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.

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.

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.

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.

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.

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.

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.

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.

High-resolution crystal structures of ribosome-bound chloramphenicol and erythromycin provide the ultimate basis for their competition.

The 70S ribosome is a major target for antibacterial drugs. Two of the classical antibiotics, chloramphenicol (CHL) and erythromycin (ERY), competitively bind to adjacent but separate sites on the bacterial ribosome: the catalytic peptidyl transferase center (PTC) and the nascent polypeptide exit tunnel (NPET), respectively. The previously reported competitive binding of CHL and ERY might be due either to a direct collision of the two drugs on the ribosome or due to a drug-induced allosteric effect. Because of the resolution limitations, the available structures of these antibiotics in complex with bacterial ribosomes do not allow us to discriminate between these two possible mechanisms. In this work, we have obtained two crystal structures of CHL and ERY in complex with the Thermus thermophilus 70S ribosome at a higher resolution (2.65 and 2.89 Å, respectively) allowing unambiguous placement of the drugs in the electron density maps. Our structures provide evidence of the direct collision of CHL and ERY on the ribosome, which rationalizes the observed competition between the two drugs.

Polypyridyl-Based Copper Phenanthrene Complexes: Combining Stability with Enhanced DNA Recognition.

We report a series of copper(II) artificial metallo-nucleases (AMNs) and demonstrate their DNA damaging properties and in-vitro cytotoxicity against human-derived pancreatic cancer cells. The compounds combine a tris-chelating polypyridyl ligand, di-(2-pycolyl)amine (DPA), and a DNA intercalating phenanthrene unit. Their general formula is Cu-DPA-N,N' (where N,N'=1,10-phenanthroline (Phen), dipyridoquinoxaline (DPQ) or dipyridophenazine (DPPZ)). Characterisation was achieved by X-ray crystallography and continuous-wave EPR (cw-EPR), hyperfine sublevel correlation (HYSCORE) and Davies electron-nuclear double resonance (ENDOR) spectroscopies. The presence of the DPA ligand enhances solution stability and facilitates enhanced DNA recognition with apparent binding constants (Kapp ) rising from 105 to 107 m-1 with increasing extent of planar phenanthrene. Cu-DPA-DPPZ, the complex with greatest DNA binding and intercalation effects, recognises the minor groove of guanine-cytosine (G-C) rich sequences. Oxidative DNA damage also occurs in the minor groove and can be inhibited by superoxide and hydroxyl radical trapping agents. The complexes, particularly Cu-DPA-DPPZ, display promising anticancer activity against human pancreatic tumour cells with in-vitro results surpassing the clinical platinum(II) drug oxaliplatin.

A harmonized and standardized in vitro approach produces reliable results on silver nanoparticles toxicity in different cell lines.

Despite the widespread use of silver nanoparticles (AgNPs) in different fields and the amount of investigations available, to date, there are many contradictory results on their potential toxicity. In the present study, extensively characterized 20-nm AgNPs were investigated using optimized protocols and standardized methods to test several toxicological endpoints in different cell lines. The agglomeration/aggregation state of AgNPs in culture media was measured by dynamic light scattering (DLS). DNA and chromosomal damage on BEAS-2B and RAW 264.7 cells were evaluated by comet and micronucleus assays, while oxidative DNA damage by modified comet assay and 8-oxodG/8-oxodA detection. We also investigated immunotoxicity and immunomodulation by cytokine release and NO production in RAW 264.7 and MH-S cells, with or without lipopolysaccharide (LPS) stimulus. Transmission electron microscope (TEM) analysis was used to analyze cellular uptake of AgNPs. Our results indicate different values of AgNPs hydrodynamic diameter depending on the medium, some genotoxic effect just on BEAS-2B and no or slight effects on function of RAW 264.7 and MH-S in absence or presence of LPS stimulus. This study highlights the relevance of using optimized protocols and multiple endpoints to analyze the potential toxicity of AgNPs and to obtain reliable and comparable results.

Biomarker-Driven Analysis Using High-Throughput Approaches in Neuroinflammation and Neurodegenerative Diseases.

Aging is responsible for homeostatic dysregulation and the primary risk for neurodegenerative diseases. The main signaling pathways may regulate inflammatory-related disorders and neurodegeneration include genomic instability, cell senescence, and mitochondria dysfunction. The use of high-throughput technologies has emerged as a powerful approach to the rapid discovery of many candidate biomarkers for age-related diseases. Various types of molecules, such as nucleic acids, proteins, or metabolites, can serve as soluble factors in clinical practice with deviations in their normal biological levels being an indication of an underlying disease state. The development of multifactorial biomarkers based on models involving molecular alterations in complex disorders may also provide specific challenges for translating biological findings and targeted diagnostic tools. As diseases are often regulated by a multiset of markers that coordinate and interact each other in a complex signaling network to maintain holistic processes within a cell, potent network-based approaches to data-driven biomarker identification are required. System-based biomarker discovery pipelines can offer an extraordinary adjustment opportunity for data heterogeneity and limitation, whereas integrated analysis of distinct networks clusters  can provide important information for the early detection of intracellular pathogenic processes as well as for monitoring the response to treatment.

Bioinformatics Approaches for Parkinson's Disease in Clinical Practice: Data-Driven Biomarkers and Pharmacological Treatment.

Parkinson's disease is a gradually progressive neurodegenerative disorder characterized by a selective loss of dopaminergic neurons in the midbrain area called the substantia nigra pars compacta and cytoplasmic alpha-synuclein-rich inclusions termed Lewy bodies. The etiology and pathogenesis remain incompletely understood. The development of reliable biomarkers for the early and accurate diagnosis, including biochemical, genetic, clinical, and neuroimaging markers, is crucial for unraveling the pathogenic processes of the disease as well as patients' progress surveillance. High-throughput technologies and system biology methodologies can support the identification of potent molecular fingerprints together with the establishment of dynamic network biomarkers. Emphasis is given on multi-omics datasets and dysregulated pathways associated with differentially expressed transcripts, modified protein motifs, and altered metabolic profiles. Although there is no therapy that terminates the neurodegenerative process and dopamine replacement strategy with L-DOPA represents the most effective treatment, numerous therapeutic protocols such as dopamine receptor agonists, MAO-B inhibitors, and cholinesterase inhibitors represent candidate treatments providing at the same time valuable network-based approaches to drug repositioning. Computational methodologies and bioinformatics platforms for visualization, clustering, and validating of molecular and clinical datasets provide important insights into diagnostic processing and therapeutic pipeline.

Oxygen Dependent Purine Lesions in Double-Stranded Oligodeoxynucleotides: Kinetic and Computational Studies Highlight the Mechanism for 5',8-Cyclopurine Formation.

The reaction of HO• radical with DNA is intensively studied both mechanistically and analytically for lesions formation. Several aspects related to the reaction paths of purine moieties with the formation of 5',8-cyclopurines (cPu), 8-oxopurines (8-oxo-Pu), and their relationship are not well understood. In this study, we investigated the reaction of HO• radical with a 21-mer double-stranded oligodeoxynucleotide (ds-ODNs) in γ-irradiated aqueous solutions under various oxygen concentrations and accurately quantified the six purine lesions (i.e., four cPu and two 8-oxo-Pu) by LC-MS/MS analysis using isotopomeric internal standards. In the absence of oxygen, 8-oxo-Pu lesions are only ∼4 times more than cPu lesions. By increasing oxygen concentration, the 8-oxo-Pu and the cPu gradually increase and decrease, respectively, reaching a gap of ∼130 times at 2.01 × 10-4 M of O2. Kinetic treatment of the data allows to estimate the C5' radical competition between cyclization and oxygen trapping in ds-ODNs, and lastly the rate constants of the four cyclization steps. Tailored computational studies by means of dispersion-corrected DFT calculations were performed on the CGC and TAT in their double-strand models for each cPu diastereoisomer along with the complete reaction pathways of the cyclization steps. Our findings reveal unheralded reaction mechanisms that resolve the long-standing issues with C5' radical cyclization in purine moieties of DNA sequences.

Fatty Acid Remodeling of Membrane Glycerophospholipids Induced by Bleomycin and Iron Oxide Nanoparticles in Human Embryonic Kidney Cells.

Bleomycin has a long-studied mechanism of action through the formation of a complex with metals, such as iron. The bleomycin-iron complex was recently shown to induce membrane damage by free radical reactivity. Because the use of Fe nanoparticles is spreading for drug delivery strategies, molecular mechanisms of cell damage must include different compartments in order to observe the progression of the cell reactivity. In this study, human embryonic kidney (HEK-293) cells were exposed for 24 h to bleomycin and polymeric iron oxide nanoparticles (Fe-NPs), alone or in combination. The fatty acid-based membrane lipidomic analysis evidenced the fatty acid remodeling in response to the treatments. Bleomycin alone caused the increase of saturated fatty acid (SFA) moieties in cell membrane glycerophospholipids with concomitant diminution of monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acid levels. Under Fe-NPs treatment, omega-6 PUFA decreased and trans fatty acid isomers increased. Under coadministration bleomycin and Fe-NPs, all membrane remodeling changes disappeared compared to those of the controls, with only an increase of omega-6 PUFA that elevates peroxidation index remaining. Our results highlight the important role of fatty-acid-based membrane lipidome monitoring to follow up the fatty acid reorganization induced by the drug, to be considered as a side effect of the pharmacological activity, suggesting the need of an integrated approach for the investigation of drug and carrier molecular mechanisms.

Transcriptomics and Metabolomics in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease involving progressive and selective loss of motor neurons, muscle weakness, paralysis and death. The pathogenesis of ALS is not clearly understood, while reliable prognostic markers have not been identified to detect symptoms at earlier time points. The rapid development of microarray technology offers great potential for simultaneous analysis of the transcriptional expression of thousands of genes, aiming to determine novel candidate targets for efficient treatment. Additionally, metabolomics, as a high-throughput approach, is gaining significant attention in ALS research providing an opportunity to develop predictive biomarkers that may be utilized as indicators of clinical symptoms of ALS. In this review, recent evidences from gene expression profiling studies in ALS are illustrated in order to examine molecular signatures related to the disease's pathogenesis and potential discovery of therapeutic targets. Moreover, potent challenges are presented regarding the utilization of the metabolomics approach as a diagnostic tool in context with distinctive biomarkers' identification.