HIV broadly neutralizing antibody escapability drives the therapeutic efficacy of vectored immunotherapy.

Broadly neutralizing antibodies (bNAbs) have shown great promise for prevention and treatment of HIV infection. Breadth of bNAb neutralization, measured in vitro across panels of diverse viral isolates, is often used as a predictor of clinical potential. However, recent prevention studies demonstrate that the clinical efficacy of a broad and potent bNAb (VRC01) is undermined by neutralization resistance of circulating strains. Using HIV-infected humanized mice, we find that therapeutic efficacy of bNAbs delivered as Vectored ImmunoTherapy (VIT) is a function of both the fitness cost and resistance benefit of mutations that emerge during viral escape, which we term 'escapability'. Applying this mechanistic framework, we find that the sequence of the envelope V5-loop alters the resistance benefits of mutants that arise during escape, thereby impacting the therapeutic efficacy of VIT-mediated viral suppression. We also find that an emtricitabine-based antiretroviral drug regimen dramatically enhances the efficacy of VIT, by reducing the fitness of mutants along the escape path. Our findings demonstrate that bNAb escapability is a key determinant to consider in the rational design of antibody regimens with maximal efficacy and illustrates a tractable means of minimizing viral escape from existing bNAbs.

Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning.

Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole-derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time-series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.

The way we walk ­ our 'gait' ­ is a key indicator of health. Gait irregularities like limping, shuffling or a slow pace can be signs of muscle or joint problems. Assessing a patient's gait is therefore an important element in diagnosing these conditions, and in evaluating whether treatments are working. Gait is often assessed via a simple visual inspection, with patients being asked to walk back and forth in a doctor's office. While quick and easy, this approach is highly subjective and therefore imprecise. 'Objective gait analysis' is a more accurate alternative, but it relies on tests being conducted in specialised laboratories with large-scale, expensive equipment operated by highly trained staff. Unfortunately, this means that gait laboratories are not accessible for everyday clinical use. In response, Wipperman et al. aimed to develop a low-cost alternative to the complex equipment used in gait laboratories. To do this, they harnessed wearable sensor technologies ­ devices that can directly measure physiological data while embedded in clothing or attached to the user. Wearable sensors have the advantage of being cheap, easy to use, and able to provide clinically useful information without specially trained staff. Wipperman et al. analysed data from classic gait laboratory devices, as well as 'digital insoles' equipped with sensors that captured foot movements and pressure as participants walked. The analysis first 'trained' on data from gait laboratories (called force plates) and then applied the method to gait measurements obtained from digital insoles worn by either healthy participants or patients with knee problems. Analysis of the pressure data from the insoles confirmed that they could accurately predict which measurements were from healthy individuals, and which were from patients. The gait characteristics detected by the insoles were also comparable to lab-based measurements ­ in other words, the insoles provided similar type and quality of data as a gait laboratory. Further analysis revealed that information from just a single step could reveal additional information about the subject's walking. These results support the use of wearable devices as a simple and relatively inexpensive way to measure gait in everyday clinical practice, without the need for specialised laboratories and visits to the doctor's office. Although the digital insoles will require further analytical and clinical study before they can be widely used, Wipperman et al. hope they will eventually make monitoring muscle and joint conditions easier and more affordable.

Glutaminase 2 knockdown reduces hyperammonemia and associated lethality of urea cycle disorder mouse model.

Amino acids, the building blocks of proteins in the cells and tissues, are of fundamental importance for cell survival, maintenance, and proliferation. The liver plays a critical role in amino acid metabolism and detoxication of byproducts such as ammonia. Urea cycle disorders with hyperammonemia remain difficult to treat and eventually necessitate liver transplantation. In this study, ornithine transcarbamylase deficient (Otcspf-ash ) mouse model was used to test whether knockdown of a key glutamine metabolism enzyme glutaminase 2 (GLS2, gene name: Gls2) or glutamate dehydrogenase 1 (GLUD1, gene name: Glud1) could rescue the hyperammonemia and associated lethality induced by a high protein diet. We found that reduced hepatic expression of Gls2 but not Glud1 by AAV8-mediated delivery of a short hairpin RNA in Otcspf-ash mice diminished hyperammonemia and reduced lethality. Knockdown of Gls2 but not Glud1 in Otcspf-ash mice exhibited reduced body weight loss and increased plasma glutamine concentration. These data suggest that Gls2 hepatic knockdown could potentially help alleviate risk for hyperammonemia and other clinical manifestations of patients suffering from defects in the urea cycle.