Biology subjects

Bacalini, M. G.

Publications and source records attributed to Bacalini, M. G..

11 recordsLinked to original sources

bioRxiv2026

Plasmatic immune extracellular vesicle profiles identify prodromal and early stages of Parkinson's disease

Extracellular vesicles (EVs) hold promise as minimally invasive biomarkers for neurodegenerative proteinopathies, but disease- and stage-specific profiles remain unclear. For this study, we enrolled 378 participants across five centers and the MJFF-BioFIND cohort: 100 healthy controls [HC], 64 isolated REM sleep behavior disorder [iRBD], 41 DeNovo Parkinsons Disease [PD], 89 Late PD, 32 other Synucleinopathies, and 52 Tauopathies. All participants underwent clinical evaluation and blood collection. The 77 subjects from the BioFIND cohort also provided CSF samples. EV concentration and size were assessed by nanoparticle tracking analysis; flow cytometry quantified tetraspanins (CD9/CD63/CD81) and 37 surface markers. Multivariable logistic regression, receiver operating characteristic analyses (ROC), and repeated random forest (rRF) classifiers evaluated diagnostic utility. Late PD showed the highest EV concentrations compared to HC and other disease groups. Participants exhibited distinct EV surface immunophenotypes, with the iRBD group displaying the most extensive immune activation signature vs HC, followed by PD patients. Multivariate logistic regression analysis identified diagnostic marker panels: CD3/CD9/CD25/CD56 for iRBD, SSEA4 for Late PD, CD146/CD209 for Synucleinopathies, and CD8/CD45/CD62P for Tauopathies. ROC confirmed good discriminatory performance, with CD56 emerging as the strongest single predictor for iRBD vs HC, SSEA4 showing high sensitivity for Late PD, and marker combinations providing optimal balance for Synucleinopathy/Tauopathy classification vs HC. In the CSF BioFIND subset, Late PD EVs exhibited increased myeloid (CD1c), adhesion (CD29), activation (CD69), and epithelial (CD326) markers compared to HC. Among these, CD326 was independently associated with Late PD diagnosis. Machine learning classifiers using all 37 surface antigens achieved excellent training performance (91.7-94.3% accuracy for iRBD/Synucleinopathies vs HC) and maintained robust validation accuracy, particularly for iRBD (77.8%) and DeNovo PD (76.6%) vs HC. EV immuno-phenotyping reveals distinct signatures across the neurodegenerative proteinopathies spectrum, with the highest diagnostic utility for prodromal iRBD detection. Longitudinal validation and cell-of-origin refinement represent key next steps toward clinical translation.

neuroscience↗

bioRxiv2026

Alterations of gut microbiota in Down syndrome and their association with Alzheimer's disease

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSAdults with Down syndrome (DS) have a higher risk of developing Alzheimers disease (AD). As gut microbiota (GM) alterations have been reported in AD, we investigated their association with cognitive decline and plasma AD biomarkers in DS. METHODSFecal and plasma samples were collected from 58 adults with DS (21-75 years) and 30 euploid controls (CTRL; 25-83 years). GM was profiled using 16S rRNA sequencing. Major Neurocognitive Disorder (NcD) was diagnosed according to DSM-5 criteria. Plasma levels of p-Tau181, NfL, and GFAP were measured using the Simoa platform. RESULTSCompared with CTRL, DS showed significant changes in UBA1819 and Intestinibacter genera, previously reported to be associated with mild cognitive impairment. Furthermore, DS with NcD were characterized by a reduced abundance of Roseburia genus, which was also negatively associated with plasma levels of AD biomarkers. CONCLUSIONAdults with DS display AD-associated changes in GM partially resembling those previously reported in euploid AD patients

microbiology↗

bioRxiv2023

Increased hippocampal epigenetic age in the Ts65Dn Mouse Model of Down Syndrome

Down syndrome (DS) is a segmental progeroid genetic disorder associated to multi-systemic precocious ageing phenotypes, which are particularly evident at the immune and nervous systems. Accordingly, people with DS show an increased biological age as measured by epigenetic clocks. Ts65Dn trisomic mouse, which harbors extra-numerary copies of Hsa21-syntenic regions, was shown to recapitulate several progeroid features of DS, but no biomarkers of age have been applied to it so far. Here we used a mouse specific epigenetic clock to measure epigenetic age of hippocampi from Ts65Dn and euploid mice at 20 weeks. Ts65Dn mice showed an increased hippocampal epigenetic age respect to controls, and the observed changes in DNA methylation partially recapitulated those observed in hippocampi from people with DS. Collectively, our results support the use of the Ts65Dn model to decipher the molecular mechanisms underlying the progeroid DS phenotypes.

molecular biology↗

bioRxiv2023

Epigenetic features of far northern Yakutian population

Yakuts are one of the indigenous populations of the subarctic and arctic territories of Siberia characterized by a continental subarctic climate with severe winters, with the regular January average temperature in the regional capital city of Yakutsk dipping below -40{degrees}C. The epigenetic mechanisms of adaptation to such ecologies and environments and, in particular, epigenetic age acceleration in the local population have not been studied before. This work reports the first epigenetic study of the Yakutian population using whole blood DNA methylation data, supplemented with the comparison to the residents of Central Russia. Gene set enrichment analysis revealed, among others, geographic region-specific differentially methylated regions associated with adaptation to climatic conditions (water consumption, digestive system regulation), aging processes (actin filament activity, cell fate), and both of them (channel activity, regulation of steroid and corticosteroid hormone secretion). Further, it is demonstrated that the epigenetic age acceleration of the Yakutian representatives is significantly higher than that of Central Russia counterparts. For both geographic regions, we showed that epigenetically males age faster than females, whereas no significant sex differences were found between the regions.

genetics↗

bioRxiv2022

Deep Learning vs Gradient Boosting in age prediction on immunology profile

BackgroundThe aging process affects all systems of the human body, and the observed increase in inflammatory components affecting the immune system in old age can lead to the development of age-associated diseases and systemic inflammation. ResultsWe propose a small clock model SImAge based on a limited number of immunological biomarkers. To regress the chronological age from cytokine data, we first use a baseline Elastic Net model, gradient-boosted decision trees models, and several deep neural network architectures. For the full dataset of 46 immunological parameters, DANet, SAINT, FT-Transformer and TabNet models showed the best results for the test dataset. Dimensionality reduction of these models with SHAP values revealed the 10 most age-associated immunological parameters, taken to construct the SImAge small immunological clock. The best result of the SImAge model shown by the FT-Transformer deep neural network model has mean absolute error of 6.94 years and Pearson{rho} = 0.939 on the independent test dataset. Explainable artificial intelligence methods allow for explaining the model solution for each individual participant. ConclusionsWe developed an approach to construct a model of immunological age based on just 10 immunological parameters, coined SImAge, for which the FT-Transformer deep neural network model had proved to be the best choice. The model shows competitive results compared to the published studies on immunological profiles, and takes a smaller number of features as an input. Neural network architectures outperformed gradient-boosted decision trees, and can be recommended in the further analysis of immunological profiles.

bioinformatics↗

bioRxiv2022

Disease classification for whole blood DNA methylation: meta-analysis, missing values imputation, and XAI

BackgroundDNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific pipeline for joint data processing. ResultsWe propose a comprehensive approach of combined DNA methylation datasets to classify controls and patients. The solution includes data harmonization, construction of machine learning classification models, dimensionality reduction of models, imputation of missing values, and explanation of model predictions by explainable artificial intelligence (XAI) algorithms. We show that harmonization can improve classification accuracy by up to 20% when preprocessing methods of the training and test datasets are different. The best accuracy results were obtained with tree ensembles, reaching above 95% for Parkinsons disease. Dimensionality reduction can substantially decrease the number of features, without detriment to the classification accuracy. The best imputation methods achieve almost the same classification accuracy for data with missing values as for the original data. Explainable artificial intelligence approaches have allowed us to explain model predictions from both populational and individual perspectives. ConclusionsWe propose a methodologically valid and comprehensive approach to the classification of healthy individuals and patients with various diseases based on whole blood DNA methylation data using Parkinsons disease and schizophrenia as examples. The proposed algorithm works better for the former pathology, characterized by a complex set of symptoms. It allows to solve data harmonization problems for meta-analysis of many different datasets, impute missing values, and build classification models of small dimensionality.

bioinformatics↗

bioRxiv2021

Accelerated epigenetic aging and inflammatory/immunological profile (ipAGE) in patients with chronic kidney disease

Chronic kidney disease (CKD) is defined by reduced estimated glomerular filtration rate (eGFR). This failure can be related to a phenotype of accelerated aging. In this work we considered 76 subjects with end-stage renal disease (ESRD) and 83 healthy controls. We evaluated two measures that can be informative of the rate of aging, i.e. whole blood DNA methylation using the Illumina Infinium EPIC array and plasma levels of a selection of inflammatory/immunological proteins using Multiplex Immunoassays. We demonstrated accelerated aging in terms of the most common epigenetic age estimators in CKD patients. We developed a new predictor of age based on inflammatory/immunological profile (ipAGE) and confirmed age acceleration in CKD patients. Finally, we evaluated the relationship between epigenetic age predictors and ipAGE and further identified the inflammatory/immunological biomarkers differentially expressed between cases and controls. In summary, our data show an accelerated aging phenotype in CKD patients sustained by inflammatory processes.

bioinformatics↗

bioRxiv2021

Epigenomic signature of the progeroid Cockayne syndrome exposes distinct and common features with physiological ageing

Cockayne syndrome (CS) and UV-sensitivity syndrome (UVSS) are rare genetic disorders caused by mutation of the DNA repair and chromatin remodelling proteins CSA or CSB, but only CS patients display a progeroid and neurodegenerative phenotype. As epigenetic modifications constitute a well-established hallmark of ageing, we characterized genome-wide DNA methylation (DNAm) of fibroblasts from CS versus UVSS patients and healthy donors. The analysis of differentially methylated positions and regions revealed a CS-specific epigenetic signature, enriched in developmental transcription factors, transmembrane transporters, and cell adhesion factors. The CS-specific signature compared to DNAm changes in other progeroid diseases and regular ageing, identifyied commonalities and differences in epigenetic remodelling. CS shares DNAm changes with normal ageing more than other progeroid diseases do, and according to the methylation clock CS samples show up to 13-fold accelerated ageing. Thus, CS is characterized by a specific epigenomic signature that partially overlaps with and exacerbates DNAm changes occurring in physiological aging. Our results unveil new genes and pathways that are potentially relevant for the progeroid/degenerative CS phenotype.

molecular biology↗

bioRxiv2020

Disentangling age-dependent DNA methylation: deterministic, stochastic, and nonlinear

DNA methylation variability arises due to concurrent genetic and environmental influences. Each of them is a mixture of regular and noisy sources, whose relative contribution has not been satisfactorily understood yet. We conduct a systematic assessment of the age-dependent methylation by the signal-to-noise ratio and identify a wealth of "deterministic" CpG probes (about 90%), whose methylation variability likely originates due to genetic and general environmental factors. The remaining 10% of "stochastic" CpG probes are arguably governed by the biological noise or incidental environmental factors. Investigating the mathematical functional relationship between methylation levels and variability, we find that in about 90% of the age-associated differentially methylated positions, the variability changes as the square of the methylation level, whereas in the most of the remaining cases the dependence is linear. Furthermore, we demonstrate that the methylation level itself in more than 15% cases varies nonlinearly with age (according to the power law), in contrast to the previously assumed linear changes. Our findings present ample evidence of the ubiquity of strong DNA methylation regulation, resulting in the individual age-dependent and nonlinear methylation trajectories, whose divergence explains the cross-sectional variability. It may also serve a basis for constructing novel nonlinear epigenetic clocks.

systems biology↗

bioRxiv2020

Age-dependent DNA methylation Parenclitic Networks in family-based cohort patients with Down Syndrome

Despite the fact that the cause of Down Syndrome (DS) is well established, the underlying molecular mechanisms that contribute to the syndrome and the phenotype of accelerated aging remain largely unknown. DNA methylation profiles are largely altered in DS, but it remains unclear how different methylation regions and probes are structured into a network of interactions. We develop and generalize the Parenclitic Networks approach that enables finding correlations between distant CpG probes (which are not pronounced as stand-alone biomarkers) and quantifies hidden network changes in DNA methylation. DS and a familybased cohort (including healthy siblings and mothers of persons with DS) are used as a case study. Following this approach, we constructed parenclitic networks and obtained different signatures that indicate (i) differences between individuals with DS and healthy individuals; (ii) differences between young and old healthy individuals; (iii) differences between DS individuals and their age-matched siblings, and (iv) difference between DS and the adult population (their mothers). The Gene Ontology analysis showed that the CpG network approach is more powerful than the single CpG approach in identifying biological processes related to DS phenotype. This includes the processes occurring in the central nervous system, skeletal muscles, disorders in carbohydrate metabolism, cardiopathology, and oncogenes. Our open-source software implementation is accessible to all researchers. The software includes a complete workflow, which can be used to construct Parenclitic Networks with any machine learning algorithm as a kernel to build edges. We anticipate a broad applicability of the approach to other diseases.

bioinformatics↗

bioRxiv2020

Complex sex-specific age-related changes in DNA methylation including variability, epimutations and entropy

In humans, females live longer than males but experience a worse longevity, as genome-wide autosomal DNA methylation differences between males and females have been reported. So far, few studies have investigated if DNA methylation is differently affected by aging in males and females. We performed a meta-analysis of 4 large whole blood datasets, comparing 4 aspects of epigenetic age-dependent remodeling between the two sexes: differential methylation, variability, epimutations and entropy. We reported that a large fraction (43%) of sex-associated probes undergoes age-associated DNA methylation changes, and that a limited number of probes shows age-by-sex interaction. We experimentally validated 2 regions mapping in FIGN and PRR4 genes, and showed sex-specific deviations of their methylation patterns in models of decelerated (centenarians) and accelerated (Down syndrome) aging. While we did not find sex differences in the age-associated increase in epimutations and in entropy, we showed that the number of probes showing age-related increase in methylation variability is 15 times higher in males compared to females. Our results can offer new epigenetic tools to study the interaction between aging and sex and can pave the way to the identification of molecular triggers of sex differences in longevity and age-related diseases prevalence.

genomics↗