NEST-Scoring, a Novel miRNA Target Gene Profile Network Analysis of Placenta-Derived Extracellular Vesicles in a Transgenic Rat Model of Preeclampsia

NEST-Scoring, a Novel miRNA Target Gene Profile Network Analysis of Placenta-Derived Extracellular Vesicles in a Transgenic Rat Model of Preeclampsia | 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 NEST-Scoring, a Novel miRNA Target Gene Profile Network Analysis of Placenta-Derived Extracellular Vesicles in a Transgenic Rat Model of Preeclampsia View ORCID Profile Brian M. Westwood , Jonathan Ray , Xuming Sun , Yixin Su , Nildris Cruz-Diaz , Sangeeta Singh , NyKea L. Lewis , Ashish Kumar , Gagan Deep , Victor M. Pulgar , View ORCID Profile Liliya M. Yamaleyeva doi: https://doi.org/10.1101/2025.09.22.675363 Brian M. Westwood 1 Department of Surgery/Hypertension and Vascular Research, Wake Forest University School of Medicine , Winston-Salem, North Carolina, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Brian M. Westwood For correspondence: Brian.Westwood{at}advocatehealth.org Liliya.Yamaleyeva{at}advocatehealth.org Jonathan Ray 1 Department of Surgery/Hypertension and Vascular Research, Wake Forest University School of Medicine , Winston-Salem, North Carolina, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xuming Sun 1 Department of Surgery/Hypertension and Vascular Research, Wake Forest University School of Medicine , Winston-Salem, North Carolina, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yixin Su 2 Department of Internal Medicine, Gerontology and Geriatric Medicine, Wake Forest University School of Medicine , Winston-Salem, North Carolina, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nildris Cruz-Diaz 1 Department of Surgery/Hypertension and Vascular Research, Wake Forest University School of Medicine , Winston-Salem, North Carolina, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sangeeta Singh 2 Department of Internal Medicine, Gerontology and Geriatric Medicine, Wake Forest University School of Medicine , Winston-Salem, North Carolina, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site NyKea L. Lewis 1 Department of Surgery/Hypertension and Vascular Research, Wake Forest University School of Medicine , Winston-Salem, North Carolina, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ashish Kumar 2 Department of Internal Medicine, Gerontology and Geriatric Medicine, Wake Forest University School of Medicine , Winston-Salem, North Carolina, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gagan Deep 2 Department of Internal Medicine, Gerontology and Geriatric Medicine, Wake Forest University School of Medicine , Winston-Salem, North Carolina, United States 3 Atrium health Wake Forest Baptist Comprehensive Cancer Center, Wake Forest University School of Medicine , Winston-Salem, North Carolina, United States 4 Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest University School of Medicine , Medical Center Blvd, Winston-Salem, North Carolina, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Victor M. Pulgar 5 Pharmaceutical & Clinical Sciences, Campbell University , Buies Creek, North Carolina, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site Liliya M. Yamaleyeva 1 Department of Surgery/Hypertension and Vascular Research, Wake Forest University School of Medicine , Winston-Salem, North Carolina, United States Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Liliya M. Yamaleyeva For correspondence: Brian.Westwood{at}advocatehealth.org Liliya.Yamaleyeva{at}advocatehealth.org Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Preeclampsia is a multisystem hypertensive disorder of pregnancy and a leading cause of maternal morbidity and mortality. Despite its increasing incidence and the debilitating nature of its cerebrovascular complications, the underlying mechanisms remain incompletely understood. The goals of this study were to 1) determine whether middle cerebral artery (MCA) hemodynamics are altered in late gestation (LG) or two months postpartum in transgenic rats with preeclampsia compared to normal pregnant Sprague-Dawley (SD) rats, and 2) evaluate the microRNA (miRNA) profiles of circulating placental-derived extracellular vesicles (EV PD ) using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway framework using NEST-Scoring [Node, Edge, System, Topology], a novel target gene profile network analysis. Our data showed elevated blood pressure in transgenic preeclampsia rats at both LG and two months post-partum (PP-2M) compared to normal pregnant SD rats in the setting of hypertension. The heart rate was also significantly higher in preeclampsia rats at LG. No differences were observed in left MCA (LMCA) velocities between strains or time points. However, LMCA resistance and pulsatility indexes were lower at PP-2M in transgenic preeclampsia rats compared to SD rats. Exposure to preeclampsia differentially altered the miRNA profiles of circulating EV PD in transgenic preeclampsia rats at both LG and PP-2M. The identified miRNA targets were associated with key vascular and cellular regulatory pathways, suggesting a mechanistic link between placental dysfunction and postpartum vascular alterations in this preeclampsia model. Overall, this study suggests that the TGA-PE rat model could be a valuable platform to study the mechanistic link between preeclampsia and cerebrovascular disease. INTRODUCTION Preeclampsia is a multisystem hypertensive disorder of pregnancy and a leading cause of maternal morbidity and mortality [ 1 ]. Although the pathogenesis of preeclampsia remains incompletely understood, end-organ damage is at least partially attributed to systemic vascular dysfunction which is induced by placental factors released in response to hypoxia and oxidative stress [ 2 - 4 ]. The maternal cerebral vasculature is particularly vulnerable to these alterations and cerebrovascular disease remains the most common cause of mortality in patients with preeclampsia [ 5 - 7 ]. Importantly, cerebrovascular complications extend beyond pregnancy. A history of preeclampsia is associated with long-term consequences, including increased risk of stroke [ 8 - 11 ], vascular dementia and early cognitive dysfunction [ 12 - 14 ], which may occur years after the pregnancy has ended. Despite the rising incidence of preeclampsia and the debilitating nature of its cerebrovascular complications, the underlying mechanisms remain poorly understood. Given a critical role of extracellular vesicles (EVs), membrane bound vesicles secreted by all cells, in intercellular signaling, we hypothesize that the microRNA profiles and associated target pathways will differ in circulating placental-derived EVs (EV PD ) isolated from transgenic rats with preeclampsia compared to normal pregnant Sprague-Dawley (SD) rats at late gestation (LG) and 2 months postpartum (PP-2M) revealing novel molecular targets for future postpartum therapeutic interventions. Multi-omics modeling methods [ 15 - 17 ] can be complex; we have been developing simpler receiver operating characteristic tests of first through fifth neighbors in KEGG pathway maps [ 18 , 19 ]. This NEST-Scoring method - a simple graphical scoring metric to index the spectrum of associated gene targets relative to unrelated pathway elements, only requires the evaluation of the first neighbor of genes of interest. NEST-Scoring defines how diffuse or consolidated the signal of interest is. The input is genes of interest with highest confidence (baseline capacity), genes of moderately high confidence (identified as with excitatory potential) and genes that are not of interest as targets of miRNA (identified as with inhibitory potential) [ 20 ]. METHODS Animals The study was approved by the Institutional Animal Care and Use Committee of the Wake Forest University School of Medicine (A21-057), and all experiments and methods were performed in accordance with relevant guidelines and regulations. Female transgenic rats with overexpression of human angiotensinogen (TGA) or male transgenic rats with overexpression of human renin (TGR) were obtained from the colony maintained by the Department of Surgery/Hypertension and Vascular Research Center, Wake Forest University School of Medicine, Winston-Salem, NC. Sprague-Dawley (SD) rats purchased from Charles River Laboratories (Wilmington, MA, USA) were used in control experiments. All rats were housed at a constant room temperature, humidity, and light cycle (12:12-h light-dark), fed a standard rodent chow (Lab Diet 5P00 – Prolab RMH 3000, PMI Nutrition International, INC, Brentwood, MO) and were given water ad libitum throughout the experimental protocols. Study timeline Female TGA rats were mated to male TGR rats to develop preeclampsia-like features (TGA-PE). SD females were bred to SD male rats. TGA-PE and SD rats at 11-26 weeks of age during late-gestation were used for this study. The majority of females used were first-time pregnant. Gestation day (GD) was estimated by embryonic features detectable by transabdominal ultrasound [ 21 ]. Plasma was collected at LG and 2-months postpartum (PP-2M) from SD and TGA-PE rats. Blood pressure and heart rate recordings, and transcranial Doppler (TCD) ultrasound analysis of the left middle cerebral artery (LMCA) were performed during late-gestation (GD16-19) and at 2-months post-delivery. Blood pressure and heart rate were recorded one day prior to TCD ultrasound recordings. Blood pressure and heart rate measurements Blood pressure (systolic, diastolic, and mean) and heart rate were recorded in trained, conscious rats by the tail-cuff method using the Non-invasive Blood Pressure (NIBP) Monitoring System (Columbus Instruments, Columbus, OH). Data were averaged for each animal and reported as mean±SEM. Transcranial Doppler ultrasound High frequency ultrasound was used to determine the pulsatility (P.I.) and resistance indexes (R.I.) of the LMCA. Animals were placed on a temperature-controlled platform. Temporal hair was removed using a depilatory cream (Nair, Church & Dwight Co., Ewing, NJ). Ultrasound was performed using a Vevo LAZR Ultrasound and Photoacoustic System and LZ250 transducer (FujiFilm, VisualSonics, Toronto, Canada) under 1.5% isoflurane anesthesia. The LMCA was visualized in color Doppler mode by directing the transducer through the rat temporal foramen as previously described [ 22 ]. Maximum (V max ), minimum (V min ), and mean (V mean ) blood flow velocities were determined by pulse wave Doppler mode and were averaged over three cardiac cycles. P.I. and R.I. were calculated as follows: P.I. = V max -V min /V mean ; R.I. = V max -V min /V max . Data were analyzed using Vevo LAB software version 5.7.1 for Windows (FujiFilm, VisualSonics, Toronto, Canada). Isolation of EV PD Total small EV were isolated from plasma via sequential centrifugation and ExoQuick precipitation as reported by us previously [ 23 - 25 ]. Total EVs were characterized by nanoparticle tracking analyses (NTA) using Nanosight NS300 (Malvern Instruments, UK) for size and concentration) as reported by us earlier [ 25 , 26 ]. Next, 2,500 μg of total EVs were incubated with 5 μg of biotin-tagged PLAP antibody (MA5-38111; ThermoFisher) overnight at 4°C with constant mixing. PLAP antibody was labeled with biotin using FluoReporter Mini-Biotin-XX protein labeling Kit (ThermoFisher, Cat. No. F6347). Streptavidin-tagged magnetic beads were added to the samples and incubated for 2 hrs at RT with continuous mixing. Beads were then washed twice with 0.1% TBST and PLAP+ EV PD were eluted from the beads by incubating with 200 μl elution buffer for 10 min at RT. Beads were then magnetized and supernatant with EV PD was collected in 10% v/v 1M Tris buffer (pH 9) to neutralize the pH. Nano-flow cytometry Total EVs were analyzed using nano-flow cytometry following the method described by us previously [ 24 ]. Briefly, total EVs were incubated with fluorescently labeled PLAP antibody (ThermoFisher, Cat. No. MA5-38111), for 2 h at room temperature in the dark. PLAP antibody was fluorescently labeled using APC conjugation kit (Abcam, Cat. No. ab201807) as per the manufacturer’s recommendations. Following antibody incubation, membrane-labeling dye CellBrite steady 488 (CellBrite steady membrane staining Kit, Biotium, Fremont, CA, USA) was diluted 200-fold in 0.1 micron filtered PBS, and 50 uL of diluted dye was added to the total EVs for 15 min incubation in the dark. Samples were diluted 100-to 200-fold in 0.1% BSA in PBS filtered through a 0.1-micron filter to achieve an abort ratio of less than 10%. Total EVs were acquired on CytoFlex (Beckman Coulter Life Science, Indianapolis, United States) for 60s at a low flow rate. Filtered PBS was run for 60s in between the samples. Total EVs without dye was used to set the gate for dye-positive events, and total EVs labeled with dye but without fluorescent antibody were used to set the gate for AF647-positive events. EV PD -RNA for sequencing analysis Total RNA from EV PD was isolated using Trizol based method as detailed previously [ 25 ]. Briefly, 200 μl of EV PD were incubated with 20 μl RNAse A for 30 min at RT to remove any non-EV RNA. Next, 800 μl TRIZol was added to the samples and incubated at RT for 10 min with occasional vortexing in between. Following incubation, 150 μl of chloroform was added and samples were incubated for 2 min before centrifugation at 14,000g for 15 min at 4°C. The top clear phase was collected and mixed with 2 volumes of ethanol for overnight incubation at -20°C. Afterwards samples were purified using RNA isolation columns (Qiagen, Germantown, Maryland, USA), following kit’s instruction, and finally eluted in RNAse free water. The concentration of RNA was measured by NanoDrop (ThermoFisher Scientific). Small RNA sequencing using 350 ng of EV PD -RNA was performed by LC Sciences (Houston, TX, USA). IQR-score [ 18 ] top-ranked miRNAs were clustered using discrete correlate summation (DCS) [ 27 ], followed by the functional enrichment of target gene sets via DAVID [ 28 , 29 ] and KEGG pathway analyses [ 30 ] (summary in supplemental table). miRNA targets were stratified by miRDB target score (MTS) [ 31 ], selecting high-confidence targets (MTS≥95) related to blood pressure and pregnancy as annotated in Swiss-Prot [ 32 ] (n = 146; “95BpPreg”), and compared against general high-confidence targets (MTS≥80; n=672; “MTS80”; Figure 1 ). Download figure Open in new tab Figure 1. EV PD miRNA target gene enrichment pipeline for NEST-Scoring evaluation. 53 KEGG pathways NEST-Scored ( Figure 2 and supplemental plate) for miRDB targets score (MTS)≥95 (yellow nodes) and Swiss-Prot blood pressure (cyan nodes) and pregnancy (magenta nodes) MTS≥80 genes relative to all other IQR-score/DCS MTS80 gene targets (gray nodes). Supplemental plate with all scoring networks analyzed in imageJ [ 33 ]. Download figure Open in new tab Figure 2. Figure 2 . Cartoon example of NEST-Scoring evaluation of miRNA used for 53 KEGG pathways. Nodes in KEGG pathways are yellow (95) if miRDB score >95, masked cyan-bp, magenta-preg, otherwise gray (green are rat genome). Scoring: CMY nodes=1, edge from CMY-gray = 0.25 / CMY-green = -0.25. RESULTS Systolic and diastolic blood pressures were elevated in late gestation and PP-2M in TGA-PE vs. SD rat ( Figure 3A and B ). Within each strain, blood pressure decreased at PP-2M vs. LG in TGA-PE, while no differences in blood pressures were observed in SD rat at PP-2M vs. LG. The heart rate was elevated in TGA-PE at LG but was not different at PP-2M post-delivery vs. SD ( Figure 3C ). Download figure Open in new tab Figure 3. Blood pressure in TGA-PE vs. SD rats at LG and 2M postpartum (PP-2M). Data are mean±SEM, n=10-19. There were no differences in left MCA (LMCA) velocities at LG or PP-2M between strains or studied time points ( Figure 4A-C ). However, LMCA resistance and pulsatility indexes were lower 2-months postdelivery in TGA-PE vs. SD rat ( Figure 4D-E ). Download figure Open in new tab Figure 4. MCA pulsatility and resistance indexes in TGA-PE vs. SD rats at LG and 2M postpartum (PP-2M). Data are mean±SEM, n=10-19. Isolated total EVs from the plasma (n=3/ group) were first analyzed for size and concentration using nanoparticle tracking analysis, which confirmed the size range of small EVs (120 – 160 nm). Interestingly, the size of the total EVs at late gestation in TGA-PE (size Mean ± SEM = 128.73 ± 3.72) was found to be significantly smaller compared to SD (size Mean ± SEM = 160.03±10.06), while SD mice showed significant smaller EVs at PP-2M (size Mean ± SEM =121.66 ± 4.91) compared to late gestation. No significant difference was noted in TGA-PE mice between LG vs. PP-2M (size Mean ± SEM = 126.9 ± 3.21), though at both timepoints, represented smaller size compared to SD at LG ( Figure 5A ). However, no difference in the total EVs concentration was observed between both groups at any timepoint ( Figure 5B ). Furthermore, nanoflow cytometry revealed that 6.53% ± 0.24% of EVs were positive for PLAP in total EVs (% of EV PD in total EVs), though no significant difference was observed among the groups ( Figure 5C ). Download figure Open in new tab Figure 5. Total EVs, isolated from plasma, were first analyzed for (A-B) size and concentration using nanoparticle tracking analysis. Data is presented as mean ± SEM. (B) Further total EVs were analyzed for the presence of presence of PLAP+ EVs (EVPD) using nano-flow cytometry and presented as percentage of PLAP+ EVs in total EVs. * p<0.05, ** <0.005. The miRNA seq of EV PD revealed 29/2436 miRNA with significant alterations between LG or PP-2M for TGA-PE in comparison with SD (17 up- and 12 downregulated miRNAs). KEGG pathway enrichment of target genes (95BpPreg) revealed seven pathways with elevated nest scores (mean±SD: 9.2±4.6), including MAPK signaling pathway, regulation of actin cytoskeleton, Ras signaling pathway, lipid and atherosclerosis, cellular senescence, focal adhesion, TNF signaling ( Figure 6 and Figure 7A ). Download figure Open in new tab Figure 6. DAVID gene-enrichment analysis of KEGG pathway output by NEST-Score in miRNA from EV PD . Download figure Open in new tab Download figure Open in new tab Figure 7. KEGG pathway/EV-PD miRNA target gene network (Panel A) and STRING-GO network (Panel B). Target gene color scheme from Figure 1 . Mapped in Cytoscape [ 35 ]. STRING-based network analysis [ 34 ] of the 39 MTS≥95 target genes from these pathways via Markov clustering identified a primary cluster consisting of 17 highly interconnected genes. Gene Ontology (GO) enrichment analysis of targets with signals exceeding the upper inner fence and false discovery rate (FDR) < 0.05 yielded the pathways involved in vascular remodeling, cellular proliferation, migration, angiogenesis such as epidermal growth factor receptor; vascular endothelial growth factor; transmembrane receptor protein tyrosine kinase; blood vessel morphogenesis; angiogenesis. These GO terms were predominantly associated with miRNAs that were downregulated in EV-PD at LG and upregulated at PP-2M in TGA-PE animals ( Figure 7B ). DISCUSSION A history of preeclampsia is recognized by the American Heart Association and American Stroke Association as an independent risk factor for stroke, underscoring the long-term cerebrovascular consequences of the condition [ 11 ]. According to AHA guidelines, women with a history of preeclampsia face double the risk of stroke and quadruple the risk of developing hypertension later in life [ 36 ]. Despite the increasing incidence of preeclampsia and the debilitating nature of its cerebrovascular complications, the underlying mechanisms remain poorly understood. Our data demonstrated altered middle cerebral artery (MCA) resistance in transgenic rats with preeclampsia (TGA-PE) under sustained hypertension at both late gestation (LG) and two months postpartum, compared with normal pregnant Sprague-Dawley (SD) rats [ 37 ]. These findings suggest that this transgenic strain exhibit cerebrovascular dysfunction at 2 months postdelivery similar to that observed in human preeclamptic patients [ 38 ]. Furthermore, lower MCA resistance data observed in our study align with clinical studies showing that women with preeclampsia exhibit characteristic changes on the ultrasound evaluation including decreased cerebrovascular reactivity, dysfunctional autoregulation of the cerebral blood flow, and decreased pulsatility index which is a measure of vascular resistance [ 39 - 41 ]. Some of these changes in the ultrasound evaluation can persist for years after pregnancy [ 38 ]. These hemodynamic changes are likely contributing factors to the pathogenesis of cerebrovascular disease post-preeclampsia long-term. Recent studies have elucidated the pivotal role of small non-coding RNAs, particularly microRNAs, in modulating gene expression, cellular dynamics, and mediating intercellular communication within the maternal–fetal interface. These regulatory RNAs exert significant influence on both placental and maternal physiological processes. miRNAs have also emerged as promising molecular biomarkers for pregnancy-related pathologies such as preeclampsia and fetal growth restriction and are implicated in the regulation of key signaling cascades governing trophoblast motility, invasiveness, and endothelial cell differentiation [ 42 - 44 ]. Moreover, gestational progression is accompanied by distinct, temporally regulated shifts in miRNA expression profiles within the human placenta. Since dysfunctional placenta contributes to the development of preeclampsia and circulating placenta-derived extracellular vesicles are important mediators of preeclamptic pathologies during pregnancy and long-term, we determined the miRNA profiles and target pathways in placental-derived EVs at LG and PP-2M. The exposure to preeclampsia differentially altered miRNA profiles of circulating placental-derived EVs in TGA-PE rats versus SD at both LG and PP-2M. We have developed a NEST-Scoring analysis that serves as an exploratory tool for investigating changes in pathway systems networks. The bootstrapping paradigm begins by identifying gene targets with moderate to high confidence. These initial sets are then refined using KEGG pathway enrichment analysis via DAVID, which helps eliminate spurious targets. By applying the NEST score to each pathway, this approach offers a true systems-level perspective, highlighting the coordinated biological “chords” orchestrated by miRNA changes. The identified circulating placental-derived miRNA targets included key vascular and cell function regulatory pathways suggesting a link between the dysfunctional placenta and postpartum vascular alterations in this model of preeclampsia. Overall, a novel NEST-Scoring analysis provides an alternative method for investigating alterations in biological pathway networks. This study also suggests that the TGA-PE rat model could be a valuable platform to study the mechanistic link between preeclampsia and cerebrovascular disease. ACKNOWLEDGMENT Supported by NIH R01HL155420 (to LMY), 1R21HD114073 (to LMY), WFUSM CTSI Ignition Funds Pilot Award (to LMY), 1R01AG084696 and R01AG068629 (to GD) and in part by R. Odell Farley Research Fund. Funder Information Declared NIH Common Fund , R01HL155420 , 1R01AG084696 , R01AG068629 WFUSM CTSI Ignition Funds Pilot Award R. Odell Farley Research Fund REFERENCES 1. ↵ Say L , Chou D , Gemmill A , Tuncalp O , Moller AB , Daniels J , et al. Global causes of maternal death: a WHO systematic analysis . Lancet Glob Health . 2014 ; 2 ( 6 ): e323 – 33 . Epub 20140505. DOI: 10.1016/S2214-109X(14)70227-X . PubMed PMID: 25103301 . OpenUrl CrossRef PubMed 2. ↵ Goulopoulou S , Davidge ST . Molecular mechanisms of maternal vascular dysfunction in preeclampsia . Trends Mol Med . 2015 ; 21 ( 2 ): 88 – 97 . Epub 20141202. DOI: 10.1016/j.molmed.2014.11.009 . PubMed PMID: 25541377 . OpenUrl CrossRef PubMed 3. Bridges JP , Gilbert JS , Colson D , Gilbert SA , Dukes MP , Ryan MJ , et al. Oxidative stress contributes to soluble fms-like tyrosine kinase-1 induced vascular dysfunction in pregnant rats . Am J Hypertens . 2009 ; 22 ( 5 ): 564 – 8 . Epub 20090305. DOI: 10.1038/ajh.2009.24 . 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