The mammalian intestine is a complex biological system that exhibits functional plasticity in its response to diverse stimuli to maintain homeostasis. To improve our understanding of this plasticity, we performed a high-level data integration of 14 whole-genome transcriptomics datasets from samples of intestinal mouse mucosa. We used the tool Centrality based Pathway Analysis (CePa), along with information from the Reactome database..The results show an integrated response of the mouse intestinal mucosa to challenges with agents introduced orally that were expected to perturb homeostasis. We observed that a common set of pathways respond to different stimuli, of which the most reactive was the Regulation of Complement Cascade pathway. Altered expression of the Regulation of Complement Cascade pathway was verified in mouse organoids challenged with different stimuli in vitroResults of the integrated transcriptomics analysis and data driven experiment suggest an important role of epithelial production of complement and host complement defence factors in the maintenance of homeostasis. The mammalian gastrointestinal (GI) tract is crucial for the digestion and absorption of nutrients, energy metabolism, and homeostasis of the gut barrier and mucosal immunity. A number of specialized adaptations of the mammalian mucosal immune system have evolved to maintain a peaceful co-existence with the microbial symbionts while responding appropriately to prevent infection by enteric pathogens , 2. ChanThe gut functionalities described above are attributed to groups of genes organised into various functional pathways –24 respoTherefore, it is essential to investigate the functional plasticity of mucosal tissues at the functional genomic level in terms of pathways. Such an approach may aid to identify key sets of biosynthetic and signalling pathways involved in the mucosal responses, but also to identify the commonalities and differences in the expression of pathways responding to various environmental and physiological perturbations. To investigate this, we used publicly available gene expression datasets generated from mouse intestinal tissues exposed to orally administered challenges. From the results of the analysis on these datasets we identified the pathway “Regulation of Complement Cascade” that appears to play an important role in the functional plasticity of the intestinal epithelial response to different nutritional, microbial, and chemical challenges.The complement system consists of several inactive pre-proteins produced in the liver that circulate in the blood which are crucial for efficient clearance of invading organisms. It is part of the innate immune system and activation of the complement cascade plays a key role in the opsonisation of micro-organisms to increase phagocytosis by macrophages and neutrophils at the sites of infection . To avoiWe identified 14 publicly available datasets meeting the search criteria. We classified them into three broad stimulation categories: Diet (7 experiments); Drug (3 experiments); and Immune Challenge (4 experiments) based on the type of intervention. Stimulations that were given as part of the feed of the animal were classified as ‘Diet’. Stimulation with a substance that is used as medication was classified into the ‘Drug’ category. Any substance that elicits a strong immune response was classified as an ‘Immune Challenge’. It could be argued that the DSS challenge belongs to the Drug category rather than Immune Challenge category as it can be used as a drug. However, since the effects of DSS likely result from a primary epithelial damage leading to the translocation of bacterial antigens, here we have chosen to classify it as an Immune Challenge.These 14 experiments comprised of 37 experimental conditions  algorithm  and the p-values of these pathways. The majority of these pathways (84%) were significant in both centrality measures whereas about 11% were significant only in the ‘in-reach’ centrality and 5% significant in only the ‘out-reach’ centrality. An overview of the pathway analysis results is given in Additional file Diet.Fat.20.P.SI). The average number of signifcantly regulated pathways is highest in the conditions belonging to the Diet category with 132 pathways, the second highest is Immune Challenge with 101 pathways followed by Drug with 87 pathways on average.For all 37 conditions analysed, 710 pathways were significantly enriched, see Additional file athways 8% were siThere was a large overlap in the results between the three categories Fig. . In addiIn order to investigate the most differently regulated pathways among the three conditions, the 212 common pathways were ranked based on a Difference Score. The Difference Score was calculated based on the mean node scores of the pathway nodes. The node scores of the pathway are a t-statistic which is a differential value between the experimental condition and the control. A mean of the node scores in the pathway under experimental conditions within one challenge category was calculated to end up with three scores for a pathway. The sum of the difference between the three mean values, the Difference Score was used to rank the list of common pathways. Table Drug.Penicillin.SI), expression of the complement pathway genes highlighted in Fig. Drug.HerbalDrug.SI, Drug.HerbalDrug.SPF.C). The biggest change was in the Immune Challenge category infection, where Salmonella upregulated the complement factors C4, C2, C3. These complement factors are required for activation of the complement cascade via the classical pathway and mannose lectin pathway  and a pharmacological agonist of PPARα a transcription factor and a major regulator of lipid metabolism.Salmonella) induces expression of all complement related genes except C5, C8GH and CR2  pathways. To the best of our knowledge, this type of study, has not yet been performed on this scale, focusing on one tissue and different types of challenges. By grouping the different challenge conditions in categories comprising drugs, dietary ingredients or potentially inflammatory agents, we grouped the responses of the mucosal tissue to facilitate broader comparisons. The results revealed pathways which are regulated in all categories. In addition, we observed large differences in the expression profiles of pathway genes between different exposure conditions, in some cases, irrespective of the challenge category. By focussing on the commonly regulated pathways, we show that the gut mucosa employs similar pathway systems. However, these pathway systems are used in different combinations and with different intra-pathway gene expression profiles, to respond to different exposures.The most important criteria for dataset selection was the age at which the mice were sampled, because the mucosal immune system and intestinal microbiota of mammals is known to change dramatically around weaning . This hoWe used the CePa algorithm that considers a pathway’s topology by using different network centrality measures. Based on the biological information behind the pathways, we decided to use two centrality calculations, ‘In-reach’ and ‘Out-reach’ to capture regulation of pathways down-stream  and up-stream (for metabolic pathways) respectively.Most of the results were significant in both the centrality calculations irrespective of the type of pathway. This apparent indifference to topology is also observed by Bayerlova et al.  in a difThe results of the integrated pathway analysis show a notable overlap in the pathway response between the three challenge categories. The Diet category has the highest number of leaf pathway results, and also the highest number of experimental conditions. Surprisingly, most of the results from Drug and Immune Challenges were also shared by the Diet class. The results demonstrate that there is a group of pathways that are commonly regulated by interventions which perturb homeostasis. These common pathways contribute to a major extent towards the capability of the intestinal mucosa to display a high-level of functional plasticity. Most of the transcriptomics data used in this study came from intestinal scrapings which is greatly enriched for different types of epithelial cells involved in innate immunity and cross-talk with the immune cells in the lamina propria . The difOne of the 212 common pathways which responds differently to the challenge categories is the ‘Regulation of Complement Cascade’. This pathway showed the largest difference in node expression profiles between the three challenge categories as shown in Table Complement factors involved in complement activation by one of three pathways are produced in the liver and enter the circulation. Complement factors reach tissue sites of infection through acute inflammation which results in the exudation of fluid and plasma proteins and an emigration of leukocytes into the extravascular compartment. Our observation that key complement factors involved in the complement activation pathways , complement control and host protection , are increased in expression by epithelial cells exposed to infectious challenge, suggests that local complement production may be needed in the intestine as an early defence mechanism against encounters with microorganisms due to a dysfunctional barrier or infection. This idea has been proposed before , 45 espeIn the Diet category of challenges high fat diets strongly upregulated transcription of complement pathway genes, which may be due to a low-grade inflammation and a hyperpermeable gut , 50. IntC2, C3 and regulatory proteins Cfi, Cd46 and Cd55, whereas expression of C5, C8 and Cfh was not significantly changed and Cd59 was significantly down-regulated. The agonist of PPARα significantly up-regulated transcription of regulatory proteins Cd55, Cr2, Cd46, and Cfi and significantly down-regulated expression of Cd59. Surprisingly, flagellin . TNFα is known to be induced by invasive infection by enteric infection with Salmonella , which well line . It has ell line . Having ell line . InteresCD55 and CD46 have other functions, besides their role in protection of host cells from complement activation which may be relevant for intestinal homeostasis. CD55 binds to the neutrophil receptor CD97 expressed on neutrophils to promote neutrophil migration through the epithelium . BindingIn conclusion, high-level data integration of transcriptomics datasets from intestinal tissue from in vivo experiments was a valuable approach to identify common pathways associated with functional plasticity and intestinal homeostasis. The identified pathways are regulated in different combinations to generate different physiological responses, or genes within a pathway are differentially regulated contributing further to the plasticity. The “Regulation of Complement Cascade” pathway is one of many pathways regulated by multiple intestinal challenges suggesting it is an important mechanism in the periphery of the intestine, which might have poor access to circulating complement components from the blood. Epithelial expression of complement factors involved in opsonisation and chemotaxis of host phagocytes, but not formation of the MAC complex, indicates a primary function in opsonisation of microbes and chemotaxis of host immune cells. Coincident with the increased intestinal expression of complement factors is the expression of host factors involved in complement control and protection such as CD55 and CD46 which have secondary functions in innate immunity and wound healing.GEOmetadb  [The analysis of all the datasets was performed using pathways from the Reactome database [RRID:SCR_003485], a freely accessible and a manually curated database available in different formats. Pathways from Reactome were downloaded in the BioPAX [RRID:SCR_009881]   format , which uses pathways as networks where the nodes in a pathway could be small molecules (compounds), macro-molecules (proteins or RNA) or complexes (more than one protein). The topological information of the pathway is used to assign weights to each node using centralities. The user can choose between one of four centrality measures, in-degree (number of edges that are directed towards the node), out-degree (number of edges that are directed outwards from the node), in-reach (longest path that brings information to the node) and out-reach (longest path that directs out of the node), with another option of giving equal weight to all nodes. This centrality information is used along with the expression data to give a list of significantly enriched pathways for given conditions vs their controls. There are two methods of using the expression data in the CePa package, Over-Representation Analysis (ORA) and Gene Set Analysis (GSA). ORA usually takes a list of differentially expressed genes which could be ranked with The gene expression data is mapped to the nodes of the pathways, when the node is a protein the expression value of the corresponding gene is used as such. When the node is a complex, the largest component from a Principle Component Analysis of the expression values of all the corresponding proteins is assigned as the node expression value. Subsequently, these expression values are inputted in a t-statistic to obtain a differential expression value for each node, which can be positive or negative based on the up- or down-regulation of that protein. This differential node value is multiplied with the centrality-based weight of the node to obtain a final node value. This calculation is performed for each of the nodes in the pathway and all these values are averaged to obtain a pathway level score. The pathway level score is then tested for significance by substituting random expression values in the same pathway calculations and comparing the obtained value with the original dataset and the value obtained with the randomized data. The fraction of the iterations on which a higher score is obtained with the randomized data is used to represent the p-value. This p-value calculation was modified from the original CePa function which randomizes the replicates of the tested conditions. As we work with a minimum of three samples per condition, we modified this calculation to be able to handle smaller sample sizes. The original algorithm randomized the data across samples in order to calculate the significance of a pathway score. We decided to randomize the expression values by genes so that there is a larger chance of the values being truly random and thus without a biological signal. Because the hierarchical nature of the pathway database implies dependence between the pathways, we decided not to perform a multiple testing correction.p-values was set at 0.01 to compensate for the lack of multiple testing correction.We weighted the nodes with the in-reach and out-reach centrality calculations, because they assign higher weights to the nodes down-stream and up-stream of the pathway respectively. In this way, we can capture signalling pathways, where the effectors are more likely to be down-stream of the pathway. However, we did not rule out the metabolic pathways where the enzymes are generally up-stream in a pathway. The threshold of the Three dimensional (3-D) crypt derived murine intestinal organoids were grown as described in literature , 76–78. The 3-D organoids were stimulated with TNFα (10 ng/ml), a PPARα agonist (WY14643 0.1% v/v), and flagellin (200 ng/ml) for 6 h before total RNA was extracted with the Qiagen mini-kit according to manufacturer’s instructions along with a 15 min DNAse step. Purity and integrity measurements were performed on a DS-11 spectrophotometer (DeNovix) and 1 μg total RNA was reverse transcribed into cDNA using a QScript kit (Quantabio). Quantification of gene expression (RT-qPCR) was performed using a Rotor-gene Q2 plex RT-cycler (Qiagen) on primers specified in Table C8GH, CFI, CFH, CR1, and C2) were chosen because their expression varied substantially under the 17 experimental conditions where the ‘Regulation of Complement Cascade’ pathway was significantly affected. Complement Factor H and Factor I are involved in the regulation of complement activation, C2 is a component required for activation of the classical and alternate pathways. The murine CR2 contains 25 exons; a common first exon is spliced to exon 2 and to exon 9 in transcripts encoding CR1 and CR2 which encode receptors binding complement complexes on host immune cells.Untreated 3-D organoids were used as control to obtain relative gene expression values of the 10 chosen complement pathway genes when stimulated by the three treatments. Genes encoding C3 and C5 were selected as they are key factors in the three complement activation pathways. Genes encoding CD46, CD55 and CD59 were chosen because they are involved in protection of host membranes when complement pathway is activated. The other five genes (Additional file 1: Table S1. Description of the 37 conditions. This table describes the abbreviated names of experimental conditions as used in the paper and the control conditions in each of the experiments.Additional file 2: Table S2. Significant pathways with p-values in each experimental condition both centralities. This table has the p-values of all the significant leaf pathways in the 37 experimental conditions in both the in-reach and out-reach centrality calculations. If the p-value of the pathway was above the threshold of 0.01 an empty space is shown in the table. The p-values of at least one of the centrality calculations have to be lower than the threshold.Additional file 3: Table S3. Differential expression of genes in “Regulation of Complement Cascade” pathway. This table contains the differential gene expression values of 24 genes in 17 experimental conditions. The differential values were obtained with a T test of the experimental condition vs the control in that experiment.Additional file 4: Figure S1. PCA of all the significant pathways over the experimental conditions. Green points represent p-values from the pathway analysis from the small intestine and the red ones from the large intestine. Circles represent experimental conditions from the Diet category, the triangles are from the Drug category and squares from the Immune Challenge category.Additional file 5: Figure S2. Network of Reactome root pathway. The nodes in this network represent the 27 root pathways as present in Reactome v51 and the edges indicate the ‘leaf’ pathways shared by connected root pathways. The thickness of the edges indicates the number of leaf pathways shared by the nodes. The nodes are labelled with the names of the root pathways and the number of enclosed leaf pathways is given between brackets. The inset shows a simplified example of root and leaf pathways, where the cartoon has one root pathway with three leaf pathways.


As protein is the basic unit of cell function and biological pathway, shotgun proteomics, the large-scale analysis of proteins, is contributing greatly to our understanding of disease mechanisms. Proteomics study could detect the changes of both protein expression and modification. With the releases of large-scale cancer proteome studies, how to integrate acquired proteomic and phosphoproteomic data in more comprehensive pathway analysis becomes implemented, but remains challenging. Integrative pathway analysis at proteome level provides a systematic insight into the signaling network adaptations in the development of cancer.Here we integrated proteomic and phosphoproteomic data to perform pathway prioritization in breast cancer. We manually collected and curated breast cancer well-known related pathways from the literature as target pathways (TPs) or positive control in method evaluation. Three different strategies including Hypergeometric test based over-representation analysis, Kolmogorov-Smirnov (K-S) test based gene set analysis and topology-based pathway analysis, were applied and evaluated in integrating protein expression and phosphorylation. In comparison, we also assessed the ranking performance of the strategy using information of protein expression or protein phosphorylation individually. Target pathways were ranked more top with the data integration than using the information from proteomic or phosphoproteomic data individually. In the comparisons of pathway analysis strategies, topology-based method outperformed than the others. The subtypes of breast cancer, which consist of Luminal A, Luminal B, Basal and HER2-enriched, vary greatly in prognosis and require distinct treatment. Therefore we applied topology-based pathway analysis with integrating protein expression and phosphorylation profiles on four subtypes of breast cancer. The results showed that TPs were enriched in all subtypes but their ranks were significantly different among the subtypes. For instance, p53 pathway ranked top in the Basal-like breast cancer subtype, but not in HER2-enriched type. The rank of Focal adhesion pathway was more top in HER2- subtypes than in HER2+ subtypes. The results were consistent with some previous researches.The results demonstrate that the network topology-based method is more powerful by integrating proteomic and phosphoproteomic in pathway analysis of proteomics study. This integrative strategy can also be used to rank the specific pathways for the disease subtypes.The online version of this article (10.1186/s12918-018-0646-y) contains supplementary material, which is available to authorized users. Following the quick accumulation of large-scale genome, transcriptome and other omics data, some studies or approaches integrating multiple omics data into pathway analysis have been reported –4. Mass-Signal pathways describe a group of molecular in a cell that work together to control one or more cell functions, such as cell division or cell death. Pathway analysis gives an insight into the underlying mechanism in a given condition and makes it more explanatory in comparison with the studies at individual gene or protein level. Pathway analysis methods include gene set analysis and topology-based analysis. Gene set methods only consider the set of genes/proteins in the pathways while the topology-based methods use both genes/proteins and the interactions among them. Gene set methods consist of Over-Representation Analysis (ORA) based on the Hypergeometric test or Fisher exact test , 9 and FIn this work, we tried to integrate proteomic and phosphoproteomic data in pathway analysis in breast cancer and its subtypes. The results showed that integrating protein and phosphorylation differential expression with the network-topology based method can identify the target pathways more accurately. What’s more, we also identified the top ranked pathways in four subtypes of breast cancer specifically.The proteomic and phosphoproteomic data of breast cancer in this study included 77 tumor samples and 3 normal breast tissue samples, which were downloaded from Clinical Proteomic Tumor Analysis Consortium (CPTAC). The process of quality control and normalization for both the proteomic and phosphoproteomic data was presented in Mertin et al.’s work . As the P-values for pathway analysis resulting from the permutation (n = 2000) were provided in Additional File Since ORA, GSEA and SPIA are the representatives of three kinds of pathway analysis, which are Over-Representation analysis, Functional Class Score and topology-based pathway analysis, we used these three strategies to do pathway analysis. We used R package ‘HTSanalyzeR’  to do ORp < 0.05). The input file for GSEA method in our study was the list of all proteins/phosphoproteins with fold change between the case and control. We summed up and sorted the fold changes for the overlapping proteins in the protein expression and phosphorylation profiles as the integrated information for GSEA. As for SPIA, the input files consisted of the topology of the pathways downloaded from KEGG database and the DE proteins with their fold change. The topology changes of the pathways could be calculated by the ‘SPIA’ R package. The input for SPIA was the intersection list of the DE proteins and DE phosphoproteins with the sum of their fold change.Different methods of pathway analysis require different input data. For ORA, the input file is the list of DE proteins/modifications or the intersection of the DE protein and phosphoprotein as an integration  are correlated strongly with poor tumor differentiation and significantly associated with HER2 overexpression in breast cancer . The higPI3K/AKT/mTOR pathway is a key intracellular signaling system that drives cellular growth and survival. Hyperactivation of this pathway is implicated in the tumorigenesis of ER+ breast cancer –45. BesiMany studies have established that mTOR pathway has tightly interaction with PI3K-AKT and MAPK signaling pathways. Inhibition of mTORC1, an important part of mTOR pathway, leads to MAPK pathway activation through a PI3K-dependent feedback in human cancer . It can We also took a look at the top 20 ranked pathways for each subtype of breast cancer. There were 7 common pathways among the four subtypes. Besides two TPs cell cycle and pathways in cancer, the other common pathways have been reported to be related with breast cancer pathways which consist of Fanconi anemia pathway , ProgestExpression and modification describe the in vivo changes of proteins in cancer proteome at different views. The pathway analysis based on the information at single level, such as protein expression or protein phosphorylation alone, often brings high risk of both false positive and false negative due to technological limitations. To the best of our knowledge, the integration proteomic and phosphoproteomic data in pathway analysis in cancer has not been evaluated and reported. In this study, the pathway analysis was performed and compared using the integration of proteomic and phosphoproteomic data in CPTAC’s breast cancer dataset. Moreover we tried to find the different patterns in pathway ranking among the subtypes.Our results suggested that both differential expression of proteins and phosphorylation were useful for identifying the important pathways in cancer or cancer subtypes. Furthermore, the integration of protein expression and modification profiles could provide more comprehensive information and rank TPs more accurately. Although the ranking lists of three kinds of pathway analysis were different, some consistent results were observed since the expression change of proteins and phosphoproteins are used in all of strategies. While the GSEA requires the fold change of all proteins, it has more complete information reflecting the expression profile. SPIA needs the topology information of the pathways in addition, which can provide detailed influence between the nodes of pathways.We also tested the performance using the union of DE proteins and phosphoproteins information in pathway ranking, but poor accuracy was obtained. It’s possibly because of too much noise in individual omics data. In order to control the risk of false positive, the intersection of the DE proteins or DE PTMs were used as input in this study that might be too conservative. Because only one dataset was tested here, for some new pathways in top ranking list, more independent proteomics datasets in cancer need to be processed and validated in the future.Integrative pathway analysis by combing the information from protein expression, protein modification and the topology of protein interaction network is more efficient way to identify key pathway in breast cancer. Pathway ranking in certain subgroup of patients can provide insight into the specific mechanisms and be helpful for the precision medicine for each subtype.Additional file 1:Table S1. The results of different pathway ranking methods. (XLSX 89 kb)Additional file 2:Figure S1. Ranks of TPs in subtypes of breast cancer. (DOCX 17 kb)


The phosphoinositide 3-kinase (PI3K) growth factor signaling pathway plays an important role in embryonic development and in many physiological processes, for example the generation of an immune response. The pathway is frequently activated in cancer, driving cell division and influencing the activity of other signaling pathways, such as the MAPK, JAK-STAT and TGFβ pathways, to enhance tumor growth, metastasis, and therapy resistance. Drugs that inhibit the pathway at various locations, e.g., receptor tyrosine kinase (RTK), PI3K, AKT and mTOR inhibitors, are clinically available. To predict drug response versus resistance, tests that measure PI3K pathway activity in a patient sample, preferably in combination with measuring the activity of other signaling pathways to identify potential resistance pathways, are needed. However, tests for signaling pathway activity are lacking, hampering optimal clinical application of these drugs. We recently reported the development and biological validation of a test that provides a quantitative PI3K pathway activity score for individual cell and tissue samples across cancer types, based on measuring Forkhead Box O (FOXO) transcription factor target gene mRNA levels in combination with a Bayesian computational interpretation model. A similar approach has been used to develop tests for other signaling pathways . The potential utility of the test is discussed, e.g., to predict response and resistance to targeted drugs, immunotherapy, radiation and chemotherapy, as well as (pre-) clinical research and drug development. A limited number of signal transduction pathways are evolutionarily well conserved to control basic cellular processes, such as cell division, cell differentiation and migration ,2,3. SigFrom a clinical perspective, the PI3K-AKT-mTOR signal transduction pathway is certainly one of the most important signaling pathways as it regulates key processes such as cell division, migration, metabolism, and DNA repair in a large variety of cell types, to an important extent also by recruiting and influencing the activity of one or more of the other signaling pathways ,31,32,332-adrenergic receptor with its ligand epinephrine, and members of the receptor tyrosine kinase family  with ligands such as the epidermal growth factor (EGF) and heregulin. Multiple isoforms of PI3K signaling enzymes exist. Class I PI3K isoforms are relevant for cancer, i.e., p110α (encoded by the PIK3CA gene), p110β (PIK3CB gene), p110γ (PIK3CG gene), and p110δ (PIK3CD gene). They catalyze the formation of the phospholipid phosphatidylinositol  triphosphate P3 or PIP3) by the addition of a third phosphate group to the phosphatidylinositol bisphosphate  [The PI3K pathway consists of a membrane receptor, which, upon binding an appropriate ligand, activates a downstream PI3K signaling molecule, resulting in the activation of the AKT and mTOR effector proteins and the AKT-mediated inhibition of the Forkhead Box O (FOXO) transcription factor. Membrane receptors are G-protein coupled receptors (GPCR) such as the βor PIP2) . Phosphaor PIP2) ,30. CompThe PI3K pathway is linked via the activation of AKT kinase to FOXO transcription factors. FOXO family members are ubiquitously expressed transcription factors ,38,39,40BCL-2 family) and proliferation  proteins [Phosphorylated AKT also mediates the activation of the mammalian target of rapamycin complex 1 (mTORC1), which facilitates the assembly of the eIF4F protein complex required for protein translation ,42. Thisproteins . By thisIn addition, activation of the PI3K pathway can lead directly or indirectly to activation of other signaling pathways, for example growth factor pathways, such as the ERK-MAPK and JAK-STAT pathways, but also developmental pathways, for example, Wnt, TGFβ, and Hedgehog pathways ,46,47,48HER2), gene deletion or loss-of-function mutations in genes encoding inhibitory proteins , or specific activating mutations in one of the key pathway-activating components  [The PI3K pathway is the most frequently activated signaling pathway in cancer. In physiological processes in healthy cells the pathway is typically activated by growth factors. However, in many types of cancer the pathway is activated by genomic aberrations, such as gene amplification  . In many PIK3CA) ,54. Of high relevance to cancer in view of recent developments in immunotherapy is the role of the PI3K pathway in the differentiation of immune cells and the generation of a proper anti-cancer immune response ,55. ImmuTargeted drugs that inhibit the PI3K pathway are clinically available and more are in development stages or in clinical trials for many types of cancer ,57,58. TIn cancer, drugs targeting the PI3K pathway are generally only effective in a subgroup of patients, and their use is associated with frequent resistance and more or less severe side effects ,59. ResiHER2 fluorescent in situ hybridization (FISH) tests are available to select patients for HER2-inhibitory drugs such as trastuzumab. When correctly performed and interpreted they are good predictors for response to these drugs [PIK3CA, AKT mutations, PTEN loss) and AKT phosphorylation status appeared not to be sufficiently precise in predicting response to PI3K pathway inhibitors, while to the best of our knowledge predictive mRNA profiles are not clinically available [Tests to measure PI3K pathway activity in cancer tissue or cell samples are needed to improve prediction (and monitoring) of PI3K pathway-targeted therapy response in patients ,61. Humase drugs . Howeverse drugs . Moreovese drugs . Gene muvailable ,63,64.We have recently described a novel approach to develop tests to quantitatively measure the functional activity of signaling pathways in individual tissue and cell samples, across cancer types ,66. UsinSOD2/MnSOD), was successfully used to distinguish between the two states of FOXO transcriptional activity [SOD2 mRNA level . In such a cellular stress condition, our PI3K pathway test cannot formally infer PI3K pathway activity. However, the PI3K pathway is most likely active and probably not the only active signaling pathway contributing to cell division, e.g., other growth factor pathways such as the MAPK and JAK-STAT pathways can also be activated [Although activity of the FOXO transcription factor can be reliably measured with this method, interpretation with respect to PI3K pathway activity is not always straightforward. In cancer tissue, FOXO can be alternatively activated by cellular oxidative stress . Oxidatiactivity . Oxidatictivated ,69.Comparison of the here discussed PI3K pathway test with other approaches to develop a signature for pathway activity revealed important differences ,67. MultPIK3CA, PTEN, RTK such as HER2) may reveal a genomic cause for the identified activity of the PI3K pathway, providing the complementary information needed to choose the type of PI3K pathway inhibitor drug. Since PI3K, MAPK, and JAK-STAT pathways may co-activate each other depending on cell type and cellular condition, use of targeted MAPK and JAK-STAT pathway inhibitors may also result in a decrease in PI3K pathway activity score, reflecting pathway crosstalk [The envisioned clinical use of the PI3K pathway test lies in predicting and monitoring the response to therapy, especially PI3K pathway inhibition, across cancer types. In the case of an active pathway, (targeted) mutation or copy number analysis  in the anti-cancer immune response, which may be of help to predict response to immunotherapy ,67. It may also be useful in combination with the measurement of other signaling pathway activities to improve the characterization of tumor pathophysiology, to identify co-existing pathway activities and to provide information on their oncogenic versus tumor suppressive mode of action, as well as a potential role in therapy resistance ,67. As dSOD2 mRNA [PI3K pathway activity has also been linked to chemoradiation resistance ,76. ManyOD2 mRNA . In the OD2 mRNA . Taken tFinally, it was recently reported that many cell lines differ in their transcriptome, compromising the reproducibility of drug response experiments . In lifeThe PI3K growth factor signaling pathway plays an important role in progression and treatment resistance of many types of cancer. Multiple drugs targeting the pathway are available and being developed. However, pathway activity varies on an individual patient basis and cannot be reliably assessed by currently available tests, including DNA mutation analysis. As a consequence, there is a need for improved tests to measure pathway activity. The here discussed mRNA-based PI3K pathway assay measures functional activity of the pathway in a quantitative manner. Upon further clinical validation it is expected to contribute to improved prediction of response to cancer treatments, including PI3K pathway targeting drugs, as well as be of use in drug development.


Ketogulonicigenium vulgare has been widely used in vitamin C two-step fermentation. Four K. vulgare strains  have been completely genome-sequenced, however, less attention was paid to elucidate the reason for the differences in 2-KGA yield on genetic level. Here, a novel K. vulgare SPU B805 with higher 2-keto-L-gulonic acid (2-KGA) yield, was genome-sequenced to confirm harboring one circular chromosome with plasmid free. Comparative genome analyses showed that the absence of plasmid 2 was an important factor for its high 2-KGA productivity. The amino acid biosynthetic pathways in strain SPU B805 are much more complete than those in other K. vulgare strains. Meanwhile, strain SPU B805 harbored a complete PPP and TCA route, as well as a disabled EMP and ED pathway, same as to strain SKV, whereas strain WSH-001, Y25 and Hbe602 harbored complete PPP, ED, TCA pathway and a nonfunctional EMP pathway. The transcriptome of strain SPU B805 validated the carbon metabolism in cytoplasm mainly through the PPP pathway due to its higher transcriptional levels. This is the first time to elucidate the underlying mechanism for the difference in 2-KGA yield, and it is of great significance for strain improvement in the industrial fermentation. Ketogulonicigenium vulgare has been widely used for the bioconversion of L-sorbose to 2-keto-L-gulonic acid (2-KGA), the key precursor of vitamin C in the industrial production. In this process, K. vulgare is usually co-cultured with companion strain, Bacillus megatherium, which is generally considered to provide nutrients or metabolic substances for the growth of K. vulgare1. The known 2-KGA-producing strains, K. vulgare Y25, K. vulgare WSH-001, K. vulgare SKV and K. vulgare Hbe602 have been completely genome-sequenced and reported5. The genome-scale metabolic model of K. vulgare WSH-001 was reconstructed and analyzed by flux balance analysis (FBA)6. Total 116 genes, located on the chromosome, were identified as the essential genes for the growth of K. vulgare WSH-0016. Among them, approximately half of the essential genes participated in the metabolism of nucleotides and amino acids. Some specific amino acids significantly affected the growth of cells and the accumulation of 2-KGA8; moreover, the uptake rates of amino acids had a linear positive correlation with cell growth rate6. Additional, K. vulgare WSH-001 had a powerful transporting system to absorb nutrients and various amino acids3. Although many researches on metabolic pathways have been carried out, the understanding of the K. vulgare is still a partial picture due to its unique growth characters. Therefore, it is necessary to discover the panorama of whole metabolism features of different 2-KGA-producing strains.9. However, these routes are different in the ATP yield, cofactors and chemical intermediates. The EMP pathway is a process by which glucose is not completely oxidized to pyruvate under anaerobic conditions10. In the classical EMP pathway, glucose is phosphorylated twice and cleaved into glyceraldehyde 3-phosphate (G3P) and dihydroxyacetone phosphate (DHAP), and then two G3P are formed by the reversible interconversion between DHAP and G3P. G3P is metabolized to pyruvate in the subsequent glycolysis and coupled with ATP production through the substrate-level phosphorylation. So, one mole of glucose is oxidized to produce a net of two moles of ATP and two moles of NADH through the EMP pathway.In prokaryotes, sugar has a variety of oxidation decomposition pathways, the most common of which are the Embden-Meyerhoff-Parnass (EMP) pathway, the Entner-Doudoroff (ED) pathway and the pentose phosphate pathway (PPP)12. In the ED pathway, glucose is phosphorylated to form glucose 6-phosphate (G6P), and then oxidized to 6-phosphogluconate (6PG) by glucose-6-phosphate dehydrogenase (Zwf)11. 6PG is then oxidized to 2-keto-3-deoxy-6-phosphogluconic acid (KDPG) by KDPG aldolase (Eda), which is then cleaved to one pyruvate and one G3P. G3P is used to produce ATP through substrate-level phosphorylation in latter glycolysis. KDPG is exclusively generated in the ED pathway and Eda is the key enzyme unique to the ED pathway10. The ED pathway can achieve the same conversion rate as the EMP pathway do, but fewer enzymes are required10. During the process, only one mole of ATP as well as one mole of NADH and one mole of NADPH are produced when one mole of glucose is decomposed.The ED pathway is a wildly distributed glucose catabolic route in nature13. In the oxidative branch, G6P is metabolized into ribulose 5-phosphate (Ru5P), NADPH and carbon dioxide by the enzyme Zwf and 6-phosphate gluconate dehydrogenase (Gnd). In the non-oxidative branch, the glycolytic intermediates fructose 6-phosphate (F6P), G3P and sedoheptulose 7-phosphate (S7P) are rearranged to generate ribose 5-phosphate (R5P) and other sugar phosphate for the subsequent biosynthesis of nucleic acids and amino acids13. Zwf plays an important role in the fine-tuning of PPP pathway, but it is not the unique to the PPP pathway because of its product 6PG can be channeled into ED pathway for further metabolism11. Unlike the EMP and ED pathway, one mole of glucose is decomposed to produce a net of two moles of NADPH through the PPP pathway.The PPP pathway, as a fundamental component in the carbon metabolism, can be divided into the oxidative branch and non-oxidative branchK. vulgare have been genome-sequenced, annotated and published5, but their attentions were mainly focused on the elucidation of the metabolic pathway of 2-KGA biosynthesis and symbiosis mechanism with companion strain14. Less attention was paid to explore the mechanism of different 2-KGA yield on the genetic level in different strains. Therefore, in this work we studied the differences in genome and carbon metabolism pathway between K. vulgare SPU B805 and the four reported K. vulgare strains to understand the variations in the growth and 2-KGA production, as well as the advantages of K. vulgare SPU B805. K. vulgare SPU B805, a new 2-KGA-producing strain, contains only a circular chromosome without any plasmid. The COG and RAST annotation revealed that the genes encoding gluconate 2-dehydrogenase (GA2DH), one of L-sorbosone dehydrogenase (SNDH) and the key enzymes (Eda and Edd) unique to the ED pathway were all absent from the genome of strain SPU B805, which may be the underling mechanism that contributes to its high 2-KGA productivity. The complete pathways of glycine, serine, threonine and proline in SPU B805, whereas absent from other K. vulgare strains, may be another reason for high 2-KGA yield of SPU B805. Meanwhile, the comparative genome analysis showed that strain SPU B805 and SKV contained a complete PPP and TCA cycle, as well as a disabled EMP and ED pathway. Whereas, strain WSH-001, Y25 and Hbe602 harbor complete PPP, ED, TCA pathways and a nonfunctional EMP pathway. The transcriptome of strain SPU B805 validated that the PPP pathway was of major importance for cytoplasmic carbon oxidation. Our findings provide a complementary platform for understanding the underlying mechanism for the different central carbon metabolic features and 2-KGA yield of different K. vulgare strains. It also provides a useful framework for further physiology and gene engineering research. This is of great significance for K. vulgare strain improvement and genetic engineering in the 2-KGA industrial fermentation.Although several strains of K. vulgare SPU B805 was fermented with B. megatherium SPU B806 in 5-L bioreactor for 2-KGA production. The 2-KGA production was 82.74 ± 0.77 g/L with a 95.97% conversion rate at the end of the fermentation, and the absorbance at 660 nm was 5.63 ± 0.17, which means the cell concentration is 52 ± 1.4 × 108 CFU/mL (Table p < 0.001) compared with K. vulgare Hbe602 in the 2-KGA yield , productivity (increased by 241.82%), 2-KGA conversion rate (increased by 25.55%) and fermentation cycle (decreased by 172.73%); similarly, strain SPU B805 showed significantly differences (p < 0.001) compared with K. vulgare WSH-001 in the 2-KGA yield , productivity (increased by 102.15%), 2-KGA conversion rate (increased by 8.48%) and fermentation cycle (decreased by 59.09%). The cell growth (OD660) of SPU B805 was also significantly higher than that of WSH-001 (p < 0.05). These superior qualities aroused our great interest to understand the mechanism. So, the genome of K. vulgare SPU B805 was sequenced, annotated and compared with all of the reported 2-KGA-producing strains .mL Table . BecauseK. vulgare SPU B805 was sequenced and deposited in the GenBank database with the accession number CP017622. The genome schema was drawn as Supplementary Fig. K. vulgare SPU B805 is highly homologous to a reported 2-KGA-producing strain of K. vulgare SKV5  and tRNA (58) are the same as those in other strains except for tRNA (59) in WSH-001. The number of sorbose dehydrogenase (sdh) and sorbosone dehydrogenase (sndh), key genes responsible for the bioconversion of L-sorbose to 2-KGA, is different among all the strains, shown in Table sdh genes  and 1 sndh gene (KVC_0605) in strain SPU B805.The complete genome of K. vulgare SPU B805 is more similar with K. vulgare SKV  were selected to compare the distribution of COG classification with SPU B805  genes is also found to be located on plasmid 2. The over-expression of SNDH (encoded by plasmid 2) in K. vulgare Hbe602 produced an obvious byproduct , so as to decrease the 2-KGA yield15. Moreover, the key genes unique to ED pathway, 2-keto-3-deoxy-6-phosphogluconate aldolase (Eda) and phosphogluconate dehydratase (Edd), are annotated to be located on plasmid 2. The deletion of edd and eda genes from Gluconobacter oxydans 621H resulted in a lower overall sugar uptake by cells in the cytoplasm, and led to more sugar being left in the periplasm and converted to the end-product (2-keto-gluconate) with high yield16. The loss of ED pathway maybe has a similar effect on 2-KGA production in K. vulgare. Fortunately, these genes  located on the indigenous plasmid 2, are all absent from the genome of K. vulgare SPU B805, which may be a reason for its high 2-KGA production. Therefore, presumably, elimination of the above genes or indigenous plasmid 2 is an effective method to enhance 2-KGA production by K. vulgare. Moreover, to construction an XFP-PTA pathway as described by Wang, et al.17 to decrease the carbon flux towards ED pathway with lower carbon loss is another pathway to promote the 2-KGA yield.On the basis of the COG and RAST annotation, the genes on plasmid 2 of strain Hbe602, WSH-001 and Y25 account for about 7% of the genome. A majority of them are related to amino acid transport and metabolism (E), and assigned as “an ABC transporter”  Fig. . Howeverpfk leads to the incompleteness of the EMP route even though all the other enzymes are present. Similarly, Eda and Edd are the key enzymes unique to the ED pathway, therefore the absence of eda and edd imply the ED pathway is disabled in strain SPU B805. Thus, the PPP pathway was speculated as the main carbohydrate metabolic pathway in strain SPU B805.Generally, the prokaryote carbohydrate metabolic pathways are diverse, including the EMP pathway, ED pathway and PPP pathway. However, the genomic annotation revealed that the genes coding 6-phosphofructokinase , 2-keto-3-deoxy-6-phosphogluconate aldolase  and phosphogluconate dehydratase  were missing in strain SPU B805. Because Pfk is an enzyme specific to the EMP pathway, the absence of pfk , and the median point was 195.564  was employed to represent the gene transcriptional levelsdh   were obviously higher than those of the central carbon degradation related genes, which partially explained that the majority of L-sorbose was converted to 2-KGA in strain SPU B805. Although the gene sndh  had a relative lower transcriptional level, it did not affect the 2-KGA production rate because SDH was a dual-function enzyme, which could oxidize L-sorbose to L-sorbosone and further oxidize to 2-KGA20. Therefore, the central carbon metabolism, as well as the transcription level , were put together in Fig. ga2dh), therefore the 2-KGA production can reach a higher level in SPU B805 than that in Hbe602 and WSH-001. A minor part of L-sorbose as carbon source is catabolized to F6P and subsequently to 6PG in cytoplasm, and then enters the PPP pathway for energy and biomass production. In the oxidative branch, 6PG is converted into ribulose 5-phosphate (Ru5P), carbon dioxide and NADPH. NADPH is the major reducing power used to maintain the redox balance under stress situation21. The formed Ru5P is converted to R5P by ribose 5-phosphate isomerase  or X5P by ribulose 5-phosphate epimerase  in the non-oxidative branch. R5P and X5P undergo carbon rearrangement to generate sedoheptulose 7-phosphate (S7P), glyceraldehyde 3-phosphate (G3P) and erythorse 4-phosphate (E4P). R5P and E4P, as the precursors, were used for the biosynthesis of RNA, DNA, as well as aromatic amino acids13.In fermentation, the FPKM of the five zwf (KVC_1674), gnd (KVC_0890), transketolase  and transaldolase B  were conducted by RT-qPCR. Compared with internal standard (polA), all of the four genes showed a relative high transcription level, and the changed trends in different time-points were in accordance with the transcriptome results 6. Liu, et al.7 reported that glycine, proline, threonine and isoleucine play vital roles in K. vulgare growth and 2-KGA production, and the addition of these amino acids increased the 2-KGA productivity by 20.4%, 17.2%, 17.2% and 11.8%, respectively. Zhang, et al.8 also determined the key factors affected 2-KGA fermentation by orthogonal design experiments, and the results indicated that glycine and threonine were the key components affecting the mixed cell growth; serine, glycine, and proline were the key components that affected the 2-KGA production. Therefore, the more complete amino acid biosynthetic pathway, the more contribution to the high 2-KGA production of K. vulgare SPU B805. Moreover, the absence of the SNDH on plasmid 2 can avoid the unknown byproduct in the formation of 2-KGA. Besides, the gene ga2dh, responsible for the subsequent decomposition of 2-KGA to idonate, was also absent from strain SPU B805, which was a very advantageous factor for the 2-KGA accumulation. Additionally, the higher transcriptional level of five sdh  helps to explain the high L-sorbose conversion ability of strain SPU B805.For a long time, researchers have mainly concentrated on elucidating the symbiosis mechanism of K. vulgare was versatile. Strain WSH-001, Y25 and Hbe602 contained all the genes involved in the ED, the PPP and the TCA cycle, meaning that these pathways were complete4. While the pfk gene was absent from strain WSH-001, Y25 and Hbe602 genome, indicating that the EMP pathway was nonfunctional in these strains. Whereas, in strain SPU B805 and SKV, all the genes referred to the PPP and the TCA cycle were found, meaning that the PPP and the TCA pathway were complete5. The pfk, as well as eda and edd genes were simultaneously missing from the genome of strain SPU B805 and SKV, which suggested that neither of the EMP and the ED pathway were functional. Therefore, the PPP pathway is the exclusively pathway for cytoplasmic carbohydrate decomposition in strain SPU B805 and SKV. In strain WSH-001, Y25 and Hbe602, the ED and the PPP pathways are paralleled for cytoplasmic carbohydrate catabolism. Although the ED pathway was complete in strain WSH-001, Y25 and Hbe602 genome, the carbon flux through this pathway is presumably negligible because of the lower ATP productivity ability of the ED pathway10. Moreover, in G. oxydans 621H, the deletion of gnd to inactive the PPP pathway resulted in a reduced final biomass and a decreased end-product 2-keto-gluconate, whereas, the inactivation of the ED pathway by deleting the edd-eda genes caused a lower overall sugar uptake by cells for the formation of biomass and energy in the cytoplasm, and led to a larger fraction of the sugar being converted to the final product of 2-keto-gluconate in the periplasm16. This research indicated that the PPP pathway was of the major importance for cytoplasmic carbohydrate catabolism, whereas the ED pathway is dispensable. The loss of ED pathway maybe has a similar effect on 2-KGA production in K. vulgare. Although higher ATP productivity in the EMP pathway than in the ED pathway, the EMP requires more enzymes to sustain the equivalent carbon flux10. If the protein synthesis is a limiting factor for cell growth, the bacterium usually does not synthesize additional protein only for one more energy23.The genome and transcriptome analysis showed that the central carbon metabolism of 13. The PPP pathway can provide various intermediate metabolites for the biosynthesis of nucleotide and amino acids, such as G3P, E4P, R5P, S7P, and generate NADPH for the maintenance of redox balance under the stress situations21. K. vulgare as an aerobic bacterium, its growth and metabolism are inevitably subject to reactive oxygen species (ROS)25, therefore a reducing environment is an essential requirement for cell metabolism. It is well known that NADPH is the major reducing power in cell, and plays an important role in the GSH and thioredoxin metabolism28. Besides, a large number of anti-oxidant enzymes are coupled with NADPH as cofactor29, thus the PPP pathway may help K. vulgare SPU B805 to defeat intracellular ROS. Moreover, the biomass of K. vulgare was composed of protein, DNA, RNA, lipids, peptidoglycans, lipopolysaccharides, glycogen and soluble pool30. It is conceivable that the PPP pathway may provide a variety of intermediates as precursors to satisfy the demands for the biosynthesis of cellular components and facilitate to curb different types of environmental hardship in the process of growth.The PPP pathway, as a fundamental component in cellular metabolism, plays an important role in maintain carbon homoeostasisK. vulgare SPU B805 (2-KGA-producing strain) and B. megatherium SPU B806 (the helper strain), which were stored in Microbial Resource Center of Shenyang Pharmaceutical University, were used for 2-KGA fermentation in this study.Two strains, 2PO4, 3 g/L yeast powder, 0.2 g/L MgSO4, 3 g/L beef extract, 1 g/L urea, 10 g/L peptone, 3 g/L corn steep liquor and 20 g/L agar, pH 6.8. The plates were cultured at 28–30 °C for 4 days. The seed medium (S medium) contained 20 g/L L-sorbose, 1 g/L KH2PO4, 3 g/L yeast powder, 0.2 g/L MgSO4, 12 g/L urea, 10 g/L corn steep liquor, pH 6.8. Seeds were cultured at 28–30 °C, 250 rpm for 18 h. The fermentation medium (F medium) contained 80 g/L L-sorbose, 20 g/L corn steep liquor, 1 g/L KH2PO4, 0.2 g/L MgSO4, 12 g/L urea. The fermentation was conducted in 5-L fermenter containing 3 L F medium and 300 mL co-inoculated seeds of K. vulgare SPU B805 and B. megatherium SPU B806. The fermentation pH was maintained automatically at 6.8–7.0 by addition of 30% (m/v) Na2CO3 solution. The temperature was controlled at 30 °C, and the agitation speed was 400 rpm with the air flow rate of 1.5 L/min.The plate medium (P medium) contained 20 g/L L-sorbose, 1 g/L KHK. vulgare, the fermentation broth was diluted and spread on P plate, and the growth curve was drawn according to the CFU/mL. Meanwhile, the cell density was measured spectrophotometrically at 660 nm.To calculate the Colony Forming Units (CFUs) of K. vulgare SPU B805 was cultured in 250 mL flasks with 50 mL S medium at 30 °C for 18 h at 220 rpm. The genome of strain SPU B805 was extracted with the TIANamp Genomic DNA Kit (Tiangen Biotech (Beijing) Co., Ltd.) according to the instruction. The concentration and purity of DNA sample were qualified by Agilent 2100 BioAnalyzer . The integrity of genomic DNA was detected by agarose gel electrophoresis. The DNA samples with a 260/280 nm absorbance ration of 1.8–2.0 and a 260/230 nm absorbance ration of 2.0–2.2 were considered pure and then used for the library construction and sequencing.31, and the gaps between the contigs were closed by PCR amplification, followed by DNA sequencing. Finally, the contigs were assembled into a circular chromosome, and no plasmid was found. The complete genome of SPU B805 had an error rate of less than 0.2 in a 10-kb sequence as determined by sequence assembly and quality assessment using the Phred32/Phrap33/Consed34 software package.The complete genome sequence was determined by the Chinese National Human Genome Center  using 454 single-end sequencing technology. It yielded a total of 116,450 reads with a 17.29-fold coverage of the genome. The reads were assembled into 26 large contigs (>500 nucleotides) and 3 small contigs (100–500 nucleotides) by using the 454 Newbler assembler . The relationships between the contigs were determined by ContigScape35 and ZCURVE36, followed by manual inspection. Each gene was functionally classified by assigning a Cluster of Orthologous groups (COG) number. Both tRNA and rRNA genes were identified by tRNA scan-SE37 and RNAmmer38, respectively. Functional annotations of ORFs were performed by searching against public databases, including NCBI (http://www.ncbi.nlm.nih.gov/), KEGG39 and SWISS-Prot40. Basic metabolic pathways were reconstructed by KEGG and MetaCyc41. The Rapid Annotation using Subsystem Technology (RAST) server42 was used to re-annotate the genome of K. vulgare SPU B805. Then, RAST annotation was put through the Model SEED pipeline to obtain the initial network reconstruction43.Protein-coding genes were predicted by combining the results of Glimmer 3.02K. vulgare SPU B805 and B. megatherium SPU B806 were co-cultured in 5-L fermenter. The fermentation processes of 0 h (seed), 8 h, 16 h, 24 h, 32 h and 40 h were chosen to represent different growth phase. The co-culture samples were subjected to a centrifugation of 4,000 rpm for 5 min at 4 °C to eliminate the cells of B. megatherium SPU B806. The RNA of K. vulgare SPU B805 was extracted with the RNAprep Pure Bacteria Kit . The concentration and purity of RNA sample were qualified by a Agilent 2100 BioAnalyzer . The integrity was monitored on 1% agarose gels. The RNA samples with a 260/280 nm absorbance ration of 1.8–2.1 were considered pure, and a rRNA ration (28S/18S) ≥1.5 and RIN ≥7 were considered integrity. The NEBNext Ultra RNA Library Prep Kit  for Illumina was used to construct sequencing libraries. The rRNA was removed from total RNA using a Ribo-Zero Magnetia Kit  to enrich the mRNA and then the cDNA was synthesized. The library preparation was sequenced on Illumina Hiseq 2500 platform. The genome of K. vulgare SPU B805 was used as a reference.2PO4  was used as mobile phase with a flow rate of 0.6 mL/min.The concentration of 2-KGA and L-sorbose in the fermentation broth were analyzed by the High-Performance Liquid Chromatography (HPLC)  using an amino column (Agilent Zorbax NH2) with an ultraviolet detector at 210 nm. Cetonitrile-KHpolA) gene was used as an internal standard. The fold change of gene expression was calculated by the 2−ΔΔCT method44.The primers used for real time quantitative PCR (RT-qPCR) were listed in Supplementary Table Supplementary material


Cancer develops when pathways controlling cell survival, cell fate or genome maintenance are disrupted by the somatic alteration of key driver genes. Understanding how pathway disruption is driven by somatic alterations is thus essential for an accurate characterization of cancer biology and identification of therapeutic targets. Unfortunately, current cancer pathway analysis methods fail to fully model the relationship between somatic alterations and pathway activity.To address these limitations, we developed a multi-omics method for identifying biologically important pathways and genes in human cancer. Our approach combines single-sample pathway analysis with multi-stage, lasso-penalized regression to find pathways whose gene expression can be explained largely in terms of gene-level somatic alterations in the tumor. Importantly, this method can analyze case-only data sets, does not require information regarding pathway topology and supports personalized pathway analysis using just somatic alteration data for a limited number of cancer-associated genes. The practical effectiveness of this technique is illustrated through an analysis of data from The Cancer Genome Atlas using gene sets from the Molecular Signatures Database.Novel insights into the pathophysiology of human cancer can be obtained from statistical models that predict expression-based pathway activity in terms of non-silent somatic mutations and copy number variation. These models enable the identification of biologically important pathways and genes and support personalized pathway analysis in cases where gene expression data is unavailable.The online version of this article (10.1186/s12859-018-2476-8) contains supplementary material, which is available to authorized users. High-throughput genomic assays have revolutionized our understanding of cancer. Projects such as The Cancer Genome Atlas (TCGA)  and the To interpret cancer genomic data at the level of biological pathways, researchers have developed a large number of pathway analysis methods, many specifically customized for cancer data . For theAlthough the pathways most commonly impacted by somatic alterations in cancer have been identified  and signIdentify pathways that play an important role in the pathophysiology of human cancers.Identify genes whose somatic alterations are significantly associated with pathway activity.Support personalized pathway analysis using only somatic alteration data for known cancer genes.To address these limitations, we have developed a new multi-omics pathway analysis method for cancer genomic data that aims to: To achieve these aims, our method jointly analyzes gene expression and somatic alteration data from human tumors to build statistical models that predict the subject-level pathway activity in terms of the somatic alterations of known cancer genes. Importantly, this method does not require data from matched controls and can analyze pathways lacking topological information. This design is motivated by our hypothesis that biologically important pathways are those for which expression-level activity of the genes in the pathway relative to other cancers of the same type can be well predicted by the somatic alterations present in the tumor. To realize our method, we leveraged three important advances in cancer genomics and biostatistics, namely the development of large cancer genomics data sets that combine gene expression and somatic alteration data, e.g., TCGA , the crehttp://www.dartmouth.edu/hrfrost/MutPath/. This website also contains detailed information on the regression models fit using this approach. Reflecting the data sources used to evaluate our method, Fig. Our approach, illustrated in Fig. Our method first determines the activity of each candidate pathway within each tumor relative to other tumors of the same cancer type. This step is performed using the single-sample pathway analysis method gene set variation analysis (GSVA)  which taOur approach next determines how well the expression-based activity of each pathway can be predicted from gene-level somatic alterations. This step is performed via the regression of the single-sample pathway scores computed in Step 1 on gene-level indicators of non-silent somatic mutations and copy number variation (CNV) values. These models are estimated using LASSO-penalized multiple linear regression  with theentclass1pt{minimaUsing the regression models estimated in Step 2, one or more of the primary aims can be addressed:Aim 1. To identify biologically important pathways, the pathways are ranked according to the mean Aim 2. To identify genes whose somatic alteration is associated with pathway activity for a specific cancer type, the estimated coefficients for the mutation and CNV predictors in the unpenalized pathway regression models are inspected. If somatic alteration of a gene is retained as a significant predictor in the model for a specific pathway, the gene is deemed to be a potential driver for that pathway in the analyzed cancer type. A list of inferred driver genes for each cancer type can be generated by summarizing predictor significance across all pathway models while taking into account model predictive performance.Aim 3. The regression models estimated in the second step enable personalized pathway analysis using just somatic alteration data for a limited number of cancer-associated genes. Specifically, given tumor-specific mutational status and CNV values for the genes with non-zero coefficients in the LASSO-penalized models, it is possible to predict the activity of each evaluated pathway in that patient. When predictions are based on the unpenalized regression models, an approximate prediction interval can also be computed.R2 values generated for the pathways in the C2.CP collection for each supported TCGA cancer type  Does model predictive performance identify biologically important pathways?(Aim 2) Can the models be used to identify cancer driver genes?(Aim 3) Is model predictive performance sufficient for personalized pathway analysis?Can the models be used to characterize cancer subtypes?To evaluate our proposed method, we analyzed 20 TCGA cancer types using gene sets from the MSigDB curated canonical (C2.CP) and oncogenic signatures (C6) collections  for the predictor in the unpenalized model) and model To ascertain if the models can identify known cancer driver genes, we ranked the somatic alteration predictors for each cancer type according to predictor significance in the pathway models. Specifically, alterations were ranked according to a weight computed as the average across all pathway models in a specific MSigDB collection of the product of predictor significance , errors in the gene-level estimates of non-silent somatic mutations and copy number variation, sparsity of the somatic alteration data, and the fact that the employed non-silent mutation indicators fail to distinguish between gain-of-function and loss-of-function mutations. An additional limitation associated with the leveraged TCGA data is the fact that somatic alteration data types like methylation, mutations of non-protein coding genes and structure features such as fusions and translations are not included as predictors in the regression models. Although the COSMIC cancer gene census provides a comprehensive list of genes with a known cancer association, the census does not quantify the degree or direction of association, provides only approximate cancer type associations for each gene, and likely misses many genes that have a true link to cancer. Limitations of the gene set collections in MSigDB include the variable quality of annotations, bias in pathway annotations , the fact that the analyzed MSigDB pathways do not represent all potential cancer-related pathways, and overlaps between the members of many pathways. An important implication of our use of curated pathways from MSigDB is that our method is unlikely to identify truly novel pathway-cancer associations. The statistical model used to predict pathway activity only includes copy number alterations and indicators of non-silent somatic alterations as predictors; other genomic features that are known to impact gene expression such as epigenetic changes , translocations, gene fusions and mutation of non-protein coding genes are ignored. Given the unknown marginal and joint distribution of the Possible extensions to this method include support for additional data types, modifications to the statistical model, exploration of cancer subtypes, validation on other cancer genomics data sets and creation of tools to support personalized pathway analysis on new tumor genomic data. To more accurately model the somatic alterations that drive gene expression, the current approach can be expanded to include epigenetic modifications , mutations of non-protein coding genes, and features such as gene fusions and translocations as predictors in the regression model. An important issue that must be address in future efforts to integration additional predictor variables will be the limited available of many of these additional genomic data types. Potential enhancements of the statistical model include the addition of interaction terms, predictor weighting based on prior knowledge regarding the role of specific genes in cancer and modification of predictor weights to account for the overlap between pathways. To support formal statistical analysis of the We have developed a new approach for the pathway-based analysis of multi-omics cancer data. Our approach combines single-sample pathway analysis with multi-stage, lasso-penalized regression to find pathways whose gene expression can be explained largely in terms of the non-silent somatic mutations and copy number variations present in the tumor. This method enables the identification of biologically important pathways and genes and can be used for personalized pathway analysis in cases where gene expression data is unavailable. Importantly, this method can be used on case-only data sets and does not require information regarding pathway topology. An analysis of 20 human cancer types using TCGA genomic data and MSigDB gene sets illustrates the effectiveness of our technique. These analysis results also provide cancer researchers with ranked lists of pathways and genes that likely play a key role in the etiology of these cancer types, information that can be used to generate hypotheses for more detailed experimental exploration of cancer pathways and novel driver genes.Additional file 1Additional results and details on the computational pipeline, analyzed data sets and logic used to generate all tables and figures. (PDF 1772 kb)


During the past decade, a vast amount of information on different organisms, at both the genetic and metabolic levels, has been accumulated and systematically stored in various specific databases, such as KEGG, ENZYME, BRENDA, EcoCyc and MetaCyc. These data have made it feasible to address such an essential problem. In this paper, we have analyzed known regulatory pathways in humans by extracting different  features from each of the 17,069 protein-formed systems, of which 169 are positive pathways, i.e., known regulatory pathways taken from KEGG; while 16,900 were negative, i.e., not formed as a biologically meaningful pathway. Each of these protein-forming systems was represented by 352 features, of which 88 are graph features and 264 biological features. To analyze these features, the “Minimum Redundancy Maximum Relevance” and the “Incremental Feature Selection” techniques were utilized to select a set of 22 optimal features to query whether a protein-forming system is able to form a biologically meaningful pathway or not. It was found through cross-validation that the overall success rate thus obtained in identifying the positive pathways was 79.88%. It is anticipated that, this novel approach and encouraging result, although preliminary yet, may stimulate extensive investigations into this important topic. Given a protein-forming system,  During the past decade, the continuous development of high-throughput experimental technologies has increased the sizes of large-scale datasets, including both metagenomes and personal genomes, which necessitate renewed efforts to develop computational technologies for better biological interpretation of all this data. A vast amount of information about different organisms, both on the genetic and metabolic levels, has been accumulated and systematically stored in specific databases that are available on various websites including KEGG ,2, ENZYMKEGG (Kyoto Encyclopedia of Genes and Genomes) ,2,7 is aMany studies from various research laboratories around the world have indicated that mathematical analysis, computational modeling, and the introduction of novel physical concepts to solve important problems in biology and medicine, such as protein structural class prediction ,14, modeFor most pathways stored in the KEGG server, it is barely possible to acquire their graph characteristics by manual query execution. The present study was devoted to the development of a new approach to address this problem that maybe of use for in-depth study of the various pathway network systems.The data of regulatory pathways was collected from the public available database KEGG (ftp://ftp.genome.jp/pub/kegg/xml). Those pathways without GO information or biological properties were removed. Pathways involving less than three proteins were also excluded. As a result, 169 regulatory pathways, or protein-forming systems, were obtained and they are termed as “positive pathways”. The 169 positive pathways as well as the protein codes contained in each of such pathways are given in Online The negative pathways data was generated by the following two routes: first, proteins were randomly picked as the nodes of a graph, followed by the creation of some arcs between these proteins in a random manner. The number of arcs in each pathway was assigned according to the size distribution of the arcs in the positive pathways. Second, about half of proteins were replaced by other proteins in each positive pathway, and the arcs between the proteins, including both the original and the replaced ones, left unchanged. Since positive pathways are very rare in comparison with the vast majority of negative pathways, in this study the number of negative pathways thus generated was 100 times as big as that of the positive ones. The 16,900 negative pathways thus obtained are given in Online The use of graphic approaches to study biological systems can provide useful intuitive insights, as indicated by many previous studies on a variety of important biological topics, such as enzyme-catalyzed reactions ,38,39,40i.e. one protein P1 can regulate another protein P2 while P2 cannot always regulate P1. In this study, 88 graph features were extracted from each directed graph that represents a pathway, and 264 features of biological properties were derived from biochemical properties and physicochemical properties, including amino acid compositions, hydrophobicity, normalized van der Waals volume, polarity, polarizability, solvent accessibility and secondary structure. Thus, we have a total of (88 + 264) = 352 features altogether. For the codes of the 352 features and how they were used to quantitatively define each of the 169 positive pathways, see Online In this study, both graphic features and biological properties were used to code each pathway. We downloaded the human KGML (KEGG XML) files from KEGG FTP site (ftp://ftp.genome.jp/pub/ kegg/xml) and parsed them into graphs using KEGGgraph , an inteActually, many graph features were derived in ,53,54, wG =  where V represent the vertices and E the arcs. The size of the graph is the number of proteins in the pathway. Suppose |E|max = |V|2 is the theoretical maximum number of possible arcs in G. The graph density is defined as |E| divided by |E|max . It is possible when w(e) = 0 for some arc e∈E; we extracted features in two cases: (a) all arcs in graph were considered including those with zero weights, and that mean and variance of those weights being taken as the features; (b) arcs with non-zero weights were considered so as to take mean and variance of the non-zero weights as features . Let iF = {f0, f1 … if} (0 ≤ I ≤ N-1) be the i-th feature set taken from F. For every i (0 ≤ i ≤ N-1), we executed NN algorithm with the features in iF and obtained an accuracy of correctly predicting the positive pathways, evaluated by jackknife cross-validation. As a result, a curve named IFS curve, with identification accuracy as its y-axis and the index i of iF as its x-axis, was obtained.From mRMR, we obtained an ordered feature list http://research.janelia.org/peng/proj/mRMR/. It was run with default parameters. The following two feature lists were obtained through the mRMR program: (1) MaxRel features list; (2) mRMR features list . Shown in Shown in Shown in In this work, we present a novel KEGG pathway network analysis method based on hybrid properties, the graph properties and biochemical and physicochemical properties. It was found that the features contributing most in forming pathways were the “out_local_density” and “in_local_density”, both of which were involved with the change of the number of the edges when different weight cutoffs were applied to the graph. Therefore, more edges might remain in the positive graph when higher weight cutoffs were applied. The other graph feature with more contribution to the pathway is the “topological mean”, reflecting various proteins topologies in the regulatory pathway. For a non-broken graph, linear graph (proteins in the graph form a linear path) has a minimum topological mean, while a complete graph has a maximum topological mean. A densely-connected graph always has higher topological mean, indicating a higher likelihood to form a regulatory pathway. The “in_degree_variance”, “out_degree_variance”, and “out_degree_correlation_max” represent the difference of similarity between each of the protein pairs. Most of the forefront features with the dominant contribution are graph features, indicating that graph features are the most important ones. The biochemical and physicochemical properties, including “polarity_composition_P_max”, “secondary_structure_distribution_P-1.0_mean”, “secondary_structure_distribution_P-1.0_max”, “secondary_structure_distribution_P-0.0_max”, “polarizability_distribution_N-1.0_max”, and “AA_composition_ C_mean” also had considerable contributions in determining the regulatory networks. The distribution of the polarity of proteins structures had strong impact on the conformation of proteins, and hence their interactions as well as their binding sites. We analyzed 352 features extracted from each of the generated positive pathways and negative pathways. Of the 352 features, 88 were graph ones, meaning that each pathway was treated as a graph; and 264 were derived from protein biological properties. The mRMR (minimum redundancy maximum relevance) and IFS  techniques were employed to analyze these features. Nearest neighbor algorithm and jackknife test were used to evaluate the accuracy of our model in searching for the positive pathways. As a result, 22 features were found to be the important features for the classification. These findings might be of use for stimulating further studies on such an important and challenging topic.


P value < 0.005 and |log2(fold change)| > 1 were identified between ITP patient and control groups. One hundred and eighty-three target genes associated with ITP were finally identified. The functional annotations of target genes were found to be related to transcription, cytosol, protein binding, and so on. Twenty-four pathways including cell cycle, estrogen signaling pathway, and MAPK signaling pathway were significantly enriched. MDM2 was the core gene and other several genes including TP53, MAPK1, CDKN1A, MYC, and DDX5 were the key gens in the gene-pathway network of YQSX for treatment of ITP. The results indicated that YQSX’s effects against ITP may relate to regulation of immunological function through the specific biological processes and the related pathways. This study demonstrates the application of network pharmacology in evaluating mechanisms of action and molecular targets of complex herbal formulations.Yiqi Shexue formula (YQSX) is traditionally used to treat primary immune thrombocytopenia (ITP) in clinical practice of traditional Chinese medicine. However, its mechanisms of action and molecular targets for treatment of ITP are not clear. The active compounds of YQSX were collected and their targets were identified. ITP-related targets were obtained by analyzing the differential expressed genes between ITP patients and healthy individuals. Protein–protein interaction (PPI) data were then obtained and PPI networks of YQSX putative targets and ITP-related targets were visualized and merged to identify the candidate targets for YQSX against ITP. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis were carried out. The gene-pathway network was constructed to screen the key target genes. In total, 177 active compounds and 251 targets of YQSX were identified. Two hundred and thirty differential expressed genes with an  Primary immune thrombocytopenia (ITP) is the most common autoimmune cytopenia characterized by transient or persistent decreased platelet count . The occIn recent years, traditional Chinese medicine (TCM) has been regarded as a potential effective auxiliary strategy to treat the chronic diseases, including ITP . Huang eGinseng Radix et Rhizoma , Poria , Atractylodis Macrocephalae Rhizoma , Glycyrrhizae Radix et Rhizoma , Angelicae Sinensis Radix , Chuanxiong Rhizoma , Paeoniae Radix Alba , Rehmanniae Radix Praeparata , and Asini Corii Colla . In TCM, BZD is frequently used to treat the deficiency of qi and blood which is characterized by many symptoms, including anemia, asthenia, dizziness, chronic abscess, fatigue, irregular menstruation, palpitations, fatigue of the muscles, and pale complexion , an improved formula of Bazhen decoction (BZD), is a mixture of 9 Chinese medicine extracts including mplexion . It has mplexion . YQSX ismbopenia . In TCM,In TCM, complex herbal formulations that consist of multiple herbs are used and these complex chemical mixtures include numerous potential bioactive components that can interact with multiple therapeutic targets. This multi-component, multi-target, and multi-pathway approach may be ideal for the treatment of diseases with complex pathophysiology and therapeutic targets, but also present a tremendous challenge in understanding of the interactions between various components, their mechanisms of action and molecular targets. In the present study, a network pharmacology approach was used to explore the mechanisms of action and molecular targets of YQSX for treatment of ITP. The active compounds of YQSX and their targets were firstly identified using drugbank database. Then ITP-related targets were obtained by analyzing the differential expressed genes between ITP patients and healthy individuals. The mechanisms of action underlying YQSX for the treatment of ITP were analyzed by gene ontology (GO) and pathway analysis.http://lsp.nwu.edu.cn/tcmsp.php) and select candidate compounds which has oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18  ≥ 0.18 . One hun) ≥ 0.18 . Eventuahttps://www.drugbank.ca/) to identify the corresponding targets of YQSX. One hundred and forty-eight compounds were finally selected after removing 29 compounds which did not link to any targets. And the targets of 148 compounds were collected. Two thousand one hundred and seventy-seven targets were identified, 204 in GRR, 28 in P, 20 in AMR, 1272 in RRG, 106 in ASR, 36 in CR, 99 in PRA, 58 in RRP, and 354 in ACC. A total of 251 targets were collected after removing duplication.The 177 candidate compounds were imported into the DrugBank database  . Genes with a P value < 0.005 and |log 2(fold change)| > 1 were considered to be of significantly differential expression and ITP-related targets.The differential expressed genes of ITP patients were obtained from GEO database , Biological General Repository for Interaction Datasets (BioGRID), Human Protein Reference Database (HPRD), IntAct Molecular Interaction Database (IntAct), Molecular INTeraction database (MINT), and biomolecular interaction network database (BIND) using the plugin Bisogenet  of CytosThe PPI networks of YQSX putative targets and ITP-related targets were merged with Cytoscape software. And the nodes with topological importance in the interaction network were screened by calculating Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Eigenvector Centrality (EC), Local average connectivity-based method (LAC), and Network Centrality (NC) with the Cytoscape plugin CytoNCA. These parameters represent the topological importance and they have been reported about their definitions and computational formulas and used in network pharmacology and systems pharmacology .https://david.ncifcrf.gov, v6.8)  . FunctioOne hundred and forty-eight compounds of YQSX , 98 in biological process, 29 in cellular component, and 45 in molecular function. The data of GO analysis were shown in The pathways that were significantly influenced by YQSX in the process of treating ITP were identified by KEGG pathway analysis. Twenty-four significantly enriched pathways (FDR < 0.05) including Epstein-Barr virus infection, cell cycle, estrogen signaling pathway, pathway in cancer, and MAPK signaling pathway were identified. The data of KEGG pathway analysis were shown in The gene-pathway network was constructed based on the significantly enriched pathways and genes that regulated these pathways, which was presented in spleen to manage blood. Guipi decoction and BZD were the most common prescribed formulas in the treatment of ITP in TCM. YQSX, the improved formula of BZD, is an empirical formula to treat ITP in TCM clinical practice, and has demonstrated significant clinical effects. Compared with BZD, ACC is added to YQSX. ACC has been shown to enrich the blood and to improve hemorheology, hemostasis, and immunological regulation, and its addition has further strengthened the BZD’s effects for the treatment of ITP. TCM adopts a holistic approach focusing on overall functional recovery and elimination of the cause of the disease. The concept of network pharmacology is comparable to TCM theory and is therefore appropriate to be used for the research on unknown components and mechanism of action of complex TCM herbal formulations supported by a variety of databases and software available.The unique medical theory of TCM has been formed and developed over thousands of years in China for the treatment and preventions of diseases. Multiple compatible herbs are usually used as complex herbal formulations to improve therapeutic effect through synergism . In TCM,In the present study, a compound-target network of YQSX was constructed using the 148 compounds and 251 compound targets. The results suggested that most compounds of YQSX affected multiple targets, for example, kaempferol, glycine, and stigmasterol acted on 118, 99, and 87 targets, respectively. Therefore, they were very likely to be the crucial pleiotropically active compounds for YQSX. Although the number of putative targets in each single herb was different, the overlapping targets in different herbs were numerous. In another word, multiple compounds of YQSX may have the same target providing synergistic effects. Kaempferol is a representative flavonoid and has been shown to exert multiple pharmacological activities, such as antioxidant, anti-inflammatory, anti-cancer, anti-diabetic, anti-osteoarthritic, and immunomodulatory properties . Lin et The PPI networks of YQSX putative targets and ITP-related targets were structured and merged to obtain the candidate targets for YQSX against ITP. In order to get the more accurate targets, 6 parameters including DC, BC, and CC were set to screen nodes and structure a new network. One hundred and eighty-three targets were finally identified and used to carry out the bioinformatic analysis to elucidate the mechanisms underlying the anti-ITP effects of YQSX.The targets of YQSX against ITP were enriched in biological processes, cellular components, and molecular function by GO enrichment analysis. Results suggested that YQSX regulated some biological processes, such as gene silencing, gene expression, apoptotic process, and signal transduction by p53 class mediator. ITP is an autoimmune disease characterized by an abnormality in T cell immunity and T cell mechanism has been proved to be an important pathophysiologic mechanism in ITP . It has TCM is multi-component, multi-target, and multi-pathway. YQSX, as a TCM formula, also has the same characteristic. Therefore, it can be sure that YQSX treats ITP through multi-pathway. In the present study, a total of 24 KEGG pathways including MAPK signaling pathway and PI3K-AKT signaling pathway were significantly enriched. MAPKs can regulate gene expression, immune response, cell proliferation, apoptosis, and response to oxidative stress, which is one of the mechanisms of immune regulation . ResearcGene-pathway network was constructed to investigate the core and key target genes for YQSX against ITP. Results suggested that MDM2 had the maximum BC and it might be the core target gene. Other top 5 genes  were selected as the key target gens. MDM2 can negatively regulate p53 which is a central cell cycle regulator and has a negatively regulatory effect on autoimmunity . MDM2 caThe mechanisms of action and molecular targets of YQSX for ITP were explored using a network pharmacology approach in this study. Kaempferol, glycine, and stigmasterol regulated the most targets associated with ITP. YQSX may regulate immunological function through the specific biological processes including gene silencing, gene expression, apoptotic process, and signal transduction by p53 class mediator and the related pathways including MAPK signaling pathway and PI3K-AKT signaling pathway. In addition, YQSX may exert its regulatory function in the pathogenesis of ITP and the regulation of pathways including proteoglycans in cancer, prostate cancer, glioma, and thyroid hormone and estrogen signaling pathways which are associated with the occurrence of ITP. MDM2, TP53, MAPK1, CDKN1A, MYC, and DDX5 were the key target gens in the gene network of YQSX for treatment of ITP. The network pharmacology appears to be a suitable approach for the study of complex TCM formulations.YJ and NL performed main analysis and drafted the manuscript. JL and XH designed the research. SZ helped for introduction and discussion. DC assisted in the preparation of the manuscript. All authors wrote, read, and approved the manuscript.This work was supported by the National Basic Research Program of China  and National Natural Science Foundation of China (81803772).NL was employed by Beijing Increase Research for Drug Efficacy and Safety 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.The handling editor declared a shared affiliation, though no other collaboration, with one of the authors YJ at the time of the review.