Biology subjects

Xie, P.

Publications and source records attributed to Xie, P..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

A Label-free Nanowell-based Impedance Sensor for Ten-minute SARS-CoV-2 Detection

This work explores label-free biosensing as an effective method for biomolecular analysis, ensuring the preservation of native conformation and biological activity. The focus is on a novel electronic biosensing platform utilizing micro-fabricated nanowell-based impedance sensors, offering rapid, point-of-care diagnosis for SARS-CoV-2 (COVID-19) detection. The nanowell sensor, constructed on a silica substrate through a series of microfabrication processes including deposition, patterning, and etching, features a 5x5 well array functionalized with antibodies. Real-time impedance changes within the nanowell array enable diagnostic results within ten minutes using small sample volumes (<5 {micro}L). The research includes assays for SARS-CoV-2 spike proteins in Phosphate-buffered saline (PBS) and artificial saliva buffers to mimic real human SARS-CoV-2 samples, covering a wide range of concentrations. The sensor exhibits a detection limit of 0.2 ng/mL (1.5 pM) for spike proteins. Middle East Respiratory Syndrome (MERS-CoV) spike proteins are differentiated from SARS-CoV-2 spike proteins, demonstrating specificity. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/627986v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@1a9f2a7org.highwire.dtl.DTLVardef@79d5acorg.highwire.dtl.DTLVardef@bb1bc1org.highwire.dtl.DTLVardef@1b5098_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2024

scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity by providing gene expression data at single-cell resolution, uncovering insights into rare cell populations, cell-cell interactions, and gene regulation. Foundation models pretrained on large-scale scRNA-seq datasets have shown great promise in analyzing such data, but existing approaches are often limited to modeling a small subset of highly expressed genes and lack the integration of external genespecific knowledge. To address these limitations, we present sc-Long, a billion-parameter foundation model pretrained on 48 million cells. sc-Long performs self-attention across the entire set of 28,000 genes in the human genome. This enables the model to capture long-range dependencies between all genes, including lowly expressed ones, which often play critical roles in cellular processes but are typically excluded by existing foundation models. Additionally, sc-Long integrates gene knowledge from the Gene Ontology using a graph convolutional network, enriching its contextual understanding of gene functions and relationships. In extensive evaluations, sc-Long surpasses both stateof-the-art scRNA-seq foundation models and task-specific models across diverse tasks, including predicting transcriptional responses to genetic and chemical perturbations, forecasting cancer drug responses, and inferring gene regulatory networks.

bioinformatics↗

bioRxiv2024

Nuclear effects play an important role in determining codon usage-dependent human gene expression

Codon usage biases play a significant role in determining gene expression levels. Codon usage was thought to primarily influence translation-dependent processes. Here we show that codon usage optimality correlates genome-wide with nuclear mRNA and transcription levels in human cells, suggesting a nuclear role for codon usage. A genome-wide non-biased CRISPR-Cas9 screen was performed to identify factors involved in codon usage effects on gene expression. The identified factors include the CCR4-NOT complex subunit CNOT4 and many nuclear factors, including nuclear RNA exosome, PAXT complex components, and transcription factors. The CCR4-NOT complex has been shown to affect codon usage-dependent co-translational mRNA decay in yeast. Surprisingly, human CNOT4 was found to be highly enriched in nucleus and its influence on codon usage-dependent gene expression is largely caused by its impact on nuclear RNA levels due to transcriptional effect. On the other hand, nuclear exosome and PAXT complex, regulates nuclear mRNA stability through the RNA quality control pathway, leading to preferential cytoplasmic accumulation of mRNAs with optimal codon usage. Overall, our findings suggest that codon usage biases are due to selection on both translation-dependent and independent processes; different nuclear mechanisms play a key role in determining nucleotide composition-dependent gene expression levels in human cells.

genomics↗

bioRxiv2024

ProteinAligner: A Multi-modal Pretraining Framework for Protein Foundation Models

Protein foundation models, particularly protein language models, have demonstrated strong success in learning meaningful representations of proteins using transformer architectures pretrained on large-scale protein datasets with self-supervised learning. These representations have been highly effective for downstream tasks such as predicting protein functions and properties. However, most current protein foundation models focus on pretraining with amino acid sequences, often neglecting additional modalities like protein structures and related literature, both of which provide valuable insights. To address this gap, we propose a multi-modal pretraining approach that integrates three key modalities - protein sequences, structures, and literature text. In our framework, the protein sequence modality serves as the anchor, with the other two modalities aligned to it, enhancing the models capacity to capture more comprehensive protein information. ProteinAligner out-performed state-of-the-art protein foundation models in predicting protein functions and properties across diverse down-stream tasks.

bioinformatics↗

bioRxiv2024

Multi-Modal Large Language Model Enables All-Purpose Prediction of Drug Mechanisms and Properties

Accurately predicting the mechanisms and properties of potential drug molecules is essential for advancing drug discovery. However, traditional methods often require the development of specialized models for each specific prediction task, resulting in inefficiencies in both model training and integration into work-flows. Moreover, these approaches are typically limited to predicting pharmaceutical attributes represented as discrete categories, and struggle with predicting complex attributes that are best described in free-form texts. To address these challenges, we introduce DrugChat, a multi-modal large language model (LLM) designed to provide comprehensive predictions of molecule mechanisms and properties within a unified framework. DrugChat analyzes the structure of an input molecule along with users queries to generate comprehensive, free-form predictions on drug indications, pharmacodynamics, and mechanisms of action. Moreover, DrugChat supports multi-turn dialogues with users, facilitating interactive and in-depth exploration of the same molecule. Our extensive evaluation, including assessments by human experts, demonstrates that DrugChat significantly outperforms GPT-4 and other leading LLMs in generating accurate free-form predictions, and exceeds state-of-the-art specialized prediction models.

pharmacology and toxicology↗

bioRxiv2024

Decoding Specificity of Cyanobacterial MysDs in Mycosporine-Like Amino Acid Biosynthesis through Heterologous Expression in Saccharomyces cerevisiae

Mycosporine-like amino acids (MAAs) are potent natural UV-protectants, but their industrial production is hindered by efficiency and sustainability issues of large-scale extraction of their native hosts. Heterologous expression of MAA biosynthesis pathway genes in chassis organisms provides a promising alternative route, though the substrate promiscuity of the ATP-grasp ligase MysD complicates the biosynthesis of specific MAAs. In this study, we developed a Saccharomyces cerevisiae strain with enhanced capacity of producing mycosporine-glycine (MG), through genomic expression of biosynthesis pathway genes and knockout of competing pathway genes. This strain serves as an efficient MysD expression platform, which converts MG into shinorine and porphyra-334. Through structural modelling, site-directed mutagenesis and mutant characterization, we identified two residues on the omega-loop of MysD involved in determining product specificity. We further characterized the product specificity of 20 MysDs from diverse cyanobacterial lineages and confirmed the residue pattern-product specificity correlation. Our findings provide guidance for screening, selecting, and designing novel MysDs for industrial-scale MAA production through heterologous expression.

synthetic biology↗

bioRxiv2024

Multi-Modal Large Language Model Enables Protein Function Prediction

Predicting the functions of proteins can greatly accelerate biological discovery and applications, where deep learning methods have recently shown great potential. However, these methods predominantly predict protein functions as discrete categories, which fails to capture the nuanced and complex nature of protein functions. Furthermore, existing methods require the development of separate models for each prediction task, a process that can be both resource-heavy and time-consuming. Here, we present ProteinChat, a versatile, multi-modal large language model that takes a proteins amino acid sequence as input and generates comprehensive narratives describing its function. ProteinChat is trained using over 1,500,000 (protein, prompt, answer) triplets curated from the Swiss-Prot dataset, covering diverse functions. This novel model can universally predict a wide range of protein functions, all within a single, unified framework. Furthermore, ProteinChat supports interactive dialogues with human users, allowing for iterative refinement of predictions and deeper exploration of protein functions. Our experimental results, evaluated through both human expert assessment and automated metrics, demonstrate that ProteinChat outperforms general-purpose LLMs like GPT-4, one of the flagship LLMs, by over ten-fold. In addition, ProteinChat exceeds or matches the performance of task-specific prediction models.

bioinformatics↗

bioRxiv2024

PTEN neddylation aggravates CDK4/6 inhibitor resistance in breast cancer

The gradual emergence of a novel therapeutic approach lies in the restoration of tumor suppressive machinery. PTEN is a crucial negative regulator of the PI3K/Akt signaling pathway. Protein neddylation modification contributes to PTEN inactivation and fuels breast cancer progression. Here, we highlight that an elevated level of PTEN neddylation is markedly associated with resistance to palbociclib, a CDK4/6 inhibitor used in luminal subtype breast cancer patients. Mechanistically, PTEN neddylation activates the PI3K/Akt signaling pathway, and more notably, upregulates the activity of the AP-1 transcription factor. Our data showed that PTEN neddylation stabilizes JUND, a transcription factor involved in the AP-1 complex, by disrupting its interaction with the E3 ubiquitin ligase ITCH. Consequently, activated JUND leads to the release of cytokines and chemokines, which in turn may drive an inflammatory tumor microenvironment, potentially contributing to drug resistance. Then, we identified Echinacoside as a potent inhibitor of PTEN neddylation both in vivo and in vitro by disrupting its interaction with XIAP, the E3 ligase responsible for PTEN neddylation. Combination of Echinacoside effectively overcome resistance to palbociclib in breast cancer treatment. These findings highlight targeting PTEN neddylation as a promising strategy for restoring tumor suppressor activity and overcoming resistance.

cell biology↗

bioRxiv2024

Epigenetic delineation of the earliest cardiac lineage segregation by single-cell multi-omics

The mammalian heart is formed from multiple mesoderm-derived cell lineages. However, it remains largely unknown when and how the specification of mesoderm towards cardiac lineages is determined. Here, we systematically depict the transcriptional trajectories toward cardiomyocyte in early mouse embryo, and characterize the epigenetic landscapes underlying the early mesodermal lineage specification by single-cell multi-omics analyses. The analyses also reveal distinct core regulatory networks (CRN) in controlling specification of mesodermal lineages. We further demonstrate the essential role HAND1 and FOXF1 in driving the earliest cardiac progenitors specification. These key transcription factors occupy at distinct enhancers, but function synergistically and hierarchically to regulate the expression of cardiac-specific genes. In addition, HAND1 is required for exiting from the nascent mesoderm program, while FOXF1 is essential for driving cardiac differentiation during juxta-cardiac field (JCF) specification. Our findings establish transcriptional and epigenetic determinants specifying the early cardiac lineage, providing insights for the investigation of congenital heart defects.

molecular biology↗

bioRxiv2023

Mammalian circadian clock proteins form dynamic interacting microbodies distinct from phase separation

Liquid-liquid phase separation (LLPS) underlies diverse biological processes. Because most LLPS studies were performed in vitro or in cells that overexpress protein, the physiological relevance of LLPS is unclear. PERIOD proteins are central mammalian circadian clock components and interact with other clock proteins in the core circadian negative feedback loop. Different core clock proteins were previously shown to form large complexes. Here we show that when transgene was stably expressed, PER2 formed nuclear phosphorylation-dependent LLPS condensates that recruited other clock proteins. Super-resolution microscopy of endogenous PER2, however, revealed formation of circadian-controlled, rapidly diffusing microbodies that were resistant to protein concentration changes, hexanediol treatment, and loss of phosphorylation, indicating that they are distinct from the LLPS condensates caused by overexpression. Surprisingly, only a small fraction of endogenous PER2 microbodies transiently interact with endogenous BMAL1 and CRY1, a conclusion that was confirmed in cells and in mice tissues, suggesting an enzyme-like mechanism in the circadian negative feedback process. Together, these results demonstrate that the dynamic interactions of core clock proteins is a key feature of mammalian circadian clock mechanism and the importance of examining endogenous proteins in LLPS and circadian studies.

genetics↗

bioRxiv2023

TransGeneSelector: A Transformer-based Approach Tailored for Key Gene Mining with Small Plant Transcriptomic Datasets

Gene mining, particularly from small sample sizes such as in plants, remains a challenge in life sciences. Traditional methods often omit significant genes, while deep learning techniques are hindered by small sample constraints and lack specialized gene mining approaches. This paper presents TransGeneSelector, the first deep learning method tailored for key gene mining in small transcriptomic datasets, ingeniously integrating data augmentation, sample filtering, and a Transformer-based classifier. Tested on Arabidopsis thaliana seeds germination classification using just 79 samples, it not only achieves classification performance on par with, if not superior to, Random Forest and SVM but also excels in identifying upstream regulatory genes that Random Forest might miss, and these pinpointed genes more accurately reflect the metabolic processes inherent in seed germination. TransGeneSelectors ability to mine vital genes from limited datasets signifies its potential as the current state-of-the-art in gene mining in small sample scenarios, providing an efficient and versatile solution for this critical research area.

bioinformatics↗

bioRxiv2023

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimers disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

neuroscience↗

bioRxiv2023

Breaking Down Cell-Free DNA Fragmentation: A Markov Model Approach

Cell-free DNA (cfDNA) is released into the bloodstream from cells in various physiological and pathological conditions. The use of cfDNA as a non-invasive biomarker has attracted attention, but the dynamics of cfDNA fragmentation in the bloodstream are not well understood and have not been previously studied computationally. To address this issue, we present a Markovian model called FRIME (Fragmentation, Immigration, and Exit) that captures three leading mechanisms governing cfDNA fragmentation in the bloodstream. The FRIME model enables the simulation of cfDNA fragment profiles by sampling from the stationary distribution of FRIME processes. By varying the parameters of our model, we generate fragment profiles similar to those observed in liquid biopsies and provide insight into the underlying biological mechanisms driving the fragmentation dynamics. To validate our model, we compare simulated FRIME profiles with mitochondrial and genomic cfDNA fragment profiles. Our simulation results are consistent with experimental and clinical observations and highlight potential physicochemical differences between mitochondrial and genomic cfDNA. The FRIME simulation framework provides an initial step towards an improved computational understanding of DNA fragmentation dynamics in the bloodstream and may aid in the analysis of liquid biopsy data. Author summaryCell-free DNA (cfDNA) released into the bloodstream from cells in different conditions is the basis for liquid biopsies, thus being non-invasive biomarkers of potential interest. However, the dynamics of cfDNA fragmentation in the bloodstream remain poorly understood and are yet to be studied computationally. To address this issue, we developed a Markovian model which captures the three leading mechanisms governing cfDNA fragmentation in the bloodstream: FRagmentation, IMmigration, and Exit (FRIME). The FRIME model enables the simulation of cfDNA fragment profiles by sampling from the stationary distribution of FRIME processes. Simulation results are compared with mitochondrial and genomic cfDNA fragment profiles, which show consistency with experimental and clinical observations. The FRIME simulation framework is a significant step towards understanding DNA fragmentation dynamics in the bloodstream, uncovering potential physicochemical differences between mitochondrial and genomic cfDNA, and aiding the analysis of liquid biopsy data.

bioinformatics↗

bioRxiv2023

The Complexity of Functional Connectivity Profiles of the Subgenual Anterior Cingulate Cortex and Dorsal Lateral Prefrontal Cortex in Major Depressive Disorder: a DIRECT Consortium Study

BackgroundThe subgenual anterior cingulate cortex (sgACC) plays a central role in the pathophysiology of major depressive disorder (MDD), and its functional interactive profile with the left dorsal lateral prefrontal cortex (DLPFC) is associated with transcranial magnetic stimulation (TMS) treatment outcomes. Nevertheless, previous research on sgACC functional connectivity (FC) in MDD has yielded inconsistent results, partly due to small sample sizes and limited statistical power. Furthermore, calculating sgACC-FC to target TMS individually is challenging. MethodsLeveraging a large multi-site cross-sectional sample (1660 MDD patients vs. 1341 healthy controls) from Phase II of the Depression Imaging REsearch ConsorTium (DIRECT), we systematically delineated case-control difference maps of sgACC-FC. Then, we explored the potential impact of such group-level abnormality profiles on the TMS target localization and clinical efficacy. Next, we developed an MDD big data-guided individualized TMS targeting algorithm to integrate group-level statistical maps with individual-level brain activity to localize TMS targets individually. ResultsWe found an enhanced sgACC-DLPFC FC in MDD patients compared to healthy controls (HC). Such group differences altered the position of the sgACC anti-correlation peak in the left DLPFC. In two independent clinical samples, we showed that the magnitude of TMS targets case-control differences in sgACC FC was related to clinical improvement. The MDD big data-guided individualized TMS targeting algorithm may generate individualized TMS targets that are clinically superior to group-level targets. InterpretationWe reliably delineated MDD-related abnormalities of sgACC-FC profiles in a large, independently ascertained sample and demonstrated the potential impact of such case-control differences on FC-guided localization of TMS targets. FundingMinistry of Science and Technology of the Peoples Republic of China, National Natural Science Foundation of China, and Chinese Academy of Sciences

neuroscience↗

bioRxiv2023

Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome

Major depressive disorder (MDD) is the most burdensome psychiatric disorder characterized by remarkably heterogeneous clinical phenotypes. It remains challenging to delineate the heterogeneity of neurobiological abnormalities underlying the clinical variance and, on this basis, to identify neurophysiological subtypes of MDD patients. Here, using a large multisite resting-state functional MRI data from 1,148 MDD patients and 1,079 healthy controls, we generated lifespan normative models of functional connectivity strengths, mapped the heterogeneity of patients individual deviations, and identified neurobiological MDD subtypes. MDD patients showed positive deviations mainly in the default mode and subcortical areas, and negative deviations widely distributed over the cortex. However, there was a great inter-subject heterogeneity as indicated by that no more than 3.14% of patients deviated from the normative range for any brain region. Two neurophysiological MDD subtypes were identified. Subtype 1 showed severe deviations with positive deviations in the default mode, limbic, and subcortical areas, and negative deviations in the sensorimotor, dorsal and ventral attention areas, while subtype 2 showed a moderate but conversed deviation pattern. The severe-deviation subtype had older age, higher medicated proportion, and higher Suicide item score, while the moderate-deviation subtype showed higher Work and Activities and Depressed Mood item scores. Moreover, the baseline deviations in the severe-deviation subtype were predictive of 6-month antidepressant treatment effects in a subsample. To our knowledge, the current study is the largest multisite analysis of neurophysiological MDD subtyping to date and the findings shed light on our understanding of the biological mechanisms underlying the intersubject heterogeneity of clinical phenotypes, which are informative for the development of personalized treatments for this disorder.

neuroscience↗

bioRxiv2022

JBrowse 2: A modular genome browser with views of synteny and structural variation

We present JBrowse 2, a general-purpose genome annotation browser offering enhanced visualization of complex structural variation and evolutionary relationships. JBrowse 2 retains the core features of the open-source JavaScript genome browser JBrowse while adding new views for synteny, dotplots, breakpoints, gene fusions, and whole-genome overviews. The software readily allows users to share sessions, open multiple genomes or views, and navigate quickly between these views. It can be embedded in a web page, used as a standalone desktop application, or run from Jupyter notebooks or R sessions. Using a plugin framework, developers can create new data adapters, track types, and visualizations. These improvements are enabled by a ground-up redesign of the JBrowse architecture using modern web technology. We describe application functionality, use cases, performance benchmarks, and implementation notes for web administrators and developers.

bioinformatics↗

bioRxiv2022

JBrowse Jupyter: A Python interface to JBrowse 2

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSJBrowse Jupyter is a package that aims to close the gap between Python programming and genomic visualization. Web-based genome browsers are routinely used for publishing and inspecting genome annotations. Historically they have been deployed at the end of bioinformatics pipelines, typically decoupled from the analysis itself. However, emerging technologies such as Jupyter notebooks enable a more rapid iterative cycle of development, analysis and visualization. ResultsWe have developed a package that provides a python interface to JBrowse 2s suite of embeddable components, including the primary Linear Genome View. The package enables users to quickly set up, launch and customize JBrowse views from Jupyter notebooks. In addition, users can share their data via Googles Colab notebooks, providing reproducible interactive views. AvailabilityJBrowse Jupyter is released under the Apache License and is available for download on PyPI. Source code and demos are available on GitHub at https://github.com/GMOD/jbrowse-jupyter. Contactihh@berkeley.edu

bioinformatics↗

bioRxiv2022

Regional and cell type-specific afferent and efferent projections of the mouse claustrum

The claustrum (CLA) is a conspicuous subcortical structure interconnected with cortical and subcortical regions. However, its regional anatomy and cell-type-specific connections in the mouse remain largely undetermined. Here, we accurately delineated the boundary of the mouse CLA and quantitatively investigated its inputs and outputs brain-wide using anterograde and retrograde viral tracing and fully reconstructed single claustral principal neurons. At a population level, the CLA reciprocally connects with all isocortical modules. It also receives inputs from at least 35 subcortical structures but sends projections back to only a few of them. We found that cell types projecting to the CLA are differentiated by cortical areas and layers. We classified single CLA principal neurons into at least 9 cell types that innervate the diverse sets of functionally linked cortical targets. Axons of interneurons within the CLA arborize along almost its entire anteroposterior extent. Together, this detailed wiring diagram of the cell-type-specific connections of the mouse CLA lays a foundation for studying its functions.

neuroscience↗