The major obstacle in successfully treating triple-negative breast cancer (TNBC) is resistance to cytotoxic chemotherapy, the mainstay of treatment in this disease. Previous preclinical models of chemoresistance in TNBC have suffered from a lack of clinical relevance. Using a single high dose chemotherapy treatment, we developed a novel MDA-MB-436 cell-based model of chemoresistance characterized by a unique and complex morphologic phenotype, which consists of polyploid giant cancer cells giving rise to neuron-like mononuclear daughter cells filled with smaller but functional mitochondria and numerous lipid droplets. This resistant phenotype is associated with metabolic reprogramming with a shift to a greater dependence on fatty acids and oxidative phosphorylation. We validated both the molecular and histologic features of this model in a clinical cohort of primary chemore-sistant TNBCs and identified several metabolic vulnerabilities including a dependence on PLIN4, a perilipin coating the observed lipid droplets, expressed both in the TNBCresistant cells and clinical chemoresistant tumors treated with neoadjuvant doxorubicin-based chemotherapy. These findings thus reveal a novel mechanism of chemotherapy resistance that has therapeutic implications in the treatment of drug-resistant cancer.Implications: These findings underlie the importance of a novel morphologic-metabolic phenotype associated with chemotherapy resistance in TNBC, and bring to light novel therapeutic targets resulting from vulnerabilities in this phenotype, including the expression of PLIN4 essential for stabilizing lipid droplets in resistant cells.

No abstract

Marine nonindigenous species (NIS) are spreading at an alarming rate internationally through anthropogenic activities such as shipping and aquaculture, affecting local biodiversity and negatively impacting the ecosystem and human well‐being. Countries and international organizations have recognized this global threat and have begun implementing biosecurity management programs to ensure early detection, effective surveillance, and mitigation of marine NIS spread. Molecular techniques based on environmental DNA and RNA (eDNA/eRNA), collectively referred to as environmental nucleic acids (eNAs), have become a popular noninvasive tool for detecting NIS and monitoring biodiversity locally and globally. However, uncertainties about eNAs detection probabilities and the location of the source population impede the broad uptake of this tool in marine biosecurity programs. It's been hypothesized that most of these uncertainties can be explained by studying the molecules' dynamics within a marine environment and implementing eNAs distribution models. To contribute to further knowledge development in this area, our study reviews data from 20 recent reports on the degradation mechanisms and fate of eNAs in the marine environment. We classified the critical factors influencing eNAs' persistence that should be considered by biosecurity practitioners, outlining the complex interaction between the molecules' degradation processes and particular environmental conditions. To help guide the parameterization of eNAs distribution models, this review also summarizes and standardizes the marine decay rates of eDNA/eRNA from the literature. Finally, this manuscript outlines guidelines to help calculate accurate decay rates to build appropriate “fit‐for‐purpose” marine biosecurity tools for improved target detectability and greater resolution in assessing biodiversity.

Advances in high-throughput sequencing (HTS) technologies and their increasing affordability have fueled environmental DNA (eDNA) metabarcoding data generation from freshwater, marine and terrestrial ecosystems. Research institutions worldwide progressively employ HTS for biodiversity assessments, new species discovery and ecological trend monitoring. Moreover, even non-scientists can now collect an eDNA sample, send it to a specialized laboratory for analysis and receive in-depth biodiversity record from a sampling site. This offers unprecedented opportunities for biodiversity assessments across wide temporal and spatial scales. The large volume of data produced by metabarcoding also enables incidental detection of species of concern, including non-indigenous and pathogenic organisms. We introduce an online app—Pest Alert Tool—for screening nuclear small subunit 18S ribosomal RNA and mitochondrial cytochrome oxidase subunit I datasets for marine non-indigenous species as well as unwanted and notifiable marine organisms in New Zealand. The output can be filtered by minimum length of the query sequence and identity match. For putative matches, a phylogenetic tree can be generated through the National Center for Biotechnology Information’s BLAST Tree View tool, allowing for additional verification of the species of concern detection. The Pest Alert Tool is publicly available at https://pest-alert-tool-prod.azurewebsites.net/.

Molecular biosecurity surveillance programs increasingly use environmental DNA (eDNA) for detecting marine non-indigenous species (NIS). However, the current molecular detection workflow is cumbersome, prone to errors and delays, and is limited in providing knowledge about eDNA beyond the spatial and temporal extent of the sampling. These limitations can hinder management efforts and restrict the “opportunity window” for a rapid response to new marine NIS incursions. Emerging innovative field-deployable digital droplet PCR (ddPCR) systems offer improved workflow efficiency by autonomously analyzing targeted free-floating extra-cellular eDNA (free-eDNA) signals. Despite their potential, these systems have not been tested in marine environments. Thus, an aquarium study was conducted with three distinct marine NIS: the Mediterranean fanworm Sabella spallanzanii, the ascidian clubbed tunicate Styela clava, and the brown bryozoan Bugula neritina to evaluate the detectability of free-eDNA in seawater. The detectability of targeted free-eDNA was assessed by directly analyzing aquarium water samples using an optimized species-specific ddPCR assay, without filtration or DNA extraction, so-called, “direct-ddPCR”. The results demonstrated the consistent detection of Sabella spallanzanii and Bugula neritina free-eDNA when these organisms were present in high abundance. Once organisms were removed, the free-eDNA signal exponentially declined, noting that free-eDNA persisted between 24-72 hours. Results indicate that organism biomass, specimen characteristics (e.g., stress and viability), and species-specific biological differences may influence free-eDNA detectability. These results are critical for implementing in-situ nucleic acid automated continuous sensing systems for marine biosurveillance, enabling point-of-need detection and rapid management response to biosecurity threats.

<p>Table S18: Two-way ANOVA</p>

<p>Table S18: Two-way ANOVA</p>

<p>Table S1: List of regions of genes with increased CNV (>= 0.5) and gene expression (>= 0.5) in DOXO-R cells compared to parental cells Table S2: List of all fold change and pvalue of DOXO-R C1 and C8 (vs parental cells) from gene expression analysis. Table S3. GSEA using the C5 module for the 1852 genes with gene expression fold change > 1.5 (pval < 0.05) in DOXO-R C1 and C8 Table S4. GSEA using the C5 module for the 407 genes with gene expression fold change > 1.5 (pval < 0.05) and CNV change > 0.5 Table S5: Mean LOG2FC of genes commonly up-regulated in C1 and C8 Table S6: Proteins that are significantly upregulated according to the following criteria. (i) only present in one or two of the Dox resistant cell lines (**) with at least 3 and 2 unique peptides, respectively. (ii) At least 1.5-fold upregulated in both cell lines with a maximum relative standard deviation (RSD) of 31% across control replicates and 25% RSD across resistant cell line replicates. Table S7: GSEA using the C5 module for the 98 overexpressed proteins in DOXO-R vs parental cells Table S8: mRNA fold change (POST vs PRE NAC) for 9 chemoresistant TNBC patients. Table S9: GSEA using the C5 module for NEO2 up regulated genes > 2.5 FC. The complete list of genes are listed below the GSEA analysis. Table S10: GSEA using the C5 module for NEO24 up regulated genes > 2.5 FC. The complete list of genes are listed below the GSEA analysis. Table S11: GSEA using the C5 module for NEO25 up regulated genes > 2.5 FC. The complete list of genes are listed below the GSEA analysis. Table S12: GSEA using the C5 module for NEO27 up regulated genes > 2.5 FC. The complete list of genes are listed below the GSEA analysis. Table S13: GSEA using the C5 module for NEO28 up regulated genes > 2.5 FC. The complete list of genes are listed below the GSEA analysis. Table S14: GSEA using the C5 module for NEO30 up regulated genes > 2.5 FC. The complete list of genes are listed below the GSEA analysis. Table S15: GSEA using the C5 module for NEO31 up regulated genes > 2.5 FC. The complete list of genes are listed below the GSEA analysis. Table S16: GSEA using the C5 module for NEO35 up regulated genes > 2.5 FC. The complete list of genes are listed below the GSEA analysis. Table S17: GSEA using the C5 module for NEO44 up regulated genes > 2.5 FC. The complete list of genes are listed below the GSEA analysis.</p>