In this study, we used the large number of cases in the FDA adverse-event reporting system (FAERS) database to investigate risk factors for drug-induced hiccups and to explore the relationship between hiccups and gender. From 11,810,863 adverse drug reactions reported between the first quarter of 2004 and the first quarter of 2020, we extracted only those in which side effects occurred between the beginning and end of drug administration. Our sample included 1454 adverse reactions for hiccups, with 1159 involving males and 257 involving females (the gender in 38 reports was unknown). We performed univariate analyses of the presence or absence of hiccups for each drug and performed multivariate analysis by adding patient information. The multivariate analysis showed nicotine products to be key suspect drugs for both men and women. For males, the risk factors for hiccups included older age, lower body weight, nicotine, and 14 other drugs. For females, only nicotine and three other drugs were extracted as independent risk factors. Using FAERS, we were thus able to extract new suspect drugs for drug-induced hiccups. Furthermore, this is the first report of a gender-specific analysis of risk factors for hiccups that provides novel insights into drug-induced hiccups, and it suggests that the mechanism responsible is strongly related to gender. Thus, this study can contribute to elucidating the mechanism underlying this phenomenon. Hiccups are occasionally experienced by most individuals, and they are mainly caused by diaphragmatic myoclonus . AlthougHiccups are a brief involuntary twitching of the diaphragm muscles accompanied by the coordinated contraction of the glottic-closure group of muscles . It has Based on their duration, hiccup bouts are classified as transient (lasting for less than 48 h), persistent (lasting less than 1 month but more than 48 h), or intractable (lasting more than 1 month) . No repoSeveral studies have investigated drug-induced hiccups. In particular, cisplatin and dexamethasone—used in chemotherapy for cancer treatment—have been reported as important suspect drugs ,8,9,10. The total number of adverse drug reactions reported between the first quarter of 2004 and the first quarter of 2020 was 11,810,863. Of these, we used 8,184,203 , for which there was a clear relationship between drug use and adverse drug reactions, as the analysis table. In the analysis table, hiccups occurred in 1454 reports (843 cases). The reported rate of hiccups was approximately 0.06%. The top three countries by number of reports for hiccups were the United States, Japan, and the United Kingdom, with 399, 113, and 47 reports, respectively .p < 0.001). The hiccup incidence rates in the separate male and female data tables and the percentage of all hiccups reports that are male are plotted as functions of age in p < 0.001). The mean weights of the individuals in the hiccup and non-hiccup groups were 74.37 ± 1.36 kg and 73.11 ± 0.01 kg, respectively, i.e., there was no significant difference (p = 0.36).The relationships between patient information and hiccups in the data tables for all reports, for males only, and for females only are shown in p = 0.04). Similarly, the mean weights of the individuals in the hiccup and non-hiccup groups were 75.81 ± 1.58 kg and 78.51 ± 0.06 kg, respectively, with no significant difference (p = 0.09).In the male data table, hiccups were reported in 622 cases . The meap < 0.001). Similarly, the mean weights of the individuals in the hiccup and non-hiccup groups were 69.72 ± 2.88 kg and 69.08 ± 0.05 kg, respectively, with no significant difference (p = 0.823).In the female data table, hiccups were reported in 193 cases . The meax-axis represents the natural logarithm of the reporting odds ratio (lnROR). The positive x-direction indicates that adverse effects associated with hiccups were reported more than other adverse effects. The y-axis represents the negative logarithm of the p-value [−log10] from Fisher’s exact test. The positive y-direction represents a strongly significant difference. In other words, for medications plotted in the upper right side of this figure, both the ROR and the significant differences were large. We defined such medications as high-signal drugs. The data for each scatter plot are attached as We produced an encyclopedic scatter plot of the relationship between the reporting odds ratio (ROR) and the significant differences in order to examine medicines suspected of causing hiccups and other drugs. This scatter plot, produced from the analysis tables, is shown in p-values of less than 0.001, and more than 5000 reported adverse effects. We then performed multivariate analyses on those drugs with the patient information. The results are shown in From these data, we extracted drugs with reporting odds ratios greater than unity, Fisher’s exact test From the scatter plot of the male data table , nicotinp-value of less than 0.001, with more than 3000 reported adverse effects. We performed multivariate analyses on those drugs with gender and weight in the male data table. The results are shown in We extracted drugs with an ROR greater than unity and a Fisher’s exact test p-value of <0.001, with more than 5000 reported adverse effects. We performed multivariate analyses on those drugs using gender and weight from the female data table. The results are shown in From the scatter plot for the female group, nicotine showed a particularly high signal. Unlike the male group, anticancer drugs, steroids, and psychotropic drugs were not extracted as high signal drugs in the female group. We extracted drugs with an ROR greater than unity and a Fisher’s exact test FAERS is a global database of adverse-event reports from the U.S., Japan, EU countries, etc. This database is much larger than other adverse drug reaction databases. In the analysis table used in this study, the United States had the highest number of reports, 756,468 (55.1%), followed by France with 99,088 (7.2%), and Japan with 96,960 (7.1%).The adverse-event reporting systems are different in each country. In JADER, Japan’s Adverse Drug Event Report database, medical practitioners and pharmaceutical companies report adverse drug reactions. On the other hand, in FAERS, which we used in this study, patients can report their own side effects. It is thus necessary to keep in mind not only the size of the database but also the characteristics of the data. Adverse drug reactions reported only by medical personnel may be more severe cases. On the other hand, minor side effects may be included if they are reported by the patients themselves.In this study, the number of hiccups reports was 1454 (843 cases), and the hiccup reporting rate was about 0.06% of all adverse drug event reports. Both the number of cases and the percentage of reports of hiccups were higher for FAERS. There is no point in making simple comparisons between the JADER and FAERS data because they were obtained at different times. The reason for the larger number of cases in FAERS is because it is a larger data set than JADER and because there are differences in the systems for reporting adverse drug events. As hiccups are not a life-threatening symptom, they are rarely reported by medical professionals, but they tend to be more severe when they are so reported. On the other hand, because these are unpleasant symptoms for the patients, the number of self-reports is higher, and minor hiccups may be included in reports.We also surveyed the countries reporting on hiccups and found some reporting bias but no findings of regional differences. In any case, it is difficult to examine regional differences in the incidence of hiccups from the FAERS data because of the large difference in the number of reports in each country.From the results of this study, approximately 74% of the reports of hiccups involved men. The incidence of hiccups in males tends to be less than the results obtained using JADER . As mentIn the results of multivariate analysis, older age was extracted as a risk factor for hiccups. A retrospective study of hiccups in hospitalized patients reported a trend towards a higher incidence of hiccups in infants with low birth weights and in patients after the age of 40 years . In the In this study, we found no association between hiccups and body weight, which is among the physical information included in FAERS. In previous studies, we have reported a relationship between low BMI and tall stature and hiccups ,12. The We performed an exhaustive univariate analysis of the association between hiccups and drugs using FAERS. We visualized the results using scatter plots . As hiccAs noted above, nicotine has been revealed as an important suspect drug for hiccups in both men and women, although there is only one case report of an association between nicotine and hiccups . The preIt is empirically known that smoking can cause hiccups, but the mechanism responsible for this is not clear. The inhalation of substances from smoking may irritate the lungs and induce hiccups as a vagal reflex. However, the sources of nicotine in FAERS are pharmaceutical gums or patches. Thus, the present study shows that the intake of nicotine other than by smoking can induce hiccups. This suggests that nicotine itself or its metabolites may be causing hiccups in the body.B agonist, has been shown to suppress hiccups, suggesting that the hiccup center is inhibited by GABA  was greater than 0.9, we concluded that there was an internal correlation. When there was no internal correlation, we treated these items as independent factors. All analyses were performed with JMP1Pro14 .We calculated means (±standard deviations) for all the continuous variables. We considered a In this study, we obtained new findings on risk factors for hiccups using FAERS, a worldwide database. There are many unanswered questions about drug-induced hiccups, and this study may help to elucidate the mechanism of drug-induced hiccups because it looked at the suspect drugs comprehensively. In addition, this is the first study to show that suspected drugs differ between male and female groups. This is a very important finding for the pathogenesis of drug-induced hiccups, and will aid future research on hiccups.In this study, we were able to report new suspect drugs for hiccups. In particular, nicotine was extracted as a common suspect drug for both males and females, which may indicate a new pathway for hiccups. We believe that further molecular–chemical investigations of the suspect drugs will lead to the elucidation of the mechanism responsible for drug-induced hiccups.We were able to show that the data mining method used in this study is a useful method for extracting suspect drugs for side effects that are rarely reported and for understanding patient characteristics.Elucidation of the risk factors and of the mechanisms that cause the onset and suppression of hiccups can contribute to a patient’s QOL in terms of prevention and treatment.


Idiopathic pulmonary fibrosis (IPF) is a devastating disease with a high clinical burden. The molecular signatures of IPF were analyzed to distinguish molecular subgroups and identify key driver genes and therapeutic targets.Thirteen datasets of lung tissue transcriptomics including 585 IPF patients and 362 normal controls were obtained from the databases and subjected to filtration of differentially expressed genes (DEGs). A functional enrichment analysis, agglomerative hierarchical clustering, network-based key driver analysis, and diffusion scoring were performed, and the association of enriched pathways and clinical parameters was evaluated.A total of 2,967 upregulated DEGs was filtered during the comparison of gene expression profiles of lung tissues between IPF patients and healthy controls. The core molecular network of IPF featured p53 signaling pathway and cellular senescence. IPF patients were classified into two molecular subgroups  via unsupervised clustering. C1 was more enriched in the p53 signaling pathway and ciliated cells and presented a worse prognostic score, while C2 was more enriched for cellular senescence, profibrosing pathways, and alveolar epithelial cells. The p53 signaling pathway was closely correlated with a decline in forced vital capacity and carbon monoxide diffusion capacity and with the activation of cellular senescence. CDK1/2, CKDNA1A, CSNK1A1, HDAC1/2, FN1, VCAM1, and ITGA4 were the key regulators as evidence by high diffusion scores in the disease module. Currently available and investigational drugs showed differential diffusion scores in terms of their target molecules.An integrative molecular analysis of IPF lungs identified two molecular subgroups with distinct pathobiological characteristics and clinical prognostic scores. Inhibition against CDKs or HDACs showed great promise for controlling lung fibrosis. This approach provided molecular insights to support the prediction of clinical outcomes and the selection of therapeutic targets in IPF patients.The online version contains supplementary material available at 10.1186/s12890-021-01749-3. Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by progressive scarring of the lung parenchyma associated with a steady worsening of respiratory symptoms and a decline in pulmonary function, ultimately leading to death . IPF is Molecular subgroups exist within some fibrotic diseases in which different clinical phenotypes or outcomes are presented –12. GeneIn this study, we compiled lung tissue transcriptome datasets from public data repositories to establish an IPF compendium and characterized the cellular and molecular features in detail. The samples were separated using data-driven, unsupervised clustering methods, and the clustered subgroups were subjected to prognostic profiling. Finally, we employed an integrative network-based approach and Bayesian inference to identify key drivers of the disease and evaluated the impact of current and investigational drugs in the context of the disease module.We used the keywords “idiopathic pulmonary fibrosis,” “interstitial lung disease,” “lung,” “transcriptomics, microarray, or RNA-sequencing,” “dataset” in Google Scholar and PubMed to find relevant publications on the topic of lung gene signatures of patients with IPF. We retrieved all publications that were accompanied by high-throughput datasets and ultimately selected 13 datasets with the GEO series (GSE) IDs GSE10667, GSE21369, GSE24206, GSE32537, GSE35145, GSE47460, GSE53845, GSE72073, GSE83717, GSE99621, GSE110147, GSE124685 and GSE150910  method (R package affy) was applied to the image data of a set of replicates for background correction, normalization, and probe-set summarization . For duap value < 0.01 as the significance threshold (R package limma) [In order to identify the differentially expressed genes (DEGs), we employed three independent methods: (a) an empirical Bayesian method (eBayes) using the Benjamini–Hochberg procedure with adjusted e limma) ; (b) thee limma) ; (c) mule limma) . An absop value was made using the Benjamini–Hochberg method for correction for multiple hypotheses testing and the terms were considered significant if the adjusted p value was less than 0.05. For biological processes or signaling pathways, gene-set enrichment analysis (GSEA) was conducted using Broad Institute software to assess overrepresentation [We performed a functional enrichment analysis focusing on upregulated DEGs using Enrichr software ; adjusteentation . Gene-seentation –27; the entation . The enrentation . The enrentation . To intuTo test for gene enrichment in individual samples, we used a single-sample gene-set enrichment analysis, which defines an enrichment score as the degree of absolute enrichment of a gene set in each sample within a given dataset . InformaTo assess the interconnectivity of DEGs in the IPF lung samples, we constructed a protein–protein interaction network based on the human interactome database . Graph tk), we computed the silhouette scores and the within-cluster sum of squared error for each k [t-distributed stochastic neighborhood embedding was used to confirm the unsupervised clustering results [To categorize the IPF patients into subgroups based on their molecular signatures, agglomerative hierarchical clustering was performed with the dissimilarity matrix given by Euclidean distance and Ward’s method was used to join similar clusters . To inter each k . The max results , a powerp value based on the simulated null distribution. False discovery rates were estimated using the Benjamini–Hochberg method and genes with values below 0.01 were considered key drivers.When mapped onto the protein–protein interaction network, disease-associated genes tend to co-localize and form networks of functionally related genes called disease modules. To predict genes that regulate the disease module, we performed a key driver analysis (KDA) using a previously defined algorithm that mathematically identifies causal modulators of the regulatory state of functionally interconnected gene groups (R package mergeomics) . The sigz-scaled Monte-Carlo method loaded in the R package diffuStats was used to calculate the diffusion score of the nodes based on the genes across the largest connected component of the network with default parameters [To quantify the leverage of each gene in the disease module, we employed label propagation and the network diffusion algorithm, which models heat flow from the seed genes through interactions in a protein–protein interaction network. The rameters .t-test. Categorical or dichotomous variables were compared using Fisher’s exact test. Correlation analysis between variables was carried out using Pearson’s method and Bonferroni correction. All analyses were conducted in R .For continuously distributed data, between-group comparisons were performed using the unpaired DSP gene, a variant of which are known to confer the risk of developing IPF [CCL18, CD28, CHI3L1, CLU, CXCL13, HSPA4, KRT19, MMP1, MMP7, MUC16, POSTN, SPP1, TNFSF13B, and VCAM1) [DSP) and three biomarkers .A total of 2,967 upregulated DEGs was filtered during the comparison of gene expression profiles of lung tissues between IPF patients and healthy controls  , 45. Thed VCAM1) . The LCCP = 1.37 × 10−11) and PI3K-Akt (P = 7.15 × 10−4). The most significant pathway, p53 signaling pathway, was confirmed by GSEA  , while cellular senescence, FoxO signaling pathway, PI3K-Akt signaling pathway, and TGFβ signaling pathway were more activated in C2 , whereas alveolar epithelial cells type I (AEC1), alveolar epithelial cells type II (AEC2), fibroblasts and macrophages were more populated in C2  and ciliated cell markers (FOXJ1 and MUC5B) were also highly expressed in C1  between the two groups  was most strongly correlated with the p53 and IL-17 signaling pathway   and apoptosis   were more highly expressed in C1 than in C2. The most differentially expressed gene in C1 was DSP , which was followed by MUC16 , a strong predictor for disease progression and mortality in IPF [P = 6.922 × 10−10)  and checkpoint kinase 1 (CHEK1) with high diffusion scores. CDK1 and CDK2 were KDGs and key elements of the p53 signaling pathway and cellular senescence.We collected information on the target molecules of 11 current treatment drugs and two investigational drugs  from a publicly available database and literature , 51–53 aIn the present study, we built a comprehensive transcriptomic compendium of IPF lung tissue and performed an integrative analysis to better understand the relationship between cellular and molecular expression patterns and clinical parameters. An unsupervised cluster analysis of the IPF transcriptomic profiles yielded two subgroups with different cellular and pathologic activities and prognostic risk profiles. Finally, we identified KDGs and molecules that may serve as promising targets for therapeutic intervention based on network-based Bayesian inference.CO, p53 and IL-17 signaling pathway activity could be an intriguing biomarker bridging the mechanistic feature and clinical condition in IPF.Biological functions in living organisms are orchestrated by the cooperative interactions of genes, proteins, and chemical compounds. Likewise, a complex disease is rarely the consequence of an abnormality in a single gene but rather results from the aberrant activation of pathways or disease modules by dysregulated genes and their linked neighbors. We identified the p53 signaling pathway as the most significant dysregulated pathway in IPF, and its enrichment score had a close correlation with cellular senescence and apoptosis in the lung tissue of IPF patients. This finding aligns with previously reported upregulation of p53 and activation of the p53 signaling pathway in response to the proliferation and hyperactivity of AECs leading to AEC apoptosis and senescence, the pathologic hallmarks of IPF lungs , 54, 55.MMP7, MUC16, and SPP1, which were powerful predictors of IPF progression [We identified two novel molecular subtypes of IPF using an unsupervised clustering method. C1 subgroup was more enriched with p53 signaling pathway and ciliated cell signature, indicating further transformation into fibrosed structural change, honeycombing state. Histologically, honeycomb cysts are lined with ciliated cells that express a variety of epithelial markers  and thisgression , 50. ThiCDK1, CDK2, CDKN1A, CDKN2A, and MDM2 were identified as KDGs involving both the p53 signaling pathway and cellular senescence. To better understand the KDGs in the disease module network, we calculated their diffusion scores. Fibronectin (FN1) is responsible for mediating cell–matrix adhesion and is essential in driving myofibroblast differentiation. Inhibition of FN1 deposition attenuated fibrosis in hepatic and cardiac fibrosis models [Current IPF therapies cannot effectively modify the disease’s clinical course and their efficacy is inconsistent, although the anti-fibrotic drugs pirfenidone and nintedanib have demonstrated to ability to significantly slow respiratory deterioration in some IPF patients , 2. Theis models , 59. FN1s models , 60. HDAs models . These rN-acetylcysteine, imatinib, and nintedanib ranked higher than did other drugs and were assumed to be more effective owing to their multiple targets. The performance of pirfenidone was likely underestimated because its exact targets and mechanisms are not clearly defined. Notably, the investigational drug UCN-01 (7-hydroxystaurosporine) showed good diffusion scores for its targets. UCN-01 targets CDK1, CDK2, and CHEK1, the main components of the p53 signaling pathway and cellular senescence, and reactivates FoxO3 to control its inappropriate proliferation and differentiation [To evaluate the use of current drug therapies in the disease module, we compared the diffusion scores of their target molecules. ntiation , 53. In This study had several limitations. First, the combination of multiple datasets inevitably caused the loss of genes that overlapped only among some datasets, and the correction of the batch effect was not ideal. Second, we did not address the association with clinical factors, such as radiographic pattern or fibrosis score, due to the lack of this information. Third, minority signatures of specific cell subsets might have been diluted because the gene expression signature was at the bulk tissue level. Fourth, the datasets did not provide detailed background medications for individual patients. Surgical lung biopsy protocol , 63, theIPF represents a major medical challenge with high unmet treatment needs. Our network-based integrative approach described discrete IPF subtypes with distinct cellular and molecular characteristics and revealed their significance in terms of clinical prognostic scales. Patient stratification could be leveraged to formulate customized therapies and improve clinical trial design. KDGs and target molecules were identified in the defined disease module, and particularly, inhibition against CDKs or HDACs afforded great promise of successful anti-fibrotic drugs. This not only explained the limitations of current pharmacotherapies but also provided insights into navigating new drug therapies.Additional file 1: Table S1. A summary of the datasets used in the study.Additional file 2: Figure S1. Principal component analysis on the compendium of IPF lung tissue transcriptomics before and after normalization and batch correction. Figure S2. Refinement of the DEGs. Figure S3. Pathway enrichment scores according to IPF subgroups. Figure S4. Correlation between pulmonary function parameters, pathway, and cell subset enrichment score. Figure S5. Volcano plot of expressed genes between two subgroups. Figure S6. Enrichment scores of prognostic markers according to IPF subgroups by gene-set enrichment analysis.


GBA2. This gene codes for the non-lysososomal β-glucosylceramidase, which is involved in sphingolipid metabolism through its catalytic role in the degradation of glucosylceramide. However, the mechanism by which GBA2 variants lead to the development of SA is still unclear.Spastic ataxias (SAs) encompass a group of rare and severe neurodegenerative diseases, characterized by an overlap between ataxia and spastic paraplegia clinical features. They have been associated with pathogenic variants in a number of genes, including GBA2 c.1780G > C missense variant. We further exploit DEGs in pathway analyses in order to elucidate candidate molecular mechanisms that are implicated in the development of the GBA2 gene-associated SA.In this work, we perform next-generation RNA-sequencing (RNA-seq), in an attempt to discover differentially expressed genes (DEGs) in lymphoblastoid, fibroblast cell lines and induced pluripotent stem cell-derived neurons derived from patients with SA, homozygous for the Our data reveal a total of 5217 genes with significantly altered expression between patient and control tested tissues. Furthermore, the most significant extracted pathways are presented and discussed for their possible role in the pathogenesis of the disease. Among them are the oxidative stress, neuroinflammation, sphingolipid signaling and metabolism, PI3K-Akt and MAPK signaling pathways.GBA2-associated SA patients and suggests pathways and pathway synergies that could possibly have a role in SA pathogenesis. Lastly, it provides a list of DEGs and pathways that could be further validated towards the discovery of disease biomarkers.Overall, our work examines for the first time the transcriptome profiles of The online version contains supplementary material available at 10.1186/s13578-022-00754-1. Spastic ataxias (SAs) comprise a group of rare neurodegenerative diseases that are characterized by an overlap between ataxia and spastic paraplegia clinical features. Their main symptoms include spasticity and weakness in the limbs, as well as gait and balance impairment, which are often accompanied by additional neurological or extra-neurological features. These diseases are also characterized by an early age of onset, usually before the age of 20 years. The neuronal structures that are mainly degenerated are the cerebellum, the brainstem, the spinocerebellar tract and/or the sensory tracts of the spinal cord, as well as the corticospinal tracts , 2.SPG7, SACS, FXN, ATXN1, CYP27A1, CAPN1, SETX, SYNE1, CACNA1A, AFG3L2, GBA2 and other genes  could possibly affect the identification of significant findings and under the hypothesis that tissue specific pathological mechanisms might exist, a pairwise analysis of the FCLs and LCLs was also considered as significant. Indeed, this analysis enabled the identification of additional interesting findings that could be associated with neurodegeneration, based on the existing knowledge. These include the top scored KEGG 2019 pathway “Proteasome”, the top scored Reactome 2016 pathway “Synthesis of 15-eicosatetraenoic acid derivatives”, as well as the “Nicotinate metabolism”, “Cholesterol biosynthesis”, “Nicotinamide salvaging”, “MAPK1/3 (ERK2/1) activation”, “Ubiquitin-dependent degradation of Cyclin D/D1” and “Regulation of cholesterol biosynthesis by SREBP”, which interestingly have been among the top ten enriched Reactome 2016 pathways, and some of them can be grouped under more common general pathways as discussed below.The ubiquitin–proteasome system (UPS), supported by both databases findings, is responsible for protein degradation. Its dysregulation can result to neurotoxic protein accumulation and aggregation, a major characteristic of several neurodegenerative diseases, such as AD, PD, and Huntington's disease (HD) . The “SyThe “Nicotinate and nicotinamide metabolism” describes a general pathway of vitamins and cofactors metabolism and involves the nicotinamide-adenine dinucleotide (NAD+) and nicotinamide-adenine dinucleotide phosphate (NADP+) precursors, nicotinate and nicotinamide . The NAD+ and NADP+ are essential coenzymes in redox reactions of respiration and are thus involved in energy production, while they also participate in other biological processes, such as gene expression, cell cycle, DNA repair and cell death . Both ni“Cholesterol biosynthesis” is part of the lipid metabolism; cholesterol, together with sphingolipids and phospholipids are key elements of membrane lipid bilayers . Under tFinally, the “MAPK signaling pathway” has been previously associated with a number of neurodegenerative diseases, including AD, PD and ALS, while it also contributes to neuroinflammation. Studies have also shown that the molecular mechanisms behind neuronal damage induced by oxidative stress involve a death mode in which MAPK signaling pathways are strongly implicated , 122.GBA2-caused SA and highlighted possible pathway synergies that could be further examined to draw valid conclusions.In addition to the above pathway analyses, the currently produced data have been further compared with our previous work data  towards GBA2-caused SA, this work presents information based on a limited sample size of iPSC-derived neurons (one patient and one control), FCLs (two patients and two controls) and LCLs (three patients and six controls). For this reason, two different approaches of pathway analysis were performed, and the results were evaluated and compared with our previous work on a large number of ataxia and spasticity phenotype gene expression datasets, as well as the bibliography. This work discusses the possible implication of the identified pathways in GBA2-caused SA pathogenesis; nevertheless, additional studies should be performed using a larger sample of patients, in order to establish stronger connections of the suggested pathways to the disease.Due to the rarity of GBA2 c.1780G > C variant, as well as of control individuals, and presents the identification of DEGs through patient-control comparisons. In addition, pathway analyses using the DEGs data highlight the most interesting pathways as candidates for the GBA2-associated SA, after performing further assessment using our previous work data and bibliography reports. Our results support the implication of oxidative stress to the disease that has previously been proposed as a pathogenic mechanism in other neurodegenerative diseases, and also provide information about the possible involvement of the MAPK, the PI3K-Akt/mTOR signaling pathways, as well as the neuroinflammation and the lipid metabolism dysregulation. In addition, our work strengthens the implication of the sphingolipid metabolism, in which GBA2 participates, and supports a possible cross-talk between the sphingolipid metabolism with some of the proposed pathways, as indicated by the in-silico network analysis. These results suggest that several pathways rather than a single, could be deregulated thus leading to the SA phenotype. Since some of the pathways were found to be connected through the in-silico approach performed, the effect of each pathway deregulation, as well as the synergies between these pathways could be investigated and confirmed or excluded through experimental studies. Moreover, because of the limitation of the small sample size that is included in the current study, additional similar or even larger sample-sized gene expression studies on SA patients diagnosed with GBA2 mutations could be performed, thus enabling comparison with the current findings or repetition of the data analyses after pooling more sample data (including this study samples). Furthermore, in case of lack of additional SA patient data, animal models and/or human cell lines could be used to functionally study the proposed pathways in disease and compare with control state. Such approaches might enable the extraction of more specific and significant findings including a possible exclusion of some pathways or the identification of novel. Furthermore, this study provides a list of common DEGs and pathways within tissues of SA patients that could be further validated in a larger sample size and in additional more easily accessible tissues (i.e. blood) towards the discovery of disease biomarkers. Overall, our work indicates GBA2-associated SA candidate pathways in human for the first time, and provides knowledge that could contribute to the disease pathogenetic mechanism determination.In conclusion, our work examines for the first time the transcriptome profile of SA patients homozygous for the Additional file 1: Figure S1. A) Bright-field microscopy of CTRL DANs at 45 days of differentiation showing typical morphology with elongated and branching processes B) ICC of the pan-neuronal marker TUJ1 expressed by CTRL DANs at 35 days of differentiation C) ICC showing expression of the neuronal (TUJ1) and dopaminergic (TH) markers in DANs cultures at 50 days of differentiation from one control and one patient’s line. Most neuronal cells exhibit co-expression of TUJ1 and TH, demonstrating their dopaminergic identity. Abbreviations: CTRL: control, DANs: dopaminergic neurons, TH: tyrosine hydroxylase, TUBB3: beta-III tubulin.Additional file 2.  Mapping percentages. The file includes the percentage of RNA-Seq reads mapped successfully against the reference genome for each sample analyzed.Additional file 3. Raw counts. The file contains the raw counts produced for each gene mapped against the reference genome.Additional file 4. Differentially expressed genes. The file contains the list of differentially expressed genes from the comparison between patients and control individuals for each cell line . The gene name is provided, along with the fold change (FC), the logFC, the log counts per million (CPM), the F-value provided by the statistical test, the P-value, the false discovery rate (FDR) and the absolute logFC (abslogFC).Additional file 5. Validation of selected genes—real-time PCR. The file contains the fold change (FC) and p-value of selected DEGs calculated from RNA-Seq and real-time PCR experiments. Statistical analysis was performed only for the LCL tissue.Additional file 6. HCA and HSP genes. The file contains HCA and HSP genes found differentially expressed in each of the three cell lines analyzed .Additional file 7. Common DEGs between the three cell lines. The file contains the common DEGs between the three cell lines and also between each pair of cell lines.Additional file 8. Enrichment analysis of the top 500 DEGs. The file contains the pathways that resulted from the enrichment analyses of the top 500 DEGs of each of the three cell lines analyzed .Additional file 9.  Enrichment analysis of the common DEGs between cell lines. The file contains the pathways that resulted from the enrichment analyses of the common DEGs between each pair of cell lines.


Arnebia. At present, there is no systematic analysis of its mechanism. Therefore, we used the method of network pharmacology to analyze the mechanism of RosA. In our study, PubChem database was used to search for the chemical formula and the Chemical Abstracts Service (CAS) number of RosA. Then, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to evaluate the pharmacodynamics of RosA, and the Comparative Toxicogenomics Database (CTD) was used to identify the potential target genes of RosA. In addition, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of target genes were carried out by using the web-based gene set analysis toolkit . At the same time, we uploaded the targets to the STRING database to obtain the protein interaction network. Then, we carried out a molecular docking about targets and RosA. Finally, we used Cytoscape to establish a visual protein-protein interaction network and drug-target-pathway network and analyze these networks. Our data showed that RosA has good biological activity and drug utilization. There are 55 target genes that have been identified. Then, the bioinformatics analysis and network analysis found that these target genes are closely related to inflammatory response, tumor occurrence and development, and other biological processes. These results demonstrated that RosA can act on a variety of proteins and pathways to form a systematic pharmacological network, which has good value in drug development and utilization.Rosmarinic acid (RosA) is a natural phenolic acid compound, which is mainly extracted from Labiatae and  Arnebia plants such as Rosmarinus officinalis, Melissa officinalis, and Hyssopus cuspidatus Boriss [Traditional Chinese medicine and natural compounds contain a large number of active components. This provides more possibilities and opportunities for the development and the use of drugs. Rosmarinic acid (RosA) is one of the water-soluble natural phenolic acid compounds . It is ws Boriss . Increass Boriss –7. Moreos Boriss –10. So Rs Boriss , 12. ThiIn consequence, we systematically analyzed the pharmacological effects of RosA by using the method of network pharmacology. Firstly, the server of TCMSP was used to evaluate the biological characteristics of RosA. In addition, we predicted the potentially related target genes by chemical-gene interaction analysis. Furthermore, these potential target genes were used for GO enrichment analysis, KEGG enrichment analysis, and molecular docking. Finally, we systematically explained the potential targets and mechanism of RosA by constructing the pharmacological relationship network of RosA. https://pubchem.ncbi.nlm.nih.gov/) is an open and accessible database resource, which contains important information resources such as drug discovery and chemical biology research [PubChem database  is one of the phytochemical databases. The database includes pharmacokinetic characteristics of natural compounds, such as oral bioavailability (OB), drug similarity (DL), blood-brain barrier (BBB), and water solubility [TCMSP Database and Analysis Platform (lubility . Most ofhttp://ctdbase.org/) is a database that aims to clarify the relationship between genes, drugs, and diseases. It provides key information about the interrelation between genes, gene interactions, disease, and chemical phenotype. The information in the database is selected from peer-reviewed scientific literature [CTD  is one of the online databases that can collect, score, and integrate all publicly available sources of information about protein-protein interactions. It can use computational predictions to supplement the existing information on protein-protein interactions. It achieves a comprehensive and objective global network, including direct  and indirect  interactions [The STRING 11.0 version  is one of the popular software devices for functional and pathway enrichment analysis [WebGestalt  and saved in PDB format. The solvent molecules and ligands were removed by PyMOL software. AutoDock software was used to add hydrogenation, electron, and root and carry out other operations. And the formats of compounds and target proteins were converted to pdbqt format. During molecular docking, the protein structure was set as a rigid macromolecule, and the algorithm was Genetic Algorithm Parameters. For the group with the lowest binding energy of each protein in the results, PyMOL was used to draw 3D images, and one online tool (https://proteins.plus/) was used to generate 2D graphics.Firstly, the chemical structure formula of RosA was downloaded from the CTD database and saved in mol2 format. Then, the 3D structures of target gene-associated proteins were downloaded from PDB data  , cellular component (CC), and molecular function (MF). BP enrichment mainly contained the following target genes: biological regulation (52/55), response to stimulus (51/55), metabolic process (49/55), multicellular organismal process (47/55), cell communication (43/55), developmental process (43/55), cellular component organization (38/55), localization (38/55), multiorganism process (34/55), and cell proliferation (26/55). CC enrichment was mainly involved in the following target genes: membrane-enclosed lumen (33/55), membrane (32/55), cytosol (32/55), vesicle (28/55), extracellular space (27/55), nucleus (27/55), endomembrane system (27/55), and protein-containing complex (21/55). MF enrichment was mainly engaged in the following target genes: protein binding (49/55) and ion binding (30/55). All the results are shown in At the same time, we carried out KEGG enrichment analysis of these candidate genes by WebGestalt tool. KEGG pathway enrichment analysis displayed that 55 potential target genes were enriched and 120 signal pathways were significantly related with the target genes (FDR ≤ 0.05). In In order to further clearly show the relationship between compound (RosA), targets, and pathway clearly, we constructed a drug-target-pathway network diagram by Cytoscape 3.7.2 version. It is generally believed that the lower the energy of the conformational stability of the ligand and the receptor, the greater the possibility of interaction. At present, there is no unified standard for target screening of active molecules. The active components with binding energy ≤−5.0 kJ/mol were selected as the basis for screening. The results of molecular docking showed that 13 of the selected target proteins had an affinity of less than −5.0 kJ/mol to RosA. The figure shows the best four docking results with the lowest binding energy. The results are shown in At present, the method of network pharmacology has been paid more and more attention during the process of drug development and utilization. This method can, firstly, evaluate, screen, and optimize some important characteristics of drugs, so as to speed up or simplify the process of drug discovery , 12. UsiLipinski's Rule of Five is a rule of thumb. It is mainly used to evaluate whether a compound can be used as a drug or whether a compound with pharmacology or biological activity can become a human oral drug. Lipinski's rule of five mainly includes the following criteria: the molecular weight (MW) ≤500 g/mol; an octanol-water partition coefficient log P (Log P) ≤5; the donor of hydrogen bond (nitrogen or oxygen atoms with one or more hydrogen atoms) ≤5; the number of hydrogen bond receptors (nitrogen or oxygen atoms) ≤10 , 25. TheDL is used to evaluate the “drug-like” degree of expected compounds. This helps to optimize pharmacokinetics and drug properties . The DL level of a compound is ≥0.18, which can be used as one of the screening criteria of the traditional Chinese herbs . The DL −1 as the screening criterion, we found that the binding energy of 13 of the 18 targets was less than −5.0 kJ·mol−1; that is, RosA may interact directly with these targets. So, they may be the key targets for the pharmacological action of RosA.In the process of drug discovery, the search for target genes is a very important link. More and more molecules, compounds, and drugs have been found to have complex relationships with many genes and proteins –40. At tWe did further GO analysis and pathway analysis of these potential genes through WebGestalt. According to the analysis of BP items, RosA is closely related to biological regulation, response to stimulus, metabolic process, multicellular organismal process, cell communication, developmental process, and so on. These results indicate that RosA may be related to anti-infection and antitumor. What is more, 9 of the first 20 enriched KEGG pathways are related to bacterial, viral, and parasite infections, such as pertussis, Chagas disease (American trypanosomiasis), salmonella infection, hepatitis B, amoebiasis, and toxoplasmosis. These results suggested that RosA may have antibacterial, antiviral, and anti-infective effects. Some of these pathways are related to inflammatory diseases and tumor diseases, for example, the AGE-RAGE signaling pathway in diabetic complications, colorectal cancer. Other studies suggested that RosA can inhibit the growth of cancer cells –43. In aOur studies showed that RosA has many pharmacological activities. At the same time, we analyzed the possible mechanism of action of RosA, which can be used to further develop safe and effective anti-inflammatory and anticancer drugs. Our work provides a new idea for the research, development, and clinical application of RosA. However, its shortcomings such as low oral availability and not easy to pass through the BBB also bring new challenges.


During the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. The prediction of an early warning signal (pre-disease state) before such deterioration is very important in clinical practice, especially for a single sample. The single-sample landscape entropy (SLE) was proposed to tackle this issue. However, the PPI used in SLE was lack of definite biological meanings. Besides, the calculation of multiple correlations based on limited reference samples in SLE is time-consuming and suspect.Abnormal signals generally exert their effect through the static definite biological functions in signaling pathways across the development of diseases. Thus, it is a natural way to study the propagation of the early-warning signals based on the signaling pathways in the KEGG database. In this paper, we propose a signaling perturbation method named SSP, to study the early-warning signal in signaling pathways for single dynamic time-series data. Results in three real datasets including the influenza virus infection, lung adenocarcinoma, and acute lung injury show that the proposed SSP outperformed the SLE. Moreover, the early-warning signal can be detected by one important signaling pathway PI3K-Akt.These results all indicate that the static model in pathways could simplify the detection of the early-warning signals. Theoretical considerations and computational studies suggest that many types of complex dynamical systems may have a critical points between an ordered and a disordered dynamical regime , 2. ThisMathematically, the dynamic behavior of complex systems can be characterized by state space. One state can be represented as the behavior of the composite elements in the system. In a relatively long time, some initial states of the system will eventually settle down to one of a limited set of stable states. Bifurcation theory demonstrates that complex systems may undergo a sudden state transition under some critical continuous perturbations of various internal or externals. Such a change often occurs at a critical threshold, or the so-called ‘‘tipping point’’, at which the system shifts abruptly from one state to another.The time evolution of complex diseases may follow the bifurcation theory, that is, a sudden health deterioration will occur during these diseases’ gradual progression at a tripping point time –17. For The execution of various physiological processes in cells is carried out by complex biomolecular systems. The massively parallel dynamics of complex molecular networks furnish the cell with the ability to process information from its environment and mount appropriate responses , 27. TheSignaling pathways in the KEGG database include a series of validated enzymatic reactions from biological experiments. And they can transmit the extracellular molecular signals into cells to exert effects through specific biological functions. The development of diseases results from the continuous perturbation of the various abnormal signals in some of these pathways. Therefore, Tarca et al. proposed a signaling pathway impact analysis (SPIA) method to determine significant disease-related signaling pathways by normal and disease samples . From thhttps://www.kegg.jp/kegg/pathway.html). The pathways in KEGG dataset are all hand-painted and validated by scientific pieces of literature. In this section, we compare the early-warning signals detection of the proposed SSP with SLE using all these signaling pathways.Complex diseases arise is due to the accumulation of differential expressions and signal perturbations in a subgroup of genes allowing uncontrolled biological functions. The accumulation of differential expressions and signal perturbations in signaling pathways is an invertible dynamic process before the biological functions become uncontrolled. Thus, when the accumulation changes in signaling pathways, it will signal a piece of pre-disease information. We use three single-sample datasets to illustrate how SSP works, including influenza virus infection (GSE30550), lung adenocarcinoma (LUAD) from the TCGA database, and acute lung injury (GSE2565). In this paper, we download 178 signaling pathways of human and 174 mmu signaling pathways from the KEGG PATHWAY dataset , the gene expression profiles of the first four time points, i.e., Baseline, 0, 5, and 12 h are regarded as reference samples. Figure Figure Figure As the perturbations of upstream genes usually contribute abnormal signals in a signaling pathway, we present the fold change of the 30 upstream genes in the PI3K-Akt signaling pathway in Fig. For dataset LUAD, the 58 tumor adjacent (TA) samples are considered as reference samples. Because of no individual-based samples across all time points, the expressions of genes are obtained by their average value in each time point. Metastasis is the culprit behind most cancer-related deaths and the ultimate challenge in our effort to fight cancer as a life-threatening disease . Stage IFigure For the acute lung injury dataset GSE2565, the samples collected from the air- or phosgene-exposed mice at 0 h are considered as reference samples. Because of no individual-based samples across all time points, the expressions of genes are obtained by their average value in each time point.Figure In the progression of complex diseases, a sudden health deterioration (pre-disease state) usually occurs due to the gradual accumulating effect of various internal or external factors. An early warning signal before this deterioration is essentially important for clinical practice. Some effective treatments could be applied to prevent the happening of the irreversible process of diseases. In recent years, Chen et al. developed a new framework to tackle this issue. Especially the SLE method can detect early warning signals based on the time point expression data of an individual through the correlation changes between genes.In this paper, we apply the signal perturbation in pathways to this framework to predict the early warning signals of complex diseases. Results from three datasets demonstrate that the proposed method SSP outperforms the SLE method in both the influenza virus infection dataset and LUAD dataset, and equals in the acute lung injury dataset. The consideration of the static model of pathways provides the proposed SSP method a relatively concrete biological mechanism to detect the essential changes. In addition, it reduces the computation complexity of the proposed SSP compared with the SLE method. Finally, based on the result by only the PI3K-Akt signaling pathway, the proposed method SSP can be further simplified by using some crucial pathways if enough data is available.However, the SSP method still has its limitations. SSP mainly relies on the signaling pathways from KEGG datasets. The biological information included in these pathways is incomplete. Thus, the SSP score may have a fluctuation under some circumstances. The noise in initial gene expression data is also the reason for the fluctuation of the SSP score. In addition, the strength of the interactions between genes in pathways is not considered in SSP. This may result in the loss of some real biological information.In this paper, a method called SSP to identify the pre-disease state of complex diseases is presented. SSP adapted a static biological model  to a dynamic system to character the dynamic change of system for an individual sample, which has a better performace than previous method SLE. Besides, benefiting from the use of static signaling pathways, the calculation of SSP method takes less time than previous method SLE. Therefore, SSP can identify the pre-disease state in less time that previous methods cannot.www.ncbi.nlm.nih.gov/geo), and data of the lung adenocarcinoma (LUAD) from the TCGA database. For all these omics genomic data, we discard the probes without the corresponding NCBI Entrez gene symbol. For each gene mapped by multiple probes, the average value is employed as the gene expression.We download three time-course or stage-course datasets including the the microarray data of influenza virus infection process (GSE30550) and acute lung injury induced by phosgene inhalation(GSE2565) from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30550) records influenza virus infection of 16 human adult subjects inoculated with live H3N2/ Wisconsin influenza virus [The dataset GSE30550 (The lung adenocarcinoma (LUAD) dataset includes 518 tumor samples and 58 tumor-adjacent samples. The tumor samples are divided into different stages based on clinical (stage) information from TCGA. Based on clinical information, the samples are grouped into ten stages, i.e., stage I, IA, IB, II, IIA, IIB, IIIA, IIIB, and IV of lung cancer (Table https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE2565) comprises expression profiles of the mouse with acute lung injury induced by phosgene inhalation. These lung tissues are collected from air- or phosgene-exposed mice at 0, 0.5, 1, 4, 8, 12, 24, 48, and 72 h after exposure  method to determine the significant disease-related signaling pathways by normal and disease samples . They deSignaling pathways in the KEGG database can transmit the extracellular molecular signals into cellcells to exert effects through specific biological functions. Complex diseases always associate with the abnormal biological functions that direct impacted by the most downstream genes. The accumulation of differential expressions and signal perturbations in signaling pathways is an invertible dynamic process before the biological functions become uncontrolled. The upstream genes in signaling may be very differential expression in the pre-disease state. As demonstrated by SPIA, the amount of signal perturbations in signaling pathways are directly impact by the expression value of upstream genes. Therefore, following SPIA, we adapt the dynamic time-series expression data to static signaling pathwaysin the same wayinstead of the correlations calculated in SLE.Then, the weighted signal perturbation (SSP score) for all genes in all signaling pathways at the time point If the SSP score has a sharp increase from the previous time point to a time point


Serum long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) interaction network was discovered to exert an important role in liver cirrhosis while little is known in mild hepatic encephalopathy (MHE). Therefore, we aim to systematically evaluate the serum lncRNA-mRNA network and its regulatory mechanism in MHE.  The data of serum mRNAs and lncRNAs were derived from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were calculated between 11 cirrhotic patients with and without MHE. Next, the biological functions and underlined pathways of DEGs were determined through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Finally, an interactive network between lncRNAs and mRNAs was built, and hub genes were identified, respectively.  A total of 64 differentially expressed lncRNAs (dif-lncRNAs) were found between patients with and without MHE, including 30 up- and 34 downregulated genes. 187 differentially expressed mRNAs (dif-mRNAs) were identified, including 84 up- and 103 downregulated genes. Functional enrichment analysis suggested that the regulatory pathways involved in MHE mainly consisted of a series of immune and inflammatory responses. Several hub mRNAs involved in regulatory network were identified, including CCL5, CCR5, CXCR3, CD274, STAT1, CXCR6, and EOMES. In addition, lnc-FAM84B-8 and lnc-SAMD3-1 were found to regulate these above hub genes through building a lncRNA-mRNA network.  This is the first study to construct the serum lncRNA-mRNA network in MHE, demonstrating the critical role of lncRNAs in regulating inflammatory and immunological profiles in the developing of MHE, suggesting a latent mechanism in this pathophysiological process. Mild hepatic encephalopathy (MHE) is a neuropsychiatric complication of severe liver cirrhosis accompanied by attention deficit, mild cognitive impairment, intellectual disability, and coordination disorders . It was Gene microarrays provide a large amount of analyzable data, which made it widely used in brain disorders . At presLong noncoding RNA (lncRNA), with length more than 200 nucleotides, plays an important role by regulating the gene expression. Those lncRNAs do not encode proteins but exert their action by interfering gene transcription involving inflammation, apoptosis, and oxidative stress –10. At pIn this study, serum transcriptome data of cirrhotic patients were obtained from the GEO database (GSE149741). Firstly, the differentially expressed genes (DEGs) of MHE were identified including differentially expressed lncRNAs (dif-lncRNAs) and differentially expressed mRNAs (dif-mRNAs). Then, the pathophysiological processes regulated by dif-lncRNAs were analyzed through Gene Ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Finally, the core lncRNAs were identified and their regulatory network with hub mRNAs was explored by constructing mRNA-lncRNA interaction network.https://www.ncbi.nlm.nih.gov/geo/), including 5 cases without MHE and 6 cases with MHE [limma package (v. 3.40.6) of R (v. 4.0.4).The original microarray dataset (GSE149741) was downloaded from the GEO database  | >1.5 were used as cut-off criteria. Heatmaps on dif-lncRNAs and dif-mRNAs were drawn using pheatmap R package. Volcano plots were generated using ggplot2 R package.The identification of differential gene expression was carried out using the  package . P < 0.0clusterProfiler package [P < 0.05 was regarded as cut-off criteria [GO function and KEGG pathway analyses of dif-lncRNAs were conducted by using  package . The GO criteria , 20.https://string-db.org/) is a database for interactive gene search to construct the protein–protein interactive network [The STRING database , activation of macrophage, and C-X-C motif chemokine ligand 5 (CXCL5) [As far as we know, liver fibrosis is often accompanied by systemic inflammatory responses, which could influence the brain function and play important roles in the pathogenesis of HE . In this (CXCL5) –33. Addiβ, interferon γ, tumor necrosis factor α, and ionized calcium binding adaptor molecule 1 (Iba1). In addition, as an important event of neuroinflammation, microglial activation was also detected in BDL rats. To ameliorate the inflammation, we tried to stimulate meningeal lymphangiogenesis by injecting adeno-associated virus–vascular endothelial growth factor C (AAV-VEGF-C), a potent lymphangiogenesis stimulatory factor, and we were surprised to find that the expression of those inflammatory factors was decreased by increasing lymph efflux and the motor function of the rats were recovered in some extent. Besides, we also found that the level of ammonia in the brain tissue of BDL rats did not increase significantly, suggesting that the inflammation may play a dominant role in the development of HE. In our study, the regulatory functions of lncRNAs in MHE were also found related to immune inflammatory processes. Considering trigger role of inflammation and immunophenotype in the development of MHE, lncRNAs might be a crucial component in this process with its immunoregulatory functions.As a common complication of cirrhosis, HE is largely influenced and deteriorated by alterations of the immune and inflammatory systems. D'Mello et al. discovered that the increase of circulating inflammatory factors induced by liver injury altered the neurological function in mice significantly . Görg etβ, IL-6, IFNγ, IL-17α, IFNλ2, and IFNλ3 [α [The lncRNAs-related pathways discovered in the enrichment analysis were mainly consisted of immune and inflammatory factors, which were implicated in HE according to preceding studies, including Toll-like receptors, steroid, cytokines, chemokines, cell adhesion molecules, AKT/JAK-STAT pathways, and antigen processing and presentation. For example, Jayakumar et al.  discovernd IFNλ3 . FurtherIFNλ3 [α . RegardiIFNλ3 [α , 42. SolIFNλ3 [α . MoreoveIFNλ3 [α  and JAK-IFNλ3 [α  showed sThis study still has certain limitations. First of all, only GEO database data was employed in our analysis, which lacked clinical samples to confirm. Meanwhile, the mechanism of regulatory network in MHE also needs to be further explored. In future investigations, rat MHE models should also be constructed in order to fortify the related molecular pathway mechanisms.In summary, based on bioinformatics analysis, our study showed that the serum lncRNAs-mRNA interaction network may play a critical role in cirrhosis-associated MHE by regulating the inflammatory and immunological systems, implying a latent mechanism in the pathophysiological process of MHE. Additionally, two core lncRNAs were identified including lnc-FAM84B-8 and lnc-SAMD3-1, which may serve as key targets for intervention in MHE. Additional research is required to demonstrate the therapeutic effectiveness of these lncRNAs.


Infantile hemangiomas (IHs) are the most frequent vascular tumors that occur during infancy. Microribonucleic acids (miRNAs) have been demonstrated as critical regulators of gene expression in various diseases. However, the function of miRNAs in IH still remains largely unknown. In the present study, we performed a miRNA microarray analysis of IH and identified 68 differentially expressed miRNAs (DEMs). In addition, miRNA-gene networks and protein-protein interactions were constructed, and the hub miRNAs and genes of IH were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used for biological analysis of DEMs and differentially expressed genes (DEGs). The pathway enrichment analysis of DEMs revealed several tumor-related pathways, including proteoglycans in cancer, signaling pathway regulating pluripotency of stem cells and TGF-beta signaling pathway. DEGs were mainly enriched in biological processes, including intracellular signal transduction, cell adhesion, and cell death. KEGG pathway analysis indicated that DEGs were enriched in tumorigenesis- and angiogenesis-related pathways such as proteoglycans in cancer, MAPK signaling pathway and Rap1 signaling pathway. Collectively, this study first established a comprehensive miRNA-gene network in IH, which should provide novel insights into IH pathogenesis and be beneficial to the understanding of neovascularization-related disorders. Infantile hemangiomas (IHs) are the most frequent vascular tumors that occur during infancy and childhood with a high incidence rate of 3–10%. They are more prevalent in females, low-birth weight, and premature infants . IHs typProliferative IH consists of massive proliferating capillaries and endothelial cells . It has Micro-ribonucleic acids (miRNAs) are small noncoding RNA molecules containing about 22 nucleotides that function in post-transcriptional regulation of gene expression. An extensive number of studies have demonstrated that miRNAs are involved in various biological processes, including tumor angiogenesis and vascular-related disorders . In addiThis study was approved by the Institutional Ethics Review Board of Shandong provincial hospital (No. 2016–309). Informed consents were obtained from the parents of the enrolled patients.Six samples were collected from October 2016 to June 2017 at the Shandong provincial hospital, including three proliferative IH tissues and three adjacent normal tissues. The diagnosis and phase of IH samples were confirmed by two independent doctors. All samples were immediately immersed in RNALater  overnight at 4°C and preserved at −80°C.Total RNA was extracted from frozen samples with TRIzol reagent  according to the manufacturer’s instructions. RNA concentration was determined by Nanodrop 2000 . The quality of RNA was measured using an Agilent 2100 bioanalyzer and an associated RNA 6000 Nano and Pico Lab Chip kits . MiRNA microarray was performed utilizing Gene Chip miRNA 4.0 . The complete microarray data were deposited in Gene Expression Omnibus (GEO) database [accession number GSE100682 (NCBI tracking system #18530520)].http://statweb.stanford.edu/∼tibs/SAM/), which calculates permutation-based FDR values (miRNA (GSE100682) profiles were used for identification of differentially expressed miRNAs (DEMs) in IH samples. Raw data from the miRNA microarray were normalized and summarized using the log scale robust multi -array analysis (RMA) method . The staR values . MiRNAs R values . The intR values  were theR values .p < 0.05 was considered statistically significant.Diana mirPath v3.0, a miRNA pathway web-server, was used for pathway enrichment analysis of DEMs . The DatSearch Tool for the Retrieval of Interacting Genes/Proteins (STRING) was used to explore the interaction of DEGs in IH . InteracThe deregulated miRNAs from proliferative IH and normal skin tissues as identified by microarray are shown as The heatmap of pathway analysis showed hub DEMs were mainly involved in proteoglycans in cancer, estrogen signaling pathway, TGF-beta signaling pathway, signaling pathways regulating pluripotency of stem cells, MAPK signaling pathway, etc .As shown in MYC), PH domain and leucine rich repeat protein phosphatase (PHLPP1), B-cell lymphoma 2 (BCL2), epidermal growth factor (EGFR), transforming growth factor beta 1 (TGFB1), integrin beta 1 (ITGB1), erb-b2 receptor tyrosine kinase (ERBB2), intracellular adhesion molecule 1 (ICAM1), protein tyrosine kinase 2 (PTK2), and vascular cell adhesion molecule 1 (VCAM1). A modular DEG analysis was performed with the plug-in MCODE of Cytoscape. A module consisting of 37 nodes and 113 edges was identified  and the constitutive photomophogenesis nine gene (COP9)  was significantly up-regulated in IH, which was consistent with the findings of e (COP9) . MiR-21-e (COP9) . Severale (COP9) . Over-exe (COP9) . Pathwaye (COP9) . The TGFe (COP9) .According to our study, a total of 475 DEGs were identified in IH, including 228 up-regulated and 247 down-regulated genes. GO analysis indicated DEGs were closely related to intracellular signal transduction pathways, cell adhesion, and cell death. KEGG analysis showed DEGs were mainly involved in the MAPK, Rap1, and HIF-1 pathways. Substantial evidence has suggested that IH proliferation was extensively associated with the HIF-1-VEGF angiogenic axis . The hubIn conclusion, we identified the miRNA profile of IH and constructed DEMs-DEGs regulating network by integration of miRNA and mRNA profiles. Several hub miRNAs and genes of IH were identified by bioinformatics, and the key pathways, which were closely related to tumorigenesis and angiogenesis, were elucidated. This study should be beneficial to comprehension of IH pathogenesis and may provide latent targets for IH diagnosis and therapy. Nevertheless, further pathological mechanism and clinical studies containing large samples are required in the future.


The relevance and implication of the expression of NUDT16 under septic conditions and the putative functional roles of this enzyme are discussed.Sepsis is an aberrant systemic inflammatory response mediated by the acute activation of the innate immune system. Neutrophils are important contributors to the innate immune response that controls the infection, but harbour the risk of collateral tissue damage such as thrombosis and organ dysfunction. A better understanding of the modulations of cellular processes in neutrophils and other blood cells during sepsis is needed and can be initiated via transcriptomic profile investigations. To that point, the growing repertoire of publicly accessible transcriptomic datasets serves as a valuable resource for discovering and/or assessing the robustness of biomarkers. We employed systematic literature mining, reductionist approach to gene expression profile and empirical  ADPadenosine diphosphateDCP1mRNA‐decapping enzymesGEOGene Expression OmnibusGXBGene Expression BrowserIL‐6interleukin 6LPSlipopolysaccharideNETsNeutrophil extracellular trapsPGNpeptidoglycanTNFαtumour necrosis factor alpha1Sepsis is an aberrant systemic inflammatory response mediated by the acute activation of the innate immune system.The anti‐inflammatory and immunosuppressive state of late‐phase sepsis resemble endotoxin tolerance in which the TLR4‐dependent pathway is non‐responsive to gram‐negative bacterial lipopolysaccharide  being consistently upregulated in septic patients compared with healthy individuals across multiple transcriptomic datasets, and 2) the absence of overlap between NUDT16 and sepsis in the current literature.In this study, 22.1Public repository of articles and data, such as PubMed and GEO, represents a vast resource but can be complicate to explore. Here, we present a logical reductionist approach to investigate putative novel biomarkers in sepsis. The approach can be applied to any field of research.The steps consist of (1) identifying a gene of interest based on its differential expression in the pathological/physiological context of interest, (2) validating the reproducibility of the initial observation, (3) determining the current body of literature linking the gene and topic, (4) extracting the known biological concepts regarding to the gene and (5) inferring putative novel roles to the gene with literature support.2.2GSE60424) was used to assess the expression of NUDT16 across leukocyte populations. The dataset consists of RNA‐Seq profiles of neutrophils, monocytes, B cells, CD4+ T cells, CD8+ T cells and NK cells isolated from blood of healthy controls, patients with type 1 diabetes, amyotrophic lateral sclerosis, sepsis or multiple sclerosis prior to and 24 hours post treatment with interferon beta (up to 20 subjects/cell type). NUDT16 expression profile is accessible for this dataset via the GXB data browsing web application (link). Significant differences (p < 0.0001) were determined by one‐way ANOVA with Tukey's multiple comparisons test.A dataset that we have previously generated and deposited in GEO .Datasets were obtained from GEO to be used to conduct independent validation of the initial finding in a relevant clinical setting. Validation data were selected without a priori knowledge of 2.52 for the desired time. For transcript abundance, the 6 h incubation was optimally selected based on previously reported time‐course data for NUDT16 expression (GSE3284).Heparinized diluted whole blood (WB) from healthy donors was exposed to media (control) or combined LPS/PGS at 100 ng/ml and 5 ug/ml, respectively, to simulate septic conditions. Samples were collected at 6 h post‐stimulation for total RNA and overnight for protein expression analyses. Briefly, whole blood  from each donor was collected via venipuncture into a heparin‐sulphate vacutainer (Becton Dickinson) and mixed at a 1:1 ratio with RPMI (Gibco). 500 ul of WB:RPMI mixture was added to cryovial or microwell already containing the specific stimulation. The samples were incubated in tissue culture incubator at 37°C with 5% CO2.6NUDT16 (NM_152395.3) ‐ F: 5’‐TTTCTCCTCCTGTTAGCCATTC‐3’, R: 5’ CCCTACCTGGCTCTTCTATACT‐3’: GAPDH (NM_002046.7) ‐ F: 5’‐GAAGGTGAAGGTCGGAGTC 3’, R: 5’‐GAAGATGGTGATGG GATTTC‐3’. Target gene expressions were normalized to GAPDH expression and are shown relative to the control samples (ΔΔCt method). The experiments were performed in duplicates and repeated twice.At the time of collection, cultured whole blood samples were mixed with PAXgene™ reagent (Qiagen), at a sample‐to‐reagent ratio of 1:2.76, and then gently inverted and stored at −80°C within two hours. Total RNA extraction was performed with PAXgene™ Blood RNA Kit (Qiagen) according to manufacturer's protocol. cDNA was synthesized from 500 ng of total RNA using SuperScript™ III First‐Strand Synthesis System (Invitrogen) and analysed by qPCR using SYBR Green PCR Master Mix (Thermo Fisher) and on a QuantStudio (Thermo Fisher) with the following thermal cycles: initial denaturation at 95°C for 10 min; 40 cycles of denaturation at 95°C for 15 s and annealing/extension at 60°C for 1min. For Melt curve analysis, the following thermal parameters were used as follows: (1) 95°C for 15 s, (2) 60°C for 1 min, (3) 95°C for 15 s and (4) 4°C, hold. Transcript of interest (RefSeq accession number in parentheses) was detected using the following primer pairs : 2.7Cultured whole blood phenotyping and immunostaining for surface and intra‐cellular markers were performed using the standardized protocols published by Cytobank.2.8GraphPad Prism v.5 (Graphpad Prism Software) was used for all plots and statistical analyses. Outliers determined by removal of outliers (ROUT) method (Q = 1%) and remove from median calculation. The Mann–Whitney test was used to identify significant differences between groups.3NUDT16 as the candidate gene based on its expression in healthy neutrophils exposed to plasma from septic patients compared with healthy individuals. This observation was made in a dataset generated in our previous work, which is publicly available in GEO under accession GSE49755,link). In brief, the study was conducted in the Northeast of Thailand and involved adult subjects who were hospitalized with symptoms of sepsis and showed culture positive results for either Burkholderia pseudomallei, the etiological agent of melioidosis, or other bacterial species. In vitro cultures of neutrophils isolated from blood of two healthy donors were exposed to serum samples fractionated from blood of healthy controls (n = 6) and individuals hospitalized with bacterial sepsis (n = 12). After 6 h of exposure to the serum, neutrophils were lysed, and total RNA extracted for profiling on the Illumina HT12 Bead arrays. We downloaded the data and processed them for outlier identification (ROUT method). NUDT16 expression profile .We identified e Figure  showed sNUDT16 is expressed in several immune cell types with the highest expression level in granulocytes and monocytes, suggesting a greater contribution from these cell types to the expression measured in whole blood . This expression pattern among blood cells is further supported by data from the Human Protein Atlas . A total of 38 articles was returned as of 6 January, 2022. However, we found no overlaps between NUDT16 literature with sepsis, inflammation or neutrophils  composed of different tissue matrixes and cohorts (summarized in Table p < 0.01) increases in NUDT16 expression in septic compared with non‐septic patients were observed in six datasets .SysInflam HuDB, we found that NUDT16 expression was consistently increased in patients with shock , and was higher in bacterial infections compared with other types of infections  . Melioidosis patients also exhibited higher NUDT16 expression compared with other causes of severe infection .The robustness of the increase in ts Table . Differein vitro experiments to have a closer look at NUDT16 expression in whole blood. We measured the NUDT16 levels in cultured whole blood  exposed to a combination of lipopolysaccharide (LPS) and peptidoglycan (PGN) or culture medium (control); we observed a 6.6‐fold increase in NUDT16 expression with stimulation to unstim  and summarized on the basis of their related biological concepts (Table (‘Nudix hydrolase 16’ [tw] OR NUDT16 [tw] OR H29K [tw]) AND (adp OR bp OR nudix OR proteins OR ‘adp ribose’ OR ‘ADP‐ribosylation’ OR ribosylation) (38 articles). When searching for ‘Human NUDT’ or ‘RNA decapping’, 35 and 38 articles were returned, respectively; combining these two concepts returned the same 38 articles as for ‘ADP‐ribose/ribosylation’. Using the same strategy, the concept ‘Cell growth and arrest’ returned 15 articles.To gain a general overview of the biological functions of NUDT16, the literature mining approach was re‐utilized. Biological concepts were manually extracted from the NUDT16 literature by using the NUDT16 query presented earlier, but this time restricting the search to title by using [ti] (instead of [tw] = ‘text words’). This search returned 10 articles. Keywords were identified and clustered using term frequency–inverse document frequency (TF‐IDF) and cosine distance function of Document Clustering package in R  AND inflammation identified 8 articles linking indirectly NUDT16 with inflammation  and returned 1396, 172, 506 articles respectively , which is a known mediator of pro‐inflammatory cytokine inhibition during sepsis.The NUDT16 protein is primarily associated with The role of NUDT16 in the context of sepsis or inflammation exhibits a knowledge gap and should be further explored experimentally. However, we showed that a joint interpretation of available literature and transcript profiling data conducted herein, along with basic experimental data, permitted inferences as to what role NUDT16 may play during inflammation and sepsis. We provided support for an indirect link between NUDT16 and inflammatory processes through ADP‐ribosylation and RNA decapping activities and infer the potential role for NUDT16 in the degradation of mRNAs and/or post‐translational regulation of inflammatory molecules during sepsis. These findings raise the question on what are the signalling pathways implicated in the modulation of NUDT16 expression in blood cells during sepsis.The authors confirm that there are no conflicts of interest.Shih Yin Huang: Data curation ; Investigation ; Validation ; Visualization ; Writing – original draft ; Writing – review & editing . Darawan Rinchai: Conceptualization ; Data curation (supporting); Investigation (supporting); Methodology ; Project administration ; Validation (supporting); Visualization (supporting); Writing – review & editing (supporting). Mohammed Toufiq: Data curation ; Investigation ; Software ; Validation ; Visualization ; Writing – review & editing . Basirudeen Syed Ahamed Kabeer: Data curation ; Methodology (supporting); Validation ; Writing – review & editing . Jessica Roelands: Data curation ; Validation ; Writing – review & editing . Wouter Hendrickx: Resources ; Writing – review & editing (supporting). Sabri Boughorbel: Software (lead); Writing – review & editing . Davide Bedognetti: Resources (supporting); Writing – review & editing (supporting). Nicholas Van Panhuys: Methodology (supporting); Resources ; Writing – review & editing . Damien Chaussabel: Conceptualization ; Data curation (supporting); Investigation (supporting); Methodology ; Project administration ; Resources ; Validation (supporting); Visualization (supporting); Writing – original draft ; Writing – review & editing . Mathieu Garand: Conceptualization ; Data curation ; Formal analysis ; Investigation ; Methodology ; Project administration ; Supervision ; Validation ; Visualization ; Writing – original draft ; Writing – review & editing .Susie The study was approved by Sidra Medicine's Institutional Review Board. Written informed consents were obtained from the healthy donors.All the authors consent for publication.Fig S1‐S2Click here for additional data file.