Biology subjects

Shen, R.

Publications and source records attributed to Shen, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Harnessing Data-Intelligence-Intensive Multi-Agent System for Life Science Research

Advancements in high-throughput sequencing technologies and artificial intelligence offer unprecedented opportunities for groundbreaking discoveries in bioinformatics research. However, the challenges of exponential growth of omics data and the rapid development of artificial intelligence technologies require automated big biological data analysis capability and interdisciplinary knowledge-driven scientific insight. Here we propose a data-intelligence-intensive bioinformatics copilot (Bio-Copilot) system that synergizes AI capabilities with human expertise to facilitate hypothesis-free exploratory research and inspire novel scientific insights in large-scale omics studies. Bio-Copilot forms high-quality intensive intelligence through close collaboration between multiple agents, driven by large language models (LLMs), and human experts. To augment the capabilities of Bio-Copilot, this study devises an agent group management strategy, an effective human-agent interaction mechanism, a shared interdisciplinary knowledge database, and continuous learning strategies for the agents. We comprehensively compare Bio-Copilot against GPT-4o and several leading AI agents across diverse bioinformatics tasks, using a broad range of evaluation metrics. Bio-Copilot achieves the overall state-of-the-art performance across all tasks, while showcases exceptional task completeness. Furthermore, in the application of constructing a large-scale human lung cell atlas, Bio-Copilot not only reproduces the intricate data integration process detailed in a seminal study but also introduces a hierarchical annotation strategy to capture the continuous nature of cellular states and uncovers the characteristics of rare cell types, highlighting its potential to unravel hidden complexities in biological systems. Beyond the technical achievements, this study also underscores the profound implications of integrating AI capabilities with expert knowledge in accelerating impactful biological discoveries and exploring uncharted territories in life sciences.

bioinformatics↗

bioRxiv2024

Machine learning predictions improve identification of real-world cancer driver mutations

Characterizing and validating which mutations influence development of cancer is challenging. Machine learning has delivered significant advances in protein structure prediction, but its utility for identifying cancer drivers is less explored. We evaluated multiple computational methods for identifying cancer driver alterations. For identifying known drivers, methods incorporating protein structure or functional genomic data outperformed methods trained only on evolutionary data. We further validated VUSs annotated as pathogenic by testing their association with overall survival in two cohorts of patients with non-small cell lung cancer (N=7,965 and 977). "Pathogenic" VUSs in KEAP1 and SMARCA4 identified by several methods were associated with worse survival, unlike "benign" VUSs. "Pathogenic" VUSs exhibited mutual exclusivity with known oncogenic alterations at the pathway level, further suggesting biological validity. Despite training primarily on germline, rather than somatic, mutation data, computational predictions contribute to a more comprehensive understanding of tumor genetics as validated by real-world data.

genomics↗

bioRxiv2024

IOBR2: Multidimensional Decoding Tumor Microenvironment for Immuno-Oncology Research

The use of large transcriptome datasets has greatly improved our understanding of the tumor microenvironment (TME) and helped develop precise immunotherapies. The increasing popularity of multi-omics sequencing, single-cell transcriptome sequencing (scRNA), and spatial transcriptome sequencing has led to numerous new discoveries. However, these findings require clinical phenotypic validation with a large sample size. To enhance the integration of multi-omics in advancing research on the tumor microenvironment, we have developed a systematic and comprehensive analytical tool (Immuno-Oncology Biological Research 2, IOBR2) based on our prior work. IOBR2 offers six modules for TME analysis based on multi-omics data. These modules cover data preprocessing, TME estimation, TME infiltrating patterns, cellular interactions, genome and TME interaction, and visualization for TME relevant features, as well as modelling based on key features. IOBR2 integrates multiple vital microenvironmental analysis algorithms and signature estimation methods, simplifying the analysis and downstream visualization of the TME. In addition to providing a quick and easy way to construct gene signatures from single-cell data, IOBR2 also provides a way to construct a reference matrix for TME deconvolution from single-cell RNAseq. The analysis pipeline and feature visualization are user-friendly and provide a comprehensive description of the complex TME, offering insights into tumor-immune interactions. A comprehensive gitbook (https://iobr.github.io/book/) is available with a user-friendly manual and complete analysis workflow for each module.

cancer biology↗

bioRxiv2023

Structural Basis of Voltage-Dependent Gating in BK Channels and Its Coupling to the Calcium Sensor

The allosteric communication between pore domain, voltage sensors, and Ca2+ binding sites in the Ca2+-activated K+ channel (BK) shapes its multiple physiological roles as the preeminent signal integrator in excitable systems. BK displays shallow voltage sensitivity with very fast gating charge kinetics, yet little is known about the molecular underpinnings of this distinctive behavior. Here, we explore the mechanistic basis of coupling between voltage-sensing domains (VSDs) and calcium sensors in Aplysia BK by locking the VSDs in their resting (R196Q and R199Q) and activated (R202Q) states, with or without calcium. Cryo-EM structures of these mutants reveal unique tilts at the S4 C-terminal end, together with large side-chain rotameric excursions of the gating charges. Importantly, the VSD resting structure (R202Q) also revealed BK in its elusive fully closed state, highlighting the reciprocal relation between calcium and voltage sensors. These structures provide a plausible mechanism where voltage and Ca2+ binding converge physically and couple energetically to define the conformation of the pore domain and thus, BK full functional range.

biophysics↗

bioRxiv2023

Adaptation of a Mutual Exclusivity Framework to Identify Driver Mutations within Biological Pathways

Distinguishing genomic alterations in cancer genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage has important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering novel functional candidates beyond the existing knowledge-base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. A limited memory BFGS algorithm is used to facilitate large scale optimization. We use simulations to study the operating characteristics of the method and assess false positive and false negative rates in driver nomination. When applied to a large study of primary melanomas the method accurately identified the known driver genes within the RTK-RAS pathway and nominated a number of rare variants with previously unknown biological and clinical relevance as prime candidates for functional validation.

cancer biology↗

bioRxiv2023

Physical structure and interstitial flows govern microbial life in microenvironments

Most microbial life on Earth is found in localized microenvironments that collectively exert a crucial role in maintaining ecosystem health and influencing global biogeochemical cycles. In many habitats such as biofilms in aquatic systems, bacterial flocs in activated sludge, periphyton mats, or particles sinking in the ocean, these microenvironments experience sporadic or continuous flow. Depending on their microscale structure, pores and channels through the microenvironments permit localized flow that shifts the relative importance of diffusive and advective mass transport. How this flow alters nutrient supply, facilitates waste removal, drives the emergence of different microbial niches, and impacts the overall function of the microenvironments remains unclear. Here, we quantify how pores through microenvironments that permit flow can elevate nutrient supply to the resident bacterial community using a microfluidic experimental system and gain further insights from coupled population-based and computational fluid dynamics simulations. We find that the microscale structure determines the relative contribution of advection versus diffusion, and even a modest flow through a pore in the range of 10 {micro}m s-1 can increase the carrying capacity of a microenvironment by 10%. Recognizing the fundamental role that microbial hotspots play in the Earth system, developing frameworks that predict how their heterogeneous morphology and potential interstitial flows change microbial function and collectively alter global scale fluxes is critical.

microbiology↗

bioRxiv2023

Forsythoside A blocks the infection of Avian Infectious Bronchitis Virus through binding S1 subunit

Avian infectious bronchitis (IB) is an acute highly contagious respiratory infectious disease caused by avian infectious bronchitis virus (IBV), causing serious economic losses to poultry industry. Forsythiaside A (FTA) is a phenylethanol glycoside compound in the fruit of Forsythia suspensa, which is widely used for inflammatory respiratory tract disease, including viral infected inflammation. In this study, the mechanism of FTA against IBV was researched. To determine the inhibitory effect of FTA on IBV infection and the interaction between FTA and IBV particles, immunofluorescence assay, plaque assay, RT-qPCR and enzyme-linked immunosorbent assay (ELISA) were performed and it was discovered that FTA inhibited IBV infection and viral attachment to host receptor under non-cytotoxic concentrations, and FTA may bind to virus particles. Then ELISA and isothermal titration calorimetry (ITC) were employed to further study the interaction between FTA and S protein. The results indicated that FTA bound to S1 subunit rather S2 subunit. Besides, the molecular docking showed that FTA form hydrogen bonds with eight amino acids in the S1 subunit. When these amino acids were mutated. the affinity disappeared between the S1 mutant and FTA. In conclusion, FTA prevents IBV infection by blocking viral attachment and contacts with the S1, but not the S2 subunit of IBV. At last, the bind sites were identified by comparing the affinity of wild S1 and mutant S1.

pharmacology and toxicology↗

bioRxiv2023

Conservation and divergence of regulatory architecture in nitrate-responsive plant gene circuits

Nitrogen is an essential element for all life processes in plants. As such, plant roots dynamically respond to nitrogen availability below-ground by executing a signaling and transcriptional cascade resulting in altered plant growth, optimized for nutrient uptake. The NIN-LIKE PROTEIN 7 (NLP7) transcription factor senses nitrogen and along with its closely related paralog NLP6, partially coordinates these transcriptional responses. While post-translational regulation of NLP6/7 is well established, its upstream transcriptional regulation remains understudied in Arabidopsis and other plant species. Here, we dissect a previously identified sub-circuit upstream of NLP6/7 in Arabidopsis and which was predicted to contain multiple multi-node feedforward loops suggestive of an optimized design principle of nitrogen transcriptional regulation. This sub-circuit comprises AUXIN RESPONSE FACTOR 18 (ARF18), ARF9, DEHYDRATION RESPONSE ELEMENT BINDING-PROTEIN 26 (DREB26), A NAC-DOMAIN CONTAINING PROTEIN 32 (ANAC032), NLP6 and NLP7 and their regulation of NITRITE REDUCTASE 1 (NIR1). Conservation and divergence of this circuit and their influence on N-dependent root system architecture are similarly assessed in Solanum lycopersicum. The specific binding sites of these factors within their respective promoters and their putative cis-regulatory architecture are identified. The direct or indirect nature of these interactions are validated in planta. The resulting models were genetically validated in varying concentrations of available nitrate by measuring the transcriptional output of the network revealing rewiring of nitrogen regulation across distinct plant lineages. Significance StatementNitrogen is a critical nutrient for plant growth and yield. While external N has facilitated modern agriculture, over-application of N-containing fertilizers has drastic ecological and environmental consequences. Here, we experimentally validate a six gene regulatory circuit with extensive feedforward loops identified to act upstream of the critical NIN-LIKE PROTEIN 6/7 transcription factors which regulates a nitrogen metabolic enzyme. Our results indicate conservation and divergence in these circuits between Arabidopsis and tomato despite the similar role of NLP7 in N-dependent changes in root system architecture. The resulting network models complement existing knowledge of NLP7 regulation by providing a framework for targeted transcriptional engineering to increase plant nitrogen use efficiency.

plant biology↗

bioRxiv2023

Uncovering the hidden structure of dynamic T cell composition in peripheral blood during cancer immunotherapy: a topic modeling approach

Immune checkpoint inhibitors (ICIs), now mainstays in the treatment of cancer treatment, show great potential but only benefit a subset of patients. A more complete understanding of the immunological mechanisms and pharmacodynamics of ICI in cancer patients will help identify the patients most likely to benefit and will generate knowledge for the development of next-generation ICI regimens. We set out to interrogate the early temporal evolution of T cell populations from longitudinal single-cell flow cytometry data. We developed an innovative statistical and computational approach using a Latent Dirichlet Allocation (LDA) model that extends the concept of topic modeling used in text mining. This powerful unsupervised learning tool allows us to discover compositional topics within immune cell populations that have distinct functional and differentiation states and are biologically and clinically relevant. To illustrate the models utility, we analyzed [~]17 million T cells obtained from 138 pre- and on-treatment peripheral blood samples from a cohort of melanoma patients treated with ICIs. We identified three latent dynamic topics: a T-cell exhaustion topic that recapitulates a LAG3+ predominant patient subgroup with poor clinical outcome; a naive topic that shows association with immune-related toxicity; and an immune activation topic that emerges upon ICI treatment. We identified that a patient subgroup with a high baseline of the naive topic has a higher toxicity grade. While the current application is demonstrated using flow cytometry data, our approach has broader utility and creates a new direction for translating single-cell data into biological and clinical insights.

bioinformatics↗

bioRxiv2023

Formate Promotes Invasion and Metastasis by Activating Fatty Acid Synthesis and Matrix Metalloproteinases

Metabolic rewiring is essential to enable cancer onset and progression. One important metabolic pathway that is often hijacked by cancer cells is the one-carbon cycle, in which the third carbon of serine is oxidized to formate. We have previously shown that formate production in cancer cells often exceeds the anabolic demand, resulting in formate overflow. Furthermore, we observed that high extracellular formate promotes the in vitro invasiveness of glioblastoma (GBM) cells. However, additional data supporting this in vitro observation and mechanistic details remained elusive so far. In the present study, we now demonstrate that inhibition of formate overflow results in a decreased invasiveness of GBM cells ex vivo and in vivo. Additionally, we observed that exposure to exogeneous formate can induce a transiently stable pro-invasive phenotype that results in increased metastasis formation in vivo. All in all, these results suggest that a local formate increase within the tumor microenvironment may be one factor that can promote cancer cell motility and dissemination. Mechanistically, we uncover a previously undescribed interplay where formate acts as a trigger to alter fatty acid metabolism and matrix metalloproteinase (MMP) activity which in turn impacts cancer cell invasiveness. We thus highlight the role of formate as a pro-invasive metabolite. Gaining a deeper understanding of formate overflow and how it promotes invasion in cancer, may open new therapeutic opportunities to prevent cancer cell dissmination.

cancer biology↗

bioRxiv2022

Dynamics of activation in the voltage-sensing domain of Ci-VSP

The Ciona intestinalis voltage-sensing phosphatase (Ci-VSP) is a membrane protein containing a voltagesensing domain (VSD) that is homologous to VSDs from voltage-gated ion channels responsible for cellular excitability. Two crystal structures of Ci-VSD in putative resting and active conformations suggest a helical-screw voltage sensing mechanism in which the S4 helix translocates and rotates to enable exchange of salt-bridge partners. By combining extensive molecular dynamics simulations with a computational framework based on dynamical operators, we elucidate the microscopic mechanism of the resting-active transition at physiological membrane potential. Sparse regression reveals a small set of coordinates that distinguish intermediates hidden from electrophysiological measurements. The intermediates arise from a noncanonical helical-screw mechanism in which translocation, rotation, and side-chain movement of the S4 helix are only loosely coupled. These results provide new insights into existing experimental and computational findings on voltage sensing and suggest ways of further probing its mechanism.

biophysics↗

bioRxiv2022

High-resolution de novo structure prediction from primary sequence

Recent breakthroughs have used deep learning to exploit evolutionary information in multiple sequence alignments (MSAs) to accurately predict protein structures. However, MSAs of homologous proteins are not always available, such as with orphan proteins or fast-evolving proteins like antibodies, and a protein typically folds in a natural setting from its primary amino acid sequence into its three-dimensional structure, suggesting that evolutionary information and MSAs should not be necessary to predict a proteins folded form. Here, we introduce OmegaFold, the first computational method to successfully predict high-resolution protein structure from a single primary sequence alone. Using a new combination of a protein language model that allows us to make predictions from single sequences and a geometry-inspired transformer model trained on protein structures, OmegaFold outperforms RoseTTAFold and achieves similar prediction accuracy to AlphaFold2 on recently released structures. OmegaFold enables accurate predictions on orphan proteins that do not belong to any functionally characterized protein family and antibodies that tend to have noisy MSAs due to fast evolution. Our study fills a much-encountered gap in structure prediction and brings us a step closer to understanding protein folding in nature.

bioinformatics↗

bioRxiv2022

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding

Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets. A series of quantitative comparison experiments on public available SRT datasets demonstrate the superiority of Spatial-ID compared with other state-of-the-art methods. Besides, the application of Spatial-ID on a SRT dataset with 3D spatial dimension measured by Stereo-seq shows its advancement on the large field tissues with subcellular spatial resolution.

genomics↗

bioRxiv2022

State Dependent Anionic Pore Currents Conducted by Single Countercharge Mutants in a Voltage-Sensing Phosphatase

Mutating gating charge residues in the S4 segment of voltage-sensing domains (VSDs) can cause ionic leak currents through the VSDs. These leak currents, known as gating pore or omega currents, play important pathophysiological role in many diseases. Here, we show that mutations in a key countercharge residue, D129, in the Ciona intestinalis voltage-sensing phosphatase (Ci-VSP) facilitate conduction of unique anionic omega currents. Neutralization of D129 causes a dramatic positive shift of activation, facilitates the formation of a continuous water path through the intermediate state VSD, and creates a positive electrostatic potential landscape inside the VSD leading to anion selectivity. Increasing the population or duration of the conducting state by a high external pH or an engineered Cd2+ bridge markedly increases the current magnitude. Our findings uncover a new role of countercharge residues and could inform on the mechanisms of channelopathies linked to countercharge residue mutations.

biophysics↗

bioRxiv2022

Mechanism of Voltage Gating in the Voltage-Sensing Phosphatase Ci-VSP

The conformational changes in voltage-sensing domain (VSD) are driven by the transmembrane electric field acting on charges and countercharges. Yet, the overall energetics and detailed mechanism of this process are not fully understood. Here, we determined free energy and displacement charge landscapes, as well as major conformations corresponding to a complete functional gating cycle in the isolated voltage-sensing domain of the phosphatase Ci-VSP (Ci-VSD) comprising four transmembrane helices (segments S1-S4). Molecular dynamics simulations highlight the extent of S4 movements. In addition to the crystallographically determined activated Up and resting Down states, the simulations predict two novel Ci-VSD conformations: a deeper resting state ( Down-minus) and an extended activated ( Up-plus) state. These additional conformations were experimentally probed via systematic cysteine mutagenesis with metal-ion bridges and the engineering of proton conducting mutants at hyperpolarizing voltages. These results show that voltage activation involves sequentially populating these four states in a stepwise way, translating one arginine across the membrane electric field per step, transferring ~3 e0 charges.

biophysics↗

bioRxiv2022

A model of functionally buffered deleterious mutations can lead to signatures of positive selection distinguishable from an evolutionary conflict model

AbstractSelective pressures on DNA sequences often result in departures from neutral evolution that can be captured by the McDonald-Kreitman (MK) test. However, the nature of such selective forces often remains unknown to experimentalists. Amino acid fixations driven by natural selection in protein coding genes are commonly associated with a genetic arms race or changing biological purposes, leading to proteins with new functionality. Here, we evaluate the expectations of a buffering mechanism driving selective amino acids to fixation, which is motivated by an observed phenotypic rescue of otherwise deleterious nonsynonymous substitutions at bag of marbles (bam) and Sex lethal (Sxl). These two important genes in Drosophila melanogaster were shown to experience strong episodic bursts of natural selection potentially due to infections of the endosymbiotic bacteria Wolbachia observed among multiple Drosophila species. Using simulations to implement and evaluate the evolutionary dynamics of a Wolbachia buffering model, we demonstrate that selectively fixed amino acid replacements will occur, but that proportion of adaptive amino acid fixations and the statistical power of the MK test to detect the departure from an equilibrium neutral model are both significantly lower than seen for an arms race/change-in-function model that favors proteins with diversified amino acids.

genetics↗

bioRxiv2021

Harbingers of Aggressive Prostate Cancer: Precision Oncology Case Studies

Primary prostate cancer (PCa) can show marked molecular heterogeneity. However, systematic analyses comparing primary PCa and matched metastases in individual patients are lacking. We aimed to address the molecular aspects of metastatic progression while accounting for heterogeneity of primary PCa. In this pilot study, we collected 12 radical prostatectomy (RP) specimens from men who subsequently developed metastatic castration-resistant prostate cancer (mCRPC). We used histomorphology (Gleason grade, focus size, stage) and immunohistochemistry (IHC) (ERG and p53) to identify independent tumors and/or distinct subclones of primary PCa. We then compared molecular profiles of these primary PCa areas to matched metastatic samples using whole exome sequencing (WES) and amplicon-based DNA and RNA sequencing. Based on combined pathology and molecular analysis, seven (58%) RP specimens harbored monoclonal and topographically continuous disease, albeit with some degree of intra-tumor heterogeneity; four (33%) specimens showed true multifocal disease; and one displayed monoclonal disease with discontinuous topography. Early (truncal) events in primary PCa included SPOP p.F133V (one patient), BRAF p.K601E (one patient), and TMPRSS2:ETS rearrangements (nine patients). Activating AR alterations were seen in eight (67%) mCRPC patients, but not in matched primary PCa. Hotspot TP53 mutations, found in metastases from three patients, were readily present in matched primary disease. Alterations in genes encoding epigenetic modifiers were observed in several patients (either shared between primary foci and metastases or in metastatic samples only). WES-based phylogenetic reconstruction and/or clonality scores were consistent with the index focus designated by pathology review in six out of nine (67%) cases. The three instances of discordance pertained to monoclonal, topographically continuous tumors, which would have been considered as unique disease in routine practice. Overall, our results emphasize pathologic and molecular heterogeneity of primary PCa, and suggest that comprehensive IHC-assisted pathology review and genomic analysis are highly concordant in nominating the "index" primary PCa area.

cancer biology↗

bioRxiv2021

Cryo-EM Structures of Prestin and the Molecular Basis of Outer Hair Cell Electromotility

The voltage-dependent motor protein, Prestin (SLC26A5) is responsible for the electromotive behavior of outer hair cells (OHCs). Here, we determined the structure of dolphin Prestin in six distinct states using single particle cryo-electron microscopy. Structural and functional data suggest that Prestin adopts a unique and complex set of states, tunable by the identity of bound anions. Complexes with the inhibitor salicylate show that it competes for the anion-binding site of Prestin. These conformations reveal a novel mechanism of area expansion that depends on the helix flexibility and conformational transitions at the membrane protein interface and putatively affects the physical state of the surrounding membrane. These observations illuminate the structural basis of Prestin electromotility, a key component of the mammalian cochlear amplifier.

neuroscience↗