Biology subjects

Qin, J.

Publications and source records attributed to Qin, J..

8 recordsLinked to original sources

bioRxiv2026

Mechanistic modeling of bacterial translation initiation across growth conditions

Translation frequency in bacteria depends on how ribosomes, mRNAs, and initiation factors are allocated across growth conditions. Here, we developed a mechanistic ODE-based model of Escherichia coli translation that represents initiation, elongation, termination, and coupled auxiliary processes. Growth-dependent abundances were derived from physiological relationships and reprocessed omics data, and simulated outputs were compared with translation-frequency and active-ribosome references. The model predicts a continuous shift from complex-formation-limited toward ribosome-limited behavior as growth increases. This shift is characterized by a decline in free-ribosome abundance, whereas initiation-factor pools remain largely unbound and do not become depleted in parallel. Together with the implemented IF-dependent kinetic term, this preserved availability provides a model-internal route through which productive initiation can be maintained despite increasing ribosome utilization. Consistently, transcript-wide ribosome loading remains below its theoretical maximum, while COG-level simulations reveal distinct sector-specific translation-frequency trajectories. The study therefore provides a resource-allocation framework for interpreting how mRNA--ribosome interactions shape bacterial translation across growth conditions.

systems biology↗

bioRxiv2018

TCR-pMHC bond length controls TCR ligand discrimination

T-cell receptors (TCRs) detect specifically and sensitively a small number of agonist peptide-major histocompatibility complexes (pMHCs) from an ocean of structurally similar self-pMHCs to trigger antigen-specific adaptive immune responses1-4. Despite intense efforts, the mechanism underlying TCR ligand discrimination remains a major unanswered question in immunology. Here we show that a TCR discriminates between closely related peptides by forming TCR-pMHC bonds with different lengths, which precisely control the accessibility of CD3{zeta} immunoreceptor tyrosine-based activation motifs (ITAMs) for phosphorylation. Using in situ fluorescence resonance energy transfer (FRET)3,5, we measured the intermolecular length of single TCR-pMHC bonds and the intramolecular distance of individual TCR-CD3{zeta} complexes at the membrane of live primary T cells. We found that an agonist forms a short TCR-pMHC bond to pull the otherwise sequestered CD3{zeta} off the inner leaflet of the plasma membrane, leading to full exposure of its ITAMs for strong phosphorylation. By contrast, a structurally similar weaker peptide forms a longer bond with the TCR, resulting in partial dissociation of CD3{zeta} from the membrane and weak phosphorylation. Furthermore, we found that TCR-pMHC bond length determines 2D TCR binding kinetics and affinity, T-cell calcium signaling and T-cell proliferation, governing the entire process of signal reception, transduction and regulation. Thus, our data reveal the fundamental mechanism by which a TCR deciphers the structural differences between foreign antigens and self-peptides via TCR-pMHC bond length to initiate different TCR signaling for ligand discrimination.

immunology↗

bioRxiv2018

Gut microbiome of an unindustrialized population have characteristic enrichment of SNPs in species and functions with the succession of seasons

Most studies investigating human gut microbiome dynamics are conducted in modern populations. However, unindustrialized populations are arguably better subjects in answering human-gut microbiome coevolution questions due to their lower exposure to antibiotics and higher dependence on natural resources. Hadza hunter-gatherers in Tanzania have been found to exhibit high biodiversity and seasonal patterns in their gut microbiome composition at family level, where some taxa disappear in one season and reappear at later time. However, such seasonal changes have previously been profiled only according to species abundances, with genome-level variant dynamics unexplored. As a result it is still elusive how microbial communities change at the genome-level under environmental pressures caused by seasonal changes. Here, a strain-level SNP analysis of Hadza gut metagenome is performed for 40 Hadza fecal samples collected in three seasons. First, we benchmarked three SNP calling tools based on simulated sequencing reads, and selected VarScan2 that has highest accuracy and sensitivity after a filtering step. Second, we applied VarScan2 on Hadza gut microbiome, with results showing that: with more SNP presented in wet season in general, eight prevalent species have significant SNP enrichments in wet season of which only three species have relatively high abundances. This indicates that SNP characteristics are independent of species abundances, and provides us a unique lens towards microbial community dynamics. Finally, we identify 83 genes with the most characteristic SNP distributions between wet season and dry season. Many of these genes are from Ruminococcus obeum, and mainly from metabolic pathways like carbon metabolism, pyruvate metabolism and glycolysis, as shown by KEGG annotation. This implies that the seasonal changes might indirectly impact the mutational patterns for specific species and functions for gut microbiome of an unindustrialized population, indicating the role of these variants in their adaptation to the changing environment and diets.\n\nImportanceBy analyzing the changes of SNP enrichments in different seasons, we have found that SNP characteristics are independent of species abundances, and could provide us a unique lens towards microbial community dynamics at the genomic level. Many of the genes in microbiome also presented characteristic SNP distributions between wet season and dry season, indicating the role of variants in specific species in their adaptation to the changing environment for an unindustrialized population.

microbiology↗

bioRxiv2018

Automated literature mining and hypothesis generation through a network of Medical Subject Headings

The scientific literature is vast, growing, and increasingly specialized, making it difficult to connect disparate observations across subfields. To address this problem, we sought to develop automated hypothesis generation by networking at scale the MeSH terms curated by the National Library of Medicine. The result is a Mesh Term Objective Reasoning (MeTeOR) approach that tallies associations among genes, drugs and diseases from PubMed and predicts new ones.\n\nComparisons to reference databases and algorithms show MeTeOR tends to be more reliable. We also show that many predictions based on the literature prior to 2014 were published subsequently. In a practical application, we validated experimentally a surprising new association found by MeTeOR between novel Epidermal Growth Factor Receptor (EGFR) associations and CDK2. We conclude that MeTeOR generates useful hypotheses from the literature (http://meteor.lichtargelab.org/).\n\nAUTHOR SUMMARYThe large size and exponential expansion of the scientific literature forms a bottleneck to accessing and understanding published findings. Manual curation and Natural Language Processing (NLP) aim to address this bottleneck by summarizing and disseminating the knowledge within articles as key relationships (e.g. TP53 relates to Cancer). However, these methods compromise on either coverage or accuracy, respectively. To mitigate this compromise, we proposed using manually-assigned keywords (MeSH terms) to extract relationships from the publications and demonstrated a comparable coverage but higher accuracy than current NLP methods. Furthermore, we combined the extracted knowledge with semi-supervised machine learning to create hypotheses to guide future work and discovered a direct interaction between two important cancer genes.

bioinformatics↗

bioRxiv2018

Task-Related EEG Source Localization via Graph Regularized Low-Rank Representation Model

To infer brain source activation patterns under different cognitive tasks is an integral step to understand how our brain works. Traditional electroencephalogram (EEG) Source Imaging (ESI) methods usually do not distinguish task-related and spurious non-task-related sources that jointly generate EEG signals, which inevitably yield misleading reconstructed activation patterns. In this research, we argue that the task-related source signal intrinsically has a low-rank property, which is exploited to to infer the true task-related EEG sources location. Although the true task-related source signal is sparse and low-rank, the contribution of spurious sources scattering over the source space with intermittent activation patterns makes the actual source space lose the low-rank property. To reconstruct a low-rank true source, we propose a novel ESI model that involves a spatial low-rank representation and a temporal Laplacian graph regularization, the latter of which guarantees the temporal smoothness of the source signal and eliminate the spurious ones. To solve the proposed model, an augmented Lagrangian objective function is formulated and an algorithm in the framework of alternating direction method of multipliers is proposed. Numerical results illustrate the effectiveness of the proposed method in terms of reconstruction accuracy with high effciency.

bioinformatics↗

bioRxiv2017

Single Molecule Sequencing of Cell-free DNA from Maternal Plasma for Noninvasive Trisomy Detection

The demand of non-invasive prenatal testing for autosomal aneuploidy using cell-free fetal DNA (cffDNA) in maternal plasma is a highly sought-after diagnostic, with a rapidly growing market. Current approaches developed by next generation sequencing (NGS) need PCR amplifcation during sample preparation, which results in amplification bias in GC-rich areas of the human genome. With these approaches, the minimum fetal fraction in maternal plasma is 4% for the small differences in circulating cfDNA between trisomic and disomic pregnancies to be detectable. In this paper, we performed single molecule sequencing of cell-free DNA from maternal plasma for noninvasive trisomy 13, 18 and 21 detections using the GenoCare platform. We found that single molecule sequencing is sensitive enough to detect these chromosome abnormalities when the fetal DNA fraction is as low as 2%. Compared to the Hiseq2500 platform, no significant GC bias was observed. The improved sensitivity and unbiased GC readout make GenoCare a promising platform for autosomal aneuploidy detections, even in the very early stage of pregnancy.

genomics↗

bioRxiv2017

NFATc2 enhances tumor-initiating phenotypes through the NFATc2/SOX2/ALDH axis in lung adenocarcinoma

Cancers display intratumoral genetic and molecular heterogeneity with tumor initiating cells (TIC) showing enhanced tumor phenotypes. In this study, we show the calcium pathway transcription factor NFATc2 is a novel regulator of lung TIC through the NFATc2/SOX2/ALDH1A1 regulatory axis. In vitro and in vivo cancer cell modeling demonstrated supportive evidences including cell renewal, tumorigenicity at limiting dose, cell motility, resistance to cytotoxic chemotherapy and EGFR targeted therapy. In human lung cancers, high NFATc2 expression predicts poor tumor differentiation, adverse recurrence-free and overall patient survivals. Mechanistic investigations identified NFATc2 response elements in the SOX2 3 enhancer region, and NFATc2/SOX2 coupling upregulates ALDH1A1 by binding to its 5 enhancer. Through this axis, oxidative stresses and reactive oxygen species induced by cancer drug treatment are attenuated, accounting for a mutation-independent mechanism of drug resistance. Targeting this axis provides a novel approach for the long term treatment of lung cancer through TIC elimination.

cancer biology↗

bioRxiv2017

Deficiency of Voltage-gated Proton Channel Hv1 Leads to hypoinsulinaemia, hyperglycemia and glucose intolerance in mice

Here, we demonstrate that the voltage-gated proton channel Hv1 represents a regulatory mechanism for insulin secretion of pancreatic islet {beta} cell. In vivo, Hv1-de[fi]cient mice display hyperglycemia and glucose intolerance due to reduced insulin secretion, but normal peripheral insulin sensitivity. In vitro, islets of Hv1-de[fi]cient and heterozygous mice, INS-1 (832/13) cells with siRNA-mediated knockdown of Hv1 exhibit a marked defect in glucose- and K+-induced insulin secretion. Hv1 de[fi]ciency decreases both insulin and proinsulin contents, and limits glucose-induced Ca2+ entry and membrane depolarization. Furthermore, loss of Hv1 increases insulin-containing granular pH and decreases cytosolic pH. In addition, histologic studies show a decrease in {beta} cell mass in islets of Hv1-deficient mice. Collectively, our results indicate that Hv1 supports insulin secretion in the {beta} cell by calcium entry, membrane depolarization and intracellular pH regulation.

cell biology↗