Fluoromodule-based reporter/probes designed for in vivo fluorescence imaging

Optical imaging of whole, living animals has proven to be a powerful tool in multiple areas of preclinical research and has allowed noninvasive monitoring of immune responses, tumor and pathogen growth, and treatment responses in longitudinal studies. However, fluorescence-based studies in animals are challenging because tissue absorbs and autofluoresces strongly in the visible light spectrum. These optical properties drive development and use of fluorescent labels that absorb and emit at longer wavelengths. Here, we present a far-red absorbing fluoromodule-based reporter/probe system and show that this system can be used for imaging in living mice. The probe we developed is a fluorogenic dye called SC1 that is dark in solution but highly fluorescent when bound to its cognate reporter, Mars1. The reporter/probe complex, or fluoromodule, produced peak emission near 730 nm. Mars1 was able to bind a variety of structurally similar probes that differ in color and membrane permeability. We demonstrated that a tool kit of multiple probes can be used to label extracellular and intracellular reporter-tagged receptor pools with 2 colors. Imaging studies may benefit from this far-red excited reporter/probe system, which features tight coupling between probe fluorescence and reporter binding and offers the option of using an expandable family of fluorogenic probes with a single reporter gene

Association and interaction of PPAR-complex gene variants with latent traits of left ventricular diastolic function

<p>Abstract</p> <p>Background</p> <p>Abnormalities in myocardial metabolism and/or regulatory genes have been implicated in left ventricular systolic dysfunction. However, the extent to which these modulate left ventricular diastolic function (LVDF) is uncertain.</p> <p>Methods</p> <p>Independent component analysis was applied to extract latent LVDF traits from 14 measured echocardiography-derived endophenotypes of LVDF in 403 Caucasians. Genetic association was assessed between measured and latent LVDF traits and 64 single nucleotide polymorphisms (SNPs) in three peroxisome proliferator-activated receptor <it>(PPAR)</it>-complex genes involved in the transcriptional regulation of fatty acid metabolism.</p> <p>Results</p> <p>By linear regression analysis, 7 SNPs (4 in <it>PPARA</it>, 2 in <it>PPARGC1A</it>, 1 in <it>PPARG</it>) were significantly associated with the latent LVDF trait, whereas a range of 0-4 SNPs were associated with each of the 14 measured echocardiography-derived endophenotypes. Frequency distribution of <it>P </it>values showed a greater proportion of significant associations with the latent LVDF trait than for the measured endophenotypes, suggesting that analyses of the latent trait improved detection of the genetic underpinnings of LVDF. Ridge regression was applied to investigate within-gene and gene-gene interactions. In the within-gene analysis, there were five significant pair-wise interactions in <it>PPARGC1A </it>and none in <it>PPARA </it>or <it>PPARG</it>. In the gene-gene analysis, significant interactions were found between rs4253655 in <it>PPARA </it>and rs1873532 (p = 0.02) and rs7672915 (p = 0.02), both in <it>PPARGC1A</it>, and between rs1151996 in <it>PPARG </it>and rs4697046 in <it>PPARGC1A </it>(p = 0.01).</p> <p>Conclusions</p> <p>Myocardial metabolism <it>PPAR</it>-complex genes, including within and between genes interactions, may play an important role modulating left ventricular diastolic function.</p

Recommendations for the design of laboratory studies on non-target arthropods for risk assessment of genetically engineered plants

This paper provides recommendations on experimental design for early-tier laboratory studies used in risk assessments to evaluate potential adverse impacts of arthropod-resistant genetically engineered (GE) plants on non-target arthropods (NTAs). While we rely heavily on the currently used proteins from Bacillus thuringiensis (Bt) in this discussion, the concepts apply to other arthropod-active proteins. A risk may exist if the newly acquired trait of the GE plant has adverse effects on NTAs when they are exposed to the arthropod-active protein. Typically, the risk assessment follows a tiered approach that starts with laboratory studies under worst-case exposure conditions; such studies have a high ability to detect adverse effects on non-target species. Clear guidance on how such data are produced in laboratory studies assists the product developers and risk assessors. The studies should be reproducible and test clearly defined risk hypotheses. These properties contribute to the robustness of, and confidence in, environmental risk assessments for GE plants. Data from NTA studies, collected during the analysis phase of an environmental risk assessment, are critical to the outcome of the assessment and ultimately the decision taken by regulatory authorities on the release of a GE plant. Confidence in the results of early-tier laboratory studies is a precondition for the acceptance of data across regulatory jurisdictions and should encourage agencies to share useful information and thus avoid redundant testing

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery