Breast cancer, the second deadliest among women, kills about 40,000 individuals worldwide per year. Such lethality is due, in part, to the inherent heterogeneity of breast cancer disease, posing challenges for clinicians to accurately diagnose and subtype tumors. Recently, RNA‐seq technology has made it possible to subtype and classify tumors using gene expression profiling. In this experiment, we validated the efficacy of using RNA‐seq gene expression technology as a diagnostic tool for breast tumor subtyping in the context of an undergraduate course. Publicly available datasets from the Sequence Read Archive (SRA) of MCF‐7 breast cancer cells were subjected to quality control analysis using the Green Line of DNA Subway (Tuxedo Pipeline, FAST‐X, FAST‐QC, Tophat, and CuffDiff algorithms). Log‐transformed gene expression ratios were subjected to the BiNGO gene ontology algorithm. Significant over‐expression of genes for metabolic and cellular processes pathways included cell death and cell division (p<0.01 Binomial). Significantly down‐regulated pathways included those in development (organ development, etc), regulation (stimulus response, inflammatory response, etc), and signaling (cellular communication, etc; p<0.01 Binomial). Furthermore, using previously established biomarker genes, we subtyped the cells as basal‐like ‐ showing little change in expression for Estrogen Receptor (ER), Her2/neu (HER), or Ki‐67 biomarkers, but a significant over‐expression of the progesterone receptor (q = .0031). Using the Mammaprint suite of biomarkers we diagnosed a highly progressed tumor, wherein 60% and 73% of the Mammaprint mid‐hallmark genes and 38% late hallmarks were differentially expressed. This analysis was corroborated by previous datasets from DNA microarrays. Taken together, our findings suggest that RNA‐seq sequence data can be a valid approach for subtyping human breast cancers, and determining progression of disease.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.
Rapid advances in RNAseq technology and bioinformatic analysis of gene expression has normalized the use of precision diagnostics and recurrence risk testing for breast cancer patients. While these diagnostic approaches have numerous benefits, patients often lack the scientific literacy to evaluate data models ‐ and their uncertainties ‐ for enhancing their health decision‐making. To increase access to scientific knowledge for stakeholders, we designed an educational module that evaluates the validity of RNA‐seq gene expression as a tool for subtyping and determining the prognostic signature of a human breast tumor. Using a crowdsourced approach in the context of an undergraduate course, we recreated the workflow of prominent precision oncology companies ‐ we prepared a whole transcriptome library from a single breast cancer patient, and used multiplex sequencing on twelve uniquely barcoded Illumina preparations. Datasets obtained from the sequencing were subjected to quality control analysis using the Green Line of DNA Subway (FAST QC, FAST X, Kallisto, Sleuth algorithms). Log transformed gene expression ratios were used to determine the molecular subtype of the tumor. To calculate the recurrence risk, the Oncotype DX model was used, including 16 cancer genes and 5 reference genes. Results from the proof‐of‐concept experiment indicated significant overexpression of ER and PR genes (p<0.01) and concomitant underexpression of HER2 and Ki67 genes (p<0.01), consistent with a Luminal A tumor subtype. The Oncotype DX score was >25, suggesting high risk for 10 year distant recurrence (95% CI). Further exploration of the model indicated that normalization to aberrantly expressed reference genes can greatly skew the recurrence risk calculation, highlighting limits to the predictive power of this tool. Taken together, our findings illustrate the validity of RNA‐seq‐based tumor subtyping through an educational module, and underscores the importance of equitable access to genomics education as a form of patient self‐empowerment and decision‐making. We discuss the future implications of this work for education and policy.
Common buckthorn, Rhamnus carthartica, is an invasive species that outcompetes native species throughout North America. Introduced from Europe for horticultural purposes, this decorative shrub is fast‐growing, shade tolerant, and depletes soil nutrients. Given its exquisite fitness and negative consequences to biodiversity, an approach to mitigate the detrimental effects of buckthorn is essential. Can buckthorn biomass be turned into energy? The goal of this project was to evaluate the efficacy and feasibility of transforming buckthorn into combustible ethanol as a long‐term conservation strategy. We asked two central questions in the context of a course‐based undergraduate research experience: 1) Which tissue ‐ leaves, stems, or berries ‐ is the optimal source of biomass? 2) Does pre‐freezing enhance ethanol yield? Tissue harvested from 20 locations in the upper‐midwest were subjected to cellulase pretreatment followed by fermentation. Crude samples were then filtered and subjected to simple and fractional distillation to 95% purity. The ethanol from all sources was combustible. Although berries contained the highest amount of pre‐cellulase glucose, all biomass produced comparable amounts of final ethanol yields (ANOVA p=.93). Freeze‐thaw pretreatment of these tissues produced significantly lower ethanol yields (T‐test p=.00063). Based on this data, we propose a scalable, sustainable, and economically viable buckthorn‐to‐biofuel conversion program that can be implemented in a variety of community contexts.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.
Biological invasions of exotic species pose a significant threat to local biodiversity and ecosystem services. Redistribution of plant species in particular disrupts key structure‐function relationships in the soil by altering the composition of microbial taxa. This study combines environmental DNA analysis with a goat‐grazing intervention to investigate the taxonomic structure of soil microbiomes interacting with the invasive plant, Rhamnus cathartica (common Buckthorn). 16S (515F/806R) metabarcodes were amplified from soil DNA harvested before and after a goat‐grazing regimen on buckthorn‐invaded and control plots. Samples were sequenced on an Illumina iSeq, then analyzed using the DNA Subway Purple Line (QIIME 2.0) to process sequencing reads and calculate diversity indices. PCoA visualizations of Jaccard Beta diversity patterns revealed differences in community composition between buckthorn and control soils, but no effects from goat grazing. Five dominant taxa were identified in no‐buckthorn control soils, accounting for 35 +/‐ 9.6 SD% of the average relative abundance of total sequences. Genus DA101 notably demonstrated the largest decrease in average relative abundance in the buckthorn and goat‐grazed soils (% decrease = 6.4 +/‐ 2 SD), whereas the evenness of other taxa increased. Overall, buckthorn soil showed significant increases in Pielou’s taxonomical evenness (Kruskal‐Wallis H(2, N=29) = 10.9; p=0.004), but goat grazing had no impact on this score (p>0.01). Faith’s phylogenetic diversity scores showed no significant differences between buckthorn, goat‐grazed, and control soils (Kruskal‐Wallis H(2, N=31) = 2.2; p=0.32). Taken together, these preliminary results illustrate that buckthorn increases taxonomical evenness but has no effect on overall phylogenetic diversity of soil communities. Moreover, buckthorn goat‐grazing had little impact on soil community structure. Future studies will investigate the role of DA101 on buckthorn suppression as a possible mitigation strategy for this invasive species.
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