A large net carbon loss attributed to anthropogenic and natural disturbances in the Amazon Arc of Deforestation

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric measurements suggest that it has been releasing more carbon than it has absorbed because of deforestation and forest degradation. Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes. We used a unique, randomized, repeated, very high-resolution airborne laser scanning survey to provide a direct, detailed, and high-resolution partitioning of aboveground carbon gains and losses in the Brazilian Arc of Deforestation. Our analysis revealed that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7%, respectively. Extrapolating the lidar-based statistics to the study area (544,300 km2), we found that 24.1, 24.2, and 14.5 Tg C y-1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y-1) and other disturbances (50.3 Tg C y-1) were partially counterbalanced by forest growth (44.1 Tg C y-1). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y-1 for the study region attributable to both anthropogenic and natural processes. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system.

Real-space visualization of a defect-mediated charge density wave transition

We study the coupled charge density wave (CDW) and insulator-to-metal transitions in the 2D quantum material 1T-TaS2. By applying in situ cryogenic 4D scanning transmission electron microscopy with in situ electrical resistance measurements, we directly visualize the CDW transition and establish that the transition is mediated by basal dislocations (stacking solitons). We find that dislocations can both nucleate and pin the transition and locally alter the transition temperature Tc by nearly ~75 K. This finding was enabled by the application of unsupervised machine learning to cluster five-dimensional, terabyte scale datasets, which demonstrate a one-to-one correlation between resistance-a global property-and local CDW domain-dislocation dynamics, thereby linking the material microstructure to device properties. This work represents a major step toward defect-engineering of quantum materials, which will become increasingly important as we aim to utilize such materials in real devices.

Acoustically shaped DNA-programmable materials

Recent advances in DNA nanotechnology allow for the assembly of nanocomponents with nanoscale precision, leading to the emergence of DNA-based material fabrication approaches. Yet, transferring these nano- and micron-scale structural arrangements to the macroscale morphologies remains a challenge, which limits the development of materials and devices based on DNA nanotechnology. Here, we demonstrate a materials fabrication approach that combines DNA-programmable assembly with actively driven processes controlled by acoustic fields. This combination provides a prescribed nanoscale order, as dictated by equilibrium assembly through DNA-encoded interactions, and field-shaped macroscale morphology, as regulated by out-of-equilibrium materials formation through specific acoustic stimulation. Using optical and electron microscopy imaging and x-ray scattering, we further revealed the nucleation processes, domain fusion, and crystal growth under different acoustically stimulated conditions. The developed approach provides a pathway for the fabrication of complexly shaped macroscale morphologies for DNA-programmable nanomaterials by controlling spatiotemporal characteristics of the acoustic fields.

Offshore wind and wave energy can reduce total installed capacity required in zero-emissions grids

As the world races to decarbonize power systems to mitigate climate change, the body of research analyzing paths to zero emissions electricity grids has substantially grown. Although studies typically include commercially available technologies, few of them consider offshore wind and wave energy as contenders in future zero-emissions grids. Here, we model with high geographic resolution both offshore wind and wave energy as independent technologies with the possibility of collocation in a power system capacity expansion model of the Western Interconnection with zero emissions by 2050. In this work, we identify cost targets for offshore wind and wave energy to become cost effective, calculate a 17% reduction in total installed capacity by 2050 when offshore wind and wave energy are fully deployed, and show how curtailment, generation, and transmission change as offshore wind and wave energy deployment increase.

Electrical switching of spin-polarized light-emitting diodes based on a 2D CrI3/hBN/WSe2 heterostructure

Spin-polarized light-emitting diodes (spin-LEDs) convert the electronic spin information to photon circular polarization, offering potential applications including spin amplification, optical communications, and advanced imaging. The conventional control of the emitted light's circular polarization requires a change in the external magnetic field, limiting the operation conditions of spin-LEDs. Here, we demonstrate an atomically thin spin-LED device based on a heterostructure of a monolayer WSe2 and a few-layer antiferromagnetic CrI3, separated by a thin hBN tunneling barrier. The CrI3 and hBN layers polarize the spin of the injected carriers into the WSe2. With the valley optical selection rule in the monolayer WSe2, the electroluminescence exhibits a high degree of circular polarization that follows the CrI3 magnetic states. Importantly, we show an efficient electrical tuning, including a sign reversal, of the electroluminescent circular polarization by applying an electrostatic field due to the electrical tunability of the few-layer CrI3 magnetization. Our results establish a platform to achieve on-demand operation of nanoscale spin-LED and electrical control of helicity for device applications.

Pressure induced structural and electronic band transition in CsPbBr3

Cesium lead bromide (CsPbBr3) is a prominent halide perovskite with extensive optoelectronic applications. In this study, we report the pressure modulation of CsPbBr3's crystal structure and electronic properties at room temperature up to 5 GPa. We have observed a crystal structure transition from the orthorhombic Pnma space group to a new monoclinic phase in the space group P21/c at 2.08 GPa. The structure is associated with ~8% of density jump across the transition boundary. DFT calculations have suggested that the structure transition leads to a change in the electronic band structure, and there is an emergent indirect bandgap at the Pnma-P21/c phase transition boundary at 2.08 GPa. Across the transition boundary, the electronic band gap of CsPbBr3 increased from 2.07 eV to 2.38 eV, which explains its pressure-induced color change. Our study demonstrates the importance of using in-situ crystal structure in the electronic band structure calculations in halide perovskites.

Restructuring dynamics of surface species in bimetallic nanoparticles probed by modulation excitation spectroscopy

Restructuring of metal components on bimetallic nanoparticle surfaces in response to the changes in reactive environment is a ubiquitous phenomenon whose potential for the design of tunable catalysts is underexplored. The main challenge is the lack of knowledge of the structure, composition, and evolution of species on the nanoparticle surfaces during reaction. We apply a modulation excitation approach to the X-ray absorption spectroscopy of the 30 atomic % Pd in Au supported nanocatalysts via the gas (H2 and O2) concentration modulation. For interpreting restructuring kinetics, we correlate the phase-sensitive detection with the time-domain analysis aided by a denoising algorithm. Here we show that the surface and near-surface species such as Pd oxides and atomically dispersed Pd restructured periodically, featuring different time delays. We propose a model that Pd oxide formation is preceded by the build-up of Pd regions caused by oxygen-driven segregation of Pd atoms towards the surface. During the H2 pulse, rapid reduction and dissolution of Pd follows an induction period which we attribute to H2 dissociation. Periodic perturbations of nanocatalysts by gases can, therefore, enable variations in the stoichiometry of the surface and near-surface oxides and dynamically tune the degree of oxidation/reduction of metals at/near the catalyst surface.

Identification and characterization of a small-molecule metallophore involved in lanthanide metabolism

Many bacteria secrete metallophores, low-molecular-weight organic compounds that bind ions with high selectivity and affinity, in order to access essential metals from the environment. Previous work has elucidated the structures and biosynthetic machinery of metallophores specific for iron, zinc, nickel, molybdenum, and copper. No physiologically relevant lanthanide-binding metallophore has been discovered despite the knowledge that lanthanide metals (Ln) have been revealed to be essential cofactors for certain alcohol dehydrogenases across a diverse range of phyla. Here, we report the biosynthetic machinery, the structure, and the physiological relevance of a lanthanophore, methylolanthanin. The structure of methylolanthanin exhibits a unique 4-hydroxybenzoate moiety which has not previously been described in other metallophores. We find that production of methylolanthanin is required for normal levels of Ln accumulation in the methylotrophic bacterium Methylobacterium extorquens AM1, while overexpression of the molecule greatly increases bioaccumulation and adsorption. Our results provide a clearer understanding of how Ln-utilizing bacteria sense, scavenge, and store Ln; essential processes in the environment where Ln are poorly bioavailable. More broadly, the identification of this lanthanophore opens doors for study of how biosynthetic gene clusters are repurposed for additional functions and the complex relationship between metal homeostasis and fitness.

Identification and characterization of a skin microbiome on Caenorhabditis elegans suggests environmental microbes confer cuticle protection

In the wild, C. elegans are emersed in environments teeming with a veritable menagerie of microorganisms. The C. elegans cuticular surface serves as a barrier and first point of contact with their microbial environments. In this study, we identify microbes from C. elegans natural habitats that associate with its cuticle, constituting a simple "skin microbiome." We rear our animals on a modified CeMbio, mCeMbio, a consortium of ecologically relevant microbes. We first combine standard microbiological methods with an adapted micro skin-swabbing tool to describe the skin-resident bacteria on the C. elegans surface. Furthermore, we conduct 16S rRNA gene sequencing studies to identify relative shifts in the proportion of mCeMbio bacteria upon surface-sterilization, implying distinct skin- and gut-microbiomes. We find that some strains of bacteria, including Enterobacter sp. JUb101, are primarily found on the nematode skin, while others like Stenotrophomonas indicatrix JUb19 and Ochrobactrum vermis MYb71 are predominantly found in the animal's gut. Finally, we show that this skin microbiome promotes host cuticle integrity in harsh environments. Together, we identify a skin microbiome for the well-studied nematode model and propose its value in conferring host fitness advantages in naturalized contexts.

IMPORTANCE

The genetic model organism C. elegans has recently emerged as a tool for understanding host-microbiome interactions. Nearly all of these studies either focus on pathogenic or gut-resident microbes. Little is known about the existence of native, nonpathogenic skin microbes or their function. We demonstrate that members of a modified C. elegans model microbiome, mCeMbio, can adhere to the animal's cuticle and confer protection from noxious environments. We combine a novel micro-swab tool, the first 16S microbial sequencing data from relatively unperturbed C. elegans, and physiological assays to demonstrate microbially mediated protection of the skin. This work serves as a foundation to explore wild C. elegans skin microbiomes and use C. elegans as a model for skin research.

A combinatorially complete epistatic fitness landscape in an enzyme active site

Protein engineering often targets binding pockets or active sites which are enriched in epistasis-nonadditive interactions between amino acid substitutions-and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences-a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.

Barcoded overexpression screens in gut Bacteroidales identify genes with roles in carbon utilization and stress resistance

A mechanistic understanding of host-microbe interactions in the gut microbiome is hindered by poorly annotated bacterial genomes. While functional genomics can generate large gene-to-phenotype datasets to accelerate functional discovery, their applications to study gut anaerobes have been limited. For instance, most gain-of-function screens of gut-derived genes have been performed in Escherichia coli and assayed in a small number of conditions. To address these challenges, we develop Barcoded Overexpression BActerial shotgun library sequencing (Boba-seq). We demonstrate the power of this approach by assaying genes from diverse gut Bacteroidales overexpressed in Bacteroides thetaiotaomicron. From hundreds of experiments, we identify new functions and phenotypes for 29 genes important for carbohydrate metabolism or tolerance to antibiotics or bile salts. Highlights include the discovery of a D-glucosamine kinase, a raffinose transporter, and several routes that increase tolerance to ceftriaxone and bile salts through lipid biosynthesis. This approach can be readily applied to develop screens in other strains and additional phenotypic assays.

Host-imposed control mechanisms in legume-rhizobia symbiosis

Legumes are ecologically and economically important plants that contribute to nutrient cycling and agricultural sustainability, features tied to their intimate symbiosis with nitrogen-fixing rhizobia. Rhizobia vary dramatically in quality, ranging from highly growth-promoting to non-beneficial; therefore, legumes must optimize their symbiosis with rhizobia through host mechanisms that select for beneficial rhizobia and limit losses to non-beneficial strains. In this Perspective, we examine the considerable scientific progress made in decoding host control over rhizobia, empirically examining both molecular and cellular mechanisms and their effects on rhizobia symbiosis and its benefits. We consider pre-infection controls, which require the production and detection of precise molecular signals by the legume to attract and select for compatible rhizobia strains. We also discuss post-infection mechanisms that leverage the nodule-level and cell-level compartmentalization of symbionts to enable host control over rhizobia development and proliferation in planta. These layers of host control each contribute to legume fitness by directing host resources towards a narrowing subset of more-beneficial rhizobia.

Dynamic two-dimensional covalent organic frameworks

Porous covalent organic frameworks (COFs) enable the realization of functional materials with molecular precision. Past research has typically focused on generating rigid frameworks where structural and optoelectronic properties are static. Here we report dynamic two-dimensional (2D) COFs that can open and close their pores upon uptake or removal of guests while retaining their crystalline long-range order. Constructing dynamic, yet crystalline and robust frameworks requires a well-controlled degree of flexibility. We have achieved this through a 'wine rack' design where rigid π-stacked columns of perylene diimides are interconnected by non-stacked, flexible bridges. The resulting COFs show stepwise phase transformations between their respective contracted-pore and open-pore conformations with up to 40% increase in unit-cell volume. This variable geometry provides a handle for introducing stimuli-responsive optoelectronic properties. We illustrate this by demonstrating switchable optical absorption and emission characteristics, which approximate 'null-aggregates' with monomer-like behaviour in the contracted COFs. This work provides a design strategy for dynamic 2D COFs that are potentially useful for realizing stimuli-responsive materials.

Metabolic complexity drives divergence in microbial communities

Microbial communities are shaped by environmental metabolites, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. Here we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but they diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.

Proton-triggered topological transformation in superbase-mediated selective polymerization enables access to ultrahigh-molar-mass cyclic polymers

The selective synthesis of ultrahigh-molar-mass (UHMM, >2 million Da) cyclic polymers is challenging as an exceptional degree of spatiotemporal control is required to overcome the possible undesired reactions that can compete with the desired intramolecular cyclization. Here we present a counterintuitive synthetic methodology for cyclic polymers, represented here by polythioesters, which proceeds via superbase-mediated ring-opening polymerization of gem-dimethylated thiopropiolactone, followed by macromolecular cyclization triggered by protic quenching. This proton-triggered linear-to-cyclic topological transformation enables selective, linear polymer-like access to desired cyclic polythioesters, including those with UHMM surpassing 2 MDa. In addition, this method eliminates the need for stringent conditions such as high dilution to prevent or suppress linear polymer contaminants and presents the opposite scenario in which protic-free conditions are required to prevent cyclic polymer formation, which is capitalized to produce cyclic polymers on demand. Furthermore, such UHMM cyclic polythioester exhibits not only much enhanced thermostability and mechanical toughness, but it can also be quantitatively recycled back to monomer under mild conditions due to its gem-disubstitution.

Solution-phase sample-averaged single-particle spectroscopy of quantum emitters with femtosecond resolution

The development of many quantum optical technologies depends on the availability of single quantum emitters with near-perfect coherence. Systematic improvement is limited by a lack of understanding of the microscopic energy flow at the single-emitter level and ultrafast timescales. Here we utilize a combination of fluorescence correlation spectroscopy and ultrafast spectroscopy to capture the sample-averaged dynamics of defects with single-particle sensitivity. We employ this approach to study heterogeneous emitters in two-dimensional hexagonal boron nitride. From milliseconds to nanoseconds, the translational, shelving, rotational and antibunching features are disentangled in time, which quantifies the normalized two-photon emission quantum yield. Leveraging the femtosecond resolution of this technique, we visualize electron-phonon coupling and discover the acceleration of polaronic formation on multi-electron excitation. Corroborated with theory, this translates to the photon fidelity characterization of cascaded emission efficiency and decoherence time. Our work provides a framework for ultrafast spectroscopy in heterogeneous emitters, opening new avenues of extreme-scale characterization for quantum applications.

Conventional superconductivity in the doped kagome superconductor Cs(V0.86Ta0.14)3Sb5 from vortex lattice studies

A hallmark of unconventional superconductors is a complex electronic phase diagram where intertwined orders of charge-spin-lattice degrees of freedom compete and coexist. While the kagome metals such as CsV3Sb5 also exhibit complex behavior, involving coexisting charge density wave order and superconductivity, much is unclear about the microscopic origin of the superconducting pairing. We study the vortex lattice in the superconducting state of Cs(V0.86Ta0.14)3Sb5, where the Ta-doping suppresses charge order and enhances superconductivity. Using small-angle neutron scattering, a strictly bulk probe, we show that the vortex lattice exhibits a strikingly conventional behavior. This includes a triangular symmetry with a period consistent with 2e-pairing, a field dependent scattering intensity that follows a London model, and a temperature dependence consistent with a uniform superconducting gap. Our results suggest that optimal bulk superconductivity in Cs(V1-xTax)3Sb5 arises from a conventional Bardeen-Cooper-Schrieffer electron-lattice coupling, different from spin fluctuation mediated unconventional copper- and iron-based superconductors.

Adaptative survival of Aspergillus fumigatus to echinocandins arises from cell wall remodeling beyond β-1,3-glucan synthesis inhibition

Antifungal echinocandins inhibit the biosynthesis of β-1,3-glucan, a major and essential polysaccharide component of the fungal cell wall. However, the efficacy of echinocandins against the pathogen Aspergillus fumigatus is limited. Here, we use solid-state nuclear magnetic resonance (ssNMR) and other techniques to show that echinocandins induce dynamic changes in the assembly of mobile and rigid polymers within the A. fumigatus cell wall. The reduction of β-1,3-glucan induced by echinocandins is accompanied by a concurrent increase in levels of chitin, chitosan, and highly polymorphic α-1,3-glucans, whose physical association with chitin maintains cell wall integrity and modulates water permeability. The rearrangement of the macromolecular network is dynamic and controls the permeability and circulation of the drug throughout the cell wall. Thus, our results indicate that echinocandin treatment triggers compensatory rearrangements in the cell wall that may help A. fumigatus to tolerate the drugs' antifungal effects.

Genomic insights into redox-driven microbial processes for carbon decomposition in thawing Arctic soils and permafrost

Climate change is rapidly transforming Arctic landscapes where increasing soil temperatures speed up permafrost thaw. This exposes large carbon stocks to microbial decomposition, possibly worsening climate change by releasing more greenhouse gases. Understanding how microbes break down soil carbon, especially under the anaerobic conditions of thawing permafrost, is important to determine future changes. Here, we studied the microbial community dynamics and soil carbon decomposition potential in permafrost and active layer soils under anaerobic laboratory conditions that simulated an Arctic summer thaw. The microbial and viral compositions in the samples were analyzed based on metagenomes, metagenome-assembled genomes, and metagenomic viral contigs (mVCs). Following the thawing of permafrost, there was a notable shift in microbial community structure, with fermentative Firmicutes and Bacteroidota taking over from Actinobacteria and Proteobacteria over the 60-day incubation period. The increase in iron and sulfate-reducing microbes had a significant role in limiting methane production from thawed permafrost, underscoring the competition within microbial communities. We explored the growth strategies of microbial communities and found that slow growth was the major strategy in both the active layer and permafrost. Our findings challenge the assumption that fast-growing microbes mainly respond to environmental changes like permafrost thaw. Instead, they indicate a common strategy of slow growth among microbial communities, likely due to the thermodynamic constraints of soil substrates and electron acceptors, and the need for microbes to adjust to post-thaw conditions. The mVCs harbored a wide range of auxiliary metabolic genes that may support cell protection from ice formation in virus-infected cells.

IMPORTANCE

As the Arctic warms, thawing permafrost unlocks carbon, potentially accelerating climate change by releasing greenhouse gases. Our research delves into the underlying biogeochemical processes likely mediated by the soil microbial community in response to the wet and anaerobic conditions, akin to an Arctic summer thaw. We observed a significant shift in the microbial community post-thaw, with fermentative bacteria like Firmicutes and Bacteroidota taking over and switching to different fermentation pathways. The dominance of iron and sulfate-reducing bacteria likely constrained methane production in the thawing permafrost. Slow-growing microbes outweighed fast-growing ones, even after thaw, upending the expectation that rapid microbial responses to dominate after permafrost thaws. This research highlights the nuanced and complex interactions within Arctic soil microbial communities and underscores the challenges in predicting microbial response to environmental change.

Transport coefficient approach for characterizing nonequilibrium dynamics in soft matter

Nonequilibrium states in soft condensed matter require a systematic approach to characterize and model materials, enhancing predictability and applications. Among the tools, X-ray photon correlation spectroscopy (XPCS) provides exceptional temporal and spatial resolution to extract dynamic insight into the properties of the material. However, existing models might overlook intricate details. We introduce an approach for extracting the transport coefficient, denoted as [Formula: see text], from the XPCS studies. This coefficient is a fundamental parameter in nonequilibrium statistical mechanics and is crucial for characterizing transport processes within a system. Our method unifies the Green-Kubo formulas associated with various transport coefficients, including gradient flows, particle-particle interactions, friction matrices, and continuous noise. We achieve this by integrating the collective influence of random and systematic forces acting on the particles within the framework of a Markov chain. We initially validated this method using molecular dynamics simulations of a system subjected to changes in temperatures over time. Subsequently, we conducted further verification using experimental systems reported in the literature and known for their complex nonequilibrium characteristics. The results, including the derived [Formula: see text] and other relevant physical parameters, align with the previous observations and reveal detailed dynamical information in nonequilibrium states. This approach represents an advancement in XPCS analysis, addressing the growing demand to extract intricate nonequilibrium dynamics. Further, the methods presented are agnostic to the nature of the material system and can be potentially expanded to hard condensed matter systems.

A theory of phonon-induced friction on molecular adsorbates

In this manuscript, we provide a general theory for how surface phonons couple to molecular adsorbates. Our theory maps the extended dynamics of a surface's atomic vibrational motions to a generalized Langevin equation, and by doing so captures these dynamics in a single quantity: the non-Markovian friction. The different frequency components of this friction are the phonon modes of the surface slab weighted by their coupling to the adsorbate degrees of freedom. Using this formalism, we demonstrate that physisorbed species couple primarily to acoustic phonons while chemisorbed species couple to dispersionless local vibrations. We subsequently derive equations for phonon-adjusted reaction rates using transition state theory and demonstrate that these corrections improve agreement with experimental results for CO desorption rates from Pt(111).

Stochastic machine learning via sigma profiles to build a digital chemical space

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available.

Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling

Engineering metabolism to efficiently produce chemicals from multi-step pathways requires optimizing multi-gene expression programs to achieve enzyme balance. CRISPR-Cas transcriptional control systems are emerging as important tools for programming multi-gene expression, but poor predictability of guide RNA folding can disrupt expression control. Here, we correlate efficacy of modified guide RNAs (scRNAs) for CRISPR activation (CRISPRa) in E. coli with a computational kinetic parameter describing scRNA folding rate into the active structure (rS = 0.8). This parameter also enables forward design of scRNAs, allowing us to design a system of three synthetic CRISPRa promoters that can orthogonally activate (>35-fold) expression of chosen outputs. Through combinatorial activation tuning, we profile a three-dimensional design space expressing two different biosynthetic pathways, demonstrating variable production of pteridine and human milk oligosaccharide products. This RNA design approach aids combinatorial optimization of metabolic pathways and may accelerate routine design of effective multi-gene regulation programs in bacterial hosts.

Disentangling plant- and environment-mediated drivers of active rhizosphere bacterial community dynamics during short-term drought

Mitigating the effects of climate stress on crops is important for global food security. The microbiome associated with plant roots, the rhizobiome, can harbor beneficial microbes that alleviate stress, but the factors influencing their recruitment are unclear. We conducted a greenhouse experiment using field soil with a legacy of growing switchgrass and common bean to investigate the impact of short-term drought severity on the recruitment of active bacterial rhizobiome members. We applied 16S rRNA and 16S rRNA gene sequencing for both crops and metabolite profiling for switchgrass. We included planted and unplanted conditions to distinguish environment- versus plant-mediated rhizobiome drivers. Differences in community structure were observed between crops and between drought and watered and planted and unplanted treatments within crops. Despite crop-specific communities, drought rhizobiome dynamics were similar across the two crops. The presence of a plant more strongly explained the rhizobiome variation in bean (17%) than in switchgrass (3%), with a small effect of plant mediation during drought observed only for the bean rhizobiome. The switchgrass rhizobiome was stable despite changes in rhizosphere metabolite profiles between planted and unplanted treatments. We conclude that rhizobiome responses to short-term drought are crop-specific, with possible decoupling of plant exudation from rhizobiome responses.