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Breast cancer is the second-leading cause of cancer-related deaths in women, but the details of how it begins remain elusive. Increasing evidence supports the association of aggressive triple-negative (TN) breast cancer with heightened expression of the Polycomb group protein Enhancer of Zeste Homolog 2 (EZH2) and increased tumor-initiating cells (TICs). However, mechanistic links between EZH2 and TICs are unclear, and direct demonstration of a tumorigenic function of EZH2 in vivo is lacking. Here, we identify an unrecognized EZH2/NOTCH1 axis that controls breast TICs in TN breast carcinomas. EZH2 overexpression increases NOTCH1 expression and signaling, and inhibition of NOTCH1 activity prevents EZH2-mediated stem cell expansion in nontumorigenic breast cells. We uncover a unique role of EZH2 in activating, rather than repressing, NOTCH1 signaling through binding to the NOTCH1 promoter in TN breast cancer cells. EZH2 binding is independent of its catalytic histone H3 lysine 27 methyltransferase activity and of the Polycomb Repressive Complex 2 but corresponds instead to transcriptional activation marks. In vivo, EZH2 knockdown decreases the onset and volume of xenografts derived from TN breast TICs. Conversely, transgenic EZH2 overexpression accelerates mammary tumor initiation and increases NOTCH1 activation in mouse mammary tumor virus-neu mice. Consonant with these findings, in clinical samples, high levels of EZH2 are significantly associated with activated NOTCH1 protein and increased TICs in TN invasive carcinomas. These data reveal a functional and mechanistic link between EZH2 levels, NOTCH1 signaling activation, and TICs, and provide previously unidentified evidence that EZH2 enhances breast cancer initiation.

Metaplastic breast carcinoma (MBC) is a highly aggressive form of triple-negative cancer (TNBC), defined by the presence of metaplastic components of spindle, squamous, or sarcomatoid histology. The protein profiles underpinning the pathological subtypes and metastatic behavior of MBC are unknown. Using multiplex quantitative tandem mass tag-based proteomics we quantify 5798 proteins in MBC, TNBC, and normal breast from 27 patients. Comparing MBC and TNBC protein profiles we show MBC-specific increases related to epithelial-to-mesenchymal transition and extracellular matrix, and reduced metabolic pathways. MBC subtypes exhibit distinct upregulated profiles, including translation and ribosomal events in spindle, inflammation- and apical junction-related proteins in squamous, and extracellular matrix proteins in sarcomatoid subtypes. Comparison of the proteomes of human spindle MBC with mouse spindle ( CCN6 knockout) MBC tumors reveals a shared spindle-specific signature of 17 upregulated proteins involved in translation and 19 downregulated proteins with roles in cell metabolism. These data identify potential subtype specific MBC biomarkers and therapeutic targets. Metaplastic breast carcinoma (MBC) is among the most aggressive subtypes of triple-negative breast cancer (TNBC) but the underlying proteome profiles are unknown. Here, the authors characterize the protein signatures of human MBC tissue samples and their relationship to TNBC and normal breast tissue.

Background Cancers exhibit complex transcriptomes with aberrant splicing that induces isoform-level differential expression compared to non-diseased tissues. Transcriptomic profiling using short-read sequencing has utility in providing a cost-effective approach for evaluating isoform expression, although short-read assembly displays limitations in the accurate inference of full-length transcripts. Long-read RNA sequencing (Iso-Seq), using the Pacific Biosciences (PacBio) platform, can overcome such limitations by providing full-length isoform sequence resolution which requires no read assembly and represents native expressed transcripts. A constraint of the Iso-Seq protocol is due to fewer reads output per instrument run, which, as an example, can consequently affect the detection of lowly expressed transcripts. To address these deficiencies, we developed a concatenation workflow, PacBio Full-Length Isoform Concatemer Sequencing (PB_FLIC-Seq), designed to increase the number of unique, sequenced PacBio long-reads thereby improving overall detection of unique isoforms. In addition, we anticipate that the increase in read depth will help improve the detection of moderate to low-level expressed isoforms. Results In sequencing a commercial reference (Spike-In RNA Variants; SIRV) with known isoform complexity we demonstrated a 3.4-fold increase in read output per run and improved SIRV recall when using the PB_FLIC-Seq method compared to the same samples processed with the Iso-Seq protocol. We applied this protocol to a translational cancer case, also demonstrating the utility of the PB_FLIC-Seq method for identifying differential full-length isoform expression in a pediatric diffuse midline glioma compared to its adjacent non-malignant tissue. Our data analysis revealed increased expression of extracellular matrix (ECM) genes within the tumor sample, including an isoform of the Secreted Protein Acidic and Cysteine Rich ( SPARC ) gene that was expressed 11,676-fold higher than in the adjacent non-malignant tissue. Finally, by using the PB_FLIC-Seq method, we detected several cancer-specific novel isoforms. Conclusion This work describes a concatenation-based methodology for increasing the number of sequenced full-length isoform reads on the PacBio platform, yielding improved discovery of expressed isoforms. We applied this workflow to profile the transcriptome of a pediatric diffuse midline glioma and adjacent non-malignant tissue. Our findings of cancer-specific novel isoform expression further highlight the importance of long-read sequencing for characterization of complex tumor transcriptomes.

Overexpression of EZH2 in estrogen receptor negative (ER-) breast cancer promotes metastasis. EZH2 has been mainly studied as the catalytic component of the Polycomb Repressive Complex 2 (PRC2) that mediates gene repression by trimethylating histone H3 at lysine 27 (H3K27me3). However, how EZH2 drives metastasis despite the low H3K27me3 levels observed in ER- breast cancer is unknown. Here we show that in human invasive carcinomas and distant metastases, cytoplasmic EZH2 phosphorylated at T367 is significantly associated with ER- disease and low H3K27me3 levels. p38-mediated EZH2 phosphorylation at T367 promotes EZH2 cytoplasmic localization and potentiates EZH2 binding to vinculin and other cytoskeletal regulators of cell migration and invasion. Ectopic expression of a phospho-deficient T367A-EZH2 mutant is sufficient to inhibit EZH2 cytoplasmic expression, disrupt binding to cytoskeletal regulators, and reduce EZH2-mediated adhesion, migration, invasion, and development of spontaneous metastasis. These results point to a PRC2-independent non-canonical mechanism of EZH2 pro-metastatic function. Polycomb group protein EZH2 is overexpressed in ER- breast cancer, promoting metastasis. Here, the authors show that independent of the polycomb group, phosphorylation of EZH2 at T367 by p38 promotes cytoplasmic localization of EZH2, binding to vinculin and other regulators of cell migration and invasion.

The main goal of this study is to compare the effects of pH, uranium concentration, and background electrolyte (seawater and NaClO4 solution) on the speciation of uranium(VI) associated with the marine bacterium Idiomarina loihiensis MAH1. This was done at the molecular level using a multidisciplinary approach combining X-ray Absorption Spectroscopy (XAS), Time-Resolved Laser-Induced Fluorescence Spectroscopy (TRLFS), and High Resolution Transmission Electron Microscopy (HRTEM). We showed that the U(VI)/bacterium interaction mechanism is highly dependent upon pH but also the nature of the used background electrolyte played a role. At neutral conditions and a U concentration ranging from 5·10(-4) to 10(-5) M (environmentally relevant concentrations), XAS analysis revealed that uranyl phosphate mineral phases, structurally resembling meta-autunite [Ca(UO2)2(PO4)2 2-6H2O] are precipitated at the cell surfaces of the strain MAH1. The formation of this mineral phase is independent of the background solution but U(VI) luminescence lifetime analyses demonstrated that the U(VI) speciation in seawater samples is more intricate, i.e., different complexes were formed under natural conditions. At acidic conditions, pH 2, 3 and 4.3 ([U] = 5·10(-4) M, background electrolyte  = 0.1 M NaClO4), the removal of U from solution was due to biosorption to Extracellular Polysaccharides (EPS) and cell wall components as evident from TEM analysis. The LIII-edge XAS and TRLFS studies showed that the biosorption process observed is dependent of pH. The bacterial cell forms a complex with U through organic phosphate groups at pH 2 and via phosphate and carboxyl groups at pH 3 and 4.3, respectively. The differences in the complexes formed between uranium and bacteria on seawater compared to NaClO4 solution demonstrates that the actinide/microbe interactions are influenced by the three studied factors, i.e., the pH, the uranium concentration and the chemical composition of the solution.

Elevated extracellular potassium (K+) in the tumor microenvironment (TME) of breast and other cancers is increasingly recognized as a critical factor influencing tumor progression and immune suppression. Current methods for noninvasive mapping of the potassium distribution in tumors are limited. Here, we employed photoacoustic chemical imaging (PACI) with a solvatochromic dye-based, potassium-sensitive nanoprobe (SDKNP) to quantitatively visualize extracellular potassium levels in an orthotopic metaplastic breast cancer mouse model, Ccn6-KO. Tumors of three distinct sizes (5 mm, 10 mm, and 20 mm) were imaged using multi-wavelength photoacoustic imaging at five laser wavelengths (560, 576, 584, 605, and 625 nm). Potassium concentration maps derived from spectral unmixing of the photoacoustic images at the five laser wavelengths revealed significantly increased potassium levels in larger tumors, confirmed independently by inductively coupled plasma mass spectrometry (ICP-MS). The PACI results matched ICP-MS measurements, validating PACI as a robust, noninvasive imaging modality for potassium mapping in tumors in vivo. This work establishes PACI as a promising tool for studying the chemical properties of the TME and provides a foundation for future studies evaluating the immunotherapy response through ionic biomarker imaging.

While 3D cellular models are useful to study biological processes, gel-embedded organoids have large variability. This paper describes high-yield production of large (~1 mm diameter), scaffold-free, highly-spherical organoids in a one drop-one organoid format using MCF10A cells, a non-tumorigenic breast cell line. These organoids display a hollow lumen and secondary acini, and express mammary gland-specific and progenitor markers, resembling normal human breast acini. When subjected to treatment with TGF-β, the hypoxia-mimetic reagent CoCl 2 , or co-culture with mesenchymal stem/stromal cells (MSC), the organoids increase collagen I production and undergo large phenotypic and morphological changes of neoplastic progression, which were reproducible and quantifiable. Advantages of this scaffold-free, 3D breast organoid model include high consistency and reproducibility, ability to measure cellular collagen I production without noise from exogenous collagen, and capacity to subject the organoid to various stimuli from the microenvironment and exogenous treatments with precise timing without concern of matrix binding. Using this system, we generated organoids from primary metaplastic mammary carcinomas of MMTV-Cre; Ccn6 fl/fl mice, which retained the high grade spindle cell morphology of the primary tumors. The platform is envisioned to be useful as a standardized 3D cellular model to study how microenvironmental factors influence breast tumorigenesis, and to potential therapeutics.

Excess weight gain throughout adulthood can lead to adverse clinical outcomes and are influenced by complex factors that are difficult to measure in free-living individuals. Metabolite profiling offers an opportunity to systematically discover new predictors for weight gain that are relatively easy to measure compared to traditional approaches. Using baseline metabolite profiling data of middle-aged individuals from the Framingham Heart Study (FHS; n = 1,508), we identified 42 metabolites associated (p < 0.05) with longitudinal change in body mass index (BMI). We performed stepwise linear regression to select 8 of these metabolites to build a metabolite risk score (MRS) for predicting future weight gain. We replicated the MRS using data from the Mexico City Diabetes Study (MCDS; n = 768), in which one standard deviation increase in the MRS corresponded to ~0.03 increase in BMI (kg/m2) per year (i.e. ~0.09 kg/year for a 1.7 m adult). We observed that none of the available anthropometric, lifestyle, and glycemic variables fully account for the MRS prediction of weight gain. Surprisingly, we found the MRS to be strongly correlated with baseline insulin sensitivity in both cohorts and to be negatively predictive of T2D in MCDS. Genome-wide association study of the MRS identified 2 genome-wide (p < 5 × 10-8) and 5 suggestively (p < 1 × 10-6) significant loci, several of which have been previously linked to obesity-related phenotypes. We have constructed and validated a generalizable MRS for future weight gain that is an independent predictor distinct from several other known risk factors. The MRS captures a composite biological picture of weight gain, perhaps hinting at the anabolic effects of preserved insulin sensitivity. Future investigation is required to assess the relationships between MRS-predicted weight gain and other obesity-related diseases.

Background To achieve optimal outcomes, adolescents with chronic or end-stage kidney disease must undergo healthcare transition (HCT) preparation from a pediatric- to an adult-focused setting. The pediatric nephrology group at the Fundación Valle del Líli in Cali, Colombia, collaborated with the University of North Carolina Chapel Hill STAR x Program and Hospital Infantil de México Federico Gómez to start an HCT preparation program using their tools. The objective of this study was to evaluate the efficacy of the “ ALL YOU NEED IS LOVE” syllabus (Spanish version) and its effects on HCT readiness in young patients with kidney failure, including renal transplants. Methods We conducted a pre-test/post-test quasi-experimental study without control group in 11- to 21-year-old consecutive patients with kidney failure followed at the Fundación Valle de Lili. Using the TRxANSITION Index, we measured HCT readiness skills before and after implementing the “ALL YOU NEED IS LOVE” syllabus, an educational curriculum delivered in three monthly 2-hour sessions. Analysis was performed using linear mixed models in R Studio software to evaluate intervention effects while accounting for participant characteristics. Results We enrolled 35 patients (57% female, median age 15.4 years [IQR: 12.6–17.1]). Most patients (77%) had received dialysis pre-transplant and 68% had congenital anomalies of the kidneys and urinary tract. Mothers were primary caregivers for 85% of patients. Linear mixed models showed that post-intervention scores increased significantly across all measures (β = 3.28, 95% CI 2.64–3.92, p  < 0.001 for transition scores; β = 1.93, 95% CI 1.48–2.38, p  < 0.001 for parent scores; β = 9.31, 95% CI 7.66–10.96, p  < 0.001 for total scores). College education was associated with higher baseline scores (β = 2.38, 95% CI 0.42–4.35, p  = 0.019 for transition scores; β = 7.58, 95% CI 1.23–13.93, p  = 0.021 for total scores). Male participants showed slightly lower initial scores (β = − 0.88, 95% CI − 1.76 to 0.00, p  = 0.051). Conclusion In this cohort of youth with kidney failure from Cali, Colombia, implementation of the Spanish version of the “ALL YOU NEED IS LOVE” syllabus was associated with significant improvements in HCT readiness. Linear mixed models demonstrated robust intervention effects across all domains, with educational level emerging as a significant moderator of intervention effectiveness. Further longitudinal studies are needed to evaluate the long-term impact and sustainability of these improvements in transition readiness scores. Trial Registration This study was retrospectively registered at ClinicalTrials.gov (NCT06836544, https://clinicaltrials.gov/ct2/show/NCT06836544 ) on February 10, 2025.

Metabolomics is a powerful approach for discovering biomarkers and for characterizing the biochemical consequences of genetic variation. While untargeted metabolite profiling can measure thousands of signals in a single experiment, many biologically meaningful signals cannot be readily identified as known metabolites nor compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To overcome these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets and to generate metabolic pathway annotations for these signals. To pair up signals measured in different datasets, where retention times (RT) are often not comparable or even available, we implemented an imputation-based approach that only requires mass-to-charge ratios (m/z). As validation, we treated each shared known metabolite as an unmatched signal and showed that PAIRUP-MS correctly matched 70-88% of these metabolites from among thousands of signals, equaling or outperforming a standard m/z- and RT-based approach. We performed further validation using genetic data: the most stringent set of matched signals and shared knowns showed comparable consistency of genetic associations across datasets. Next, we developed a pathway reconstitution method to annotate unknown signals using curated metabolic pathways containing known metabolites. We performed genetic validation for the generated annotations, showing that annotated signals associated with gene variants were more likely to be enriched for pathways functionally related to the genes compared to random expectation. Finally, we applied PAIRUP-MS to study associations between metabolites and genetic variants or body mass index (BMI) across multiple datasets, identifying up to ~6 times more significant signals and many more BMI-associated pathways compared to the standard practice of only analyzing known metabolites. These results demonstrate that PAIRUP-MS enables analysis of unknown signals in a robust, biologically meaningful manner and provides a path to more comprehensive, well-powered studies of untargeted metabolomics data.