Exploring the toxicological mechanisms of reduced fertility in dairy cows due to nonesterified fatty acids on the basis of network toxicology, transcriptomics and molecular docking

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There are few studies on the independent toxic effects of NEFA-mediated RFC. This study aimed to investigate the toxicological effects of NEFA-mediated RFC systematically via network toxicology, transcriptomics, and molecular docking techniques. A total of 403 potential targets of NEFA-mediated RFC toxicity were screened by comprehensively analyzing the GeneCards, OMIM, ChEMBL and Swiss Target Prediction databases. Further analysis via the GEO (GSE165476 dataset), STRING databases and Cytoscape software yielded eight hub targets, including MMP2, MAPK1, PRKACA and PRKCB. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these targets were involved in pathways related to metabolism, endocrine processes, cell death, and signal transduction, such as the AGE-RAGE signaling pathway in diabetic complications, the GnRH signaling pathway, and the MAPK signaling pathway. Molecular docking further confirmed the potential interactions between NEFAs and these hub targets. This study revealed that NEFAs may exacerbate the occurrence of RFC by interfering with endocrine regulation, inducing inflammatory responses, affecting angiogenesis and tissue remodeling, regulating apoptosis, and disrupting metabolic balance. The results of this study provide novel molecular insights into the mechanism of NEFA-mediated RFC toxicity and provide a scientific basis for emphasizing the importance of metabolite toxicity in dairy farming health management. network toxicology NEFAs reduced fertility in cows transcriptomics molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction A decrease in reproductive performance is the main reason for the premature culling of dairy cows worldwide, which not only leads to a reduction in milk production and calves but also causes significant economic losses. Losses of 70–220 dollars per cow per year have been reported in the United States when the calving-to-conception interval is 130–160 days [ 1 ]. Transitional cows reduce their dry matter intake prior to calving, require more energy for milk production than they consume, and generally develop an NEB metabolic state due to deficiencies in vitamins A and E [ 2 ]. In response to these conditions, cows mobilize substantial amounts of lipids, which are broken down to generate large quantities of NEFAs; these then diffuse into the bloodstream to supply energy for the entire body. However, high levels of NEFAs can cause anorexia in cows, which may exacerbate NEB deterioration and promote lipid metabolism and NEFA loading [ 3 , 4 ]. NEFA is a collective term for a class of compounds, among which the most abundant fatty acids are oleic acid (OA; C18:1), palmitic acid (PA; C16:0) and stearic acid (SA; C18:0) [ 5 ]. Currently, numerous studies have shown that excess NEFAs are strongly associated with reproductive disorders and thus may lead to RFC. Ribeiro et al. demonstrated that NEFA concentrations in the first 10 days post-partum in dairy cows are strongly negatively correlated with the development of uterine diseases [ 6 ]. Kaneene et al. reported that elevated NEFA concentrations increase the risk of metritis and placenta retention [ 7 ]. Macmillan et al. demonstrated that higher serum NEFA concentrations in early postpartum cows were associated with lower fertility [ 8 ]. Therefore, high concentrations of NEFAs are not only energy-related indicators of massive lipid mobilization but also potential metabolic toxicants. Previous studies have focused mostly on the impact of NEFAs on single indicators such as the conception rate in dairy cows [ 6 , 9 , 10 ]. Although some studies have demonstrated the toxic effects of high concentrations of NEFAs on oocytes [ 11 , 12 ], the specific and systematic molecular mechanisms of toxicity are not clear. In addition, NEB status in cows is accompanied by multiple hormonal and metabolic changes, making it difficult to accurately isolate the independent toxic effects of NEFAs and their interaction networks in this complex context. Network toxicology integrates the principles of network pharmacology and systems biology and involves constructing a network of compounds, toxins and targets with the help of bioinformatics, big data analysis and multiomics technology to reveal complex biological mechanisms [ 13 ]. Transcriptomics can directly reveal differential expression profiles at the genome-wide level in tissues exposed to compounds, thereby identifying key differentially expressed genes (DEGs) and functional modules. Molecular docking computationally simulates the binding capacity of targets and compounds, validating their direct interaction and providing molecular-level evidence to support predictive results from network toxicology and transcriptomics [ 14 ]. By combining these three approaches, the mechanism of the reproductive toxicity of NEFAs can thus be systematically revealed more efficiently and reliably. This study aims to systematically investigate the potential molecular toxicity mechanisms of NEFA-mediated RFC through network toxicology, transcriptomics, and molecular docking to provide new toxicological insights for understanding the effects of NEFAs on RFC. The detailed experimental procedure is shown in Fig. 1 . Materials and methods Identification of NEFA target genes We retrieved the molecular information and simplified molecular input line entry system (SMILES) representations of PA, OA, and SA from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). On the basis of the chemical names and SMILES of PA, OA, and SA, we used the ChEMBL (https://www.ebi.ac.uk/chembl/), Swiss Target Prediction (http://www.swisstargetprediction.ch/), OMIM (https://omim.org/), and GeneCards (https://www.genecards.org/) databases to identify potential targets of NEFAs. The thresholds described in the previous study were referenced and slightly modified [15]; targets with p > 0.1 were selected from the Swiss Target Prediction; the top 5% of the targets were selected from the OMIM; and the top 15% of the targets by score were selected from the GeneCards. To improve the accuracy of the targets and species, after integrating the targets obtained from the 4 databases and removing duplicates, we used the UniProt database (https://www.uniprot.org/) to calibrate the targets, with the species selected as “ Bos taurus ”. Identification of RFC target genes To include as many RFC-related targets as possible, the keywords “reduced fertility of females” or “reduced reproduction of females” were used to identify potential targets in the GeneCards and OMIM databases. Among these, the top 10% of the targets by score were selected from the GeneCards, and the top 10% of the targets were selected from the OMIM. After integrating the targets obtained from the 2 databases and removing duplicates, we used the UniProt database (https://www.uniprot.org/) to calibrate the targets, with the species selected as “ Bos taurus ”. Cross-analysis of target sets The obtained RFC target sets and NEFA target sets were cross-analyzed via the DrawVennDiagram online tool (https://bioinformatics.psb.ugent.be/webtools/Venn/) to explore overlapping genes, which were designated potential targets for subsequent analyses. The statistical significance of this overlap was confirmed via a hypergeometric test. Screening of hub targets and construction of the protein–protein interaction (PPI) network The potential targets aforementioned were entered into the STRING database (https://cn.stringdb.org/) to construct a PPI network, with the confidence threshold set to the highest confidence (≥ 0.9) and targets without connections hidden to identify biologically significant PPIs, thereby ensuring the validity of interactions in the network. The PPI network was analyzed and visualized via Cytoscape v3.10.2 software, with nodes representing NEFA or RFC targets and edges representing interactions between the target proteins. The PPI network modules were subsequently clustered via the Molecular Complex Detection (MCODE) plugin to identify gene clusters with similar or identical biological functions; the MCODE score is proportional to their importance in the entire network. The parameters for MCODE analysis were set as follows: degree cutoff = 2, node score cutoff = 0.2, k-core = 2, and maximum depth = 100. The PPI network was ranked according to the MCODE score values, with larger circles and darker nodes representing higher scores. In addition, five algorithms of the CytoHubba plugin—maximum clique centrality (MCC), maximum neighborhood component (MNC), degree, betweenness centrality, and closeness centrality—were used to calculate the overlapping genes among the top 15 genes, and these overlapping genes served as hub genes. Mining of hub genes via transcriptomics Determination of the dataset To further expand the scope of the hub genes, we used the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and selected the dataset GSE165476, which contains bovine ovarian tissues exposed to high concentrations of NEFAs and control samples. Tissue sample preparation for GSE165476: Ovarian cortical strips were isolated from abattoir-sourced bovine ovaries. The natural follicles were removed, and the remaining cortical strips were randomly assigned to the control tissue culture medium (CON) group or the high-NEFA group (containing high concentrations of free fatty acids: PA, SA and OA). Quality control and preprocessing of data To ensure data reliability, we downloaded the raw data of dataset GSE165476 (SRA Study: SRP303183) from the SRA database (https://www.ncbi.nlm.nih.gov/sra) and processed the data as follows: Quality control and preprocessing of the raw data were performed via the fastp v0.23.4 tool to remove low-quality sequences and adapter contamination, with the first 12 bp of the reads subsequently trimmed. The processed high-quality reads were aligned to the reference genome ( Bos taurus .ARS-UCD2.0, release 114) via STAR v2.7.10b software to determine gene expression. Next, the expression of each gene in the samples was calculated via the featureCounts v2.0.1 tool to generate an expression matrix. On average, 88.11% of the reads were mapped to the genome, with an average of 82.73% uniquely mapped. Finally, principal component analysis (PCA) was performed and visualized via R software and the “ggplot2” package. Screening of DEGs and hub targets To identify DEGs, we applied the “limma” package in R software for statistical analysis. The p value was adjusted via the Benjamini–Hochberg method, and the screening criteria were set as |log 2 fold change| ≥ 1 and p ≤ 0.05. A volcano plot was generated via the “ggplot2” package in R software to visualize the transcriptional differences between the NEFA and control groups. A heatmap of the DEGs was generated via the ChiPlot tool (https://www.chiplot.online/) to visualize the expression profiles of the DEGs. Finally, the DEGs were cross-analyzed with the two target sets aforementioned, and the overlapping genes of the three datasets served as our new hub genes. GO and KEGG enrichment analyses GO functional annotation and KEGG pathway enrichment analyses were performed and visualized via the “clusterProfiler”, “org.Bt.eg.db”, and “ggplot2” packages in R. The screening criterion was set as a false discovery rate (FDR) < 0.05 to explore the potential roles of biological processes, molecular functions, cellular components and signaling pathways associated with the potential targets. In addition, to better explore the key mechanisms of local network modules as well as hub targets in NEFA-induced RFC toxicity, we performed GO term enrichment analysis for MCODE cluster 1, which had the highest modular score, and KEGG enrichment analysis for the hub genes. Finally, to further explore the pathways of NEFA-mediated RFC, we performed cross-analysis of the KEGG analysis results of the potential targets and hub targets aforementioned. Molecular docking The PDB files of the hub genes were downloaded from the AlphaFold Protein Structure Database (https://alphafold.com/), and the SDF files of OA, PA, and SA were downloaded from the PubChem database. Molecular docking analysis was performed via the CB-Dock2 platform (https://cadd.labshare.cn/cb-dock2/index.php), and the binding energy was used to assess the molecular docking interactions of the receptors and ligands. A binding energy < -5.0 kcal/mol is generally considered to indicate stable binding. Finally, a heatmap of binding energy was generated via the ChiPlot tool, and the molecular docking results (binding energy < -5.0 kcal/mol) were visualized via the CB-Dock2 platform [16]. Results Identification of NEFA-related targets A total of 1220 targets were obtained by comprehensive analysis of the ChEMBL, Swiss Target Prediction, OMIM and GeneCards databases, and with calibration in the UniProt database, 526 potential NEFA-related targets were ultimately obtained (Supplementary Table 1). Identification of RFC-related targets A total of 1933 targets were obtained by integrating the results of the GeneCards and OMIM databases. After calibration via the UniProt database, 816 potential RFC-related targets were ultimately obtained (Supplementary Table 2). Results of screening the DEGs PCA was used to assess the overall distribution and variability of gene expression data between the NEFA and control groups. As shown in Figure 2A, the first principal component (PC1) (70.34%) and PC2 (15.89%) together explained 86.23% of the total variance, indicating a good principal component explanation rate. However, the two samples did not show a clear cluster pattern in the space constituted by these two principal components, suggesting that the intergroup variation in gene expression may be smaller than the intragroup variation. We used a volcano plot to further validate the transcriptional differences between the NEFA and control groups. The volcano plot (Figure 2B) revealed a total of 55 DEGs, which included 40 upregulated genes (red points) and 15 downregulated genes (blue points). In addition, a heatmap of the DEGs was generated to visualize the expression levels of these genes in different samples. As shown in Figure 3, the expression of genes within the group was highly consistent, indicating distinct expression profiles between the NEFA group and the control group. Cross-analysis results of the target sets and DEGs The NEFA target library, RFC target library and 55 DEGs were cross-analyzed via the DrawVennDiagram online tool, and a Venn diagram was generated to visualize the results. There were 403 potential target genes overlapping between the NEFA and RFC target sets (Figure 4A; Supplementary Table 3), 4 of which were DEGs (Figure 4B). These DEGs were MMP2, CTSB, CTSK, and SOD2, which were all upregulated in the high-concentration NEFA group (Table 1). Results of screening the hub targets The PPI network of 403 potential targets was constructed via the STRING database and visualized with Cytoscape v3.10.2 software (Figure 5). This network contains 259 nodes and 540 edges, with an average node degree of 3.16 and a PPI enrichment p value of < 1.0e -16 . A total of 12 gene clusters were obtained via the MCODE plugin (Supplementary Table 4). Through comprehensive analysis of five algorithms (MCC, MNC, degree, betweenness centrality, and closeness centrality) of the CytoHubba plugin, 4 hub genes (PRKACA, PRKCB, IL6, and MAPK1) were identified (Figure 6A~F). These targets, together with the 4 DEGs aforementioned, constitute the 8 hub targets identified in this study. Results of the GO and KEGG enrichment analyses The bubble plot (Figure 7A) revealed the top 10 most enriched GO terms for 403 potential targets. The lollipop plot (Figure 7B) revealed the top 30 most enriched KEGG pathways for 403 potential targets. The Sankey–Lollipop plot (Figure 7C) revealed the 11 most significantly enriched KEGG pathways for the 8 hub targets. All the above rankings are sorted by ascending FDR values. The GO enrichment analysis of 403 potential targets revealed 229 biological process (BP), 24 cellular component (CC) and 47 molecular function (MF) terms; the KEGG enrichment analysis revealed 201 potential pathways (Supplementary Table 5). GO enrichment analysis (Figure 7A; Supplementary Table 5) revealed that BPs were involved mainly in metabolic regulation, physiological homeostasis and other related pathways; CCs were involved mainly in pathways related to membrane signal transduction and neuronal function; and MFs were involved mainly in biological processes such as enzyme catalysis, molecular binding, and transport. KEGG enrichment analysis (Figure 7B; Supplementary Table 5) revealed that the AGE-RAGE signaling pathway in diabetic complications, lipids and atherosclerosis, proteoglycans in cancer, and the HIF-1 signaling pathway were the most significantly enriched pathways, suggesting their potential significance in the regulatory mechanism and development of NEFA-induced RFC toxicity. The GO enrichment analysis of MCODE cluster 1 (Table 3) is shown in Figure 7D, and the genes of this module were involved mainly in the biological process of the steroid hormone signaling pathway, which includes key processes: hormone signaling and cellular communication, kinase signaling hubs, hormone signaling perception, and activation and transcriptional regulation. KEGG enrichment analysis of the hub genes (Figure 7C) revealed that the GnRH signaling pathway and the AGE-RAGE signaling pathway in diabetic complications were the most significantly enriched pathways associated with the genes, emphasizing the importance of reproductive hormones and advanced glycation end products (AGEs) in NEFA-induced RFC toxicity. In addition, the KEGG pathways of the 403 potential targets and 8 hub targets highly and significantly overlapped, emphasizing the important role of the 8 hub targets among the 403 potential targets (Figure 7E; Supplementary Table 2). Results of molecular docking The molecular docking results (Figure 8A) revealed that the binding energies of MMP2, MAPK1, PRKACA and PRKCB to NEFAs were all < -5.0 kcal/mol, suggesting that these proteins can spontaneously bind to NEFAs and exhibit high stability. Further analysis revealed that NEFAs formed hydrogen bonds with these four target proteins (Figure 8B~E), further emphasizing the strong binding affinity between these targets and NEFAs. These findings validate and underscore the critical role of these targets in the molecular mechanism of NEFA-induced RFC toxicity. Discussion High circulating concentrations of NEFAs, although normal metabolites of cows in the NEB state, are thought to be strongly associated with reproductive diseases, ketosis, gastric shift, and fatty liver in dairy cows [ 3 , 4 , 17 ]. In this study, a total of 403 potential targets and 55 DEGs were identified through comprehensive analysis of the ChEMBL, Swiss Target Prediction, GeneCards, OMIM, and GEO databases. A total of 8 hub targets, SOD2, CTSB, CTSK, MMP2, IL6, MAPK1, PRKACA and PRKCB, were obtained via the STRING database and Cytoscape software. In particular, the binding stability of MMP2, MAPK1, PRKACA and PRKCB to NEFAs was validated via molecular docking (< -5.0 kcal/mol; Figs. 7B ~ E), emphasizing that these proteins may play key bridging roles between NEFAs and RFC. Our study provides a novel scientific theoretical basis and potential molecular targets for the study of NEFA-mediated RFC toxicity. MMP2 is a member of the matrix metalloproteinase family, and its main function is to participate in the remodeling and degradation of the extracellular matrix [ 18 ]. The extracellular matrix is tightly regulated by proteins such as MMP2, and disruption of this regulatory balance may lead to abnormal embryonic implantation or infertility [ 19 ]. In addition, MMP2 also plays a role in determining the distribution of growth factors required for embryonic development in the endometrium [ 20 ]. Previous studies have shown that the mRNA and protein levels of MMP2 are significantly increased in a bovine endometritis model induced by Escherichia coli and Staphylococcus aureus [ 21 ]; in humans, the upregulation of MMP2 is strongly associated with endometriosis, infertility, and miscarriage [ 22 , 23 ]. In this study, MMP2 was both a common target of NEFAs and RFC, and an upregulated DEG after treatment with high concentrations of NEFAs (Table 1 ), suggesting that excess NEFAs may induce MMP2 overexpression, which subsequently disrupts the balance between the remodeling and degradation of reproductive tract tissues, ultimately leading to RFC. Mitogen-activated protein kinases (MAPKs) and cyclic adenosine monophosphate (cAMP)-dependent protein kinase A (PKA) are involved in the regulation of the processes of oocyte maturation [ 24 , 25 ]. MAPK1, a key member of the MAPK family, can be activated by various cytokines, growth factors, reactive oxygen species, and high glucose to promote cell differentiation, proliferation, growth, and apoptosis [ 26 , 27 ]. MAPK1 also synergizes with maturation-promoting factors in the regulation of bovine oocyte maturation [ 28 ]. In addition, MAPK1 activation is closely related to bovine blastocyst quality and ectodermal gene expression [ 29 ]. PRKACA (protein kinase cAMP-activated catalytic subunit alpha), the major catalytic subunit of PKA, can directly inhibit MPF activity through the cAMP/PKA pathway, thereby arresting meiosis resumption in bovine oocytes [ 25 ]. PRKACA is also related to the follicle-stimulating hormone-induced proliferation of granulosa cells [ 30 ]. In addition, dysregulation of PRKACA is strongly associated with human reproductive disorders such as high-grade ovarian serous carcinomas, endometrial cancers, cervical cancers, and recurrent miscarriages [ 31 – 34 ]. These findings suggest that MAPK1 and PRKACA may play a synergistic role in the development of NEFA-mediated RFC. Investigating the underlying association between NEFA exposure and the dysregulation of MAPK1 or PRKACA could help elucidate the molecular toxicological molecular mechanisms underlying NEFA-mediated RFC. PRKCB is a key member of the protein kinase C (PKC) family, encoding PKC-β, which is dependent on calcium ions and diacylglycerol for its phosphorylation to regulate cell proliferation, apoptosis, and metabolism [ 35 ]. Currently, studies on the association between PRKCB and RFC are limited, and only a few studies have shown that dysregulation of PRKCB is associated with ovarian lymphoma, cervical cancer, abnormal placental function and other conditions in humans [ 36 – 38 ]. Notably, PRKCB is a key regulatory gene for diabetes and its complications [ 35 , 39 ], and high concentrations of NEFAs are closely associated with dairy cow ketosis (a major complication of diabetes) [ 40 ]. This may serve as a link between PRKCB and RFC, although verification in future studies is needed. Therefore, exploring the relationship between high levels of NEFAs and PRKCB expression could further refine and elucidate the mechanism of RFC toxicity caused by NEFAs. We performed PCA to assess the overall variation and distribution of gene expression data between the normal (CON) and NEFA groups. However, PCA has limitations in identifying transcriptional differences: as a statistical method, PCA can identify directions that explain the maximum variance in high-dimensional datasets, but such separation relies on global gene expression rather than individual genes; even though PCA captures an overall trend of variance, it does not yield specific DEGs [ 41 – 43 ]. Therefore, the interpretation of the PCA results should be combined with subsequent analysis of the DEGs, enrichment analysis, etc. [ 44 ]. In this study, even though the PCA results were unsatisfactory, on the basis of the subsequent expression profiles of four DEGs (MMP2, CTSB, CTSK and SOD2) (Fig. 3 ) and enrichment analysis of eight hub targets (Figs. 6C and 6E), the DEG group exhibited a consistent trend of gene expression, and the enrichment analyses revealed RFC-associated pathways, including the AGE-RAGE signaling pathway in diabetic complications and the GnRH signaling pathway. Thus, our screening of DEGs was biologically meaningful. GO and KEGG pathway enrichment analyses revealed the pathways that may be affected by NEFA exposure and the molecular processes involved in the progression of RFC, thereby enabling a deeper understanding of the biological mechanisms underlying NEFA exposure leading to RFC. The combined GO and KEGG enrichment analyses of 403 potential targets and 8 hub targets suggested that NEFAs may affect RFC through multiple pathways, including the AGE-RAGE signaling pathway in diabetic complications, the GnRH signaling pathway, lipid metabolism disorders, apoptosis, the MAPK signaling pathway, the VEGF signaling pathway, and the FoxO signaling pathway. These pathways are involved in key processes, including hormone regulation, inflammation, oxidative stress, angiogenesis, tissue remodeling, apoptosis, cell survival and energy metabolism. Notably, among these pathways, the GnRH signaling pathway can inhibit the release of gonadotropins and impair the development and maturation of germ cells; lipid metabolism disorders may exacerbate lipid deposition and inflammatory responses; apoptosis may impair vascular endothelial function in reproductive tissues, subsequently affecting cell development and the blood supply; the MAPK signaling pathway may exacerbate apoptosis and inflammatory responses; the VEGF signaling pathway may inhibit endometrial angiogenesis and embryo implantation; and the FoxO signaling pathway may promote germ tissue damage through oxidative stress. In addition, the GO enrichment analysis of MCODE cluster 1, the highest-scoring gene cluster via the MCODE method, involved mainly the steroid hormone signaling pathway, suggesting that NEFAs may potentially affect the synthesis and secretion of steroid hormones, emphasizing the importance of hormone regulation in NEFA-mediated RFC toxicity. The AGE-RAGE signaling pathway in diabetic complications, which was the most significantly enriched pathway in the KEGG enrichment analysis of potential targets and hub targets, highlights the possibility that AGEs may be a key factor in the pathological development of NEFA-mediated RFC toxicity. AGEs are irreversible products formed through nonenzymatic reactions between reducing sugars and molecules such as proteins, lipids, and nucleic acids [ 45 ]. Studies have demonstrated that AGEs may exacerbate endothelial dysfunction, infertility, endometritis, and oxidative stress in ovarian cells [ 45 – 47 ]. The binding of AGEs to the RAGE receptor induces MAPKs upregulation by activating key transcription factors such as NF-κB and AP-1, which subsequently induce the expression of proinflammatory genes such as IL-1β, IL-6, and TNF-α, ultimately exacerbating the inflammatory response and oxidative stress [ 45 ]. In this study, NEFAs may promote the production and accumulation of AGEs, exacerbating inflammatory responses and oxidative stress through AGE/RAGE/MAPK cascade reactions and ultimately promoting the development of RFC. Notably, the transcriptomic validation in our study was based on the GSE165476 dataset, which was generated from bovine ovarian tissues experimentally treated with high concentrations of NEFAs. Although accessed through a public repository, these data originate from real biological experiments and therefore serve as experimental evidence supporting our study. Nonetheless, because the dataset was not produced independently in our laboratory, further in vitro and in vivo experiments will be necessary to strengthen the causal inference between NEFA exposure and RFC. Conclusions This study is the first to integrate network toxicology, transcriptomics, and molecular docking approaches to systematically explore the potential molecular mechanisms of the endogenous metabolite NEFA-mediated RFC. By integrating these three approaches, we established a complete systematic chain of screening-prediction-verification, which not only advances the understanding of NEFA-mediated reproductive toxicity but also provides potential biomarkers and molecular targets for improving reproductive management strategies in dairy cows. Future studies should focus on animal and cell modeling experiments, multiomics studies, and molecular dynamic simulations to explore and validate the potential effects of these targets on RFC across multiple levels. These studies provide deeper and more comprehensive insights into targeting the inhibition of NEFA toxicity to RFC to ultimately improve reproductive performance and reduce culling rates in dairy cows. Abbreviations NEFAs Nonesterified fatty acids NEB Negative energy balance RFC Reduced fertility in cows GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes OA Oleic acid PA Palmitic acid SA Stearic acid DEGs Differentially expressed genes SMILES Simplified molecular input line entry system PPI Protein–protein interaction MCODE Molecular Complex Detection MCC Maximum clique centrality MNC Maximum neighborhood component PCA Principal component analysis FDR False discovery rate BP Biological process CC Cellular component MF Molecular function AGEs Advanced glycation end products MAPKs Mitogen-activated protein kinases cAMP cyclic adenosine monophosphate PKA Protein kinase A PKC Protein kinase C Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material. All other data generated or analyzed during this study are provided in the supplementary material. Competing interests The authors declare that they have no competing interests. Funding This work was financially supported by grants from the Science and Technology Department of Xinjiang Uygur Autonomous Region (2024AB034) and the National Key Research and Development Program of China (2023YFE0107600). Authors’ contributions JW: Conceptualization, Investigation, Formal analysis, Software, Visualization, Writing–original draft. WW: Methodology, Validation, Writing–review & editing. XK: Formal analysis, Visualization, Writing–review & editing. YL: Software, Formal analysis, Writing–review & editing. LL: Methodology, Supervision, Writing–review & editing. YL: Funding acquisition, Supervision, Writing–review & editing. 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Gene name Ensembl ID log 2 (fold change) p value SDF2L1 ENSBTAG00000000067 1.46 1.98E -02 ATP10D ENSBTAG00000000473 2.88 2.49E -02 GPNMB ENSBTAG00000000604 1.89 1.22E -08 ADAMTS5 ENSBTAG00000000648 1.59 6.23E -04 LGALS3 ENSBTAG00000002326 2.12 1.15E -03 ADAMTSL4 ENSBTAG00000003604 2.67 4.08E -02 MYO5C ENSBTAG00000003763 2.24 1.14E -02 DPAGT1 ENSBTAG00000005371 2.72 2.52E -02 SEMA3C ENSBTAG00000006138 2.53 1.30E -06 SUOX ENSBTAG00000006160 2.75 3.31E -02 MYO5A ENSBTAG00000006489 1.32 4.01E -02 SOD2 ENSBTAG00000006523 1.39 7.79E -05 LOC100139670 ENSBTAG00000007881 1.57 8.48E -03 SEL1L ENSBTAG00000008083 1.34 1.04E -02 SLC38A2 ENSBTAG00000011105 1.07 2.85E -02 CKAP4 ENSBTAG00000011913 1.36 3.50E -02 CTSB ENSBTAG00000012442 1.03 3.50E -02 NFE2L1 ENSBTAG00000013653 1.35 3.31E -02 SPCS3 ENSBTAG00000014146 1.55 2.52E -02 CD9 ENSBTAG00000014764 2.15 1.39E -02 TM4SF1 ENSBTAG00000015163 1.28 2.85E -02 BTBD2 ENSBTAG00000016108 2.91 2.17E -02 FAM180A ENSBTAG00000017427 3.25 2.37E -03 CHI3L1 ENSBTAG00000018223 1.65 1.09E -03 ANXA8L1 ENSBTAG00000018499 2.98 9.83E -03 S100A4 ENSBTAG00000019203 2.49 7.85E -15 MMP2 ENSBTAG00000019267 1.18 1.52E -02 FBP2 ENSBTAG00000019554 2.81 1.81E -02 DHRS7 ENSBTAG00000020729 2.30 2.52E -02 CTSK ENSBTAG00000021035 1.33 1.86E -07 BCAS4 ENSBTAG00000038929 3.13 2.80E -03 LOC112444563 ENSBTAG00000043222 1.88 4.31E -02 NA ENSBTAG00000043582 1.76 2.31E -06 CFB ENSBTAG00000046158 2.34 2.08E -05 ADAMTSL5 ENSBTAG00000046263 2.83 2.17E -02 MT1A ENSBTAG00000054808 2.16 3.31E -02 NA ENSBTAG00000057732 1.27 1.23E -04 NA ENSBTAG00000057824 2.72 3.31E -02 NA ENSBTAG00000061247 1.35 2.90E -10 HERPUD1 ENSBTAG00000076584 1.41 1.02E -02 NA, not applicable. Table 2 Transcription results of downregulated DEGs ( p value ≤ 0.05, log 2 (fold change) ≤ -1). Gene name Ensembl ID log 2 (fold change) p value TTI2 ENSBTAG00000015463 -3.03 4.98E -05 SORBS2 ENSBTAG00000016486 -2.53 2.36E -05 GRB14 ENSBTAG00000019291 -3.03 4.98E -05 TRIM7 ENSBTAG00000020954 -3.29 8.57E -06 DUS4L ENSBTAG00000021849 -2.90 7.65E -05 ECSCR ENSBTAG00000030518 -3.29 5.01E -06 LOC112448304 ENSBTAG00000042475 -1.48 5.16E -05 NA ENSBTAG00000056594 -3.47 1.35E -06 NA ENSBTAG00000059357 -1.54 3.31E -05 NA ENSBTAG00000063983 -2.97 6.39E -05 TLR1 ENSBTAG00000064150 -2.96 5.15E -05 NA ENSBTAG00000067673 -3.52 1.32E -06 NA ENSBTAG00000069885 -2.17 1.57E -07 NA ENSBTAG00000073706 -3.08 2.85E -05 5S_rRNA ENSBTAG00000076020 -3.24 1.52E -06 NA, not applicable. Table 3 Information on MCODE cluster 1. MCODE clusters Targets MCODE node status MCODE score Closeness centrality Betweenness centrality Degree Cluster 1 ESR2 Seed 6.0 0.28 0.00 6 ESR1 Clustered 6.0 0.31 0.03 12 FOS Clustered 6.0 0.29 0.00 9 JUN Clustered 6.0 0.32 0.03 11 MAPK1 Clustered 6.0 0.33 0.10 26 PRKACA Clustered 6.0 0.33 0.11 24 RPS6KB1 Clustered 6.0 0.29 0.00 8 Additional Declarations No competing interests reported. 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11:03:09","extension":"xml","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":156931,"visible":true,"origin":"","legend":"","description":"","filename":"8fc50cef35fc486488ec5461c8ba3b241structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/4fc89dddfabf3d23ca361c38.xml"},{"id":93035213,"identity":"85e96cf6-82d8-4469-91f8-b39d16a689a5","added_by":"auto","created_at":"2025-10-08 10:55:10","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165494,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/bbc4811de03b9c83e8be1bf1.html"},{"id":93035177,"identity":"5f3fb3ab-18d0-4a06-8daa-251c91f299e4","added_by":"auto","created_at":"2025-10-08 10:55:09","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":581384,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of study design.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/58f10a238e55f5c8a804ed75.jpeg"},{"id":93035176,"identity":"6bda447e-061e-46f9-bb12-27788de8f976","added_by":"auto","created_at":"2025-10-08 10:55:09","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131505,"visible":true,"origin":"","legend":"\u003cp\u003ePCA and DEG analysis of the GSE165476 dataset.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) PCA of the GSE165476 dataset. CON, control. (\u003cstrong\u003eB\u003c/strong\u003e) Volcano plot of the DEGs between the NEFA and control groups (\u003cem\u003ep\u003c/em\u003e value ≤ 0.05, |log\u003csub\u003e2\u003c/sub\u003e fold change| ≥ 1).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/d7fa19245bc26ddae915ab54.jpeg"},{"id":93035178,"identity":"ec44a9cc-d063-487b-8384-bb2e512949db","added_by":"auto","created_at":"2025-10-08 10:55:09","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":645898,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of intragroup consistency analysis of the DEGs. The gene name is represented by an official symbol, and the gene without an official symbol is replaced by an Ensembl ID.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/7b46f4b633600e0084f1e63b.jpeg"},{"id":93035489,"identity":"45ad04f3-8cf2-42f0-8474-046a03c6fc12","added_by":"auto","created_at":"2025-10-08 11:03:09","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":184404,"visible":true,"origin":"","legend":"\u003cp\u003eCross-analysis of target sets. (\u003cstrong\u003eA\u003c/strong\u003e) Cross-analysis of the NEFA and RFC target sets. (\u003cstrong\u003eB\u003c/strong\u003e) Cross-analysis of DEGs, NEFAs and RFC target sets.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/960164f940dc2d7af7614df2.jpeg"},{"id":93035490,"identity":"a75bc9d2-c172-4aac-b32c-a098ffd97354","added_by":"auto","created_at":"2025-10-08 11:03:09","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1612552,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of the 403 potential targets shared between the NEFA and RFC target sets. Nodes are colored and sized according to their MCODE score, with darker colors and larger circles indicating higher MCODE scores.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/5a703d05145caebe3afa6fc8.jpeg"},{"id":93035195,"identity":"3c0df541-f6d8-4b9e-9908-5928f66ccafb","added_by":"auto","created_at":"2025-10-08 10:55:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4030492,"visible":true,"origin":"","legend":"\u003cp\u003eThe top 15 targets and cross-analysis of the PPI network via the MCC, MNC, degree, betweenness centrality and closeness centrality algorithms. (\u003cstrong\u003eA\u003c/strong\u003e) The top 15 targets of the PPI network via the MCC algorithm. (\u003cstrong\u003eB\u003c/strong\u003e) The top 15 targets of the PPI network via the MNC algorithm. (\u003cstrong\u003eC\u003c/strong\u003e) The top 15 targets of the PPI network via the degree algorithm. (\u003cstrong\u003eD\u003c/strong\u003e) The top 15 targets of the PPI network via the betweenness centrality algorithm. (\u003cstrong\u003eE\u003c/strong\u003e) The top 15 targets of the PPI network via the closeness centrality algorithm. (\u003cstrong\u003eF\u003c/strong\u003e) Cross-analysis of the top 15 targets via five algorithms.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/abee64518ee240bd7190e4e8.png"},{"id":93035204,"identity":"f0319f65-21f5-47db-b2ac-db53ad7b0a48","added_by":"auto","created_at":"2025-10-08 10:55:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1562615,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment analyses of 403 potential targets, 8 hub targets and MCODE cluster 1. (\u003cstrong\u003eA\u003c/strong\u003e) Bubble plot of the results of the GO enrichment analysis of 403 potential targets. (\u003cstrong\u003eB\u003c/strong\u003e) Lollipop plot of the results of the KEGG enrichment analysis of 403 potential targets. (\u003cstrong\u003eC\u003c/strong\u003e) Sankey–Lollipop plot of the top 11 enriched genes identified via KEGG analysis of the 8 hub targets. (\u003cstrong\u003eD\u003c/strong\u003e) Bubble plot of the GO enrichment analysis of MCODE cluster 1. (\u003cstrong\u003eE\u003c/strong\u003e) Cross-analysis of KEGG enrichment results between 8 hub targets and 403 potential targets.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/e8c7a3ed87c20e008a3bb3bf.png"},{"id":93035191,"identity":"8fcbb067-4e8d-42a9-9eb7-9503f19dba01","added_by":"auto","created_at":"2025-10-08 10:55:09","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9107539,"visible":true,"origin":"","legend":"\u003cp\u003eThe binding energy and visual results of molecular docking. (\u003cstrong\u003eA\u003c/strong\u003e) Heatmap of the molecular docking affinity of 8 hub targets and NEFAs (OA, PA and SA). (\u003cstrong\u003eB\u003c/strong\u003e) Molecular docking visualization of MMP2 with NEFAs. (\u003cstrong\u003eC\u003c/strong\u003e) Molecular docking visualization of MAPK1 with NEFAs. (\u003cstrong\u003eD\u003c/strong\u003e) Molecular docking visualization of PRKACA with NEFAs. (\u003cstrong\u003eE\u003c/strong\u003e) Molecular docking visualization of PRKCB with NEFAs. In figures B~E, “a”, “b”, and “c” indicate OA, PA and SA, respectively; connecting lines with yellow, blue, pale blue and gray indicate ionic interactions, hydrogen bonds, weak hydrogen bonds, and hydrophobic contacts, respectively.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/0e51dc1742d23737dbc8308c.jpeg"},{"id":101692023,"identity":"82ae0bfa-8eb8-4e34-af68-52d925c52e81","added_by":"auto","created_at":"2026-02-02 16:16:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18662987,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/70b42515-8e5d-4592-ad85-88fb5a429d07.pdf"},{"id":93035179,"identity":"de7fa0b2-ebd3-4b2a-8a52-d3189f675aa1","added_by":"auto","created_at":"2025-10-08 10:55:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":114932,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/4d6ac21812672718076ddc22.docx"},{"id":93035186,"identity":"89ee071d-9dad-4eaa-b3a7-81fcf8698a19","added_by":"auto","created_at":"2025-10-08 10:55:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":147400,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/bb7a2b552b7b43de74cb16d9.docx"},{"id":93035183,"identity":"08edca09-e533-44c7-a50b-84b061cc074f","added_by":"auto","created_at":"2025-10-08 10:55:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":85159,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/6951881e6fc6cb62e13263e3.docx"},{"id":93035487,"identity":"b60d8d48-11e7-4946-a5e6-8ed93368a008","added_by":"auto","created_at":"2025-10-08 11:03:09","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":22189,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/3a64649ab1f3ed81eae2c78f.docx"},{"id":93035188,"identity":"8edf217f-823d-43d4-9925-21a7934e84f7","added_by":"auto","created_at":"2025-10-08 10:55:09","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":102815,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-7524568/v1/4b90d510988a8c0e4710168a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the toxicological mechanisms of reduced fertility in dairy cows due to nonesterified fatty acids on the basis of network toxicology, transcriptomics and molecular docking","fulltext":[{"header":"Introduction","content":"\u003cp\u003eA decrease in reproductive performance is the main reason for the premature culling of dairy cows worldwide, which not only leads to a reduction in milk production and calves but also causes significant economic losses. Losses of 70\u0026ndash;220 dollars per cow per year have been reported in the United States when the calving-to-conception interval is 130\u0026ndash;160 days [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Transitional cows reduce their dry matter intake prior to calving, require more energy for milk production than they consume, and generally develop an NEB metabolic state due to deficiencies in vitamins A and E [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In response to these conditions, cows mobilize substantial amounts of lipids, which are broken down to generate large quantities of NEFAs; these then diffuse into the bloodstream to supply energy for the entire body. However, high levels of NEFAs can cause anorexia in cows, which may exacerbate NEB deterioration and promote lipid metabolism and NEFA loading [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNEFA is a collective term for a class of compounds, among which the most abundant fatty acids are oleic acid (OA; C18:1), palmitic acid (PA; C16:0) and stearic acid (SA; C18:0) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Currently, numerous studies have shown that excess NEFAs are strongly associated with reproductive disorders and thus may lead to RFC. Ribeiro et al. demonstrated that NEFA concentrations in the first 10 days post-partum in dairy cows are strongly negatively correlated with the development of uterine diseases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Kaneene et al. reported that elevated NEFA concentrations increase the risk of metritis and placenta retention [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Macmillan et al. demonstrated that higher serum NEFA concentrations in early postpartum cows were associated with lower fertility [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, high concentrations of NEFAs are not only energy-related indicators of massive lipid mobilization but also potential metabolic toxicants.\u003c/p\u003e\u003cp\u003ePrevious studies have focused mostly on the impact of NEFAs on single indicators such as the conception rate in dairy cows [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although some studies have demonstrated the toxic effects of high concentrations of NEFAs on oocytes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the specific and systematic molecular mechanisms of toxicity are not clear. In addition, NEB status in cows is accompanied by multiple hormonal and metabolic changes, making it difficult to accurately isolate the independent toxic effects of NEFAs and their interaction networks in this complex context.\u003c/p\u003e\u003cp\u003eNetwork toxicology integrates the principles of network pharmacology and systems biology and involves constructing a network of compounds, toxins and targets with the help of bioinformatics, big data analysis and multiomics technology to reveal complex biological mechanisms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Transcriptomics can directly reveal differential expression profiles at the genome-wide level in tissues exposed to compounds, thereby identifying key differentially expressed genes (DEGs) and functional modules. Molecular docking computationally simulates the binding capacity of targets and compounds, validating their direct interaction and providing molecular-level evidence to support predictive results from network toxicology and transcriptomics [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. By combining these three approaches, the mechanism of the reproductive toxicity of NEFAs can thus be systematically revealed more efficiently and reliably.\u003c/p\u003e\u003cp\u003eThis study aims to systematically investigate the potential molecular toxicity mechanisms of NEFA-mediated RFC through network toxicology, transcriptomics, and molecular docking to provide new toxicological insights for understanding the effects of NEFAs on RFC. The detailed experimental procedure is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eIdentification of NEFA target genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe retrieved the molecular information and simplified molecular input line entry system (SMILES) representations of PA, OA, and SA from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). On the basis of the chemical names and SMILES of PA, OA, and SA, we used the ChEMBL (https://www.ebi.ac.uk/chembl/), Swiss Target Prediction (http://www.swisstargetprediction.ch/), OMIM (https://omim.org/), and GeneCards (https://www.genecards.org/) databases to identify potential targets of NEFAs. The thresholds described in the previous study were referenced and slightly modified [15]; targets with \u003cem\u003ep \u003c/em\u003e\u0026gt; 0.1 were selected from the Swiss Target Prediction; the top 5% of the targets were selected from the OMIM; and the top 15% of the targets by score were selected from the GeneCards. To improve the accuracy of the targets and species, after integrating the targets obtained from the 4 databases and removing duplicates, we used the UniProt database (https://www.uniprot.org/) to calibrate the targets, with the species selected as \u0026ldquo;\u003cem\u003eBos taurus\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of RFC target genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo include as many RFC-related targets as possible, the keywords \u0026ldquo;reduced fertility of females\u0026rdquo; or \u0026ldquo;reduced reproduction of females\u0026rdquo; were used to identify potential targets in the GeneCards and OMIM databases. Among these, the top 10% of the targets by score were selected from the GeneCards, and the top 10% of the targets were selected from the OMIM. After integrating the targets obtained from the 2 databases and removing duplicates, we used the UniProt database (https://www.uniprot.org/) to calibrate the targets, with the species selected as \u0026ldquo;\u003cem\u003eBos taurus\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-analysis of target sets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe obtained RFC target sets and NEFA target sets were cross-analyzed via the DrawVennDiagram online tool (https://bioinformatics.psb.ugent.be/webtools/Venn/) to explore overlapping genes, which were designated potential targets for subsequent analyses. The statistical significance of this overlap was confirmed via a hypergeometric test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening of hub targets and construction of the protein\u0026ndash;protein interaction (PPI) network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe potential targets aforementioned were entered into the STRING database (https://cn.stringdb.org/) to construct a PPI network, with the confidence threshold set to the highest confidence (\u0026ge; 0.9) and targets without connections hidden to identify biologically significant PPIs, thereby ensuring the validity of interactions in the network. The PPI network was analyzed and visualized via Cytoscape v3.10.2 software, with nodes representing NEFA or RFC targets and edges representing interactions between the target proteins. The PPI network modules were subsequently clustered via the Molecular Complex Detection (MCODE) plugin to identify gene clusters with similar or identical biological functions; the MCODE score is proportional to their importance in the entire network. The parameters for MCODE analysis were set as follows: degree cutoff = 2, node score cutoff = 0.2, k-core = 2, and maximum depth = 100. The PPI network was ranked according to the MCODE score values, with larger circles and darker nodes representing higher scores. In addition, five algorithms of the CytoHubba plugin\u0026mdash;maximum clique centrality (MCC), maximum neighborhood component (MNC), degree, betweenness centrality, and closeness centrality\u0026mdash;were used to calculate the overlapping genes among the top 15 genes, and these overlapping genes served as hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMining of hub genes via transcriptomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDetermination of the dataset\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further expand the scope of the hub genes, we used the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and selected the dataset GSE165476, which contains bovine ovarian tissues exposed to high concentrations of NEFAs and control samples.\u003c/p\u003e\n\u003cp\u003eTissue sample preparation for GSE165476: Ovarian cortical strips were isolated from abattoir-sourced bovine ovaries. The natural follicles were removed, and the remaining cortical strips were randomly assigned to the control tissue culture medium (CON) group or the high-NEFA group (containing high concentrations of free fatty acids: PA, SA and OA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eQuality control and preprocessing of data\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure data reliability, we downloaded the raw data of dataset GSE165476 (SRA Study: SRP303183) from the SRA database (https://www.ncbi.nlm.nih.gov/sra) and processed the data as follows: Quality control and preprocessing of the raw data were performed via the fastp v0.23.4 tool to remove low-quality sequences and adapter contamination, with the first 12 bp of the reads subsequently trimmed. The processed high-quality reads were aligned to the reference genome (\u003cem\u003eBos taurus\u003c/em\u003e.ARS-UCD2.0, release 114) via STAR v2.7.10b software to determine gene expression. Next, the expression of each gene in the samples was calculated via the featureCounts v2.0.1 tool to generate an expression matrix. On average, 88.11% of the reads were mapped to the genome, with an average of 82.73% uniquely mapped. Finally, principal component analysis (PCA) was performed and visualized via R software and the \u0026ldquo;ggplot2\u0026rdquo; package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eScreening of DEGs and hub targets\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify DEGs, we applied the \u0026ldquo;limma\u0026rdquo; package in R software for statistical analysis. The \u003cem\u003ep\u003c/em\u003e value was adjusted via the Benjamini\u0026ndash;Hochberg method, and the screening criteria were set as |log\u003csub\u003e2\u003c/sub\u003e fold change| \u0026ge; 1 and \u003cem\u003ep \u003c/em\u003e\u0026le; 0.05. A volcano plot was generated via the \u0026ldquo;ggplot2\u0026rdquo; package in R software to visualize the transcriptional differences between the NEFA and control groups. A heatmap of the DEGs was generated via the ChiPlot tool (https://www.chiplot.online/) to visualize the expression profiles of the DEGs. Finally, the DEGs were cross-analyzed with the two target sets aforementioned, and the overlapping genes of the three datasets served as our new hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO and KEGG enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO functional annotation and KEGG pathway enrichment analyses were performed and visualized via the \u0026ldquo;clusterProfiler\u0026rdquo;, \u0026ldquo;org.Bt.eg.db\u0026rdquo;, and \u0026ldquo;ggplot2\u0026rdquo; packages in R. The screening criterion was set as a false discovery rate (FDR) \u0026lt; 0.05 to explore the potential roles of biological processes, molecular functions, cellular components and signaling pathways associated with the potential targets. In addition, to better explore the key mechanisms of local network modules as well as hub targets in NEFA-induced RFC toxicity, we performed GO term enrichment analysis for MCODE cluster 1, which had the highest modular score, and KEGG enrichment analysis for the hub genes. Finally, to further explore the pathways of NEFA-mediated RFC, we performed cross-analysis of the KEGG analysis results of the potential targets and hub targets aforementioned.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PDB files of the hub genes were downloaded from the AlphaFold Protein Structure Database (https://alphafold.com/), and the SDF files of OA, PA, and SA were downloaded from the PubChem database. Molecular docking analysis was performed via the CB-Dock2 platform (https://cadd.labshare.cn/cb-dock2/index.php), and the binding energy was used to assess the molecular docking interactions of the receptors and ligands. A binding energy \u0026lt; -5.0 kcal/mol is generally considered to indicate stable binding. Finally, a heatmap of binding energy was generated via the ChiPlot tool, and the molecular docking results (binding energy \u0026lt; -5.0 kcal/mol) were visualized via the CB-Dock2 platform [16].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of NEFA-related targets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1220 targets were obtained by comprehensive analysis of the ChEMBL, Swiss Target Prediction, OMIM and GeneCards databases, and with calibration in the UniProt database, 526 potential NEFA-related targets were ultimately obtained (Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of RFC-related targets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1933 targets were obtained by integrating the results of the GeneCards and OMIM databases.\u0026nbsp;After calibration via the UniProt database, 816 potential RFC-related targets were ultimately obtained (Supplementary Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of screening the DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePCA was used to assess the overall distribution and variability of gene expression data between the NEFA and control groups. As shown in Figure 2A, the first principal component (PC1) (70.34%) and PC2 (15.89%) together explained 86.23% of the total variance, indicating a good principal component explanation rate. However, the two samples did not show a clear cluster pattern in the space constituted by these two principal components, suggesting that the intergroup variation in gene expression may be smaller than the intragroup variation.\u003c/p\u003e\n\u003cp\u003eWe used a volcano plot to further validate the transcriptional differences between the NEFA and control groups. The volcano plot (Figure 2B) revealed a total of 55 DEGs, which included 40 upregulated genes (red points) and 15 downregulated genes (blue points). In addition, a heatmap of the DEGs was generated to visualize the expression levels of these genes in different samples. As shown in Figure 3, the expression of genes within the group was highly consistent, indicating distinct expression profiles between the NEFA group and the control group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-analysis results of the target sets and DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NEFA target library, RFC target library and 55 DEGs were cross-analyzed via the DrawVennDiagram online tool, and a Venn diagram was generated to visualize the results. There were 403 potential target genes overlapping between the NEFA and RFC target sets (Figure 4A; Supplementary Table 3), 4 of which were DEGs (Figure 4B). These DEGs were MMP2, CTSB, CTSK, and SOD2, which were all upregulated in the high-concentration NEFA group (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of screening the hub targets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PPI network of 403 potential targets was constructed via the STRING database and visualized with Cytoscape v3.10.2 software (Figure 5). This network contains 259 nodes and 540 edges, with an average node degree of 3.16 and a PPI enrichment \u003cem\u003ep\u003c/em\u003e value of \u0026lt; 1.0e\u003csup\u003e-16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA total of 12 gene clusters were obtained via the MCODE plugin (Supplementary Table 4). Through comprehensive analysis of five algorithms (MCC, MNC, degree, betweenness centrality, and closeness centrality) of the CytoHubba plugin, 4 hub genes (PRKACA, PRKCB, IL6, and MAPK1) were identified (Figure 6A~F). These targets, together with the 4 DEGs aforementioned, constitute the 8 hub targets identified in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of the GO and KEGG enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bubble plot (Figure 7A) revealed the top 10 most enriched GO terms for 403 potential targets. The lollipop plot (Figure 7B) revealed the top 30 most enriched KEGG pathways for 403 potential targets. The Sankey\u0026ndash;Lollipop plot (Figure 7C) revealed the 11 most significantly enriched KEGG pathways for the 8 hub targets. All the above rankings are sorted by ascending FDR values.\u003c/p\u003e\n\u003cp\u003eThe GO enrichment analysis of 403 potential targets revealed 229 biological process (BP), 24 cellular component (CC) and 47 molecular function (MF) terms; the KEGG enrichment analysis revealed 201 potential pathways (Supplementary Table 5). GO enrichment analysis (Figure 7A; Supplementary Table 5) revealed that BPs were involved mainly in metabolic regulation, physiological homeostasis and other related pathways; CCs were involved mainly in pathways related to membrane signal transduction and neuronal function; and MFs were involved mainly in biological processes such as enzyme catalysis, molecular binding, and transport. KEGG enrichment analysis (Figure 7B; Supplementary Table 5) revealed that the AGE-RAGE signaling pathway in diabetic complications, lipids and atherosclerosis, proteoglycans in cancer, and the HIF-1 signaling pathway were the most significantly enriched pathways, suggesting their potential significance in the regulatory mechanism and development of NEFA-induced RFC toxicity.\u003c/p\u003e\n\u003cp\u003eThe GO enrichment analysis of MCODE cluster 1 (Table 3) is shown in Figure 7D, and the genes of this module were involved mainly in the biological process of the steroid hormone signaling pathway, which includes key processes: hormone signaling and cellular communication, kinase signaling hubs, hormone signaling perception, and activation and transcriptional regulation.\u003c/p\u003e\n\u003cp\u003eKEGG enrichment analysis of the hub genes (Figure 7C) revealed that the GnRH signaling pathway and the AGE-RAGE signaling pathway in diabetic complications were the most significantly enriched pathways associated with the genes, emphasizing the importance of reproductive hormones and advanced glycation end products (AGEs) in NEFA-induced RFC toxicity.\u003c/p\u003e\n\u003cp\u003eIn addition, the KEGG pathways of the 403 potential targets and 8 hub targets highly and significantly overlapped, emphasizing the important role of the 8 hub targets among the 403 potential targets (Figure 7E; Supplementary Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of molecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe molecular docking results (Figure 8A) revealed that the binding energies of MMP2, MAPK1, PRKACA and PRKCB to NEFAs were all \u0026lt; -5.0 kcal/mol, suggesting that these proteins can spontaneously bind to NEFAs and exhibit high stability. Further analysis revealed that NEFAs formed hydrogen bonds with these four target proteins (Figure 8B~E), further emphasizing the strong binding affinity between these targets and NEFAs. These findings validate and underscore the critical role of these targets in the molecular mechanism of NEFA-induced RFC toxicity.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHigh circulating concentrations of NEFAs, although normal metabolites of cows in the NEB state, are thought to be strongly associated with reproductive diseases, ketosis, gastric shift, and fatty liver in dairy cows [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this study, a total of 403 potential targets and 55 DEGs were identified through comprehensive analysis of the ChEMBL, Swiss Target Prediction, GeneCards, OMIM, and GEO databases. A total of 8 hub targets, SOD2, CTSB, CTSK, MMP2, IL6, MAPK1, PRKACA and PRKCB, were obtained via the STRING database and Cytoscape software. In particular, the binding stability of MMP2, MAPK1, PRKACA and PRKCB to NEFAs was validated via molecular docking (\u0026lt; -5.0 kcal/mol; Figs.\u0026nbsp;7B\u0026thinsp;~\u0026thinsp;E), emphasizing that these proteins may play key bridging roles between NEFAs and RFC. Our study provides a novel scientific theoretical basis and potential molecular targets for the study of NEFA-mediated RFC toxicity.\u003c/p\u003e\u003cp\u003eMMP2 is a member of the matrix metalloproteinase family, and its main function is to participate in the remodeling and degradation of the extracellular matrix [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The extracellular matrix is tightly regulated by proteins such as MMP2, and disruption of this regulatory balance may lead to abnormal embryonic implantation or infertility [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, MMP2 also plays a role in determining the distribution of growth factors required for embryonic development in the endometrium [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Previous studies have shown that the mRNA and protein levels of MMP2 are significantly increased in a bovine endometritis model induced by \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; in humans, the upregulation of MMP2 is strongly associated with endometriosis, infertility, and miscarriage [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, MMP2 was both a common target of NEFAs and RFC, and an upregulated DEG after treatment with high concentrations of NEFAs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that excess NEFAs may induce MMP2 overexpression, which subsequently disrupts the balance between the remodeling and degradation of reproductive tract tissues, ultimately leading to RFC.\u003c/p\u003e\u003cp\u003eMitogen-activated protein kinases (MAPKs) and cyclic adenosine monophosphate (cAMP)-dependent protein kinase A (PKA) are involved in the regulation of the processes of oocyte maturation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. MAPK1, a key member of the MAPK family, can be activated by various cytokines, growth factors, reactive oxygen species, and high glucose to promote cell differentiation, proliferation, growth, and apoptosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. MAPK1 also synergizes with maturation-promoting factors in the regulation of bovine oocyte maturation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, MAPK1 activation is closely related to bovine blastocyst quality and ectodermal gene expression [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. PRKACA (protein kinase cAMP-activated catalytic subunit alpha), the major catalytic subunit of PKA, can directly inhibit MPF activity through the cAMP/PKA pathway, thereby arresting meiosis resumption in bovine oocytes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. PRKACA is also related to the follicle-stimulating hormone-induced proliferation of granulosa cells [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In addition, dysregulation of PRKACA is strongly associated with human reproductive disorders such as high-grade ovarian serous carcinomas, endometrial cancers, cervical cancers, and recurrent miscarriages [\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These findings suggest that MAPK1 and PRKACA may play a synergistic role in the development of NEFA-mediated RFC. Investigating the underlying association between NEFA exposure and the dysregulation of MAPK1 or PRKACA could help elucidate the molecular toxicological molecular mechanisms underlying NEFA-mediated RFC.\u003c/p\u003e\u003cp\u003ePRKCB is a key member of the protein kinase C (PKC) family, encoding PKC-β, which is dependent on calcium ions and diacylglycerol for its phosphorylation to regulate cell proliferation, apoptosis, and metabolism [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Currently, studies on the association between PRKCB and RFC are limited, and only a few studies have shown that dysregulation of PRKCB is associated with ovarian lymphoma, cervical cancer, abnormal placental function and other conditions in humans [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Notably, PRKCB is a key regulatory gene for diabetes and its complications [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and high concentrations of NEFAs are closely associated with dairy cow ketosis (a major complication of diabetes) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This may serve as a link between PRKCB and RFC, although verification in future studies is needed. Therefore, exploring the relationship between high levels of NEFAs and PRKCB expression could further refine and elucidate the mechanism of RFC toxicity caused by NEFAs.\u003c/p\u003e\u003cp\u003eWe performed PCA to assess the overall variation and distribution of gene expression data between the normal (CON) and NEFA groups. However, PCA has limitations in identifying transcriptional differences: as a statistical method, PCA can identify directions that explain the maximum variance in high-dimensional datasets, but such separation relies on global gene expression rather than individual genes; even though PCA captures an overall trend of variance, it does not yield specific DEGs [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Therefore, the interpretation of the PCA results should be combined with subsequent analysis of the DEGs, enrichment analysis, etc. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In this study, even though the PCA results were unsatisfactory, on the basis of the subsequent expression profiles of four DEGs (MMP2, CTSB, CTSK and SOD2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and enrichment analysis of eight hub targets (Figs.\u0026nbsp;6C and 6E), the DEG group exhibited a consistent trend of gene expression, and the enrichment analyses revealed RFC-associated pathways, including the AGE-RAGE signaling pathway in diabetic complications and the GnRH signaling pathway. Thus, our screening of DEGs was biologically meaningful.\u003c/p\u003e\u003cp\u003eGO and KEGG pathway enrichment analyses revealed the pathways that may be affected by NEFA exposure and the molecular processes involved in the progression of RFC, thereby enabling a deeper understanding of the biological mechanisms underlying NEFA exposure leading to RFC. The combined GO and KEGG enrichment analyses of 403 potential targets and 8 hub targets suggested that NEFAs may affect RFC through multiple pathways, including the AGE-RAGE signaling pathway in diabetic complications, the GnRH signaling pathway, lipid metabolism disorders, apoptosis, the MAPK signaling pathway, the VEGF signaling pathway, and the FoxO signaling pathway. These pathways are involved in key processes, including hormone regulation, inflammation, oxidative stress, angiogenesis, tissue remodeling, apoptosis, cell survival and energy metabolism. Notably, among these pathways, the GnRH signaling pathway can inhibit the release of gonadotropins and impair the development and maturation of germ cells; lipid metabolism disorders may exacerbate lipid deposition and inflammatory responses; apoptosis may impair vascular endothelial function in reproductive tissues, subsequently affecting cell development and the blood supply; the MAPK signaling pathway may exacerbate apoptosis and inflammatory responses; the VEGF signaling pathway may inhibit endometrial angiogenesis and embryo implantation; and the FoxO signaling pathway may promote germ tissue damage through oxidative stress. In addition, the GO enrichment analysis of MCODE cluster 1, the highest-scoring gene cluster via the MCODE method, involved mainly the steroid hormone signaling pathway, suggesting that NEFAs may potentially affect the synthesis and secretion of steroid hormones, emphasizing the importance of hormone regulation in NEFA-mediated RFC toxicity.\u003c/p\u003e\u003cp\u003eThe AGE-RAGE signaling pathway in diabetic complications, which was the most significantly enriched pathway in the KEGG enrichment analysis of potential targets and hub targets, highlights the possibility that AGEs may be a key factor in the pathological development of NEFA-mediated RFC toxicity. AGEs are irreversible products formed through nonenzymatic reactions between reducing sugars and molecules such as proteins, lipids, and nucleic acids [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Studies have demonstrated that AGEs may exacerbate endothelial dysfunction, infertility, endometritis, and oxidative stress in ovarian cells [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The binding of AGEs to the RAGE receptor induces MAPKs upregulation by activating key transcription factors such as NF-κB and AP-1, which subsequently induce the expression of proinflammatory genes such as IL-1β, IL-6, and TNF-α, ultimately exacerbating the inflammatory response and oxidative stress [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In this study, NEFAs may promote the production and accumulation of AGEs, exacerbating inflammatory responses and oxidative stress through AGE/RAGE/MAPK cascade reactions and ultimately promoting the development of RFC.\u003c/p\u003e\u003cp\u003eNotably, the transcriptomic validation in our study was based on the GSE165476 dataset, which was generated from bovine ovarian tissues experimentally treated with high concentrations of NEFAs. Although accessed through a public repository, these data originate from real biological experiments and therefore serve as experimental evidence supporting our study. Nonetheless, because the dataset was not produced independently in our laboratory, further in vitro and in vivo experiments will be necessary to strengthen the causal inference between NEFA exposure and RFC.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study is the first to integrate network toxicology, transcriptomics, and molecular docking approaches to systematically explore the potential molecular mechanisms of the endogenous metabolite NEFA-mediated RFC. By integrating these three approaches, we established a complete systematic chain of screening-prediction-verification, which not only advances the understanding of NEFA-mediated reproductive toxicity but also provides potential biomarkers and molecular targets for improving reproductive management strategies in dairy cows. Future studies should focus on animal and cell modeling experiments, multiomics studies, and molecular dynamic simulations to explore and validate the potential effects of these targets on RFC across multiple levels. These studies provide deeper and more comprehensive insights into targeting the inhibition of NEFA toxicity to RFC to ultimately improve reproductive performance and reduce culling rates in dairy cows.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNEFAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eNonesterified fatty acids\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNEB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eNegative energy balance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eReduced fertility in cows\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eOleic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003ePalmitic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eStearic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eDifferentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSMILES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eSimplified molecular input line entry system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eProtein\u0026ndash;protein interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMCODE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eMolecular Complex Detection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eMaximum clique centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMNC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eMaximum neighborhood component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003ePrincipal component analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eFalse discovery rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eBiological process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eCellular component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eMolecular function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAGEs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eAdvanced glycation end products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMAPKs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eMitogen-activated protein kinases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ecAMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003ecyclic adenosine monophosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePKA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eProtein kinase A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePKC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003eProtein kinase C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material. All other data generated or analyzed during this study are provided in the supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by grants from the Science and Technology Department of Xinjiang Uygur Autonomous Region (2024AB034) and the National Key Research and Development Program of China (2023YFE0107600).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJW: Conceptualization, Investigation, Formal analysis, Software, Visualization, Writing\u0026ndash;original draft. WW: Methodology, Validation, Writing\u0026ndash;review \u0026amp; editing. XK: Formal analysis, Visualization, Writing\u0026ndash;review \u0026amp; editing. YL: Software, Formal analysis, Writing\u0026ndash;review \u0026amp; editing. LL: Methodology, Supervision, Writing\u0026ndash;review \u0026amp; editing. YL: Funding acquisition, Supervision, Writing\u0026ndash;review \u0026amp; editing. HH: Funding acquisition, Project administration,\u0026nbsp;Supervision,\u0026nbsp;Writing\u0026ndash;review \u0026amp; editing. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKhemarach S, Yammuen-art S, Punyapornwithaya V, Nithithanasilp S, Jaipolsaen N, Sangsritavong S. 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Prev Vet Med. 1997;31:59\u0026ndash;72. https://doi.org/10.1016/s0167-5877(96)01141-5.\u003c/li\u003e\n\u003cli\u003eMacmillan K, Gobikrushanth M, Helguera IL, Behrouzi A, Colazo MG. Relationships between early postpartum nutritional and metabolic profiles and subsequent reproductive performance of lactating dairy cows. Theriogenology. 2020;151:52\u0026ndash;7. https://doi.org/10.1016/j.theriogenology.2020.03.034.\u003c/li\u003e\n\u003cli\u003eL\u0026uuml;ttgenau J, Purschke S, Tsousis G, Bruckmaier RM, Bollwein H. Body condition loss and increased serum levels of nonesterified fatty acids enhance progesterone levels at estrus and reduce estrous activity and insemination rates in postpartum dairy cows. Theriogenology. 2016;85:656\u0026ndash;63. https://doi.org/10.1016/j.theriogenology.2015.10.003.\u003c/li\u003e\n\u003cli\u003eCardoso FC, LeBlanc SJ, Murphy MR, Drackley JK. Prepartum nutritional strategy affects reproductive performance in dairy cows. 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Bovine endometrial metallopeptidases MMP14 and MMP2 and the metallopeptidase inhibitor TIMP2 participate in maternal preparation of pregnancy. Mol Cell Endocrinol. 2011;332:48\u0026ndash;57. https://doi.org/10.1016/j.mce.2010.09.009.\u003c/li\u003e\n\u003cli\u003eWu J, Bai F, Mao W, Liu B, Yang X, Zhang J, et al. Anti-inflammatory effects of the prostaglandin D2/prostaglandin DP1 receptor and lipocalin-type prostaglandin D2 synthase/prostaglandin D2 pathways in bacteria-induced bovine endometrial tissue. Vet Res. 2022;53:98. https://doi.org/10.1186/s13567-022-01100-6.\u003c/li\u003e\n\u003cli\u003eSkrzypczak J, Wirstlein P, Mikołajczyk M, Ludwikowski G, Zak T. TGF superfamily and MMP2, MMP9, TIMP1 genes expression in the endometrium of women with impaired reproduction. Folia Histochem Cytobiol. 2007;45 Suppl 1:S143-148.\u003c/li\u003e\n\u003cli\u003eMalvezzi H, Aguiar VG, Paz CCPD, Tanus-Santos JE, Penna IADA, Navarro PA. 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Biol Reprod. 2013;88:57. https://doi.org/10.1095/biolreprod.112.105106.\u003c/li\u003e\n\u003cli\u003eGong X, Wang Z, You J, Gao J, Chen K, Chu J, et al. Pyroptosis-associated genes and tumor immune response in endometrial cancer. Discov Onc. 2024;15:433. https://doi.org/10.1007/s12672-024-01315-3.\u003c/li\u003e\n\u003cli\u003eGuo W, Yu H, Zhang L, Chen X, Liu Y, Wang Y, et al. Effect of hyperoside on cervical cancer cells and transcriptome analysis of differentially expressed genes. Cancer Cell Int. 2019;19:235. https://doi.org/10.1186/s12935-019-0953-4.\u003c/li\u003e\n\u003cli\u003eAhmadi K, Reiisi S, Habibi Z. Comparison of the gene expression profiles of endometrial and trophoblastic cells in women with recurrent miscarriage: A bioinformatics approach. IJRM. 2024;22:495\u0026ndash;506. https://doi.org/10.18502/ijrm.v22i6.16800.\u003c/li\u003e\n\u003cli\u003eTurnham RE, Scott JD. Protein kinase A catalytic subunit isoform PRKACA; history, function and physiology. Gene. 2016;577:101\u0026ndash;8. https://doi.org/10.1016/j.gene.2015.11.052.\u003c/li\u003e\n\u003cli\u003eKawakami T, Kawakami Y, Kitaura J. Protein kinase C (PKC ): Nomal functions and dieases. J Biochem. 2002;132:677\u0026ndash;82. https://doi.org/10.1093/oxfordjournals.jbchem.a003273.\u003c/li\u003e\n\u003cli\u003eGao Q, Li H, Ding H, Fan X, Xu T, Tang J, et al. Hyper-methylation of AVPR1A and PKC\u0026Beta; gene associated with insensitivity to arginine vasopressin in human pre-eclamptic placental vasculature. Ebiomedicine. 2019;44:574\u0026ndash;81. https://doi.org/10.1016/j.ebiom.2019.05.056.\u003c/li\u003e\n\u003cli\u003eXu H, Duan N, Wang Y, Sun N, Ge S, Li H, et al. The clinicopathological and genetic features of ovarian diffuse large B-cell lymphoma. Pathology. 2020;52:206\u0026ndash;12. https://doi.org/10.1016/j.pathol.2019.09.014.\u003c/li\u003e\n\u003cli\u003eYu J, Gui X, Zou Y, Liu Q, Yang Z, An J, et al. A proteogenomic analysis of cervical cancer reveals therapeutic and biological insights. Nat Commun. 2024;15:10114. https://doi.org/10.1038/s41467-024-53830-0.\u003c/li\u003e\n\u003cli\u003eLi Q, Park K, Li C, Rask-Madsen C, Mima A, Qi W, et al. Induction of vascular insulin resistance and endothelin-1 expression and acceleration of atherosclerosis by the overexpression of protein kinase C-\u0026beta; isoform in the endothelium. Circ Res. 2013;113:418\u0026ndash;27. https://doi.org/10.1161/CIRCRESAHA.113.301074.\u003c/li\u003e\n\u003cli\u003eMartens H. Invited review: Increasing milk yield and negative energy balance: A gordian knot for dairy cows? Animals-basel. 2023;13:3097. https://doi.org/10.3390/ani13193097.\u003c/li\u003e\n\u003cli\u003eRingn\u0026eacute;r M. What is principal component analysis? Nat Biotechnol. 2008;26:303\u0026ndash;4. https://doi.org/10.1038/nbt0308-303.\u003c/li\u003e\n\u003cli\u003eJolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Phil Trans R Soc A. 2016;374:20150202. https://doi.org/10.1098/rsta.2015.0202.\u003c/li\u003e\n\u003cli\u003eLever J, Krzywinski M, Altman N. Principal component analysis. Nat Methods. 2017;14:641\u0026ndash;2. https://doi.org/10.1038/nmeth.4346.\u003c/li\u003e\n\u003cli\u003eConesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13. https://doi.org/10.1186/s13059-016-0881-8.\u003c/li\u003e\n\u003cli\u003eAdamopoulos C, Piperi C, Gargalionis AN, Dalagiorgou G, Spilioti E, Korkolopoulou P, et al. Advanced glycation end products upregulate lysyl oxidase and endothelin-1 in human aortic endothelial cells via parallel activation of ERK1/2\u0026ndash;NF-\u0026kappa;B and JNK\u0026ndash;AP-1 signaling pathways. 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Cells. 2022;11:1312. https://doi.org/10.3390/cells11081312.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Transcription results of upregulated DEGs (\u003cem\u003ep\u003c/em\u003e value \u0026le; 0.05, log\u003csub\u003e2\u003c/sub\u003e(fold change) \u0026ge; 1).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eEnsembl ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003elog\u003csub\u003e2\u003c/sub\u003e(fold change)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSDF2L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000000067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.98E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eATP10D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000000473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.49E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGPNMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000000604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.22E\u003csup\u003e-08\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eADAMTS5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000000648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e6.23E\u003csup\u003e-04\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eLGALS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000002326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.15E\u003csup\u003e-03\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eADAMTSL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000003604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4.08E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMYO5C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000003763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.14E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eDPAGT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000005371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.52E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSEMA3C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000006138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.30E\u003csup\u003e-06\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSUOX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000006160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3.31E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMYO5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000006489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4.01E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSOD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000006523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e7.79E\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eLOC100139670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000007881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e8.48E\u003csup\u003e-03\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSEL1L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000008083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.04E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSLC38A2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000011105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.85E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCKAP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000011913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3.50E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCTSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000012442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3.50E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNFE2L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000013653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3.31E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSPCS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000014146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.52E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000014764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.39E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eTM4SF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000015163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.85E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eBTBD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000016108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.17E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eFAM180A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000017427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.37E\u003csup\u003e-03\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCHI3L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000018223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.09E\u003csup\u003e-03\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eANXA8L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000018499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e9.83E\u003csup\u003e-03\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eS100A4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000019203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e7.85E\u003csup\u003e-15\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMMP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000019267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.52E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eFBP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000019554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.81E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eDHRS7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000020729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.52E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCTSK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000021035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.86E\u003csup\u003e-07\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eBCAS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000038929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.80E\u003csup\u003e-03\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eLOC112444563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000043222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e4.31E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000043582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.31E\u003csup\u003e-06\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCFB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000046158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.08E\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eADAMTSL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000046263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.17E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMT1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000054808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3.31E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000057732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.23E\u003csup\u003e-04\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000057824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e3.31E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000061247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e2.90E\u003csup\u003e-10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eHERPUD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000076584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e1.02E\u003csup\u003e-02\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNA, not applicable.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Transcription results of downregulated DEGs (\u003cem\u003ep\u003c/em\u003e value \u0026le; 0.05, log\u003csub\u003e2\u003c/sub\u003e(fold change) \u0026le; -1).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eEnsembl ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003elog\u003csub\u003e2\u003c/sub\u003e(fold change)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eTTI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000015463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e4.98E\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSORBS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000016486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e2.36E\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eGRB14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000019291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e4.98E\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eTRIM7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000020954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e8.57E\u003csup\u003e-06\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eDUS4L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000021849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e7.65E\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eECSCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000030518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e5.01E\u003csup\u003e-06\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eLOC112448304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000042475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e5.16E\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000056594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e1.35E\u003csup\u003e-06\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000059357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e3.31E\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000063983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e6.39E\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eTLR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000064150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e5.15E\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000067673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e1.32E\u003csup\u003e-06\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000069885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e1.57E\u003csup\u003e-07\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000073706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e2.85E\u003csup\u003e-05\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5S_rRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eENSBTAG00000076020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003e-3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e1.52E\u003csup\u003e-06\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNA, not applicable.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Information on MCODE cluster 1.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eMCODE clusters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eTargets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eMCODE node status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eMCODE score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCloseness centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eBetweenness centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eDegree\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCluster 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eESR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eSeed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eESR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eFOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eJUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eMAPK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003ePRKACA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eRPS6KB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eClustered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"network toxicology, NEFAs, reduced fertility in cows, transcriptomics, molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-7524568/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7524568/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh concentrations of nonesterified fatty acids (NEFAs) are normal metabolites of high-producing dairy cows in a state of negative energy balance (NEB), but they are thought to be strongly associated with reproductive disorders in dairy cows, which may contribute to reduced fertility in cows (RFC). There are few studies on the independent toxic effects of NEFA-mediated RFC. This study aimed to investigate the toxicological effects of NEFA-mediated RFC systematically via network toxicology, transcriptomics, and molecular docking techniques. A total of 403 potential targets of NEFA-mediated RFC toxicity were screened by comprehensively analyzing the GeneCards, OMIM, ChEMBL and Swiss Target Prediction databases. Further analysis via the GEO (GSE165476 dataset), STRING databases and Cytoscape software yielded eight hub targets, including MMP2, MAPK1, PRKACA and PRKCB. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these targets were involved in pathways related to metabolism, endocrine processes, cell death, and signal transduction, such as the AGE-RAGE signaling pathway in diabetic complications, the GnRH signaling pathway, and the MAPK signaling pathway. Molecular docking further confirmed the potential interactions between NEFAs and these hub targets. This study revealed that NEFAs may exacerbate the occurrence of RFC by interfering with endocrine regulation, inducing inflammatory responses, affecting angiogenesis and tissue remodeling, regulating apoptosis, and disrupting metabolic balance. The results of this study provide novel molecular insights into the mechanism of NEFA-mediated RFC toxicity and provide a scientific basis for emphasizing the importance of metabolite toxicity in dairy farming health management.\u003c/p\u003e","manuscriptTitle":"Exploring the toxicological mechanisms of reduced fertility in dairy cows due to nonesterified fatty acids on the basis of network toxicology, transcriptomics and molecular docking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 10:55:04","doi":"10.21203/rs.3.rs-7524568/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-27T06:48:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T05:08:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T06:56:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97526477693478192951361877310432096612","date":"2025-10-08T09:44:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127809891425990363798492404625193947080","date":"2025-10-06T12:38:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180573818179454584612644581186376151039","date":"2025-10-03T18:10:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106603888769588490346720023761784630446","date":"2025-10-03T16:49:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"636789256539709524121319842963653767","date":"2025-09-25T14:57:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197599344977307957235979105560010375130","date":"2025-09-25T14:06:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-25T09:43:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T09:49:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T08:51:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T08:50:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-09-03T08:12:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56824a89-ef2b-4281-8c40-186bd5378858","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:13:52+00:00","versionOfRecord":{"articleIdentity":"rs-7524568","link":"https://doi.org/10.1186/s12864-025-12444-6","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2026-01-29 15:58:20","publishedOnDateReadable":"January 29th, 2026"},"versionCreatedAt":"2025-10-08 10:55:04","video":"","vorDoi":"10.1186/s12864-025-12444-6","vorDoiUrl":"https://doi.org/10.1186/s12864-025-12444-6","workflowStages":[]},"version":"v1","identity":"rs-7524568","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7524568","identity":"rs-7524568","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}