Loss of low-molecular-weight protein tyrosine phosphatase shows limited improvement in glucose tolerance but causes mild cardiac hypertrophy in mice.

Insulin resistance is a major public health burden that often results in other comorbidities including type 2 diabetes, nonalcoholic fatty liver disease (NAFLD), and cardiovascular disease. An insulin sensitizer has the potential to become a disease-modifying therapy. It remains an unmet medical need to identify therapeutics that target the insulin signaling pathway to treat insulin resistance. Low-molecular-weight protein tyrosine phosphatase (LMPTP) negatively regulates insulin signaling and has emerged as a potential therapeutic target for insulin sensitization. Genetic studies have demonstrated that LMPTP is positively associated with obesity in humans and promotes insulin resistance in rodents. A recent study showed that pharmacological inhibition or genetic deletion of LMPTP protects mice from high-fat diet-induced insulin resistance and diabetes. Here, we show that loss of LMPTP by genetic deletion has no significant effects on improving glucose tolerance in lean or diet-induced obese mice. Furthermore, our data demonstrate that LMPTP deficiency potentiates cardiac hypertrophy that leads to mild cardiac dysfunction. Our findings suggest that the development of LMPTP inhibitors for the treatment of insulin resistance and type 2 diabetes should be reevaluated, and further studies are needed to characterize the molecular and pathophysiological role of LMPTP.NEW & NOTEWORTHY Inhibition of LMPTP with a small-molecule inhibitor, Cmpd23, improves glucose tolerance in mice as reported earlier. However, genetic deficiency of the LMPTP-encoding gene, Acp1, has limited effects on glucose metabolism but leads to mild cardiac hypertrophy in mice. The findings suggest the potential off-target effects of Cmpd23 and call for reevaluation of LMPTP as a therapeutic target for the treatment of insulin resistance and type 2 diabetes.

Soft windowing application to improve analysis of high-throughput phenotyping data.

MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce 'soft windowing', a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.