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Nie, P.

Publications and source records attributed to Nie, P..

9 recordsLinked to original sources

Mechanistic insights into coronavirus inhibition by a distinct base-modified adenosine analog targeting the viral polymerase

Nucleoside analog (NA) inhibitors of viral polymerases are a chemically diverse and prominent class of antivirals, with most agents bearing sugar modifications to induce chain termination. Compounds with a modified base yet natural ribose are comparatively rare and typically incorporated to act as virus mutagens, raising significant safety concerns. How nucleobase modifications interfere with polymerization and the molecular basis of this effect remain poorly understood. We here define the mechanism of 6-ethyl-modified 7-deazaadenosine (EDA), a recently identified coronavirus (CoV) inhibitor that does not induce virus mutagenesis. Its strong activity spans across multiple CoVs and cell models and is dictated by the 6-ethyl substituent, with poor tolerance for smaller, larger or heteroatom-containing substituents. At the level of SARS-CoV-2 RNA-dependent RNA polymerase (RdRp), EDA-triphosphate incorporates as a mimic of ATP but not other NTPs, inducing immediate stalling but also excision by the viral 3'-to-5' exonuclease. While EDA incorporation prevents extension with a second EDA residue, the stalling of natural NTP incorporation is transient and still allows the formation of longer products. A similar phenomenon is observed with template-embedded EDA. Cryo-EM analysis with SARS-CoV-2 RdRp revealed that 3'-terminal EDA is accommodated without distortion of the template:primer duplex, but its 6-ethyl group induces rearrangements in the NTP binding pocket that prevent stable binding of the incoming nucleotide. The mechanistic findings were validated through antiviral studies with SARS-CoV-2 mutants bearing core substitutions in the RdRp or exonuclease. Together, these findings position EDA as a mechanistically distinct base-modified NA prototype that has relevance beyond coronaviruses and underscores the ever-expanding versatility of NA-based polymerase inhibitors.

microbiology↗

DDTRN: Predicting Bacterial Transcriptional Regulatory Networks Based on Gene Sequences using Dual Descriptor

Accurate computational reconstruction of bacterial transcriptional regulatory network (TRN) from sequence information alone remains a fundamental challenge in systems biology, particularly for non-model organisms lacking extensive transcriptomic data. We present DDTRN, a sequence-driven framework that formulates TRN inference as a binary classification task over concatenated regulator-target gene sequence pairs and employs a Dual Descriptor (DD) model to predict regulatory interactions. The DD architecture represents a sequence into two learnable components: Composition Weight Map (CWM) and Position Weight Function (PWF). We comprehensively evaluate DDTRN against six conventional machine learning baselines across eight benchmark bacterial datasets, including E. coli (DREAM5, RegulonDB), B. subtilis, S. enterica, C. glutamicum, M. tuberculosis, P. aeruginosa, and S. coelicolor. DDTRN achieves superior overall performance, attaining average AUROC and AUPR scores of 0.869 and 0.868, respectively, with particularly pronounced advantages at lower descriptor ranks where positional weighting compensates for limited sequence context. Systematic sensitivity analyses of rank, embedding dimension, and basis function count reveal stable optimal operating regimes, while subsampling experiments demonstrate strong robustness even with limited training data. Interpretability analyses show that PWF learns distinct periodic contributions across different rank granularities and that CWM preferentially weights meaningful k-mers. A case study on E. coli dataset further illustrates that DDTRN identifies method-specific candidate targets complementary to those proposed by conventional approaches. By operating solely on genomic sequence, DDTRN provides a scalable, interpretable, and data-efficient framework for bacterial TRN inference in species where expression data are scarce, and it establishes a foundation for future multimodal integration with condition-specific regulatory information.

bioinformatics↗

Cell types associated with human brain functional connectomes and their implications in psychiatric diseases

Cell types are fundamental to the functional organization of the human brain, yet the specific cell clusters contributing to functional connectomes remain unclear. Using human whole-brain single-cell RNA sequencing data, we investigated the relationship between cortical cell cluster distribution and functional connectomes. Our analysis identified dozens of cell clusters significantly associated with resting-state network connectivity, with excitatory neurons predominantly driving positive correlations and inhibitory neurons driving negative correlations. Many of these cell clusters are also conserved in macaques. Notably, functional network connectivity is predicted by cellular communication among these clusters. We further identified cell clusters linked to various neuropsychiatric disorders, with several clusters implicated in multiple conditions. Comparative analysis of schizophrenia and autism spectrum disorder revealed distinct expression patterns, highlighting disease-specific cellular mechanisms. These findings underscore the critical role of specific cell clusters in shaping functional connectomes and their implications for neuropsychiatric diseases.

neuroscience↗

Organizational principles of the primate cerebral cortex at the single-cell level

The primate cerebral cortex, the major organ for cognition, consists of an immense number of neurons. However, the organizational principles governing these neurons remain unclear. By accessing the single-cell spatial transcriptome of over 25 million neuron cells across the entire macaque cortex, we discovered that the distribution of neurons within cortical layers is highly non-random. Strikingly, over three-quarters of these neurons are located in distinct neuronal clusters. Within these clusters, different cell types tend to collaborate rather than function independently. Typically, excitatory neuron clusters mainly consist of excitatory-excitatory combinations, while inhibitory clusters primarily contain excitatory-inhibitory combinations. Both cluster types have roughly equal numbers of neurons in each layer. Importantly, most excitatory and inhibitory neuron clusters form spatial partnerships, indicating a balanced local neuronal network and correlating with specific functional regions. These findings suggest that different brain regions of the primate cortex may exhibit similar mechanisms at the neuronal population level.

neuroscience↗

Feeding disruptions lead to a significant increase in disease modules in adult mice

Feeding disruption is intimately associated with numerous diseases, yet its molecular mechanisms remain to be elucidated in circadian biology. We discovered that in the liver of adult mice, feeding disruption can lead to a substantial increase in disease modules in the transcriptome. This increase in disease modules occurs far earlier than the individuals aging and disease onset. In contrast, calorie restriction significantly reduces the formation of disease modules. This offers a critical missing link between feeding disruption and the molecular systems mechanisms of disease.

neuroscience↗

Noninvasive cancer detection by extracting and integrating multi-modal data from whole-methylome sequencing of plasma cell-free DNA

AbstractPlasma cell-free DNA (cfDNA) methylation and fragmentation signatures have been shown to be valid biomarkers for blood-based cancer detection. However, conventional methylation sequencing assays are inapplicable for fragmentomic profiling due to bisulfite-induced DNA damage. Here using enzymatic conversion-based low-pass whole-methylome sequencing (WMS), we developed a novel approach to comprehensively interrogate the genome-wide plasma methylation, fragmentation, and copy number profiles for sensitive and noninvasive multi-cancer detection. With plasma WMS data from a clinical cohort comprising 497 healthy controls and 780 patients with both early- and advanced-stage cancers of the breast, colorectum, esophagus, stomach, liver, lung, or pancreas, genomic features including methylation, fragmentation size, copy number alteration, and fragment end motif were extracted individually and subsequently integrated to develop an ensemble cancer classifier, called THEMIS, using machine learning algorithms. THEMIS outperformed individual biomarkers for differentiating cancer patients of all seven types from healthy individuals and achieved a combined area under the curve value of 0.971 in the independent test cohort, translating to a sensitivity of 86% and early-stage (I and II) sensitivity of 77% at 99% specificity. In addition, we built a cancer signal origin classifier with true-positive cancer samples at 100% specificity based on methylation and fragmentation profiling of tissue-specific accessible regulatory elements, which localized cancer-like signal to a limited number of clinically informative sites with 66% accuracy. Overall, this proof-of-concept work demonstrates the feasibility of extracting and integrating multi-modal biomarkers from a single WMS run for noninvasive detection and localization of common cancers across stages.

cancer biology↗

Gene networks under circadian control exhibit diurnal organization in primate organs

Mammalian organs are individually controlled by autonomous circadian clocks. At the molecular level, this process is defined by the cyclical co-expression of both core transcription factors and off-target genes across time. While interactions between these molecular clocks are likely necessary for proper homeostasis, these features remain undefined. Here, we utilize integrative analysis of a baboon diurnal transcriptome atlas to characterize the properties of gene networks under circadian control. We found that 53.4% (8,120) of baboon genes are rhythmically expressed body-wide. In addition, >30% of gene-gene interactions exhibit periodic co-expression patterns, with core circadian genes more cyclically co-expressed than others. Moreover, two basic network modes were observed at the systems level: daytime and nighttime mode. Daytime networks were enriched for genes involved in metabolism, while nighttime networks were enriched for genes associated with growth and cellular signaling. A substantial number of diseases only form significant disease modules at either daytime or nighttime. In addition, we found that 216 of 313 genes encoding products that interact with SARS-CoV-2 are rhythmically expressed throughout the body. Importantly, more than 80% of SARS-CoV-2 related genes enriched modules are rhythmically expressed, and have significant network proximities with circadian regulators. Our data suggest that synchronization amongst circadian gene networks is necessary for proper homeostatic functions and circadian regulators have close interactions with SARS-CoV-2 infection.

systems biology↗

IDEA: a web server for Interactive Differential Expression Analysis with R Packages

Differential expression (DE) analysis is a fundamental task in the downstream analysis of the next-generation sequencing (NGS) data. Up to now, a number of R packages have been developed for detecting differentially expressed genes. Although R language has an interaction-oriented programming design, for many biology researchers, a lack of basic programming skills has greatly hindered the application of these R packages. To address this issue, we developed the Interactive Differential Expression Analyzer (IDEA), a Shiny-based web application integrating the differential expression analysis related R packages into a graphical user interface (GUI), allowing users to run the analysis without writing any new code. A wide variety of charts and tables are generated to facilitate the interpretation of the results. In addition, IDEA also provides a combined analysis framework which helps to reconcile any discrepancy from different computational methods. As a public data analysis server, IDAE is implemented in HTML, CSS and JavaScript, and is freely available at http://idea.renlab.org.

bioinformatics↗