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Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.

Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns.

All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.

 

Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits ( R 2 = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening. 

Various intracellular degradation organelles, including autophagosomes, lysosomes, and endosomes, work in tandem to perform autophagy, which is crucial for cellular homeostasis. Altered autophagy contributes to the pathophysiology of various diseases, including cancers and metabolic diseases. This paper aims to describe an approach to reproducibly identify and distinguish subcellular structures involved in macroautophagy. Methods are provided that help avoid common pitfalls. How to distinguish between lysosomes, lipid droplets, autolysosomes, autophagosomes, and inclusion bodies are also discussed. These methods use transmission electron microscopy (TEM), which is able to generate nanometer-scale micrographs of cellular degradation components in a fixed sample. Serial block face-scanning electron microscopy is also used to visualize the 3D morphology of degradation machinery using the Amira software. In addition to TEM and 3D reconstruction, other imaging techniques are discussed, such as immunofluorescence and immunogold labeling, which can be used to classify cellular organelles, reliably and accurately. Results show how these methods may be used to accurately quantify cellular degradation machinery under various conditions, such as treatment with the endoplasmic reticulum stressor thapsigargin or ablation of the dynamin-related protein 1. 

Logjams in a stream create backwater conditions and locally force water to flow through the streambed, creating zones of transient storage within the surface and subsurface of a stream. We investigate the relative importance of logjam distribution density, logjam permeability, and discharge on transient storage in a simplified experimental channel. We use physical flume experiments in which we inject a salt tracer, monitor fluid conductivity breakthrough curves in surface water, and determine breakthrough‐curve skewness to characterize transient storage. We then develop a companion numerical model in HydroGeoSphere to reveal flow paths through the subsurface (or hyporheic zone) that contribute to some of the longest transient‐storage timescales. In both the flume experiments and numerical simulations, we observe backwater formation and an increase in hyporheic exchange at logjams. Observed complexities in transient storage behavior depend largely on surface water flow in the backwater zone. As expected, multiple successive logjams provide more pervasive hyporheic exchange by distributing the head drop at each jam, leading to distributed but shallow flow paths. Decreasing the permeability of a logjam or increasing the discharge both facilitate greater surface water storage and volumetric rate of hyporheic exchange. Understanding how logjam characteristics affect solute transport through both the channel and hyporheic zone has important management implications for rivers in forested, or historically forested, environments.

 

Persons Excluded from science because of Ethnicity and Race (PEERs) face chronic exposure to interpersonal stressors, such as social discrimination, throughout their scientific careers, leading to a long-term decline in physical and mental health. Many PEERs exhibit John Henryism, a coping mechanism to prolonged stress where an individual expends higher levels of effort and energy at the cost of their physical and mental health. In this article, we discuss how social dominance may increase John Henryism within the STEM community; the causes, effects and costs of John Henryism; and highlight solutions to combat these social adversity stressors within the academic institution.