Insights from multi-omic modeling of neurodegeneration in xeroderma pigmentosum using an induced pluripotent stem cell system.

Xeroderma pigmentosum (XP) is caused by defective nucleotide excision repair of DNA damage. This results in hypersensitivity to ultraviolet light and increased skin cancer risk, as sunlight-induced photoproducts remain unrepaired. However, many XP patients also display early-onset neurodegeneration, which leads to premature death. The mechanism of neurodegeneration is unknown. Here, we investigate XP neurodegeneration using pluripotent stem cells derived from XP patients and healthy relatives, performing functional multi-omics on samples during neuronal differentiation. We show substantially increased levels of 5',8-cyclopurine and 8-oxopurine in XP neuronal DNA secondary to marked oxidative stress. Furthermore, we find that the endoplasmic reticulum stress response is upregulated and reversal of the mutant genotype is associated with phenotypic rescue. Critically, XP neurons exhibit inappropriate downregulation of the protein clearance ubiquitin-proteasome system (UPS). Chemical enhancement of UPS activity in XP neuronal models improves phenotypes, albeit inadequately. Although more work is required, this study presents insights with intervention potential.

Lipidomic Analysis of Plasma Extracellular Vesicles Derived from Alzheimer's Disease Patients.

Analysis of blood-based indicators of brain health could provide an understanding of early disease mechanisms and pinpoint possible intervention strategies. By examining lipid profiles in extracellular vesicles (EVs), secreted particles from all cells, including astrocytes and neurons, and circulating in clinical samples, important insights regarding the brain's composition can be gained. Herein, a targeted lipidomic analysis was carried out in EVs derived from plasma samples after removal of lipoproteins from individuals with Alzheimer's disease (AD) and healthy controls. Differences were observed for selected lipid species of glycerolipids (GLs), glycerophospholipids (GPLs), lysophospholipids (LPLs) and sphingolipids (SLs) across three distinct EV subpopulations (all-cell origin, derived by immunocapture of CD9, CD81 and CD63; neuronal origin, derived by immunocapture of L1CAM; and astrocytic origin, derived by immunocapture of GLAST). The findings provide new insights into the lipid composition of EVs isolated from plasma samples regarding specific lipid families (MG, DG, Cer, PA, PC, PE, PI, LPI, LPE, LPC), as well as differences between AD and control individuals. This study emphasizes the crucial role of plasma EV lipidomics analysis as a comprehensive approach for identifying biomarkers and biological targets in AD and related disorders, facilitating early diagnosis and potentially informing novel interventions.

Machine Learning Analysis of Alzheimer's Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions.

Advancements in molecular biology have revolutionized our understanding of complex diseases, with Alzheimer's disease being a prime example. Single-cell sequencing, currently the most suitable technology, facilitates profoundly detailed disease analysis at the cellular level. Prior research has established that the pathology of Alzheimer's disease varies across different brain regions and cell types. In parallel, only machine learning has the capacity to address the myriad challenges presented by such studies, where the integration of large-scale data and numerous experiments is required to extract meaningful knowledge. Our methodology utilizes single-cell RNA sequencing data from healthy and Alzheimer's disease (AD) samples, focused on the cortex and hippocampus regions in mice. We designed three distinct case studies and implemented an ensemble feature selection approach through machine learning, also performing an analysis of distinct age-related datasets to unravel age-specific effects, showing differential gene expression patterns within each condition. Important evidence was reported, such as enrichment in central nervous system development and regulation of oligodendrocyte differentiation between the hippocampus and cortex of 6-month-old AD mice as well as regulation of epinephrine secretion and dendritic spine morphogenesis in 15-month-old AD mice. Our outcomes from all three of our case studies illustrate the capacity of machine learning strategies when applied to single-cell data, revealing critical insights into Alzheimer's disease.

In Silico Structural Analysis Exploring Conformational Folding of Protein Variants in Alzheimer's Disease.

Accurate protein structure prediction using computational methods remains a challenge in molecular biology. Recent advances in AI-powered algorithms provide a transformative effect in solving this problem. Even though AlphaFold's performance has improved since its release, there are still limitations that apply to its efficacy. In this study, a selection of proteins related to the pathology of Alzheimer's disease was modeled, with Presenilin-1 (PSN1) and its mutated variants in the foreground. Their structural predictions were evaluated using the ColabFold implementation of AlphaFold, which utilizes MMseqs2 for the creation of multiple sequence alignments (MSAs). A higher number of recycles than the one used in the AlphaFold DB was selected, and no templates were used. In addition, prediction by RoseTTAFold was also applied to address how structures from the two deep learning frameworks match reality. The resulting conformations were compared with the corresponding experimental structures, providing potential insights into the predictive ability of this approach in this particular group of proteins. Furthermore, a comprehensive examination was performed on features such as predicted regions of disorder and the potential effect of mutations on PSN1. Our findings consist of highly accurate superpositions with little or no deviation from experimentally determined domain-level models.

Assessing and Modelling of Post-Traumatic Stress Disorder Using Molecular and Functional Biomarkers.

Post-traumatic stress disorder (PTSD) is a complex psychological disorder that develops following exposure to traumatic events. PTSD is influenced by catalytic factors such as dysregulated hypothalamic-pituitary-adrenal (HPA) axis, neurotransmitter imbalances, and oxidative stress. Genetic variations may act as important catalysts, impacting neurochemical signaling, synaptic plasticity, and stress response systems. Understanding the intricate gene networks and their interactions is vital for comprehending the underlying mechanisms of PTSD. Focusing on the catalytic factors of PTSD is essential because they provide valuable insights into the underlying mechanisms of the disorder. By understanding these factors and their interplay, researchers may uncover potential targets for interventions and therapies, leading to more effective and personalized treatments for individuals with PTSD. The aforementioned gene networks, composed of specific genes associated with the disorder, provide a comprehensive view of the molecular pathways and regulatory mechanisms involved in PTSD. Through this study valuable insights into the disorder's underlying mechanisms and opening avenues for effective treatments, personalized interventions, and the development of biomarkers for early detection and monitoring are provided.

Setting Up a Bio-AFM to Study Protein Misfolding in Neurodegenerative Diseases.

The clinical pathology of neurodegenerative diseases suggests that earlier onset and progression are related to the accumulation of protein aggregates due to misfolding. A prominent way to extract useful information regarding single-molecule studies of protein misfolding at the nanoscale is by capturing the unbinding molecular forces through forced mechanical tension generated and monitored by an atomic force microscopy-based single-molecule force spectroscopy (AFM-SMFS). This AFM-driven process results in an amount of data in the form of force versus molecular extension plots (force-distance curves), the statistical analysis of which can provide insights into the underlying energy landscape and assess a number of characteristic elastic and kinetic molecular parameters of the investigated sample. This chapter outlines the setup of a bio-AFM-based SMFS technique for single-molecule probing. The infrastructure used as a reference for this presentation is the Bruker ForceRobot300.

Protein Structure Prediction for Disease-Related Insertions/Deletions in Presenilin 1 Gene.

More than 450 mutations, some of which have unknown toxicity, have been reported in the presenilin 1 gene, which is the most common cause of Alzheimer's disease (AD) with an early onset. PSEN1 mutations are thought to be responsible for approximately 80% of cases of monogenic AD, which are characterized by complete penetrance and an early age of onset. It is still unknown exactly how mutations in the presenilin 1 gene can cause dementia and neurodegeneration; however, both conditions have been linked to these changes. In this chapter, well-known computational analysis servers and accessible databases such as Uniprot, iTASSER, and PDBeFold are examined for their ability to predict the functional domains of mutant proteins and quantify the effect that these mutations have on the three-dimensional structure of the protein.

Computational and Functional Insights of Protein Misfolding in Neurodegeneration.

Protein folding is the process by which a polypeptide chain self-assembles into the correct three-dimensional structure, so that it ends up in the biologically active, native state. Under conditions of proteotoxic stress, mutations, or cellular aging, proteins can begin to aggregate into non-native structures such as ordered amyloid fibrils and plaques. Many neurodegenerative diseases involve the misfolding and aggregation of specific proteins into abnormal, toxic species. Experimental approaches including crystallography and AFM (atomic force microscopy)-based force spectroscopy are used to exploit the folding and structural characterization of protein molecules. At the same time, computational techniques through molecular dynamics, fold recognition, and structure prediction are widely applied in this direction. Benchmarking analysis for combining and comparing computational methodologies with functional studies can decisively unravel robust interactions between the side groups of the amino acid sequence and monitor alterations in intrinsic protein dynamics with high precision as well as adequately determine potent conformations of the folded patterns formed in the polypeptide structure.

Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data.

System-level network-based approaches are an emerging field in the biomedical domain since biological networks can be used to analyze complicated biological processes and complex human disorders more efficiently. Network biomarkers are groups of interconnected molecular components causing perturbations in the entire network topology that can be used as indicators of pathogenic biological processes when studying a given disease. Although in the last years computational systems-based approaches have gained ground on the path to discovering new network biomarkers, in complex diseases like Alzheimer's disease (AD), this approach has still much to offer. Especially the adoption of single-cell RNA sequencing (scRNA-seq) has now become the dominant technology for the study of stochastic gene expression. Toward this orientation, we propose an R workflow that extracts disease-perturbed subpathways within a pathway network. We construct a gene-gene interaction network integrated with scRNA-seq expression profiles, and after network processing and pruning, the most active subnetworks are isolated from the entire network topology. The proposed methodology was applied on a real AD-based scRNA-seq data, providing already existing and new potential AD biomarkers in gene network context.

A Consensus Gene Regulatory Network for Neurodegenerative Diseases Using Single-Cell RNA-Seq Data.

Gene regulatory network (GRN) inference from gene expression data is a highly complex and challenging task in systems biology. Despite the challenges, GRNs have emerged, and for complex diseases such as neurodegenerative diseases, they have the potential to provide vital information and identify key regulators. However, every GRN method produced predicts results based on its assumptions, providing limited biological insights. For that reason, the current work focused on the development of an ensemble method from individual GRN methods to address this issue. Four state-of-the-art GRN algorithms were selected to form a consensus GRN from their common gene interactions. Each algorithm uses a different construction method, and for a more robust behavior, both static and dynamic methods were selected as well. The algorithms were applied to a scRNA-seq dataset from the CK-p25 mus musculus model during neurodegeneration. The top subnetworks were constructed from the consensus network, and potential key regulators were identified. The results also demonstrated the overlap between the algorithms for the current dataset and the necessity for an ensemble approach. This work aims to demonstrate the creation of an ensemble network and provide insights into whether a combination of different GRN methods can produce valuable results.

A Sensor-Based Platform for Early-Stage Parkinson's Disease Monitoring.

Biosensing platforms have gained much attention in clinical practice screening thousands of samples simultaneously for the accurate detection of important markers in various diseases for diagnostic and prognostic purposes. Herein, a framework for the design of an innovative methodological approach combined with data processing and appropriate software in order to implement a complete diagnostic system for Parkinson's disease exploitation is presented. The integrated platform consists of biochemical and peripheral sensor platforms for measuring biological and biometric parameters of examinees, a central collection and management unit along with a server for storing data, and a decision support system for patient's state assessment regarding the occurrence of the disease. The suggested perspective is oriented on data processing and experimental implementation and can provide a powerful holistic evaluation of personalized monitoring of patients or individuals at high risk of manifestation of the disease.

Signature-Based Computational Drug Repurposing for Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a late-onset fatal neurodegenerative disease characterized by progressive loss of the upper and lower motor neurons. There are currently limited approved drugs for the disorder, and for this reason the strategy of repositioning already approved therapeutics could exhibit a successful outcome. Herein, we used CMAP and L1000CDS2 databases which include gene expression profiles datasets (genomic signatures) to identify potent compounds and classes of compounds which reverse disease's signature which could in turn reverse its phenotype. ALS signature was obtained by comparing gene expression of muscle biopsy specimens between diseased and healthy individuals. Statistical analysis was conducted to explore differentially transcripts in patients' samples. Then, the list of upregulated and downregulated genes was used to query both databases in order to determine molecules which downregulate the genes which are upregulated by ALS and vice versa. These compounds, based on their chemical structure along with known treatments, were clustered to reveal drugs with novel and potentially more effective mode of action with most of them predicted to affect pathways heavily involved in ALS. This evidence suggests that these compounds are strong candidates for moving to the next phase of the drug repurposing pipeline which is in vitro and in vivo experimental evaluation.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Assessing the Formation of Purine Lesions in Mitochondrial DNA of Cockayne Syndrome Cells.

Mitochondrial (mt) DNA and nuclear (n) DNA have known structures and roles in cells; however, they are rarely compared under specific conditions such as oxidative or degenerative environments that can create damage to the DNA base moieties. Six purine lesions were ascertained in the mtDNA of wild type (wt) CSA (CS3BE-wtCSA) and wtCSB (CS1AN-wtCSB) cells and defective counterparts CS3BE and CS1AN in comparison with the corresponding total (t) DNA (t = n + mt). In particular, the four 5',8-cyclopurine (cPu) and the two 8-oxo-purine (8-oxo-Pu) lesions were accurately quantified by LC-MS/MS analysis using isotopomeric internal standards after an enzymatic digestion procedure. The 8-oxo-Pu levels were found to be in the range of 25-50 lesions/107 nucleotides in both the mtDNA and tDNA. The four cPu were undetectable in the mtDNA both in defective cells and in the wt counterparts (CSA and CSB), contrary to their detection in tDNA, indicating a nonappearance of hydroxyl radical (HO•) reactivity within the mtDNA. In order to assess the HO• reactivity towards purine nucleobases in the two genetic materials, we performed γ-radiolysis experiments coupled with the 8-oxo-Pu and cPu quantifications on isolated mtDNA and tDNA from wtCSB cells. In the latter experiments, all six purine lesions were detected in both of the DNA, showing a higher resistance to HO• attack in the case of mtDNA compared with tDNA, likely due to their different DNA helical topology influencing the relative abundance of the lesions.

Effects of Aging and Disease Conditions in Brain of Tumor-Bearing Mice: Evaluation of Purine DNA Damages and Fatty Acid Pool Changes.

The consequences of aging and disease conditions in tissues involve reactive oxygen species (ROS) and related molecular alterations of different cellular compartments. We compared a murine model of immunodeficient (SCID) xenografted young (4 weeks old) and old (17 weeks old) mice with corresponding controls without tumor implantation and carried out a compositional evaluation of brain tissue for changes in parallel DNA and lipids compartments. DNA damage was measured by four purine 5',8-cyclo-2'-deoxynucleosides, 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxo-dG), and 8-oxo-7,8-dihydro-2'-deoxyadenosine (8-oxo-dA). In brain lipids, the twelve most representative fatty acid levels, which were mostly obtained from the transformation of glycerophospholipids, were followed up during the aging and disease progressions. The progressive DNA damage due to age and tumoral conditions was confirmed by raised levels of 5'S-cdG and 5'S-cdA. In the brain, the remodeling involved a diminution of palmitic acid accompanied by an increase in arachidonic acid, along both age and tumor progressions, causing increases in the unsaturation index, the peroxidation index, and total TFA as indicators of increased oxidative and free radical reactivity. Our results contribute to the ongoing debate on the central role of DNA and genome instability in the aging process, and on the need for a holistic vision, which implies choosing the best biomarkers for such monitoring. Furthermore, our data highlight brain tissue for its lipid remodeling response and inflammatory signaling, which seem to prevail over the effects of DNA damage.

Effects of Oxygen Tension for Membrane Lipidome Remodeling of Cockayne Syndrome Cell Models.

Oxygen is important for lipid metabolism, being involved in both enzymatic transformations and oxidative reactivity, and is particularly influent when genetic diseases impair the repair machinery of the cells, such as described for Cockayne syndrome (CS). We used two cellular models of transformed fibroblasts defective for CSA and CSB genes and their normal counterparts, grown for 24 h under various oxygen tensions (hyperoxic 21%, physioxic 5% and hypoxic 1%) to examine the fatty acid-based membrane remodeling by GC analysis of fatty acid methyl esters derived from membrane phospholipids. Overall, we first distinguished differences due to oxygen tensions: (a) hyperoxia induced a general boost of desaturase enzymatic activity in both normal and defective CSA and CSB cell lines, increasing monounsaturated fatty acids (MUFA), whereas polyunsaturated fatty acids (PUFA) did not undergo oxidative consumption; (b) hypoxia slowed down desaturase activities, mostly in CSA cell lines and defective CSB, causing saturated fatty acids (SFA) to increase, whereas PUFA levels diminished, suggesting their involvement in hypoxia-related signaling. CSB-deprived cells are the most sensitive to oxidation and CSA-deprived cells are the most sensitive to the radical-based formation of trans fatty acids (TFA). The results point to the need to finely differentiate biological targets connected to genetic impairments and, consequently, suggest the better definition of cell protection and treatments through accurate molecular profiling that includes membrane lipidomes.

Antioxidant and cytotoxic activities of selected salicylidene imines: experimental and computational study.

Selected salicylidene imines were evaluated for their antioxidant and cytotoxic potentials. Several of them exerted potent scavenging capacity towards ABTS radical and hydrogen peroxide. The insight into the preferable antioxidative mechanism was reached employing density functional theory. In the absence of free radicals, the SPLET mechanism is dominant in polar surroundings, while HAT is prevailing in a non-polar environment. The results obtained for the reactions of the most active compounds with some medically relevant radicals pointed out competition between HAT and SPLET mechanisms. The assessment of their cytotoxic properties revealed inhibition of ER-a human breast adenocarcinoma cells or estrogen-independent prostate cancer cells. Molecular docking study with the cyclooxygenase (COX) 2 enzyme was performed to examine the most probable bioactive conformations and possible interactions between the tested derivatives and COX-2 binding pocket.

A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes.

Parkinson's disease (PD) is a progressive neurodegenerative disorder associated with dysfunction of dopaminergic neurons in the brain, lack of dopamine and the formation of abnormal Lewy body protein particles. PD is an idiopathic disease of the nervous system, characterized by motor and nonmotor manifestations without a discrete onset of symptoms until a substantial loss of neurons has already occurred, enabling early diagnosis very challenging. Sensor-based platforms have gained much attention in clinical practice screening various biological signals simultaneously and allowing researchers to quickly receive a huge number of biomarkers for diagnostic and prognostic purposes. The integration of machine learning into medical systems provides the potential for optimization of data collection, disease prediction through classification of symptoms and can strongly support data-driven clinical decisions. This work attempts to examine some of the facts and current situation of sensor-based approaches in PD diagnosis and discusses ensemble techniques using sensor-based data for developing machine learning models for personalized risk prediction. Additionally, a biosensing platform combined with clinical data processing and appropriate software is proposed in order to implement a complete diagnostic system for PD monitoring.

Incorporation of 5',8-cyclo-2'deoxyadenosines by DNA repair polymerases via base excision repair.

5',8-cyclo-2-deoxy nucleosides (cdPus) are the smallest tandem purine lesions including 5',8-cyclo-2'-deoxyadenosine (cdA) and 5',8-cyclo-2'-deoxyguanosine (cdG). They can inhibit DNA and RNA polymerases causing mutations, DNA strand breaks, and termination of DNA replication and gene transcription. cdPus can be removed by nucleotide excision repair with low efficiency allowing them to accumulate in the genome. Recent studies suggest that cdPus can be induced in damaged nucleotide pools and incorporated into the genome by DNA polymerases. However, it remains unknown if and how DNA polymerases can incorporate cdPus. In this study, we examined the incorporation of cdAs by human DNA repair polymerases, DNA polymerases ß (pol ß), and pol η during base excision repair. We then determined the efficiency of cdA incorporation by the polymerases using steady-state kinetics. We found that pol ß and pol η incorporated cdAs opposite dT and dC with low efficiency, and incorporated cdAs were readily extended and ligated into duplex DNA. Using molecular docking analysis, we found that the 5',8-covalent bond in cdA disrupted its hydrogen bonding with a template base suggesting that the phosphodiester bond between the 3'-terminus nucleotide and the α-phosphate of cdATP were generated in the absence of hydrogen bonding. The enzyme kinetics analysis further suggests that pol ß and pol η increased their substrate binding to facilitate the enzyme catalysis for cdA incorporation. Our study reveals unique mechanisms underlying the accumulation of cdPu lesions in the genome resulting from nucleotide incorporation by repair DNA polymerases.