Biology subjects

Blaby, I.

Publications and source records attributed to Blaby, I..

8 recordsLinked to original sources

bioRxiv2026

Expanding the Promoter Toolbox for Metabolic Engineering in the Lignocellulolytic Thermophile Anaerocellum bescii

Anaerocellum (formerly Caldicellulosiruptor) bescii, an anaerobic, extremely thermophilic (Topt [~]78 {degrees}C) lignocellulolytic bacterium, is a promising chassis for metabolic engineering and next-generation bioprocessing. Yet, a lack of well-characterized genetic parts in A. bescii has hampered metabolic engineering efforts. Here, using a previously developed hyperthermophilic {beta}-galactosidase reporter system, we screened a diverse panel of putative A. bescii promoter sequences, identifying promoters that drove reporter output across a broad range. For a select subset, we mapped their transcriptional start sites (TSSs) and evaluated ribosome binding site (RBS) regions using chimeric promoter constructs. By constructing truncated promoter variants, we defined functional regions within the widely used, high-expression S-layer protein promoter (Pslp) and engineered a compact 99 bp variant that retained substantial reporter activity. Finally, we demonstrated that these new promoters can be used for metabolic engineering by using two newly characterized promoters to express an established thermostable alcohol dehydrogenase from Thermoclostridium stercorarium to drive ethanol production in A. bescii. Together, this work expands and diversifies the A. bescii genetic toolkit, opening doors to future metabolic engineering efforts in this species.

Synthetic Biology↗

bioRxiv2026

Mapping Genome-wide Transcription Factor Binding Sites in two Rhodanobacter strains Isolated from Extreme Environments

Identification of target genes for bacterial transcription factors (TFs) provide a deeper understanding of bacterial response to environmental conditions. We focused on two Rhodanobacter sp. isolates from the subsurface of the U.S. Department of Energy Oak Ridge Reservation (ORR), an environment characterized by high-concentration mixed-waste contamination plumes. Bacterial strains in this subsurface ecosystem experience changing and complex environmental conditions, including variable nutrient and oxygen availability, fluctuating pH, nitrate and metals. We examined 91 DNA-binding TFs from 17 TF families in two Rhodanobacter strains, R. denitrificans FW104-10B01 and R. thiooxydans FW510-R12, using both DNA affinity purification sequencing (DAP-seq) and comparative genomics. Target genes regulated by 31 TFs were identified. Despite being isolated from different wells, the two strains shared a highly similar core regulatory network, with orthologous TFs regulating nearly identical sets of genes. These conserved networks governed essential survival functions, including nutrient uptake (e.g., phosphate), carbohydrate metabolism, motility, and stress responses to heavy metals and oxidative environment. TF-binding sequence motifs were also inferred for each of the 31 TFs. Key results include the elucidation of a global regulator Clp which regulates a large number of genes, particularly those involved in biofilm formation and motility. Functional validation through RT-qPCR confirmed that Clp activates the expression of genes critical for type IV pilin structure and flagellar regulation. Our work highlights the advantages of synergistically employing DAP-seq and comparative genomics, which enabled us to corroborate findings and obtain a larger view of the regulatory landscape of these non-model denitrifying bacteria.

microbiology↗

bioRxiv2026

DAP-seq Reveals Cluster-Situated Regulator Control of Numerous Streptomyces Natural Product Biosynthetic Genes

Natural products (NPs) are a rich source of therapeutic and agricultural compounds. Unfortunately, many promising metabolites are not expressed under standard laboratory conditions. Deepening our understanding of the regulatory networks governing NP biosynthetic genes is essential for unlocking this hidden chemical diversity. Cluster-situated regulators (CSRs) are transcription factors involved in the regulation of NPs, but their full regulatory range has remained elusive due to limited genome-wide data. Using DNA Affinity Purification Sequencing (DAP-seq), we defined the predicted regulons for 84 CSR homologs across 78 Streptomyces strains. CSRs in this cohort exerted influence across multiple cellular processes, with particularly strong impacts on other transcription factors throughout the genome. Approximately 30% of predicted NP biosynthetic gene clusters (BGCs) contained CSR-regulated genes. In strains encoding multiple CSR homologs, we observed substantial overlap in BGC regulation. Together, these results greatly expand the genomic landscape of CSR activity and provide a foundation for improved bioinformatic strategies to predict and interpret regulatory control of NP biosynthesis.

genetics↗

bioRxiv2025

Sorghum Metabolic Atlas: Large-Scale Mapping of Subcellular Enzyme Localization in Sorghum bicolor

Plant metabolism drives traits essential for productivity and resilience, yet understanding metabolic networks requires subcellular, cellular, and tissue-level spatial context that remains limited, particularly in crop species. Experimentally-derived subcellular localization data for enzymes are sparse, constraining analyses of metabolic organization in the cell. We developed a high-throughput protoplast transformation and fluorescent protein (FP) tagging system optimized for Sorghum bicolor, a climate-resilient C4 crop. Using this platform, we experimentally determined the subcellular localization of 234 metabolic enzymes spanning 184 pathways. The sorghum enzymes we characterized localize to 12 subcellular compartments. Comparison with computational predictions highlights variable accuracy across compartments, and cross-species comparison with Arabidopsis thaliana shows partial agreement with available experimental data. All data are accessible through the Sorghum Metabolic Atlas (www.sorghummetabolicatlas.org) web platform, enabling search, visualization, and download. This study presents a large-scale experimental dataset of enzyme localization in sorghum, providing a resource for studies of plant metabolic organization and comparative analyses.

plant biology↗

bioRxiv2024

Synthetic genomes unveil the effects of synonymous recoding

Engineering the genetic code of an organism provides the basis for (i) making any organism safely resistant to natural viruses and (ii) preventing genetic information flow into and out of genetically modified organisms while (iii) allowing the biosynthesis of genetically encoded unnatural polymers1-4. Achieving these three goals requires the reassignment of multiple of the 64 codons nature uses to encode proteins. However, synonymous codon replacement--recoding--is frequently lethal, and how recoding impacts fitness remains poorly explored. Here, we explore these effects using whole-genome synthesis, multiplexed directed evolution, and genome-transcriptome-translatome-proteome co-profiling on multiple recoded genomes. Using this information, we assemble a synthetic Escherichia coli genome in seven sections using only 57 codons to encode proteins. By discovering the rules responsible for the lethality of synonymous recoding and developing a data-driven multi-omics-based genome construction workflow that troubleshoots synthetic genomes, we overcome the lethal effects of 62,007 synonymous codon swaps and 11,108 additional genomic edits. We show that synonymous recoding induces transcriptional noise including new antisense RNAs, leading to drastic transcriptome and proteome perturbation. As the elimination of select codons from an organisms genetic code results in the widespread appearance of cryptic promoters, we show that synonymous codon choice may naturally evolve to minimize transcriptional noise. Our work provides the first genome-scale description of how synonymous codon changes influence organismal fitness and paves the way for the construction of functional genomes that provide genetic firewalls from natural ecosystems and safely produce biopolymers, drugs, and enzymes with an expanded chemistry.

synthetic biology↗

bioRxiv2024

Functional genomic screening in Komagataella phaffii enabled by high-activity CRISPR-Cas9 library

CRISPR-based high-throughput genome-wide loss-of-function screens are a valuable approach to functional genetics and strain engineering. The yeast Komagataella phaffii is a host of particular interest in the biopharmaceutical industry and as a metabolic engineering host for proteins and metabolites. Here, we design and validate a highly active 6-fold coverage genome-wide sgRNA library for this biotechnologically important yeast containing 30,848 active sgRNAs targeting over 99% of its coding sequences. Conducting fitness screens in the absence of functional non-homologous end joining (NHEJ), the dominant DNA repair mechanism in K. phaffii, provides a quantitative means to assess the activity of each sgRNA in the library. This approach allows for the experimental validation of each guides targeting activity, leading to more precise screening outcomes. We used this approach to conduct growth screens with glucose as the sole carbon source and identify essential genes. Comparative analysis of the called gene sets identified a core set of K. phaffii essential genes, many of which relate to protein production, secretion, and glycosylation. The high activity, genome-wide CRISPR library developed here enables functional genomic screening in K. phaffii, applied here to gene essentiality classification, and promises to enable other genetic screens. HighlightsO_LIDesigned and validated a high activity genome-wide CRISPR-Cas9 library for K. phaffii C_LIO_LIDisabling NHEJ DNA repair enables the generation of genome-wide guide activity profiles C_LIO_LIActivity-corrected fitness screens identify a high confidence set of essential genes in K. phaffii C_LIO_LIProtein production, secretion, and glycosylation pathways are essential in K. phaffii but not in other yeasts C_LI

systems biology↗

bioRxiv2022

Sensitivity analysis of genome-scale metabolic flux prediction

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with Transcription Factor (TF)-gene regulatory network (TRN), where the TRN is modeled via Bayesian network (BN). In this paper, we focus on sensitivity analysis of metabolic flux prediction considering potential model uncertainty in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty when learning from given transcriptomic expression data and analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yield of interest. The obtained sensitivity analyses can provide a useful guidance for Optimal Experimental Design (OED) to help acquire new data that can enhance TRN modeling and effectively achieve specific metabolic engineering objectives, including metabolite yield alterations. We have performed simulation experiments to demonstrate the effectiveness of our developed sensitivity analysis strategy and its potential to effectively guide OED. ACM Reference FormatPuhua Niu, Maria J. Soto, Shuai Huang, Byung-Jun Yoon, Edward R. Dougherty,, Francis J. Alexander, Ian Blaby, Xiaoning Qian. 2018. Sensitivity analysis of genome-scale metabolic flux prediction. In Proceedings of Make sure to enter the correct conference title from your rights confirmation email (CNB-MAC 2022). ACM, New York, NY, USA, 9 pages. https://doi.org/XXXXXXX.XXXXXXX

bioinformatics↗

bioRxiv2021

TRIMER: Transcription Regulation Integrated with MEtabolic Regulation

Advances in bioengineering have enabled numerous bio-based commodities. Yet most traditional approaches do not extend beyond a single metabolic pathway and do not attempt to modify gene regulatory networks in order to buffer metabolic perturbations. This is despite access to near universal technologies allowing genome-scale engineering. To help overcome this limitation, we have developed a pipeline enabling analysis of Transcription Regulation Integrated with MEtabolic Regulation (TRIMER). TRIMER utilizes a Bayesian network (BN) inferred from transcriptomic data to model the transcription factor regulatory network. TRIMER then infers the probabilities of gene states that are of relevance to the metabolism of interest, and predicts metabolic fluxes resulting from deletion of transcription factors at the genome scale. Additionally, we have developed a simulation framework to mimic the TF-regulated metabolic network, capable of generating both gene expression states and metabolic fluxes, thereby providing a fair evaluation platform for benchmarking models and predictions. Here, we present this computational pipeline. We demonstrate TRIMERs applicability to both simulated and experimental data and show that it outperforms current approaches on both data types.

bioinformatics↗