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.

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.

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.

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.

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.

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 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.

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.

In Silico Structural Analysis Predicting the Pathogenicity of PLP1 Mutations in Multiple Sclerosis

The X chromosome gene PLP1 encodes myelin proteolipid protein (PLP), the most prevalent protein in the myelin sheath surrounding the central nervous system. X-linked dysmyelinating disorders such as Pelizaeus-Merzbacher disease (PMD) or spastic paraplegia type 2 (SPG2) are typically caused by point mutations in PLP1. Nevertheless, numerous case reports have shown individuals with PLP1 missense point mutations which also presented clinical symptoms and indications that were consistent with the diagnostic criteria of multiple sclerosis (MS), a disabling disease of the brain and spinal cord with no current cure. Computational structural biology methods were used to assess the impact of these mutations on the stability and flexibility of PLP structure in order to determine the role of PLP1 mutations in MS pathogenicity. The analysis showed that most of the variants can alter the functionality of the protein structure such as R137W variants which results in loss of helix and H140Y which alters the ordered protein interface. In silico genomic methods were also performed to predict the significance of these mutations associated with impairments in protein functionality and could suggest a better definition for therapeutic strategies and clinical application in MS patients.

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.

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.

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.

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 β (pol β), 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 β 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 β 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.

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.

Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression

The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.

On the relevance of hydroxyl radical to purine DNA damage

Hydroxyl radical (HO^•) is the most reactive toward DNA among the reactive oxygen species (ROS) generated in aerobic organisms by cellular metabolisms. HO^• is generated also by exogenous sources such as ionizing radiations. In this review we focus on the purine DNA damage by HO^• radicals. In particular, emphasis is given on mechanistic aspects for the various lesion formation and their interconnections. Although the majority of the purine DNA lesions like 8-oxo-purine (8-oxo-Pu) are generated by various ROS (including HO^•), the formation of 5',8-cyclopurine (cPu) lesions in vitro and in vivo relies exclusively on the HO^• attack. Methodologies generally utilized for the purine lesions quantification in biological samples are reported and critically discussed. Recent results on cPu and 8-oxo-Pu lesions quantification in various types of biological specimens associated with the cellular repair efficiency as well as with distinct pathologies are presented, providing some insights on their biological significance.

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 (K_app ) 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.

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.

Oxygen-Dependent Accumulation of Purine DNA Lesions in Cockayne Syndrome Cells

Cockayne Syndrome (CS) is an autosomal recessive neurodegenerative premature aging disorder associated with defects in nucleotide excision repair (NER). Cells from CS patients, with mutations in CSA or CSB genes, present elevated levels of reactive oxygen species (ROS) and are defective in the repair of a variety of oxidatively generated DNA lesions. In this study, six purine lesions were ascertained in wild type (wt) CSA, defective CSA, wtCSB and defective CSB-transformed fibroblasts under different oxygen tensions (hyperoxic 21%, physioxic 5% and hypoxic 1%). 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. cPu levels were found comparable to 8-oxo-Pu in all cases (3–6 lesions/10⁶ nucleotides), slightly increasing on going from hyperoxia to physioxia to hypoxia. Moreover, higher levels of four cPu were observed under hypoxia in both CSA and CSB-defective cells as compared to normal counterparts, along with a significant enhancement of 8-oxo-Pu. These findings revealed that exposure to different oxygen tensions induced oxidative DNA damage in CS cells, repairable by NER or base excision repair (BER) pathways. In NER-defective CS patients, these results support the hypothesis that the clinical neurological features might be connected to the accumulation of cPu. Moreover, the elimination of dysfunctional mitochondria in CS cells is associated with a reduction in the oxidative DNA damage.

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.

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.

Identification of biomarkers associated with Parkinson's disease by gene expression profiling studies and bioinformatics analysis

Parkinson's disease (PD) is associated with a selective loss of the neurons in the midbrain area called the substantia nigra pars compacta and the loss of projecting nerve fibers in the striatum. Predominant pathological hallmarks of PD are the degeneration of discrete neuronal populations and progressive accumulation of α-synuclein-containing intracytoplasmic inclusions called Lewy bodies and dystrophic Lewy neuritis. There is currently no therapy to terminate or delay the neurodegenerative process as the exact mechanisms underlying the pathogenesis of PD require further investigation. The identification and validation of novel biomarkers for the diagnosis of PD is a great challenge using contemporary approaches and optimizing sampling handling as well as interpretation using bioinformatics analysis. In this review, recent evidences associated with multi-omic data-sets and molecular mechanisms underlying PD are examined. A combined mapping of several transcriptional evidences could establish a patient-specific signature for early diagnose of PD though eligible systems biology tools, which can also help develop effective drug-based therapeutic approaches.

New Insights into the Reaction Paths of Hydroxyl Radicals with Purine Moieties in DNA and Double-Stranded Oligodeoxynucleotides

The reaction of hydroxyl radical (HO^•) with DNA produces many primary reactive species and many lesions as final products. In this study, we have examined the optical spectra of intermediate species derived from the reaction of HO^• with a variety of single- and double-stranded oligodeoxynucleotides and ct-DNA in the range of 1 μs to 1 ms by pulse radiolysis using an Intensified Charged Coupled Device (ICCD) camera. Moreover, we applied our published analytical protocol based on an LC-MS/MS system with isotopomeric internal standards to enable accurate and precise measurements of purine lesion formation. In particular, the simultaneous measurement of the four purine 5′,8-cyclo-2′-deoxynucleosides (cPu) and two 8-oxo-7,8-dihydro-2′-deoxypurine (8-oxo-Pu) was obtained upon reaction of genetic material with HO^• radicals generated either by γ-radiolysis or Fenton-type reactions. Our results contributed to the debate in the literature regarding absolute level of lesions, method of HO^• radical generation, 5′R/5′S diastereomeric ratio in cPu, and relative abundance between cPu and 8-oxo-Pu.

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.

Transcriptomics in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is an adult-onset, incurable neurodegenerative disease characterized by the selective death of upper and lowers motor neurons in the spinal cord, brainstem and motor cortex, which ultimately leads to paralysis and death within 2-3 years of onset. ALS is poorly understood, although multiple studies have been proposed to explain the pathophysiological mechanisms of the disorder. The development of microarray technology, for simultaneous analysis of the transcriptional expression of thousands of genes, has provided new possibilities to get better insights into the pathogenesis of ALS, and most important, potential new candidate targets for novel treatments. The present review illustrates current evidences from transcriptomic studies in animal models and human samples, related to ALS pathogenesis in parallel to molecular targets associated with the disease progression. Additionally, alteration of RNA metabolism was identified as a major dysregulated pathway in ALS and via this study, new insights into the contribution of altered transcriptional profiles of microRNAs and ALS-associated ribosomal binding proteins have been investigated, in an effort to understand the functional consequences of widespread RNA dysregulation in the disease's pathological mechanism.

Radical-induced purine lesion formation is dependent on DNA helical topology

Herein we report the quantification of purine lesions arising from gamma-radiation sourced hydroxyl radicals (HO^•) on tertiary dsDNA helical forms of supercoiled (SC), open circular (OC), and linear (L) conformation, along with single-stranded folded and non-folded sequences of guanine-rich DNA in selected G-quadruplex structures. We identify that DNA helical topology and folding plays major, and unexpected, roles in the formation of 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxo-dG) and 8-oxo-7,8-dihydro-2'-deoxyadenosine (8-oxo-dA), along with tandem-type purine lesions 5',8-cyclo-2'-deoxyguanosine (5',8-cdG) and 5',8-cyclo-2'-deoxyadenosine (5',8-cdA). SC, OC, and L dsDNA conformers together with folded and non-folded G-quadruplexes d[TGGGGT]₄ (TG4T), d[AGGG(TTAGGG)₃] (Tel22), and the mutated tel24 d[TTGGG(TTAGGG)₃A] (mutTel24) were exposed to HO^• radicals and purine lesions were then quantified via stable isotope dilution LC-MS/MS analysis. Purine oxidation in dsDNA follows L > OC ≫ SC indicating greater damage towards the extended B-DNA topology. Conversely, G-quadruplex sequences were significantly more resistant toward purine oxidation in their unfolded states as compared with G-tetrad folded topologies; this effect is confirmed upon comparative analysis of Tel22 (∼50% solution folded) and mutTel24 (∼90% solution folded). In an effort to identify the accessibly of hydroxyl radicals to quadruplex purine nucleobases, G-quadruplex solvent cavities were then modeled at 1.33 Å with evidence suggesting that folded G-tetrads may act as potential oxidant traps to protect against chromosomal DNA damage.

Syntheses, antiproliferative activity and theoretical characterization of acitretin-type retinoids with changes in the lipophilic part

Acitretin analogs, incorporating changes in the lipophilic part, were efficiently synthesized from commercially available aromatic aldehydes or methyl ketones using the Wittig or Horner-Wadsworth-Emmons reaction. Their antiproliferative activity was evaluated against human breast MCF-7 epithelial cells. Analogs 3, 4, 8 and 11 exhibited strong, dose-dependent, antiproliferative activity on the tested cell line. Analog 3, incorporating three methoxy groups in the aromatic ring, exhibited the strongest inhibitory effect at 10 μM. High-level all electron conventional ab initio and density functional theory quantum chemical calculations were performed to obtain the molecular structure, electron charge distribution and polarization properties of all compounds of interest in this work. The most active analogs were planar and were characterized by larger dipole moments than the other synthesized molecules. Another factor of importance to the analysis of the activity of these molecules is the dipole polarizability.

Dissecting the ribosomal inhibition mechanism of a new ketolide carrying an alkyl-aryl group at C-13 of its lactone ring

Ketolides are effective not only against macrolide-sensitive bacteria but also against some macrolide-resistant strains. Here we present data regarding a new ketolide with an alkyl-aryl side chain at C-13 of its lactone ring. It behaves as a strong inhibitor of protein synthesis in a model coupled transcription/translation system, although it does not affect the accuracy of translation. In addition, detailed kinetic analysis shows that it slowly forms a very tight, slowly reversible complex with prokaryotic ribosomes, a property that could be correlated with its superior activity compared with erythromycin against Escherichia coli both in vivo and in vitro.