Genome-wide association studies (GWAS) have been widely used to identify phenotype-related genetic variants using many statistical methods, such as logistic and linear regression. However, GWAS-identified SNPs, as identified with stringent statistical significance, explain just a small portion of the overall estimated genetic heritability. To address this ‘missing heritability’ issue, gene- and pathway-based analysis, and biological mechanisms, have been used for many GWAS studies. However, many of these methods often neglect the correlation between genes and between pathways.We constructed a hierarchical component model that considers correlations both between genes and between pathways. Based on this model, we propose a novel pathway analysis method for GWAS datasets, Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA). HisCoM-PCA first summarizes the common variants of each gene, first at the gene-level, and then analyzes all pathways simultaneously by ridge-type penalization of both the gene and pathway effects on the phenotype. Statistical significance of the gene and pathway coefficients can be examined by permutation tests.Using the simulation data set of Genetic Analysis Workshop 17 (GAW17), for both binary and continuous phenotypes, we showed that HisCoM-PCA well-controlled type I error, and had a higher empirical power compared to several other methods. In addition, we applied our method to a SNP chip dataset of KARE for four human physiologic traits: (1) type 2 diabetes; (2) hypertension; (3) systolic blood pressure; and (4) diastolic blood pressure. Those results showed that HisCoM-PCA could successfully identify signal pathways with superior statistical and biological significance.Our approach has the advantage of providing an intuitive biological interpretation for associations between common variants and phenotypes, via pathway information, potentially addressing the missing heritability conundrum. However, such analyses ignore genetic information from the SNPs that are not selected [Genome-wide association studies (GWAS) have greatly advanced our understanding of the association between sets of genetic variants (genotypes) and traits of interest (phenotypes). GWAS typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits (phenotypes), such as type 2 diabetes (T2D) . To idenselected –6. In adselected .Considering these deficiencies, a hierarchical component model has been constructed, PHARAOH . PHARAOH performs pathway analysis for rare variants using a single hierarchical model, and includes a collapsing step for rare variants, whose data are usually sparse. PHARAOH gene-level summary statistics are obtained by a special weight approach for rare variants, and analyzes entire genes and pathways by adding ridge-type penalties on both gene and pathway effects on traits . PHARAOHHisCoM-PCA has several distinctive features. First, HisCoM-PCA can identify associations between a distinct trait and entire pathways, using a single model. It can simultaneously quantify both the effects of pathways and genes to the phenotype. Second, HisCoM-PCA performs pathway analysis using gene-level summary statistics from SNPs within the same genes. Third, HisCoM-PCA allows potential correlations between genes and between pathways by adding ridge-type penalties to both genes and pathways effects. In addition, HisCoM-PCA may not only be used for binary phenotypes, but also continuous phenotypes. Overall, HisCoM-PCA can identify associated genes and pathways, by controlling correlations within them.In this study, we applied HisCoM-PCA for two binary phenotypes, type 2 diabetes (T2D) and hypertension (HT), and two continuous phenotypes, systolic blood pressure (SBP) and diastolic blood pressure (DBP), using large-scale SNP data from a Korean population study, KARE 8840 samples) , and the0 samplesp-values < 10−6. Thus, we only kept the subjects with gender consistencies, and those whose calling rates were > 90%. After such quality control processes, missing values were imputed only for existing variants.The Korea Association REsource (KARE project) is a nearly 9000-participant cohort GWAS study of Korean populations from Ansan and Ansung, representing city and countryside populations, respectively [An individual is defined as T2D, according to the following criteria: 1) under treatment for T2D; (2) fasting plasma glucose (FPG) ≥ 126 mg/dL, 2-h postprandial blood glucose (Glu120) ≥ 200 mg/dL, or glycated hemoglobin (HbA1c) ≥ 6.5%; and (3) age of disease onset ≥40 years. Resultantly, a total of 1288 subjects were diagnosed as T2D, among 8840 individuals, with another 3687 individuals selected as normal subjects by the inclusion criteria: (1) FPG < 100 mg/dL, Glu120 < 140 mg/dL and HbA1c < 5.7%; and (2) no history of diabetes [ under trA total of 2008 individuals were defined as hypertensive, according to the following criteria: (1) SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg; and (2) treatment with antihypertension medication, while 4569 individuals were defined as normotensive controls according to the criteria: SBP < 120 mmHg and DBP < 80 mmHg. Subjects with pre-hypertensive status were excluded from the analysis. For quantitative trait analysis of SBP and DBP, 1019 subjects were excluded due to hypertensive therapy or drug treatments, variables that influence blood pressure . The basThe first step of HisCoM-PCA reduces the dimensions of the common variants, located in the same genes, by PCA. After PCA was performed for each gene, part of principle components (PCs), as gene-level summary statistics, are chosen to represent the corresponding genes. In order to reduce the high dimension of SNP data, we only select a small number of PCs for each gene for the next pathway analysis. In order to determine the number of PCs for used for the pathway analysis, we use the following simple criteria: (1) using only the first PC and (2) using the PCs whose cumulative proportion of variances are more than 30%. We use a R’s function prcomp in the stats package to conduct PCA.After reducing the dimensions of common variants for each gene, pathway analysis is performed, using the selected PCs, with a hierarchical component model, as previously used for pathway analysis of rare variants . Before yj as the phenotype of the jth subject and assume that phenotype independently follow an exponential family distribution . Let K be the number of pathways, Tk be the number of genes in the kth pathway and Nkt be the number of PCs for the tth gene in the kth pathway. Let gkti denote the ith PC derived from PCA at the step 1 . These gkti s represent the genes and have continuous values. Let wkti denote a weight assigned to gkti and βk denote the coefficient connecting the kth pathway to the phenotype. For each individual, the relationships between PCs and binary phenotype are established in such a way that:In this model, pathways are defined as a weighted component of a set of PCs Fig. . Let us To estimate the parameters in HisCoM-PCA, we use the alternating least squares (ALS) algorithm. The ALS algorithm was originally proposed by de Leeuw et al.  and adopStep2 − 1: For fixing the weight coefficient estimates wkti, update the pathway coefficient estimates βk, in the sense of least squares.Step2 − 2: For fixing pathway coefficient estimates βk, update the weight coefficient estimates wkti, in the sense of least squares.p is the probability distribution for the phenotype of the jth individual, and λg and λp are ridge parameters for genes and pathways, respectively. After estimation, we performed permutation testing by resampling the phenotypes, to test the significance of the parameters. Here, we use a tool called WISARD (Workbench for Integrated Superfast Association study with Related Data) [To take into account potential correlations between genes and between pathways, we utilize a penalization approach. In this study, we adopt a ridge-type penalty to control multi-collinearity between genes and between pathways. Then, we sought to maximize the penalized log-likelihood function, given as follows:ed Data) , which wTo check the power and type Ι error rate of HisCoM-PCA, a simulation study was performed using simulation data from the Genetic Analysis Workshop 17 (GAW17) . In brieλg and λp, were optimized based on five-fold CV.To check the power and type I error of HisCoM-PCA, we performed a simulation study using the GAW17 dataset, for both binary and continuous types of a Q1 trait. For binary phenotypes, we transformed the continuous values of Q1 to binary values, using the median. Each SNP was then assigned to a gene, if its location was in, or within 20 kb of, the gene, and the KEGG database then used to map genes and pathways. In the simulation study, we chose the first PCs and PCs whose cumulative proportion of variances was more than 30%, after PCA of each gene. The tuning parameters of our method, To investigate where the type I error rate is controlled, we examined type I error by the proportion of identifying a null pathway whose number of genes was the same as the VEGF pathway. We checked the type I errors of HisCoM-PCA, sARTP, competitive version of MAGMA, self-contained version of MAGMA, and GSA-SNP2 . To test the pathways’ significance, we performed permutation tests by generating 1000 permuted phenotypes.For KARE data, PLINK 1.90  was usedp-values for each pathway. When HisCoM-PCA was used for the first PCs, only three pathways had FDR corrected p-values less than 0.1 for HT. However, none of pathways passed this threshold for other phenotypes. When HisCoM-PCA was used for the PCs whose cumulative proportion of variance is more than 30%, two pathways for T2D and one pathway for HT passed the same threshold of FDR corrected p-value, respectively. None of pathways passed this threshold for SBP and DBP.HisCoM-PCA, using the first PCs, successfully identified 14 pathways for T2D, 15 pathways for HT, 3 pathways for SBP, and 9 pathways for DBP, respectively, at a 5% significance level. HisCoM-PCA, using the 4486 PCs, identified 13 pathways for T2D, 20 pathways for HT, 6 pathways for SBP, and 7 pathways for DBP, respectively, at the same significance level. These different PC selection criteria provided very consistent results. Both identified 10 common pathways for T2D, 14 common pathways for HT, three common pathways for SBP, and five common pathways for DBP. The summary results of GSA-SNP2, MAGMA (competitive version) and HisCoM-PCA for KARE data analysis are shown in Additional file For T2D analysis, HisCoM-PCA successfully identified several well-known pathways biologically related to T2D. For example, pathways such as calcium signaling, the renin-angiotensin system, and phosphatidylinositol signaling, are known to be related to insulin resistance or insulin sensitivity –27. Of t2+ channels, members of the calcium signaling pathway. Moreover, the kallikrein-kinin system (KKS) importantly regulates BP by influencing vascular tone and renal salt processing. It is also well known that KKS is a large picture of the complement and coagulation cascades pathway. Other disease pathways, such as maturity onset diabetes of the young (MODY) and hypertrophic cardiomyopathy (HCM) were identified by HisCoM-PCA, and previous studies have shown that MODY and HCM may also associate with HP [The pathways related to blood pressure (BP) were also identified by HisCoM-PCA using the phenotypes HT, SBP, and DBP. In that regard, calcium signaling pathway and the complement and coagulation cascades pathway were previously shown to be related to BP regulation , 35. BP  with HP –38. Thesp-values. Beside pathway analysis, we performed gene analysis using HisCoM-PCA at the same time. The reported genes identified by HisCoM-PCA at the 5% nominal significant level are summarized in Additional file HisCoM-PCA is a novel method for pathway analysis of GWAS data. By applying HisCoM-PCA to a large population study dataset (KARE), we identified several biologically associated pathways for type-2 diabetes (T2D) and blood pressure (BP). For BP, we used three phenotypes: hypertension (HT), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Whether the phenotype of interest is continuous or binary, HisCoM-PCA can successfully detect associated pathways with statistical significance. As self-directed validation, some pathways related to HT were also identified for SBP or DBP, simultaneously providing significant Other existing pathway methods revealed numbers of significant pathways. As shown in simulation studies, however, they have high chance of being false positives. On the other hand, some pathways identified by HisCoM-PCA were previously reported to be related to T2D or BP, while these pathways were not significant by other pathway identification methods we used for comparison. In addition, some pathways were jointly identified by other methods and HisCoM-PCA. Real data analysis showed that HisCoM-PCA can provide new candidates that other methods cannot successfully identify.We also examined empirical power and type I error rate for both binary and continuous phenotypes, using the Genetic Analysis Workshop 17 (GAW17) simulation dataset for GWAS. Compared to several methods, HisCoM-PCA controlled type I error well and showed high statistical power. However, some methods, such as sARTP and the self-contained version of MAGMA, did not control type I error well. Moreover, the methods that well controlled type I error showed lower power than HisCoM-PCA. In the simulation study, HisCoM-PCA analysis of the first PC showed similar power to HisCoM-PCA with PCs whose cumulative proportion of variance was more than 30%. This may indicate that the power is similar, using multiple PCs, which can save a lot of computing time.p-values or test statistics of univariate analysis. These are gene-level summary measures and are used as inputs to perform pathway analysis. However, since the values of these summaries do not directly represent the raw genetic data, this issue probably leads to false discoveries. In HisCoM-PCA, we can obtain gene-level summary statistics, by PCA, for each gene. These statistics are a linear combination of SNPs from the raw data. Using these values, subsequent analysis for genes and pathways may decrease the possibility of false discoveries.In this study, we proposed a novel pathway analysis method HisCoM-PCA for GWAS datasets. Over the existing methods, HisCoM-PCA has several advantages. HisCoM-PCA performs gene-based and pathway-based analysis directly from raw data, while GSA-SNP2 and MAGMA use only summary measures such as HisCoM-PCA also considers correlations between pathways, an aspect usually neglected by other methods. Correlation between pathways may influence the combined effect of pathways on traits, similar to when correlations exist between genes in a specific pathway. To allow correlation between genes and between pathways, HisCoM-PCA applies a ridge-type penalization approach on coefficient estimation for both genes and pathways by analyzing entire pathways simultaneously. Cross-validation is then used to detect the optimal tuning parameters of ridge-type penalties. Note that other methods can only analyze one pathway at a time. For the mapped 186 pathways in our KARE data, HisCoM-PCA analyzed them with one big model, while other methods performed single pathway analyses 186 times.Furthermore, only HisCoM-PCA have an ability to perform conditional inference for identifying a novel pathway given known pathways. For example, for a given well-known pathway related to a trait of interest, HisCoM-PCA can only identify additional pathways for this given pathway. HisCoM-PCA can also perform stepwise selection of pathways by adding one pathway sequentially given the selected pathways.In addition to the above advantages, HisCoM-PCA has high flexibility for users. First, PC selection criteria may be defined by the user. Second, users can perform both non-target and target pathway analysis. Since HisCoM-PCA controls the correlation between pathways, it is useful to detect associated pathways having similar molecular mechanisms. However, HisCoM-PCA has higher computational burden than other methods due to permutation test. We are currently working on developing an asymptotic test which can replace the permutation test. We strongly believe that our method, HisCoM-PCA, can be applied to any number of GWAS studies, resulting in the successful identification of genes and pathways associated with specific phenotypes.Additional file 1: Table S1. Reported genes identified by HisCoM-PCA (the first PC) for type 2 diabetes. Table S2. Reported genes identified by HisCoM-PCA (the first PC) for blood pressure. Figure S1. Summary for number of SNPs in each gene excluding genes with one SNP. Figure S2. Summary of significant results from GSA-SNP2, MAGMA and HisCoM-PCA for four phenotypes: T2D, HT, SBP and DBP.


Background: Ionizing radiation from galactic cosmic rays (GCR) is one of the major risk factors that will impact the health of astronauts on extended missions outside the protective effects of the Earth’s magnetic field. The NASA GeneLab project has detailed information on radiation exposure using animal models with curated dosimetry information for spaceflight experiments. Methods: We analyzed multiple GeneLab omics datasets associated with both ground-based and spaceflight radiation studies that included in vivo and in vitro approaches. A range of ions from protons to iron particles with doses from 0.1 to 1.0 Gy for ground studies, as well as samples flown in low Earth orbit (LEO) with total doses of 1.0 mGy to 30 mGy, were utilized. Results: From this analysis, we were able to identify distinct biological signatures associating specific ions with specific biological responses due to radiation exposure in space. For example, we discovered changes in mitochondrial function, ribosomal assembly, and immune pathways as a function of dose. Conclusions: We provided a summary of how the GeneLab’s rich database of omics experiments with animal models can be used to generate novel hypotheses to better understand human health risks from GCR exposures. Space is a hostile environment, and human space travel will entail effects at many levels of biological organization leading to related physiological effects. One of the major environmental factors in space is radiation. Ionizing radiation in space is composed of a complex mixture of particles including protons from the sun and galactic cosmic rays (GCRs) originating from outside the solar system. The GCR is composed of atomic nuclei, including a component of heavy charged and energetic (HZE) particles that are relatively few in number but highly ionizing. A major risk-factor for human spaceflight is exposure GCR HZE that can cause DNA damage that is unrepaired or mis-repaired . Current research has shown measurable impact on in several areas affecting human health bone loss , skeletaAt the molecular level, space radiation from high energy photons and atomic nuclei can cause damage to cellular components, in particular deoxyribonucleic acid (DNA). Damaged DNA, which is inadequately repaired, can led to mutations and chromosomal aberrations that have potential to propagate into RNA, potentially leading to cancer. Simulations using ground-based datasets representative of a combined space radiation and microgravity exposure have demonstrated that oxidative stress processes and mitochondrial dysfunctions are important contributors to biological effects from spaceflight. Simulated microgravity studies have demonstrated an inhibitory effect on cell cycle and cell proliferation . These sA substantial amount of work has also been generated in the area of omics. For several decades, omics animal-based studies have been performed to understand the human health risks due to spaceflight missions using the International Space Station, the space shuttle, biosatellites, and ground-based experiments. NASA’s growing GeneLab omics database now hosts almost 250 experimental studies on spaceflight exposure and analogous ground-based experiments from microbes, plants, animal models, and human/animal-derived cell cultures . These dIn this study, a meta-analysis of these accumulated transcriptomic datasets in GeneLab relevant to spaceflight was undertaken to uncover potential dose-dependent biological responses that will help in understanding the human health risks experienced in the spaceflight environment. The studies identified were from human- and animal-derived tissues or cells exposed to protons and GCR HZEs at doses ranging from 0.1 to 1.0 Gy for ground studies and using samples flown to the International Space Station or shuttle missions with total doses between 1.0 and 30 mGy. Ground experiments are a well-established analog for space flight, but are limited in that they are relatively high dose, high dose rate, and in most cases use single particle species, in contrast to the low dose, low dose rate mixed radiation in space. Further work is needed to elucidate platform-dependent differences  datasets were collected . Multiplp < 0.05; p < 0.05, corrected) in spaceflight animals compared to ground controls . Further investigation of the mismatches with respect to each chromosome identified error rates to be near 0.5%; however, there was no difference between the spaceflight and ground control groups A,B. Intecontrols D,F.Each individual transcriptome dataset was analyzed by comparing flight/radiation exposure versus ground/control treatment. Utilizing standard bioinformatics pipelines for RNA-sequencing and microarray analyses, we obtained expression data for each dataset for use in gene set enrichment analysis (GSEA) . The resTo observe if any trends or batch issues arise from this type of analysis, we compared the normalized enrichment scores (NES) for all the gene sets resulting from each molecular signature database collection . PrincipTo compare the number of genes sets with the annotated gene sets, we provided heatmaps and clustering for all the datasets based on the NES for each molecular signature database collection for the analysis with the FDR < 0.25 . As expeTo observe the impact of radiation exposure, transcriptional data from experiments spanning 1 to 1000 mGy doses in animal models and cell cultures were examined for alteration in multiple biological pathways. This analysis combined statistically significant biological pathways that resulted from GSEA analysis for each individual dataset . This inUsing Cytoscape’s enrichment mapper, 63 statistically significant GSEA gene sets from the Gene Ontology (C5) database had a FDR < 25% in 50% or more of the GeneLab datasets analyzed. Using the auto-annotate clustering tool, these 63 gene sets represented 7 major biological pathways including: (1) ribosome assembly, (2) mitochondrial function, (3) interferon-gamma response, (4) chemotaxis, (5) cellular differentiation, (6) extracellular organization, and (7) cytokine production A. Each ok-means clustering of the heatmaps representing each clustering nominal enrichment scores, six major annotated clusters were overwhelmingly represented this data, including (1) mitochondrial pathways, (2) leukocyte and adaptive immune response, (3) wound healing and VEGF pathways, (4) ribosome, (5) lipid pathways, and (6) mitotic nuclear division  database that had an FDR < 0.05 in only three of the GeneLab datasets analyzed. Although the FDR was set at a lower threshold, the lower number of required datasets with an FDR < 0.05 threshold  would reclusters . The GAMclusters . As expeclusters  and decrclusters . This inTo expand these results, the canonical pathways (C2) molecular signatures database collection was examined in a similar manner to the gene ontology collection (C5) above. For this analysis, there were 30 total datasets as opposed to 28 for the C5 gene set analysis. We determined 411 significantly regulated C2 gene sets using Cytoscape’s enrichment mapper with an FDR ≤ 25% in 50% of the GeneLab datasets analyzed. Due to the C2 GSEA database containing more gene sets, a larger number of gene sets and auto-annotated terms was expected. From these terms, we observed several key pathways, including mitotic and cell cycle pathways, cancer-related pathways, hypoxia-related pathways, interferon pathways, and pathways related to radiation impact on cells A. Cell ck-means clustering for heatmaps representing each clustering nominal enrichment scores, 15 major annotated clusters overwhelmingly represented these data, including (1) hypoxia, MYC, and phospholipid pathways; (2) EGF, TNF, and hematopoietic pathways; (3) E2F3, EGFR2, p53, and cell cycle pathways; (4) luminal, miR-21 targets, KRAS, and transformation pathways; (5) RB1, metastasis, mitotic division, and progenitor pathways; (6) hematopoiesis, interferons, and methylation pathways; (7) obesity-, oxidation-, and adipogenic-related pathways; (8) mitochondrial and adipogenesis pathways; (9) complex EIF binding; (10) progesterone; (11) apoptosis and phospholipid pathways; (12) inflammatory, invasion, NOTCH, and HNF1a pathways; (13) carcinoma, hypoxia, and IL1a pathways; (14) PKD1, ZMPSTE24, and cardiac dysfunctions; and (15) ICP with H3K27ME3 pathway  KRAS- and LEF1-related pathways; (2) p53-, mTOR-, KRAS-, and CRX-related pathways; (3) PDGF- and TGFβ-related pathways; (4) TBK1, VEGFA, and ERBB2 pathways; and (5) embryonic stem cells (ESC) pathway  identified to be common across the spaceflight missions (1.0 to 30 mGy) and ground studies (0.1 to 1.0 Gy) were plotted as a function of dose and average log2 fold-change and fit to a complex polynomial model. These genes, which were expressed across the various tissue types, displayed no significant expression effects from spaceflight up to 100 mGy. At doses higher than 100 mGy, a slight deviation from baseline expression was observed  at doses from 100 to 1000 mGy. By combining 25 GeneLab datasets over these doses, this work represents the first time a study has provided a comprehensive biological analysis and produced relevant and novel results indicating which relevant biological processes are being regulated as a function of space radiation exposure. These results may be further utilized to generate health risk assessment models and uncover potential targets for mitigation of space radiation effects via novel therapeutics.Our hypothesis for this work suspected that there was a tissue independent dysregulation as a function of space radiation-absorbed dose that impacted key biological pathways associated with increased health risks such as induction of cancer. Because the GeneLab Data System contains spaceflight transcriptomic data from mice and tissues flown in low Earth orbit (LEO) for various time durations, we were able to obtain accumulated space radiation doses from 1 to 30 mGy. To obtain doses that will be more relevant to long-term deep space travel ,14, we cWe chose to first look at the distribution of mapping errors within the RNA-sequencing results from liver samples by examining whether mice exposure to spaceflight resulted in detectable changes compared to ground controls. We chose to focus on liver tissue datasets from GeneLab with mice exposed to space radiation because on in the GLDS liver is the organ with the largest number of sequenced tissue thus far from rodent research missions . This exA major difference potentially impacting the observed differences between GLDS-168 and GLDS-242 was the animal sacrificing time at sample collection, which occurred during spaceflight for GLDS-168, but was 2 weeks after returning to Earth for GLDS-242. The recovery time for the mice after 2 weeks back on Earth might possibly allow recovery of the base substitutions and error rates observed during exposure to space radiation. The ability to detect single nucleotide variants (SNVs) in RNA and DNA datasets has been performed previously on the basis of the assumption of a homogeneous genomic sample for example from a tumor , whereasNext, purely on the basis of statistical analysis, we were able to determine key biological functions that were being dysregulated as a function of space radiation. For example, in the C5 Gene Ontology (GO) terms C, ribosoWe observed a clear trend of increased activation of mitochondrial-related pathways in multiple different pathway databases, which include both the C5 GO terms C and C2 A distinct elicitation of inflammatory and immune-related cellular functions was also observed in this analysis with alteration in chemokine, cytokine, and interferon expression C. IndeedWe also explored the potential cancer risk as a function of space radiation. The datasets we utilized for this study were from both in vitro and in vivo samples that did not have any cancer present and were not prone to induction of cancer. From our analysis, we observed several interesting results indicative of increased risk for the induction of cancer. In our analysis on the C6 Oncogenic Signature database, we observed that PDGF and TGFβ were clustered together, purely on the basis of the statistics, and both were decreasing with increasing dose  specifically related to fibroblast serum . FibroblThese observed trends as a function of space radiation exposure revealed unique pathways that seem to be specifically related to GCR, whereas some pathways from our analysis are known to be universal responses to radiation. The mechanisms of damage are common to highly ionizing radiation regardless of the source ; what isThis technique may also be utilized to determine potential targets for therapeutic intervention. For example, because we report that mitochondrial pathways decrease as a function of space radiation, this argues for additional validation and research on specific mitochondrial targets for countermeasure development. In addition, from this analysis we can extrapolate to specific doses that may pose greater health risks. This analysis can only be done by utilizing a large number of datasets that are available from platforms such as NASA’s GeneLab. The resolution obtained from this analysis can easily be lost once we lower the number datasets that will be used. Our analysis has indicated several different health risks that may occur with increasing exposure to space radiation. Through an unbiased technique purely driven by statistics and utilizing a large amount of transcriptomic data from NASA’s GeneLab platform, we were able to identify tissue-independent biological pathways that are dysregulated as a function of space radiation dose. We believe this technique can further be used with other datasets and databases to generate more hypotheses that will assist with determining optimal therapeutic countermeasures and targets that can mitigate space radiation response in humans.https://github.com/FelixKrueger/TrimGalore/blob/master/Docs/Trim_Galore_User_Guide.md) to remove adapters and bases falling below a Q20 Phred quality score. Paired reads were aligned to the mouse mm10 reference genome using bowtie2  and the output piped through samtools to generate a bam file. The bam file had read group information added and was coordinate sorted using Picard tools . Mapping metrics were calculated using Picard tools and final mismatches (MN flag) extracted with genomic coordinates using Bedtools . Data were analyzed in R to determine the frequency of mismatches with respect to chromosome number and plotted. Mapping quality data from Picard tools was analyzed using GraphpadPrism . Two mouse liver RNA-sequencing datasets (GLDS-168 and GLDS-242) from NASA spaceflight experiments were specifically analyzed for chromosomal or substitution error rates. Compressed fastq files were cleaned using TRIM galore . There was a total of 25 GeneLab datasets (GLDS) with 30 experimental comparisons from RNA-sequencing or microarray platforms (56Fe), or silicon (26Si) particles (0.5 Gy or 1 Gy each). RNA extraction was executed 1 hr after exposure and the transcriptome data were evaluate by microarray technology using the Illumina HumanWG-6 V2 BeadChip  [The transcriptome datasets used for this study were from openly available data housed by NASA’s GeneLab platform  . The rawhttp://baderlab.org/Software/AutoAnnotate) in Cytoscape to group-related gene set nodes together. The newly annotated gene sets were then used for further analysis.Fold-change values between spaceflight/radiation exposure versus ground/control samples were calculated and used for gene set enrichment analysis  with three molecular signature dataset collections  . These rhttps://cran.r-project.org/web/packages/pca3d/pca3d.pdf) [https://cran.r-project.org/web/packages/Rtsne/Rtsne.pdf). Further characterization was performed by hierarchical k-means clustering on the NES. Heatmaps were generated using the R-program pheatmap . The auto-annotated gene sets for each k-means cluster were then plotted with the NES versus the dose for associated for each GeneLab dataset (https://cran.r-project.org/web/packages/mgcv/mgcv.pdf)The GSEA-produced nominal enrichment scores (NES) were compared using principle component analysis (PCA) plots from the R-program pca3d  , and t-d dataset  with R-p2 fold-change was computed as the ratio of the spaceflight measurement to a ground control value. In studies where terrestrial measurements for ground control values were made in both an animal enclosure module  and a vivarium, the AEM ground control value was used due to the similar module conditions experienced by tissue in the spaceflight AEMs. For the terrestrial studies that simulated space radiation, the experimental values were the samples that were irradiated with a specific ion  compared to the sham unirradiated samples closest to the time of irradiation. Across all of the studies, the most common genes probed were chosen, which produced nine genes present with all the GLDS sets used for this manuscript. A polynomial of degree 2 was fit to the data in the range 1–1000 mGy for all datasets. For fits involving only spaceflight samples and only muscle tissues, due to the smaller range of doses up to 30 mGy, data were fit using a polynomial of degree 1. All fits were performed using a chi-square method via CERN’s ROOT toolkit  [A total of 19 spaceflight studies and 3 ground studies were selected from the GeneLab database. .18/02,) . The nin.18/02,)  to deterThis work is an example of how GeneLab’s rich database of omics experiments with animal models can be exploited to better understand the effects of ionizing radiation and the resulting potential risks to humans. This large-scale analysis of multiple transcriptome datasets including RNA-sequencing and microarray datasets provides a valuable examination of dose-dependent perturbations of cellular function across multiple tissue types in animal models. Individually, these experiments were limited by flight duration, resulting in a single accumulated dose from a micture of particles or, in ground-based studies, exposure to a single particle species. When analyzing multiple molecular databases using our approach to many omics datasets, a more comprehensive analysis of significantly impacted biological factors can be obtained than was previously possible.


Angiopoietin-like protein 1 (ANGPTL1) has been proved to suppress tumor metastasis in several cancers. However, its extracellular effects on the pre-metastatic niches (PMNs) are still unclear. ANGPTL1 has been identified in exosomes, while its function remains unknown. This study was designed to explore the role of exosomal ANGPTL1 on liver metastasis in colorectal cancer (CRC).Exosomes were isolated by ultracentrifugation. The ANGPTL1 level was detected in exosomes derived from human CRC tissues. The effects of exosomal ANGPTL1 on CRC liver metastasis were explored by the intrasplenic injection mouse model. The liver PMN was examined by vascular permeability assays. Exosomal ANGPTL1 localization was validated by exosome labeling. The regulatory mechanisms of exosomal ANGPTL1 on Kupffer cells were determined by RNA sequencing. qRT-PCR, Western Blot, and ELISA analysis were conducted to examine gene expressions at mRNA and protein levels.ANGPTL1 protein level was significantly downregulated in the exosomes derived from CRC tumors compared with paired normal tissues. Besides, exosomal ANGPTL1 attenuated liver metastasis and impeded vascular leakiness in the liver PMN. Moreover, exosomal ANGPTL1 was mainly taken up by KCs and regulated the KCs secretion pattern, enormously decreasing the MMP9 expression, which finally prevented the liver vascular leakiness. In mechanism, exosomal ANGPTL1 downregulated MMP9 level in KCs by inhibiting the JAK2-STAT3 signaling pathway.Taken together, exosomal ANGPTL1 attenuated CRC liver metastasis and impeded vascular leakiness in the liver PMN by reprogramming the Kupffer cell and decreasing the MMP9 expression. This study suggests a suppression role of exosomal ANGPTL1 on CRC liver metastasis and expands the approach of ANGPTL1 functioning.The online version contains supplementary material available at 10.1186/s13046-020-01816-3. Colorectal cancer (CRC) is one of the most common digestive tract malignancies worldwide with high prevalence and mortality . DistantIn our previous study , we founNevertheless, previous researches about ANGPTL1’s function limited in primary tumors , 16, 18.In this study, we focused on the role of exosomal ANGPTL1 in CRC metastasis. We studied the protein level of ANGPTL1 in exosomes derived from tumor and normal tissues in CRC patients. Both In vivo and in vitro models were applied to characterize the effects of exosomal ANGPTL1 on CRC liver metastasis and PMNs formation. We demonstrated that exosomal ANGPTL1 attenuates CRC liver metastasis by regulating the kupffer cell secretion pattern and impeding vascular leakiness in the liver PMN.n = 8) in the Second Affiliated Hospital of Zhejiang University School of Medicine. This project was approved by the ethical committee of the Second Affiliated Hospital of Zhejiang University School of Medicine. Informed consent was obtained from all patients.The clinical CRC and paired normal tissues were obtained from CRC patients  were obtained from the American Type Culture Collection . The ANGPTL1 stably overexpressed cell line (SW620-ANGPTL1) and its control cell line (SW620-Ctrl) were established in the previous study [Exosomes were collected by sequential ultracentrifugation. CRC cells were cultured in the exosome-depleted  complete medium for 72 h. The supernatants were collected and centrifugate at 500×g for 10 min to remove cell contamination, then at 3000×g for 20 min to remove apoptotic bodies and large cell debris, followed by centrifugation at 12,000×g for 20 min to remove large microvesicles. Next, exosomes were collected by 100,000×g centrifugation for 70 min (Beckman Ti70). The exosome pellet was resuspended in 20 mL of phosphate-buffered saline (PBS) and collected by 100,000×g ultracentrifugation for 70 min (Beckman Ti70). Exosome preparation was verified by Transmission electron microscopy (TEM). Exosome size was measured by dynamic light scattering (DLS) analysis using Zetasizer Nano ZSE .For tissue-derived exosome collection, the CRC tumors and paired normal tissues were cut into 1 mm × 1 mm pieces and cultured in 15 mL of FBS-free RMPI-1640 medium for 24 h. Then the supernatant was harvested for further isolation of exosomes.Purified exosomes were injected into the mouse retro-orbital venous sinus in a total volume of 100 μL PBS. For in vivo education experiments, mice received 5 μg of exosomes every other day for 21 days. Retro-orbital injection of PBS was used in control groups. For in vitro education, exosomes (10 μg/mL) were added into the culture medium (CM) of ImKC for 3, 6, 12, 24 h. For exosome-tracking experiments, exosomes were labeled using PKH67 membrane dye , followed by 100,000×g ultracentrifugation for 70 min, and labeled exosomes were resuspended in PBS. In experiments involving the evaluation of exosome incorporation, labeled exosomes were injected retro-orbitally into the mice or added into the CM of ImKC 24 h before immunofluorescence analysis for exosome cells.6 SW620 cells were injected into the mice spleen as described in the previous study [To analyze the role of exosomal ANGPTL1 in CRC liver metastasis, we pre-educated the 6–8-week-old scid-beige mice  retro-orbitally with exosomes for 21 days . Then, 2 × 10us study , 30. A sus study . LiposomAfter pre-education with exosomes for 21 days, mice were injected with the recombinant mouse MMP9  intravenously. The rmMMP9 was preactivated using 1 mM aminophenylmercuricacetate  for 2 h at 37 °C. One hour after rmMMP9 injection, FITC-Dextran  was injected through the tail vein. After one hour, mice were sacrificed and perfused with PBS to remove the excess dye. Liver tissues were embedded in Tissue-Tek O.C.T. Compound  to make frozen blocks for sectioning and immunofluorescent staining. Stained sections were viewed and photographed with a fluorescence microscope. The intensity of fluorescence was measured using ImageJ software (ImageJ software v1.8.0).4) were seeded on transwell filters . After reaching confluence, HUVECs were treated with CM from ImKC educated by PBS, Ctrl-Exo, or ANGPTL1-Exo  for 48 h. Then, FITC-Dextran (1 mg/mL) was added to the top well. 40 μL medium in the bottom well was taken for fluorescence measurement every 30 min using a SpectraMax microplate reader  at 488 nm excitation and 520 nm emission. The fluorescence intensity represents the passage of FITC-Dextran, which represents the the HUVECs layer permeability.HUVECs . The MMP9 siRNA and scramble siRNA were added into the ImKC 24 h after exosome pre-education. The siRNA transfection was performed with Lipofectamine 3000  according to the manufacturer’s instructions. The siRNA sequences were as follows: MMP9 siRNA, 5′-CAAGACAAAGCCUAUUUCUTT-3′ (sense), 5′-AGAAAUAGGCUUUGUCUUGTT-3′ (antisense).Total RNAs were isolated from cells and mouse livers with TRIzol reagent  and evaluated using NanoDrop 2000 spectrophotometer . qRT-PCR was conducted using a standard SYBR-Green PCR kit protocol  with a 7500 Fast Real-Time PCR System . The primers were synthesized by Tsingke biological technology . The sequences of all primers are listed in Table Total RNA of ImKC cells educated by PBS or exosomes for 24 h in vitro was extracted and subjected to BGI  for RNA sequencing. The DEGseq R package was used to analyze differentially expressed genes based on the conditions of a fold change (FC) ≥ 1 and Q-values ≤0.001. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explore the significant pathways. All mRNA sequencing data were uploaded to NCBI (accession number PRJNA656088).The tissue lysates were prepared using RIPA lysis buffer, while the cell and exosome lysates were prepared using 3× Loading Buffer. All samples were performed as depicted previously . ExosomeIn order to detect the protein level in the CM of ImKC through ELISA, cells were cultured in the presence of exosomes (10 μg/mL) for 24 h before supernatants were collected, and the MMP9 level was measured by mouse MMP9 ELISA kit .For histological analysis, tissues were dissected and fixed in a mix of 2% Paraformaldehyde (PFA) and 20% sucrose solution overnight, followed by embedding and section. Then 6 μm O.C.T. tissue cryosections were stained with antibodies against F4/80 , αSMA , CD31 , MMP9 . Secondary antibodies conjugated to Alexa Fluor 488, 555, or 594 were used . Fluorescent images were obtained using a Zeiss LSM 710 laser confocal microscope  and analyzed using Zen software (ZEN 3.0).For ZO-1 detection in HUVECs, cell immunofluorescence was conducted as described previously . Cells wP < 0.05 was considered statistically significant.All data are presented as the mean ± standard error of the mean (SEM) or mean ± standard deviation of the mean (SD). Statistical analyses were performed with Student’s t-test for comparisons between two groups and with ANOVA for more than two groups using Prism 8.0. P = 0.03, Fig. To study the ANGPTL1 expression in CRC-derived exosomes, we first detected the ANGPTL1 protein level in exosomes secreted by CRC tumors (TDEs) and paired normal tissues (NDEs). We collected eight pairs of samples from our oncology center and isolated the tissue-derived exosomes by ultracentrifugation. The characteristic of TDEs and NDEs were accessed by TEM, DLS, and Western Blot (WB) . The six genes were significantly downregulated in ImKC educated by ANGPTL1-Exo, and all of them were reported to be involved in PMNs formation. Taken together, we found that exosomal ANGPTL1 was mainly taken up by KCs and regulated KCs secretion pattern, which may result in liver PMNs remodeling.The above results implied that exosomal ANGPTL1 might inhibit CRC liver metastasis through regulating KCs. To further investigate the effect of exosomal ANGPTL1 on KCs, we educated ImKC with PBS, Ctrl-Exo, and ANGPTL1-Exo in vitro for 24 h and analyzed gene expression by mRNA sequencing. GO enrichment analysis showed that the changing genes between ImKC educated by Ctrl-Exo and ANGPTL1-Exo mainly belonged to the extracellular region Fig. d, which P < 0.05). We also confirmed that the MMP9 decrease in ANGPTL1-Exo educated mouse livers  is intimately involved in regulating vascular integrity in PMNs . To explSince the tight junction proteins (TJs) were proved to regulate the EC layer permeability , we examTo further investigate how exosomal ANGPTL1 downregulated MMP9 expression in KCs, we conducted KEGG enrichment to analyze the RNA sequencing data. The results showed that the changing genes between ImKC educated by Ctrl-Exo and ANGPTL1-Exo were mainly enriched in IL-17, TNF, Toll-like receptor, and JAK-STAT signaling pathway Fig. a. Since ANGPTL1 has been reported to suppress tumor metastasis in several cancers , while iIn this study, we found that ANGPTL1 expression was downregulated in the exosomes derived from CRC tumor tissues than paired normal colorectal tissues. The exosomes containing more ANGPTL1 proteins attenuated liver metastasis and impeded vascular leakiness. Further exploration showed that exosomal ANGPTL1 regulated the KCs secretion pattern, significantly decreased the MMP9 expression by inhibiting the JAK2-STAT3 pathway, which in turn normalized vascular leakiness in live PMN Fig. e.An increasing number of studies have found that ANGPTL1 could be secreted in exosomes. Sinha A et al. identified ANGPTL1 in human ovarian cancer cell-derived exosomes by mass spectrometry . ANGPTL1Our functional experiments in mouse models of CRC proved that the exosomal ANGPTL1 upregulation inhibited liver metastasis. Consistently, early studies have proved that ANGPTL1 hindered tumor metastasis in lung cancer , hepatocOur study found that KC was the predominant cell that took up CRC-derived exosomes in the liver, which was also approved by Shao Yingkuan et al. . It indiLike proteins or microRNAs, exosomal cargos can regulate the recipient cells’ physiological activities . We demoHowever, how exosomal ANGPTL1 inhibited the JAK2-STAT3 pathway still needs further investigation. The way that exosomes transfer the content or induce signals may involve ligand-receptor interaction or cytomembrane fusion . SeveralThis study aimed to explore the function of exosomal ANGPTL1 in CRC liver metastasis. Our experimental results demonstrated that ANGPTL1 was downregulated in CRC derived exosomes. More importantly, exosomal ANGPTL1 attenuated CRC liver metastasis and impeded vascular leakiness. In mechanism, we found exosomal reprogrammed the Kupffer cell and decreased MMP9 expression through inhibiting the JAK2-STAT3 signaling pathway. These findings suggest a suppression role of exosomal ANGPTL1 on CRC progression, and expand the approach of ANGPTL1 functioning, enriching the mechanisms of CRC liver metastasis.Additional file 1: Fig. S1. Characterization of exosomes derived from SW620-Ctrl and SW620-ANGPTL1 cells. a Electron microscopy analysis of Ctrl-Exo and ANGPTL1-Exo. Scale bar, 100 nm. b The number of particles and median diameter was determined by dynamic light scattering (DLS) analysis. n = 3. Data were shown as mean ± standard deviation of three technical replicates. c Western blot analysis of exosomes positive biomarkers , negative biomarkers , and exosomal ANGPTL1.Additional file 2: Fig. S2. Immunofluorescent validation of the KC-specific localization of exosomes. a, b Immunofluorescence analysis of the colocalization of PKH67-labelled exosome (green) with αSMA+ Hepatic stellate cells (hStCs) (A) or CD31+ endothelial cells (ECs) (B) (red). Arrows indicate PKH67-labelled exosomes without fusion with hStCs or ECs. c Mice were injected with PBS (up) or liposome clodronate (low) for 2 days. Immunostaining of F4/80+ KCs (red) showed complete ablation of KCs in mouse liver after treated with liposome clodronate. d In vitro exosome uptake by ImKC at 2 h was observed on confocal microscopy. Phase contrast (left) and merged (right) pictures are shown.Additional file 3: Fig. S3. qRT-PCR, immunofluorescent, ELISA validation of MMP9 expression. a qRT-PCR validation of MMP9 expression in liver tissue from the mice educated by PBS, Ctrl-Exo or ANGPTL1-Exo, n = 3. b Immunofluorescence analysis of MMP9 in liver slices from the mice education by PBS, Ctrl-Exo or ANGPTL1-Exo, n = 3. c qRT-PCR (left) and ELISA (right) validation of MMP9 silencing in ImKC. Data are presented as the mean ± SD of three independent experiment, and analyzed using t-test. *P < 0.05.Additional file 4: Table S1. The stages of CRC patients and the corresponding exosomal ANGPTL1 index calculated by ImageJ from WB brands. The CRC stage was based on 7th revised edition of the AJCC Colorectal Cancer.


A pharmacogenomics-based pathway represents a series of reactions that occur between drugs and genes in the human body after drug administration. PG-path is a pharmacogenomics-based pathway that standardizes and visualizes the components (nodes) and actions (edges) involved in pharmacokinetic and pharmacodynamic processes. It provides an intuitive understanding of the drug response in the human body. A pharmacokinetic pathway visualizes the absorption, distribution, metabolism, and excretion (ADME) at the systemic level, and a pharmacodynamic pathway shows the action of the drug in the target cell at the cellular-molecular level. The genes in the pathway are displayed in locations similar to those inside the body. PG-path allows personalized pathways to be created by annotating each gene with the overall impact degree of deleterious variants in the gene. These personalized pathways play a role in assisting tailored individual prescriptions by predicting changes in the drug concentration in the plasma. PG-path also supports counseling for personalized drug therapy by providing visualization and documentation. These diagrams have proven to be powerful tools to organize, share and discuss knowledge. One type of diagram that visually illustrates the flow and actions of a drug in the human body is a pharmacogenomics (PGx)-based pathway, which represents a series of reactions that occur between drugs and genes in the human body after drug administration.  ResearchPGx studies analyze the genes involved in PK and PD and interpret how variations in these genes alter the function of proteins. Based on this interpretation, PGx studies aim to maximize the safety and efficacy of drugs by adjusting the dosage or changing the drugs themselves. –7 A PGx-As research on the interaction between drugs and genes has progressed alongside advancements in the Internet and systems biology,  many PGxPathway resources that utilize PK and PD in relation to drug-related genes include the Pharmacogenomics Knowledgebase (PharmGKB)  and the We propose the new knowledge-based PGx pathway: PG-path. We collected scattered drug pathways and standardized and visualized them to help users intuitively understand the PK and PD of a specific drug. In addition to its use in research, PG-path is designed to be used to assist personalized drug prescriptions by predicting the change in plasma drug concentration using the overall impact degree of deleterious variants in a gene, in the clinical field.The components of PG-path were generated on reference DrugBank 5.0.1.  The compUsing sequence homology, SIFT predicts whether an amino acid substitution will affect the protein function and consequently alter the phenotype. –18The 1000 Genomes Project is a comprehensive, open-access database of genetic variants from 2,504 individuals from 26 populations across the globe. , 20Variations in a gene that interacts with a drug can affect the function and activity of the protein encoded by the gene, resulting in an altered phenotype. The altered phenotype affects the drug's ADME, resulting in changed drug concentrations in the plasma, which shows different drug efficacy and toxicity from person to person. The GVB score, which indicates the degree of protein damage, is applied to the pathways, which is one method of the uses of PG-path. This GVB score was developed using the SIFT score.  The GVB Each PK/PD pathway is developed by applying the following method :Extract the drug-gene pair, drug interaction type, and drug action from DrugBank 5.0.1 for the selected drug Create a standard frame for the PK/PD pathway and produce a set of symbols Draw the nodes and edges of the PK/PD pathway using PathVisio software Save the data in the Graphical Pathway Markup Language (GPML) format and store the information in a PK/PD GPML folderDraw the background image for the PK/PD pathway using Illustrator CS6 Save the data in the Portable Network Graphics (PNG) format and store the information in the PK/PD PNG folderConvert the GPML file to the Scalable Vector Graphics (SVG) format using PathVisioRPC (PK/PD pathway)Merge the SVG file with the background image PNG file (PK/PD pathway) Link to an SVG + Hypertext Markup Language (HTML) file with gene and drug information windows and a PD description windowGenerate the complete PK/PD pathwayPathway diagrams were generated in the GPML format using PathVisio 3.2.4, and background images were created in the PNG format using Adobe Illustrator CS6. Each pathway diagram saved in the GPML format was converted to the SVG format using PathVisioRPC, an XML-RPC interface, after code modification.PG-path is a human-centered drug pathway, although antibiotic, antifungal, and antiviral drugs are included in PG-paths. Each pathway is produced by dividing the PK pathway, representing the ADME in the human body, and the PD pathway, describing the action of the drug at the target cell. PK pathways have been elucidated at the systemic level based on metabolism and transport, and PD pathways have been characterized at the cellular and molecular- level based on drug actions and responses.Prodrugs become active when they are absorbed and metabolized by the human body, and active drugs are already active in their present form. These two categories are distinguished by the same shape with different colors. Metabolites produced through enzymatic metabolism of a drug are also divided into inactive metabolites and active metabolites. These two categories are distinguished by the same shape in different colors as a drug. The genes encoding enzymes, transporters, and carriers play different roles; therefore, they differ in shape and color. After defining each node, we standardized the edges representing the actions between the nodes. The actions consist of metabolism, transportation, binding, and excretion. The edge corresponding to each action is selected using PathVisio's molecular interaction map (MIM) tool.  in your submission contain copyrighted images. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. 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Please note that we cannot proceed with consideration of your article until this information has been declared.Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” . The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The present manuscript describes a novel pharmacogenomics-based pathway, called PG-path that could help users to assist personalized drug prescriptions and counseling for personalized drug therapy. There is a lot of statistics and informatics in this manuscript, and I'm not qualified to go into all the algorithms used to generate the software. However, I really enjoied reading the manuscript and do believe that instruments that visually show all the pharmacokinetic and pharmacodynamic processes could be of help in the field.I have only some suggestions to improve the quality of the article:1) It is not clear to this reviewer if PG-path will be a commercially available tool or a free downloadable one. Please clarify.2) PG-path was designed based on available gene-drug information, and on data from the 1000 genome project. How can this pathway be continuosly updated with the growing body of information that NGS technology provides every day?3) Variants of the gene sequence are not the only ones that can impair the function and the activity of proteins involved in PK and PD processes. Is there any way to implement the path with epigenetic modifications and gene expression levels?Reviewer #2: The present manuscript entitled “PG-path: Modeling and Personalizing Pharmacogenomics-based Pathways” proposes a new method to improve, personalize and visualize pharmacogenomics pathways. Its approach is interesting and could enrich the pathways analysis tools. The manuscript is technically sound, written in clear and correct language, and the data support the conclusions.However, this article could be improved in some domains.1. It is not clear how users can access to these new personalized pathways, applying the method described in “Materials and Methods” paragraph. Is there a new tool, or is it being planned, to develop PK / PD pathways?2. The authors used SIFT to predict the effects of genetic variants. Since we talk about predictions without any support coming from functional studies, having only one predictive program could be limiting. Other predictive programs should also be evaluated, for example, VarSome, an analysis tool for human genetic variation that reports prediction scores from 20 different algorithms.3. The authors affirmed that this approach “addresses only genetic elements”. It would be advisable to speculate on this limit.4. The use of “gene deleteriousness” is inappropriate. It is more correct to state that the genetic variants are deleterious.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  6 Feb 2020Reviewer #1: 1) It is not clear to this reviewer if PG-path will be a commercially available tool or a free downloadable one. Please clarify.The objective of this study is modeling the pharmacogenomic pathway, so we do not determine yet whether PG-path will be available commercially or freely.2) PG-path was designed based on available gene-drug information, and on data from the 1000 genome project. How can this pathway be continuously updated with the growing body of information that NGS technology provides every day?We are currently working on the best methodology to incorporate the updates on time, though the detailed process will take time to determine.3) Variants of the gene sequence are not the only ones that can impair the function and the activity of proteins involved in PK and PD processes. Is there any way to implement the path with epigenetic modifications and gene expression levels?Epigenetic modifications are measured using chromatin immunoprecipitation (ChIP) and bisulfite-based methods, ChIP-chip and ChIP-seq, or RNA-seq. Gene expression is analyzed using microarray or RNA-seq. With our platform, the pathway is analyzed using DNA-seq. Therefore, it is not currently possible to apply epigenetic modification and gene expression level to PG-path. However, we will think carefully about these factors.Reviewer #2: 1) It is not clear how users can access these new personalized pathways, applying the method described in the “Materials and Methods” paragraph. Is there a new tool, or is it being planned, to develop PK / PD pathways?This paper only covers the steps of proposing a method for producing individual pathways. If the number of pathways increases, we are considering the creation of a website that can show results on the screen by searching distinct pathways in HTML format.2) The authors used SIFT to predict the effects of genetic variants. Since we talk about predictions without any support coming from functional studies, having only one predictive program could be limiting. Other predictive programs should also be evaluated, for example, VarSome, an analysis tool for human genetic variation that reports prediction scores from 20 different algorithms.In this study, pathway personalization was performed using the GVB score, defined as the geometric mean of the SIFT scores for the set of coding variants in a gene. However, evaluating the GVB score itself by applying various predictive scoring algorithms to GVB is an enormous task and should be conducted separately from this study.3) The authors affirmed that this approach “addresses only genetic elements”. It would be advisable to speculate on this limit.As mentioned, changes in drug concentration are influenced not only by genetic factors but also by clinical and environmental factors. These various factors will be investigated through further research.4) The use of “gene deleteriousness” is inappropriate. It is more correct to state that the genetic variants are deleterious.We have revised the term as you advise.AttachmentResponse to Reviewer.docxSubmitted filename: Click here for additional data file. 13 Mar 2020PG-path: Modeling and personalizing pharmacogenomics-based pathwaysPONE-D-19-31446R1Dear Dr. Kim,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact With kind regards,Giuseppe NovelliAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 23 Mar 2020PONE-D-19-31446R1 PG-path: Modeling and personalizing pharmacogenomics-based pathways Dear Dr. Kim:I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. For any other questions or concerns, please email Thank you for submitting your work to PLOS ONE.With kind regards,PLOS ONE Editorial Office Staffon behalf ofProf. Giuseppe Novelli Academic EditorPLOS ONE


To understand the function of the neocortex, which is a hierarchical distributed network, it is useful giving meaning to the signals transmitted between these areas from the computational viewpoint. The overall anatomical structure or organs related to this network, including the neocortex, thalamus, and basal ganglia, has been roughly revealed, and much physiological knowledge, though often fragmentary, is being accumulated. The computational theories involving the neocortex have also been developed considerably. By introducing the assumption “The signals transmitted by interarea axonal projections of pyramidal cells in the neocortex carry different meanings for each cell type, common to all areas,” derived from its nature as a distributed network in the neocortex, allows us to specify the computational meanings of interarea signals. In this paper, first, the types of signals exchanged between neocortical areas are investigated, taking into account biological constraints, and employing theories such as predictive coding, reinforcement learning, representation emulation theory, and BDI logic as theoretical starting points, two types of feedforward signals (observation and deviation) and three types of feedback signals  are identified. Next, based on the anatomical knowledge of the neocortex and thalamus, the pathways connecting the areas are organized and summarized as three corticocortical pathways and two thalamocortical pathways. Using this summation as preparation, this paper proposes a hypothesis that gives meaning to each type of signals transmitted in the different pathways in the neocortex, from the viewpoint of their functions. This hypothesis reckons that the feedforward corticocortical pathway transmits observation signals, the feedback corticocortical pathway transmits prediction signals, and the corticothalamic pathway mediated by core relay cells transmits deviation signals. The thalamocortical pathway, which is mediated by matrix relay cells, would be responsible for transmitting the signals that activate a part of prediction signals as intentions, due to the reason that the nature of the other available feedback pathways are not sufficient for conveying plans and intentions as signals. The corticocortical pathway, which is projected from various IT cells to the first layer, would be responsible for transmitting signals that activate a part of prediction signals as plans. Understanding the neocortex, which is responsible for cognition and behavior generation in the brain, is of major interest in neuroscience. At the same time, it is useful as a reference for constructing systems with human-like intelligence. The cerebral neocortex, if one considers a large number of areas  to be nodes, is organized as a hierarchical, distributed network that operates in a highly parallel manner  Model, a deep learning method, has been successfully engineered  values because of the use of neural activity spikes. However, in order to realize prediction coding, it is necessary to use a prediction error signal with both positive and negative codes. Although the mechanism for transmitting error signals between the neocortical areas has been investigated  task at a time properly while concentrating resources on it. This form of human behavior is based on the belief-desire-intent (BDI) logic Bratman, , as wellTaken together, reinforcement learning has provided a framework for value-addition using reward in the generation of action. The emulation theory of representation has shown that action is part of prediction, and the BDI logic has distinguished what is committed in the plan as an intention and what is immediately executable in the intention as a present-directed intention. Notably, plans and intentions in BDI theory implicitly include predictions for the future, since they are used by agents when they are feasible.Considering the above information, the feedback signals involved in the generation of action can be summarized as follows see . PredictFinally, In this chapter, based on anatomical findings for the neocortex and thalamus, all types of interarea pathways will be identified see . AlthougExcitatory cells of the neocortex are categorized into three major classes based on differences in axonal projection patterns and information coding , which we will mention later. For this reason, we do not consider it as a pathway that transmits information between areas of the neocortex, and exclude it in this paper.In this paper, the directionality of hierarchy are described as “ff” for feedforward and “fb” for feedback. Furthermore, corticocortical connections are abbreviated to “CC,” and thalamocortical connections are abbreviated to “TC.” For example, the feedforward corticocortical pathway is written “ffCC pathway.” In The largest class of excitatory neurons is intratelencephalic (IT) neurons, which project axons only within the terminal brain . Projection of IT neurons allows rapid information transfer by forming corticocortical connections that directly exchange signals between different areas, and by utilizing spike timing of neural activity. These IT neurons are the only type of excitatory cells that project to the contralateral cortex  in microcircuits. The axons projected into this layer are diverse IT neurons L2IT, L3ITL5IT in a wide range of neocortical areas and matrix-type thalamic relay cells  neurons in the sixth layer that project to these relay cells receive, in the local circuits, projections from a number of IT neurons, including L5 IT, and project mainly to the ipsilateral core-type thalamic relay cells. Core relay neurons project to the fourth layer, targeting a single cortical area  neurons (L5 PT) in the fifth layer project to these relay nuclei. This subtype is located downstream of IT neurons in the local circuit, and this group of neurons produces initial preparatory activities that last until the onset of motions . The other sub-type of L5 PT projects to the spinal cord, etc., and outputs pre-action preparatory activities and motor commands.In this chapter, five computationally meaningful signals (see Chapter 2) are assigned to five pathways (see Chapter 3) that connect the neocortex areas. Through this work, we make computational sense of the signals exchanged through each pathway.For feedforward signals, we observe that it is reasonable to assign observation signals to the ffCC pathway, and deviation signals to the ffTC pathway.Below is the rationale for assigning observation signals to the ffCC pathway.In order for a system to operate in response to the changing external world, it is desirable to update its beliefs about the state of the world with as little delay as possible. It should also be capable of conveying the information of a rich observation signal. Therefore, a feedforward corticocortical pathway should be assigned to the transmission of the observed signal with less delay and degradation of the information transmission. In other words, it would be reasonable to assign the observed signal to the ffCC pathway.Neocognitron, which is a visual information processing model, has alternating layers of S cells that extract features of figures and C cells that absorb the misalignment of features. This corresponds to L3 IT cells and L4 cells in the ffCC pathway, as shown in We state the rationale that it is appropriate to assign deviation signals to the ffTC pathway. The simple reason is that the only remaining feedforward path to be assigned is the ffTC pathway.As mentioned in section 2.3, the bottom-up attention realized by deviation signals involves processes such as selection and gain control of observation signals. These processes would be realized as a modulation on the pathway to receive the observed signal. The terminal end of the ffTC pathway mediated by core relay neurons is suitable for achieving bottom-up attention, for it projects to this fourth layer.For bottom-up attention to perform its function of selecting the observation signal, the deviation signal must be transmitted a parallel feedforward pathway to the observation signal. It is known that the ffTC path is a feedforward path that is roughly parallel to the ffCC path that conveys the observed information , the plan signals could be produced by activating a part of the prediction signals with the plannize signals. Since the intention signal is a desirable and committed part of the prediction signals (see section 2.4), intention signals could be produced by activating a part of the prediction signals with intentionize signals.Clearly, from the above discussion, it is appropriate to assign the prediction signals to the fbCC1 pathway.We explain below the rationale for assigning the conversion of plan signals into intention signals to the fbTC pathway.It is traditionally known that in the brain, behavioral decisions at various levels, such as the motor, oculomotor, prefrontal, orbitofrontal, and cingulate levels, are made in the basal ganglia loop that is governed by reward signals . This pathway is utilized in the basal ganglia of the cerebrum, which appear only in vertebrates in terms of phylogenetic development. Probably even before the basal ganglia emerged, plannize signals had been transmitted through the fbCC2 pathway that transmits from L5 IT directly to L1 layers in other areas. However, this pathway does not allow selection based on delayed reward and timing-control of plans to be executed. The basal ganglia and the fbTC pathway were probably added to improve this point.Therefore, both the role of the fbTC pathway in transmitting deviation signals, and the role of the fbTC pathway in transforming plan signals into intention signals, are likely to have been added later in terms of phylogeny.Although the corticocortical pathway is a direct connection that has sufficient capacity for transmitting observation and prediction signals, this pathway has difficulty, as the other side of it, in dynamically controlling the flow of information. To address this weakness, a mechanism called the synchronous coupling mechanism has been proposed  of the thalamus. The feedback of the reticular nucleus to the thalamus is related to the overall synchronization of thalamic neurons in slow-wave phases of sleep and in a certain form of epilepsy , which act as top-down attention, and the other involving high-frequency oscillations (gamma waves) of the ffTC pathway, which act as bottom-up attention, in the reticular nucleus of the thalamus.The neocortex is a system that recognizes and generates actions by a hierarchical network of representation. In this paper, two presuppositions are assumed. First, a neocortex consists of areas formed by uniform local circuits. Second, different pathways that connect areas transmit different meaning signals.Several theories, such as predictive coding, reinforcement learning, representation emulation theory, and BDI logic, are used as starting points for the study, and signals exchanged between domains are classified into five types, taking into account biological constraints. The following types of signals have been defined: observation signals that transmit information about the external world feedforwardly, prediction signals that transmit inference about the external world as feedback, deviation signals that transmit deviation of observation signals from prediction signals feedforwardly, plan signals that transmit desirable prediction to the system as feedback, and intention signals that transmit prediction which are both desirable and committed to its realization as feedback.Then, based on the anatomical knowledge of the neocortex and the thalamus, we identified the pathways connecting areas as three types of corticocortical pathways and two types of thalamocortical pathways. Then, as to the pathway for feedforward, we reckoned that the corticocortical pathway, which originates at IT cells in the third layer (L3IT) in an area and projects to cells in the higher-order fourth layer(L4), transmits observation signals, and the thalamocortical pathway, which originates at cells in the corticothalamic cells in the sixth layer(L6CT) in an area and connects to cells in the higher-order fourth layer via thalamic core relay cells, transmits observation signals. Of the three feedback pathways that exist, the one with the properties required to convey prediction signals is the corticocortical pathway, which originates at IT cells of the second layer(L2IT) of an area and connects to IT cells in the lower-order second layer. Therefore, we assigned prediction signals to this pathway.Thus, the thalamocortical pathway projecting from fifth-layer pyramidal tract cells (L5PTs) in the area through thalamic matrix relay cells to the lower first layer is assigned an intentional signal that activates a part of the predicted signal as an intention. The plannize signal, which activates a part of the prediction signals as plans, was assigned to cortical pathways that project from various IT cells in areas to the first layer in lower-order areas.In this paper, based on the above examination, a hypothesis that provides computational meaning to the five types of interarea signals in the neocortex was proposed. In the future, further examinations would be required to reveal the meaning of signals exchanged in brain organs such as the neocortex, thalamus, and basal ganglia from the perspectives of anatomy, physiology, and computational theory, based on their meanings in this hypothesis.In this paper, study of projections from the layer 6 pyramidal neurons, are limited only for projection to the matrix-type relay cells in the thalamus (ffTC pathway). There exists various subtypes of pyramidal neurons in layer 6 Thomson, . PyramidThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Hiccups are common; however, hiccups caused by sarcoidosis have rarely been reported. An unusual case involving a patient with persistent hiccups possibly caused by hilar/mediastinal lymph node enlargement due to sarcoidosis prompted us to perform a literature search. Eight case reports relating hiccups to sarcoidosis were found and in only one case were the hiccups thought to be due to thoracic lymphadenopathy (LAD). Most cases were attributed to involvement of the central nervous system (CNS) with sarcoidosis. Management of hiccups in general is unclear and only chlorpromazine is approved by the Food and Drug Administration (FDA) for treatment; multiple other pharmacological agents have been advocated mostly being ineffective. This case report describes a patient whose hiccups were likely caused by thoracic sarcoidosis. It reviews the mechanisms of hiccups, explores co‐morbid conditions associated with hiccups (including sarcoidosis), and provides some recommended treatments. Hiccups caused by sarcoidosis have rarely been reported. We present an unusual case of sarcoidosis presenting with persistent hiccups that we believe is caused by his hilar and mediastinal lymph node enlargement. Sarcoidosis is a chronic granulomatous disease most common in young adults and especially African Americans. According to the U.S. census data, there are over 25,000 new cases of sarcoidosis diagnosed in the United States annually, with 185,000 patients seeking medical care. Most patients have pulmonary involvement, although many other organs can be affected such as the heart, skin, eyes, and central and peripheral nerves. Here, we present an unusual case of sarcoidosis presenting with persistent hiccups that we believe is caused by his hilar and mediastinal lymph node enlargement.A 64‐year‐old male was referred to the pulmonary clinic because of an abnormal radiograph found during an evaluation for persistent hiccups. The hiccups developed after a procedure to excise a cyst on his left thumb. To determine the hiccup aetiology, the patient had a computed tomography (CT) scan of his chest, abdomen, and pelvis. He was found to have mildly enlarged mediastinal/hilar lymph nodes and pulmonary nodules as well as a left renal mass. Other than hiccups, the patient had no cough, sputum production, shortness of breath, chest pain, and no constitutional symptoms such as fever, night sweats, decreased appetite, or weight loss. Past medical history and review of systems were unremarkable.Physical examination demonstrated a healthy appearing male in no acute distress; his vital signs were normal; the skin had a large keloid on the anterior chest wall (long‐standing from an ingrown hair that was excised), no rashes, and no lacrimal gland enlargement; the lungs were clear both to auscultation and percussion; the heart showed a regular rhythm with no murmurs or gallops; the abdomen was soft and non‐tender, no masses or organomegaly were palpated; the extremities were without clubbing, cyanosis, or oedema; neurological examination showed no cranial nerve abnormalities with sensation and reflexes intact and no muscle weakness; and there was no supraclavicular, cervical, or axillary lymphadenopathy (LAD).Chest CT scan showed fibronodular changes in the lung apices, likely reflecting prior granulomatous disease, also seen were scattered pulmonary nodules and hilar LAD and mild right pre‐tracheal LAD Fig. . A positA pathological analysis of the pulmonary nodules and enlarged mediastinal lymph nodes was sought to make sure the patient did have not have metastatic renal cancer.The hiccups persisted despite multiple therapies, including proton pump inhibitors, a course of prednisone, and a variety of muscle relaxants. He first underwent endobronchial ultrasound without a definitive diagnosis. He then had a mediastinoscopy with removal of the pre‐tracheal lymph nodes, which made the diagnosis of sarcoidosis.Interestingly, after the mediastinoscopy, the patient's hiccups resolved. No further treatment was necessary, and the patient remained without further episodes of hiccups. The patient underwent a nephrectomy to remove the renal mass, which was a renal cell carcinoma. He is presently without any evidence of cancer or active disease with sarcoidosis, and he remains without further episodes of hiccups.A hiccup is defined as an involuntary contraction of the diaphragm causing inspiration, followed by closure of the glottis resulting in the “hic”cup sound, a protective reflex to prevent significant hyperventilation.Hiccups are commonly associated with abnormalities involving the neurological, gastrointestinal, and respiratory systems. Its mechanism involves the central and peripheral nerves, both afferent and efferent signalling. The reflex arc involves the afferent limb which includes the glossopharyngeal, vagus, and phrenic nerves, and sympathetic chain. Central mediators for hiccups are not well understood but are believed to involve the midbrain, including the medulla oblongata, periaqueductal grey matter, subthalamic nuclei, phrenic nerve nuclei, reticular formation, and hypothalamus. The hiccup centre is thought to be located in the midbrain (medulla) or high cervical spinal cord between C3 and C5. The efferent limb involves the nerves stimulating the glottis, the phrenic nerve, and inspiratory intercostal muscles. Although there is a lack of studies, hiccups generally result from lesions within the hiccup reflex limb , 11, 12.Persistent hiccups are defined as lasting for more than 48 h to one month, and chronic lasting for more than one month. The Guinness medical record breakers state that the longest attack of hiccups was by Charles Osborne (1894–1991) of Anthon, Iowa. He started hiccupping in 1922 while attempting to weigh a hog before slaughtering it. Physicians were unable to find a cure and he continued hiccupping until February 1990, for a total of 68 years.Serious health complications of hiccups involve malnutrition, weight loss, dehydration, insomnia, and even death in extreme situations. It is estimated that 4000 hospital admissions are due to hiccups annually. A report of 18 cases of persistent hiccups demonstrated the following co‐morbid conditions: vascular lesions (20%), post‐operative causes (18%), central nervous system (CNS) disease (17%), and duodenal ulcer (5%). Central causes of hiccups include various types of strokes such as lateral medullary infarcts, and other injuries to the CNS. Peripheral causes include mediastinal tumours, myocardial ischaemia, gastroesophageal reflux diseaseand chest trauma , 11. IntThere have been eight case reports prior to ours demonstrating a link between sarcoidosis and hiccups Table . Five ofAnother case  was a patient without CNS involvement but who had right hilar LAD and multiple mediastinal lymph node involvement on chest CT scan . HiccupsOur case report highlights a patient with persistent hiccups likely due to enlarged mediastinal lymph nodes; with removal of the mediastinal lymph nodes after mediastinoscopy, the hiccups subsided.The response to steroids in patients with hiccups due to sarcoidosis has been variable. In the small number of cases reported with predominantly CNS sarcoidosis, more than half responded to steroids alone or in combination with another drug . AlternaThe only case in the literature that attributed hiccups to sarcoid LAD  was unable to tolerate steroids . Kondo eAppropriate written informed consent was obtained for publication of this case report and accompanying images.