The proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia

Human stem cell-based models to study synaptic dysfunction and cognition in schizophrenia: A narrative review

Cognitive impairment is the strongest predictor of functional outcomes in schizophrenia and is hypothesized to result from synaptic dysfunction. However, targeting synaptic plasticity and cognitive deficits in patients remains a significant clinical challenge. A comprehensive understanding of synaptic plasticity and the molecular basis of learning and memory in a disease context can provide specific targets for the development of novel therapeutics targeting cognitive impairments in schizophrenia. Here, we describe the role of synaptic plasticity in cognition, summarize evidence for synaptic dysfunction in schizophrenia and demonstrate the use of patient derived induced-pluripotent stem cells for studying synaptic plasticity in vitro. Lastly, we discuss current advances and future technologies for bridging basic science research of synaptic dysfunction with clinical and translational research that can be used to predict treatment response and develop novel therapeutics.

Proteogenomic network analysis reveals dysregulated mechanisms and potential mediators in Parkinson's disease

Parkinson's disease is highly heterogeneous across disease symptoms, clinical manifestations and progression trajectories, hampering the identification of therapeutic targets. Despite knowledge gleaned from genetics analysis, dysregulated proteome mechanisms stemming from genetic aberrations remain underexplored. In this study, we develop a three-phase system-level proteogenomic analytical framework to characterize disease-associated proteins and dysregulated mechanisms. Proteogenomic analysis identified 577 proteins that enrich for Parkinson's disease-related pathways, such as cytokine receptor interactions and lysosomal function. Converging lines of evidence identified nine proteins, including LGALS3, CSNK2A1, SMPD3, STX4, APOA2, PAFAH1B3, LDLR, HSPB1, BRK1, with potential roles in disease pathogenesis. This study leverages the largest population-scale proteomics dataset, the UK Biobank Pharma Proteomics Project, to characterize genetically-driven protein disturbances associated with Parkinson's disease. Taken together, our work contributes to better understanding of genome-proteome dynamics in Parkinson's disease and sets a paradigm to identify potential indirect mediators connected to GWAS signals for complex neurodegenerative disorders.

Mitochondrial protein prohibitin promotes learning memory recovery in mice following intracerebral hemorrhage via CAMKII/CRMP signaling pathway

Prohibitin (PHB) is a mitochondrial inner membrane protein with neuroprotective, antioxidant, and apoptosis-reducing effects. This study aimed to explore the role of PHB in pathological symptoms, behavioral deficits, and cognitive impairment in a collagenase-IV-induced intracerebral hemorrhage (ICH) murine model. In this study, mice that received collagenase IV injection were pretreated with PHB or saline 21 days prior to modeling. The role of PHB in memory and learning ability was monitored using the Morris water maze, Y-maze, and rotarod, social, startle, and nest-building tests. The effect of PHB on depression-like symptoms was examined using the forced swimming, tail suspension, and sucrose preference tests. Subsequently, mouse samples were analyzed using immunohistochemistry, western blotting, Perls staining, Nissl staining, and gene sequencing. Results showed that collagenase IV significantly induced behavioral deficits, brain edema, cognitive impairment, and depressive symptoms. PHB overexpression effectively alleviated memory, learning, and motor deficits in mice with ICH. PHB markedly inhibited the number of terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick-end labeling-positive cells and protein levels of ionized calcium-binding adapter molecule 1, glial fibrillary acidic protein, and interleukin-1β in the perihematomal region of ICH mice. PHB overexpression also remarkably promoted production of neurologin1 (NLGL1), and upregulated levels of Ca2+-calmodulin-dependent kinase II (CaMKII) and collapsin response mediator protein-1 (CRMP1) proteins. In conclusion, PHB overexpression can effectively alleviate the neurological deficits and neurodegeneration around the hematoma region. This may play a protective role by upregulating the expression of NLGL1 and promoting expression of CaMKII and CRMP1.

Changes in neurotransmitter levels, brain structural characteristics, and their correlation with PANSS scores in patients with first-episode schizophrenia

BACKGROUND

In patients with schizophrenia, the brain structure and neurotransmitter levels change, which may be related to the occurrence and progression of this disease.

AIM

To explore the relationships between changes in neurotransmitters, brain structural characteristics, and the scores of the Positive and Negative Symptom Scale (PANSS) in patients with first-episode schizophrenia.

METHODS

The case group comprised 97 patients with schizophrenia, who were evaluated using the Canadian Neurological Scale and confirmed by laboratory tests at Ningbo Mental Hospital from January 2020 to July 2022. The control group comprised 100 healthy participants. For all participants, brain structural characteristics were explored by measuring brain dopamine (DA), glutamic acid (Glu), and gamma-aminobutyric acid (GABA) levels, with magnetic resonance imaging. The case group was divided into negative and positive symptom subgroups using PANSS scores for hierarchical analysis. Linear correlation analysis was used to analyze the correlations between neurotransmitters, brain structural characteristics, and PANSS scores.

RESULTS

Patients in the case group had higher levels of DA and lower levels of Glu and GABA, greater vertical and horizontal distances between the corpus callosum and the inferior part of the fornix and larger ventricle area than patients in the control group (P < 0.05). Patients with positive schizophrenia symptoms had significantly higher levels of DA, Glu, and GABA than those with negative symptoms (P < 0.05). In patients with positive schizophrenia symptoms, PANSS score was significantly positively correlated with DA, vertical and horizontal distances between the corpus callosum and the infrafornix, and ventricular area, and was significantly negatively correlated with Glu and GABA (P < 0.05). In patients with negative schizophrenia symptoms, PANSS score was significantly positively correlated with DA, vertical distance between the corpus callosum and the infrafornix, horizontal distance between the corpus callosum and the infrafornix, and ventricular area, and was significantly negatively correlated with Glu and GABA (P < 0.05).

CONCLUSION

In patients with first-episode schizophrenia, DA levels increased, Glu and GABA levels decreased, the thickness of the corpus callosum increased, and these variables were correlated with PANSS scores.

Changes in neurotransmitter levels, brain structural characteristics, and their correlation with PANSS scores in patients with first-episode schizophrenia

BACKGROUND

In patients with schizophrenia, the brain structure and neurotransmitter levels change, which may be related to the occurrence and progression of this disease.

AIM

To explore the relationships between changes in neurotransmitters, brain structural characteristics, and the scores of the Positive and Negative Symptom Scale (PANSS) in patients with first-episode schizophrenia.

METHODS

The case group comprised 97 patients with schizophrenia, who were evaluated using the Canadian Neurological Scale and confirmed by laboratory tests at Ningbo Mental Hospital from January 2020 to July 2022. The control group comprised 100 healthy participants. For all participants, brain structural characteristics were explored by measuring brain dopamine (DA), glutamic acid (Glu), and gamma-aminobutyric acid (GABA) levels, with magnetic resonance imaging. The case group was divided into negative and positive symptom subgroups using PANSS scores for hierarchical analysis. Linear correlation analysis was used to analyze the correlations between neurotransmitters, brain structural characteristics, and PANSS scores.

RESULTS

Patients in the case group had higher levels of DA and lower levels of Glu and GABA, greater vertical and horizontal distances between the corpus callosum and the inferior part of the fornix and larger ventricle area than patients in the control group (P < 0.05). Patients with positive schizophrenia symptoms had significantly higher levels of DA, Glu, and GABA than those with negative symptoms (P < 0.05). In patients with positive schizophrenia symptoms, PANSS score was significantly positively correlated with DA, vertical and horizontal distances between the corpus callosum and the infrafornix, and ventricular area, and was significantly negatively correlated with Glu and GABA (P < 0.05). In patients with negative schizophrenia symptoms, PANSS score was significantly positively correlated with DA, vertical distance between the corpus callosum and the infrafornix, horizontal distance between the corpus callosum and the infrafornix, and ventricular area, and was significantly negatively correlated with Glu and GABA (P < 0.05).

CONCLUSION

In patients with first-episode schizophrenia, DA levels increased, Glu and GABA levels decreased, the thickness of the corpus callosum increased, and these variables were correlated with PANSS scores.

Consolidation of metabolomic, proteomic, and GWAS data in connective model of schizophrenia

Despite of multiple systematic studies of schizophrenia based on proteomics, metabolomics, and genome-wide significant loci, reconstruction of underlying mechanism is still a challenging task. Combination of the advanced data for quantitative proteomics, metabolomics, and genome-wide association study (GWAS) can enhance the current fundamental knowledge about molecular pathogenesis of schizophrenia. In this study, we utilized quantitative proteomic and metabolomic assay, and high throughput genotyping for the GWAS study. We identified 20 differently expressed proteins that were validated on an independent cohort of patients with schizophrenia, including ALS, A1AG1, PEDF, VTDB, CERU, APOB, APOH, FASN, GPX3, etc. and almost half of them are new for schizophrenia. The metabolomic survey revealed 18 group-specific compounds, most of which were the part of transformation of tyrosine and steroids with the prevalence to androgens (androsterone sulfate, thyroliberin, thyroxine, dihydrotestosterone, androstenedione, cholesterol sulfate, metanephrine, dopaquinone, etc.). The GWAS assay mostly failed to reveal significantly associated loci therefore 52 loci with the smoothened p < 10^-5 were fractionally integrated into proteome-metabolome data. We integrated three omics layers and powered them by the quantitative analysis to propose a map of molecular events associated with schizophrenia psychopathology. The resulting interplay between different molecular layers emphasizes a strict implication of lipids transport, oxidative stress, imbalance in steroidogenesis and associated impartments of thyroid hormones as key interconnected nodes essential for understanding of how the regulation of distinct metabolic axis is achieved and what happens in the conditioned proteome and metabolome to produce a schizophrenia-specific pattern.

Bioinformatics and System Biology Techniques to Determine Biomolecular Signatures and Pathways of Prion Disorder

Prion disorder (PD) is caused by misfolding and the formation of clumps of proteins in the brain, notably Prion proteins resulting in a steady decrease in brain function. Early detection of PD is difficult due to its unpredictable nature, and diagnosis is limited regarding specificity and sensitivity. Considering the uncertainties, the current study used network-based integrative system biology approaches to reveal promising molecular biomarkers and therapeutic targets for PD. In this study, brain transcriptomics gene expression microarray datasets (GSE160208 and GSE124571) of human PD were evaluated and 35 differentially expressed genes (DEGs) were identified. By employing network-based protein-protein interaction (PPI) analysis on these DEGs, 10 central hub proteins, including SPP1, FKBP5, HPRT1, CDKN1A, BAG3, HSPB1, SYK, TNFRSF1A, PTPN6, and CD44, were identified. Employing bioinformatics approaches, a variety of transcription factors (EGR1, SSRP1, POLR2A, TARDP, and NR2F1) and miRNAs (hsa-mir-8485, hsa-mir-148b-3p, hsa-mir-4295, hsa-mir-26b-5p, and hsa-mir-16-5p) were predicted. EGR1 was found as the most imperative transcription factor (TF), and hsa-mir-16-5p and hsa-mir-148b-3p were found as the most crucial miRNAs targeted in PD. Finally, resveratrol and hypochlorous acid were predicted as possible therapeutic drugs for PD. This study could be helpful in better understanding of molecular systems and prospective pharmacological targets for developing effective PD treatments.

Tissue-wide cell-specific proteogenomic modeling reveals novel candidate risk genes in autism spectrum disorders

Autism spectrum disorders (ASD) are a set of complex neurodevelopmental diseases characterized with repetitive behavioral patterns and communication disabilities. Using a systems biology method called MAPSD (Markov Affinity-based Proteogenomic Signal Diffusion) for joint modeling of proteome dynamics and a wide array of omics datasets, we identified a list of candidate ASD risk genes. Leveraging the collected biological signals as well as a large-scale protein-protein interaction network adjusted based on single cell resolution proteome properties in four brain regions, we observed an agreement between the known and the newly identified candidate genes that are spatially enriched in neuronal cells within cerebral cortex at the protein level. Moreover, we created a detailed subcellular localization enrichment map of the known and the identified genes across 32 micro-domains and showed that neuronal cells and neuropils share the largest fraction of signal enrichment in cerebral cortex. Notably, we showed that the identified genes are among the transcriptional biomarkers of inhibitory and excitatory neurons in human frontal cortex. Intersecting the identified genes with a single cell RNA-seq data on ASD brains further evidenced that 20 candidate genes, including GRIK1, EMX2, STXBP6, and KCNJ3 are disrupted in distinct cell-types. Moreover, we showed that ASD risk genes are predominantly distributed in certain human interactome modules, and that the identified genes may act as the regulator for some of the known ASD loci. In summary, our study demonstrated how tissue-wide cell-specific proteogenomic modeling can reveal candidate genes for brain disorders that can be supported by convergent lines of evidence.

Prohibitins: A Key Link between Mitochondria and Nervous System Diseases

Prohibitins (PHBs) are conserved proteins in eukaryotic cells, which are mainly located in the inner mitochondrial membrane (IMM), cell nucleus, and cell membrane. PHBs play crucial roles in various cellular functions, including the cell cycle regulation, tumor suppression, immunoglobulin M receptor binding, and aging. In addition, recent in vitro and in vivo studies have revealed that PHBs are important in nervous system diseases. PHBs can prevent apoptosis, inflammation, mitochondrial dysfunction, and autophagy in neurological disorders through different molecules and pathways, such as OPA-1, PINK1/Parkin, IL6/STAT3, Tau, NO, LC3, and TDP43. Therefore, PHBs show great promise in the protection of neurological disorders. This review summarizes the relevant studies on the relationship between PHBs and neurological disorders and provides an update on the molecular mechanisms of PHBs in nervous system diseases.

Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction

Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.

A protein-centric view of in vitro biological model systems for schizophrenia

Schizophrenia (SCZ) is a severe brain disorder, characterized by psychotic, negative, and cognitive symptoms, affecting 1% of the population worldwide. The precise etiology of SCZ is still unknown; however, SCZ has a high heritability and is associated with genetic, environmental, and social risk factors. Even though the genetic contribution is indisputable, the discrepancies between transcriptomics and proteomics in brain tissues are consistently challenging the field to decipher the disease pathology. Here we provide an overview of the state of the art of neuronal two-dimensional and three-dimensional model systems that can be combined with proteomics analyses to decipher specific brain pathology and detection of alternative entry points for drug development.

Using biochemistry and biophysics to extinguish androgen receptor signaling in prostate cancer

Castration resistant prostate cancer (CRPC) continues to be androgen receptor (AR) driven. Inhibition of AR signaling in CRPC could be advanced using state-of-the-art biophysical and biochemical techniques. Structural characterization of AR and its complexes by cryo-electron microscopy would advance the development of N-terminal domain (NTD) and ligand-binding domain (LBD) antagonists. The structural basis of AR function is unlikely to be determined by any single structure due to the intrinsic disorder of its NTD, which not only interacts with coregulators but likely accounts for the constitutive activity of AR-splice variants (SV), which lack the LBD and emerge in CRPC. Using different AR constructs lacking the LBD, their effects on protein folding, DNA binding, and transcriptional activity could reveal how interdomain coupling explains the activity of AR-SVs. The AR also interacts with coregulators that promote chromatin looping. Elucidating the mechanisms involved can identify vulnerabilities to treat CRPC, which do not involve targeting the AR. Phosphorylation of the AR coactivator MED-1 by CDK7 is one mechanism that can be blocked by the use of CDK7 inhibitors. CRPC gains resistance to AR signaling inhibitors (ARSI). Drug resistance may involve AR-SVs, but their role requires their reliable quantification by SILAC-mass spectrometry during disease progression. ARSI drug resistance also occurs by intratumoral androgen biosynthesis catalyzed by AKR1C3 (type 5 17β-hydroxysteroid dehydrogenase), which is unique in that its acts as a coactivator of AR. Novel bifunctional inhibitors that competitively inhibit AKR1C3 and block its coactivator function could be developed using reverse-micelle NMR and fragment-based drug discovery.

Cell-Type-Specific Proteogenomic Signal Diffusion for Integrating Multi-Omics Data Predicts Novel Schizophrenia Risk Genes

Accumulation of diverse types of omics data on schizophrenia (SCZ) requires a systems approach to model the interplay between genome, transcriptome, and proteome. We introduce Markov affinity-based proteogenomic signal diffusion (MAPSD), a method to model intra-cellular protein trafficking paradigms and tissue-wise single-cell protein abundances. MAPSD integrates multi-omics data to amplify the signals at SCZ risk loci with small effect sizes, and reveal convergent disease-associated gene modules in the brain. We predicted a set of high-confidence SCZ risk loci followed by characterizing the subcellular localization of proteins encoded by candidate SCZ risk genes, and illustrated that most are enriched in neuronal cells in the cerebral cortex as well as Purkinje cells in the cerebellum. We demonstrated how the identified genes may be involved in neurodevelopment, how they may alter SCZ-related biological pathways, and how they facilitate drug repurposing. MAPSD is applicable in other polygenic diseases and can facilitate our understanding of disease mechanisms.

Chemometrics: a complementary tool to guide the isolation of pharmacologically active natural products

Chemometrics offers an important complementary tool to enhance the searching and isolation of bioactive natural products from natural sources.