Browse Articles

A simplified and efficient method for isolating small extracellular vesicles for comparative and comprehensive translational research

Abstract Small extracellular vesicles (sEVs) can provide information about the pathophysiology of the cells; therefore, sEVs have attracted considerable interest as possible diagnostic biomarkers. A key challenge lies in the necessity for simple and cost-effective sEV isolation methods to achieve high purity and yield suitable for research and clinical applications. We are introducing a comprehensive study on isolating sEVs using a novel cocktail strategy that integrates chemical precipitation and ultrafiltration with a two-step filtering process to ensure a highly pure and homogeneous population and further compared with PEG-based precipitation, ultra-centrifugation, and size-exclusion-chromatography columns. The isolated sEVs from each protocol are quantified for size and yield using nanoparticle tracking analysis, morphologically characterized through transmission electron microscopy, and validated by quantifying the expression profiles of sEV surface biomarkers. Furthermore, the study explores the applicability of our method for downstream multi-omics analyses. The results highlight the efficacy of the proposed protocol, demonstrating the ease and efficiency of isolating sEVs from different biofluids with minimal laboratory requirements and confirming the compatibility with multi-omics analyses. These findings position our method as particularly valuable for translational research, offering a promising avenue for advancing the study and application of sEVs in diagnostic and therapeutic research.

Early processing of traumatic material and contextual information in posttraumatic stress disorder and its relation to memory impairments

Let-7 restrains an epigenetic circuit in AT2 cells to prevent fibrogenic intermediates in pulmonary fibrosis

Designer artificial environments for membrane protein synthesis

Abstract Protein synthesis in natural cells involves intricate interactions between chemical environments, protein-protein interactions, and protein machinery. Replicating such interactions in artificial and cell-free environments can control the precision of protein synthesis, elucidate complex cellular mechanisms, create synthetic cells, and discover new therapeutics. Yet, creating artificial synthesis environments, particularly for membrane proteins, is challenging due to the poorly defined chemical-protein-lipid interactions. Here, we introduce MEMPLEX (Membrane Protein Learning and Expression), which utilizes machine learning and a fluorescent reporter to rapidly design artificial synthesis environments of membrane proteins. MEMPLEX generates over 20,000 different artificial chemical-protein environments spanning 28 membrane proteins. It captures the interdependent impact of lipid types, chemical environments, chaperone proteins, and protein structures on membrane protein synthesis. As a result, MEMPLEX creates new artificial environments that successfully synthesize membrane proteins of broad interest but previously intractable. In addition, we identify a quantitative metric, based on the hydrophobicity of the membrane-contacting amino acids, that predicts membrane protein synthesis in artificial environments. Our work allows others to rapidly study and resolve the “dark” proteome using predictive generation of artificial chemical-protein environments. Furthermore, the results represent a new frontier in artificial intelligence-guided approaches to creating synthetic environments for protein synthesis.

Wearable continuous diffusion-based skin gas analysis

Fabricating supramolecular pre-emergence herbicide CPAM-BPyHs for farming herbicide-resistant rice

Abstract Controlling weeds before their emergence is crucial for minimizing their impacts on crop yield and quality. Bipyridyl herbicides (BPyHs), a class of highly effective and broad-spectrum herbicides, cannot be used as pre-emergence herbicides because they can be absorbed and inactivated by negatively charged soil after application. Here, we design and fabricate an adsorbed-but-active supramolecular pre-emergence herbicide consisting of cationic polyacrylamide and bipyridyl herbicides (CPAM-BPyHs). CPAM is a positively charged polymer. It can preferentially bind to soil particles and shift their electric potential to a more positive value. Thus, it prevents not only runoff but also inactivation of BPyHs. We also develop a BPyHs-resistant rice line by mutation of the gene encoding L-type amino acid transporter 5 (OsLAT5). Field trial results show that the weed control efficiency of CPAM-diquat for direct-seeded herbicide-resistant rice line exceeds 90%. The herbicidal activity can maintain up to one month with only one application. This work offers a method for rice weed control and provides insights into the design of pesticides to prevent soil inactivation and runoff.

3D-printed spines for programmable liquid topographies and micromanipulation

Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials

Abstract Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have demonstrated remarkable accuracy and generalizability. However, the computational cost of MLIPs limits their applicability to chemically disordered systems requiring large simulation cells or to sample-intensive statistical methods. Here, we report the use of continuous and differentiable alchemical degrees of freedom in atomistic materials simulations, exploiting the fact that graph neural network MLIPs represent discrete elements as real-valued tensors. The proposed method introduces alchemical atoms with corresponding weights into the input graph, alongside modifications to the message-passing and readout mechanisms of MLIPs, and allows smooth interpolation between the compositional states of materials. The end-to-end differentiability of MLIPs enables efficient calculation of the gradient of energy with respect to the compositional weights. With this modification, we propose methodologies for optimizing the composition of solid solutions towards target macroscopic properties, characterizing order and disorder in multicomponent oxides, and conducting alchemical free energy simulations to quantify the free energy of vacancy formation and composition changes.

T cell memory response to MPXV infection exhibits greater effector function and migratory potential compared to MVA-BN vaccination

Abstract In 2022, a global mpox outbreak occurred, and remains a concern today. The T cell memory response to MPXV (monkeypox virus) infection has not been fully investigated. In this study, we evaluate this response in convalescent and MVA-BN (Modified Vaccinia Ankara - Bavarian Nordic) vaccinated individuals using VACV-infected cells. Strong CD8+ and CD4+ T cell responses are observed, and T cell responses are biased towards viral early expressed proteins. We identify seven immunodominant HLA-A*02:01 restricted MPXV-specific epitopes and focus our detailed phenotypic and scRNAseq analysis on the immunodominant HLA-A*02:01-G5R18-26-specific CD8+ T cell response. While tetramer+CD8+ T cells share similar differentiation and activation phenotypes, T cells from convalescent individuals show greater cytotoxicity, migratory potential to site of infection and TCR clonal expansion. Our data suggest that effective functional profiles of MPXV-specific memory T cells induced by Mpox infection may have an implication on the long-term protective responses to future infection.

Structural basis of TMPRSS11D specificity and autocleavage activation

Abstract Transmembrane Protease, Serine-2 (TMPRSS2) and TMPRSS11D are human proteases that enable SARS-CoV-2 and Influenza A/B virus infections, but their biochemical mechanisms for facilitating viral cell entry remain unclear. We show these proteases spontaneously and efficiently cleave their own zymogen activation motifs, activating their broader protease activity on cellular substrates. We determine TMPRSS11D co-crystal structures with a native and an engineered activation motif, revealing insights into its autocleavage activation and distinct substrate binding cleft features. Leveraging this structural data, we develop nanomolar potency peptidomimetic inhibitors of TMPRSS11D and TMPRSS2. We show that a broad serine protease inhibitor that underwent clinical trials for TMPRSS2-targeted COVID-19 therapy, nafamostat mesylate, was rapidly cleaved by TMPRSS11D and converted to low activity derivatives. In this work, we develop mechanistic insights into human protease viral tropism and highlight both the strengths and limitations of existing human serine protease inhibitors, informing future drug discovery efforts targeting these proteases.

Anti-Müllerian hormone signalling sustains circadian homeostasis in zebrafish

Streamlined metal-based hydrogel facilitates stem cell differentiation, extracellular matrix homeostasis and cartilage repair in male rats

Comprehensive synthesis and anticoagulant evaluation of a diverse fucoidan library

A minimally invasive thrombotic model to study stroke in awake mice

Mapping the interaction surface between CaVβ and actin and its role in calcium channel clearance

Abstract Defective ion channel turnover and clearance of damaged proteins are associated with aging and neurodegeneration. The L-type CaV1.2 voltage-gated calcium channel mediates depolarization-induced calcium signals in heart and brain. Here, we determined the interaction surface between actin and two calcium channel subunits, CaVβ2 and CaVβ4, using cross-linking mass spectrometry and protein-protein docking, and uncovered a role in replenishing conduction-defective CaV1.2 channels. Computational and in vitro mutagenesis identified hotspots in CaVβ that decreased the affinity for actin but not for CaV1.2. When coexpressed with CaV1.2, none of the tested actin-association-deficient CaVβ mutants altered the single-channel properties or the total number of channels at the cell surface. However, coexpression with the CaVβ2 hotspot mutant downregulated current amplitudes, and with a concomitant reduction in the number of functionally available channels, indicating that current inhibition resulted from a build-up of conduction silent channels. Our findings established CaVβ2–actin interaction as a key player for clearing the plasma membrane of corrupted CaV1.2 proteins to ensure the maintenance of a functional pool of channels and proper calcium signal transduction. The CaVβ–actin molecular model introduces a potentially druggable protein-protein interface to intervene CaV-mediated signaling processes.

RNA transcripts serve as a template for double-strand break repair in human cells

Abstract Double-strand breaks (DSBs) are toxic lesions that lead to genome instability. While canonical DSB repair pathways typically operate independently of RNA, growing evidence suggests that RNA:DNA hybrids and nearby transcripts can influence repair outcomes. However, whether transcript RNA can directly serve as a template for DSB repair in human cells remains unclear. In this study, we develop fluorescence and sequencing-based assays to show that RNA-containing oligonucleotides and messenger RNA can serve as templates during DSB repair. We conduct a CRISPR/Cas9-based genetic screen to identify factors that promote RNA-templated DSB repair (RT-DSBR). Of the candidate polymerases, we identify DNA polymerase zeta (Polζ) as a potential reverse transcriptase that facilitates RT-DSBR. Furthermore, analysis of cancer genome sequencing data reveals whole intron deletions - a distinct genomic signature of RT-DSBR that occurs when spliced mRNA guides repair. Altogether, our findings highlight RT-DSBR as an alternative pathway for repairing DSBs in transcribed genes, with potential mutagenic consequences.

Phenotypic drug susceptibility testing for Mycobacterium tuberculosis variant bovis BCG in 12 hours

Abstract Drug-resistant tuberculosis (DR-TB) kills ~200,000 people every year. A contributing factor is the slow turnaround time (TAT) associated with drug susceptibility diagnostics. The prevailing gold standard for phenotypic drug susceptibility testing (pDST) takes at least two weeks. Here we show that growth-based pDST for slow-growing mycobacteria can be conducted in 12 h. We use Mycobacterium tuberculosis variant bovis Bacillus Calmette-Guérin (BCG) and Mycobacterium smegmatis as the mycobacterial pathogen models and expose them to antibiotics used in (multidrug-resistant) tuberculosis (TB) treatment regimens - i.e ., rifampicin (RIF), isoniazid (INH), ethambutol (EMB), linezolid (LZD), streptomycin (STR), bedaquiline (BDQ), and levofloxacin (LFX). The bacterial growth in a microfluidic chip is tracked by time-lapse phase-contrast microscopy. A deep neural network-based segmentation algorithm is used to quantify the growth rate and to determine how the strains responded to drug treatments. Most importantly, a panel of susceptible and resistant M. bovis BCG are tested at critical concentrations for INH, RIF, STR, and LFX. The susceptible strains could be identified in less than 12 h. These findings are comparable to what we expect for pathogenic M. tuberculosis as they share 99.96% genetic identity.

Surface lipoprotein sorting by crosstalk between Lpt and Lol pathways in gram-negative bacteria

Globally increased cropland soil exposure to climate extremes in recent decades

The unexplored diversity of rough-seeded lupins provides rich genomic resources and insights into lupin evolution

Abstract Lupin crops provide nutritious seeds as an excellent source of dietary protein. However, extensive genomic resources are needed for crop improvement, focusing on key traits such as nutritional value and climate resiliency, to ensure global food security based on sustainable and healthy diets for all. Such resources can be derived either from related lupin species or crop wild relatives, which represent a large and untapped source of genetic variation for crop improvement. Here, we report genome assemblies of the cross-compatible species Lupinus cosentinii (Mediterranean) and its pan-Saharan wild relative L. digitatus, which are well adapted to drought-prone environments and partially domesticated. We show that both species are tetraploids, and their repetitive DNA content differs considerably from that of the main lupin crops L. angustifolius and L. albus. We present the complex evolutionary process within the rough-seeded lupins as a species-based model involving polyploidization and rediploidization. Our data also provide the foundation for a systematic analysis of genomic diversity among lupin species to promote their exploitation for crop improvement and sustainable agriculture.