Aberrant Notch signaling is implicated in several cancers, including breast cancer. However, the mechanistic details of the specific receptors and function of ligand-mediated Notch signaling that promote breast cancer remains elusive. In our studies we show that DLL1, a Notch signaling ligand, is significantly overexpressed in ERα+ luminal breast cancer. Intriguingly, DLL1 overexpression correlates with poor prognosis in ERα+ luminal breast cancer, but not in other subtypes of breast cancer. In addition, this effect is specific to DLL1, as other Notch ligands (DLL3, JAGGED1, and JAGGED2) do not influence the clinical outcome of ERα+ patients. Genetic studies show that DLL1-mediated Notch signaling in breast cancer is important for tumor cell proliferation, angiogenesis, and cancer stem cell function. Consistent with prognostic clinical data, we found the tumor-promoting function of DLL1 is exclusive to ERα+ luminal breast cancer, as loss of DLL1 inhibits both tumor growth and lung metastasis of luminal breast cancer. Importantly, we find that estrogen signaling stabilizes DLL1 protein by preventing its proteasomal and lysososmal degradations. Moreover, estrogen inhibits ubiquitination of DLL1. Together, our results highlight an unexpected and novel subtype-specific function of DLL1 in promoting luminal breast cancer that is regulated by estrogen signaling. Our studies also emphasize the critical role of assessing subtype-specific mechanisms driving tumor growth and metastasis to generate effective subtype-specific therapeutics.

The spatial organization of microbial communities arises from a complex interplay of biotic and abiotic interactions, and is a major determinant of ecosystem functions. Here we design a microfluidic platform to investigate how the spatial arrangement of microbes impacts gene expression and growth. We elucidate key biochemical parameters that dictate the mapping between spatial positioning and gene expression patterns. We show that distance can establish a low-pass filter to periodic inputs and can enhance the fidelity of information processing. Positive and negative feedback can play disparate roles in the synchronization and robustness of a genetic oscillator distributed between two strains to spatial separation. Quantification of growth and metabolite release in an amino-acid auxotroph community demonstrates that the interaction network and stability of the community are highly sensitive to temporal perturbations and spatial arrangements. In sum, our microfluidic platform can quantify spatiotemporal parameters influencing diffusion-mediated interactions in microbial consortia.

2The spatial organization of microbial communities arises from a complex interplay of biotic and 3 abiotic interactions and is a major determinant of ecosystem functions. We design a microfluidic 4 platform to investigate how the spatial arrangement of microbes impacts gene expression and 5 growth. We elucidate key biochemical parameters that dictate the mapping between spatial 6 positioning and gene expression patterns. We show that distance can establish a low-pass filter 7 to periodic inputs, and can enhance the fidelity of information processing. Positive and negative 8 feedback can play disparate roles in the synchronization and robustness of a genetic oscillator 9 distributed between two strains to spatial separation. Quantification of growth and metabolite 10 release in an amino-acid auxotroph community demonstrates that the interaction network and 11 stability of the community are highly sensitive to temporal perturbations and spatial arrangements. 12In sum, our microfluidic platform can quantify spatiotemporal parameters influencing diffusion-13 mediated interactions in microbial consortia. 14 15 16 INTRODUCTION 17 18Spatial organization is a prevalent feature of microbiomes ranging from soil 1 to the human 19 gastrointestinal tract 2 . This spatial structure is a major driver of microbial community behaviors, 20 stability, and responses to environmental perturbations [3][4][5][6][7] . The spatial organization of 21 microbiomes span multiple scales: variation in environmental (abiotic) parameters dictate 22 behaviors over longer length scales (centimeter to meter), whereas inter-microbial interactions 23 impact community behaviors on shorter length scales (tens of microns to centimeters) 8,9 . It is not 24 currently understood how a microbiome's spatial structure impacts community function and 25 stability. 26The proximity of members within a community is a major determinant of the costs and 27 benefits of microbial interactions, and shapes how the ecological network evolves over time 10,11 . 28Spatial structure is known to provide ecological benefits, such as promoting population survival 29 through local public good production 12 and enhancing biofilm resilience to environmental 30 perturbations 5,13 . Spatial heterogeneity was shown to reduce the propensity for a prisoner's 31 dilemma by enabling the stable coexistence of cooperator and cheater populations 3 . Ecosystem 32properties are shaped by the balance between cooperation and competition, which have been 33 shown to dominate in different spatial regimes 14 . The degree of mixing of strains impacts the 34 concentrations of resources and toxins in local microenvironments, which in turn dictates the 35 outcome of invasion of non-resident organisms into the community 15,16 . 36The majority of microbial interactions are mediated by diffusible compounds including 37 metabolites, chemical signals, or proteins 7 . These biomolecular mediators can enhance or inhibit 38 community member's growth rates, as well as modify the activities of intracellular si...

Mass spectrometry-based proteomics is a powerful and robust platform for studying the interactions between biological systems during health and disease. Bacterial infections represent a significant threat to global health and drive the pursuit of novel therapeutic strategies to combat emerging and resistant pathogens. During infection, the interplay between a host and pathogen determines the ability of the microbe to survive in a hostile environment and promotes an immune response by the host as a protective measure. It is the protein-level changes from either biological system that define the outcome of infection, and mass spectrometry-based proteomics provides a rapid and effective platform to identify such changes. In particular, proteomics detects alterations in protein abundance, quantifies protein secretion and/or release, measures an array of post-translational modifications that influence signaling cascades, and profiles protein-protein interactions through protein complex and/or network formation. Such information provides new insight into the role of known and novel bacterial effectors, as well as the outcome of host cell activation. In this Review, we highlight the diverse applications of mass spectrometry-based proteomics in profiling the relationship between bacterial pathogens and the host. Our work identifies a plethora of strategies for exploring mechanisms of infection from dual perspectives (i.e., host and pathogen) and we suggest opportunities to extrapolate the current knowledgebase to other biological systems for applications in therapeutic discovery.

Single cell genetic heterogeneity is ubiquitous in microbial populations and an important aspect of microbial biology. However, we lack a broadly applicable and accessible method to study this heterogeneity at the single cell level. Here, we introduce a simple, robust, and generalizable platform for quantitative and massively parallel single cell sequencing of target genetic loci in microbes using ultrahigh-throughput droplet microfluidics (Droplet Targeted Amplicon Sequencing or DoTA-seq). Using DoTA-seq, we elucidate the highly diverse single cell ON/OFF states of the phase-variable capsule synthesis operons in the prevalent human gut species Bacteroides fragilis. In addition, we quantify the shifts in antibiotic resistance gene abundances in different species in a 25 member human gut microbial community in response to antibiotics. By sequencing tens of thousands of single-cells derived from a human fecal sample, we identify links between plasmid replicons and the taxonomic lineages of their associated hosts. In sum, DoTA-seq is an accessible and broadly applicable tool for profiling single-cell genetic variation in microbiomes.