Systematic molecular evolution enables robust biomolecule discovery.

Evolution occurs when selective pressures from the environment shape inherited variation over time. Within the laboratory, evolution is commonly used to engineer proteins and RNA, but experimental constraints have limited the ability to reproducibly and reliably explore factors such as population diversity, the timing of environmental changes and chance on outcomes. We developed a robotic system termed phage- and robotics-assisted near-continuous evolution (PRANCE) to comprehensively explore biomolecular evolution by performing phage-assisted continuous evolution in high-throughput. PRANCE implements an automated feedback control system that adjusts the stringency of selection in response to real-time measurements of each molecular activity. In evolving three distinct types of biomolecule, we find that evolution is reproducibly altered by both random chance and the historical pattern of environmental changes. This work improves the reliability of protein engineering and enables the systematic analysis of the historical, environmental and random factors governing biomolecular evolution.

Gibbs free energy of protein-protein interactions correlates with ATP production in cancer cells.

In this paper, we analyze several cancer cell types from two seemingly independent angles: (a) the over-expression of various proteins participating in protein-protein interaction networks and (b) a metabolic shift from oxidative phosphorylation to glycolysis. We use large data sets to obtain a thermodynamic measure of the protein-protein interaction network, namely the associated Gibbs free energy. We find a strong inverse correlation between the percentage of energy production via oxidative phosphorylation and the Gibbs free energy of the protein networks. The latter is a measure of functional dysregulation within the cell. Our findings corroborate earlier indications that signaling pathway upregulation in cancer cells is linked to the metabolic shift known as the Warburg effect; hence, these two seemingly independent characteristics of cancer phenotype may be interconnected.

A Practical Guide to Phage- and Robotics-assisted Near-continuous Evolution.

Robotics-accelerated Evolution techniques improve the reliability and speed of evolution using feedback control, improving the outcomes of protein and organism evolution experiments. In this article, we present a guide to setting up the hardware and software necessary to implement Phage- and Robotics-assisted Near-continuous Evolution (PRANCE). PRANCE combines fast phage-based molecular evolution with the ability to run hundreds of independent, feedback-controlled evolution experiments simultaneously. This paper will describe the hardware requirements and setup for PRANCE, including a liquid-handling instrument, a plate reader, auxiliary pumps, heaters, and 3D-printed containers. We describe how to configure the liquid handling robot to be compatible with Python-based open-source software. Finally, we provide suggestions for the first two experiments that can be conducted with a newly constructed PRANCE system that exercises its capabilities and validates that the system is ready to conduct multiplexed evolution. This guide is intended to serve as a handbook for navigating the considerable equipment setup associated with conducting robotics-accelerated evolution.

Proximity labeling of endogenous protein interactions enabled by directed evolution.

Enhanced ascorbate peroxidase 2 (APEX2) is a protein generated with directed evolution by Lam et al. that has transformed our understanding of subcellular entities and phenomena. The rapid kinetics of this engineered protein highlights the power of directed evolution to expand the molecular toolkit for biologists.

PyLabRobot: An Open-Source, Hardware Agnostic Interface for Liquid-Handling Robots and Accessories.

Liquid handling robots are often limited by proprietary programming interfaces that are only compatible with a single type of robot and operating system, restricting method sharing and slowing development. Here we present PyLabRobot, an open-source, cross-platform Python interface capable of programming diverse liquid-handling robots, including Hamilton STARs, Tecan EVOs, and Opentron OT-2s. PyLabRobot provides a universal set of commands and representations for deck layout and labware, enabling the control of diverse accessory devices. The interface is extensible and can work with any robot that manipulates liquids within a Cartesian coordinate system. We validated the system through unit tests and several application demonstrations, including a browser-based simulator, a position calibration tool, and a path-teaching tool for complex movements. PyLabRobot provides a flexible, open, and collaborative programming environment for laboratory automation.

High-throughput approaches to uncover synergistic drug combinations in leukemia.

We report a comprehensive drug synergy study in acute myeloid leukemia (AML). In this work, we investigate a panel of cell lines spanning both MLL-rearranged and non-rearranged subtypes. The work comprises a resource for the community, with many synergistic drug combinations that could not have been predicted a priori, and open source code for automation and analyses. We base our definitions of drug synergy on the Chou-Talalay method, which is useful for visualizations of synergy experiments in isobolograms, and median-effects plots, among other representations. Our key findings include drug synergies affecting the chromatin state, specifically in the context of regulation of the modification state of histone H3 lysine-27. We report open source high throughput methodology such that multidimensional drug screening can be accomplished with equipment that is accessible to most laboratories. This study will enable preclinical investigation of new drug combinations in a lethal blood cancer, with data analysis and automation workflows freely available to the community.

The Transcriptional Response of Soil Bacteria to Long-Term Warming and Short-Term Seasonal Fluctuations in a Terrestrial Forest.

Terrestrial ecosystems are an important carbon store, and this carbon is vulnerable to microbial degradation with climate warming. After 30 years of experimental warming, carbon stocks in a temperate mixed deciduous forest were observed to be reduced by 30% in the heated plots relative to the controls. In addition, soil respiration was seasonal, as was the warming treatment effect. We therefore hypothesized that long-term warming will have higher expressions of genes related to carbohydrate and lipid metabolism due to increased utilization of recalcitrant carbon pools compared to controls. Because of the seasonal effect of soil respiration and the warming treatment, we further hypothesized that these patterns will be seasonal. We used RNA sequencing to show how the microbial community responds to long-term warming (~30 years) in Harvard Forest, MA. Total RNA was extracted from mineral and organic soil types from two treatment plots (+5°C heated and ambient control), at two time points (June and October) and sequenced using Illumina NextSeq technology. Treatment had a larger effect size on KEGG annotated transcripts than on CAZymes, while soil types more strongly affected CAZymes than KEGG annotated transcripts, though effect sizes overall were small. Although, warming showed a small effect on overall CAZymes expression, several carbohydrate-associated enzymes showed increased expression in heated soils (~68% of all differentially expressed transcripts). Further, exploratory analysis using an unconstrained method showed increased abundances of enzymes related to polysaccharide and lipid metabolism and decomposition in heated soils. Compared to long-term warming, we detected a relatively small effect of seasonal variation on community gene expression. Together, these results indicate that the higher carbohydrate degrading potential of bacteria in heated plots can possibly accelerate a self-reinforcing carbon cycle-temperature feedback in a warming climate.