Exogenous interleukin-1 beta stimulation regulates equine tenocyte function and gene expression in three-dimensional culture which can be rescued by pharmacological inhibition of interleukin 1 receptor, but not nuclear factor kappa B, signaling.

We investigated how Interleukin 1 beta (IL-1ß) impacts equine tenocyte function and global gene expression in vitro and determined if these effects could be rescued by pharmacologically inhibiting nuclear factor-κB (NF-KB) or interleukin 1 signalling. Equine superficial digital flexor tenocytes were cultured in three-dimensional (3D) collagen gels and stimulated with IL-1ß for two-weeks, with gel contraction and interleukin 6 (IL6) measured throughout and transcriptomic analysis performed at day 14. The impact of three NF-KB inhibitors on gel contraction and IL6 secretion were measured in 3D culture, with NF-KB-P65 nuclear translocation by immunofluorescence and gene expression by qPCR measured in two-dimensional (2D) monolayer culture. In addition, daily 3D gel contraction and transcriptomic analysis was performed on interleukin 1 receptor antagonist-treated 3D gels at day 14. IL-1ß increased NF-KB-P65 nuclear translocation in 2D culture and IL6 secretion in 3D culture, but reduced daily tenocyte 3D gel contraction and impacted > 2500 genes at day 14, with enrichment for NF-KB signaling. Administering direct pharmacological inhibitors of NF-KB did reduce NF-KB-P65 nuclear translocation, but had no effect on 3D gel contraction or IL6 secretion in the presence of IL-1ß. However, IL1Ra restored 3D gel contraction and partially rescued global gene expression. Tenocyte 3D gel contraction and gene expression is adversely impacted by IL-1ß which can only be rescued by blockade of interleukin 1 receptor, but not NF-KB, signalling.

Subphenotyping of Mexican Patients With COVID-19 at Preadmission To Anticipate Severity Stratification: Age-Sex Unbiased Meta-Clustering Technique.

BACKGROUND: The COVID-19 pandemic has led to an unprecedented global health care challenge for both medical institutions and researchers. Recognizing different COVID-19 subphenotypes-the division of populations of patients into more meaningful subgroups driven by clinical features-and their severity characterization may assist clinicians during the clinical course, the vaccination process, research efforts, the surveillance system, and the allocation of limited resources. OBJECTIVE: We aimed to discover age-sex unbiased COVID-19 patient subphenotypes based on easily available phenotypical data before admission, such as pre-existing comorbidities, lifestyle habits, and demographic features, to study the potential early severity stratification capabilities of the discovered subgroups through characterizing their severity patterns, including prognostic, intensive care unit (ICU), and morbimortality outcomes. METHODS: We used the Mexican Government COVID-19 open data, including 778,692 SARS-CoV-2 population-based patient-level data as of September 2020. We applied a meta-clustering technique that consists of a 2-stage clustering approach combining dimensionality reduction (ie, principal components analysis and multiple correspondence analysis) and hierarchical clustering using the Ward minimum variance method with Euclidean squared distance. RESULTS: In the independent age-sex clustering analyses, 56 clusters supported 11 clinically distinguishable meta-clusters (MCs). MCs 1-3 showed high recovery rates (90.27%-95.22%), including healthy patients of all ages, children with comorbidities and priority in receiving medical resources (ie, higher rates of hospitalization, intubation, and ICU admission) compared with other adult subgroups that have similar conditions, and young obese smokers. MCs 4-5 showed moderate recovery rates (81.30%-82.81%), including patients with hypertension or diabetes of all ages and obese patients with pneumonia, hypertension, and diabetes. MCs 6-11 showed low recovery rates (53.96%-66.94%), including immunosuppressed patients with high comorbidity rates, patients with chronic kidney disease with a poor survival length and probability of recovery, older smokers with chronic obstructive pulmonary disease, older adults with severe diabetes and hypertension, and the oldest obese smokers with chronic obstructive pulmonary disease and mild cardiovascular disease. Group outcomes conformed to the recent literature on dedicated age-sex groups. Mexican states and several types of clinical institutions showed relevant heterogeneity regarding severity, potentially linked to socioeconomic or health inequalities. CONCLUSIONS: The proposed 2-stage cluster analysis methodology produced a discriminative characterization of the sample and explainability over age and sex. These results can potentially help in understanding the clinical patient and their stratification for automated early triage before further tests and laboratory results are available and even in locations where additional tests are not available or to help decide resource allocation among vulnerable subgroups such as to prioritize vaccination or treatments.

Deciphering Rhizosphere Microbiome Assembly of Castanea henryi in Plantation and Natural Forest.

Based on the importance and sensitivity of microbial communities to changes in the forest ecosystem, soil microorganisms can be used to indicate the health of the forest system. The metagenome sequencing was used to analyze the changes of microbial communities between natural and plantation Castanea henryi forests for understanding the effect of forest types on soil microbial communities. Our result showed the soil microbial diversity and richness were higher in the natural forests than in the plantation. Proteobacteria, Actinobacteria, and Acidobacteria are the dominant categories in the C. henryi rhizosphere, and Proteobacteria and Actinobacteria were significantly enriched in the natural forest while Acidobacteria was significantly enriched in the plantation. Meanwhile, the functional gene diversity and the abundance of functions in the natural forest were higher than that of the plantation. Furthermore, we found that the microbial network in the natural forests had more complex than in the plantation. We also emphasized the low-abundance taxa may play an important role in the network structure. These results clearly showed that microbial communities, in response to different forest types, provide valuable information to manipulate microbiomes to improve soil conditions of plantation.