Common genetic signatures of Alzheimer's disease in Down Syndrome

RNAseq analysis of olfactory neuroepithelium cytological samples in individuals with Down syndrome compared to euploid controls: a pilot study

Down syndrome is a common genetic disorder caused by partial or complete triplication of chromosome 21. This syndrome shows an overall and progressive impairment of olfactory function, detected early in adulthood. The olfactory neuronal cells are located in the nasal olfactory mucosa and represent the first sensory neurons of the olfactory pathway. Herein, we applied the olfactory swabbing procedure to allow a gentle collection of olfactory epithelial cells in seven individuals with Down syndrome and in ten euploid controls. The aim of this research was to investigate the peripheral gene expression pattern in olfactory epithelial cells through RNAseq analysis. Validated tests (Sniffin' Sticks Extended test) were used to assess olfactory function. Olfactory scores were correlated with RNAseq results and cognitive scores (Vineland II and Leiter scales). All Down syndrome individuals showed both olfactory deficit and intellectual disability. Down syndrome individuals and euploid controls exhibited clear expression differences in genes located in and outside the chromosome 21. In addition, a significant correlation was found between olfactory test scores and gene expression, while a non-significant correlation emerged between olfactory and cognitive scores. This first preliminary step gives new insights into the Down syndrome olfactory system research, starting from the olfactory neuroepithelium, the first cellular step on the olfactory way.

Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a rare progressive neurodegenerative disease that affects upper and lower motor neurons. As the molecular basis of the disease is still elusive, the development of high-throughput sequencing technologies, combined with data mining techniques and machine learning methods, could provide remarkable results in identifying pathogenetic mechanisms. High dimensionality is a major problem when applying machine learning techniques in biomedical data analysis, since a huge number of features is available for a limited number of samples. The aim of this study was to develop a methodology for training interpretable machine learning models in the classification of ALS and ALS-subtypes samples, using gene expression datasets.

METHODS

We performed dimensionality reduction in gene expression data using a semi-automated preprocessing systematic gene selection procedure using Statistically Equivalent Signature (SES), a causality-based feature selection algorithm, followed by Boosted Regression Trees (XGBoost) and Random Forest to train the machine learning classifiers. The SHapley Additive exPlanations (SHAP values) were used for interpretation of the machine learning classifiers. The methodology was developed and tested using two distinct publicly available ALS RNA-seq datasets. We evaluated the performance of SES as a dimensionality reduction method against: (a) Least Absolute Shrinkage and Selection Operator (LASSO), and (b) Local Outlier Factor (LOF).

RESULTS

The proposed methodology achieved 85.18% accuracy for the classification of cerebellum or frontal cortex samples as C9orf72-related familial ALS, sporadic ALS or healthy samples. Importantly, the genes identified as the most determinative have also been reported as disease-associated in ALS literature. When tested in the evaluation dataset, the methodology achieved 88.89% accuracy for the classification of sporadic ALS motor neuron samples. When LASSO was used as feature selection method instead of SES, the accuracy of the machine learning classifiers ranged from 74.07 to 96.30%, depending on tissue assessed, while LOF underperformed significantly (77.78% accuracy for the classification of pooled cerebellum and frontal cortex samples).

CONCLUSIONS

Using SES, we addressed the challenge of high dimensionality in gene expression data analysis, and we trained accurate machine learning ALS classifiers, specific for the gene expression patterns of different disease subtypes and tissue samples, while identifying disease-associated genes.

Asynchronous excitatory neuron development in an isogenic cortical spheroid model of Down syndrome

The intellectual disability (ID) in Down syndrome (DS) is thought to result from a variety of developmental deficits such as alterations in neural progenitor division, neurogenesis, gliogenesis, cortical architecture, and reduced cortical volume. However, the molecular processes underlying these neurodevelopmental changes are still elusive, preventing an understanding of the mechanistic basis of ID in DS. In this study, we used a pair of isogenic (trisomic and euploid) induced pluripotent stem cell (iPSC) lines to generate cortical spheroids (CS) that model the impact of trisomy 21 on brain development. Cortical spheroids contain neurons, astrocytes, and oligodendrocytes and they are widely used to approximate early neurodevelopment. Using single cell RNA sequencing (scRNA-seq), we uncovered cell type-specific transcriptomic changes in the trisomic CS. In particular, we found that excitatory neuron populations were most affected and that a specific population of cells with a transcriptomic profile resembling layer IV cortical neurons displayed the most profound divergence in developmental trajectory between trisomic and euploid genotypes. We also identified candidate genes potentially driving the developmental asynchrony between trisomic and euploid excitatory neurons. Direct comparison between the current isogenic CS scRNA-seq data and previously published datasets revealed several recurring differentially expressed genes between DS and control samples. Altogether, our study highlights the power and importance of cell type-specific analyses within a defined genetic background, coupled with broader examination of mixed samples, to comprehensively evaluate cellular phenotypes in the context of DS.

Dynamic Changes in Central and Peripheral Neuro-Injury vs. Neuroprotective Serum Markers in COVID-19 Are Modulated by Different Types of Anti-Viral Treatments but Do Not Affect the Incidence of Late and Early Strokes

The balance between neurodegeneration, neuroinflammation, neuroprotection, and COVID-19-directed therapy may underly the heterogeneity of SARS-CoV-2's neurological outcomes. A total of 105 patients hospitalized with a diagnosis of COVID-19 had serum collected over a 6 month period to assess neuroinflammatory (MIF, CCL23, MCP-1), neuro-injury (NFL, NCAM-1), neurodegenerative (KLK6, τ, phospho τ, amyloids, TDP43, YKL40), and neuroprotective (clusterin, fetuin, TREM-2) proteins. These were compared to markers of nonspecific inflammatory responses (IL-6, D-dimer, CRP) and of the overall viral burden (spike protein). Data regarding treatment (steroids, convalescent plasma, remdasavir), pre-existing conditions, and incidences of strokes were collected. Amyloid β42, TDP43, NF-L, and KLK6 serum levels declined 2-3 days post-admission, yet recovered to admission baseline levels by 7 days. YKL-40 and NCAM-1 levels remained elevated over time, with clusters of differential responses identified among TREM-2, TDP43, and YKL40. Fetuin was elevated after the onset of COVID-19 while TREM-2 initially declined before significantly increasing over time. MIF serum level was increased 3-7 days after admission. Ferritin correlated with TDP-43 and KLK6. No treatment with remdesivir coincided with elevations in Amyloid-β40. A lack of convalescent plasma resulted in increased NCAM-1 and total tau, and steroidal treatments did not significantly affect any markers. A total of 11 incidences of stroke were registered up to six months after initial admission for COVID-19. Elevated D-dimer, platelet counts, IL-6, and leukopenia were observed. Variable MIF serum levels differentiated patients with CVA from those who did not have a stroke during the acute phase of COVID-19. This study demonstrated concomitant and opposite changes in neurodegenerative and neuroprotective markers persisting well into recovery.

Towards a Consensus on Alzheimer's Disease Comorbidity?

Alzheimer's disease (AD) is often comorbid with other pathologies. First, we review shortly the diseases most associated with AD in the clinic. Then we query PubMed citations for the co-occurrence of AD with other diseases, using a list of 400 common pathologies. Significantly, AD is found to be associated with schizophrenia and psychosis, sleep insomnia and apnea, type 2 diabetes, atherosclerosis, hypertension, cardiovascular diseases, obesity, fibrillation, osteoporosis, arthritis, glaucoma, metabolic syndrome, pain, herpes, HIV, alcoholism, heart failure, migraine, pneumonia, dyslipidemia, COPD and asthma, hearing loss, and tobacco smoking. Trivially, AD is also found to be associated with several neurodegenerative diseases, which are disregarded. Notably, our predicted results are consistent with the previously published clinical data and correlate nicely with individual publications. Our results emphasize risk factors and promulgate diseases often associated with AD. Interestingly, the comorbid diseases are often degenerative diseases exacerbated by reactive oxygen species, thus underlining the potential role of antioxidants in the treatment of AD and comorbid diseases.