Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Single-cell and spatial omics: exploring hypothalamic heterogeneity

Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

Emerging Biomarkers and Advanced Diagnostics in Chronic Kidney Disease: Early Detection Through Multi-Omics and AI

Chronic kidney disease (CKD) remains a significant global health burden, often diagnosed at advanced stages due to the limitations of traditional biomarkers such as serum creatinine and estimated glomerular filtration rate (eGFR). This review aims to critically evaluate recent advancements in novel biomarkers, multi-omics technologies, and artificial intelligence (AI)-driven diagnostic strategies, specifically addressing existing gaps in early CKD detection and personalized patient management. We specifically explore key advancements in CKD diagnostics, focusing on emerging biomarkers-including neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), soluble urokinase plasminogen activator receptor (suPAR), and cystatin C-and their clinical applications. Additionally, multi-omics approaches integrating genomics, proteomics, metabolomics, and transcriptomics are reshaping disease classification and prognosis. Artificial intelligence (AI)-driven predictive models further enhance diagnostic accuracy, enabling real-time risk assessment and treatment optimization. Despite these innovations, challenges remain in biomarker standardization, large-scale validation, and integration into clinical practice. Future research should focus on refining multi-biomarker panels, improving assay standardization, and facilitating the clinical adoption of precision-driven diagnostics. By leveraging these advancements, CKD diagnostics can transition toward earlier intervention, individualized therapy, and improved patient outcomes.

Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications

Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image-based techniques to contemporary sequence-based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell-cell interaction analysis, and 3D multi-slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research.

Regulatory role of CD177+ neutrophils in BMP signaling pathway and its implications for inflammatory bowel disease, sepsis, and intestinal tumors

OBJECTIVES

Inflammatory bowel disease (IBD), sepsis, and intestinal tumors are major health threats. This study aimed to explore the regulatory role of CD177+ neutrophils in the BMP signaling pathway and its impact on the onset, progression, and treatment of these diseases.

METHODS

Gene expression data from the Gene Expression Omnibus (GEO) database for IBD and sepsis were retrieved. Bioinformatics methods like background correction, normalization, and differential expression analysis were used. Weighted gene co-expression network analysis (WGCNA), gene functional enrichment analysis, pan-cancer analysis, single-cell analysis, and in vitro experiments including Caco-2 cell culture, cell proliferation assay (CCK-8), flow cytometry apoptosis analysis, quantitative real-time PCR (qRT-PCR), and plate colony formation assay were performed.

RESULTS

Key genes associated with IBD and sepsis, such as BMP2, BMP4, BMP6, BMP8A, and CD177, were identified. WGCNA in sepsis found two significant modules related to key clinical outcomes. Core gene screening revealed seven shared genes between IBD and sepsis, and enrichment analysis showed involvement in important biological processes and pathways. Pan-cancer analysis showed diverse gene expression patterns and correlations with immune dynamics. Single-cell transcriptomics provided insights into the tumor microenvironment. In vitro experiments demonstrated that CD177 knockdown affected BMP signaling pathway-related gene expression, ROS production, apoptosis, and cell proliferation.

CONCLUSION

CD177+ neutrophils play a crucial role in regulating the BMP signaling pathway in IBD, sepsis, and intestinal tumors. These findings offer potential therapeutic targets, but further clinical validation is required to translate them into effective treatment strategies.

Role of metabolic dysfunction and inflammation along the liver-brain axis in animal models with obesity-induced neurodegeneration

The interaction between metabolic dysfunction and inflammation is central to the development of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. Obesity-related conditions like type 2 diabetes and non-alcoholic fatty liver disease exacerbate this relationship. Peripheral lipid accumulation, particularly in the liver, initiates a cascade of inflammatory processes that extend to the brain, influencing critical metabolic regulatory regions. Ceramide and palmitate, key lipid components, along with lipid transporters lipocalin-2 and apolipoprotein E, contribute to neuroinflammation by disrupting blood-brain barrier integrity and promoting gliosis. Peripheral insulin resistance further exacerbates brain insulin resistance and neuroinflammation. Preclinical interventions targeting peripheral lipid metabolism and insulin signaling pathways have shown promise in reducing neuroinflammation in animal models. However, translating these findings to clinical practice requires further investigation into human subjects. In conclusion, metabolic dysfunction, peripheral inflammation, and insulin resistance are integral to neuroinflammation and neurodegeneration. Understanding these complex mechanisms holds potential for identifying novel therapeutic targets and improving outcomes for neurodegenerative diseases.

The Neurodegenerative Disease Knowledge Portal: Propelling Discovery Through the Sharing of Neurodegenerative Disease Genomic Resources

Although large-scale genetic association studies have proven useful for the delineation of neurodegenerative disease processes, we still lack a full understanding of the pathologic mechanisms of these diseases, resulting in few appropriate treatment options and diagnostic challenges. To mitigate these gaps, the Neurodegenerative Disease Knowledge Portal (NDKP) was created as an open-science initiative with the aim to aggregate, enable analysis, and display all available genomic datasets of neurodegenerative disease, while protecting the integrity and confidentiality of the underlying datasets. The portal contains 218 genomic datasets, including genotyping and sequencing studies, of individuals across 10 different phenotypic groups, including neurologic conditions such as Alzheimer disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson disease. In addition to securely hosting large genomic datasets, the NDKP provides accessible workflows and tools to effectively use the datasets and assist in the facilitation of customized genomic analyses. Here, we summarize the genomic datasets currently included within the portal, the bioinformatics processing of the datasets, and the variety of phenotypes captured. We also present example use cases of the various user interfaces and integrated analytic tools to demonstrate their extensive utility in enabling the extraction of high-quality results at the source, for both genomics experts and those in other disciplines. Overall, the NDKP promotes open science and collaboration, maximizing the potential for discovery from the large-scale datasets researchers and consortia are expending immense resources to produce and resulting in reproducible conclusions to improve diagnostic and therapeutic care for patients with neurodegenerative disease.

A New Perspective on Precision Medicine: The Power of Digital Organoids

Precision medicine is a personalized medical model based on the individual's genome, phenotype, and lifestyle that provides tailored treatment plans for patients. In this context, tumor organoids, a 3-dimensional preclinical model based on patient-derived tumor cell self-organization, combined with digital analysis methods, such as high-throughput sequencing and image processing technology, can be used to analyze the genome, transcriptome, and cellular heterogeneity of tumors, so as to accurately track and assess the growth process, genetic characteristics, and drug responsiveness of tumor organoids, thereby facilitating the implementation of precision medicine. This interdisciplinary approach is expected to promote the innovation of cancer diagnosis and enhance personalized treatment. In this review, the characteristics and culture methods of tumor organoids are summarized, and the application of multi-omics, such as bioinformatics and artificial intelligence, and the digital methods of organoids in precision medicine research are discussed. Finally, this review explores the main causes and potential solutions for the bottleneck in the clinical translation of digital tumor organoids, proposes the prospects of multidisciplinary cooperation and clinical transformation to narrow the gap between laboratory and clinical settings, and provides references for research and development in this field.

Lysosomal acidification impairment in astrocyte-mediated neuroinflammation

Astrocytes are a major cell type in the central nervous system (CNS) that play a key role in regulating homeostatic functions, responding to injuries, and maintaining the blood-brain barrier. Astrocytes also regulate neuronal functions and survival by modulating myelination and degradation of pathological toxic protein aggregates. Astrocytes have recently been proposed to possess both autophagic activity and active phagocytic capability which largely depend on sufficiently acidified lysosomes for complete degradation of cellular cargos. Defective lysosomal acidification in astrocytes impairs their autophagic and phagocytic functions, resulting in the accumulation of cellular debris, excessive myelin and lipids, and toxic protein aggregates, which ultimately contributes to the propagation of neuroinflammation and neurodegenerative pathology. Restoration of lysosomal acidification in impaired astrocytes represent new neuroprotective strategy and therapeutic direction. In this review, we summarize pathogenic factors, including neuroinflammatory signaling, metabolic stressors, myelin and lipid mediated toxicity, and toxic protein aggregates, that contribute to lysosomal acidification impairment and associated autophagic and phagocytic dysfunction in astrocytes. We discuss the role of lysosomal acidification dysfunction in astrocyte-mediated neuroinflammation primarily in the context of neurodegenerative diseases along with other brain injuries. We then highlight re-acidification of impaired lysosomes as a therapeutic strategy to restore autophagic and phagocytic functions as well as lysosomal degradative capacity in astrocytes. We conclude by providing future perspectives on the role of astrocytes as phagocytes and their crosstalk with other CNS cells to impart neurodegenerative or neuroprotective effects.

A novel integrative multimodal classifier to enhance the diagnosis of Parkinson's disease

Parkinson's disease (PD) is a complex, progressive neurodegenerative disorder with high heterogeneity, making early diagnosis difficult. Early detection and intervention are crucial for slowing PD progression. Understanding PD's diverse pathways and mechanisms is key to advancing knowledge. Recent advances in noninvasive imaging and multi-omics technologies have provided valuable insights into PD's underlying causes and biological processes. However, integrating these diverse data sources remains challenging, especially when deriving meaningful low-level features that can serve as diagnostic indicators. This study developed and validated a novel integrative, multimodal predictive model for detecting PD based on features derived from multimodal data, including hematological information, proteomics, RNA sequencing, metabolomics, and dopamine transporter scan imaging, sourced from the Parkinson's Progression Markers Initiative. Several model architectures were investigated and evaluated, including support vector machine, eXtreme Gradient Boosting, fully connected neural networks with concatenation and joint modeling (FCNN_C and FCNN_JM), and a multimodal encoder-based model with multi-head cross-attention (MMT_CA). The MMT_CA model demonstrated superior predictive performance, achieving a balanced classification accuracy of 97.7%, thus highlighting its ability to capture and leverage cross-modality inter-dependencies to aid predictive analytics. Furthermore, feature importance analysis using SHapley Additive exPlanations not only identified crucial diagnostic biomarkers to inform the predictive models in this study but also holds potential for future research aimed at integrated functional analyses of PD from a multi-omics perspective, ultimately revealing targets required for precision medicine approaches to aid treatment of PD aimed at slowing down its progression.

THz Waves Improve Spatial Working Memory by Increasing the Activity of Glutamatergic Neurons in Mice

Terahertz (THz) waves, a novel type of radiation with quantum and electronic properties, have attracted increasing attention for their effects on the nervous system. Spatial working memory, a critical component of higher cognitive function, is coordinated by brain regions such as the infralimbic cortex (IL) region of the medial prefrontal cortex and the ventral cornu ammonis 1 (vCA1) of hippocampus. However, the regulatory effects of THz waves on spatial working memory and the underlying mechanisms remain unclear. In this study, the effects of 0.152 THz waves on glutamatergic neuronal activity and spatial working memory and the related mechanisms were investigated in cell, brain slice, and mouse models. Cellular experiments revealed that THz waves exposure for 60 min significantly increased the intrinsic excitability of primary hippocampal neurons, enhanced glutamatergic neuron activity, and upregulated the expression of molecules involved in glutamate metabolism. In brain slice experiments, THz waves markedly elevated neuronal activity, promoted synaptic plasticity, and increased glutamatergic synaptic transmission within the IL and vCA1 regions. Molecular dynamics simulations found that THz waves could inhibit the ion transport function of glutamate receptors. Moreover, Y-maze tests demonstrated that mice exposed to THz waves exhibited significantly improved spatial working memory. Multiomics analyses indicated that THz waves could induce changes in chromatin accessibility and increase the proportion of excitatory neurons. These findings suggested that exposure to 0.152 THz waves increased glutamatergic neuronal activity, promoted synaptic plasticity, and improved spatial working memory, potentially through modifications in chromatin accessibility and excitatory neuron proportions.

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Therapeutic targeting of obesity-induced neuroinflammation and neurodegeneration

Obesity is a major modifiable risk factor leading to neuroinflammation and neurodegeneration. Excessive fat storage in obesity promotes the progressive infiltration of immune cells into adipose tissue, resulting in the release of pro-inflammatory factors such as cytokines and adipokines. These inflammatory mediators circulate through the bloodstream, propagating inflammation both in the periphery and in the central nervous system. Gut dysbiosis, which results in a leaky intestinal barrier, exacerbates inflammation and plays a significant role in linking obesity to the pathogenesis of neuroinflammation and neurodegeneration through the gut-brain/gut-brain-liver axis. Inflammatory states within the brain can lead to insulin resistance, mitochondrial dysfunction, autolysosomal dysfunction, and increased oxidative stress. These disruptions impair normal neuronal function and subsequently lead to cognitive decline and motor deficits, similar to the pathologies observed in major neurodegenerative diseases, including Alzheimer's disease, multiple sclerosis, and Parkinson's disease. Understanding the underlying disease mechanisms is crucial for developing therapeutic strategies to address defects in these inflammatory and metabolic pathways. In this review, we summarize and provide insights into different therapeutic strategies, including methods to alter gut dysbiosis, lifestyle changes, dietary supplementation, as well as pharmacological agents derived from natural sources, that target obesity-induced neuroinflammation and neurodegeneration.

New potential ligand-receptor axis involved in tissue repair as therapeutic targets in progressive multiple sclerosis

Progressive multiple sclerosis (MS) represents the worsening phase of the disease, characterized by increasing neurodegeneration and disability and mainly refractory to current treatments. Finding therapeutic options remains challenging partially not only because of the lack of understanding of pathogenic mechanisms but also because the early dogma was centered on neuroinflammation, overshadowing the critical role of the tissue repair process. The tissue repair target should start early in disease development, and therapeutic strategies for progressive MS should combine anti-inflammatory and neuroprotective aspects. Increasing preclinical evidence, together with the new era of omics applied on frozen human brain tissue, has shed light on some ligand receptor pairs, such as growth-arrest-specific 6 (GAS6)/protein tyrosine kinase receptor (TYRO3) and protein S (PROS1)/AXL receptor tyrosine kinase (AXL), required to dampen inflammation, promote tissue repair, and engage remyelination. Understanding the role of these proteins in the early stages of MS is a critical step toward preventing or stopping neurodegeneration. Herein, we will discuss the receptor/ligand pairs that might be targetable for therapeutic intervention in progressive MS. SIGNIFICANCE STATEMENT: The aim for progressive multiple sclerosis treatment should be to combine anti-inflammatory and neuroprotective therapeutic strategies based on early intervention. Targeting the TYRO3, AXL, and MER tyrosine kinase receptor (TAM) signaling axis, particularly as growth-arrest-specific 6/TYRO3 and protein S/AXL, which are involved in tempering inflammation, promoting tissue repair, and engaging remyelination, could significantly benefit patients in the early stages of progressive multiple sclerosis.

Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens

Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models. We explore the roles of common and rare genetic variants, gene-gene and gene-environment interactions, and epigenetic influences in shaping disease phenotypes. Additionally, we emphasize the importance of multi-omics layers including genomic, transcriptomic, proteomic, epigenetic, and metabolomic data in elucidating the molecular mechanisms underlying neurodegeneration. Special attention is given to missing heritability and the contribution of rare variants, particularly in the context of pleiotropy and network pleiotropy. We examine the application of single-cell omics technologies, transcriptome-wide association studies, and epigenome-wide association studies as key approaches for dissecting disease mechanisms at tissue- and cell-type levels. Our review introduces the OmicPeak Disease Trajectory Model, a conceptual framework for understanding the genetic architecture of neurodegenerative disease progression, which integrates multi-omics data across biological layers and time points. This review highlights the critical importance of adopting a systems genetics approach to unravel the complex genetic architecture of neurodegenerative diseases. Finally, this emerging holistic understanding of multi-omics data and the exploration of the intricate genetic landscape aim to provide a foundation for establishing more refined genetic architectures of these diseases, enhancing diagnostic precision, predicting disease progression, elucidating pathogenic mechanisms, and refining therapeutic strategies for neurodegenerative conditions.

Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry

Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.

Neurobehavioral dysfunction in a mouse model of Down syndrome: upregulation of cystathionine β-synthase, H2S overproduction, altered protein persulfidation, synaptic dysfunction, endoplasmic reticulum stress, and autophagy

Down syndrome (DS) is a genetic condition where the person is born with an extra chromosome 21. DS is associated with accelerated aging; people with DS are prone to age-related neurological conditions including an early-onset Alzheimer's disease. Using the Dp(17)3Yey/ + mice, which overexpresses a portion of mouse chromosome 17, which encodes for the transsulfuration enzyme cystathionine β-synthase (CBS), we investigated the functional role of the CBS/hydrogen sulfide (H2S) pathway in the pathogenesis of neurobehavioral dysfunction in DS. The data demonstrate that CBS is higher in the brain of the DS mice than in the brain of wild-type mice, with primary localization in astrocytes. DS mice exhibited impaired recognition memory and spatial learning, loss of synaptosomal function, endoplasmic reticulum stress, and autophagy. Treatment of mice with aminooxyacetate, a prototypical CBS inhibitor, improved neurobehavioral function, reduced the degree of reactive gliosis in the DS brain, increased the ability of the synaptosomes to generate ATP, and reduced endoplasmic reticulum stress. H2S levels in the brain of DS mice were higher than in wild-type mice, but, unexpectedly, protein persulfidation was decreased. Many of the above alterations were more pronounced in the female DS mice. There was a significant dysregulation of metabolism in the brain of DS mice, which affected amino acid, carbohydrate, lipid, endocannabinoid, and nucleotide metabolites; some of these alterations were reversed by treatment of the mice with the CBS inhibitor. Thus, the CBS/H2S pathway contributes to the pathogenesis of neurological dysfunction in DS in the current animal model.

Transforming Clinical Research: The Power of High-Throughput Omics Integration

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

A Comprehensive Review of Biomarkers for Chronic Kidney Disease in Older Individuals: Current Perspectives and Future Directions

Chronic kidney disease (CKD) is a progressive condition characterized by a gradual loss of kidney function, leading to significant health complications and an increased risk of cardiovascular events. Early detection and effective management are crucial for slowing disease progression and improving patient outcomes. Biomarkers are valuable tools in CKD diagnosis, prognosis, and treatment. Traditional biomarkers, such as serum creatinine and urine protein, are widely used, but emerging biomarkers like cystatin C, kidney injury molecule-1 (KIM-1), and neutrophil gelatinase-associated lipocalin (NGAL) offer enhanced diagnostic precision and insights into disease severity. These advanced biomarkers are particularly important in older adults, who may present with age-related physiological changes and comorbid conditions that complicate CKD management. This review explores the current state of biomarker research in CKD, focusing on their application in older populations. It highlights the role of traditional and emerging biomarkers, discusses their relevance for early detection and prognosis, and examines future directions in biomarker research, including technological innovations and personalized medicine approaches. By integrating biomarkers into clinical practice, healthcare providers can achieve more accurate diagnoses, tailor treatments to individual patient needs, and potentially improve the overall management of CKD. Continued research and development in this field are essential for addressing the complexities of CKD and advancing patient care.

The Potential of Metabolomics to Find Proper Biomarkers for Addressing the Neuroprotective Efficacy of Drugs Aimed at Delaying Parkinson's and Alzheimer's Disease Progression

The first objective is to highlight the lack of tools to measure whether a given intervention affords neuroprotection in patients with Alzheimer's or Parkinson's diseases. A second aim is to present the primary outcome measures used in clinical trials in cohorts of patients with neurodegenerative diseases. The final aim is to discuss whether metabolomics using body fluids may lead to the discovery of biomarkers of neuroprotection. Information on the primary outcome measures in clinical trials related to Alzheimer's and Parkinson's disease registered since 2018 was collected. We analysed the type of measures selected to assess efficacy, not in terms of neuroprotection since, as stated in the aims, there is not yet any marker of neuroprotection. Proteomic approaches using plasma or CSF have been proposed. PET could estimate the extent of lesions, but disease progression does not necessarily correlate with a change in tracer uptake. We propose some alternatives based on considering the metabolome. A new opportunity opens with metabolomics because there have been impressive technological advances that allow the detection, among others, of metabolites related to mitochondrial function and mitochondrial structure in serum and/or cerebrospinal fluid; some of the differentially concentrated metabolites can become reliable biomarkers of neuroprotection.

Acidic Nanoparticles Restore Lysosomal Acidification and Rescue Metabolic Dysfunction in Pancreatic β-Cells under Lipotoxic Conditions

Type 2 diabetes (T2D), a prevalent metabolic disorder lacking effective treatments, is associated with lysosomal acidification dysfunction, as well as autophagic and mitochondrial impairments. Here, we report a series of biodegradable poly(butylene tetrafluorosuccinate-co-succinate) polyesters, comprising a 1,4-butanediol linker and varying ratios of tetrafluorosuccinic acid (TFSA) and succinic acid as components, to engineer lysosome-acidifying nanoparticles (NPs). The synthesized NPs are spherical with diameters of ≈100 nm and have low polydispersity and good stability. Notably, TFSA NPs, which are composed entirely of TFSA, exhibit the strongest degradation capability and superior acidifying properties. We further reveal significant downregulation of lysosomal vacuolar (H+)-ATPase subunits, which are responsible for maintaining lysosomal acidification, in human T2D pancreatic islets, INS-1 β-cells under chronic lipotoxic conditions, and pancreatic tissues of high-fat-diet (HFD) mice. Treatment with TFSA NPs restores lysosomal acidification, autophagic function, and mitochondrial activity, thereby improving the pancreatic function in INS-1 cells and HFD mice with lipid overload. Importantly, the administration of TFSA NPs to HFD mice reduces insulin resistance and improves glucose clearance. These findings highlight the therapeutic potential of lysosome-acidifying TFSA NPs for T2D.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle

Intramuscular fat (IMF) deposition profoundly influences meat quality and economic value in beef cattle production. Meanwhile, contemporary developments in epigenetics have opened new outlooks for understanding the molecular basics of IMF regulation, and it has become a key area of research for world scholars. Therefore, the aim of this paper was to provide insight and synthesis into the intricate relationship between epigenetic mechanisms and IMF deposition in beef cattle. The methodology involves a thorough analysis of existing literature, including pertinent books, academic journals, and online resources, to provide a comprehensive overview of the role of epigenetic studies in IMF deposition in beef cattle. This review summarizes the contemporary studies in epigenetic mechanisms in IMF regulation, high-resolution epigenomic mapping, single-cell epigenomics, multi-omics integration, epigenome editing approaches, longitudinal studies in cattle growth, environmental epigenetics, machine learning in epigenetics, ethical and regulatory considerations, and translation to industry practices from perspectives of IMF deposition in beef cattle. Moreover, this paper highlights DNA methylation, histone modifications, acetylation, phosphorylation, ubiquitylation, non-coding RNAs, DNA hydroxymethylation, epigenetic readers, writers, and erasers, chromatin immunoprecipitation followed by sequencing, whole genome bisulfite sequencing, epigenome-wide association studies, and their profound impact on the expression of crucial genes governing adipogenesis and lipid metabolism. Nutrition and stress also have significant influences on epigenetic modifications and IMF deposition. The key findings underscore the pivotal role of epigenetic studies in understanding and enhancing IMF deposition in beef cattle, with implications for precision livestock farming and ethical livestock management. In conclusion, this review highlights the crucial significance of epigenetic pathways and environmental factors in affecting IMF deposition in beef cattle, providing insightful information for improving the economics and meat quality of cattle production.

Blood-Brain Barrier-Targeting Nanoparticles: Biomaterial Properties and Biomedical Applications in Translational Neuroscience

Overcoming the blood-brain barrier (BBB) remains a significant hurdle in effective drug delivery to the brain. While the BBB serves as a crucial protective barrier, it poses challenges in delivering therapeutic agents to their intended targets within the brain parenchyma. To enhance drug delivery for the treatment of neurological diseases, several delivery technologies to circumvent the BBB have been developed in the last few years. Among them, nanoparticles (NPs) are one of the most versatile and promising tools. Here, we summarize the characteristics of NPs that facilitate BBB penetration, including their size, shape, chemical composition, surface charge, and importantly, their conjugation with various biological or synthetic molecules such as glucose, transferrin, insulin, polyethylene glycol, peptides, and aptamers. Additionally, we discuss the coating of NPs with surfactants. A comprehensive overview of the common in vitro and in vivo models of the BBB for NP penetration studies is also provided. The discussion extends to discussing BBB impairment under pathological conditions and leveraging BBB alterations under pathological conditions to enhance drug delivery. Emphasizing the need for future studies to uncover the inherent therapeutic properties of NPs, the review advocates for their role beyond delivery systems and calls for efforts translating NPs to the clinic as therapeutics. Overall, NPs stand out as a highly promising therapeutic strategy for precise BBB targeting and drug delivery in neurological disorders.

Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity

There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.

Identification of Molecular Correlations of GSDMD with Pyroptosis inAlzheimer's Disease

AIM

An analysis of bioinformatics and cell experiments was performed to verify the relationship between gasdermin D (GSDMD), an executive protein of pyroptosis, and Alzheimer's disease (AD).

METHODS

The training set GSE33000 was utilized to identify differentially expressed genes (DEGs) in both the AD group and control group, as well as in the GSDMD protein high/low expression group. Subsequently, the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) regression analysis were conducted, followed by the selection of the key genes for the subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The association between GSDMD and AD was assessed and confirmed in the training set GSE33000, as well as in the validation sets GSE5281 and GSE48350. Immunofluorescence (IF) was employed to detect the myelin basic protein (MBP), a distinctive protein found in the rat oligodendrocytes (OLN-93 cells). A range of concentrations (1-15 μmol/L) of β-amyloid 1-42 (Aβ1-42) were exposed to the cells, and the subsequent observations were made regarding cell morphology. Additionally, the assessments were conducted to evaluate the cell viability, the lactate dehydrogenase (LDH) release, the cell membrane permeability, and the GSDMD protein expression.

RESULTS

A total of 7,492 DEGs were screened using GSE33000. Subsequently, WGCNA analysis identified 19 genes that exhibited the strongest correlation with clinical traits in AD. Additionally, LASSO regression analysis identified 13 key genes, including GSDMD, AFF1, and ATOH8. Furthermore, the investigation revealed that the key genes were associated with cellular inflammation based on GO and KEGG analyses. Moreover, the area under the curve (AUC) values for the key genes in the training and validation sets were determined to be 0.95 and 0.70, respectively. Significantly, GSDMD demonstrated elevated levels of expression in AD across both datasets. The positivity of MBP expression in cells exceeded 95%. As the concentration of Aβ1-42 action gradually escalated, the detrimental effects on cells progressively intensified, resulting in a gradual decline in cell survival rate, accompanied by an increase in lactate dehydrogenase release, cell membrane permeability, and GSDMD protein expression.

CONCLUSION

The association between GSDMD and AD has been observed, and it has been found that Aβ1-42 can induce a significant upregulation of GSDMD in OLN-93 cells. This suggests that Aβ1-42 has the potential to induce cellular pyroptosis and can serve as a valuable cellular pyroptosis model for the study of AD.

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

Multimodal deep learning as a next challenge in nutrition research: tailoring fermented dairy products based on cytidine diphosphate-diacylglycerol synthase-mediated lipid metabolism

Deep learning is evolving in nutritional epidemiology to address challenges including precise nutrition and data-driven disease modeling. Fermented dairy products consumption as the implementation of specific dietary priority contributes to a lower risk of all-cause mortality, cardiovascular disease, and obesity. Various lipid types play different roles in cardiometabolic health and fermentation process changes the lipid profile in dairy products. Leveraging the power of multiple biological datasets can provide mechanistic insights into how proteins impact lipid pathways, and establish connections among fermentation-lipid biomarkers-protein. The recent leap of deep learning has been performed in food category recognition, agro-food freshness detection, and food flavor prediction and regulation. The proposed multimodal deep learning method includes four steps: (i) Forming data matrices based on data generated from different omics layers. (ii) Decomposing high-dimensional omics data according to self-attention mechanism. (iii) Constructing View Correlation Discovery Network to learn the cross-omics correlations and integrate different omics datasets. (iv) Depicting a biological network for lipid metabolism-centered quantitative multi-omics data analysis. Relying on the cytidine diphosphate-diacylglycerol synthase-mediated lipid metabolism regulates the glycerophospholipid composition of fermented dairy effectively. Innovative processing strategies including ohmic heating and pulsed electric field improve the sensory qualities and nutritional characteristics of the products.

Lysosomal acidification dysfunction in microglia: an emerging pathogenic mechanism of neuroinflammation and neurodegeneration

Microglia are the resident innate immune cells in the brain with a major role in orchestrating immune responses. They also provide a frontline of host defense in the central nervous system (CNS) through their active phagocytic capability. Being a professional phagocyte, microglia participate in phagocytic and autophagic clearance of cellular waste and debris as well as toxic protein aggregates, which relies on optimal lysosomal acidification and function. Defective microglial lysosomal acidification leads to impaired phagocytic and autophagic functions which result in the perpetuation of neuroinflammation and progression of neurodegeneration. Reacidification of impaired lysosomes in microglia has been shown to reverse neurodegenerative pathology in Alzheimer's disease. In this review, we summarize key factors and mechanisms contributing to lysosomal acidification impairment and the associated phagocytic and autophagic dysfunction in microglia, and how these defects contribute to neuroinflammation and neurodegeneration. We further discuss techniques to monitor lysosomal pH and therapeutic agents that can reacidify impaired lysosomes in microglia under disease conditions. Finally, we propose future directions to investigate the role of microglial lysosomal acidification in lysosome-mitochondria crosstalk and in neuron-glia interaction for more comprehensive understanding of its broader CNS physiological and pathological implications.