Biology subjects

Song, M.

Publications and source records attributed to Song, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Super-pangenome analysis of 3562 human and animal papillomavirus isolates illuminates their genome and pathogenicity evolution

Papillomaviruses (PVs) are groups of ubiquitous DNA viruses infecting a wide range of vertebrate hosts, including human. The infection of some human PVs (i.e., HPVs) are strongly associated with cancers, but it remains elusive how such pathogenicity evolved at the genomic level. Here we performed a super-pangenome analysis on a compendium of well-curated 3562 human and animal PV genomes. We found their global genomic diversity being strongly associated with their body sites and tissue origins, but not with their host, geographical, or disease specificity. By reconciling time-resolved phylogenies of PVs and their hosts, we revealed a high-resolution view on how virus-host cospeciation, horizontal host switches, and duplication/loss turnovers collectively shaped the evolution of PVs. We identified key selection-driven mutations that defined the genus-specific divergence of PVs, with those underlying the rise of Alpha-PVs highly enriched in the N-terminal domain of E1 gene. Especially, we identified a group of risk-defining variant sites showing contrasting genotypes between low-risk and high-risk HPVs. With four independent PV genome cohorts, we further validated their high sensitivity and specificity on HPV risk classification even at single site level, underscoring their application values in large-scale HPV screening tests. Finally, with cervical and oropharyngeal cancer patient cohorts assembled by this study, we also evaluated the prognostic value of specific HPV16 variants in predicting patient survival. Taken together, such illumination on the genome and pathogenicity evolution of PVs paves the road for better prevention, control, and treatment of PV-related cancers. Significance statementPapillomaviruses (PVs) are ancient and widespread DNA viruses co-evolved with many vertebrate hosts, including human. Some human PVs (e.g., HPV16 and HPV18) bear strong association with cancers, raising a pressing public health challenge. A better understanding of how such pathogenicity evolved holds the key in addressing this challenge. Here we systematically investigated the genomic and evolutionary diversity of PVs via a pangenome study on over 3500 human and animal PV isolates, which revealed key selection-driven mutations shaping their genome and pathogenicity evolution. With independently acquired HPV-associated cancer cohorts, we further demonstrated how some of these important HPV variants can serve as clinically informative predictive and prognostic biomarkers. Such comprehensive characterization on PVs genome diversity, pathogenicity evolution, and clinical implication paves the road for better prevention, control, and treatment of PV-related cancers.

genomics↗

bioRxiv2025

EVscope: A Comprehensive Bioinformatics Pipeline for Accurate and Robust Analysis of Total RNA Sequencing from Extracellular Vesicles

MotivationExtracellular vesicle (EV) RNA sequencing has emerged as a powerful approach for studying RNA biomarkers and intercellular communication. Nevertheless, the extremely low abundance, fragmented nature and ubiquitous tissue origin of EV RNAs, alongside potential contamination from co-isolated materials, such as free DNA and bacterial RNA, pose substantial analytical challenges. These complexities highlight a pressing need for a standardized, computational workflow that ensures robust quality control and EV RNA characterization. ResultsHere, we present EVscope, an open-source bioinformatics pipeline designed specifically for processing EV RNA-seq datasets. EVscope employs an optimized genome-wide expectation-maximization (EM) algorithm that significantly improves multi-mapping read assignment at single-base resolution by effectively leveraging alignment scores (AS) and local read coverage, specifically tailored for fragmented and low-abundance EV RNAs. Notably, EVscope uniquely generates EM-based BigWig files for downstream analysis, a capability currently unavailable in existing EM-based BigWig quantification tools. The pipeline systematically integrates 27 major steps, including quality control, analysis of library structure, contamination assessment, read alignment, read strandedness detection, UMI-based deduplication, RNA quantification, genomic DNA (gDNA) contamination correction, cellular and tissue source inference and visualization with a comprehensive HTML report. EVscope incorporates a comprehensive, updated annotation covering 19 distinct RNA biotypes, encompassing protein-coding genes, lncRNAs, miRNAs, piRNAs, retrotransposons (LINEs, SINEs, ERVs), and additional non-coding RNAs (tRNAs, rRNAs, snoRNAs). Furthermore, it leverages two highly balanced circRNA detection algorithms for robust circular RNA identification. Notably, a downstream module enables the inference of the tissue/cellular origins of EV RNAs using bulk and single-cell RNA-seq reference datasets. EVscope is implemented as a convenient, single-command Bash pipeline leveraging Conda-managed standard software packages and custom scripts, ensuring reproducibility and straightforward deployment. Availability and implementationCode, documentation, and tutorials are available at GitHub (https://github.com/TheDongLab/EVscope) and archived on Zenodo (https://zenodo.org/records/15577789).

bioinformatics↗

bioRxiv2025

GABA-independent activation of GABAB receptor by mechanical forces

The heterodimeric GABAB receptor, composed of GB1 and GB2 subunits, is a metabotropic G protein-coupled receptor (GPCR) activated by the neurotransmitter GABA. GABA binds to the extracellular domain of GB1 to activate G proteins through GB2. Here we show that GABAB receptors can be activated by mechanical forces, such as traction force and shear stress, in a GABA-independent manner. This GABA-independent mechano-activation of GABAB receptor is mediated by a direct interaction between integrins and the extracellular domain of GB1, indicating that GABAB receptor and integrin form a novel type of mechano-transduction complex. Mechanistically, shear stress promotes the binding of integrin to GB1 and induces an allosteric re-arrangement of GABAB receptor transmembrane domains towards an active conformation, culminating in receptor activation. Furthermore, we demonstrate that shear stress-induced GABAB receptor activation plays a crucial role in astrocyte remodeling. These findings reveal a previously unrecognized function of GABAB receptor in mechano-transduction, uncovering a novel ligand-independent activation mechanism for GPCRs.

neuroscience↗

bioRxiv2025

Digital Twin Brain Predicts rTMS Effects on Brain State Dynamics in Chronic Tinnitus

Predicting repetitive transcranial magnetic stimulation (rTMS) effects on whole-brain dynamics in clinical populations is crucial for developing personalized therapies and advancing precision medicine in brain disorders. This study provides the first proof-of-concept demonstrating that the Digital Twin Brain (DTB) can forecast rTMS effects on brain state dynamics in individuals with brain disorders (chronic tinnitus). First, we identified two aberrant brain states that predominantly overlapped with the somatomotor and default mode networks, respectively. Subsequently, we developed DTB for patients and derived regional responses for each brain region, revealing distinct roles of the parieto-occipital and frontal regions. Mechanistically, we examined the biological plausibility using tinnitus-specific risk genes and investigated the multi-scale neurobiological relevance. Clinically, we found that DTB can predict rTMS effects in an independent, longitudinal dataset (all r > 0.78). Particularly, the predictive capacity exhibits a state-specific nature. Overall, this work proposes a novel DTB-based framework for predicting rTMS effects in clinical populations and provides the first empirical evidence supporting its clinical utility. This approach may be generalizable to other brain disorders and neuromodulation techniques, promoting broader advancements in brain health.

neuroscience↗

bioRxiv2025

Whole-Proteome Screening and Multi-Modal Profiling of Antigen-Specific CD4+ T Cells at Single-Cell Resolution

Systematic whole-proteome screening and comprehensive profiling of antigen-specific CD4+ T cells are crucial for advancing our understanding of CD4+ T cell immunity, yet such efforts remain technically challenging. Here, we present a high-throughput platform that employs large-scale class II single-chain trimer libraries to detect antigen-specific CD4+ T cells, while simultaneously profiling their antigen specificity, TCR/{beta} sequences, MHC restriction, whole transcriptomes, and patient/timepoint origins at single-cell resolution. We benchmarked SCTs against conventional pMHCs and validated the SCT library-based approach in direct ex vivo identification of antigen-specific CD4+ T cells in healthy donors. We then applied the platform to screen the entire SARS-CoV-2 receptor-binding domain in a longitudinal patient cohort, identifying 2,188 antigen-specific CD4+ T cells and revealing key features that define antigen immunogenicity. Extending to cancer, we performed whole-proteome screening of HPV-16 E6/E7 for TCR repertoire profiling in a precancerous cohort, uncovering functional heterogeneity of HPV-specific TCRs. By integrating high-throughput antigen screening with high-dimensional, multi-modal cellular characterization, our approach offers an unprecedented window into CD4+ T cell immunity across diverse disease contexts and empowers the development of new therapies.

bioengineering↗

bioRxiv2025

THA_1941 from Thermosipho africanus: A Thermostable β-1,3-Glucan Phosphorylase for Efficient β-1,3-Glucan Synthesis

{beta}-1,3-Glucan phosphorylases capable of utilizing glucose as a priming substrate are key biocatalysts for the synthesis of functional {beta}-1,3-glucan. In this study, we identified THA_1941 from Thermosipho africanus (Ta{beta}GP) as a GH161 {beta}-1,3-glucan phosphorylase exhibiting robust synthetic activity towards glucose, as confirmed by 13C nuclear magnetic resonance, liquid chromatography-mass spectrometry, and sequence and structural analyses. Ta{beta}GP displayed exceptional thermostability, retaining 93% of its activity at 60 {degrees}C for 180 h, and showed broad pH tolerance ranging from pH 5.0 to 10.0, surpassing the performance of previously reported homologs. In addition, Ta{beta}GP exhibited broad substrate flexibility, accepting both - and {beta}-linked disaccharides, and demonstrated strong resistance to metal ions and lignocellulose-derived inhibitors. In the presence of 150 mM glucose 1-phosphate as the donor substrate, Ta{beta}GP synthesized {beta}-1,3-glucan with a tunable average degree of polymerization (10-32), depending on the concentration of glucose used as the primer. The combination of thermostability, inhibitor resistance, and substrate versatility makes Ta{beta}GP a promising biocatalyst for the economically viable and environmentally sustainable synthesis of {beta}-1,3-glucan from non-food biomass sources.

biochemistry↗

bioRxiv2025

deepTFBS: Improving within- and cross-species prediction of transcription factor binding using deep multi-task and transfer learning

The precise prediction of transcription factor binding sites (TFBSs) is crucial in understanding gene regulation. In this study, we present deepTFBS, a comprehensive deep learning (DL) framework that builds a robust DNA language model of TF binding grammar for accurately predicting TFBSs within and across plant species. Taking advantages of multi-task DL and transfer learning, deepTFBS is capable of leveraging the knowledge learned from large-scale TF binding profiles to enhance the prediction of TFBSs under small-sample training and cross-species prediction tasks. When tested using available information on 359 Arabidopsis TFs, deepTFBS outperformed previously described prediction strategies, including position weight matrix, deepSEA and DanQ, with a 244.49%, 49.15%, and 23.32% improvement of the area under the precision-recall curve (PRAUC), respectively. Further cross-species prediction of TFBS in wheat showed that deepTFBS yielded a significant PRAUC improvement of 30.6% over these three baseline models. deepTFBS can also utilize information from gene conservation and binding motifs, enabling efficient TFBS prediction in species where experimental data availability is limited. A case study, focusing on the WUSCHEL (WUS) transcription factor, illustrated the potential use of deepTFBS in cross-species applications, in our example between Arabidopsis and wheat. deepTFBS is publically available at https://github.com/cma2015/deepTFBS. One sentence summaryThe high-performing deep learning framework deepTFBS can flexibly perform within- and cross-species prediction of TF binding, transferring the transcription regulation from model to non-model species.

bioinformatics↗

bioRxiv2025

Enzyme Co-Scientist: Harnessing Large Language Models for Enzyme Kinetic Data Extraction from Literature

The extraction of molecular annotations from scientific literature is critical for advancing data-driven research. However, traditional methods, which primarily rely on human curation, are labor-intensive and error-prone. Here, we present an LLM-based agentic workflow that enables automatic and efficient data extraction from literature with high accuracy. As a demonstration, our workflow successfully delivers a dataset containing over 91,000 enzyme kinetics entries from around 3,500 papers. It achieves an average F1 score above 0.9 on expert-annotated subsets of protein enzymes and can be extended to the ribozyme domain in fewer than 3 days at less than $90. This method opens up new avenues for accelerating the pace of scientific research.

biochemistry↗

bioRxiv2025

Physical Activity and Depressive Mood Shared the Structural Connectivity between Motor and Reward Network: a population-based study from the UK Biobank

BackgroundExercise has been revealed to leave a positive effect on alleviating depressive symptoms by various existing studies. However, the neural basis behind this phenomenon remains unknown, as well as its underlying biological mechanism.AimsUsing a large neuroimaging cohort from UK Biobank, this study aimed to identify structural connectivity (SC) patterns simultaneously linked with physical activity and depression, as well as its biological interpretation.Method492 participants with major depression disorder (MDD) and 535 healthy controls (HC) were extracted from the UK Biobank dataset. Partial least squares regression was first utilized to explore a replicable SC pattern simultaneously associated with physical activity and depressive mood. The neuromaps toolbox was applied to interpret the biological ontologies of the identified SC pattern. Neuroimaging-transcriptome association and enrichment analyses were conducted to explore its underlying genetic basis, pathways and cell types. The reproducibility and generalizability were tested on another independent MDD dataset (N=3,496) and bipolar disorder (BD) dataset (N=81).ResultsA SC pattern linked with exercise was identified to be both significantly correlated with depressive mood and group discriminative between MDDs and HCs, which primarily located between the motor-related regions and reward-related regions. This pattern was associated with multiple neurotransmitter receptors, such as serotonin and GABA receptors, and enriched in pathways like synaptic signaling and astrocytes cell type. The SC pattern and genetic results were also replicated in another independent MDD dataset and present commonalities with BD.ConclusionsThis study not only initially identified a reproducible shared SC pattern between physical activity and depressive mood, but also elucidated the underlying biological mechanisms, which enhanced our understanding of how exercise helps alleviate depression and may inform the development of novel neuromodulation targets.

neuroscience↗

bioRxiv2024

Human Stem Cell-derived Kidney Collecting Duct Model via Epithelial Microphysiological Analysis Platform: Epi-MAP

The kidney epitheliums pivotal role in molecular filtration, metabolism, and excretion highlights the crucial importance of understanding kidney physiology in drug development. However, our knowledge is largely derived from non-human or non-physiological models, potentially limiting its applicability to humans. To address this significant gap, we have pioneered a human kidney epithelial microphysiological analysis platform (Epi-MAP) designed to establish, mature, and monitor renal functions of the human collecting epithelium within a physiologically relevant microenvironment. We first demonstrate the highly mature collecting duct physiology derived from human stem cells, enabled by the Epi-MAPs microenvironments that recapitulate in vivo asymmetries in fluidic and biochemical conditions. The integrated biosensors of the Epi-MAP provide long-term, time-resolved epithelial maturation trajectories, revealing advanced integrity and functional maturity with transepithelial metrics. Furthermore, Epi-MAPs electrophysiological analytics for measuring water flux, in conjunction with transepithelial potential and resistance, allow for real-time decoding of intricate epithelial responses to substance stimulation, showcasing its effectiveness as a robust pharmacological test model. This human cell-derived, physiologically advanced model on a chip stands as a robust in vitro tool, offering comprehensive insights into human kidney biology and significantly enhancing drug discovery process based on human physiology.

bioengineering↗

bioRxiv2024

Dynamic Kidney Organoid Microphysiological Analysis Platform

Kidney organoids, replicating human development, pathology, and drug responses, are a promising model for advancing bioscience and pharmaceutical innovation. However, reproducibility, accuracy, and quantification challenges hinder their broader utility for advanced biological and pharmaceutical applications. Herein, we present a dynamic kidney organoid microphysiological analysis platform (MAP), designed to enhance organoid modeling and assays within physiologically relevant environments, thereby expanding their utility in advancing kidney physiology and pathology research. First, precise control of the dynamic microenvironment in MAP enhances the ability to fine-tune nephrogenic intricacies, facilitating high-throughput and reproducible human kidney organoid development. Also, MAPs miniaturization of kidney organoids significantly advances pharmaceutical research by allowing for detailed analysis of entire nephron segments, which is crucial for assessing the nephrotoxicity and safety of drugs. Furthermore, the MAPs application in disease modeling faithfully recapitulates pathological development and functions as a valuable testbed for therapeutic exploration in polycystic kidney diseases. We envision the kidney organoid MAP enhancing pharmaceutical research, standardizing processes, and improving analytics, thereby elevating the quality and utility of organoids in biology, pharmacology, precision medicine, and education.

bioengineering↗

bioRxiv2024

Benchmarking copy number aberrations inference tools using single-cell multi-omics datasets

Copy number aberrations (CNAs) are an important type of genomic variation which play a crucial role in the initiation and progression of cancer. With the explosion of single-cell RNA sequencing (scRNA-seq), several computational methods have been developed to infer CNAs from scRNA-seq studies. However, to date, no independent studies have comprehensively benchmarked their performance. Herein, we evaluated five state-of-the-art methods based on their performance in tumor vs normal cell classification, CNAs profile accuracy, tumor subclone inference and aneuploidy identification in non-malignant cells. Our results showed that Numbat outperformed others across most evaluation criteria, while CopyKAT excelled in scenarios when expression matrix alone was used as input. In specific tasks, SCEVAN showed the best performance in clonal breakpoint detection and Numbat showed high sensitivity in copy number neutral LOH (cnLOH) detection. Additionally, we investigated how referencing settings, inclusion of tumor microenvironment cells, tumor type, and tumor purity impact the performance of these tools. This study provides a valuable guideline for researchers in selecting the appropriate methods for their datasets.

bioinformatics↗

bioRxiv2024

Multi-View Integrative Approach For Imputing Short-Chain Fatty Acids and Identifying Key factors predicting Blood SCFA

Short-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of dietary fiber within gastrointestinal tract. SCFAs produced by gut microbiotas (GMs) are absorbed by host, reach bloodstream, and are distributed to different organs, thus influencing host physiology. However, due to the limited budget or the poor sensitivity of instruments, most studies on GMs have incomplete blood SCFA data, limiting our understanding of the metabolic processes within the host. To address this gap, we developed an innovative multi-task multi-view integrative approach (M2AE, Multi-task Multi-View Attentive Encoders), to impute blood SCFA levels using gut metagenomic sequencing (MGS) data, while taking into account the intricate interplay among the gut microbiome, dietary features, and host characteristics, as well as the nuanced nature of SCFA dynamics within the body. Here, each view represents a distinct type of data input (i.e., gut microbiome compositions, dietary features, or host characteristics). Our method jointly explores both view-specific representations and cross-view correlations for effective predictions of SCFAs. We applied M2AE to two in-house datasets, which both include MGS and blood SCFAs profiles, host characteristics, and dietary features from 964 subjects and 171 subjects, respectively. Results from both of two datasets demonstrated that M2AE outperforms traditional regression-based and neural-network based approaches in imputing blood SCFAs. Furthermore, a series of gut bacterial species (e.g., Bacteroides thetaiotaomicron and Clostridium asparagiforme), host characteristics (e.g., race, gender), as well as dietary features (e.g., intake of fruits, pickles) were shown to contribute greatly to imputation of blood SCFAs. These findings demonstrated that GMs, dietary features and host characteristics might contribute to the complex biological processes involved in blood SCFA productions. These might pave the way for a deeper and more nuanced comprehension of how these factors impact human health.

bioinformatics↗

bioRxiv2024

Brainwaves Monitoring via Human Midbrain Organoids Microphysiological Analysis Platform: MAP

Understanding the development and pathogenesis of the human midbrain is critical for developing diagnostics and therapeutics for incurable neurological disorders including Parkinsons disease (PD)1-3. While organoid models are introduced to delineate midbrain-related pathogenesis based on experimental flexibility4-6, there is currently a lack of tools with high fidelity for tracing the long-term dynamics of intact brain networks-- an essential portrait of physiological states7,8. Here, we report a brain organoid microphysiological analysis platform (MAP) designed for long-term physiological development and in-situ real-time monitoring, akin to electroencephalogram (EEG), of midbrain organoids. We successfully achieved the on-chip homogeneous organogenesis of midbrain organoids and in-situ, non-disturbing electrophysiological tracking of the midbrain network activities. Throughout our long-term EEG monitoring via MAP, we captured the early-stage electrophysiological evolution of midbrain development, transitioning from discontinuous brief brainwave bursts to complex broadband activities. Furthermore, our midbrain organoid MAP facilitated the modeling and monitoring of neurotoxin-induced Parkinsonism, replicating the pathological dynamics of midbrain circuitry and exhibiting PD-like alterations in beta oscillation. We envision that the modeling and monitoring of brain organoid MAP will significantly enhance our understanding of human neurophysiology, neuropathogenesis, and drug discovery of neurodegenerative diseases.

bioengineering↗

bioRxiv2024

Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping

The ability to spatially map multiple layers of the omics information over different time points allows for exploring the mechanisms driving brain development, differentiation, arealization, and alterations in disease. Herein we developed and applied spatial tri-omic sequencing technologies, DBiT ARP-seq (spatial ATAC-RNA-Protein-seq) and DBiT CTRP-seq (spatial CUT&Tag- RNA-Protein-seq) together with multiplexed immunofluorescence imaging (CODEX) to map spatial dynamic remodeling in brain development and neuroinflammation. A spatiotemporal tri-omic atlas of the mouse brain was obtained at different stages from postnatal day P0 to P21, and compared to the regions of interest in the human developing brains. Specifically, in the cortical area, we discovered temporal persistence and spatial spreading of chromatin accessibility for the layer-defining transcription factors. In corpus callosum, we observed dynamic chromatin priming of myelin genes across the subregions. Together, it suggests a role for layer specific projection neurons to coordinate axonogenesis and myelination. We further mapped the brain of a lysolecithin (LPC) neuroinflammation mouse model and observed common molecular programs in development and neuroinflammation. Microglia, exhibiting both conserved and distinct programs for inflammation and resolution, are transiently activated not only at the core of the LPC lesion, but also at distal locations presumably through neuronal circuitry. Thus, this work unveiled common and differential mechanisms in brain development and neuroinflammation, resulting in a valuable data resource to investigate brain development, function and disease.

neuroscience↗

bioRxiv2024

Viral delivery of an RNA-guided genome editor for transgene-free plant germline editing

AbstractGenome editing is transforming plant biology by enabling precise DNA modifications. However, delivery of editing systems into plants remains challenging, often requiring slow, genotype-specific methods such as tissue culture or transformation. Plant viruses, which naturally infect and spread to most tissues, present a promising delivery system for editing reagents. But most viruses have limited cargo capacities, restricting their ability to carry large CRISPR-Cas systems. Here, we engineered tobacco rattle virus (TRV) to carry the compact RNA-guided TnpB enzyme ISYmu1 and its guide RNA. This innovation allowed transgene-free editing of Arabidopsis thaliana in a single step, with edits inherited in the subsequent generation. By overcoming traditional reagent delivery barriers, this approach offers a novel platform for genome editing, which can greatly accelerate plant biotechnology and basic research.

plant biology↗

bioRxiv2024

Targeting protein tyrosine phosphatase non-receptor type 2 with a novel inhibitor for AML therapy

Acute myeloid leukemia (AML) is a fatal disease characterized by a bleak prognosis. For over four decades, treatment options for AML have been constrained to administering high-dose cytotoxic chemotherapy. However, the emergence of drug resistance and the resultant toxic side effects have created an urgent necessity for identifying novel therapeutic targets. In this study, non-receptor protein tyrosine phosphatase type 2 (PTPN2) is highly expressed in AML. Remarkably, knock down of PTPN2 expression markedly mitigated AML burden both in vitro and in vivo. Additionally, we unveiled the direct interaction between PTPN2 and C-MYC, establishing C-MYC as a direct acting substrate of PTPN2. Then, we performed a small-molecule compound screening to identify K73, a selective inhibitor of PTPN2. At the same time, K73 showed a good safety profile and exhibited strong activity against AML in vivo. In conclusion, we have pinpointed a considerable therapeutic potential in targeting PTPN2 for AML treatment and discovered a novel class of selective PTPN2 inhibitors suitable for AML therapy.

cancer biology↗

bioRxiv2024

Molecular basis for the interactions of eIF2β with eIF5, eIF2B, and 5MP1 and their regulation by CK2

The heterotrimeric GTPase eukaryotic translation initiation factor 2 (eIF2) delivers the initiator Met-tRNAi to the ribosomal translation preinitiation complex (PIC). eIF2{beta} has three lysine-rich repeats (K-boxes), important for binding to the GTPase-activating protein eIF5, the guanine nucleotide exchange factor eIF2B, and the regulator eIF5-mimic protein (5MP). Here, we combine X-ray crystallography with NMR to understand the molecular basis and dynamics of these interactions. The crystal structure of yeast eIF5-CTD in complex with eIF2{beta} K-box 3 reveals an extended binding site on eIF2{beta}, far beyond the K-box. We show that eIF2{beta} contains three distinct binding sites, centered on each of the K-boxes, and human eIF5, eIF2B{varepsilon}, and 5MP1 can bind to all three sites, while reducing each others affinities. Our results reveal how eIF2B speeds up the dissociation of eIF5 from eIF2-GDP to promote nucleotide exchange; and how 5MP1 can destabilize eIF5 binding to eIF2 and the PIC, to promote stringent start codon selection. All these affinities are increased by CK2 phosphomimetic mutations, highlighting the role of CK2 in both remodeling and stabilizing the translation apparatus.

biochemistry↗