Placental immune factors change during the first half of healthy pregnancy

Placental immune factors change during the first half of healthy pregnancy | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Placental immune factors change during the first half of healthy pregnancy View ORCID Profile Maria Obolenska , View ORCID Profile Olexandr Lykhenko , View ORCID Profile Yevhenii Kukuruza , View ORCID Profile Volodymyr Martynenko , View ORCID Profile Yehor Poliakov , View ORCID Profile Olga Martsenyuk , View ORCID Profile Volodymyr Bezguba , View ORCID Profile Berthold Huppertz doi: https://doi.org/10.1101/2025.10.16.682794 Maria Obolenska 1 Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine , Kyiv, Ukraine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maria Obolenska For correspondence: o.k.lykhenko{at}imbg.org.ua Olexandr Lykhenko 1 Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine , Kyiv, Ukraine 2 Kyiv Academic University (KAU) , Kyiv, Ukraine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Olexandr Lykhenko Yevhenii Kukuruza 1 Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine , Kyiv, Ukraine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yevhenii Kukuruza Volodymyr Martynenko 2 Kyiv Academic University (KAU) , Kyiv, Ukraine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Volodymyr Martynenko Yehor Poliakov 1 Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine , Kyiv, Ukraine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yehor Poliakov Olga Martsenyuk 1 Institute of Molecular Biology and Genetics, National Academy of Sciences of Ukraine , Kyiv, Ukraine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Olga Martsenyuk Volodymyr Bezguba 2 Kyiv Academic University (KAU) , Kyiv, Ukraine Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Volodymyr Bezguba Berthold Huppertz 3 Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz , Graz, Austria Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Berthold Huppertz For correspondence: o.k.lykhenko{at}imbg.org.ua Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract During gestation, the human placenta develops as a fetal organ in direct contact with maternal cells and tissues. Most pregnancy complications, such as preeclampsia and fetal growth restriction, have their roots early in pregnancy, most probably in dysregulation of placental development and/or disturbances in the interaction with maternal (immune) cells. Here, we applied an integrative analysis of open-access gene expression data on the human placenta, comparing expression levels between the first and second trimesters of healthy pregnancy, with a focus on differentially expressed genes related to immune processes. The holistic approach of our study revealed differentially expressed genes involved in the recognition and elimination of potential PAMPs and DAMPs, transendothelial cell migration, cytokine production, transplacental IgG transport, and maintenance of immune tolerance during the transition from the first to the second trimester of placental development. Most DEGs show an increased expression. Only a few genes, DEFB1 , SLPI , and LCN2, which encode antimicrobial proteins, homeostatic chemokines, and a pleiotropic PAEP that maintains tolerance, display maximal expression in the first trimester, followed by further down-regulation. The cell types deconvolutions revealed the typical representation of placental cells. Introduction The human placenta is a temporary fetal organ that develops into a multifunctional morphological structure, maintaining a close connection between mother and fetus throughout pregnancy and ensuring its successful completion. Disturbances and alterations of placental development and functioning are the cause of pregnancy complications, including fetal growth restriction (FGR) (3-7% of all pregnancies) and preeclampsia (5-10% of all pregnancies 1 , 2 , 3 ), which can have long-term effects on both the mother and the fetus 4 . The early-onset subgroup of preeclampsia (high blood pressure or/and proteinuria observed starting around the 20 th week of gestation) accounts for about 15-20% of all preeclampsia cases. Due to the lack of a reliable, cost-effective early screening test, well-established primary prevention measures or specific treatment options are a pressing issue 5 . The etiologies of the early-onset subgroups of FGR and preeclampsia are considered related to adverse placentation and trophoblast invasion, where immune processes and cellular interactions play pivotal roles 1 , 6 . The maternal organism has to tolerate the persistence of paternal alloantigens without affecting the anti-infectious response against potential pathogens or damaged self-material 7 , 8 . So far, case-control studies on the human placenta have investigated the expression of single immune-related genes. Other studies have characterized biological processes as part of transcriptome profiling and have identified a number of genes participating in these processes 8 , 9 . This study aims to analyze the changes in large-scale gene expression related to immune processes in the human placenta during the critical first half of a healthy pregnancy. To address this challenge, we used gene expression data on the human placenta in health and disease from open-access databases. This data is available for secondary analysis. We identified 250 differentially expressed genes (DEGs) between the first and second trimesters and 54 DEGs related to the immune processes. We grouped the genes of the immune cluster according to their predominant functions and identified changes in gene expression in the complement system, receptors responsible for recognition and further elimination of pathogen- and damage-associated patterns, activated diapedesis and proinflammatory chemokine and cytokine production, and maintenance of immune tolerance pointing to an activated proinflammatory response. The described changes in gene expression in the human placenta during the transition period from the first to the second trimester may serve as a reference point for investigating the processes in the pathology-affected placenta. 2. Methods (see Document S1. Table S1) Data for the analysis was obtained from the Gene Expression Omnibus (GEO) and BioStudies (ArrayExpress) databases. As a first step, we developed the specialized IGEA (integrative gene expression analysis) database [ https://molecularbiology.ink/igea/ ] containing the metadata for the genome-wide transcriptome profiling of more than 1000 samples of normal and pathology-affected human placentas. The metadata include information on the biological specimen, diagnosis, gestational age, mode of delivery, and other characteristics, which we previously standardized 10 , 11 . Methods used in this study are described in Lykhenko et al., 2021a,b 12 , 13 . In short, raw microarray data was extracted from GEO and BioStudies (ArrayExpress) databases, complemented with standardized metadata from the IGEA database, and integrated into one dataset using the empirical Bayes method for cross-normalization and batch effect removal. The method of integrative analysis overcomes the problem of small sample sizes in experiments, reduces the bias of various processing strategies, and essentially, presents the transcript levels in quantitative relative units 13 , 14 . To assess the quality of batch effect removal, we employed principal component analysis (PCA) to project the original multi-dimensional data into a two-dimensional space ( Fig. 1 ). Figure 1A depicts the data distribution before batch effect removal. It is apparent that, in terms of gene expression, samples from datasets GSE122214 and GSE9984 differ most both from each other and from the samples of the other two datasets (principal components dim1 and dim2), even though all expression data belong to the first and second trimesters. These datasets introduce the greatest dispersion (dim1 and dim2). The technical variability disappears after batch effect removal ( Fig. 1B ). By contrast, the difference between first- and second-trimester samples obscured by batch effect ( Fig. 1C ) became the primary source of variability after its removal ( Fig. 1D ). Moreover, the gene expression data can also be grouped by fetal sex, as shown along components dim3 and dim4 ( Fig. 1E ). Download figure Open in new tab Figure 1: The verification of the batch effect removal during the integration of gene expression data. The PCA plots depict the data distribution before (A and D) and after (B, D and E) batch effect removal. We used generalized linear models implemented in the R package limma to identify DEGs and the Benjamini – Hochberg method to determine the corrected p-value (false discovery rate, FDR) of the difference in gene expression between two time points 15 , 16 .. For this study, we focused on differences between trimesters. Therefore, we included pregnancy trimester and fetal sex as covariates at the batch effect removal stage to preserve as much biological variation as possible and prevent sex differences overcorrection as batch effects. However, when determining differentially expressed genes in this article, we grouped our expression data by trimester. In our earlier work, we identified moderate autosomal differences (PUDP, CACFD1, NCAM2, YIPF6, PLXDC1, BTBD1, and GRINA) in placentas during the first half of pregnancy that correlate with fetus sex 17 , 18 . We constructed a graph of association and interaction between proteins encoded by DEGs with data from the STRINGdb 19 using the R STRINGdb package. Then, we clustered and visualized the graph using the fast greedy algorithm from the R igraph package 20 . In the resulting clusters, we identified functionally enriched groups of genes based on the Gene Ontology database using a hypergeometric test, as in Franceschini et al., 2013 21 . The R packages used for the pipeline were obtained from the Bioconductor website bioconductor.org . Cell type proportions in placental samples were estimated using the nonnegative least squares method of cell-type deconvolution, as implemented in the R MuSiC package 116 , and the placental deconvolution reference developed by Campbell et al. (Seurat object in GSE182381) 117 . Gene Set Enrichment Analysis (GSEA) was performed using the gseGO function from the clusterProfiler R package, focusing on Gene Ontology Biological Process (BP) terms 118 . Genes for GSEA analysis were ranked by π value, a parameter calculated by multiplying logFC and adjusted p-value. 3. Results and Discussion To analyze changes in gene expression levels between the first and second trimesters in healthy placentas, we used samples of villous tissues from 6.4 to 9 weeks of gestation and 13 to 19 weeks. The samples were obtained after uncomplicated elective termination. The authors of the original papers stated that the electively terminated first-trimester pregnancies had no maternal or fetal clinical complications. The mothers were not exposed to drug or steroid hormones and had a record of regular menstruation. They had no clinically confirmed miscarriages in their medical reproductive history. No fetal anomalies were detected for the second-trimester placentas [see Document S1, Table S1, and references therein]. Analysis of gene expression data on the human placenta from the first and second trimesters revealed 250 differentially expressed genes (DEGs). Here, we focused on genes related to immune processes due to the unique immunological complexity of the maternal-fetal interaction during the transition between the first and second trimester of healthy pregnancies [see Document S1, Table S2]. Since the complex features of biological processes are often caused by the combined effects of quantitatively small but numerous changes in gene expression and more substantial but less numerous ones 22 , we selected the DEGs with a fold change of logarithm to base 2 │LogFC│>1.0 and set the threshold for the applied adjusted p-value at <0.05. Focusing on the immune processes, we arranged the genes into functional groups according to their primary previously annotated functions. We decided to analyze only genes that have been proven to play a role in the immune response. However, it is quite probable that other multifunctional proteins indirectly contribute to the reaction to pregnancy as well, They will not be covered in this study, as even the well-established immune pathways are complex enough to warrant further investigation. In the text below, the DEGs, their corresponding proteins, and LogFC values are highlighted in parentheses and bold font format. 3.1. Recognition and elimination of potential pathogen-associated and damage-associated molecular patterns The genes assigned to this group recognize and eliminate potential pathogen-associated and damage-associated molecular patterns, PAMPs and DAMPs, by different mechanisms through host proteins specifically bound to the antigens (opsonin receptors) or by direct binding (non-opsonin receptors). The involvement of the complement system in placental immune response Our data revealed the involvement of the complement system’s classical, lectin, and alternative pathways in the placental immune response. The C1QA ( 1.37 ), C1QB ( 1.33 ), and C1QC ( 1.12) genes encode opsonin receptors that initiate the classical pathway ( Fig. 2 ). The respective proteins combine into the C1q complex that is instrumental in recognizing the antigen-antibody immune complexes and initiates autocatalytic activation of the classic complement cascade 23 , 24 . C1q, as a primary link between innate and acquired immunity 25 , is predominantly detected in the maternal part of the placenta between endovascular trophoblasts invading maternal vessels and decidual endothelial cells 26 , 27 . Download figure Open in new tab Figure 2: The participation of differentially expressed genes (DEGs) in the complement system. The schematic drawing displays the complement system with its three pathways, the classical pathway via C1, the lectin pathway via MBL, and the alternative pathway via C3-H 2 O. DEGs are shown with the fold change in red (increased expression in the second trimester) or black (decreased expression in the second trimester compared to the first trimester) under the gene name. Anaphylatoxins are marked with a green frame. Schematic drawing adapted from Yednock et al., 2022 32 . Unlike these proteins, C4b-binding protein alpha ( C4BPA, -2.20 ) inhibits the major fragment C4b of the central protein C4 in the classical and lectin pathways 23 . A decrease in C4BPA gene expression might reduce (or eliminate) the inhibitory activity of its protein and thus support the further reactions of the complement cascade with the active C4b fragment 28 . Complement factor B ( CFB , 1.04 ) facilitates the catalytic activity of C3 convertase in the alternative pathway 29 . C3 convertase cleaves C3 into its active components, C3a and C3b. C3a elicits activity via the C3A receptor 1 ( C3AR1, 1.19} and C3A/ C3AR1 may activate T cells and macrophages and stimulate angiogenesis, chemotaxis, cytokine release, and mast cell degranulation 30 , 31 . The C7 protein ( C7 , 2.06 ) is a component of the MAC complex, which destroys the pathogen’s membrane and the pathogen itself. The elevated expression of C7 aligns with the results of Bulla et al. 2012 27 , 32 . The recognition receptors during the first to second trimester transition The scavenger receptors are non-opsonin receptors and directly bind to PAMPs and DAMPs 33 . Platelet glycoprotein 4 ( CD36, 1.72) , for example may recognize various bacteria, viruses, and numerous endogenous ligands such as lipoproteins and HDL (high-density lipoprotein). CD36 may interact with transmembrane proteins like TLR2, TLR4, and TLR6 in joint pathogen recognition and signal transduction 34 , 35 . The CD163 receptor (CD163 , 1.29 ) is a gram-positive and gram-negative bacteria sensor 36 and inducer of local inflammation by producing cytokines, e.g., IL-6 and IL-10. In the human placenta, CD163 was detected in Hofbauer cells. A cleaved soluble form of the CD163 receptor , sCD163, was identified in maternal blood as a risk-predictor for developing gestational diabetes mellitus 37 . The soluble CD93 receptor ( CD93, 1.02 ) recognizes bacterial DNA rich in unmethylated CpG dinucleotides and delivers it to the endosome. CD93 is detected in the cytotrophoblast and placental vessels 38 . The mannose receptor C-type 1 ( MRC1, 1.17 ) binds mannose-rich structures on the surface of pathogenic viruses, bacteria, and fungi (e.g., Candida albicans) and interacts with glycoproteins released into the circulation during the inflammatory response. The FPR1 receptor ( FPR1, 1.43 ) interacts with N-formyl-methionine-peptides, by which degraded bacteria and host cells’ damaged mitochondria signal to immune cells: “Find me, Catch me, and Eat me.” The timely reaction to these signals transmitted via FPR1 promotes phagocytosis preventing the destruction of the damaged cell’s plasma membrane, hydrolytic enzyme leakage, and the development of autoimmunity 39 . Two genes encode for G-protein coupled receptors . GPR34 ( 1.55 ) is located on the X-chromosome and encodes the orphan receptor. The gene GPR183 ( 1.54 ) is tissue-specifically highly expressed by immune cells in the human placenta and brain. The immune cells are driven by oxysterol gradient to locations of high ligand concentration 40 . All structures bound to recognition receptors mentioned above are eliminated by phagocytosis. The toll-like receptor 7 ( TLR7 , 1.4) is located in the endosome; it recognizes ssRNA (viral) RNA and transmits a signal to the nucleus via transcription factor IRF (interferon regulatory factor). IRF, in turn, induces transcription of the IFNα/β and type III IFN genes, and an antiviral response 41 , 42 . TLR7 is detected in first-trimester cytotrophoblast and second-trimester Hofbauer cells 43 . Elimination of potential pathogens and damaged cells’ material Most recognition receptors trigger the internalization of particles to their destruction in the phagolysosome where NADPH oxidase (NOX)-derived reactive oxygen species (ROS) provide antimicrobial activity 44 , 45 , 46 . The formation of NOX is initiated by phagocytosis via the assembly of six subunits, among which are cytochrome b-245 heavy chain ( CYBB, 1.47), neutrophil cytosol factor 2 ( NCF2, 1.10 ), and neutrophil cytosol factor 4 ( NCF4, 1.03 ). NOX shuttles electrons from cytoplasmic NADPH to molecular oxygen in phagosomes and the extracellular space to produce oxidants (O 2 −, HO•, and H 2 O 2 ) with bactericidal activity. The ROS might also be implicated in signal transduction and regulation of proliferation, differentiation, migration, and cell death 44 , 47 . NOX as the primary source of superoxide in first trimester chorionic villi may be required for redox adaptation to increased oxygen pressure after the 12 th week of gestation when hemotrophic nutrition is established 48 , 49 . Upregulated RNase 6 ( 1.13 ) encodes an enzyme that acts against gram-positive and gram-negative bacteria 50 , 51 . In humans, RNase 6 expression is mainly localized in the placenta and the urinary tract during urinary tract infections 52 , 53 . RNase 6 acts as a chemoattractant, damage-associated molecule or alarmin, immune cell activator, and opsonin 50 , 51 . The BST2 gene ( 1.01 ) encodes tetherin , which retains virions in infected cells near the cell membrane to prevent their spread inside and outside the cell. The BST2 protein can activate interferon-stimulated genes in response to infection by enveloping viruses 54 . However, not all “protective” bactericidal genes are upregulated during this period. The level of beta-defensin 1 ( DEFB1, -3.23), secretory leukocyte protease inhibitor ( SLPI, -2.18), and neutrophil gelatinase-associated lipocalin , NGAL ( LCN2, -1.98) might be highest in the first trimester and decline later. DEFB 1 is mainly expressed in epithelial and immune cells of the reproductive tract 55 , 56 . DEFB1 may directly inactivate and inhibit the replication of various viruses, kill bacteria, or inhibit their growth 57 . DEFB1 expression has been detected in the chorion, decidua, and amnion 58 . Peak endometrial mRNA expression of the DEFB1 gene coincides with implantation and menstruation, suggesting its protective function during these critical events 58 . Like DEFB1, SLPI is a natural antimicrobial of the innate immune response. SLPI guards the host from infection and maintains homeostasis 59 , 60 , 61 while excessive SLPI expression increases epithelial tumors’ metastatic potential 61 . In the human placenta, SLPI has been detected in the decidua and amnion 58 , where it may limit proteinase activities during the implantation period and regulate the extent of trophoblast invasion 62 . NGAL is an active bacteriostatic that deprives prokaryotic cells of iron, thus delaying their growth and reproduction. NGAL is a neutrophil attractant, involved in apoptosis, innate immunity, and kidney development, and found in neutrophil and macrophage granules 63 . The high starting expression levels of these three genes might be the result of the protective innate immune response to the destruction of the endometrium during implantation. In contrast, the subsequent reduced expression concords with the increasing expression of pro-inflammatory factors. 3.2 Immune cell migration, cytokines, and immune tolerance (see Document S1, Table S3) Transendothelial immune cell migration ( Fig. 3 ) Several DEGs are directly or indirectly involved in diapedesis, cell transmigration from the bloodstream into the surrounding tissue 64 , 65 . Histamine is a potent vasodilator that aids in the diapedesis of leukocytes 66 . Diamine oxidase (DAO) ( AOC1, 1.65 ) is the only extracellular enzyme capable of inactivating histamine in humans and preventing its accumulation in the placenta, thus decreasing the risk of premature uterine contractions and fetal death 67 . Download figure Open in new tab Figure 3: Receptors involved in regulating transendothelial cell migration. The schematic drawing shows the migration of a leukocyte (violet, center) between two endothelial cells (orange, margin). Differentially expressed genes (DEGs) are shown with the fold change in red (increased expression in the second trimester) or black (decreased expression in the second trimester compared to the first trimester) under the gene name. Schematic drawing adapted from Strell & Entschladen, 2008 125 . The platelet endothelial cell adhesion molecule 1 ( PECAM1, 1.46 ) and junctional adhesion molecules B ( JAM-2, 1.56 ) and C ( JAM-3, 1.09 ) belong to the immunoglobulin superfamily and are expressed by leukocytes, platelets, and endothelial cells. Endothelial PECAM1 interacts with leukocyte PECAM1 and supports leukocyte diapedesis by dephosphorylation of VE-cadherin 68 . JAM proteins are located in tight junctions between endothelial cells. JAM-B binds to α4β1 integrin and JAM-C to αMβ2 integrin on leukocytes. Endothelial JAM-B can connect with leukocyte JAM-C 69 . The concentration of these proteins in the human placental villous cytotrophoblast and endothelium is ten times higher than the average concentration in other tissues [Human Protein Atlas, HPA] thus enhancing the diapedesis. Upregulated Nectin-3 ( 1.48 ) plays a significant part in facilitating the movement of T-lymphocytes and monocytes across the endothelial barrier 70 . Two other proteins, endomucin ( EMCN, - 1.25 ) and epithelial cell adhesion molecule ( EpCAM, -3.08), might decline from the first to the second trimester. Endomucin is mainly synthesized by endothelial cells of postcapillary venules, where it prevents leukocyte adhesion and diapedesis. Reduced expression of the EMCN gene suggests a potential increase in the number of leukocyte attachments and diapedesis during this period. EpCAM on the surface of an epithelial cell binds to EpCAM on the surface of a neighboring cell, keeping the cells together. The decline in EpCAM expression suggests an increase in the facilitation of diapedesis through the placental barrier. The maximum of EpCAM expression level was detected in 4–5 week placentas. After that, it quickly diminished in the late first trimester 71 , as our integrative analysis confirmed. Chemokines in cell migration ( Fig. 4 ) The migration of immune cells from the bloodstream into the surrounding tissue is regulated by chemokines, a large family of small, secreted proteins that transmit signals through cell surface G protein-coupled chemokine receptors. The differentially expressed genes encoding chemokines demonstrate remarkable synchronicity. The genes for homeostatic chemokines, CCL21 ( -1.24 ), CXCL12 ( -1.17 ) , CXCL13 ( -2.18 ), and CCR7 receptor ( -1.26 ) are downregulated, while the genes for pro-inflammatory chemokines CCL2 ( 1.6 5 ), CCL8 ( 1.14 ), CCL13 ( 1.51 ), CXCL1 ( 1.53 ), CXCL2 ( 1.37 ), CXCL4 ( 1.98 ), CXCL7 ( 2.0 ), and CXCL8 (i.e. IL8 ) ( 1.74 ) are upregulated. Their function is not limited by chemotaxis. The homeostatic CCL21 and CCR7 interaction promotes epithelial-to-mesenchymal transition (EMT) in a trophoblast-derived cell line 72 . The level of CXCL12 expression in all trophoblast subtypes is closely related to the invasive depth of trophoblast cells. The placental CXCL12 may promote Th2 immunity 73 , 74 . СXCL13 is expressed in decidual stromal cells, fetal membranes, and the umbilical vein, where it may recruit T and B cells and regulate the adaptive immune response. The homeostatic genes CXCL12 , CXCL13 , CCL21, and CCR7 receptor contain the hypoxia response element (HSE) in their promoters. They might be the targets of heterodimeric transcription factor HIF-1a–HIF-1b, which is activated by hypoxia typical for the human placenta before the 10 th /12 th week of gestation and the onset of the maternal blood supply 119 , 120 . Download figure Open in new tab Figure 4: Changes in the expression of genes encoding homeostatic and pro-inflammatory chemokines in the human placenta between the first and second trimesters of healthy pregnancy. During the second trimester, the expression of the pro-inflammatory chemokines increases, while the expression of the homeostatic chemokines is downregulated. Download figure Open in new tab Figure 5: Differentially expressed genes of the IL-6/IL-12 family. The IL6/IL12 cytokine family displays a multitude of different receptor and ligand pairs and interactions. Two differentially expressed genes in combinatorial ligand/receptor complexes of the IL6/IL12 cytokine family were identified. Schematic drawing adapted from Floss et al., 2020. 99 The gradual decline of homeostatic gene expression appears to give way to inflammatory chemokines usually formed under potential pro-inflammatory stimuli of Il-1, TNF-α, LPS, and viruses 75 . Inflammatory chemokines attract immune cells to inflammatory sites and induce the release of superoxide ions, acting in inflammation, proliferation, metabolism, and angiogenesis 75 , 76 , 77 . Interleukin 8 (chemokine CXCL8) is a major pro-inflammatory cytokine. It was localized in villous trophoblast and Hofbauer cells, decidual stromal cells, decidual lymphocytes, and endometrial gland cells 78 . Unlike many other chemokines, IL-8 has distinct target specificity for neutrophils, with weak effects on other blood cells 79 . It enhances the oxidative metabolism and generation of ROS, possibly leading to oxidative stress 79 , 80 . Studies on placental villous explants from the first and second trimesters show that IL-8 secretion increases during gestation, with maximal production in the second trimester and at term 81 . Thus, on IL-8 mRNA level, our data confirms and complements the previously obtained results. CCL2 , localized in trophoblast and decidual cells, may attract macrophages, T cells, basophils, mast cells, and NK cells. These cells assemble at the maternal-fetal interface, guaranteeing the immune microenvironment and tolerance 82 . CXCL4 and CXCL7 mRNAs originate from maternal platelets adhering to the surface of the placental syncytiotrophoblast 83 . Their corresponding proteins, platelet factor 4 ( PF4 ) and pro-platelet basic protein ( PPBP ), are released upon platelet activation. PF4 promotes fibrinoid deposition on the syncytiotrophoblast 84 , 85 , interacts with the upregulated blood coagulation factor XIIIA ( F13A1, 1.29 ), and regulates trophoblast invasion (by activating CCR1 and the macrophage inflammatory protein-1 ( CCL3, 1.52 ) 83 , 86 . The protein PPBP has been shown to influence the synthesis of DNA, hyaluronic acid and sulfated glycosaminoglycan, prostaglandin E2 secretion, and impact glycolysis. The genes S100A8 ( 1.87 ) and S100A9 ( 1.70 ) encode proteins constitutively expressed in neutrophils and monocytes. In response to inflammatory stimuli, S100A8 and S100A9 form a complex (calprotectin), which interacts with TLR4, RAGE, and CD33 receptors and acts as an alarmin or DAMP. Calprotectin may induce neutrophil degranulation with bactericidal effects, production and release of pro-inflammatory chemokines, cytokines, and ROS, leukocyte recruitment to the inflammatory site, generating a positive, pro-inflammatory feedback loop 87 , 88 . The proteoglycan serglycin ( SRGN, 1.02 ) is considered a chemokine as it interacts with numerous pro-inflammatory mediators and regulates their retention and secretion from secretory granules 89 . The IL-6/IL-12 and the CSF cytokine families with shared ligand/receptor elements The cytokine families IL-6/IL-12 and CSF play a pivotal role in pregnancy-related processes. The combination of common and specific components in the heteromeric structures of these cytokines and their receptors ensures the variability of cytokine/receptor complexes with distinct biological functions. The IL-6/IL-12 family includes IL-6, IL-12, IL-23, IL-27, IL-35, and IL-39 cytokines. Interleukin-6 ( IL-6, 1.69 ) is a major driver of physiological and pathological pregnancy-related processes 79 . It is implicated in organ development, acute-phase response, immune response, and metabolic regulation. Elevated levels of IL-6 point to inflammatory response 79 . In the human placenta, IL-6 90 and IL-6R 91 were detected in the syncytiotrophoblast and extravillous trophoblast, suggesting trophoblast response for IL-6/IL-6R interaction. IL-6 production begins as early as late 6 th week of gestation, with a subsequent increase until term 92 , which is partially confirmed by our data on the mRNA level increase from the first to the second trimester. The heterodimeric receptors of IL-6/IL-12 family cytokines share the common glycoprotein 130 signaling subunit and interleukin 12 receptor, beta 2 subunit ( IL-12RВ2, 1.25 ) 93 . In contrast, IL-27, IL-35, and IL-39 cytokines share the EBI3 subunit encoded by Epstein-Barr virus-induced gene 3 ( EBI3, 1.89), which is expressed by the syncytiotrophoblast and extravillous trophoblast throughout pregnancy 94 ,, 99 . The binding of IL-6/IL-12 cytokines to their receptors activates JAK-STAT signaling pathways 93 , 95 . Similar to the receptors of IL-6/IL-12 family cytokines, the receptors of colony-stimulating factors share the β signaling subunit ( CSF2RB, 1.44 ) between GM-CSF, IL-3, and IL-5 receptors 96 . Each cytokine binds to a cytokine-specific α chain of a heterodimeric receptor and recruits the primary signaling CSF2RB chain that connects the receptor to downstream signaling 95 , 96 ( Figure 6 ). The CSF2RB protein was detected in the human placenta, particularly in the cells of primitive hematopoiesis in the early placenta 97 , 98 . Download figure Open in new tab Figure 6: The differentially expressed gene CSF2RB in combinatorial ligand/receptor complexes of the CSF cytokine family. The β common ([βc]/CD131) family of cytokines uses different alpha-subunits of receptors with a common beta subunit (CSF2RB). This differentially expressed gene (CSF2RB) facilitates active signaling in combinatorial ligand/receptor complexes of the CSF cytokine family. Schematic drawing adapted from https://www.creative-diagnostics.com/common-beta-chain-receptor-family.htm . Transplacental transport of IgG and immune tolerance The FCGR2B ( FCGR2B, 3.0 ), a receptor for the Fc fragment of IgGs, plays a crucial role in the transplacental transfer of maternal IgGs to fetal blood. This process, which commences around the 13 th week of gestation and peaks at the end of the third trimester, is instrumental in providing the fetus and newborn with passive protection against infectious diseases, especially when their humoral response is still developing 100 . The FCGR2B protein also functions as a negative regulator of immune cell activity, preventing excessive immune reactions and maintaining placental immune tolerance 101 . This activity might be supported by the leukocyte-associated immunoglobulin-like receptor 1 (LAIR1, 1.34) and leukocyte immunoglobulin like receptor B5 (LILRB5, 1.27), which maintain placental immune tolerance by a mechanism similar to that of FCGR2B 102 , 103 . LILRB5 may recognize self-molecules represented by histocompatibility complexes of class I (HLA Class I) and inhibit the various functions of effector immune cells like migration, proliferation, synthesis and secretion of cytokines, and phagocytosis 103 . FCGR2B is expressed in the endothelium of villous vessels 104 while LAIR1 — by Hofbauer cells, dendritic cells, macrophages, monocytes, granulocytes, and NK cells [HPA]. The other protein, the progestagen-associated endometrial protein or glycodelin ( PAEP, -4.43 ), also plays a substantial role in fetomaternal tolerance, particularly during the earliest gestational period. It regulates blastocyst implantation, trophoblast differentiation, and placental development 105 The PAEP ’s isoform glycodelin-A is abundant in the secretory endometrium, decidua, and amniotic fluid 106 . It is secreted from the endometrial glands starting right after the implantation, peaking between 6 and 12 weeks of gestation, and declining after that 106 . Our analysis of the changes in gene expression level is in line with these observations. 3.3 Placental cell type deconvolution ( Fig. 7 , see Document S1, Table S4) The bulk placental tissue-level gene expression measurements represent a convolution of gene expression signals from individual cells and cell types and depend on their ratios. Although we accompanied most genes’ descriptions with cellular localization information, we were unaware of cell-type proportions. The application of the nonnegative least squares (NNLS) method revealed that fetal fibroblasts constitute 26% to 42% of cells, fetal mesenchymal stem cells — 20% to 29%, and proliferative cytotrophoblasts – invariably 23-25% across placental samples. The lower percentage of cytotrophoblasts (∼15.7%) compared to proliferative ones, and the extremely low amount of syncytiotrophoblasts (1% to 3%) and nucleated red blood cells, align with the results of other studies 121 . Download figure Open in new tab Figure 7: Cell-type proportions as estimated based on nonnegative least squares method of cell-type deconvolution, using the reference dataset developed by Campbell et al 117 . The color scale represents the percentage of each cell type in a sample. Download figure Open in new tab Figure 8: Overview of the changes in immune-related gene expression of the human placenta during the first half of pregnancy. The blue triangles represent an increased and the purple triangles a decreased expression level during the transition from the first to the second trimester of healthy pregnancy. All DEGs listed as examples have been quantified in this study. On top of the schematic drawing, the duration of pregnancy with the last menstrual cycle, implantation, onset of maternal blood flow through the placenta as well as delivery are depicted. According to Campbell et al. 117 , proliferative cytotrophoblasts are distinguished from other cytotrophoblasts by overexpression of genes related to cytoplasmic translation and mitotic sister chromatin segregation, indicative of their proliferative phenotype. The GSEA analysis applied to differentially expressed genes between the first and second trimesters revealed the highly active cellular proliferation enriched in cell cycle and division processes, chromosome segregation, sister chromatid segregation, nuclear division, and metabolic activity. It is visualized using a treeplot based on semantic similarity (pairwise_termsim) with the enrichplot package (Document S1, Table S4). Thus, it is very likely that the proliferative cytotrophoblasts are responsible for the cellular mitotic activity; the lack of maternal cells in the estimate further suggests that that the analyzed samples are from placenta tissue and not the surrounding maternal tissue. Summary. The holistic approach of our study revealed differentially expressed genes involved in the recognition and elimination of potential PAMPs and DAMPs, transendothelial cell migration, cytokine production, transplacental IgG transport, and maintenance of immune tolerance during the transition from the first to the second trimester of placental development. Most DEGs show an increased expression. Only a few genes, DEFB1 , SLPI , and LCN2, which encode antimicrobial proteins, homeostatic chemokines, and a pleiotropic PAEP that maintains tolerance, display maximal expression in the first trimester, followed by further downregulation. This data aligns with the general concept of immune activation, tolerance maintenance, active proliferation, and morphogenetic processes during early pregnancy 110 . The idea is supported by data from numerous studies, including those that explore transcriptome dynamics using microarray technology and RNA sequencing. For example, comparing our results (four studies) with those of the other three independent studies, which also explored gene expression in placental tissue from the first trimester to midgestation, we observed immune and inflammatory responses, an enriched category of antiviral immunity, extracellular matrix remodeling, responses to cytokines, cell adhesion, and signal transduction activity 111 , 112 , 113 . Overall, between 6–10 weeks’ and 11–23 weeks’ gestation, RNAseq profiling of transcriptional dynamics at an individual transcript resolution identified similar changes, together with cell migration, membrane, and cell adhesion processes, cytokine-related signaling, 114 , 115 , 122 . Compared to microarray technology, RNA sequencing provides more detailed information about the transcriptome. Along with classical differential gene expression (DEGs), RNA sequencing enables the characterization of transcriptome profiling through differential transcript (isoform) expression (DTE) and differential transcript usage (DTU), which captures changes in transcript proportions 113 . For instance, RNA sequencing has shown that CD36 (1.72) has six isoforms, with only one of them being responsible for the upregulation during early gestation 113 . Precise knowledge of DTU is helpful for a better understanding of molecular processes. Though RNA sequencing has a wider dynamic range, the expression of the PAEP gene decreases equally between the first and second trimesters, with logFC values of -4.43 and -4.33, respectively 114 , indicating the relevance of both methods in detecting principal parameters. Significant downregulation of DEFB1 , SLPI , LCN2, homeostatic chemokines, and PAEP is preceded by their elevated expression during the earliest physiological state of low oxygen in placental development. The genes for homeostatic cytokines and DEFB1 contain a response element HRE in their promoters, and they are more likely to be regulated by the HIF1α-HIF1b transcription factor, which is active during this period 123 . LCN2 is capable of orchestrating the HIF1α pathway 124 . The allocation of a separate stage during the first trimester is consonant with a demarcation of three distinct temporal phases in early placenta, detected by RNA sequencing: early gestation (6-10 weeks), a transition phase (11-13 weeks) and early second trimester to mid gestation (14-23 weeks), with chracterisitc RNA prorfiles for each phase 114 . The maternal blood flow to the placenta between the 10th and 12th weeks triggers a significant change in gene expression, as observed in our studies and others, manifesting as a pronounced proinflammatory and tolerogenic response to initial trophoblast invasion and ongoing uterine remodeling, as well as the recognition of fetal antigens by maternal immune cells. As demonstrated in our study, the activated expression of alarmins or DAMPs, including S100A8, S100A9 , and RNASE6, may play a crucial role in initiating and maintaining a proinflammatory response. The data on gene expression were accompanied by localization of their mRNA and corresponding proteins in placental cells when possible. The described changes in immune-related gene expression in the human placenta during the transition period from the first to the second trimester provide only an indication of potential protein levels and function due to differential translation and posttranslational modifications. Nonetheless, changes in their gene expression are essential constituents within major physiological transformations in the human placenta. The study’s strengths. This study focuses on a critical period of pregnancy during which many pathologies are believed to originate. Placental gene expression during the transition from the first to the second trimester remains understudied compared to the term placenta, primarily because biological samples, particularly those from the second trimester, are challenging to obtain. Addressing this topic is still relevant. The inquiry into the microarray data in open-access sources enabled us to summarize and interpret the valuable accumulated data. Focusing on the immune processes, we analyzed the differential expression of genes within previously annotated functional groups that had been poorly reported in detail in other studies. We allocated a distinct temporal phase in early pregnancy characterized by a low oxygen state, with corresponding immune–related genes predominantly regulated by HIF1α. We highlighted the special molecular interactions within functional groups, enabling these groups to achieve their optimal operating effectiveness. In the complement system, the activation of effector gene expression occurs alongside the downregulation of the inhibitor gene, thus promoting the efficiency of the complement system. An impressive example of synchronicity and harmonization reveals the expression of chemokine genes. The homeotic chemokines regulated by HIF1α are synchronously downregulated, leading to the following synchronous upregulation of proinflammatory chemokines. The regulatory mechanisms of coexpression of chemokine genes, similar to those regulating other immune-related genes and the cell set in this study, pose a further challenge. The study’s limitations. As NGS and its various applications, such as single-cell technology and spatial transcriptomics, become increasingly popular, it appears that microarrays are already becoming obsolete. RNA sequencing provides comprehensive coverage of all RNA sequences present in a sample, offering a wider dynamic range and heightened sensitivity that make it the preferred choice for identifying novel transcripts and unraveling complex gene expression patterns. Unlike RNA sequencing, microarray technology detects predefined sequences present on the platform and utilizes hybridization as its primary method for detecting RNA abundance. The data from RNA sequencing is presented in absolute values, while that of microarray is in relative values. Compared with RNA-Seq, the RNA microarray is simpler in both experimental and data analysis processes. The choice of method depends on the goal of the biological research and the financial and instrumental capabilities. Here, we had no choice as we wanted to summarize the valuable data obtained by microarray technology and identify the most pronounced functional changes spanning the periods between the first and second trimesters, and between the second and third trimesters. This study, which focused on immune-related processes in early gestation, was the first step in this investigation. The other limitation concerns the limited number of samples. The impact of both limitations is partly mitigated by comparing our results with available data obtained using microarray technology, which were not included in the study, and corresponding data obtained by RNA sequencing. We realize that data verification by classical RT-PCR, Western blot or histochemistry would be greatly appreciated. Conclusions Our study is based on publicly available large-scale studies and their integrative analysis to enhance the statistical power of conclusions, mitigate the bias of various processing strategies, and consolidate and unify the accumulated results. The periodic integration and updating of large-scale data are necessary steps as new data accumulates. View this table: View inline View popup Download powerpoint Table 1. Differentially expressed genes in recognition and elimination of potential pathogen- or damage-associated molecular patterns Author contributions (following the CReDiT Taxonomy) MO headed conceptualization, writing of the original draft, project administration, supervision, and was involved in funding acquisition. OL contributed to conceptualization, data curation, formal analysis, methodology, database development, and validation. YK contributed to data deconvolution. OM contributed to data curation, formal analysis, methodology, and validation. VM contributed to GSEA analysis. YP contributed to updating the codebase and deploying the web interface of the database to the IGEA web portal. BH contributed to conceptualization and funding acquisition, and headed reviewing and editing of the draft and revised/figures. Declaration of interests Nothing to declare. Data availability Data for the analysis was obtained from the Gene Expression Omnibus (GEO) and BioStudies (ArrayExpress) databases. We developed the specialized IGEA (integrative gene expression analysis) database [ https://molecularbiology.ink/igea/ ] containing the metadata for the genome-wide transcriptome profiling of more than 1000 samples of normal and pathology-affected human placentas. For direct inquiries, please contact the O. Lykhenko author. The entire pipeline code used in this study and the supplementary files are available via GitHub: https://github.com/Sashkow/r-affymetrix-integration-pipeline . It is a pipeline for Affymetrix microarray expression data integration, with an example of integrating 4 GEO datasets with samples of the first- and second-trimester human placentas. Document S1 Table S1. Datasets’ accession numbers (codes), sample information and references Table S1 is related to the main text, section 2 , Methods Table S2. Differentially expressed immune-related genes in the human placenta between the first and second trimesters. Table S2 is related to the main text, section 3 , Results and Discussion. Excel file containing additional data is too large to fit in a PDF. adj.P.Val – adjusted P-value. Table S3. Differentially expressed genes in the migration of immune cells, cytokine activation, and immune tolerance. Table S3 is related to the main text, section 3.2, Immune cell migration, cytokines, and immune tolerance Acknowledgements This study was supported by the OeAD grant (# UA 06/2023). Funder Information Declared Austrian Agency for Education and Internationalisation , № 0123U104257 Footnotes Lead author Berthold Huppertz, Division of Cell Biology, Histology and Embryology Gottfried Schatz Research Center, Medical University of Graz Neue Stiftingtalstr. 6, 8010 Graz Austria, Email: berthold.huppertz{at}medunigraz.at Reference list 1. ↵ Huppertz , B . ( 2008 ). Placental origins of preeclampsia: challenging the current hypothesis . Hypertension : doi: 10.1161/HYPERTENSIONAHA.107.107607 OpenUrl CrossRef 2. ↵ Staff A.C . ( 2019 ), The two-stage placental model of preeclampsia: An update . J Reprod Immunol. Sep ; 134-135 : 1 – 10 . doi: 10.1016/j.jri.2019.07.004.3 . OpenUrl CrossRef 3. ↵ Kosińska-Kaczyńska , K . ( 2022 ), Placental Syndromes-A New Paradigm in Perinatology . Int. .J Environ Res Public Health . 19 ( 12 ): 7392 . doi: 10.3390/ijerph19127392 . OpenUrl CrossRef 4. ↵ Yang , C. , Baker , P.N. , Granger , J.P. , Davidge , S.T. , Tong , C . ( 2023 ), Long-Term Impacts of Preeclampsia on the Cardiovascular System of Mother and Offspring . Hypertension .; 80 ( 9 ): 1821 – 1833 . doi: 10.1161/HYPERTENSIONAHA.123.21061 . OpenUrl CrossRef PubMed 5. ↵ Chaemsaithong , P. , Sahota , D.S. , Poon , L.C . ( 2022 ). First trimester preeclampsia screening and prediction . Am J Obstet Gynecol . 226 ( 2S ): S1071 – S1097 .e2. doi: 10.1016/j.ajog.2020.07.020 . OpenUrl CrossRef PubMed 6. ↵ Huppert , B .. ( 2020 ), Traditional and New Routes of Trophoblast Invasion and Their Implications for Pregnancy Diseases . Int. J. Mol. Sci . 21 ( 1 ), 289 . doi: 10.3390/ijms21010289 OpenUrl CrossRef 7. ↵ Zhu , L. , Luo , F. , Hu , W. , Han , Y. , Wang , Y. , Zheng , H. , Guo , X. , Qin , J. ( 2018 ). Bacterial Communities in the Womb During Healthy Pregnancy . Front Microbiol . 9 : 2163 . doi: 10.3389/fmicb.2018.02163 . OpenUrl CrossRef PubMed 8. ↵ Ding , .J , Maxwell , A. , Adzibolosu , N. , Hu , A. , You . Y , Liao . A ,, Mor , G. ( 2022 ). Mechanisms of immune regulation by the placenta: Role of type I interferon and interferon-stimulated genes signaling during pregnancy . Immunol Rev . 308 ( 1 ): 9 – 24 . doi: 10.1111/imr.13077 . OpenUrl CrossRef PubMed 9. ↵ Liu , F. , Zhu , W. , Shoaito , H. , Chissey , A. , Degrelle , S. A. , & Fournier , T . ( 2020 ). Mining of combined human placental gene expression data across pregnancy, applied to PPAR signaling pathway . Placenta . 99 , 157 – 165 . doi: 10.1016/j.placenta.2020.07.02 OpenUrl CrossRef PubMed 10. ↵ Lykhenko , O. , Frolova , A. , Obolenska , M. ( 2017a ). Designing the database for microarray experiments metadata. 2017 IEEE International Young Scientists Forum on Applied Physics and Engineering (YSF) . IEEE . doi: 10.1109/YSF.2017.8126658 OpenUrl CrossRef 11. ↵ Lykhenko , O. , Frolova , A. , & Obolenskaya , M .( 2017b ). Creation of gene expression database on preeclampsia-affected human placenta. Biopolym . Cell , 33 ( 6 ), 442 – 452 . doi: 10.7124/bc.000967 OpenUrl CrossRef 12. ↵ Lykhenko , O. , Frolova , A. , Obolenskaya , M . ( 2021a ). Consecutive integration of available microarray data for analysis of differential gene expression in human placenta . Biotechnologia Acta 14 ( 1 ): 38 – 45 .10.15407/biotech14.01.38 OpenUrl 13. ↵ Lykhenko , O. , Frolova , A. , Obolenskaya , M . ( 2021b ). Changes in the human placental transcriptome during the physiological course of pregnancy. Biopolym . Cell . 37 ( 1 ): 73 – 82 . doi: 10.7124/bc.000A4D OpenUrl CrossRef 14. ↵ Taminau , J. , Lazar , C. , Meganck , S. , Nowé , A . ( 2014 ). Comparison of merging and meta-analysis as alternative approaches for integrative gene expression analysis . ISRN Bioinform . 2014 : 345106 . doi: 10.1155/2014/345106 . OpenUrl CrossRef 15. ↵ Benjamini , Y. and Hochberg , Y . ( 1995 ). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing . J. R. Stat. Ser.B (Methodological ), 57 ( 1 ): 289 – 300 . Available at: http://www.jstor.org/stable/2346101 . OpenUrl 16. ↵ Smyth , G. K . ( 2005 ). Bioinformatics and Computational Biology Solutions Using R and Bioconductor. limma: Linear Models for Microarray Data . Springer , pp. 397 – 420 . doi: 10.1007/0–387–29362–0_23 . OpenUrl CrossRef 17. ↵ Lykhenko , OK. , Obolenskaya , MYu. ( 2021 ). The knowledge of chromosomal sex is important for large-scale analysis of gene expression . Dopov. Nac. akad. nauk Ukr . 1 : 100 —109. doi: 10.15407/dopovidi2021.01.100 . OpenUrl CrossRef 18. ↵ Buckberry , S. , et al. ( 2014 ) massiR: a method for predicting the sex of samples in gene expression microarray datasets . Bioinformatics . 30 ( 14 ): 2084 – 5 . doi: 10.1093/bioinformatics/btu161 OpenUrl CrossRef PubMed 19. ↵ Szklarczyk , D. , et al. ( 2019 ). STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets . NAR . 47 ( Database ), D607 . doi: 10.1093/nar/gky1131 . OpenUrl CrossRef PubMed 20. ↵ Clause , A. , Newman , M.E.J. , Moore , C. ( 2004 ). Finding community structure in very large networks, Phys . Rev. E 70 , 066111 / doi: 10.1103/Phys.RevE.70.066111 . OpenUrl CrossRef 21. ↵ Franceschini , A. , Szklarczyk , D. , Frankild , S. , Kuhn , M. , Simonovic , M. ,. Roth , A. , Lin , J. , Minguez , P. , Bork , P. , von Mering , C. , Jensen , L.J. ( 2013 ). STRING v9.1: protein-protein interaction networks, with increased coverage and integration . NARes . 41 ( Database issue ): D808 – 15 . doi: 10.1093/nar/gks1094 . OpenUrl CrossRef PubMed Web of Science 22. ↵ Ward , LD. , Kellis , M . ( 2012 ) Interpreting noncoding genetic variation in complex traits and human disease . Nat Biotechnol . 30 ( 11 ): 1095 – 106 . doi: 10.1038/nbt.2422 . OpenUrl CrossRef PubMed 23. ↵ Bohlson , S.S. , Fraser , D.A. , Tenner , A.J. ( 2007 ). Complement proteins C1q and MBL are pattern recognition molecules that signal immediate and long-term protective immune functions . Mo.l Immunol ; 44 ( 1-3 ): 33 – 43 . doi: 10.1016/j.molimm.2006.06.021 . OpenUrl CrossRef PubMed Web of Science 24. ↵ Lu , J.H. , Teh , B.K. , Wang , Ld ., Wang , Y.N. , Tan , Y.S. , Lai , M.C. , Reid , K.B .. ( 2008 ). The classical and regulatory functions of C1q in immunity and autoimmunity . Cell. Mol. Immunol . 5 ( 1 ): 9 – 21 . doi: 10.1038/cmi.2008.2 . OpenUrl CrossRef PubMed 25. ↵ Sarma , J. ,V., Ward , P. ,A. ( 2011 ). The complement system . Cell Tissue Res . 343 ( 1 ): 227 – 35 . doi: 10.1007/s00441-010-1034-0 . OpenUrl CrossRef PubMed Web of Science 26. ↵ Bulla , R. , Agostinis , C. , Bossi , F. , Rizzi , L. , Debeus , A. , Tripodo , C. , Radillo , , O. , De Seta , F. , Ghebrehiwet , B. , Tedesco , F. ( 2008 ). Decidual endothelial cells express surface-bound C1q as a molecular bridge between endovascular trophoblast and decidual endothelium . Mol. Immunol .; 45 ( 9 ): 2629 – 40 . doi: 10.1016/j.molimm.2007.12.025 . OpenUrl CrossRef PubMed 27. ↵ Bulla , R. , Bossi , F. , Tedesco , F . ( 2012 ). The complement system at the embryo implantation site: friend or foe? Front Immunol . 19 ; 3 : 55 . doi: 10.3389/fimmu.2012.00055 . OpenUrl CrossRef 28. ↵ Varghese , P.M. , Murugaiah , V. , Beirag , N. , Temperton , N. , Khan , H.A. , Alrokayan , S.H. , Al-Ahdal , M.N. , Nal , B. Al. ,- Mohanna , F.A. , Sim , R.B. and Kishore , U. ( 2021 ). C4b Binding Protein Acts as an Innate Immune Effector Against Influenza A Virus .. Front. Immunol . 11 : 585361 . doi: 10.3389/fimmu.2020.585361 . OpenUrl CrossRef PubMed 29. ↵ Velickovic , I. , Dalloul , M. , Wong , K. A. , Bakare , O. , Schweis , F. , Garala , M. , Alam , A. , Medranda , G. , Lekovic , J. , Shuaib , W. et al. ( 2015 ). Complement factor B activation in patients with preeclampsia . J Reprod Immunol . 109 , 94 – 100 . doi: 10.1016/j.jri.2014.12.002 . OpenUrl CrossRef PubMed 30. ↵ Pierik , E. , Prins , J.R. , van Goor , H. , Dekker , G.A. , Daha , M.R. , Seelen , M.A.J. , Scherjon , S.A. ( 2020 ). Dysregulation of Complement Activation and Placental Dysfunction: A Potential Target to Treat Preeclampsia? Front Immunol . 10 : 3098 . doi: 10.3389/fimmu.2019.03098 . OpenUrl CrossRef PubMed 31. ↵ Denny , K.J. , Woodruff , T.M. , Taylor , S.M. , Callaway , L.K . ( 2013 ) Complement in pregnancy: a delicate balance . Am J Reprod Immunol . 69 : 3 – 11 . doi: 10.1111/aji.12000 OpenUrl CrossRef PubMed 32. ↵ Yednock , T. , Fong , D.S. and Lad , E.M . ( 2022 ). C1q and the classical complement cascade in geographic atrophy secondary to age-related macular degeneration . Int J Retin Vitr . 8 , 79 . doi: 10.1186/s40942-022-00431-y OpenUrl CrossRef 33. ↵ Taban , Q. , Mumtaz , P.T. , Masoodi , K.Z. , Haq , E , Ahmad , S.M . ( 2022 ). Scavenger receptors in host defense: from functional aspects to mode of action . Cell Commun Signal . 20 ( 1 ): 2 . doi: 10.1186/s12964-021-00812-0 . OpenUrl CrossRef PubMed 34. ↵ Banesh , S. , Trivedi , V . ( 2020 ). Therapeutic Potentials of Scavenger Receptor CD36 Mediated Innate Immune Responses Against Infectious and Non-Infectious Diseases . Curr Drug Discov Technol . 17 ( 3 ): 299 – 317 . 0.2174/1570163816666190802153319. OpenUrl PubMed 35. ↵ Silverstein , R.L. , Febbraio , M . ( 2009 ). CD36, a scavenger receptor involved in immunity, metabolism, angiogenesis, and behavior . Sci Signal . 26 ;2( 72 ): re3 . doi: 10.1126/scisignal.272re3 . OpenUrl Abstract / FREE Full Text 36. ↵ Alquraini , A. , El Khoury , J . ( 2020 ). Scavenger receptors . Curr Biol . 20; 30 ( 14 ): R790 – R795 . doi: 10.1016/j.cub.2020.05.051 . OpenUrl CrossRef PubMed 37. ↵ Bari , M ,F., Weickert , M.O. , Sivakumar , K. , James , S.G. , Snead , D.R. , Tan , B.K. , Randeva , H.S. , Bastie , C.C. , Vatish , M. ( 2014 ). Elevated soluble CD163 in gestational diabetes mellitus: secretion from human placenta and adipose tissue . PLoS One . 1; 9 ( 7 ): e101327 . doi: 10.1371/journal.pone.0101327 . OpenUrl CrossRef PubMed 38. ↵ Fantone , S. , Tossetta , G. , Di Simone , N. , Tersigni , C. , Scambia , G. , Marcheggiani , F. , Giannubilo , S.R. , Marzioni , D. ( 2022 ). CD93 a potential player in cytotrophoblast and endothelial cell migration . Cell Tissue Res . 387 , 123 – 130 . doi: 10.1007/s00441-021-03543-3 . OpenUrl CrossRef PubMed 39. ↵ Li , S. , Li , A. , Zhai , L. , Sun , Y. , Yu , L. , Fang , Z. , Zhang , L. , Peng , Y. , Zhang , M. , Wang , X . ( 2022 ). Suppression of FPR2 expression inhibits inflammation in preeclampsia by improving the biological functions of trophoblast via NF-κB pathway . J Assist Reprod Genet . 39 ( 1 ): 239 – 250 . doi: 10.1007/s10815-022-02395-2 . OpenUrl CrossRef PubMed 40. ↵ Chen , H. , Huang , W , Li , X . ( 2022 ). Structures of oxysterol sensor EBI2/GPR183, a key regulator of the immune response . Structure . 30 ( 7 }: 1016 – 1024 .e5 , doi: 10.1016/j.str.2022.04.006 . OpenUrl CrossRef 41. ↵ Zhang , Z. , Ohto , U. , Shibata , T. , Krayukhina , E. , Taoka , M. , Yamauch , i Y. , Tanji , H. , Isobe , T. , Uchiyama , S. , Miyake , K. , et al. ( 2016 ). Structural Analysis Reveals that Toll-like Receptor 7 Is a Dual Receptor for Guanosine and Single-Stranded RNA . Immunity . 45 ( 4 ), 737 – 748 . doi: 10.1016/j.immuni.2016.09.011 . OpenUrl CrossRef PubMed 42. ↵ Sameer , A ,S,, Nissar , S. T ( 2021 ). Toll-Like Receptors (TLRs): Structure, Functions, Signaling, and Role of Their Polymorphisms in Colorectal Cancer Susceptibility . Biomed Res Int . 2021: 1157023 doi: 10.1155/2021/1157023 . OpenUrl CrossRef PubMed 43. ↵ Pudney , J. , He , X. , Masheeb , Z. , Kindelberger , D.W. , Kuohung , W. , Ingalls , R.R. ( 2016 ). Differential expression of toll-like receptors in the human placenta across early gestation . Placenta . 46 : 1 – 10 . doi: 10.1016/j.placenta.2016.07.005 . OpenUrl CrossRef PubMed 44. ↵ Nauseef , W.M . ( 2019 ). The phagocyte NOX2 NADPH oxidase in microbial killing and cell signaling . Curr Opin Immunol . 60 : 130 – 140 . doi: 10.1016/j.coi.2019.05.006 . OpenUrl CrossRef PubMed 45. ↵ Panday , A. , Sahoo , M. , Osorio , D. Batra , S. ( 2015 ). NADPH oxidases: an overview from structure to innate immunity-associated pathologies . Cell Mol. Immunol . 12 , 5 – 23 doi: 10.1038/cmi.2014.89 OpenUrl CrossRef PubMed 46. ↵ Rosales , C. , Uribe-Querol , E . ( 2017 ). Phagocytosis: A Fundamental Process in Immunity . Biomed. Res. Int . 2017: 9042851 . doi: 10.1155/2017/9042851 . OpenUrl CrossRef PubMed 47. ↵ Brown , D. I. and Griendling , K. K . ( 2009 ). Nox proteins in signal transduction . Free Radic Biol Med . 47 ( 9 ), 1239 – 1253 . doi: 10.1016/j.freeradbiomed.2009.07.023 OpenUrl CrossRef PubMed Web of Science 48. ↵ Hernandez , I ,, Fournier , T. , Chissey , A. , Therond , P. , Slama , A. , Beaudeux , J.L. , Zerrad-Saad , A. ( 2019 ). NADPH oxidase is the major source of placental superoxide in early pregnancy: association with MAPK pathway activation . Sci Rep . 9 ( 1 ): 13962 . doi: 10.1038/s41598-019-50417-4 . OpenUrl CrossRef PubMed 49. ↵ Mortimer , P.M. , Mc Intyre , S.A. and Thomas , D.C . ( 2021 ). Beyond the Extra Respiration of Phagocytosis: NADPH Oxidase 2 in Adaptive Immunity and Inflammation . Front. Immunol . 12 : 733918 . doi: 10.3389/fimmu.2021.733918 OpenUrl CrossRef 50. ↵ Becknell , B. , Eichler , T.E ,, Beceiro , S. , Li , B. , Easterling , R.S. , Carpenter , A.R. , James , C.L. , McHugh , K.M. , Hains , D.S. , Partida-Sanchez , S. , Spencer , J.D. ( 2015 ). Ribonucleases 6 and 7 have antimicrobial function in the human and murine urinary tract . Kidney Int . 87 ( 1 ): 151 – 61 . doi: 10.1038/ki.2014.268 . OpenUrl CrossRef PubMed 51. ↵ Becknell , B. , Ching , C. , Spencer , J.D . ( 2019 ). The Responses of the Ribonuclease A Superfamily to Urinary Tract Infection . Front Immunol .; 10 : 2786 . doi: 10.3389/fimmu.2019.02786 . eCollection 2019. OpenUrl CrossRef PubMed 52. ↵ Koczera P , Martin L , Marx G , Schuerholz T . The Ribonuclease A Superfamily in Humans: Canonical RNases as the Buttress of Innate Immunity . Int J Mol Sci . 2016 Aug 5; 17 ( 8 ): 1278 . doi: 10.3390/ijms17081278 . PMID: 27527162 . OpenUrl CrossRef PubMed 53. ↵ Rosenberg , H.F. and Dyer , K.D . ( 1996 ). Molecular cloning and characterization of a novel human ribonuclease (RNase k6): increasing diversity in the enlarging ribonuclease gene family . N AR . 24 ( 18 ): 3507 – 3513 . doi: 10.1093/nar/24.18.3507 OpenUrl CrossRef PubMed Web of Science 54. ↵ Zhao , Y. , Zhao , K. , Wang , S. Du , J. ( 2022 ). Multi-functional BST2/tetherin against HIV-1, other viruses and LINE-1 . Front. Cell. Infect. Microbiol., Sec. Virus and Host . Vol. 12 . doi: 10.3389/fcimb.2022.979091 OpenUrl CrossRef PubMed 55. ↵ Zhai , Y ,J., Feng , Y. , Ma , X. , Ma , F. ( 2023 ). Defensins: defenders of human reproductive health , Human Reproduction Update . 29 ( 1 ): 126 – 154 ,. doi: 10.1093/humupd/dmac032 . OpenUrl CrossRef PubMed 56. ↵ Agakidou , E. , Agakidis , C. , Kontou , A. , Chotas , W. , Sarafidis , K . ( 2022 ). Antimicrobial Peptides in Early-Life Host Defense, Perinatal Infections, and Necrotizing Enterocolitis-An Update . J Clin Med . 11 ( 17 ): 5074 . doi: 10.3390/jcm11175074 . OpenUrl CrossRef PubMed 57. ↵ Xu , D. and Lu , W . ( 2020 ) Defensins: A Double-Edged Sword in Host Immunity . Front. Immunol . 11 : 764 . doi: 10.3389/fimmu.2020.00764 OpenUrl CrossRef PubMed 58. ↵ King ,, A.E. , Paltoo , A. , Kelly , R.W. , Sallenave , J.-M. , Bocking , A.D. , Challis , J.R.G. ( 2007 ). Expression of Natural Antimicrobials by Human Placenta and Fetal Membranes . Placenta .. 28 ( 2–3 ): 161 – 169 . doi: 10.1016/j.placenta.2006.01.006 . OpenUrl CrossRef PubMed Web of Science 59. ↵ Doumas , S. , Kolokotronis , A. , Stefanopoulo , P . ( 2005 ). Anti-inflammatory and antimicrobial roles of secretory leukocyte protease inhibitor . Infect Immun . 73 ( 3 ): 1271 – 4 . : doi: 10.1128/IAI.73.3.1271-1274.2005 . OpenUrl FREE Full Text 60. ↵ Sallenave , J.M . ( 2010 ). Secretory leukocyte protease inhibitor and elafin/trappin-2: versatile mucosal antimicrobials and regulators of immunity . Am J Respir Cell Mol Biol . 42 ( 6 ): 635 – 43 . doi: 10.1165/rcmb.2010-0095RT . OpenUrl CrossRef PubMed Web of Science 61. ↵ Nugteren , S. and Samsom , J. N . ( 2021 ). Secretory Leukocyte Protease Inhibitor (SLPI) in mucosal tissues: Protects against inflammation, but promotes cancer . Cytokine Growth Factor Rev . 59 , 22 – 35 . doi: 10.1016/j.cytogfr.2021.01.005 . OpenUrl CrossRef PubMed 62. ↵ Salamonsen , LA . ( 1999 ). Role of proteases in implantation . Rev Reprod . 4 ( 1 ): 11 – 22 . doi: 10.1530/ror.0.0040011 . OpenUrl Abstract 63. ↵ Romejko , K. , Markowska , M. , Niemczyk , S . ( 2023 ). The Review of Current Knowledge on Neutrophil Gelatinase-Associated Lipocalin (NGAL) . Int. J. Mol. Sci . 24 ( 13 ): 10470 . doi: 10.3390/ijms241310470 . OpenUrl CrossRef 64. ↵ Yeh , Y.-T. , Serrano , R. , François , J. , Chiu , J.-J. , Li , Y.-S. J. del Álamo , J. C. , Chien , S. , Lasheras , J. C. ( 2017 ). Three-dimensional forces exerted by leukocytes and vascular endothelial cells dynamically facilitate diapedesis . PNAS . 115 ( 1 ), 133 – 138 . doi: 10.1073/pnas.1717489115 . OpenUrl Abstract / FREE Full Text 65. ↵ Filippi , M.D . ( 2019 ). Neutrophil transendothelial migration: updates and new perspectives . Blood .; 133 ( 20 ): 2149 – 2158 . doi: 10.1182/blood-2018-12-844605 . OpenUrl Abstract / FREE Full Text 66. ↵ Doukas , J. , Shepro , D. , Hechtman , H. B . ( 1987 ). Vasoactive Amines Directly Modify Endothelial Cells to Affect Polymorphonuclear Leukocyte Diapedesis In Vitro . Blood . 69 ( 6 ): 1563 – 1569 . doi: 10.1182/blood.V69.6.1563.1563 . OpenUrl Abstract / FREE Full Text 67. ↵ Velicky , P. , Windsperger , K. , Petroczi K , Pils S , Reiter B , Weiss T , Vondra S , Ristl R , Dekan S , Fiala C , Cantonwine DE , McElrath , T.F. , Jilma , B. , Knöfler , M. , Boehm , T. , Pollheimer , J . ( 2018 ). Pregnancy-associated diamine oxidase originates from extravillous trophoblasts and is decreased in early-onset preeclampsia . Sci Rep . 8 ( 1 ): 6342 . doi: 10.1038/s41598-018-24652-0 . OpenUrl CrossRef PubMed 68. ↵ Arif , N ,, Zinnhardt , M. , Nyamay, Antu , A. , Teber , D. , Brückner , R. , Schaefer , K. , Li , YT. , Trappmann , B. , Grashoff , C. , Vestweber , D. ( 2021 ). PECAM-1 supports leukocyte diapedesis by tension-dependent dephosphorylation of VE-cadherin . EMBO J . 40 ( 9 ): e106113 . doi: 10.15252/embj.2020106113 . OpenUrl CrossRef PubMed 69. ↵ Kummer , D. , Ebnet , K . ( 2018 ). Junctional Adhesion Molecules (JAMs): The JAM-Integrin Connection . Cells .; 7 ( 4 ): 25 . doi: 10.3390/cells7040025 . OpenUrl CrossRef 70. ↵ Takai , Y. , Irie , K. , Shimizu , K. , Sakisaka , T. , Ikeda , W . ( 2003 ). Nectins and nectin-like molecules: roles in cell adhesion, migration, and polarization . Cancer Sci . 94 ( 8 ): 655 – 67 . doi: 10.1111/j.1349-7006.2003.tb01499.x OpenUrl CrossRef PubMed Web of Science 71. ↵ Wong , F.T.M. , Lin , C. , Co , B.J . ( 2019 ). Cellular systems biology identifies dynamic trophoblast populations in early human placentas . Placenta . 76 : 10 – 18 . doi: 10.1016/j.placenta.2018.12.012 . Epub 2019 Jan 11. OpenUrl CrossRef PubMed 72. ↵ Liu , Z. , He , J. , Jin , P. , Ran , Y. , Yin , N. , Qi , H . ( 2023 ). CCL21/CCR7 Axis Contributes to Trophoblastic Cell Migration and Invasion in Preeclampsia by Affecting the Epithelial Mesenchymal Transition via the ERK1/2 Signaling Pathway . Biology (Basel ). 12 ( 2 ): 150 . doi: 10.3390/biology12020150 . OpenUrl CrossRef PubMed 73. ↵ Piao , H.L. , Tao , Y. , Zhu , R. , Wang , S.C ,, Tang , C.L. , Fu , Q. , Du , M.R. , Li , D.J. ( 2012 ). The CXCL12/CXCR4 axis is involved in the maintenance of Th2 bias at the maternal/fetal interface in early human pregnancy . Cell. Mol .Immunol . 9 ( 5 ): 423 – 30 . doi: 10.1038/cmi.2012.23 . OpenUrl CrossRef PubMed 74. ↵ Zhang , S. , Ding , J. , Zhang , Y. , Liu , S. , Yang , J. , Yin , T. ( 2022 ). Regulation and Function of Chemokines at the Maternal–Fetal Interface . Front. Cell Dev. Biol. Sec. Molecular and Cellular Reproduction . Vol. 10 – 2022. doi: 10.3389/fcell.2022.826053 . OpenUrl CrossRef 75. ↵ Hughes , C.E. , Nibbs , R.J.B . ( 2018 ). A guide to chemokines and their receptors . FEBS J . 285 ( 16 ): 2944 – 2971 . doi: 10.1111/febs.14466 . OpenUrl CrossRef PubMed 76. ↵ Capucetti , A. , Albano , F. , & Bonecchi , R. ( 2020 ). Multiple Roles for Chemokines in Neutrophil Biology . Front. Immuno . 11 . doi: 10.3389/fimmu.2020.01259 . OpenUrl CrossRef PubMed 77. ↵ Li , H. , Wu , M. , Zhao , X . ( 2022 ). Role of chemokine systems in cancer and inflammatory diseases . Med.Comm . 3 ( 2 ) e147 . doi: 10.1002/mco2.147 . OpenUrl CrossRef 78. ↵ Saito , S. , Kasahara , T. , Sakakura , S. , Umekage , H. , Harada , N. , Ichijo , M . ( 1994 ). Detection and localization of interleukin-8 mRNA and protein in human placenta and decidual tissues . J. Reprod. Immunol . 27 ( 3 ): 161 – 72 . doi: 10.1016/0165-0378(94)90001-9 . OpenUrl CrossRef PubMed Web of Science 79. ↵ Vilotić , A. , Nacka-Aleksić , M. , Pirković , A. , Bojić-Trbojević , Ž. , Dekanski , D. , Jovanović Krivokuća , M. . ( 2022 ). IL-6 and IL-8: An Overview of Their Roles in Healthy and Pathological Pregnancies . Int J Mol Sci . 23 ( 23 ): 14574 . doi: 10.3390/ijms232314574 . OpenUrl CrossRef 80. ↵ Bréchar , S. , Bueb , JL. , Tschirhart , EJ . ( 2005 ). Interleukin-8 primes oxidative burst in neutrophil-like HL-60 through changes in cytosolic calcium . Cell Calcium . 37 ( 6 ): 531 – 40 . doi: 10.1016/j.ceca.2005.01.019 . OpenUrl CrossRef PubMed 81. ↵ Shimoya , K. , Moriyama , A. , Matsuzaki , N. , Ogata , I. , Koyama , M. , Azuma , C. , Saji , F. , Murata , Y. ( 1999 ). Human placental cells show enhanced production of interleukin (IL)-8 in response to lipopolysaccharide (LPS), IL-1 and tumour necrosis factor (TNF)-alpha, but not to IL-6 . Mo.l Hum. Repro.d . 5 ( 9 ): 885 . doi: 10.1093/molehr/5.9.885 . OpenUrl CrossRef PubMed Web of Science 82. ↵ Lin , Z. , Sh , i JL. , Chen , M. , Zheng , Z.M. , Li , M.Q. , Shao , J. ( 2023 ). CCL2: An important cytokine in normal and pathological pregnancies: A review . Front. Immunol . 13 : 1053457 . doi: 10.3389/fimmu.2022.1053457 . eCollection 2022. OpenUrl CrossRef PubMed 83. ↵ Forstner , D. , Guettler , J. , Gauster , M . ( 2021 ). Changes in Maternal Platelet Physiology during Gestation and Their Interaction with Trophoblasts . Int. J. Mol. Sci . 22 ( 19 ): 10732 . doi: 10.3390/ijms221910732 . OpenUrl CrossRef PubMed 84. ↵ Mose , G. , Guettler , J. , Forstne , r D. , Gauster , M. ( 2019 ). Maternal Platelets—Friend or Foe of the Human Placenta? Int. J. Mo.l Sci . 20 ( 22 ): 5639 . doi: 10.3390/ijms20225639 . PMID: 31718032 . OpenUrl CrossRef PubMed 85. ↵ Guettler , J. , Forstner , D. , Gauster , M . ( 2022 ). Maternal platelets at the first trimester maternal-placental interface - Small players with great impact on placenta development . Placenta . 125 : 61 – 67 . doi: 10.1016/j.placenta.2021.12.009 . OpenUrl CrossRef PubMed 86. ↵ Sat , Y. , Fujiwara , H. , Zeng , B.X. , Higuch , i T. , Yoshioka , S. , Fujii , S. ( 2005 ). Platelet-derived soluble factors induce human extravillous trophoblast migration and differentiation: platelets are a possible regulator of trophoblast infiltration into maternal spiral arteries . Blood . 106 ( 2 ): 428 – 35 . OpenUrl Abstract / FREE Full Text 87. ↵ Wang , S. , Song , R. , Wang , Z. , Jing , Z. , Wang , S. , Ma , J. ( 2018 ). S100A8/A9 inInflammation . Front Immunol . 9 : 1298 . doi: 10.3389/fimmu.2018.01298 . OpenUrl CrossRef 88. ↵ Garcia , V. , Perera , Y..R. , Chazin , W.J . ( 2022 ). A .structural perspective on calprotectin as a ligand of receptors mediating inflammation and potential drug target . Biomolecules . 12 , 519 – 532 , doi: 10.3390/biom12040519 OpenUrl CrossRef PubMed 89. ↵ Korpetinou , A. , Skandalis , SS. , Labropoulou , V.T. , Smirlaki , G. , Noulas , A. , Karamanos , N.K. , Theocharis , A.D . ( 2014 ). Serglycin: at the crossroad of inflammation and malignancy . Front. Oncol . 3 : 327 . doi: 10.3389/fonc.2013.00327 . OpenUrl CrossRef PubMed 90. ↵ Jauniaux , E. , Gulbis , B. , Schandene , L. , Collette , J. , Hustin , J . ( 1996 ). Distribution of interleukin-6 in maternal and embryonic tissues during the first trimester . Mol. Hum. Reprod . 2 ( 4 ): 239 – 43 . doi: 10.1093/molehr/2.4.239 . OpenUrl CrossRef PubMed 89. Saito , S . ( 2000 ). Cytokine network at the feto-maternal interface . J. Reprod. Immunol . 47 ( 2 ): 87 – 103 . doi: 10.1016/S0165-0378(00)00060-7 . OpenUrl CrossRef PubMed Web of Science 90. Murakami , M. , Narazaki , M. , Hibi , M. , Yawata , H. , Yasukawa , K. , Hamaguchi , M. , Taga , T. and Kishimoto , T. ( 1991 ). Critical Cytoplasmic Region of the Interleukin 6 Signal Transducer gp130 is Conserved in the Cytokine Receptor Family . PNAS . 88 ( 24 ), 11349 – 11353 . doi: 10.1073/pnas.88.24.11349 . OpenUrl Abstract / FREE Full Text 91. ↵ Nishino , E. , Matsuzaki , N. , Masuhiro , K. , Kameda , T. , Taniguchi , T. , Takagi , T. , Saji , F. , Tanizawa , O . ( 1990 ). Trophoblast-derived interleukin-6 (IL-6) regulates human chorionic gonadotropin release through IL-6 receptor on human trophoblasts . J. Clin. Endocrinol. Metab . 71 ( 2 ): 436 – 41 . doi: 10.1210/jcem-71-2-436 . OpenUrl CrossRef PubMed Web of Science 92. ↵ Agarwal , R. , Loganath , A. , Roy , A.C. , Wong , Y.C. , Lindoff , C. , Ng , S.C . ( 2000 ). Increased expression of interleukin 6 in term compared to the first trimester human placental villi . Horm. Metab. Res . 32 ( 5 ): 164 – 8 . doi: 10.1055/s-2007-978615 . 5. OpenUrl CrossRef PubMed Web of Science 93. ↵ Tait Wojno , E.D. , Hunter , C.A. , Stumhofer , S. ( 2019 ). The Immunobiology of the Interleukin-12 Family: Room for Discovery . Immunity . 50 ( 4 ): 851 – 870 . doi: 10.1016/j.immuni.2019.03.011 . OpenUrl CrossRef PubMed 94. ↵ Devergne , O. , Coulomb-L’Herminé , A. , Capel , F. , Moussa , M. , Capron , F. ( 2001 ). Expression of Epstein-Barr virus-induced gene 3, an interleukin-12 p40-related molecule, throughout human pregnancy: involvement of syncytiotrophoblasts and extravillous trophoblasts . Am J Pathol . 159 ( 5 ): 1763 – 76 . doi: 10.1016/S0002-9440(10)63023-4 . OpenUrl CrossRef PubMed Web of Science 95. ↵ Kang , S. , Narazaki , M. , Metwally , H. , Kishimoto , T. ( 2020 ). Historical overview of the interleukin-6 family cytokine . J. Exp. Med . 217 ( 5 ): e20190347 . doi: 10.1084/jem.20190347 . Erratum in: J Exp Med. 2020 May 4;217(5):jem.2019034704212020c. 10.1084/jem.2019034704212020c. OpenUrl CrossRef PubMed 96. ↵ Miyajima , A . ( 1992 ). Molecular structure of the IL-3, GM-CSF and IL-5 receptors . The International Journal of Cell Cloning . 10 ( 3 ): 126 – 134 . OpenUrl PubMed 95. Hercus , T.R. , Kan , W.L.T. , Broughton , S.E. , Tvorogov , D. , Ramshaw , H.S. , Sandow , J.. , Nero , T.L. , Dhagat , U. , Thompson , E.J. , Shing , K.S.C.T. et al. ( 2018 ). Role of the β Common (βc) Family of Cytokines in Health and Disease . Cold Spring Harb Perspect Biol . 10 ( 6 ): a028514 . doi: 10.1101/cshperspect.a028514 . OpenUrl Abstract / FREE Full Text 96. Dougan , M. , Dranoff , . , Dougan , S.K . ( 2019 ). GM-CSF, IL-3, and IL-5 Family of Cytokines: Regulators of Inflammation . Immunity . 50 ( 4 ): 796 – 811 . doi: 10.1016/j.immuni.2019.03.022 . OpenUrl CrossRef PubMed 97. ↵ Enzler , T. and Dranoff , G. ( 2003 ). Granulocyte–macrophage colony-stimulating factor . Chapter 21. in The Cytokine Handbook ( Fourth Edition) Edited by Angus W. Thompson and Michael T. Lotze . Academic Press . pp. 503 – 524 . 98. ↵ Gearing , A.J . ( 1989 ). New cytokines with potential for in vitro manipulation of cells . Curr Opin Immunol .. 1 ( 5 ): 937 – 9 . doi: 10.1016/0952-7915(89)90076-9 . OpenUrl CrossRef PubMed 99. ↵ Floss , D.M. , Moll , J.M. and cheller , J. ( 2020 ). IL-12 and IL-23—Close Relatives with Structural Homologies but Distinct Immunological Functions . Cells , 9 ( 10 ), 2184 . doi: 10.3390/cells9102184 OpenUrl CrossRef 100. ↵ Saji , F. , Samejima , Y. , Kamiura , S. , Koyama , M . ( 1999 ). Dynamics of immunoglobulins at the feto-maternal interface . Rev.Reprod . 14 ( 2 ): 81 – 89 . OpenUrl 101. ↵ Anania , J. C. , Chenoweth , A. M. , Wines , B. D. , & Hogarth , P. M . ( 2019 ). The Human FcγRII (CD32) Family of Leukocyte FcR in Health and Disease . Front. Immunol . 10 . doi: 10.3389/fimmu.2019.00464 OpenUrl CrossRef PubMed 102. ↵ Meyaard , L. , Adema , G.J. , Chang , C. , Woollatt , E. , Sutherland , G.R. , Lanier , L.L. , Phillips , J.H. ( 1997 ). LAIR-1, a novel inhibitory receptor expressed on human mononuclear leukocytes . Immunity . 7 ( 2 ): 283 – 90 . doi: 10.1016/s1074-7613(00)80530-0 . OpenUrl CrossRef PubMed Web of Science 103. ↵ Zhang , Z. , Hatano , H. , Shaw , J. , Olde Nordkamp , M. , Jiang , G. , Li , D. , Kollnberger , S. ( 2015 ). The Leukocyte Immunoglobulin-Like Receptor Family Member LILRB5 Binds to HLA-Class I Heavy Chains . PLoS One . 10 ( 6 ): e0129063 . doi: 10.1371/journal.pone.0129063 OpenUrl CrossRef PubMed 103. Zhang , J. , Mai , S. , Chen , H.M. , Kang , K. , Li , X.C. , Chen , S.H. , Pan P.Y . ( 2017 ). Leukocyte immunoglobulin-like receptors in human diseases: an overview of their distribution, function, and potential application for immunotherapies . J. Leukoc. Biol . 102 ( 2 ): 351 – 360 . : doi: 10.1189/jlb.5MR1216-534R . OpenUrl CrossRef PubMed 104. ↵ Sedmak , D.D. , Orosz , C.G. ( 1991 ). The role of vascular endothelial cells in transplantation . Arch. Pathol. Lab.Med . 115 ( 3 ): 260 – 5 . OpenUrl PubMed Web of Science 105. ↵ Lee , C-L. , Lam , K.K.W. , Vijayan , M. , Koistinen , H. , Seppala , M. , Ng , E.H.Y. , Yeung , W.S.B. , Chiu , P.C.N. ( 2016 ). The pleiotropic effect of glycodelin-A in early pregnancy . Am. J. Reprod. Immunol . 75 : 290 – 297 . doi: 10.1111/aji.12471 OpenUrl CrossRef PubMed 106. ↵ Toth , B. , Roth , K. , Kunert-Keil C. , Scholz , C. , Schulze , S. , Mylonas , I. , Friese , K. , Jeschke , U . ( 2008 ). Glycodelin protein and mRNA is downregulated in human first trimester abortion and partially upregulated in mole pregnancy . J.Histochem. Cytochem . 56 ( 5 ): 477 – 85 . doi: 10.1369/jhc.2008.950600 . OpenUrl CrossRef PubMed 107. Wickramage , I. , VanWye , J. , Max , K. , Lockhart , J.H. , Hortu , I. , Mong , E.F. , Canfield , J. , LamabaduWarnakulasuriya Patabendige , H.M. et al. ( 2023 ). SINE RNA of the imprinted miRNA clusters mediates constitutive type III interferon expression and antiviral protection in hemochorial placentas . Cell. Host. Microbe . 31 ( 7 ): 1185 – 1199 .e10. doi: 10.1016/j.chom.2023.05.018 . OpenUrl CrossRef PubMed 108. La , X. , Wang , Y , Xiong , X. , Shen , L. , Chen , W. , Zhang , L. , Yang , F. , Cai , X. , Zheng , H. , Jiang , H . ( 2022 ). The Composition of Placental Microbiota and Its Association With Adverse Pregnancy Outcomes . Front Microbiol . 13 : 911852 . doi: 10.3389/fmicb.2022.911852 . OpenUrl CrossRef PubMed 109. Goldberg , K. , Lobov , A. , Antonello , P. et al. ( 2025 ). Cell-autonomous innate immunity by proteasome-derived defence peptides . Nature 639 , 1032 – 1041 . doi: 10.1038/s41586-025-08615-w . OpenUrl CrossRef PubMed 110. ↵ Burton , GJ. , Jauniaux , E . ( 2023 ). The human placenta: new perspectives on its formation and function during early pregnancy . Proc Biol Sci . 290 ( 1997 ): 20230191 . doi: 10.1098/rspb.2023.0191 . OpenUrl CrossRef 111. ↵ Khan , MA. , Kar , M. , Mittal , S. , Kumar , S. , Bharagava , VL. , Sengupta , J. , Ghoshi , D . ( 2010 ). Small scale transcript expression profile of Human first trimester placental villi analyzed by a custom-tailored cDNA array . Indian J Physiol Pharmacol . 54 ( 3 ): 235 – 54 . OpenUrl PubMed 112. ↵ Soncin , F. , Khater , M. , To , C. , Pizzo , D. , Farah , O. , Wakeland , A. , Arul Nambi Rajan , K. , Nelson , KK. , Chang , CW. , Moretto-Zita , M. , Natale , DR. , Laurent , LC. , Parast , MM. ( 2018 ). Comparative analysis of mouse and human placentae across gestation reveals species-specific regulators of placental development . Development . 29; 145 ( 2 ): dev156273 . doi: 10.1242/dev.156273 . OpenUrl Abstract / FREE Full Text 113. ↵ Zhang , B. , Feng , L. , Guo , H. , Li , H. , Wang , Y. , Zhang , K. , Yu , X. , Cheng , S . ( 2017 ). Villi-specific Gene Expression Reveals Novel Prognostic Biomarkers in Multiple Human Cancers . J Cancer . 8 ( 14 ): 2793 – 2801 . doi: 10.7150/jca.19787 OpenUrl CrossRef PubMed 114. ↵ Breen , J. ; McAninch , D. ; Jankovic-Karasoulos , T. ; McCullough , D. ; Smith , M.D. ; Bogias , K.J. ; Wan , Q. ; Choudhry , A. ; Hin , N. ; Pederson , S.M. et al. ( 2020 ). Temporal placental genome wide expression profiles reflect three phases of utero-placental blood flow during early to mid human gestation . medRxiv , 1 – 18 . 115. ↵ Bogias KJ. , Pederson SM. , Leemaqz S. , Smith MD. , McAninch D. , Jankovic-Karasoulos T. , McCullough D. , Wan Q. , Bianco-Miotto T. , Breen J. , Roberts CT . ( 2022 ). Placental Transcription Profiling in 6-23 Weeks’ Gestation Reveals Differential Transcript Usage in Early Development . Int J Mol Sci . ; 23 ( 9 ): 4506 . doi: 10.3390/ijms23094506 . OpenUrl CrossRef PubMed 116. ↵ Wang , X. , Park , J. , Susztak , K. , Zhang , N. R. , & Li , M. ( 2019 ). Bulk tissue cell type deconvolution with multi-subject single-cell expression reference . Nat Commun . 10 ( 1 ), 380 . doi: 10.1038/s41467-018-08023-x116 . OpenUrl CrossRef PubMed 117. ↵ Campbell , K.A. , Colacino , J.A. , Puttabyatappa , M. et al. ( 2023 ). Placental cell type deconvolution reveals that cell proportions drive preeclampsia gene expression differences . Commun Biol 6 , 264 . doi: 10.1038/s42003-023-04623-6 . OpenUrl CrossRef PubMed 118. ↵ https://learn.gencore.bio.nyu.edu/rna-seq-analysis/gene-set-enrichment-analysis/ 119. ↵ Rajakumar , A. , Conrad , KP. ( 2000 ). Expression, ontogeny, and regulation of hypoxia-inducible transcription factors in the human placenta . Biol Reprod . 63 ( 2 ): 559 – 69 . doi: 10.1095/biolreprod63.2.559 . OpenUrl CrossRef PubMed Web of Science 120. ↵ Korbecki , J. , Kojder , K. , Kapczuk , P. , Kupnicka , P. , Gawrońska-Szklarz , B. , Gutowska , I. , Chlubek , D. , Baranowska-Bosiacka , I . ( 2021 ). The Effect of Hypoxia on the Expression of CXC Chemokines and CXC Chemokine Receptors-A Review of Literature . Int J Mol Sci . 15; 22 ( 2 ): 843 . doi: 10.3390/ijms22020843 . OpenUrl CrossRef PubMed 121. ↵ Derisoud , E ,, Jiang , H ,, Zhao , A ,, Chavatte-Palmer , P ,, Deng , Q. ( 2024 ). Revealing the molecular landscape of human placenta: a systematic review and meta-analysis of single-cell RNA sequencing studies . Hum Reprod Update . 1; 30 ( 4 ): 410 – 441 . doi: 10.1093/humupd/dmae006 . OpenUrl CrossRef 122. ↵ Prater , M. ,, Hamilton , RS. , Wa Yung , H. , Sharkey , AM. , Robson , P. , Abd Hamid , NE. , Jauniaux , E. , Charnock-Jones , DS. , Burton , GJ. , Cindrova-Davies , T. ( 2021 ). RNA-Seq reveals changes in human placental metabolism, transport and endocrinology across the first-second trimester transition . Biol Open . 10 ( 6 ): bio058222 . doi: 10.1242/bio.058222 . OpenUrl CrossRef PubMed 123. ↵ Kelly , CJ. , Glover , LE. , Campbell , EL. , Kominsky , DJ. , Ehrentraut , SF. , Bowers , BE. , Bayless , AJ. , Saeedi , BJ. , Colgan , SP . ( 2013 ). Fundamental role for HIF-1α in constitutive expression of human β defensin-1 . Mucosal Immunol . 6 ( 6 ): 1110 – 8 . doi: 10.1038/mi.2013.6 . OpenUrl CrossRef PubMed 124. ↵ Yang , YH. , Yan , F. , Shi , PS. , Yang , LC. , Cui , DJ . ( 2024 ). HIF-1α Pathway Orchestration by LCN2: A Key Player in Hypoxia-Mediated Colitis Exacerbation . Inflammation . 47 ( 4 ): 1491 – 1519 . doi: 10.1007/s10753-024-01990-y . OpenUrl CrossRef PubMed 125. ↵ Strell , C. , Entschladen , F . ( 2008 ). Extravasation of leukocytes in comparison to tumor cells . Cell Commun. Signal . 6 : 10 . doi: 10.1186/1478-811X-6-10 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted October 16, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Placental immune factors change during the first half of healthy pregnancy Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Placental immune factors change during the first half of healthy pregnancy Maria Obolenska , Olexandr Lykhenko , Yevhenii Kukuruza , Volodymyr Martynenko , Yehor Poliakov , Olga Martsenyuk , Volodymyr Bezguba , Berthold Huppertz bioRxiv 2025.10.16.682794; doi: https://doi.org/10.1101/2025.10.16.682794 Share This Article: Copy Citation Tools Placental immune factors change during the first half of healthy pregnancy Maria Obolenska , Olexandr Lykhenko , Yevhenii Kukuruza , Volodymyr Martynenko , Yehor Poliakov , Olga Martsenyuk , Volodymyr Bezguba , Berthold Huppertz bioRxiv 2025.10.16.682794; doi: https://doi.org/10.1101/2025.10.16.682794 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Bioinformatics Subject Areas All Articles Animal Behavior and Cognition (7618) Biochemistry (17637) Bioengineering (13864) Bioinformatics (41853) Biophysics (21403) Cancer Biology (18540) Cell Biology (25429) Clinical Trials (138) Developmental Biology (13356) Ecology (19862) Epidemiology (2067) Evolutionary Biology (24287) Genetics (15585) Genomics (22464) Immunology (17701) Microbiology (40300) Molecular Biology (17142) Neuroscience (88440) Paleontology (666) Pathology (2825) Pharmacology and Toxicology (4814) Physiology (7633) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4285) Systems Biology (9809) Zoology (2268)