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Li, H.

Publications and source records attributed to Li, H..

At least 289 records · Page 16Linked to original sources

Mineral guided molecular enrichment: An interfacial driving force for protocell emergence on early Earth

The emergence of protocells from a dilute prebiotic environment is a fundamental challenge in origins-of-life research. which requires the simultaneous overcoming of molecular dilution, the establishment of metabolic cycles and the formation of selectively permeable compartments. But where and how such critical conditions are satisfied is very obscure and debated. We demonstrate, using contemporary model proteins and enzymatic systems, that mineral surfaces can facilitate the co-adsorption and spatial arrangement of nucleotides, proteins, and lipids into locally enriched, protocell-like assemblies under the examined laboratory conditions. We showed that geochemically relevant minerals efficiently co-adsorb and colocalize diverse biomolecules, creating crowded interfacial microenvironments that support enzyme cascades with substrate channeling-like behavior. Subsequently, these mineral-protein complexes template the assembly of lipid membranes, leading to the formation of discrete compartments that maintain metabolic activity while permitting molecular exchange. This mineral-guided surface enrichment (MSE) mechanism integrates key aspects of "metabolism-first" and "membrane-first" scenarios into a unified pathway, reconciling long-standing conceptual divides in prebiotic chemistry. Our findings establish mineral interfaces as active organizers of protocell emergence, offering a geochemically plausible framework for the origin of cellular life.

evolutionary biology↗

Enabling regiospecific di-halogenation in one-pot reactions using an engineered single-component flavin-dependent tryptophan halogenase

Installation of halogen atoms to organic compounds through flavin-dependent halogenases (FDHs) catalyzed reactions has emerged as an attractive strategy in organic biochemistry and synthetic biology. A cyanobacterial FDH termed AetF that contains FDH and flavin reductase in the same polypeptide chain could become a useful tool enzyme as the need of including a reductase is omitted. Notably, AetF exploits a unique substrate-interaction network to consecutively brominate tryptophan (Trp) on C5 and then C7 to generate di-brominated Trp. In this study, structure-based engineering is used to eliminate the C7 halogenation capacity of AetF. The resulting variant AetF-AIF that contains three residue substitutions mainly catalyzes bromination or iodination on C5 of Trp. We also show that combining AetF-AIF in tandem with the wild type AetF enables the generation of heterogeneously halogenated Trp in a one-pot reaction. The final products, 5-Br-7-I-Trp or 5-I-7-Br-Trp depending on the addition order of halides, account for a high ratio in the reaction mixture without additional purification step. These results display the potentials of single-component Trp-FDHs in a wide application of the halogenation reactions. Table of Content Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/687318v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@101bd34org.highwire.dtl.DTLVardef@12ca797org.highwire.dtl.DTLVardef@aee280org.highwire.dtl.DTLVardef@1984907_HPS_FORMAT_FIGEXP M_FIG A single-component flavin-dependent halogenase that catalyzes di-bromination of tryptophan was engineered as a mono-halogenase and coupled in a one-pot reaction for the synthesis of heterogeneously halogenated tryptophan. C_FIG

molecular biology↗

The deubiquitinating enzyme Otu1 releases substrates from the conserved initiation complex of the Cdc48/p97 ATPase for proteasomal degradation

Many eukaryotic proteins are modified with a polyubiquitin chain and then recruited to either the Cdc48 ATPase (p97 or VCP in mammals) or the 26S proteasome by conserved cofactors. They can then shuttle between the Cdc48 ATPase and the 26S proteasome before being degraded. How substrates avoid being trapped on the Cdc48 ATPase complex is incompletely understood, as they can undergo repeated cycles of translocation through the ATPase pore. Here, we show that the deubiquitinating enzyme (DUB) Otu1 (Yod1 in mammals) can break this futile cycle. Otu1 trims the ubiquitin chain of the substrate before its translocation through the Cdc48 pore is initiated, allowing transfer to the proteasome and subsequent degradation. A cryo-EM structure shows that the mammalian homolog Yod1 binds to p97 simultaneously with other Cdc48 cofactors. As in the yeast system, polypeptide translocation through the ATPase pore is initiated by the unfolding of a ubiquitin molecule, suggesting that the mechanism of substrate processing is conserved in all eukaryotes.

biochemistry↗

Microcompartments in Hodarchaeales: a bioenergetic engine that could have fuelled eukaryogenesis

Eukaryotic intracellular compartmentalization is a key innovation in the evolution of complex cellular life. While microcompartments enable metabolic specialization in many bacteria, to our knowledge, no analogous systems have been identified in Archaea. Here, we report the discovery of archaeal microcompartments (AMCs) in Hodarchaeales, an order within the phylum Promethearchaeati (Asgard archaea) that includes the closest known archaeal relatives of eukaryotes. Phylogenetic and structural analyses indicate that these catabolic AMCs, which are specialized for sugar-phosphate metabolism, were acquired by horizontal gene transfer from deep-rooted bacteria of the phylum Myxococcota. The shell pentamers of AMCs are fused to lysine/arginine-rich intrinsically disordered regions that capture cytosolic DNA, facilitating nutrient scavenging. Reaction-diffusion modelling predicts that enzyme colocalization and substrate channelling within AMCs can increase the NADH flux approximately 100-fold. Thus, the AMCs substantially boost energy production in the cell and might have primed the archaeal host for eukaryogenesis.

microbiology↗

Blood originates in hypoblasts during embryonic development

Blood is essential for oxygen supply throughout life. The emergence of blood in the human embryo remains poorly understood. Our study leverages multiple stem cell embryo models and advanced lineage barcoding to unveil that hypoblast, originally regarded as forming the yolk sac wall, is heterogeneous and contributes to CDX2+ extraembryonic mesoderm, followed by hemoglobin+ cells as the first blood cells. CDX2 marks the hypoblast-to-hemoglobin+ cell trajectory that functionally sustains oxygen levels in embryo models. These hemoglobin+ cells molecularly and functionally resemble phagocytes. We show that the erythro-core regulatory network is poised in hypoblasts, and its boost endows erythropoiesis to both hypoblasts and phagocytes. Hypoblasts are the origin of the first blood in humans and non-human primates, providing a conceptual framework that earlier blood generation than expected fills the gap in the establishment of circulation. Further, the hypoblast is a place where primates may repurpose the phagocyte program to carry oxygen throughout embryos. HighlightsO_LIBarcode-traced hypoblast fate in human embryo models; C_LIO_LIThe first blood comes from the hypoblast that contributes to hemoglobin+ phagocyte-like cells; C_LIO_LICDX2 marks hypoblast blood that sustains oxygen supply in embryos before heart formation; C_LIO_LIErythro-core regulatory network endows erythropoiesis to human hypoblasts and phagocytes. C_LI

developmental biology↗

Rewiring protein function through genetically encoded oxidative chemistry

Oxidative chemistry underlies many endogenous signaling pathways but remains underutilized as a programmable strategy for regulating protein function in living cells. Here we establish genetically encoded oxidative chemistry as a tunable framework for modulating diverse proteins by coupling a photosensitizer to defined intracellular contexts. Using miniSOG to generate reactive oxygen species (ROS), we show that controlled intracellular oxidation increases the fluorescence of the redox reporter HyPerRed and activates redox-sensitive TRP ion channels, with strong responses in TRPA1 and TRPV1 but not TRPV4. Pathway-selective scavengers reveal differential coupling of soluble and membrane targets to distinct oxidative processes, supporting selectivity by context rather than uniform oxidative perturbation. Modulation strength and kinetics are quantitatively tunable through illumination parameters, expression ratios, and subcellular localization, with membrane targeting enhancing coupling to membrane effectors. Finally, fusion targeting of miniSOG to TRPV1 and modulation of endogenous TRPA1 in human fibroblasts extend this approach to protein-proximal and native cellular settings. Together, these results position genetically encoded oxidative chemistry as a versatile and spatially organized modality for engineering protein function in living cells within a defined operating regime.

synthetic biology↗

A multi-tissue atlas of genetic regulatory effects in sheep

Characterizing the impact of genomic variants on genome function and ultimately complex traits in livestock is essential for the development of sustainable precision agriculture and comparative genomics. Here, as part of the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we present the pilot phase of the SheepGTEx resource through analyzing 6,761 RNA-sequencing samples of 51 primary tissues in a multi-breed population of sheep. We identify millions of regulatory variants associated with seven types of molecular phenotypes, and fine-map 322,468 primary and 114,017 non-primary effects, revealing a high degree of regulatory allelic heterogeneity. We systematically characterize the pleiotropic effects of these variants on molecular phenotypes, assess their context-specific regulatory patterns across tissues, breeds, sexes, and developmental stages, as well as explore their evolutionary constraints across mammals. Finally, we demonstrate the substantial potential of the SheepGTEx resource (https://sheepgtex.farmgtex.org), by providing examples of regulatory mechanisms underpinning 34 complex traits, population divergence between European and Asian breeds, and adaptive evolution in sheep over the past ten millennia.

genomics↗

Redeployment of Archaeal ESCRT Systems Reveals Conserved Mechanisms Underlying Eukaryotic Complexity

Eukaryotic ESCRT complexes remodel membranes at the plasma membrane and internal compartments, yet how these systems evolved from prokaryotic precursors functioning at the cellular membrane remains unresolved. Here, we show that ESCRT-II, ESCRT-III, VPS4, and ubiquitin from Candidatus Prometheoarchaeum syntrophicum (MK-D1), an Asgard archaeon closely related to eukaryotes, are recruited to canonical ESCRT sites in human cells, including centrosomes, midbodies, nuclear envelopes, endosomes despite the absence of these structures from MK-D1 morphology. In addition to MK-D1 ESCRT II recruitment at these regions, MK-D1 ESCRT-III and VPS4 form ring assemblies at midbodies and target reforming nuclear envelope regions. Futhermore, expression of MK-D1 ESCRT modules enhances efficiency of viral budding in human cells. These findings provide direct experimental evidence that archaeal ESCRT modules integrate into diverse eukaryotic structures, supporting a model in which ancestral membrane-remodeling systems were redeployed during eukaryogenesis to organize novel internal architectures.

evolutionary biology↗

Evolutionary landscape of oral microbiome over 100,000 years

The human oral microbiome represents an intimate and enduring partnership between host and microbial communities, yet its evolutionary trajectory across deep time remains largely uncovered. Here we reconstruct the global history of the oral microbiome over 102,400 years using a global collection of 1854 oral samples spanning 61 countries. We reveal that major cultural transitions of the Neolithic Revolution, industrialization, and recent medical advances have driven a directional, globally coordinated co-evolution of oral microbial ecology, function, and population structure. Network analysis identifies two universal microbial modules of dental calculus modules: the commensal-enriched DCM1 module and the pathogen-enriched DCM2, both exhibiting progressive ecological polarization. DCM2 surged around 4,000 years ago with agricultural intensification but subsequently declined in modern European populations while persisting at high levels in African and some American groups, a geographic divide mirroring socioeconomic inequities. Species-level analyses demonstrate widespread directional changes, with obligate anaerobes declining as aerotolerant taxa expand, reflecting a carbohydrate-associated functional remodeling toward starch metabolism. Phylogenomic reconstruction reveals contrasting evolutionary trajectories. Pauljensenia mediterranea experienced near-extinction following a severe bottleneck, while Actinomyces israelii underwent a selective sweep with rapid population expansion 1,000 years ago. Critically, the oral resistome has accelerated in the antibiotic era, resulting in [~]33-50-fold surge of highly mobile, clinically relevant antimicrobial resistance genes in the last century, with Streptococcus acting as a central hub of dissemination. Collectively, these results position the oral microbiome as an active participant that has co-evolved with human subsistence, urbanization and medical practice, with direct implications for global oral health, antimicrobial resistance and interventions aimed at restoring resilient microbial states.

microbiology↗

Decoding the Oral Microbiome: Metagenomic Insights into Host Metabolic Health

The oral microbiome may capture system-specific information about host metabolic health, yet large-scale, multi-system evidence remains scarce. We analyzed 9,431 participants in the Human Phenotype Project (HPP), integrating buccal-swab oral whole-metagenome profiles with 44 metabolic measures spanning liver ultrasound, continuous glucose monitoring (CGM), and dual-energy X-ray absorptiometry (DXA). Using a microbiome-wide association study (MWAS) framework, we constructed a multilayer map across strains, gene families and pathways, revealing widespread associations: 213 strains, 124,603 gene families and 299 pathways were significantly associated with metabolic measures. Prioritizing the strongest and cross-phenotype signals, we identified multiple oral features with most significant associations to metabolic health. For example, acyl carrier protein (ACP) was associated with lower liver inflammation and reduced adiposity, whereas polyamine biosynthesis and ceramide -oxidation tracked higher glucose variability and adverse liver and adiposity phenotypes. Leveraging these MWAS-derived signals, we trained disease classification models using phenotype-selected oral features, which outperformed full-feature oral models across six metabolic diseases. These association signals were also robust in oral-health sensitivity analyses in HPP, and key BMI and waist-circumference associations directionally replicated at the genus level in an independent cohort (n = 20,293). Together, these findings provide a population-scale oral-metabolic association map and highlight the potential of oral microbial markers as non-invasive tools for metabolic risk stratification.

microbiology↗

Diploid donor-specific assembly enhances somatic structural variant detection in cancer genomes

Somatic structural variants (SVs) play a crucial role in tumor development and evolution, yet their identification remains challenging, partly due to limitations in current reference genomes. We present a systematic evaluation of diploid donor-specific assemblies (DSAs)--generated based on hybrid long-read sequencing data--as the reference genome for detecting somatic SVs. We analyzed six tumor-normal cell line pairs, using the EchoSV tool we developed to consolidate haplotype-based SVs into a single DSA-based set and to compare SVs across reference genomes. Across Illumina, PacBio HiFi, and Oxford Nanopore Technology (ONT) data, DSA-based analysis improved read-mapping quality, identified over 20% additional SVs compared to GRCh38 and CHM13, and reduced germline artifacts. Most DSA-specific SVs were isolated deletions or insertions in repetitive elements, especially in satellite regions. By tracking sequence-context differences, we identified scenarios in which DSAs enabled detection of SVs missed on GRCh38/CHM13, and confirmed their functional impact with RNA-seq. These results highlight the value of integrating diploid DSAs into somatic SV analysis.

genomics↗

Improving long-read somatic structural variant calling with pangenome and de novo personal genome assembly

Accurate detection of mosaic and somatic structural variants (SVs) provides early diagnostic and therapeutic evidence for cancers. While long-read whole-genome sequencing leads to more accurate SV detection than short read sequencing, existing long-read SV callers only look at alignment against a single reference genome and are susceptible to systematic false discovery caused by germline differences between the individual genome and the reference genome. Here we develop a new SV calling method that jointly considers the alignment against a pangenome and the de novo assembly of the germline genome. It dramatically reduces false positive mosaic SVs in normal samples and somatic SVs in cancer cell lines with little loss in sensitivity. Our study highlights the essential need for pangenome or personal genome assembly to integrate SV calls for both SV discoveries and clinical diagnostics. SignificanceWe introduced a novel long-read SV calling method that leverages pangenome and personal genome and greatly improves the accuracy of somatic SV calling.

genomics↗

In Silico Post-screening of Anti-polymerization Agents to Treat Sickle Cell Disease

Sickle cell disease (SCD) is a genetic disorder that affects approximately 100,000 individuals in the United States and millions globally. Although curative therapies, such as stem cell transplant and gene therapy, are available, their application is limited by high cost and donor availability. Conseuently, drug therapy remains the most feasible treatment option for the majority of patients with SCD. To date, only four drugs have been approved by the FDA, but none of these treatments comprehensively address all SCD-related symptoms or crises, suggesting the pressing need for developing new drugs. Several high-throughput screening campaigns have been performed for SCD drug discovery based on the in vitro sickling of red blood cells (RBCs) and they have identified several hits. However, it is challenging to replicate the organ-specific oxygen level and physiological deoxygenation time in these in vitro RBC sickling assays. Furthermore, these assays do not consider the pharmacokinetics (PK) and pharmacodynamics (PD) of the identified drugs, which are essential to determine whether the drugs can provide robust and sustained efficacy in treated patients with SCD. To address these technical gaps, we have developed a computational platform to perform post-screening analysis of potential anti-sickling agents. This platform sequentially combines PK/PD models with a kinetic model of RBC sickling, enabling efficient prediction of the dosage-dependent therapeutic efficacy of various anti-sickling agents based on patient-specific hematological factors and organ-specific oxygen levels. We first demonstrate the effectiveness of our integrated platform by showcasing the therapeutic efficacy of two FDA-approved drugs, Hydroxyurea (HU) and voxelotor. Next, we evaluate the therapeutic efficacy of two potential anti-sickling agents under clinical trial, namely Bitopertin and osivelotor. Our findings suggest that Bitopertin exhibits anti-sickling effects that are considerably less pronounced than those of HU and voxelotor. On the other hand, osivelotor can achieve similar anti-sickling effects as voxelotor with significantly lower doses due to its improved PK properties. Furthermore, we show the versatility of the proposed platform in predicting the anti-sickling effect of multi-agent therapies and evaluate the conse-quences of drug noncompliance. In particular, our analysis indicates that noncompliance with voxelotor may result in rapid increases in RBC sickling, whereas osivelotor is likely to mitigate noncompliance-induced adverse effects due to improved PK properties. We further quantify the relationship between drug dosage and the duration of noncompliance that leads to loss of thera-peutic efficacy for voxelotor and osivelotor, providing guidance for optimizing dosage strategies to reduce the risk associated with noncompliance. In summary, our in silico platform serves as a valuable tool for post-screening analysis of potential anti-sickling agents by considering their PK and anti-sickling efficacy under patient-specific hemoglobin level and organ-specific oxygen level, thereby gaining insights into their potential therapeutic efficacy alone or in combination before clinical trials.

bioengineering↗

Accurate protein structure determination from cryo-EM maps using deep learning and structure prediction

Cryo-electron microscopy (cryo-EM) has become the main-stream technique for macromolecular structure determination. However, due to intrinsic resolution heterogeneity, accurate modeling of all-atom structure from cryo-EM maps remains challenging even for maps at near-atomic resolutions. Addressing the challenge, we present EMProt, a fully automated method for accurate protein structure determination from cryo-EM maps by efficiently integrating map information and structure prediction with a three-track attention network. EMProt is extensively evaluated on a diverse test set of 177 experimental cryo-EM maps with up to 54 chains in a case at <4.0 [A] resolutions, and compared with state-of-the-art methods including DeepMainmast, ModelAngelo, phenix.dock_and_rebuild, and AlphaFold3. It is shown that EMProt significantly outperforms the existing methods in recovering the protein structure and building the complete structure. In addition, the built models by EMProt also exhibit a high accuracy in model-to-map fit and structure validations. EMProt is freely available at http://huanglab.phys.hust.edu.cn/EMProt/.

bioinformatics↗

FuFiHLA: A tool for Full-Field HLA typing from long read data

0.MotivationAllele typing for Human Leukocyte Antigen (HLA) genes has many important clinical applications. Popular short-read typing can only accurately distinguish alleles at the peptide level, which potentially limit our understanding of the effect of variants in non-coding region. Recently, a few methods were declared to distinguish full-field HLA alleles from de-novo assemblies, which motivates us to develop an accurate HLA typing method directly from long reads. ResultsWe developed FuFiHLA, a lightweight open-source software, to type HLA alleles. Currently it supports typing alleles of six HLA genes (HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DQA1, and HLA-DQB1) from long reads. Evaluation using 47 PacBio HiFi samples from HPRC shows that FuFiHLA achieves 99.57% accuracy in the full field allele typing and QV as 50.1 for consensus allele sequence construction. Additional testing on four Nanopore R10 reads demonstrates slightly reduced accuracy in the fourth field. Availability and implementationFuFiHLA is available on GitHub (https://github.com/jingqing-hu/FuFiHLA) under MIT License.

bioinformatics↗

Gradient-based Optimization for mRNA Sequence Design

MotivationOptimization of mRNA sequences presents fundamental challenges to balance multiple physicochemical/biological properties--including accessibility, stability, and translation efficiency--while preserving amino acid sequences. The discrete nature of RNA sequence design hinders direct application of gradient-based methods, despite their potential for leveraging modern deep learning predictors of the properties in biological sequence design. ResultsWe present the Input Data Differentiable Designer (ID3) framework, a unified computational approach for mRNA sequence optimization that enables gradient-based optimization of discrete RNA sequences through innovative mathematical techniques. ID3 framework encompasses 12 constrained variants across four base configurations and three constraint mechanisms: Codon Profile Constraint, Amino Matching Softmax, and Lagrangian multipliers. The ID3 framework treats trained models as fixed differentiable functions while optimizing input data through continuous probability distributions. We also provide convergence analyses from the perspective of trained model input optimization. Availability and implementationhttps://github.com/Li-Hongmin/ID3.git Contactlihongmin@edu.k.u-tokyo.ac.jp, terai@edu.k.u-tokyo.ac.jp, asai@k.u-tokyo.ac.jp

bioinformatics↗

Automatic Classification of Circulating Blood Cell Clusters based on Multi-channel Flow Cytometry Imaging

Circulating blood cell clusters (CCCs) containing red blood cells (RBCs), white blood cells (WBCs), and platelets are significant biomarkers linked to conditions like thrombosis, infection, and inflammation. Flow cytometry, paired with fluorescence staining, is commonly used to analyze these cell clusters, revealing cell morphology and protein profiles. While computational approaches based on machine learning have advanced the automatic analysis of single-cell flow cytometry images, there is a lack of effort to build tools to automatically analyze images containing CCCs. Unlike single cells, cell clusters often exhibit irregular shapes and sizes. In addition, these cell clusters often consist of heterogeneous cell types, which require multi-channel staining to identify the specific cell types within the clusters. This study introduces a new computational framework for analyzing CCC images and identifying cell types within clusters. Our framework uses a two-step analysis strategy. First, it categorizes images into cell cluster and non-cluster groups by fine-tuning the You Only Look Once(YOLOv11) model, which outperforms traditional convolutional neural networks (CNNs), Vision Transformers (ViT). Then, it identifies cell types by overlaying cluster contours with regions from multi-channel fluorescence stains, enhancing accuracy despite cell debris and staining artifacts. This approach achieved over 95% accuracy in both cluster classification and phenotype identification. In summary, our automated framework effectively analyzes CCC images from flow cytometry, leveraging both bright-field and fluorescence data. Initially tested on blood cells, it holds potential for broader applications, such as analyzing immune and tumor cell clusters, supporting cellular research across various diseases.

bioinformatics↗

Spatial learning in multi-scale environments: Roles of hippocampus, orbitofrontal cortex, and retrosplenial cortex

Navigation cognition involves the learning of multi-scale environments and the formation of cognitive maps. How do humans build cognitive maps in multi-scale environments? Cognitive maps are thought to be organized hierarchically, with local representations for subareas and global representations for the entire environment. However, it remains unclear how spatial learning influences the representations of multi-scale environments and their underlying neural mechanisms across different levels of representation. In the current study, we built a virtual environment (VE) with four orthogonally positioned rectangular rooms, each containing eight objects at the corners. Twenty-three healthy subjects completed a four-session spatial memory experiment conducted over two weeks. We measured their brain activity by using BOLD-fMRI at two stages: pre-learning stage and post-learning, when they were judging the relative direction between the objects within the VE. We found that with the progression of learning, the subjects shifted from relying on local, directional cues to using more global representations of the environment. At the neural level, the hippocampus (HIP), retrosplenial cortex (RSC), and orbitofrontal cortex (OFC) played distinct roles in encoding spatial information across the two learning stages. Specifically, after learning, the HIP shifted from local to global representations, while the RSC and OFC supported the integration of spatial information across these representational levels. In addition, the anterior cingulate cortex was involved in forming global representations, facilitating efficient spatial processing as learning advanced. These findings revealed how spatial learning leads to adaptive shifts in brain activity, contributing to the formation of cognitive maps in complex, multi-scale environments. HighlightsO_LIUsing fMRI to study neural mechanisms of cognitive map formation in a multi-scale environment. C_LIO_LIDetected that local and global spatial representations coexist and evolve during spatial learning. C_LIO_LIFound that learning progress is associated with activation changes in the entorhinal cortex, hippocampus, and retrosplenial cortex. C_LIO_LIRevealed the complementary roles of hippocampus and orbitofrontal cortex in representing the environment. C_LIO_LIShowed that the retrosplenial cortex improves both local and global representations, supporting the integration of multi-scale spatial information. C_LI

neuroscience↗