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The Authors wish to correct an error in Figure 6. In Figure 6B the panel label should read ‘Ku + DNA-PKcs + LINP1, 6 kbp bridge’ instead of ‘Ku + DNA-PKcs + PAXX, 6 kbp bridge’. The published article has been updated. This error does not affect the conclusions of the article.
Abstract Mutations within the mtrR gene are commonly found amongst multidrug resistant clinical isolates of Neisseria gonorrhoeae, which has been labelled a superbug by the Centers for Disease Control and Prevention. These mutations appear to contribute to antibiotic resistance by interfering with the ability of MtrR to bind to and repress expression of its target genes, which include the mtrCDE multidrug efflux transporter genes and the rpoH oxidative stress response sigma factor gene. However, the DNA-recognition mechanism of MtrR and the consensus sequence within these operators to which MtrR binds has remained unknown. In this work, we report the crystal structures of MtrR bound to the mtrCDE and rpoH operators, which reveal a conserved, but degenerate, DNA consensus binding site 5′-MCRTRCRN4YGYAYGK-3′. We complement our structural data with a comprehensive mutational analysis of key MtrR-DNA contacts to reveal their importance for MtrR-DNA binding both in vitro and in vivo. Furthermore, we model and generate common clinical mutations of MtrR to provide plausible biochemical explanations for the contribution of these mutations to multidrug resistance in N. gonorrhoeae. Collectively, our findings unveil key biological mechanisms underlying the global stress responses of N. gonorrhoeae.
The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.
Data visualization is a pivotal component of a structural biologist’s arsenal. The Mol* Viewer makes molecular visualizations available to broader audiences via most web browsers. While Mol* provides a wide range of functionality, it has a steep learning curve and is only available via a JavaScript interface. To enhance the accessibility and usability of web-based molecular visualization, we introduce MolViewSpec (molstar.org/mol-view-spec), a standardized approach for defining molecular visualizations that decouples the definition of complex molecular scenes from their rendering. Scene definition can include references to commonly used structural, volumetric, and annotation data formats together with a description of how the data should be visualized and paired with optional annotations specifying colors, labels, measurements, and custom 3D geometries. Developed as an open standard, this solution paves the way for broader interoperability and support across different programming languages and molecular viewers, enabling more streamlined, standardized, and reproducible visual molecular analyses. MolViewSpec is freely available as a Mol* extension and a standalone Python package.
Abstract Protein activity, abundance, and stability can be regulated by post-translational modification including ubiquitination. Ubiquitination is conserved among eukaryotes and plays a central role in modulating cellular function; yet, we lack comprehensive catalogs of proteins that are modified by ubiquitin in plants. In this study, we describe an antibody-based approach to enrich ubiquitinated peptides coupled with isobaric labeling to enable quantification of up to 18-multiplexed samples. This approach identified 17,940 ubiquitinated lysine sites arising from 6,453 proteins from Arabidopsis (Arabidopsis thaliana) primary roots, seedlings, and rosette leaves. Gene ontology analysis indicated that ubiquitinated proteins are associated with numerous biological processes including hormone signaling, plant defense, protein homeostasis, and metabolism. We determined ubiquitinated lysine residues that directly regulate the stability of three transcription factors, CRYPTOCHROME-INTERACTING BASIC-HELIX-LOOP-HELIX 1 (CIB1), CIB1 LIKE PROTEIN 2 (CIL2), and SENSITIVE TO PROTON RHIZOTOXICITY1 (STOP1) using in vivo degradation assays. Furthermore, codon mutation of CIB1 to create a K166R conversion to prevent ubiquitination, via CRISPR/Cas9-derived adenosine base editing, led to an early flowering phenotype and increased expression of FLOWERING LOCUS T (FT). These comprehensive site-level ubiquitinome profiles provide a wealth of data for future functional studies related to modulation of biological processes mediated by this post-translational modification in plants.
Abstract Over the past two decades, mass spectrometric (MS)-based proteomics technologies have facilitated the study of signaling pathways throughout biology. Nowhere is this needed more than in plants, where an evolutionary history of genome duplications has resulted in large gene families involved in posttranslational modifications and regulatory pathways. For example, at least 5% of the Arabidopsis thaliana genome (ca. 1,200 genes) encodes protein kinases and protein phosphatases that regulate nearly all aspects of plant growth and development. MS-based technologies that quantify covalent changes in the side-chain of amino acids are critically important, but they only address one piece of the puzzle. A more crucially important mechanistic question is how noncovalent interactions—which are more difficult to study—dynamically regulate the proteome’s 3D structure. The advent of improvements in protein 3D technologies such as cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography has allowed considerable progress to be made at this level, but these methods are typically limited to analyzing proteins, which can be expressed and purified in milligram quantities. Newly emerging MS-based technologies have recently been developed for studying the 3D structure of proteins. Importantly, these methods do not require protein samples to be purified and require smaller amounts of sample, opening the wider proteome for structural analysis in complex mixtures, crude lysates, and even in intact cells. These MS-based methods include covalent labeling, crosslinking, thermal proteome profiling, and limited proteolysis, all of which can be leveraged by established MS workflows, as well as newly emerging methods capable of analyzing intact macromolecules and the complexes they form. In this review, we discuss these recent innovations in MS-based “structural” proteomics to provide readers with an understanding of the opportunities they offer and the remaining challenges for understanding the molecular underpinnings of plant structure and function.
Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.
Deep learning is increasingly used in medical imaging, improving many steps of the processing chain, from acquisition to segmentation and anomaly detection to outcome prediction. Yet significant challenges remain: (i) image-based diagnosis depends on the spatial relationships between local patterns, something convolution and pooling often do not capture adequately; (ii) data augmentation, the de facto method for learning three-dimensional pose invariance, requires exponentially many points to achieve robust improvement; (iii) labelled medical images are much less abundant than unlabelled ones, especially for heterogeneous pathological cases; and (iv) scanning technologies such as magnetic resonance imaging can be slow and costly, generally without online learning abilities to focus on regions of clinical interest. To address these challenges, novel algorithmic and hardware approaches are needed for deep learning to reach its full potential in medical imaging.
Rewetting of seasonally dry soils induces a burst of microbial activity and carbon mineralization that changes nutrient availability and leads to succession. Yet the microbial functions that underpin this succession are not well described. Further, it’s unclear how previous precipitation frames microbial capacities after rewetting and how long these effects persist. We used isotopically-labeled water to rewet seasonally dry annual grassland soil that experienced either mean annual or reduced precipitation during the previous two years, and sampled at five subsequent time points. We used quantitative stable isotope probing (qSIP)-informed genome- resolved metagenomics to identify growing microorganisms, predict their capabilities, and analyze how these traits differed over time and between precipitation treatments. Organisms that grew after wetup were more abundant than non-growing organisms prior to the wet-up, suggesting that traits that initiate succession are pre-defined at the end of the prior plant growing season or via survival over the summer. Fast-growing organisms had fewer carbohydrate active enzyme (CAZy) genes per genome than slower-growing organisms, suggesting that although fast-growers were capable of degrading complex C, they may not specialize in this process. Differential abundance of CAZy genes in growing organisms throughout the succession implies that substrate availability varied with time. In contrast, the abundance of peptidases in growing organisms differed between precipitation treatments, but not over time following wet-up. Before wet-up, the soil organisms’ gene inventories were different between the two precipitation treatments. Surprisingly, this legacy effect waned after just one week. Thus, pre-wetup differences in microbial functional capacity converged shortly after rewetting.
We present an unsupervised learning framework for detecting anomalous superconducting radio-frequency (SRF) cavity behavior at the Continuous Electron Beam Accelerator Facility (CEBAF), emphasizing its initial performance and effectiveness. Key to the system’s success was the development of data acquisition systems (DAQs) that capture fast-sampled, information-rich signals, essential for detecting transient effects. The approach involves creating daily cavity-specific models using principal component analysis to handle variations in rf signal behavior and mitigate performance degradation from data drift. This unsupervised method eliminates the need for expensive labeling by continuously updating models with recent data. Deployed and operational for 3 months before a scheduled shutdown, the system successfully identified several issues with DAQ signals, confirming its effectiveness. Despite access to only a fraction of CEBAF’s SRF cavity signals, the framework efficiently detected several instances requiring intervention, demonstrating a significant improvement over traditional, labor-intensive methods of manual plot inspection. Published by the American Physical Society 2025
Auxiliary field quantum Monte Carlo simulations of interacting fermions require sampling over a Hubbard-Stratonovich field h introduced to decouple the interactions. The weight for a given configuration involves the products of the determinant of matrices $M_σ(h)$ where σ labels the species, and hence is typically not positive definite. Indeed, the average sign $\langle \mathscr {L} \rangle$ of the determinants goes to zero exponentially with increasing spatial size and decreasing temperature for most Hamiltonians of interest. This statement, however, does not explicitly separate two possible origins for the vanishing of $\langle \mathscr {L} \rangle$. Does $\langle \mathscr {L} \rangle$ → 0 because randomly chosen field configurations have det[M(h)] < 0, or does the sign problem arise because the specific subset of configurations chosen by the weighting function have a greater preponderance of negative values? In the latter case, the process of weighting the configurations with |det[M(h)]| might steer the simulation to a region of configuration space of h where positive and negative determinants are equally likely, even though randomly chosen h would preferentially have determinants with a single dominant sign. Here in this paper, we address the relative importance of these two mechanisms for the vanishing of $\langle \mathscr {L} \rangle$ in quantum simulations.
In subleading powers of soft-collinear effective theory (SCET), the Lagrangian contains couplings between soft quarks and hard-collinear quarks. Matrix elements of the hard-collinear parts of these couplings are radiative jet functions. In the position-space formulation of SCET, the Lagrangians are constructed from operators that appear to be gauge invariant. Nevertheless, we find violations of gauge invariance arise in the hard-collinear sector because gauge transformations can shift the momentum of a hard-collinear quark field from the hard-collinear sector to the soft sector, where the hard-collinear fields, by definition, have no support. The violations of gauge invariance are manifested in perturbation theory in the hard-collinear sector through the absence of certain Feynman diagrams that would be present in full QCD. A consequence of the absence of these diagrams is that the radiative jet functions that follow directly from the position-space Lagrangians are not gauge invariant, and we demonstrate this through explicit calculations in lower-order perturbation theory. We obtain gauge-invariant Lagrangians by adding to existing position-space Lagrangians terms that are proportional to the soft-quark equation of motion. These gauge-invariant Lagrangians are valid for nonzero, as well as zero, quark masses. We also remark briefly on the gauge invariance of certain Lagrangians that have been constructed in the label-momentum formulation of SCET. Published by the American Physical Society 2024
The topology of photons in vacuum is interesting because there are no photons with k = 0, creating a hole in momentum space. We show that while the set of all photons forms a trivial vector bundle $γ$ over this momentum space, the R and L photons form topologically nontrivial subbundles $γ±$ with first Chern numbers ∓2. In contrast, $γ$ has no linearly polarized subbundles, and there is no Chern number associated with linear polarizations. It is a known difficulty that the standard version of Wigner’s little group method produces singular representations of the Poincaré group for massless particles. By considering representations of the Poincaré group on vector bundles we obtain a version of Wigner’s little group method for massless particles which avoids these singularities. Here we show that any massless bundle representation of the Poincaré group can be canonically decomposed into irreducible bundle representations labeled by helicity, which in turn can be associated to smooth irreducible Hilbert space representations. This proves that the R and L photons are globally well defined as particles and that the photon wave function can be uniquely split into R and L components. This formalism offers a method of quantizing the electromagnetic field without invoking discontinuous polarization vectors as in the traditional scheme. We also demonstrate that the spin-Chern number of photons is not a purely topological quantity. Lastly, there has been an extended debate on whether photon angular momentum can be split into spin and orbital parts. Our work explains the precise issues that prevent this splitting. Photons do not admit a spin operator; instead, the angular momentum associated with photons’ internal degree of freedom is described by a helicity-induced subalgebra corresponding to the translational symmetry of $γ$.
We propose new cross section ratios in ultraperipheral A + A and p + A collisions (UPCs) as a potential experimental signal of saturation physics. We consider the ratio R 1 of elastic vector meson photoproduction cross section to the inclusive hadron or jet photoproduction cross section. The ratio can be measured in the γ + A and γ + p collisions taking place in the UPCs. We label the ratios R 1 ( A ) and R 1 ( p ) , respectively. Constructing the double ratio R UPC ( A ) = R 1 ( A ) / R 1 ( p ) , and performing a small- x calculation both in the quasiclassical approximation and by including small- x evolution, we observe that R UPC ( A ) exhibits a markedly different dependence on the nuclear atomic number inside and outside the saturation region. This result indicates that R UPC ( A ) and R 1 ( A ) measurements in UPCs may help in the experimental searches for the evidence of saturation physics. Published by the American Physical Society 2024
We present universal relations between entanglement entropy, which quantifies the quantum correlation between subsystems, and the cross section, which is the primary observable for high-energy particle scattering, by employing a careful formulation of wave packets for the incoming particles. For 2-to-2 elastic scattering with no initial entanglement and subdividing the system along particle labels, we show that both the Rényi and Tsallis entropies in the final states are directly proportional to the elastic cross section in units of the transverse size for the initial wave packets, which is then interpreted as the elastic scattering probability. The relations do not depend on the underlying dynamics of the quantum field theory and are valid to all orders in coupling strengths. Furthermore, computing quantum correlations between momentum and nonkinematic data leads to entanglement entropies expressed as various semi-inclusive elastic cross sections. Our result gives rise to a novel “area law” for entanglement entropy in a two-body system. Published by the American Physical Society 2025
The construction of gauge-invariant states of SU(3) lattice gauge theories has garnered new interest in recent years, but implementing them is complicated by the need for SU(3) Clebsch-Gordon coefficients. In the loop-string-hadron (LSH) approach to lattice gauge theories, the elementary excitations are strictly gauge invariant, and constructing the basis requires no knowledge of Clebsch-Gordon coefficients. Originally developed for SU(2), the LSH formulation was recently generalized to SU(3), but limited to one spatial dimension. In this work, we generalize the LSH approach to constructing the basis of SU(3) gauge-invariant states at a trivalent vertex—the essential building block to multidimensional space. A direct generalization from the SU(2) vertex yields a legitimate basis; however, in certain sectors of the Hilbert space, the naive LSH basis vectors so defined suffer from being nonorthogonal. The issues with orthogonality are directly related to the “missing label” or “outer multiplicity” problem associated with SU(3) tensor products and may also be phrased in terms of Littlewood-Richardson coefficients or the need for a “seventh Casimir” operator. The states that are unaffected by the problem are orthonormalized in closed form. For the sectors that are afflicted, we discuss the nonorthogonal bases and their orthogonalization. A few candidates for seventh Casimir operators are readily constructed from the suite of LSH gauge-singlet operators. The diagonalization of a seventh Casimir represents one prescriptive solution toward obtaining a complete orthonormal basis, but a closed-form general solution remains to be found. Published by the American Physical Society 2025
The Rothman-Keller color-gradient (CG) lattice Boltzmann method is a popular method to simulate two-phase flow because of its ability to deal with fluids with large viscosity contrasts and a wide range of interfacial tensions. Here, two fluids are labeled red and blue, and the gradient in the color difference is used to compute the effect of interfacial tension. It is well known that finite-difference errors in the color-gradient calculation lead to anisotropy of interfacial tension and errors such as spurious currents. Here, we investigate the accuracy of the CG calculation for interfaces between fluids with several radii of curvature and find that the standard CG calculations lead to significant inaccuracy. Specifically, we observe significant anisotropy of the color gradient of order 7% for high curvature of an interface such as when a pinchout occurs. We derive a second order accurate color gradient and find that the diagonal nearest neighbors can be weighted differently than in the usual color-gradient calculation such that anisotropy is minimized to a fraction of a percent. The optimal weights that minimize anisotropy for the smallest radius of curvature interface are found to be w = (0.298, 0.284, 0.275) for diagonal nearest neighbors for the cases of the interface smoothing parameter β = (0.5, 0.7, 0.99), somewhat higher than the w = 0.25 value derived by Leclaire et al. [Leclaire, Reggio, and Trepanier, Computers and Fluids 48, 98 (2011)] based on obtaining isotropic errors to second order. We find that use of these optimal w values yields over a factor of 10 decrease in anisotropy and over a factor of 30 decrease in mean anisotropy relative to using the standard w = 1 value. And we find a factor of about 2 decrease in the anisotropic error and up to factor 15 decrease in mean anisotropic error relative to the choice of w = 0.25 for small radius of curvature interfaces. The improved CG calculations will allow the method to be more reliably applied to studies of phenomenology and pore scale processes such as viscous and capillary fingering, and droplet formation where surface-tension isotropy of narrow fingers and small droplets plays a crucial role in correctly capturing phenomenology. We present an example illustrating how different phenomena can be captured using the improved color-gradient method. Namely, we present simulations of a wetting fluid invading a fluid filled pipe where the viscosity ratio of fluids is unity in which droplets form at the transition to fingering using the improved CG calculations that are not captured using the standard CG calculations. We present an explanation of why this is so which relates to anisotropy of the surface tension, which inhibits the pinchouts needed to form droplets.
Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.