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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 L1000CDS 2 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., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

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., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

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., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

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., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

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., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

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., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

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., (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

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., (© 2021. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

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., (© 2021. The Author(s), under exclusive license to Springer Nature Switzerland AG.)

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.