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ABSTRACT Penicillin-binding proteins (PBPs) play critical roles in cell wall construction, cell shape maintenance, and bacterial replication. Bacteria maintain a diversity of PBPs, indicating that despite their apparent functional redundancy, there is differentiation across the PBP family. Apparently-redundant proteins can be important for enabling an organism to cope with environmental stressors. In this study, we evaluated the consequence of environmental pH on PBP enzymatic activity inBacillus subtilis. Our data show that a subset of PBPs inB. subtilischange activity levels during alkaline shock and that one PBP isoform is rapidly modified to generate a smaller protein (i.e., PBP1a to PBP1b). Our results indicate that a subset of the PBPs are favored for growth under alkaline conditions, while others are readily dispensable. Indeed, we found that this phenomenon could also be observed inStreptococcus pneumoniae, implying that it may be generalizable across additional bacterial species and further emphasizing the evolutionary benefit of maintaining many, seemingly-redundant periplasmic enzymes. IMPORTANCEMicrobes adapt to ever-changing environments and thrive over a vast range of conditions. While bacterial genomes are relatively small, significant portions encode for “redundant” functions. Apparent redundancy is especially pervasive in bacterial proteins that reside outside of the inner membrane. While conditions within the cytoplasm are carefully controlled, those of the periplasmic space are largely determined by the cell’s exterior environment. As a result, proteins within this environmentally exposed region must be capable of functioning under a vast array of conditions, and/or there must be several similar proteins that have evolved to function under a variety of conditions. This study examines the activity of a class of enzymes that is essential in cell wall construction to determine if individual proteins might be adapted for activity under particular growth conditions. Our results indicate that a subset of these proteins are preferred for growth under alkaline conditions, while others are readily dispensable. 

Abstract Seed-mediated synthesis strategies, in which small gold nanoparticle precursors are added to a growth solution to initiate heterogeneous nucleation, are among the most prevalent, simple, and productive methodologies for generating well-defined colloidal anisotropic nanostructures. However, the size, structure, and chemical properties of the seeds remain poorly understood, which partially explains the lack of mechanistic understanding of many particle growth reactions. Here, we identify the majority component in the seed solution as an atomically precise gold nanocluster, consisting of a 32-atom Au core with 8 halide ligands and 12 neutral ligands constituting a bound ion pair between a halide and the cationic surfactant: Au₃₂X₈[AQA⁺•X^-]₁₂(X = Cl, Br; AQA = alkyl quaternary ammonium). Ligand exchange is dynamic and versatile, occurring on the order of minutes and allowing for the formation of 48 distinct Au₃₂clusters with AQAX (alkyl quaternary ammonium halide) ligands. Anisotropic nanoparticle syntheses seeded with solutions enriched in Au₃₂X₈[AQA⁺•X^-]₁₂show narrower size distributions and fewer impurity particle shapes, indicating the importance of this cluster as a precursor to the growth of well-defined nanostructures. 

Nanoenabled strategies have recently attracted attention as a sustainable platform for agricultural applications. Here, we present a mechanistic understanding of nanobiointeraction through an orthogonal investigation. Pristine (nS) and stearic acid surface-modified (cS) sulfur nanoparticles (NPs) as a multifunctional nanofertilizer were applied to tomato (Solanum lycopersicumL.) through soil. Both nS and cS increased root mass by 73% and 81% and increased shoot weight by 35% and 50%, respectively, compared to the untreated controls. Bulk sulfur (bS) and ionic sulfate (iS) had no such stimulatory effect. Notably, surface modification of S NPs had a positive impact, as cS yielded 38% and 51% greater shoot weight compared to nS at 100 and 200 mg/L, respectively. Moreover, nS and cS significantly improved leaf photosynthesis by promoting the linear electron flow, quantum yield of photosystem II, and relative chlorophyll content. The time-dependent gene expression related to two S bioassimilation and signaling pathways showed a specific role of NP surface physicochemical properties. Additionally, a time-dependent Global Test and machine learning strategy applied to understand the NP surface modification domain metabolomic profiling showed that cS increased the contents of IA, tryptophan, tomatidine, and scopoletin in plant leaves compared to the other treatments. These findings provide critical mechanistic insights into the use of nanoscale sulfur as a multifunctional soil amendment to enhance plant performance as part of nanoenabled agriculture. 

There is a need to develop new and sustainable agricultural technologies to help provide global food security, and nanoscale materials show promising results in this area. In this study, mesoporous silica nanoparticles (MSNs) and chitosan-coated mesoporous silica nanoparticles (CTS-MSNs) were synthesized and applied to soybeans (Glycine max) by two different strategies in greenhouse and field studies to study the role of dissolved silicic acid and chitosan in enhancing plant growth and suppressing disease damage caused by Fusarium virguliforme. Plant growth and health were assessed by measuring the soybean biomass and chlorophyll content in both healthy and Fusarium-infected plants at harvest. In the greenhouse study, foliar and seed applications with 250 mg/L nanoparticle treatments were compared. A single seed treatment of MSNs reduced disease severity by 30% and increased chlorophyll content in both healthy and infected plants by 12%. Based on greenhouse results, seed application was used in the follow-up field study and MSNs and CTS-MSNs reduced disease progression by 12 and 15%, respectively. A significant 32% increase was observed for chlorophyll content for plants treated with CTS-MSNs. Perhaps most importantly, nanoscale silica seed treatment significantly increased (23–68%) the micronutrient (Zn, Mn, Mg, K, B) content of soybean pods, suggesting a potential sustainable strategy for nano-enabled biofortification to address nutrition insecurity. Overall, these findings indicate that MSN and CTS-MSN seed treatments in soybeans enable disease suppression and increase plant health as part of a nano-enabled strategy for sustainable agriculture. 

Nanoparticles (NPs) are increasingly being used in medical, electronic, energy, and agricultural applications due to their unique properties that often arise due to the high surface area-to-volume ratio. However, this characteristic along with the high reactivity of NPs make these materials highly dynamic in environmental settings. Thus, several transformations can take place when these materials enter the environment that determines their transport, toxicity, and fate of them in our environment. These transformations, and more specifically oxidative dissolution and sulfidation, are directly impacted by the characteristics that a NP has in addition to the surrounding environmental conditions. Therefore, this review aims to summarize how NP characteristics (size, coatings, etc.) and other important environmentally relevant conditions (oxic/anoxic waters, natural organic matter, etc.) impact the oxidative dissolution and sulfidation of several metal and metal oxide NPs. The impact of these factors is crucial to understanding and predicting the environmental risks of these materials in a wide range of applications. 

A suite of acyl chloride structural isomers (C6H11OCl) was used to effect gas-phase esterification of starch-based phytoglycogen nanoparticles (PhG NPs). The surface degree of substitution (DS) was quantified using X-ray photoelectron spectroscopy, while the overall DS was quantified using 1H NMR spectroscopy. Gas-phase modification initiates at the NP surface, with the extent of surface and overall esterification determined by both the reaction time and the steric footprint of the acyl chloride reagent. The less sterically hindered acyl chlorides diffuse fully into the NP interior, while the branched isomers are restricted to the near-surface region and form self-limiting hydrophobic shells, with shell thicknesses decreasing with increasing steric footprint. These differences in substitution were also reflected in the solubility of the NPs, with water solubility systematically decreasing with increasing DS. The ability to separately control both the surface and overall degree of functionalization and thereby form thin hydrophobic shells has significant implications for the development of polysaccharide-based biopolymers as nanocarrier delivery systems. 

Prediction of organismal viability upon exposure to a nanoparticle in varying environments─as fully specified at the molecular scale─has emerged as a useful figure of merit in the design of engineered nanoparticles. We build on our earlier finding that a bag of artificial neural networks (ANNs) can provide such a prediction when such machines are trained with a relatively small data set (with ca. 200 examples). Therein, viabilities were predicted by consensus using the weighted means of the predictions from the bags. Here, we confirm the accuracy and precision of the prediction of nanoparticle viabilities using an optimized bag of ANNs over sets of data examples that had not previously been used in the training and validation process. We also introduce the viability strip, rather than a single value, as the prediction and construct it from the viability probability distribution of an ensemble of ANNs compatible with the data set. Specifically, the ensemble consists of the ANNs arising from subsets of the data set corresponding to different splittings between training and validation, and the different bags (k-folds). A k−1k machine uses a single partition (or bag) of k – 1 ANNs each trained on 1/k of the data to obtain a consensus prediction, and a k-bag machine quorum samples the k possible k−1k machines available for a given partition. We find that with increasing k in the k-bag or k−1k machines, the viability strips become more normally distributed and their predictions become more precise. Benchmark comparisons between ensembles of 4-bag machines and 34 fraction machines suggest that the 34 fraction machine has similar accuracy while overcoming some of the challenges arising from divergent ANNs in the 4-bag machines. 

High-throughput phenotypic profiling assays, popular for their ability to characterize alternations in single-cell morphological feature data, have been useful in recent years for predicting cellular targets and mechanisms of action (MoAs) for different chemicals and novel drugs. However, this approach has not been extensively used in environmental toxicology due to the lack of studies and established methods for performing this kind of assay in environmentally relevant species. Here, we developed a multiplexed algal cytological imaging (MACI) assay, based on the subcellular structures of the unicellular microalgae, Raphidocelis subcapitata, a toxicology and ecological model species. Several different herbicides and antibiotics with unique MoAs were exposed to R. subcapitata cells, and MACI was used to characterize cellular impacts by measuring subtle changes in their morphological features, including metrics of area, shape, quantity, fluorescence intensity, and granularity of individual subcellular components. This study demonstrates that MACI offers a quick and effective framework for characterizing complex phenotypic responses to environmental chemicals that can be used for determining their MoAs and identifying their cellular targets in plant-type organisms.