Biology subjects

Lu, J.

Publications and source records attributed to Lu, J..

At least 145 records · Page 8Linked to original sources

bioRxiv2024

MOSAIC: A Pipeline for MicrobiOme Studies Analytical Integration and Correction

Large-scale and consortium microbiome studies have enabled identification of reliable population-level biomedical signals, wherein integration is essential to eliminate unwanted variations between batches or studies and retain biological signals. Many strategies, each with distinct advantages and limitations, have been adapted or developed for microbiome data. The optimal strategy for a given study needs to be determined on a data-specific, case-by-case basis. Here, we develop the first-of-its-kind MicrobiOme Studies Analytical Integration and Correction (MOSAIC) pipeline to enable a convenient, fair, and comprehensive comparison of integration strategies. It includes modules for pre-processing, integration, and evaluation of artifact removal and signal preservation, using metrics relevant to common microbiome analyses, including alpha and beta diversities, disease prediction, and differential abundance analysis. We applied MOSAIC to extensive real-world and simulated data and found that though no single strategy excels in all aspects, yet certain strategies, the ComBat and ConQuR families, perform better overall.

bioinformatics↗

bioRxiv2024

Behavioral and neural evidence for perceptual predictions in social interactions

The ability to predict others behavior is crucial for social interactions. The goal of the present study was to test whether predictions are derived during observation of social interactions and whether these predictions influence how the whole-body emotional expressions of the agents are perceived. Using a novel paradigm, we induced social predictions in participants by presenting them with a short video of a social interaction in which a person approached another person and greeted him by touching the shoulder in either a neutral or an aggressive fashion. The video was followed by a still image showing a later stage in the interaction and we measured participants behavioral and neural responses to the still image, which was either congruent or incongruent with the emotional valence of the touching. We varied the strength of the induced predictions by parametrically reducing the saliency of emotional cues in the video. Behaviorally, we found that reducing the emotional cues in the video led to a significant decrease in participants ability to correctly judge the appropriateness of the emotional reaction in the image. At the neural level, EEG recordings revealed that observing an angry reaction elicited significantly larger N170 amplitudes than observing a neutral reaction. This emotion effect was only found in the high prediction condition (where the context in the preceding video was intact and clear), not in the mid and low prediction conditions. We further found that incongruent conditions elicited larger N300 amplitudes than congruent conditions only for the neutral images. Our findings provide evidence that viewing the initial stages of social interactions triggers predictions about their outcome in early cortical processing stages.

neuroscience↗

bioRxiv2024

Evolutionarily guided transcription factor design programs novel T cell states

Protein-coding genes in the human genome evolved via modular rearrangement of domains from ancestral genes1. Here, we develop a scalable, evolutionarily guided method to assemble novel protein-coding genes from constituent domains within a protein family, termed DESynR (Domain Engineered via Synthesis and Recombination) genes. Using primary human chimeric antigen receptor T cells as a model system, we find that the expression of DESynR Activator Protein-1 (AP-1) transcription factors (TFs) significantly outperforms the overexpression of natural AP-1 TFs in multiple functional assays in vitro and in vivo. Top DESynR AP-1 TFs exhibit non-intuitive architectures of constituent domains, including from TFs that are not canonically expressed in T cells. DESynR AP-1 TFs induce broad transcriptional and epigenetic reprogramming of T cells and, in some cases, lead to the development of non-natural T cell states, engaging gene expression modules from disparate human cell types. Taken together, we demonstrate that novel configurations of existing protein domains may uncover non-evolved genes that program cell states with therapeutically relevant functions.

genetics↗

bioRxiv2024

YY1 Enhances the Stability of HIF-1α Protein by Interacting with NUSAP1 in Macrophages within the Prostate Cancer Microenvironment

Immune checkpoint therapy for prostate cancer (PCa) has failed in clinical trials; however, the precise underlying mechanisms involved remain elusive. PCa, a classic "immune-cold" tumor, is characterized by an immunosuppressive tumor microenvironment. Within this milieu, macrophages, the predominant immune cell population, have a propensity to infiltrate the hypoxic zones of tumors. In a previous study, we showed that Yin Yang 1 (YY1) is highly expressed in macrophages in PCa tissues. Here, through multiplexed imaging mass cytometry (IMC) of a PCa tissue microarray, we further demonstrate that YY1+ macrophages aggregate in hypoxic areas of tumors and that hypoxia promotes the phase separation of YY1 in the nucleus by increasing YY1 tyrosine phosphorylation in macrophages. Furthermore, YY1 binds to NUSAP1 and promotes the SUMOylation of HIF-1, which promotes phase separation and stabilization of the HIF-1 protein. We also demonstrated that either treatment with a small molecule inhibitor (tenapanor) to decrease the YY1-NUSAP1-HIF-1 interaction or myeloid-specific YY1 gene knockout impairs subcutaneous PCa tumor formation. Furthermore, we present a first-generation tetrahedral DNA nanostructure (TDN) based on the proteolysis targeting chimera (PROTAC) technique, named YY1-DcTAC, which targets and degrades YY1 in tumor-associated macrophages. In a PCa mouse model, YY1-DcTAC exhibited prolonged drug efficacy, robust macrophage-specific responsiveness, potent antitumor effects, and increased CD8+ T cell tumor infiltration. In summary, our findings underscore the pivotal role of YY1 within the hypoxia/HIF-1 pathway in tumor-associated macrophages and affirm the therapeutic potential of targeting YY1 for treating PCa.

cancer biology↗

bioRxiv2024

NOLC1 Suppresses Immuno-chemotherapy by Inhibiting p53-mediated Ferroptosis in Gastric Cancer

Gastric cancer (GC) is one of the most malignant cancers, and cisplatin (Cis)-based chemotherapy remains the main clinical treatment for GC. However, Cis resistance often occurs, largely limiting its therapeutic efficacy in tumors. Therefore, a better understanding of the drug resistance mechanism could reveal new approaches for improving GC treatment efficacy. Here, we define the integrative role of nucleolar and coiled-body phosphoprotein 1 (NOLC1), a molecular chaperone that is significantly upregulated in GC tissues and Cis-resistant GC cells. Knocking down NOLC1 increased GC sensitivity to Cis by regulating ferroptosis. Mechanistically, NOLC1 binds to the p53 DNA binding domain (DBD), decreasing p53 nuclear accumulation stimulated by Cis and suppressing p53 transcriptional functions. Then, the p53-mediated ferroptosis is suppressed. Furthermore, the silence of NOLC1 promoted ferroptosis-induced immunogenic cell death (ICD) and reprogrammed the immunosuppressive tumor microenvironment, thereby increasing sensitivity to anti-programmed cell death-1 (PD-1) therapy plus Cis. The combination of anti-PD-1 plus Cis effectively inhibited GC growth without significant side effects. In summary, our findings reveal that targeting NOLC1 may be a novel therapeutic strategy for GC and may increase the efficacy of chemotherapy combined with immune checkpoint inhibitor (ICI) therapy.

cancer biology↗

bioRxiv2024

Mechanism of N-Myc Downstream Regulated Gene-1 regulating Integrin expression to inhibit gastric cancer metastasis

BackgroundN-myc downstream regulated gene-1 (NDRG1), the first member of the NDRG family, is widely distributed in various tissues and organs. In gastric cancer, the hypo-expression of NDRG1 has been positively associated with malignant biological behavior and poor prognosis. In-depth investigation of the potential regulatory mechanism of NDRG1 on gastric cancer is crucial for understanding the pathogenesis of this disease, identifying new therapeutic targets, and developing personalized treatment strategies. This study aims to explore the significance of NDRG1 in regulating the expression of integrins during the progression of gastric cancer. MethodsThe expression of NDRG1 in gastric cancer was analyzed using R software v4.0.3, Boxplot, and the Kaplan-Meier Plotter. Stable cell lines with NDRG1 knockdown and overexpression were subjected to wound healing assays, transwell migration assays, CCK8 assays, and colony formation assays, in order to determine the effects of NDRG1 on the migration and proliferation of gastric cancer cells. Western blotting was employed to examine the impact of NDRG1 on the expression of integrin-related proteins. Immunostaining was used to comprehensively analyze the in vitro experimental results. ResultsThe mRNA and protein levels of NDRG1 were downregulated substantially in gastric cancer, which was inversely correlated with the malignancy of pathological behavior and poor prognosis in gastric cancer. Knockdown of the NDRG1 gene significantly enhanced the proliferation and migration capabilities of gastric cancer cells, whereas overexpression of NDRG1 reduced their metastatic potential and inhibited both migration and proliferation. NDRG1 was found to regulate the expression of fibronectin 1, which in turn influenced the expression of a series of integrins, including v, 5, {beta}1, and {beta}3. This regulatory mechanism may play a crucial role in the early distant metastasis observed in gastric cancer. ConclusionsThis study demonstrated that NDRG1 could regulate the expression levels of integrins through specific signaling pathways, thereby interfering the adhesive and migratory capacities of gastric cancer cells. NDRG1 could serve as a valuable biomarker for the diagnosis and prognosis of gastric cancer, offering new potential targets for its treatment.

cancer biology↗

bioRxiv2024

Quantitative Resolving Cell Fate in the Early Embryogenesis of Caenorhabditis elegans

The nematode Caenorhabditis elegans exhibits an invariant cell lineage during its development where the gene-molecular network that regulates the development is crucial for the biological process. While there are many molecular cell atlases describing the phenomena and key molecules involved in cell transformation, the underlying mechanisms from a systems biology perspective have received less attention. Based on an endogenous molecular-cellular theory that relates the molecular mechanisms to biological phenotypes, we constructed a model of core endogenous network to describe the early stages of embryonic development of the nematode. Different cell types and intermediate cell states during development from zygotes to founder cells correspond to the steady states of the network as a nonlinear stochastic dynamical system. Connections between steady states form a topological landscape that encompasses known developmental lineage trajectories. By regulating the expression of agents in the network, we quantitatively simulated the effects of the Wnt and Notch signaling pathway on cell fate transitions and predicted the possible trajectories of transdifferentiation of the AB cell across the lineage. The success of the current study may help advance our understanding of the fundamental principles of developmental biology and cell fate determination, offering an effective tool for quantitative analysis of cellular processes.

developmental biology↗

bioRxiv2024

Cancer-associated adipocytes mediate CD8+T cell dysfunction via FGF21-driven lipolysis

Cancer-associated adipocytes (CAAs) reprogram metabolic status of tumor microenvironment (TME). The metabolic crosstalk between CAAs and CD8+T cells in TME remains unclear. Here we report that CAAs undergo lipolysis, releasing free fatty acids that promote lipid peroxidation and disturb mitochondrial homeostasis in CD8+T cells, leading to their functional exhaustion. Importantly, we uncover that fibroblast growth factor 21 (FGF21) autocrinally drives CAA lipolysis through upregulating adipose triglyceride lipase (ATGL) via FGFR1/KLB-p38 signaling. FGF21 deletion in adipose tissue or ATGL inhibition impedes CAA lipolysis, mitigates lipid peroxidation, normalizes mitochondrial dynamics of CD8+T cells, and restores their effector function, consequently blunting tumor growth. Moreover, combining ATGL inhibitor with anti-PD-1 therapy synergistically enhances the antitumor activity of CD8+T cells, yielding greater therapeutic efficacy. Our findings highlight the pivotal role of CAA lipol ysis in CD8+T cell dysfunction within TME, suggesting that targeting CAA lipolysis represents a valuable avenue for improving cancer immunotherapy.

cancer biology↗

bioRxiv2024

Dissecting Regulatory Non-Coding Heart Disease GWAS Loci with High-Resolution 3D Chromatin Interactions Reveals Causal Genes with Pathophysiological Relevance to Heart Failure

Heart failure is caused in part by cardiac remodeling processes that include the death of cardiac myocytes and their replacement by cardiac fibroblasts. We hypothesized that these two cell types may harbor epigenetic contexts in which heart disease-associated non-coding SNPs perturb gene expression relevant to disease. Accordingly, we generated high-resolution Hi-C data layered with chromatin accessibility and transcriptomic information to annotate and link putative distal regulatory elements in heart disease-associated loci to gene promoters. Our analysis identified several target genes with established roles in cardiac fibrosis and/or heart disease (GJA1, TBC1D32, CXCL12, IL6R, and FURIN). Perturb-seq in cardiac fibroblasts to knock out putative regulatory elements confirmed regulatory relationships involving GJA1, CXCL12, and FURIN, as gene editing led to changes in transcriptomic signatures associated with fibroblasts in heart failure. Our results demonstrate how integrative multi-omic approaches can delineate pathophysiologically relevant regulatory circuits that connect protein-coding genes to non-coding genetic variants associated with disease.

genomics↗

bioRxiv2024

Scaling laws of plasmids across the microbial tree of life

Plasmids play a critical role in shaping the dynamics and evolution of microbial communities. The capacity of a plasmid to express genes is constrained by two parameters: length and copy number. However, the interplay between these parameters and their constraints on plasmid evolution have remained elusive due to the absence of comprehensive quantitative analyses. To address this gap, we developed Probabilistic Iterative Read Assignment (PIRA), a new computational method that overcomes previous computational bottlenecks, enabling rapid and accurate determination of plasmid copy numbers at an unprecedented scale. Applying PIRA to all microbial genomes in the NCBI RefSeq database with linked short-read sequencing data in the Sequencing Read Archive (SRA), we analyzed 4,317 bacterial and archaeal genomes encompassing 11,338 plasmids, spanning the microbial tree of life. Our analysis reveals three scaling laws of plasmids: first, an inverse power-law correlation between plasmid copy number and plasmid length; second, a positive linear correlation between protein-coding genes and plasmid length; and third, a positive correlation between metabolic genes per plasmid and plasmid length, particularly for large plasmids. These scaling laws imply fundamental constraints on plasmid evolution and functional organization, indicating that as plasmids increase in length, they converge toward chromosomal characteristics in copy number and functional content. Our findings not only advance the understanding of plasmid dynamics but also have implications for microbial evolution, biotechnology, and the design of synthetic plasmids. SignificanceBy discovering universal scaling laws and developing a new computational method to compute plasmid copy numbers across the microbial tree of life, we show that as plasmids increase in length, they converge to chromosomes in their copy number and their coding and metabolic properties. This insight reveals fundamental principles governing plasmid evolution and has implications for biotechnology and medicine.

microbiology↗

bioRxiv2024

Gas1-Mediated Suppression of Hepatoblastoma Tumorigenesis

Background and AimsHepatoblastoma (HB), the most common pediatric liver cancer, often dysregulates the Wnt/{beta}-catenin, Hippo and NFE2L2/NRF2 pathways. Pairwise combinations of oncogenically active forms of the terminal transcription factor effectors of these pathways, namely {beta}-catenin (B), YAP (Y) and NRF2 (N) generate HBs in mice, with the triple combination (B+Y+N) being particularly potent. Each tumor group alters the expression of thousands of B-,Y- and N-driven unique and common target genes. Identifying those most responsible for transformation is thus an important question as it might reveal new mechanistic insights and therapeutic opportunities. Approach and ResultsTranscriptional profiling of >60 murine HBs driven by the above oncogenic combinations and different B mutants and in genetic backgrounds that impair tumor growth rates but not initiation has revealed a common set of 22 "BYN genes" that are similarly deregulated in all cases. Many are associated with multiple "Cancer Hallmarks" and their expression levels often correlate with survival in human HBs, hepatocellular carcinomas and other cancers. Among the most down-regulated of these is Gas1, which encodes a Glycosylphosphatidylinositol (GPI)-linked outer membrane protein. We show here that restoring Gas1 expression impairs B+Y+N-driven HB tumor growth in vivo and in HB-derived immortalized cell lines in vitro in a manner than requires membrane anchoring of the protein via its GPI moiety. ConclusionsOur findings implicate Gas1 as a proximal mediator of HB pathogenesis and validate the BYN gene set as one deserving of closer additional scrutiny in future studies.

cancer biology↗

bioRxiv2024

Efficient Derivation of Immortalized, Isogenic Cell Lines from Genetically Defined Murine Hepatoblastomas

Background & AimsMolecularly, hepatoblastoma (HB), the most common childhood liver cancer, is the simplest of all human neoplasms, with the vast majority deregulating the Wnt/{beta}-catenin, Hippo and/or NFE2/NRF2 signaling pathways. Murine HBs can be generated by over-expressing any pairwise or triple combination of mutant forms of these pathways terminal effectors, namely {beta}-catenin (B), YAP (Y) and NFE2L2/NRF (N). Each molecular subtypes displays distinct features resembling those of human HBs. However, research has been hampered by a paucity of established cell lines of any species. MethodsWe show here that immortalized cell lines can be routinely established from murine HBs that over-express B+Y and B+Y+N. This is facilitated by the concurrent in vivo, Crispr-mediated inactivation of the Cdkn2a tumor suppressor locus. ResultsEight BY and 3 BYN cell lines have been generated and characterized and are available to the HB research community. Ten of these lines can be regrown as subcutaneous and metastatic lung tumors in the immuno-competent mice from which they originated while retaining their original histologic features. During maintenance as spheroids in vitro, or during in vivo propagation, tumor cells express endothelial cell markers, particularly in regions that are hypoxic and/or in proximity to incipient blood vessels. ConclusionsThe ability to generate isogenic HB cell lines with defined oncogenic drivers should facilitate studies that are best performed in vitro. The approach may also be useful for deriving HB cell lines associated with less common molecular drivers and from human tumors. SynopsisThe derivation of multiple immortalized murine hepatoblastoma cell lines driven by defined oncogenes is described. These lines are isogenic, retain their tumorigenicity in immuno-competent mice, readily form spheroids and express endothelial markers in response to hypoxia. They will allow studies that have heretofore been difficult or impossible to perform in vivo.

cancer biology↗

bioRxiv2024

Deep learning facilitates precise identification of disease-resistance genes in plants

The identification of plant disease-resistance genes is essential for understanding the plant immune system and accelerating crop breeding with disease resistance. Therefore, there is a pressing need for a method capable of accurately and comprehensively annotating resistance genes at a genome-wide scale. In this study, we propose Evolutionary Scale Modeling for LRR (ESM-LRR), a novel approach based on the deep protein language model to accurately identify LRR domains which are substantially variable structures in disease-resistance proteins. ESM-LRR achieved its highest F1 score of 0.80 on a test set using the 90% identity as the matching threshold. Building upon ESM-LRR, we developed the Plant Disease-Resistance Gene Predictor (R-Predictor), a framework designed to simultaneously annotate 15 diverse domain topologies, extensively covering recently characterised resistance genes across the entire genome. R-Predictor integrates four modules, each employing superior methods that outperform existing methods (F1 score of 0.89 for RLKs and 0.88 for NLRs), demonstrating its high accuracy and practicality in annotating disease-resistance genes (R genes). R-Predictor were also applied to identify R genes in rice, tomato, and grapevine. Furthermore, AlphaFold3 was employed to screen interactions between 1,116 protein pairs involving three NLRs identified exclusively by R-Predictor and 372 literature-reported plant-pathogen effectors, resulting in the identification of 15 putative NLR-effector complexes. Overall, this study presents a novel approach for advancing our understanding of plant immune mechanisms.

bioinformatics↗

bioRxiv2024

Somatic mtDNA Mutation Burden Shapes Metabolic Plasticity in Leukemogenesis

The role of somatic mitochondrial DNA (mtDNA) mutations in leukemogenesis remains poorly characterized. To determine the impact of somatic mtDNA mutations on the process, we assessed the leukemogenic potential of hematopoietic progenitor cells (HPCs) from mtDNA mutator mice (Polg D257A) with or without NMyc overexpression. We observed a higher incidence of spontaneous leukemogenesis in recipients transplanted with heterozygous Polg HPCs and a lower incidence of NMyc-driven leukemia in those with homozygous Polg HPCs compared to controls. Although mtDNA mutations in heterozygous and homozygous HPCs caused similar baseline impairments in mitochondrial function, only heterozygous HPCs responded to and supported altered metabolic demands associated with NMyc overexpression. Homozygous HPCs showed altered glucose utilization with pyruvate dehydrogenase inhibition due to increased phosphorylation, exacerbated by NMyc overexpression. The impaired growth of NMyc-expressing homozygous HPCs was partially rescued by inhibiting pyruvate dehydrogenase kinase, highlighting a relationship between mtDNA mutation burden and metabolic plasticity in leukemogenesis. TEASERSomatic mtDNA mutations as drivers of metabolic change in the development of leukemia.

cancer biology↗

bioRxiv2024

The glia club: Validation of polarization biomarkers for human microglia (HMC3) using quantitative real time RT-qPCR.

Microglia are the primary immune cells of the brain and play critical roles in neurodevelopment, neuroprotection, the maintenance of homeostasis, and neurotoxicity. Classification of microglia polarization, however, remains contentious. Identifying suitable biomarkers for gene expression analysis of microglia polarization is crucial for characterizing the biological significance of microglia in health and disease. In this study, we use human microglia clone 3 (HMC3) cells to validate and test suitable internal controls (GAPDH, PKM, 18S, ACTB, PGK1, TKT1, TPI1), homeostatic (CD68, TGF-{beta}, IBA1, BIN1, RGS10), proinflammatory (IL-6, CXCL10, CCL5, SAA, IL-1{beta}) and anti-inflammatory (CCL2, SOCS3, IL-10, CD200R1, ARG1) markers along with their transcriptional profiles upon interferon-gamma (IFN-{gamma}) stimulation to characterize the microglia polarization spectrum. Our study is the first to present a comprehensive list of biomarkers with detailed methodology on gene selection, primer design, RT-qPCR parameters, and transcript abundance in baseline and polarized HMC3 cells. Our study will increase the rigor of gene expression analysis and target selection in a widely used brain macrophage.

cell biology↗

bioRxiv2024

ICOS limits memory-like properties and function of exhausted PD-1+ CD8 T cells

During persistent antigen stimulation, PD-1+CD8 T cells are maintained by progenitor exhausted PD-1+TCF-1+CD8 T cells (Tpex). Tpex respond to PD-1 blockade, and regulation of Tpex differentiation into more functional Tex is of major interest for cancer immunotherapies. Tpex express high levels of Inducible Costimulator (ICOS), but the role of ICOS for PD-1+CD8 T cell responses has not been addressed. In chronic infection, ICOS-deficiency increased both number and quality of virus-specific CD8 T cells, with accumulation of effector-like Tex due to enhanced survival. Mechanistically, loss of ICOS signaling potentiated FoxO1 activity and memory-like features of Tpex. In mice with established chronic infection, ICOS-Ligand blockade resulted in expansion of effector-like Tex and reduction in viral load. In a mouse model of hepatocellular carcinoma, ICOS inhibition improved cytokine production by tumor-specific PD-1+CD8 T cells and delayed tumor growth. Overall, we show that ICOS limits CD8 T cell responses during chronic antigen exposure.

immunology↗

bioRxiv2024

Decoding pattern formation rules by integrating mechanistic modeling and deep learning

Predictive programming of self-organized pattern formation using living cells is challenging in major part due to the difficulty in navigating through the high-dimensional design space effectively. The emergence and characteristics of patterns are highly sensitive to both system and environmental parameters. Often, the optimal conditions able to generate patterns represent a small fraction of the possible design space. Furthermore, the experimental generation and quantification of patterns is typically labor intensive and low throughput, making it impractical to optimize pattern formation solely based on trials and errors. To this end, simulations using a well-formulated mechanistic model can facilitate the identification of optimal experimental conditions for pattern formation. However, even a moderately complex system can make these simulations computationally prohibitive when applied to a large parameter space. In this study, we demonstrate how integrating mechanistic modeling with machine learning can significantly accelerate the exploration of design space for patterning circuits and aid in deriving human-interpretable design rules. We apply this strategy to program self-organized ring patterns in Pseudomonas aeruginosa using a synthetic gene circuit. Our approach involved training a neural network with simulated data to predict pattern formation 10 million times faster than the mechanistic model. This neural network was then used to predict pattern formation across a vast array of parameter combinations, far exceeding the size of the training dataset and what was computationally feasible using the mechanistic model alone. By doing so, we identified many parameter combinations able to generate desirable patterns, which still represent an extremely small fraction of explored parametric space. We next used the mechanistic model to validate top candidates and identify coarse-grained rules for patterning. We experimentally demonstrated the generation and control of patterning guided by the learned rules. Our work highlights the effectiveness in integrating mechanistic modeling and machine learning for rational engineering of complex dynamics in living cells.

synthetic biology↗

bioRxiv2024

A forward genetic screen identifies Sirtuin1 as a driver of neuroendocrine prostate cancer

Although localized prostate cancer is relatively indolent, advanced prostate cancer manifests with aggressive and often lethal variants, including neuroendocrine prostate cancer (NEPC). To identify drivers of aggressive prostate cancer, we leveraged Sleeping Beauty (SB) transposon mutagenesis in a mouse model based on prostate-specific loss-of-function of Pten and Tp53. Compared with control mice, SB mice developed more aggressive prostate tumors, with increased incidence of metastasis. Notably, a significant percentage of the SB prostate tumors display NEPC phenotypes, and the transcriptomic features of these SB mouse tumors recapitulated those of human NEPC. We identified common SB transposon insertion sites (CIS) and prioritized associated CIS-genes differentially expressed in NEPC versus non-NEPC SB tumors. Integrated analysis of CIS-genes encoding for proteins representing upstream, post-translational modulators of master regulators controlling the transcriptional state of SB-mouse and human NEPC tumors identified sirtuin 1 (Sirt1) as a candidate mechanistic determinant of NEPC. Gain-of-function studies in human prostate cancer cell lines confirmed that SIRT1 promotes NEPC, while its loss-of-function or pharmacological inhibition abrogates NEPC. This integrative analysis is generalizable and can be used to identify novel cancer drivers for other malignancies. SummaryUsing an unbiased forward mutagenesis screen in an autochthonous mouse model, we have investigated mechanistic determinants of aggressive prostate cancer. SIRT1 emerged as a key regulator of neuroendocrine prostate cancer differentiation and a potential target for therapeutic intervention.

cancer biology↗