Transcriptomics of maternal and fetal membranes can discriminate between gestational-age matched preterm neonates with and without cognitive impairment diagnosed at 18-24 months

In utero and post-natal opioid exposure followed by mild traumatic brain injury contributes to cortical neuroinflammation, mitochondrial dysfunction, and behavioral deficits in juvenile rats

Maternal opioid use poses a significant health concern not just to the expectant mother but also to the fetus. Notably, increasing numbers of children born suffering from neonatal opioid withdrawal syndrome (NOWS) further compounds the crisis. While epidemiological research has shown the heightened risk factors associated with NOWS, little research has investigated what molecular mechanisms underly the vulnerabilities these children carry throughout development and into later life. To understand the implications of in utero and post-natal opioid exposure on the developing brain, we sought to assess the response to one of the most common pediatric injuries: minor traumatic brain injury (mTBI). Using a rat model of in utero and post-natal oxycodone (IUO) exposure and a low force weight drop model of mTBI, we show that not only neonatal opioid exposure significantly affects neuroinflammation, brain metabolites, synaptic proteome, mitochondrial function, and altered behavior in juvenile rats, but also, in conjunction with mTBI these aberrations are further exacerbated. Specifically, we observed long term metabolic dysregulation, neuroinflammation, alterations in synaptic mitochondria, and impaired behavior were impacted severely by mTBI. Our research highlights the specific vulnerability caused by IUO exposure to a secondary stressor such as later life brain injury. In summary, we present a comprehensive study to highlight the damaging effects of prenatal opioid abuse in conjunction with mild brain injury on the developing brain.

The use of data independent acquisition based proteomic analysis and machine learning to reveal potential biomarkers for autism spectrum disorder

Autism spectrum disorder (ASD) is a complex neurological developmental disorder in children, and is associated with social isolation and restricted interests. The etiology of this disorder is still unknown. There is neither any confirmed laboratory test nor any effective therapeutic strategy to diagnose or cure it. We performed data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis of plasma from children with ASD and controls. The result showed that 45 differentially expressed proteins (DEPs) were identified between autistic subjects and controls. Among these, only one DEP was down-regulated in ASD; other DEPs were up-regulated in ASD children's plasma. These proteins are found associated with complement and coagulation cascades, vitamin digestion and absorption, cholesterol metabolism, platelet degranulation, selenium micronutrient network, extracellular matrix organization and inflammatory pathway, which have been reported to be related to ASD. After MRM verification, five key proteins in complement pathway (PLG, SERPINC1, and A2M) and inflammatory pathway (CD5L, ATRN, SERPINC1, and A2M) were confirmed to be significantly up-regulated in ASD group. Through the screening of machine learning model and MRM verification, we found that two proteins (biotinidase and carbonic anhydrase 1) can be used as early diagnostic markers of ASD (AUC = 0.8, p = 0.0001). SIGNIFICANCE: ASD is the fastest growing neurodevelopmental disorder in the world and has become a major public health problem worldwide. Its prevalence has been steadily increasing, with a global prevalence rate of 1%. Early diagnosis and intervention can achieve better prognosis. In this study, data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis was applied to analyze the plasma proteome of ASD patients (31 (±5) months old), and 378 proteins were quantified. 45 differentially expressed proteins (DEPs) were identified between the ASD group and the control group. They mainly were associated with platelet degranulation, ECM proteoglycar, complement and coagulation cascades, selenium micronutrient network, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), cholesterol metabolism, vitamin metabolism, and inflammatory pathway. Through the integrated machine learning methods and the MRM verification of independent samples, it is considered that biotinidase and carbon anhydrase 1 have the potential to become biomarkers for the early diagnosis of ASD. These results complement proteomics database of the ASD patients, broaden our understanding of ASD, and provide a panel of biomarkers for the early diagnosis of ASD.

Maternal plasma cytokines and the subsequent risk of uterine atony and postpartum hemorrhage

OBJECTIVES

To determine whether the maternal plasma concentrations of cytokines are higher in pregnant women with postpartum hemorrhage (PPH) compared to pregnant women without PPH.

METHODS

A retrospective case-control study included 36 women with PPH and 72 matched controls. Cases and controls were matched for gestational age at delivery, labor status, delivery route, parity, and year of sample collection. Maternal plasma samples were collected up to 3 days prior to delivery. Comparison of the plasma concentrations of 29 cytokines was performed by using linear mixed-effects models and included adjustment for covariates and multiple testing. A false discovery rate adjusted p-value <0.1 was used to infer significance. Random forest models with evaluation by leave-one-out and 9-fold cross-validation were used to assess the combined value of the proteins in predicting PPH.

RESULTS

Concentrations of interleukin (IL)-16, IL-6, IL-12/IL-23p40, monocyte chemotactic protein 1 (MCP-1), and IL-1β were significantly higher in PPH than in the control group. This difference remained significant after adjustment for maternal age, clinical chorioamnionitis, and preeclampsia. Multi-protein random forest proteomics models had moderate cross-validated accuracy for prediction of PPH [area under the ROC curve, 0.69 (0.58-0.81) by leave-one-out cross validation and 0.73 (0.65-0.81) by 9-fold cross-validation], and the inclusion of clinical and demographic information did not increase the prediction performance.

CONCLUSIONS

Pregnant women with severe PPH had higher median maternal plasma concentrations of IL-16, IL-6, IL-12/IL-23p40, MCP-1, and IL-1β than patients without PPH. These cytokines could serve as biomarkers or their pathways may be therapeutic targets.

The amniotic fluid proteome changes with gestational age in normal pregnancy: a cross-sectional study

The cell-free transcriptome in amniotic fluid (AF) has been shown to be informative of physiologic and pathologic processes in pregnancy; however, the change in AF proteome with gestational age has mostly been studied by targeted approaches. The objective of this study was to describe the gestational age-dependent changes in the AF proteome during normal pregnancy by using an omics platform. The abundance of 1310 proteins was measured on a high-throughput aptamer-based proteomics platform in AF samples collected from women during midtrimester (16-24 weeks of gestation, n = 15) and at term without labor (37-42 weeks of gestation, n = 13). Only pregnancies without obstetrical complications were included in the study. Almost 25% (320) of AF proteins significantly changed in abundance between the midtrimester and term gestation. Of these, 154 (48.1%) proteins increased, and 166 (51.9%) decreased in abundance at term compared to midtrimester. Tissue-specific signatures of the trachea, salivary glands, brain regions, and immune system were increased while those of the gestational tissues (uterus, placenta, and ovary), cardiac myocytes, and fetal liver were decreased at term compared to midtrimester. The changes in AF protein abundance were correlated with those previously reported in the cell-free AF transcriptome. Intersecting gestational age-modulated AF proteins and their corresponding mRNAs previously reported in the maternal blood identified neutrophil-related protein/mRNA pairs that were modulated in the same direction. The first study to utilize an aptamer-based assay to profile the AF proteome modulation with gestational age, it reveals that almost one-quarter of the proteins are modulated as gestation advances, which is more than twice the fraction of altered plasma proteins (~ 10%). The results reported herein have implications for future studies focused on discovering biomarkers to predict, monitor, and diagnose obstetrical diseases.

A Potential ceRNA Network for Neurological Damage in Preterm Infants

Objective

This study is aimed at identifying key genes involved in neurological damage in preterm infants and at determining their potential circRNA-miRNA-mRNA regulatory mechanisms.

Methods

Differentially expressed miRNAs, mRNAs, and circRNAs were downloaded from the GEO database. GO and KEGG enrichment analyses were used to determine possible relevant functions of differentially expressed mRNAs. The TTRUST database was used to predict differential TF-mRNA regulatory relationships. Then, CircMIR, miRDB, TargetScan and miRTarBase were then used to map circRNA/miRNA-TF/mRNA interaction networks. Finally, GSEA enrichment analysis was performed on the core transcription factors.

Results

A total of 640 mRNAs, 139 circRNAs, and 206 differentially expressed miRNAs associated with neurological injury in preterm infants were obtained. Based on the findings of Cytoscape and PPI network analysis, the hsa_circ_0008439-hsa-mir-3665-STAT3-MMP3 regulatory axis was established. GSEA analysis revealed that suppressed expression levels of STAT3 were associated with upregulated oxidative phosphorylation pathways in the neurological injury group of preterm infants.

Conclusions

The circRNA-miRNA-TF-mRNA regulatory network of neurological injury in preterm infants can be used to elucidate on the pathogenesis of brain injury and help us with the early detection of brain injury in preterm infants.

Integrative analysis of shared genetic pathogenesis by autism spectrum disorder and obsessive-compulsive disorder

Many common pathological features have been observed for both autism spectrum disorders (ASDs) and obsessive-compulsive disorder (OCD). However, no systematic analysis of the common gene markers associated with both ASD and OCD has been conducted so far. Here, two batches of large-scale literature-based disease–gene relation data (updated in 2017 and 2019, respectively) and gene expression data were integrated to study the possible association between OCD and ASD at the genetic level. Genes linked to OCD and ASD present significant overlap (P-value <2.64e-39). A genetic network of over 20 genes was constructed, through which OCD and ASD may exert influence on each other. The 2017-based analysis suggested six potential common risk genes for OCD and ASD (CDH2, ADCY8, APOE, TSPO, TOR1A, and OLIG2), and the 2019-based study identified two more genes (DISP1 and SETD1A). Notably, the gene APOE identified by the 2017-based analysis has been implicated to have an association with ASD in a recent study (2018) with DNA methylation analysis. Our results support the possible complex genetic associations between OCD and ASD. Genes linked to one disease are worth further investigation as potential risk factors for the other.

Impact of Apolipoprotein E gene polymorphism during normal and pathological conditions of the brain across the lifespan

The central nervous system (CNS) is the cellular substrate for the integration of complex, dynamic, constant, and simultaneous interactions among endogenous and exogenous stimuli across the entire human lifespan. Numerous studies on aging-related brain diseases show that some genes identified as risk factors for some of the most common neurodegenerative diseases - such as the allele 4 of APOE gene (APOE4) for Alzheimer's disease (AD) - have a much earlier neuro-anatomical and neuro-physiological impact. The impact of APOE polymorphism appears in fact to start as early as youth and early-adult life. Intriguingly, though, those same genes associated with aging-related brain diseases seem to influence different aspects of the brain functioning much earlier actually, that is, even from the neonatal periods and earlier. The APOE4, an allele classically associated with later-life neurodegenerative disorders as AD, seems in fact to exert a series of very early effects on phenomena of neuroplasticity and synaptogenesis that begin from the earliest periods of life such as the fetal ones.We reviewed some of the findings supporting the hypothesis that APOE polymorphism is an early modifier of various neurobiological aspects across the entire human lifespan - from the in-utero to the centenarian life - during both normal and pathological conditions of the brain.

The plasma metabolome of women in early pregnancy differs from that of non-pregnant women

BACKGROUND

In comparison to the non-pregnant state, the first trimester of pregnancy is characterized by systemic adaptation of the mother. The extent to which these adaptive processes are reflected in the maternal blood metabolome is not well characterized.

OBJECTIVE

To determine the differences between the plasma metabolome of non-pregnant and pregnant women before 16 weeks gestation.

STUDY DESIGN

This study included plasma samples from 21 non-pregnant women and 50 women with a normal pregnancy (8-16 weeks of gestation). Combined measurements by ultrahigh performance liquid chromatography/tandem mass spectrometry and by gas chromatography/mass spectrometry generated molecular abundance measurements for each sample. Molecular species detected in at least 10 samples were included in the analysis. Differential abundance was inferred based on false discovery adjusted p-values (FDR) from Mann-Whitney-Wilcoxon U tests <0.1 and a minimum median abundance ratio (fold change) of 1.5. Alternatively, metabolic data were quantile normalized to remove sample-to-sample differences in the overall metabolite abundance (adjusted analysis).

RESULTS

Overall, 637 small molecules met the inclusion criteria and were tested for association with pregnancy; 44% (281/637) of small molecules had significantly different abundance, of which 81% (229/281) were less abundant in pregnant than in non-pregnant women. Eight percent (14/169) of the metabolites that remained significant in the adjusted analysis also changed as a function of gestational age. A pathway analysis revealed enrichment in steroid metabolites related to sex hormones, caffeine metabolites, lysolipids, dipeptides, and polypeptide bradykinin derivatives (all, FDR < 0.1).

CONCLUSIONS

This high-throughput mass spectrometry study identified: 1) differences between pregnant vs. non-pregnant women in the abundance of 44% of the profiled plasma metabolites, including known and novel molecules and pathways; and 2) specific metabolites that changed with gestational age.

Spinning convincing stories for both true and false association signals

When interpreting genome‐wide association peaks, it is common to annotate each peak by searching for genes with plausible relationships to the trait. However, “all that glitters is not gold”—one might interpret apparent patterns in the data as plausible even when the peak is a false positive. Accordingly, we sought to see how human annotators interpreted association results containing a mixture of peaks from both the original trait and a genetically uncorrelated “synthetic” trait. Two of us prepared a mix of original and synthetic peaks of three significance categories from five different scans along with relevant literature search results and then we all annotated these regions. Three annotators also scored the strength of evidence connecting each peak to the scanned trait and the likelihood of further studying that region. While annotators found original peaks to have stronger evidence (p_Bonferroni = 0.017) and higher likelihood of further study ( p_Bonferroni = 0.006) than synthetic peaks, annotators often made convincing connections between the synthetic peaks and the original trait, finding these connections 55% of the time. These results show that it is not difficult for annotators to make convincing connections between synthetic association signals and genes found in those regions.

Soluble inflammatory mediators induce transcriptional re-organization that is independent of dna methylation changes in cultured human chorionic villous trophoblasts

The studies proposed here were undertaken to test the hypothesis that, under specific circumstances (e.g., a strong enough inflammatory stimulus), genes that are repressed at the maternal-fetal interface via DNA methylation might be de-methylated, allowing either a maternal immune response to the semi-allogenic fetus or the onset of early labor. Chorionic trophoblasts (CT) were isolated from fetal membranes, followed by incubation with medium from LPS-activated PBMC or resting PBMC medium for 2 h. RNA and DNA were isolated from the cells for RNA-seq and DNA methylation studies. Two hrs after being exposed to conditioned medium from LPS-activated PBMC, CT showed differential expression of 114 genes, all but 2 of which showed higher expression in the stimulated cells than is the unstimulated cells. We also identified 318 differentially methylated regions (DMRs) that associated with 306 genes (155 protein coding genes) in the two groups, but the observed methylation changes had negligible impact on the observed transcriptional changes in CT.

CT display complex patterns of transcription in response to inflammation. DNA methylation does not appear to be an important regulator of the observed transcriptional changes.

Human blood gene signature as a marker for smoking exposure: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge

Crowdsourcing has emerged as a framework to address methodological challenges in omics data analysis and assess the extent to which omics data are predictive of phenotypes of interest. The sbv IMPROVER Systems Toxicology Challenge was designed to leverage crowdsourcing to determine whether human blood gene expression levels are informative of current and past smoking. Participating teams were invited to use a training gene expression dataset to derive parsimonious models (up to 40 genes) that can accurately classify subjects into exposure groups: smokers, former smokers that quit for at least one year, and never-smokers. Teams were ranked based on two classification performance metrics evaluated on a blinded test dataset. The analytical approaches of the first- and third-ranked teams, that are presented in detail in this article, involved feature selection by moderated t-test or LASSO regression and linear discriminant analysis (LDA) and logistic regression classifiers, respectively. While the 12-gene signature of the top team allowed the classification of current smokers with 100% sensitivity at 93% specificity, discriminating former smokers from never-smokers was much more challenging (65% sensitivity at 57% specificity). Gene ontology molecular functions and KEGG pathways associated with current smoking included G protein-coupled receptor activity, signaling receptor activity, calcium ion binding, and the Neuroactive ligand-receptor interaction pathway. Selection of marker genes by either moderated t-test or multivariate LASSO regression followed by LDA or logistic regression, are robust approaches to classification with omics data, confirming in part findings of previous sbv IMPROVER challenges. While current smoking is accurately identified based on blood mRNA levels, smoking cessation for more than one year is accompanied by a "normalization" of the expression of certain mRNAs, making it difficult to distinguish former smokers from never-smokers.

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

OBJECTIVE

Pregnancy is accompanied by dramatic physiological changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age and determine biological processes that are perturbed in normal pregnancy.

STUDY DESIGN

A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median, 5) were profiled to measure the abundance of 1125 proteins using multiplex assays. Linear mixed-effects models with polynomial splines were used to model protein abundance as a function of gestational age, and the significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having the following: (1) >1.5-fold change between 8 and 40 weeks of gestation; and (2) a false discovery rate-adjusted value of P < .1. Gene ontology enrichment analysis was used to identify biological processes overrepresented among the proteins that changed with advancing gestation.

RESULTS

The following results were found: (1) Ten percent (112 of 1125) of the profiled proteins changed in abundance as a function of gestational age; (2) of the 1125 proteins analyzed, glypican-3, sialic acid-binding immunoglobulin-type lectin-6, placental growth factor, C-C motif-28, carbonic anhydrase 6, prolactin, interleukin-1 receptor 4, dual-specificity mitogen-activated protein kinase 4, and pregnancy-associated plasma protein-A had more than a 5-fold change in abundance across gestation (these 9 proteins are known to be involved in a wide range of both physiological and pathological processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc); and (3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis, and leukocyte migration (false discovery rate, 10%).

CONCLUSION

The plasma proteome of normal pregnancy demonstrates dramatic changes in both the magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy and the development of biomarkers to differentiate normal vs abnormal pregnancy and determine the response to interventions.

Human fetal membranes at term: Dead tissue or signalers of parturition?

Various endocrine, immune, and mechanical factors produced by feto-maternal compartments at term increase intrauterine inflammatory loads to induce labor. The role of fetal (placental) membranes (amniochorion) as providers of parturition signals has not been well investigated. Fetal membranes line the intrauterine cavity and grow with and protect the fetus. Fetal membranes exist as an entity between the mother and fetus and perform unique functions during pregnancy. Membranes undergo a telomere-dependent p38 MAPK-induced senescence and demonstrate a decline in functional and mechanical abilities at term, showing signs of aging. Fetal membrane senescence is also allied with completion of fetal maturation at term as the fetus readies for delivery, which may also indicate the end of independent life and longevity of fetal membranes as their functional role concludes. Fetal membrane senescence is accelerated at term because of oxidative stress and increased stretching. Senescent fetal membranes cells produce senescence-associated secretory phenotype (SASP-inflammation) and also release proinflammatory damage-associated molecular patterns (DAMPs), namely HMGB1 and cell-free fetal telomere fragments. In a feedback loop, SASP and DAMPs increase senescence and enhance the inflammatory load to promote labor. Membranes increase the inflammatory load to disrupt homeostatic balance to transition quiescent uterine tissues toward a labor phenotype. Therefore, along with other well-described labor-promoting signals, senescent fetal membranes may also contribute to human term parturition.

Genome-Wide Profiling of RNA from Dried Blood Spots: Convergence with Bioinformatic Results Derived from Whole Venous Blood and Peripheral Blood Mononuclear Cells

Genome-wide transcriptional profiling has emerged as a powerful tool for analyzing biological mechanisms underlying social gradients in health, but utilization in population-based studies has been hampered by logistical constraints and costs associated with venipuncture blood sampling. Dried blood spots (DBS) provide a minimally invasive, low-cost alternative to venipuncture, and in this article we evaluate how closely the substantive results from DBS transcriptional profiling correspond to those derived from parallel analyses of gold-standard venous blood samples (PAXgene whole blood and peripheral blood mononuclear cells [PBMC]). Analyses focused on differences in gene expression between African-Americans and Caucasians in a community sample of 82 healthy adults (age 18-70 years; mean 35). Across 19,679 named gene transcripts, DBS-derived values correlated r = .85 with both PAXgene and PBMC values. Results from bioinformatics analyses of gene expression derived from DBS samples were concordant with PAXgene and PBMC samples in identifying increased Type I interferon signaling and up-regulated activity of monocytes and natural killer (NK) cells in African-Americans compared to Caucasian participants. These findings demonstrate the feasibility of DBS in field-based studies of gene expression and encourage future studies of human transcriptome dynamics in larger, more representative samples than are possible with clinic- or lab-based research designs.