Drynariae Rhizoma (DR) has been widely used in the prevention and treatment of various fractures. However, the specific mechanisms of DR's active ingredients have not been elucidated. The purpose of this study was to explore the synergistic mechanisms of DR for treating fracture.  A network pharmacology approach integrating ingredient screening, target exploration, active ingredients-gene target network construction, protein-protein interaction network construction, molecular docking, gene-protein classification, gene ontology (GO) functional analysis, KEGG pathway enrichment analysis, and signaling pathway integration was used.  This approach identified 17 active ingredients of DR, interacting with 144 common gene targets and 143 protein targets of DR and fracture. NCOA1, GSK3B, TTPA, and MAPK1 were identified as important gene targets. Five most important protein targets were also identified, including MAPK1, SRC, HRAS, RXRA, and NCOA1. Molecular docking found that DR has a good binding potential with common protein targets. GO functional analysis indicated that common genes involve multiple processes, parts and functions in biological process, cellular component, and molecular function, including positive regulation of transcription from RNA polymerase II promoter, signal transduction, cytosol, extracellular exosome, cytoplasm, and protein binding. The KEGG pathway enrichment analysis indicated that common gene targets play a role in repairing fractures in multiple signaling pathways, including MAPK, PI3K/AKT, Ras, and VEGF signaling pathways. MAPK and PI3K/AKT signaling pathways were involved in osteoblast formation, Ras signaling pathway was involved in enhancing mesenchymal stromal cell migration, and VEGF signaling pathway was involved in angiogenesis.  The study revealed the correlation between DR and fracture and the potential synergistic mechanism of different targets of DR in the treatment of fractures, which provides a reference for the development of new drugs. Fracture is a common and frequent disease that occurs in patients with various injuries or osteoporosis . In ChinRecently, DR, one of the plants from Davalliaceae and Davallia Sm., has been widely used in the prevention and treatment of various fractures due to excellent treatment, low side effects, extensive use, and safety . Animal Network pharmacology based on systems biology and polypharmacology has achieved a paradigm shift from “one drug, one goal” to “multi-ingredient therapy, biological network,” which has attracted the attention of Chinese medicine researchers and has been recognized as an effective tool for elucidating multiple components, targets, synergistic effects, and mechanisms of Chinese medicine –10. It ihttp://lsp.nwu.edu.cn/, Version 2.3) Database and Analysis Platform includes chemicals, targets, and drug-target-disease networks, as well as pharmacokinetic properties involving oral bioavailability, druglikeness, blood-brain-barrier, and so on [Traditional Chinese Medicine Systems Pharmacology  to download a chemical structure and save it in sdf format, or the PubChem compound was put into the Zinc database (https://zinc.docking.org/) to download a chemical structure and save it in mol2 format. The related SMILES of potential active ingredients was received from TCMSP or PubChem or Zinc database. Then, the SMILES was put into the Swiss Target Prediction database (http://www.swisstargetprediction.ch/) to obtain the related drug target and save it.The structure of the potential active ingredients of DR was downloaded from TCMSP and stored in mol2 format. If there was no chemical structure, the PubChem compound was put into the PubChem  has a collection of drug molecules and targetable human proteins [Z-score in the DRAR-CPI server [Z-score <−0.5 was recommended by DRAR-CPI as a potential protein target for human-targetable drugs [Z-score <−0.5 to select potential protein targets for DR. A total of 1760 proteins with Z-score <−0.5 and 355 protein targets were obtained after deletion of the duplicate data. The PDB ID of the protein targets were inputted into UniProt KB (http://www.uniprot.org/uniprot/) of the UniProt database, and the “popular organisms” was selected as human to obtain the gene targets associated with the potential active ingredient of DR.The DRAR-CPI server , Therapeutic Targets Database (http://bidd.nus.edu.sg/BIDD-Databases/TTD/TTD.asp), PharmGkb database (https://www.pharmgkb.org/), GeneCards database (http://www.genecards.org/), and OMIM database (http://www.ncbi.nlm.nih.gov/omim). Then, the duplicate data and false-positive genes were deleted. Finally, the Venny tool  was used to identify the common gene targets of DR and fracture, which may be the potential targets for DR to treat fractures.The following electronic databases were searched to identify the genes related to fractures: Genetic Association Database  to construct an ingredient-target network of ntrality . The degntrality .https://string-db.org/, Version 10.5) is a database containing known and predicted PPIs, which collect and integrate a large number of protein interactions involving 9,643,763 proteins and 1,380,838,440 interactions, including experimental data and interactive prediction data derived from bioinformatic methods [The String database  is a web server for network pharmacology-based prediction and analysis that could be used to illustrate the role of ligands on a complex molecular network [SystemsDock  is widely used for annotation of gene function, providing detailed annotations of gene function in terms of biological process (BP), cellular component (CC), and molecular function (MF), respectively [https://david.ncifcrf.gov/, Version 6.8) is a functional genomic annotation database that provides bioinformatics annotation for genes or proteins based on the gene annotation function of the GO database and the signaling pathway information of the KEGG database [Homo sapiens” in species. Step 5: submit list. Step 6: download the results of BP, CC, and MF in the gene ontology. Step 7: download the results of KEGG pathway in the pathways. Step 8: targets with P < 0.05 were screened and sorted by count (number of targets), and the top-ranked biological processes or KEGG pathways were extracted. Step 9: BP, CC, and MF were designed using GraphPad Prism 5.0 software. The KEGG pathways were designed by the advanced bubble chart of the omicshare tool (http://omicshare.com/tools/Home/Soft/getsoft/type/index).GO  is a discovery platform that contains one of the largest publicly available genes and variants associated with human disease. It could be used to analyze the properties of disease genes and investigate the molecular basis of specific diseases and their comorbidities, as well as adverse drug reactions [DisGeNET (eactions . We usedhttp://www.kegg.jp/) to retrieve some pathways of DR for fractures and then integrate into a final pathway map. The procedure was as follows: Step 1: used the UniProt KB search function of the UniProt database to retrieve the UniprotID of the common gene targets. Step 2: import the UniProt ID of the common gene targets. Step 3: set the parameters: search against: hsa, primary ID: NCBI-UniProt ID, and examples: Homo sapiens pathway. Step 4: download the PI3K-AKT, MAPK, Ras, and VEGF signaling pathways. Step 5: integrate the signal path.We used the KEGG Mapper tool in the KEGG database  was removed because it could not be recognized by the PubChem or Zinc database. The remaining 17 active ingredients are shown in A total of 303 gene targets associated with the potential active ingredients of DR were retrieved in the UniProt database. A total of 3,173 fracture-related genes were received, and 3,054 genes remained after deletion of the duplicate and false-positive genes. Common gene target screening for fracture and DR are shown in The active ingredients and gene targets of DR was inputted into Cytoscape software to construct the ingredient-target network, as shown in The PPI network of DR is shown in Degree in the network indicates the number of proteins that a protein has interacting with. In other words, top-degree protein targets screened in PPI plays a pivotal role in the treatment of fractures with DR. Five important protein targets with top degree of DR were identified in the PPI network and are shown in Three important protein targets with top degree of DR were identified by SystemsDock, including SRC, RXRA, and NCOA1. Dock-IN score of these three proteins docked with 17 active ingredients of DR are shown in Enriched gene ontology terms for BP, CC, and MF of potential therapeutic fracture targets from the main active ingredients of DR are shown in P=7.82 × 10−6. The PI3K-Akt signaling pathway involved 17 (11.81%) potential therapeutic fracture targets, the Rap1 signaling pathway involved 14 (9.72%) potential therapeutic fracture targets, the Ras signaling pathway involved 14 (9.72%) potential therapeutic fracture targets, and the signaling pathways regulating pluripotency of stem cells involved 12 (9.03%) potential therapeutic fracture targets.Enriched KEGG pathways of potential targets for treating fracture from the main active ingredients of DR are shown in The protein class corresponding to potential targets for treating fracture from the main active ingredients of DR is presented in Four pathways associated with the potential targets of DR main active ingredients for treating fracture are presented in In order to reveal the relationship between fracture and the active ingredients involved in the DR, we predicted the mechanism of DR treatment fractures by constructing a biological network of interactions between active ingredients and common gene targets and common protein targets from a molecular level. A total of 17 active ingredients of DR were received in our study, including (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one, aureusidin, eriodictyol (flavanone), stigmasterol, beta-sitosterol, kaempferol, naringenin, (+)-catechin, eriodictyol, digallate, luteolin, 22-stigmasten-3-one, cyclolaudenol acetate, cycloartenone, cyclolaudenol, davallioside A_qt, and xanthogalenol. Most of them were polyphenolic compounds, which are also called flavonoids. Flavonoids are considered to be the main active ingredients of DR and have been reported to reduce bone loss in ovariectomized rats . In addiα- and AP-1-mediated VEGFa transcription [In our study, 144 common gene targets of DR and fracture were received, and 774 interactions between the active ingredients of DR and common gene targets were found. Some gene targets have been confirmed by clinical trials or animal experiments. For example, Guimarães et al. found that polymorphisms in the FGFR1 and BMP4 genes were associated with fracture nonunion in patients . And ourcription . Galli ecription . Fujita cription . Matsushcription . These gcription .To identify the interactions of proteins corresponding to common genes, we conducted a PPI network. A total of 143 common protein targets for DR and fracture were received, with 315 PPIs. In addition, MAPK1, SRC, HRAS, RXRA, and NCOA1 were identified as the five most important target proteins. Previous studies have found that MAPK1 and SRC could promote proliferation and differentiation of myeloid cells and inhibit apoptosis , 33. CliIn order to identify the function of the common gene, we performed GO functional analysis on these genes. The results showed that the common gene involves multiple processes, parts and functions in BP, CC, and MF, which was consistent with existing studies about DR and fracture repair. For example, in the BP, 33 (22.92%) gene targets were involved in positive regulation of transcription from the RNA polymerase II promoter, and 30 (20.84%) gene targets were involved in signal transduction. Previous studies have shown that the promoter activates the polymerase to bind precisely to the template DNA and has the specificity of transcription initiation . The RNAIn order to identify the synergistic mechanism of DR for fracture, we performed KEGG pathway enrichment analysis and summarized some important signaling pathways, which provides direction for future research. In the KEGG pathway enrichment analysis, 17 (11.81%) gene targets were involved in MAPK signaling pathway, 17 (11.81%) gene targets were involved in PI3K-Akt signaling pathway, 14 (9.72%) gene targets were involved in Ras signaling pathway, and 6 (4.17%) gene targets were involved in VEGF signaling pathway, which suggest that common gene targets play a role in repairing fractures in multiple signaling pathways. MAPK and PI3K/AKT signaling pathways have been demonstrated to promote osteoblastic bone formation . Zhang eWe collected the gene and protein targets of fractures and active ingredients of DR and then used network pharmacology to reveal the correlation between drugs and diseases and the potential synergistic mechanism of different targets of DR in the treatment of fractures, which provides a reference for the development of new drugs.


Gene regulatory networks (GRNs) are often inferred based on Gaussian graphical models that could identify the conditional dependence among genes by estimating the corresponding precision matrix. Classical Gaussian graphical models are usually designed for single network estimation and ignore existing knowledge such as pathway information. Therefore, they can neither make use of the common information shared by multiple networks, nor can they utilize useful prior information to guide the estimation. In this paper, we propose a new weighted fused pathway graphical lasso (WFPGL) to jointly estimate multiple networks by incorporating prior knowledge derived from known pathways and gene interactions. Based on the assumption that two genes are less likely to be connected if they do not participate together in any pathways, a pathway-based constraint is considered in our model. Moreover, we introduce a weighted fused lasso penalty in our model to take into account prior gene interaction data and common information shared by multiple networks. Our model is optimized based on the alternating direction method of multipliers (ADMM). Experiments on synthetic data demonstrate that our method outperforms other five state-of-the-art graphical models. We then apply our model to two real datasets. Hub genes in our identified state-specific networks show some shared and specific patterns, which indicates the efficiency of our model in revealing the underlying mechanisms of complex diseases. Most biological processes within cells involve multiple genes . InferriGaussian graphical model (GGM) is an attractive paradigm to depict the associations among biomolecules . In GGM,However, as biological systems are highly dynamic , we are Although the above methods for learning the structure of multiple GGMs have been successfully used to estimate the regulatory relationships among genes, their performance may be limited since they do not consider the existing knowledge about genes and their regulatory relationships. For example, a pathway is a set of components that interact with each other to perform specific biological tasks. Researches have found that many diseases arose from the joint action of multiple genes within a pathway . Therefohttps://github.com/NuosiWu/WFPGL.To address the above problems, in this paper, we proposed a novel weighted fused pathway graphical lasso (WFPGL) to jointly estimate multiple gene networks as well as their difference by incorporating prior knowledge derived from known pathways and gene interactions. In particular, given a set of pathways, we first assume that regulatory relationships will not take place between genes that belong to different pathways. Here, the pathway information is assumed to be able to provide us prior knowledge that certain edges are unlikely to be present . Under tThe rest of this paper is organized as follows. In the section Weighted Fused Pathway Graphic Lasso, we formulate our WFPGL and describe the optimization processes. In the section Simulation Studies, we illustrate the performance of WFPGL and another five state-of-the-art methods on synthetic datasets. The section Real Data Analysis provides studies on two real datasets, and the section Conclusion discusses the utilization of the method.X(1), …, X(K) are K groups of sample sets that measure the gene expression levels of p genes across K different states. Each X(k) ∈ Rp×nk may have a different sample size kn, and samples within each group (or state) are independent and follow same multivariate Gaussian distribution. Then maximizing the likelihood functions of multiple Gaussian graphical model is equivalent to solving the following optimization problem (Suppose  problem :(k)S is the sample covariance matrix for X(k) and Θ(k) is the inverse covariance matrix (or precision matrix).where (k) (which represent networks) for k = 1, …, K, additional penalty is often required to guarantee structural recovery especially for high-dimensional setting. Fused lasso penalty, which imposes sparse penalties not only on individual networks but also on the differences between each pair of networks, has been proven to be effective on joint estimation of multiple networks , and considering the pathway constraints and the knowledge of prior gene interaction network, the objective function of our WFPGL model is as follows:Suppose there are F({Θ(k)}) takes the form of function (2).where J pathways, let (k), which models the sub-graph within the tp genes in t-th pathway (p1 + ⋯ + Jp = p). After some permutations of the rows and columns under the pathway constraints, Θ(k) can be rearranged into the following form:Given 1 and Θother, which is a square matrix containing all links among the genes that co-occur in the pathways as well as the remaining pathways. Applying the Schur complement decomposition, we havewhere where via maximizing likelihood function only requires information from the corresponding pathway itself. We define a function with such property to be pathway separable.where the matrix with subscript denotes the sub-matrix corresponding to the first pathway. Notice that (7) is exactly the likelihood function for updating Problem (4) is converted into the following equivalent problem:which can be solved with the ADMM method  by minimA, B> = tr (TAB),‖A‖ denotes the Frobenius norm of matrix A, μ > 0 is a penalty parameter, and Y(k) is a Lagrangian multiplier. We let V(k) = Y(k)/μ, and (9) can be rewritten as the ultimate objective function:where <ADMM solves above problem by three steps in every iteration, as shown in Algorithm 1.(k) (It is easy to verify that f Θ(k) . By updat of a matrix denotes the index of the sub-matrix that corresponds to the t-th pathway. TUCU, where TUDU represents the eigendecomposition of C is a diagonal matrix with i-th diagonal element to be Here, the subscript Z(k) as the minimizer ofThe second step is to update (k) as V(k) + Θ(k) − Z(k). We stop the algorithm until The solution to this problem can be obtained by the method introduced in via D-trace loss (Dtrace)  graphical lasso (GL) , which i(Dtrace) , which i(Dtrace) , which i1 and λ2 and the weight of prior information w. For w, as suggested by w = 0.3 in the following experiments.WFPGL has three tuning parameters, i.e., the sparsity controllers λijθ and To evaluate the performance of various, we adopt four evaluation metrics named true-positive rate (TPR), false-positive rate (FPR), true-positive differential rate (TPDR), and false-positive differential rate (FPDR). Let According to the above definitions, TPR and FPR measure the accuracy of network estimation, whereas TPDR and FPDR measure the accuracy of differential network estimation.In this section, we first consider the situation when there are two groups of samples corresponding to two different states. The main generation procedure of the synthetic datasets is described as follows.STEP 1: PATHWAY DEFINITIONnol = 5 features overlapped in neighbor pathway. Let tP  represent a pathway set in which the element indicates the index of feature. In such configuration, pathways are generated as P1 = {1, 2, …, 45}, P2 = . M(1) with each non-zero element substituted by a uniform distribution value on  ∪ . Then r = 30% non-zero elements vanished.We first build a random scale-free network for the state 1, denoted by its adjacency binary matrix STEP 3: PRIOR NETWORK GENERATIONη) of edges from M(1) and connect the corresponding features in prior gene interaction network G.We select a proportion  in To evaluate the performance of various methods without prior network information or prior pathway information, we show the results of various methods without prior network information . The precision matrix for state 3 is copied from state 2, with the removed elements in state 2 being reassigned by uniform distributed values  ∪  with the possibility of 0.5.We generate three groups of samples (corresponding to three different states) with p = 200 features and n = 50 samples per group, covered by five pathways with ovn = 8. The first three networks are generated the same way as the above section except that the different rate is set to be r = 20%. Then we randomly deleted 20% non-zero elements from state 1 to build the network of state 4. To further test the robustness of WFPGL, we generated a dataset with four different states. In particular, we generated four groups of samples with η = 0.8. Because Dtrace and WDtrace cannot handle the estimation of multiple differential networks, we compare WFPGL with GL, PGL, and FGL. WFPGL still outperforms others on both individual and differential network estimations, followed by PGL, which uses pathway information only. FGL and GL do not perform well on these datasets, though FGL performs slightly better than GL on individual network estimation.Similarly, 50 random datasets are generated for the above two situations, and 1, λ2) are determined based on the following Akaike information criterion (AIC):In this section, we use WFPGL to estimate the gene regulatory networks of type 2 diabetes patients and breast cancer patients, with 8,444 known gene interactions from TRRUST database  as prior1 and λ2, and 1 and λ2, which obtain the outputs that minimize AIC to generate the final estimation of Θ(k).where We downloaded the gene expression data of type 2 diabetes patients from , which cw = 0.3 and choose λ1 (ranging from 0.05 to 0.5) and λ2 (ranging from 0.1 to 1) based on AIC. Networks for both IR and IS samples are built, and we calculate their difference to identify the network rewiring associated with insulin resistance.We normalize each gene to have 0 mean and 1 standard deviation. By merging rows with the same gene name based on their average, 442 genes are covered by the prior pathway information. According to the experiences in simulation studies, we set For real data analysis, due to the lack of ground truth, it is hard to know whether the predicted regulatory relationships are real. As the changes of gene regulatory relationships are often derived by the aberrations of certain genes, we assess the performance of our method by quantifying how hub nodes in our estimated differential networks capture known functionally important genes. We consider the gene expression data from The Cancer Genome Atlas  that meaw = 0.3 but choose λ1 ranging from 0.01 to 0.1 and λ2 ranging from 0.001 to 0.01. We build four state-specific networks corresponding to four breast cancer subtypes. We still set The genes appeared in all subtypes are significantly associated with breast cancer. For example, IGF-1 gene is identified as a key player in major signaling pathways involved in breast cancer growth ; TP53 geIn addition, the genes that emerged only in one subtype are regarded as potential subtype-specific genes, which may have diverse functions across subtypes. In this result, there are three subtype-specific hub genes identified by our method, i.e., CDKN2B in basal-like, and IL1R1 and TNFRSF10B in luminal B. CDKN2B protein is a cyclin-dependent kinase inhibitor that functions as a cell growth regulator, and its methylation is part of triple negative breast cancer (TNBC) profile . This reIn this paper, we propose a novel weighted fused pathway graphical lasso (WFPGL) that can effectively incorporate additional knowledge including pathway information and gene interaction networks to jointly estimate multiple gene regulatory networks. These two kinds of prior information have different effects on our algorithm. We incorporate gene interaction priors by assigning a weight matrix to the estimated individual networks and differential networks. When there is no prior gene interaction information, all elements in the weight matrix are set to 1, and our model degenerates to a fused pathway graphical lasso model. For prior pathway information, we utilize the information by imposing constraints that a pair of genes can be connected to each other only if they co-occur in at least one pathway. The constraints have the potential of improving structure learning of gene regulatory networks. First, it can accelerate the optimization of the algorithm, leading to acceptable results when dealing with high-dimensional data. Second, making use of such prior information in learning the structures of networks can yield results that are more meaningful and interpretable. Moreover, as our algorithm is a flexible framework, the “pathway” here does not need to be an exact biological pathway. The “pathway” here stands for a partition of genes such that genes that belong to different “pathways” are less likely to have regulatory relationships. On the one hand, if we can collect additional information, such as the transcript factors (TFs) of genes in a given pathway, we could combine these TFs and their regulated genes into a new “pathway” such that the regulatory relationships are more likely to take place between genes within the same “pathways.” On the other hand, if the pathway information is not comprehensive or remains unknown to us, we could treat all genes as a pathway, and our model degenerates to a weighted fused graphical lasso model.https://www.genome.jp/; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40234; https://portal.gdc.cancer.gov/Publicly available datasets were analyzed in this study. This data can be found here: NW conceived and designed the study, performed the statistical analysis, and drafted the manuscript. LO-Y and ZZ conceived the study, and participated in its design and coordination, and helped to draft the manuscript. JH and X-FZ participated in the design of the study, performed the statistical analysis, and helped to revise the manuscript. SH and WX participated in the design of the study and helped to revise the manuscript. All authors read and approved the final manuscript.This work is supported by the National Natural Science Foundation of China under grants No. 61602309, 61871272, 61575125, 11871026 and 61402190, Shenzhen Fundamental Research Program, under grant JCYJ20170817095210760 and JCYJ20170302154328155, Natural Science Foundation of SZU [2017077], Guangdong Special Support Program of Topnotch Young Professionals, under grants 2014TQ01X273, and 2015TQ01R453, Guangdong Foundation of Outstanding Young Teachers in Higher Education Institutions, under grant Yq2015141, Natural Science Foundation of Hubei province [ZRMS2018001337] and Funding of Shenzhen Institute of Artificial Intelligence and Robotics for Society.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Systemic sclerosis (SSc), a multi-organ disorder, is characterized by vascular abnormalities, dysregulation of the immune system, and fibrosis. The mechanisms underlying tissue pathology in SSc have not been entirely understood. This study intended to investigate the common and tissue-specific pathways involved in different tissues of SSc patients.An integrative gene expression analysis of ten independent microarray datasets of three tissues was conducted to identify differentially expressed genes (DEGs). DEGs were mapped to the search tool for retrieval of interacting genes (STRING) to acquire protein–protein interaction (PPI) networks. Then, functional clusters in PPI networks were determined. Enrichr, a gene list enrichment analysis tool, was utilized for the functional enrichment of clusters.A total of 12, 2, and 4 functional clusters from 619, 52, and 119 DEGs were determined in the lung, peripheral blood mononuclear cell (PBMC), and skin tissues, respectively. Analysis revealed that the tumor necrosis factor (TNF) signaling pathway was enriched significantly in the three investigated tissues as a common pathway. In addition, clusters associated with inflammation and immunity were common in the three investigated tissues. However, clusters related to the fibrosis process were common in lung and skin tissues.Analysis indicated that there were common pathological clusters that contributed to the pathogenesis of SSc in different tissues. Moreover, it seems that the common pathways in distinct tissues stem from a diverse set of genes. Systemic sclerosis (SSc) is a rare, multisystemic, autoimmune disease that involves the skin and various internal organs, including the lungs, gastrointestinal tract, heart, and kidneys. The exact pathogenesis of SSc remains unknown, but it seems that vascular abnormalities, inflammation, dysregulation of immune system, and extracellular matrix (ECM) deposition can lead to progressive connective tissue fibrosis. Organ failures that arise from fibrosis are the most significant causes of mortality in SSc patients , 2.Although the etiopathogenesis of SSc has not been well identified, accumulated evidence suggests that multiple genes and their interactions with environmental factors play important roles in this context , 4. TradMicroarrays have been extensively applied for understanding biological mechanisms, discovering new drug targets, and evaluating drug responses , 10. In Skin involvement is one of the most common clinical manifestations of SSc and is known to be a key marker of disease activity . The lunThe method procedures and steps are illustrated in Fig. https://www.ncbi.nlm.nih.gov/geo/) was searched for gene expression datasets regarding SSc [Gene Expression Omnibus (GEO)  using the Benjamini-Hochberg (BH) procedure and the logarithm of fold change (logFC) > ± 0.5. A total of 52, 619, and 119 DEGs between the SSc group and healthy controls were identified in PBMC, lung, and skin tissues, respectively. A complete list of DEGs in lung, PBMC, and skin datasets are provided as Additional file DEGs between healthy controls and patients in PBMCs as well as lung and skin tissues were identified using the Limma package . DEGs wehttps://string-db.org) database, which integrates both known and predicted PPIs, can be applied to predict functional interactions of proteins [Homo sapiens” and an interaction score > 0.4 were applied to construct the PPI networks. Cytoscape software version 3.6.1 was used to visualize the PPI network. To detect highly connected regions of the network, ClusterONE 1.0 software was used based on the following criteria: minimum size = 5, minimum density = 0.05, and edge weights = combined_score. Minimum size is the minimum size of each cluster; minimum density represents the average edge weight within the cluster if missing edges are supposed to have a weight of zero, and edge weights determine the weight of each edge [Homo sapiens” and a combined score > 0.4, were applied.The search tool for retrieval of interacting genes (STRING) (proteins . To seekach edge . In the http://amp.pharm.mssm.edu/Enrichr/) for clusters obtained from different SSc tissues. Enrichr is a web-based tool that allows the evaluation of annotations with its extensive gene-set libraries [p-value < 0.05. The five most significant  GO biological processes and KEGG pathways in each cluster of lungs, PBMC, and skin datasets are listed in Tables Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using Enrichr  and proliferation.Clusters L.4, L.5, and L.10 all contained biological processes and pathways dependent upon G-protein coupled receptor (GPCR) signaling. GPCRs are a major family of cell surface receptors that are involved in the physiological processes, including regulation of immune systems, cellular motility, and differentiation .The enrichment of ECM organization terms and pathways was observed to be associated with cluster L.6. For example, ECM organization, collagen fibril organization, and protein complex subunit organization terms as well as protein digestion and absorption and ECM-receptor interaction pathways were enriched in cluster L.6. Likewise, cluster L.11 was observed to be more represented in ECM degradation terms, such as proteolysis and ECM disassembly. However, no meaningful KEGG pathways were assigned to this cluster.As shown in Table The PPI network of PBMC was divided into two significant clusters. These two clusters and the biological processes and pathways relevant to the PPI network of PBMC are listed in Table Four significant clusters were found in the skin PPI network. Table Analysis showed that the TNF signaling pathway was enriched significantly in cluster L.1 and cluster L.2 of lung tissue, cluster P.2 of PBMC, and cluster S.2 of skin tissue. Moreover, it was detected that the TNF signaling arose from the function of different sets of genes in individual tissues. The TNF signaling-related genes in each cluster of the three evaluated tissues are represented in Table Despite the vast amount of research on SSc, its etiopathogenesis has not yet been fully clarified. Consequently, an effective systemic or targeted therapy does not exist . Genome-The GO and KEGG pathway analyses of clusters in PBMC as well as lung and skin tissues indicated the clusters contained biological processes and pathways, including extracellular matrix organization, immune response, inflammatory response, cell proliferation, and apoptosis, which may play roles in SSc pathogenesis.Although no shared DEGs were detected among the three evaluated tissues, TNF signaling pathway was enriched significantly in all three as a common pathway. Therefore, it seems that this common pathway arises from diverse sets of genes in distinct tissues. TNF has a pivotal role in response to infections and in the pathogenesis of different immune-mediated disorders, like rheumatoid arthritis (RA) and spondyloarthritis (SpA) , 36. HowAmong the cytokines with increased levels in SSc, transforming growth factor (TGF)-β, interleukin (IL)-6, and IL-4 are considered as main fibrogenic cytokines in this disease. Different immune cell types, such as macrophages, T cells, B cells, and dendritic cells (DCs) have also been implicated in the immunopathogenesis of SSc. The involvement of multiple cytokines, cell types, and organs makes it difficult to clarify the precise pathogenesis of SSc. However, it seems that activation of the immune system and initiation of the autoimmunity trigger the tissue fibrosis , 44. ConCheadle and colleagues have performed an integrative analysis of microarray data from PAH, pulmonary hypertension (PH), and idiopathic arterial hypertension (IPAH), SSc diseases. Their analysis showed that erythrocyte signature was enriched significantly in PBMCs from PH patients in comparison with SSc and healthy individuals . HoweverAlthough all the biological processes and pathways obtained from the PPI network analysis of PBMC were implicated mostly in clusters that can be labeled as immunity or inflammatory, the lung and skin tissues were enriched in other clusters in addition to immunity and inflammatory clusters. For example, some other lung and skin clusters could be labeled as extracellular matrix (ECM) organization. As expected, the terms and pathways associated with fibrosis containing ECM organization, ECM-receptor interaction, and collagen fibril organization were common in lung and skin tissues. The lung PPI network analysis showed that its terms and pathways not only could be labeled as the inflammation, immunity, and ECM organization, but could also be implicated in cell proliferation and cell death, regulation of bone morphogenetic proteins (BMPs), GPCR signaling, and metabolic processes.The detection of clusters related to cell proliferation and cell death as well as regulation of BMPs is compatible with previous studies that have indicated enrichment of the cell cycle, proliferation, and p53 signaling in SSc . BMPs arThe altered expression of G protein-coupled receptors (GPCRs) and their ligands has been associated with multiple immune-mediated disorders, like pulmonary arterial hypertension (PAH) and RA , 50. TheRecent studies have indicated that there is an association between metabolic pathways and immune-mediated disorders , 53, 54.Based on the current results, it seems that common pathological pathways contribute to the pathogenesis of SSc in different tissues. However, tissue type may make it susceptible to the initiation of more complicated pathways. Areas for future exploration may include determining the role of TNF signaling pathway in the initiation and progression of SSc, the role of GPCRs in the pathophysiology of SSc, the metabolomics profiling of SSc in different tissues, and the role of the metabolic process in SSc pathogenesis. Ultimately, sampling from diverse patients should be conducted tissue by tissue in different stages of the disease to perform more accurate tissue comparisons and design effective systemic or targeted therapies for SSc in the future.Additional file 1: Table S1. DEGs in SSc lungAdditional file 2: Table S2. DEGs in SSc PBMCAdditional file 3: Table S3. DEGs in SSc skin


Four basic modules are presented as the backbone of the package: feature mapping module, which standardizes the metabolite names provided by users and maps them to pathways; dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores; feature selection module, which helps to select the significant pathway features related to the disease phenotypes; and classification and prediction module, which offers various machine learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at:  Metabolomics has been increasingly employed as a systematic approach to investigate the relationship between cellular signals and phenotypes . Non-tarCurrently, there are multitudes of secondary metabolomics analysis tools, primarily in the form of web tools. Very few comprehensive packages exist in R/Bioconductor, the dominant bioinformatics scripting language, in order to support metabolomics data analysis. Various modules of metabolomics pipelines have been implemented in other programming languages, including preprocessing , compounIn the pathway analysis area, various approaches have been proposed to analyze metabolomics data, such as MetPA , IMPaLA To fill the void above, we introduce a new R package called Lilikoi (the Hawaiian name for passion fruit), which specializes in personalized pathway measurement and classification prediction models. We present this tool in four modules: feature-pathway mapper, which standardizes metabolite ID and maps them to pathways; dimension transformation, which derives personalized pathway deregulation scores from metabolite profiles; feature selection, which provides the user with a range of feature selection algorithms to select significant features related to phenotypes; classification and prediction, which lists a series of classification algorithms to derive machine learning models and give predictions on testing datasets.Pathifier algorithm [features in the training set that are significantly related to phenotypes. The final classification module builds a classification model on the training set based on various algorithms, including random forest (RF), support vector machine (SVM), linear discriminate analysis (LDA), logistic regression (LOG), prediction analysis for microarray (PAM), generalized boosted model (GBM), and recursive partitioning and regression analysis (RPART). It then performs prediction and quantitative evaluations on testing sets using various metrics. The details of each module are discussed in the following sections.The Lilikoi package can be divided into four functional modules Fig. : featurelgorithm . The thiReporting proper compounds with standard accession numbers is of paramount importance, and ownstream metabolomics data analysis is only possible with unique metabolite IDs. The non-standardized synonyms create many issues for data analysis and thus must be dealt with. A few tools have been developed to address this issue. The Chemical Translation Service is a web-based tool for metabolite ID conversion . BridgeDThe feature mapping process consists of three steps Fig. . In stepPathifier algorithm to perform the metabolites-pathway dimension transformation [Pathifier on biomarker studies (prognosis or diagnosis) can be found in our earlier publications [Lilikoi applies the ormation . This alormation . For eacications , 18.P(i)D was calculated for each pathway P and each sample i, based on the intensities of the metabolites in pathway P. This score estimates the extent to which the pathway P in sample i deviates from the control. Briefly, in the high-dimensional space Pd made of metabolite vectors (where each metabolite belongs to pathway P), all samples form a data cloud, where sample i is a data point xi. The principle curve P′S in this space Pd is then calculated using Hastie and Stuetzle's algorithm [i is projected onto the principle curve P′S. The deregulation score P(i)D of sample i is then defined as the distance from the start of the principle curve to the projected point on this curve.The PDS score lgorithm . For eacRWeka package is required for the feature selection module [Lilikoi allows the user to provide training and testing datasets, as well phenotype information for the samples. For the training set, Lilikoi provides two major feature selection algorithms: information gain  and gain ratio, which select the most significant pathway-level features related to the phenotype. The n module . Informacaret for automatic parameter tuning of all the algorithms [Seven widely used machine learning algorithms, including LDA, SVM, RF, RPART, PAM, LOG, and GBM, are supported by Lilikoi to build classification models. These methods have been widely used in the metabolomics community and reported in various research articles , 21–23. gorithms . An n-fogorithms . ReceiveCaret PR package. This function ranks features based on their contribution to the model performance.To rank the importance of pathway features in the classification model, we used the variable importance function implemented in the The user can add any clinical factors such as age, sex, and ethnicity to the model. All of these factors are normalized between 0 and 1 by scaling between minimum and maximum values so that they are compatible with the PDS score.For demonstration, we present two metabolomics datasets. One set is from the City of Hope Hospital that was published earlier . This daFor illustration purposes, we applied Lilikoi to two metabolomics datasets of breast cancers. The first dataset is the plasma samples of breast cancer vs normal controls, which also have clinical information such as age, sex, and ethnicity . The secA good practice of a metabolomics report is to have standardized identifiers. However, in reality, currently different metabolomics research laboratories/preprocessing tools generate metabolomics profiles using different naming standards, and this causes big problems for downstream bioinformatics analysis. To cope with it, Lilikoi first transforms the metabolite names to standard IDs. It allows the user to input any kind of metabolite IDs, their synonyms, KEGG IDs, HMDB IDs, or PubChem IDs. Moreover, Lilikoi embeds comprehensive databases, including more than 18,000 metabolites and 100,000 synonyms, in accordance with other types of input IDs. Another major user-friendly characteristic of Lilikoi is the implementation of a fuzzy matching algorithm, which allows better mapping of uncertain metabolites by calculating the string similarity score of the input metabolite name with those in the databases. These features of Lilikoi greatly improve its usability. In the first plasma sample set, 182 of 227 metabolites are mapped to standard HMDB IDs; in the second breast cancer tissue sample set, 120 of 162 metabolites are mapped to standard HMDB IDs.After transforming metabolites to standardized IDs, the metabolomics profile of the training set is transformed to a pathway-based profile through module 2: dimension transformation, with additional phenotype input (cancer/control) also provided by users. In the first dataset, the metabolites are mapped to 224 pathways, and the resulting PDS score (ranging from 0 to 1) based matrix with 224 pathways (rows) and 207 patients (columns) are shown in Fig. P value and adjusted P value), and the number of metabolites involved in each pathway that were included in the metabolites dataset. Aminoacyl-tRNA biosynthesis (P = 5.8 e-09), biochemical pathways part I (P = 4.4 e-34), and protein digestion and absorption (P = 3.15 e-20) are the most significant pathways for MetPA, IMPaLA, and MEPA, respectively. Notably, although these methods retrieve significant pathways, these pathways cannot be used as the input features for downstream statistical modeling and classification in the following sections. Thus, Lilikoi is a unique pathway-based metabolomics analysis package that enables rigorous biomarker predictive modeling.On the contrary, other pathway-based methods, such as MetPA , IMPaLA We conducted similar clustering of pathways on dataset 2 Fig.  and founThe next step is the feature selection module, using the PDS matrix and phenotypes of the training set as input. We then split each dataset into 80% training and 20% hold-out testing set. The user can choose either information gain  or gain ratio to select key pathway attributes. Lilikoi plots a bar plot of selected features and their relevance to phenotype labels. Lilikoi enables the output of information gain score, a measure of feature relevance to phenotype for each selected attribute Fig.  and 4C. In dataset 2, six pathways are identified as feature pathways in the breast tissue training set Fig. . Among tThe last step is classification model construction and prediction. This module builds a model from the selected pathway features and allows the users to select among seven different classification algorithms with n-fold cross-validation. The user can compare performance measurements and choose the best classifier as the model of choice Fig. . This moNext, we checked the importance of these pathway features Fig. , C relatAdjusting the fitted model using the clinical factors (if available) is a critical step in metabolomics-based biomarker research. In this step, Lilikoi builds three models using metabolomics data, or clinical data, or the combination of the two types of data, and plots their ROC curves on the corresponding testing sets Fig. . Model 1Metabolomics biomarker discoveries have gained an increasing amount of attention recently in a variety of applications such as disease diagnosis and progression. Currently, most of the biomarker features in the metabolomics field are represented as individual metabolites, which suffer from inconsistency among studies. On the other hand, most pathway-based methods in the metabolomics field are not personalized and they are merely used for graphical mapping and enrichment analysis. None of these metabolomics pathway-based tools employ pathways as features for downstream biomarker modeling. Lilikoi addresses all of these issues with personalized pathway deregulation measurements (PDS scores) and offers a standardized classification model for biomarker prediction. Compared to the traditional way of identifying individual metabolites as biomarkers, pathway-based biomarkers are more tolerant of population heterogeneity. Additional advantages of Lilikoi include the flexibility of its feature selection methods, the use of various machine learning classification algorithms, and its automatic tuning of parameters to generate the best model for a specific algorithm.As an R package that will undergo active improvements, Lilikoi can potentially benefit from other technical tweaking. Currently, a small percentage (20%) of the metabolites still cannot be mapped to the standard names in databases. One possible reason for this mismatching is that we used the Levenshtein distance as a measure of the similarity between the user's query metabolites and the metabolites stored in Lilikoi's database. However, regardless of mapping facilitation, the first line of reporting practice is to always use metabolite standard identifiers. Lilikoi uses standard IDs such as PubChem CIDs, HMDB IDs, InChiKey or METLIN IDs for the mapping process. Additionally, although the parameters in each classification model are automatically optimized, there is no automatic algorithm (AutoML) implemented that selects the best overall classification model; rather it depends on the user's subjective preference of a machine learning method. It would be beneficial to automatically provide users with references for classification algorithm selection, without human supervision , 32. We Project name: Lilikoihttps://github.com/lanagarmire/lilikoiProject home page: Operating system(s): Windows and LinuxProgramming language: e.g., ROther requirements: e.g., R3.5.1License: GPLv3https://cran.r-project.org/web/packages/lilikoi/. The source code is also freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi. We made a web tool for Lilikoi to facilitate programming-free use of the package. The GUI link is: Lilikoi.garmiregroup.org. Additionally, Docker image and binder image can be accessed at: https://mybinder.org/v2/gh/FADHLyemen/lilikoi_Fadhl/masterThe R package for Lilikoi is accessible at GigaScience repository, GigaDB [Additional supporting data, which includes R package scripts and snapshots of the code, including Docker image and binder image, are available in the , GigaDB .Table S1: the enrichment analysis results of other pathway methods on data set 1: MetPA, IMPaLA, and MPEA.AUC: area under the curve; CRAN: The Comperhensive R Archive Network; ER: estrogen receptor; GBM: generalized boosted model; GSEA: gene set enrichment analysis; HDMB:; KEGG: Kyoto Encyclopedia of Genes and Genomes; IMPaLA: pathway analysis with transcriptomics and metabolomics data; LDA: linear discriminate analysis; LOG: logistic regression; METLIN: metabolite and tandem MS database; MPEA: metabolite pathway enrichment analysis; MetPA: a web-based metabolomics tool for pathway analysis and visualization; PAM: prediction analysis for microarray; PDS: pathway deregulation score; RF: random forest; ROC: receiver operating characteristic; RPART: recursive partitioning and regression analysis; SEN: sensitivity; SPEC: specificity; SVM: support vector machine.The authors declare that they have no competing interests.www.bd2k.nih.gov), P20 COBRE GM103457 awarded by National Institute of Health/ National Institue of General Medical Sciences (NIH/NIGMS), R01 LM012373 awarded by National Library of Medicine (NLM), R01 HD084633 awarded by Eunice Kennedy Shriver National Institue of Health (NICHD) to L.X.G.This research was supported by grants K01ES025434 awarded by National Institute of Enviromental Health Sciences (NIEHS) through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative  -- Jeff Xia4/11/2018 ReviewedClick here for additional data file.Reviewer_1_Report_Revision_1 -- Jeff Xia8/3/2018 ReviewedClick here for additional data file.Reviewer_3_Report_ -- Zhibo Yang4/25/2018 ReviewedClick here for additional data file.Reviewer_3_Report_Revision_1 -- Zhibo Yang7/30/2018 ReviewedClick here for additional data file.giy136_Supplement_Table_S1Click here for additional data file.


YWHAZ and other genes coding 14–3-3 proteins are identified as important DEGs in signaling pathways and in protein-protein interaction networks (PPIN). Perturbed pathways also include mitochondrial dysfunction and oxidative stress. There was a significant overlap in DEGs between PD and AD, and over 99% of these were differentially expressed in the same up or down direction across the diseases. REST was identified as an upstream regulator in both diseases. Our study demonstrates that PD and AD share significant common DEGs and pathways, and identifies novel genes, pathways and upstream regulators which may be important targets for therapy in both diseases.Parkinson’s disease (PD) and Alzheimer’s disease (AD) are the most common neurodegenerative diseases and have been suggested to share common pathological and physiological links. Understanding the cross-talk between them could reveal potentials for the development of new strategies for early diagnosis and therapeutic intervention thus improving the quality of life of those affected. Here we have conducted a novel meta-analysis to identify differentially expressed genes (DEGs) in PD microarray datasets comprising 69 PD and 57 control brain samples which is the biggest cohort for such studies to date. Using identified DEGs, we performed pathway, upstream and protein-protein interaction analysis. We identified 1046 DEGs, of which a majority (739/1046) were downregulated in PD. The online version of this article (10.1186/s13041-019-0436-5) contains supplementary material, which is available to authorized users. Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease (ND) effecting approximately 145,000 people in the UK . PD primAlzheimer’s disease (AD) is the most common ND accounting for 60–80% of dementia cases, characterised pathologically by deposits of intracellular tau neurofibrillary tangles and accumulation of extracellular amyloid β (Aβ) plaques in the brain . The mosAPOE4, has been shown to be related to cognitive decline in PD [There is increasing evidence that PD and AD both have several common characteristics . Around ne in PD . There ine in PD . There hne in PD . Complexne in PD .The largest RNA sequencing (RNA-seq) study in the PD brain was performed using prefrontal cortex tissue, and subset of these samples were tested using proteomics . This stA recent review has highlighted the previous transcriptomics studies published about PD . This reIn this study we carry out an integrated study to give insight into the genomics, genetics and molecular mechanisms that underlie the features of PD, and reveal the relationship with AD. Here we apply a novel meta-analysis approach we proposed  to discohttps://www.ebi.ac.uk/arrayexpress/) and NCBI GEO (Gene Expression Omnibus) (http://www.ncbi.nlm.nih.gov/geo/) databases using the keywords “Parkinson AND substantia nigra” to find mRNA expression studies of human post-mortem brain tissue from the SN related to PD. Studies were included if they: (1) used clinically diagnosed idiopathic PD patients; (2) used brain tissue samples and (3) had cohorts with more than three samples in either disease or control conditions. If a patient had duplicate samples analysed using different platforms or multiple samples from within the SN, only one of them was used.We searched arrayExpress (http://www.bioconductor.org) [Data processing is shown in Fig. tor.org) . Boxplottor.org) .Fig. 1Wop-value of the Anderson-Darling normality test using the nortest R package [The first step of pre-filtering was using detection (Present/Absent) call generated by the affy microarray suite version 5 (MAS5) algorithm to remove data that was not reliably detected. For each study, probesets with absent calls across a chosen percentage of samples were removed. This was repeated in 5% intervals removing probesets with 5% up to 95% of samples absent. The percentage absent cut-off used was set to minimize the  package  and givehttps://gtexportal.org/home/datasets). Genes that mapped to more than one gene symbol and any duplicated gene symbols were removed. All RMA normalized microarray control data were merged using the ComBat function [The Genotype-Tissue Expression (GTEx) database  containsfunction  from thefunction . The Peahttps://github.com/chingtoe365/metaUnion). This meta-analysis method calculates the combined effect size across studies to identify DEGs with the assumption of a normal distribution of the data. Our approach works on the combined gene sets from all the studies included in the meta-analysis, rather than the genes that are common between all datasets as other approaches have done [Meta-analysis was performed using the novel metaUnion R package previously proposed by us   software was used to analyse canonical pathways and upstream regulator analysis (URA) [p-values below 0.05 and upstream regulators with p-values below 0.01 are considered significant.The QIAGEN Ingenuity® Pathway Analysis   of the DA protein-protein interaction network (PPIN) is used to analyse the interaction of DEGs at the protein level. The PPIN from the Human Protein Reference Database  is downloaded and visualized in Cytoscape v.3.6.1  to creatOur results were compared to our previous study using similar methodology on AD frontal cortex microarray data . The sigOur search criteria found 7 Affymetrix chip datasets which included 69 PD and 57 control samples. Information about the datasets is shown in Additional file p-value < 0.05), of which 307 were upregulated and 739 were downregulated. A full list of the 1046 DEGs are shown in Additional file https://figshare.com/s/508c83677f885ced28dc). Table p-value, of which only three are up-regulated.Meta-analysis identified 1046 DEGs from the initial pool of 10,362 genes after false discovery rate (FDR) correction .A recent meta-analysis of GWAS data identified 69 risk genes for PD  only 49 p-value = 2.18E-07, ratio = 34/283) and 14–3-3 mediated Signalling . Using the downregulated DEGs 81 significant pathways were found , including Adipogenesis pathway  and STAT3 pathway . Using down-regulated DEGs IPA identified 17 upstream regulators , which regulates six down regulated genes . Using up-regulated DEGs IPA identified 25 upstream regulators including HSF1  which regulates 8 upregulated DEGs.After Benjamini-Hochberg correction IPA identified 54 canonical pathways that were significant for the 1046 DEGs  which is connected to 122 other genes in the subnetwork including 6 down and 4 upregulated DEGs. Figure A PPIN was created to understand relationships among top DEGs at a protein level. From the top 30 DEGs, 21 were mapped to the PPIN and first neighbour nodes (FNN) extracted. This subnetwork contains 248 nodes and 912 edges, and included 2 GWAS genes, SNCA, YWHAZ and MAPT. DEGs were over-represented in the GWAS PPI sub-network . The largest hub of the GWAS gene PPI subnetwork was MAPT which had 46 mapped genes, followed by DLG4 and SNCA.Of the 69 GWAS genes previously identified, 37 mapped to the PPIN created. The subnetwork created had 331 nodes and 1245 edges that included 45 DEGs, including p-value = < 2.2e-16, Fisher Exact test). This means around 42% of PD DEGs were found in AD and around 14% of AD DEGs were found in PD. Over 99% (432) of the shared DEGs were differentially expressed in the same up or down direction. PIK3R3, LIMK2, CD55 and MAPT were the only genes not dysregulated in the same direction between diseases. It is interesting that the majority of DEGs in common between AD and PD were significantly distributed towards downregulation   and 14–3-3-mediated Signalling . The top five pathways identified using DEGs unique to PD were all among the common pathways between AD and PD. In contrast, only two of the top ten pathways identified by AD unique DEGs were also perturbed in PD, i.e., HIPPO Signalling and Sirtuin Signalling pathway. It is interesting that, of the top five perturbed pathways for the 2688 AD unique DEGs, neuroinflammation signalling pathway, complement system and NF-kB signalling were not perturbed in PD.IPA identified 54 affected pathways in PD and 107 pathways in AD, with 27 shared between these two  has been implicated as an important regulator of neurons in the normal aging brain, closely correlating with cognitive longevity . In AD aeviously . In celleviously . It has e models . Reductie models . Here boThe Sirtuin Signalling pathway was revealed to be perturbed in AD and PD and modulating their activities can alter the course of both diseases in both cell and animal models . In PD SThe SN was chosen as the brain region of interest in this study as neuron degeneration in this region is a hallmark of PD and it is the region with the most data for the meta-analysis . We haveAlthough RNA-seq has demonstrated itself as a superior approach , there iSLC18A2, FGF13, AASDHPPT) are identified in their results. However, pathways associated with oxidative phosphorylation, cardiac hypertrophy and the cytoskeleton were shared. A possible explanation for this is the very low power of the RNA-seq study, which only used three control and three PD samples and the fact that these samples were not age and gender matched. This is particularly important as age and gender are some of the largest risk factors for PD. The control samples had an average age of 87.3 (+/− 5.5) and were all females, and the PD samples had an average age of 79.0 (+/− 5.6) and only one sample was female.For PD there has been a limited application of RNA-seq to identify DEGs, in fact for the analysis of the SN only one RNA-seq study has been completed . There aA limitation of this study is that the SN is affected early in PD development, and by the time symptoms manifest much of the SN can be lost. This means our results reflect the perturbed genes and pathways present once the disease has been established, and not the changes that take place that lead to PD. To investigate early changes in disease more accessible tissues, such as blood and cerebrospinal fluid, would have to be investigated. Currently, there is no reliable way of diagnosing PD before it has had a substantial effect. As a result, investigating the perturbed pathways at this point in the disease would be difficult without development of effective early diagnosis biomarkers. Nonetheless identifying genes and pathways perturbed in the later stages of the disease can still help identify therapeutically important information and compare to similarly late stages of AD. A large limitation in this meta-analysis is the limited number of PD samples. As only 69 PD and 57 control samples are included in this study, the statistical power would be lower than that of our previous meta-analysis for AD which included 450 AD and 212 control samples [YWHAZ and 14–3-3 proteins in PD, through their down regulation, involvement in perturbed pathways and as hubs in PPIN. We demonstrate that PD and AD share significant number of DEGs that are differentially expressed in the same direction and perturbed pathways that indicate some novel shared pathogenesis between the two diseases. These insights suggest several new areas for mechanistic research into PD and cross-talk between AD and PD.In conclusion, our meta-analysis strategy is the largest study of its type in PD SN tissue to date. We highlight REST as an important upstream regulator in PD and AD through the perturbation of Wnt signalling . Our resAdditional file 1:Table S1. Information about each study used in our meta-analysis after removal of outlier samples. Table S2. Differentially expressed genes identified in our meta-analysis that have been identified as PD risk genes in a recent GWAS meta-analysis [Table S3. IPA canonical pathway analysis for significant pathways identified using all PD DEGs, included with the information for pathways shared with those identified as significant using all AD DEGs. Table S4. IPA canonical pathway analysis for significant pathways identified using down-regulated PD DEGs. Table S5. IPA upstream regulator analysis for up and down regulated PD DEGs analysed separately. Table S6. Top 10 hubs found in the protein-protein interaction network (PPIN) analysis subnetwork created using the top 30 PD DEGs. Table S7. The direction of differential expression between the common DEGs found between AD and PD. Figure S1. Selecting filtering threshold for microarray data. The percentage of studies called absent in a mas5 present absent call for each probe was calculated, and threshold determined by minimizing Anderson-Darling normality tests and giving optimal Q-Q plot of the Z-scores after meta-analysis. The Q-Q plot for (A) 5%, (B) 10%, (C) 15%, (D) 20% and (E) 30% filtering. After 15% filtering A-D p-values were minimized (F) and the 15% Q-Q plot gave closest values to normality. A-D is Anderson-Darling normality test. Figure S2. RNAseq data vs. microarray gene expression data. Average absolute expression level of RNA-seq log2(TPM) of SN tissue from GTEx database plotted against RMA normalised and filtered intensity of microarray control and PD data used in this meta-analysis. The Pearson correlation coefficient between the control microarray data and healthy RNA-seq data (A) is 0.70  showing that the expression values of genes between microarray and RNA-seq are correlated and expression data distribution is similar. The Pearson correlation between the healthy RNA-seq and PD microarray data (B) is actually higher than between RNA-seq and control microarray at 0.73 , when it would be expected to be lower due to some genes being differentially expressed. When using only DEGs, correlation between healthy RNA-seq and control microarray (C) and PD microarray (D) data this difference in correlation is minimised to 0.65  and 0.66  respectively, suggesting that the difference in correlation could be due to the larger sample size of the PD data. (DOCX 1024 kb)analysis . Table SAdditional file 2:DEGs identified in this study. A full list of the 1046 DEGs identified in this meta-analysis in an Excel file. (XLSX 120 kb)


We aimed to provide an integrated resource of RNA sequencing and global proteomics and metabolomics. Gene ontology analysis, KEGG pathway analysis, and fuzzy c‐means clustering were used to identify up‐ or downregulated biological processes and metabolic pathways on all 3 levels, and Ingenuity pathway analysis (Qiagen) was used to identify upstream regulators. Differential expression was observed for 8547 mRNAs and for 1199 of 2285 quantified proteins. Furthermore, 151 metabolites with significant changes were identified. Differentially regulated metabolic pathways include branched chain amino acid degradation (upregulated at P23), fatty acid metabolism  as well as the HMGCS (HMG‐CoA [hydroxymethylglutaryl‐coenzyme A] synthase)–mediated mevalonate pathway and ketogenesis (transiently activated). Pharmacological inhibition of HMGCS in primary neonatal cardiomyocytes reduced the percentage of BrdU‐positive cardiomyocytes, providing evidence that the mevalonate and ketogenesis routes may participate in regulating the cardiomyocyte cell cycle.Mouse ventricular tissue samples taken on postnatal day 1 (P01), P04, P09, and P23 were analyzed with This study is the first systems‐level resource combining data from genomewide transcriptomics with global quantitative proteomics and untargeted metabolomics analyses in the mouse heart throughout the early postnatal period. These integrated data of molecular changes associated with the loss of cardiac regeneration may open up new possibilities for the development of regenerative therapies. This study is the first combining transcriptomics with untargeted proteomic and global metabolomic analyses over several time points in the early postnatal heart and, as such, provides an extensive resource of molecule abundance for future mechanistic studies.This report is the first showing temporal regulation of mevalonate and ketone body metabolism in the postnatal heart and identifies their potential roles in the regulation of neonatal cardiomyocyte cell cycle.These integrated molecule‐level data may open up new possibilities for the development of regenerative therapies.The work highlights the importance of metabolic pathways as potential novel drug targets for cardioregenerative therapeutics.Adult human hearts possess a negligible regenerative capacity; therefore, cell loss in response to myocardial infarction (MI) leads to scar formation, remodeling of the surrounding myocardium, progressive impairment of cardiac function, and eventually to heart failure  and from Tnni1 to Tnni3  within the early postnatal period , Rpl4 , Rpl32 , Tbp (TATA‐box binding protein), Oaz1 (ornithine decarboxylase antizyme 1), and Pgk1 (phosphoglycerate kinase 1) did not change significantly, whereas several other genes either generally used or recommended as control genes,Gapdh , were up‐ or downregulated (q<0.01 and fold change >1.5) in at least 1 time‐point comparison. Furthermore, genes known to play a role in cardiomyocyte proliferation , metabolic switch from glycolysis to fatty acid oxidation , and mitochondrial maturation  exhibited expected changes To quantify protein abundances in ventricular tissue, we used a shotgun proteomics approach. On average, 1140 and 1337 distinct proteins were quantified in individual samples in sample sets 1 and 2, respectively Figure B; this wThe postnatal increased activity of energy metabolism and the shift from carbohydrates to fatty acids as the primary source of ATP were well represented in the proteomics data. Several components (ENO1 (enolase 1), PFKL , PGK1 (phosphoglycerate kinase 1)) of the HIF1 signaling pathway that promotes anaerobic metabolism in the embryonic heart were downregulated after birth Table . FurtherAnalysis of key proteins of cell cycle regulation and signaling pathway activation with proteomics is challenging because of the low abundance of these proteins. Nevertheless, expression of proteins related to microtubule dynamics during mitosis (DYNC1H1 (dynein cytoplasmic 1 heavy chain 1), PAFAH1B1 (platelet activating factor acetylhydrolase 1b regulatory subunit 1), RHOA (ras homolog family member A), and BUB3 ) decreased with increasing postnatal age Table . Most ofFor the untargeted metabolomics analysis, we used 2 complementary techniques to increase the metabolite coverage, namely, liquid chromatography‐mass spectrometry (LC‐MS) and 2‐dimensional gas chromatography coupled to MS (GC×GC‐MS), and combined the data from the 2 sample sets using the linear mixed effects model. Quality control results are available in Data In line with the transcriptomics and proteomics results, the metabolomics data show a distinct transition from carbohydrates to lipids as the main source of energy. The abundances of glucose and sugar derivatives  were downregulated at P23 compared with P01 Figure B, whereaIn addition to the general increase in fatty acid abundance, various lipid species  displayed interesting changes in abundance Figure A. For phThe levels of most amino acids displayed an initial increase at P04 and/or P09 followed by a decrease at P23 .Appendix , which iAppendix . FurtherAppendix —correlatBckdha, and the protein phosphatase responsible for BCKDH activation  were significantly upregulated on the mRNA level at P23, and BCKDHA protein abundance was upregulated at P23 ; ACSLs (long‐chain acyl‐CoA [acyl‐coenzyme A] synthetases), which attach CoA to free fatty acids; and ACOTs (acyl‐CoA thioesterases), which remove the CoA from acyl‐CoA, thus releasing free fatty acid Figure A. The ren Figure C and mRNn Figure D and thun Figure . These dHmgcs1 and the mitochondrial Hmgcs2 isoforms were downregulated at the mRNA level at P09 and P23 compared with P01, whereas there was no change in the mRNA levels of the upstream enzyme ACAT1 , Pmvk , and Idi1 (isopentenyl‐diphosphate δ‐isomerase 1) were downregulated with increasing postnatal age. Of these enzymes, ACAT1, HMCSS2, HMGCL, and OXCT1 (3‐oxoacid CoA‐transferase 1) were reliably quantified in proteomics  was the most significantly upregulated protein from P01 to P04 and cholesterol biosynthesis was one of the enriched metabolism‐related biological processes in GO enrichment analysis, the HMGCS‐mediated mevalonate pathway and ketogenesis were investigated in more detail. The HMGCS‐catalyzed synthesis of HMG‐CoA serves as a substrate for HMGCR (HMG‐CoA reductase) in the mevalonate pathway and HMGCL (HMG‐CoA lyase) in ketogenesis Figure A. Both ts Figure C. The abs Figure D, correls Figure D. CollecTo evaluate the role of mevalonate pathway and ketogenesis in cardiomyocyte proliferation, we investigated the effects of pharmacological HMGCS and HMGCR inhibition on the viability and proliferation of neonatal rat ventricular cardiomyocytes. The HMGCS inhibitor hymeglusinTherapeutic strategies to promote regeneration of the adult human hearts are urgently sought.According to previous reports and the present data, the cardiac energy metabolism changes drastically after birth in response to altered nutrient availability and transition to oxygen‐rich environment. Increased oxidative metabolism gives rise to reactive oxygen species causing oxidative stress and DNA damage, which is thought to contribute to cardiomyocyte cell cycle withdrawal.Another key finding of this work, not previously described in the context of postnatal heart development, is the temporal regulation of the mevalonate pathway, which was strongly activated immediately after birth and downregulated after the regenerative window. The mevalonate pathway plays a role in cancer cell proliferation and is upregulated by several oncogenic signaling routes.Parallel to the mevalonate pathway, we observed transient postnatal activation of ketogenesis in the mouse heart. The circulating levels of β‐hydroxybutyrate, which is mainly produced in the liver, increase temporarily after birth and provide an important source of energy for the developing brain.Unlike other amino acids, the BCAAs valine, leucine, and isoleucine are metabolized mainly in organs other than the liver, such as skeletal and cardiac muscle. They provide nitrogen for maintaining glutamate, alanine, and glutamine pools and function as signaling molecules activating mTOR  signaling, which regulates cardiac homeostasis and plays a crucial role in cardiac pathophysiology.With respect to methodological limitations, proteomic and metabolomic analyses can detect only a fraction of proteins and metabolites. By using shotgun proteomics, we were able to perform relative quantification of >2000 proteins. Hydrophobic (eg transmembrane) proteins and proteins with very low abundance, for example, were often below detection. To increase metabolite coverage, we applied 2 complementary MS‐based methods for the metabolomics analyses. Metabolite identification was confined to previously reported metabolites in spectral libraries; therefore, numerous detected metabolic features with interesting abundance patterns remain unidentified. In general, the correlation between mRNA and protein levels is only modest, which can be caused by, for example, variable protein turnover rates. Integration of protein turnover analysis to transcriptomics and proteomics was recently shown to increase the yield of identified disease gene candidates in pathological cardiac hypertrophy in mice,Considering the multicellular composition of the heart, the present study cannot elucidate cardiomyocyte‐specific phenomena because the data represent tissue‐level changes. In cell volume, cardiomyocytes build up 70% to 80% of the cardiac tissue,In summary, we used an integrative systems biology approach with 3 levels of omics analyses to characterize changes in molecule abundances throughout the early postnatal period and to identify metabolic pathways associated with cardiac regeneration. To our knowledge, this study is the first combining transcriptomics with untargeted proteomics and global metabolomics analyses over several time points in the early postnatal heart and, as such, provides an extensive resource for molecule abundances for future mechanistic studies. Furthermore, we present several examples of metabolic pathways with correlative changes on all omics levels. These include the well‐established metabolic switch from glycolysis to fatty acid β‐oxidation as well as many previously unreported changes in cardiac metabolic pathways, such as the mevalonate pathway and ketogenesis. Finally, we identified a biological function for mevalonate and ketone body metabolism in the heart with a potential role in the regulation of neonatal cardiomyocyte proliferation. This integrated molecule‐level data may open up new possibilities for the development of regenerative therapies.This study was supported by Business Finland ; the Finnish Foundation for Cardiovascular Research; the Sigrid Jusélius Foundation; and the Academy of Finland (project no. 2666621).None.Data S1. Supplemental methods.Table S1. Expression of Selected Genes Linked to Cardiac Regeneration and the Postnatal Metabolic SwitchTable S2. ProteomicsFigure S1. Individual factor maps of the RNA sequencing data principal component (PC) analysis shows perfect separation of sample groups.Figure S2. Top 10 down‐ and upregulated genes in the postnatal mouse heart.Figure S3. Ion channel and control gene expression in the postnatal mouse ventricular tissue.Figure S4. Proteomic changes in neonatal mouse hearts.Figure S5. Individual factor maps of the proteomics data principal component (PC) analysis of sample set 1 (A) and sample set 2 (B).Figure S6. The abundance profiles of all individual transcripts, proteins, and metabolites from fuzzy c‐means clustering of RNA sequencing, proteomics, and metabolomics data.Figure S7. Fuzzy clustering comparison.Figure S8. Postnatal changes in glycolysis and gluconeogenesis in the mouse heart.Figure S9. Postnatal changes in the pyruvate pathway in the mouse heart.Figure S10. Postnatal changes in fatty acid degradation in the mouse heart.Click here for additional data file.Appendix S1. RNA sequencing and differential expression analysis.Click here for additional data file.Appendix S2. Gene set enrichment analysis.Click here for additional data file.Appendix S3. Proteomics.Click here for additional data file.Appendix S4. Metabolomics.Click here for additional data file.Appendix S5. Fuzzy clustering  and upstream regulator analysis .Click here for additional data file.


Cynoglossus semilaevis) is determined by both genetic sex determination (GSD) and environmental sex determination (ESD), making it an ideal model to study the relationship between sex-determination and temperature. In the present study, transcriptomes of undifferentiated gonads from genetic females and males, as well as differentiated gonads from males, females, and pseudomales under high and normal temperature treatments were generated for comparative transcriptomic analysis. A mean of 68.24 M high-quality clean reads was obtained for each library. Differentially expressed genes (DEGs) between different sexes and environmental treatments were identified, revealing that the heat shock protein gene family was involved in the high temperature induced sex reversal. The Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were enriched in pseudomale and genetic female comparison included neuroactive ligand-receptor interaction, cortisol synthesis and secretion, and steroid hormone biosynthesis. Furthermore, weighted gene co-expression network analyses were conducted on all samples, and two modules were positive correlated with pseudomale under high temperature. An illustrated protein-protein interaction map of the module identified a hub gene, hsc70. These findings provide insights into the genetic network that is involved in sex determination and sexual differentiation, and improve our understanding of genes involved in sex reversal under high temperature.The sex of Chinese tongue sole ( Oreochromis niloticus) exposed to elevated temperature can override genetic sex determination, and the females can be reversed to produce pseudomales (Oreochromis aureus), Japanese flounder , European sea bass (Dicentrarchus labrax), turbot  and Atlantic silverside (Menidia menidia) also undergo temperature induced ESR  and environmental sex determination (ESD) . In GSD,udomales . Blue tiuced ESR .cyp19a1a promoter, which correlated to the masculinization of these individuals (Trachemys scripta elegans) (ESD), high temperature lead KDM6B, the histone H3 lysine 27 demethylase, to promote the transcription of the male sex-determining gene dmrt1. Therefore, a direct genetic link between epigenetic mechanisms and temperature-dependent sex determination has been shown in a turtle species  and epigenetic regulators appear to be downstream factors in the ESR related pathways. Therefore, the identification of more upstream genes responding to the change in temperature is critical to further our understanding of ESR. Transcriptomic analyses can serve as a powerful tool to identify potential upstream regulators. The gonad transcriptome of Nile tilapia revealed numerous genes involved in temperature dependent ESR (Odontesthes bonariensis), transcriptomes of the developing gonads identified pathways involved in sex differentiation affected by temperature  is an economically important marine flatfish with ZZ/ZW sex chromosome system (GSD) and the genome has been sequenced  loci associated with pseudomales, allowing for the detection of male, female, and pseudomale C. semilaevis (Chinese tongue sole (equenced . Genotypequenced . Therefomilaevis . Gene exmilaevis .via GO term and KEGG pathway enrichment. The heat shock protein (hsp) gene family and several pathways, such as steroid hormone biosynthesis, cortisol synthesis and secretion, and neuroactive ligand-receptor interaction, were differentially expressed. Weighted gene co-expression network analysis (WGCNA) was employed to identify pseudomale-related genetic modules and hsc70 was identified as hub gene regulates the formation of pseudomales. These results provide new insights into the genes involved in sex determination and ESR in C. semilaevis.In the present study, transcriptomic analysis was conducted on undifferentiated gonads from genetic females and males, plus differentiated gonads from males, females, and pseudomales under high temperature and normal temperature treatment. A large number of differentially expressed genes (DEGs) between female, male, and pseudomales under high and normal temperature were obtained. DEGs were functionally analyzed C. semilaevis were kept in Laizhou Mingbo Aquatic Co., Ltd., in Yantai, China. Approximately 300 larvae from the same family were fed twice a day with commercial pellets and were reared in filtered seawater at 22°C. For the undifferentiated group (30 dpf_F and 30dpf_M), twenty larvae were sampled at 30-day post fertilization (dpf). The remaining larvae were divided into two equal groups and cultured in two 300 L tanks. The control group was cultured at 22°C , and the high temperature group was cultured at 28°C  until 3-month post fertilization (mpf) when sex differentiation was completed. Gonads were collected from the 30 dpf and 3 mpf C. semilaevis individuals for RNA extraction and immediately stored in liquid nitrogen. The remaining part of each fish was stored in ethanol to identify the genetic sex.dmrt1 was measured to further confirm male, female, and pseudomale individuals by real-time quantitative reverse transcription PCR (qRT-PCR)  against sex-specific simple sequence repeat (SSR) marker with the primers described previously . PseudomqRT-PCR) .Total RNA was isolated from each gonad sample using the Trizol reagent  according to the manufacturer’s instructions. The qualities of the RNA samples were measured with the Agilent 2100RNA 6000 Nano kit . Three biological replicate samples for each group were generated. Thus, 24 samples of 30 dpf_F1-3, 30 dpf_M1-3, C22_F1-3, C22_M1-3, C22_P1-3, C28_F1-3, C28_M1-3, and C28_P1-3 were acquired. RNA sequencing was conducted with a BGISEQ-500 by PTM Biolabs .C. semilaevis genome (NCBI Cse_v1.0) via HISAT2 v2.1.0  < 0.001 were considered to be significant DEGs. DEGs were then subjected to an enrichment analysis for GO function and KEGG pathways using Phyper in R . Abundances of transcripts were quantified by RNA-Seq by Expectation Maximization (RSEM) , and genper in R , with p p value < 0.05 was defined as the threshold for significance. The protein–protein interaction network was predicted using DIAMOND . Then, qRT-PCR was performed using QuantiNova™ SYBR Green PCR Kit  in 20-µl reactions, containing 10 µl 2 × SYBR Green PCR Master Mix, 2 µl QN ROX Reference Dye, 0.7 µM forward primer, 0.7 µM reverse primer, and 1 µl cDNA. The cycling program was carried out at 95°C for 2 min, followed by 40 cycles of 95°C for 5 s and 60°C for 10 s; this was followed by a melting curve analysis in an ABI StepOnePlus Real-Time PCR system . Reactions were performed in triplicate. The relative expression fold changes of nine genes in female, pseudomale, and male gonads under normal and high temperature were analyzed using the 2−ΔΔCt method.Nine candidate genes related to high temperature induced sex-reversal from the female, pseudomale, and male between the heat versus control temperature were validated with qRT-PCR. Primers Table S1 control . One micdmrt1 was high in males, low in females, and moderate in pseudomales  (C. semilaevis genome. The transcriptome data has been uploaded to NCBI Sequence Read Archive (SRA). The accession number was PRJNA576366.A total of 24 libraries, named C22_F 1-3, C22_M 1-3, C22_P 1-3, C28_F 1-3, C28_M 1-3, C28_P 1-3, 30dpf_F 1-3, 30dpf_M 1-3, were constructed using total RNA from gonads. A mean of 68.24 M filtered clean reads with a Q20 above 96.81% was obtained from each library Table S2p value of less than 0.001 and an absolute fold change greater than 2 as the threshold, over 1500 DEGs were obtained for each comparison, and the number of DEGs were highly variable, depending on the sex and environmental temperature treatment , including complement and coagulation cascades, calcium signaling pathway, and apelin signaling pathway . Including DNA integration, microtubule-based process, DNA conformation change, nucleosome assembly, and protein-DNA complex assembly (p < 0.05). Among these, nine pathways were significantly enriched (q < 0.05), including pancreatic secretion, calcium signaling pathway, circadian entrainment, and oocyte meiosis , including DNA binding, aminoglycan metabolic process, ion gated channel activity, DNA metabolic process, and temperature homeostasis (q < 0.05), including neuroactive ligand-receptor interaction, cell cycle, p53 signaling pathway, and ovarian steroidogenesis , including cell cycle, oocyte meiosis, hedgehog signaling pathway, calcium signaling pathway, neuroactive ligand-receptor interaction, and pancreatic secretion , including nitrogen metabolism, glucagon signaling pathway, phototransduction, adipocytokine signaling pathway, FoxO signaling pathway, and hippo signaling pathway  member 13), hspa5 (heat shock protein family A (Hsp70) member 5), hsp90b1 (heat shock protein 90 beta family member 1), hspd1 (heat shock protein family D (Hsp60) member 1), and hspb9  member 9) were up-regulated in C28_P compared to C22_P. The male-related genes dmrt1 and sox6 were also up-regulated in C28_P compared to C22_P. Other genes that were up-regulated in the high temperature treated pseudomales included the vasopressin V2 receptor (LOC103384583), which is involved in signal transduction, the bag3 and BAG family molecular chaperone regulator 4-like (LOC103398065) which were co-chaperones for HSP70 and HSC70 chaperone proteins, and cyp11b-like, which is responsible for glucocorticoid and mineralocorticoid biosynthesis. KEGG enrichment analysis of this group of genes found 24 enriched pathways (q < 0.05). These pathways included steroid hormone biosynthesis, metabolism of xenobiotics by cytochrome P450, and carbon metabolism (hsd17b3-like (testosterone 17-beta-dehydrogenase 3-like), fshr (follicle stimulating hormone receptor), aromatase-like, female-related foxl2 and wnt5b, and 11β-hydroxysteroid dehydrogenases hsd11b1-like, which were down-regulated in C28_P compared to C22_P. These DEGs had 30 significantly enriched KEGG pathways (q < 0.05), including neuroactive ligand-receptor interaction, cortisol synthesis and secretion, progesterone-mediated oocyte maturation, and estrogen signaling pathway  was conducted on all samples. Based on the expression values of each gene, we ultimately obtained six modules, and genes within each module had highly interconnected expression patterns (p < 0.05). The enriched KEGGs included Wnt signaling pathway, hippo signaling pathway, glycosphingolipid biosynthesis-globo and isoglobo series, adherens junction, and melanogenesis ] was significantly up-regulated in the pseudomale under high temperature, while fshr (follicle stimulating hormone receptor) was significantly down-regulated. Vasopressin is produced and released by the pituitary gland, and mediates peripheral and central physiological functions that are important for osmoregulation and reproduction. Receptors of vasopressin are up-regulated in the Thalassoma bifasciatum testis  304 DEGs that were up-regulated in high temperature compared to normal temperature, and (2) 188 DEGs that were down-regulated in high temperature compared to normal temperature. The first category included several members of the heat shock protein family, including  species . In addiand lhx6 . Thus, Hreaction . Reducedm testis . fshr ist gonads . The neupatterns . The exphsd17b3-like (LOC103398902), aromatase-like (LOC103378914) was also found in the steroid hormone biosynthesis pathway. In teleosts, aromatase activity is correlated with sex steroid levels, converting testosterone into estrogen and thus may play a role in homeostasis of the accumulated androgen.The steroid hormone biosynthesis pathway plays a critical role in sex differentiation across vertebrates . In addiSalvelinus fontinalis) , a major androgen in fish that promotes spermatogenesis  . In additinalis) . In our ogenesis . The downeration . These eneration .hsc70-like (heat shock cognate 70 kDa protein-like) as the hub gene for pseudomale formation. HSC70 is a regulator of HSF1 (heat-shock factor 1), which plays an essential role in mediating the appropriate cellular response to physiological stresses  and transcript LOC103398065 (BAG family molecular chaperone regulator 4-like) were up-regulated in C28_P compared to C22_P, indicating the linkage between of the heat shock protein family and steroid hormones.WGCNA analysis gave rise to two modules, although found in one biological replicate, correlated to pseudomale formation under high temperature. These two modules were enriched in KEGG pathways that were involved in signal transduction and cell developmental processes. In particular, protein-protein network analysis of the yellow module in WGCNA revealed stresses . In humaific SRY . A previific SRY . In our C. semilaevis.Based on these findings, we propose a mechanism for how the heat signal causes sex reversal. Members of heat shock protein family may detect external high temperature. Then a series of processes including neuroactive ligand-receptor interaction pathway, steroid hormone biosynthesis pathway, and cortisol synthesis and secretion pathway were activated. This leads to changes in the relative amount of sex steroid hormones (androgen and estrogen), promoting the development of pseudomales in C. semilaevis. Future studies incorporating detailed functional survey of these networks and hub genes will further improve our understanding of sex reversal under external high temperature in fish.The present study provides important insights into ESR induced pseudomale formation in The transcriptome data used in this study has been uploaded to NCBI Sequence Read Archive (SRA) with accession number PRJNA576366.The collection and handling of C. semilaevis were approved by the Animal Care and Use Committee at the Chinese Academy of Fishery Sciences, and all the experimental procedures were performed in accordance with the guidelines for the Care and Use of Laboratory Animals at the Chinese Academy of Fishery Sciences.dmrt1 gene. LT participated in the fish sampling. QL and FP revised the manuscript. CS was in charge of the guidance and revision of this manuscript.QW analyzed the transcriptome sequencing data and wrote the manuscript. KL and WL reared the fish. BF and ZZ completed fish sampling and SNP locus sequencing. RW finished the qRT-PCR of This work was supported by National Nature Science Foundation of China , National Key R&D Program of China (2018YFD0900301), the AoShan Talents Cultivation Program Supported by Qingdao National Laboratory for Marine Science and Technology (2017ASTCP-ES06), the Taishan Scholar Project Fund of Shandong of China to CS, the National Ten-Thousands Talents Special Support Program to CS, and the International Scientific Partnership Program ISPP at King Saud University (No. 0050).Author WL was employed by company Laizhou Mingbo Aquatic Co., Ltd. . The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.