Plasticity mechanisms of genetically distinct Purkinje cells.

Despite its uniform appearance, the cerebellar cortex is highly heterogeneous in terms of structure, genetics and physiology. Purkinje cells (PCs), the principal and sole output neurons of the cerebellar cortex, can be categorized into multiple populations that differentially express molecular markers and display distinctive physiological features. Such features include action potential rate, but also their propensity for synaptic and intrinsic plasticity. However, the precise molecular and genetic factors that correlate with the differential physiological properties of PCs remain elusive. In this article, we provide a detailed overview of the cellular mechanisms that regulate PC activity and plasticity. We further perform a pathway analysis to highlight how molecular characteristics of specific PC populations may influence their physiology and plasticity mechanisms.

A patient-based murine model recapitulates human STAT3 gain-of-function syndrome.

STAT3 gain-of-function (GOF) variants results in a heterogeneous clinical syndrome characterized by early onset immunodeficiency, multi-organ autoimmunity, and lymphoproliferation. While 191 documented cases with STAT3 GOF variants have been reported, the impact of individual variants on immune regulation and the broad clinical spectrum remains unclear. We developed a Stat3p.L387R mouse model, mirroring a variant identified in a family exhibiting common STAT3 GOF symptoms, and rare phenotypes including pulmonary hypertension and retinal vasculitis. In vitro experiments revealed increased STAT3 phosphorylation, nuclear migration, and DNA binding of the variant. Our Stat3p.L387R model displayed similar traits from previous Stat3GOF strains, such as splenomegaly and lymphadenopathy. Notably, Stat3p.L387R/+ mice exhibited heightened embryonic lethality compared to prior Stat3GOF/+ models and ocular abnormalities were observed. This research underscores the variant-specific pathology in Stat3p.L387R/+ mice, highlighting the ability to recapitulate human STAT3 GOF syndrome in patient-specific transgenic murine models. Additionally, such models could facilitate tailored treatment development.

Association of blood cell-based inflammatory markers with gut microbiota and cancer incidence in the Rotterdam study.

The immune response-gut microbiota interaction is implicated in various human diseases, including cancer. Identifying the link between the gut microbiota and systemic inflammatory markers and their association with cancer will be important for our understanding of cancer etiology. The current study was performed on 8090 participants from the population-based Rotterdam study. We found a significant association (false discovery rate [FDR] ≤0.05) between lymphocytes and three gut microbial taxa, namely the family Streptococcaceae, genus Streptococcus, and order Lactobacillales. In addition, we identified 95 gut microbial taxa that were associated with inflammatory markers (p < 0.05). Analyzing the cancer data, we observed a significant association between higher systemic immune-inflammation index (SII) levels at baseline (hazard ratio (HR): 1.65 [95% confidence interval (CI); 1.10-2.46, p ≤ 0.05]) and a higher count of lymphocytes (HR: 1.38 [95% CI: 1.15-1.65, p ≤ 0.05]) and granulocytes (HR: 1.69 [95% CI: 1.40-2.03, p ≤ 0.05]) with increased risk of lung cancer after adjusting for age, sex, body mass index (BMI), and study cohort. This association was lost for SII and lymphocytes after additional adjustment for smoking (SII = HR:1.46 [95% CI: 0.96-2.22, p = 0.07] and lymphocytes = HR: 1.19 [95% CI: 0.97-1.46, p = 0.08]). In the stratified analysis, higher count of lymphocyte and granulocytes at baseline were associated with an increased risk of lung cancer in smokers after adjusting for age, sex, BMI, and study cohort (HR: 1.33 [95% CI: 1.09-1.62, p ≤0.05] and HR: 1.57 [95% CI: 1.28-1.92, p ≤0.05], respectively). Our study revealed a positive association between gut microbiota, higher SII levels, and higher lymphocyte and granulocyte counts, with an increased risk of developing lung cancer.

Bioinformatic meta-analysis reveals novel differentially expressed genes and pathways in sarcoidosis.

Introduction: Sarcoidosis is a multi-system inflammatory disease of unknown origin with heterogeneous clinical manifestations varying from a single organ non-caseating granuloma site to chronic systemic inflammation and fibrosis. Gene expression studies have suggested several genes and pathways implicated in the pathogenesis of sarcoidosis, however, due to differences in study design and variable statistical approaches, results were frequently not reproducible or concordant. Therefore, meta-analysis of sarcoidosis gene-expression datasets is of great importance to robustly establish differentially expressed genes and signalling pathways. Methods: We performed meta-analysis on 22 published gene-expression studies on sarcoidosis. Datasets were analysed systematically using same statistical cut-offs. Differentially expressed genes were identified by pooling of p-values using Edgington's method and analysed for pathways using Ingenuity Pathway Analysis software. Results: A consistent and significant signature of novel and well-known genes was identified, those collectively implicated both type I and type II interferon mediated signalling pathways in sarcoidosis. In silico functional analysis showed consistent downregulation of eukaryotic initiation factor 2 signalling, whereas cytokines like interferons and transcription factor STAT1 were upregulated. Furthermore, we analysed affected tissues to detect differentially expressed genes likely to be involved in granuloma biology. This revealed that matrix metallopeptidase 12 was exclusively upregulated in affected tissues, suggesting a crucial role in disease pathogenesis. Discussion: Our analysis provides a concise gene signature in sarcoidosis and expands our knowledge about the pathogenesis. Our results are of importance to improve current diagnostic approaches and monitoring strategies as well as in the development of targeted therapeutics.

Proteomic biomarkers related to obesity in heart failure with reduced ejection fraction and their associations with outcome.

OBJECTIVE: Heart failure (HF) pathophysiology in patients with obesity may be distinct. To study these features, we identified obesity-related biomarkers from 4210 circulating proteins in patients with HF with reduced ejection fraction (HFrEF) and examined associations of these proteins with HF prognosis and biological mechanisms. METHODS: In 373 patients with trimonthly blood sampling during a median follow-up of 2.1 (25th-75th percentile: 1.1-2.6) years, we applied an aptamer-based multiplex approach measuring 4210 proteins in baseline samples and the last two samples before study end. Associations between obesity (BMI > 30 kg/m2) and baseline protein levels were analyzed. Subsequently, associations of serially measured obesity-related proteins with biological mechanisms and the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, left ventricular assist device implantation, and heart transplantation) were examined. RESULTS: Obesity was identified in 26% (96/373) of patients. A total of 30% (112/373) experienced a PEP (with obesity: 26% [25/96] vs. without obesity: 31% [87/277]). A total of 141/4210 proteins were linked to obesity, reflecting mechanisms of neuron projection development, cell adhesion, and muscle cell migration. A total of 50/141 proteins were associated with the PEP, of which 12 proteins related to atherosclerosis or hypertrophy provided prognostic information beyond clinical characteristics, N-terminal pro-B-type natriuretic peptide, and high-sensitivity troponin T. CONCLUSIONS: Patients with HFrEF and obesity show distinct proteomic profiles compared to patients with HFrEF without obesity. Obesity-related proteins are independently associated with HF outcome. These proteins carry potential to improve management of obesity-related HF and could be leads for future research.