Biology subjects

Jiang, X.

Publications and source records attributed to Jiang, X..

At least 217 records · Page 12Linked to original sources

bioRxiv2023

Dehydrozaluzanin C- derivative protects septic mice by alleviating over-activated inflammatory response and promotes the phagocytosis of macrophages

Host-directed therapy (HDT) is a new adjuvant strategy that interfere with host cell factors that are required by a pathogen for replication or persistence. In this study, we assessed the effect of dehydrozaluzanin C-derivative (DHZD), a modified compound from dehydrozaluzanin C (DHZC), as a potential HDT agent for severe infection. LPS-induced septic mouse model and Carbapenem resistant Klebsiella pneumoniae (CRKP) infection mouse model was used for testing in vivo. RAW264.7 cells, mouse primary macrophages, and DCs were used for in vitro experiments. Dexamethasone (DXM) was used as a positive control agent. DHZD ameliorated tissue damage (lung, kidney, and liver) and excessive inflammatory response induced by LPS or CRKP infection in mice. Also, DHZD improved the hypothermic symptoms of acute peritonitis induced by CRKP, inhibited heat-killed CRKP (HK-CRKP)-induced inflammatory response in macrophages, and upregulated the proportions of phagocytic cell types in lungs. In vitro data suggested that DHZD decreases LPS-stimulated expression of IL-6, TNF- and MCP-1 via PI3K/Akt/p70S6K signaling pathway in macrophages. Interestingly, the combined treatment group of DXM and DHZD had a higher survival rate and lower level of IL-6 than those of the DXM-treated group; the combination of DHZD and DXM played a synergistic role in decreasing IL-6 secretion in sera. Moreover, the phagocytic receptor CD36 was increased by DHZD in macrophages, which was accompanied by increased bacterial phagocytosis in a clathrin- and actin-dependent manner. This data suggests that DHZD may be a potential drug candidate for treating bacterial infections.

pathology↗

bioRxiv2023

MiceVAPORDot: A novel automated approach for high-throughput behavioral characterization during E-cigarette exposure in mice

The prosperity of electronic nicotine delivery systems (ENDS) or e-cigarette use has been regarded to lead an increasing risk of nicotine addiction, especially among youth. Understanding and evaluating the behaviors induced by ENDS are fundamental to the study of neuropsychiatric effects of e-cigarettes. However, little is known regarding the behavioral features during e-cigarette exposure in mice. Current behavioral assessments for nicotine addiction are based on nicotine withdrawal-induced anxiety which can be only performed after ENDS exposures. Here we developed MiceVAPORDot, a novel high-throughput tool for automated in situ behavioral characterization during e-cigarette exposure. The integration of a deep learning-based animal pose tracking method by MiceVAPORDot allows precise characterization on behavioral phenotypes of e-vapor exposed mice, which were unable revealed by traditional evaluation methodology such as conditioned place preference and elevated plus maze tests. The behavioral fingerprints recognized by MiceVAPORDot can be used for high-throughput screening on incentive nature of e-cigarette flavors as well as medications for smoking cessation.

animal behavior and cognition↗

bioRxiv2023

TEclass2: Classification of transposable elements using Transformers

MotivationTransposable elements (TEs) are interspersed repetitive sequences that are major constituents of most eukaryotic genomes and are crucial for genome evolution. Despite the existence of multiple tools for their classification and annotation, none of them can achieve completely reliable results making it a challenge for genomic studies. In this work, we introduce TEclass2, a new software that uses a deep learning approach based upon a linear Transformer architecture with a k-mer to-kenizer and further adaptations to handle DNA sequences. This software has an easy configuration that allows training models on new datasets and the classification of TE models providing multiple metrics for a reliable evaluation of the results. ResultsThis work shows a successful adaptation of deep learning with Transformers for the classification of TE models from consensus sequences, and these results lay a foundation for novel methodologies in bioinformatics. We provide a tool for the training of models and the classification of consensus sequences from TE models on custom data and a web page interface with a pre-trained dataset based on curated and non-curated TE libraries allowing a fast and simple classification of TEs. Availabilityhttps://bioinformatics.uni-muenster.de/tools/teclass2/index.pl Contactwojmak@uni-muenster.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2023

Biosynthesis of the azoxy compound azodyrecin from Streptomyces mirabilis P8-A2

Azoxy compounds are a distinctive group of bioactive secondary metabolites, characterized by a unique RN=N+(O-)R moiety. The azoxy moiety is present in various classes of metabolites that exhibit various biological activities. The enzymatic mechanisms underlying azoxy bond formation remain enigmatic. Azodyrecins are cytotoxic azoxy metabolites produced by Streptomyces mirabilis P8-A2. Here we cloned and confirmed the putative azd biosynthetic gene cluster through CATCH cloning followed by expression and production of azodyrecins in two heterologous hosts, S. albidoflavus J1074 and S. coelicolor M1146, respectively. We explored the function of 14 enzymes in azodyrecin biosynthesis through gene knock-out using CRISPR-Cas9 base editing in the native producer, S. mirabilis P8-A2. The key intermediates were analyzed in the mutants through MS/MS fragmentation studies, revealing azoxy bond formation via the conversion of hydrazine to azo compound; followed by further oxygenation. Additionally, N-oxygenase and dehydrogenase activities were confirmed among 8 core biosynthetic genes and five helper genes. Moreover, the distribution of the azoxy biosynthetic gene clusters across Streptomyces spp. genomes is explored, highlighting the presence of these clusters in over 20% of the Streptomyces spp. genomes and revealing that azoxymycin and valanimycin are scarce, while azodyrecin and KA57A like clusters are widely distributed across the phylogenetic tree.

microbiology↗

bioRxiv2023

FedGMMAT: Federated Generalized Linear Mixed Model Association Tests

Increasing genetic and phenotypic data size is critical for understanding the genetic determinants of diseases. Evidently, establishing practical means for collaboration and data sharing among institutions is a fundamental methodological barrier for performing high-powered studies. As the sample sizes become more heterogeneous, complex statistical approaches, such as generalized linear mixed effects models, must be used to correct for confounders that may bias results. On another front, due to the privacy concerns around Protected Health Information (PHI), genetic information is restrictively protected by sharing according to regulations such as Health Insurance Portability and Accountability Act (HIPAA). This limits data sharing among institutions and hampers efforts around executing high-powered collaborative studies. Federated approaches are promising to alleviate the issues around privacy and performance, since sensitive data never leaves the local sites. Motivated by these, we developed FedGMMAT, a federated genetic association testing tool that utilizes a federated statistical testing approach for efficient association tests that can correct for arbitrary fixed and random effects among different collaborating sites. Genetic data is never shared among collaborating sites, and the intermediate statistics are protected by homomorphic encryption. Using simulated and real datasets, we demonstrate FedGMMAT can achieve the virtually same results as pooled analysis under a privacy-preserving framework with practical resource requirements.

bioinformatics↗

bioRxiv2023

Systematic Investigation of Machine Learning on Limited Data: A Study on Predicting Protein-Protein Binding Strength

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigate the impact of data limitations and make significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirmed the significant improvements achieved by our models in predicting protein-protein binding affinity. The source codes for these three models are available at https://github.com/minghuilab/BindPPI.

bioinformatics↗

bioRxiv2023

ProxyTyper: Generation of Proxy Panels for Privacy-aware Outsourcing of Genotype Imputation

One of the major challenges in genomic data sharing is protecting the privacy of participants. Numerous studies demonstrated that genetic data and related summary statistics can be used for identifying individuals. These studies led to a strong chilling effect on researchers that hindered collaborative data sharing. Consequently, population-level genetic databases are often siloed in central repositories with complex and burdensome data usage agreements. While cryptographic methods that are provably secure have been developed, these methods require high-level expertise in security and depend on large computational resources. To fill the methodological gap in this domain, we present ProxyTyper, a set of data protection mechanisms to generate "proxy-panels" from sensitive haplotype-level genetic datasets. ProxyTyper uses haplotype sampling, allele hashing, and anonymization to protect the genetic variant coordinates, genetic maps, and chromosome-wide haplotypes. These mechanisms can provide strong deterrence against honest-but-curious entities and well-known re-identification and linking attacks. The proxy panels can be used as input to existing tools without any modifications to the underlying algorithms. We focus on building proxy-panels for genotype imputation to protect typed and untyped variants. We demonstrate that proxy-based imputation provides protection against well-known attacks with a minor decrease of imputation accuracy for variants in wide range of allele frequencies.

bioinformatics↗

bioRxiv2023

Human disturbance increases spatiotemporal associations among mountain forest terrestrial mammal species

Spatial and temporal associations between sympatric species underpin biotic interactions, structure ecological assemblages, and sustain ecosystem functioning and stability. However, the resilience of interspecific spatiotemporal associations to human activity remains poorly understood, particularly in mountain forests where anthropogenic impacts are often pervasive. Here we applied context-dependent Joint Species Distribution Models to a systematic camera-trap survey dataset from a global biodiversity hotspot in eastern Himalaya to understand how prominent human activities in mountain forests influence species associations within terrestrial mammal communities. We obtained 10,388 independent detections of 17 focal species (12 carnivores and five ungulates) from 322 stations over 43,163 camera days of effort. We identified a higher incidence of positive associations in habitats with higher levels of human modification (87 %) and human presence (83 %) compared to those located in habitats with lower human modification (64 %) and human presence (65 %) levels. We also detected a significant reduction of pairwise encounter time at increasing levels of human disturbance, corresponding to more frequent encounters between pairs of species. Our findings indicate that human activities can push mammals together into more frequent encounters and associations, which likely influences the coexistence and persistence of wildlife, with potential far-ranging ecological consequences.

ecology↗

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

CellPolaris: Decoding Cell Fate through Generalization Transfer Learning of Gene Regulatory Networks

Cell fate changes are determined by gene regulatory network (GRN), a sophisticated system regulating gene expression in precise spatial and temporal patterns. However, existing methods for reconstructing GRNs suffer from inherent limitations, leading to compromised accuracy and application generalizability. In this study, we introduce CellPolaris, a computational system that leverages transfer learning algorithms to generate high-quality, cell-type-specific GRNs. Diverging from conventional GRN inference models, which heavily rely on integrating epigenomic data with transcriptomic information or adopt causal strategies through gene co-expression networks, CellPolaris employs high-confidence GRN sources for model training, relying exclusively on transcriptomic data to generate previously unknown cell-type-specific GRNs. Applications of CellPolaris demonstrate remarkable efficacy in predicting master regulatory factors and simulating in-silico perturbations of transcription factors during cell fate transition, attaining state-of-the-art performance in accurately predicting candidate key factors and outcomes in cell reprogramming and spermatogenesis with validated datasets. It is worth noting that, with a transfer learning framework, CellPolaris can perform GRN based predictions in all cell types even across species. Together, CellPolaris represents a significant advancement in deciphering the mechanisms of cell fate regulation, thereby enhancing the precision and efficiency of cell fate manipulation at high resolution.

bioinformatics↗

bioRxiv2023

A Stapled Peptide Inhibitor of METTL3-METTL14 for Cancer Therapy

METTL3, a primary methyltransferase catalyzing RNA N6-methyladenosine (m6A) modification, has been identified as an oncogene in several cancer types and thus nominated as a potentially effective target for therapeutic inhibition, although current options using this strategy are limited. In this study, we targeted protein-protein interactions at the METTL3-METTL14 binding interface to inhibit complex formation and subsequent catalysis of RNA m6A modification. Among candidate peptides, RM3 exhibited the highest anti-cancer potency, inhibiting METTL3 activity while also facilitating its proteasomal degradation. We then designed a stapled peptide inhibitor (RSM3) with enhanced peptide stability and formation of the -helical secondary structure required for METTL3 interaction. Functional and transcriptomic analysis in vivo indicated that RSM3 induced upregulation of programmed cell death-related genes while inhibiting cancer-promoting signals. Furthermore, tumor growth was significantly suppressed while apoptosis was enhanced upon RSM3 treatment, accompanied by in-creased METTL3 degradation, and reduced global RNA methylation levels in two in vivo tumor models. This peptide inhibitor thus exploits a mechanism distinct from other competitive-binding small molecules to inhibit oncogenic METTL3 activity. Our findings collectively highlight the potential of targeting METTL3 in cancer therapies through peptide-based inhibition of complex formation and proteolytic degradation.

biochemistry↗

bioRxiv2023

The longitudinal single-cell panorama of cynomolgus monkey ovary throughout lifespan revealed a conserved ovarian clock between primates

Ovarian function is critical for female fertility and impacts reproductive longevity. It is of great importance to accurately predict the aging process within the ovary for fertility assessment and disease diagnosis. However, cell metrics for evaluating the ovarian aging rate are still in urgent need, and molecular insights into ovarian development and dysfunction during the primate life course are also limited. Here, we reported large-scale ovarian cell atlas of consecutive development of cynomolgus monkeys across 22 years with 20 time points, covering the foetal, newborn, prepubertal, pubertal, adult, perimenopausal and menopausal stages. We characterized and validated distinct molecular signatures of each cluster of cells within primate ovaries, and uncovered a previously undocumented RHOXF1-positive oocyte type during primordial follicle assembly in primates. Furthermore, the constitution and developmental trajectories of primate germ cells, granulosa cells and stromal / theca cells were also elucidated, and their precursors were identified. More importantly, dynamics of cellular compositions were unravelled through the ovarian development, featured by granulosa, epithelial, stromal, and immune cells that showed strong temporal heterogeneity spanning lifetime, whilst referred to the key function during the corresponding stages. Based on the correlations of each cell type with age and stage-specific molecular dynamics, we further constructed a transcriptomic ovarian clock which could perceive an effective biological age prediction of the ovary and further applied to humans. The findings reveal granulosa, epithelial, and stromal cells as the highest performance predictors of ovarian biological age, while highlighting the crucial role of AGE-RAGE and Relaxin signaling pathways in regulating ovarian aging. Our work not only provide valuable resource for obtaining insights into the development, aging and dysfunction of key organs, but also establish a transcriptomic clock to predict biological ovarian aging thus to be potential clinical implementation in future.

developmental biology↗

bioRxiv2023

Characterization of N-glycosylation and its Functional Role in SIDT1-Mediated RNA Uptake

The mammalian SID-1 transmembrane family members, SIDT1 and SIDT2, are multi-pass transmembrane proteins that mediate the cellular uptake and intracellular trafficking of nucleic acids, playing important roles in the immune response and tumorigenesis. Previous work has suggested that human SIDT1 and SIDT2 are N-glycosylated, but the precise site-specific N-glycosylation information and its functional contribution remain unclear. In this study, we employ high-resolution liquid chromatography tandem mass spectrometry to comprehensively map the N-glycosites and quantify the N-glycosylation profiles of SIDT1 and SIDT2. Further molecular mechanistic probing elucidates the essential role of N-linked glycans in regulating cell surface expression, RNA binding, protein stability, and RNA uptake of SIDT1. Our results provide crucial information about the potential functional impact of N-glycosylation in the regulation of SIDT1-mediated RNA uptake and provide insights into the molecular mechanisms of this promising nucleic acid delivery system, with potential implications for therapeutic applications.

biochemistry↗

bioRxiv2023

Artificial Intelligence Helps to Predict Recurrence and Mortality for Prostate Cancer using Histology Images

Besides grading, deep learning could improve expert consensus to predict prostate cancer (PCa) recurrence. We developed a novel PCa recurrence prediction system based on artificial intelligence (AI). We validated it using multi-institutional and international datasets comprising 2,647 PCa patients with at least a 10-year follow-up. Survival analyses were performed and goodness-of-fit of multivariate models was evaluated using partial likelihood ratio tests, Akaikes test, or Bayesian information criteria to determine the superiority of our system over existing grading systems. Comprehensive survival analyses demonstrated the effectiveness of our AI- system in categorizing PCa into four distinct risk groups. The system was independent and superior to the existing five grade groups for malignancies. A high consensus level was observed among five blinded genitourinary pathology experts in ranking images according to our prediction system. Therefore, AI may help develop an accurate and clinically interpretable PCa recurrence prediction system, facilitating informed decision-making for PCa patients.

pathology↗

bioRxiv2023

Topoisomerase IIα C-terminal Domain Mutations and Catalytic Function

Topoisomerase II is a nuclear enzyme needed for dealing with topological entanglements in the DNA arising from replication and transcription. The N-terminal region and core of the protein are utilized in the catalytic cycle of the enzyme, which generates a transient double-stranded break in one segment of DNA and passes another segment through the break. The C-terminal domain is a large, intrinsically disordered region that appears to be involved in regulating the function of the enzyme both in terms of substrate selection and the level of activity of the enzyme. In a previous study, we explored eleven targeted mutations to the C-terminal domain. This present study explores six of these mutants to determine whether there are any defects in closure of the N-terminal clamp and whether an experimental compound known as a Cu(II)-thiosemicarbazone affects DNA cleavage with the mutants. Based upon our results, the mutants are able to close the N-terminal clamp, but some of the mutants that displayed the least clamp closing activity also had the lowest catalytic activity. Further, Cu-APY-ETSC did impact the ability of the enzymes to cleave DNA to similar levels as seen with the WT enzyme. These results lay the groundwork for additional analyses of the C-terminal domain and indicate the C-terminal domain regions tested did not influence the action of Cu-APY-ETSC except at the level of coordination between the two active sites.

biochemistry↗

bioRxiv2023

Species -Shared and -Unique Gyral Peaks on Human and Macaque Brains

Cortical folding is an important feature of primate brains that plays a crucial role in various cognitive and behavioral processes. Extensive research has revealed both similarities and differences in folding morphology and brain function among primates including macaque and human. The folding morphology is the basis of brain function, making cross-species studies on folding morphology important for understanding brain function and species evolution. However, prior studies on cross-species folding morphology mainly focused on partial regions of the cortex instead of the entire brain. Previously, we defined a whole-brain landmark based on folding morphology: the gyral peak. It was found to exist stably across individuals and ages in both human and macaque brains. In this study, we identified shared and unique gyral peaks in human and macaque, and investigated the similarities and differences in the spatial distribution, anatomical morphology, and functional connectivity of them.

neuroscience↗

bioRxiv2023

Disease-associated gut microbiome and metabolome changes in chronic low back pain patients with bone marrow lesions

Chronic low back pain (LBP) is the leading cause of global disability. Vertebral bone marrow lesions (BMLs), one etiological factor for chronic LBP, are MRI signal changes in the vertebral bone marrow that extend from the disc endplate. The adipogenesis of bone marrow mesenchymal stem cells (BM-MSCs) could explain fatty replacement (FR) in normal bone marrow. FR is the most common type of BMLs. Here we show how the gut microbiome and serum metabolome change and how they interact in LBP patients with or without FR. The serum metabolome of chronic LBP patients with FR is characterized by decreased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has important capability to regulate BCAA degradation pathway. Ruminococcus gnavus, Roseburia hominis and Lachnospiraceae bacterium 8 1 57FAA are identified as the main species driving the association between biosynthesis of BCAAs and BM-MSCs metabolism in LBP with FR individuals. In vitro work demonstrates that BCAAs can induce the adipogenesis of BM-MSCs by activating the SIRT4 pathway. Our findings provide a deep insight into understanding the role of the disturbed gut ecosystem in FR and LBP.

cell biology↗

bioRxiv2023

Source-Free Random Forest Model Calibration for Myoelectric Control

ObjectiveMost existing machine learning models for myoelectric control require a large amount of data to learn user-specific characteristics of the electromyographic (EMG) signals, which is burdensome. Our objective is to develop an approach to enable the calibration of a pre-trained model with minimal data from a new myoelectric user. ApproachWe trained a random forest model with EMG data from 20 people collected during the performance of multiple hand grips. To adapt the decision rules for a new user, first, the branches of the pre-trained decision trees were pruned using the validation data from the new user. Then new decision trees trained merely with data from the new user were appended to the pruned pre-trained model. ResultsReal-time myoelectric experiments with 18 participants over two days demonstrated the improved accuracy of the proposed approach when compared to benchmark user-specific random forest and the linear discriminant analysis models. Furthermore, the random forest model that was calibrated on day one for a new participant yielded significantly higher accuracy on day two, when compared to the benchmark approaches, which reflects the robustness of the proposed approach. SignificanceThe proposed model calibration procedure is completely source-free, that is, once the base model is pre-trained, no access to the source data from the original 20 people is required. Our work promotes the use of efficient, explainable, and simple models for myoelectric control.

neuroscience↗