Use of Routinely Collected Amniotic Fluid for Whole-Genome Expression Analysis of Polygenic Disorders

Characterizing neuroinflammation and identifying prenatal diagnostic markers for neural tube defects through integrated multi-omics analysis

BACKGROUND

Neural Tube Defects (NTDs) are congenital malformations of the central nervous system resulting from the incomplete closure of the neural tube during early embryonic development. Neuroinflammation refers to the inflammatory response in the nervous system, typically resulting from damage to neural tissue. Immune-related processes have been identified in NTDs, however, the detailed relationship and underlying mechanisms between neuroinflammation and NTDs remain largely unclear. In this study, we utilized integrated multi-omics analysis to explore the role of neuroinflammation in NTDs and identify potential prenatal diagnostic markers using a murine model.

METHODS

Nine public datasets from Gene Expression Omnibus (GEO) and ArrayExpress were mined using integrated multi-omics analysis to characterize the molecular landscape associated with neuroinflammation in NTDs. Special attention was given to the involvement of macrophages in neuroinflammation within amniotic fluid, as well as the dynamics of macrophage polarization and their interactions with neural cells at single-cell resolution. We also used qPCR assay to validate the key TFs and candidate prenatal diagnostic genes identified through the integrated analysis in a retinoic acid-induced NTDs mouse model.

RESULTS

Our analysis indicated that neuroinflammation is a critical pathological feature of NTDs, regulated both transcriptionally and epigenetically within central nervous system tissues. Key alterations in gene expression and pathways highlighted the crucial role of STATs molecules in the JAK-STAT signaling pathway in regulating NTDs-associated neuroinflammation. Furthermore, single-cell resolution analysis revealed significant polarization of macrophages and their interaction with neural cells in amniotic fluid, underscoring their central role in mediating neuroinflammation associated with NTDs. Finally, we identified a set of six potential prenatal diagnostic genes, including FABP7, CRMP1, SCG3, SLC16A10, RNASE6 and RNASE1, which were subsequently validated in a murine NTDs model, indicating their promise as prospective markers for prenatal diagnosis of NTDs.

CONCLUSIONS

Our study emphasizes the pivotal role of neuroinflammation in the progression of NTDs and underlines the potential of specific inflammatory and neural markers as novel prenatal diagnostic tools. These findings provide important clues for further understanding the underlying mechanisms between neuroinflammation and NTDs, and offer valuable insights for the future development of prenatal diagnostics.

Identification of novel nutrient-sensitive gene regulatory networks in amniocytes from fetuses with spina bifida

Neural tube defects (NTDs) remain among the most common congenital anomalies. Contributing risk factors include genetics and nutrient deficiencies, however, a comprehensive assessment of nutrient-gene interactions in NTDs is lacking. We applied a nutrient-focused gene expression analysis pipeline to identify nutrient-sensitive gene regulatory networks in amniocyte gene expression data (GSE4182) from fetuses with NTDs (cases; n = 3) and fetuses with no congenital anomalies (controls; n = 5). Differentially expressed genes (DEGs) were screened for having nutrient cofactors. Nutrient-dependent transcriptional regulators (TRs) that regulated DEGs, and nutrient-sensitive miRNAs with a previous link to NTDs, were identified. Of the 880 DEGs in cases, 10% had at least one nutrient cofactor. DEG regulatory network analysis revealed that 39% and 52% of DEGs in cases were regulated by 22 nutrient-sensitive miRNAs and 10 nutrient-dependent TRs, respectively. Zinc- and B vitamin-dependent gene regulatory networks (Zinc: 10 TRs targeting 50.6% of DEGs; B vitamins: 4 TRs targeting 37.7% of DEGs, 9 miRNAs targeting 17.6% of DEGs) were dysregulated in cases. We identified novel, nutrient-sensitive gene regulatory networks not previously linked to NTDs, which may indicate new targets to explore for NTD prevention or to optimise fetal development.

Recursive Feature Elimination-based Biomarker Identification for Open Neural Tube Defects

Background: Open spina bifida (myelomeningocele) is the result of the failure of spinal cord closing completely and is the second most common and severe birth defect. Open neural tube defects are multifactorial, and the exact molecular mechanism of the pathogenesis is not clear due to disease complexity for which prenatal treatment options remain limited worldwide. Artificial intelligence techniques like machine learning tools have been increasingly used in precision diagnosis. Objective: The primary objective of this study is to identify key genes for open neural tube defects using a machine learning approach that provides additional information about myelomeningocele in order to obtain a more accurate diagnosis. Materials and Methods: Our study reports differential gene expression analysis from multiple datasets (GSE4182 and GSE101141) of amniotic fluid samples with open neural tube defects. The sample outliers in the datasets were detected using principal component analysis (PCA). We report a combination of the differential gene expression analysis with recursive feature elimination (RFE), a machine learning approach to get 4 key genes for open neural tube defects. The features selected were validated using five binary classifiers for diseased and healthy samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbour (KNN) with 5-fold cross-validation. Results: Growth Associated Protein 43 (GAP43), Glial fibrillary acidic protein (GFAP), Repetin (RPTN), and CD44 are the important genes identified in the study. These genes are known to be involved in axon growth, astrocyte differentiation in the central nervous system, post-traumatic brain repair, neuroinflammation, and inflammation-linked neuronal injuries. These key genes represent a promising tool for further studies in the diagnosis and early detection of open neural tube defects. Conclusion: These key biomarkers help in the diagnosis and early detection of open neural tube defects, thus evaluating the progress and seriousness in diseases condition. This study strengthens previous literature sources of confirming these biomarkers linked with open NTD's. Thus, among other prenatal treatment options present until now, these biomarkers help in the early detection of open neural tube defects, which provides success in both treatment and prevention of these defects in the advanced stage.

Systems biology analysis of human genomes points to key pathways conferring spina bifida risk

Genetic investigations of most structural birth defects, including spina bifida (SB), congenital heart disease, and craniofacial anomalies, have been underpowered for genome-wide association studies because of their rarity, genetic heterogeneity, incomplete penetrance, and environmental influences. Our systems biology strategy to investigate SB predisposition controls for population stratification and avoids much of the bias inherent in candidate gene searches that are pervasive in the field. We examine both protein coding and noncoding regions of whole genomes to analyze sequence variants, collapsed by gene or regulatory region, and apply machine learning, gene enrichment, and pathway analyses to elucidate molecular pathways and genes contributing to human SB.

Spina bifida (SB) is a debilitating birth defect caused by multiple gene and environment interactions. Though SB shows non-Mendelian inheritance, genetic factors contribute to an estimated 70% of cases. Nevertheless, identifying human mutations conferring SB risk is challenging due to its relative rarity, genetic heterogeneity, incomplete penetrance, and environmental influences that hamper genome-wide association studies approaches to untargeted discovery. Thus, SB genetic studies may suffer from population substructure and/or selection bias introduced by typical candidate gene searches. We report a population based, ancestry-matched whole-genome sequence analysis of SB genetic predisposition using a systems biology strategy to interrogate 298 case-control subject genomes (149 pairs). Genes that were enriched in likely gene disrupting (LGD), rare protein-coding variants were subjected to machine learning analysis to identify genes in which LGD variants occur with a different frequency in cases versus controls and so discriminate between these groups. Those genes with high discriminatory potential for SB significantly enriched pathways pertaining to carbon metabolism, inflammation, innate immunity, cytoskeletal regulation, and essential transcriptional regulation consistent with their having impact on the pathogenesis of human SB. Additionally, an interrogation of conserved noncoding sequences identified robust variant enrichment in regulatory regions of several transcription factors critical to embryonic development. This genome-wide perspective offers an effective approach to the interrogation of coding and noncoding sequence variant contributions to rare complex genetic disorders.

Identifying biomarkers for prenatal diagnosis of neural tube defects based on "omics"

Neural tube defects (NTDs) are the most severe birth defects and the main cause of newborn death; posing a great challenge to the affected children, families, and societies. Presently, the clinical diagnosis of NTDs mainly relies on ultrasound images combined with certain indices, such as alpha-fetoprotein levels in the maternal serum and amniotic fluid. Recently, the discovery of additional biomarkers in maternal tissue has presented new possibilities for prenatal diagnosis. Over the past 20 years, "omics" techniques have provided the premise for the study of biomarkers. This review summarizes recent advances in candidate biomarkers for the prenatal diagnosis of fetal NTDs based on omics techniques using maternal biological specimens of different origins, including amniotic fluid, blood, and urine, which may provide a foundation for the early prenatal diagnosis of NTDs.

Key Modules and Hub Genes Identified by Coexpression Network Analysis for Revealing Novel Biomarkers for Spina Bifida

Spina bifida is a common neural tube defect (NTD) accounting for 5–10% of perinatal mortalities. As a polygenic disease, spina bifida is caused by a combination of genetic and environmental factors, for which the precise molecular pathogenesis is still not systemically understood. In the present study, we aimed to identify the related gene module that might play a vital role in the occurrence and development of spina bifida by using weighted gene co-expression network analysis (WGCNA). Transcription profiling according to an array of human amniocytes from patients with spina bifida and healthy controls was downloaded from the Gene Expression Omnibus database. First, outliers were identified and removed by principal component analysis (PCA) and sample clustering. Then, genes in the top 25% of variance in the GSE4182 dataset were then determined in order to explore candidate genes in potential hub modules using WGCNA. After data preprocessing, 5407 genes were obtained for further WGCNA. Highly correlated genes were divided into nineteen modules. Combined with a co-expression network and significant differentially expressed genes, 967 candidate genes were identified that may be involved in the pathological processes of spina bifida. Combined with our previous microRNA (miRNA) microarray results, we constructed an miRNA–mRNA network including four miRNAs and 39 mRNA among which three key genes were, respectively, linked to two miRNA-associated gene networks. Following the verification of qRT-PCR and KCND3 was upregulated in the spina bifida. KCND3 and its related miR-765 and miR-142-3p are worthy of further study. These findings may be conducive for early detection and intervention in spina bifida, as well as be of great significance to pregnant women and clinical staff.

LST1: A multifunctional gene encoded in the MHC class III region

The LST1 gene is located in the MHC class III cluster between the MHC class I and II regions. While most genes in this cluster have been sufficiently characterised, a definitive function and expression pattern for LST1 still remains elusive. In the present review we describe its promotor, gene organisation, splice variants and expression in human tissues, cell lines and cancer. We focus on LST1 expression in inflammation and discuss known correlations with autoimmune diseases and cancer. Current data on LST1 polymorphisms and their known associations with pathologies are also discussed in detail. We summarize the potential functions that have been described for the full-length LST1 protein including its function as a transmembrane adaptor protein with inhibitory signal transduction and its role as a membrane scaffold facilitating the formation of tunnelling nanotubes. We also discuss further potential functions by compiling all known LST1-interacting proteins. Furthermore, we address knowledge gaps and conflictive issues regarding disease association, non-hematopoietic expression and the discrepancy between RNA and protein expression data.

Lst1 deficiency has a minor impact on course and outcome of the host response to influenza A H1N1 infections in mice

BACKGROUND

Previously, we performed a quantitative trait locus (QTL) mapping study in BXD recombinant inbred mice to identify host genetic factors that confer resistance to influenza A virus infection. We found Lst1 (leukocyte specific transcript 1) as one of the most promising candidate genes in the Qivr17-2 locus because it is non-functional in DBA/2 J mice. Several studies have proposed that LST1 plays a role in the immune response to inflammatory diseases in humans and has additional immune-regulatory functions. Here, we evaluated the relevance of LST1 for the host response to influenza A infection in B6-Lst1 (-/-) mutant mice.

FINDINGS

To investigate the role of LST1, we infected B6-Lst1 (-/-) mutant and C57BL/6 N wild-type mice with a low-virulent influenza A virus (PR8M; H1N1). Lst1 deficient mice exhibited significantly increased body weight loss at days 5 and 6 after infection and slightly increased lethality compared to infected wild-type mice. Determination of viral loads, histopathological examination and analysis of immune cell composition in bronchoalveolar lavage of infected lungs did not reveal any obvious differences between KO and wild-type mice.

CONCLUSIONS

The absence of Lst1 leads to a slightly more susceptible phenotype. However, deletion of Lst1 in DBA/2 J mice alone does not explain the high susceptibility of this strain to PR8M influenza infections.

Standardization of amniotic fluid leptin levels and utility in maternal overweight and fetal undergrowth

OBJECTIVE

Leptin is an adipokine that regulates energy homeostasis. The objective of this study was to establish a gestational age-specific standard for amniotic fluid leptin (AFL) levels and examine the relationship between AFL, maternal overweight and fetal growth restriction.

STUDY DESIGN

Amniotic fluid was obtained at mid-gestation from singleton gravidas, and leptin was quantified using enzyme-linked immunosorbent assay. Amniotic fluid samples from 321 term pregnancies were analyzed. Clinical data, including fetal ultrasound measurements and maternal and infant characteristics, were available for a subset of patients (n=45).

RESULTS

The median interquartile range AFL level was significantly higher at 14 weeks' gestation (2133 pg ml(-1) (1703 to 4347)) than after 33 weeks' gestation (519 pg ml(-1) (380 to 761), P trend<0.0001), an average difference of 102 pg ml(-1) per week. AFL levels were positively correlated with maternal pre-pregnancy body mass index (BMI) (r=0.36, P=0.03) adjusting for gestational age at measurement, but were not associated with fetal growth.

CONCLUSIONS

AFL levels are higher at mid-gestation than at late gestation, and are associated with maternal pre-pregnancy BMI.

An upstream open reading frame regulates LST1 expression during monocyte differentiation

The regulation of gene expression depends on the interplay of multiple factors at the transcriptional and translational level. Upstream open reading frames (uORFs) play an important role as translational repressors of main ORFs and their presence or usage in transcripts can be regulated by different mechanisms. The main objective of the present study was to assess whether uORFs regulate the expression of the MHC class III gene LST1. We report that expression of LST1 is tightly regulated by alternative transcription initiation and the presence of an uORF in the 5′-UTR of transcripts. Specifically, using EGFP reporter constructs in human HeLa and HEK-293T cells and flow cytometry as well as western blot analysis we found the uORF to reduce the expression of the main ORF by roughly two-thirds. Furthermore, we were able to correlate a previously detected increase in LST1 protein expression during monocyte differentiation with an increase of transcription initiation at an alternative exon that does not contain an uORF.

Proteomic technologies for prenatal diagnostics: advances and challenges ahead

Proteomics-based identification of biomarkers for fetal abnormalities in maternal plasma, amniotic fluid and reproductive fluids has made significant progress in the past 5 years. This is attributed mainly to advances in various technology platforms associated with mass spectrometry-based techniques. As these techniques are highly sensitive and require only small quantities of body fluids, it is hoped that they will pave the way for the development of effective noninvasive approaches, without subjecting the developing fetus to the same degree of harm as current invasive procedures (e.g., amniocentesis). It is possible that these developments will include same-day analyses, thereby permitting rapid intervention when necessary. To date, a host of body fluids, such as maternal serum and plasma, amniotic fluid, cervical fluid, vaginal fluid, urine, saliva or fetal material, such as placental trophoblast, fetal membranes or cord blood, have been used successfully in the quest to develop markers for a number of pregnancy-related pathologies. In the current review update we focus on the emergence of proteomics as a major platform technology in studying various types of fetal conditions and developing markers for pregnancy-related disorders, such fetal aneuploidy, preterm birth, preeclampsia, intra-amniotic infection and fetal stress. Should the development of these markers be successful, then it is to be envisaged that proteomic approaches will become standard of care for a number of disease conditions associated with feto-maternal health.

[mRNA expression analysis and classification of colonic biopsy samples using oligonucleotide and cDNA microarray techniques]

Despite tremendous progress in the past few decades, certain important aspects regarding the diagnosis, therapy, and follow-up of colorectal cancer still remain unsolved. In our work we searched for biomarkers of the development of colorectal carcinoma, and performed gene expression analysis for colorectal disease classification. We have established that the oligonucleotide microarray analyses of biopsy samples wholly fulfil the Affymetrix quality requirements, are highly standard and reproducible and the Taqman microfluidic card system is suitable for high-throughput, quick and cost efficient real-time-PCR validation of gene expression changes. We have shown that the sequential overexpression of osteopontin and osteonectin mRNAs and proteins significantly correlates with the progression of the colorectal adenoma-dysplasia-carcinoma sequence. We have identified and validated ten novel markers with continuously increasing mRNA expression in line with the adenoma-dysplasia-carcinoma transition. We have identified the top 27, 13 and 10 genes associated with adenoma, colorectal cancer, and inflammatory bowel diseases.

Protein microchips in biomedicine and biomarker discovery

Protein microarray technology is of high recent interest, especially for generating confirmatory and complementary information for transcriptomic studies. In this paper, the advantages, technical limitations, main application fields, and some early results of protein microarrays are reviewed. Today protein microchip technology is mostly available in the form of printed glass slides, bioaffinity surfaces, and tissue microarray (TMA)-based techniques. The advantages of glass slide-based microchips are the simplicity of their application and their relatively low cost. Affinity surface-based protein chip techniques are applicable to minute amounts of starting material (< 1 microg), but interrogation of these chips requires expensive instrumentation, such as mass spectrometers. TMAs are useful for parallel testing of antibody specificities on a broad range of histological specimens in a single slide. Protein microarrays have been successfully implemented for serum tumor marker profiling, cell physiology studies, and mRNA expression study verification. Some of the bottlenecks of the technology are protein instability, problems with nonspecific interactions, and the lack of amplification techniques to generate sufficient amounts of the lower abundance proteins. In spite of the current difficulties, protein microchips are envisioned to be available for routine biomedical and diagnostic applications provided that the ongoing technological developments are successful in improving sensitivity, specificity, and reducing costs.