Project description:In this project we aim to construct a tyrosine-producing E. coli strain through iterative steps of genome engineering. High PEP availability through knockout of the PTS was combined with the precise, in-place genomic integration of several engineering interventions, known to increase L-tyrosine production yields, to create a tyrosine-overproducing E. coli strain that can function as a platform for further engineering and optimization. Utilizing a design-build-test-learn (DBTL) cycle, an evolved pts-knockout E. coli strain was equipped with optimizations of the aroG, aroB and tyrA genes and cultivated under batch and fed-batch conditions. Subsequently, metabolomics, transcriptomics and proteomics samples from the fed-batch experiments were analyzed to inform the design of new genomic interventions.
2025-07-01 | GSE264483 | GEO
Project description:Batch test focusing on kinetic behavior, energy harvest, and microbial Self-adaptation
Project description:This submission includes the raw data analyzed and search results described in our manuscript “Proteome-Scale Recombinant Standards And A Robust High-Speed Search Engine To Advance Cross-Linking MS-Based Interactomics”. In this study, we develop a strategy to generate a well-controlled XL-MS standard by systematically mixing and cross-linking recombinant proteins. The standard can be split into independent datasets, each of which has the MS2-level complexity of a typical proteome-wide XL-MS experiment. The raw datasets included in this submission were used to (1) guide the development of Scout, a machine learning-based search engine for XL-MS with MS-cleavable cross-linkers (batch 1), test different LC-MS acquisition methods (batch 2), and directly compare Scout to widely used XL-MS search engines (batches 3 and 4).
Project description:High resolution HiC libraries are usually lightly sequenced before investing in a deep sequencing. We modeled HiC resolution in function of the sequencing depth to predict accurately the resolution of any high resolution HiC library given a small sequnecing batch of the library. To test our tool, we used public datasets as well as a newly generated dataset using Arima kit on mouse purified rods photoreceptors.
Project description:Purpose: We compare small noncoding RNA (ncRNA) profiles between exosomes and whole plasma to test a better source of small ncRNA biomarkers. We hypothesize that exosome is a better source of small ncRNA biomarkers compared to whole plasma. Methods: Small ncRNA sequencings were done using Illumina NextSeq500. Single-end 76 bp reads were explored using FASTQC. GeneGlobe Data Analysis Center, an online platform from Qiagen was used for the small ncRNA-seq data analysis. The mapped read counts for miRNA, piRNA and tRNA were used for differential expression analysis using DESeq2 Bioconductor in R. The small RNA species with logFC > |1| and adj.P-value <0.05 were called differentially expressed. Conclusion: Small ncRNAs extracted from exosomes were found to have the most consistent profiles among healthy individuals. Whole plasma RNA profiles contain high concentrations of blood-derived miRNAs. These miRNA levels can create batch effects among samples due to different levels of hemolysis. Our finding suggests the importance of using purified exosomes as a better source of small ncRNA biomarkers to avoid the batch effect.