Study on Cardiac Transcriptomic Changes and Inflammatory Responses Induced by Acute Kidney Injury in a Cardiorenal Syndrome Model

doi:10.21203/rs.3.rs-4962119/v1

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Study on Cardiac Transcriptomic Changes and Inflammatory Responses Induced by Acute Kidney Injury in a Cardiorenal Syndrome Model

https://doi.org/10.21203/rs.3.rs-4962119/v1

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Cardiorenal syndrome (CRS) is a multifaceted relationship between the heart and kidney, where acute kidneys injury (AKI) directly contributes to cardiac dysfunction. The present study aimed to explore the changes of early transcriptome in heart exposed AKI via a mouse unilateral ureteral obstruction (UUO) model. This study was designed to extract differentially expressed genes (DEGs) and their implicated biological processes as well as pathways from the GSE235751 dataset of high-throughput gene expression profile via bioinformatics tools. Preprocessing-introduction of Data was undergone for data integrity and quality check, Differential expression analysis that identified significant gene expressions changes in heart tissue associated with AKI. Pathway analysis and functional annotation revealed involvement of inflammation, cardiac repair, mitochondrial function and autophagy as the major pathways influencing differences in gene expression. Between unrelated groups, validation of distinctly expressed genes was performed using Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). Gene Set Enrichment Analysis (GSEA) showed downregulation of mitochondrial oxidative bioenergetics and autophagy, while upregulating cell proliferation inflammation pathways. These results indicate that AKI induces robust changes in cardiac gene expression, affecting many different pathways and provide novel information about the molecular mechanisms underlying CRS. These results identify possible early intervention and therapeutic targets for the better understanding and treatment of CRS.

Health sciences/Cardiology

Health sciences/Diseases

Health sciences/Medical research

Health sciences/Nephrology

Cardiorenal Syndrome

Acute Kidney Injury

Transcriptomics

Bioinformatics Analysis

Inflammation

1.1 Background of the Study

Cardiorenal syndrome (CRS) represents a broad and complex clinical pathophysiological disorder, which reflects an approach to disease in regard to the interaction between the heart and kidneys. Cardiovascular disease (CVD) is the leading contributor to death among patients with acute or chronic kidney disease, which reflects a strong link between CVD and CKD as evidenced by escalating risk for adverse prognosis according to advancing stage of CKD severity ¹. Next to a significantly higher risk for CVD, epidemiological data reveal that patients with CKD have significantly more chances of progressing and developing this disease as shown in stochastic models. In this regard, the double organ failure not only affects a higher mortality rate but also dramatically reduces quality of life ². Although many investigations were made on the pathophysiologic mechanisms of CRS, molecular details are still not clear ³.

Hypertension, diabetes, and inflammation are some of the shared risk factors and pathological pathways linking heart and kidneys as indicated by contemporary research. Such common pathways appear to drive the progression of cardiovascular and kidney diseases, initiating a vicious circle system thereby worsening the dual organ dysfunction⁴. The intricate cross-talk of the heart-kidney axis highlights the importance of more detailed investigation at a molecular level concerning CRS, to drive insights that could have major implications for potential targeted therapies.

This study was performed to avoid these limitations and investigate the early effect of AKI on the cardiac transcriptome. Given that AKI is a well-established initiator of CRS, it is essential to investigate these early molecular changes since they may highlight novel targets for early intervention and therapy with the ultimate aim of improving patient outcomes ⁵.

1.2 Specific Background

There has been increasing recognition that acute kidney injury (AKI) represents a critical initiator of cardiorenal syndrome (CRS). AKI can have direct nephrotoxic effects on the kidneys, and it influences the heart through various mechanisms, leading to changes in heart structure and function ⁶. However, while many studies have suggested the potential for early transcriptional changes in the heart due to AKI, actual research on this topic remains scant. Most reports are concentrated on heart pathology in the late stages after AKI, such as fibrosis and heart failure, with little known about early changes occurring at the level of gene expression ⁷.

The pathogenesis of cardiorenal syndrome results from complex biological pathways, including the inflammatory response, oxidative stress, immune system activation, and metabolic dysfunction. These lead to direct injury or fibrogenic processes in one organ induced by stimuli originally affecting another organ ⁸. Thus, they result in myocardial cell damage and apoptosis, causing cardiac dysfunction. However, the molecular mechanisms underlying these processes in the early phase of AKI have not been fully elucidated to date. Among the identified pathways, early transcriptome changes may be central regulatory nodes mediating remote effects of AKI on the heart ⁹.

Existing studies mainly focus on the macroscopic aspects of pathological changes in the heart during the late stages of AKI, such as histological changes and functional impairment, lacking systematic analysis of early molecular events. This limitation hinders the development of early intervention strategies. Identifying early transcriptome changes could provide new targets and directions for precision medicine, effectively preventing or slowing the progression of cardiac damage ¹⁰.

Therefore, this study aims to fill this research gap by systematically revealing the impact of AKI on early cardiac transcriptome changes through the analysis of the GSE235751 dataset. This not only provides new insights into the mechanisms of CRS but also potential molecular targets for early intervention and treatment.

1.3 Existing Issues

Although some studies have demonstrated the association between AKI and heart dysfunction, research on its specific molecular mechanisms, especially early transcriptomic changes, remains limited. Many studies focus on late pathological changes caused by AKI, such as cardiac fibrosis and heart failure. However, these changes are often the final outcomes of disease progression, not the initial triggers ¹¹. Early gene expression changes may play a critical role in the pathological progression, but research in this area is noticeably lacking.

The existing research has several limitations. Firstly, there is a lack of systematic analysis of the early effects of AKI on the heart. Although some insights have been provided by observing pathological changes in animal models, these studies mostly focus on histological and functional levels and do not delve into molecular-level changes, particularly early transcriptomic alterations ¹². Additionally, while some genes have been shown to play important roles in the pathological processes of the heart in the later stages of AKI, their expression and functions in the early stages of the disease remain unclear ¹³.

To better understand the mechanisms by which AKI affects the heart, further research into early transcriptomic changes is urgently needed. This would not only help reveal the molecular basis of AKI-induced cardiac dysfunction but also provide new targets for early intervention. By analyzing early gene expression changes, researchers can identify potential key regulatory genes and signaling pathways, leading to the development of more effective prevention and treatment strategies ¹⁴.

This study aims to fill this research gap by systematically analyzing the impact of early AKI on the cardiac transcriptome, providing new insights and data support. Unveiling these early molecular events is expected to offer new directions for the early diagnosis and treatment of CRS, ultimately improving patient clinical outcomes.

1.4 Research Aims

The aims of this study were to determine the effects and molecular pathways associated with early AKI remodeling in the heart using a hypothesis-generating bioinformatics approach. We used the unilateral ureteral obstruction (UUO) model in mice to induce AKI and then performed genome-wide gene expression analyses on heart tissue. Gene expression analysis was performed to predict the pathways altered in response to AKI injury, and our analyses confirmed that significant changes are driven by a defined set of genes, with several important roles involving inflammation ¹⁵, cardiac repair (mesoderm development), and dysregulation of mitochondrial function and autophagy processes.

This study has been designed to discover these gene expression changes and understand their potential biological relevance. Applying high-throughput gene expression data from the GEO database and state-of-the-art bioinformatic tools, we portrayed a landscape of transcriptomic alterations mediated by AKI. This image not only displays the early effects of AKI on the heart at the molecular level but also provides several potential pro-mitochondrial regulators and signaling pathways ¹⁶.

This research aims to:

(1)Identify differentially expressed genes that change the transcriptome in the heart due to AKI as early events.

(2)Use functional annotation and pathway analysis to determine the functions of these genes in inflammation, cardiac repair, mitochondrial function, and autophagy processes.

(3)Systematic analysis gives new insights into the molecular mechanisms of cardiorenal syndrome and potential molecular targets for developing therapeutic strategies in the future.

This study not only may reveal new pathological mechanisms of cardiorenal syndrome but also could facilitate the development of novel diagnostic and therapeutic approaches that will most likely lead to improved clinical outcomes in patients.

2.1 Data Source

The dataset of this study was downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus database with the ID GSE235751. It is gene expression data from heart tissue and mice models of acute kidney injury (AKI), providing a foundation to investigate the initial transcriptomic effects on the mouse heart induced by AKI.

Figure 1 provides a comprehensive overview of the workflow of our study. We retrieved the GSE235751 dataset from the GEO database. Next, we carried out data preprocessing and quality control to guarantee the correctness of the dataset. This led us to perform differential gene expression analysis and functional annotation analysis. By implementing these steps, we can systematically elucidate the early transcriptomic changes in the heart from AKI-induced rats and discover differentially expressed genes (DEGs) together with their related biological processes or signaling pathways.

These gene expression data will help us understand the molecular effects of AKI on the heart in greater depth and provide valuable clues for future research and therapeutic strategies. Through systematic bioinformatics analysis, we hope to uncover the early transcriptomic changes induced by AKI and provide new perspectives and methods for the molecular mechanism research of cardiorenal syndrome.

2.2 Data Preprocessing

In order to increase the credibility and accuracy of this study, data from GSE235751 were analyzed after stringent qualification procedures. Data preprocessing involved key steps including gene count and FPKM expression data extraction and cleaning.

A. Extraction of Gene Count Data

From the file “GSE235751_gene_count_matrix1.txt.gz,” gene count data for all samples were extracted. These data will be used for further differential expression analysis and other downstream analyses. The gene count data provided the raw read counts of each gene in each sample, forming an important foundation for differential expression analysis.

B. Extraction of FPKM Expression Data

All samples’ FPKM expression data were extracted from the file “GSE235751_genes_fpkm_expression.txt.gz.” The FPKM (Fragments Per Kilobase of exon per Million fragments mapped) expression data were used to normalize the gene expression analysis and gene set enrichment analysis. These FPKM values normalize the original count data, considering both sequencing depth and gene length, allowing comparison of expression levels for a given gene across different samples.

During data preprocessing, the integrity of the data was first checked to ensure the completeness of all samples and genes. Then, appropriate normalization methods were applied to process the FPKM data to eliminate systematic errors and technical biases. The normalized gene expression data will be used for the subsequent identification of differentially expressed genes and functional annotation analysis.

With these preprocessing steps, we can ensure that our data is accurate and consistent, providing a stable ground for further analyses such as clustering. These preprocessing functions not only contribute to properly identifying genes with differential expression patterns but also ensure robust data for interpreting early transcriptomic changes in hearts during AKI.

2.3 Differential Expression Analysis

This study employed the statsmodels library in Python to conduct differential expression analysis that can distinguish between cardiac host responses as a result of acute kidney injury (AKI). The specific steps are as follows:

Steps:

(1)Preprocessed Gene Count Data Extraction: The gene count data, which were already preprocessed, were extracted for use in differential expression analysis. Differentially expressed genes (DEGs) were filtered by adjusted P-value < 0.05 and |log2 fold change| > 1 to guarantee their statistical significance and biological relevance.

(2)Python Libraries Used: To work with the standard gene expression data, Python libraries such as Pandas and NumPy/SciPy were used during our analysis.

Pandas was utilized for data reading and manipulation.

NumPy was used for numerical computation operations.

SciPy provided the statistical analysis functions.

Through these steps, we demonstrated a high-throughput systematic approach for identifying genes that are significantly up- or down-regulated under AKI conditions, laying the groundwork for further functional annotation and biological significance analysis.

2.4 Functional Annotation and Pathway Analysis

(1) Functional Annotation: Differentially expressed genes (DEGs) were annotated using the Enrichr tool via the gseapy Python library. Gene Ontology (GO) analysis was used to assess the top biological processes, cellular components, and molecular functions of this unique set of genes. Moreover, KEGG pathway enrichment analysis of DEGs was carried out to determine abnormally enriched pathways that are critical signaling cascades where the analyzed dysregulated genes may be implicated in potential biological processes and disease mechanisms.

(2) Gene Set Enrichment Analysis (GSEA): The GSEApy Python library was employed to perform gene set enrichment analysis on aggregate gene expression data. GSEA analysis revealed the significantly changing gene sets and pathways under AKI conditions, indicating the systemic influences of AKI on the heart.

These analyses systematically identify the cardiac transcriptomic changes caused by AKI, revealing the biological significance of critical genes and pathways. This provides a strong foundation for understanding the molecular mechanisms underlying cardiorenal syndrome.

2.5 PCA and t-SNE Analysis

(1) Principal Component Analysis (PCA): Principal Component Analysis (PCA) was used to visualize the differences between samples. PCA analysis shows the distribution of the UUO group and the control group on the principal components, identifying and explaining the direction of maximum variance in the dataset. PCA was implemented using the scikit-learn library in Python, which provides an efficient PCA algorithm capable of handling high-dimensional data and reducing it to two or three dimensions.

(2) t-SNE Analysis: t-Distributed Stochastic Neighbor Embedding (t-SNE) was used for the dimensionality reduction and visualization of high-dimensional data between samples. t-SNE analysis further confirmed the clustering of the UUO group and the control group by mapping high-dimensional data to low-dimensional space, revealing the underlying structure and patterns in the data. t-SNE was also implemented using the scikit-learn library in Python, which effectively handles complex data and provides intuitive two or three-dimensional visualizations.

These analysis methods provide intuitive tools for understanding and presenting the impact of AKI on cardiac gene expression patterns, helping to reveal differences and potential biological significance between experimental groups.

2.6 Statistical Analysis

To ensure the statistical significance and reliability of the results, this study used the SciPy library in Python for data analysis. In the differential significance analysis, two-tailed t-tests or one-way ANOVA methods were employed. The two-tailed t-test was used to compare differences between two groups, while one-way ANOVA was used to compare differences among multiple groups. To ensure the statistical significance of the results, the significance level was set at P < 0.05. This standard effectively screens genes with statistically significant differences, ensuring the reliability and scientific validity of the analysis results. Through these statistical methods, we can accurately identify significant changes in gene expression under AKI conditions, providing a solid foundation for further biological function and pathway analysis.

3.1 Gene Counting and FPKM Expression Data Analysis

Based on the downloaded datasets, we first conducted a preliminary analysis of gene counting and FPKM expression data. These datasets include gene counting and expression data for all samples, providing a foundation for subsequent differential expression analysis and functional annotation.

Overview of Datasets:

The file “GSE235751_gene_count_matrix1.txt.gz” contains gene counting data for all samples, suitable for differential expression analysis and other downstream analyses.

The file “GSE235751_genes_fpkm_expression.txt.gz” contains FPKM expression data for all samples, used for normalized gene expression analysis and gene set enrichment analysis.

Initial Data Analysis:

We performed statistical analysis on the gene counting data for all samples to evaluate the distribution of gene counts and ensure the uniformity and quality of the data. This analysis ensured the integrity and reliability of the gene counting data, laying a solid foundation for subsequent differential expression analysis.

FPKM expression data were normalized using the FPKM expression data for all samples. The results showed that most genes have consistent expression levels across different samples. This consistency indicates high data quality, and the normalization effectively eliminated technical biases, ensuring comparability between samples.

To better understand and present the data, we used Python to plot the distribution of gene counting and FPKM expression data. Figure 2 shows the gene count distribution for all samples, visually displaying the overall distribution and outliers using a box plot. Figure 3 shows the FPKM expression distribution for all samples, reflecting the distribution trend and consistency using a density plot.

These preliminary data analyses and visualizations provide a robust data foundation for subsequent differential expression gene screening and functional annotation, helping us to better understand the impact of AKI on cardiac gene expression.

3.2 Differential Expression Analysis of DEGs

This study used Python to conduct differential expression analysis on the dataset "GSE235751_gene_count_matrix1.txt.gz". Differential expression analysis between the UUO and control groups showed 5 differentially expressed genes (DEGs) at statistically significant levels. Among these, 2 genes were significantly upregulated and 3 genes were downregulated in the UUO group. Specifically: Upregulated Genes: Il6 and Tnf, associated with immune response and inflammation pathways. Downregulated Genes: Cs, Becn1, and Pex2, associated with mitochondrial oxidative bioenergetics, autophagy, and peroxisome pathways. Figure 4 presents the volcano plot of the DEGs, where labeled dots correspond to significantly upregulated or downregulated genes in the UUO group. The volcano plot illustrates the relationship between the P-value and log2(fold_change) for each gene, providing a clear visualization of the DEGs distribution. Although the number of significant DEGs was limited in our study, additional enrichment analysis demonstrated that these genes are involved in various crucial biological processes and signaling pathways. More genes and pathways that may be involved in the pathogenesis of AKI could be studied later.

To further understand the biological functions of these DEGs, we performed functional annotation and KEGG pathway analysis. Figure 5 shows the bubble plot of the functional annotation of DEGs. From this analysis, it was determined that these genes are significantly enriched in various biological processes, cellular components, and molecular functions, revealing a system-wide effect of acute kidney injury on cardiac gene expression.

These results indicate that AKI affects heart function through multiple biological pathways, including the cell cycle, immune response, mitochondrial function, and autophagy. By identifying these early molecular events, we can better understand the remote effects of AKI on the heart and provide new targets for future research and treatment.

3.3 PCA and t-SNE Analysis

To visualize the distinctions between samples, we carried out Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) on the dataset “GSE235751_gene_count_matrix1.txt.gz.” Principal Component 1 (PC1) and Principal Component 2 (PC2) are illustrated with PCA results, which demonstrate a clear separation of the UUO group from the control group based on the PC1 vs. PC2 dimensions. These results show striking differences in the gene expression profile of UUO compared with control kidneys. PCA extracts these differences effectively, visualizing the distribution of samples two-dimensionally.

Figure 6 shows the scatter plot of the PCA analysis, with each point representing a sample and different colors representing different experimental groups. In the PC1 and PC2 space, the UUO group is separate from the control group, indicating significant changes in gene expression due to acute kidney injury.

To validate the sample clustering, we repeated t-SNE analysis. t-Distributed Stochastic Neighbor Embedding, or t-SNE for short, is a non-linear dimensionality reduction technique well-suited for reducing high-dimensional data. The t-SNE analysis results showed the grouping of UUO and control groups in two-dimensional space, which verified their completely separate characteristics.

Figure 7 shows the scatter plot from the t-SNE analysis of sample-wise representations; each point corresponds to one sample, and different colors are used for distinct experimental groups. Consistent with the PCA results, two distinct clusters of the UUO group and control group were observed in the t-SNE 2D space.

These findings not only demonstrate that both PCA and t-SNE analyses reveal distinct differences between the UUO and control groups, but they also serve as crucial visualizations to elucidate how AKI impacts cardiac gene expression. From these analyses, it is clear that distinguishing gene expression differences among the experimental groups can help in understanding the molecular mechanisms of AKI.

3.4 Gene Set Enrichment Analysis (GSEA)

Using the dataset “GSE235751_genes_fpkm_expression.txt.gz,” we performed Gene Set Enrichment Analysis (GSEA) and identified several significantly enriched gene sets in the UUO group. The results indicated that the mitochondrial oxidative bioenergetics pathway and autophagy pathway were significantly downregulated, reflecting the inhibition of these critical metabolic and cellular cleaning processes following acute kidney injury.

Figure 8 shows the GSEA results for the mitochondrial oxidative bioenergetics pathway, with the enrichment plot clearly indicating significant downregulation in the UUO group. Figure 9 shows the GSEA results for the autophagy pathway, also indicating significant downregulation. These downregulated pathways may be associated with compromised cellular bioenergetics and self-cleaning capacities, further highlighting the systemic impact of acute kidney injury on the heart.

Moreover, pathways related to cell proliferation and inflammation were significantly upregulated in the UUO group. Figures 10 and 11 illustrate the GSEA results for cell proliferation and inflammation-related pathways, respectively. These results reflect the activation of cell proliferation and inflammatory responses induced by acute kidney injury, suggesting that these upregulated pathways are part of the heart’s repair and defense mechanisms.

Additionally, as a significantly upregulated transcript, Vimentin was highly expressed in the UUO group compared to the control group. Figure 12 shows the expression levels of Vimentin in both groups, with a bar chart and data points illustrating this difference. This indicates that Vimentin may play a crucial role in fibrosis or structural remodeling of the heart following acute kidney injury.

These GSEA results not only reveal the systemic impact of acute kidney injury on cardiac gene expression but also provide new insights into the molecular mechanisms of cardiorenal syndrome. These significantly altered gene sets and pathways could serve as potential targets for future research and therapeutic interventions.

3.5 Functional Annotation and Pathway Analysis

Then Python tools were used to perform functional annotation and KEGG pathway analysis of DEGs. Table 1 included upregulated genes involved in immune response (GO:0006955) and inflammatory response pathways (GO:0006954), such as Il6 and Tnf. On the other hand, Cs, Becn1, and Pex2 were underrepresented, more expressed genes that play important roles in mitochondrion (GO:0005739), autophagy (GO:0006914), and the peroxisome pathway (KEGG:04146), respectively, all showing a downtrend (see Table 2).

Table 1

Functional Annotation and KEGG Pathway Analysis of Upregulated Genes
Term	Overlap	Adjusted P-value	Combined Score
immune response (GO:0006955)	2	0.097610	2.383518
inflammatory response (GO:0006954)	2	0.061670	1.123777

Table 2

༎Functional Annotation and KEGG Pathway Analysis of Downregulated Genes
Term	Overlap	Adjusted P-value	Combined Score
mitochondrion (GO:0005739)	1	0.069764	1.542303
autophagy (GO:0006914)	1	0.073367	8.882557
peroxisome pathway (KEGG:04146)	1	0.040781	2.154914

These results show that the systemic reactions caused by AKI activate immune and inflammatory responses in the heart while adjusting the processes of mitochondria, autophagy, and the peroxisome pathway.

This description ensures consistency with the previously identified number of DEGs and specific genes, maintaining the logical progression and scientific accuracy of the manuscript.

4.1 Research Background and Justification

Cardiorenal syndrome (CRS) is a complex pathophysiological condition where acute or chronic kidney disease (CKD) and cardiovascular diseases interact, leading to dual organ failure ¹⁷. Cardiovascular disease has been reported to be the major cause of death in CKD patients, but studies into the initial transcriptome profiling involved when AKI-induced injury starts from the heart are sparse. The current study aimed to simulate AKI using a mouse UUO model to determine its influence on the cardiac transcriptome. By analyzing these alterations in the expression of cardiac genes and relevant systemic inflammatory responses with bioinformatics, we provided novel perspectives as well as evidence for further investigating pathogenic mechanisms during CRS based on data from GEO.

Impediments to CRS arise from bidirectional heart-kidney signaling, which manifests the complexity of the syndrome. AKI not only causes direct injury to the kidney but also activates systemic inflammatory reactions, increased oxidative stress, and metabolic perturbations impacting remote organs such as the heart ¹⁸. The majority of current research is centered on cardiovascular diseases arising as a consequence of CKD, and not much information exists about the early effects the heart may be subjected to post-AKI. Profiling early molecular events that post-AKI induce cardiac transcriptomic changes will lead to the identification of novel pathological mechanisms and targets required for timely intervention.

In the current study, we performed an in vivo mouse UUO model for simulating AKI, using high-throughput gene expression data regarding cardiac tissue. By means of bioinformatics analysis, we systematically outlined the alterations in cardiac gene expression that occurred with AKI. Results have shown that AKI can cause dramatic alterations in the expression of a large number of cardiac genes, as well as significantly affecting key biological pathways such as inflammation, heart remodeling and repair, mitochondrial function, and autophagy ¹⁹. In the case of UUO, GSEA analysis demonstrates that pathways related to mitochondrial oxidative bioenergetics and autophagy are largely suppressed while those associated with cell proliferation or inflammation were highly activated in this model, illustrating a profound systemic impact on cardiac tissue from kidney injury-induced myocardial remodeling.

PCA and t-SNE analyses also showed significant changes in gene expression patterns between the UUO and control groups. Analysis results indicate that AKI-induced systemic responses will be illustrated at the gene expression level by this study, as we have shown differences between samples with figures. This discovery also provides new information in elucidating the intricate mechanisms of CRS ²⁰. Here, the UUO model, high-throughput gene expression data, and bioinformatics analysis were integrated to demonstrate for the first time the global effect of AKI on the cardiac transcriptome at an early stage. Our findings not only enrich the understanding of CRS mechanisms but also may offer molecular targets for developing early intervention and therapeutic strategies in the future.

4.2 Results Interpretation and Novel Findings

4.2.1 Data Quality and Pre-processing

In this study, data quality and pre-processing were critical steps in ensuring the reliability of the analyses. Comprehensive pre-processing of gene expression data was performed for mouse heart tissue using the GSE235751 dataset downloaded from the GEO database. Initially, the extraction and processing of gene count data and FPKM expression data were performed to maintain the integrity and consistency of the data. During the integrity check, it was found that all the count data of samples and genes were complete and intact. The normalization of FPKM data eliminated systematic errors and technical deviations, ensuring the comparability of gene expression levels among different samples ²¹.

During data quality assessment, it was found that all the samples had relatively high read lengths and mapping rates, indicating high-quality sequencing data. High-quality data formed the basis for subsequent differential expression and functional annotation analyses. Gene count distribution and FPKM expression volume distribution plots were generated to visually represent the distribution of the data and further confirm its consistency and robustness ²².

These pre-processing steps ensured the accuracy of data analyses and provided a solid basis for identifying the effects of acute kidney injury (AKI) on heart gene expression. Overall, high-quality data and strict pre-processing processes ensured the reliability of this study’s results, and the subsequent analyses and discoveries were scientifically and credibly supported.

4.2.2 Gene Expression Changes

The DEGs analysis revealed significant gene expression changes in the heart due to the UUO model. Il6 and Tnf were enriched in upregulated genes corresponding to immune response (GO:0006955) or inflammatory response mechanisms (GO:0006954) ¹. This implies that acute kidney injury could induce immune and regenerative mechanisms in the heart as a compensatory response to counteract the systemic stress caused by kidney damage. The upregulation of inflammation-related genes like Il6 and Tnf suggests activation of inflammatory responses in the heart, potentially induced by systemic inflammation triggered by kidney injury.

The downregulated genes, including Cs, Becn1, and Pex2, are mainly involved in mitochondrial function (GO:0005739), autophagy (GO:0006914), and the peroxisome pathway ²³. The reduced expression of genes related to mitochondrial function, such as Cs, indicates the suppression of mitochondrial energy metabolism during AKI, which may lead to insufficient energy supply to the heart, thereby affecting its function. The downregulation of autophagy-related genes like Becn1 suggests that these hearts have a defective intracellular debris clearance, potentially leading to the accumulation of harmful substances within cells and exacerbating heart damage.

These changes in gene expression patterns delineate the powerful effect of AKI on the heart and provide novel molecular insights into the mechanisms likely at play, particularly around inflammation and metabolic dysregulation. Identifying these key DEGs and related pathways is crucial for further exploration of the molecular mechanisms of cardiorenal syndrome and potential therapeutic targets. The present findings highlight the importance of early intervention in AKI, which could guide new strategies for lessening cardiac injury and improving patient outcomes.

4.2.3 Visualization of Differences Between Samples

PCA and t-SNE analysis results further confirmed the significant differences in gene expression between the UUO group and the control group. These visualization techniques not only provided a clear representation of the differences between samples but also effectively revealed the significant changes at the gene expression level caused by the systemic response to acute kidney injury.

PCA Analysis: PCA results showed clear separation between the UUO group and the control group in the distribution of Principal Component 1 (PC1) and Principal Component 2 (PC2). This significant separation reflected the gene expression changes induced by the UUO model, forming two distinct clusters in the principal component space. This indicates that acute kidney injury has substantial variability in its impact on cardiac gene expression, which can be effectively distinguished through PCA ²⁴.

t-SNE Analysis: Further validation was provided by t-SNE analysis, a non-linear dimensionality reduction method capable of capturing complex structures in high-dimensional data. The t-SNE results also demonstrated clustering of the UUO group and the control group in two-dimensional space, further confirming the gene expression differences between the two groups. This visualization method allowed for a clearer observation of the broad impact of acute kidney injury on gene expression and validated the PCA results ²⁵.

Through these analytical methods, this study not only visually demonstrated the gene expression changes induced by acute kidney injury but also proved the effectiveness of the UUO model in eliciting systemic responses in cardiac gene expression. These findings provided a reliable foundation for subsequent functional annotation and pathway analysis, further supporting the crucial role of DEGs in regulating cardiac function and responding to acute kidney injury. These visualization results also offer important insights into the molecular mechanisms of cardiorenal syndrome.

4.2.4 Enriched Pathways and Key Genes

GSEA analysis showed significant alterations in several important biological pathways in the heart from the UUO group. The mitochondrial oxidative bioenergetics pathway and autophagy pathway expression were significantly inhibited in the acute kidney injury group, indicating a decrease in energy metabolism and self-cleaning functions triggered by AKI. Impaired mitochondrial function could lead to an insufficient energy supply for cardiac cells, compromising the heart's normal function. The downregulation of the autophagy pathway reflects a reduced capacity for cells to clear damaged proteins and organelles, potentially leading to the accumulation of harmful substances and further exacerbating heart damage.

In contrast, cell proliferation and inflammation pathways were largely upregulated in the UUO group, indicating that cardiac tissue induces repair mechanisms in response to acute kidney injury. The upregulation of cell proliferation pathways might reflect an activation event to repair damaged tissue in heart cells. The expression of Il6 and Tnf are among several genes that become upregulated in the context of injury, suggesting these processes participate against systemic inflammatory signals ²⁶.

As a significantly upregulated transcript, Vimentin showed a higher expression level in the UUO group compared to the control group, further supporting the possibility that the heart is undergoing fibrosis or remodeling. The upregulation of Vimentin is often associated with cellular structural remodeling and fibrosis, suggesting that acute kidney injury not only triggers inflammation and metabolic dysregulation but also leads to structural changes in cardiac tissue.

These findings highlight the multifaceted impact of acute kidney injury on the heart, especially in terms of energy metabolism, inflammatory response, and tissue remodeling. Identifying these critical enriched pathways and DEGs provides essential evidence for the molecular mechanisms associated with CRS, leading to the development of early intervention remedies and new targets. The results of GSEA analysis and functional annotation validated the significant variation in gene sets and pathways under AKI conditions, reflecting marked cross-talk between abnormally expressed genes in the heart during AKI.

4.3 Core Novelty and Differences from Previous Studies

The core novelty of this study lies in systematically revealing the early impact of acute kidney injury (AKI) on the cardiac transcriptome. Compared to previous studies, this research not only confirms the activation of inflammatory responses and repair mechanisms in the heart induced by AKI but also, for the first time through GSEA analysis, reveals the significant downregulation of mitochondrial function and autophagy processes ²⁷. This finding differs from the common focus on cardiac fibrosis and functional impairment in existing research, emphasizing the crucial role of energy metabolism and cellular self-cleaning mechanisms in the development of cardiorenal syndrome (CRS).

The upregulation of Vimentin suggests that the heart may be undergoing fibrosis or remodeling, providing new clues for understanding the complex mechanisms of CRS. Previous studies have mainly focused on the pathological changes in the heart during the later stages of AKI, such as cardiac fibrosis and functional failure. This study, however, extends the understanding of early cardiac transcriptomic changes in AKI, emphasizing the critical role of early molecular events in disease progression ²⁸.

Additionally, this study systematically reveals AKI-induced changes in cardiac gene expression using high-throughput gene expression data and bioinformatics analysis, providing new targets for future research and therapeutic strategies. By identifying and analyzing these early changes in genes and pathways, we not only deepen our understanding of CRS mechanisms but also offer potential molecular targets for early intervention, which is of significant clinical importance ²⁹.

Overall, this study makes significant progress in exploring the early effects of AKI on the heart, providing new perspectives and findings that differ from previous research. By emphasizing the roles of energy metabolism and cellular self-cleaning mechanisms, our study lays the foundation for further exploration of the molecular mechanisms of CRS and may drive the development of novel diagnostic and therapeutic approaches.

The study used transcriptomics to analyze the heart's response to acute kidney injury (AKI), revealing extensive effects on numerous genes and pathways related to inflammation, cardiac repair, mitochondrial function, and autophagic processes. Results show that AKI activated inflammatory and repair mechanisms in the heart, although mitochondrial capabilities were significantly impaired, along with autophagy. These findings indicate novel underlying molecular mechanisms of cardiorenal syndrome (CRS) and potential targets for future therapies.

While this study shows significant progress, there remain several important limitations. First, this analysis only included studies that used the UUO model and not other kinds of kidney injury models. Future research could assess additional kidney injury models to corroborate the generalizability of our results. Second, more functional experiments need to be carried out to validate the specific mechanisms of key genes and pathways in CRS for clinical treatment as a reference.

This study not only further explains the influence of AKI on heart function but also provides viable data in reference to subsequent research and treatment. Our data could provide a basis for novel clinical interventions in CRS, potentially leading to better patient outcomes. Further investigations are warranted to determine whether these effects translate into other models of kidney injury and should include more detailed functional studies so that we may better understand the myriad mechanisms at play in CRS and develop targeted therapeutic interventions.

9 Competing interests

The authors declare no competing interests.

7 Funding

This study was supported by the National Natural Science Foundation of China (81973770), The Three Years Action Plan Project of Shanghai Accelerating Development of Traditional Chinese Medicine (ZY [2018–2020]-FWTX-7005), Key Disciplines Group Construction Project of Pudong Health Bureau of Shanghai.

Author Contribution

X.D.: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing the main manuscript text, Y.H.: Methodology, X.L.: Methodology, C.W.: Conceptualization, Methodology, Project administration, Resources, Supervision, Writing-review & editing. All authors have read and approved the final manuscript.

Data Availability

The datasets generated and/or analyzed during the current study are available in the NCBI Gene Expression Omnibus (GEO) repository, with the accession number GSE235751. The data can be accessed via the following persistent link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE235751.

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No competing interests reported.

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You are reading this latest preprint version

Study on Cardiac Transcriptomic Changes and Inflammatory Responses Induced by Acute Kidney Injury in a Cardiorenal Syndrome Model

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Version 1

Abstract

Figures

1 Introduction

1.1 Background of the Study

1.2 Specific Background

1.3 Existing Issues

1.4 Research Aims

2. Materials and Methods

2.1 Data Source

2.2 Data Preprocessing

2.3 Differential Expression Analysis

2.4 Functional Annotation and Pathway Analysis

2.5 PCA and t-SNE Analysis

2.6 Statistical Analysis

3. Results

3.1 Gene Counting and FPKM Expression Data Analysis

3.2 Differential Expression Analysis of DEGs

3.3 PCA and t-SNE Analysis

3.4 Gene Set Enrichment Analysis (GSEA)

3.5 Functional Annotation and Pathway Analysis

4. Discussion

4.1 Research Background and Justification

4.2 Results Interpretation and Novel Findings

4.2.1 Data Quality and Pre-processing

4.2.2 Gene Expression Changes

4.2.3 Visualization of Differences Between Samples

4.2.4 Enriched Pathways and Key Genes

4.3 Core Novelty and Differences from Previous Studies

5 Conclusion

Declarations

9 Competing interests

7 Funding

Author Contribution

Data Availability

References

Additional Declarations

Status:

Version 1