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Biology subjects

Huang, Z.

Publications and source records attributed to Huang, Z..

At least 127 records · Page 7Linked to original sources

Neural connectivity of a computational map for fly flight control.

Nervous systems rely on sensory feature maps, where the tuning of neighboring neurons for some ethologically-relevant parameter varies systematically, to control behavior1,2. Such maps can be organized topographically or based on some computational principle. However, it is unclear how the central organization of a sensory system corresponds to the functional logic of the motor system. This problem is exemplified by insect flight, where sub-millisecond modifications in wing-steering muscle activity are necessary for stability and maneuverability. Although the muscles that control wing motion are anatomically and functionally stratified into distinct motor modules3-7, comparatively little is known about the architecture of the sensory circuits that regulate their precise firing times. Here, we leverage an existing volume of an adult female VNC of the fruit fly Drosophila melanogaster8,9 to reconstruct the complete population of afferents in the haltere-natures only biological "gyroscope"10,11-and their synaptic partners. We morphometrically classify these neurons into distinct subtypes and design split-GAL4 lines that help us determine the peripheral locations from which each subtype originates. We find that each subtype, rather than originating from the same anatomical location, is comprised of multiple regions on the haltere. We then trace the flow of rapid mechanosensory feedback from the peripheral haltere receptors to the central motor circuits that control wing kinematics. Our work demonstrates how a sensory systems connectivity patterns construct a neural map that may facilitate rapid processing by the motor system.

neuroscience↗

Fat-rich diet reprograms intrapulmonary neutrophils to boost tissue-specific antitumor immunity

Neutrophils adapt to tissue-specific signals and exert innate defense functions against infections and malignancies. Fat-rich diet (FRD), such as high-fat diet (HFD) and ketogenic diet (KD), has complex impacts on immunity. However, whether and how FRD shapes tissue-specific functions of neutrophils remain unclear. Here we show that both isocaloric HFD- and KD-fed mice demonstrate enhanced neutrophil-mediated pulmonary tumor resistance than chow diet-fed mice. Intrapulmonary but not systemic neutrophils in FRD-fed mice bear enhanced potential of reactive oxygen species (ROS) production and ROS-dependent tumor cytotoxicity. Mechanistically, FRD-induced increased serum saturated fatty acids and cholesterol stimulate lung vascular endothelial cells (LVECs), which reprogram intrapulmonary neutrophils via contact- and intercellular adhesion molecule-1 (ICAM-1)-dependent mechanisms. Analysis on human lung single-cell RNA sequencing data showed that intensified cell adhesion and priming signals from human LVECs are associated with enhanced antitumor functions in intrapulmonary neutrophils. Our findings highlight the roles of dietary fats in shaping neutrophil functions in a tissue-specific manner. Dietary intervention targeting tissue-specific reprogramming of neutrophils therefore represents a potential strategy against malignancies in the lungs.

immunology↗

Chip-Based 3D Interferometric Nanoscopy

Ultra-high resolution 3D single-molecule localization microscopy (SMLM) traditionally requires complex dual-objective lenses (4Pi) configurations to enhance axial (z) precision through interferometry. Here we present a streamlined chip-based alternative, Silicon-assisted interferometric Localization Microscopy (SiLM), which achieves comparable performance using a single-objective lens design. By combining tunable axial structured illumination field, arising from surface-generated excitation interference, with asynchronous interferometry, SiLM enhances axial localization precision to approximately twice that of the lateral (xy), comparable to 4Pi-based methods. Additionally, SiLM provides intrinsic axial self-referencing, offering dramatically improved robustness against mechanical drift. Our method is readily implementable on standard SMLM-capable microscopes and supports a broad range of applications including dual-color imaging, extended-depth imaging, and live-cell 3D single-molecule tracking. Using SiLM, we demonstrate accurate mapping of the stratified nanoscale architecture of integrin-based focal adhesions, establishing it as a powerful and accessible method for high-precision 3D structural cell biology.

biophysics↗

Non-redundant roles of the HDAC3 corepressor complex subunits SMRT and NCOR in controlling inflammatory and metabolic macrophage pathways

Transcription factors and coregulators coordinate inflammatory and metabolic pathways in macrophages through epigenetic and transcriptional mechanisms. The HDAC3 corepressor complex plays fundamental roles in these mechanisms, with the homologous subunits SMRT and NCOR being critical for complex assembly and interactions with transcription factors and chromatin. However, the relative contribution of SMRT and NCOR in controlling complex-dependent macrophage pathways remains poorly understood. Here, we assessed their genome-wide roles in mouse macrophage RAW264.7 cells and in bone marrow-derived macrophages. Transcriptome analysis upon corepressor depletion identified six differentially expressed gene clusters. SMRT depletion primarily upregulated inflammation-related pathways, NCOR depletion primarily upregulated metabolism-related pathways. Epigenome analysis revealed that corepressor depletion differentially altered chromatin accessibility and H3K27 acetylation, consistent with transcriptome changes. Cistrome analysis revealed that both corepressors differentially influence each other at chromatin. SMRT uniquely controls the chromatin binding and nuclear localization of NCOR, GPS2 and HDAC3, thus acting as the chromatin anchor for the corepressor complex. Finally, corepressor depletion differentially modulated macrophage reprogramming in response to TLR4, IL4 and LXR signaling. Overall, our study reveals a hitherto underappreciated non-redundant role of SMRT and NCOR in coordinating chromatin accessibility, H3K27 acetylation, enhancer activity and transcription to differentially regulate inflammatory and metabolic macrophage pathways. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/653058v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1d9f2dforg.highwire.dtl.DTLVardef@1d81386org.highwire.dtl.DTLVardef@e9e536org.highwire.dtl.DTLVardef@1c74c15_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

TidyMass2: Advancing LC-MS Untargeted Metabolomics Through Metabolite Origin Inference and Metabolic Feature-based Functional Module Analysis

Untargeted metabolomics provides a direct window into biochemical activities but faces critical challenges in determining metabolite origins and interpreting unannotated metabolic features. Here, we present TidyMass2, an enhanced computational framework for Liquid Chromatography-Mass Spectrometry (LC-MS) untargeted metabolomics that addresses these limitations. TidyMass2 introduces three major innovations compared its predecessor, TidyMass: (1) a comprehensive metabolite origin inference capability that traces metabolites to human, microbial, dietary, pharmaceutical, and environmental sources through integration of 11 metabolite databases containing 532,488 metabolites with source information; (2) a metabolic feature-based functional module analysis approach that bypasses the annotation bottleneck by leveraging metabolic network topology to extract biological insights from unannotated metabolic features; and (3) an intuitive graphical interface that makes advanced metabolomics analyses accessible to researchers without programming expertise. Applied to longitudinal urine metabolomics data from human pregnancy, TidyMass2 identified diverse metabolites originating from human, microbiome, and environment, and uncovered 27 dysregulated metabolic modules. It increased the proportion of biologically interpretable metabolic features from 5.8% to 58.8%, revealing coordinated changes in steroid hormone biosynthesis, carbohydrate metabolism, and amino acid processing. By expanding biological interpretation beyond conventionally MS2 spectra-based annotated metabolites, TidyMass2 enables more comprehensive metabolic phenotyping while upholding strict open-source principles of reproducibility, traceability, and transparency.

bioinformatics↗

Modeling Dyslexia in Neurotypical Adults by Combining Neuroimaging and Neuromodulation Techniques: A Hypothesis Paper

Dyslexia is a prevalent developmental disorder marked by deficits in literacy skills. Given that the core deficits of dyslexia are uniquely human, animal models have not been as useful in dyslexia research as they have been in other areas of research. While significant progress has been made through behavioral and neuroimaging studies, a viable model could facilitate controlled investigations into the neural mechanisms underlying dyslexia and accelerate the development of targeted interventions. In this hypothesis article, we propose a two-pronged approach to model dyslexia in neurotypical adults using neuroimaging and neuromodulation techniques. First, we propose using functional and structural MRI data to cluster individuals into neuropathologically derived subgroups in order to facilitate the classification of dyslexia subtypes based on neuropathological characteristics. Second, we propose employing transcranial temporal interference stimulation (tTIS) to temporarily downregulate activity in brain regions specified in the clustering analysis, inducing subtype-specific dyslexic symptoms in neurotypical individuals. This approach enables the establishment of causal or probabilistic relationships between neuropathologies and dyslexia subtypes, while at the same time creating dyslexia models to facilitate investigation into subtype-specific interventions. By integrating neuroimaging and neuromodulation, we hope to offer a viable substitute for animal models in dyslexia and accelerate the development of personalized therapeutic strategies for dyslexia.

neuroscience↗

Integrated Classification of Cortical Cells and Quantitative Projectomic Mapping Unveil Organizational Principles of Brain-Wide Connectomes at Single Cell Level

Molecularly defined cortical cell types have recently been linked to whole neuronal morphology (WNM), particularly those characterized by whole-brain-wide projections, such as intratelencephalic (IT), extratelencephalic (ET), and corticothalamic (CT) neurons. In contrast, classical morphological classifications (e.g., tufted TPC, small tufted SPC, and stellate SSC) are based primarily on local dendrosomatic and axonal structures, especially apical dendrites. This study bridges these perspectives by establishing a new neuronal taxonomy, analyzing the connectomes of defined cortical cell types, and comparing them with those obtained from bulk anterograde injections. Neurons were sparsely labeled via tamoxifen-inducible Cre lines with GFP reporters, and 1,419 WNM cells were comprehensively reconstructed with Vaa3D-TeraVR from [~]15 areas across six functional regions of molecularly labeled brains imaged with 2p-fMOST. These cells were newly classified by integrating current molecular-WNM and classical morphological perspectives, with sample size augmented by 1,455 publicly available WNM cells reconstructed from the Mouse-Light project and CEBSIT. This effort defined ten combined molecular-WNM-classical morphological cell types: L5ET_TPC, L6CT_NPC, L6b_HPC, and seven IT types--L2/3IT_TPC, L4IT_SSC, L4IT_UPC, L4IT_TPC, L5IT_SPC, L6IT_IPC, and L6IT_car3PC. Clustering, quantitative analyses and random Forest classifier objectively validated these types and revealed their distinct connectomes, along with convergent, topographic, and hierarchical organizations across their projection brain regions. At the single-cell level, multiple organizational principles governing cortico-cortical (C-C) and cortico-subcortical (C-subC) connectomes emerged with unprecedented detail, offering a precise GPS-like tool for in vivo recordings and robust datasets for neuronal network modeling. Comparisons with bulk anterograde injection data underscored the limitations of traditional methods in identifying projection targets. Overall, our approach provides significant insights into cortical circuitry and elucidates the complex interplay between neuronal molecular identity, whole morphology, and classical morphological classification.

neuroscience↗

Alcohol Attenuates CRF-Induced Excitatory Effects from the Extended Amygdala to Dorsostriatal Cholinergic Interneurons

Alcohol relapse is associated with corticotropin-releasing factor (CRF) signaling and altered reward pathway function, though the precise mechanisms remain unclear. Here, we investigated how CRF modulates cholinergic interneurons (CINs) in the dorsal striatum, a region critical in mediating cognitive flexibility and action selection. Using monosynaptic and retrograde circuit tracing, we identified direct inputs from CRF-expressing (CRF) neurons in the central amygdala (CeA) and bed nucleus of the stria terminalis (BNST) to dorsal striatal CINs. We showed that CINs express CRF receptor 1 (CRFR1) and established their functional connectivity with CeA/BNST CRF projections. Functional recordings revealed that CRF enhanced CIN excitability and promoted acetylcholine release in the dorsal striatum. However, acute alcohol exposure and withdrawal attenuated the excitatory effect of CRF on CIN firing, suggesting a mechanism by which alcohol disrupts CRF-dependent neuromodulation. These findings reveal a previously unrecognized CRF-CIN pathway linking the extended amygdala to the dorsal striatum and provide new insight into how CRF and alcohol interact to impair striatal function. This work highlights CRF signaling as a potential target for understanding stress-induced changes to the reward pathway. HighlightsO_LIDorsal striatal CINs receive monosynaptic CRF+ inputs from CeA and BNST neurons. C_LIO_LICRFR1 is expressed in striatal CINs, and CRF+ fibers are present in the dorsal striatum. C_LIO_LICRF enhances dorsal striatal CIN activity via CRFR1 signaling. C_LIO_LIAcute alcohol exposure impairs CRF-induced cholinergic activity. C_LI Significance StatementThe dorsal striatum regulates goal-directed behavior and is implicated in alcohol use disorder (AUD). Within this region, cholinergic interneurons (CINs) support cognitive flexibility and receive input from limbic areas, including the central amygdala (CeA) and bed nucleus of the stria terminalis (BNST). In this study, we identified direct projections from CRF-producing neurons in the CeA and BNST to dorsal striatal CINs, a subset of which express CRF receptor 1 (CRFR1). Electrophysiological recordings confirmed these projections provide functional input that is disrupted by acute alcohol exposure. These findings lay the groundwork for future studies on how CRF and alcohol interact to impair striatal function.

neuroscience↗

AI-driven computational pathology for recurrence risk assessment in early-stage breast cancer

Recurrence related to poor prognosis is a leading cause of mortality in patients with breast cancer (BC). The MammaPrint (MP) genomic assay is designed to stratify recurrence risk and evaluate chemotherapy benefits for early-stage HR+/HER2- BC patients. However, MP fails to reveal spatial tumor morphology and is limited by high costs. In this study, we establish a BC MP cohort and develop CPMP, a weakly supervised agent-attention transformer model, to predict MP recurrence risk from annotation-free BC histopathological slides. CPMP achieves an AUROC of 0.824 {+/-} 0.03 in predicting MP risk groups. We further leveraged CPMP for spatial and morphological analyses to explore histological patterns associated with MP risk groups. The model reveals tumor spatial localization at the whole-slide level and highlights distinct intercellular interaction patterns of MP groups. It also characterizes the diversity in tumor morphology and uncovers MP High-specific, Low-specific, and colocalized morphological phenotypes that differ in quantitative cellular composition. Prognostic evaluation in the external cohort exhibits significant stratification of distant metastasis risk (HR: 3.14, p-value = 0.0014), underscoring the prognostic power of CPMP. These findings demonstrate the capability of CPMP in MP risk prediction, offering a flexible supplement to genomic risk assessment in early-stage BC.

pathology↗

Optogenetic translocation to subcellular compartments through regulation of protein avidity

Inducible translocation to subcellular compartments is a common strategy for protein switches that control a variety of cell behaviors. However, existing switches achieve translocation through induced dimerization, requiring constitutive anchoring of one component into the target compartment and optimization of relative expression levels between the two components. We present a simpler, single-component strategy called Avidity-assisted targeting (Aviatar). Aviatar achieves translocation with only a single protein by converting low-affinity monomers into high-avidity assemblies through inducible clustering. We demonstrated the Aviatar concept and its generality using optogenetic clustering to drive translocation to the plasma membrane, endosomes, golgi, endoplasmic reticulum, and microtubules using binding domains for lipids or endogenous proteins that were specific to those compartments. Aviatar recruitment regulated actin polymerization at the cell periphery and revealed compartment-specific signaling of receptor tyrosine kinase fusions associated with cancer. Finally, GFP-targeting Aviatar probes allowed inducible localization to any GFP-tagged target, including endogenously-tagged stress granule proteins. Aviatar is a straightforward platform that can be rapidly adapted to a broad array of targets without the need for their prior modification or disruption.

bioengineering↗

EasyMultiProfiler: An Efficient Multi-Omics Data Integration and Analysis Workflow for Microbiome Research

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/649266v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@14dd0b1org.highwire.dtl.DTLVardef@1c04a79org.highwire.dtl.DTLVardef@159894dorg.highwire.dtl.DTLVardef@1454a01_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG HighlightsO_LIEasyMultiProfiler provides unified integration of microbial multi-omics data C_LIO_LIEasyMultiProfiler enables natural language-style analytical workflows C_LIO_LIEasyMultiProfiler solves key challenges in standardization and reproducibility C_LIO_LIEasyMultiProfiler extracts meaningful biological insights efficiently C_LI Host-microbiome interactions are crucial in maintaining physiological homeostasis and influencing disease progression. While traditional microbiome research, focused on microbial diversity and abundance, provided essential frameworks, the integration of multi-omics approaches established a far more comprehensive, systems-level understanding of microbiome functionality. However, significant methodological challenges in multi-omics data integration remain, including inconsistent sample coverage, heterogeneous data formats, and complex downstream analytical workflows. These challenges impact the reproducibility and reliability of results and underscore the critical requirement for developing standardized, systematic workflows. Thus, we developed the EasyMultiProfiler (EMP) workflow, a streamlined and efficient analytical framework for multi-omics data analysis. EMP provides a comprehensive infrastructure based on the SummarizedExperiment and MultiAssayExperiment classes, creating a unified framework for storing and analyzing omics data. The frameworks architecture comprises five interconnected functional modules: data extraction, preparation, support, analysis, and visualization. These modules are smoothly integrated into natural language-style analytical workflows, offering users an efficient and standardized solution for multi-omics analysis. EMP addresses critical challenges in multi-omics data analysis, including data integration, workflow standardization, and result reproducibility. The platforms modular architecture and intuitive interface provide researchers and clinicians with a robust, flexible workflow to systematically extract biologically relevant insights from complex multi-omics datasets.

bioinformatics↗

Optogenetic control of mechanotransduction based on light-induced homodimerization of talin

Integrin-based cell adhesions (IACs) serve as primary sites where piconewton-scale actomyosin-generated mechanical forces are transmitted to the extracellular matrix (ECM), generating traction forces that drive cell-ECM responses including adhesion, migration, and mechano-signaling. Talin, a large (270 kDa) cytosolic adaptor protein, is the principal force-transmission protein in integrin-based adhesions, containing multiple mechanosensitive domains and protein-protein interaction sites that orchestrate molecular events in mechanosensing. As a highly modular multi-domain protein, talin has been identified as an effective target for chemogenetic and optogenetic manipulation of integrin-based mechanotransduction. However, a key limitation of previous approaches is the reliance on heterodimerization modules to control talin function, requiring the expression of two modified talin fragments. In practice, achieving precise expression levels in such a 2-component approach can be challenging, particularly when combined with other genetic tools. Since talin naturally contains a C-terminal dimerization domain that forms part of its actin-binding site, we reasoned that the molecularly engineered talin with a C-terminal optically-controlled homodimerizer could enable single-component optogenetic control of mechanotransduction. This approach would facilitate multiplexing with other molecular perturbations or experimental techniques. Here, we describe an opto-homodimerizable talin based on the pdDronpa1.2 optogenetic module, which enables optogenetic control of talin by a single construct. We demonstrate that light-induced talin dimerization promotes talin recruitment to IACs, adhesion formation, actin retrograde flow engagement, and downstream mechanotransduction signaling. Conversely, light-induced talin monomerization rapidly disassembles focal adhesions, disrupts talin-actin linkages, and accelerates actin retrograde flow, underscoring the critical roles of talin dimerization. Furthermore, our single-construct design allows facile multiplexing of optogenetic modulation of integrin-mediated mechanotransduction with super-resolution single-molecule tracking, revealing the essential role of talin dimerization for integrin v{beta}5 engagement.

cell biology↗

Pulsatory response of the BcLOV4 photoreceptor through intramolecular feed-forward regulation

Proteins are information processors, but their computations are typically considered at steady state. Here we find that individual proteins can dynamically encode information about their environment and that such response dynamics have been conserved throughout evolution. The fungal protein BcLOV4 exhibits pulsatory light responses shaped by the magnitude of environmental light and temperature. Response adaptation resulted from competitive interactions between domains that sensed either light or temperature. Temperature-sensing was encoded in a modular domain and could be tuned by mutations within co-evolved loops. Photo-thermal response dynamics were conserved in homologues from fungi that diverged >300 million years ago, and the characteristic temperature of pulsatory responses had adapted to match the ecological niche of the hosts, ranging from Antarctica to thermal ponds. These findings uncover a class of dynamic proteins, determine molecular principles of time-varying protein activation, and suggest functional importance for light- and temperature-conditioned protein activity pulses. One-Sentence SummaryIndividual proteins can dynamically encode information through interactions between their component domains, revealing principles for complex signal processing in natural and engineered proteins.

systems biology↗

Lack of Bcl6 promotes anti-tumor immunity by activating glycolysis to rescue CD8 T cell function

T cells are one of the most powerful weapons to fight cancer; however, T cell exhaustion and dysfunction restrict their long-lasting function in anti-tumor immunity. B-cell lymphoma 6 (BCL6) has many functions in CD8 T cells but it is unclear how it regulates the effector function and exhaustion of CD8 cells. Overall, a low level of BCL6 mRNA in human cancer samples is associated with better outcomes. We found that BCL6 deficiency in activated CD8 T cells enhanced tumor repression in multiple mouse models. More IL-2-expressing CD8 T cells and reduced proportions of exhausted or dysfunctional CD8 T cells were detected within tumors when Bcl6 was knocked out upon T cell activation. Glycolysis was promoted in BCL6-deficient CD8 T cells owing to derepression of glucose transporter GLUT3 (encoded by Slc2a3). The BCL6 inhibitor Fx1 promoted anti-tumor immunity in a T cell-dependent manner. These findings suggest a novel pathway to restore effector function of CD8 T cells by changing their energy utilization pathways to facilitate long-term tumor resistance. Summary BlurbBCL6 limits CD8 T cell responses to tumors by inhibiting Glut3 expression. Conditional deletion of Bcl6 in activated CD8 T cells or pharmacological inhibition of BCL6 in mice represses tumor growth.

immunology↗

Allo-PED: Leveraging protein language models and structure features for allosteric site prediction

Allosteric regulation plays a pivotal role in modulating protein function and allosteric sites represent a promising target for drug discovery. However, identifying allosteric sites remains challenging due to their structural and evolutionary diversity. Here, we present AlloPED, a novel framework that combines protein language models and machine learning to predict allosteric sites with high accuracy. AlloPED consists of two modules: AlloPED-pocket, an ensemble model leveraging physicochemical features to predict allosteric pockets; and AlloPED-site, a dilated convolutional neural network (DCNN) augmented with a comprehensive attention mechanism for residue-level prediction. AlloPED-pocket achieves state-of-the-art performance on benchmark datasets, yielding an MCC of 0.544 and an AUC of 0.920, outperforming existing methods such as AllositePro and PARS. AlloPED-site further refines predictions using high-dimensional sequence embeddings from the ProtT5 protein language model, achieving a precision of 0.601, a recall of 0.422, and a specificity of 0.661. These results highlight the effectiveness of integrating ensemble learning and deep learning for allosteric site prediction. AlloPED also identifies critical determinants of allosteric sites, including residue clustering coefficients, van der Waals volume, and hydrophobic microenvironments. In summary, this framework provides a robust tool for advancing our understanding of allosteric regulation and facilitating structure-based drug design.

bioinformatics↗

Functional landscape of circular RNAs in human cancer cells

Circular RNAs (circRNAs) constitute a novel class of noncoding RNAs showcasing distinct tissue- and cell-specific expression patterns. Despite the extensive profiling of circRNAs, their individual functions remain poorly understood. To fill this gap, we designed a genome-wide library of 65,300 shRNAs, targeting 9,663 clinically relevant circRNAs and 3,981 of their linear parental genes, and conducted functional screening in seven types of human cancer. We identified a total of 1,342 essential circRNAs (13.9% screened) that impact cell proliferation in at least one cell line, and in 96.5% of the cases, the linear counterparts are not essential. While a shared common subset emerges as functional regulators across all examined cell lines, the majority of circRNAs are functional in a cell type-specific manner. For a comprehensive presentation of the functional circRNA landscape in cancer, we introduce FunCirc, an online database encompassing functional circRNAs across cancer cell lines, coupled with circRNA expression profiles from diverse cancer and tissue types. Our work enhances the understanding of circRNA functions in cancer and provides the scientific community with a resource to further investigate their intricate roles.

cancer biology↗

Circular RNA profiling and functional screening in breast cancer identify circNSD1 as a suppressor of tumor autophagy

Circular RNAs (circRNAs) have emerged as critical regulators of cell biology. However, their function in breast cancer remains elusive. Herein, through circRNA profiling of 38 breast tumors and 10 benign breast tissues, we identified 509 of differentially expressed circRNAs. Integration with transcriptome-wide functional screening of approximately 10,000 circRNAs pinpointed circNSD1(6) as a top ranked tumor suppressor in breast cancer. circNSD1(6) is downregulated in tumors and suppression of the circular form, but not the linear form, promotes breast cancer proliferation and tumor growth. Mechanistically, circNSD1(6) interacts with autophagy receptor SQSTM1/p62 and inhibits the oligomerization of p62, thereby suppressing p62 body formation and p62-dependent autophagy. Furthermore, circNSD1(6) ameliorates p62-mediated Keap1 sequestration, hence suppresses the Nrf2 pathway and oxidative stress tolerance. Our study provides a landscape view of the transcription and function of circRNA in breast cancer, and uncovers the crucial role of circNSD1(6) in autophagy and antioxidant stress.

cancer biology↗

Synergistic effects of commensals and phage predation in combating pathogen infections: a shrimp model

Colonization resistance is a fundamental mechanism by which microbiomes suppress pathogen invasion, yet the factors governing its efficacy remain unclear. By constructing a defined microbial consortium and employing in vivo models, we provide evidence supporting that phage-mediated pathogen suppression interacts with competitive dynamics within microbial communities to enhance colonization resistance. While individual species conferred limited protection, combining key taxa with phage treatment extensively improved pathogen exclusion. Phage-induced pathogen killing amplified nutrient competition among commensals, however, the timing intervention emerged as a crucial determinant of efficacy. Guided by these ecological principles, we formulated a minimal, synergistic consortium that robustly enhanced colonization resistance in vivo. These findings advance our understanding of microbiome-driven pathogen resistance and provide a strategic framework for designing protective microbial consortia with potential applications in human and animal health.

microbiology↗