Discovery of novel serum biomarkers for prenatal Down syndrome screening by integrative data mining

Application of intelligent algorithms in Down syndrome screening during second trimester pregnancy

BACKGROUND

Down syndrome (DS) is one of the most common chromosomal aneuploidy diseases. Prenatal screening and diagnostic tests can aid the early diagnosis, appropriate management of these fetuses, and give parents an informed choice about whether or not to terminate a pregnancy. In recent years, investigations have been conducted to achieve a high detection rate (DR) and reduce the false positive rate (FPR). Hospitals have accumulated large numbers of screened cases. However, artificial intelligence methods are rarely used in the risk assessment of prenatal screening for DS.

AIM

To use a support vector machine algorithm, classification and regression tree algorithm, and AdaBoost algorithm in machine learning for modeling and analysis of prenatal DS screening.

METHODS

The dataset was from the Center for Prenatal Diagnosis at the First Hospital of Jilin University. We designed and developed intelligent algorithms based on the synthetic minority over-sampling technique (SMOTE)-Tomek and adaptive synthetic sampling over-sampling techniques to preprocess the dataset of prenatal screening information. The machine learning model was then established. Finally, the feasibility of artificial intelligence algorithms in DS screening evaluation is discussed.

RESULTS

The database contained 31 DS diagnosed cases, accounting for 0.03% of all patients. The dataset showed a large difference between the numbers of DS affected and non-affected cases. A combination of over-sampling and under-sampling techniques can greatly increase the performance of the algorithm at processing non-balanced datasets. As the number of iterations increases, the combination of the classification and regression tree algorithm and the SMOTE-Tomek over-sampling technique can obtain a high DR while keeping the FPR to a minimum.

CONCLUSION

The support vector machine algorithm and the classification and regression tree algorithm achieved good results on the DS screening dataset. When the T21 risk cutoff value was set to 270, machine learning methods had a higher DR and a lower FPR than statistical methods.

Placental development and function in trisomy 21 and mouse models of Down syndrome: Clues for studying mechanisms underlying atypical development

Down syndrome (DS) is the most common genetic disorder leading to developmental disability. The phenotypes associated with DS are complex and vary between affected individuals. Placental abnormalities in DS include differences in cytotrophoblast fusion that affect subsequent conversion to syncytiotrophoblast, atypical oxidative stress/antioxidant balance, and increased expression of genes that are also upregulated in the brains of individuals with Alzheimer's disease. Placentas in DS are prematurely senescent, showing atypical evidence of mineralization. Fetuses with DS are especially susceptible to adverse obstetric outcomes, including early in utero demise, stillbirth and growth restriction, all of which are related to placental function. The placenta, therefore, may provide key insights towards understanding the phenotypic variability observed in individuals with DS and aid in identifying biomarkers that can be used to evaluate phenotypic severity and prenatal treatments in real time. To address these issues, many different mouse models of DS have been generated to identify the mechanisms underlying developmental changes in many organ systems. Little is known, however, regarding placental development in the currently available mouse models of DS. Based upon the relative paucity of data on placental development in preclinical mouse models of DS, we recommend that future evaluation of new and existing models routinely include histologic and functional assessments of the placenta. In this paper we summarize studies performed in the placentas of both humans and mouse models with DS, highlighting gaps in knowledge and suggesting directions for future research.

Proteomic profile of serum of pregnant women carring a fetus with Down syndrome using nano uplc Q-tof ms/ms technology

INTRODUCTION

Prenatal diagnosis of Down syndrome (DS) is based on the calculated risk of maternal age, biochemical and ultrasonographic markers and recently by cfDNA. Differences in proteomic profiles may give an opportunity to find new biomarkers.

OBJECTIVE

Characterize proteome of serum of mothers carrying DS fetus.

MATERIAL AND METHODS

Blood serum samples of three groups of women were obtained, (a) 10 non-pregnant, (b) 10 pregnant with healthy fetus by ultrasound evaluation, (c) nine pregnant with DS fetus. Sample preparation was as follows: Albumin/IgG depletion, desalting, and trypsin digestion; the process was performed in nanoUPLC MS/MS. Data analysis was made with Mass Lynx 4.1 and ProteinLynx Global Server 3.0, peptide and protein recognition by MASCOT algorithm and UNIPROT-Swissprot database.

RESULTS

Each group showed different protein profiles. Some proteins were shared between groups. Only sera from pregnant women showed proteins related to immune and clot pathways. Mothers with DS fetus had 42 specific proteins.

CONCLUSIONS

We found a different serum protein profile in mothers carrying DS fetuses that do not reflect expression of genes in the extra chromosome. Further studies will be necessary to establish the role of these proteins in aneuploid fetus and analyze their possible use as potential biomarkers.

The developmental basis of epigenetic regulation of HTR2A and psychiatric outcomes

The serotonin receptor 5-HT2A (encoded by HTR2A) is an important regulator of fetal brain development and adult cognitive function. Environmental signals that induce epigenetic changes of serotonin response genes, including HTR2A, have been implicated in adverse mental health outcomes. The objective of this perspective article is to address the medical implications of HTR2A epigenetic regulation, which has been associated with both infant neurobehavioral outcomes and adult mental health. Ongoing research has identified a region of the HTR2A promoter that has been associated with a number of medical outcomes in adults and infants, including bipolar disorder, schizophrenia, chronic fatigue syndrome, borderline personality disorder, suicidality, and neurobehavioral outcomes. Epigenetic regulation of HTR2A has been studied in several different types of tissues, including the placenta. The placenta is an important source of serotonin during fetal neurodevelopment, and placental epigenetic variation of HTR2A has been associated with infant neurobehavioral outcomes, which may represent the basis of adult mental health disorders. Further analysis is needed to identify intrinsic and extrinsic factors that modulate HTR2A methylation, and the mechanism by which this epigenetic variation influences fetal growth and leads to altered brain development, manifesting in psychiatric disorders.

PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more

PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized ‘Given X, find all associated Ys’ query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: ‘Find all diseases associated with Bisphenol A’. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation.

Predictive performance of a seven-plex antibody array in prenatal screening for Down Syndrome

We evaluated the use of multiplex antibody array methodology for simultaneous measurement of serum protein markers for first trimester screening of Down Syndrome (DS) and other pregnancy outcomes such as preeclampsia. For this purpose, we constructed an antibody array for indirect (“sandwich”) measurement of seven serum proteins: pregnancy-associated plasma protein-A (PAPP-A), free beta subunit of human chorionic gonadotropin (fβ-hCG), alpha-fetoprotein (AFP), angiopoietin-like 3 (ANGPTL3), epidermal growth factor (EGF), insulin-like growth factor 2 (IGFII), and superoxide dismutase 1 (SOD1). This array was tested using 170 DS cases and 510 matched controls drawn during the 8th–13th weeks of pregnancy. Data were used for prediction modelling and compared to previously obtained AutoDELFIA immunoassay data for PAPP-A and fβ-hCG. PAPP-A and fβ-hCG serum concentrations obtained using antibody arrays were highly correlated with AutoDELFIA data. Moreover, DS prediction modeling using (log-MoMmed) antibody array and AutoDELFIA data gave comparable results. Of the other markers, AFP and IGFII showed significant changes in concentration, although adding these markers to a prediction model based on prior risk, PAPP-A and fβ-hCG did not improve the predictive performance. We conclude that implementation of antibody arrays in a prenatal screening setting is feasible but will require additional first trimester screening markers.

Maternal plasma and amniotic fluid chemokines screening in fetal Down syndrome

Objective. Chemokines exert different inflammatory responses which can potentially be related to certain fetal chromosomal abnormalities. The aim of the study was to determine the concentration of selected chemokines in plasma and amniotic fluid of women with fetal Down syndrome. Method. Out of 171 amniocentesis, we had 7 patients with confirmed fetal Down syndrome (15th–18th weeks of gestation). For the purpose of our control, we chose 14 women without confirmed chromosomal aberration. To assess the concentration of chemokines in the blood plasma and amniotic fluid, we used a protein macroarray, which allows the simultaneous determination of 40 chemokines per sample. Results. We showed significant decrease in the concentration of 4 chemokines, HCC-4, IL-28A, IL-31, and MCP-2, and increase in the concentration of CXCL7 (NAP-2) in plasma of women with fetal Down syndrome. Furthermore, we showed decrease in concentration of 3 chemokines, ITAC, MCP-3, MIF, and increase in concentration of 4 chemokines, IP-10, MPIF-1, CXCL7, and 6Ckine, in amniotic fluid of women with fetal Down syndrome. Conclusion. On the basis of our findings, our hypothesis is that the chemokines may play role in the pathogenesis of Down syndrome. Defining their potential as biochemical markers of Down syndrome requires further investigation on larger group of patients.

Vitrification alters rabbit foetal placenta at transcriptomic and proteomic level

Although numerous studies have demonstrated that cryopreservation alters gene expression, less is known about those embryos that implanted successfully and continued in gestation. To raise the question of the neutrality of this technique, we examine the effects of vitrification through gestation in rabbit before and after the implantation. We monitored the distribution of losses of 569 vitrified morulae, observing that embryos which reach the last pre-implantatory stage are able to implant. However, we found that not all implanted embryos had the ability to continue with their gestation. The results reveal that vitrification decreased foetus and maternal placenta weights at mid-gestation, but led to a higher offspring birth weight. A novel finding is that while no differences in gene expression were detected in pre-implantatory embryos at day 6, vitrification affects a gene and protein expression in the placenta at day 14. Our results for first time reveal strong evidence of modifications in implanted embryos subjected to vitrification, suggesting that the crucial step that vitrified embryos must overcome is the placenta formation. On the basis of these findings, our work leaves the question open as to whether the effects we observed that cause vitrification during foetal development could give rise to some type of physiological or metabolic alteration in adulthood.

Maternal serum protein profile and immune response protein subunits as markers for non-invasive prenatal diagnosis of trisomy 21, 18, and 13

OBJECTIVES

To use proteomics to identify and characterize proteins in maternal serum from patients at high-risk for fetal trisomy 21, trisomy 18, and trisomy 13 on the basis of ultrasound and maternal serum triple tests.

METHODS

We performed a comprehensive proteomic analysis on 23 trisomy cases and 85 normal cases during the early second trimester of pregnancy. Protein profiling along with conventional sodium dodecyl sulfate polyacrylamide gel electrophoresis/Tandem mass spectrometry analysis was carried out to characterize proteins associated with each trisomy condition and later validated using Western blot.

RESULTS

Protein profiling approach using surface enhanced laser desorption/ionization time-of-flight mass (SELDI-TOF/MS) spectrometry resulted in the identification of 37 unique hydrophobic proteomic features for three trisomy conditions. Using sodium dodecyl sulfate polyacrylamide gel electrophoresis followed by Matrix Assisted Laser Desorption Ionization - Time of Flight/Time of Flight (MALDI-TOF/TOF) and western blot, glyco proteins such as alpha-1-antitrypsin, apolipoprotein E, apolipoprotein H, and serum carrier protein transthyretin were identified as potential maternal serum markers for fetal trisomy condition. The identified proteins showed differential expression at the subunit level.

CONCLUSIONS

Maternal serum protein profiling using proteomics may allow non-invasive diagnostic testing for the most common trisomies and may complement ultrasound-based methods to more accurately determine pregnancies with fetal aneuploidies.

Mining biomarker information in biomedical literature

BACKGROUND

For selection and evaluation of potential biomarkers, inclusion of already published information is of utmost importance. In spite of significant advancements in text- and data-mining techniques, the vast knowledge space of biomarkers in biomedical text has remained unexplored. Existing named entity recognition approaches are not sufficiently selective for the retrieval of biomarker information from the literature. The purpose of this study was to identify textual features that enhance the effectiveness of biomarker information retrieval for different indication areas and diverse end user perspectives.

METHODS

A biomarker terminology was created and further organized into six concept classes. Performance of this terminology was optimized towards balanced selectivity and specificity. The information retrieval performance using the biomarker terminology was evaluated based on various combinations of the terminology's six classes. Further validation of these results was performed on two independent corpora representing two different neurodegenerative diseases.

RESULTS

The current state of the biomarker terminology contains 119 entity classes supported by 1890 different synonyms. The result of information retrieval shows improved retrieval rate of informative abstracts, which is achieved by including clinical management terms and evidence of gene/protein alterations (e.g. gene/protein expression status or certain polymorphisms) in combination with disease and gene name recognition. When additional filtering through other classes (e.g. diagnostic or prognostic methods) is applied, the typical high number of unspecific search results is significantly reduced. The evaluation results suggest that this approach enables the automated identification of biomarker information in the literature. A demo version of the search engine SCAIView, including the biomarker retrieval, is made available to the public through http://www.scaiview.com/scaiview-academia.html.

CONCLUSIONS

The approach presented in this paper demonstrates that using a dedicated biomarker terminology for automated analysis of the scientific literature maybe helpful as an aid to finding biomarker information in text. Successful extraction of candidate biomarkers information from published resources can be considered as the first step towards developing novel hypotheses. These hypotheses will be valuable for the early decision-making in the drug discovery and development process.

Identification of novel candidate maternal serum protein markers for Down syndrome by integrated proteomic and bioinformatic analysis

OBJECTIVE

This study aimed to identify candidate protein biomarkers from maternal serum for Down syndrome (DS) by integrated proteomic and bioinformatics analysis.

METHODS

A pregnancy DS group of 18 women and a control group with the same number were prepared, and the maternal serum proteins were analyzed by isobaric tags for relative and absolute quantitation and mass spectrometry, to identify DS differentially expressed maternal serum proteins (DS-DEMSPs). Comprehensive bioinformatics analysis was then employed to analyze DS-DEMSPs both in this paper and seven related publications.

RESULTS

Down syndrome differentially expressed maternal serum proteins from different studies are significantly enriched with common Gene Ontology functions, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, transcription factor binding sites, and Pfam protein domains, However, the DS-DEMSPs are less functionally related to known DS-related genes. These evidences suggest that common molecular mechanisms induced by secondary effects may be present upon DS carrying. A simple scoring scheme revealed Alpha-2-macroglobulin, Apolipoprotein A1, Apolipoprotein E, Complement C1s subcomponent, Complement component 5, Complement component 8, alpha polypeptide, Complement component 8, beta polypeptide and Fibronectin as potential DS biomarkers.

CONCLUSION

The integration of proteomics and bioinformatics studies provides a novel approach to develop new prenatal screening methods for noninvasive yet accurate diagnosis of DS.

Bioinformatics characterization of differential proteins in serum of mothers carrying Down syndrome fetuses: combining bioinformatics and ELISA

INTRODUCTION

Characterization of novel proteins in maternal serum derived from mothers carrying Down syndrome (DS) fetuses.

MATERIAL AND METHODS

Based on last comparative proteomic analysis, five significant differences of expressed proteins in serum from four groups have been confirmed by ELISA. DAVID and GeneGo MetaCore were used to bioinformatically analyze candidate protein markers.

RESULTS

The serum levels of ceruloplasmin (CP) and complement factor B (CFB) were significantly increased in mother carried DS fetuses (346.5 ng/ml and 466.8 ng/ml vs. 248.6 ng/ml and 293.5 ng/ml, p< 0.05). Twenty-nine proteins were mainly categorized into binding, catalytic activity and enzyme regulator activity proteins, and their biological roles were involved in biological regulation, metabolic processes, cellular processes, and response to stimuli. The immune response alternative complement pathway was the most significant GeneGo Pathway related to DS.

CONCLUSIONS

These 29 proteins have relations with the development of Down syndrome, especially CP and CFB play more important roles.

Integrative data mining to identify novel candidate serum biomarkers for pre-eclampsia screening

OBJECTIVE

Pre-eclampsia (PE) is a serious complication that affects approximately 2% of pregnant women worldwide. At present, there is no sufficiently reliable test for early detection of PE in a screening setting that would allow timely intervention. To help future experimental identification of serum biomarkers for early onset PE, we applied a data mining approach to create a set of candidate biomarkers.

METHODS

We started from the disease etiology, which involves impaired trophoblast invasion into the spiral arteries. On the basis of this, we used a three-stage filtering strategy consisting of selection of tissue-specific genes, textmining for further gene prioritization, and identifying blood-detectable markers.

RESULTS

This approach resulted in 38 candidate biomarkers. These include the best three first-trimester serum biomarkers for PE found to date LGALS13 (placental protein 13, PP13), PAPPA (pregnancy-associated plasma protein-A, PAPP-A), and PGF (placental growth factor, PlGF), as well as five proteins previously identified as biomarker after the first-trimester or disease onset. This substantiates the effectiveness of our approach and provides an important indication that the list will contain several new biomarkers for PE.

CONCLUSIONS

We anticipate this list can serve in prioritization of future experimental studies on serum biomarkers for early onset PE.

2D DIGE analysis of maternal plasma for potential biomarkers of Down Syndrome

Background

Prenatal screening for Down Syndrome (DS) would benefit from an increased number of biomarkers to improve sensitivity and specificity. Improving sensitivity and specificity would decrease the need for potentially risky invasive diagnostic procedures.

Results

We have performed an in depth two-dimensional difference gel electrophoresis (2D DIGE) study to identify potential biomarkers. We have used maternal plasma samples obtained from first and second trimesters from mothers carrying DS affected fetuses compared with mothers carrying normal fetuses. Plasma samples were albumin/IgG depleted and expanded pH ranges of pH 4.5 - 5.5, pH 5.3 - 6.5 and pH 6 - 9 were used for two-dimensional gel electrophoresis (2DE). We found no differentially expressed proteins in the first trimester between the two groups. Significant up-regulation of ceruloplasmin, inter-alpha-trypsin inhibitor heavy chain H4, complement proteins C1s subcomponent, C4-A, C5, and C9 and kininogen 1 were detected in the second trimester in maternal plasma samples where a DS affected fetus was being carried. However, ceruloplasmin could not be confirmed as being consistently up-regulated in DS affected pregnancies by Western blotting.

Conclusions

Despite the in depth 2DE approach used in this study the results underline the deficiencies of gel-based proteomics for detection of plasma biomarkers. Gel-free approaches may be more productive to increase the number of plasma biomarkers for DS for non-invasive prenatal screening and diagnosis.

Gene expression profiling in a mouse model identifies fetal liver- and placenta-derived potential biomarkers for Down Syndrome screening

Background

As a first step to identify novel potential biomarkers for prenatal Down Syndrome screening, we analyzed gene expression in embryos of wild type mice and the Down Syndrome model Ts1Cje. Since current Down Syndrome screening markers are derived from placenta and fetal liver, these tissues were chosen as target.

Methodology/Principal Findings

Placenta and fetal liver at 15.5 days gestation were analyzed by microarray profiling. We confirmed increased expression of genes located at the trisomic chromosomal region. Overall, between the two genotypes more differentially expressed genes were found in fetal liver than in placenta. Furthermore, the fetal liver data are in line with the hematological aberrations found in humans with Down Syndrome as well as Ts1Cje mice. Together, we found 25 targets that are predicted (by Gene Ontology, UniProt, or the Human Plasma Proteome project) to be detectable in human serum.

Conclusions/Significance

Fetal liver might harbor more promising targets for Down Syndrome screening studies. We expect these new targets will help focus further experimental studies on identifying and validating human maternal serum biomarkers for Down Syndrome screening.

Biomarker discovery for Alzheimer's disease, frontotemporal lobar degeneration, and Parkinson's disease

Ante-mortem diagnosis of neurodegenerative disorders based on clinical features alone is associated with variable sensitivity and specificity, and biomarkers can potentially improve the accuracy of clinical diagnosis. In patients suspected of having Alzheimer's disease (AD), alterations in cerebrospinal fluid (CSF) biomarkers that reflect the neuropathologic changes of AD strongly support the diagnosis, although there is a trade-off between sensitivity and specificity due to similar changes in cognitively healthy subjects. Here, we review the current approaches in using CSF AD biomarkers (total tau, p-tau(181), and Abeta42) to predict the presence of AD pathology, and our recent work using multi-analyte profiling to derive novel biomarkers for biofluid-based AD diagnosis. We also review our use of the multi-analyte profiling strategy to identify novel biomarkers that can distinguish between subtypes of frontotemporal lobar degeneration, and those at risk of developing cognitive impairment in Parkinson's disease. Multi-analyte profiling is a powerful tool for biomarker discovery in complex neurodegenerative disorders, and analytes associated with one or more diseases may shed light on relevant biological pathways and potential targets for intervention.

Down syndrome screening: imagining the screening test of the future

Prenatal screening for Down syndrome (DS) is performed by risk calculation based on biochemical and biometric parameters. This way, approximately 75-85% of all DS cases can be detected. A way to improve detection rates is to search for new screening markers. Since the majority of biomarkers used in current DS screening are predominantly produced by the placenta, and the presence of an extra chromosome (as in DS) complicates placental development and function, it is plausible to assume that new potential screening markers may also originate from the placenta. Any alterations in these markers can be attributed to abnormal placental development and function. This article focuses on normal early placental development and function compared with that in DS pregnancies. Using this knowledge, we reason towards candidate biomarkers that may be useful in screening for DS.