Small-Molecule Morphogenesis Modulators Enhance the Ability of 14-Helical ß-Peptides To Prevent Candida albicans Biofilm Formation.

Candida albicans is an opportunistic fungal pathogen responsible for mucosal candidiasis and systemic candidemia in humans. Often, these infections are associated with the formation of drug-resistant biofilms on the surfaces of tissues or medical devices. Increased incidence of C. albicans resistance to current antifungals has heightened the need for new strategies to prevent or eliminate biofilm-related fungal infections. In prior studies, we designed 14-helical ß-peptides to mimic the structural properties of natural antimicrobial α-peptides (AMPs) in an effort to develop active and selective antifungal compounds. These amphiphilic, cationic, helical ß-peptides exhibited antifungal activity against planktonic C. albicans cells and inhibited biofilm formation in vitro and in vivo Recent studies have suggested the use of antivirulence agents in combination with antifungals. In this study, we investigated the use of compounds that target C. albicans polymorphism, such as 1-dodecanol, isoamyl alcohol, and farnesol, to attempt to improve ß-peptide efficacy for preventing C. albicans biofilms. Isoamyl alcohol, which prevents hyphal formation, reduced the minimum biofilm prevention concentrations (MBPCs) of ß-peptides by up to 128-fold. Combinations of isoamyl alcohol and antifungal ß-peptides resulted in less than 10% hemolysis at the antifungal MBPCs. Overall, our results suggest potential benefits of combination therapies comprised of morphogenesis modulators and antifungal AMP peptidomimetics for preventing C. albicans biofilm formation.

Proliferation history and transcription factor levels drive direct conversion.

The sparse and stochastic nature of reprogramming has obscured our understanding of how transcription factors drive cells to new identities. To overcome this limit, we developed a compact, portable reprogramming system that increases direct conversion of fibroblasts to motor neurons by two orders of magnitude. We show that subpopulations with different reprogramming potentials are distinguishable by proliferation history. By controlling for proliferation history and titrating each transcription factor, we find that conversion correlates with levels of the pioneer transcription factor Ngn2, whereas conversion shows a biphasic response to Lhx3. Increasing the proliferation rate of adult human fibroblasts generates morphologically mature, induced motor neurons at high rates. Using compact, optimized, polycistronic cassettes, we generate motor neurons that graft with the murine central nervous system, demonstrating the potential for in vivo therapies.

Evaluation of Lee et al.: Clarity and interpretation of mutual information in promoter transfer functions.

One snapshot of the peer review process for "Mapping the dynamic transfer functions of eukaryotic gene regulation" (Lee et al., 2021) appears below.

Engineering cell fate: Applying synthetic biology to cellular reprogramming.

Cellular reprogramming drives cells from one stable identity to a new cell fate. By generating a diversity of previously inaccessible cell types from diverse genetic backgrounds, cellular reprogramming is rapidly transforming how we study disease. However, low efficiency and limited maturity have limited the adoption of in vitro-derived cellular models. To overcome these limitations and improve mechanistic understanding of cellular reprogramming, a host of synthetic biology tools have been deployed. Recent synthetic biology approaches have advanced reprogramming by tackling three significant challenges to reprogramming: delivery of reprogramming factors, epigenetic roadblocks, and latent donor identity. In addition, emerging insight from the molecular systems biology of reprogramming reveal how systems-level drivers of reprogramming can be harnessed to further advance reprogramming technologies. Furthermore, recently developed synthetic biology tools offer new modes for engineering cell fate.

Understanding and Engineering Chromatin as a Dynamical System across Length and Timescales.

Connecting the molecular structure and function of chromatin across length and timescales remains a grand challenge to understanding and engineering cellular behaviors. Across five orders of magnitude, dynamic processes constantly reshape chromatin structures, driving spaciotemporal patterns of gene expression and cell fate. Through the interplay of structure and function, the genome operates as a highly dynamic feedback control system. Recent experimental techniques have provided increasingly detailed data that revise and augment the relatively static, hierarchical view of genomic architecture with an understanding of how dynamic processes drive organization. Here, we review how novel technologies from sequencing, imaging, and synthetic biology refine our understanding of chromatin structure and function and enable chromatin engineering. Finally, we discuss opportunities to use these tools to enhance understanding of the dynamic interrelationship of chromatin structure and function.

Data-Driven Development of Predictive Models for Sustained Drug Release.

Mathematical modeling of drug release can aid in the design and development of sustained delivery systems, but the parameter estimation of such models is challenging owing to the nonlinear mathematical structure and complexity and interdependency of the physical processes considered. Highly parameterized models often lead to overfitting, strong parameter correlations, and as a consequence, inaccurate model predictions for systems not explicitly part of the fitting database. Here, we show that an efficient stochastic optimization algorithm can be used not only to find robust estimates of global minima to such complex problems but also to generate metadata that allow quantitative evaluation of parameter sensitivity and correlation, which can be used for further model refinement and development. A practical methodology is described through the analysis of a predictive drug release model on published experimental data sets. The model is then used to design a zeroth-order release profile in an experimental system consisting of an antibody fragment in a poly(lactic-co-glycolic acid) solvent depot, which is validated experimentally. This approach allows rational decision-making when developing new models, selecting models for a specific application, or designing formulations for experimental trials.