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Inflammatory bowel disease (IBD), comprising Crohn’s disease (CD) and ulcerative colitis (UC), arises from complex genetic and environmental interactions. Here, we integrate genome-wide association study (GWAS) meta-analyses with tissue-specific expression data from GTEx to map the polygenic architecture of IBD and its systemic implications. We identified 69 genome-wide significant single-nucleotide polymorphisms (SNPs) across 26 genes shared by CD and UC, revealing an almost equal partition of subtype-specific (50.7%) and shared (49.3%) risk variants. IL23R exhibited the highest allelic heterogeneity—three UC-specific, one CD-specific, and three shared SNPs—while ATG16L1′s four CD-specific variants underscored autophagy’s pivotal role in CD. Chromosomal mapping revealed distinct regulatory hotspots: UC-only loci on chromosomes 1 and 6, CD-only loci on chromosomes 10 and 16, and shared loci on chromosomes 7 and 19. Pathway enrichment emphasized IL-23/IL-17, Th17 differentiation, NF-κB, and JAK–STAT signaling as central to IBD pathogenesis. GTEx analyses showed uniformly high expression of IBD genes in gastrointestinal tissues, but pronounced heterogeneity across brain regions, including the cerebellum, frontal cortex, and hippocampus. This neuro-expression, together with enrichment of neurotrophin and neurodegeneration pathways and a nearly two-fold gene overlap with autism spectrum disorder, schizophrenia, and depression (FDR < 0.05), provides integrative evidence for gut–brain axis involvement in IBD. These findings consolidate prior work while extending perspectives on systemic disease implications. This study consolidates and systematizes dispersed genetic and transcriptomic findings into a unified reference framework. Our results highlight recurrent immune-regulatory and neuro-inflammatory pathways shared between gut and brain, offering a resource to guide future mechanistic, clinical, and translational investigations in IBD and related disorders.

Autism Spectrum Disorder (ASD) is characterized mainly by social and sensory-motor abnormal and repetitive behavior patterns. Over hundreds of genes and thousands of genetic variants were reported to be highly penetrant and causative of ASD. Many of these mutations cause comorbidities such as epilepsy and intellectual disabilities (ID). In this study, we measured cortical neurons derived from induced pluripotent stem cells (iPSCs) of patients with four mutations in the genes GRIN2B, SHANK3, UBTF, as well as chromosomal duplication in the 7q11.23 region and compared them to neurons derived from a first-degree relative without the mutation. Using a whole-cell patch-clamp, we observed that the mutant cortical neurons demonstrated hyperexcitability and early maturation compared to control lines. These changes were characterized by increased sodium currents, increased amplitude and rate of excitatory postsynaptic currents (EPSCs), and more evoked action potentials in response to current stimulation in early-stage cell development (3–5 weeks post differentiation). These changes that appeared in all the different mutant lines, together with previously reported data, indicate that an early maturation and hyperexcitability may be a convergent phenotype of ASD cortical neurons.

This research aimed to develop a machine learning algorithm to predict suicide risk in bipolar disorder (BD) patients using RNA sequencing analysis of lymphoblastoid cell lines (LCLs). By identifying differentially expressed genes (DEGs) between high and low risk patients and their enrichment in relevant pathways, we gained insights into the molecular mechanisms underlying suicide risk. LCL gene expression analysis revealed pathway enrichment related to primary immunodeficiency, ion channels, and cardiovascular defects. Notably, genes such as LCK, KCNN2 , and GRIA1 emerged as pivotal, suggesting their potential roles as biomarkers. Machine learning algorithms trained on a subset of the patients and tested on others demonstrated high accuracy in distinguishing low and high risk of suicide in BD patients. Additionally, the study explored the genetic overlap between suicide-related genes and several psychiatric disorders. Our study enhances the understanding of the complex interplay between genetics and suicidal behaviour, providing a foundation for prevention strategies.

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease. Primary symptoms of PD arise with the loss of dopaminergic (DA) neurons in the Substantia Nigra Pars Compacta, but PD also affects the hippocampus and cortex, usually in its later stage. Approximately 15% of PD cases are familial with a genetic mutation. Two of the most associated genes with autosomal recessive (AR) early-onset familial PD are PINK1 and PRKN . In vitro studies of these genetic mutations are needed to understand the neurophysiological changes in patients’ neurons that may contribute to neurodegeneration. In this work, we generated and differentiated DA and hippocampal neurons from human induced pluripotent stem cells (hiPSCs) derived from two patients with a double mutation in their PINK1 and PRKN (one homozygous and one heterozygous) genes and assessed their neurophysiology compared to two healthy controls. We showed that the synaptic activity of PD neurons generated from patients with the PINK1 and PRKN mutations is impaired in the hippocampus and dopaminergic neurons. Mutant dopaminergic neurons had enhanced excitatory post-synaptic activity. In addition, DA neurons with the homozygous mutation of PINK1 exhibited more pronounced electrophysiological differences compared to the control neurons. Signaling network analysis of RNA sequencing results revealed that Focal adhesion and ECM receptor pathway were the top two upregulated pathways in the mutant PD neurons. Our findings reveal that the phenotypes linked to PINK1 and PRKN mutations differ from those from other PD mutations, suggesting a unique interplay between these two mutations that drives different PD mechanisms.

Several mutations that cause Parkinson’s disease (PD) have been identified over the past decade. These account for 15–25% of PD cases; the rest of the cases are considered sporadic. Currently, it is accepted that PD is not a single monolithic disease but rather a constellation of diseases with some common phenotypes. While rodent models exist for some of the PD-causing mutations, research on the sporadic forms of PD is lagging due to a lack of cellular models. In our study, we differentiated PD patient-derived dopaminergic (DA) neurons from the induced pluripotent stem cells (iPSCs) of several PD-causing mutations as well as from sporadic PD patients. Strikingly, we observed a common neurophysiological phenotype: neurons derived from PD patients had a severe reduction in the rate of synaptic currents compared to those derived from healthy controls. While the relationship between mutations in genes such as the SNCA and LRRK2 and a reduction in synaptic transmission has been investigated before, here we show evidence that the pathogenesis of the synapses in neurons is a general phenotype in PD. Analysis of RNA sequencing results displayed changes in gene expression in different synaptic mechanisms as well as other affected pathways such as extracellular matrix-related pathways. Some of these dysregulated pathways are common to all PD patients (monogenic or idiopathic). Our data, therefore, show changes that are central and convergent to PD and suggest a strong involvement of the tetra-partite synapse in PD pathophysiology.

Bipolar disorder (BD) and schizophrenia are psychiatric disorders that manifest unusual mental, behavioral, and emotional patterns leading to suffering and disability. These disorders span heterogeneous conditions with variable heredity and elusive pathophysiology. Mood stabilizers such as lithium and valproic acid (VPA) have been shown to be effective in BD and, to some extent in schizophrenia. This review highlights the efficacy of lithium and VPA treatment in several randomized, controlled human trials conducted in patients suffering from BD and schizophrenia. Furthermore, we also address the importance of using induced pluripotent stem cells (iPSCs) as a disease model for mirroring the disease’s phenotypes. In BD, iPSC-derived neurons enabled finding an endophenotype of hyperexcitability with increased hyperpolarizations. Some of the disease phenotypes were significantly alleviated by lithium treatment. VPA studies have also reported rescuing the Wnt/β-catenin pathway and reducing activity. Another significant contribution of iPSC models can be attributed to studying the molecular etiologies of schizophrenia such as abnormal differentiation of patient-derived neural stem cells, decreased neuronal connectivity and neurite number, impaired synaptic function, and altered gene expression patterns. Overall, despite significant advances using these novel models, much more work remains to fully understand the mechanisms by which these disorders affect the patients’ brains.

Neurodevelopmental and psychiatric disorders, such as Autism Spectrum Disorder (ASD) and Bipolar Disorder (BD), have been linked to genetic and molecular changes that disrupt normal brain function and development. These disorders often involve complex interactions between genes and environmental factors, leading to significant cognitive and emotional dysregulation. In this thesis, my findings demonstrate that hippocampal neurons carrying the SLC1A4mutation exhibit early maturation, characterized by heightened synaptic activity and excitability during early developmental stages compared to healthy controls. However, as these neurons age, their excitability significantly decreases. Clinically, the SLC1A4mutation is associated with symptoms such as developmental delays, intellectual disability (ID), speech impairments, hypotonia (reduced muscle tone), and motor dysfunction, reflecting the broad impact of this mutation on both cognitive and physical development. This pattern of early hyperexcitability followed by reduced neuronal activity over time mirrors phenotypes observed in other ASD-associated mutations. The metabolic profiling data further supports these electrophysiological findings, shedding light on the underlying biochemical alterations specifically, revealing alterations in energy metabolism, neurotransmitter precursors, and other metabolites that are critical for SLC1A4function. Additionally, this thesis also delves into Smith-Magenis Syndrome (SMS), which is caused by a deletion or mutation in the RAI1gene and is linked to ID, sleep disturbances, and behavioral abnormalities. The study shows a promising response to retinoic acid (RA) treatment, which ameliorates some of the deficits in excitability and synaptic function observed in SMS-mutant neurons.Beyond neurodevelopmental studies, the thesis delves into RNA sequencing analysis of lymphoblastoid cell lines (LCLs) from BD patients to investigate genetic signatures associated with suicide risk. Differentially expressed genes (DEGs) identified in high-risk suicide patients reveal significant involvement in pathways related to immune, ion channel, and cardiovascular dysfunctions. Machine learning (ML) models developed in the study demonstrate high predictive accuracy for suicide risk in BD. This comprehensive approach advances our understanding of both neurodevelopmental disorder: SLC1A4and RAI1mutation and psychiatric disorder such as BD facets of disorders, offering new molecular insights and potential therapeutic strategies.

Schizophrenia affects approximately 1% of the world population. Genetics, epigenetics, and environmental factors are known to play a role in this psychiatric disorder. While there is a high concordance in monozygotic twins, about half of twin pairs are discordant for schizophrenia. To address the question of how and when concordance in monozygotic twins occur, we have obtained fibroblasts from two pairs of schizophrenia discordant twins (one sibling with schizophrenia while the second one is unaffected by schizophrenia) and three pairs of healthy twins (both of the siblings are healthy). We have prepared iPSC models for these 3 groups of patients with schizophrenia, unaffected co-twins, and the healthy twins. When the study started the co-twins were considered healthy and unaffected but both the co-twins were later diagnosed with a depressive disorder. The reprogrammed iPSCs were differentiated into hippocampal neurons to measure the neurophysiological abnormalities in the patients. We found that the neurons derived from the schizophrenia patients were less arborized, were hypoexcitable with immature spike features, and exhibited a significant reduction in synaptic activity with dysregulation in synapse-related genes. Interestingly, the neurons derived from the co-twin siblings who did not have schizophrenia formed another distinct group that was different from the neurons in the group of the affected twin siblings but also different from the neurons in the group of the control twins. Importantly, their synaptic activity was not affected. Our measurements that were obtained from schizophrenia patients and their monozygotic twin and compared also to control healthy twins point to hippocampal synaptic deficits as a central mechanism in schizophrenia.

Schizophrenia (SCZ) is a highly heritable, polygenic neuropsychiatric disease, which disables the patients as well as decreases their life expectancy and quality of life. Common and Rare variants studies on SCZ subjects have provided more than 100 genomic loci that hold importance in the context of SCZ pathophysiology. Transcriptomic studies from clinical samples have informed about the differentially expressed genes (DEGs) and non-coding RNAs in SCZ patients. Despite these advancements, no causative genes for SCZ were found and hence SCZ is difficult to recapitulate in animal models. In the last decade, induced Pluripotent Stem Cells (iPSCs)-based models have helped in understanding the neural phenotypes of SCZ by studying patient iPSC-derived 2D neuronal cultures and 3D brain organoids. Here, we have aimed to provide a simplistic overview of the current progress and advancements after synthesizing the enormous literature on SCZ genetics and SCZ iPSC-based models. Although further understanding of SCZ genetics and mechanisms using these technological advancements is required, the recent approaches have allowed to delineate important cellular mechanisms and biological pathways affected in SCZ.

This research investigates the genetic signatures associated with a high risk of suicide in Bipolar disorder (BD) patients through RNA sequencing analysis of lymphoblastoid cell lines (LCLs). By identifying differentially expressed genes (DEGs) and their enrichment in pathways and disease associations, we uncover insights into the molecular mechanisms underlying suicidal behavior. LCL gene expression analysis reveals significant enrichment in pathways related to primary immunodeficiency, ion channel, and cardiovascular defects. Notably, genes such as LCK, KCNN2, and GRIA1 emerged as pivotal in these pathways, suggesting their potential roles as biomarkers. Machine learning models trained on a subset of the patients and then tested on other patients demonstrate high accuracy in distinguishing low and high-risk of suicide in BD patients. Moreover, the study explores the genetic overlap between suicide-related genes and several psychiatric disorders. This comprehensive approach enhances our understanding of the complex interplay between genetics and suicidal behavior, laying the groundwork for future prevention strategies.