Inhaled "Muco-Trapping" Monoclonal Antibody Effectively Treats Established Respiratory Syncytial Virus (RSV) Infections.

Respiratory syncytial virus (RSV) causes substantial morbidity and mortality in infants, the immunocompromised, and the elderly. RSV infects the airway epithelium via the apical membrane and almost exclusively sheds progeny virions back into the airway mucus (AM), making RSV difficult to target by systemically administered therapies. An inhalable "muco-trapping" variant of motavizumab (Mota-MT), a potent neutralizing mAb against RSV F is engineered. Mota-MT traps RSV in AM via polyvalent Fc-mucin bonds, reducing the fraction of fast-moving RSV particles in both fresh pediatric and adult AM by ≈20-30-fold in a Fc-glycan dependent manner, and facilitates clearance from the airways of mice within minutes. Intranasal dosing of Mota-MT eliminated viral load in cotton rats within 2 days. Daily nebulized delivery of Mota-MT to RSV-infected neonatal lambs, beginning 3 days after infection when viral load is at its maximum, led to a 10 000-fold and 100 000-fold reduction in viral load in bronchoalveolar lavage and lung tissues relative to placebo control, respectively. Mota-MT-treated lambs exhibited reduced bronchiolitis, neutrophil infiltration, and airway remodeling than lambs receiving placebo or intramuscular palivizumab. The findings underscore inhaled delivery of muco-trapping mAbs as a promising strategy for the treatment of RSV and other acute respiratory infections.

The Cervicovaginal Microbiota-Host Interaction Modulates Chlamydia trachomatis Infection.

The mechanism(s) by which Lactobacillus-dominated cervicovaginal microbiota provide a barrier to Chlamydia trachomatis infection remain(s) unknown. Here we evaluate the impact of different Lactobacillus spp. identified via culture-independent metataxonomic analysis of C. trachomatis-infected women on C. trachomatis infection in a three-dimensional (3D) cervical epithelium model. Lactobacillus spp. that specifically produce d(-) lactic acid were associated with long-term protection against C. trachomatis infection, consistent with reduced protection associated with Lactobacillus iners, which does not produce this isoform, and with decreased epithelial cell proliferation, consistent with the observed prolonged protective effect. Transcriptomic analysis revealed that epigenetic modifications involving histone deacetylase-controlled pathways are integral to the cross talk between host and microbiota. These results highlight a fundamental mechanism whereby the cervicovaginal microbiota modulates host functions to protect against C. trachomatis infection.IMPORTANCE The vaginal microbiota is believed to protect women against Chlamydia trachomatis, the etiologic agent of the most prevalent sexually transmitted infection (STI) in developed countries. The mechanism underlying this protection has remained elusive. Here, we reveal the comprehensive strategy by which the cervicovaginal microbiota modulates host functions to protect against chlamydial infection, thereby providing a novel conceptual mechanistic understanding. Major implications of this work are that (i) the impact of the vaginal microbiota on the epithelium should be considered in future studies of chlamydial infection and other STIs and (ii) a fundamental understanding of the cervicovaginal microbiota's role in protection against STIs may enable the development of novel microbiome-based therapeutic strategies to protect women from infection and improve vaginal and cervical health.

Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D.

Particle tracking is a powerful biophysical tool that requires conversion of large video files into position time series, i.e., traces of the species of interest for data analysis. Current tracking methods, based on a limited set of input parameters to identify bright objects, are ill-equipped to handle the spectrum of spatiotemporal heterogeneity and poor signal-to-noise ratios typically presented by submicron species in complex biological environments. Extensive user involvement is frequently necessary to optimize and execute tracking methods, which is not only inefficient but introduces user bias. To develop a fully automated tracking method, we developed a convolutional neural network for particle localization from image data, comprising over 6,000 parameters, and used machine learning techniques to train the network on a diverse portfolio of video conditions. The neural network tracker provides unprecedented automation and accuracy, with exceptionally low false positive and false negative rates on both 2D and 3D simulated videos and 2D experimental videos of difficult-to-track species.