Exploring The Molecular Basis Of Preterm Birth Based On Multi-omics Data

Preterm birth（PTB）is defined as delivery at less than 37 weeks of gestation.It is the leading cause of neonatal death and the second leading cause of death in children under five,posing a serious threat to maternal and fetal health and quality of life.As a multifactorial syndrome,the specific molecular mechanisms of preterm birth are still unknown.This greatly limits the development and implementation of clinical prediction and prevention strategies,leaving the majority of pregnant women and their fetuses still exposed to potential risks.Therefore,systematic research from multiple perspectives and levels to explore the mechanism of its development,so as to build a more accurate diagnosis and treatment system,is an important cornerstone and key breakthrough to improve the clinical dilemma.At present,with the explosive development of high-throughput sequencing technology,precision medicine and omics research provide reliable support for solving the preterm birth puzzle.This project aims to explore the molecular basis of preterm birth based on multi-omics data and bioinformatic strategies,analyze the relevant biological changes and their regulatory networks at the macro level,and systematically investigate the molecular mechanism from a dynamic and holistic perspective at the multi-level of transcription,protein and metabolism,which is expected to analyze the key processes of preterm birth and explore potential therapeutic targets,thus bringing breakthrough reforms to perinatal diagnosis and treatment and effectively safeguarding the health of mother and fetus.Objective: To identify the preterm birth-related proteins and metabolic molecules,and to investigate their underlying biological mechanisms.Methods: Peripheral blood samples were collected from preterm and normal pregnant women and subjected to DIA protein mass spectrometry and UPLC-MS metabolic profiling,respectively.To reveal the overall protein and metabolic characteristics of preterm and normal pregnant women,and screen and identify the protein and metabolic molecules associated with preterm birth by bioinformatics strategies such as PLS-DA,difference analysis,cluster analysis,and WGCNA.Based on database and functional enrichment analysis to explore their potential roles in the occurrence of preterm birth.Results: We revealed the expression patterns of 475 plasma proteins in preterm and normal pregnant women,and identified 65 preterm birth-associated proteins.These proteins may be involved in preterm birth onset mainly through activation of immune inflammation-related pathways.Based on the abundance of 201 metabolites,we found significant metabolic differences between preterm and normal pregnant women.Multiple screening revealed that 21 of them may be key metabolites in the onset of preterm birth.Conclusion: The omics strategies revealed the presence of protein and metabolic disorders in preterm pregnant women from a macroscopic perspective,which may provide new targets for preterm birth prediction,diagnosis,and treatment.Objective: To explore the potential transcriptomic characteristics of PTB in the short term（<7 days）and to develop a clinical model for predicting PTB.Methods: Based on the m RNA microarray data,WGCNA,GSEA,and immune analysis were used to reveal the molecular biological basis of PTB within 7 days in TPTL women.LASSO and SVM algorithms were used to screen core genes from them.Then,the association between core genes and clinical features was explored using Pearson correlation analysis and plotting KM curves.Finally,clinical prediction models based on these genes were developed and validated in a prospective cohort.Results: We identified the 1090 key genes involved in PTB < 7 days in TPTL women and found their biological basis of immune-inflammatory activation（e.g.,IFN-γ,TNF-α signaling）as well as immune cell disorders（e.g.,monocytes,Th17 cells）.Then,four core genes（JOSD1,IDNK,ZMYM3,and IL1B）were screened and a prediction model with an AUC of0.907 was constructed,which was validated in a larger population（AUC =0.783）.Moreover,the predictive value（AUC = 0.957）and clinical feasibility of this model were verified through the clinical prospective cohort we established.Conclusion: We revealed the transcriptomic characteristics of PTB within 7 days,and developed and validated an effective clinical prediction model based on this.Our work offers the possibility to significantly improve the accuracy of clinical assessment of PTB risk,which is beneficial for the precise management of TPTL women,reducing maternal-fetal damage,and avoiding unnecessary waste of medical resources.Objective: To investigate the biological role of circ RNA,an emerging nucleic acid molecule,in the occurrence of PTB and its feasibility as a biomarker of PTB.Methods: RNA-seq data from the peripheral blood of preterm and term pregnant women were analyzed to reveal circ RNA disorders associated with PTB.Then,bioinformatic strategies such as constructing circ RNA-mi RNA-m RNA（ce RNA）networks,functional enrichment analysis,and Hub gene screening were used to identify the potential mechanisms of circ RNA in PTB.Finally,the sample size was expanded to assess the value of candidate circ RNAs as PTB biomarkers,which is validated using circ RNA microarray data.Results: There were 211 circ RNA expression disorders in PTB,of which 68 increased and 143 decreased.Bioinformatics analysis revealed that the top 20 differential circ RNAs competitively bind 68 mi RNAs,thereby regulating 622 m RNAs mainly related to immune inflammation,and nerve activity,which may ultimately contribute to the occurrence of PTB.Moreover,6 regulatory pairs,including hsa-MORC3₀001 —hsa-mi R-1248—CHRM2 were the core parts of this mechanism network,which might be therapeutic targets for PTB.Besides,ROC analysis indicated that hsa-ANKFY1₀025,hsa-FAM13B₀019,and hsa-NUSAP1₀010（AUC = 0.7138,0.9589,1.000）have an excellent discrimination ability for PTB.Conclusion: We explored for the first time the circRNA expression profile in peripheral blood of PTB,and preliminarily analyzed its regulatory mechanism and predictive value for PTB,thus bringing new light to the diagnosis and treatment of PTB.Objective: To reveal the overall expression pattern and potential role of circ RNAs within the maternal-fetal system in preterm birth.Methods: Based on whole transcriptome data,we extracted and analyzed the circ RNA expression profiles in maternal and fetal samples of preterm or term pregnancies,including maternal plasma,maternal monocytes,myometrium,chorion,placenta,and fetal peripheral blood.We identified the PTB-related circ RNAs in different tissues through bioinformatic strategies and explored their relationship from the perspective of the overall maternal-fetal system.Furthermore,the mechanisms of circ RNAs in PTB were revealed by analyzing their co-expressed m RNA,target micro RNAs（mi RNAs）,and RNA-binding proteins（RBPs）.In the end,we investigated the special biofunctions of circ RNAs in different tissues and their common features and communication in PTB.Results: Significant differences in circ RNA types and expression levels between preterm and term groups have been proved,as well as between tissues.Nevertheless,there were still several PTB-related differentially expressed circ RNAs（DECs）shared by these tissues.The functional enrichment analysis showed that the DECs have essential tissue-specific biofunctions through their target mi RNA and co-expressed m RNAs,which contribute to the signature pathologic changes of each maternal and fetal tissue in PTB（e.g.,the contraction of the myometrium）.Moreover,DECs in different tissues have some common biological activities,which are mainly the activation of immune-inflammatorypathways（e.g.,TGF-β,TLRs,NF-κB,and complement）.Conclusion: For the first time,we provide a macroscopic map for the expression and possible roles of circ RNAs in PTB,which develops a necessary guideline and lays the foundation for future research on the mechanisms of circ RNAs in PTB.