Synthetic Biology Approaches for Improving Chemical Production in Cyanobacteria

Biological chemical production has gained traction in recent years as a promising renewable alternative to traditional petrochemical based synthesis. Of particular interest in the field of metabolic engineering are photosynthetic microorganisms capable of sequestering atmospheric carbon dioxide. CO₂ levels have continued to rise at alarming rates leading to an increasingly uncertain climate. CO₂ can be sequestered by engineered photosynthetic microorganisms and used for chemical production, representing a renewable production method for valuable chemical commodities such as biofuels, plastics, and food additives. The main challenges in using photosynthetic microorganisms for chemical production stem from the seemingly inherent limitations of carbon fixation and photosynthesis resulting in slower growth and lower average product titers compared to heterotrophic organisms. Recently, there has been an increase in research around improving photosynthetic microorganisms as renewable chemical production hosts. This review will discuss the various efforts to overcome the intrinsic inefficiencies of carbon fixation and photosynthesis, including rewiring carbon fixation and photosynthesis, investigating alternative carbon fixation pathways, installing sugar catabolism to supplement carbon fixation, investigating newly discovered fast growing photosynthetic species, and using new synthetic biology tools such as CRISPR to radically alter metabolism.

Development of a multiomics and functional drug sensitivity platform to investigate cell type specific drug effects in AML

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Background: Understanding the heterogeneity of AML is necessary for developing targeted drugs and diagnostics. A key measure of heterogeneity is the variance in response to treatments. Previously, we developed an ex vivo flow cytometry drug sensitivity assay (DSA) that predicted response to treatments in myelodysplastic syndrome. Unlike bulk cell viability measures of other drug sensitivity assays, our flow cytometry assay provides single cell resolution. The assay measures a drug’s effect on the viability or functional state of specific cell types. Here we present the development of this technology for AML, with additional measurements of DNA-Seq and RNA-Seq. Using the data from this assay, we aim to characterize the heterogeneity in AML drug sensitivity and the molecular mechanisms that drive it. Methods: As an initial feasibility analysis, we assayed 1 bone marrow and 3 peripheral blood AML patient samples. For the DSA, the samples were cultured with six AML standard of care (SOC) compounds across seven doses, in addition to two combinations. The cells were stained to detect multiple cell types including tumor blasts, and drug response was measured by flow cytometry. For the multi-omics, the cells were magnetically sorted to enrich for blasts and then assayed using a targeted 400 gene DNA-Seq panel and whole bulk transcriptome RNA-Seq. For comparison with BeatAML, Pearson correlations between gene expression and venetoclax sensitivity were investigated. Results: In our drug sensitivity assay, we measured dose response curves for the six SOC compounds, for each different cell type across each sample. The dose responses had cell type specific effects, including differences in drug response between CD11b+ blasts, CD11b- blasts, and other non-blast populations. Integrating with the DNA-Seq and RNA-Seq data, known associations between ex vivo drug response and gene expression were identified with additional cell type specificity. For example, BCL2A1 expression was negatively correlated with venetoclax sensitivity in CD11b- blasts but not in CD11b+ blasts. To further corroborate, among the top 1000 genes associated with venetoclax sensitivity in BeatAML, 93.7% had concordant directionality in effect. Conclusions: Here we describe the development of an integrated ex vivo drug sensitivity assay and multi-omics dataset. The data demonstrated that ex vivo responses to compounds differ between cell types, highlighting the importance of measuring drug response in specific cell types. In addition, we demonstrated that integrating these data will provide unique insights on molecular mechanisms that affect cell type specific drug response. As we continue to expand the number of patient samples evaluated with our multi-dimensional platform, this dataset will provide insights for novel drug target discovery, biomarker development, and, in the future, informing treatment decisions.