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Moradi, P.

Publications and source records attributed to Moradi, P..

3 recordsLinked to original sources

Paroxysmal Slow Wave Events as a diagnostic and predictive biomarker for post-traumatic epilepsy

Traumatic brain injury (TBI) is a major global health concern, affecting more than 40 million people annually. While most cases are mild and present with light symptoms, repeated mild injuries can result in delayed brain pathologies, including cognitive decline, neuropsychiatric complications, and post-traumatic epilepsy (PTE). PTE refers to recurring, unprovoked seizures occurring at least one week after TBI. While the link between moderate to severe TBI and PTE is well established, the epileptogenesis after repetitive mild TBI (rmTBI) is seldom studied. Currently, there are no biomarkers to identify those at risk of developing PTE, and its diagnosis is challenging. Here, we used a rat model to study PTE following rmTBI and assessed human EEG data to identify potential biomarkers for PTE. We employed a closed head TBI model to induce rmTBI, and recorded brain activity using electrocorticography (ECoG) between 2- and 6-months post-injury. Behavioral assessments and post-mortem analysis were also conducted. In humans, we analyzed EEG recordings from the Temple University database to investigate the potential of EEG-derived features for diagnosing PTE. At 6 months post injury, 70% of rmTBI animals developed PTE, compared to 22% in the control group (P=0.01). While neurological assessments following injury did not predict PTE, paroxysmal slow wave events (PSWEs) were found to be a reliable biomarker for PTE prediction. In humans, the percentage time in PSWEs was significantly elevated in PTE patients with epileptiform activity. In conclusion, we suggest PSWEs as a non-invasive, cost-effective biomarker for PTE in rodents and human patients.

neuroscience↗

A human DNA methylation atlas reveals principles of cell type-specific methylation and identifies thousands of cell type-specific regulatory elements

DNA methylation is a fundamental epigenetic mark that governs chromatin organization, cell identity, and gene expression. Here we describe a human methylome atlas, based on deep whole-genome bisulfite sequencing allowing fragment-level analysis across thousands of unique markers for 39 cell types sorted from 207 healthy tissue samples. Replicates of the same cell-type are >99.5% identical, demonstrating robustness of cell identity programs to genetic variation and environmental perturbation. Unsupervised clustering of the atlas recapitulates key elements of tissue ontogeny, and identifies methylation patterns retained since gastrulation. Loci uniquely unmethylated in an individual cell type often reside in transcriptional enhancers and contain DNA binding sites for tissue-specific transcriptional regulators. Uniquely hyper-methylated loci are rare and are enriched for CpG islands, polycomb targets, and CTCF binding sites, suggesting a novel role in shaping cell type-specific chromatin looping. The atlas provides an essential resource for interpretation of disease-associated genetic variants, and a wealth of potential tissue-specific biomarkers for use in liquid biopsies. Summary paragraphDNA methylation, a fundamental epigenetic mark, governs chromatin organization and gene expression1, thus defining the molecular identity of cells and providing a window into developmental processes with wide-ranging physiologic and clinical ramifications. Current DNA methylation datasets have limitations, typically including only a fraction of methylation sites, many from cell lines that underwent massive changes in culture or from tissues containing unspecified mixtures of cells2-6. We present a human methylome atlas based on deep whole-genome bisulfite sequencing of 39 sorted, primary cell types and use this dataset to address fundamental questions in developmental biology, physiology and pathology. Biological replicates are >99.5% identical, demonstrating unappreciated robustness to genetic variation and environmental perturbations. Clustering recapitulates key elements of tissue ontogeny, identifying methylation patterns retained since gastrulation. Loci uniquely unmethylated in individual cell types identify novel transcriptional enhancers and are enriched for tissue-specific transcription factors binding motifs. In contrast, loci uniquely hyper-methylated in specific cell types are rare, enriched for CpG islands and polycomb targets, and overlap CTCF binding sites, suggesting a novel role in shaping cell-type-specific chromatin looping. Finally, the atlas facilitates fragment-level deconvolution of tissue and plasma methylomes across thousands of cell-type specific regions to quantify their individual components at unprecedented resolution. The human cell-type-specific methylation atlas provides an essential resource for studying gene regulation by defining cell-type-specific distal enhancers and regulators of 3D organization, for identifying pathological changes in DNA methylation, and for the interpretation of methylation-based liquid biopsies. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY O_LIA deep methylation atlas of 39 human cell types, sorted from healthy samples C_LIO_LIMethylomes record developmental history of cells C_LIO_LIThousands of novel cell type-specific methylation markers C_LIO_LIHypo-methylation uncovers cell type-specific regulatory map of distal enhancers C_LIO_LIHyper-methylation across CTCF sites C_LIO_LICell type-specific biomarkers facilitate fragment-level deconvolution of tissues and cfDNA C_LI

genomics↗

Enhanced Waddington Landscape Model with Cell-CellCommunication Can Explain Molecular Mechanisms ofSelf-Organization

The molecular mechanisms of self-organization that orchestrate embryonic cells to create astonishing patterns have been among major questions of developmental biology. It is recently shown that embryonic stem cells (ESCs), when cultured on particular micropatterns, can self-organize and mimic early steps of pre-implantation embryogenesis. A systems-biology model to address this observation from a dynamical systems perspective is essential. Here, we propose a multicellular mathematical model for pattern formation during in vitro gastrulation of human ESCs. This model enhances the basic principles of Waddington epigenetic landscape with cell-cell communication, in order to enable pattern and tissue formation. To prevent overfitting of the model, there is a minimal number of parameters in the model, which are sufficient to address different experimental observations such as the formation of three germ layers and trophectoderm, responses to altered culture conditions and micropattern diameters, and unexpected spotted forms of the germ layers under certain conditions. This model provides a basis for in-silico modeling of self-organization.

systems biology↗