Oleic acid is an endogenous ligand of TLX/NR2E1 that triggers hippocampal neurogenesis.

Neural stem cells, the source of newborn neurons in the adult hippocampus, are intimately involved in learning and memory, mood, and stress response. Despite considerable progress in understanding the biology of neural stem cells and neurogenesis, regulating the neural stem cell population precisely has remained elusive because we have lacked the specific targets to stimulate their proliferation and neurogenesis. The orphan nuclear receptor TLX/NR2E1 governs neural stem and progenitor cell self-renewal and proliferation, but the precise mechanism by which it accomplishes this is not well understood because its endogenous ligand is not known. Here, we identify oleic acid (18:1ω9 monounsaturated fatty acid) as such a ligand. We first show that oleic acid is critical for neural stem cell survival. Next, we demonstrate that it binds to TLX to convert it from a transcriptional repressor to a transcriptional activator of cell-cycle and neurogenesis genes, which in turn increases neural stem cell mitotic activity and drives hippocampal neurogenesis in mice. Interestingly, oleic acid-activated TLX strongly up-regulates cell cycle genes while only modestly up-regulating neurogenic genes. We propose a model in which sufficient quantities of this endogenous ligand must bind to TLX to trigger the switch to proliferation and drive the progeny toward neuronal lineage. Oleic acid thus serves as a metabolic regulator of TLX activity that can be used to selectively target neural stem cells, paving the way for future therapeutic manipulations to counteract pathogenic impairments of neurogenesis.

Metabolic bioactivation of antidepressants: advance and underlying hepatotoxicity.

Many drugs that serve as first-line medications for the treatment of depression are associated with severe side effects, including liver injury. Of the 34 antidepressants discussed in this review, four have been withdrawn from the market due to severe hepatotoxicity, and others carry boxed warnings for idiosyncratic liver toxicity. The clinical and economic implications of antidepressant-induced liver injury are substantial, but the underlying mechanisms remain elusive. Drug-induced liver injury may involve the host immune system, the parent drug, or its metabolites, and reactive drug metabolites are one of the most commonly referenced risk factors. Although the precise mechanism by which toxicity is induced may be difficult to determine, identifying reactive metabolites that cause toxicity can offer valuable insights for decreasing the bioactivation potential of candidates during the drug discovery process. A comprehensive understanding of drug metabolic pathways can mitigate adverse drug-drug interactions that may be caused by elevated formation of reactive metabolites. This review provides a comprehensive overview of the current state of knowledge on antidepressant bioactivation, the metabolizing enzymes responsible for the formation of reactive metabolites, and their potential implication in hepatotoxicity. This information can be a valuable resource for medicinal chemists, toxicologists, and clinicians engaged in the fields of antidepressant development, toxicity, and depression treatment.

SPA-STOCSY: an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra.

MOTIVATION: Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistical TOtal Correlation SpectroscopY (SPA-STOCSY), which overcomes challenges faced when analyzing NMR data and identifies metabolites in a sample with high accuracy. RESULTS: As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset. It first investigates the covariance pattern among datapoints and then calculates the optimal threshold with which to cluster datapoints belonging to the same structural unit, i.e. the metabolite. Generated clusters are then automatically linked to a metabolite library to identify candidates. To assess SPA-STOCSY's efficiency and accuracy, we applied it to synthesized spectra and spectra acquired on Drosophila melanogaster tissue and human embryonic stem cells. In the synthesized spectra, SPA outperformed Statistical Recoupling of Variables (SRV), 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 biological data, SPA-STOCSY performed comparably to the operator-based Chenomx analysis while avoiding operator bias, and it required <7 min of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. It may thus accelerate the use of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making. AVAILABILITY AND IMPLEMENTATION: The codes of SPA-STOCSY are available at https://github.com/LiuzLab/SPA-STOCSY.

MALDI Imaging Mass Spectrometry Visualizes the Distribution of Antidepressant Duloxetine and Its Major Metabolites in Mouse Brain, Liver, Kidney, and Spleen Tissues.

Imaging mass spectrometry (IMS) is a powerful tool for mapping the spatial distribution of unlabeled drugs and metabolites that may find application in assessing drug delivery, explaining drug efficacy, and identifying potential toxicity. This study focuses on determining the spatial distribution of the antidepressant duloxetine, which is widely prescribed despite common adverse effects (liver injury, constant headaches) whose mechanisms are not fully understood. We used high-resolution IMS with matrix-assisted laser desorption/ionization to examine the distribution of duloxetine and its major metabolites in four mouse organs where it may contribute to efficacy or toxicity: brain, liver, kidney, and spleen. In none of these tissues is duloxetine or its metabolites homogeneously distributed, which has implications for both efficacy and toxicity. We found duloxetine to be similarly distributed in spleen red pulp and white pulp but differentially distributed in different anatomic regions of the liver, kidney, and brain, with dose-dependent patterns. Comparison with hematoxylin and eosin staining of tissue sections reveals that the ion images of endogenous lipids help delineate anatomic regions in the brain and kidney, while heme ion images assist in differentiating regions within the spleen. These endogenous metabolites may serve as a valuable resource for examining the spatial distribution of other drugs in tissues when staining images are not available. These findings may facilitate future mechanistic studies of the therapeutic and adverse effects of duloxetine. In the current work, we did not perform absolute quantification of duloxetine, which will be reported in due course. SIGNIFICANCE STATEMENT: The study utilized imaging mass spectrometry to examine the spatial distribution of duloxetine and its primary metabolites in mouse brain, liver, kidney, and spleen. These results may pave the way for future investigations into the mechanisms behind duloxetine's therapeutic and adverse effects. Furthermore, the mass spectrometry images of specific endogenous metabolites such as heme could be valuable in analyzing the spatial distribution of other drugs within tissues in scenarios where histological staining images are unavailable.

Microglia Actively Remodel Adult Hippocampal Neurogenesis through the Phagocytosis Secretome.

During adult hippocampal neurogenesis, most newborn cells undergo apoptosis and are rapidly phagocytosed by resident microglia to prevent the spillover of intracellular contents. Here, we propose that phagocytosis is not merely passive corpse removal but has an active role in maintaining neurogenesis. First, we found that neurogenesis was disrupted in male and female mice chronically deficient for two phagocytosis pathways: the purinergic receptor P2Y12, and the tyrosine kinases of the TAM family Mer tyrosine kinase (MerTK)/Axl. In contrast, neurogenesis was transiently increased in mice in which MerTK expression was conditionally downregulated. Next, we performed a transcriptomic analysis of the changes induced by phagocytosis in microglia 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 microglia act as a sensor of local cell death, modulating the balance between proliferation and survival in the neurogenic niche through the phagocytosis secretome, thereby supporting the long-term maintenance of adult hippocampal neurogenesis.SIGNIFICANCE STATEMENT Microglia are the brain professional phagocytes and, in the adult hippocampal neurogenic niche, they remove newborn cells naturally undergoing apoptosis. Here we show that phagocytosis of apoptotic cells triggers a coordinated transcriptional program that alters their secretome, limiting neurogenesis both in vivo and in vitro In addition, chronic phagocytosis disruption in mice deficient for receptors P2Y12 and MerTK/Axl reduces adult hippocampal neurogenesis. In contrast, inducible MerTK downregulation transiently increases neurogenesis, suggesting that microglial phagocytosis provides a negative feedback loop that is necessary for the long-term maintenance of adult hippocampal neurogenesis. Therefore, we speculate that the effects of promoting engulfment/degradation of cell debris may go beyond merely removing corpses to actively promoting regeneration in development, aging, and neurodegenerative diseases.

A question of fate.

Ever since the discovery of neural stem cells in the mammalian brain, the possibility of brain tissue regeneration has captured the minds of scientists, clinicians, and the public. Neural stem cells have been envisioned as a source of donor cells for transplantation and vectors for the delivery of gene therapy. Over the past decade, many researchers have contributed to characterizing these cells and their lineages, providing the foundation for their utilization as therapeutic devices. In a new study, Azim and colleagues took a different approach: using pharmacogenomics to focus on neural stem cell lineage, they identified specific compounds that can direct neural stem cell fate toward a specific lineage in vivo, both in physiological and pathological conditions. Their work opens new avenues for treatment of neurodegenerative and demyelinating disorders.

Neuronal Hyperactivity Disturbs ATP Microgradients, Impairs Microglial Motility, and Reduces Phagocytic Receptor Expression Triggering Apoptosis/Microglial Phagocytosis Uncoupling.

Phagocytosis is essential to maintain tissue homeostasis in a large number of inflammatory and autoimmune diseases, but its role in the diseased brain is poorly explored. Recent findings suggest that in the adult hippocampal neurogenic niche, where the excess of newborn cells undergo apoptosis in physiological conditions, phagocytosis is efficiently executed by surveillant, ramified microglia. To test whether microglia are efficient phagocytes in the diseased brain as well, we confronted them with a series of apoptotic challenges and discovered a generalized response. When challenged with excitotoxicity in vitro (via the glutamate agonist NMDA) or inflammation in vivo (via systemic administration of bacterial lipopolysaccharides or by omega 3 fatty acid deficient diets), microglia resorted to different strategies to boost their phagocytic efficiency and compensate for the increased number of apoptotic cells, thus maintaining phagocytosis and apoptosis tightly coupled. Unexpectedly, this coupling was chronically lost in a mouse model of mesial temporal lobe epilepsy (MTLE) as well as in hippocampal tissue resected from individuals with MTLE, a major neurological disorder characterized by seizures, excitotoxicity, and inflammation. Importantly, the loss of phagocytosis/apoptosis coupling correlated with the expression of microglial proinflammatory, epileptogenic cytokines, suggesting its contribution to the pathophysiology of epilepsy. The phagocytic blockade resulted from reduced microglial surveillance and apoptotic cell recognition receptor expression and was not directly mediated by signaling through microglial glutamate receptors. Instead, it was related to the disruption of local ATP microgradients caused by the hyperactivity of the hippocampal network, at least in the acute phase of epilepsy. Finally, the uncoupling led to an accumulation of apoptotic newborn cells in the neurogenic niche that was due not to decreased survival but to delayed cell clearance after seizures. These results demonstrate that the efficiency of microglial phagocytosis critically affects the dynamics of apoptosis and urge to routinely assess the microglial phagocytic efficiency in neurodegenerative disorders.

Correction: Neuronal Hyperactivity Disturbs ATP Microgradients, Impairs Microglial Motility, and Reduces Phagocytic Receptor Expression Triggering Apoptosis/Microglial Phagocytosis Uncoupling.

[This corrects the article DOI: 10.1371/journal.pbio.1002466.].

Correction: Neuronal Hyperactivity Disturbs ATP Microgradients, Impairs Microglial Motility, and Reduces Phagocytic Receptor Expression Triggering Apoptosis/Microglial Phagocytosis Uncoupling.

[This corrects the article DOI: 10.1371/journal.pbio.1002466.].

Heterogeneity of Stem Cells in the Hippocampus.

The discovery of neural stem cells in the adult mammalian hippocampus has attracted attention and controversy, which both continue to this day. Hippocampal neural stem cells and their immediate progeny, amplifying neuroprogenitor cells, give rise to neurons and astrocytes in the region. Envisioned as possible key for tissue regeneration, whether mobilized endogenously or transplanted exogenously, neural stem cells have been in the eye of both public and science over the course of the past 20 years. These cells are a heterogeneous population, and here, we review different aspects of their heterogeneity from morphology to metabolism and response to different stimuli.

MECP2-related disorders while gene-based therapies are on the horizon.

The emergence of new genetic tools has led to the discovery of the genetic bases of many intellectual and developmental disabilities. This creates exciting opportunities for research and treatment development, and a few genetic disorders (e.g., spinal muscular atrophy) have recently been treated with gene-based therapies. MECP2 is found on the X chromosome and regulates the transcription of thousands of genes. Loss of MECP2 gene product leads to Rett Syndrome, a disease found primarily in females, and is characterized by developmental regression, motor dysfunction, midline hand stereotypies, autonomic nervous system dysfunction, epilepsy, scoliosis, and autistic-like behavior. Duplication of MECP2 causes MECP2 Duplication Syndrome (MDS). MDS is found mostly in males and presents with developmental delay, hypotonia, autistic features, refractory epilepsy, and recurrent respiratory infections. While these two disorders share several characteristics, their differences (e.g., affected sex, age of onset, genotype/phenotype correlations) are important to distinguish in the light of gene-based therapy because they require opposite solutions. This review explores the clinical features of both disorders and highlights these important clinical differences.

PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk RNA-seq data.

Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism; yet, its regulation and impact on human diseases remain understudied. Existing bulk RNA sequencing (RNA-seq)-based APA methods predominantly rely on predefined annotations, severely impacting their ability to decode novel tissue- and disease-specific APA changes. Furthermore, they only account for the most proximal and distal cleavage and polyadenylation sites (C/PASs). Deconvoluting overlapping C/PASs and the inherent noisy 3' UTR coverage in bulk RNA-seq data pose additional challenges. To overcome these limitations, we introduce PolyAMiner-Bulk, an attention-based deep learning algorithm that accurately recapitulates C/PAS sequence grammar, resolves overlapping C/PASs, captures non-proximal-to-distal APA changes, and generates visualizations to illustrate APA dynamics. Evaluation on multiple datasets strongly evinces the performance merit of PolyAMiner-Bulk, accurately identifying more APA changes compared with other methods. With the growing importance of APA and the abundance of bulk RNA-seq data, PolyAMiner-Bulk establishes a robust paradigm of APA analysis.

Generation of five induced pluripotent stem cell lines from patients with MECP2 Duplication Syndrome.

MECP2 Duplication Syndrome (MDS) is a rare, severe neurodevelopmental disorder arising from duplications in the Xq28 region containing the MECP2 gene that predominantly affects males. We generated five human induced pluripotent stem cell (iPSC) lines from the fibroblasts of individuals carrying between 0.355 and 11.2 Mb size duplications in the chromosomal locus containing MECP2. All lines underwent extensive testing to confirm MECP2 duplication and iPSC-related features such as morphology, pluripotency markers, and trilineage differentiation potential. These lines are a valuable resource for molecular and functional studies of MDS as well as screening for a variety of therapeutic approaches.

Rigor and reproducibility in human brain organoid research: Where we are and where we need to go.

Human brain organoid models have emerged as a promising tool for studying human brain development and function. These models preserve human genetics and recapitulate some aspects of human brain development, while facilitating manipulation in an in vitro setting. Despite their potential to transform biology and medicine, concerns persist about their fidelity. To fully harness their potential, it is imperative to establish reliable analytic methods, ensuring rigor and reproducibility. Here, we review current analytical platforms used to characterize human forebrain cortical organoids, highlight challenges, and propose recommendations for future studies to achieve greater precision and uniformity across laboratories.

Metabolomic approach to human brain spectroscopy identifies associations between clinical features and the frontal lobe metabolome in multiple sclerosis.

Proton magnetic resonance spectroscopy ((1)H-MRS) is capable of noninvasively detecting metabolic changes that occur in the brain tissue in vivo. Its clinical utility has been limited so far, however, by analytic methods that focus on independently evaluated metabolites and require prior knowledge about which metabolites to examine. Here, we applied advanced computational methodologies from the field of metabolomics, specifically partial least squares discriminant analysis and orthogonal partial least squares, to in vivo (1)H-MRS from frontal lobe white matter of 27 patients with relapsing-remitting multiple sclerosis (RRMS) and 14 healthy controls. We chose RRMS, a chronic demyelinating disorder of the central nervous system, because its complex pathology and variable disease course make the need for reliable biomarkers of disease progression more pressing. We show that in vivo MRS data, when analyzed by multivariate statistical methods, can provide reliable, distinct profiles of MRS-detectable metabolites in different patient populations. Specifically, we find that brain tissue in RRMS patients deviates significantly in its metabolic profile from that of healthy controls, even though it appears normal by standard MRI techniques. We also identify, using statistical means, the metabolic signatures of certain clinical features common in RRMS, such as disability score, cognitive impairments, and response to stress. This approach to human in vivo MRS data should promote understanding of the specific metabolic changes accompanying disease pathogenesis, and could provide biomarkers of disease progression that would be useful in clinical trials.

Members of the high mobility group B protein family are dynamically expressed in embryonic neural stem cells.

Neural Stem Cells (NSCs) are a distinct group of cells present in the embryonic and adult mammalian central nervous system (CNS) that are able to differentiate into neurons, astrocytes and oligodendrocytes. As NSC proliferation declines with age, factors that regulate this process need to be defined. To search for NSC regulatory factors, we performed a quantitative shotgun proteomics study that revealed that members of the High Mobility Group B (HMGB) family are highly expressed in NSCs. Using a neurosphere assay, we report the differential expression of HMGB 1, 2, 3, and 4 mRNAs in proliferating NSCs isolated from various time points during embryonic development, as well as the dynamic expression of HMGB1 and B2 mRNAs and proteins in differentiating embryonic NSCs. Expression of HMGB2 underwent the most dramatic changes during the developmental ages examined; as a result, we assessed its role in NSC proliferation and differentiation. We report the predominance of small diameter HMGB2-/- neurospheres in comparison to wild-type, which correlated with increased proliferation in these smaller HMGB2-/- neurospheres. Our data suggest that HMGB2 plays a regulatory role in NSC cell proliferation and maintenance pathways.

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.

Mass spectrometry imaging as an emerging tool for studying metabolism in human brain organoids.

Human brain organoids are emerging models to study human brain development and pathology as they recapitulate the development and characteristics of major neural cell types, and enable manipulation through an in vitro system. Over the past decade, with the advent of spatial technologies, mass spectrometry imaging (MSI) has become a prominent tool for metabolic microscopy, providing label-free, non-targeted molecular and spatial distribution information of the metabolites within tissue, including lipids. This technology has never been used for studies of brain organoids and here, we set out to develop a standardized protocol for preparation and mass spectrometry imaging of human brain organoids. We present an optimized and validated sample preparation protocol, including sample fixation, optimal embedding solution, homogenous deposition of matrices, data acquisition and processing to maximize the molecular information derived from mass spectrometry imaging. We focus on lipids in organoids, as they play critical roles during cellular and brain development. Using high spatial and mass resolution in positive- and negative-ion modes, we detected 260 lipids in the organoids. Seven of them were uniquely localized within the neurogenic niches or rosettes as confirmed by histology, suggesting their importance for neuroprogenitor proliferation. We observed a particularly striking distribution of ceramide-phosphoethanolamine CerPE 36:1; O2 which was restricted within rosettes and of phosphatidyl-ethanolamine PE 38:3, which was distributed throughout the organoid tissue but not in rosettes. This suggests that ceramide in this particular lipid species might be important for neuroprogenitor biology, while its removal may be important for terminal differentiation of their progeny. Overall, our study establishes the first optimized experimental pipeline and data processing strategy for mass spectrometry imaging of human brain organoids, allowing direct comparison of lipid signal intensities and distributions in these tissues. Further, our data shed new light on the complex processes that govern brain development by identifying specific lipid signatures that may play a role in cell fate trajectories. Mass spectrometry imaging thus has great potential in advancing our understanding of early brain development as well as disease modeling and drug discovery.

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 3'UTR 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 Alzheimer's Disease. Our method, PolyAMiner-Bulk, creates a paradigm for future alternative polyadenylation analysis from bulk RNA-seq data.

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-STOCSY’s 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.