👤 Madeline J Dyke

Koolen-de Vries Syndrome (KdVS) is a neurodevelopmental disorder (NDD) caused by KANSL1 haploinsufficiency with no treatment options. To investigate neuronal network activity in KdVS, human induced pluripotent stem cell (hiPSC)-derived neurons from KdVS patients and controls were cultured on microelectrode arrays (MEAs). KdVS networks exhibited reduced burst rates and increased variability in burst rhythmicity. To bridge molecular and functional aspects of the syndrome, we applied MEA-seq, integrating electrophysiological recordings with high-throughput transcriptome profiling. This analysis revealed a negative correlation between the NDD-associated gene CLCN4 and network burst rate. Knockdown of CLCN4 in KdVS neurons restored network bursting toward control levels, highlighting how transcriptome profiling can identify mediators linking genetic defects to relevant physiological phenotypes. We also identified significant correlations between mitochondrial gene expression and network activity and consequently confirmed impaired mitochondrial function in KdVS hiPSC-derived neurons. Using the KdVS transcriptomic signature for computational screening against the LINCS drug perturbation database, we predicted compounds capable of reversing dysregulated gene expression. Ten candidates were prioritized for experimental validation, focusing on mitochondrial function. Among these, the antioxidant phloretin improved multiple aspects of the KdVS-related network activity phenotype, reduced reactive oxygen species, and rescued synaptic density across patient lines, revealing its potential as a therapeutic candidate. Together, these findings demonstrate that integrative MEA-seq profiling can connect molecular and electrophysiological alterations in KdVS, providing a robust framework for identifying novel drugs and druggable pathways for KdVS and potentially other neurodevelopmental disorders. Show less

Objectives: Periodontal disease (PD) and rheumatoid arthritis (RA) are known chronic conditions with sustained inflammation leading to osteolysis. Cardiovascular diseases (CVD) are frequent comorbidities that may arise from sustained inflammation associated with both PD and RA. In order to determine CVD risk, alterations at the molecular level need to be identified. The objective of this study, therefore, was to assess the relationship of CVD associated biomarkers in RA patients and how it is influenced by PD. Methods: The study consisted of patient (26 RA with PD, 21 RA without PD, 51 patients with PD only) and systemically and periodontally healthy control (n = 20) groups. Periodontal parameters bleeding on probing, probing pocket depth, and marginal bone loss were determined to characterize the patient groups. Proteomic analysis of 92 CVD-related protein biomarkers was performed using a multiplex proximity extension assay. Biomarkers were clustered using the search tool for retrieval of interacting genes (STRING) to determine protein−protein interaction (PPI) networks. Results: RA patients with PD had higher detection levels for 47% of the measured markers (ANGPT1, BOC, CCL17, CCL3, CD4, CD84, CTRC, FGF-21, FGF-23, GLO1, HAOX1, HB-EGF, hOSCAR, HSP 27, IL16, IL-17D, IL18, IL-27, IL6, LEP, LPL, MERTK, MMP12, MMP7, NEMO, PAPPA, PAR-1, PARP-1, PD-L2, PGF, PIgR, PRELP, RAGE, SCF, SLAMF7, SRC, THBS2, THPO, TNFRSF13B, TRAIL-R2, VEGFD, VSIG2, and XCL1) as compared to RA without PD. Furthermore, a strong biological network was identified amongst these proteins (clustering coefficient = 0.52, PPI enrichment p-value < 0.0001). Coefficients for protein clusters involved in CVD (0.59), metabolic (0.53), and skeletal (0.51) diseases were strongest in the PD group. Conclusion: Periodontal disease augments CVD-related biomarkers in RA through shared pathological clusters, concurrently enhancing metabolic and skeletal disease protein interactions, independent of autoimmune status. Show less