In vivo selection of synthetic nucleocapsids for tissue targeting.

Controlling the biodistribution of protein- and nanoparticle-based therapeutic formulations remains challenging. In vivo library selection is an effective method for identifying constructs that exhibit desired distribution behavior; library variants can be selected based on their ability to localize to the tissue or compartment of interest despite complex physiological challenges. Here, we describe further development of an in vivo library selection platform based on self-assembling protein nanoparticles encapsulating their own mRNA genomes (synthetic nucleocapsids or synNCs). We tested two distinct libraries: a low-diversity library composed of synNC surface mutations (45 variants) and a high-diversity library composed of synNCs displaying miniproteins with binder-like properties (6.2 million variants). While we did not identify any variants from the low-diversity surface library that yielded therapeutically relevant changes in biodistribution, the high-diversity miniprotein display library yielded variants that shifted accumulation toward lungs or muscles in just two rounds of in vivo selection. Our approach should contribute to achieving specific tissue homing patterns and identifying targeting ligands for diseases of interest.

Cascading indirect genetic effects in a clonal vertebrate.

Understanding how individual differences arise and how their effects propagate through groups are fundamental issues in biology. Individual differences can arise from indirect genetic effects (IGE): genetically based variation in the conspecifics with which an individual interacts. Using a clonal species, the Amazon molly (Poecilia formosa), we test the hypothesis that IGE can propagate to influence phenotypes of the individuals that do not experience them firsthand. We tested this by exposing genetically identical Amazon mollies to conspecific social partners of different clonal lineages, and then moving these focal individuals to new social groups in which they were the only member to have experienced the IGE. We found that genetically different social environments resulted in the focal animals experiencing different levels of aggression, and that these IGE carried over into new social groups to influence the behaviour of naive individuals. These data reveal that IGE can cascade beyond the individuals that experience them. Opportunity for cascading IGE is ubiquitous, especially in species with long-distance dispersal or fission-fusion group dynamics. Cascades could amplify (or mitigate) the effects of IGE on trait variation and on evolutionary trajectories. Expansion of the IGE framework to include cascading and other types of carry-over effects will therefore improve understanding of individual variation and social evolution and allow more accurate prediction of population response to changing environments.

Engineering Self-Assembling Protein Nanoparticles for Therapeutic Delivery.

Despite remarkable advances over the past several decades, many therapeutic nanomaterials fail to overcome major in vivo delivery barriers. Controlling immunogenicity, optimizing biodistribution, and engineering environmental responsiveness are key outstanding delivery problems for most nanotherapeutics. However, notable exceptions exist including some lipid and polymeric nanoparticles, some virus-based nanoparticles, and nanoparticle vaccines where immunogenicity is desired. Self-assembling protein nanoparticles offer a powerful blend of modularity and precise designability to the field, and have the potential to solve many of the major barriers to delivery. In this review, we provide a brief overview of key designable features of protein nanoparticles and their implications for therapeutic delivery applications. We anticipate that protein nanoparticles will rapidly grow in their prevalence and impact as clinically relevant delivery platforms.

Creating a Metabolic Syndrome Research Resource using the National Health and Nutrition Examination Survey.

Metabolic syndrome (MetS) is multifaceted. Risk factors include visceral adiposity, dyslipidemia, hyperglycemia, hypertension and environmental stimuli. MetS leads to an increased risk of cardiovascular disease, type 2 diabetes and stroke. Comparative studies, however, have identified heterogeneity in the pathology of MetS across groups though the etiology of these differences has yet to be elucidated. The Metabolic Syndrome Research Resource (MetSRR) described in this report is a curated database that provides access to MetS-associated biological and ancillary data and pools current and potential biomarkers of MetS extracted from relevant National Health and Nutrition Examination Survey (NHANES) data from 1999-2016. Each potential biomarker was selected following the review of over 100 peer-reviewed articles. MetSRR includes 28 demographics, survey and known MetS-related variables, including 9 curated categorical variables and 42 potentially novel biomarkers. All measures are captured from over 90 000 individuals. This biocuration effort provides increased access to curated MetS-related data and will serve as a hypothesis-generating tool to aid in novel biomarker discovery. In addition, MetSRR provides the ability to generate and export ethnic group-/race-, sex- and age-specific curated datasets, thus broadening participation in research efforts to identify clinically evaluative MetS biomarkers for disparate populations. Although there are other databases, such as BioM2MetDisease, designed to explore metabolic diseases through analysis of miRNAs and disease phenotypes, MetSRR is the only MetS-specific database designed to explore etiology of MetS across groups, through the biocuration of demographic, biological samples and biometric data. Database URL:  http://www.healthdisparityinformatics.com/MetSRR.