Biology subjects

Maletic-Savatic, M.

Publications and source records attributed to Maletic-Savatic, M..

8 recordsLinked to original sources

bioRxiv2025

MECP2 Duplication Uncouples Mitochondrial and Purine Metabolism During neuronal maturation

Mitochondria and nucleotide metabolism are critical for cellular and developmental homeostasis, yet their potential interdependence and role in neurodevelopmental disease remain unclear. In MECP2 Duplication Syndrome (MDS), we identify a conserved correlation between mitochondrial function and purine metabolism that is disrupted across human, organoid, and mouse models. Multiomics integration reveals Complex III as the focal point of mitochondrial collapse, leading to redox stress, DNA damage, and hyperactivation of the de novo purine biosynthesis via purinosome assembly. The breakdown of mitochondria-purinosome coupling compromises genome stability, impairs radial glia proliferation, and delays neuronal maturation. By linking a defined genetic dosage imbalance to metabolic network failure, our study positions the mitochondria-purinosome coordination as a fundamental control axis for neurodevelopment and a therapeutic entry point across metabolic and neurodevelopmental disorders. Metabolic control is fundamental to cellular function, influencing energy production, signaling, epigenetic regulation, and tissue homeostasis1. Nowhere is this more critical than in the brain, where tightly regulated metabolic networks sustain high energetic demands and support neuronal development, synaptic plasticity, and circuit formation2. Disruptions in these networks are increasingly implicated across a spectrum of neurodevelopmental disorders3-5, yet their precise metabolic signatures and mechanistic contributions remain poorly understood.

neuroscience↗

bioRxiv2024

Leveraging clinical sleep data across multiple pediatric cohorts for insights into neurodevelopment: the Retrospective Analysis of Sleep in Pediatric (RASP) cohorts study

Sleep disturbances are prominent across neurodevelopmental disorders (NDDs) and may reflect specific abnormalities in brain development and function. Overnight polysomnography (PSG) allows for detailed investigation of sleep architecture, offering a unique window into neurocircuit function. A better understanding of sleep in NDDs compared to typically developing children could therefore define mechanisms underlying abnormal development in NDDs and provide avenues for the development of therapeutic interventions to improve sleep quality and developmental outcomes. Here, we introduce and characterize a collection of 1527 pediatric overnight PSGs across five different sites. We first developed an automated stager trained on independent pediatric sleep data, which yielded better performance compared to a stager trained on adults. Using consistent staging across cohorts, we derived a panel of EEG micro-architectural features. This unbiased approach replicated broad trajectories previously described in typically developing sleep architecture. Further, we found sleep architecture disruptions in children with Downs Syndrome (DS) that were consistent across independent cohorts. Finally, we built and evaluated a model to predict age from sleep EEG metrics, which recapitulated our previous findings of younger predicted brain age in children with DS. Altogether, by creating a resource pooled from existing clinical data we expanded the available datasets and computational resources to study sleep in pediatric populations, specifically towards a better understanding of sleep in NDDs. This Retrospective Analysis of Sleep in Pediatric (RASP) cohorts dataset, including staging annotation derived from our automated stager, will be deposited at https://sleepdata.org. Statement of significanceWe introduce the RASP cohorts, a collection of 1527 clinical pediatric overnight polysomnographies that includes typically developing and neurodevelopmental disorder cases. As a first step towards addressing the analytic bottleneck inherent in manual sleep staging, we developed and validated a pediatric-specific sleep stager. Leveraging the retrospective RASP cohorts dataset, we redemonstrated known developmental trajectories in sleep architecture. To summarize changes in brain function reflected in sleep, we developed a model to predict brain age from sleep measures. We recapitulate younger predicted age in RASP Downs Syndrome cases. This work not only enhances our understanding of sleep disturbances in NDDs, but also provides a valuable resource for future research and underscores the utility of existing clinical polysomnography studies.

neuroscience↗

bioRxiv2023

NMRQNet: a deep learning approach for automatic identification and quantification of metabolites using Nuclear Magnetic Resonance (NMR) in human plasma samples

Nuclear Magnetic Resonance is a powerful platform that reveals the metabolomics profiles within biofluids or tissues and contributes to personalized treatments in medical practice. However, data volume and complexity hinder the exploration of NMR spectra. Besides, the lack of fast and accurate computational tools that can handle the automatic identification and quantification of essential metabolites from NMR spectra also slows the wide application of these techniques in clinical. We present NMRQNet, a deep-learning-based pipeline for automatic identification and quantification of dominant metabolite candidates within human plasma samples. The estimated relative concentrations could be further applied in statistical analysis to extract the potential biomarkers. We evaluate our method on multiple plasma samples, including species from mice to humans, curated using three anticoagulants, covering healthy and patient conditions in neurological disorder disease, greatly expanding the metabolomics analytical space in plasma. NMRQNet accurately reconstructed the original spectra and obtained significantly better quantification results than the earlier computational methods. Besides, NMRQNet also proposed relevant metabolites biomarkers that could potentially explain the risk factors associated with the condition. NMRQNet, with improved prediction performance, highlights the limitations in the existing approaches and has shown strong application potential for future metabolomics disease studies using plasma samples.

bioinformatics↗

bioRxiv2023

SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra

Nuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSYs efficiency and accuracy, we applied it to synthesized and real NMR data obtained from Drosophila melanogaster brains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.

bioinformatics↗

bioRxiv2023

PolyAMiner-Bulk: A Machine Learning Based Bioinformatics Algorithm to Infer and Decode Alternative Polyadenylation Dynamics from bulk RNA-seq data

More than half of human genes exercise alternative polyadenylation (APA) and generate mRNA transcripts with varying 3 untranslated regions (UTR). However, current computational approaches for identifying cleavage and polyadenylation sites (C/PASs) and quantifying 3UTR length changes from bulk RNA-seq data fail to unravel tissue- and disease-specific APA dynamics. Here, we developed a next-generation bioinformatics algorithm and application, PolyAMiner-Bulk, that utilizes an attention-based machine learning architecture and an improved vector projection-based engine to infer differential APA dynamics accurately. When applied to earlier studies, PolyAMiner-Bulk accurately identified more than twice the number of APA changes in an RBM17 knockdown bulk RNA-seq dataset compared to current generation tools. Moreover, on a separate dataset, PolyAMiner-Bulk revealed novel APA dynamics and pathways in scleroderma pathology and identified differential APA in a gene that was identified as being involved in scleroderma pathogenesis in an independent study. Lastly, we used PolyAMiner-Bulk to analyze the RNA-seq data of post-mortem prefrontal cortexes from the ROSMAP data consortium and unraveled novel APA dynamics in Alzheimers Disease. Our method, PolyAMiner-Bulk, creates a paradigm for future alternative polyadenylation analysis from bulk RNA-seq data.

bioinformatics↗

bioRxiv2020

Oleic acid triggers hippocampal neurogenesis by binding to TLX/NR2E1

Adult hippocampal neurogenesis underpins learning, memory, and mood, but diminishes with age and illness. The orphan nuclear receptor TLX/NR2E1 is known to regulate neural stem and progenitor cell self-renewal and proliferation, but the precise mechanism by which it accomplishes this is unknown. We found that neural stem and progenitor cells require monounsaturated fatty acids to survive and proliferate. Specifically, oleic acid (18:1{omega}9) binds to TLX to convert it from a transcriptional repressor to a transcriptional activator of cell cycle and neurogenesis genes. We propose a model in which sufficient quantities of this endogenous ligand must bind to TLX to trigger the switch to proliferation. These findings pave the way for future therapeutic manipulations to counteract pathogenic impairments of neurogenesis.

neuroscience↗

bioRxiv2019

Microglia actively remodels adult hippocampal neurogenesis through the phagocytosis secretome

During adult hippocampal neurogenesis, the majority of newborn cells undergo apoptosis and are rapidly phagocytosed by resident microglia to prevent the spillover of their intracellular contents. Here, we propose that phagocytosis is not merely a passive process of corpse removal but has an active role in maintaining adult hippocampal neurogenesis. First, we found that neurogenesis was disrupted in mice chronically deficient for two microglial phagocytosis pathways (P2Y12 and MerTK/Axl), but was transiently increased in mice in which MerTK expression was conditionally downregulated. Next, we performed a transcriptomic analysis of microglial phagocytosis in vitro and identified genes involved in metabolism, chromatin remodeling, and neurogenesis-related functions. Finally, we discovered that the secretome of phagocytic microglia limits the production of new neurons both in vivo and in vitro. Our data suggest that reprogrammed phagocytic microglia act as a sensor of local cell death, modulating the balance between cell proliferation and cell survival in the neurogenic niche, thereby supporting the long-term maintenance of adult hippocampal neurogenesis.\n\nGRAPHICAL ABSTRACT

cell biology↗

bioRxiv2018

Quantitative Real-Time Imaging of Glutathione with Sub-Cellular Resolution

Quantitative imaging of glutathione with high spatial and temporal resolution is essential for studying the roles of glutathione in redox biology. We developed a fluorescent glutathione probe--HaloRT--that targets organelles of interest through expressing organelle-specific HaloTag proteins. Using HaloRT, we quantitatively measure the glutathione concentrations in the nucleus and the cytosol and find no appreciable concentration gradient between these two organelles, challenging the view of nuclear compartmentalization of glutathione.

cell biology↗