Proteins that bind to a target protein of interest, termed "binders," are essential components of biological research reagents and therapeutics. Target proteins present multiple binding surfaces with varying interaction potential. High-potential surfaces, or "hot spots," are experimentally identified as the most probable binding sites in de novo discovery campaigns. However, hot spots and their default binding modes do not always confer the desired specificity. Related proteins or isoforms often share similar hot spots, resulting in promiscuous binding. Interaction with a hot spot may also fail to elicit the intended biological outcome. Consequently, methods that direct de novo binder discovery toward targets with defined specificity are critically needed. We recently developed phage-assisted non-continuous selection of binders (PANCS-Binders), a selection platform with unparalleled speed and sequence-function fidelity that enables routine de novo binder discovery within days. However, because PANCS-Binder selections enrich variants based primarily on affinity, secondary screening is unlikely to identify binders to lesser hot spots because of the high likelihood of convergence. These alternative binding surfaces with weaker inherent interactions may possess desirable specificity profiles. Here, we develop PANCS-spec-Binders, which incorporates simultaneous selection and counterselection to control the specificity of enriched binders. We demonstrate PANCS-spec-Binders in two proof-of-concept applications: (1) discovery of isoform-selective binders that bind HRAS with >100-fold higher affinity than the highly related KRAS isoform, and (2) discovery of epitope-specific binders that either target or avoid the LIR interaction region of LC3B. PANCS-spec-Binders enables rapid identification of binders with defined specificity within days.

Precise temporal control of protein abundance is essential for dissecting dynamic cellular processes. While degron-based systems enable rapid protein depletion in eukaryotic cells, comparable tools are lacking for bacterial effectors delivered into host cells during infection. Here, we establish AIDE (Auxin-Inducible Degradation of Effectors), a host-directed degradation platform that harnesses the ubiquitin-proteasome system to selectively eliminate secreted bacterial proteins, including membrane-integrated effectors. By integrating a minimal auxin-inducible degron (AID) tag into effector genes, AIDE enables rapid, reversible, and spatially confined degradation while preserving native expression and secretion. Applied to Chlamydia trachomatis, AIDE revealed that the membrane-integrated deubiquitinase Cdu1 suppresses autophagy early and later promotes developmental transitions, whereas the integral membrane fusogen IncA is continuously required for inclusion integrity. This AIDE platform provides minute-scale, spatiotemporal control over bacterial effector activity, offering a broadly applicable framework for dissecting virulence mechanisms and host-pathogen interactions across diverse secretion-dependent pathogens.

Understanding protein function at the molecular level requires connecting residue-level annotations with physical and structural properties. This can be cumbersome and error-prone when functional annotation, computation of physicochemical properties, and structure visualization are separated. To address this, we introduce ProCaliper, an open-source Python library for computing and visualizing physicochemical properties of proteins. It can retrieve annotation and structure data from UniProt and AlphaFold databases, compute residue-level properties such as charge, solvent accessibility, and protonation state, and interactively visualize the results of these computations along with user-supplied residue-level data. Additionally, ProCaliper incorporates functional and structural information to construct and optionally sparsify networks that encode the distance between residues and/or annotated functional sites or regions.

The conjugative transfer of plasmid-mediated antibiotic resistance genes (ARGs) plays a key role in the spread of antibiotic resistance, posing a major global public health threat. The tire rubber antioxidant N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD) and its quinone derivative (6PPD-Q) are emerging pollutants that are widely present in the environment and pose significant ecological risks. However, their potential impact on plasmid transfer remains poorly understood. Here, we established in vitro conjugation models under simulated aquatic conditions to evaluate the effects of environmentally relevant concentrations of 6PPD and 6PPD-Q on RP4 plasmid transfer in Escherichia coli(E. coli). Our results demonstrate that both compounds significantly enhance plasmid transfer, with 6PPD-Q exhibiting a markedly greater impact. The primary mechanisms underlying this enhancement include increased reactive oxygen species (ROS) production, enhanced membrane permeability, improved cell adhesion, and promoted adenosine triphosphate (ATP) synthesis. Transcriptomic analysis, nontargeted metabolomics profiling, and molecular docking simulations corroborate these findings. Notably, 6PPD-Q significantly upregulated ATP synthesis-related genes in donor bacteria and altered metabolic processes in the conjugation system. This study provides novel insights into how 6PPD and 6PPD-Q facilitate the conjugative transfer of ARGs and highlights the potential environmental impact of 6PPD-Q in promoting the spread of ARGs.

Filamentous cyanobacteria of the Nostocaceae family can differentiate into multicellular forms to adapt to environmental stresses, and members can establish symbiosis with various embryophytes. Representative laboratory strains are typically grown under continuous light to maintain stable metabolic conditions; however, this departure from a natural diel cycle can result in extended stress. Early genomic examination of Nostoc punctiforme suggests the genetic potential for a circadian clock, but we lack insight into global cellular dynamics through the natural diel cycle for this model organism. Here, we comprehensively assess changes in expression of core cellular processes and the mobilome of accessory genetic elements during diel growth of N. punctiforme PCC 73102. The primary transcriptome confirmed that multicellular cyanobacteria precisely coordinate photosynthesis and carbon assimilation for cell division during the day, while control of DNA recombination and repair appeared to be sequestered to darkness. Moreover, we expanded the known repertoire of light-sensing proteins to uncover a putative regulator of circadian rhythm that itself exhibits striking oscillation between day-night expression. This was in sharp contrast to the arrhythmic pattern observed for a homolog of the canonical circadian input kinase in unicellular cyanobacteria. Looking beyond cellular coordination of diel growth, we uncovered dynamic mobile elements and, notably, targeted hypermutation by retroelements that are likely maintained for conflict mitigation, which is crucial for a multicellular lifestyle.

Agrobacterium-mediated transformation (AMT) is the primary means of genetic engineering in plants and many fungi, but the factors that control transformation outcomes - efficiency, transgene insertion number, and transgene integrity - remain poorly characterized. Although transformation outcomes dictate an event's potential utility in both industrial and academic contexts, AMT remains largely unoptimized for these metrics. Here, we systematically analyze the impact of the transgene-harboring binary vector on transformation outcomes across plant and fungal species. Through a comparison of different plasmid origin of replication (ORI) families and engineered copy number variants, our results reveal that the ORI family - not plasmid copy number - dictates T-DNA insertion number, backbone inclusion, and transformation efficiency, while plasmid copy-number tuning alters efficiency without changing ORI family-specific signatures. Independent of plasmid copy number across kingdoms, the most widely used pVS1 ORI-based vectors (e.g. pCambia) result in significantly more insertions per transformant and high levels of transgene silencing compared to the less-utilized pSa ORI family, which enriches for more uniform single insertion events. Furthermore, we demonstrate that ORI-dependent transformation outcomes in yeast predictably reflect those in Arabidopsis. Together, these results lay the foundation for future binary vector design aimed at achieving more predictable, controllable, and optimized transformation outcomes across diverse eukaryotic hosts.

Iron is a critical micronutrient for both plants and humans, yet its declining availability across agricultural systems threatens global food security and health. Biofortification of food crops has emerged as a promising strategy to combat iron deficiency and anemia, leveraging both crop breeding and microbiome-based approaches to enhance iron mobilization and uptake. Advances in plant and bacterial synthetic biology could enable the precise programming of iron homeostasis and acquisition mechanisms, offering tailored solutions across diverse species and environments. Here, we outline key biomolecules, genes, and biosynthetic and transport pathways that represent underexplored synthetic biology targets for improving crop iron acquisition. We highlight opportunities to tune expression strength, tissue specificity, and cross-host pathway transfer to enhance chelation- and reduction-mediated solubilization of soil iron and augment plant uptake. Finally, we emphasize the broader importance of developing plant–microbe–metal actuators as modular components in genetic circuit design and discuss how their deployment across diverse plant and microbial chassis could accelerate agricultural biofortification and improve global nutrition.

CRISPR-dCas tools have widespread applications for rapidly manipulating and dissecting gene function across the microbial tree of life. However, despite their theoretical suitability for use in a broad range of species, CRISPR-dCas tools that are often initially optimized for use in model cell lines and model organisms still frequently require extensive modifications to enable their application in specific microbial organisms. Here, we review different iterations of CRISPR-dCas in microbes and the application of these techniques. We further discuss common obstacles faced and troubleshooting approaches while developing and applying CRISPR-dCas systems to a microbial organism. Finally, we suggest enhancements that can be made that may help improve the applicability of a CRISPR-dCas tool developed for nonmodel microbial organisms.

Under osmotic stress, bacteria express a heterotetrameric protein complex, KdpFABC, which functions as an ATP-dependent K+ pump to maintain intracellular potassium levels. The subunit KdpA belongs to the superfamily of K+ transporters and adopts pseudo fourfold symmetry with a membrane-embedded selectivity filter as seen in K+ channels. KdpB belongs to the superfamily of P-type ATPases with a conserved binding site for ions within the membrane domain and three cytoplasmic domains that orchestrate ATP hydrolysis via an aspartyl phosphate intermediate. Previous work hypothesized that K+ moves parallel to the membrane plane through a 40 Å long tunnel that connects the selectivity filter of KdpA with a canonical binding site in KdpB. In the current work, we have reconstituted KdpFABC into lipid nanodiscs and used cryo-EM to image the wild-type pump under turnover conditions. We present a 2.1 Å structure of the E1~P·ADP conformation, which reveals new features of the conduction pathway. This map shows strong densities within the selectivity filter and at the canonical binding site, consistent with K+ bound at each of these sites in this conformation. Many water molecules occupy a vestibule and the proximal end of the tunnel, which becomes markedly hydrophobic and dewetted at the subunit interface. We go on to use ATPase and ion transport assays to assess effects of numerous mutations along this proposed conduction pathway. The results confirm that K+ ions pass through the tunnel and support the existence of a low-affinity site in KdpB for releasing these ions to the cytoplasm. Taken together, these data shed new light on the unique partnership between a transmembrane channel and an ATP-driven pump in maintaining the large electrochemical K+ gradient essential for bacterial survival. 

The Michaelis constant (Km) is central to enzyme kinetics, guiding variant selection, inhibitor screening, and metabolic modeling. However, Km obtained by nonlinear regression can be substantially inaccurate even when the reported standard error (SE) appears small. Common software reports SE but provides no accuracy metric. This gap is addressed by extending the accuracy confidence interval (ACI) framework to Km (ACI-Km) through a binding-isotherm formulation of the velocity–substrate fit. Given confidence intervals for concentration accuracy, the method quantifies how residual systematic uncertainties in enzyme and substrate concentrations ( E0 and S0) propagate into the determined Km values and provides a probabilistic interval expected to enclose the accurate value. The approach requires no additional kinetic experiments and is directly applicable to existing datasets. Concentration-accuracy intervals can be estimated from calibration data, reagent specifications, or quality-control records. ACI-Km is valid across a wide range of E0/Km conditions, including relatively high E0. A free web application (https://aci.sci.yorku.ca) implements ACI-Km. Tests on synthetic and experimental datasets show that Km ± SE can severely underestimate uncertainty, whereas ACI provides more reliable accuracy bounds for decision-making, complementing rather than replacing traditional precision metrics by providing quantitative diagnostic bounds for concentration-related uncertainties in Km determination.

Custom DNA constructs have never been more common or important in the life sciences. Many researchers therefore devote substantial time and effort to molecular cloning, aided by abundant computer-aided design tools. However, support for managing and documenting the construction process, and for effectively handling and reducing the frequency of setbacks, is lacking. To address this need, we developed CloneCoordinate, a free, open-source electronic laboratory notebook specifically designed for cloning and fully implemented in Google Sheets. By maintaining a real-time, automatically prioritized task list, a uniform physical sample inventory, and standardized data structures, CloneCoordinate enables productive, collaborative cloning for individuals or teams. We demonstrate how the information captured by CloneCoordinate can be leveraged to troubleshoot assembly problems and provide data-driven insights into cloning efficiency, setting the stage for automated recommendations based on actual track records. CloneCoordinate offers a new and uniquely accessible model for how to carry out, and iteratively improve on, real-world DNA assembly.

Escherichia coli Nissle 1917 (EcN) is a well-characterized Gram-negative probiotic distinguished by its unique, strain-specific physiology. Genome-scale metabolic models (GEMs) are powerful tools for elucidating metabolic traits and predicting genotype-phenotype relationships. Although several EcN GEMs have been published, none have explicitly represented its probiotic physiology. Here, we present a manually curated GEM of EcN that, for the first time, incorporates the energetic costs associated with its cryptic plasmids. Inclusion of a plasmid-specific module improved biomass yield predictions and overall model accuracy, providing a more physiologically realistic representation of EcN metabolism. Using COBRA methodologies and possibilistic metabolic flux analysis, this model and previous EcN reconstructions were systematically compared to evaluate the trade-off between model complexity and predictive performance. The analysis revealed that increased structural detail does not necessarily enhance quantitative accuracy and that predictive reliability depends on both computational methodology and model context. Metabolomic profiling under gut-like anaerobic conditions further showed that EcN exhibits a distinctive metabolic phenotype, characterized by elevated amino acid consumption and enhanced short-chain fatty acid production. These findings highlight the unique probiotic physiology of EcN and demonstrate the utility of metabolic modeling for reproducing and exploring such traits. Overall, this study provides a quantitatively reliable and physiologically relevant framework for modeling E. coli Nissle 1917 and related commensal bacteria, supporting advances in probiotic engineering, synthetic biology, and bioprocess design.

Heme is an essential molecule required for critical biochemical processes in most vertebrates and bacteria. During infections, vertebrate hosts sequester heme away from invading pathogens, a process known as nutritional immunity, driving bacteria to evolve diverse mechanisms to evade this immunity and cause diseases. This review explores the functions of heme at the host–pathogen interface. We discuss the multifaceted roles of heme in bacterial pathogenesis and the potential for heme-targeting antimicrobial therapies. Beyond serving as a source of iron in the host environment, where iron bioavailability is limited, heme contributes to the structural stability and enzymatic functions of hemoproteins. We examine the regulatory mechanisms governing bacterial heme homeostasis in the host environment including sensing, detoxification, acquisition, utilization, and degradation pathways. Understanding how heme influences bacterial survival and virulence can lead to the development of novel therapeutic strategies that target the various essential and conserved mechanisms of heme homeostasis in bacterial pathogens. Given the rising challenge of antibiotic resistance, heme-based therapeutic interventions are promising strategies for the treatment of bacterial infections.