Biology subjects

Jiao, F.

Publications and source records attributed to Jiao, F..

15 recordsLinked to original sources

bioRxiv2026

Inter-domain flexibility in F-Box proteins revealed by integrative modeling

F-box proteins, the substrate-recognition modules of the SCF (Skp1-Cul1-F-box) ubiquitin ligase complex, play a central role in targeted protein degradation by enabling the selective recruitment of diverse substrates. F-box proteins share a common architecture with a conserved anchor domain linked to the substrate-binding domain. Despite extensive structural characterization of F-box proteins, their potential conformational dynamics remain poorly understood. Here, we employ an integrative modeling approach combining cryo-EM structures, cross-linking mass spectrometry (XL-MS), and deep learning-based conformational ensemble prediction to profile the accessible conformations of F-box proteins. Our analyses reveal a significant inter-domain flexibility in FBXL2 and FBXL17 that enables sampling of a broad range of conformations within assembled SCF and CSN-SCF complexes. By contrast, other F-box proteins, including FBXL1 and FBXO22, exhibit intrinsic flexibility but are restricted to a considerably narrower conformational landscape. Together, our work highlights inter-domain plasticity as a tunable architectural feature of F-box proteins, which may shape their functional versatility within SCF complexes.

molecular biology↗

bioRxiv2026

Optimization of Search and Acquisition Strategies for DSSO Crosslinking Mass Spectrometry using Protein Prospector and Touchstone.

Crosslinking mass spectrometry (XL-MS) enables residue-level characterization of protein-protein interactions (PPIs) in both purified assemblies and complex biological systems. Cleavable crosslinkers such as DSSO are widely used to facilitate analysis of complex samples, yet there remains limited consensus on how best to leverage their properties in both acquisition and data analysis workflows. In part, this reflects differences in scoring, classification, and false discovery rate (FDR) estimation across analysis pipelines. Therefore, an integrated XL-MS data analysis pipeline that enables confident identification of MS-cleavable cross-linked peptides through improved scoring and rigorous FDR assessment is essential for systematically comparing and benchmarking workflows across different sample preparations. To this end, we implemented a new capability within the Protein Prospector search engine to improve MS2-based identification of MS-cleavable cross-linked peptides. Next, we developed Touchstone, a framework for re-scoring, classification, FDR-controlled summarization, and visualization of Prospector XL-MS search results. Using this approach, we evaluate the contributions of DSSO-specific fragmentation features and compare multiple acquisition strategies across datasets ranging from purified macromolecular assemblies to proteome-scale samples. We find that the primary advantage of DSSO arises from enhanced backbone fragmentation of both linked peptides, rather than from diagnostic ion formation, and that incorporating diagnostic ion pairs into MS2-based scoring or filtering does not improve identification performance. Benchmarking against established datasets shows that Prospector-Touchstone performs comparably to leading methods for non-cleavable crosslinkers and provides improved performance for DSSO analysis. We further compare acquisition strategies and find that for purified protein assemblies, MS2.stHCD characterizes more residue-pairs than ETD-based, MS3-based, and hybrid methods. In contrast, for proteome-scale XL-MS, MS3-based acquisition yields similar numbers of inter-protein PPIs with lower error rates despite reduced sampling depth. Together, these results highlight complementary strengths of MS2- and MS3-based workflows and provide practical guidance for MS-cleavable XL-MS experiments with varying sample complexities.

bioinformatics↗

bioRxiv2026

Extending structural surfaceomics to identify aberrant conformations of tumor surface proteins as potential immunotherapy targets

The complement of tumor cell surface proteins, or "surfaceome", is a rich source of potential immunotherapy targets. To move beyond expression-based target discovery, we previously described "structural surfaceomics," combining crosslinking mass spectrometry (XL-MS) with surface protein biotinylation to identify conformation-selective targets. In our prior work, we applied this method to a single model of acute myeloid leukemia (AML), identifying active integrin beta-2 as a promising target. Here, we expand structural surfaceomics to identify additional immunotherapy targets and surface protein biology across additional models of AML, multiple myeloma, and prostate cancer, as well as donor peripheral blood mononuclear cells. Utilizing these models and different chemical crosslinkers, we compile an extensive database of 5,209 crosslinks. We characterize both shared and unique crosslink-based features, identifying 1,612 disease model-specific crosslinks, including 212 potentially defining tumor-specific conformations based on distance constraint violations relative to AlphaFold predictions. We further implement a suite of emerging modeling tools to predict tumor-specific protein structures. We probe crosslinking patterns suggesting multiple myeloma-specific CD48 and AML-specific integrin 1/{beta}4 heterodimer conformations. This work establishes a resource for cancer structural biology by implementation of structural surfaceomics. Our findings also point toward more realistic protein design models, potentially enabling systematic detection of targetable cancer-specific epitopes for next-generation immunotherapies.

cancer biology↗

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

Orthosteric Molecular Glue Inhibits COP9 Signalosome with Substrate-Dependent Potency

Orthosteric inhibitors block enzyme active sites and prevent substrates from binding. Enhancing their specificity through substrate dependence seems inherently unlikely, as their mechanism hinges on direct competition rather than selective recognition. Here, we show that a molecular glue mechanism unexpectedly imparts substrate-dependent potency to CSN5i-3, an orthosteric inhibitor of the COP9 signalosome (CSN). We first confirm that CSN5i-3 inhibits CSN, which catalyzes NEDD8 deconjugation from the cullin-RING ubiquitin ligases (CRLs), by occupying the active site of its catalytic subunit, CSN5, and directly competing with the iso-peptide bond substrate. Curiously, the orthosteric inhibitor binds free CSN with only micromolar affinity, yet achieves nanomolar potency in blocking its deneddylase activity. Cryo-EM structures of the enzyme-substrate-inhibitor complex reveal that active site-engaged CSN5i-3 occludes the substrate iso-peptide linkage while simultaneously extending an NEDD8-binding exosite of CSN5, acting as a molecular glue to cement the NEDD8-CSN5 interaction. The cooperativity of this tri-molecular CSN5i-3-NEDD8-CSN5 assembly, in turn, sequesters CSN5i-3 at its binding site, conferring high potency to the orthosteric inhibitor despite its low affinity for the free enzyme. Together, our findings highlight the modest affinity requirements of molecule glues for individual target proteins and establish "orthosteric molecular glue inhibitors" as a new class of substrate-dependent enzyme antagonists.

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

Stochastic Modeling and Transient Analysis of Epidemic Extinction Dynamics

Understanding extinction probabilities in branching processes is pivotal for epidemiology and population dynamics. Traditional models often assume a fixed generation time, resulting in extinction probabilities determined solely by offspring distributions and remaining unchanged over time. By contrast, our study incorporates the generation time into the analysis, considering how the timing of each generation influences extinction dynamics. By focusing on the finite-time risk of extinction, our approach reveals that shorter generation times can lead to a temporary increase in the risk of population or epidemic die-out. We support our findings with precise fixed-point analyses and numerical integration techniques based on real data from various infectious diseases. Although the long-term probability of extinction does not change, the transient dynamics show that a faster-paced transmission process may elevate early extinction risk. The study highlights the crucial role of transmission timing in epidemic modeling and indicates that accounting for generation time can provide new perspectives for developing effective public health strategies and outbreak control measures.

systems biology↗

bioRxiv2025

Multi-omics reveals lipid metabolism, mitochondrial and extracellular matrix dysregulation of diabetic cardiomyopathy in the human heart

Diabetic cardiomyopathy (DbCM) is a condition characterized by myocardial dysfunction in diabetes. Although the clinical recognition of DbCM is increasing, its underlying molecular mechanisms remain poorly understood. We aimed to conduct a comprehensive multi-omics analysis of heart tissue from cardiomyopathy patients (both with and without diabetes),alongside tissue from healthy donors, by integrating transcriptomic, 4D-DIA proteomic, and full-spectrum widely targeted metabolomic data. Differential analyses revealed a significant metabolic pattern in patients with DbCM, characterized by increasing utilization of triglyceride-derived fatty acids. Correlation analyses highlighted impaired BNIP3L-mediated mitophagy as a potential contributor to the disruption of fatty acid metabolism and mitochondrial dysfunction in DbCM. Protein interaction and fibrotic trichrome analyses showed that patients with DbCM had impaired cardiac matrix repair compared to patients with cardiomyopathy without diabetes. These findings provide a system-level understanding of DbCM and reveal molecular signatures driving its progression, offering insights for targeted therapeutic strategies against diabetic heart disease.

bioinformatics↗

bioRxiv2025

Activation-Free Upgrading of Carboxylic Acids to Aldehydes and Alcohols

Advances in organic and gas waste valorization have enabled high-yield production of carboxylic acids, positioning them as promising feedstocks for the bioeconomy. However, carboxylic acids must be activated before downstream use, typically requiring ATP, CoA, or reduced ferredoxin to overcome unfavorable thermodynamics. These activators are costly to generate and divert carboxylic acids into CO2-releasing pathways, reducing carbon efficiency. Here, we demonstrate that aldehyde dehydrogenases (ALDHs) can directly reduce carboxylic acids to aldehydes without prior activation, a process previously thought to be biologically inaccessible. Screening 133 ALDHs revealed that this activity is remarkably widespread within the protein family, enabling production of aliphatic aldehydes and alcohols, diols, and aromatic alcohols, at titers >1 g/L, in some cases, after optimization of thermodynamic driving forces. Additionally, we applied this system to upgrade waste-derived carboxylic acid effluent streams from wastewater sludge, food waste, and waste gas (CO2). This activation-free process, termed "reverse aldehyde oxidation" (rAOX), establishes a broadly applicable, energy-efficient platform for carboxylic acid valorization at 100% carbon yield. Analogous to the reverse tricarboxylic acid cycle (rTCA) and reverse {beta}-oxidation (rBOX), rAOX exemplifies that metabolic reactions classically defined as unidirectional may have unexpected plasticity to operate in reverse and open new avenues in biomanufacturing.

synthetic biology↗

bioRxiv2025

XL-Ranker: A Computational Workflow for Prioritizing Protein-Protein Interactions from Cross-Linking Mass Spectrometry Data

Protein-protein interactions (PPIs) are central to virtually all biological processes, and their disruption can lead to a wide spectrum of human diseases. Cross-linking mass spectrometry (XL-MS) enables proteome-scale detection of interacting peptide pairs, from which PPIs can be inferred. However, interpretating XL-MS data to assign homologous cross-linked peptide pairs to specific protein interactions can be challenging. A major hurdle arises when a single peptide can map to multiple proteins, and when two such peptides are paired, the number of possible protein-protein interactions increases combinatorially. This mapping ambiguity can lead to inflated interaction networks that could compromise downstream analysis and biological interpretation. However, this problem has often been overlooked or addressed using heuristic approaches in previous XL-MS studies. To tackle this challenge, we developed XL-Ranker, a computational framework that combines a set cover graph algorithm and machine learning to systematically resolve peptide-mapping ambiguity and infer high-confidence PPIs from XL-MS data. Applied to an XL-MS dataset from HEK293 cells, XL-Ranker identified a high-confidence network with 880 PPIs involving 964 unique genes. Among all possible PPIs, the ones selected by XL-Ranker for inclusion in the final network had significantly higher interaction scores in the STRING database than the excluded ones. Network analysis further demonstrated that these interactions form biologically meaningful clusters, supporting the accuracy of our approach. In summary, XL-Ranker provides a practical solution to a key analytical challenge in XL-MS data interpretation, enhancing the reliability of PPI discovery.

bioinformatics↗

bioRxiv2024

Geometry theory of distribution shapes for autoregulatory gene circuits

In this study, we provide a complete mathematical characterization of the phase diagram of distribution shapes in an extension of the two-state telegraph model of stochastic gene expression in the presence of positive or negative autoregulation. Using the techniques of second-order difference equations and nonlinear discrete dynamical systems, we prove that the feedback loop can only produce three shapes of steady-state protein distributions (decaying, bell-shaped, and bimodal), corresponding to three distinct parameter regions in the phase diagram. The boundaries of the three regions are characterized by two continuous curves, which can be constructed geometrically by the contour lines of a series of ratio operators. Based on the geometric structure of the phase diagram, we then provide some simple and verifiable sufficient and/or necessary conditions for the existence of the bimodal parameter region, as well as the conditions for the steady-state distribution to be decaying, bell-shaped, or bimodal. Finally, we also investigate how the phase diagram is affected by the strength of positive or negative feedback.

biophysics↗

bioRxiv2023

What can we learn when fitting a complex gene expressionmodel to a simple telegraph model?

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

biophysics↗

bioRxiv2022

Inhibition of c-Jun in AgRP neurons mediates chronic stress-induced anxiety-like behaviors and colitis susceptibility

Psychiatric disorders, such as anxiety, are frequently associated with inflammatory bowel diseases (IBD), however, the neural mechanisms are unknown. Here, we showed that hypothalamic agouti-related protein (AgRP) neuronal activity was suppressed under chronic restraint stress (CRS), a condition known to induce anxiety-like behaviors and increase colitis susceptibility. Consistently, chemogenic activation (inhibition) of AgRP neurons reversed (mimicked) CRS-induced anxiety-like behaviors and colitis susceptibility. Furthermore, CRS inhibited AgRP neuronal activity by suppressing the expression of c-Jun. As expected, overexpression of c-Jun in these neurons protected against the CRS-induced these effects and knockdown of c-Jun in AgRP neurons (c-Jun{Delta}AgRP) promoted anxiety-like behaviors and colitis. Moreover, relieving the anxiety with cyamemazine (an anxiolytic drug) alleviated colitis susceptibility in c-Jun{Delta}AgRP mice. Finally, according to a proteomic analysis, the levels of the secreted protein thrombospondin 1 (THBS1) were negatively associated with the increased anxiety-like behaviors and colitis susceptibility, supplementing recombinant THBS1 rescued colitis in c-Jun{Delta}AgRP mice. Taken together, these results reveal a critical role of hypothalamic AgRP neuron-derived c-Jun in orchestrating chronic stress-induced anxiety-like behaviors and colitis susceptibility. These results provide a new perspective for understanding the neuronal mechanisms and potential therapeutic target for the comorbidity of psychiatric disorders, such as anxiety, and IBD.

neuroscience↗

bioRxiv2021

Framework estimation of stochastic gene activation using transcription average level

Gene activation is usually a non-Markovian process that has been modeled as various frameworks that consist of multiple rate-limiting steps. Understanding the exact activation framework for a gene of interest is a central problem for single-cell studies. In this paper, we focus on the dynamical data of the average transcription level M (t), which is typically neglected when deciphering gene activation. Firstly, the smooth trend lines of M (t) data present rich, visually dynamic features. Secondly, tractable analysis of M (t) allows the establishment of bijections between M (t) dynamics and system parameter regions. Because of these two clear advantages, we can rule out frameworks that fail to capture M (t) features and we can further test potential competent frameworks by fitting M (t) data. We implemented this procedure to determine an exact activation framework for a large number of mouse fibroblast genes under tumor necrosis factor induction; the cross-talk between the signaling and basal pathways is crucial to trigger the first peak of M (t), while the following damped gentle M (t) oscillation is regulated by the multi-step basal pathway. Moreover, the fitted parameters for the mouse genes tested revealed two distinct regulation scenarios for transcription dynamics. Taken together, we were able to develop an efficient procedure for using traditional M (t) data to estimate the gene activation frameworks and system parameters. This procedure, together with sophisticated single-cell transcription data, may facilitate a more accurate understanding of stochastic gene activation. Author SummaryIt has been suggested that genes randomly transit between inactive and active states, with mRNA produced only when a gene is active. The gene activation process has been modeled as a framework of multiple rate-limiting steps listed sequentially, parallel, or in combination. The system step numbers and parameters can be predicted by computationally fitting sophisticated single-cell transcription data. However, current algorithms require a prior hypothetical framework of gene activation. We found that the prior estimation of the framework can be achieved using the traditional dynamical data of mRNA average level M (t) which present easily discriminated dynamical features. The theory regarding M (t) profiles allows us to confidently rule out other frameworks and to determine optimal frameworks by fitting M (t) data. We successfully applied this procedure to a large number of mouse fibroblast genes and confirmed that M (t) is capable of providing a reliable estimation of gene activation frameworks and system parameters.

biophysics↗

bioRxiv2020

Design of Biologically Active Binary Protein 2D Materials

Proteins that assemble into ordered two-dimensional arrays such as S-layers1,2 and designed analogues3-5 have intrigued bioengineers,6,7 but with the exception of a single lattice formed through non-rigid template streptavidin linkers,8 they are constituted from just one protein component. For modulating assembly dynamics and incorporating more complex functionality, materials composed of two components would have considerable advantages.9-12 Here we describe a computational method to generate de-novo binary 2D non-covalent co-assemblies by designing rigid asymmetric interfaces between two distinct protein dihedral building-blocks. The designed array components are soluble at mM concentrations, but when combined at nM concentrations, rapidly assemble into nearly-crystalline micrometer-scale p6m arrays nearly identical to the computational design model in vitro and in cells without the need of a two-dimensional support. Because the material is designed from the ground up, the components can be readily functionalized, and their symmetry reconfigured, enabling formation of ligand arrays with distinguishable surfaces to drive extensive receptor clustering, downstream protein recruitment, and signaling. Using quantitative microscopy we show that arrays assembled on living cells have component stoichiometry and likely structure similar to arrays formed in vitro, suggesting that our material can impose order onto fundamentally disordered substrates like cell membranes. We find further that in sharp contrast to previously characterized cell surface receptor binding assemblies such as antibodies and nanocages, which are rapidly endocytosed, large arrays assembled at the cell surface suppress endocytosis in a tunable manner, with potential therapeutic relevance for extending receptor engagement and immune evasion. Our work paves the way towards synthetic cell biology, where a new generation of multi-protein macroscale materials is designed to modulate cell responses and reshape synthetic and living systems. One Sentence SummaryCo-assembling binary 2D protein crystals enables robust formation of complex large scale ordered biologically active materials

synthetic biology↗