An atlas of epithelial cell states and plasticity in lung adenocarcinoma.

Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention.

USP7 substrates identified by proteomics analysis reveal the specificity of USP7.

Deubiquitylating enzymes (DUBs) remove ubiquitin chains from proteins and regulate protein stability and function. USP7 is one of the most extensively studied DUBs, since USP7 has several well-known substrates important for cancer progression, such as MDM2, N-MYC, and PTEN. Thus, USP7 is a promising drug target. However, systematic identification of USP7 substrates has not yet been performed. In this study, we carried out proteome profiling with label-free quantification in control and single/double-KO cells of USP7and its closest homolog, USP47 Our proteome profiling for the first time revealed the proteome changes caused by USP7 and/or USP47 depletion. Combining protein profiling, transcriptome analysis, and tandem affinity purification of USP7-associated proteins, we compiled a list of 20 high-confidence USP7 substrates that includes known and novel USP7 substrates. We experimentally validated MGA and PHIP as new substrates of USP7. We further showed that MGA deletion reduced cell proliferation, similar to what was observed in cells with USP7 deletion. In conclusion, our proteome-wide analysis uncovered potential USP7 substrates, providing a resource for further functional studies.

GDF5+ chondroprogenitors derived from human pluripotent stem cells preferentially form permanent chondrocytes.

It has been established in the mouse model that during embryogenesis joint cartilage is generated from a specialized progenitor cell type, distinct from that responsible for the formation of growth plate cartilage. We recently found that mesodermal progeny of human pluripotent stem cells gave rise to two types of chondrogenic mesenchymal cells in culture: SOX9+ and GDF5+ cells. The fast-growing SOX9+ cells formed in vitro cartilage that expressed chondrocyte hypertrophy markers and readily underwent mineralization after ectopic transplantation. In contrast, the slowly growing GDF5+ cells derived from SOX9+ cells formed cartilage that tended to express low to undetectable levels of chondrocyte hypertrophy markers, but expressed PRG4, a marker of embryonic articular chondrocytes. The GDF5+-derived cartilage remained largely unmineralized in vivo. Interestingly, chondrocytes derived from the GDF5+ cells seemed to elicit these activities via non-cell-autonomous mechanisms. Genome-wide transcriptomic analyses suggested that GDF5+ cells might contain a teno/ligamento-genic potential, whereas SOX9+ cells resembled neural crest-like progeny-derived chondroprogenitors. Thus, human pluripotent stem cell-derived GDF5+ cells specified to generate permanent-like cartilage seem to emerge coincidentally with the commitment of the SOX9+ progeny to the tendon/ligament lineage.

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders.

As the most complex organ of the human body, the brain is composed of diverse regions, each consisting of distinct cell types and their respective cellular interactions. Human brain development involves a finely tuned cascade of interactive events. These include spatiotemporal gene expression changes and dynamic alterations in cell-type composition. However, our understanding of this process is still largely incomplete owing to the difficulty of brain spatiotemporal transcriptome collection. In this study, we developed a tensor-based approach to impute gene expression on a transcriptome-wide level. After rigorous computational benchmarking, we applied our approach to infer missing data points in the widely used BrainSpan resource and completed the entire grid of spatiotemporal transcriptomics. Next, we conducted deconvolutional analyses to comprehensively characterize major cell-type dynamics across the entire BrainSpan resource to estimate the cellular temporal changes and distinct neocortical areas across development. Moreover, integration of these results with GWAS summary statistics for 13 brain-associated traits revealed multiple novel trait-cell-type associations and trait-spatiotemporal relationships. In summary, our imputed BrainSpan transcriptomic data provide a valuable resource for the research community and our findings help further studies of the transcriptional and cellular dynamics of the human brain and related diseases.

Innately expressed estrogen-related receptors in the skeletal muscle are indispensable for exercise fitness.

Transcriptional determinants in the skeletal muscle that govern exercise capacity, while poorly defined, could provide molecular insights into how exercise improves fitness. Here, we have elucidated the role of nuclear receptors, estrogen-related receptor alpha and gamma (ERRα/γ) in regulating myofibrillar composition, contractility, and exercise capacity in skeletal muscle. We used muscle-specific single or double (DKO) ERRα/γ knockout mice to investigate the effect of ERRα/γ deletion on muscle and exercise parameters. Individual knockout of ERRα/γ did not have a significant impact on the skeletal muscle. On the other hand, DKO mice exhibit pale muscles compared to wild-type (WT) littermates. RNA-seq analysis revealed a predominant decrease in expression of genes linked to mitochondrial and oxidative metabolism in DKO versus WT muscles. DKO muscles exhibit marked repression of oxidative enzymatic capacity, as well as mitochondrial number and size compared to WT muscles. Mitochondrial function is also impaired in single myofibers isolated from DKO versus WT muscles. In addition, mutant muscles exhibit reduced angiogenic gene expression and decreased capillarity. Consequently, DKO mice have a significantly reduced exercise capacity, further reflected in poor fatigue resistance of DKO mice in in vivo contraction assays. These results show that ERRα and ERRγ together are a critical link between muscle aerobic capacity and exercise tolerance. The ERRα/γ mutant mice could be valuable for understanding the long-term impact of impaired mitochondria and vascular supply on the pathogenesis of muscle-linked disorders.

Long-Term Erythromycin Treatment Alters the Airway and Gut Microbiota: Data from Chronic Obstructive Pulmonary Disease Patients and Mice with Emphysema.

INTRODUCTION: Although long-term macrolide antibiotics could reduce the recurrent exacerbation of chronic obstructive pulmonary disease (COPD), the side effect of bacterial resistance and the impact on the microbiota remain concerning. We investigated the influence of long-term erythromycin treatment on the airway and gut microbiota in mice with emphysema and patients with COPD. METHODS: We conducted 16S rRNA gene sequencing to explore the effect of erythromycin treatment on the lung and gut microbiota in mice with emphysema. Liquid chromatography-mass spectrometry was used for lung metabolomics. A randomized controlled trial was performed to investigate the effect of 48-week erythromycin treatment on the airway and gut microbiota in COPD patients. RESULTS: The mouse lung and gut microbiota were disrupted after cigarette smoke exposure. Erythromycin treatment depleted harmful bacteria and altered lung metabolism. Erythromycin treatment did not alter airway or gut microbial diversity in COPD patients. It reduced the abundance of pathogens, such as Burkholderia, in the airway of COPD patients and increased levels of symbiotic bacteria, such as Prevotella and Veillonella. The proportions of Blautia, Ruminococcus, and Lachnospiraceae in the gut were increased in COPD patients after erythromycin treatment. The time to the first exacerbation following treatment was significantly longer in the erythromycin treatment group than in the COPD group. CONCLUSION: Long-term erythromycin treatment reduces airway and gut microbe abundance in COPD patients but does not affect microbial diversity and restores microbiota balance in COPD patients by reducing the abundance of pathogenic bacteria.

DeepFun: a deep learning sequence-based model to decipher non-coding variant effect in a tissue- and cell type-specific manner.

More than 90% of the genetic variants identified from genome-wide association studies (GWAS) are located in non-coding regions of the human genome. Here, we present a user-friendly web server, DeepFun (https://bioinfo.uth.edu/deepfun/), to assess the functional activity of non-coding genetic variants. This new server is built on a convolutional neural network (CNN) framework that has been extensively evaluated. Specifically, we collected chromatin profiles from ENCODE and Roadmap projects to construct the feature space, including 1548 DNase I accessibility, 1536 histone mark, and 4795 transcription factor binding profiles covering 225 tissues or cell types. With such comprehensive epigenomics annotations, DeepFun expands the functionality of existing non-coding variant prioritizing tools to provide a more specific functional assessment on non-coding variants in a tissue- and cell type-specific manner. By using the datasets from various GWAS studies, we conducted independent validations and demonstrated the functions of the DeepFun web server in predicting the effect of a non-coding variant in a specific tissue or cell type, as well as visualizing the potential motifs in the region around variants. We expect our server will be widely used in genetics, functional genomics, and disease studies.

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.

Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.

Genome-wide CRISPR screens reveal cyclin C as synthetic survival target of BRCA2.

Poly (ADP-ribose) polymerase inhibitor (PARPi)-based therapies initially reduce tumor burden but eventually lead to acquired resistance in cancer patients with BRCA1 or BRCA2 mutation. To understand the potential PARPi resistance mechanisms, we performed whole-genome CRISPR screens to discover genetic alterations that change the gene essentiality in cells with inducible depletion of BRCA2. We identified that several RNA Polymerase II transcription Mediator complex components, especially Cyclin C (CCNC) as synthetic survival targets upon BRCA2 loss. Total mRNA sequencing demonstrated that loss of CCNC could activate the transforming growth factor (TGF)-beta signaling pathway and extracellular matrix (ECM)-receptor interaction pathway, however the inhibition of these pathways could not reverse cell survival in BRCA2 depleted CCNC-knockout cells, indicating that the activation of these pathways is not required for the resistance. Moreover, we showed that the improved survival is not due to restoration of homologous recombination repair although decreased DNA damage signaling was observed. Interestingly, loss of CCNC could restore replication fork stability in BRCA2 deficient cells, which may contribute to PARPi resistance. Taken together, our data reveal CCNC as a critical genetic determinant upon BRCA2 loss of function, which may help the development of novel therapeutic strategies that overcome PARPi resistance.

Comprehensive characterization of tumor immune landscape following oncolytic virotherapy by single-cell RNA sequencing.

An important mechanism of oncolytic virotherapy in ameliorating cancer immunotherapy is by inducing significant changes in the immune landscape in the tumor microenvironment (TME). Despite this notion and the potential therapeutic implications, a comprehensive analysis of the immune changes in carcinomas induced by virotherapy has not yet been elucidated. We conducted single-cell RNA sequencing analysis on carcinomas treated with an HSV-2-based oncolytic virus to characterize the immunogenic changes in the TME. We specifically analyzed and compared the immune cell composition between viral treated and untreated tumors. We also applied CellChat to analyze the complex interactions among the infiltrated immune cells. Our data revealed significant infiltration of B cells in addition to other important immune cells, including CD4+, CD8+, and NK cells following virotherapy. Further analysis identified distinct subset compositions of the infiltrated immune cells and their activation status upon virotherapy. The intensive interactions among the infiltrated immune cells as revealed by CellChat analysis may further shape the immune landscape in favor of generating antitumor immunity. Our findings will facilitate the design of new strategies in incorporating immunotherapy into virotherapy for clinical translation. Moreover, the significant infiltration of B cells makes it suitable for combining virotherapy with immune checkpoint inhibitors.

Hypoxia-inducible factor-dependent induction of myeloid-derived netrin-1 attenuates natural killer cell infiltration during endotoxin-induced lung injury.

Sepsis and sepsis-associated lung inflammation significantly contribute to the morbidity and mortality of critical illness. Here, we examined the hypothesis that neuronal guidance proteins could orchestrate inflammatory events during endotoxin-induced lung injury. Through a targeted array, we identified netrin-1 as the top upregulated neuronal guidance protein in macrophages treated with lipopolysaccharide (LPS). Furthermore, we found that netrin-1 is highly enriched in infiltrating myeloid cells, particularly in macrophages during LPS-induced lung injury. Transcriptional studies implicate hypoxia-inducible factor HIF-1α in the transcriptional induction of netrin-1 during LPS treatment. Subsequently, the deletion of netrin-1 in the myeloid compartment (Ntn1loxp/loxp LysM Cre) resulted in exaggerated mortality and lung inflammation. Surprisingly, further studies revealed enhanced natural killer cells (NK cells) infiltration in Ntn1loxp/loxp LysM Cre mice, and neutralization of NK cell chemoattractant chemokine (C-C motif) ligand 2 (CCL2) reversed the exaggerated lung inflammation. Together, these studies provide functional insight into myeloid cell-derived netrin-1 in controlling lung inflammation through the modulation of CCL2-dependent infiltration of NK cells.

Decoding regulatory structures and features from epigenomics profiles: A Roadmap-ENCODE Variational Auto-Encoder (RE-VAE) model.

The development of chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing (ChIP-seq) technologies has promoted generation of large-scale epigenomics data, providing us unprecedented opportunities to explore the landscape of epigenomic profiles at scales across both histone marks and tissue types. In addition to many tools directly for data analysis, advanced computational approaches, such as deep learning, have recently become promising to deeply mine the data structures and identify important regulators from complex functional genomics data. We implemented a neural network framework, a Variational Auto-Encoder (VAE) model, to explore the epigenomic data from the Roadmap Epigenomics Project and the Encyclopedia of DNA Elements (ENCODE) project. Our model is applied to 935 reference samples, covering 28 tissues and 12 histone marks. We used the enhancer and promoter regions as the annotation features and ChIP-seq signal values in these regions as the feature values. Through a parameter sweep process, we identified the suitable hyperparameter values and built a VAE model to represent the epigenomics data and to further explore the biological regulation. The resultant Roadmap-ENCODE VAE (RE-VAE) model contained data compression and feature representation. Using the compressed data in the latent space, we found that the majority of histone marks were well clustered but not for tissues or cell types. Tissue or cell specificity was observed only in some histone marks (e.g., H3K4me3 and H3K27ac) and could be characterized when the number of tissue samples is large (e.g., blood and brain). In blood, the contributive regions and genes identified by RE-VAE model were confirmed by tissue-specificity enrichment analysis with an independent tissue expression panel. Finally, we demonstrated that RE-VAE model could detect cancer cell lines with similar epigenomics profiles. In conclusion, we introduced and implemented a VAE model to represent large-scale epigenomics data. The model could be used to explore classifications of histone modifications and tissue/cell specificity and to classify new data with unknown sources.

TSEA-DB: a trait-tissue association map for human complex traits and diseases.

Assessing the causal tissues of human traits and diseases is important for better interpreting trait-associated genetic variants, understanding disease etiology, and improving treatment strategies. Here, we present a reference database for trait-associated tissue specificity based on genome-wide association study (GWAS) results, named Tissue-Specific Enrichment Analysis DataBase (TSEA-DB, available at https://bioinfo.uth.edu/TSEADB/). We collected GWAS summary statistics data for a wide range of human traits and diseases followed by rigorous quality control. The current version of TSEA-DB includes 4423 data sets from the UK Biobank (UKBB) and 596 from other resources (GWAS Catalog and literature mining), totaling 5019 unique GWAS data sets and 15 770 trait-associated gene sets. TSEA-DB aims to provide reference tissue(s) enriched with the genes from GWAS. To this end, we systematically performed a tissue-specific enrichment analysis using our recently developed tool deTS and gene expression profiles from two reference tissue panels: the GTEx panel (47 tissues) and the ENCODE panel (44 tissues). The comprehensive trait-tissue association results can be easily accessed, searched, visualized, analyzed, and compared across the studies and traits through our web site. TSEA-DB represents one of the many timely and comprehensive approaches in exploring human trait-tissue association.

Diverse types of genomic evidence converge on alcohol use disorder risk genes.

BACKGROUND: Alcohol use disorder (AUD) is one of the most common forms of substance use disorders with a strong contribution of genetic (50%-60%) and environmental factors. Genome-wide association studies (GWAS) have identified a number of AUD-associated variants, including those in alcohol metabolism genes. These genetic variants may modulate gene expression, making individuals more susceptible to AUD. A long-term alcohol consumption can also change the transcriptome patterns of subjects via epigenetic modulations. METHODS: To explore the interactive effect of genetic and epigenetic factors on AUD, we conducted a secondary analysis by integrating GWAS, CNV, brain transcriptome and DNA methylation data to unravel novel AUD-associated genes/variants. We applied the mega-analysis of OR (MegaOR) method to prioritise AUD candidate genes (AUDgenes). RESULTS: We identified a consensus set of 206 AUDgenes based on the multi-omics data. We demonstrated that these AUDgenes tend to interact with each other more frequent than chance expectation. Functional annotation analysis indicated that these AUDgenes were involved in substance dependence, synaptic transmission, glial cell proliferation and enriched in neuronal and liver cells. We obtained a multidimensional evidence that AUD is a polygenic disorder influenced by both genetic and epigenetic factors as well as the interaction of them. CONCLUSION: We characterised multidimensional evidence of genetic, epigenetic and transcriptomic data in AUD. We found that 206 AUD associated genes were highly expressed in liver, brain cerebellum, frontal cortex, hippocampus and pituitary. Our studies provides important insights into the molecular mechanism of AUD and potential target genes for AUD treatment.

CNet: a multi-omics approach to detecting clinically associated, combinatory genomic signatures.

MOTIVATION: Genome-wide multi-omics profiling of complex diseases provides valuable resources and opportunities to discover associations between various measures of genes and diseases. Currently, a pressing challenge is how to effectively detect functional genes associated with or causing phenotypic outcomes. We developed CNet to identify groups of genomic signatures whose combinatory effect is significantly associated with clinical and phenotypical outcomes. RESULTS: CNet builds on a generalized sequential feedforward method, augmented by a down-sampling bootstrap strategy to reduce random hitchhiking signatures. It further applies a dynamic trimming procedure to remove relatively less informative signatures at every step. CNet can manage heterogeneous genomic signature profiles simultaneously and select the best signature to represent a specific gene. To deal with various forms of clinical and phenotypical measurements, we introduced four models to deal with continuous, categorical and censored data. We tested CNet using drug-response data, multidimensional cancer genomics data and genome-wide association study data for multiple traits. Our results demonstrated that in various scenarios, CNet could effectively identify signatures that are associated with the outcomes. In addition, we applied CNet to identify likely disease-causing chains involving somatic mutations, pathway activities and patient outcomes. With appropriate setting, CNet can be applied in many biological conditions. AVAILABILITY AND IMPLEMENTATION: CNet can be downloaded at https://github.com/bsml320/CNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

deTS: tissue-specific enrichment analysis to decode tissue specificity.

MOTIVATION: Diseases and traits are under dynamic tissue-specific regulation. However, heterogeneous tissues are often collected in biomedical studies, which reduce the power in the identification of disease-associated variants and gene expression profiles. RESULTS: We present deTS, an R package, to conduct tissue-specific enrichment analysis with two built-in reference panels. Statistical methods are developed and implemented for detecting tissue-specific genes and for enrichment test of different forms of query data. Our applications using multi-trait genome-wide association studies data and cancer expression data showed that deTS could effectively identify the most relevant tissues for each query trait or sample, providing insights for future studies. AVAILABILITY AND IMPLEMENTATION: https://github.com/bsml320/deTS and CRAN https://cran.r-project.org/web/packages/deTS/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Repeated fed-batch strategy and metabolomic analysis to achieve high docosahexaenoic acid productivity in Crypthecodinium cohnii.

BACKGROUND: Docosahexaenoic acid (DHA) is essential for human diet. However, high production cost of DHA using C. cohnii makes it currently less competitive commercially, which is mainly caused by low DHA productivity. In recent years, repeated fed-batch strategies have been evaluated for increasing the production of many fermentation products. The reduction in terms of stability of culture system was one of the major challenges for repeated fed-batch fermentation. However, the possible mechanisms responsible for the decreased stability of the culture system in the repeated fed-batch fermentation are so far less investigated, restricting the efforts to further improve the productivity. In this study, a repeated fed-batch strategy for DHA production using C. cohnii M-1-2 was evaluated to improve DHA productivity and reduce production cost, and then the underlying mechanisms related to the gradually decreased stability of the culture system in repeated fed-batch culture were explored through LC- and GC-MS metabolomic analyses. RESULTS: It was discovered that glucose concentration at 15-27 g/L and 80% medium replacement ratio were suitable for the growth of C. cohnii M-1-2 during the repeated fed-batch culture. A four-cycle repeated fed-batch culture was successfully developed and assessed at the optimum cultivation parameters, resulting in increasing the total DHA productivity by 26.28% compared with the highest DHA productivity of 57.08 mg/L/h reported using C. cohnii, including the time required for preparing seed culture and fermentor. In addition, LC- and GC-MS metabolomics analyses showed that the gradually decreased nitrogen utilization capacity, and down-regulated glycolysis and TCA cycle were correlated with the decreased stability of the culture system during the long-time repeated fed-batch culture. At last, some biomarkers, such as Pyr, Cit, OXA, FUM, L-tryptophan, L-threonine, L-leucine, serotonin, and 4-guanidinobutyric acid, correlated with the stability of culture system of C. cohnii M-1-2 were identified. CONCLUSIONS: The study proved that repeated fed-batch cultivation was an efficient and energy-saving strategy for industrial production of DHA using C. cohnii, which could also be useful for cultivation of other microbes to improve productivity and reduce production cost. In addition, the mechanisms study at metabolite level can also be useful to further optimize production processes for C. cohnii and other microbes.

Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics.

BACKGROUND: Genome-wide association studies (GWAS) have been successful in identifying disease-associated genetic variants. Recently, an increasing number of GWAS summary statistics have been made available to the research community, providing extensive repositories for studies of human complex diseases. In particular, cross-trait associations at the genetic level can be beneficial from large-scale GWAS summary statistics by using genetic variants that are associated with multiple traits. However, direct assessment of cross-trait associations using susceptibility loci has been challenging due to the complex genetic architectures in most diseases, calling for advantageous methods that could integrate functional interpretation and imply biological mechanisms. RESULTS: We developed an analytical framework for systematic integration of cross-trait associations. It incorporates two different approaches to detect enriched pathways and requires only summary statistics. We demonstrated the framework using 25 traits belonging to four phenotype groups. Our results revealed an average of 54 significantly associated pathways (ranged between 18 and 175) per trait. We further proved that pathway-based analysis provided increased power to estimate cross-trait associations compared to gene-level analysis. Based on Fisher's Exact Test (FET), we identified a total of 24 (53) pairs of trait-trait association at adjusted pFET < 1 × 10- 3 (pFET < 0.01) among the 25 traits. Our trait-trait association network revealed not only many relationships among the traits within the same group but also novel relationships among traits from different groups, which warrants further investigation in future. CONCLUSIONS: Our study revealed that risk variants for 25 different traits aggregated in particular biological pathways and that these pathways were frequently shared among traits. Our results confirmed known mechanisms and also suggested several novel insights into the etiology of multi-traits.

PMN-MDSCs Enhance CTC Metastatic Properties through Reciprocal Interactions via ROS/Notch/Nodal Signaling.

Intratumoral infiltration of myeloid-derived suppressor cells (MDSCs) is known to promote neoplastic growth by inhibiting the tumoricidal activity of T cells. However, direct interactions between patient-derived MDSCs and circulating tumors cells (CTCs) within the microenvironment of blood remain unexplored. Dissecting interplays between CTCs and circulatory MDSCs by heterotypic CTC/MDSC clustering is critical as a key mechanism to promote CTC survival and sustain the metastatic process. We characterized CTCs and polymorphonuclear-MDSCs (PMN-MDSCs) isolated in parallel from peripheral blood of metastatic melanoma and breast cancer patients by multi-parametric flow cytometry. Transplantation of both cell populations in the systemic circulation of mice revealed significantly enhanced dissemination and metastasis in mice co-injected with CTCs and PMN-MDSCs compared to mice injected with CTCs or MDSCs alone. Notably, CTC/PMN-MDSC clusters were detected in vitro and in vivo either in patients' blood or by longitudinal monitoring of blood from animals. This was coupled with in vitro co-culturing of cell populations, demonstrating that CTCs formed physical clusters with PMN-MDSCs; and induced their pro-tumorigenic differentiation through paracrine Nodal signaling, augmenting the production of reactive oxygen species (ROS) by PMN-MDSCs. These findings were validated by detecting significantly higher Nodal and ROS levels in blood of cancer patients in the presence of naïve, heterotypic CTC/PMN-MDSC clusters. Augmented PMN-MDSC ROS upregulated Notch1 receptor expression in CTCs through the ROS-NRF2-ARE axis, thus priming CTCs to respond to ligand-mediated (Jagged1) Notch activation. Jagged1-expressing PMN-MDSCs contributed to enhanced Notch activation in CTCs by engagement of Notch1 receptor. The reciprocity of CTC/PMN-MDSC bi-directional paracrine interactions and signaling was functionally validated in inhibitor-based analyses, demonstrating that combined Nodal and ROS inhibition abrogated CTC/PMN-MDSC interactions and led to a reduction of CTC survival and proliferation. This study provides seminal evidence showing that PMN-MDSCs, additive to their immuno-suppressive roles, directly interact with CTCs and promote their dissemination and metastatic potency. Targeting CTC/PMN-MDSC heterotypic clusters and associated crosstalks can therefore represent a novel therapeutic avenue for limiting hematogenous spread of metastatic disease.