Citations

CD4 + effector T helper 1 (T H 1) cells drive inflammatory bowel disease (IBD). Here, we investigated how integrated signaling, metabolic, and epigenetic programs sustain their pathogenic persistence. We found that enforcing mitochondrial pyruvate metabolism with methyl pyruvate (MePyr) suppressed T H 1 cell effector function and induced the acquisition of a regulatory-like phenotype. MePyr suppressed the activity of glycogen synthase kinase 3β (GSK3β), promoting a metabolic shift from glycolysis toward oxidative phosphorylation (OXPHOS) and limiting the accumulation of intracellular acetyl-CoA. This metabolic reprogramming decreased histone H3 acetylation at genes encoding inflammatory cytokines, thereby decreasing T H 1 cell–associated gene expression. Restoration of inflammatory cytokine production by acetate supplementation or histone deacetylase inhibition established acetyl-CoA–dependent histone acetylation as a mechanistic link between metabolic flux and effector function. Conversely, the cytokine interleukin-21 enhanced T H 1 cell effector activity by promoting a GSK3β-dependent shift from OXPHOS to glycolysis. Single-cell transcriptomic analysis of Crohn’s disease tissue revealed enrichment of glycolytic gene programs in nonregulatory CD4 + T cells. Consistent with these findings, pharmacologic inhibition of GSK3β attenuated disease severity in a T cell–transfer mouse model of colitis. Together, these findings identify a GSK3β-dependent metabolic-epigenetic axis that sustains T H 1 cell pathogenicity and highlight GSK3β as a potential therapeutic target for IBD.

Abstract Recent evidence implicates somatic mutations in disease-associated genes across multiple neurodegenerative disorders, including the identification of two somatic TARDBP variants as a putative cause of frontotemporal lobar degeneration with TDP-43 pathology type C (FTLD-TDP type C). However, these findings remain anecdotal. We performed single-nucleus amplicon sequencing of 39,800 neurons from the superior temporal gyrus of sporadic FTLD-TDP type C patients (34,738 neurons, 52 individuals) and non-demented controls (5,062 neurons, 26 individuals) using the Mission Bio Tapestri platform and investigated somatic variants in TARDBP and other FTLD-TDP-associated genes in a case-control setting. We uncovered an extensive landscape of ultra-low-frequency (<1%) somatic mutations across all analysed genes, including TARDBP . Rare somatic occurrences of known ALS/FTD-associated germline TARDBP variants, including p.N267S and p.M337V, were identified exclusively in patients. Among all targeted genes, TARDBP had the highest proportion of neurons harbouring a somatic variant, and this mutational burden was lower in individuals who died at an older age. Strikingly, the C-terminal domain of TDP-43, where most ALS/FTD germline pathogenic variants reside, exhibited a lower neuronal mutational burden than its other domains. This suggests that neurons harbouring damaging mutations may undergo progressive loss prior to autopsy, leaving only rare survivors at frequencies too low to detect by bulk or small-scale single-cell approaches. Together, our findings provide an unprecedented resolution into the somatic mutational landscape of post-mitotic neurons in FTLD-TDP type C and reveal a gene-specific layer of neuronal mosaicism that current single-cell WGS studies fail to detect.

Objectives: Pulmonary function tests (PFTs) are essential for diagnosis, management, and follow-up of a wide range of respiratory conditions as they provide quantitative insights into various aspects of lung function. Precision in ordering and scheduling PFTs, like any test, is crucial as errors can lead to misdiagnoses, delayed treatments, unnecessary retesting, inflated healthcare costs, and potentially compromised patient care. Hence, the aim of this quality improvement initiative was to reduce the PFT ordering error rate to below 5% by redesigning the ordering workflow and implementing a standardized order set tailored to specific clinical scenarios. Methods: The quality improvement initiative was implemented from August 2023 to March 2024. Data was collected from the electronic medical record (EMR) using predefined operational definitions to identify incorrect PFT orders and scheduling errors. A redesigned orderset was created to align with specific clinical presentations, including symptoms, suspected diagnosis, confirmed diagnosis, and presurgical evaluation. Scheduling was also restructured to match the expected testing time for each category. Education sessions were conducted with ordering providers, and ongoing feedback loops were established. Results: Among 575 preintervention PFT orders, 130 (22.6%) contained errors. Following implementation, review of 607 postintervention orders showed a reduction in error rates to 4.1%, representing an ∼80% improvement ( P <0.01). There was no increase in unnecessary testing, supporting the effectiveness of the intervention without unintended negative consequences. Conclusions: Implementation of a knowledge-based CDSS with standardized order sets significantly reduced PFT ordering errors and variability, improving guideline adherence and care quality. Continued refinement and integration with evolving technologies are essential for broader applicability.

Assessing balance is crucial in neurological rehabilitation, yet while wearable sensors enable real-world monitoring, identifying reliable digital biomarkers remains challenging. This study utilized a high-fidelity instrumented walkway to determine which gait parameters best predict balance impairment, providing robust targets for future wearable applications. We analyzed 49 steady-state gait metrics from 140 individuals with diverse neurological conditions. Using statistical analysis and machine learning, we evaluated these parameters against objective force plate sway scores and clinical fall-history labels. Group analysis identified 16 parameters significantly distinguishing fallers from non-fallers, and a neural network classified fallers with an area under the curve of 0.75. Across all analytical approaches, overall gait variability, e.g., Stride Width S.D. and the Gait Variability Index, emerged as a universal predictor of balance impairment and fall risk. Furthermore, while traditional linear models emphasized spatial postural control, machine learning classification uniquely identified inter-limb asymmetry as a premier driver of fall prediction. These findings indicate that instrumented gait analysis effectively identifies digital biomarkers for balance deficits. Isolating these specific metrics provides a clear blueprint for meaningful metrics required for continuous objective monitoring and future development of personalized, adaptive rehabilitation strategies.