Biology subjects

Liao, Y.

Publications and source records attributed to Liao, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

CRISPR Screens Reveal Epstein-Barr Virus-activated JunB as a Key Lymphoblastoid B cell Dependency Factor that Represses Cyclin Dependent Kinase Inhibitor P18INK4c

Epstein-Barr virus (EBV) persistently infects over 95% of adults worldwide and is associated with a range of cancers, including lymphomas and epithelial malignancies. Despite advances in understanding EBV biology, targeted therapies for EBV-associated cancers remain limited. To identify novel dependencies in EBV-infected cancers, we performed genome-wide CRISPR-Cas9 loss-of-function screens in EBV+ lymphoblastoid versus Burkitt lymphoma cells, which differ by EBV latency programs. JunB emerged as a critical LCL-selective host dependency factor. LCL JunB knockout significantly decreased proliferation, with reduced G2/M progression, but without inducing apoptosis. JunB was more highly expressed in B cells with the EBV latency III than latency I program and correlated with LMPist1 levels in newly infected B cells. LMP1 stimulated JunB expression in a manner dependent on its cytoplasmic tail TES1/CTAR1 region and on canonical NF-{kappa}B. EBV-activated JunB played an obligatory role in repression of the G1/S phase inhibitor CDKN2C/p18INK4c in LCLs but not Burkitt B cells. These findings establish an LMP1-JunB-p18INK4c axis as essential for EBV-driven lymphoblastoid B cell proliferation, suggest JunB-mediated cross-talk between Epstein-Barr nuclear antigens and LMP1, and highlight JunB as a potential therapeutic target for EBV-associated lymphoproliferative disorders.

microbiology↗

bioRxiv2026

eBiota: Designing microbial communities from large seed pools with desired function using rapid optimization and deep learning

Designing microbial communities to generate target products is crucial for biotechnology, agriculture, and disease treatment. However, rationally designing such communities from large seed pools has become a major challenge, as the rapidly expanding number of complete microbial genomes greatly expands the search space and sharply increases the required screening time and computational cost. Here, we introduce eBiota, a platform for ab initio design of microbial communities from a pool of 21,514 strains to generate target products. eBiota not only identifies optimal strain combinations but also simulates community behaviors, including microbial interactions and relative abundances. eBiota integrates three modules: CoreBFS, a graph-based search algorithm that rapidly screens for bacteria with complete metabolic pathways related to the target product; ProdFBA, an extended flux balance analysis that identifies microbial consortia with maximal production efficiency; and DeepCooc, a deep learning model trained on 23,323 microbiome samples across various environments to infer co-occurrence patterns. We validated eBiotas capabilities in microbial community design and production efficiency calculation using public microbiome datasets, ranging from single strains to six-member consortia. Further in vitro experiments involving 94 strains confirmed eBiotas ability to identify species that inhibit pathogen growth and to accurately model the relative abundances within complex microbial communities. As an initial digital twin, eBiota provides a powerful platform for the rational design of functional microbial communities, offering new opportunities for metabolic engineering and synthetic biology.

bioengineering↗

bioRxiv2026

A CRISPR-Cas9 screen Reveals STEEP1 as a Key Host Dependency Factor for Epstein-Barr Virus Latent Membrane Protein 1 Trafficking and Signaling

The Epstein-Barr virus (EBV) oncogene Latent membrane protein 1 (LMP1) is essential for B-cell transformation into continuously growing lymphoblastoid cell lines. LMP1 traffics to plasma membrane and intracellular signaling sites to mimic aspects of signaling by the B cell co-receptor CD40. LMP1 is expressed in many EBV-associated cancers, including post-transplant lymphoma, Hodgkin lymphoma, T/NK lymphoma and nasopharyngeal carcinoma, where it activates key growth and survival pathways. LMP1 signaling is also implicated in multiple sclerosis pathogenesis. To identify host dependency factors that support LMP1 trafficking and signaling, we performed a human genome-wide CRISPR-Cas9 screen in B cells. The screen identified both known and previously uncharacterized mediators of LMP1 signaling. The ER resident protein STEEP1, implicated in DNA sensor STING trafficking and signaling, was a top screen hit. Importantly, STEEP1 did not score in our prior B cell CRISPR screen for factors that support CD40 signaling, suggesting specificity. STEEP1 depletion strongly impaired LMP1 signaling, including activation of NF-kB and MAP kinase pathways. Mechanistically, STEEP1 associated with LMP1 in a manner dependent on the N-terminal cytoplasmic tail and supported LMP1 egress from the ER to signaling sites in both B and epithelial cells. Collectively, these findings reveal STEEP1 as a key host factor that supports trafficking of newly synthesized LMP1 molecules to intracellular signaling sites and highlights LMP1/STEEP1 interaction as a novel therapeutic target. ImportanceEpstein-Barr virus (EBV) infects most people worldwide. While infection is often benign, it causes infectious mononucleosis, is associated with a range of lymphomas, nasopharyngeal and gastric carcinoma and is a major trigger for autoimmune disease, including multiple sclerosis. The EBV encoded oncogene LMP1 is a key driver of EBV pathogenesis, and its signaling is necessary for viral immortalization of B lymphocytes into continuously growing lymphoblasts (LCLs). Here, we performed a CRISPR genetic screen to identify host factors that support continuous, ligand-independent signaling by LMP1. This analysis identified an ER-resident protein called STEEP1, previously implicated in support of trafficking of the DNA sensor STING, as a key LMP1 partner. We found that STEEP1 associates with LMP1 and supports LMP1 trafficking out of the endoplasmic reticulum to cellular signaling sites. As STEEP1 knockout impaired LMP1 function and LCL survival, our study identifies the STEEP1/LMP1 complex as a therapeutic target.

microbiology↗

bioRxiv2026

Paired in situ and molecular analyses identify mechanisms of pathogen persistence within environmental communities

Natural environments are a key reservoir for many opportunistic pathogens, yet mechanisms enabling persistence within complex microbial communities remain poorly defined. Current methods do not adequately link molecular and functional understanding of bacterial persistence to the activities of bacteria within their natural microbial community. Here we combine in situ culture-independent techniques, including metagenomics and metatranscriptomics, with isolate-level functional genomics and mutant studies to understand how opportunistic pathogens from the genera Escherichia, Klebsiella, and Enterococcus survive in urban-adjacent freshwater ecosystems. . Our results show that potential pathogens from each of these genera are both present and active within freshwater microbial communities. We further investigate a mouse-virulent isolate of the E. coli UPEC lineage ST73 isolated directly from this ecosystem, showing that this isolate persists in freshwater microcosms for at least one month. Paired transcriptomics and genome-scale fitness screening in creek water containing autochthonous microbiota identified E. coli genes required for persistence, including those involved in amino acid metabolism, nucleotide biosynthesis and biogenesis of curli. Isolate-resolved metatranscriptomics analysis supported these findings by revealing that many of these genes were highly expressed in situ. Deletion of curli structural genes resulted in reduced biofilm formation and a fitness defect specifically in the presence of freshwater microbiota, indicating that these structures promote environmental survival by improving E. coli competitiveness. Our study deepens our understanding of E. coli survival in waterways, while providing a broadly applicable framework for interrogating mechanisms of pathogen persistence in complex environments and communities.

microbiology↗

bioRxiv2026

DIA-CLIP: a universal representation learning framework for zero-shot DIA proteomics

Data-independent acquisition mass spectrometry has increasingly emerged as a cornerstone for characterizing highly heterogeneous biological systems, such as single-cell proteomics, metaproteomics, and spatial proteomics, offering unparalleled identification depth and quantification reproducibility. Current DIA analysis frameworks, however, require semi-supervised training within each run for peptide-spectrum match (PSM) re-scoring, which is prone to overfitting and lacks generalizability across diverse species and experimental conditions. Here, we present {pi}DIA-CLIP, a generalized framework shifting the DIA analysis strategy from semi-supervised training to zero-shot cross-modal representation learning through integrating dual-encoder contrastive learning and encoder-decoder architectures to establish a unified, high-precision representation for spectral features and peptides. Notably, the generalized zero-shot nature of {pi}DIA-CLIP facilitates an inference-only architecture, streamlining the analysis to achieve exceptional computational efficiency. Extensive evaluations across five distinct benchmarks demonstrate that {pi}DIA-CLIP consistently outperforms existing tools, yielding an up to 44.6% increase in protein identification alongside a reduction in entrapment identifications reaching a maximal 52.5%. Furthermore, the enhanced identification depth facilitates the discovery of novel biomarkers and the elucidation of intricate cellular mechanisms.

bioinformatics↗

bioRxiv2026

PRISM: Niche-informed Deciphering of Incomplete Spatial Multi-Omics Data

Spatial multi-omics data, characterizing the knowledge of diverse molecular layers, have become indispensable for the in situ analysis of tissue architecture and complex biological processes. Nevertheless, current spatial multi-omics sequencing protocols are often hindered by incompatible protocols, resulting in incomplete spatial multi-omics pairing due to inconsistent field-of-view or spatial resolution. To address this, we present PRISM, a novel computational method tailored for this scenario. PRISM leverages a niche-informed graph to propagate information from paired to unpaired regions, jointly achieving the spatial domain identification and spatial omics imputation. Extensive benchmarking on five diverse simulated and real experimental datasets demonstrated that PRISM outperformed existing methods in spatial multi-omics analysis tasks. Application to the human Parkinsons disease data revealed that PRISM accurately recovered dopamine-associated spatial domains and metabolite distributions masked by incomplete data gaps. PRISM offers a robust solution for bridging the integration gap inherent in incompatible sequencing protocols, thereby facilitating more accurate downstream biological interpretation.

bioinformatics↗

bioRxiv2026

A moments-based approach for inferring mechanisms of transcriptional regulation using nascent RNA data

AbstractUnraveling the kinetics of transcriptional regulation is a central challenge in molecular biology. Traditional inference methods based on cell-to-cell mRNA variability are limited because mRNA levels reflect not only transcriptional activity but also post-transcriptional processes. Recent advances in quantifying nascent RNAs across isogenic cells provide a more direct readout of transcriptional dynamics. Here, we present a moments-based theoretical framework that leverages nascent RNA data to infer transcriptional kinetics. We derive closed-form expressions for the first two time-dependent moments of nascent RNA across three canonical transcription models and demonstrate that nascent RNA exhibits distinct transient overshoots and higher Fano factors compared to mature mRNA. Through analysis of synthetic datasets, we show that these features enable more accurate estimation of transcriptional kinetics and allow reliable model selection, distinguishing single from multi-pathway promoter activation with over 75% accuracy even from small sample sizes. We further validated our model selection method using real experimental transcription datasets in yeast and E. coli cells, with nascent RNA demonstrating superior performance in identifying cross-talk pathway regulation. Our framework thus offers a computationally efficient and mechanistically informative approach for decoding transcriptional dynamics, emphasizing the advantages of time-dependent nascent RNA measurements over mature mRNA in capturing the true kinetics of transcriptional regulation.

biophysics↗

bioRxiv2025

Microbiome-host interactions drive a trade-off between sleep quality and lifespan in Drosophila

Understanding the interactions between various aging processes and the resulting heterogeneity in aging is crucial for promoting healthy aging. Here, we provide evidence that heterogeneity in microbiome and host interactions contributes to diversifying aging phenotypes in sleep, gut integrity, and longevity in Drosophila. Aged flies exhibiting sleep fragmentation preserve gut integrity, accompanied by a shift in microbiota composition, particularly an increase in Acinetobacter junii. A. junii induces sleep fragmentation via its metabolite, urocanic acid, through serotonin receptor-dependent dopamine upregulation. In parallel, A. junii exploits the host response to promote its growth, leading to lifespan extension, which is recapitulated by genetically modified Escherichia coli, suggesting a trade-off between sleep quality and lifespan. Our study demonstrates a systematic mechanism underlying aging heterogeneity, suggesting interventions through bacterial supplements.

animal behavior and cognition↗

bioRxiv2025

6mA demethylase Mi-NMAD-1 modulates nematode development and parasitism through the transcription factor Mi-nhr-118

N6-methyladenine (6mA) DNA methylation has emerged as an important epigenetic mark in eukaryotes, but its biological role in plant-parasitic nematodes remains largely unexplored. Here, we demonstrate that 6mA methylation is essential for the embryonic development and parasitic ability of Meloidogyne incognita. Silencing of the 6mA demethylase genes minmad-1 significantly reduced egg hatching, revealing that 6mA-mediated regulation is required for nematode development. Transcriptome analyses showed that minmad-1 knockdown downregulated genes involved in embryogenesis, RNA biosynthesis, and cell differentiation, suggesting a critical role of 6mA in developmental gene regulation. Among the transcription factors affected by minmad-1 silencing, Mi-nhr-118, a nuclear hormone receptor orthologous to C. elegans nhr-118, was identified as a key regulator of embryonic development. Functional assays confirmed that RNAi or host-induced gene silencing (HIGS) of Mi-nhr-118 led to developmental arrest, reduced gall and egg mass formation, and impaired infectivity across multiple Meloidogyne species. ChIP-seq analysis revealed that Mi-nhr-118 directly binds to promoters of genes associated with cellular regulation and developmental signaling. Several downstream targets, including transcription factors (Mi_23621.1, Mi_39647.1) and genes implicated in metabolic and signaling processes (Mi_03067.1, Mi_08398.1), were shown to be essential for both embryogenesis and parasitism. Functional enrichment analyses of Mi-nhr-118 target genes indicated suppression of immune-related signaling, carbohydrate recognition, and glycoprotein biosynthetic pathways following gene silencing. Collectively, these findings establish Mi-nhr-118 as a central regulator linking 6mA-mediated epigenetic control to development and parasitism in M. incognita.

molecular biology↗

bioRxiv2025

Using the simple telegraph model to decipher transcriptional burst regulation across genome-wide data

Gene transcription is a stochastic bursting process, with burst frequency and burst size as core parameters. Deciphering its genome-wide regulation is challenging, as complex models (capturing detailed biology) are impractical for large-scale use due to unstable inference and high computation. We proposed a simple telegraph model-based framework to analyze genome-wide scRNA-seq and smFISH datasets, addressing these limitations. When sample size [≥] 500 and burst parameter change [≥] 3-fold, its inferred burst frequency- and size-dominated variations reliably proxy true regulation. Analyses across mouse cells (CAST/C57 alleles, fibroblasts vs. embryonic stem cells) and healthy/hypertrophic cardiomyopathy (HCM) human heart tissues revealed key rules: 1) Burst frequency- or size-dominated regulation was primary (over 70% of variable genes), with more bursty-regulated genes in HCM; 2) TATA-initiator synergy (enhancing burst size-dominated regulation) was lost in HCM; 3) Burst frequency-dominated genes enriched in genome stability/cell cycle/apoptosis (via TFs like Foxo4/Mcm2), while burst size-dominated ones in signaling/metabolism/proteostasis (via TFs like Zfp322a/Smchd1/Ppargc1a), and their interdependent dysregulation accelerated HCM. This study establishes the simple telegraph model as a scalable framework linking transcriptional burst dynamics to cell fate and pathology.

molecular biology↗

bioRxiv2025

Epstein-Barr Virus Encoded lncRNAs Control the Viral Lytic Switch

Epstein-Barr virus (EBV) uses a biphasic lifecycle, switching between latent and lytic phases to persistently infect most adults. Latency is observed in most tumor cells of the 200,000 EBV-associated cancers/year. EBV reactivation is increasingly implicated in autoimmune diseases, including multiple sclerosis. However, mechanisms that regulate EBV reactivation have remained incompletely understood. Here, we leveraged multi-omic approaches to reveal the existence of pro-latency and a pro-lytic viral long noncoding RNAs (lncRNAs) that counter-regulate the lytic switch. Known reactivation triggers rapidly induced expression of the pro-lytic lncRNA, which encodes an RNA G-quadruplex that mediated its interaction with CTCF. The pro-lytic lncRNA occupies viral origin of lytic replication enhancers and promotes their looping to the immediate early lytic promoter to trigger reactivation. The pro-latency lncRNA duplexes with the pro-lytic RNA to impede its interactions with CTCF. These studies lay a foundation for therapeutic approaches to manipulate the EBV lytic switch.

microbiology↗

bioRxiv2025

Arginine Metabolism Supports De Novo Pyrimidine Biosynthesis to Block DNA Damage and Maintain Epstein-Barr Virus Latency

Incompletely understood mechanisms serve to maintain Epstein-Barr virus (EBV) latency in most B-cell states, in which viral oncogene(s) are expressed but lytic antigens are repressed. Shortly after EBVs discovery and even before it was named, early pioneers Werne and Gertrude Henle identified that restriction of extracellular arginine de-represses EBV lytic antigens within Burkitt lymphoma tumor cells. However, for nearly 60 years, it has remained unknown how arginine metabolism supports EBV latency. To gain insights, we performed an amino acid restriction screen in Burkitt cell lines. This confirmed that arginine restriction was sufficient to trigger EBV reactivation in Burkitt B-cells and gastric carcinoma models. Arginine restriction strongly impaired de novo pyrimidine biosynthesis, and CRISPR or chemical genetic blockade of pyrimidine biosynthesis enzymes induced EBV immediate early and early lytic gene expression. However, arginine restriction blocked EBV lytic DNA replication and consequently also late gene expression, suggesting an abortive lytic cycle. Arginine restriction triggered DNA damage, which was an important driver of arginine restriction-driven EBV reactivation. Arginine restriction and DNA hypomethylation synergistically increased EBV reactivation. Together, our results highlight arginine and pyrimidine metabolism as potential targets for EBV lytic antigen induction therapy in B and epithelial cell contexts. ImportanceAltered metabolism is a hallmark of cancer, frequently increasing transformed cell dependence on extracellular amino acid supply. Despite current interest in EBV lytic antigen induction therapy, in which viral lytic reactivation sensitizes tumors to the highly cytotoxic effects of the antiviral ganciclovir, there has been no systemic study of extracellular amino acid that controls EBV latency. We identified that arginine uptake was important for the maintenance of EBV latency in both Burkitt lymphoma and gastric carcinoma contexts. Metabolic pathway analyses highlighted that arginine uptake and metabolism was required to supply pyrimidines. Disruption of arginine metabolism or de novo pyrimidine synthesis caused DNA damage. As arginine restriction was also found to cause Burkitt DNA hypermethylation, we provide evidence that the combination of arginine restriction and DNA hypomethylation by decitabine or by CRISPR approaches together induced EBV reactivation more highly than either alone, suggesting a therapeutic approach.

microbiology↗

bioRxiv2025

Global Mapping of Combinatorial Chromatin Regulatory Events Using Hi-Plex CUT&Tag

Epigenetic modulators, transcription factors, and chromatin-associated proteins collaboratively regulate essential genomic functions, such as transcription, repression, and DNA damage repair. However, the intricate cross-talk or the interaction between these chromatin regulatory factors (CRFs) remains poorly understood, largely due to limitations in experimental methods, which traditionally interrogate the genomic locations of only one CRF. Although the latest methods can study multiple CRFs simultaneously, they cannot examine the interaction between these factors. Inferring biological interactions often requires integrating data from multiple experiments, which can be labor-intensive and imprecise since the measurement is not from the same DNA molecule. To address these challenges, we developed Hi-Plex CUT&Tag, an advanced technology that enables the robust detection of co-localization events across over 600 pairs of CRFs using 36 barcoded monoclonal antibodies (mAbs), while minimizing background signals and cross-contamination. Hi-Plex CUT&Tag facilitates comprehensive pairwise analysis of epigenetic modifiers, including histone post-translational modifications (PTMs), writers, and transcription factors (TFs). Each Tn5 tagmentation fragment provides detailed insights, capturing the identities of two co-localized events, the underlying genomic sequences, and the molecular distance between them. For the first time, numerous novel bivalent events, epigenetic-context-dependent transcriptional regulation, and specific chromatin mark combinations with significant impacts on gene regulation can be detected in a single experiment. Furthermore, single-cell Hi-Plex CUT&Tag enables the analysis of synergistic interactions between CRF pairs at the single-cell level, providing unprecedented resolution for studying chromatin dynamics.

genetics↗

bioRxiv2025

Go/z-biased coupling profile of the dopamine D3 receptor

Dopamine receptors are G protein coupled receptors (GPCRs) that serve as key targets for FDA-approved drugs used to treat various neuropsychiatric disorders. Notably, [~]11% of all marketed GPCR-targeting drugs act on dopamine receptors. Five GPCRs mediate the effects of endogenous dopamine and compounds used to treat Parkinsons disease, schizophrenia, and other conditions. However, on-target side effects associated with these medications highlight the need to analyze dopamine receptor signaling to design safer, more effective therapeutics. We characterized the G protein coupling of dopamine D2-like receptors and observed the striking inability of D3R to engage with Gi proteins while effectively activating Go and Gz subtypes. Applying orthogonal cell-based assays that utilize wild-type G proteins both in parental and {Delta}Gi/o/z cells, we conclusively established that D3R does not activate Gi proteins. Further analysis of Gi2:GoA and D2R:D3R chimeras revealed that this selective inability is driven by molecular determinants located within the 5 helix of Gi and the intracellular loop 2 (ICL2) of D3R. Guided by cryo-EM structures, we modeled the interface between these regions to better understand the structural basis of this selectivity. Finally, we treated hippocampal neurons in acute brain slices with selective agonists for D2R and D3R and observed marked differences in their ability to regulate endogenous adenylyl cyclase to produce cAMP, highlighting the neurophysiological significance of our findings.

pharmacology and toxicology↗

bioRxiv2025

Neural mechanisms of structural inference: an EEG investigation of linguistic phrase structure categorization

Language comprehension is a complex cognitive process of understanding spoken or written language, during which the brain derives meanings and interprets messages by constructing hierarchical structures from word sequences. A key component underlying this process is structural inference. This computation determines the syntactic category ("head") of phrases, such as noun phrases (NP) and verb phrases (VP). Categorization confers distinct semantic and distributional properties. However, isolating neural correlates of structural inference is challenging, as modulations of syntactic category usually alter lexical content. Here, we collected the electroencephalography (EEG) data while participants read Mandarin NPs and VPs, which differ in their syntactic "headedness" but with lexical-semantic, lexical-syntactic categories and word order strictly conserved. We found significant theta power increases at left central-parietal scalp sites in NPs relative to VPs around the presentation (0-210 ms) of the nouns, where only the nouns in NPs project a head. Both NPs and VPs relative to one-word phrases exhibited spatiotemporally consistent low-frequency power increases at the point of linguistic composition, consistent with previous studies. Overall, our findings offer novel constraints on neurocomputational accounts of linguistic structure-building through these previously undocumented signatures of syntactic inference.

neuroscience↗

bioRxiv2025

Developmental dynamic transcriptomics reveals multiple effectors and transcription factors critical for Ditylenchus destructor parasitism

Plant-parasitic nematodes (PPNs) pose a major threat to global agricultural production, yet fundamental research on their biology remains limited. The origin and evolutionary trajectory of PPNs remain elusive, largely due to the scarcity of chromosome-level genomic data. Among them, migratory PPNs are considered a key transitional form between free-living and obligate parasitic lifestyles, as they exhibit both plant parasitism and fungal feeding behaviors. In this study, we assembled a chromosome-level genome of the sweet potato rot nematode Ditylenchus destructor and confirmed the presence of four chromosomes through Hi-C scaffolding and karyotype analysis. Comparative genomic analysis with two other migratory PPNs, Bursaphelenchus xylophilus and Aphelenchoides besseyi, revealed that the Nigon elements in B. xylophilus are largely conserved with those of the model organism Caenorhabditis elegans, while D. destructor and A. besseyi exhibit extensive Nigon element rearrangements. These rearrangements were strongly correlated with patterns of protein sequence collinearity. Moreover, transcriptomic profiling across five developmental stages of D. destructor identified numerous stage-specific effectors and transcription factors. Functional analysis via RNA interference demonstrated that many of these genes play essential roles in embryogenesis and parasitic activity. Together, our results provide valuable genomic and transcriptomic resources for studying PPNs, uncovering critical insights into their genome evolution and parasitism-related gene functions, and laying a crucial foundation for advancing the understanding of PPN biology and their impact on agricultural systems.

genomics↗

bioRxiv2025

Comparative assessment of co-folding methods for molecular glue ternary structure prediction

Molecular glues (MGs) represent an emerging therapeutic paradigm capable of inducing or stabilizing protein-protein interactions, with broad applications in creating neomorphic interactomes and targeted protein degradation. However, current discovery efforts remain largely confined to experimental screening, while in silico rational design of MGs persists as a formidable challenge. A critical step toward rational design lies in accurate ternary complex modeling. Given the scarcity of such complexes in the Protein Data Bank for training specialized predictive models, we tested the ability of recently developed co-folding models, including AlphaFold 3, Boltz-1, Chai-1, Protenix, and RoseTTAFold All-Atom in building the complex models. We systematically curated a dataset, named MG-PDB, with 221 non-covalent MG-engaged ternary complexes. MGBench were further introduced as a comprehensive benchmark set, which comprises 88 ternary structures excluded from co-folding models training data through rigorous time-based partitioning. Our benchmark results demonstrated that AlphaFold3 achieved the best overall performance among co-folding methods, in terms of both protein-protein interaction interface prediction (50.6% success rate) and MG-protein interaction recovery (32.9% success rate). However, our homology study showed that most of their successful predictions actually stemmed from memorization. Further analysis revealed three phenomena of current co-folding methods for MG ternary structure prediction. Firstly, these methods struggle to accurately model large interaction interfaces. Secondly, their predictive accuracy is notably reduced for domain-domain complexes compared to domain-motif interactions. Lastly, they face specific challenges in modeling MG degrader complexes with sufficient accuracy. We showcased they relied on the existing interaction patterns, and highlighted the need for further improved in novel E3 ligase systems. These findings reveal fundamental gaps in existing methods to learn atomic-level interaction rules for MG-engaged ternary complex modeling. MG-PDB and MGBench provide critical resources and mechanistic insights to advance computational MG discovery. Table of Contents Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/655997v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@172b19borg.highwire.dtl.DTLVardef@ea4844org.highwire.dtl.DTLVardef@12ea246org.highwire.dtl.DTLVardef@9fb4bc_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

EasyMultiProfiler: An Efficient Multi-Omics Data Integration and Analysis Workflow for Microbiome Research

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/649266v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@14dd0b1org.highwire.dtl.DTLVardef@1c04a79org.highwire.dtl.DTLVardef@159894dorg.highwire.dtl.DTLVardef@1454a01_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG HighlightsO_LIEasyMultiProfiler provides unified integration of microbial multi-omics data C_LIO_LIEasyMultiProfiler enables natural language-style analytical workflows C_LIO_LIEasyMultiProfiler solves key challenges in standardization and reproducibility C_LIO_LIEasyMultiProfiler extracts meaningful biological insights efficiently C_LI Host-microbiome interactions are crucial in maintaining physiological homeostasis and influencing disease progression. While traditional microbiome research, focused on microbial diversity and abundance, provided essential frameworks, the integration of multi-omics approaches established a far more comprehensive, systems-level understanding of microbiome functionality. However, significant methodological challenges in multi-omics data integration remain, including inconsistent sample coverage, heterogeneous data formats, and complex downstream analytical workflows. These challenges impact the reproducibility and reliability of results and underscore the critical requirement for developing standardized, systematic workflows. Thus, we developed the EasyMultiProfiler (EMP) workflow, a streamlined and efficient analytical framework for multi-omics data analysis. EMP provides a comprehensive infrastructure based on the SummarizedExperiment and MultiAssayExperiment classes, creating a unified framework for storing and analyzing omics data. The frameworks architecture comprises five interconnected functional modules: data extraction, preparation, support, analysis, and visualization. These modules are smoothly integrated into natural language-style analytical workflows, offering users an efficient and standardized solution for multi-omics analysis. EMP addresses critical challenges in multi-omics data analysis, including data integration, workflow standardization, and result reproducibility. The platforms modular architecture and intuitive interface provide researchers and clinicians with a robust, flexible workflow to systematically extract biologically relevant insights from complex multi-omics datasets.

bioinformatics↗