Fully-automated, high-throughput micro-computed tomography analysis of body composition enables therapeutic efficacy monitoring in preclinical models.

BACKGROUND: The ability to non-invasively measure body composition in mouse models of obesity and obesity-related disorders is essential for elucidating mechanisms of metabolic regulation and monitoring the effects of novel treatments. These studies aimed to develop a fully automated, high-throughput micro-computed tomography (micro-CT)-based image analysis technique for longitudinal quantitation of adipose, non-adipose and lean tissue as well as bone and demonstrate utility for assessing the effects of two distinct treatments. METHODS: An initial validation study was performed in diet-induced obesity (DIO) and control mice on a vivaCT 75 micro-CT system. Subsequently, four groups of DIO mice were imaged pre- and post-treatment with an experimental agonistic antibody specific for anti-fibroblast growth factor receptor 1 (anti-FGFR1, R1MAb1), control immunoglobulin G antibody, a known anorectic antiobesity drug (rimonabant, SR141716), or solvent control. The body composition analysis technique was then ported to a faster micro-CT system (CT120) to markedly increase throughput as well as to evaluate the use of micro-CT image intensity for hepatic lipid content in DIO and control mice. Ex vivo chemical analysis and colorimetric analysis of the liver triglycerides were performed as the standard metrics for correlation with body composition and hepatic lipid status, respectively. RESULTS: Micro-CT-based body composition measures correlate with ex vivo chemical analysis metrics and enable distinction between DIO and control mice. R1MAb1 and rimonabant have differing effects on body composition as assessed by micro-CT. High-throughput body composition imaging is possible using a modified CT120 system. Micro-CT also provides a non-invasive assessment of hepatic lipid content. CONCLUSIONS: This work describes, validates and demonstrates utility of a fully automated image analysis technique to quantify in vivo micro-CT-derived measures of adipose, non-adipose and lean tissue, as well as bone. These body composition metrics highly correlate with standard ex vivo chemical analysis and enable longitudinal evaluation of body composition and therapeutic efficacy monitoring.

Fitness valleys constrain HIV-1's adaptation to its secondary chemokine coreceptor.

Fitness valleys, in which mutations at different loci are singly deleterious but jointly beneficial, arise because of reciprocal sign epistasis. Recent theoretical work provides analytical approximations of times to cross fitness valleys via three mechanisms: sequential fixation, stochastic tunnelling and recombination. These times depend critically on the effective population size (N(e)). Human immunodeficiency virus type 1 (HIV-1) encounters fitness valleys in adapting to its secondary cell-surface chemokine coreceptor, CXCR4. Adaptation to CXCR4 tends to occur late in infection and only in about 50% of patients and is associated with disease progression. It has been hypothesized that the need to cross fitness valleys may explain the delayed and inconsistent adaptation to CXCR4. We have identified four fitness valleys from a previous study of fitness epistasis in adaptation to CXCR4 and use estimates of the within-patient variance N(e) for different patient treatment statuses and infection stages (conditions) to estimate times to cross the valleys. These valleys may be crossed predominantly by stochastic tunnelling, although mean crossing times are consistently longer than the durations of the conditions for which they are calculated. These results were confirmed with stochastic simulation. Simulations show that crossing times for a given condition are highly variable and that for each condition there is a low probability of crossing each valley. These findings support the hypothesis that fitness valleys constrain the adaptation of HIV-1 to CXCR4. This study provides the first detailed analysis of the evolutionary dynamics associated with empirical fitness valleys.