Biology subjects

Bush, E.

Publications and source records attributed to Bush, E..

3 recordsLinked to original sources

bioRxiv2021

Re-convolving the compositional landscape of primary and recurrent glioblastoma using single nucleus RNA sequencing

Glioblastoma (GBM) is an aggressive diffusely infiltrating neoplasm that spreads beyond surgical resection margins, where it intermingles with non-neoplastic brain cells. This complex microenvironment harboring infiltrating glioma and non-neoplastic brain cells is the origin of tumor recurrence. Thus, understanding the cellular and molecular features of the glioma microenvironment is therapeutically and prognostically important. We used single-nucleus RNA sequencing (snRNAseq) to determine the cellular composition and transcriptional states in primary and recurrent glioma and identified three compositional tissue-states defined by the observed patterns of cohabitation between neoplastic and non-neoplastic brain cells. These comprise states enriched in A) neurons and non-neoplastic glia, B) reactive astrocytes and inflammatory cells, and C) proliferating tumor cells. The tissue states also showed distinct associations with the different transcriptional states of GBM cells. Spatial transcriptomics revealed that the cell-types/transcriptional-states associated with each tissue state colocalize in space. Tissue states are clinically significant because they correlate with radiographic, histopathologic, and prognostic features. Importantly, we found that our compositionally-defined tissue states are enriched in distinct metabolic pathways. One such pathway is fatty acid biosynthesis, which was enriched in tissue state B - a state enriched in recurrent glioblastoma and associated with shorter overall survival- and composed of astrocyte-like/mesenchymal glioma cells, reactive astrocytes, and monocyte-like myeloid cells. We showed that treating acute slices of GBM with a fatty acid synthesis inhibitor is sufficient to deplete the transcriptional signature of tissue state B. Our findings define a novel compositional approach to analyze glioma-infiltrated tissue which allows us to discover prognostic and targetable features, paving the way to new mechanistic and therapeutic discoveries.

pathology↗

bioRxiv2021

Maximum Parsimony Reconciliation in the DTLOR Model

BackgroundAnalyses of microbial evolution often use reconciliation methods. However, the standard duplication-transfer-loss (DTL) model does not account for the fact that species trees are often not fully sampled and thus, from the perspective of reconciliation, a gene family may enter the species tree from the outside. Moreover, within the species tree, genes are often rearranged, causing them to move to new syntenic "regions." ResultsWe extend the DTL model to account for two events that commonly arise in the evolution of microbes: evolution occurring outside the sampled species tree and changes in the syntenic regions of genes in the genome due to rearrangement. We describe an efficient algorithm for maximum parsimony reconciliation in this new DTLOR model and then show how it can be extended to account for non-binary gene trees. Finally, we describe preliminary experimental results from the integration of our algorithm into the existing xenoGI tool for reconstructing the histories of genomic islands in closely related bacteria. ConclusionsReconciliation in the DTLOR model can offer new insights into the evolution of microbes that is not currently possible under the DTL model.

bioinformatics↗

bioRxiv2021

Metagenomic sequencing for rapid identification of Xylella fastidiosa from leaf samples

Xylella fastidiosa (Xf) is a globally distributed plant pathogenic bacterium. The primary control strategy for Xf diseases is eradicating infected plants; therefore, timely and accurate detection is necessary to prevent crop losses and further pathogen dispersal. Conventional Xf diagnostics primarily relies on quantitative PCR (qPCR) assays. However, these methods do not consider new or emerging variants due to pathogen genetic recombination and sensitivity limitations. We developed and tested a metagenomics pipeline using in-house short-read sequencing as a complementary approach for affordable, fast, and highly accurate Xf detection. We used metagenomics to identify Xf to strain level in single and mixed infected plant samples at concentrations as low as one picogram of bacterial DNA per gram of tissue. We also tested naturally infected samples from various plant species originating from Europe and the United States. We identified Xf subspecies in samples previously considered inconclusive with real-time PCR (Cq > 35). Overall, we showed the versatility of the pipeline by using different plant hosts and DNA extraction methods. Our pipeline provides taxonomic and functional information for Xf diagnostics without extensive knowledge of the disease. We hope this pipeline can be used for early detection of Xf and incorporated as a tool to inform disease management strategies. IMPORTANCEXylella fastidiosa (Xf) destructive outbreaks in Europe highlight this pathogens capacity to expand its host range and geographical distribution. The current disease diagnostic approaches are limited by a multiple-step process, biases to known sequences, and detection limits. We developed a low-cost, user-friendly metagenomic sequencing tool for Xf detection. In less than three days, we were able to identify Xf subspecies and strains in field-collected samples. Overall, our pipeline is a diagnostics tool that could be easily extended to other plant-pathogen interactions and implemented for emerging plant threat surveillance.

microbiology↗