G, S. | bioRxiv Science

bioRxiv2024

Heterologous Expression of the carbon monoxide dehydrogenase Gene from Clostridium sp. to Enhance Acetic Acid and Alcohol Production from CO2

This study evaluates the performance of carbon monoxide dehydrogenase (codh) embedded strains in bench-scale microbial electrochemical systems (MES) for CO2 reduction to biofuels and biochemicals. CO2 fermentation efficiency was evaluated by comparing the wild-type Clostridium acetobutylicum (Wild), a negative control E. coli strain lacking the codh gene (NC-BL21), and engineered E. coli strain (Eng) alone and with IPTG induction (Eng+IPTG). Four electrochemical systems were used viz. Wild+E, NC-BL21+E, Eng+E, and Eng+IPTG+E, with a poised potential of 0.6 V applied to the working electrode. CO2 and bicarbonate were supplemented to a total inorganic carbon (IC) concentration of 40 g/L, with a retention time of 60 h. The engineered strains demonstrated enhanced metabolic performance compared to the wild-type and negative control strains, yielding a maximum formic acid concentration of 2.1 g/L and acetic acid concentration of 7.8 g/L under the Eng+IPTG condition. Ethanol yield was highest at 3.9 g/L under the Eng+IPTG+E condition, substantially exceeding the 2.4 g/L acetic acid yield observed in the wild-type strain. The engineered strains showed superior cumulative yields (0.4075 g/g), improved codh charge flux stability (60 vs. 5 for Wild), and upregulated expression of genes in the Wood-Ljungdahl pathway. Bioelectrochemical performance analysis demonstrated elevated reductive catalytic currents, enhanced CO2 reduction, and optimal charge transfer kinetics. This study highlights the effectiveness of genetic and process engineering, particularly codh overexpression and IPTG induction, in optimizing microbial electrosynthesis for biofuel and biochemical production from C1 gases.

bioengineering↗

bioRxiv2023

An explainable machine learning data analytics method using TIGIT-linked genes for identifying biomarker signatures to clinical outcomes.

In the last decade, immunotherapies targeting immune checkpoint inhibitors have been extremely effective in eliminating subsets of some cancers in some patients. Multi-modal immune and non-immune factors that contribute to clinical outcomes have been utilized for predicting response to therapies and developing diagnostics. However, these data analytic methods involve a combination of complex mathematical data analytics, and even-more complex biological mechanistic pathways. In order to develop a method for data analytics of transcriptomics data sets, we have utilized an explainable machine learning (ML) model to investigate the genes involved in the signaling pathway of T-cell-immunoreceptor with immunoglobulin and ITIM domain (TIGIT). TIGIT is a receptor on T, NK, and T-regulatory cells, that has been classified as a checkpoint inhibitor due to its ability to inhibit innate and adaptive immune responses. We extracted gene whole genome sequencing data of 1029 early breast cancer patient tumors, and adjacent normal tissues from the TCGA and UCSC Xena Data Hub public databases. We followed a workflow which involved the following steps: i) data acquisition, processing, and visualization followed by ii) developed of a predictive prognostic model using input (gene expression data) and output (survival time) parameters iii) model interpretation was performed by calculating SHAP (Shapely-Additive-exPlanations); iv) the application of the model involved a Cox-regression model, trained with L-2 regularization and optimization using 5 fold cross validation. The model identified gene signatures associated with TIGIT that predicted survival outcome with a test set with a score of 0.601. In summary, we have utilized this case study of TIGIT-mediated signaling pathways to develop a roadmap for biologists to harness ML methods effectively.

cancer biology↗