Biology subjects

Jian, R.

Publications and source records attributed to Jian, R..

10 recordsLinked to original sources

bioRxiv2025

Remodeling of the immune-metabolic landscape triggered by long-term high-altitude exposure

Growing evidence indicates that immunological and metabolic outcomes are key mediators of long-term high-altitude exposure (LTHAE) adaption, but the underlying mechanisms remain poorly understood. This study employs plasma metabolomics and peripheral blood single-cell transcriptomic sequencing to analyze the metabolic and immune dynamic regulation in 46 young male lowlanders following a 90-day adaptation period at high altitude. Single-cell analysis shows a pattern of "innate immune activation and adaptive immune suppression" under LTHAE, characterized by facilitated maturation of neutrophils, enhanced cytotoxicity of CD56dim NK cells, and increased immune responsiveness of cDC2 and pDC, while inhibited maturation of plasmablasts and suppressed immune responsiveness of CD8{square}TEM and CD4+ T cells. Plasma metabolic analysis reveals significant alterations, involving enhanced steroid hormone synthesis, unsaturated fatty acid and amino acid metabolism under LTHAE, which in turn are associated with immune remodeling. Moreover, transcriptomic-metabolic integration analysis indicates the molecular mechanisms of enhanced aerobic oxidation efficiency under LTHAE. Collectively, these findings provide integrated insights into immune-metabolic landscape remodeling and suggest potential mutual regulatory relationship between immune and metabolic state following LTHAE, offering a molecular foundation for high-altitude adaptation research.

genomics↗

bioRxiv2025

Comprehensive Characterization of the Promoter Proximal Proteome of Single Copy Locus FOXP2

Determining the proteins interacting with sequence-defined chromatin segments is a critical step in understanding gene expression and developing experimental interventions in the process. We used genetically targeted proximity labeling with dCas9-APEX2 to specifically biotinylate the promoter proximal proteome of the single copy locus FOXP2 in live HEK293 cells. To identify labeled proteins in a discovery-based manner, we utilized quantitative mass spectrometry. Specifically, online 2D-LC coupled directly to a tribrid mass spectrometer to enable real-time database searching synchronous precursor selection MS3 provided deep proteome coverage and accurate quantitation via isobaric tandem mass tags. We inferred 6,039 proteins from our sample using Proteome Discoverer and performed bioinformatic analysis on quantified proteins to identify 373 significantly enriched proteins at the active promoter (Storey-q<.05, FC>1.2). These proteins were enriched for transcription factors and components of the spliceosome. To validate our candidate transcriptional regulators, we utilized computationally predicted transcription factor binding and the >200 ChIP-Seq experiments performed in HEK293 cells by ENCODE. In addition to validating several candidate transcription factors as binders of the targeted genomic locus, we identify IRF2BP2 as a negative regulator of FOXP2 transcription.

genetics↗

bioRxiv2024

Improving design and normalization of multiplex proteomics study

Advances in multiplex mass spectrometry-based technologies have enabled high-throughput, quantitative proteome profiling of large cohort. However, certain experimental design configurations can amplify sample variability and introduce systematic biases. To address these challenges, we incorporated two novel features in a recent proteogenomic investigation: (1) the inclusion of two reference samples within each mass spectrometry run to serve as internal standards, and (2) the analysis of each specimen as technical replicates across two distinct mass spectrometry runs. Building on these enhancements, we present ProMix, a flexible analytical framework designed to fully leverage these supplementary experimental components. Using both simulated and real-world datasets, we demonstrate the improved performance of ProMix and highlight the advantages conferred by these refined experimental design strategies.

bioinformatics↗

bioRxiv2024

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study HighlightsO_LIThe stability of the human microbiome varies among individuals and body sites. C_LIO_LIHighly individualized microbial genera are more stable over time. C_LIO_LIAt each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected. C_LIO_LIIndividuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions. C_LI

microbiology↗

bioRxiv2023

Integrative Multi-omic Profiling of Two Human Decedents Receiving Pig Heart Xenografts Reveals Strong Perturbations in Early Immune-Cell and Cellular Metabolism Responses.

BackgroundRecent advances in xenotransplantation in living and decedent humans using pig xenografts have laid promising groundwork towards future emergency use and first in human trials. Major obstacles remain though, including a lack of knowledge of the genetic incompatibilities between pig donors and human recipients which may led to harmful immune responses against the xenograft or dysregulation of normal physiology. In 2022 two pig heart xenografts were transplanted into two brain-dead human decedents with a minimized immunosuppression regime, primarily to evaluate onset of hyper-acute antibody mediated rejection and sustained xenograft function over 3 days. MethodsWe performed multi-omic profiling to assess the dynamic interactions between the pig and human genomes in the first two pig heart-xenografts transplants into human decedents. To assess global and specific biological changes that may correlate with immune-related outcomes and xenograft function, we generated transcriptomic, lipidomic, proteomic and metabolomics datasets, across blood and tissue samples collected every 6 hours over the 3-day procedures. ResultsSingle-cell datasets in the 3-day pig xenograft-decedent models show dynamic immune activation processes. We observe specific scRNA-seq, snRNA-seq and geospatial transcriptomic changes of early immune-activation leading to pronounced downstream T-cell activity and hallmarks of early antibody mediated rejection (AbMR) and/or ischemia reperfusion injury (IRI) in the first xenograft recipient. Using longitudinal multiomic integrative analyses from blood in addition to antigen presentation pathway enrichment, we also observe in the first xeno-heart recipient significant cellular metabolism and liver damage pathway changes that correlate with profound physiological dysfunction whereas, these signals are not present in the other xenograft recipient. ConclusionsSingle-cell and multiomics approaches reveal fundamental insights into early molecular immune responses indicative of IRI and/or early AbMR in the first human decedent, which was not evident in the conventional histological evaluations.

systems biology↗

bioRxiv2022

Neuron Navigator 1 Regulates Learning, Memory, and the Response to Multiple Potentially Addictive Drugs

Genetic variation accounts for much of the risk for developing a substance use disorder (SUD). Inbred mouse strains exhibit substantial and heritable differences in the extent of voluntary cocaine intravenous self-administration (IVSA). Computational genetic analysis of IVSA data obtained from an inbred strain panel identified Nav1, a member of the neuron navigator family that regulates dendrite formation and axonal guidance, as a candidate gene. To test this hypothesis, we generated and characterized Nav1 knockout (KO) mice. Nav1 KO mice exhibited increased cocaine intake during IVSA testing. Surprisingly, Nav1 KO mice also displayed a reduced susceptibility to become opioid dependent or develop opioid-induced hyperalgesia after chronic morphine administration, and had impaired spatial learning/memory. Immunohistochemistry and electrophysiology studies revealed that inhibitory synapse density in the cortex of Nav1 KO mice was reduced, and excitatory synaptic transmission was increased in the Nav1 KO cortex and hippocampus. Transcriptomic analysis revealed that Nav1 KO mice had a marked increase in excitatory neurons in a deep cortical layer. Collectively, our results indicate that Nav1 regulates learning, memory, and the response to multiple addictive drugs, and that changes in the excitatory and inhibitory synaptic balance in the cortex and hippocampus could possibly mediate these phenotypic effects.

genetics↗

bioRxiv2021

Transcriptome variation in human tissues revealed by long-read sequencing

Regulation of transcript structure generates transcript diversity and plays an important role in human disease. The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure. In this paper, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from GTEx tissues and cell lines, complementing the GTEx resource. We identified just under 100,000 new transcripts for annotated genes, and validated the protein expression of a similar proportion of novel and annotated transcripts. We developed a new computational package, LORALS, to analyze genetic effects of rare and common variants on the transcriptome via allele-specific analysis of long reads. We called allele-specific expression and transcript structure events, providing novel insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we use this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns.

genomics↗

bioRxiv2019

A genome-wide almanac of co-essential modules assigns function to uncharacterized genes

A central remaining question in the post-genomic era is how genes interact to form biological pathways. Measurements of gene dependency across hundreds of cell lines have been used to cluster genes into co-essential pathways, but this approach has been limited by ubiquitous false positives. Here, we develop a statistical method that enables robust identification of gene co-essentiality and yields a genome-wide set of functional modules. This almanac recapitulates diverse pathways and protein complexes and predicts the functions of 102 uncharacterized genes. Validating top predictions, we show that TMEM189 encodes plasmanylethanolamine desaturase, the long-sought key enzyme for plasmalogen synthesis. We also show that C15orf57 binds the AP2 complex, localizes to clathrin-coated pits, and enables efficient transferrin uptake. Finally, we provide an interactive web tool for the community to explore the results (coessentiality.net). Our results establish co-essentiality profiling as a powerful resource for biological pathway identification and discovery of novel gene functions.

genomics↗

bioRxiv2019

A Quantitative Proteome Map of the Human Body

Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from 12,627 genes across 32 normal human tissue types prepared by the GTEx project. Known and new tissue specific or enriched proteins (5,499) were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode highly tissue specific proteins. Discordance in the sites of RNA expression and protein detection also revealed potential sites of synthesis and action of protein signaling molecules. Overall, these results provide an extraordinary resource, and demonstrate that understanding protein levels can provide insights into metabolism, regulation, secretome, and human diseases.\n\nSummaryQuantitative proteome study of 32 human tissues and integrated analysis with transcriptome data revealed that understanding protein levels could provide in-depth knowledge to post transcriptional or translational regulations, human metabolism, secretome, and diseases.

systems biology↗

bioRxiv2019

RobNorm: Model-Based Robust Normalization for High-Throughput Proteomics from Mass Spectrometry Platform

MotivationData normalization is an important step in processing proteomics data generated in mass spectrometry (MS) experiments, which aims to reduce sample-level variation and facilitate comparisons of samples. Previously published methods for normalization primarily depend on the assumption that the distribution of protein expression is similar across all samples. However, this assumption fails when the protein expression data is generated from heterogenous samples, such as from various tissue types. This led us to develop a novel data-driven method for improved normalization to correct the systematic bias meanwhile maintaining underlying biological heterogeneity. MethodsTo robustly correct the systematic bias, we used the density-power-weight method to down-weigh outliers and extended the one-dimensional robust fitting method described in the previous work of (Windham, 1995, Fujisawa and Eguchi, 2008) to our structured data. We then constructed a robustness criterion and developed a new normalization algorithm, called RobNorm. ResultsIn simulation studies and analysis of real data from the genotype-tissue expression (GTEx) project, we compared and evaluated the performance of RobNorm against other normalization methods. We found that the RobNorm approach exhibits the greatest reduction in systematic bias while maintaining across-tissue variation, especially for datasets from highly heterogeneous samples. Availabilityhttps://github.com/mwgrassgreen/RobNorm Contacthuatang@stanford.edu and mpsnyder@stanford.edu

bioinformatics↗