine glucose test strips at three days after the injection. When the results revealed medium or strong positive, a random blood glucose test was performed twice for every two days. When the results revealed an equal or greater than 16 mmol/L, the rats were considered as diabetics rats. Then, these T2DM rats continued to be fed with HFD for 8 or 16 weeks. Rats in the control groups were initially fed with regular-chow for four weeks. Then, these were injected with the same volume citrate buffer instead, and continued to be fed with regular-chow for the same time duration as the T2DM rats.

Oral glucose tolerance test (OGTT)

OGTT was performed to evaluate the insulin sensitivity of rats at the end of each feeding cycle. The fasting blood glucose level was measured from the caudal vein blood after 12 hours of fasting. Then, the blood glucose value was tested at different time points (30, 60, 90 and 120 minutes) after the administration of one oral glucose load (40% glucose, 2 g/kg). Next, the blood glucose (BG) time curve was drawn, and the area under the curve (AUC) was calculated. The calculation formula was, as follows: AUC [mmol/(L·min)] = 1/2 × (BG 0 min + BG 30 min) × 30 min + 1/2 × (BG 30 min + BG 60 min) × 30 min + 1/2 × (BG 60 min + BG 90 min) × 30 min + 1/2 × (BG 90 min + BG 120 min) × 30 min.

Blood glucose and lipids measurement

The blood glucose level was measured by Glucose Blood Monitor and Glucose Oxidase assay. The total serum cholesterol (TC) levels were measured using an enzymatic colorimetric kit (CHOD-PAP kit), and the triglycerides (TG) levels were measured by glycerol phosphate oxidase (GPO-PAP). The plasma concentrations of high-density lipoprotein (HDL) and low-density lipoprotein (LDL) were measured using the pT-Mg²⁺ precipitation method and polyethylene sulfate precipitation method.

Echocardiography assessment

The cardiac structure and function of rats were evaluated using the Visual Sonics Vevo 2100 High Resolution Imaging System. Briefly, rats were anesthetized with 10% chloral hydrate (300 mg/kg, i.p.). Then, the heart rate (HR), cardiac output (CO), left ventricular ejection fraction (LVEF, %), and fractional shortening (FS, %) were measured under M-mode supersonic. Color Doppler ultrasonography was used to observe the ratio of peak velocity blood flow, from the gravity in the early diastole (the E wave) to the peak velocity flow in the late diastole caused by atrial contraction (the A wave) (E/A ratio), isovolumic relaxation time (IVRT)., the volume of end-systolic and diastolic (ESV/EDV), the left ventricle internal diameter in the end-systole and diastole (LVIDs/LVIDd), and the left ventricle posterior wall thickness in the end-systole and end-diastole (LVPWs/LVPWd).

Transcriptome sequencing and bioinformatics analysis [30]

When the T2DM animal model was confirmed, these rats were sacrificed under anesthesia at the end of the feeding cycle. Then, the heart was instantly harvested and stored in liquid nitrogen. Afterwards, a small part of the heart tissue was sent to the laboratory for transcriptome sequencing. Total RNA was extracted using the Trizol method, following the standard procedure. The RNA concentration and purity were measured using the Nanodrop 2000. The quality of the total RNA was determined using the Agilent 2100 bio-analyzer system. The cDNA library was considered well-constructed when the total RNA was qualified, according to manufacturer's instructions. Briefly, the mRNA was enriched by magnetic beads attached with oligo-T, and sliced to different sizes by adding a fragment buffer. The first-strand cDNA was synthesized using random Hexamers. The second strand synthesis was performed by adding dNTPs, RNase H and DNA polymerase I. Then, the double-strand cDNA was purified by AMPure XP beads, and the poly (A) tails were added and ligated with the sequencing adaptor in the end. The cDNA library was generated using the PCR method. The cDNA library's initial quantity was determined uisng the Qubit 2.0 fluorimeter. Then, the concentration was diluted to 1.5 ng/µl. The insert size of the cDNA library was detected using an Agilent 2100 bio-analyzer. When the insert size met the transcriptome sequencing requirement, the Q-PCR method was used to quantify the effective concentration of the cDNA library. When the result was higher than 2nM, the library was considered to be qualified. Then, high-throughput sequencing was applied, and the sequence read was the Single End 50bp (SE50).

A large amount of high-quality sequence data, called raw data, was collected using the sequencing by synthesis (SBS) technique, based on the Illumina Hi-Seq platform, and the clean reads were obtained after quality control. The TopHat2 software was used to gain the mapped reads on the reference genome/genes and the specific sequence information by comparing the clean data with the reference genome sequence. The reference genome assembly was used to determine whether the data met the requirement for the bioinformatics analysis. The Cuffquant and Cuffnorm components of the Cufflinks software were used to quantify the transcriptome and gene expression levels. The Fragments per Kilobase of transcript per Million fragments mapped (FPKM) was utilized to describe the gene expression level, and eliminate the deviation caused by sequence depth, gene length and sample difference. The FPKM formula was, as follows:

The DEG, GO annotation and enriched KEGG pathways analysis

The DESeq method was used to analyze the differential expression between the two comparison groups (control group and T2DM group), and the differentially expressed genes (DEGs) were obtained. Fold Change (FC) was used to describe the ratio of differential expression. False discovery rate (FDR) was considered as the criterion for DEG screening. When the FDR was <0.01 and the Log2 FC value was greater or less than 2, the gene was considered as significant differential expression. The DEGs were blasted against several databases, such as the NCBI non-redundant protein sequence database (NCBI, NR), Swiss-Prot, Gene Ontology database (GO), Evolutionary genealogy of genes, Non-supervised Orthologous Groups (EggNOG), and EuKaryotic Orthology Groups database (KOG). The HMMER software was used to obtain the DEGs annotation in the Protein family database (Pfam). The GO enriched analysis was performed from three aspects: the biological process, the molecular function, and the cellular component. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used to analyze the enriched DEGs function. KOBAS2.0 was used to predict the enriched KEGG pathways of DEGs.

The DCM-related gene validation by Q-PCR

Based on the bioinformatics analysis results, eight selected DCM-related genes were validated by real-time PCR, following the standard procedure[31]. The total RNA was exacted from another part of the heart tissue from the same rat, and the cDNA was synthesized using PrimeScript RT reagent kits. Then, Q-PCR was performed in the ROCH lightcycler 96, following the standard procedure. GAPDH was used as the internal control gene. The gene primers are shown (Table 6). The DCM-related genes were Ceramide synthase 1 (Cers1), Acyl-CoA thioesterase 1 (Acot1), Neurotensin receptor 1 (NTSR-1), Pyruvate Dehydrogenase Kinase 4 (PDK4), ATP-binding cassette subfamily D member 2 (Abcd2), FC receptor light chain-2 (Fcrl-2), chemokine (C-X-C motif) ligand 13 (CXCL13), and Hydroxy-3-methylglutaryl-Coenzyme A synthase 2 (Hmgcs2).

Table 6. The Q-PCR primers for DCM-related genes

Genes	Description	Primers	Primer sequence(5'→3')
GAPDH	Glyceraldehyde-3'-phosphate dehydrogenase	upstream	CCACGGCAAGTTCAACGGCACA
		downstream	ATCAGCGGAAGGGGCGGAGA
Cers1	Ceramide synthase 1	upstream	TGGATGGCGGGAGTTGAGGA
		downstream	ACTGGAAGACTTGCGGGAAT
Acot1	acyl-CoA thioesterase1	upstream	GAGCCTAAGCCTCACTCTGT
		downstream	CAGCCTTTGAATCAGCACTA
NTSR1	Neurotensin Receptor1	upstream	CGTTCAACATGACCATCGAG
		downstream	GTCCACTGTTCATCCGAGAT
PDK4	Pyruvate Dehydrogenase Kinase 4	upstream	GACCGCCTCTTTAGTTACAC
		downstream	GGTTCCTTACTTGGGATACA
Abcd2	ATP-binding cassette subfamily D member 2	upstream	AAACAGAAGTTGGAGTCGCA
		downstream	CCGCAACTAATTCGTTCGCT
Fcrl2	Fc receptor-like 2	upstream	ACTCGCTGCTTCTGGTAT
		downstream	GCTATGTGATGGCTATTCTT
CXCL13	C-X-C motif chemokine ligand 13	upstream	TCTGGAGACCCATTACAC
		downstream	TCTGGCAGTAGGATTCAC
Hmgcs2	3-hydroxy-3-methylglutaryl-CoenzymeA synthase 2	upstream	CCCGTATGGGCTTCTGTTCG
		downstream	GCAGGCGTTGGTGGTATCTATG

Statistical analysis

The data are presented as mean ± S.E.M. (n= 4-6), unless there was another indication. SPSS 16.0 was used for the statistical analysis. One-way analysis of variance (ANOVA) and t-test were used to compare the group difference. Pairwise comparative analysis was used to obtain the statistically significant differences of the DEGs, and the adjusted P-values are calculated by Benjamini-Hochberg. P<0.05 was considered statistically significant, and P<0.01 was considered statistically highly significant.

Abcd2: ATP-binding cassette subfamily D member 2

Acot1: acyl-CoA thioesterase1

ANOVA: One-way analysis of variance

AUC: Area under Curve

Cers1: Ceramide synthase 1

CO: Cardiac Output

DCM: Diabetes Cardiomyopathy

DEGs: Differentially Expressed genes

EDV: End-diastolic volume

EF: Eject Factor

eggNOG: Evolutionary genealogy of genes: Non-supervised Orthologous Groups

ESV: end-systolic volume

E/A ratio: the ratio of E to mitral peak velocity of late filling (A)

FBG: Fasting blood glucose

Fcrl2: Fc receptor-like 2

FDR: False Discovery Rate

FPKM: Fragments Per Kilo base of transcript per Million fragments mapped

FS: Fractional Shortening

GAPDH: Glyceraldehyde-3’-phosphate dehydrogenase

GO: Gene Ontology database

HOMA-IR: Homeostasis Model Assessment of Insulin Resistance

Hmgcs2: 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 2

IVRT: Ventricular Isovolumic Relaxation Time

IVSs/d: Inter ventricular septum systolic/diastolic thickness

KEGG: Kyoto Encyclopedia of Genes and Genomes

KOG: EuKaryotic Orthology Groups database

Log2FC: the logarithm of Fold Change to the base 2

LVIDs/d: Left Ventricular Internal Dimension at end-systole and at end-diastole

LVPWs/d: Left Ventricular Posterior Wall Thickness At End Systole/diastole

mRNA: message RNA

NTSR1: Neurotensin Receptor1

NCBI: National Center of Biotechnology Information

OGTT: Oral Glucose Tolerance Test

PDK4: Pyruvate Dehydrogenase Kinase 4

Q-PCR: Real-time quantitative Polymerase Chain Reaction

SBS: Sequencing By Synthesis

T2DM: type 2 diabetes mellitus

Ethics approval and consent to participate

All studies in the present study have been reviewed and approved to be appropriate and humane by institutional animal care and use committee according to the rules of Committee on Animal Research and Ethics, and approved by the Institutional Animal Care and Use Committee of Guangzhou Medical University (Permit Number: 2016-171), and fully complied with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All methods are reported in accordance with ARRIVE guidelines for the reporting of animal experiments. All efforts were made to minimize suffering of laboratory animals.

Consent for publication

Not applicable

Availability of data and materials

We declare that the dataset(s) supporting the conclusions of this article are encompassed within the article (and its additional file(s).

Competing interests

The authors declare no conflict of interest.

Funding:

This research was funded by the Natural Science Foundation of China (NSFC, Grant number: 81570751), and the Natural Science Foundation of Guangdong Province, China (Grant number: 2016A030311050).

Authors' contributions

Yin Tan analyzed sequencing data, interpreted the data, prepared figures and tables, designed and supervised the Q-PCR validation, analyzed respective data and drafted the manuscript. Yanping Lei prepared the animal models and analyzed the physiological and biochemical data of animals. Huang Ceng participated the animal model experiments and collected animal data. Xiaomei Li prepared DNA samples, helped with respective animal data analysis. Yingnan Lin and Yi Huang prepared the RNA samples and performed the Q-PCR experiments under the supervision of Yin Tan. Yan Xiong conceived of the study, oversaw the research and revised the manuscript. All authors have read and approved manuscript.

Acknowledgments:

We would like to thank Dr. Ni Qiu, Dr. Zhijie Zhang, and Prof. Zhiming. He and his lab members (Cancer Center of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China) for the kind technical support including animal models, equipment usage, experiment detail discussion and helpful suggestions.

Author information

Affiliations:

GMU and GIBH School of Life Sciences, Guangzhou Medical University, Guangzhou, China

Yin Tan

Guangzhou Institute of Snake Venom Research, Guangzhou Medical University, Guangzhou, China

Yanping Lei, Ceng Huang, Xiaomei Li，Yan Xiong

Department of Biotechnology, GMU and GIBH School of Life Sciences, Guangzhou Medical University, Guangzhou, China

Yingnan Lin, Yi Huang

The Central Lab, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou,China

Yan Xiong

Corresponding author

Correspondence to Yan Xiong

Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, Shaw JE, Bright D, Williams R. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9^th edition. Diabetes Res Clin Pract. 2019; 157:107843.https://doi.org/10.1016/j.diabres.2019.107843
Hu C, Jia W. Diabetes in China: Epidemiology and Genetic Risk Factors and Their Clinical Utility in Personalized Medication. Diabetes, 2018;67(1):3-11. https://doi.org/10.2337/dbi17-0013
Bashier A , Bin Hussain A, Abdelgadir E, Alawadi F, Sabbour H, Chilton R, Consensus recommendations for management of patients with type 2 diabetes mellitus and cardiovascular diseases. Diabetol Metab Syndr, 2019;11:80. https://doi.org/10.1186/s13098-019-0476-0
Rubler S, Dlugash J, Yuceoglu Y. Z., Kumral T, Branwood A W, Grishman A. New type of cardiomyopathy associated with diabetic glomerulosclerosis. Am J Cardiol, 1972;30(6):595-602.https://doi.org/10.1016/0002-9149(72)90595-4
Evangelista I, Nuti R, Picchioni T, Dotta F, Palazzuoli A. Molecular Dysfunction and Phenotypic Derangement in Diabetic Cardiomyopathy. Int J Mol Sci, 2019; 20(13):3264.https://doi.org/10.3390/ijms20133264
Silbiger JJ. Pathophysiology and Echocardiographic Diagnosis of Left Ventricular Diastolic Dysfunction. J Am Soc Echocardiogr. 2019;32(2):216-232.e212. https://doi.org/10.1016/j.echo.2018.11.011
Tarquini R, Pala L, Brancati S, Vannini G, De Cosmo S, Mazzoccoli G, Rotella CM. Clinical Approach to Diabetic Cardiomyopathy: A Review of Human Studies[J]. Curr Med Chem. 2018,25(13):1510-1524.https://doi.org/10.2174/0929867324666170705111356
Parim B, Sathibabu Uddandrao VV, Saravanan G. Diabetic cardiomyopathy: molecular mechanisms, detrimental effects of conventional treatment, and beneficial effects of natural therapy. Heart Fail Rev, 2019;24(2):279-299. https://doi.org/10.1007/s10741-018-9749-1
Mátyás C, Kovács A, Németh BT, Oláh A, Braun S, Tokodi M, Barta BA, Benke K, Ruppert M, Lakatos BK, Merkely B, Radovits T Comparison of speckle-tracking echocardiography with invasive hemodynamics for the detection of characteristic cardiac dysfunction in type-1 and type-2 diabetic rat models. Cardiovasc Diabetol, 2018; 17(1): 13.https://doi.org/10.1186/s12933-017-0645-0
Jia G, Hill MA, Sowers JR. Diabetic Cardiomyopathy: An Update of Mechanisms Contributing to This Clinical Entity. Circ Res, 2018;122(4):624-638. https://doi.org/10.1161/CIRCRESAHA.117.311586
Smiseth OA. Evaluation of left ventricular diastolic function: state of the art after 35 years with Doppler assessment. J Echocardiogr. 2018;16(2):55-64. https://doi.org/10.1007/s12574-017-0364-2
Furman BL. Streptozotocin-Induced Diabetic Models in Mice and Rats. Curr Protoc Pharmacol, 2015;70:5.47.41-5.47.20.https://doi.org/10.1002/0471141755.ph0547s70
Riehle C, Bauersachs J. Small animal models of heart failure. Cardiovasc Res, 2019;115(13):1838-1849.https://doi.org/10.1093/cvr/cvz161
Yu Y, Fuscoe JC, Zhao C, Guo C, Jia M, Qing T, Bannon DI, Lancashire L, Bao W, Du T, Luo H, Su Z, Jones WD, Moland CL, Branham WS, Qian F, Ning B, Li Y, Hong H, Guo L, Mei N, Shi T, Wang KY, Wolfinger RD, Nikolsky Y, Walker SJ, Duerksen-Hughes P, Mason CE, Tong W, Thierry-Mieg J, Thierry-Mieg D, Shi L, Wang C. A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages. Nat Commun. 2014; 5:3230.https://doi.org/10.1038/ncomms4230
Holland WL, Summers SA. Strong Heart, Low Ceramides. Diabetes, 2018;67(8):1457-1460.https://doi.org/10.2337/dbi18-0018
Lemaitre RN, Yu C, Hoofnagle A, Hari N, Jensen PN, Fretts AM, Umans JG, Howard BV, Sitlani CM, Siscovick DS, King IB, Sotoodehnia N, McKnight B. Circulating Sphingolipids, Insulin, HOMA-IR, and HOMA-B: The Strong Heart https://doi.org/10.2337/db17-1449
Sokolowska E, Blachnio-Zabielska A. The Role of Ceramides in Insulin Resistance. Front Endocrinol (Lausanne), 2019; 10: 577.https://doi.org/10.3389/fendo.2019.00577
Franklin MP, Sathyanarayan A, Mashek DG. Acyl-CoA Thioesterase 1 (ACOT1) Regulates PPARα to Couple Fatty Acid Flux With Oxidative Capacity During Fasting. Diabetes, 2017; 66(8): 2112-2123.https://doi.org/10.2337/db16-1519
Chen SW, Chou CT, Chang CC, Li YJ, Chen ST, Lin IC, Kok SH, Cheng SJ, Lee JJ, Wu TS, Kuo ML, Lin BR. HMGCS2 enhances invasion and metastasis via direct interaction with PPARα to activate Src signaling in colorectal cancer and oral cancer. Oncotarget, 2017;8(14): 22460-22476.https://doi.org/10.18632/oncotarget.13006
Geillon F, Gondcaille C, Raas Q, Dias AMM, Pecqueur D, Truntzer C, Lucchi G, Ducoroy P, Falson P, Savary S, Trompier D. Peroxisomal ATP-binding cassette transporters form mainly tetramers. J Biol Chem, 2017;292(17): 6965-6977.https://doi.org/10.1074/jbc.M116.772806
Fornes D, White V, Higa R, Heinecke F, Capobianco E, Jawerbaum A. Sex-dependent changes in lipid metabolism, PPAR pathways and microRNAs that target PPARs in the fetal liver of rats with gestational diabetes. Mol Cell Endocrinol, 2018, 461: 12-21.https://doi.org/10.1016/j.mce.2017.08.004
Schafer C, Young ZT, Makarewich CA, Elnwasany A, Kinter C, Kinter M, Szweda LI Coenzyme A-mediated degradation of pyruvate dehydrogenase kinase 4 promotes cardiac metabolic flexibility after high-fat feeding in mice. J Biol Chem, 2018; 293(18):6915-6924.https://doi.org/10.1074/jbc.RA117.000268
Newhardt MF, Batushansky A, Matsuzaki S, Young ZT, West M, Chin NC, Szweda LI, Kinter M, Humphries KM. Enhancing cardiac glycolysis causes an increase in PDK4 content in response to short-term high-fat diet. J Biol Chem, 2019;294(45): 16831-16845. https://doi.org/10.1074/jbc.RA119.010371
Li J, Song J, Zaytseva YY, Liu Y, Rychahou P, Jiang K, Starr ME, Kim JT, Harris JW, Yiannikouris FB, Katz WS, Nilsson PM, Orho-Melander M, Chen J, Zhu H, Fahrenholz T, Higashi RM, Gao T, Morris AJ, Cassis LA, Fan TW, Weiss HL, Dobner PR, Melander O, Jia J, Evers BM. An obligatory role for neurotensin in high-fat-diet-induced obesity. Nature, 2016;533(7603): 411-415.https://doi.org/10.1038/nature17662
Rostamzadeh, D., Kazemi, T., Amirghofran, Z., & Shabani, M. Update on Fc receptor-like (FCRL) family: new immunoregulatory players in health and diseases. Expert Opin Ther Targets, 2018,22(6): 487-502. https://doi.org/10.1080/14728222.2018.1472768
Leu CM, Davis RS, Gartland LA, Fine WD, Cooper MD. FcRH1: an activation coreceptor on human B cells. Blood, 2005;105(3):1121-1126.https://doi.org/10.1182/blood-2004-06-2344
Liu S, Liu X, Xiong H, Wang W, Liu Y, Yin L, Tu C, Wang H, Xiang X, Xu J, Duan B, Tao A, Zhao Z, Mei Z. CXCL13/CXCR5 signaling contributes to diabetes-induced tactile allodynia via activating pERK, pSTAT3, pAKT pathways and pro-inflammatory cytokines production in the spinal cord of male mice. Brain Behav Immun, 2019;80: 711-724. https://doi.org/10.1016/j.bbi.2019.05.020
Russo I, Frangogiannis NG. Diabetes-associated cardiac fibrosis: Cellular effectors, molecular mechanisms and therapeutic opportunities. J Mol Cell Cardiol, 2016,90:84-93. https://doi.org/10.1016/j.yjmcc.2015.12.011
Xiong Y, Hai CX, Fang WJ, Lei YP, Li XM, Zhou XK. Endogenous asymmetric dimethylarginine accumulation contributes to the suppression of myocardial mitochondrial biogenesis in type 2 diabetic rats. Nutr Metab (Lond), 2020;17:72. https://doi.org/10.1186/s12986-020-00486
Wilson GW, Stein LD. RNASequel: accurate and repeat tolerant realignment of RNA-seq reads. Nucleic Acids Res. 2015;43(18): e122https://doi.org/10.1093/nar/gkv594
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods, 2001;25(4):402-408.https://doi.org/10.1006/meth.2001.1262

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Analysis of differentially expressed genes related to the development of diabetic cardiomyopathy in the myocardium of type-2 diabetic rats

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