Machine learning methods predict locomotor response to MK-801 in mouse models of down syndrome

Artificial intelligence for prenatal chromosome analysis

This review article delves into the rapidly advancing domain of prenatal diagnostics, with a primary focus on the detection and management of chromosomal abnormalities such as trisomy 13 ("Patau syndrome)", "trisomy 18 (Edwards syndrome)", and "trisomy 21 (Down syndrome)". The objective of the study is to examine the utilization and effectiveness of novel computational methodologies, such as "machine learning (ML)", "deep learning (DL)", and data analysis, in enhancing the detection rates and accuracy of these prenatal conditions. The contribution of the article lies in its comprehensive examination of advancements in "Non-Invasive Prenatal Testing (NIPT)", prenatal screening, genomics, and medical imaging. It highlights the potential of these techniques for prenatal diagnosis and the contributions of ML and DL to these advancements. It highlights the application of ensemble models and transfer learning to improving model performance, especially with limited datasets. This also delves into optimal feature selection and fusion of high-dimensional features, underscoring the need for future research in these areas. The review finds that ML and DL have substantially improved the detection and management of prenatal conditions, despite limitations such as small sample sizes and issues related to model generalizability. It recognizes the promising results achieved through the use of ensemble models and transfer learning in prenatal diagnostics. The review also notes the increased importance of feature selection and high-dimensional feature fusion in the development and training of predictive models. The findings underline the crucial role of AI and machine learning techniques in early detection and improved therapeutic strategies in prenatal diagnostics, highlighting a pressing need for further research in this area.

Machine learning based analysis for intellectual disability in Down syndrome

Down syndrome (DS) or trisomy 21 is the most common genetic cause of intellectual disability (ID), but a pathogenic mechanism has not been identified yet. Studying a complex and not monogenic condition such as DS, a clear correlation between cause and effect might be difficult to find through classical analysis methods, thus different approaches need to be used. The increased availability of big data has made the use of artificial intelligence (AI) and in particular machine learning (ML) in the medical field possible. The purpose of this work is the application of ML techniques to provide an analysis of clinical records obtained from subjects with DS and study their association with ID. We have applied two tree-based ML models (random forest and gradient boosting machine) to the research question: how to identify key features likely associated with ID in DS. We analyzed 109 features (or variables) in 106 DS subjects. The outcome of the analysis was the age equivalent (AE) score as indicator of intellectual functioning, impaired in ID. We applied several methods to configure the models: feature selection through Boruta framework to minimize random correlation; data augmentation to overcome the issue of a small dataset; age effect mitigation to take into account the chronological age of the subjects. The results show that ML algorithms can be applied with good accuracy to identify variables likely involved in cognitive impairment in DS. In particular, we show how random forest and gradient boosting machine produce results with low error (MSE <0.12) and an acceptable R2 (0.70 and 0.93). Interestingly, the ranking of the variables point to several features of interest related to hearing, gastrointestinal alterations, thyroid state, immune system and vitamin B12 that can be considered with particular attention for improving care pathways for people with DS. In conclusion, ML-based model may assist researchers in identifying key features likely correlated with ID in DS, and ultimately, may improve research efforts focused on the identification of possible therapeutic targets and new care pathways. We believe this study can be the basis for further testing/validating of our algorithms with multiple and larger datasets.

In silico identification of critical proteins associated with learning process and immune system for Down syndrome

Understanding expression levels of proteins and their interactions is a key factor to diagnose and explain the Down syndrome which can be considered as the most prevalent reason of intellectual disability in human beings. In the previous studies, the expression levels of 77 proteins obtained from normal genotype control mice and from trisomic Ts65Dn mice have been analyzed after training in contextual fear conditioning with and without injection of the memantine drug using statistical methods and machine learning techniques. Recent studies have also pointed out that there may be a linkage between the Down syndrome and the immune system. Thus, the research presented in this paper aim at in silico identification of proteins which are significant to the learning process and the immune system and to derive the most accurate model for classification of mice. In this paper, the features are selected by implementing forward feature selection method after preprocessing step of the dataset. Later, deep neural network, gradient boosting tree, support vector machine and random forest classification methods are implemented to identify the accuracy. It is observed that the selected feature subsets not only yield higher accuracy classification results but also are composed of protein responses which are important for the learning and memory process and the immune system.

Bi-stream CNN Down Syndrome screening model based on genotyping array

BACKGROUND

Human Down syndrome (DS) is usually caused by genomic micro-duplications and dosage imbalances of human chromosome 21. It is associated with many genomic and phenotype abnormalities. Even though human DS occurs about 1 per 1,000 births worldwide, which is a very high rate, researchers haven't found any effective method to cure DS. Currently, the most efficient ways of human DS prevention are screening and early detection.

METHODS

In this study, we used deep learning techniques and analyzed a set of Illumina genotyping array data. We built a bi-stream convolutional neural networks model to screen/predict the occurrence of DS. Firstly, we built image input data by converting the intensities of each SNP site into chromosome SNP maps. Next, we proposed a bi-stream convolutional neural network (CNN) architecture with nine layers and two branch models. We further merged two CNN branch models into one model in the fourth convolutional layer, and output the prediction in the last layer.

RESULTS

Our bi-stream CNN model achieved 99.3% average accuracies, and very low false-positive and false-negative rates, which was necessary for further applications in disease prediction and medical practice. We further visualized the feature maps and learned filters from intermediate convolutional layers, which showed the genomic patterns and correlated SNPs variations in human DS genomes. We also compared our methods with other CNN and traditional machine learning models. We further analyzed and discussed the characteristics and strengths of our bi-stream CNN model.

CONCLUSIONS

Our bi-stream model used two branch CNN models to learn the local genome features and regional patterns among adjacent genes and SNP sites from two chromosomes simultaneously. It achieved the best performance in all evaluating metrics when compared with two single-stream CNN models and three traditional machine-learning algorithms. The visualized feature maps also provided opportunities to study the genomic markers and pathway components associated with Human DS, which provided insights for gene therapy and genomic medicine developments.

Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome

Down syndrome (DS) is a chromosomal abnormality (trisomy of human chromosome 21) associated with intellectual disability and affecting approximately one in 1000 live births worldwide. The overexpression of genes encoded by the extra copy of a normal chromosome in DS is believed to be sufficient to perturb normal pathways and normal responses to stimulation, causing learning and memory deficits. In this work, we have designed a strategy based on the unsupervised clustering method, Self Organizing Maps (SOM), to identify biologically important differences in protein levels in mice exposed to context fear conditioning (CFC). We analyzed expression levels of 77 proteins obtained from normal genotype control mice and from their trisomic littermates (Ts65Dn) both with and without treatment with the drug memantine. Control mice learn successfully while the trisomic mice fail, unless they are first treated with the drug, which rescues their learning ability. The SOM approach identified reduced subsets of proteins predicted to make the most critical contributions to normal learning, to failed learning and rescued learning, and provides a visual representation of the data that allows the user to extract patterns that may underlie novel biological responses to the different kinds of learning and the response to memantine. Results suggest that the application of SOM to new experimental data sets of complex protein profiles can be used to identify common critical protein responses, which in turn may aid in identifying potentially more effective drug targets.

Prenatal treatment of Down syndrome: a reality?

PURPOSE OF REVIEW

Down syndrome affects more than 5 million people globally. During the last 10 years, there has been a dramatic increase in the research efforts focused on therapeutic interventions to improve learning and memory in Down syndrome.

RECENT FINDINGS

This review summarizes the different functional abnormalities targeted by researchers in mouse models of Down syndrome. Three main strategies have been used: neural stem cell implantation; environmental enrichment and physical exercise; and pharmacotherapy. Pharmacological targets include the choline pathway, GABA and NMDA receptors, DYRK1A protein, oxidative stress and pathways involved in development and neurogenesis. Many strategies have improved learning and memory as well as electrophysiological and molecular alterations in affected animals. To date, eight molecules have been tested in human adult clinical trials. No studies have yet been performed on infants. However, compelling studies reveal that permanent brain alterations originate during fetal life in Down syndrome. Early prenatal diagnosis offers a 28 weeks window to positively impact brain development and improve postnatal cognitive outcome in affected individuals. Only a few approaches (Epigallocatechine gallate, NAP/SAL, fluoxetine, and apigenin) have been used to treat mice in utero; these showed therapeutic effects that persisted to adulthood.

SUMMARY

In this article, we discuss the challenges, recent progress, and lessons learned that pave the way for new therapeutic approaches in Down syndrome.

Protein profiles in Tc1 mice implicate novel pathway perturbations in the Down syndrome brain

Tc1 mouse model of Down syndrome (DS) is functionally trisomic for ∼120 human chromosome 21 (HSA21) classical protein-coding genes. Tc1 mice display features relevant to the DS phenotype, including abnormalities in learning and memory and synaptic plasticity. To determine the molecular basis for the phenotypic features, the levels of 90 phosphorylation-specific and phosphorylation-independent proteins were measured by Reverse Phase Protein Arrays in hippocampus and cortex, and 64 in cerebellum, of Tc1 mice and littermate controls. Abnormal levels of proteins involved in MAP kinase, mTOR, GSK3B and neuregulin signaling were identified in trisomic mice. In addition, altered correlations among the levels of N-methyl-D-aspartate (NMDA) receptor subunits and the HSA21 proteins amyloid beta (A4) precursor protein (APP) and TIAM1, and between immediate early gene (IEG) proteins and the HSA21 protein superoxide dismutase-1 (SOD1) were found in the hippocampus of Tc1 mice, suggesting altered stoichiometry among these sets of functionally interacting proteins. Protein abnormalities in Tc1 mice were compared with the results of a similar analysis of Ts65Dn mice, a DS mouse model that is trisomic for orthologs of 50 genes trisomic in the Tc1 plus an additional 38 HSA21 orthologs. While there are similarities, abnormalities unique to the Tc1 include increased levels of the S100B calcium-binding protein, mTOR proteins RAPTOR and P70S6, the AMP-kinase catalytic subunit AMPKA, the IEG proteins FBJ murine osteosarcoma viral oncogene homolog (CFOS) and activity-regulated cytoskeleton-associated protein (ARC), and the neuregulin 1 receptor ERBB4. These data identify novel perturbations, relevant to neurological function and to some seen in Alzheimer's disease, that may occur in the DS brain, potentially contributing to phenotypic features and influencing drug responses.

Loss of correlations among proteins in brains of the Ts65Dn mouse model of down syndrome

The Ts65Dn mouse model of Down syndrome (DS) is trisomic for orthologs of 88 of 161 classical protein coding genes present on human chromosome 21 (HSA21). Ts65Dn mice display learning and memory impairments and neuroanatomical, electrophysiological, and cellular abnormalities that are relevant to phenotypic features seen in DS; however, little is known about the molecular perturbations underlying the abnormalities. Here we have used reverse phase protein arrays to profile 64 proteins in the cortex, hippocampus, and cerebellum of Ts65Dn mice and littermate controls. Proteins were chosen to sample a variety of pathways and processes and include orthologs of HSA21 proteins and phosphorylation-dependent and -independent forms of non-HSA21 proteins. Protein profiles overall show remarkable stability to the effects of trisomy, with fewer than 30% of proteins altered in any brain region. However, phospho-proteins are less resistant to trisomy than their phospho-independent forms, and Ts65Dn display abnormalities in some key proteins. Importantly, we demonstrate that Ts65Dn mice have lost correlations seen in control mice among levels of functionally related proteins, including components of the MAP kinase pathway and subunits of the NMDA receptor. Loss of normal patterns of correlations may compromise molecular responses to stimulation and underlie deficits in learning and memory.