Bachelor of Science, Cornell University (2012)
Doctor of Philosophy, University of California Berkeley (2016)
Daniel Jarosz, Postdoctoral Faculty Sponsor
Quantitative genetics aims to map genotype to phenotype, often with the goal of understanding how organisms evolved. However, it remains unclear whether the genetic variants identified are exemplary of evolution. Here we analyzed progeny of two wild Saccharomyces cerevisiae isolates to identify 195 loci underlying complex metabolic traits, resolving 107 to single polymorphisms with diverse molecular mechanisms. More than 20% of causal variants exhibited patterns of emergence inconsistent with neutrality. Moreover, contrary to drift-centric expectation, variation in diverse wild yeast isolates broadly exhibited this property: over 30% of shared natural variants exhibited phylogenetic signatures suggesting that they are not neutral. This pattern is likely attributable to both homoplasy and balancing selection on ancestral polymorphism. Variants that emerged repeatedly were more likely to have done so in isolates from the same ecological niche. Our results underscore the power of super-resolution mapping of ecologically relevant traits in understanding adaptation and evolution.
View details for PubMedID 30874558
Biotechnological processes use microbes to convert abundant molecules, such as glucose, into high-value products, such as pharmaceuticals, commodity and fine chemicals, and energy. However, from the outset of the development of a new bioprocess, it is difficult to determine the feasibility, expected yields, and targets for engineering. In this review, we describe a methodology that uses rough estimates to assess the feasibility of a process, approximate the expected product titer of a biological system, and identify variables to manipulate in order to achieve the desired performance. This methodology uses estimates from literature and biological intuition, and can be applied in the early stages of a project to help plan future engineering. We highlight recent literature examples, as well as two case studies from our own work, to demonstrate the use and power of rough estimates. Describing and predicting biological function using estimates guides the research and development phase of new bioprocesses and is a useful first step to understand and build a new microbial factory.
View details for PubMedID 30165868
Natural biochemical systems are ubiquitously organized both in space and time. Engineering the spatial organization of biochemistry has emerged as a key theme of synthetic biology, with numerous technologies promising improved biosynthetic pathway performance. One strategy, however, may produce disparate results for different biosynthetic pathways. We use a spatially resolved kinetic model to explore this fundamental design choice in systems and synthetic biology. We predict that two example biosynthetic pathways have distinct optimal organization strategies that vary based on pathway-dependent and cell-extrinsic factors. Moreover, we demonstrate that the optimal design varies as a function of kinetic and biophysical properties, as well as culture conditions. Our results suggest that organizing biosynthesis has the potential to substantially improve performance, but that choosing the appropriate strategy is key. The flexible design-space analysis we propose can be adapted to diverse biosynthetic pathways, and lays a foundation to rationally choose organization strategies for biosynthesis.
View details for PubMedID 29844460
Self-assembling proteins are critical to biological systems and industrial technologies, but predicting how mutations affect self-assembly remains a significant challenge. Here, we report a technique, termed SyMAPS (Systematic Mutation and Assembled Particle Selection), that can be used to characterize the assembly competency of all single amino acid variants of a self-assembling viral structural protein. SyMAPS studies on the MS2 bacteriophage coat protein revealed a high-resolution fitness landscape that challenges some conventional assumptions of protein engineering. An additional round of selection identified a previously unknown variant (CP[T71H]) that is stable at neutral pH but less tolerant to acidic conditions than the wild-type coat protein. The capsids formed by this variant could be more amenable to disassembly in late endosomes or early lysosomes-a feature that is advantageous for delivery applications. In addition to providing a mutability blueprint for virus-like particles, SyMAPS can be readily applied to other self-assembling proteins.
View details for PubMedID 29643335
Prion-like proteins have the capacity to adopt multiple stable conformations, at least one of which can recruit proteins from the native conformation into the alternative fold. Although classically associated with disease, prion-like assembly has recently been proposed to organize a range of normal biochemical processes in space and time. Organisms from bacteria to mammals use prion-like mechanisms to (re)organize their proteome in response to intracellular and extracellular stimuli. Prion-like behavior is an economical means to control biochemistry and gene regulation at the systems level, and prions can act as protein-based genes to facilitate quasi-Lamarckian inheritance of induced traits. These mechanisms allow individual cells to express distinct heritable traits using the same complement of polypeptides. Understanding and controlling prion-like behavior is therefore a promising strategy to combat diverse pathologies and organize engineered biological systems.
View details for PubMedID 29725624
Bacterial microcompartments are a class of proteinaceous organelles comprising a characteristic protein shell enclosing a set of enzymes. Compartmentalization can prevent escape of volatile or toxic intermediates, prevent off-pathway reactions, and create private cofactor pools. Encapsulation in synthetic microcompartment organelles will enhance the function of heterologous pathways, but to do so, it is critical to understand how to control diffusion in and out of the microcompartment organelle. To this end, we explored how small differences in the shell protein structure result in changes in the diffusion of metabolites through the shell. We found that the ethanolamine utilization (Eut) protein EutM properly incorporates into the 1,2-propanediol utilization (Pdu) microcompartment, altering native metabolite accumulation and the resulting growth on 1,2-propanediol as the sole carbon source. Further, we identified a single pore-lining residue mutation that confers the same phenotype as substitution of the full EutM protein, indicating that small molecule diffusion through the shell is the cause of growth enhancement. Finally, we show that the hydropathy index and charge of pore amino acids are important indicators to predict how pore mutations will affect growth on 1,2-propanediol, likely by controlling diffusion of one or more metabolites. This study highlights the use of two strategies to engineer microcompartments to control metabolite transport: altering the existing shell protein pore via mutation of the pore-lining residues, and generating chimeras using shell proteins with the desired pores.
View details for PubMedID 28585808
Organizing heterologous biosyntheses inside bacterial cells can alleviate common problems owing to toxicity, poor kinetic performance, and cofactor imbalances. A subcellular organelle known as a bacterial microcompartment, such as the 1,2-propanediol utilization microcompartment of Salmonella, is a promising chassis for this strategy. Here we demonstrate de novo design of the N-terminal signal sequences used to direct cargo to these microcompartment organelles. We expand the native repertoire of signal sequences using rational and library-based approaches and show that a canonical leucine-zipper motif can function as a signal sequence for microcompartment localization. Our strategy can be applied to generate new signal sequences localizing arbitrary cargo proteins to the 1,2-propanediol utilization microcompartments.
View details for DOI 10.1002/pro.3144
View details for Web of Science ID 000400166800016
View details for PubMedCentralID PMC5405430