Biology subjects

Ji, S.

Publications and source records attributed to Ji, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

A PAM-CNN recognition approach to Muscle Fatigue of Special Operators by sEMG signals

Fatigue operation was the primary accident causes of large special equipment which could lead to serious economic loss and personnel injury or death, so the establishment of early fatigue warning methods was of great significance to reduce the accident rate. In view of the working environment and biomechanics properties of operators of special equipment, theoretical and experimental method was presented to analyze the fatigue mechanism and accumulate data samples, and then a new approach was proposed to recognize muscle fatigue by classifying these datasets. Firstly, in order to deduce fatigue mechanism, hidden Markov chain was adopted to build five-element theoretical model for studying interaction between machines and operators muscles during operation process, so that influence factors and tested points of muscles were approached; Surface electromyographic (sEMG) sensors were introduced to acquire muscle signals which were taken as sample data, and wavelet packet was adopted to extract features of these data. Subsequently, a convolutional neural network (CNN) classification model based on partial attention mechanism (PAM) was proposed to execute triple fatigue classification, moreover cross entropy-Adam algorithm was applied to train and optimize this model. Finally, large crane operators were taken as case studies to verify the proposed model. By comparison results indicated when the number of extracted features was 3 and the size of patch was [5,5], PAM-CNN model could approach the optimum solution of fatigue recognition, and the average accuracy could achieve 95.6% in test datasets, which showed a good potential in practical application.

bioengineering↗

bioRxiv2025

Molecular insights into early malignant transition of hepatocellular carcinoma

The molecular continuum of premalignant-to-malignant transition remains elusive in carcinogenesis, primarily due to the difficulty in sampling transitional lesions. Here, we present comprehensive genomic and immunological profiling of 21 very early hepatocellular carcinomas (veHCCs) arising within 17 cancer-prone DNs. Surprisingly, 82% of cancer-prone DNs harbored TERT alterations, suggesting a predisposing rather than causative role in malignancy transition. A substantial increase in CNA burden, rather than SNV, was noted, indicating a role of chromosome instability in malignancy transition. Additionally, veHCC-originating DNs showed immune inactivity, not falling into the prevailing paradigm that HCCs develop in a context of chronic inflammation. 43% of veHCCs showed an inflamed phenotype with relatively mild CNA burden but already exhibited immune evasion features. Two major evolutionary scenarios were thus proposed: 1) CNA-dominant progression and 2) early immune evasion of those with mild CNA burden. Collectively, our findings illustrate previously unexplored molecular paradigms in HCC initiation, highlighting the therapeutic potential of immunotherapy for early intervention.

cancer biology↗

bioRxiv2025

Dynamic Monomer-Dimer Transition in Ligand-induced Apelin Receptor Activation

G-protein-coupled receptors (GPCRs) are significant signal transducers that exist as monomers and in multiple oligomeric forms. However, molecular mechanism driving their dynamic interconversion to regulate intricate signaling in class A GPCRs remains elusive, compounding our understanding of their related pathophysiological functions. Here, we present a set of 12 assemblies of the apelin receptor (APLNR), including dimeric apo state, agonistic small molecule- or nanobody-bound state of monomeric and dimeric APLNR with and without G-proteins, providing a detailed dynamic view of the monomer-dimer transition. High-resolution cryo-EM structures reveal that different ligands induce varying degrees of pre-dissociation of dimers in the absence of G-protein, with G-protein coupling facilitating the transition from dimeric to monomeric receptor. Functional studies further highlight the critical role of cholesterol clusters in stabilizing the APLNR dimers. These insights enhance our understanding of the dynamic regulation of class A GPCRs across different aggregated forms and advance the rational drug design strategies aimed at selectively modulating of APLNR signaling.

molecular biology↗

bioRxiv2025

The N-terminal order-disorder transition is a critical determinant for a metamorphosis of IscU

IscU, a key scaffold protein mediating the biogenesis of iron-sulfur (Fe-S) clusters, exhibits metamorphic characteristics crucial for its versatile and efficient function. Previous studies have demonstrated that IscU has two interconverting conformations: the structured state (S-state) and the disordered state (D-state), each contributing to its distinct functionality and interaction network. Despite its physiological importance, the precise mechanism underpinning the maintenance of IscUs unique structural heterogeneity has remained elusive. In this study, we used computational and experimental approaches to reveal that the N-terminal order-disorder plays a critical role in the metamorphic modulation of IscU. We found that the N-terminal region displays greater structural plasticity, which is linked to other regions of IscU through coevolutionary relationships. Moreover, we demonstrated that the degree of orderliness in the N-terminal region correlates positively with the stabilization of IscUs S-state and negatively with its affinity for HscA. This indicates that the flexibility in the N-terminal region is finely tuned to optimize IscUs physiological efficiency and efficacy. Finally, our data suggest that a peptide mimicking the N-terminal motif can modulate IscUs metamorphic properties, suggesting a novel therapeutic potential for related pathogenic processes.

biochemistry↗

bioRxiv2024

Efficacy and mechanism of actions of cipargamin as an antibabesial drug candidate

Babesiosis is a disease brought on by intraerythrocytic parasites of the genus Babesia. Current chemotherapies are accompanied by side effects and parasite relapse. Therefore, it is crucial to develop highly effective drugs against Babesia. Cipargamin (CIP) has shown inhibition against apicomplexan parasites, mainly Plasmodium and Toxoplasma. This study evaluated the growth-inhibiting properties of CIP against Babesia spp. and investigated the mechanism of CIP on B. gibsoni. The half inhibitory concentration (IC50) values of CIP against the in vitro growth of B. bovis and B. gibsoni were 20.2 {+/-} 1.4 nM and 69.4 {+/-} 2.2 nM, respectively. CIP significantly inhibited the growth of B. microti and B. rodhaini in vivo. Resistance was conferred by L921V and L921I mutations in BgATP4, which reduced the sensitivity to CIP by 6.1- and 12.8-fold. The inhibitory potency of CIP against BgATP4-associated ATPase activity was moderately reduced in mutant strains, with a 1.3-fold and 2.4-fold decrease in BgATP4L921V and BgATP4L921I, respectively, compared to that of BgATP4WT. An in silico investigation revealed reductions in affinity for CIP binding to BgATP4L921V and BgATP4L921I compared to BgATP4WT. Resistant strains showed no significant cross-resistance to atovaquone (ATO) or tafenoquine succinate (TQ), with less than a onefold change in IC50 values. Combining CIP with TQ effectively eliminated B. microti infection in SCID mice with no relapse, and parasite DNA was not detected by qPCR within 90 days post-infection. Our findings reveal the efficacy of CIP as an anti-babesial agent, its limitations as a monotherapy due to resistance development, and the potential of combination therapy with TQ to overcome said resistance and achieve complete parasite clearance.

microbiology↗

bioRxiv2024

Pan-cancer subclonal mutation analysis of 7,827 tumors predicts clinical outcome

Intra-tumor heterogeneity is characterized by a diverse population of tumor clones and subclones which are important drivers of tumor evolution and therapeutic response. However, accurate subclonal reconstruction at scale remains challenging. We developed a machine learning tool, CliPP, and surveyed 9,972 tumors from 32 cancer types. We found that high subclonal mutation load (sML), the fraction of subclonal single nucleotide variants (SNVs) to all SNVs in the coding region, was prognostic of survival (progression free survival or overall survival) in 18 cancer types. In 14 cancers with low to moderate tumor mutation burden (TMB), high sML was associated with better prognosis. In immunotherapy trials for 42 metastatic prostate cancer (mCRPC), high sML was predictive of favorable response to ipilimumab and associated with increased CD8+ T-cell infiltration and decreased macrophage population. A validation using 613 whole-genomes of esophageal adenocarcinoma confirms the favorable effect of high sML and the observed tumor-associated macrophage. Our study identifies sML as a key feature of cancer, suggesting a biphasic relationship between evolutionary dynamics and differential immune environments. Finally, sML may serve as an orthogonal approach to identify likely responders of immune checkpoint blockade in low to moderate TMB tumors.

genomics↗

bioRxiv2024

GABA production induced by imipridones is a targetable and imageable metabolic alteration in diffuse midline gliomas

Diffuse midline gliomas (DMGs) are lethal primary brain tumors in children. The imipridones ONC201 and ONC206 induce mitochondrial dysfunction and have emerged as promising therapies for DMG patients. However, efficacy as monotherapy is limited, identifying a need for strategies that enhance response. Another hurdle is the lack of biomarkers that report on drug-target engagement at an early timepoint after treatment onset. Here, using 1H-magnetic resonance spectroscopy, which is a non-invasive method of quantifying metabolite pool sizes, we show that accumulation of {psi}-aminobutyric acid (GABA) is an early metabolic biomarker that can be detected within a week of ONC206 treatment, when anatomical alterations are absent, in mice bearing orthotopic xenografts. Mechanistically, imipridones activate the mitochondrial protease ClpP and upregulate the stress-responsive transcription factor ATF4. ATF4, in turn, upregulates glutamate decarboxylase, which synthesizes GABA, and downregulates ABAT, which degrades GABA, leading to GABA accumulation in DMG cells and tumors. Functionally, GABA secreted by imipridone-treated cells acts in an autocrine manner via the GABAB receptor to induce expression of superoxide dismutase (SOD1), which mitigates imipridone-induced oxidative stress and, thereby, curbs apoptosis. Importantly, blocking autocrine GABA signaling using the clinical stage GABAB receptor antagonist SGS-742 exacerbates oxidative stress and synergistically induces apoptosis in combination with imipridones in DMG cells and orthotopic tumor xenografts. Collectively, we identify GABA as a unique metabolic adaptation to imipridones that can be leveraged for non-invasive assessment of drug-target engagement and therapy. Clinical translation of our studies has the potential to enable precision metabolic therapy and imaging for DMG patients. One Sentence SummaryImipridones induce GABA accumulation in diffuse midline gliomas, an effect that can be leveraged for therapy and non-invasive imaging.

cancer biology↗

bioRxiv2023

Identification of a Novel Ferroptosis Inducer with Dual Modulatory Effects on GPX4 Activity and Stability

Ferroptosis is a unique form of intracellular iron-dependent cell death that differs from apoptosis, necrosis, and autophagy. GPX4, an antioxidant defense enzyme, plays a pivotal role as regulator of ferroptosis. Extensive researches suggest that targeting GPX4 holds promise for cancer therapy. However, the current GPX4 inhibitors face challenges due to unfavorable drug-like properties, which hinder their progress in clinical development. In this study, we identified a novel inhibitor called MI-2, demonstrating potent ferroptosis-inducing capacity. Mechanistically, MI-2 effectively inhibits the activity of GPX4 by direct interaction. Furthermore, MI-2 promotes the degradation of GPX4 through its well-established target, MALT1. In multiple cancer models, MI-2 has demonstrated synergistic effects when combined with sorafenib or regorafenib, resulting in enhanced ferroptosis induction. These findings highlight the dual modulatory effects of MI-2 on GPX4 activity and stability, offering a promising starting point for the development of drug-like GPX4 inhibitors with translational potential.

cell biology↗

bioRxiv2023

The DeMixSC deconvolution framework uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we introduce an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using the better-matched, i.e., benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using a benchmark dataset of healthy retinas suggest much-improved deconvolution accuracy. Further analysis of a cohort of 453 patients with age-related macular degeneration supports the broad applicability of DeMixSC. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched dataset to resolve this challenge. The developed DeMixSC framework is generally applicable for deconvolving large cohorts of disease tissues, and potentially cancer.

bioinformatics↗

bioRxiv2023

Accelerated somatic mutation calling for whole-genome and whole-exome sequencing data from heterogenous tumor samples

Accurate detection of somatic mutations in DNA sequencing data is a fundamental prerequisite for cancer research. Previous analytical challenge was overcome by consensus mutation calling from four to five popular callers. This, however, increases the already nontrivial computing time from individual callers. Here, we launch MuSE2.0, powered by multi-step parallelization and efficient memory allocation, to resolve the computing time bottleneck. MuSE2.0 speeds up 50 times than MuSE1.0 and 8-80 times than other popular callers. Our benchmark study suggests combining MuSE2.0 and the recently expedited Strelka2 can achieve high efficiency and accuracy in analyzing large cancer genomic datasets.

bioinformatics↗

bioRxiv2023

Insight into the genetic diversity, resistance, and virulence of Listeria from the marine environment: reveal the risk of hypervirulent isolates

Listeria monocytogenes is a major human foodborne pathogen and a ubiquitous environmental saprophyte. In this study, we investigated the prevalence and characteristics of Listeria spp. from beach sand in the coastal environment. Three different Listeria spp., Listeria monocytogenes (n=16), Listeria fleishmanii (n=7), and Listeria aquatica (n=3) were isolated from 769 beach sand samples and demonstrated diverse biofilm forming capacity. The L. monocytogenes isolates belonged to four distinct sequence types (ST87, ST121, ST35, and ST85) and contained the majority of virulence genes, some isolates were hypervirulent clones or had close phylogenetic relatedness with clinical cases. The ST87 isolates showed higher ability of biofilm formation in seawater than other STs strains. As a reservoir of microbes from marine environments and human/animal excrement, coastal sand would play an important role in the spread of L. monocytogenes and is an environmental risk for human listeriosis.

microbiology↗

bioRxiv2023

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19%-46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

bioinformatics↗

bioRxiv2022

Instar determination by constrained gaussian mixture models according to Dyar's rule

Despite its importance in ecological studies and pest controls, the lack of knowledge of the life cycle and the ambiguity of data challenge the accurate determination of insect nymphs regarding many insect species. Finite mixture models are often utilized to classify instars without knowing the instar number. This study derives parsimonious gaussian mixture models using parameter constraints motivated by Dyars rule. Dyars rule explains the growth pattern of larvae and nymphs of insects by assuming a constant ratio of head capsule width for every two adjacent development stages. Accordingly, every mean value of log-transformed data in each instar stage is considered a linear function, where two Dyar constants are an intercept and a slope for the instar stages, respectively, to infer the instar stage of samples. The common variance for every instar stage regarding log-transformed data can be assumed in a mixture model, as well. If valid, these assumptions will allow an efficient estimation of the model by reducing free parameters. As a result, four model hypotheses are proposed for each assumption of instar counts depending on whether these two parameter constraints are applied. After model estimation, the proposed method uses the ICL criterion to choose the optimal counts of nymphal stages, and parametric bootstrap LR tests are applied to decide the most efficient model regarding parameter constraints. The proposed method could attain the correct model settings during the simulation study. This study also discusses the interpretation of the results of real insect data sets that concord with Dyars rule or not.

ecology↗

bioRxiv2022

Quartet protein reference materials and datasets for multi-platform assessment of label-free proteomics

Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories. We developed the Quartet as a proteome reference material with built-in truths, and distributed the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 MS files were obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There was a wide dynamic range of proteomes spanning ~7 orders of magnitude (101-108 copies/cell), and the injection order had marked effects on quantitative instead of qualitative. Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.

molecular biology↗

bioRxiv2022

Development and application of an uncapped mRNA platform

A novel uncapped mRNA platform was developed. Five lipid nanoparticle (LNP)-encapsulated mRNA constructs were made to evaluate several aspects of our platform, including transfection efficiency and durability in vitro and in vivo and the activation of humoral and cellular immunity in several animal models. The constructs were eGFP-mRNA-LNP (for enhanced green fluorescence mRNA), Fluc-mRNA-LNP (for firefly luciferase mRNA), S{delta}T-mRNA-LNP (for Delta strain SARS-CoV-2 spike protein trimer mRNA), gDED-mRNA-LNP (for truncated glycoprotein D mRNA coding ectodomain from herpes simplex virus type 2 (HSV2)) and gDFR-mRNA-LNP (for truncated HSV2 glycoprotein D mRNA coding amino acids 1[~]400). Quantifiable target protein expression was achieved in vitro and in vivo with eGFP-and Fluc-mRNA-LNP. S{delta}T-mRNA-LNP, gDED-mRNA-LNP and gDFR-mRNA-LNP induced both humoral and cellular immune responses comparable to those obtained by previously reported capped mRNA-LNP constructs. Notably, S{delta}T-mRNA-LNP elicited neutralizing antibodies in hamsters against the Omicron and Delta strains. Additionally, gDED-mRNA-LNP and gDFR-mRNA-LNP induced potent neutralizing antibodies in rabbits and mice. The mRNA constructs with uridine triphosphate (UTP) outperformed those with N1-methylpseudouridine triphosphate (N1m{psi}TP) in the induction of antibodies via S{delta}T-mRNA-LNP. Our uncapped, process-simplified, and economical mRNA platform may have broad utility in vaccines and protein replacement drugs.

bioengineering↗

bioRxiv2022

BigNeuron: A resource to benchmark and predict best-performing algorithms for automated reconstruction of neuronal morphology

BigNeuron is an open community bench-testing platform combining the expertise of neuroscientists and computer scientists toward the goal of setting open standards for accurate and fast automatic neuron reconstruction. The project gathered a diverse set of image volumes across several species representative of the data obtained in most neuroscience laboratories interested in neuron reconstruction. Here we report generated gold standard manual annotations for a selected subset of the available imaging datasets and quantified reconstruction quality for 35 automatic reconstruction algorithms. Together with image quality features, the data were pooled in an interactive web application that allows users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and reconstruction data, and benchmarking of automatic reconstruction algorithms in user-defined data subsets. Our results show that image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. By benchmarking automatic reconstruction algorithms, we observed that diverse algorithms can provide complementary information toward obtaining accurate results and developed a novel algorithm to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms. Finally, to aid users in predicting the most accurate automatic reconstruction results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic reconstructions.

bioinformatics↗

bioRxiv2022

Two Competing Guilds as a Core Microbiome Signature for Chronic Diseases

Summary ParagraphOver eons of co-evolution, the gut microbiota has become an essential organ for humans1,2. However, it is unclear what core members and their ecological organization ensures the stable provision of this organs essential health-relevant functions to the host. With high quality metagenome-assembled genomes as network nodes, here we identified two competing guilds3 of the most stably and highly connected bacteria that together correlate with a wide range of host health conditions. Genomes in these two guilds kept their ecological relationship unchanged despite experiencing profound abundance changes during a 3-month high fiber intervention and 1-year follow-up in patients with type 2 diabetes (T2DM). The genomes of one guild harbored more genes for plant polysaccharide degradation and butyrate production, while the other guild had more genes for virulence or antibiotic resistance. A Random Forest regression model showed that the abundance distributions of these genomes were associated with 41 out of 43 bio-clinical parameters in the study cohort. With these genomes as reference, Random Forest modeling successfully classified case and control of T2DM, atherosclerotic cardiovascular disease, liver cirrhosis, inflammatory bowel diseases, colorectal cancer, ankylosing spondylitis, schizophrenia, and Parkinsons disease in 12 independent metagenomic datasets from 1,816 participants across ethnicity and geography. This core microbiome signature may serve as a common target for health recovery.

microbiology↗

bioRxiv2022

Differential expression of Nanog 3UTR and coding region mRNA components defines an embryonic stem cell niche

Differential expression of mRNA coding sequences (CDSs) and 3untranslated regions (UTRs) is widespread, yet whether these domains contribute independently to cellular function remains unclear. Here, using Nanog as a model, we find that Nanog mRNA domain usage is spatially organized in mouse and human pluripotent cells and in the blastocyst, with cells enriched in 3UTR transcripts marking colony borders and cells enriched for the Nanog CDS located interior. Functional perturbation reveals a marked asymmetry between mRNA domains. Loss of the Nanog 3UTR leads to defects in colony architecture, cell spreading and morphogenetic behavior, accompanied by decreased extracellular matrix modeling gene expression and ROCK dependent cytoskeletal organization. In contrast, deletion of the Nanog CDS primarily disrupts epithelial polarity-associated transcriptional programs and the expression of chromatin regulators, consistent with a dominant role for the Nanog protein in transcriptional and epigenetic control. These domain specific effects are not redundant but instead reflect separable regulatory activities encoded within a single transcript. Together, these findings demonstrate that distinct regions of a single mRNA can encode separable and asymmetric biological functions, revealing mRNA domain usage as a distinct regulatory layer through which genes can encode multiple biological outputs beyond protein coding capacity.

developmental biology↗