Biology subjects

Lu, Z. J.

Publications and source records attributed to Lu, Z. J..

8 recordsLinked to original sources

bioRxiv2024

RNA-ligand interaction scoring via data perturbation and augmentation modeling

RNA-targeting drug discovery is undergoing an unprecedented revolution. Despite recent advances in this field, developing data-driven deep learning models remains challenging due to the limited availability of validated RNA-small molecule interactions and the scarcity of known RNA structures. In this context, we introduce RNAsmol, a novel sequence-based deep learning framework that incorporates data perturbation with augmentation, graph-based molecular feature representation and attention-based feature fusion modules to predict RNA-small molecule interactions. RNAsmol employs perturbation strategies to balance the bias between true negative and unknown interaction space thereby elucidating the intrinsic binding patterns between RNA and small molecules. The resulting model demonstrates accurate predictions of the binding between RNA and small molecules, outperforming other methods with average improvements of [~]8% (AUROC) in 10-fold cross-validation, [~]16% (AUROC) in cold evaluation (on unseen datasets), and [~]30% (ranking score) in decoy evaluation. Moreover, we use case studies to validate molecular binding hotspots in the prediction of RNAsmol, proving the models interpretability. In particular, we demonstrate that RNAsmol, without requiring structural input, can generate reliable predictions and be adapted to many RNA-targeting drug design scenarios.

bioinformatics↗

bioRxiv2024

OligoFormer: an accurate and robust prediction method for siRNA design

MotivationRNA interference(RNAi) has become a widely used experimental approach for post-transcriptional regulation and is increasingly showing its potential as future targeted drugs. However, the prediction of highly efficient siRNAs(small interfering RNA) is still hindered by dataset biases, the inadequacy of prediction methods, and the presence of off-target effects. To overcome these limitations, we propose an accurate and robust prediction method, OligoFormer, for siRNA design. ResultsOligoFormer comprises three different modules including thermodynamic calculation, RNA-FM module, and Oligo encoder. Oligo encoder is the core module based on the transformer encoder. Taking siRNA and mRNA sequences as input, OligoFormer can obtain thermodynamic parameters, RNA-FM embedding, and Oligo embedding through these three modules, respectively. We carefully benchmarked OligoFormer against 5 comparable methods on siRNA efficacy datasets. OligoFormer outperforms all the other methods, with an average improvement of 9% in AUC and 10.7% in F1 score in our inter-dataset validation. We also provide a comprehensive pipeline with prediction of siRNA efficacy and off-target effects using PITA score and TargetScan score. The ablation study shows RNA-FM module and thermodynamic parameters improved the performance and accelerated convergence of OligoFormer. The saliency map by gradient backpropagation shows certain base preferences in initial and terminal region of siRNAs.

bioinformatics↗

bioRxiv2023

BATTER: Accurate Prediction of Rho-dependent and Rho-independent Transcription Terminators in Metagenomes

Bacterial transcription termination is a critical yet underexplored mechanism of gene regulation in microbial ecosystems. Existing computational tools, however, primarily focus on predicting transcript 3 ends generated by Rho-independent terminators (RITs) in model species, leaving significant gaps in understanding those generated by Rho-dependent terminators (RDTs), especially in non-model species. To address these limitations, we developed BATTER (BActeria Transcript Three Prime End Recognizer), a comprehensive computational tool for bacterial transcript 3 termini prediction. BATTER builds on the observation that conserved stem-loop structures are frequently associated with 3 ends of primary transcripts generated by both RIT and RDT mechanisms across distantly related bacterial species. BATTER demonstrated its advantage compared to existing tools. It enabled a comprehensive analysis of 42,905 representative bacterial genomes, uncovering that stem-loop structures exhibit clade-specific properties with greater variations between species than between gene families. Notably, BATTER uncovered that certain Cyanobacteria lineages, despite lacking Rho homologs, harbor Rho utilization (RUT) site-like sequences near 3 ends, with preliminary experimental validation in E. coli suggesting their partial functionality in transcription termination. Additionally, BATTER systematically identified pervasive premature termination events in antimicrobial resistance (AMR) genes, highlighting their regulatory roles in translation protection and drug efflux. This study advances our understanding of transcription termination across diverse bacterial lineages and provides a robust computational approach for exploring transcription regulation in complex microbial ecosystems.

bioinformatics↗

bioRxiv2023

Pathformer: biological pathway informed Transformer model integrating multi-modal data of cancer

Multi-omics data provide a comprehensive view of gene regulation at multiple levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer. To integrate various multi-omics data of tissue and liquid biopsies for disease diagnosis and prognosis, we developed a biological pathway informed Transformer, Pathformer. It embeds multi-omics input with a compacted multi-modal vector and a pathway-based sparse neural network. Pathformer also leverages criss-cross attention mechanism to capture the crosstalk between different pathways and modalities. We first benchmarked Pathformer with 18 comparable methods on multiple cancer datasets, where Pathformer outperformed all the other methods, with an average improvement of 6.3%-14.7% in F1 score for cancer survival prediction and 5.1%-12% for cancer stage prediction. Subsequently, for cancer prognosis prediction based on tissue multi-omics data, we used a case study to demonstrate the biological interpretability of Pathformer by identifying key pathways and their biological crosstalk. Then, for cancer early diagnosis based on liquid biopsy data, we used plasma and platelet datasets to demonstrate Pathformers potential of clinical applications in cancer screen. Moreover, we revealed deregulation of interesting pathways (e.g., scavenger receptor pathway) and their crosstalk in cancer patients blood, providing new candidate targets for cancer microenvironment study.

bioinformatics↗

bioRxiv2023

Depletion-assisted multiplexing cell-free RNA sequencing reveals distinct human and microbial signatures in plasma versus extracellular vesicle

Cell-free long RNAs in human plasma and extracellular vesicles (EVs) have shown promise as biomarkers in liquid biopsy, despite their fragmented nature. To investigate these fragmented cell-free RNAs (cfRNAs), we developed a cost-effective cfRNA sequencing method called DETECTOR-seq (depletion-assisted multiplexed cell-free total RNA sequencing). DETECTOR-seq utilized a meticulously tailored set of customized guide RNAs to remove large amounts of unwanted RNAs (i.e., fragmented ribosomal and mitochondrial RNAs) in human plasma. Early barcoding strategy was implemented to reduce costs and minimize plasma requirements. Using DETECTOR-seq, we conducted a comprehensive analysis of cell-free transcriptomes in both whole human plasma and EVs. Our analysis revealed discernible distributions of RNA types in plasma and EVs. Plasma exhibited pronounced enrichment in structured circular RNAs, tRNAs, Y RNAs, and viral RNAs, while EVs showed enrichment in mRNAs and srpRNAs. Functional pathway analysis highlighted RNA splicing-related ribonucleoproteins (RNPs) and antimicrobial humoral response genes in plasma, while EVs demonstrated enrichment in transcriptional activity, cell migration, and antigen receptor-mediated immune signals. Our study indicates the comparable potential of cfRNAs from whole plasma and EVs in distinguishing cancer patients (i.e., colorectal and lung cancer) from healthy donors. And microbial cfRNAs in plasma showed potential in classifying specific cancer types. Our comprehensive analysis of total and EV cfRNAs in paired plasma samples provides valuable insights for determining the need for EV purification in cfRNA-based studies. We envision the cost-effectiveness and efficiency of DETECTOR-seq will empower transcriptome-wide investigations in the fields of extracellular vesicles and liquid biopsy.

molecular biology↗

bioRxiv2023

Cell-free multi-omics analysis reveals tumor status-informative signatures in gastrointestinal cancer patients' plasma

During cancer development, hosts tumorigenesis and immune signals are released to and informed by circulating molecules, like cell-free DNA (cfDNA) and RNA (cfRNA) in blood. However, these two kinds of molecules are still not systematically compared in gastrointestinal cancer. Here, we profiled 4 types of cell-free omics data from colorectal and stomach cancer patients, and assayed 15 types of genomic, epi-genomic, and transcriptomic variations. First, we demonstrated that the multi-omics data were more capable of detecting cancer genes than the single-omics data, where cfRNAs were more sensitive and informative than cfDNAs in terms of detection ratio, variation type, altered number, and enriched functional pathway. Moreover, we revealed several peripheral immune signatures that were suppressed in cancer patients and originated from specific circulating and tumor-microenvironment cells. Particularly, we defined a {gamma}{delta}-T-cell score and a cancer-associated-fibroblast (CAF) score using the cfRNA-seq data of 143 cancer patients. They were informative of clinical status like cancer stage, tumor size, and survival. In summary, our work reveals the cell-free multi-molecular landscape of colorectal and stomach cancer, and provides a potential monitoring utility in blood for the personalized cancer treatment.

systems biology↗

bioRxiv2019

Selective translation by alternative bacterial ribosomes

Alternative ribosome subunit proteins are prevalent in the genomes of diverse bacterial species but their functional significance is controversial. Attempts to study microbial ribosomal heterogeneity have mostly relied on comparing wild-type strains with mutants in which subunits have been deleted, but this approach does not allow direct comparison of alternate ribosome isoforms isolated from identical cellular contexts. Here, by simultaneously purifying canonical and alternative RpsR ribosomes from Mycobacterium smegmatis, we show that alternative ribosomes have distinct translational features compared with their canonical counterparts. Both alternative and canonical ribosomes actively take part in gene translation, although they translate a subset of genes with differential efficiency as measured by ribosome profiling. We also show that alternative ribosomes have a relative defect in initiation complex formation. Our work convincingly confirms the distinct and non-redundant contribution of alternative bacterial ribosomes for adaptation to hostile environments.\n\nSignificance StatementMany organisms, including most bacteria code for multiple paralogues of some ribosomal protein subunits. The relative contribution of these alternative subunits to ribosome function and gene translation is unknown and controversial. Furthermore, many studies on alternative ribosomes have been confounded by isolation of alternative and canonical ribosomes from different strains and/ or different growth conditions, potentially confounding results. Here, we show unequivocally that one form of alternative ribosome from Mycobacterium smegmatis actively engages in gene translation, but its translational profile from an identical cellular context is subtly different from canonical ribosomes. Given the prevalence of alternative ribosomal genes in diverse organisms, our study suggests that alternative ribosomes may represent a further layer of regulation of gene translation.

microbiology↗

bioRxiv2017

Epigenetic Reprogramming of Tissue-Specific Transcription Promotes Metastasis

Tumor metastasis is the cause of death for 90% of cancer patients, and no currently-available therapies target this multi-step process in which cancer cells spread from the local tissue of a primary tumor to distant organs where they establish secondary tumors1. Although epithelial-to-mesenchymal transition2, tumor-secreted exosomes3, epigenetic regulators as well as other genes4-8 have been implicated in metastasis, little is known about how cells adapt to and colonize new tissue environments. Here, we show that the epigenetics-mediated reprogramming of tissue-specific gene transcription in cancer cells promotes metastasis. Using colorectal cancer (CRC) as a model, we found in both clinical and cell line studies that metastatic CRC cells lose their colon-specific gene transcription program and gain a liver-specific gene transcription program as they metastasize in the liver. Further, we found this transcription reprogramming is driven by a reshaped epigenetic landscape of both typical and super-enhancers. Chemical inhibition of enhancer activity disrupts the ability of cells to execute altered transcription programs and consequently inhibits metastasis. Binding motif analysis of the enhancers in liver metastatic CRC cells identified the liver-specific transcription factor FOXA2 as a key regulator, and knocking down of FOXA2 expression prevents the colonization of metastatic CRC cells in the liver of a mice xenograft model. These results, together with additional observations of similar reprogramming in several cohorts of clinical CRC tumor samples and in multiple other forms of metastatic cancers, indicate that this reprogramming may be a common feature of metastasis in multiple cancers and suggest the targeted disruption of this epigenetic reprogramming as a strategy for the development of therapies to treat metastasis, the leading cause of cancer-related mortality.

cancer biology↗