Mycobacterium tuberculosis cough aerosol culture status associates with host characteristics and inflammatory profiles.

Interrupting transmission events is critical to tuberculosis control. Cough-generated aerosol cultures predict tuberculosis transmission better than microbiological or clinical markers. We hypothesize that highly infectious individuals with pulmonary tuberculosis (positive for cough aerosol cultures) have elevated inflammatory markers and unique transcriptional profiles compared to less infectious individuals.

Tuberculosis Infectiousness is Associated with Distinct Clinical and Inflammatory Profiles.

Interrupting transmission events to prevent new acquisition of infection and disease is a critical part of tuberculosis (TB) control efforts. However, knowledge gaps in understanding the biology and determinants of TB transmission, including poor estimates of individual infectiousness and the lack of accurate and convenient biomarkers, undermine efforts to develop interventions. Cough-generated aerosol cultures have been found to predict TB transmission better than any microbiological or clinical markers in cohorts from Uganda and Brazil.

Nasal and blood transcriptomic pathways underpinning the clinical response to grass pollen immunotherapy.

Background: Allergen immunotherapy (AIT) is a well-established disease-modifying therapy for allergic rhinitis, yet the fundamental mechanisms underlying its clinical effect remain inadequately understood. Gauging Response in Allergic Rhinitis to Sublingual and Subcutaneous Immunotherapy was a randomized, double-blind, placebo-controlled trial of individuals allergic to timothy grass who received 2 years of placebo (n = 30), subcutaneous immunotherapy (SCIT) (n = 27), or sublingual immunotherapy (SLIT) (n = 27) and were then followed for 1 additional year.

Objective: We used yearly biospecimens from the Gauging Response in Allergic Rhinitis to Sublingual and Subcutaneous Immunotherapy study to identify molecular mechanisms of response.

Kimma: flexible linear mixed effects modeling with kinship covariance for RNA-seq data.

Motivation: The identification of differentially expressed genes (DEGs) from transcriptomic datasets is a major avenue of research across diverse disciplines. However, current bioinformatic tools do not support covariance matrices in DEG modeling. Here, we introduce kimma (Kinship In Mixed Model Analysis), an open-source R package for flexible linear mixed effects modeling including covariates, weights, random effects, covariance matrices, and fit metrics.