Identification of significant modules and hub genes involved in fasting using WGCNA

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Regular intermittent fasting is thought to reduce body weight and ameliorate adverse cardiovascular disease factors significantly. However, there is growing evidence that fasting positively affects body composition and biochemical parameters, but very few studies related to its mechanisms. In this study, bioinformatics network analysis was performed to investigate the effects of fasting on adipose and muscle tissues and further explore the potential mechanisms and targets of action. Methods: We downloaded the adipose tissue and muscle tissue gene expression datasets before and after fasting from the Gene Expression Omnibus (GEO) database and constructed co-expression networks by Weighted correlation network analysis (WGCNA) to identify key modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the differential genes in adipose tissue and muscle tissue-related modules, respectively. Then, we constructed protein-protein interaction (PPI) networks using the STRING database and detected the central genes in the networks. Results: Functional enrichment analysis showed that AMPK pathway and neurodegenerative disease-related pathways might be involved in the regulation of fasting in humans. PPI network construction indicated that the regulation of fasting in humans, both in adipose and muscle tissues, may be associated with two central genes, TXNIP and DLAT, and that this regulation is likely to act on human metabolism. Conclusion: Our work indicates that a total of 15 key genes, including TXNIP, DLAT, PDK4, DDIT3, and PFKFB3, may receive regulation by fasting interventions, especially TXNIP and DLAT are the basis of fasting mechanisms in adipose and muscle tissues. The pathways regulated by these key genes may provide new targets for further studies on the mechanism of fasting and the treatment of metabolic diseases. Bioinformatics Intermittent fasting TXNIP DLAT WGCNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction From 1990 to 2016, an estimated one in five people worldwide died prematurely due to poor diet[1]. Dietary interventions are widely used worldwide as one of the key tools of health promotion. For people who are obese or overweight, weight loss can be accompanied by the prevention of many primary and secondary cardiovascular diseases[2]. When it comes to eating behavior modification, fasting is one of the very important methods, which usually means that subjects need to fast for a specific period voluntarily. The beginnings of fasting originated in religion as well as in the objective conditions of the lack of material living standards. This ascetic practice is mentioned in the Old Testament and other ancient texts such as the Qur'an and the Mahabharata[3]. For most people, fasting usually means consuming little or no food, including energy drinks, for a certain period. For example, Muslims fast from dawn to dusk during Ramadan, while Christians, Jews, Buddhists, and Hindus fast on designated days or periods according to their respective traditions[4]. Fasting differs from calorie restriction in that the latter involves a long-term reduction in daily energy intake of up to 40 percent while meal frequency remains the same. Although this diet of only 800 to 1500 kcal of energy per day can achieve a negative energy balance, this energy-restricted balanced diet with an average weight loss of 0.4 to 0.5 kg per week has very poor compliance[5]. Usually, people who use calorie restriction to reduce their weight will experience a rebound after 1-4 months, while most people who use this method to lose weight will return to their original weight within one year[6]. Unlike fasting, starvation is a chronic nutritional deficiency that is often incorrectly used as a substitute for the term "fasting". Starvation can also refer to extreme forms of fasting, which can lead to impaired metabolic status or even death of the body. Moreover, starvation usually implies chronic irregular fasting, which can also lead to nutritional deficiencies and partial damage to digestive health. Although prolonged fasting is difficult for the normal population, intermittent energy restriction (IER) programs have been shown to have a high compliance rate[7]. Intermittent energy restriction is an increasingly popular dietary approach for weight loss and overall health promotion. In recent years, various intermittent energy restriction programs have gained popularity as strategies to achieve weight loss and other metabolic health benefits, including intermittent fasting (a "5 + 2" model in which subjects normally eat for five days a week and consume 25% of their energy on the remaining two days, 500 kcal/day for women and 600 kcal/day for men) and time-restricted feeding(subjects previously had a daily eating window of 14 hours or more, which was adjusted to 4-10 hours per day for several consecutive weeks). These are the two most promising intermittent energy restriction programs[8]. A recent study showed that a 25-day time-restricted diet in adult men not only improved individual serum lipids and liver characteristics but also promoted the enrichment of beneficial intestinal flora, with significant enrichment of prevotella and mimobacteriaceae. The sequencing results showed that the time-restricted diet might enhance the expression of the day-night rhythm gene by activating sirtuin-1, which was positively correlated with intestinal microbiome enrichment[9]. However, as of now, data on the health promotion of intermittent fasting are very limited. Moreover, as dietary habits change, so do body rhythms and metabolism. And there are even fewer studies on how fasting affects various molecular pathways in muscle and adipose tissue[10]. Weighted gene co-expression network analysis (WGCNA) is a widely used strategy for analyzing phylogenetic data based on pairwise correlations between variables[11]. WGCNA was used to define modules, network nodes, and intramodular hubs to determine the relationship between co-expressed modules and to compare the topology of different networks to screen for significant trait genes associated with clinical traits[12]. Currently, WGCNA has been widely used to analyze genomics and metabolomics data, including microarray data, single-cell RNA-Seq data, DNA methylation data, and non-coding RNA data[13-16]. In this study, we explored differential genes in adipose and muscle tissues after fasting to reveal the potential biological alteration process of fasting. Also, key genes were identified from the co-altered genes to investigate important targets for promising endocrine therapies. 2. Materials And Methods 2.1. Data sources Gene expression profiles associated with the fasting intervention were downloaded from the Gene Expression Omnibus (GEO) database website (www.ncbi. nlm.nih.gov/geo), and samples included in the study were screened[17]. The GSE154612 dataset was derived from adipose tissue samples. We selected 11 subjects with a total of 22 subcutaneous adipose tissue biopsies collected before and after fasting while excluding samples from animal experimental sources. The GSE55924 dataset was derived from muscle tissue samples. We selected a total of 24 skeletal muscle biopsy samples collected before and after fasting from 12 subjects with the same fasting time while excluding samples from other fasting times. We defined the post-fasting adipose tissue samples and muscle tissue samples as the fasting group (containing both adipose tissue and muscle tissue) and the pre-fasting samples as the normal group. gene expression profiling arrays for GSE154612 and GSE55924 were based on the GPL17692 (Affymetrix Human Gene 2.1 ST Array) and GPL10558 platforms, respectively Illumina HumanHT-12 V4.0 expression bead chip). 2.2. Data Preparation Data preparation was performed using R software (v4.2.2) and Bioconductor Packages. The raw expression data were processed to produce expression matrices and to match probes to their gene symbols. For those that could not be matched directly, we used the DAVID website (https://david.ncifcrf.gov/) to find the original gene id of the corresponding platform before converting and matching. Using the Affy package of the R software platform, we preprocessed and normalized the microarray dataset, and we also used interpolation when missing values were present[18]. The GEO query package is used to avoid the situation where one probe corresponds to multiple molecules. When multiple probes corresponding to the same molecule are encountered, only the probe with the largest signal value is retained. Then, we check the standardization of samples by box plot, the clustering between sample groups by PCA plot and UMAP plot, followed by the difference analysis between two groups by using the limma package. 2.3. Co-Expression Network Construction First, the co-expression network of all genes in the fasting and normal groups was constructed using the "WGCNA" package of the R platform. Second, according to the scale-free topology criterion, the "pickSoft Threshold" algorithm of "WGCNA" is used to calculate the soft power threshold to construct a biologically meaningful scale-free network; then establish the weighted adjacency matrix. The formula is a mn =|c mn | β (a mn : adjacency between gene m and gene n, c mn : Pearson’s correlation, and β: soft-power threshold)[19]. In addition, the weighted adjacency matrix is transformed into a topological overlap measure (TOM) matrix to estimate its connectivity in the network. The clustering dendrogram of the TOM matrix was constructed using the mean chain hierarchy clustering method. The minimum gene module size was set to 30 to obtain the appropriate modules, and the threshold for merging similar modules was set to 0.25. Finally, gene significance (GS) and module membership (MM) were calculated to associate modules with clinical traits and visualize the characteristic gene network[20]. 2.4. Functional enrichment analysis of common genes Venn diagrams were made with VennDiagram (v 1.6.2), to overlap the genes between adipose tissue and muscle tissue related preserve modules. Afterward, we extracted the two groups of differentially expressed genes separately to complement the relevant functions further and compare how fasting changed adipose tissue versus muscle tissue in similar and different ways. GO term enrichment and KEGG pathway analyses were performed with DAVID (https://david-d.ncifcrf.gov/). 2.5. Identification of the hub genes in functional modules and crucial gene mining Differential genes in fasted and non-fasted states with protein-protein interaction (PPI) were established by an online reciprocal gene search tool (STRING database, V 11.5 http:// string-db.org/). PPI networks were constructed using a composite score greater than 0.15 and visualized using Cytoscape version 3.8.0 software. Genes commonly found in the network were screened by maximum clique centrality (MCC), and the genes with the most interactions were referred to as hub genes, which may play a central role in disease co-morbidity. Then, the GeneCards database (http://www.genecards.org/) was used to find interactions of related genes, proteins, drugs, and diseases to identify more details of hub genes. In the GSE154612 and GSE55924 datasets, the "limma" R package was used to identify differentially expressed genes (DEGs) between fasted and non-fasted samples. The cut-off value was log2FC > |0.25|, P-value < 0.05. Hierarchical cluster analysis was performed using the R package heatmap. The volcano plots were plotted for the identified genes using enhanced Volcano, an R package version 1.2.0. 3. Results 3.1 Differentially expressed genes in adipose and muscle tissues after fasting A total of 503 genes were differentially expressed in adipose tissue after fasting compared to non-fasting state, of which 307 were down-regulated, and 196 were up-regulated. In contrast, a total of 279 genes were differentially expressed in muscle tissue, of which 134 were down-regulated, and 145 were up-regulated. Figure 1A and Figure 1C show the volcano and heat map of DETs in adipose tissue, Figure 1B and Figure 1D show the volcano and heat map of DETs in muscle tissue. 3.2 Identification of co-expression gene modules We used WGCNA to identify co-expressed gene modules in the adipose and muscle tissue datasets after fasting. First, samples from both datasets were clustered into two clusters without outliers: the fasting group (adipose tissue or muscle tissue) and the normal group. Then, based on scale independence of > 0.8, 2 and 4 were selected as soft threshold power β for GSE154612 and GSE55924, respectively, to ensure biologically significant scale-free networks (Figure 2A and 1B). The genes in GSE154612 and GSE55924 were clustered into six modules by hierarchical clustering analysis of the gene dendrogram and dynamic branching cut method (Figure 2C and 2D). 3.3 Calculation of module-trait correlations We constructed the gene network and identified modules using the one-step network construction function of the WGCNA R package. Calculation of module-trait correlations to identify fasting key modules, gene significance (GS) and module membership (MM) were calculated to relate modules to clinical traits. The MM was defined as the correlation between gene expression values and the ME. The GS was defined as the correlation between genes and samples. As shown in Figure 3A, module–trait relationships indicated that the Black modules were significantly correlated with fasting in adipose tissue. Similarly, the Green and Turquoise modules significantly correlated fasting in muscle tissue (Figure 3B). 3.4 Functional enrichment analysis of common genes The common genes between muscle tissue and adipose tissue in fasting related preserve modules were indicated by Venn diagram (Figure. 4). To reveal the underlying molecular biological processes in fasting status and, more importantly, to find the mechanisms that link fasting and metabolism, We conducted GO and KEGG pathway enrichment analysis on fasting adipose tissue samples and fasting muscle tissue samples, respectively. GO term enrichment and Kegg pathway analyses were performed with DAVID (Figure 5). These analysis results show that, for adipose tissue, the genes are mainly enriched in the cellular response to peptides. As for the cellular component, the genes were primarily enriched in the integral component of organelle membrane. Finally, regarding molecular function, the genes were mainly enriched in RNA polymerase II−specific DNA−binding transcription factor binding (Figure 5A). In muscle tissue, the genes are primarily enriched in the ribonucleotide metabolic process. As for the cellular component, the genes were mainly enriched in mitochondrial matrix. Finally, regarding molecular function, the genes were primarily enriched in DNA−binding transcription factor binding (Figure 5B). KEGG pathway analysis data was performed (Figure 4D-E). In adipose tissue, the main enriched pathways include Alzheimer's disease and Parkinson's syndrome, various neurodegenerative diseases, as well as AMPK pathway and insulin resistance pathway. In muscle tissue, the major enrichment pathways also included AMPK pathway and Parkinson's syndrome, in addition to the ferroptosis pathway, which was also significantly enriched. 3.5 Identification of hub genes in fasting status common genes in fasting status were visualized by STRING database, and only experimentally validated interactions with a combined score greater than 0.15 were reserved to construct the PPI network. The PPI network was visualized using the Cytoscape software(v 3.9.1) (Figure. 6). There were 15 nodes and 23 edges in the PPI network, where the nodes denote genes and the edges represent the interactions between them. The MCC algorithm was used to analyze the topological structure of the whole PPI network and score based on the importance of each node. Then, we identified 2 top genes (TXNIP and DLAT) as hub genes. Then, further details of the selected hub genes are shown through the GeneCards database (Table 1). Table 1 The hub genes in adipose tissue and muscular tissue. Gene Genecards Identifier* Full Name Gene-related Diseases* TXNIP GC01M145992 Thioredoxin Interacting Protein Leukostasis,Hyperglycemia,Familial Combined Hyperlipidemia,Exocervical Carcinoma,Type 2 Diabetes Mellitus DLAT GC11P112026 Dihydrolipoamide S-Acetyltransferase Pyruvate Dehydrogenase E2 Deficiency,Cholangitis,Liver Disease,Autoimmune Hepatitis,Autoimmune Cholangitis PDK4 GC07M095583 Pyruvate Dehydrogenase Kinase 4 Type 2 Diabetes Mellitus,Rhabdomyosarcoma,Diabetes Mellitus,Body Mass Index Quantitative Trait Locus 11,Dilated Cardiomyopathy DDIT3 GC12M057516 DNA Damage Inducible Transcript 3 Myxoid Liposarcoma,Liposarcoma,Fatty Liver Disease,Sarcoma,Malignant Fibrous Histiocytoma PFKFB3 GC10P006144 6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 3 Colon Adenocarcinoma,Glycogen Storage Disease 4. Discussion Metabolic health has become one of the major health concerns due to the increasing prevalence of obesity, diabetes and various cardiovascular-related metabolic diseases, and the biological clock is clearly associated with the development of these metabolic diseases. Intermittent fasting, including time-restricted eating, presents an excellent solution by extending the fasting window regularly and matching it to the biological clock through long-term adherence. This approach appears to improve metabolic health indicators and may be a therapeutic option to combat metabolic diseases[21]. In addition to this, intermittent fasting can also improve body components such as waist circumference and BMI. Some previous studies have shown that weight loss and waist circumference reduction is mainly due to the poor capacity of 300-500kcal produced by the act of fasting. Still, there is little research on the changes in molecular pathways after fasting. In conclusion, fasting specifically through which biological pathways in the human body played a beneficial change in health, is gradually becoming one of the main focuses of academic research on restrictive eating. The present study explores the possible alterations in biochemical pathways brought about by fasting in different tissues[22]. Changes in gene expression and its pathway enrichment provide new insights into metabolic mechanisms following fasting interventions and contribute to the search for possible molecular targets. In this study, we used WGCNA to explore important modules associated with fasting, both in adipose and muscle tissues. Functional enrichment analysis of genes in key modules identified alterations in the biological processes of adipose and muscle tissues by fasting, including cellular response to peptides, ribose phosphate metabolic process. We then screened two central genes (TXNIP and DLAT) from the PPI network, which are closely associated with metabolic diseases. TXNIP is one of the major regulators of human glucose homeostasis, in addition to having cellular functions performed in a redox non-dependent manner[23]. DLAT, as a drug target, can directly or indirectly activate the phosphorylation of AMPK in vivo to improve metabolism[24]. In addition, PDK4, which has a high weight in the PPI network, is also thought to play an important role in human glucose and fat metabolism[25]. Interestingly, the enrichment of KEGG pathway showed significant enrichment of AMPK pathway, which is the center of energy metabolism regulation, both in muscle and adipose tissues. AMPK inactivates and phosphorylates acetyl coenzyme A carboxylase (ACC), which is one of the key rate-limiting enzymes for fatty acid oxidation after AMP construct initiation[26]. In animal experiments, it was found that the increased phosphorylation of AMP and ACC in the liver of mice undergoing intermittent fasting compared to high-fat fed mice reflected the increased AMPK activity, in short, we can suggest that intermittent fasting may increase the expression of AMPK, a favorable gene for metabolic regulation in humans. In addition, AMPK can phosphorylate CRY, a circadian rhythm-regulated gene, and thus deregulate CRY from CLOCK-BMAL1 downstream target genes, including REV-ERBA, PER and CRY[27]. Another human study showed that a 25-day intermittent fast in adult men not only improved individual serum lipids and liver characteristics, but also promoted the enrichment of beneficial intestinal flora, with significant enrichment of Prevotella and Mimosaceae. The sequencing results showed that a time-restricted diet may enhance the expression of the day-night rhythm gene by activating sirtuin-1, positively correlated with gut microbiome enrichment[28]. Therefore, intermittent fasting may be a safe drug for humans to prevent metabolic diseases associated with dyslipidemia because it regulates the day-night rhythm related to the regulation of the intestinal microbiome[29]. There is also a significant regulatory relationship between AMPK and sirtuin-1, both of which are "star molecules" in the study of neurodegenerative diseases, which explains, in part, the significant enrichment of Parkinson's syndrome-related pathways in both muscle and adipose tissue. Previous studies have shown that intermittent fasting has a significant effect in improving plasma lipocalin and HDL-C levels in humans[30]. However, lipocalin also plays an important role in promoting glucose and lipid metabolism. High levels of lipocalin may increase insulin sensitivity and energy expenditure, thereby accelerating human weight loss. Similarly, the increase in lipocalin promotes the activation of AMPK, which, along with the activation of AMPK, may further enhance insulin sensitivity, glucose metabolism and lipolysis[31, 32]. Another animal study showed that AMPK activity was lower in lipocalin knockout mice and that these mice were more susceptible to insulin resistance, which further supports the ability of lipocalin to promote AMPK activity. Given the potential insulin resistance ameliorating effects of lipocalin, intermittent fasting may be an effective way to treat or improve insulin-resistant patients[33]. In addition, intermittent fasting attenuated the expression of pro-inflammatory genes, including TNF-α, IL6, and CXCL2, and improved the extensive infiltration of macrophages due to inflammation. Moreover, intermittent fasting also inhibited and reduced cellular pro-inflammatory factors and reduced the concentration of p-p65, p-IκB, p-p38, p-JNK, and p-ERK proteins, which counteracted the inflammatory factors in the body[34]. The reason for including both adipose tissue and muscle tissue samples in this study was to understand the similarities and differences in the alterations of biochemical pathways between the two after fasting. In previous intervention studies, several studies have shown that intermittent fasting only improves body fat percentage. At the same time, several studies have also demonstrated that a similar decrease in human muscle mass accompanies a reduction in body fat percentage. There is often a limitation that needs to be considered in research, and this study is one bioinformatics study that has not been experimentally validated. Further studies are required to explore changes in these signaling pathways in order to gain insight into the effects of fasting in humans based on animal studies and clinical studies. Conclusion Our preliminary results show similarities and differences in the changes in molecular pathways between adipose tissue and muscle tissue after fasting intervention. This may be the reason why the changes in body fat percentage and muscle content of subjects in different intervention tests are not synchronized. AMPK acts as an energy regulatory center, and intermittent or regular fasting behavior may promote AMPK phosphorylation levels and activate related channels, increasing basal metabolic rates. A total of 15 common genes may be involved in the biological processes of fat and muscle tissue in the fasting state and play a key role through AMPK signaling pathway, neurodegenerative disease-related pathway, and insulin resistance pathway. TXNIP and DLAT may be the key genes that regulate these pathway changes. Declarations Funding Statement This research received no external funding. Informed Consent Statement Not applicable. Data Availability Statement The data supporting the findings of this study are available from the corresponding authors upon reasonable request. Acknowledgments All the authors are very grateful for the data support provided by the GEO databases. Conflicts of Interest The authors declare no conflict of interest. Author contributions Concept and design: Z.Q. and Y.L. Acquisition, analysis, and interpretation of data: Z.Q, J.Z., Y.F., Y.Y. Drafting of the manuscript: Z.Q. and Y.L. Critical revision of the manuscript for important intellectual content: Z.Q., Y.L., and Y.F. Statistical analysis: Z.Q. and J.Z. Visualization, Z.Q., Y.L., Y.Y., and J.Z. All authors have read and agreed to the published version of the manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2354254","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":158237112,"identity":"d93440e9-ec39-4830-b079-9775a9603783","order_by":0,"name":"Zhengqi Qiu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYLCCBDDJfODAhx+kaWFLPDizhzS7eIwPc7ARoY6//fizBw9q7tjzS+R8OMzAwyDPL3YAvxaJMznmBgnHniXOnJG74XCBBYPhzNkJ+LUYMOSwSSSwHU4wOHN2w+EZPAwJBrcJaeF//kwi4d9he/szZx4c5mEjRotEgplEYtthxg3sPQzEaZG48cbcILHvcOKM420GwECWIOwX/v70Zw9/fDtsz9/M/PjDhx828vzSBLQAAUpcSBBUjqFlFIyCUTAKRgEmAAAqNEergb3/AgAAAABJRU5ErkJggg==","orcid":"","institution":"Macau University of Science and Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhengqi","middleName":"","lastName":"Qiu","suffix":""},{"id":158237113,"identity":"1f82d6f3-c565-48ac-9160-bf375af14139","order_by":1,"name":"Yufei Li","email":"","orcid":"","institution":"Macau University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yufei","middleName":"","lastName":"Li","suffix":""},{"id":158237114,"identity":"f22769f8-9f0e-4024-9baa-ffc55153a1d7","order_by":2,"name":"Yancheng Fu","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yancheng","middleName":"","lastName":"Fu","suffix":""},{"id":158237115,"identity":"594639e4-63ea-4307-b6e3-bceb577a2600","order_by":3,"name":"Yanru Yang","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanru","middleName":"","lastName":"Yang","suffix":""},{"id":158237116,"identity":"c4a55203-c74a-4cb4-8d35-9e242fecca7a","order_by":4,"name":"Jiafu Zhong","email":"","orcid":"","institution":"Guangdong University of Foreign Studies","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiafu","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2022-12-07 13:59:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2354254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2354254/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":30207839,"identity":"ba8862e2-be86-422b-984a-b2e5dc77847a","added_by":"auto","created_at":"2022-12-12 15:18:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":524226,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of genes in adipose tissue and muscle tissue A-B. Volcano plot of all the transcripts detected by RNA-Seq in adipose tissue and Muscle tissue. C-D. The heatmap and details of their differential expression in adipose tissue and Muscle tissue.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2354254/v1/486f71d18931d985d8717086.png"},{"id":30207840,"identity":"d1dc7ad0-9af2-4ff2-8bf0-fdad0daef2ff","added_by":"auto","created_at":"2022-12-12 15:18:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":347458,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted co-expression network related datasets construction in fasting. (A-B) In the left panel, the x-axis reflects the soft-thresholding power. The y-axis reflects the scale-free topology model fit index; In the right panel, the x-axis reflects the soft-thresholding power. The y-axis reflects the mean connectivity (degree). (C-D) Clustering dendrogram of genes with assigned module colors. The colored row underneath the dendrogram shows the module assignment determined by the Dynamic Tree Cut.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2354254/v1/b173d58bf4dfe3917dac8d1d.png"},{"id":30208744,"identity":"f73de45a-d7b2-479b-b359-31517b3fe408","added_by":"auto","created_at":"2022-12-12 15:26:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":234664,"visible":true,"origin":"","legend":"\u003cp\u003eModule–trait associations. (A, B) Module-trait relationships. Each row corresponds to a module, and each column corresponds to a trait. Each cell contains the corresponding correlation and P value. The table is color-coded by correlation according to the color legend.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2354254/v1/49d5e98974103d4d7855e4ea.png"},{"id":30207842,"identity":"79555d3c-b543-4ae5-9af4-d98355a7e77b","added_by":"auto","created_at":"2022-12-12 15:18:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96833,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of common genes in fat and muscle tissue related key modules. Venn diagram of the 20 common genes between fat and muscle tissue.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2354254/v1/5f71ffa7c126a9a59f124df5.png"},{"id":30207843,"identity":"93238005-f766-4694-9d6c-484f51fb1c07","added_by":"auto","created_at":"2022-12-12 15:18:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":571544,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG pathway annotation of differential genes in adipose and muscle tissues in selected modules after fasting intervention. As the size of the balls represents the number of genes, the color change of the balls corresponds to the different P values. (A) GO annotation of adipose tissue. (B) GO annotation of muscle tissues. (C) Significant enrichment of KEGG pathway in adipose tissue. (D) Significantly enriched KEGG pathway in muscle tissue\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2354254/v1/7547d2d7b8c03a13782e89ea.png"},{"id":30207841,"identity":"39356c15-6049-4669-9e6f-bd6cb2e37f56","added_by":"auto","created_at":"2022-12-12 15:18:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":512903,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network. A protein-protein interaction network based on the STRING database of genes common to fat and muscle tissues. The Cytoscape software constructed a visual representation of the 15 genes with the topper connectivity of Maximal Clique Centrality (MCC). There is a direct correlation between node size and the MCC of gene connectivity.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-2354254/v1/f77bbcb87ff059e0ec0fc820.png"},{"id":40422392,"identity":"2824c78d-db0b-418f-b01a-5aec672d8224","added_by":"auto","created_at":"2023-07-22 22:59:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1703063,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2354254/v1/4344784f-cae3-45fe-8f5a-f2b32ab4a497.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of significant modules and hub genes involved in fasting using WGCNA","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFrom 1990 to 2016, an estimated one in five people worldwide died prematurely due to poor diet[1]. Dietary interventions are widely used worldwide as one of the key tools of health promotion. For people who are obese or overweight, weight loss can be accompanied by the prevention of many primary and secondary cardiovascular diseases[2]. When it comes to eating behavior modification, fasting is one of the very important methods, which usually means that subjects need to fast for a specific period voluntarily. The beginnings of fasting originated in religion as well as in the objective conditions of the lack of material living standards. This ascetic practice is mentioned in the Old Testament and other ancient texts such as the Qur\u0026apos;an and the Mahabharata[3]. For most people, fasting usually means consuming little or no food, including energy drinks, for a certain period. For example, Muslims fast from dawn to dusk during Ramadan, while Christians, Jews, Buddhists, and Hindus fast on designated days or periods according to their respective traditions[4]. Fasting differs from calorie restriction in that the latter involves a long-term reduction in daily energy intake of up to 40 percent while meal frequency remains the same. Although this diet of only 800 to 1500 kcal of energy per day can achieve a negative energy balance, this energy-restricted balanced diet with an average weight loss of 0.4 to 0.5 kg per week has very poor compliance[5]. Usually, people who use calorie restriction to reduce their weight will experience a rebound after 1-4 months, while most people who use this method to lose weight will return to their original weight within one year[6]. Unlike fasting, starvation is a chronic nutritional deficiency that is often incorrectly used as a substitute for the term \u0026quot;fasting\u0026quot;. Starvation can also refer to extreme forms of fasting, which can lead to impaired metabolic status or even death of the body. Moreover, starvation usually implies chronic irregular fasting, which can also lead to nutritional deficiencies and partial damage to digestive health. Although prolonged fasting is difficult for the normal population, intermittent energy restriction (IER) programs have been shown to have a high compliance rate[7]. Intermittent energy restriction is an increasingly popular dietary approach for weight loss and overall health promotion. In recent years, various intermittent energy restriction programs have gained popularity as strategies to achieve weight loss and other metabolic health benefits, including intermittent fasting (a \u0026quot;5 + 2\u0026quot; model in which subjects normally eat for five days a week and consume 25% of their energy on the remaining two days, 500 kcal/day for women and 600 kcal/day for men) and time-restricted feeding(subjects previously had a daily eating window of 14 hours or more, which was adjusted to 4-10 hours per day for several consecutive weeks). These are the two most promising intermittent energy restriction programs[8].\u0026nbsp;A recent study showed that a 25-day time-restricted diet in adult men not only improved individual serum lipids and liver characteristics but also promoted the enrichment of beneficial intestinal flora, with significant enrichment of\u0026nbsp;prevotella and\u0026nbsp;mimobacteriaceae. The sequencing results showed that the time-restricted diet might enhance the expression of the day-night rhythm gene by activating sirtuin-1, which was positively correlated with intestinal microbiome enrichment[9].\u0026nbsp;However, as of now, data on the health promotion of intermittent fasting are very limited. Moreover, as dietary habits change, so do body rhythms and metabolism. And there are even fewer studies on how fasting affects various molecular pathways in muscle and adipose tissue[10].\u003c/p\u003e\n\u003cp\u003eWeighted gene co-expression network analysis (WGCNA) is a widely used strategy for analyzing phylogenetic data based on pairwise correlations between variables[11]. WGCNA was used to define modules, network nodes, and intramodular hubs to determine the relationship between co-expressed modules and to compare the topology of different networks to screen for significant trait genes associated with clinical traits[12]. Currently, WGCNA has been widely used to analyze genomics and metabolomics data, including microarray data, single-cell RNA-Seq data, DNA methylation data, and non-coding RNA data[13-16]. In this study, we explored differential genes in adipose and muscle tissues after fasting to reveal the potential biological alteration process of fasting. Also, key genes were identified from the co-altered genes to investigate important targets for promising endocrine therapies.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Data sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene expression profiles associated with the fasting intervention were downloaded from the Gene Expression Omnibus (GEO) database website (www.ncbi. nlm.nih.gov/geo), and samples included in the study were screened[17].\u0026nbsp;The GSE154612 dataset was derived from adipose tissue samples. We selected 11 subjects with a total of 22 subcutaneous adipose tissue biopsies collected before and after fasting while excluding samples from animal experimental sources. The GSE55924 dataset was derived from muscle tissue samples. We selected a total of 24 skeletal muscle biopsy samples collected before and after fasting from 12 subjects with the same fasting time while excluding samples from other fasting times. We defined the post-fasting adipose tissue samples and muscle tissue samples as the fasting group (containing both adipose tissue and muscle tissue) and the pre-fasting samples as the normal group. gene expression profiling arrays for GSE154612 and GSE55924 were based on the GPL17692 (Affymetrix Human Gene 2.1 ST Array) and GPL10558 platforms, respectively Illumina HumanHT-12 V4.0 expression bead chip).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData Preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData preparation was performed using R software (v4.2.2) and Bioconductor Packages. The raw expression data were processed to produce expression matrices and to match probes to their gene symbols. For those that could not be matched directly, we used the DAVID website (https://david.ncifcrf.gov/) to find the original gene id of the corresponding platform before converting and matching. Using the Affy package of the R software platform, we preprocessed and normalized the microarray dataset, and we also used interpolation when missing values were present[18]. The GEO query package is used to avoid the situation where one probe corresponds to multiple molecules. When multiple probes corresponding to the same molecule are encountered, only the probe with the largest signal value is retained. Then, we check the standardization of samples by box plot, the clustering between sample groups by PCA plot and UMAP plot, followed by the difference analysis between two groups by using the limma package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCo-Expression Network Construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, the co-expression network of all genes in the fasting and normal groups was constructed using the \u0026quot;WGCNA\u0026quot; package of the R platform. Second, according to the scale-free topology criterion, the \u0026quot;pickSoft Threshold\u0026quot; algorithm of \u0026quot;WGCNA\u0026quot; is used to calculate the soft power threshold to construct a biologically meaningful scale-free network; then establish the weighted adjacency matrix. The formula is a\u003csub\u003emn\u003c/sub\u003e=|c\u003csub\u003emn\u003c/sub\u003e|\u003csup\u003e\u0026beta;\u003c/sup\u003e(a\u003csub\u003emn\u003c/sub\u003e: adjacency between gene m and gene n, c\u003csub\u003emn\u003c/sub\u003e: Pearson\u0026rsquo;s correlation, and \u0026beta;: soft-power threshold)[19]. In addition, the weighted adjacency matrix is transformed into a topological overlap measure (TOM) matrix to estimate its connectivity in the network. The clustering dendrogram of the TOM matrix was constructed using the mean chain hierarchy clustering method. The minimum gene module size was set to 30 to obtain the appropriate modules, and the threshold for merging similar modules was set to 0.25. Finally, gene significance (GS) and module membership (MM) were calculated to associate modules with clinical traits and visualize the characteristic gene network[20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Functional enrichment analysis of common genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenn diagrams were made with VennDiagram (v 1.6.2), to overlap the genes between adipose tissue and muscle tissue related preserve modules.\u0026nbsp;Afterward, we extracted the two groups of differentially expressed genes separately to complement the relevant functions further and compare how fasting changed adipose tissue versus muscle tissue in similar and different ways. GO term enrichment and KEGG pathway analyses were performed with DAVID (https://david-d.ncifcrf.gov/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Identification of the hub genes in functional modules and crucial gene mining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential genes in fasted and non-fasted states with protein-protein interaction (PPI) were established by an online reciprocal gene search tool (STRING database, V 11.5 http:// string-db.org/). PPI networks were constructed using a composite score greater than 0.15 and visualized using Cytoscape version 3.8.0 software. Genes commonly found in the network were screened by maximum clique centrality (MCC), and the genes with the most interactions were referred to as hub genes, which may play a central role in disease co-morbidity. Then, the GeneCards database (http://www.genecards.org/) was used to find interactions of related genes, proteins, drugs, and diseases to identify more details of hub genes. In the GSE154612 and GSE55924 datasets, the \u0026quot;limma\u0026quot; R package was used to identify differentially expressed genes (DEGs) between fasted and non-fasted samples. The cut-off value was log2FC \u0026gt; |0.25|, P-value \u0026lt; 0.05. Hierarchical cluster analysis was performed using the R package heatmap. The volcano plots were plotted for the identified genes using enhanced Volcano, an R package version 1.2.0.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Differentially expressed genes in adipose and muscle tissues after fasting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 503 genes were differentially expressed in adipose tissue after fasting compared to non-fasting state, of which 307 were down-regulated, and 196 were up-regulated. In contrast, a total of 279 genes were differentially expressed in muscle tissue, of which 134 were down-regulated, and 145 were up-regulated. Figure 1A and Figure 1C show the volcano and heat map of DETs in adipose tissue, Figure 1B and Figure 1D show the volcano and heat map of DETs in muscle tissue.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Identification of co-expression gene modules\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used WGCNA to identify co-expressed gene modules in the adipose and muscle tissue datasets after fasting. First, samples from both datasets were clustered into two clusters without outliers: the fasting group (adipose tissue or muscle tissue) and the normal group. Then, based on scale independence of \u0026gt; 0.8, 2 and 4 were selected as soft threshold power \u0026beta; for GSE154612 and GSE55924, respectively, to ensure biologically significant scale-free networks (Figure 2A and 1B). The genes in GSE154612 and GSE55924 were clustered into six modules by hierarchical clustering analysis of the gene dendrogram and dynamic branching cut method (Figure 2C and 2D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCalculation of module-trait correlations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe constructed the gene network and identified modules using the one-step network construction function of the WGCNA R package. Calculation of module-trait correlations to identify fasting key modules, gene significance (GS) and module membership (MM) were calculated to relate modules to clinical traits. The MM was defined as the correlation between gene expression values and the ME. The GS was defined as the correlation between genes and samples. As shown in Figure 3A, module\u0026ndash;trait relationships indicated that the Black modules were significantly correlated with fasting in adipose tissue. Similarly, the Green and Turquoise modules significantly correlated fasting in muscle tissue (Figure 3B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Functional enrichment analysis of common genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe common genes between muscle tissue and adipose tissue in fasting related preserve modules were indicated by Venn diagram (Figure. 4). To reveal the underlying molecular biological processes in fasting status and, more importantly, to find the mechanisms that link fasting and metabolism, We conducted GO and KEGG pathway enrichment analysis on fasting adipose tissue samples and fasting muscle tissue samples, respectively. GO term enrichment and Kegg pathway analyses were performed with DAVID (Figure 5).\u003c/p\u003e\n\u003cp\u003eThese analysis results show that, for adipose tissue, the genes are mainly enriched in the cellular response to peptides. As for the cellular component, the genes were primarily enriched in the integral component of organelle membrane. Finally, regarding molecular function, the genes were mainly enriched in RNA polymerase II\u0026minus;specific DNA\u0026minus;binding transcription factor binding (Figure 5A). In muscle tissue, the genes are primarily enriched in the ribonucleotide metabolic process. As for the cellular component, the genes were mainly enriched in mitochondrial matrix. Finally, regarding molecular function, the genes were primarily enriched in DNA\u0026minus;binding transcription factor binding (Figure 5B). KEGG pathway analysis data was performed (Figure 4D-E). In adipose tissue, the main enriched pathways include Alzheimer\u0026apos;s disease and Parkinson\u0026apos;s syndrome, various neurodegenerative diseases, as well as AMPK pathway and insulin resistance pathway. In muscle tissue, the major enrichment pathways also included AMPK pathway and Parkinson\u0026apos;s syndrome, in addition to the ferroptosis pathway, which was also significantly enriched.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Identification of hub genes in fasting status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ecommon genes in fasting status were visualized by STRING database, and only experimentally validated interactions with a combined score greater than 0.15 were reserved to construct the PPI network. The PPI network was visualized using the Cytoscape software(v 3.9.1) (Figure. 6). There were 15 nodes and 23 edges in the PPI network, where the nodes denote genes and the edges represent the interactions between them. The MCC algorithm was used to analyze the topological structure of the whole PPI network and score based on the importance of each node. Then, we identified 2 top genes (TXNIP and DLAT) as hub genes. Then, further details of the selected hub genes are shown through the GeneCards database (Table 1).\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eThe hub genes in adipose tissue and muscular tissue.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable align=\"\" border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"989\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.202020202020202%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.444444444444445%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenecards\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIdentifier*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44444444444444%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.90909090909091%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene-related Diseases*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.202020202020202%\"\u003e\n \u003cp\u003eTXNIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.444444444444445%\"\u003e\n \u003cp\u003eGC01M145992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44444444444444%\"\u003e\n \u003cp\u003eThioredoxin Interacting Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.90909090909091%\"\u003e\n \u003cp\u003eLeukostasis,Hyperglycemia,Familial Combined Hyperlipidemia,Exocervical Carcinoma,Type 2 Diabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.202020202020202%\"\u003e\n \u003cp\u003eDLAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.444444444444445%\"\u003e\n \u003cp\u003eGC11P112026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44444444444444%\"\u003e\n \u003cp\u003eDihydrolipoamide S-Acetyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.90909090909091%\"\u003e\n \u003cp\u003ePyruvate Dehydrogenase E2 Deficiency,Cholangitis,Liver Disease,Autoimmune Hepatitis,Autoimmune Cholangitis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.202020202020202%\"\u003e\n \u003cp\u003ePDK4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.444444444444445%\"\u003e\n \u003cp\u003eGC07M095583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44444444444444%\"\u003e\n \u003cp\u003ePyruvate Dehydrogenase Kinase 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.90909090909091%\"\u003e\n \u003cp\u003eType 2 Diabetes Mellitus,Rhabdomyosarcoma,Diabetes Mellitus,Body Mass Index Quantitative Trait Locus 11,Dilated Cardiomyopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.202020202020202%\"\u003e\n \u003cp\u003eDDIT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.444444444444445%\"\u003e\n \u003cp\u003eGC12M057516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44444444444444%\"\u003e\n \u003cp\u003eDNA Damage Inducible Transcript 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.90909090909091%\"\u003e\n \u003cp\u003eMyxoid Liposarcoma,Liposarcoma,Fatty Liver Disease,Sarcoma,Malignant Fibrous Histiocytoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.202020202020202%\"\u003e\n \u003cp\u003ePFKFB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.444444444444445%\"\u003e\n \u003cp\u003eGC10P006144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.44444444444444%\"\u003e\n \u003cp\u003e6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.90909090909091%\"\u003e\n \u003cp\u003eColon Adenocarcinoma,Glycogen Storage Disease\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMetabolic health has become one of the major health concerns due to the increasing prevalence of obesity, diabetes and various cardiovascular-related metabolic diseases, and the biological clock is clearly associated with the development of these metabolic diseases. Intermittent fasting, including time-restricted eating, presents an excellent solution by extending the fasting window regularly and matching it to the biological clock through long-term adherence. This approach appears to improve metabolic health indicators and may be a therapeutic option to combat metabolic diseases[21].\u0026nbsp;In addition to this, intermittent fasting can also improve body components such as waist circumference and BMI. Some previous studies have shown that weight loss and waist circumference reduction is mainly due to the poor capacity of 300-500kcal produced by the act of fasting. Still, there is little research on the changes in molecular pathways after fasting. In conclusion, fasting specifically through which biological pathways in the human body played a beneficial change in health, is gradually becoming one of the main focuses of academic research on restrictive eating. The present study explores the possible alterations in biochemical pathways brought about by fasting in different tissues[22].\u003c/p\u003e\n\u003cp\u003eChanges in gene expression and its pathway enrichment provide new insights into metabolic mechanisms following fasting interventions and contribute to the search for possible molecular targets. In this study, we used WGCNA to explore important modules associated with fasting, both in adipose and muscle tissues. Functional enrichment analysis of genes in key modules identified alterations in the biological processes of adipose and muscle tissues by fasting, including cellular response to peptides, ribose phosphate metabolic process.\u0026nbsp;We then screened two central genes (TXNIP and DLAT) from the PPI network, which are closely associated with metabolic diseases. TXNIP is one of the major regulators of human glucose homeostasis, in addition to having cellular functions performed in a redox non-dependent manner[23]. DLAT, as a drug target, can directly or indirectly activate the phosphorylation of AMPK in vivo to improve metabolism[24].\u0026nbsp;In addition, PDK4, which has a high weight in the PPI network, is also thought to play an important role in human glucose and fat metabolism[25].\u003c/p\u003e\n\u003cp\u003eInterestingly, the enrichment of KEGG pathway showed significant enrichment of AMPK pathway, which is the center of energy metabolism regulation, both in muscle and adipose tissues. AMPK inactivates and phosphorylates acetyl coenzyme A carboxylase (ACC), which is one of the key rate-limiting enzymes for fatty acid oxidation after AMP construct initiation[26].\u0026nbsp;In animal experiments, it was found that the increased phosphorylation of AMP and ACC in the liver of mice undergoing intermittent fasting compared to high-fat fed mice reflected the increased AMPK activity, in short, we can suggest that intermittent fasting may increase the expression of AMPK, a favorable gene for metabolic regulation in humans. In addition, AMPK can phosphorylate CRY, a circadian rhythm-regulated gene, and thus deregulate CRY from CLOCK-BMAL1 downstream target genes, including REV-ERBA, PER and CRY[27].\u0026nbsp;Another human study showed that a 25-day intermittent fast in adult men not only improved individual serum lipids and liver characteristics, but also promoted the enrichment of beneficial intestinal flora, with significant enrichment of Prevotella and Mimosaceae. The sequencing results showed that a time-restricted diet may enhance the expression of the day-night rhythm gene by activating sirtuin-1, positively correlated with gut microbiome enrichment[28].\u0026nbsp;Therefore, intermittent fasting may be a safe drug for humans to prevent metabolic diseases associated with dyslipidemia because it regulates the day-night rhythm related to the regulation of the intestinal microbiome[29].\u0026nbsp;There is also a significant regulatory relationship between AMPK and sirtuin-1, both of which are \u0026quot;star molecules\u0026quot; in the study of neurodegenerative diseases, which explains, in part, the significant enrichment of Parkinson\u0026apos;s syndrome-related pathways in both muscle and adipose tissue.\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that intermittent fasting has a significant effect in improving plasma lipocalin and HDL-C levels in humans[30]. However, lipocalin also plays an important role in promoting glucose and lipid metabolism. High levels of lipocalin may increase insulin sensitivity and energy expenditure, thereby accelerating human weight loss. Similarly, the increase in lipocalin promotes the activation of AMPK, which, along with the activation of AMPK, may further enhance insulin sensitivity, glucose metabolism and lipolysis[31, 32]. Another animal study showed that AMPK activity was lower in lipocalin knockout mice and that these mice were more susceptible to insulin resistance, which further supports the ability of lipocalin to promote AMPK activity. Given the potential insulin resistance ameliorating effects of lipocalin, intermittent fasting may be an effective way to treat or improve insulin-resistant patients[33]. In addition, intermittent fasting attenuated the expression of pro-inflammatory genes, including TNF-\u0026alpha;, IL6, and CXCL2, and improved the extensive infiltration of macrophages due to inflammation. Moreover, intermittent fasting also inhibited and reduced cellular pro-inflammatory factors and reduced the concentration of p-p65, p-I\u0026kappa;B, p-p38, p-JNK, and p-ERK proteins, which counteracted the inflammatory factors in the body[34]. The reason for including both adipose tissue and muscle tissue samples in this study was to understand the similarities and differences in the alterations of biochemical pathways between the two after fasting. In previous intervention studies, several studies have shown that intermittent fasting only improves body fat percentage. At the same time, several studies have also demonstrated that a similar decrease in human muscle mass accompanies a reduction in body fat percentage. There is often a limitation that needs to be considered in research, and this study is one bioinformatics study that has not been experimentally validated. Further studies are required to explore changes in these signaling pathways in order to gain insight into the effects of fasting in humans based on animal studies and clinical studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur preliminary results show similarities and differences in the changes in molecular pathways between adipose tissue and muscle tissue after fasting intervention. This may be the reason why the changes in body fat percentage and muscle content of subjects in different intervention tests are not synchronized. AMPK acts as an energy regulatory center, and intermittent or regular fasting behavior may promote AMPK phosphorylation levels and activate related channels, increasing basal metabolic rates. A total of 15 common genes may be involved in the biological processes of fat and muscle tissue in the fasting state and play a key role through AMPK signaling pathway, neurodegenerative disease-related pathway, and insulin resistance pathway. TXNIP and DLAT may be the key genes that regulate these pathway changes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors are very grateful for the data support provided by the GEO databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design: Z.Q. and Y.L. Acquisition, analysis, and interpretation of data: Z.Q, J.Z., Y.F., Y.Y. Drafting of the manuscript: Z.Q. and Y.L. Critical revision of the manuscript for important intellectual content: Z.Q., Y.L., and Y.F. Statistical analysis: Z.Q. and J.Z. Visualization, Z.Q., Y.L., Y.Y., and J.Z. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWallin, M.T., et al., \u003cem\u003eGlobal, regional, and national burden of multiple sclerosis 1990\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016.\u003c/em\u003e The Lancet Neurology, 2019. \u003cstrong\u003e18\u003c/strong\u003e(3): p. 269-285.\u003c/li\u003e\n\u003cli\u003eYu, E., V.S. Malik, and F.B. 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Regular intermittent fasting is thought to reduce body weight and ameliorate adverse cardiovascular disease factors significantly. However, there is growing evidence that fasting positively affects body composition and biochemical parameters, but very few studies related to its mechanisms. In this study, bioinformatics network analysis was performed to investigate the effects of fasting on adipose and muscle tissues and further explore the potential mechanisms and targets of action.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe downloaded the adipose tissue and muscle tissue gene expression datasets before and after fasting from the Gene Expression Omnibus (GEO) database and constructed co-expression networks by Weighted correlation network analysis (WGCNA) to identify key modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the differential genes in adipose tissue and muscle tissue-related modules, respectively. Then, we constructed protein-protein interaction (PPI) networks using the STRING database and detected the central genes in the networks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFunctional enrichment analysis showed that AMPK pathway and neurodegenerative disease-related pathways might be involved in the regulation of fasting in humans. PPI network construction indicated that the regulation of fasting in humans, both in adipose and muscle tissues, may be associated with two central genes, TXNIP and DLAT, and that this regulation is likely to act on human metabolism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur work indicates that a total of 15 key genes, including TXNIP, DLAT, PDK4, DDIT3, and PFKFB3, may receive regulation by fasting interventions, especially TXNIP and DLAT are the basis of fasting mechanisms in adipose and muscle tissues. The pathways regulated by these key genes may provide new targets for further studies on the mechanism of fasting and the treatment of metabolic diseases.\u003c/p\u003e","manuscriptTitle":"Identification of significant modules and hub genes involved in fasting using WGCNA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-12 15:18:27","doi":"10.21203/rs.3.rs-2354254/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"053d9fcb-709f-4e04-b510-b742ee8ae123","owner":[],"postedDate":"December 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-07-22T22:59:14+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-12 15:18:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2354254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2354254","identity":"rs-2354254","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}