The NBIA framework will be included in the next release of BLMA Bioconductor package (http://bioconductor.org/packages/release/bioc/html/BLMA.html).With the explosion of high-throughput data, effective integrative analyses are needed to decipher the knowledge accumulated in biological databases. Existing meta-analysis approaches in systems biology often focus on hypothesis testing and neglect real expression changes, i.e. effect sizes, across independent studies. In addition, most integrative tools completely ignore the topological order of gene regulatory networks that hold key characteristics in understanding biological processes. Here we introduce a novel meta-analysis framework, Network-Based Integrative Analysis (NBIA), that transforms the challenging meta-analysis problem into a set of standard pathway analysis problems that have been solved efficiently. NBIA utilizes techniques from classical and modern meta-analysis, as well as a network-based analysis, in order to identify patterns of genes and networks that are consistently impacted across multiple studies. We assess the performance of NBIA by comparing it with nine meta-analysis approaches: Impact Analysis, GSA, and GSEA combined with classical meta-analysis methods (Fisher’s and the additive method), plus the three MetaPath approaches that employ multiple datasets. The 10 approaches have been tested on 1,737 samples from 27 expression datasets related to Alzheimer’s disease, acute myeloid leukemia (AML), and influenza. For all of the three diseases, NBIA consistently identifies biological pathways relevant to the underlying diseases while the other 9 methods fail to capture the key phenomena. The identified AML signature is also validated on a completely independent cohort of 167 AML patients. In this independent cohort, the proposed signature identifies two groups of patients that have significantly different survival profiles (Cox p-value 2 × 10 Regardless of the high-throughput platforms being used, a standard comparative analysis of expression data usually produces a set of differentially expressed (DE) genes, which are often regarded as potential biological markers. These genes are important in classifying and subtyping patients, as well as in identifying entities that may involve in biological processes of the underlying diseases6. However, taken alone, gene biomarkers are insufficient to reveal biological mechanisms. In order to translate the differential expression to biological knowledge, researchers have been developing knowledge bases8 that map genes and gene products to known functional modules and regulatory networks. Concurrently, computational approaches have been developed for the identification of biomarkers at the systems level from differential expression14.Microarray and sequencing technologies have transformed biological and medical research by allowing us to monitor the biological systems at the molecular level. Enormous volumes of molecular data have accumulated in public repositories, including Gene Expression Omnibus (GEO)16 and pathways17. Therefore, effective data integration is needed to integrate such similar studies to obtain reliable and consistent findings. For this purpose, meta-analyses have been performed at both gene21 and systems levels24. These approaches typically analyze individual studies independently to assess the significance of differential expression, either at gene or pathway level. The results from individual studies are then combined using p-value-based meta-analysis methods such as Fisher’s25, Stouffer’s26, maxP27, minP28, and addCLT29. One of the critical pitfalls of these p-value-based meta-analysis methods is that they neglect the actual expression changes, i.e. effect sizes. This might result in information loss. Although p-value is influenced by effect size, it is also greatly affected by sample size30. For datasets with large sample size, a test for differential expression will almost always result in a significant p-value, unless the effect size is exactly zero, which is very unlikely in reality. Simply combining the p-values would likely produce varying degree of false discoveries. In addition, most integrative approaches do not take into consideration the topological order of genes that hold key characteristics in understanding biological processes.Remarkably, reproducibility poses big challenges in biomarker identification. Due to measurement errors and inherent study bias, analyses of independent datasets studying the same condition often result in distinctively different sets of DE genes25 and the addCLT method29, plus 3 MetaPath approaches23. NBIA outperforms existing approaches in identifying biological processes relevant to the disease.Here we propose Network-Based Integrative Analysis (NBIA), a network-based approach that utilizes techniques from both p-values-based and effect-sizes-based methods to reliably identify genes and pathways that are likely to be impacted by the underlying disease. The meta-analysis of effect sizes accurately estimates the central tendency of expression change for individual genes. The estimated genome-scale expression change allows for topology-aware analysis, in which gene interaction and signal propagation are taken into consideration. This approach transforms the meta-analysis problem into a standard topology-aware pathway analysis problem that has been solved efficiently. We illustrate the performance of NBIA using 1,737 samples from 27 studies related to Alzheimer’s disease, influenza, and acute myeloid leukemia (AML). We compared NBIA with 9 other approaches: Impact Analysis (IA), GSEA, and GSA combined with Fisher’sThe overall pipeline consists of four main modules: (i) estimating the expression changes (i.e. standardized mean difference), standard errors, and their p-values, (ii) computing the p-values obtained from standard hypothesis testing, (iii) combining the two types of evidence to identify impacted genes and their summary statistics, and finally (iv) performing a network-based pathway analysis. The output is a set of impacted pathways and gene patterns that are consistently impacted across independent studies. These can serve as the disease signature for other downstream analyses. In Fig. 31. This overall effect size represents the gene’s expression change under the effect of the condition. We then calculate the z-scores and the p-values of observing such effect sizes. Concurrently, we also calculate the p-values obtained from classical hypothesis testing. By default, we apply the linear model and empirical Bayesian testing provided by limma32 to compute the p-values for differential expression. The two-tailed p-values are converted to one-tailed p-values (left- and right-tailed). For each gene, the one-tailed p-values across all datasets are then combined using the addCLT method29. These p-values represent how likely the differential expression is observed by chance.To estimate the effect sizes of genes across all studies, we first compute standardized mean difference (SMD) for each gene in individual studies. We next estimate the overall effect size and standard error using the random-effects modelIn the third module, we combine the two types of evidence . We want that if a p-value is found significant, then it should be significant from classical hypothesis testing point of view, and the expression change should be well beyond the range of the standard error. Finally, the impacted genes and their summary statistics  are used to compute perturbation factors (detailed below) for the NBIA-prioritized genes and pathways. These perturbation factors are formulated to take into account gene interactions and signal propagation. Through permutation, we construct the null distribution of each pathway, and then compute the p-values of pathways as the fractions that are more extreme than the observed perturbation factors. The identified impacted pathways can be considered as the signature of the disease. This signature can be used for other downstream analyses.Hedge’s g33 as the metric to measure expression change between two conditions  as metrics to measure effect sizes, instead of raw mean difference. By default, we use 21. The fix-effects model assumes that there is only one true effect size that underlies all of the studies, and the variability among studies is due to sampling error. This assumption, however, is unlikely to be correct when analyzing multiple independent datasets, since it cannot account for batch effects and heterogeneity between studies35. In contrast, the random-effects model explicitly takes into consideration the batch effects and data heterogeneity. This model decomposes the variability of effect size estimates into two variance components36. The first component represents batch effects and data heterogeneity across studies, while the second component represents the variability within each study. In other words, this model includes batch effects and data heterogeneity as a covariate in the designated formula. That is the main reason we favor the random-effects model over the fixed-effects model. See Supplementary Section The central tendency of effect sizes for a gene can be estimated either using a fixed-effects model or a random-effects modely1, y2, …, ym as Hedge’s g values computed for m studies. We can write the random-effects model as yi = μ + τi + ϵi with τi ~ N and μ is the central tendency of the effect size, τi represents the term by which the effect size in the ith study differs from the central tendency, and ϵi represents within-study variability. The τi variables represent batch effects and data heterogeneity among datasets. The overall effect size μ of the gene and its standard error σ are estimated iteratively, as described in the literature39. The algorithm stops when further iterations do not change the values of μ and σ. After the REML algorithm stops, we compute the z-score using the formula μ and p-values  represent the overall expression change of the gene and the reliability of the estimated effect size.Consider one specific gene and denote 32 to calculate the two-tailed p-values. We then convert these p-values into one-tailed p-values. We note that this step can be substituted by any other hypothesis testing methods. We favor this empirical approach to avoid relying on strong assumption about the distributions of the expression values. For each gene, the one-tailed p-values are independent and uniformly distributed under the null. We next combine the individual p-values of the m studies to obtain one left- and one right-tailed p-value for each gene.In this work, we apply the linear model and empirical Bayesian test provided by limma29. This method uses the average of p-values as the test statistic; therefore, it is robust against extreme p-values. Denoting the individual p-values to be combined as P1, P2, …, Pm, and 41: m is large, the computation of the Irwin-Hall distribution becomes unstable due to underflow/overflow of memory29. In this case, we use the Central Limit Theorem42 to estimate this distribution. From the Central Limit Theorem, the average of such m independently and identically distributed variables follows a normal distribution with mean m. The method is named “addCLT” for “additive-Central Limit Theorem”29. See Supplemental Section To combine the p-values obtained from each study, we use the addCLT method that is built on the Central Limit Theoremi: (1) the central tendency μi of effect sizes, (2) the left- and right-tailed p-values, epil and epir, obtained from the z-score (σi is the standard error), and (3) the left and right-tailed p-values obtained from Bayesian statistics, bpil and bpir. We further combine the two types of p-values as follows: After performing effect-size-based meta-analysis and classical hypothesis testing, we have the following statistics for a gene with index 27 to combine the two types of p-values is to reduce the number of potential false positives. We want to make sure that the selected DE genes are significant from the classical hypothesis testing perspective, as well as have the effect size that is outside the range of standard error. After this, we correct the p-values for multiple comparisons using Benjamini-Hochberg’s False Discovery Rate (FDR)43. By default, genes with FDR ≤ 1% are considered as genes that are significantly impacted under the effects of the disease. We note that to have a p-value of 1%, the absolute z-score must be at least 2. Therefore, with a cutoff of 1% we choose genes that are not only statistically significant using the empirical Bayesian test, but also have the absolute effect size at least twice the standard error (see Supplementary Sections The intuition behind using maxP44 combines two types of evidence: (i) the over-representation of significantly impacted genes in a given pathway, and (ii) the perturbation of the pathway, as measured by propagation expression changes through the network. These two aspects are represented by two p-values: pde and ppert. The first p-value, pde, is calculated using the hypergeometric model — this probability quantified the over-representation of DE genes in a pathway, compared to the rest of the transcriptome. The second term, ppert, is obtained from an empirical hypothesis testing in which we take into account both the identity of DE genes and their known interactions. It is calculated based on the perturbation factor in each pathway. The perturbation factor (PF) of each gene is defined as: ΔE(g), captures the signed normalized expression change of the gene, i.e. standardized mean difference (SMD). In the context of meta-analysis, we use the central tendency of effect sizes μ to represent ΔE(g). This value is estimated from multiple studies and is expected to be more robust against noise and bias than the SMD obtained from any single study. The second term is the sum of all PFs of upstream genes, normalized by the number of downstream genes. The value of βug quantifies the strength of interaction between u and g. By default, βug = 1 for activation and βug = −1 for repression. The total perturbation in the pathway is then computed as: To identify the biological processes that are impacted by the disease, the Impact Analysis (IA) methodPi, we construct the null distribution of PF(Pi) by permuting both sample and gene labels. The p-value ppert is calculated by the fraction of the null distribution of Pi that is more extreme than the observed value. The two p-values, pde and ppert, are then combined using Fisher’s method to obtain one single p-value for the pathway. This combined p-value represents how likely the pathway is impacted under the effects of the condition44. See Supplementary Section For each pathway 7 to describe the known biology and mechanisms of the underlying disease. We use these KEGG pathways to validate the methods and refer to them as target pathways. We expect that a good analysis method to identify these target pathways as significant. Supplemental Table Here we analyze 1,737 samples from 27 independent datasets related to Alzheimer’s disease, influenza, and AML. We selected these conditions for our analysis due to two main reasons. First, we were able to find multiple datasets/experiments in public repositories for each of the three diseases. Second, for each disease, there is pathway that was created in KEGG44, GSA45, GSEA9, and MetaPath23. IA is a topology-aware method while GSEA and GSA are enrichment-based methods. Since IA, GSEA, and GSA are not able to perform meta-analysis, we use addCLT29 and Fisher’s method25 to combine individual p-values. MetaPath, on the other hand, is a stand-alone meta-analysis method, which performs pathway analysis without the need of any external analysis tool. There are three MetaPath methods: (i) MetaPath_G which performs meta-analysis at the gene level, (ii) MetaPath_P which performs meta-analysis at the pathway level, and (iii) MetaPath_I which combines the results obtained from MetaPath_G and MetaPath_P. In summary, we compare NBIA with 9 different integrative approaches: 6 GSEA-, GSA-, and IA-based approaches, plus 3 MetaPath methods. We consistently set the significance threshold at 5% for all approaches. Pathways with FDR-adjusted p-values smaller than the threshold are consider significantly impacted.We compare NBIA with 4 other pathway analysis approaches: Impact Analysis (IA)9, GSA45, and Impact Analysis44 and the MetaPath methods23. In the second part, we use the pathway signature identified by NBIA as biomarkers to cluster RNA-Seq data obtained from TCGA for 167 AML patients. We show that the discovered subtypes have significantly different survival profiles using 4 different clustering methods. The Cox p-values obtained from the discovered subtypes equal to 2 × 10−4, 3 × 10−4, 4 × 10−5, and 2 × 10−6 for consensus clustering, hierarchical clustering, local shrinkage, and cluster ensemble, respectively. We also show that this would not be possible without knowing the NBIA signature.The experimental study consists of two parts. In the first part, we use NBIA for each of the diseases to identify the genes that are consistently differentially expressed. The signature genes and their effect sizes are then used to identify the biological processes at the systems level. We show that NBIA outperforms other approaches: GSEAAlzheimer’s disease, that describes the known mechanisms and biological processes involved in this disease. However, it is well known that the pathways Parkinson’s disease and Huntington’s disease share many genes and mechanisms with Alzheimer’s disease49. Therefore, we expect that good analysis methods to identify all of the three neurological disorder pathways as statistically significant and rank them on top.There is a target pathway in KEGG, Alzheimer’s disease is ranked 96th (red circle) and thus falls outside of the segment. Similarly, using GSA + Fisher, we identified 35 significant pathways. The three neurological disorder pathways, Alzheimer’s disease (red circle), Huntington’s disease (green triangle), and Parkinson’s disease (blue plus sign), are ranked at the positions 32nd, 31st, and 37th, respectively. The pathway Parkinson’s disease is not significant and thus does not fall onto the segment of significant pathways.Each of the 10 meta-analysis methods  produces a list of KEGG pathways ranked according to their p-values. Table Alzheimer’s disease, Huntington’s disease, and Parkinson’s disease are ranked at positions 74th, 48th, and 121st, respectively. Similarly, MetaPath_G and MetaPath_I also fail to identify the three neurological disorder pathways as significant. MetaPath_G produces no significant pathway and ranks the three pathways at positions 81st, 6th, and 44th, respectively. In consequences, MetaPath_I also fails to identify the three neurological disorder pathways as significant . IA + addCLT and IA + Fisher, which are topology-aware methods, rank the target pathways very low (not in top 40). IA + addCLT fails to identify any of the three neurological disorder pathways as significant. The GSA-based and GSEA-based methods appear to perform well for this disease. These methods identify the target pathways as significant. However, the two methods also identify a large number of significant pathways, among which many are likely to be false positives.The three MetaPath methods fail to identify the three neurological disorder pathways as the most significant ones. MetaPath_P identifies no significant pathway. The three pathways Alzheimer’s disease is ranked 2nd with adjusted p = 0.002.Finally, we apply NBIA to combine the 10 studies . Similarly, GSA + Fisher and GSEA + addCLT identify the target pathway as significant but likely to include many false positives as well.MetaPath_P, MetaPath_G and MetaPath_I fail to identify the target pathway as significant and ranks it at the positions 167FDR = 5%. The target pathway Influenza A is ranked 2nd with FDR = 8 × 10−5. The other significant pathways, Herpes simplex infection, Systemic lupus erythematosus, Viral carcinogenesis, Pertussis, Measles, and NOD-like receptor signaling pathway, are also known to share common mechanisms with influenza and closely associated with immune response of the body53. Table Acute myeloid leukemia. Again, we use the 10 methods to combine the 8 AML datasets. The ranking and the number of significant pathways are shown in Fig. Acute myeloid leukemia as significant. IA + addCLT and IA + Fisher succeed in identifying the target pathway as significant but rank it at a relatively low position, 25th. The 10 top pathways of NBIA are shown in Table Acute myeloid leukemia is ranked on top with FDR = 0.0066.For this disease, the target pathway is http://gdac.broadinstitute.org/.To further validate the signature identified for AML, we downloaded RNA-Seq data for 167 AML patients. The raw TCGA data was sequenced using Illumina GASeq. The processed data and the overall survival information were downloaded from the Broad Institute’s website As we reported above, NBIA identified 5 pathways that are significantly impacted in AML. The total number of genes belonging to these pathways are 364. We simply use these genes as selected features in order to refine the partitioning of the 167 AML patients. The comparison between the partitioning with and without feature selection show that the selected pathways and genes play a crucial role in identifying subtypes with significantly different survival.55 (CC), hierarchical clustering (HC), and local shrinkage56, as well as one newly developed cluster ensemble approach to cluster the gene expression data. We show that using each of the three clustering methods, we discovered subtypes that have significantly different survival profiles. Figure 57 of the discovered subtypes using the four clustering methods. The heatmaps that visualize different subtypes of AML patients on all genes and NBIA signature are shown in Supplementary Fig. Here we use three existing methods, consensus clustering−4 while HC finds 2 subtypes with p-value = 3 × 10−4. Similarly, the local shrinkage finds 4 subtypes with p-value = 4 × 10−5. We note that there is a group of patients that always belongs to the highest-survival group in the three partitionings. The cluster ensemble approach that is designed to look for common pattern between the partitionings is able to separate this group of patients from the rest. This approach identifies two groups of patients with very different survival profiles . Among the high-survival group, almost 60% of the patients survived at the end of the study (more than 8 years). In contrast, only approximately 10% of the other group survived at the end.Without feature selection, we are unable to identify subtypes with significant survival differences by using genome-wide expression values. With feature selection, CC is able to find 4 subtypes with Cox p-value = 2 × 10We also perform subtyping using the pathway signatures identified by the other meta-analysis methods. The four methods, MetaPath_I, MetaPath_G, MetaPath_P and GSA+addCLT, yield no significant pathway and thus have no pathway signture. The other five methods, IA + addCLT, IA + Fisher, GSEA + addCLT, GSEA + Fisher, and GSA + Fisher, identify 46, 77, 2, 10, and 3 pathways as significant, respectively. We use the pathway signatures of these five methods to subtype AML patients. The Kaplan-Meier survival analysis of the discovered subtypes is shown in Supplementary Fig. In this article, we present a novel network-based meta-analysis that is able to combine multiple studies and identify the signaling pathways that are significantly impacted in a given phenotype. The main innovation of NBIA is that it transforms the challenging meta-analysis problem into a set of standard analysis problems that can be solved efficiently. This approach utilizes techniques from both p-value-based and effect-size-based meta-analysis techniques in order to reliably identify a robust set of impacted genes. This set of genes serves as the input of the impact analysis (IA) approach to identify the biological processes that are significantly impacted under the effect of the disease.To evaluate this framework, we examined 1,737 samples from 27 independent datasets related to Alzheimer’s disease, acute myeloid leukemia (AML), and influenza. NBIA was compared against 9 different approaches, GSA, GSEA, and IA combined with Fisher’s method and addCLT, plus three MetaPath approaches. We demonstrated that NBIA outperforms existing approaches to consistently identify the target pathways as significant and top ranked. We also assessed NBIA’s performance in simulation studies, including Monte Carlo evaluations of batch effects, false positive rates, and discuss the relative contributions of the different quantification steps in the NBIA workflow.−6. Even though our analysis stops at disease subtyping, NBIA can be used for many other applications, such as biomarker detection, drug repurposing, drug synergy, and anti-aging. In each of these areas, identifying the correct set of biological processes that are impacted by the disease/drug is the key for success.To further validate the framework, we also used the identified signature to cluster RNA-Seq data of 167 AML patients obtained from TCGA. For any of the 4 clustering methods tested, consensus clustering, hierarchical clustering, local shrinkage, and cluster ensemble, the discovered subtypes have significant survival differences with Cox p-value as small as 2 × 10Supplementary Materials.


Hypercholesterolaemia is considered an important cause of atherosclerotic cardiovascular disease. In a previous investigation, we demonstrated that cultured hepatoma cells treated with hypercholesterolaemic sera compared with cells treated with normocholesterolaemic sera show overexpression of mRNAs related to mitochondrial 3-hydroxy-3-methylglutaryl-coenzyme A synthase (HMGCS2). In the present work, using an NMR metabolomic analysis, we demonstrate that the hypercholesterolaemic blood sera previously used to treat cultured hepatoma cells are characterized by a metabolomic profile that is significantly different from the normocholesterolaemic sera. Acetate, acetone, 2-hydroxybutyrate, cysteine, valine, and glutamine are the metabolites distinguishing the two groups. Abnormalities in the concentrations of these metabolites reflect alterations in energy-related pathways, such as pantothenate and CoA biosynthesis, pyruvate, glycolysis/gluconeogenesis, the citrate cycle, and ketone bodies. Regarding ketone bodies, the pathway is regulated by HMGCS2; therefore, serum samples previously found to be able to increase HMGCS2 mRNA levels in cultured cells also contain higher amounts of the metabolites of its encoded enzyme protein product. Excessive serum cholesterol concentration is considered an important cause of atherosclerotic cardiovascular disease . CholestNevertheless, cholesterol concentration is only one element of a general metabolic condition that includes, but is not limited to, lipidic components. This metabolic change is generally known to be the causal pathogenic mechanism resulting in the high incidence of cardiovascular diseases, which leads to the most important cause of mortality in the developed world –6.To investigate a possible correlation between this general dysmetabolic condition and specific gene regulation, we previously searched for mRNAs that are differentially expressed from cultured hepatoma cells treated with hypercholesterolaemic sera compared with cells treated with normocholesterolaemic sera . Our datExtending this study, in the present work, we report an NMR metabolomic analysis of the sera previously used to treat hepatoma cultured cells. Metabolomics is the large-scale study of metabolites within cells, biofluids, tissues, or organisms. As such, metabolomics detects systemic fluctuations of multiple metabolite concentrations in response to drugs, diet, lifestyle, environment, stimuli, and genetic modulations. A variety of compounds  are simultaneously, qualitatively, and quantitatively detected in biologic samples using NMR-based metabolomics analysis , 10. As Human serum samples were selected from October 2009 to July 2010 to study serum composition, as previously described , from thNMR sample preparation and NMR spectra acquisition were performed as previously reported , 12. Blo® 6 centrifugal concentrator to remove proteic and particulate matter. Serum supernatant was removed, and to prepare NMR samples, 425 μL of each sample was added to 25 μL of 1 M potassium phosphate buffer (pH 7.4) and 50 μL of D2O. Trimethylsilyl propionic-2,2,3,3-d4 acid, sodium salt (TSP-d4 0.1% in D2O) was used as an internal reference for alignment and quantification of NMR signals; the mixture, homogenized by vortexing for 30 s, was transferred to a 5 mm NMR tube (Bruker NMR tubes) before the analysis started [Serum was aliquoted and stored at -80°C in Greiner cryogenic vials before NMR spectrometry measurements. Before being transferred to a 5-mm heavy-walled NMR tube, samples were thawed at room temperature and then spun at 3000 rpm using a Vivaspin started .NMR experiments were carried out on a Bruker DRX600 MHz spectrometer equipped with a 5 mm triple-resonance z-gradient CryoProbe. TOPSPIN, version 3.0, was used for spectrometer control and data processing (Bruker Biospin). 1D NOESY experiments , 14 were1H frequencies, including 600 MHz (http://www.hmdb.ca). A comparison of the spectral data obtained for each serum sample with the Chenomx metabolite library results in a list of compounds together with their respective concentrations based on the known concentration of the added internal reference compound, TSP-d4.NMR spectra were manually phased and baseline corrected. Quantification of serum metabolites was achieved using Chenomx NMR-Suite v8.0 (Chenomx Inc.). Briefly, the Chenomx profiler is linked to the human metabolome database (HMDB) containing more than 250 metabolite NMR spectral signatures encoded at different spectrometer We submitted our data to the MetaboLights database with ID code MTBLS1416 .http://www.metaboanalyst.ca/) [Multivariate statistical analysis, principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were conducted with normalized metabolomics data using MetaboAnalyst 4.0 (yst.ca/) . The per1H NMR data collected in 1D NOESY [Pareto scaling, was analysed by PCA  value was 95.5%, and the predictive (Q2Y) value was 68.7% , S5 Fig.p-values from pathway enrichment analysis  and topology analyses . Darker circle colours indicate the most significant changes in metabolites in the corresponding pathway. The size of the circle corresponds to the pathway impact score and is correlated with the number of metabolites involved (number of hits). By combining pathways characterized by hits >1 and p-values <1e-6  are those most related to the reported metabolic abnormalities. Interestingly, both metabolic pathway analysis and Reactome analysis support the evidence of a relationship between HMGCS2 mRNA overexpression in the hypercholesterolaemic sera and the metabolomic profile characterized by increased levels of acetate and 2-hydroxybutyrate, which are indeed ketone body metabolites.The graph reported in analysis  and accoanalysis . The pates <1e-6 , we obsees <1e-6 . The deeAnalysis of serum components is a traditional source of reference parameters that allows for obtaining relevant diagnostic and prognostic information on numerous diseases. Additionally, the importance of serum composition comes from the fact that it is determined both from endogenous metabolism and from the nutritional intake. Serum components also actively participate in most physiological and pathological mechanisms.Considering the possibility that serum molecules, including diet-derived nutrients, directly affect pathogenic mechanisms, we previously searched for genes selectively regulated in human hepatoma cells by treatment with dyslipidaemic sera . In thesThe concentrations of 2-hydroxybutyrate are higher in patients with hypercholesterolaemia than in normocholesterolaemic patients.Dysmetabolism of 2-hydroxybutyrate reflects dysmetabolism of the propanoate pathway. This pathway is one of the most significant pathways identified by the enrichment analysis Figs  and 5, tThe concentrations of acetone and acetate are higher in the sera of patients with hypercholesterolaemia. These metabolites are part of the ketone body pathways. Analysis conducted by Reactome confirms the relevance of this pathway in the dysmetabolic framework of subjects with hypercholesterolaemia. Interestingly, this pathway is regulated by HMGCS2, the enzyme whose mRNA we found to be overexpressed in the same hypercholesterolaemia blood sera.Therefore, our present data support the hypothesis that serum samples previously found to be able to increase HMGCS2 mRNA levels in cultured cells also contain higher amounts of metabolites of its encoded enzyme protein product. The intrinsic enzymatic activity of HMGCS2, which is important within the metabolic pathway to produce ketone bodies, could then be part of the pathogenic mechanisms. These data confirm numerous pieces of evidence from the literature, indicating an important role of HMGCS2 in pathogenic mechanisms. Inhibition of HMGCS2 expression was reported to reduce endothelial damage in a diabetes mellitus model . HMGCS2 Metabolomic analysis indicated increased concentrations of some amino acids, such as cysteine, serine, histidine, and phenylalanine. Previous scientific studies have shown a fundamental role of histidine in the induction of hypercholesterolaemia , 30. SimAccording to enrichment pathway analysis, metabolites discriminating the two groups of sera reflect an alteration in the metabolic pathways of pantothenate and CoA biosynthesis, pyruvate, glycolysis/gluconeogenesis, and citrate cycle. While it is not possible to specifically relate these metabolic changes to the pathogenetic mechanisms of clinical conditions associated with hypercholesterolaemia and dyslipidaemia, it is still possible to make some hypotheses. Hypercholesterolaemic and dyslipidaemic conditions leading to cardiovascular conditions are most commonly consequent to a nutritional status with excessive energy and lipid intake. Pantothenate/CoA biosynthesis, pyruvate metabolism, glycolysis/gluconeogenesis, and the citrate cycle are all energy-related pathways. Correspondingly, ketone body pathways are typically increased when excessive energy is derived from lipids. This is consistent with the observed reduction in citrate in hypercholesterolaemic samples, also indicating a decrease in the Krebs cycle, which is the typical metabolic change leading to an increase in ketone body production to process excessive acetyl CoA, which cannot be catabolized . AdditioAdministration of human hepatoma cells with hypercholestaerolaemic blood sera previously induced an increase in the mRNA expression of HMGCS2, an enzyme involved in the ketone body pathway. In the present work, the sera previously used to treat hepatoma cells have been the object of NMR metabolomics analysis. The data thus obtained show concentration abnormalities of metabolites involved in energy related pathways. Serum samples previously found to be able to increase HMGCS2 mRNA levels in cultured cells also contain higher amounts of acetate and acetoacetate, metabolites of its encoded enzyme protein product.The data presently reported, integrated by those previously published, define a multi-omics analytical approach to prove that the transcriptional regulation of the gene HMGCS2 has an effect on the synthesis of numerous metabolites related to mitochondrial energy processes.S1 FigThe explained variance of each PC is shown in the corresponding diagonal cell.(DOC)Click here for additional data file.S2 FigThe green line on top shows the accumulated variance explained; the blue line underneath shows the variance explained by individual PC.(DOC)Click here for additional data file.S3 FigThe red circles represent features above the threshold. The values are on a log scale so that both up-regulated and down- regulated features can be plotted symmetrically.(DOC)Click here for additional data file.S4 FigThe red circles represent features above the threshold. P-values are transformed by -log10 so that the more significant features  will be plotted higher on the graph.(DOC)Click here for additional data file.S5 FigThe red star indicates the best classifier.(DOC)Click here for additional data file.S6 Figa) Pantothenate and CoA biosynthesis. C00183 = L-Valine, C0049 = L-Aspartate, C0097 = L-Cysteine. b) Pyruvate metabolism. C0022 = Pyruvate, C0033 = Acetate. c) Glycolysis / Gluconeogenesis C0033 = Acetate, C00022 = Pyruvate d) Citrate cycle (TCA). C00022 = Pyruvate, C00158 = Citrate; Citric acid; 2-Hydroxy-1,2,3-propanetricarboxylic acid; 2-Hydroxytricarballylic acid. e) Ketone bodies degradation and synthesis. C01089 = (R)-3-Hydroxybutanoate, C00164 = Acetoacetate.Pathway viewer: Light blue boxes indicate metabolites (identified by KEGG ID) that are not measured in our data, but are used as background for enrichment analysis; yellow to red colors indicate the metabolites included in our dataset with different levels of significance: (DOC)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(DOC)Click here for additional data file.S5 Table(DOC)Click here for additional data file.S6 Tabletotal Cmpd), analysed compounds involved in pathway (Hits), p-value calculated on the hits (Raw p), logarithmic p-value (-log (p)), p-value adjusted by Holm-Bonferroni (Holm adjust) p-value adjusted using False Discovery Rate (FDR), the pathway impact value calculated from pathway topology analysis (Impact).The pathway was classified according to: total number of compounds found in KEGG database involved in the pathway ((DOC)Click here for additional data file.


RAS pathway linked with the Wnt/beta‐catenin pathway responsible for stem cell maintenance, hair follicle development, wound healing and driving KA proliferation and terminal keratinization. The existence and activation of this mutated pathway may form the basis of the paradoxical emergence of KAs and SCCs in patients receiving BRAF and PD‐1 inhibitor therapy.Keratoacanthomas (KAs) are distinctive tumors that are defined by their clinical and histopathological features. Their relationship and distinction from squamous cell carcinoma (SCC), however, remain controversial. All cytogenic and immunohistochemical markers that have been applied in this quest have failed. A close relationship of KAs to hair follicles has been recognized. The descriptive term infundibulocystic or infundibular SCC was introduced to define a more broad‐based pathway encompassing KAs. The follicular infundibulum roles in respect to neoplasia and wound healing are important elements in understanding the pathogenesis of KAs. Mouse models for KA have provided insights into the relationship of KA to follicles and SCCs. These advances and together with the diverse clinical and histopathological aspects of KA have contributed to the formulation of a conceptual pathway. The central element is that ultraviolet (UV)‐mutated or activated committed infundibular stem cells are driven by the combination of a mutated oncogenic  The descriptive term infundibulocystic or infundibular SCC was introduced to define a more broad‐based pathway to SCC encompassing KAs and incorporate the follicular infundibulum as an essential element to the process.RAS mutations for neoplasia.A broad range of biomarkers, particularly cytogenic markers and immunohistochemistry including CD123 have been unsuccessful in the quest to distinguish KA from SCC.RAS pathway linking KA and squamous cell carcinoma and the Wnt/beta‐catenin pathway responsible for skin stem cell maintenance, hair follicle development, wound healing and driving KA proliferation and terminal keratinization.Early carcinogen‐induced KA mouse models2Infundibulocystic and infundibular SCC were diagnostic terms proposed to define a pathway to a distinct subset of SCC incorporating KA and a range of verrucous and infundibular precursors distinct from solar keratoses or Bowen disease.Well‐differentiated infundibulocystic SCCs including KAs dominate the pathway, but with increasing UV‐induced mutations, moderate and poorly differentiated SCCs may emerge. To be in the pathway, these tumors can only be identified by the presence of a recognizable infundibular or infundibulocystic precursors representing differentiation allied to KA. The oncogenic burden ultimately signals a departure from the committed stem cell pathway and progressive loss of recognizable committed terminal infundibular differentiation.TP53 sequencing 20 of the tumors including all of the KASPs lacked mutations and 10 of 14 more cytological atypical SCCs had detectable TP53 mutations.The increased mutational burden in the infundibulocystic pathway has been shown in the study by Ko et al.3The link of KAs to hair follicles was demonstrated by the experimental studies by Ghadially in 1961 using topical carcinogens in mice.More recently, genetically engineered mouse models for human KAs have been developed.Follicular SCCs as defined by Diaz‐Cascajo et alThe mouse model for carcinogen‐induced KAs4RAS‐mutated infundibular neoplasia is not dependent on mesenchyme stroma associated with the mutated sonic hedgehog (Shh) pathway driving basal cell carcinomas.Epidermal infundibulogenesis has to be distinguished from the complex process of trichogenesis. Pluripotential stem cells within the follicular bulge and rarely from the interfollicular dermis in adults need dermal mesenchyme signaling for trichogenesis. The process is complex and is dependent on assembling a highly integrated set of differing committed stem cells that form a follicle. The identity of committed stem cells is declared through terminal differentiation. The infundibulum is one such component. Misago et al5Trauma has been linked to the emergence of KAs in a range of clinical scenarios.In hair‐bearing skin, wound repair is accelerated by infundibular keratinocytes driven by committed infundibular stem cells. CK15  was the first molecular marker for bulge‐located stem cells. This label primarily localized to the bulge has been shown to be absent in KAs6KAs are characterized by their capacity to involute and several potential drivers for this have been proposed. The follicular hair cycle is still considered as a driver, but it is unlikely as the infundibulum does not participate in this cycle. In addition, Wnt signaling, which regulates follicular cycling, was found to be switched off utilizing immunofluorescence and real‐time PCR approaches in both human and mouse regressing KAs.Immune‐mediated involution can be a contributory factor, as progressive growth of KAs has been observed in immunosuppressed individuals. Although lichenoid inflammation can occur in all stages of KAs, this is not a universal phenomenon. In the precursor phase, epidermal and infundibular hyperplasia with lichenoid inflammation may share features with hypertrophic lichen planus. Ultimately, the basis for involution in KA may also depend on the fate of mutated committed infundibular stem cells and their daughters in driving the infundibulocystic pathway. Committed cutaneous stem cells have a finite life cycle following an amplifying phase with rapid proliferation and differentiation, which in KAs culminates with terminal keratinization. Involution in the infundibular pathway may reflect the limited lifecycle of mutated stem cells that remain within the committed pathways. On the other hand, variants of KA such as KA centrifugum marginatum and verrucous plaque forms, appearing as multiple contiguous identical tumors, may be an expression of daughter committed stem cells. These have the capacity to migrate within the epidermis and repeat committed differentiation pathways to produce identical involuting tumors resulting in these distinct variants.The role of stem cells as an essential component in neoplasia continues to be a central focus of research.7Retinoids have been used to treat and slow down the appearance of multiple KAs and SCCs particularly those that are not amenable for surgery. This modality has been investigated in a pivotal study using a mouse‐based KA model.8BRAF mutations has revolutionized the treatment of metastatic disease.The introduction of BRAF and PD‐1 inhibitors for the treatment of advanced melanoma harboring V600E RAS mutations in BRAF wild‐type tumor cells. A variety of oncogenic mutations including HRAS, TP53, CDKN2A, and TGFBR1 have been detected within the activated RAS‐RAF pathway in both KA and KA‐like SCCs.The emergence of KAs and SCCs as an unexpected side effect of BRAF inhibitor therapy has resulted in detailed investigative studies to discover the basis of this paradoxical induction. The studies have revealed that BRAF inhibitors result in paradoxical increased MAPK signaling and tumor development through the activation of RAS‐mutated cutaneous SCCs emerged as a consequence of BRAF inhibitor therapy was the risk of noncutaneous SCCs to emerge as up to 25% of these SCCs also harbor oncogenic RAS mutations. This has not eventuated and may indicate that this event is particularly restricted to the cutaneous infundibulocystic pathway. Furthermore, inherent in the infundibulocystic pathway is the capacity of KAs and the majority of the broader group of SCCs, developing as a consequence of BRAF inhibitor therapy, to involute. These tumors can be easily removed surgically or in the setting of multiple or eruptive tumors to be treated or inhibited with oral retinoids.Topical BRAF inhibitors in wound healing mice models have been shown to accelerate wound healing through similar paradoxical MAPK activation.The infundibulocystic pathway to KAs and SCCs can also be activated by programmed cell death 1 (PD‐1) inhibitors.9In essence, the conceptual infundibular and infundibulocystic pathway to SCC provides a framework to explore the controversies and paradoxes linked to KA. The term infundibulocystic was chosen on purpose to include KA as an integral member of the pathway and to highlight its infundibular and ultimately infundibulocystic structure leading to paradoxical involution. Much effort has been spent on isolating KA as a distinct entity from SCC. The introduction of a conceptual pathway means that there are no sharp boundaries and this is borne by the difficulty of setting a firm boundary between KA and SCC with the common diagnostic category KA‐like SCC. The failure of this quest supports the concept of a pathway with KA as a clinically and histopathologically recognizable member within the pathway.A comparative study of KA and SCC utilizing comparative DNA microarray analysis revealed that KAs preferentially expressed genes that upregulate the cell death/apoptosis pathway in comparison to SCCs.RAS‐mutated KA and SCC and the Wnt/beta‐catenin pathway. This is not a linear pathway as the burden of UV‐ induced RAS mutations may at any phase overcome the involutional capacity linked to infundibulogenesis.The reason for the emergence of the controversies in the relationship of KA and SCC is probably to be directly related to the complex nature of the conceptual pathway combining committed infundibular stem cells, UV‐induced The focus on committed skin stem cells was initially triggered by a rare case presentation of KA centrifugum marginatum, dominated by verrucous and acanthotic gross keratoses without infundibulogenesis, limited to a finger.The separation of the infundibulocystic stem cell pathway to SCC from other committed epidermal derived stem cell pathways to SCC may not be simple and needs further exploration. It is possible that the infundibulocystic pathway may also be secondarily activated as an innate response to SCCs emerging from these alternative mutated pathways and is not always a primarily mutated infundibulocystic event.This conceptual pathway potentially provides further areas to explore the many facets linked to the complex nature of KA particularly as a longstanding member of a broad group of tumors including SCC.The author declares no conflicts of interest.


Rhegmatogenous retinal detachment (RRD) is an ophthalmic emergency, which usually requires prompt surgery to prevent further detachment and restore sensory function. Although several individual factors have been suggested, a systems level understanding of molecular pathomechanisms underlying this severe eye disorder is lacking. To address this gap in knowledge we performed the molecular level systems pathology analysis of the vitreous from 127 patients with RRD using state-of-the art quantitative mass spectrometry to identify the individual key proteins, as well as the biochemical pathways contributing to the development of the disease. RRD patients have specific vitreous proteome profiles compared to other diseases such as macular hole, pucker, or proliferative diabetic retinopathy eyes. Our data indicate that various mechanisms, including glycolysis, photoreceptor death, and Wnt and MAPK signaling, are activated during or after the RRD to promote retinal cell survival. In addition, platelet-mediated wound healing processes, cell adhesion molecules reorganization and apoptotic processes were detected during RRD progression or proliferative vitreoretinopathy formation. These findings improve the understanding of RRD pathogenesis, identify novel targets for treatment of this ophthalmic disease, and possibly affect the prognosis of eyes treated or operated upon due to RRD. In RD the highly organized multilayer neurosensory retina separates from the underlying retinal pigment epithelium (RPE), a single-cell layer of cuboidal polarized cells at the posterior part of the eye. Retinal detachments can be rhegmatogenous (caused by a break in the retina) or non-rhegmatogenous [due to the accumulation of subretinal fluid under the retina  or scar tissue pulling on the retina ] or a combination of these. Rhegmatogenous retinal detachment (RRD) is the most common type of retinal detachment, with an annual incidence rate of ca. 1/10,000 persons. It has an increased chance of occurring in patients with increased age, myopia, previous cataract surgery or trauma3.Retinal detachment (RD) is an ocular emergency severely damaging the visual function of the patient and often requiring prompt surgery to prevent further advancement of detachment and to restore sensory function4. After retinal detachment, nutrition to the photoreceptor cells is impaired and the photoreceptor cells begin to degenerate rapidly (within hours or few days) via apoptosis and programmed cell death5. The RPE cells and fibroblasts will then abnormally and ectopically proliferate, thereby resulting in irreversible damage to visual function. In addition to structural changes in the retina, complex biomolecular mechanisms activated after RRD may also play an important role in its pathogenesis. In fact, numerous cytokines, pro-inflammatory and growth factors are released to vitreous during RRD6. It has been suggested that these molecules play a significant role in the injury-induced wound-healing process and apoptosis of retinal photoreceptors in RRD8.The connection between the RPE and photoreceptor cells is essential to the health of photoreceptor cells9. Structurally, the PVR process manifests as neuroretinal star-fold scar tissue that wrinkles the retinal architecture and is associated with preretinal, intraretinal, and/or subretinal fibrotic membranes. Despite numerous efforts, no effective preventive or pharmacological treatment strategies are available to reduce the risk of PVR fibrosis development in eyes with RRD11.As a complication, a prolonged RRD can develop to a proliferative vitreoretinopathy (PVR)—the most advanced RRD disease stage. PVR complicates 5–11% of RRD eyes, leading ultimately to the irreversible loss of sight of the affected eye12. Currently, the majority of proteomic studies characterizing disease‐induced vitreous proteome changes have been analyzed using data-dependent acquisition (DDA) method where selected number of peptides are sequentially selected for MS2 analysis17. However, the stochastic precursor selection of DDA can lead to inconsistent detection of peptides and the corresponding proteins in the sample cohort. In this current study, a data‐independent acquisition (DIA) method using SWATH-MS proteomics technique was utilized to find proteins enriched particularly in RRD vitreous. SWATH‐MS is an emerging mass spectrometric method that offers a high degree of quantitative accuracy, proteomic coverage, reproducibility of proteome coverage, and sample throughput18.Quantitative proteomics has become an indispensable method of providing molecular level information on the pathogenesis, diagnosis, and treatment of ophthalmic diseases, primarily focusing on the acellular vitreous, where protein content reflects well with the pathogenesis of the surrounding tissuesWe therefore performed molecular pathology analysis of the vitreous proteomes collected from 127 patients with RRD using SWATH-mass spectrometry. The aim of the present study was to outline the main molecules and cellular processes activated during RRD and to pinpoint potential novel targets for therapeutic use. Our data indicates unique exosome-mediated proteomic changes in RRD that have not been observed in age-related vitreoretinal interface diseases or proliferative diabetic retinopathy (PDR). In addition, our results provide a view on the role of photoreceptor death, glycolysis, and immune response as well as Wnt and MAPK signaling in the progression of RRD. A deeper understanding of the pathological changes of the entire spectrum of the clinical RRD phase could allow more effective use of existing therapeutic strategies or develop new therapies to improve the visual prognosis of RRD patients.16. Age distributions of the patients were 59.5 ± 10.1 in RRD group, 64.6 ± 3.4 in MH group, 64.8 ± 3.2 in Pucker group and 45.7 ± 14.2 in PDR-TRD group, showing that the MH and Pucker patients were slightly older and diabetic patients slightly younger than RRD patients . A 3D-image obtained with OCT from the right eye of a representative RRD-patient shows a large fovea involving retinal detachment  and hierarchical clustering of all quantified proteins from the patient’ vitreous samples. With the exception of a few outlier samples, PCA analysis demonstrated a clear separation between RRD, MH, Pucker and PDR-TRD sample groups Fig. c. A simiSince we detected clear differences in RRD proteome compared to MH, Pucker or PDR-TRD proteomes, we next performed Welch's T-test analysis to find the proteins that are present in significantly differential concentrations particularly in the vitreous of RRD patients. When the RRD samples were compared to control samples (n = 24), we detected a difference in the abundances of 406 proteins  with 313 proteins being more abundant in the RRD samples (abundance ratio > 2) and only 4 proteins more abundant in the control samples Fig. . The verTo get an understanding of the biological processes and pathways active or activated in the RRD, the enriched proteins in the RRD samples were categorized according to their involvement in different biological processes and KEGG pathways Fig.  and S5. 20. Interestingly, the detected proteins were from all activation steps, and also deactivating proteins GRK1 and ARRS, and G-proteins GBB1 and GBG1 involved in signaling pathway regulator, were detected. In addition, we observed a clear enrichment of other photoreceptor proteins, classified by a term ‘visual perception’. These include e.g. interphotoreceptor matrix proteoglycan 1 and 2 (IMPG1 and IMPG2) which interact with hyaluronan and act as a structural constituent of interphotoreceptor matrix, and also peripherin-2 and rod outer segment membrane protein 1 (ROM1), which play a role in the rod outer segment (ROS) morphogenesis.The rhodopsin mediated signaling pathway, which is an essential part of phototransduction, was the most significantly enriched process in the RRD. In our analysis, we detected 8 proteins  directly linked to rhodopsin signaling Fig. . The pho‘glycolysis’, a process of converting glucose into pyruvate and generating small amounts of ATP (energy) and NADH (reducing power). The RRD enriched glycolysis enzymes cover almost the entire pathway from glucose to lactate , complement activation (CLUS), or endopeptidase activity , suggesting that intraocular wound healing mechanism is active or activated in RRD. Additionally, several cell adhesion molecules, participating in wound healing and a scar formation, were upregulated in RRD samples compared to controls.Although inflammation events have been associated with RRD22. However, the Wnt signaling and the MAPK cascade also have pathway specific RRD upregulated proteins  pathway regulates structure of the cytoskeleton through profilin 1 (PROF1) which was also detected in RRD proteome in high level.Our data showed that two of the classical cellular signaling pathways, Wnt signaling and MAPK cascade, were enriched in RRD. A common RRD enriched factor for these two signaling pathways was the proteasome complex. The main function of ubiquitin–proteasome system in the eyes is the protein quality control, but it also has a role in signaling transduction by regulating protein stabilityins Fig. a. SFRP4 The MAPK cascade is activated by a variety of extracellular stimuli factors or signal molecules, which trigger signal transduction by activating MAPKK kinase (MAPKKK), MAPK kinase (MAPKK), and MAPK, successively. The only MAPK family membrane kinase detected in our analysis was MAPK kinase 2  which activates the ERK1 and ERK2 MAP kinases Fig. b. Howeve24. In addition, chronic RRD with PVR samples were compared to PDR-TRD samples since both diseases represent an inflammatory and fibrotic conditions but PDR-TRD is always angiogenic and has a neovascularization process. Clear differences in proteomes can be observed between sample groups, as seen in PCA and hierarchical clustering analysis , the most common complication of RRD, comprises glial and RPE cells that migrate and generate membranes usually on the lower quadrants of retina. To understand the biological processes behind PVR development, we next determined the vitreous proteins which increased from acute RRD to chronic RRD. RRD forms with less than one quadrant retinal detachment were classified here as an acute onset RRD , and RRD with PVR as a chronic RRD (n = 8). Altogether, 29 proteins were differentially detected in acute and chronic conditions , quadrant 2 (n = 29) and quadrant 3 + 4 . We identified 66 proteins with increased presence in vitreous as RRD disease progressed  of the photoreceptors and RPE cells. These two structures are connected through the numerous microvilli present on the apical surface of the RPE through which RPE provides the major metabolic and nutritional support for the photoreceptors. In RRD pathology, loss of oxygen or glucose transport along the connecting cilia leads to caspase-associated photoreceptor cell death and permanent visual loss29. Previous studies have shown that the mammalian retinal function is dependent on glycolysis30. Indications of increased energy metabolism during RRD have been shown previously17, and they suggested that retinal cells try to compensate for metabolic stress by increasing energy production after RRD. However, the consequence of this is, that these glycolytic proteins are released into vitreous as the disease progresses to a more difficult condition.Another interesting finding was the high number of proteins related to carbon metabolism and glycolysis. The visual system is one of the highest energy demanding tissue in the human body, and the photoreceptors are the most metabolically active cells in the retina7. Monocytes are the primary immune cells mediating the cytokine storm following RRD. However, only a few cytokines were detected in our analysis because these molecules are present in vitreous at very low undetectable levels. After the acute phase, neutrophils take care of phagocytosis and debridement (proteolytic enzymes). Platelets participate in immune reactions, and express proteasomes and MHC class 1 molecules31. The coagulation cascade is activated, accumulation of fibrin leads to fibrosis, and initial regenerative process leads to mature scar tissue (PVR). Several proteins involved in platelet degranulation mechanism were detected at high level in RRD samples in our analysis. The amount of these proteins increased as RRD progressed, suggesting an important role for platelets activation in the RRD process.During the RRD process and especially during the early repair phase, phagocytic cells in immune response such as neutrophils, monocytes, and macrophages can migrate to the damaged subretinal space along with migrating RPE cells32. Wnt signaling has also been linked to several vascular eye diseases, like wet age-related macular degeneration or DR33. Importantly, epithelial mesenchymal transition-related signal transduction cascades, including Wnt/beta-catenin signaling, are known to be activated in RRD eyes34. The mitogen-activated protein kinases (MAPKs) are important mediators of signal transduction and play a key role in development and inflammatory responses. In the retina, MAPKs regulate apoptosis during retinal development35. The activation of the MAPK cascade has been shown to be especially important in Müller glial cells formation during retina development36 and retinal detachment38, MAPK cascade has also been suggested to have indirect neuroprotective action for the photoreceptors in RD39. Interestingly, many of the identified signaling proteins were associated with proteasome-mediated protein degradation. The ubiquitin–proteasome system (UPS) has a key role in keeping up cellular protein homeostasis by its ability to degrade non-functional self-, foreign, or short-lived regulatory proteins40. Increasing evidence suggests that UPS dysfunction is a major pathogenic mechanism in ophthalmic degenerative disorders41.Of the main cellular signaling pathways, Wnt and MAPK cascades were the most affected signaling pathways in our analysis. Wnt signaling modulates cellular and tissue differentiation, and it has been shown to have an essential role in eye development, especially in retinal vascular morphogenesis42. Increased intravitreal expression of soluble apoptosis and adhesion molecules at the time of primary RRD surgery is also known to be associated with future development of PVR43. Our data reveals that several important alterations are present in RRD vitreous also in protein level and highlight apoptotic and fibrotic components as potential therapeutic targets to prevent RRD process and conversion of RRD into the most severe form. In conclusion, preventing harmful PVR processes in RRD eyes is of utmost importance. Deeper understanding of the complex wound-healing mechanisms would help to develop effective therapeutic immunomodulatory medication.The pathological mechanisms of RRD in the acute phase differ from the chronic phase characterized by PVR. By comparing acute RRD to chronic with PVR, we obtained evidence that proteins involved in cell adhesion, apoptotic processes, and wound processes would affect RRD progression or PVR formation. This is in line with a previous report showing that the extent of RRD significantly influences intravitreal proinflammatory, profibrotic, and proapoptotic protein expression44. Genome-wide association study (GWAS) point out the potential role for cell adhesion and migration and apoptosis of photoreceptors and RPE cells in the pathology of RRD45. We can further support these findings, because we find strong indicators of photoreceptor apoptosis in our data. Also several inflammatory response factors were listed as potential biomarkers; however, we found no evidence of this in our data, although many immune response proteins are present in our data. This might suggest a larger role of inflammatory response in PDR-TRD, Pucker and MH than in RRD. In addition, Johnston et al. listed several genes associated with syndromic and non-syndromic forms of RRD46. Some of these proteins that were indicated on genome level as potential risk factors, were also detected in our proteomic data. In particular, collagen or collagen related proteins were more abundant in RRD sample groups . Also MMP2 and COL6A1 differ significantly between RRD and MH-Pucker control group. This indicates a distorted architecture of vitreous collagen fibrils in RRD eyes that acts with an altered force at the vitreous-retinal interface which can expose to a posterior vitreous detachment or retinal tear.In a recent paper, Lauwen et al. listed several suggested biomarkers in ophthalmology recovered by high-throughput methods, including genomics, transcriptomics, and metabolomics44. TTHY is synthesized and secreted by RPE cells, contributing to the formation of fibrous membranes on the retina in PVR-RRD eyes. Previously, Shitama et al. suggested that TTHY could be a candidate biomarker of fibrosis in RRD eyes47. Because TTHY was observed at a very high level in RRD vitreous and we could see a clear increase in protein abundance during RRD progression, we believe that increased TTHY levels in RRD patients prognosticates towards PVR. In addition, histones or histone-binding proteins were found to be RRD specific molecules. Histones can be released extracellularly by oxidative stress and after tissue injury48. Because histone levels were very low in control samples, and increased significantly during RRD, these molecules could be potential candidates for RRD biomarkers.In our analysis, three previously reported biomarkers  had a statistically significant difference between RRD and control samples11. Historically to prevent fibrosis, many compounds, known to modify wound healing, have been studied including anti-inflammatory agents  as well as cytotoxic and immunosuppressive drugs 50. More recently, Fucoidan was shown to reverse TGF beta1-induced EMT of RPE cells, and suppress formation of alpha-SMA and fibrosis51. We are actively looking for new target molecules and biomarkers for RRD so that one day we can offer more individualized treatments to patients suffering from this potentially blinding eye disease.Despite numerous efforts, there are no effective preventive or pharmacological treatment strategies to reduce the risk of PVR fibrosis development in eyes with RRDIn summary, our data provides the most detailed insight into the dramatic proteome changes that occur in vitreous humor during RRD. Specifically, the proteins involved in phototransduction and glycolysis were enriched as well as signaling molecules from Wnt and MAPK pathways. Retinal regeneration without fibrosis in RRD pathology remains a challenge, and we believe that our study can help to shed light on the molecular mechanisms active prior, during and after the RRD. Understanding the molecules and the mechanisms involved (in time and space) is the necessity to develop new therapies for RRD patients.The study was conducted according to the tenets of the Declaration of Helsinki and approved by the Institutional Review Board of Helsinki University Central Hospital at the University of Helsinki in Finland. Signed informed consent was obtained from each participant before the sampling occurred. Confidentiality of the patient records was maintained when the clinical data were entered into a computer-based standardized data entry for analysis.52. The findings were compared with three previously studied ophthalmic diseases, macular hole , Pucker (n = 10) or proliferative diabetic retinopathy (PDR) eyes with tractional retinal detachment 16. Eye examination included measurement of visual acuity, intraocular pressure, axial length, and biomicroscopy of anterior and posterior segment of the eye.Patients in our study were admitted for primary vitrectomy due to RRD (n = 127). Patients originate from two separate cohorts, the first set , and the second set . Proliferative vitreoretinopathy (PVR) in RRD eyes was graded according to the classification of the Retina Society Terminology Committee (1991)The optical coherence tomography (OCT) scan was obtained with Heidelberg Eye Explorer .All vitrectomies were performed by the recruiting vitreoretinal surgeon. Undiluted vitreous samples (up to 1000 μl) were collected at the start of the conventional 3‐port pars plana vitrectomy  without an infusion of artificial fluid. The samples were immediately frozen and stored at − 70 °C. Total protein content of the samples was measured using a BCA protein assay kit .Four samples, covering the diseases RRD, MH, Pucker and PDR, were selected for the specific vitreous ion library generation. 500 µg of total protein were reduced with TCEP (Tris(2-carboxyethyl)phosphine; Sigma Aldrich), alkylated with iodoacetamide, trypsin-digested with Sequencing Grade Modified Trypsin (Promega) using a 1:100 enzyme:protein ratio at 37 °C o/n, and then desalted with C18 microspin columns (Nest Group). The formed tryptic peptides were fractionated with an Agilent 3100 OFFGEL fractionator (Agilent Technologies), using 12 cm pH 3–10 IPG strips . The strips were focused at a maximum of 8000 V, 50 µA, 200 mW, until 50 kVhrs was reached. The runs took approximately 24 h. The four adjacent fractions were combined to give a total of six fractions per samples, which were desalted with C18 columns and analyzed separately with a TripleTOF 6600 Quadrupole Time-Of-Flight (Sciex) and Orbitrap Elite EDT Hybrid MS (Thermo Scientific). TripleTOF 6600 was coupled to an Eksigent nanoLC with a Turbo V Source, working in microspray mode, and YMC-Triart C18 column  was used for peptide separation. Orbitrap Elite was coupled with an EASY-nLC II system via a nanoelectrospray ion source, and Acclaim PepMap 100 column  was used. The MS analyses were performed in data-dependent acquisition (DDA) in positive ion mode, using linear 60 min gradient.53. The original raw-files from Orbitrap Elite were converted to mgf files using the conversion tool MSConvert. Wiff-files from TripleTOF were used as such. Searches were done against the human UniProtKB database  supplemented with porcine trypsin and iRT-peptide sequences . The search parameters were as follows: sample type, identification; Cys alkylation, iodoacetamide; digestion, trypsin; special factors, none. The “Thorough ID” mode was selected, which automatically adjusts the mass tolerance to fit the high‐resolution MS and MS/MS data. The results were filtered to a maximum false discovery rate (FDR) of 5%, resulting in the human vitreous humor spectral library containing 272,218 spectra from 18,446 distinct peptides which can be used to quantify 1558 proteins.Raw data were processed with ProteinPilot software using Paragon algorithm For SWATH-analysis, 100 µg of total protein per sample was digested as described above. SWATH-MS analysis was performed using TripleTOF 6600. Precursor ion selection was done in the 400–1250 m/z range, with a variable window width strategy (from 6 to 50 Da). Peptide activation was performed using CID, using nitrogen as inert gas, with rolling collision energy, and 5 eV of energy spread. The accumulation time was set to 250 ms for MS1 and 100 ms for MS2 scan. The entire duty cycle was approximately 3.1 s.Peak extraction of the SWATH data was performed using PeakView (version 2.1) with SWATH quantitation plug-in (SCIEX). Reference peptides from the iRT-kit (Biognosys) spiked into each sample were used to calibrate the retention time of extracted peptide peaks. The settings used were as follows: the maximum number of peptides per protein, 25; the number of transitions or fragment ions per peptide, 6; a peptide confidence threshold, 90; a False Discovery Rate (FDR), 1%; XIC (Extracted Ion Chromatogram) retention time window, 10; m/z tolerance, 75 ppm; shared and modified peptides were excluded. After SWATH peak extraction, the transition ion peak areas, peptide peak areas, and protein peak areas were exported in Excel format for further statistical analysis.The protein peak areas were normalized by total area normalization, i.e. average total intensity of all samples was divided by the total area of each sample. The data was further analyzed with RStudio version 1.2. running with R version 3.6.1. To discover proteins that were significantly altered between RRD-samples and controls Welch’s t-test was used. Welch’s t-tests p-values were multiple testing corrected using the FDR method and differences with q-value < 0.1 and greater than twofold change in intensity were considered significant. Principal component analysis (PCA) and Ward hierarchical clustering were done on log-scaled and z-normalized data. Statistical analysis was done using an in-house R script. Ward Hierarchical Clustering was performed using the R function hclust with the Euclidean distance metric.https://david.ncifcrf.gov/)55.Gene Ontology (GO) annotations and KEGG pathways were obtained from DAVID bioinformatics resources (Supplementary Tables.Supplementary Figures.


The purpose of this research is to identify the mechanisms of Shen-Qi compound formula (SQC), a traditional Chinese medicine (TCM), for treating diabetes mellitus (DM) using system pharmacology.  The active components and therapeutic targets were identified, and these targets were analyzed using gene ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) analysis. Finally, an integrated pathway was constructed to show the mechanisms of SQC.  A total of 282 active components and 195 targets were identified through a database search. The component-target network was constructed, and the key components were screened out according to their degree. Through the GO, PPI, and KEGG analyses, the mechanism network of SQC treating DM was constructed.  This study shows that the mechanisms of SQC treating DM are related to various pathways and targets. This study provides a good foundation and basis for further in-depth verification and clinical application. Diabetes mellitus (DM) is an endocrine and metabolic disease characterized by persistent hyperglycemia. Its main clinical manifestations include polydipsia, polyphagia, polyuria, and weight change . Due to Although the treatment of DM has long been researched, the management of DM and its chronic complications are still big challenges in clinical practice –9. At prrén shēn), Radix Astragali Praeparata cum Melle , Radix et Rhizoma Salviae Miltiorrhizae , Radix et Rhizoma Rhei , Rhizoma Dioscoreae , Radix Trichosanthis , Radix Rehmanniae , and Fructus Corni . The composition of SC is shown in β cell function and insulin resistance [In China, the application of traditional Chinese herbal medicine for DM has a long history. Shen-Qi compound formula (SQC) is a traditional Chinese medicine (TCM) formula that is widely used in treating DM in China. It consists of eight herbal medicines: Radix et Rhizoma Ginseng  and SymMap (https://www.symmap.org/). TCMSP is a database that is widely used in the network pharmacology research of TCM. It contains information on 499 herbal medicines, 29384 ingredients, and 837 related diseases [The workflow for this network pharmacology study can be seen in diseases . This dadiseases . To furtOB represents the proportion of drugs that enter the human blood circulation after oral administration . OB playA and B represent the molecular property of the herbal ingredients and average molecular property of all the molecules in the DrugBank database (http://www.drugbank.ca/), respectively. DL ≥ 0.18, which was the average DL index of all the compounds in DrugBank, was applied as the screening criteria for the ingredients. To ensure that the results of the study are comprehensive and reliable, some compounds which do not meet the screening criteria but are reported as the main components in the literature have been retained in our research.DL is a parameter used to evaluate how “drug-like” a prospective compound is . This coAs some components with pharmacological effects may not be included in the database or do not meet the conditions of OB and DL, these components will be missed. To avoid this, we searched the medicine of SQC in PubMed for pharmacological research. MeSH terms and free text words were used as the search strategy. Each search term was connected with a Boolean operator to other terms. The time limitation was from January 1, 2000, to February 5, 2020. The full search strategy can be seen in supplementary material 1.https://www.uniprot.org/) was used to search the corresponding human gene names. UniProt Knowledgebase is a protein database with extensive amount of information and resources. It integrates data from three databases: Swiss-Prot, TrEMBL, and PIR-PSD. Its data mainly come from the protein sequence obtained after the completion of the genome sequencing project. It contains a large amount of information about the biological functions of proteins from the literature. We retrieved information about proteins and replaced the protein targets with corresponding gene targets in this database.We searched the TCMSP database for protein targets of the chemical constituents we collected. Then, the UniProt Knowledgebase (https://www.genecards.org/). GeneCards is a searchable, integrative database that provides comprehensive, user-friendly information on all annotated and predicted human genes. It integrates gene-centric data from about 150 databases, including NCBI, UCSC, and Nsembl [We then searched about the targets for DM. These were retrieved from the Human Gene Database . This database is used for searching and predicting protein interactions. It can be applied to 2031 species and includes 9.6 million protein interactions and 13.8 million protein interactions. In addition to the experimental data, the results of text mining from PubMed abstracts, and the synthesis of other database data, it also uses bioinformatic methods to predict the results. This is one of the most diverse and informative websites for PPI analysis. The line thickness indicates the strength of data support, that is, confidence. The minimum required interaction score is medium confidence (0.400), which is also the default threshold.The protein-protein interaction (PPI) network analysis helps study the molecular mechanism of diseases and discover new drug targets from a systematic perspective. We constructed a PPI network for the protein targets of SQC, which we retrieved in the TCMSP database. This was done using the string database (https://david.ncifcrf.gov/). DAVID is a robust, publicly available database that aims to illuminate how environmental chemicals affect human health [P value indicates whether the pathway is significantly related to SQC, and the count value reflects the number of targets on the pathway. The larger the count value is, the more targets SQC has on the pathway. We filtered according to a P value of <0.01 and arranged the filtered results according to the count. For the GO analysis, we selected the top five results for each item to display. For the KEGG analysis, to provide readers with a more comprehensive understanding of the mechanism of SQC, the top 25 entries in the KEGG pathways were exhibited.To elucidate the mechanism of SQC in treating DM, we collected the intersected target genes for gene ontology (GO) enrichment and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis. GO enrichment is the description of genes in different dimensions and levels such as the cellular components (CC), biological process (BP), and molecular function (MF). Through the GO analysis, we can get a better understanding of genes . KEGG ann health , 31. In To display the above results more vividly, compound-target (C-T) network and target-pathway (T-P) network diagrams for the above results were constructed. These network diagrams were drawn using Cytoscape version 3.6.1, which can graphically display the network and edit it . In thesWe excluded the pathways that were unrelated to diabetes among the top 25 pathways. The rest of the pathways were considered to be the main mechanisms. Then, we looked up the KEGG database to understand the specific processes of these mechanisms. Due to the complexity of mechanisms, only the most commonly used and representative molecules in pathways and proven interactions were selected in the construction of the signal pathway. Special attention was paid to the interaction between signal pathways. In this way, we integrated the rest of the diabetic signal pathways and constructed the pathway network of SQC in the treatment of DM.rén shēn, 19 in dì huáng, 103 in shān zhū yú, 175 in dān shēn, 52 in huáng qí, 9 in tiān huā fĕn, 154 in huái shān yào, and 19 in dahuang. After a literature search, 30 additional ingredients were added to our study including 10 in RRG , 4 in Roside Re , Lithosposide Re , Magnesioside Re , 78, andoside Re –85), 3 ioside Re , emodin oside Re , 88, andoside Re ), 2 in Roside Re , 91, Diooside Re –94), 1 ioside Re , Iridoidoside Re , Loganinoside Re , 7-O-Galoside Re , 108, tooside Re , iridoidoside Re , and corBased on the above results, we further searched the targets of these ingredients. A total of 5201 targets were retrieved. Four-hundred and forty-eight targets remained after eliminating the duplicate targets. Then, DM-related targets were searched in the platform, Genecards. A total of 1244 targets were retrieved in the database using the keyword “diabetes”. By intersecting the target of SQC with that of DM, a total of 195 overlapping target genes were screened out. The targets of SQC and DM can be viewed in supplementary P value of <0.01 was considered statistically significant. The complete results of the GO enrichment analysis are displayed in supplementary materials To further elucidate the molecular mechanism(s) of SQC in the treatment of DM, GO enrichment and KEGG pathway analyses were performed on the selected candidate targets in the CTD database. The GO analysis describes the function of candidate targets from three aspects: biological process (BP), cellular component (CC), and molecular function (MF). A Subsequently, we analyzed the KEGG pathway of the candidate genes. The results of the KEGG analysis are displayed in supplementary To show the relationship between these 25 pathways and their related genes, we have mapped the T-P network . In thisα (VEGFA), tumor necrosis factor (TNF), tumor protein P53 (TP53), caspase 3 (CASP3), mitogen-activated protein kinase 3 (MAPK3), and mitogen-activated protein kinase 8 (MAPK8). These genes are considered to play an important role in treating of DM  is an important transcription factor, which can regulate the expression of many downstream target genes and participate in hypoxia adaptation, angiogenesis, immune response, apoptosis, and other reactions. It plays an important role in diabetes. The changes of HIF-1 α expression level in DM, together with the structural and functional abnormalities caused by the covalent modification of glyoxal, ultimately lead to disorder of the signal pathway regulation, such as angiogenesis and apoptosis. These are important molecular events in the occurrence and progress of diabetes and its complications. It was found that the TNF, PI3K/Akt, and MAPK signal pathways can interact with the HIF signal pathway, leading to the gradual progress of the disease.Hypoxia-inducible factor-1 κB signal pathway, and MAPK signal pathway downstream of TNF, thus leading to inflammation and apoptosis.Among the signal pathways we obtained through the KEGG analysis, the toll-like receptor and cAMP and Ras pathways are important signal pathways for organisms, which can participate in a variety of biological processes and have mutual relationships with multiple signal pathways. For example, toll-like receptor-mediated pattern recognition can rapidly activate the immune response, NF-The PPI analysis further explored the interaction of these targets. From these results, we can know which targets play a more important role. TNF, Akt, MAPK, and other targets are the most important proteins. This result is consistent with the results of the KEGG analysis.This research systematically investigated the ingredients and targets of SQC in the treatment of DM through the combination of network pharmacology and a literature search. A total of 282 components and 195 targets were obtained. The multitarget mechanisms of SQC were studied using the GO, KEGG, and PPI analyses. This study provides a solid foundation for further experimental verification and clinical application. Further experimental verification is needed to confirm the result of this current study.