Browse Articles

Nanopore sensing of protein and peptide conformation for point-of-care applications

Abstract The global population’s aging and growth will likely result in an increase in chronic aging-related diseases. Early diagnosis could improve the medical care and quality of life. Many diseases are linked to misfolding or conformational changes in biomarker peptides and proteins, which affect their function and binding properties. Current clinical methods struggle to detect and quantify these changes. Therefore, there is a need for sensitive conformational sensors that can detect low-concentration analytes in biofluids. Nanopore electrical detection has shown potential in sensing subtle protein and peptide conformation changes. This technique can detect single molecules label-free while distinguishing shape or physicochemical property changes. Its proven sensitivity makes nanopore sensing technology promising for ultra-sensitive, personalized point-of-care devices. We focus on the capability of nanopore sensing for detecting and quantifying conformational modifications and enantiomers in biomarker proteins and peptides and discuss this technology as a solution to future societal health challenges.

Electron correlation and incipient flat bands in the Kagome superconductor CsCr3Sb5

Enzymatic Birch reduction via hydrogen atom transfer at [4Fe-4S]-OH2 and [8Fe-9S] clusters

Abstract The alkali metal- and ammonia-dependent Birch reduction is the classical synthetic method for achieving dihydro additions to arenes, typically yielding 1,4-cyclodienes. A mild biological alternative to this process are 1,5-dienoyl-coenzyme A (CoA)-forming class I and II benzoyl-CoA reductases (BCRs), widely abundant key enzymes in the biodegradation of aromatic compounds at anoxic environments. To obtain a comprehensive mechanistic understanding of class I BCR catalysis, we produced the active site subunits from a denitrifying bacterium and determined the X-ray structure of its substrate and product complexes at 1.4 Å revealing non-canonical double-cubane [8Fe-9S] and active site aqua-[4Fe-4S] clusters. Together with kinetic, spectroscopic and QM/MM studies, we provide evidence for a radical mechanism with a [4Fe-4S] cluster-bound water molecule acting as hydrogen atom and electron donor at potentials beyond the biological redox window. An analogous Birch-like radical mechanism is applied by class II BCRs with the catalytic water bound to a tungsten-bis-metallopterin cofactor. The use of activated, metal-bound water ligands as hydrogen atom donor serves as a basic blueprint for future enzymatic or biomimetic Birch reduction processes.

Author Correction: A fast approach for structural and evolutionary analysis based on energetic profile protein comparison

Epidermal electronic-tattoo for plant immune response monitoring

Abstract Real-time monitoring of plant immune responses is crucial for understanding plant immunity and mitigating economic losses from pathogen and pest attacks. However, current methods relying on molecular-level assessment are destructive and time-consuming. Here, we report an ultrathin, substrate-free, and highly conductive electronic tattoo (e-tattoo) designed for plants, enabling immune response monitoring through non-invasive electrical impedance spectroscopy (EIS). The e-tattoo’s biocompatibility, high conductivity, and sub-100 nm thickness allow it to conform to leaf tissue morphology and provide robust impedance data. We demonstrate continuous EIS analysis of live transgenic Arabidopsis thaliana plants for over 24 h, capturing the onset of NLR-mediated acute immune responses within three hours post-induction, prior to visible symptoms. RNA-seq and tissue ion leakage tests validate that EIS data accurately represent the physiological and molecular changes associated with immune activation. This non-invasive tissue-assessment technology has the potential to enhance our comprehension of immune activation mechanisms in plants and paves the way for real-time monitoring for plant health management.

Can a single brain cell be surprised?

Hydrothermal activity generated by impact melt emplacement on the rim of Ritchey crater, Mars

Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks

Continuous sensorimotor transformation enhances robustness of neural dynamics to perturbation in macaque motor cortex

Evolutionary sparse learning reveals the shared genetic basis of convergent traits

Accurate cross-species 5mC detection for Oxford Nanopore sequencing in plants with DeepPlant

Abstract Nanopore sequencing enables comprehensive detection of 5-methylcytosine (5mC), particularly in repeat regions. However, CHH methylation detection in plants is limited by the scarcity of high-methylation positive samples, reducing generalization across species. Dorado, the only tool for plant 5mC detection on the R10.4 platform, lacks extensive species testing. Here, we develop DeepPlant, a deep learning model incorporating both Bi-LSTM and Transformer architectures, which significantly improves CHH detection accuracy and performs well for CpG and CHG motifs. We address the scarcity of methylation-positive CHH training samples through screening species with abundant high-methylation CHH sites using bisulfite-sequencing and generate datasets that cover diverse 9-mer motifs for training and testing DeepPlant. Evaluated across nine species, DeepPlant achieves high whole-genome methylation frequency correlations (0.705-0.838) with BS-seq data on CHH, improved by 23.4- 117.6% compared to Dorado. DeepPlant also demonstrates superior single-molecule accuracy and F1 score, offering strong generalization for plant epigenetics research.

Selective adsorption of CO2 in TAMOF-1 for the separation of CO2/CH4 gas mixtures

Proteins with proximal-distal asymmetries in axoneme localisation control flagellum beat frequency

Abstract The 9 + 2 microtubule-based axoneme within motile flagella is well known for its symmetry. However, examples of asymmetric structures and proteins asymmetrically positioned within the 9 + 2 axoneme architecture have been identified. These occur in multiple different organisms, particularly involving the inner or outer dynein arms. Here, we comprehensively analyse conserved proximal-distal asymmetries in the uniflagellate trypanosomatid eukaryotic parasites. Building on the genome-wide localisation screen in Trypanosoma brucei we identify conserved proteins with an analogous asymmetric localisation in the related parasite Leishmania mexicana . Using deletion mutants, we find which are necessary for normal cell swimming, flagellum beat parameters and axoneme ultrastructure. Using combinatorial endogenous fluorescent tagging and deletion, we map co-dependencies for assembly into their normal asymmetric localisation. This revealed 15 proteins, 9 known and 6 novel, with a conserved proximal or distal axoneme-specific localisation. Most are outer dynein arm associated and show that there are multiple classes of proximal-distal asymmetry – one which is dependent on the docking complex. Many of these proteins are necessary for retaining the normal frequency of the tip-to-base symmetric flagellar waveform. Our comprehensive mapping reveals unexpected contributions of proximal-specific axoneme components to the frequency of waveforms initiated distally.

Ex vivo cortical circuits learn to predict and spontaneously replay temporal patterns

Abstract It has been proposed that prediction and timing are computational primitives of neocortical microcircuits, specifically, that neural mechanisms are in place to allow neocortical circuits to autonomously learn the temporal structure of external stimuli and generate internal predictions. To test this hypothesis, we trained cortical organotypic slices on two temporal patterns using dual-optical stimulation. After 24-h of training, whole-cell recordings revealed network dynamics consistent with training-specific timed prediction. Unexpectedly, there was replay of the learned temporal structure during spontaneous activity. Furthermore, some neurons exhibited timed prediction errors as revealed by larger responses when the expected stimulus was omitted compared to when it was present. Mechanistically our results indicate that learning relied in part on asymmetric connectivity between distinct neuronal ensembles with temporally-ordered activation. These findings further suggest that local cortical microcircuits are intrinsically capable of learning temporal information and generating predictions, and that the learning rules underlying temporal learning and spontaneous replay can be intrinsic to local cortical microcircuits and not necessarily dependent on top-down interactions.

A multi-modal transformer for predicting global minimum adsorption energy

Abstract The fast assessment of the global minimum adsorption energy (GMAE) between catalyst surfaces and adsorbates is crucial for large-scale catalyst screening. However, multiple adsorption sites and numerous possible adsorption configurations for each surface/adsorbate combination make it prohibitively expensive to calculate the GMAE through density functional theory (DFT). Thus, we designed a multi-modal transformer called AdsMT to rapidly predict the GMAE based on surface graphs and adsorbate feature vectors without site-binding information. The AdsMT model effectively captures the intricate relationships between adsorbates and surface atoms through the cross-attention mechanism, hence avoiding the enumeration of adsorption configurations. Three diverse benchmark datasets were introduced, providing a foundation for further research on the challenging GMAE prediction task. Our AdsMT framework demonstrates excellent performance by adopting the tailored graph encoder and transfer learning, achieving mean absolute errors of 0.09, 0.14, and 0.39 eV, respectively. Beyond GMAE prediction, AdsMT’s cross-attention scores showcase the interpretable potential to identify the most energetically favorable adsorption sites. Additionally, uncertainty quantification was integrated into our models to enhance the trustworthiness of the predictions.

Percolative sulfide core formation in oxidized planetary bodies

Whether or not to act is determined by distinct signals from motor thalamus and orbitofrontal cortex to secondary motor cortex

Bright and photostable yellow fluorescent proteins for extended imaging

Oxide-dispersion-enabled laser additive manufacturing of high-resolution copper

DNA origami signal amplification in lateral flow immunoassays

Abstract Lateral flow immunoassays (LFIAs) enable a rapid detection of analytes in a simple, paper-based test format. Despite their multiple advantages, such as low cost and ease of use, their low sensitivity compared to laboratory-based testing limits their use in e.g. many critical point-of-care applications. Here, we present a DNA origami-based signal amplification technology for LFIAs. DNA origami is used as a molecularly precise adapter to connect detection antibodies to tailored numbers of signal-generating labels. As a proof of concept, we apply the DNA origami signal amplification in a sandwich-based LFIA for the detection of cardiac troponin I (cTnI) in human serum. We show a 55-fold improvement of the assay sensitivity with 40-nm gold nanoparticle labels and an adjustable signal amplification of up to 125-fold with fluorescent dyes. The technology is compatible with a wide range of existing analytes, labels, and sample matrices, and presents a modular approach for improving the sensitivity and reliability of lateral flow testing.