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 50,8-cyclo-20-deoxynucleosides (cPu) and two 8-oxo-7,8-dihydro-20-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, 50R/50S diastereomeric ratio in cPu, and relative abundance between cPu and 8-oxo-Pu.Fil: Chatgilialoglu, Chryssostomos. Istituto Per la Sintesi Organica E la Fotoreattivita, Bologna; ItaliaFil: Krokidis, Marios G.. Consiglio Nazionale delle Ricerche; ItaliaFil: Masi, Annalisa. Consiglio Nazionale delle Ricerche; ItaliaFil: Barata Vallejo, Sebastian. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay; ArgentinaFil: Ferreri, Carla. Consiglio Nazionale delle Ricerche; ItaliaFil: Terzidis, Michael A.. Consiglio Nazionale delle Ricerche; ItaliaFil: Szreder, Tomasz. Institute of Nuclear Chemistry and Technology; PoloniaFil: Bobrowski, Krzysztof. Institute of Nuclear Chemistry and Technology; Poloni

High levels of oxidatively generated DNA damage 8,5'-cyclo-2'-deoxyadenosine accumulate in the brain tissues of xeroderma pigmentosum group A gene-knockout mice.

Xeroderma pigmentosum (XP) is a genetic disorder associated with defects in nucleotide excision repair, a pathway that eliminates a wide variety of helix-distorting DNA lesions, including ultraviolet-induced pyrimidine dimers. In addition to skin diseases in sun-exposed areas, approximately 25% of XP patients develop progressive neurological disease, which has been hypothesized to be associated with the accumulation of an oxidatively generated type of DNA damage called purine 8,5'-cyclo-2'-deoxynucleoside (cyclopurine). However, that hypothesis has not been verified. In this study, we tested that hypothesis by using the XP group A gene-knockout (Xpa-/-) mouse model. To quantify cyclopurine lesions in this model, we previously established an enzyme-linked immunosorbent assay (ELISA) using a monoclonal antibody (CdA-1) that specifically recognizes 8,5'-cyclo-2'-deoxyadenosine (cyclo-dA). By optimizing conditions, we increased the ELISA sensitivity to a detection limit of ˜one cyclo-dA lesion/106 nucleosides. The improved ELISA revealed that cyclo-dA lesions accumulate with age in the brain tissues of Xpa-/- and of wild-type (wt) mice, but there were significantly more cyclo-dA lesions in Xpa-/- mice than in wt mice at 6, 24 and 29 months of age. These findings are consistent with the long-standing hypothesis that the age-dependent accumulation of endogenous cyclopurine lesions in the brain may be critical for XP neurological abnormalities

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

An Ensemble Feature Selection Approach for Analysis and Modeling of Transcriptome Data in Alzheimer’s Disease

Data-driven analysis and characterization of molecular phenotypes comprises an efficient way to decipher complex disease mechanisms. Using emerging next generation sequencing technologies, important disease-relevant outcomes are extracted, offering the potential for precision diagnosis and therapeutics in progressive disorders. Single-cell RNA sequencing (scRNA-seq) allows the inherent heterogeneity between individual cellular environments to be exploited and provides one of the most promising platforms for quantifying cell-to-cell gene expression variability. However, the high-dimensional nature of scRNA-seq data poses a significant challenge for downstream analysis, particularly in identifying genes that are dominant across cell populations. Feature selection is a crucial step in scRNA-seq data analysis, reducing the dimensionality of data and facilitating the identification of genes most relevant to the biological question. Herein, we present a need for an ensemble feature selection methodology for scRNA-seq data, specifically in the context of Alzheimer’s disease (AD). We combined various feature selection strategies to obtain the most dominant differentially expressed genes (DEGs) in an AD scRNA-seq dataset, providing a promising approach to identify potential transcriptome biomarkers through scRNA-seq data analysis, which can be applied to other diseases. We anticipate that feature selection techniques, such as our ensemble methodology, will dominate analysis options for transcriptome data, especially as datasets increase in volume and complexity, leading to more accurate classification and the generation of differentially significant features

Exploring Promising Biomarkers for Alzheimer’s Disease through the Computational Analysis of Peripheral Blood Single-Cell RNA Sequencing Data

Alzheimer’s disease (AD) represents one of the most important healthcare challenges of the current century, characterized as an expanding, “silent pandemic”. Recent studies suggest that the peripheral immune system may participate in AD development; however, the molecular components of these cells in AD remain poorly understood. Although single-cell RNA sequencing (scRNA-seq) offers a sufficient exploration of various biological processes at the cellular level, the number of existing works is limited, and no comprehensive machine learning (ML) analysis has yet been conducted to identify effective biomarkers in AD. Herein, we introduced a computational workflow using both deep learning and ML processes examining scRNA-seq data obtained from the peripheral blood of both Alzheimer’s disease patients with an amyloid-positive status and healthy controls with an amyloid-negative status, totaling 36,849 cells. The output of our pipeline contained transcripts ranked by their level of significance, which could serve as reliable genetic signatures of AD pathophysiology. The comprehensive functional analysis of the most dominant genes in terms of biological relevance to AD demonstrates that the proposed methodology has great potential for discovering blood-based fingerprints of the disease. Furthermore, the present approach paves the way for the application of ML techniques to scRNA-seq data from complex disorders, providing new challenges to identify key biological processes from a molecular perspective

Simulation of Colloidal Stability and Aggregation Tendency of Magnetic Nanoflowers in Biofluids

A population balance model for the aggregation of iron oxide nanoflowers (IONfs) is presented. The model is based on the fixed pivot technique and is validated successfully for four kinds of aggregation kernels. The extended Derjaguin, Landau, Verwey, and Overbeek (xDLVO) theory is also employed for assessing the collision efficiency of the particles, which is pertinent to the total energy of the interaction. Colloidal stability experiments were conducted on IONfs for two dispersant cases—aqueous phosphate buffered saline solution (PBS) and simulated body fluid (SBF). Dynamic light scattering (DLS) measurements after 24-h of incubation show a significant size increase in plain PBS, whereas the presence of proteins in SBF prevents aggregation by protein corona formation on the IONfs. Subsequent simulations tend to overpredict the aggregation rate, and this can be attributed to the flower-like shape of IONfs, thus allowing patchiness on the surface of the particles that promotes an uneven energy potential and aggregation hindering. In silico parametric study on the effects of the ionic strength shows a prominent dependency of the aggregation rate on the salinity of the dispersant underlying the effect of repulsion forces, which are almost absent in the PBS case, promoting aggregation. In addition, the parametric study on the van der Waals potential energy effect—within common Hamaker-constant values for iron oxides—shows that this is almost absent for high salinity dispersants, whereas low salinity gives a wide range of results, thus underlying the high sensitivity of the model on the potential energy parameters

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 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

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

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