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Chen, Z.

Publications and source records attributed to Chen, Z..

At least 253 records · Page 14Linked to original sources

Enhancer Poising Enables Pathogenic Gene Activation by Noncoding Variants

Single nucleotide variants within enhancers--non-coding DNA elements that regulate transcription--often lead to aberrant gene activation and contribute to a wide range of genetic disorders (Claussnitzer et al. 2015; Doan et al. 2016; Turner et al. 2017; Yanchus et al. 2022; Lettice et al. 2008). The mechanism by which ectopic gene activation occurs through these gain-of-function enhancer mutations remains poorly understood. Using the ZRS, a benchmark disease-associated enhancer of Sonic hedgehog (Shh), as a model, we demonstrate that poised (i.e., accessible but inactive) chromatin sensitizes Shh to aberrant activation in anterior limb bud, leading to polydactyly. In the anterior limb cells of wild-type mice, Shh is inactive, but the ZRS is accessible and marked by enhancer-associated histone modifications. We demonstrate that this poising signature explains how over 20 independent rare variants within the ZRS cause Shh misexpression in the same anterior limb bud cell population, resulting in similar limb malformations, despite affecting binding sites for different activators and repressors. Disabling pioneer transcription factor binding to the ZRS suppresses its poised state in anterior cells, prevents aberrant activation of the ZRS by rare variants, and fully rescues limb malformations in variant knock-in mice. A thorough examination of other disease-associated enhancers with pathogenic gain-of-function variants revealed that they are all poised in tissues with ectopic activity. We use this poising signature to predict and validate in vivo ectopic forebrain activity of previously uncharacterized autism-associated non-coding variants. Our findings suggest that spatial enhancer poising, likely a byproduct of development, creates a susceptibility to non-coding mutations and offers a potential mechanistic explanation for the burden of disease-associated non-coding variants.

genomics↗

Rethinking Representation Complexity in Drug-Target Prediction via Supervised Vector Quantization

Accurate prediction of drug-target interactions (DTIs) is crucial for computational drug discovery. Although pretrained language models offer richer molecular and protein representations, their increasing complexity does not always lead to better predictive performance. In many cases, the inclusion of redundant or irrelevant features may obscure biologically relevant patterns. In this study, we systematically evaluate the contribution of complex features in DTI prediction and demonstrate that only a portion of these features is truly informative. Based on this insight, we propose a Vector Quantization (VQ)-based module that functions as a plug-and-play feature selection layer within deep learning architectures. When combined with a simple fully connected classifier, this supervised VQ (SVQ) framework not only surpasses recent state-of-the-art DTI methods in performance, but also enhances interpretability through the learning of discriminative codewords. This work highlights the importance of input feature selection in deep learning and offers a new perspective for constructing robust and interpretable DTI prediction models.

bioinformatics↗

UniCure: A Foundation Model for Predicting Personalized Cancer Therapy Response

Predicting drug efficacy across diverse patient contexts remains a major challenge in oncology, as models trained on cancer cell lines often fail to capture patient-specific biology. Emerging biological foundation models and patient-derived technologies offer a promising solution. Here, we present UniCure, the first pre-trained foundation model integrating both omics and chemical foundation models (UCE and Uni-mol) to predict transcriptomic responses to drugs across diverse cellular and tissue contexts, enabling personalized cancer therapy and drug prioritization at the individual level. Rather than encoder/decoder used in traditional models, UniCure utilizes parameter-efficient fine-tuning (PEFT) techniques for optimizing the training process, a novel FlexPert module for modeling flexible drug-cell interactions, and a Maximum Mean Discrepancy (MMD) loss for learning unpaired data. Trained on over 1.8 million perturbation RNA-seq profiles over 22,000 compounds, 166 cell types, and 24 tissues, UniCure achieves high accuracy in predicting both dose-dependent responses and drug combination effects, demonstrating strong generalization across bulk and single-cell transcriptomic data. In particular, fine-tuning on our patient-derived tumor-like clusters and real-world data of 800 profiles enables UniCure to generate individualized therapeutic predictions on patients tissue samples. In addition, UniCure enables patient stratification based on the predicted drug responses, providing a new way for subtyping patients. UniCures drug prioritization was validated across over 1000 patients from pan-cancer cohorts and supported by experiments of candidate therapeutics. By enabling the potential to screen millions of compounds per patient at scale, UniCure represents a biologically grounded tool that could advance personalized precision oncology and accelerate drug discovery.

bioinformatics↗

Predicting Spatial Transcriptomics from H&E Image by Pretrained Contrastive Alignment Learning

The intricate molecular landscape within tissues holds crucial information about cellular behavior and disease progression, yet capturing this complexity at a spatial level remains challenging. While Spatial transcriptomics (ST) offers valuable insights into gene expression patterns within their native tissue context, its widespread adoption is hindered by high costs and limited gene detection capabilities. Here we introduce CarHE (Contrastive Alignment of gene expRession for hematoxylin and eosin image), a method that overcomes these limitations by accurately predicting high-dimensional ST data (over 10,000 genes) solely from readily available H&E (Hematoxylin and Eosin) stained images. This novel pre-trained architecture employs contrastive learning through two mechanisms: cell-type-based transcriptomics information transfer and image-based histology information transfer. These mechanisms precisely align image features with spatial single-cell gene expressions, achieving prediction accuracies exceeding 0.7 (up to 1.7 folds compared to second best) across diverse tissue types and species. CarHEs superior performance extends to identifying subtle pathological features such as tertiary lymphoid structures in various cancers, including breast cancer, lung cancer, melanoma and ccRCC (clear cell renal cell carcinoma), and reconstructing 3D spatial transcriptomics from images alone, offering a cost-effective and robust alternative for large-scale spatial transcriptomics. We further validated CarHEs effectiveness by predicting DFS (Disease-Free Survival) from >1,600 lung cancer patients HE images, achieving a significantly higher AUC (Area Under the Receiver Operating Characteristic Curve) of 0.73 compared to state-of-the-art alternatives (0.58-0.64).

bioinformatics↗

Pathogenicity and transmissibility of Monkeypox virus in dormice

The global spread of the monkeypox virus (MPXV) poses a significant challenge to public health, yet reliable and consistent animal models for evaluating MPXV remain limited, which to some extent restricts the advancement of treatment strategies and transmission-blocking technologies. As natural hosts, African dormice (Graphiurus spp.) represent promising candidates. However, the biological characteristics of MPXV in dormice remain largely unexplored. This study systematically evaluated the pathogenicity and transmissibility of MPXV in dormice. Experimental results demonstrated that dormice are highly susceptible to MPXV infection. Following intranasal inoculation, MPXV induced significant weight loss, lethal infections, and multi-organ pathological damage, with robust viral replication in respiratory and liver tissues. Notably, MPXV can efficiently transmit among dormice through direct contact, with one-third of the contact-exposed dormice shedding infectious viruses and two-thirds exhibiting seropositivity. In addition, one-third of the airborne exposure dormice show seropositivity, yet no infectious viruses were detected in their respiratory tissues, indicating that the airborne transmissibility of MPXV among dormice is relatively restricted. Furthermore, it was observed that infected dormice continuously released large quantities of virus-laden aerosols, with emissions peaking on 12 dpi (3.78{+/-}1.01x106 copies/dormouse/hour of respiration). Particle size analysis revealed that the viral copies in [≥]7 m coarse particles accounted for >90.9% of exhaled viral aerosols during peak shedding (8-14 dpi). These findings demonstrate that the African dormice serve as an effective animal model for assessing MPXV infection, pathogenicity, and transmission dynamics. Simultaneously, enhanced monitoring of wild dormouse populations is critical due to their potential role in MPXV transmission chains.

microbiology↗

Multimodal Evidence for Hippocampal Engagement and Modulation by Functional Connectivity-Guided Parietal TMS

Hippocampal activity supports memory and many other brain functions. Transcranial magnetic stimulation (TMS) guided by hippocampal functional connectivity (FC) shows promise in improving memory, but direct neural evidence of its capacity to engage and modulate hippocampal activity is lacking. Here we combined TMS with intracranial electroencephalography (iEEG) in 8 neurosurgical patients and with functional magnetic resonance imaging (fMRI) in 79 neurologically healthy participants. We identified that (1) single-pulse TMS to individualized parietal cortex guided by hippocampal-FC preferentially evoked distinct temporal and spectral activity patterns in the hippocampus, (2) variability in TMS-evoked hippocampal responses related to individual differences in parietal-hippocampus FC strength, and (3) repetitive TMS to hippocampal-FC-guided parietal cortex selectively suppressed hippocampal theta oscillations. These findings provide multimodal causal neural evidence and important mechanistic insights supporting the development of personalized neuromodulation strategies aimed at improving hippocampus-dependent functions.

neuroscience↗

Uncovering Heterogeneous Effects via Localized Feature Selection

Identifying features that interact to trigger disease, while accounting for heterogeneity across diverse populations, is essential for the development of precision and targeted medicine. Despite the availability of vast and complex health-related datasets, most existing works focus on identifying disease-associated features at the population level or within a few subpopulations, often overlooking individual-level heterogeneity within these groups. To address this limitation, we propose a novel framework that utilizes localized test statistics to identify disease-associated features tailored to individual profiles. Our method leverages the recently developed knockoffs methodology to control the noise level of the selection set so that the results are replicable. Moreover, it allows for the discovery of hidden heterogeneous effects within the data, as demonstrated in an application to single-cell RNA sequencing data for Alzheimers disease. By aggregating localized feature selection results, our framework also enables powerful population-level feature selection. Our framework provides a powerful tool for exploratory studies of precision medicine, offering the potential to generate novel hypotheses for confirmatory biological experiments.

bioinformatics↗

Mapping neurogenetic characteristics of psychopathological procrastination using normative modeling in a prospective twin cohort

Procrastination, affecting over 70% global population, is pervasively incurring negative outcomes in human society. This has long been studied as a bad daily habit, but it loses in delineating neurogenetic substrates underlying its psychopathological phenotyping. Using a prospective twin adolescent cohort, we demonstrate moderate heritability of this subclinical condition - PPS. Neuroimaging normative modeling analysis, further reveals that neurodevelopmental deviations in nucleus accumbens during adolescence, are predictive of PPS in adulthood, while such deviations-PPS mappings were highly genetically shared. Beyond to regional anomalies, PPS-specific whole-brain deviation patterns, notably in the default mode network, are neurobiologically enriched with changes in cortical manifolds (gradients) and neurotransmitter systems. Integrating these neuroimaging markers with transcriptomic atlas, we capture significant PPS-specific neurogenetic signatures associated with molecular transport system, neuroimmune responses, and neuroinflammation, particularly in serotonergic and dopaminergic pathways. These findings shed light on the multisystem neurogenetic architecture underlying PPS, providing evidence to theoretically conceptualize this psychopathological phenotype as a subclinical "brain disorder". TeaserPsychopathological procrastination is not a bad daily habit solely, but a "brain disorder" associated with multiscale neurodevelopment.

neuroscience↗

Contrastive Learning-Enhanced Drug Metabolite Prediction Drives the Discovery of an Orally Bioavailable RSK4 Inhibitor for Esophageal Cancer

Drug metabolism liabilities are a major bottleneck in drug development, necessitating accurate metabolite prediction tools. While AI-driven methods have advanced, challenges remain in achieving high-precision, robustness, broad reaction coverage and seamless integration into the drug development workflow. To address this, we first constructed the most comprehensive human-specific drug metabolism database to date, encompassing 11,665 human metabolic reactions and 2,497 high-quality rules. Leveraging this resource, we developed ConMeter (Contrastive learning and feature interaction based Metabolic Reaction prediction), a novel model integrating chemical feature interaction and contrastive learning. ConMeter demonstrates significant improvements, achieving [~]80% accuracy in identifying at least one metabolite within the top 5 predictions and showing an average 25% relative enhancement in ranking true positives compared to state-of-the-art methods. The comprehensive database also expands the range of predictable metabolic reactions. To demonstrate the utility of ConMeter, we integrated it into a metabolite prediction-guided drug design strategy aimed at addressing the low oral bioavailability of a metabolically vulnerable RSK4 lead compound -- a pervasive issue among known RSK inhibitors. This strategy successfully yielded R636, a novel RSK4 inhibitor exhibiting remarkably improved bioavailability (a 63-fold increase, reaching 63% absolute bioavailability) while maintaining its potent activity. Notably, R636 showed favorable safety profile and significant anti-tumor efficacy against esophageal squamous cell carcinoma (ESCC) in vitro and in two distinct patient-derived xenograft (PDX) mouse models. This work introduces ConMeter as a powerful and practical tool for high-accuracy metabolite prediction, demonstrating its potential to accelerate lead optimization, exemplified by the discovery of R636 as a promising orally bioavailable candidate for ESCC.

cancer biology↗

Cancer Diagnostics using Machine Learning of Tumor- and Tissue- Specific RNA Transcripts

Accurate cancer diagnosis and tissue origin identification are crucial for precision oncology. We explored the potential of tumor-specific RNA transcripts (Tumor-SRTs) and tissue-specific RNA transcripts (Tissue-SRTs) as dual biomarkers using machine learning. Tumor-SRTs effectively distinguished malignant from normal tissues across training, test, and validation sets. Classifiers trained on 25 Tissue-SRTs exhibited high performance, validated externally with varying predictive accuracy across tissue types. We developed SRT-based Cancer Diagnostics (SRT-CD), an intelligent diagnostic system, to diagnose primary and metastatic cancers and determine tissue origin of Cancers of Unknown Primary. SRT-CD achieved top-1/top-3 accuracies of 80%/98.1% for primary and 76.9%/92.3% for metastatic cancers in external validation. This study establishes SRT-CD as a robust tool for clinical cancer diagnosis and tissue origin identification, and guiding personalized treatment strategies.

bioinformatics↗

Quantitative molecular cartography of emergency myelopoiesis reveals conserved modules of hematopoietic activation

Hematopoietic stem and progenitor cells (HSPC) respond to infections, inflammation, and regenerative challenges using a collection of cellular and molecular mechanisms termed emergency myelopoiesis (EM) pathways. However, it remains unclear how various EM inducers regulate HSPCs using shared or distinct molecular mechanisms. Here, we generate a comprehensive and generalizable cell annotation method (HemaScribe) and a refined quantitative model of hematopoietic differentiation (HemaScape) using single cell RNA sequencing (scRNA-seq) of HSPCs, which we apply to a broad range of EM modalities. We uncover multiple strategies to enhance myelopoiesis acting at different levels of the HSPC hierarchy, which are associated with both unique and shared transcriptional response modules. In particular, we identify a myeloid progenitor-based module of EM engagement across diverse inflammatory challenges, which informs outcome in adult and pediatric human acute myeloid leukemia. Collectively, our work illuminates fundamental regulatory mechanisms in hematopoietic regeneration that have direct translational applications in disease contexts. HIGHLIGHTSO_LINew HemaScribe method for hematopoietic progenitor annotation in scRNA-seq datasets C_LIO_LIDifferent emergency myelopoiesis (EM) inducers act at distinct hematopoiesis levels C_LIO_LIUnique and shared transcriptional response modules enacted by different EM inducers C_LIO_LIA myeloid progenitor EM module informs outcome in acute myeloid leukemia C_LI eTOC BLURBSwann et al. conduct comparative analysis of single cell RNA sequencing data from multiple emergency myelopoiesis models, finding that different perturbations act at various levels of the hematopoietic hierarchy and recruit distinct sets of molecular mechanisms to enhance myelopoiesis. In particular, they identify a conserved myeloid progenitor-based activation module across multiple disease conditions, which informs outcome in human acute myeloid leukemia. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/656712v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@ffcd62org.highwire.dtl.DTLVardef@50eaeorg.highwire.dtl.DTLVardef@6df44org.highwire.dtl.DTLVardef@12bfacf_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

TIP-seq: A Single-cell Multiomics Approach for Simultaneous Transcriptome and Intracellular Protein Profiling

Single-cell multiomics technologies have significantly advanced our understanding of cellular heterogeneity; however, the concurrent profiling of mRNA transcripts and target proteins continues to pose a substantial challenge. To bridge the gap between single-cell sequencing and proteomic methods, we developed an innovative single-cell multiomics methodology, termed TIP-seq, which facilitates the simultaneous profiling of the transcriptome and intracellular proteins without prior cell manipulation. In TIP-seq, microfluidic technology is leveraged to integrate oligonucleotide-labeled antibodies with hydrogel beads, allowing for the capture of mRNAs and proteins from individual cells within droplets, thereby enabling high-throughput and precise dual-omics data acquisition. When applied to lung cell lines and non-small cell lung cancer (NSCLC) tissues, TIP-seq analysis revealed notable cellular heterogeneity and molecular dynamics, emphasizing the distinct immune cell interactions within NSCLC tissues. Key immune checkpoint interactions in NSCLC, such as SPP1-CD44, NECTIN2-TIGIT, and NECTIN2-CTLA4, along with functional alterations in tumor-associated dendritic cells (DCs) and T cells, were identified via TIP-seq, underscoring their pivotal roles in mediating immune suppression within the tumor microenvironment. Collectively, TIP-seq represents a powerful methodology for identifying novel therapeutic targets and biomarkers, thereby holding significant potential for the advancement of precision medicine in the treatment of lung cancer and other complex diseases.

biochemistry↗

Overcoming treatment resistance mediated by the bone marrow vascular niche in acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematologic malignancy originating in the bone marrow, frequently progressing to extramedullary sites. Despite advances in molecularly targeted therapies and hematopoietic stem cell transplantation, clinical outcomes remain suboptimal. Tyrosine kinase inhibitors (TKIs) confer benefit in a subset of AML patients harboring FLT3-ITD mutations, yet relapse and resistance are common. These failures are driven by both intrinsic properties of leukemic stem cells (LSCs)--a quiescent, self-renewing population--and extrinsic cues from the tumor microenvironment. We previously demonstrated that arteriolar endothelial cells (ECs) produce miR-126, which is transferred to LSCs, promoting quiescence, treatment resistance, and niche retention. During disease progression, TNF- secreted by expanding blasts suppresses EC miR-126 production, enabling LSCs and their progeny to proliferate. Following TKI administration, blast reduction lowers TNF- levels, restoring EC miR-126 production and enabling LSCs to re-enter quiescence--thereby escaping therapy and facilitating relapse. To investigate this dynamic, we developed an agent-based computational model of the AML bone marrow microenvironment, parameterized with in vitro and in vivo data. The model captures vascular niche remodeling and the feedback between leukemic populations and endothelial signaling. Simulations reveal that LSC protection mediated by miR-126 can be overcome by combining TKIs with miRisten, a miR-126 inhibitor. When administered on a defined schedule, this combination disrupts the protective niche and enhances LSC eradication. These findings underscore the therapeutic potential of targeting microenvironmental feedback to overcome resistance and prevent AML relapse.

cancer biology↗

F-53B damages cell viability leading to impaired root development in Arabidopsis

The emerging contaminant chlorinated polyfluoroalkyl ether sulfonate (Cl-PFAES/F-53B) has been detected in many plant species, however, the impact of F-53B on the development in plants and the underpinning mechanisms remain largely unelucidated. In this study, we demonstrate by molecular biology, cytogenetics, and fluorescence microscopy mythologies that F-53B impairs cell viability leading to disrupted root development in Arabidopsis (Arabidopsis thaliana). We show that F-53B suppresses root length and the inhibitory effect is pronunanced at increasing concentrations. F-53B disrupts cell wall and microtubule-mediated cell plate formation and configuration in meristem cells, revealing its toxicity to cell division and cytoskeleton organization. F-53B induces cell death predominantly at the meristematic zone, however, gene exression and genetic studies indicate that F-53B does not trigger DNA double-strand breaks, implying a divergent effect on DNA stability across species. Moreover, we show that F-53B damages nuclei stability and viability, and induces Autophagy-Related 8 protein foci formation implying that it triggers autophagy-mediated cell responses. Taken together, this study provides cellular and molecular insights into the toxicity of F-53B to plant cells, which highlights its damage to agriculture and ecology security that deserve imperative environmental management actions. Environmental ImplicationF-53B has been detected in many plant species, which raises increasing concerns on its toxicity to plants and thus agriculture and ecology safety. However, the toxicity of F-53B to development in plants and the underpinning mechanisms remain largely unelucidated. Here, we demonstrate that F-53B damages nuclei stability and viability leading to cell death and impaired root development in the model plant species Arabidopsis thaliana. Our study provides cytogenetic and molecular insights into the F-53B toxicity to plant cells, which highlights imperative environmental management actions and can be referenced for developing F-53B control and management policies. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/653422v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@159b211org.highwire.dtl.DTLVardef@1b99a95org.highwire.dtl.DTLVardef@53b964org.highwire.dtl.DTLVardef@b089be_HPS_FORMAT_FIGEXP M_FIG C_FIG One-sentence summaryF-53B triggers DNA double-strand break-free cell unviability leading to impaired meristematic cell division and root development in plants. HIGHLIGHTSO_LIF-53B impairs root development in Arabidopsis. C_LIO_LIF-53B prohibits mitotic cell division at root tips. C_LIO_LIF-53B damages cell viability predominantly at the meristematic zone in seedlings. C_LIO_LIF-53B does not induce DNA double-strand breaks in Arabidopsis seedlings. C_LIO_LIF-53B damages nuclei viability and stability in seedlings. C_LI

developmental biology↗

Longitudinal single cell RNA-sequencing reveals evolution of micro- and macro-states in chronic myeloid leukemia

Single cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cancer, yet identifying meaningful disease states from single cell data remains challenging. Here, we systematically explore the chronic myeloid leukemia (CML) specific information content encoded in single cell versus bulk transcriptomics to resolve this paradox and clarify how discrete disease-defining states emerge from inherently noisy single cell data. We demonstrate that, while CML single cell transcriptomes exist along continuous transcriptional microstates, clinically relevant leukemia phenotypes clearly manifest only at the pseudobulk (macrostate) level. By leveraging state-transition theory, we reveal how robust disease phenotype state-transitions are governed by cell type specific contributions. Our results establish a theoretical framework explaining why discrete disease phenotypes remain hidden at the single cell scale but emerge clearly at the aggregated macrostate level, enabling previously inaccessible biological insights into leukemia evolution. By resolving how single-cell variation aggregates into macroscopic disease states, our framework provides new insight into CML progression and offers a broadly applicable strategy for exploring disease dynamics across cancers and other complex conditions.

systems biology↗

Bridging 3D Molecular Structures and Artificial Intelligence by a Conformation Description Language

Artificial intelligence, particularly language models (LMs), is reshaping research paradigms across scientific domains. In the fields of chemistry and pharmacy, chemical language models (CLMs) have achieved remarkable success in two-dimensional (2D) molecular modeling tasks by leveraging one-dimensional (1D) representations of molecules, such as SMILES and SELFIES. However, extending these successes to three-dimensional (3D) molecular modeling remains a significant challenge, largely due to the absence of effective 1D representations for capturing 3D molecular structures. To address this gap, we introduce ConfSeq, a novel molecular conformation description language that integrates SMILES with internal coordinates including dihedral angles, bond angles, and pseudo-chirality. This design naturally ensures SE(3) invariance, while preserving the human readability and conciseness characteristic of SMILES. ConfSeq enables the reformulation of a range of 3D molecular modeling tasks, such as molecular conformation prediction, 3D molecular generation, and 3D molecular representation, into sequence modeling problems. Then, by simply employing a standard Transformer architecture, we achieve state-of-the-art performance on various benchmark sets. Furthermore, compared to widely used diffusion-based approaches in 3D molecular modeling, the ConfSeq-based method offers unique advantages in inference efficiency, generation controllability, and enables scoring of generated molecules. We believe that ConfSeq can serve as a foundational tool, advancing the development of sequence-based 3D molecular modeling methods.

bioinformatics↗

Human Messenger RNA Harbors Widespread Noncoding Splice Isoforms

Pre-messenger RNA transcribed from canonical protein-coding genes is frequently alternatively spliced into diverse mature mRNA isoforms, thus translating into various protein products. However, it remains unknown whether human mRNA from a protein-coding gene harbors noncoding splice isoforms in the genome. Herein, we discovered 15,836 mRNA noncoding splice isoforms across 7,298 protein-coding genes in human. mRNA noncoding isoforms are mainly produced by alternative splicing and polyadenylation, which display tissue-specific distributions and involve in RNA processing pathways. Notably, mRNA noncoding isoforms are frequently upregulated in cancer. Differentially expressed mRNA noncoding isoforms are associated with the cancer hallmarks and can independently predict patient survival. These findings unraveled human mRNA harbors widespread noncoding splice isoforms, and highlight the dual characters of mRNA including protein-translation isoforms and noncoding splice isoforms, providing a new dimension to our understanding of mRNA functional property.

genomics↗

Transcriptome and metabolome analyses reveal novel genetic targets for L-tryptophan overproduction in Corynebacterium glutamicum

Corynebacterium glutamicum is a promising microbial chassis for the industrial production of L-tryptophan, which has exhibited increasing demand due to its diverse applications and high market value. In previous work, we developed an L-tryptophan-overproducing C. glutamicum strain TR26 through multiple rounds of rational metabolic engineering. Here, comparative transcriptome and metabolome analyses were conducted between TR26 and its progenitor strain MB001 to reveal the underlying mechanisms and potential bottlenecks for L-tryptophan production in TR26. Furthermore, by systematically down- and up-regulating differentially expressed genes of interest, two novel genetic targets, glnK and sugR, were identified as being associated with L-tryptophan synthesis. Specifically, the repression of glnK and overexpression of sugR in strain TR26 increased the titer of L-tryptophan by 6.7% and 20.9%, respectively. Gene transcription profiling and intracellular metabolite analysis further suggested that the observed improvements in L-tryptophan synthesis could be attributed to optimized nitrogen transport and metabolism, efficient reallocation of cellular resources and enhanced supply of phosphoenolpyruvate (PEP). This study advances our understanding of the regulation mechanisms governing L-tryptophan synthesis in C. glutamicum and provides valuable insights for further optimization of industrial cell factories.

synthetic biology↗