Biology subjects

Utrilla, J.

Publications and source records attributed to Utrilla, J..

7 recordsLinked to original sources

bioRxiv2026

Potential benefit of loss-of-function on bacterial fitness

Escherichia coli is a well-studied organism with extensive genomic and proteomic data. This study examines how gene loss reallocates cellular resources and impacts fitness. Genes were classified based on fitness measurements as essential, important, mean-effect, or fitness-enhancing. Using proteomic data, we analyzed the relationship between protein production cost and fitness, finding that genes with a high proteomic mass fraction are more likely to affect fitness, while fitness-enhancing deletions rarely improve fitness by reducing proteomic burden. We calculated the cumulative of proteome fractions encoded by genes classified as mean-effect and compared it with the results from the ME-model simulations. The mean-effect category constitutes 31-75% of the proteome, with the highest proportion LB, while enrichment analysis of core mean-effect genes highlighted transmembrane transport as the main functional category. Furthermore, we identified a subset of genes whose deletion increased fitness compared to the mean; they generally have low expression, and many have unknown functions. AI-assisted structural analyses identified domains and conserved features compatible with DNA-binding proteins, suggesting that some may represent putative transcriptional regulators requiring further validation. RpoS, stress sigma factor controlling up to 15% of the proteome is one of the transcriptional regulators in the fitness-enhancing category. Our findings suggest that the cost of being a generalist is linked to transcriptional regulation, while molecular transport represents a high burden for nutrient readiness. ImportanceThis study provides new insights into how gene loss benefits bacteria by identifying gene categories and their associated protein fractions whose disruption does not impose large fitness penalties. Additionally, it uncovers specific fitness-enhancing genes and generates hypotheses based on structural analyses for previously uncharacterized ones. Our findings suggest that several of these genes may encode putative transcriptional regulators, highlighting a potential role for regulatory complexity in cellular efficiency. By revealing how certain gene deletions enhance fitness and which gene categories are nonessential, this work advances our understanding of bacterial adaptation and genome streamlining. These insights have broad implications for evolutionary biology, metabolic engineering, and biotechnology, offering strategies to optimize microbial function by selectively reducing genetic and regulatory burden.

systems biology↗

bioRxiv2026

Genome-wide mapping of gene essentiality in Pseudomonas chlororaphis ATCC 9446 using transposon mutagenesis.

Pseudomonas chlororaphis ATCC 9446 is a non-pathogenic rhizobacterium with biotechnological relevance as a biocontrol agent and a promising chassis for synthetic biology. Understanding which genes are strictly required for survival is fundamental to both bacterial physiology and chassis engineering. Here, we generate a genome-wide map of genetic essentiality for P. chlororaphis using high-density Random Barcoded Transposon Mutagenesis (RB-TnSeq). To convert gene-level annotation into biological insight, we layered functional assignments from complementary annotation pipelines, integrating orthology/domain classifiers, ontology mapping, protein export, and delineation of secondary metabolism. These overlays reveal that essentiality concentrates in canonical information processing, envelope biogenesis, and central energy/cofactor and nucleotide metabolism, while large genomic regions are non-essential and therefore represent safe candidates for streamlining and pathway installation. Mapping essentiality onto biosynthetic gene clusters (BGCs) shows that most pathways are dispensable, but some essential genes co-localize, clarifying boundaries for safe editing around BGC loci. Comparison of experimentally determined essential genes with in silico predictions across additional P. chlororaphis genomes show strong overall agreement. Conversely, 32 essential gene orthogroups were found to be conserved across most genomes, yet were classified as non-essential by a prediction algorithm. Together, the resolved essential genome and its integrative functional interpretation provide a durable reference for P. chlororaphis biology and a functional blueprint that can be leveraged for rational streamlining in agricultural, biocontrol and industrial biotechnology. Data summaryThe complete genome sequence of Pseudomonas chlororaphis subsp. chlororaphis ATCC 9446 is publicly available in NCBI under RefSeq accession NZ_CP144767.1 (BioProject PRJNA224116; BioSample: SAMN39889236; assembly GCF_036689615.1). Gene annotation, essentiality calls, insertion mapping outputs, and gene-level insertion statistics are provided in Supplementary Table S1. Predicted biosynthetic gene clusters were identified using antiSMASH v8.0 (bacterial version; https://antismash.secondarymetabolites.org); an annotated GenBank file including BGC coordinates is provided as Supplementary File 3 and a summary of BGC features is provided in Supplementary Table S2. Signal peptides were predicted using SignalP 6.0 (https://services.healthtech.dtu.dk/service.php?SignalP-6.0). Protein-coding sequences were functionally annotated using eggNOG-mapper v2 with the eggNOG 5.0 database (https://github.com/eggnogdb/eggnog-mapper), producing Gene Ontology terms and COG functional classifications. Additional functional annotations, including SEED Subsystems classifications, were obtained from BV-BRC (https://www.bv-brc.org/view/Genome/333.24) and are included in Supplementary Table S1. Transposon insertion processing was performed using the FEBA pipeline (PoolStats.R and associated scripts; https://bitbucket.org/berkeleylab/feba), and essential gene inference was performed using the Bio-Tradis pipeline (https://github.com/sanger-pathogens/Bio-Tradis). Comparative essentiality predictions were obtained using DELEAT (https://github.com/jime-sg/deleat). Raw sequencing reads of transposon-genome junctions are available in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1436027.

genomics↗

bioRxiv2023

Engineering of Robust Host Strains: Enhancing Escherichia coli Abiotic Stress Resistance through Ornithine Lipid Formation

Escherichia coli is a common host for biotechnology and synthetic biology applications. During growth and fermentation, the microbes are often exposed to stress conditions, such as variations in pH or solvent concentrations. Bacterial membranes play a key role in response to abiotic stresses. Ornithine lipids (OLs) are a group of membrane lipids whose presence and synthesis have been related to stress resistance in bacteria. We wondered if this stress resistance could be transferred to bacteria not encoding the capacity to form OLs in their genome, such as E. coli. In this study, we engineered different E. coli strains to produce unmodified OLs and hydroxylated OLs by expressing the synthetic operon olsFC. Our results showed that OL formation improved pH resistance and increased biomass under phosphate limitation. Transcriptome analysis revealed that OL-forming strains differentially expressed stress- and membrane-related genes. OL-producing strains also showed better growth in the presence of the ionophore carbonyl cyanide 3-chlorophenylhydrazone (CCCP), suggesting reduced proton leakiness in OL-producing strains. Furthermore, our engineered strains showed improved heterologous violacein production at phosphate limitation and also at low pH. Overall, this study demonstrates the potential of engineering the E. coli membrane composition for constructing robust hosts with an increased abiotic stress resistance for biotechnology and synthetic biology applications. KeypointsO_LIThe E. coli membrane composition was engineered by producing ornithine lipids C_LIO_LIOrnithine lipid production increase biomass yield under phosphate limitation C_LIO_LIEngineered strains show enhanced production phenotype under low pH stress C_LIO_LITranscriptome analysis and CCCP experiments revealed reduced proton leakage C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/544863v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1e261ecorg.highwire.dtl.DTLVardef@18ae062org.highwire.dtl.DTLVardef@915b57org.highwire.dtl.DTLVardef@103b74d_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2021

Regulatory perturbations of ribosome allocation results in a trade-off between fast growth and adaptation capacity

Bacteria regulate their cellular resource allocation to enable fast growth-adaptation to a variety of environmental niches. We studied the ribosomal allocation, growth and expression profiles of two sets of fast-growing mutants of Escherichia coli K-12 MG1655 in glucose minimal medium. Mutants with only 3 of the seven copies of ribosomal RNA operons grew faster than the wild-type strain in minimal media and show similar phenotype to previously studied rpoB mutants. Higher growth rates due to increased ribosome content affected resource allocation. Expression profiles of fast-growing mutants shared downregulation of hedging functions and upregulated growth functions. Mutants showed longer diauxic shifts and reduced activity of gluconeogenic promoters during glucose-acetate shifts, suggesting reduced availability of the RNA Polymerase for expressing hedging proteome. These results show that the regulation of ribosomal allocation underlies the growth/hedging phenotypes obtained from laboratory evolution experiments. We show how two different regulatory perturbations (rRNA promoters or rpoB mutations) reshape the proteome for growth with a concomitant fitness cost HighlightsMutants with only 3 ribosomal operons grow faster than wild-type in minimal medium {Delta}4 rrn and rpoB mutants share phenotypic traits Faster growth of mutants is achieved by increased ribosome content Fast-growing mutants display reduced hedging expression and adaptation trade-offs Despite similar ribosomal content in rich medium the mutants present growth defects

systems biology↗

bioRxiv2019

A quantitative method for proteome reallocation using minimal regulatory interventions

Engineering resource allocation in biological systems for synthetic biology applications is an ongoing challenge. Wild type organisms allocate abundant cellular resources for ensuring survival in changing environments, reducing the productivity of engineered functions. Here we present a novel approach for engineering the resource allocation of Escherichia coli by rationally modifying the transcriptional regulatory network of the bacterium. Our method (ReProMin) identifies the minimal set of genetic interventions that maximise the savings in cell resources that would normally be used to express non-essential genes. To this end we categorize Transcription Factors (TFs) according to the essentiality of the genes they regulate and we use available proteomic data to rank them based on its proteomic balance, defined as the net proteomic charge they release. Using a combinatorial approach, we design the removal of TFs that maximise the release of the proteomic charge and we validate the model predictions experimentally. Expression profiling of the resulting strain shows that our designed regulatory interventions are highly specific. We show that our resulting engineered strain containing only three mutations, theoretically releasing 0.5% of their proteome, has higher proteome budget and show increased production yield of a molecule of interest obtained from a recombinant metabolic pathway. This approach shows that combining whole-cell proteomic and regulatory data is an effective way of optimizing strains in a predictable way using conventional molecular methods.\n\nImportanceBiological regulatory mechanisms are complex and occur in hierarchical layers such as transcription, translation and post-translational mechanisms. We foresee the use of regulatory mechanism as a control layer that will aid in the design of cellular phenotypes. Our ability to engineer biological systems will be dependent on the understanding of how cells sense and respond to their environment at a system level. Few studies have tackled this issue and none of them in a rational way. By developing a workflow of engineering resource allocation based on our current knowledge of E. colis regulatory network, we pursue the objective of minimizing cell proteome using a minimal genetic intervention principle. We developed a method to rationally design a set of genetic interventions that reduce the hedging proteome allocation. Using available datasets of a model bacterium we were able to reallocate parts of the unused proteome in laboratory conditions to the production of an engineered task. We show that we are able to reduce the unused proteome (theoretically 0.5%) with only three regulatory mutations designed in a rational way, which results in strains with increased capabilities for recombinant expression of pathways of interest.\n\nHighlightsO_LIProteome reduction with minimal genetic intervention as design principle\nC_LIO_LIRegulatory and proteomic data integration to identify transcription factor activated proteome\nC_LIO_LIDeletion of the TF combination that reduces the greater proteomic load\nC_LIO_LIRegulatory interventions are highly specific\nC_LIO_LIDesigned strains show less burden, improved protein and violacein production\nC_LI

synthetic biology↗

bioRxiv2018

Bacterial diversity and population dynamics during the fermentation of palm wine from Guerrero Mexico

Palm wine is obtained by fermentation of palm tree sap. In the Pacific coast of Mexico, palm wine is called Tuba and it is consumed as a traditional fermented beverage. Tuba has empirical applications such as an auxiliary in gastrointestinal diseases and a good source of nutrients. In the present study, a next-generation sequencing of the V3-V4 regions of the 16S rRNA gene was employed to analyze bacterial diversity and population dynamics during the fermentation process of Tuba, both in laboratory controlled conditions and in commercial samples from local vendors. Taxonomic identification showed that Fructobacillus was the main genus in all the samples, following by Leuconostoc, Gluconacetobacter, Sphingomonas, and Vibrio. Alpha diversity analysis demonstrated variability between all the samples. Beta diversity clustered the bacterial population according to the collection origin of the sample. Metabolic functional profile inference showed that the members of the bacterial communities may present the vitamin, antibiotic and antioxidant biosynthesis genes. Additionally, we further investigated the correlation between the predominant genera and some composition parameters of this beverage. This study provides the basis of the bacterial community composition and functionality of the fermented beverage.

microbiology↗

bioRxiv2018

Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism

Growth rate and yield are fundamental features of micro-bial growth. However, we lack a mechanistic and quantita-tive understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Es-cherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield trade-off in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mecha-nistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach.

microbiology↗