Lipid Nanoparticles Deliver the Therapeutic VEGFA mRNA In Vitro and In Vivo and Transform Extracellular Vesicles for Their Functional Extensions.

Lipid nanoparticles (LNPs) are currently used to transport functional mRNAs, such as COVID-19 mRNA vaccines. The delivery of angiogenic molecules, such as therapeutic VEGF-A mRNA, to ischemic tissues for producing new blood vessels is an emerging strategy for the treatment of cardiovascular diseases. Here, the authors deliver VEGF-A mRNA via LNPs and study stoichiometric quantification of their uptake kinetics and how the transport of exogenous LNP-mRNAs between cells is functionally extended by cells' own vehicles called extracellular vesicles (EVs). The results show that cellular uptake of LNPs and their mRNA molecules occurs quickly, and that the translation of exogenously delivered mRNA begins immediately. Following the VEGF-A mRNA delivery to cells via LNPs, a fraction of internalized VEGF-A mRNA is secreted via EVs. The overexpressed VEGF-A mRNA is detected in EVs secreted from three different cell types. Additionally, RNA-Seq analysis reveals that as cells' response to LNP-VEGF-A mRNA treatment, several overexpressed proangiogenic transcripts are packaged into EVs. EVs are further deployed to deliver VEGF-A mRNA in vitro and in vivo. Upon equal amount of VEGF-A mRNA delivery via three EV types or LNPs in vitro, EVs from cardiac progenitor cells are the most efficient in promoting angiogenesis per amount of VEGF-A protein produced. Intravenous administration of luciferase mRNA shows that EVs could distribute translatable mRNA to different organs with the highest amounts of luciferase detected in the liver. Direct injections of VEGF-A mRNA (via EVs or LNPs) into mice heart result in locally produced VEGF-A protein without spillover to liver and circulation. In addition, EVs from cardiac progenitor cells cause minimal production of inflammatory cytokines in cardiac tissue compared with all other treatment types. Collectively, the data demonstrate that LNPs transform EVs as functional extensions to distribute therapeutic mRNA between cells, where EVs deliver this mRNA differently than LNPs.

Development of an ObLiGaRe Doxycycline Inducible Cas9 system for pre-clinical cancer drug discovery.

The CRISPR-Cas9 system has increased the speed and precision of genetic editing in cells and animals. However, model generation for drug development is still expensive and time-consuming, demanding more target flexibility and faster turnaround times with high reproducibility. The generation of a tightly controlled ObLiGaRe doxycycline inducible SpCas9 (ODInCas9) transgene and its use in targeted ObLiGaRe results in functional integration into both human and mouse cells culminating in the generation of the ODInCas9 mouse. Genomic editing can be performed in cells of various tissue origins without any detectable gene editing in the absence of doxycycline. Somatic in vivo editing can model non-small cell lung cancer (NSCLC) adenocarcinomas, enabling treatment studies to validate the efficacy of candidate drugs. The ODInCas9 mouse allows robust and tunable genome editing granting flexibility, speed and uniformity at less cost, leading to high throughput and practical preclinical in vivo therapeutic testing.

In vivo imaging of lipid storage and regression in diet-induced obesity during nutrition manipulation.

Changes in adipose tissue distribution and ectopic fat storage in, liver and skeletal muscle tissue impact whole body insulin sensitivity in both humans and experimental animals. Numerous mouse models of obesity, insulin resistance, and diabetes exist; however, current methods to assess mouse phenotypes commonly involve direct harvesting of the tissues of interest, precluding the possibility of repeated measurements in the same animal. In this study, we demonstrate that whole body 3-D imaging of body fat composition can be used to analyze distribution as well as redistribution of fat after intervention by repeated assessment of intrahepatocellular lipids (IHCL), intra-abdominal, subcutaneous, and total adipose tissue (IAT, SAT, and TAT) and brown adipose tissue (BAT). C57BL/6J mice fed a cafeteria diet for 16 wk were compared with mice fed standard chow for 16 wk and mice switched from café diet to standard chow after 12 wk. MRI determinations were made at 9 and 15 wk, and autopsy was performed at 16 wk. There was a strong correlation between MRI-calculated weights in vivo at 15 wk and measured weights at 16 wk ex vivo for IAT (r = 0.99), BAT (r = 0.93), and IHCL (r = 0.97). IHCL and plasma insulin increased steeply relative to body weight at body weights above 45 g. This study demonstrates that the use of 3-D imaging to assess body fat composition may allow substantial reductions in animal usage. The dietary interventions indicated that a marked metabolic deterioration occurred when the mice had gained a certain fat mass.

Automatic segmentation of intra-abdominal and subcutaneous adipose tissue in 3D whole mouse MRI.

PURPOSE: To fully automate intra-abdominal (IAT) and total adipose tissue (TAT) segmentation in mice to replace tedious and subjective manual segmentation. MATERIALS AND METHODS: A novel transform codes each voxel with the radius of the narrowest passage on the widest possible three-dimensional (3D) path to any voxel in the target object to select appropriate IAT seed points. Then competitive region growing is performed on a distance transform of the fat mask such that competing classes meet at narrow passages effectively segmenting the IAT and subcutaneous adipose compartments. Fully automatic segmentations were conducted on 32 3D mouse images independent to those used for algorithm development. RESULTS: Automatic processing worked on all 32 images and took 28 s on a 3.6 GHz Pentium computer with 2.0 GB RAM. Manual segmentation by an experienced operator typically took 1 h per 3D image. The correlation coefficients between manual and automated segmentation of TAT and IAT were 0.97 and 0.94, respectively. CONCLUSION: The fully automatic method correlates well with manual segmentation and dramatically speeds up segmentation allowing MRI to be used in the anti-obesity drug discovery pipeline.