Current trends in pyrrole and porphyrin-derived nanoscale materials for biomedical applications

This article is written to provide an up-to-date review of pyrrole-based biomedical materials. Porphyrins and other tetrapyrrolic molecules possess unique magnetic, optical and other photophysical properties that make them useful for bioimaging and therapy. This review touches briefly on some of the synthetic strategies to obtain porphyrin- and tetrapyrrole-based nanoparticles, as well as the variety of applications in which crosslinked, self-assembled, porphyrin-coated and other nanoparticles are utilized. We explore examples of these nanoparticles' applications in photothermal therapy, drug delivery, photodynamic therapy, stimuli response, fluorescence imaging, photoacoustic imaging, magnetic resonance imaging, computed tomography and positron emission tomography. We anticipate that this review will provide a comprehensive summary of pyrrole-derived nanoparticles and provide a guideline for their further development.

Modeling cross-regulatory influences on monolignol transcripts and proteins under single and combinatorial gene knockdowns in Populus trichocarpa

Accurate manipulation of metabolites in monolignol biosynthesis is a key step for controlling lignin content, structure, and other wood properties important to the bioenergy and biomaterial industries. A crucial component of this strategy is predicting how single and combinatorial knockdowns of monolignol specific gene transcripts influence the abundance of monolignol proteins, which are the driving mechanisms of monolignol biosynthesis. Computational models have been developed to estimate protein abundances from transcript perturbations of monolignol specific genes. The accuracy of these models, however, is hindered by their inability to capture indirect regulatory influences on other pathway genes. Here, we examine the manifestation of these indirect influences on transgenic transcript and protein abundances, identifying putative indirect regulatory influences that occur when one or more specific monolignol pathway genes are perturbed. We created a computational model using sparse maximum likelihood to estimate the resulting monolignol transcript and protein abundances in transgenic Populus trichocarpa based on targeted knockdowns of specific monolignol genes. Using in-silico simulations of this model and root mean square error, we showed that our model more accurately estimated transcript and protein abundances, in comparison to previous models, when individual and families of monolignol genes were perturbed. We leveraged insight from the inferred network structure obtained from our model to identify potential genes, including PtrHCT, PtrCAD, and Ptr4CL, involved in post-transcriptional and/or post-translational regulation. Our model provides a useful computational tool for exploring the cascaded impact of single and combinatorial modifications of monolignol specific genes on lignin and other wood properties.