Biology subjects

Wu, K.

Publications and source records attributed to Wu, K..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Catastrophic drought effects on water quality and phytoplankton biomass in Nanwan Reservoir (Xinyang, China)

Climate change has led to increasingly frequent and unpredictable droughts and high-temperature events, creating extreme conditions that profoundly impact the productivity of freshwater ecosystems. This study investigates the Nanwan Reservoir, a large and deep reservoir in Xinyang, China, to evaluate the effects of extreme drought events on water quality and phytoplankton production. Field investigations were conducted during both high-temperature and drought (HTD) conditions in 2019, and during non-high-temperature and drought (NHTD) conditions in 2020. Results showed that HTD conditions significantly disrupted the thermocline and oxycline structures, leading to prolonged stratification during the HTD period. Although phosphorus concentrations remained relatively stable across both periods, nitrogen levels were markedly lower during HTD events--likely due to uptake by phytoplankton--indicating a shift in nutrient limitation from phosphorus to nitrogen. Additionally, a complex relationship between environmental variables and phytoplankton biomass was observed under HTD conditions. These findings advance our understanding of primary production responses to extreme weather events in the Nanwan Reservoir and highlight the importance of incorporating such knowledge into water resource management and ecological conservation strategies.

ecology↗

bioRxiv2025

Stroke Shifts Brain Dynamics Toward Criticality: Evidence from Intrinsic Neural Timescales

Stroke disrupts brain function beyond focal lesions, altering multiscale temporal dynamics essential for information processing. We investigated intrinsic neural timescales (INT) and other properties of long-range temporal correlations, using longitudinal fMRI data from 15 ischemic stroke patients across six months, and compared them to age-matched controls. Results show that stroke patients exhibited significantly prolonged INT in multiple cortical regions, reflecting slowed temporal dynamics and disrupted hierarchy. These dynamic changes persisted through recovery and were more pronounced in patients with poor outcomes, especially within cognitive control networks. Computational modelling suggested that stroke-induced INT prolongation driven by heightened neuronal excitability reflects a dynamic shift towards criticality. Our findings position long-range temporal correlations and INT as potential biomarkers for monitoring and predicting functional recovery. This framework provides a novel perspective on stroke-induced brain changes and suggests avenues for targeted neurorehabilitation using interventions aiming at restoring intrinsic temporal dynamics.

neuroscience↗

bioRxiv2025

A cardiac microphysiological system for screening lipid nanoparticle/mRNA complexes predicts in vivo efficacy for heart transfection

Lipid nanoparticle (LNP)/mRNA complexes have tremendous potential for treating cardiac diseases but lack the transfection efficiency required for successful heart therapeutics. A challenge preventing the development of cardiac LNPs is the lack of in vitro screening platforms that maximize transfection and predict efficacy. Here, we demonstrate that a phenotypic cardiac microphysiological system (MPS), constructed from a human induced pluripotent stem cell cardiomyocytes (hiPSC-CM) Cre-reporter line, can identify LNP/mRNA complexes that diffuse efficiently within 3D cardiac micromuscles and transfect cardiomyocytes with high efficiency. Successful LNP formulations contained an acid-degradable PEG (ADP)-lipid, where ADP-LNPs containing 10% PEG had enhanced diffusion and gene editing efficiency in the cardiac MPS. The in vivo delivery of LNP/mRNA complexes, including luciferase and CRE mRNA, into Ai6 mice confirmed the cardiac MPS screening outcomes. It revealed that ADP-LNPs achieved notably superior transfection in the heart with reduced off-target liver uptake compared to a standard LNP formulation. The cardiac MPS showed strong LNP transfection in vitro and pinpointed a promising formulation for in vivo mRNA delivery to the heart.

bioengineering↗

bioRxiv2025

Arabidopsis REM transcription factors and GDE1 shape the DNA methylation landscape through the recruitment of RNA Polymerase IV transcription complexes.

DNA methylation plays crucial roles in gene regulation and transposon silencing. In plants, the maintenance of DNA methylation is controlled by several self-reinforcing loops involving histone methylation and non-coding RNAs. However, how methylation is initially patterned at specific loci is unknown. Here, we describe four Arabidopsis REM transcription factors, VDD, VAL, REM12 and REM13, that recognize specific sequence regions, and together with the protein GENETICS DETERMINES EPIGENETICS1 (GDE1), recruit RNA polymerase IV transcription complexes to generate 24-nucleotide small interfering RNAs (24nt-siRNAs) that guide DNA methylation to specific loci. In the absence of GDE1, Pol IV transcription complexes redistribute to sites bound by a different factor called REM8. These results suggest that REM proteins act as sequence specific DNA binding proteins that pattern siRNAs and methylation at specific sites in the genome, highlighting the role of genetic information in determining epigenetic patterns.

plant biology↗

bioRxiv2024

mRNA-1273 vaccines adapted to JN.1 or KP.2 elicit cross-neutralizing responses against the JN.1 sublineages of SARS-CoV-2 in mice

The continued diversification of SARS-CoV-2 omicron lineage has given rise to the JN.1 variant and descendant strains (KP.2, KP.3, and XEC) that have prolonged the JN.1 infection wave. JN.1 and KP.2 show decreased susceptibility to neutralization sera in recipients of XBB.1.5 vaccine boosters, supporting the recent authorization of JN.1- and KP.2-matched mRNA vaccines in the United States, Europe, and other regions. We evaluated the immunogenicity of two updated monovalent variant-containing formulations of mRNA-1273 vaccines encoding the spike protein of the omicron subvariants JN.1 (mRNA-1273.167) and KP.2 (mRNA-1273.712) as compared with the monovalent XBB.1.5 vaccine (mRNA-1273.815). The vaccines were administered either as a two-dose primary series in naive mice or as a booster (third) dose in mice previously immunized with two-dose primary series of mRNA-1273 (ancestral strain). The neutralizing antibody response elicited by these vaccines against JN.1 subvariants (KP.3 and LA.2) and the recombinant strain (XEC), which achieved dominance in the United States during late 2024, was evaluated. Primary series immunization with either JN.1- or KP.2-matched vaccine elicited robust neutralizing antibody titers against the matched strains and effectively cross-neutralized KP.3, LA.2, and XEC, but not the antigenically distant XBB.1.5. Similarly, JN.1- and KP.2-matched vaccines administered as a booster (third) dose increased titers against the corresponding strains and JN.1-related subvariants, but not against XBB.1.5. These data suggest these strains are antigenically similar with relatively few spike differences between JN.1 and KP.2/JN.1-related subvariants. Our results demonstrate the potency of JN.1- and KP.2-containing mRNA-1273 vaccines in neutralizing the matched variants and their utility in cross-neutralizing JN.1-related subvariants KP.3, LA.2, and XEC. Taken together, these data suggest that the licensed JN.1 and KP.2 mRNA vaccines are likely to continue to protect against the emerging strains as the JN.1 lineage further evolves.

immunology↗

bioRxiv2024

Accurate de novo design of a voltage-gated anion channel

Ion channels gated by environmental cues play key roles in fundamental biological processes. Designing ion channels with tailored gating mechanisms remains a significant challenge due to the complexities involved in designing conformational changes in proteins. Here we report the accurate de novo design of a voltage-gated anion channel, namely VGAC. VGAC adopts a 15-helix pentameric structure featuring a constriction composed of five arginine residues within the transmembrane span. In patch-clamp experiments, VGAC showed strictly voltage-dependent currents and demonstrated selectivity for chloride anions over iodide anions. Our data suggests that the arginine constriction undergoes voltage-induced conformational changes, serving as both the voltage sensor and selectivity filter. A 2.9-[A]-resolution cryo-electron microscopy structure of VGAC closely aligns with the design model. The ability to design ion channels with custom-designed conformational changes provides new insights into our understanding of the fundamental principles of membrane biophysics and unveils a wide range of potential applications.

synthetic biology↗

bioRxiv2024

Identification of a Cluster of Benzene Activation Enzymes in a Strictly Anoxic Methanogenic Consortium

The Oil Refinery (OR) consortium is a model methanogenic enrichment culture used to study anaerobic benzene degradation. Over half of the cultures bacterial community consists of two closely related Desulfobacterota strains, designated ORM2a and ORM2b, whose mechanisms of benzene activation are unknown. Two new metagenomes, including a complete circularized metagenome-assembled genome (MAG) for ORM2a, enabled a thorough investigation of this cultures proteome. Among the proteins identified were Bam-like subunits of an ATP-independent benzoyl-CoA degradation pathway, as well as downstream {beta}-oxidation proteins yielding acetate. The most abundant proteins identified mapped to two ORM2a gene clusters of unknown function. Homologous and syntenic gene clusters were identified in genomes of ORM2b and a sulfate-reducing Pelotomaculum that also degrades benzene, as well as in nine contigs assembled from hydrothermal vent metagenomes. Extensive homology and structural predictions suggest that the first cluster - termed the "Magic" gene cluster - encodes for enzymes catalyzing the chemically challenging activation of benzene and subsequent transformation steps yielding benzoyl-CoA. The second ("Nanopod") gene cluster encodes a transmembrane complex that may facilitate benzene transport across the cell membrane. Phylogenomic analyses place ORM2a and ORM2b within a novel genus of strict anaerobes specialized for benzene degradation, which we propose naming "Candidatus Anaerobenzenivorax". IMPORTANCEBenzene is a widespread, persistent and toxic pollutant that can accumulate in anoxic environments such as groundwater and sediments. Despite decades of study, the biochemical mechanisms by which benzene is activated under anaerobic conditions remain unproven. This study provides strong genetic and proteomic evidence for a new class of enzymes that initiate anaerobic benzene activation and proposes a preliminary model for their underlying biochemistry. These findings lay a foundation for future biochemical studies and expand our understanding of how microbes carry out extreme redox chemistry in the absence of oxygen.

microbiology↗

bioRxiv2024

Chai-1: Decoding the molecular interactions of life

We introduce Chai-1, a multi-modal foundation model for molecular structure prediction that performs at the state-of-the-art across a variety of tasks relevant to drug discovery. Chai-1 can optionally be prompted with experimental restraints (e.g. derived from wet-lab data) which boosts performance by double-digit percentage points. Chai-1 can also be run in single-sequence mode with-out MSAs while preserving most of its performance. We release Chai-1 model weights and inference code as a Python package for non-commercial use and via a web interface where it can be used for free including for commercial drug discovery purposes.

synthetic biology↗

bioRxiv2024

Structural Insights into Selective and Dual Antagonism of EP2 and EP4 Prostaglandin Receptors

Prostaglandin E2 (PGE2) signaling through EP2 and EP4 receptors plays a crucial role in inflammation, pain, and cancer progression. Selective and dual antagonists of these receptors have shown promising therapeutic potential. However, the structural basis for their binding modes and selectivity remained unclear. Here, we present cryo-EM structures of human EP2 and EP4 receptors in complex with selective antagonists PF-04418948 and grapiprant, respectively, as well as the dual antagonist TG6-129. These structures reveal distinct binding pockets and interaction networks that govern antagonist selectivity and efficacy. Combining the structural data and functional studies, we uncover the key residues and structural features that differentiate EP2 and EP4 binding sites, including a unique {pi}-{pi} stacking interaction in EP2 and variations in pocket shape and charge distribution. The dual antagonist TG6-129 exhibited a novel binding mode, interacting deeply with EP2 but only shallowly with EP4 in a two-warhead manner. These findings provide a structural framework for understanding prostanoid receptor pharmacology and offer valuable insights for the rational design of improved selective and dual antagonists targeting EP2 and EP4 receptors.

biochemistry↗

bioRxiv2024

Widespread brain activity increases in frontal lobe seizures with impaired consciousness

Impaired consciousness is a serious clinical manifestation of epilepsy with negative consequences on quality of life. Little work has investigated impaired consciousness in frontal lobe seizures, a common form of focal epilepsy. In temporal lobe seizures, previous studies showed widespread cortical slow waves associated with depressed subcortical arousal and impaired consciousness. However, in frontal lobe epilepsy, it is not known whether cortical slow waves are present, or whether a very different cortical activity pattern may be related to impaired consciousness. We used intracranial EEG recordings of 65 frontal lobe seizures in 30 patients for quantitative analysis of ictal cortical activity and its relationship to impaired consciousness. Behavioral changes based on blinded review of seizure videos were used to classify focal aware, focal impaired awareness, and focal to bilateral tonic-clonic seizures. Changes in intracranial EEG power from preictal baseline were analyzed in different cortical regions and across frequency ranges in these three categories. We found that frontal lobe focal aware seizures showed approximately 40% increases in intracranial EEG power localized to the frontal lobe of seizure onset across frequency ranges, with relatively smaller changes in other cortical regions. Frontal lobe focal impaired awareness seizures showed approximately 50% increases in intracranial EEG power, not significantly different from focal aware seizures in the frontal lobe of seizure onset (P = 1.038), but significantly greater than focal aware seizures in other broad cortical regions (P < 0.001). Importantly, the widespread cortical increases in EEG power observed in focal impaired awareness versus focal aware seizures were seen not just in the frequency range of slow waves, but were also observed across other frequencies including fast activity. However, the widespread cortical increases in focal impaired awareness seizures differed from focal to bilateral tonic-clonic seizures where intracranial EEG power increased to a much higher level by approximately 600%. The large power increases in focal to bilateral tonic-clonic were significantly greater than in focal impaired awareness seizures both in the frontal lobe of seizure onset and in other cortical regions (P < 0.001). Our findings contrast with focal temporal lobe epilepsy, where impaired consciousness is associated with cortical slow waves. We can speculate that different focal seizure types produce impaired consciousness by impacting widespread cortical regions but through different physiological mechanisms. Insights gained by studying mechanisms of impaired consciousness may be the first step towards developing novel treatments to prevent this important negative consequence of epilepsy.

neuroscience↗

bioRxiv2024

Nucleation limited assembly and polarized growth of a de novo-designed allosterically modulatable protein filament

The design of inducibly assembling protein nanomaterials is an outstanding challenge. Here, we describe the computational design of a protein filament formed from a monomeric subunit that binds a peptide ligand. The cryoEM structure of the micron-scale fibers is very close to the computational design model. The ligand acts as a tunable allosteric modulator: while not part of the fiber subunit-subunit interfaces, the assembly of the filament is dependent on ligand addition, with longer peptides having more extensive interaction surfaces with the monomer, promoting more rapid growth. Seeded growth and capping experiments reveal that the filaments grow primarily from one end. We show that designed nucleators that present 12 copies of the peptide ligand promote fiber assembly at concentrations where otherwise assembly occurs very slowly, likely by generating critical local concentrations of monomer in the assembly competent conformation. Following filament assembly, the peptide ligand can be exchanged with free peptide in solution fused to any functional protein of interest, opening the door to a wide variety of tunable engineered materials.

bioengineering↗

bioRxiv2024

Mapping and Modeling Age-Related Changes in Intrinsic Neural Timescales

Intrinsic timescales of brain regions exhibit heterogeneity, escalating with hierarchical levels, and are crucial for the temporal integration of external stimuli. Normal aging, often associated with cognitive deficits, involves progressive neuronal and synaptic loss, shaping brain structure and dynamics. The impact of these structural changes on temporal integration coding in the aged brain remains largely unknown. To address this gap, we mapped differences in intrinsic timescales and gray matter volume (GMV) within brain regions using magnetic resonance imaging (MRI) in young and elderly adults. We found shorter intrinsic timescales across multiple large-scale functional networks in the elderly cohort, and a significant positive association between intrinsic timescales and GMV. Additionally, age-related decline in performance on a visual discrimination task was linked to a reduction in intrinsic timescales in the cuneus. To explain these age-related changes in GMV and intrinsic timescales, we propose an age-dependent model of spiking neuron networks. In younger subjects, brain regions were modeled in a near-critical branching regime, while in elderly subjects, regions had fewer neurons and synapses, pushing the dynamics toward a more subcritical regime. The empirical results were reproduced by the model: Neuronal networks representing brain regions in young subjects exhibited longer intrinsic timescales due to critical slowing down. These findings reveal how age-related structural brain changes may drive alterations in brain dynamics, offering testable predictions and informing future interventions for cognitive decline.

bioinformatics↗

bioRxiv2024

Leveraging large language models for metabolic engineering design

Establishing efficient cell factories involves a continuous process of trial and error due to the intricate nature of metabolism. This complexity makes predicting effective engineering targets a challenging task. Therefore, it is vital to learn from the accumulated successes of previous designs for advancing future cell factory development. In this study, we developed a method based on large language models (LLMs) to extract metabolic engineering strategies from research articles on a large scale. We created a database containing over 29006 metabolic engineering entries, 1210 products and 751 organisms. Using this extracted data, we trained a hybrid model combining deep learning and mechanistic approaches to predict engineering targets. Our model outperformed traditional metabolic engineering target prediction algorithms, excelled in predicting the effects of gene modifications, and generalized well to out-of-distribution products and multiple gene combinations. Our study provides a valuable dataset, a chatbot, and an engineering target prediction model for the metabolic engineering field and exemplifies an efficient method for leveraging existing knowledge for future predictions.

systems biology↗

bioRxiv2024

ATP-dependent remodeling of chromatin condensates uncovers distinct mesoscale effects of two remodelers

ATP-dependent chromatin remodeling enzymes mobilize nucleosomes, but how such mobilization affects chromatin condensation is unclear. Here, we investigate effects of two major remodelers, ACF and RSC using chromatin condensates and single-molecule footprinting. We find that both remodelers inhibit the formation of condensed chromatin. However, the remodelers have distinct effects on pre-formed chromatin condensates. ACF spaces nucleosomes without de-condensing the chromatin, explaining how ACF maintains nucleosome organization in transcriptionally repressed genomic regions. In contrast, RSC catalyzes ATP-dependent de-condensation of chromatin. Surprisingly, RSC also drives micron-scale movements of entire condensates. These newly uncovered activities of RSC explain its central role in transcriptional activation. The biological importance of remodelers may thus reflect both their effects on nucleosome mobilization and the corresponding consequences on chromatin dynamics at the mesoscale.

biochemistry↗

bioRxiv2024

Discovery of novel quinoline papain-like protease inhibitors for COVID-19 through topology constrained molecular generative model

The rapid emergence of drug-resistant SARS-CoV-2 variants poses a persistent challenge to current antiviral strategies. Mutations in key viral targets, including RNA-dependent RNA polymerase (RdRp), main protease (3CLpro), and papain-like protease (PLpro), have been shown to markedly reduce the efficacy of approved therapeutics, highlighting the urgent need for next-generation antivirals capable of overcoming resistance. Here, we report the discovery of a novel class of PLpro inhibitors through an AIDD strategy based on a topology-constrained molecular generative model (Tree-Invent) integrated with structure-guided optimization. Scaffold hopping from a previously reported lead enabled the identification of a quinoline-based chemical series with substantially improved metabolic stability and antiviral potency. Structure-guided optimization yielded compound GZNL-2016, which exhibited potent enzymatic inhibition of PLpro (IC50 = 10.5 nM), robust antiviral activity against multiple SARS-CoV-2 variants, including Omicron BA.5 and XBB.1, and favorable pharmacokinetic properties following oral administration. Notably, GZNL-2016 retained substantial inhibitory activity against the clinically relevant drug-resistant mutant PLpro E167K (IC50 = 480.2 nM; Ki = 439.3 nM), in contrast to previously reported inhibitors that exhibit markedly reduced potency. In a SARS-CoV-2 infection mouse model, oral administration of GZNL-2016 significantly reduced pulmonary viral titers, demonstrating in vivo antiviral efficacy. Collectively, this study establishes an AI-enabled strategy for rapid antiviral discovery and identifies GZNL-2016 as a promising lead compound to address the threat of coronavirus infections caused by drug-resistant mutant SARS-CoV-2 variants. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/611841v3_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@ecbd3eorg.highwire.dtl.DTLVardef@7c663corg.highwire.dtl.DTLVardef@11c0e37org.highwire.dtl.DTLVardef@e950f0_HPS_FORMAT_FIGEXP M_FIG C_FIG We leveraged an AI generative model to discover a novel PLpro inhibitor with excellent liver stability, low CYP, hERG inhibition and reasonable oral PK properties. At the same time, the compound 16 exhibits high efficacy for the resistance mutation E167K, which resulted in severe resistance to the previously reported inhibitor Jun12682 and PF-07957472.

microbiology↗

bioRxiv2024

Coat protein of TCV suppresses RNA decay via ubiquitination and autophagy pathways to facilitate viral infection

Highlight statementThe findings highlight that TCV manipulates 26S proteasome and autophagy pathways to obstruct antiviral RNA decay defenses and ultimately enhance its ability to infect host cells. RNA decay is a pervasive process in eukaryotic cells. Viruses utilize the host cells intracellular machinery to gain access to essential molecules and subcellular structures required for infection during the pathogenesis process. The study demonstrates that turnip crinkle virus (TCV) infection enhances the expression of Arabidopsis Dcp1 (AtDcp1), which negatively regulates the accumulation of TCV RNA, indicating its involvement in antiviral defense. Nevertheless, TCV circumvents the antiviral defense based on RNA decay, as indicated by the capsid protein (CP) of TCV stabilizing the known nonsense-mediated RNA decay targeted transcripts. In vivo, CP physically interacts with AtDcp1, promoting AtDcp1 degradation via ubiquitination and autophagy pathways. This is evidenced by the observation that the degradation is inhibited by both 26S proteasome and autophagy inhibitors. Furthermore, CP elevates the polyubiquitination of Dcp1-Flag and the quantity of pre-autophagosome or autophagosome structures. These data indicate that CP suppresses RNA decay by interacting with AtDcp1 and mediating its degradation through the 26S proteasome and autophagy pathways, effectively subduing antiviral RNA decay. This study uncovers a previously unidentified virulence strategy in the ongoing conflict between plants and viruses.

pathology↗

bioRxiv2024

Yeast-MetaTwin for Systematically Exploring Yeast Metabolism through Retrobiosynthesis and Deep Learning

Underground metabolism plays a crucial role in understanding enzyme promiscuity, cellular metabolism, and biological evolution, yet experimental exploration of underground metabolism is often sparse. Even though yeast genome-scale metabolic models have been reconstructed and curated for over 20 years, more than 90% of the yeast metabolome is still not covered by these models. To address this gap, we have developed a workflow based on retrobiosynthesis and deep learning methods to comprehensively explore yeast underground metabolism. We integrated the predicted underground network into the yeast consensus genome-scale model, Yeast8, to reconstruct the yeast metabolic twin model, Yeast-MetaTwin, covering 16,244 metabolites (92% of the total yeast metabolome), 2,057 metabolic genes and 59,914 reactions. We revealed that Km parameters differ between the known and underground network, identified hub molecules connecting the underground network and pinpointed the underground percentages for yeast metabolic pathways. Moreover, the Yeast-MetaTwin can predict the by-products of chemicals produced in yeast, offering valuable insights to guide metabolic engineering designs.

synthetic biology↗

bioRxiv2024

De novo design of Ras isoform selective binders

The proto-oncogene Ras which governs diverse intracellular pathways has four major isoforms (KRAS4A, KRAS4B, HRAS, and NRAS) with substantial sequence homology and similar in vitro biochemistry. There is considerable interest in investigating the roles of these independently as their association with different cancers vary, but there are few Ras isoform-specific binding reagents as the only significant sequence differences are in their disordered and highly charged C-termini which have been difficult to elicit antibodies against. To overcome this limitation, we use deep learning-based methods to de novo design Ras isoform-specific binders (RIBs) for all major Ras isoforms that specifically target the Ras C-terminus. The RIBs bind to their target Ras isoforms both in vitro and in cells with remarkable specificity, disrupting their membrane localization and inhibiting Ras activity, and should contribute to dissecting the distinct roles of Ras isoforms in biology and disease.

bioengineering↗