| Background Systemic lupus erythematous(SLE)is an autoimmune disorder which is characterized by inflammation in several organs,often with fluctuations in disease activity over time.Over the past ten years,our team has investigated the pathogenesis of SLE and the results indicated that the dysregulation of a variety of gene m RNAs,protein and(or)metabolite in the patients with SLE.Although these findings promoted the understanding of pathogenesis of SLE,the studies,which only focused on individual molecule,were unable to illuminate the complex biological responses.With the development of science and technology,scientists put forward the concept of omics,including transcriptome,proteome and metabolome.Omics can be used to identify and quantify as many molecules as possible in complex samples,such as tissue or blood.Then,according to the idea of case-control study,omics can be used to identify the different expressed molecules among the different groups,and further to carry out bioinformatics analysis based on these molecules.Gene ontology(GO)enrichment analysis and kyoto encyclopedia of genes and genomes(KEGG)enrichment analysis are two common methods.The former exhibited biological functions in which the different expression molecules are participated,and the latter showed signal transduction pathways in which the different expression molecules are involved.Generally,omics have focused on one type of biomolecule.For instance,transcriptome have focused on gene m RNAs;proteome have focused on proteins and metabolome have focused on metabolites.However,many biological reactions are regulated by the interaction between gene m RNAs,proteins and metabolites.Thus,it is necessary to integrate the data which obtained from different omics platforms.The study of the association between transcriptome with proteome expression data is one way.By comparing and analyzing the expression levels of gene m RNAs and protein,transcriptome with proteome can complement each other.In addition,the integration of data on transcriptome,proteome and metabolome KEGG enrichment analysis is another way,which provided new ideas to explore the pathogenesis of disease.Coagulation pathway and complement pathway are important components of the immune system.In recent years,it has been reported that coagulation pathway and complement pathway are mutually regulated and affect each other.Members of both pathways interact with each other either directly through influencing each other's activation or indirectly through the processes involving inflammatory mediators.In the patients with sepsis and/or thrombosis,the interactions between coagulation pathway and complement pathway were closely related to disease course.However,very little is known about the pathogenic role of the interactions between these pathways in SLE.In this study,by performing transcriptome,proteome and metabolome experiments,we set out to find the expression levels of gene m RNA,protein and metabolite in SLE patients and healthy controls,identify different expression molecules between the two groups,and further carry out bioinformatic analysis.In addition,we integrated data on the expression levels of molecules in transcriptome and proteome experiments to investigate the associations between gene m RNA and protein expression level.Furthermore,we integrated data on KEGG enrichment analysis in transcriptome,proteome and metabolome to find the key pathway in the pathogenesis of SLE.Finally, we investigate the associations between this pathway with SLE disease activity index(SLEDAI).The study has two parts.Part 1 The study of transcriptome,proteome and metabolome in the patients with SLEObjective To identify the different expressed gene m RNAs,proteins and metabolites in the patients with SLE and find the biological reactions and signal transduction pathways in which these molecules are involved.Methods Blood samples were collected from 24 SLE patients and 24 healthy controls.The transcriptome assays were performed by RNA sequencing(RNA-seq)using RNA extracted from leukocyte.The proteome assays were performed by isobaric tags for relative and absolute quantitation(i TRAQ)using plasma extracted from blood.The metabolome assays were performed by high liquid chromatography tandem mass spectrometry(LC-MS)using plasma extracted from blood.For the transcriptome experiment,within each group(SLE patients and healthy controls),equal amounts of sample from 8 different individuals were randomly pooled.Therefore,three biological replicate samples of SLE patient and healthy control were generated.The protocols which generate biological replicate samples in proteome assay were the same as that in transcriptome assay.In addition,in proteome assay,for each biological replicate,three technical repeats(T1;T2;T3)were processed.For the transcriptome experiment,we used the NOISeq package to identify differentially expressed genes.Probability ? 0.8 and an absolute value of log2 Ratio ? 1 were used as the threshold to judge the significance of gene expression difference.For the proteome experiment,we used the Mascot software to identify differentially expressed proteins.The differentially expressed proteins have been considered as significant if they met the following criteria:(1)fold change of ratio > 1.200 or < 0.833 and P < 0.05,(2)satisfied the first criteria in at least 2 of 3 technical replicates.Proteins that met these criteria were excluded if discordant trend in expression had emerged within the three technical replicates.For the metabolome experiment,we used the partial least-squares discriminant analysis(PLS-DA)and volcano figure to identify differentially expressed metabolites.The differentially expressed metabolites have been considered as significant if they met the following criteria: variable importance in the projection(VIP)? 1 in PLS-DA,fold change >1.500 or < 0.667 and Q value <0.05 in volcano figure.GO enrichment analysis was used to find the biological functions in which the differentially expressed molecules are involved.KEGG enrichment analysis was used to find the signal transduction pathways in which the differentially expressed molecules are involved.Then,we investigated the correlations between transcriptome and proteome expression profiles.First,we compared the protein expression profile which was obtained based on the reference gene with the transcriptome expression profile.When a certain protein was expressed at the transcriptome level,this molecule was considered to be related.Then,we used the pearson correlation coefficient to reflect the correlations of these related molecules between transcriptome and proteome expression profiles.Finally,we clustered these related molecules into four groups based on the pattern of changes at the gene m RNA and protein levels:(1)gene m RNAs and proteins have the same change trends;(2)gene m RNAs and proteins have the inverse change trends;(3)gene m RNAs are significantly changed while protein remains almost unchanged;(4) gene m RNA remains almost unchanged while proteins are significantly changed.Furthermore,we integrated data on KEGG enrichment analysis in transcriptome,proteome and metabolome to find the key pathway in the pathogenesis of SLE and identify different expression gene m RNAs,proteins and metabolites in this pathway.Additionally,a combined mapping of transcriptomic,proteomic and metabolic data to this pathway was performed to reveal the common links at the gene,protein and metabolite level for the components of this pathway.Results(1)Among the quantified gene m RNAs in transcriptome experiment,420 gene m RNAs showed significant change in expression pattern with 190 being up-regulated and 230 down-regulated in SLE patients when compared with healthy controls.GO and KEGG enrichment analysis of these differentially expressed gene m RNAs revealed biological processes such as lymphocyte activation,defense response and leukocyte differentiation as well as pathways such as T cell receptor signaling pathway,NF-kappa B signaling pathway and,of particular note,complement and coagulation cascades;(2)Among the quantified proteins in proteome experiment,87 proteins had increased expression and 24 proteins had decreased expression in SLE patients.GO enrichment analysis indicated the participation of differentially expressed proteins in a diverse array of biological function,including inflammatory response and cellular response to reactive oxygen species.KEGG enrichment analysis identified complement and coagulation cascades as the most relevant pathway;(3)In metabolome experiment,a total of 195 significantly altered metabolites were found in SLE patients.Using KEGG,metabolic pathways that were altered in SLE patients were identified,which involve steroid hormone biosynthesis,tryptophan metabolism,and,of interest,complement and coagulation cascades;(4)The transcriptional expression profiles do not necessarily correlate with the levels of corresponding proteins(r=0.174).Among the quantified proteins which correlated with gene m RNA expression profiles,the numbers of quantified proteins in the four groups as described under ‘methods' were:(1)5;(2)4;(3)15 and(4)0;(5)We integrated KEGG data from the transcriptome,proteome and metabolome and found many gene m RNAs,proteins and metabolites belong to complement and coagulation cascades are dysregulated in SLE patients.A combined mapping of transcriptomic,proteomic and metabolic data to this pathway revealed the common links at the gene,protein and metabolite level for the components of complement and coagulation signaling.Conclusion The dysregulation of a variety of gene m RNA,protein and metabolite was found in SLE patients.These molecules participate in many biological functions and signal transduction pathways.The transcriptional expression profiles do not necessarily correlate with the levels of corresponding proteins in SLE patients.The dysregulation of complement and coagulation cascades is crucial for the pathogenesis of SLE.Part 2 Study on the relationships between the coagulation and complement pathway with SLEDAIObjective To evaluate the associations between coagulation and complement pathway with SLEDAI,and test whether these associations are influenced by inflammatory responses.Methods We measured the levels of ten coagulations,seven complements and three inflammatory cytokines in SLE patients by enzyme-linked immunosorbent assay(ELISA).The coagulations included factor 7(F7),factor 9(F9),factor 12(F12),factor 13(F13),fibrinogen(FIB),thrombin-antithrombin complex(TAT),Von Willebrand factor(VWF),protein S(PS),D-dimer and antithrombin-III(ATIII).The complements included complement 3 activator(C3a),complement 4 activator(C4a),complement 5 activator(C5a),mannose-binding lectin-associated serine proteinase 2(MASP2),complement 1q(C1q),complement 7(C7)and factor I(FI).The inflammatory cytokines included tumor necrosis factor-receptor II(TNF-RII),interleukin-6(IL-6)and IL-8.In addition,the level of complement 4(C4)in SLE patients was measured by using nephelometric method.Clinical manifestations and laboratory abnormalities of SLE patients were retrieved from the medical records.A total of 112 SLE patients were recruited.We divided the 21 analytes mentioned above into two groups: the pathway activators,including C3 a,C4a,C5 a,MASP2 and C7(complement cascades);VWF,F7,TAT,FIB,F9 and D-dimer(coagulation cascades)as well as TNF-RII,IL-6 and IL-8(inflammatory responses),and pathway inhibitors or de-activators,including C1 q,FI and C4(complement cascades)as well as PS,F12,F13 and ATIII(coagulation cascades).Two approaches were used to achieve this: one use data from our proteome experiment and another was based on published literatures.The pathway activators were significantly elevated in SLE patients and their levels were positively associated with the level of the activation of the pathway.In contrast,the pathway inhibitors or de-activators were significantly decreased in SLE patients and their levels were negatively associated with the level of the activation of the pathway.The complement pathway score was calculated based on the levels of C3 a,C4a,C5 a,MASP2,C7,C1 q,FI and C4.When we calculated the complement pathway score,pathway activators were regarded as positive number while pathway inhibitors or de-activators were viewed as negative number.The details about step of the calculation of complement pathway score are as follows:(1)the normal distribution of concentration value of any analyte in 112 SLE patients was tested;(2)non-normally distributed variables were log-transformed;(3)concentration values were normalized across all samples so that the maximum value for any analyte was 1;and(4)values for each sample were then summed to derive the complement pathway score.The coagulation pathway score and inflammatory response score were calculated for each participant in a way similar to the calculation of the complement pathway score.The coagulation pathway score was calculated based on the levels of F7,F9,F12,F13,FIB,TAT,VWF,PS,D-dimer and ATIII.The inflammatory response score was calculated based on the levels of TNF-RII,IL-6 and IL-8.The normal distributions of coagulation pathway score,inflammatory response score,inflammatory response score and SLEDAI were tested by using K-S testing and the non-normally distributed variables were log-transformed.The Pearson correlation coefficient was used for correlation analyses.We assessed the effects of the independent variables on the dependent variablesby using multivariate linear regressions.Interaction was assessed as a test of a product term formed from two independent variables. Multiple group comparisons were performed,and significant differences among groups were assessed by least squares difference(LSD)test assuming equal variances or Tamhane's T2(M)test assuming unequal variances.Results(1)Coagulation pathway score and complement pathway score were independent(r=0.151,P=0.112);(2)The results of multivariate linear regression analysis for the associations between coagulation pathway score and complement pathway score on log-transformed SLEDAI(lt SLEDAI)score indicated that:(A)both coagulation pathway score(?=0.706,95%CI 0.371-1.040,P<0.001)and complement pathway score(?=0.590,95%CI 0.328-0.852,P<0.001)were significantly associated with lt SLEDAI;(B)a formal interaction test between coagulation pathway score and complement pathway score for lt SLEDAI yielded a statistically significant result(P<0.001).In the group of low complement pathway score,the relationship between coagulation pathway score and lt SLEDAI was weak;the difference in mean value of lt SLEDAI between the low with the high coagulation pathway score group was only 0.334.This difference increased to 0.831 in the group of high complement pathway score.Similarly,in the group of low coagulation pathway score,the relationship between complement pathway score and lt SLEDAI was weak;the difference in mean value of lt SLEDAI between the low with the high complement pathway score group was only 0.436.This difference increased to 0.933 in the group of high coagulation pathway score;(3)To examine the extent to which the interaction effect of the coagulation cascade and complement system on SLE disease severity varied according to the level of inflammatory response,inflammatory cytokine scores were divided into two groups by its median value(high cytokine score versus low cytokine score).In the high cytokine score group,both coagulation score(?=1.047,95%CI 0.602-1.491,P<0.001)and complement score(?=0.437,95%CI 0.130-0.744,P=0.006)were significantly associated with lt SLEDAI.Moreover,an interaction effect between the coagulation score and complement score was observed(P<0.001).Specifically,in the group of low complement score,the relationship between coagulation score and lt SLEDAI was weak;the difference in mean value of lt SLEDAI between the low with the high coagulation score group was only 0.391.This difference increased to 0.897 in the group of high complement score.Similarly,in the group of low coagulation score,the relationship between complement score and lt SLEDAI was weak;the difference in mean value of lt SLEDAI between the low with the high complement score group was only 0.359.This difference increased to 0.805 in the group of high coagulation score.In contrast,in the low cytokine score group,a formal interaction test between coagulation score and complement score for lt SLEDAI yielded a statistically non-significant result(P =0.406).Conclusion In the patients with SLE,coagulation pathway and complement pathway had an interaction effect on SLEDAI,and this effect was pronounced among patients with excess inflammation. |
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