Biology subjects

Urtecho, G.

Publications and source records attributed to Urtecho, G..

10 recordsLinked to original sources

bioRxiv2026

Culturomics-Guided Precision FMT Enables Stable Antibiotic-Independent Engraftment via Metabolic Niche Targeting

The gut microbiome's functional architecture can be conceptualized as a network of metabolic niches, yet clinical approaches to fecal microbiota transplantation (FMT) do not leverage niche-level information to guide engraftment. Here, we present a culturomics-based framework that profiles the metabolic niche landscape of individual microbiomes to enable targeted, antibiotic-independent FMT. Using a panel of 60 diverse media conditions, we show that gut communities from genetically identical mice harboring distinct microbiomes exhibit reproducible, vendor-specific niche-occupancy profiles, with each condition selecting for compositionally distinct niche-associated communities (NACs). We demonstrate that antibiotic pretreatment prior to FMT compromises community resilience, yielding communities that lose donor taxa more rapidly and recover less taxonomic diversity after secondary perturbation compared to untreated recipients. By comparing donor and recipient niche profiles, we identify vacant metabolic niches in recipient microbiomes and show that NACs targeting these vacancies achieve stable engraftment persisting for over 72 days without antibiotic pretreatment, whereas NACs targeting occupied niches fail to colonize. Furthermore, complex NACs not only fill vacant niches but reshape the surrounding native community, while simpler NACs engraft with minimal collateral impact. This culturomics-guided framework provides a scalable, mechanistic approach for rational donor-recipient matching that could advance personalized microbiome-based therapies.

microbiology↗

bioRxiv2026

Metagenome-scale Modeling to Assess Microbiome Metabolic Complementarity for Precision Microbiota Transplantation Therapies

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable, in part because existing computational models do not fully capture the metabolic compatibility between donor and recipient communities. Here, we present a metagenome-scale metabolic modeling framework that quantifies metabolic niche complementarity between donor and recipient microbiomes to predict transplantation outcomes. Using MICOM-derived community metabolic models, we show that donor taxa whose metabolic flux profiles are more dissimilar from the recipient community engraft at significantly higher rates in both murine and human FMT cohorts. In a human IBS trial, metabolic models accurately predicted post-FMT community composition via leave-one-out cross-validation and recapitulated disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to demonstrate a platform for personalized donor screening. This screen identified super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition, as quantified by a flux-derived ecological network balance index that strongly predicted engraftment potential. This framework provides a mechanistic, scalable tool for rational donor-recipient matching that could guide personalized microbiome-based therapies.

microbiology↗

bioRxiv2024

SAMPL-seq reveals micron-scale spatial hubs in the human gut microbiome

The local arrangement of microbes can profoundly impact community assembly, function, and stability. To date, little is known about the spatial organization of the human gut microbiome. Here, we describe a high-throughput and streamlined method, dubbed SAMPL-seq, that samples microbial composition of micron-scale sub-communities with split-and-pool barcoding to capture spatial colocalization in a complex consortium. SAMPL-seq analysis of the gut microbiome of healthy humans identified bacterial taxa pairs that consistently co-occurred both over time and across multiple individuals. These colocalized microbes organize into spatially distinct groups or "spatial hubs" dominated by Bacteroideceae, Ruminococceae, and Lachnospiraceae families. From a dietary perturbation using inulin, we observed reversible spatial rearrangement of the gut microbiome, where specific taxa form new local partnerships. Spatial metagenomics using SAMPL-seq can unlock new insights to improve the study of microbial communities. One Sentence SummaryHigh throughput micron-scale subcommunity sampling and sequencing identifies distinct spatial associations of gut bacteria within and across individuals.

systems biology↗

bioRxiv2024

SARS-CoV-2 Omicron BA.2.87.1 Exhibits Higher Susceptibility to Serum Neutralization Than EG.5.1 and JN.1

As SARS-CoV-2 continues to spread and mutate, tracking the viral evolutionary trajectory and understanding the functional consequences of its mutations remain crucial. Here, we characterized the antibody evasion, ACE2 receptor engagement, and viral infectivity of the highly mutated SARS-CoV-2 Omicron subvariant BA.2.87.1. Compared with other Omicron subvariants, including EG.5.1 and the current predominant JN.1, BA.2.87.1 exhibits less immune evasion, reduced viral receptor engagement, and comparable infectivity in Calu-3 lung cells. Intriguingly, two large deletions ({Delta}15-26 and {Delta}136-146) in the N-terminal domain (NTD) of the spike protein facilitate subtly increased antibody evasion but significantly diminish viral infectivity. Collectively, our data support the announcement by the USA CDC that the public health risk posed by BA.2.87.1 appears to be low.

microbiology↗

bioRxiv2022

Spatiotemporal dynamics during niche remodeling by super-colonizing microbiota in the mammalian gut

While fecal microbiota transplantation (FMT) has been shown to be effective in reversing gut dysbiosis, we lack an understanding for the fundamental processes underlying microbial engraftment in the mammalian gut. Here, we explored a murine gut colonization model leveraging natural inter-individual variations in gut microbiomes to elucidate the spatiotemporal dynamics of FMT. We identified a natural super-donor consortium that universally engrafts into diverse recipients and resists reciprocal colonization. Temporal profiling of the gut microbiome showed an ordered succession of rapid engraftment by early colonizers within 72 hours followed by a slower emergence of late colonizers over 15-30 days. Moreover, engraftment was localized to distinct compartments of the gastrointestinal tract in a species-specific manner. Spatial metagenomic characterization suggested engraftment was mediated by simultaneous transfer of spatially co-localizing species from the super-donor consortia. These results offer a mechanism of super-donor colonization by which nutritional niches are expanded in a spatiotemporally- dependent manner.

microbiology↗

bioRxiv2021

KTD1 is a yeast defense factor against K28 killer toxin

Secreted protein toxins are widely used weapons in conflicts between organisms. Killer yeast produce killer toxins that inhibit the growth of nearby sensitive yeast. We investigated variation in resistance to the killer toxin K28 across diverse natural isolates of the Saccharomyces cerevisiae population and discovered a novel defense factor, which we named KTD1, that is an important determinant of K28 toxin resistance. KTD1 is a member of the DUP240 gene family of unknown function. We uncovered a putative role of DUP240 proteins in killer toxin defense and identified a region that is undergoing rapid evolution and is critical to KTD1s protective ability. Our findings implicate KTD1 as a key factor in the defense against killer toxin K28.

genetics↗

bioRxiv2020

Discovering the Molecular Determinants of Phaeobacter inhibens susceptibility to Phaeobacter phage MD18

Bacteriophage technologies have immense potential as antibiotic therapies and in genetic engineering. Understanding the mechanisms that bacteriophages implement to infect their hosts will allow researchers to manipulate these systems and adapt them to specific bacterial targets. Here, we isolated a bacteriophage capable of infecting the marine alphaproteobacterium Phaeobacter inhibens and dissected its mechanism of infection. Phaeobacter phage MD18, a novel species of bacteriophage isolated in Woods Hole, MA, exhibits potent lytic ability against P. inhibens and appears to be of the Siphoviridae morphotype. Consistent with this finding, the sequence of the MD18 revealed significant similarity to another siphophage, the recently discovered Roseobacter phage DSS3P8. We incubated MD18 with a library of barcoded P. inhibens transposon insertion mutants and identified 22 genes that appear to be required for phage predation of this host. Network analysis of these genes using genomic position, GO term enrichment, and protein associations reveals that these genes are enriched for roles in assembly of a type IV pilus (T4P) and regulators of cellular morphology. Our results suggest that T4P serve as receptors for a novel marine virus that targets P. inhibens. ImportanceBacteriophages are useful non-antibiotic therapeutics for bacterial infections as well as threats to industries utilizing bacterial agents. This study identifies Phaeobacter phage MD18, the first documented phage of Phaeobacter inhibens, a bacterium with promising use as a probiotic for aquatic farming industries. Genomic analysis suggests that the P haeobacter phage MD18 has evolved to enhance its replication in P. inhibens by adopting favorable tRNA genes as well as through genomic sequence adaptation to resemble host codon usage. Lastly, a high-throughput analysis of P. inhibens transposon insertion mutants identifies genes that modulate host susceptibility to phage MD18 and implicates the type IV pilus as the likely receptor recognized for adsorption. This study marks the first characterization of the relationship between P. inhibens and an environmentally sampled phage, which informs our understanding of natural threats to the bacterium and may promote the development of novel phage technologies for genetic manipulation of this host.

microbiology↗

bioRxiv2020

Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems

A crucial step towards engineering biological systems is the ability to precisely tune the genetic response to environmental stimuli. In the case of Escherichia coli inducible promoters, our incomplete understanding of the relationship between sequence composition and gene expression hinders our ability to predictably control transcriptional responses. Here, we profile the expression dynamics of 8,269 rationally designed IPTG-inducible promoters that collectively explore the individual and combinatorial effects of RNA polymerase and LacI repressor binding site strengths. Using these data, we fit a statistical mechanics model that accurately models gene expression and reveals properties of theoretically optimal inducible promoters. Furthermore, we characterize three novel promoter architectures and show that repositioning binding sites within promoters influences the types of combinatorial effects observed between promoter elements. In total, this approach enables us to deconstruct relationships between inducible promoter elements and discover practical insights for engineering inducible promoters with desirable characteristics.

synthetic biology↗

bioRxiv2020

Genome-wide Functional Characterization of Escherichia coli Promoters and Regulatory Elements Responsible for their Function.

Despite decades of intense genetic, biochemical, and evolutionary characterizations of bacterial promoters, we lack the ability to identify or predict transcriptional activities of promoters using primary sequence. Even in simple, well-characterized organisms such as E. coli there is little agreement on the number, location, and strength of promoters. We use a genomically-encoded massively parallel reporter assay to perform the first full characterization of autonomous promoter activity across the E. coli genome. We measure promoter activity of >300,000 sequences spanning the entire genome and map 2,228 promoters active in rich media. Surprisingly, 944 of these promoters were found within intragenic sequences and are associated with conciliatory sequence adaptations by both the protein-coding regions and overlapping RNAP binding sites. Furthermore, we perform a scanning mutagenesis of 2,057 promoters to uncover sequence elements regulating promoter activity, revealing 3,317 novel regulatory elements. Finally, we show that despite these large datasets and modern machine learning algorithms, predicting endogenous promoter activity from primary sequence is still challenging.

microbiology↗

bioRxiv2017

Systematic Dissection of Sequence Elements Controlling σ70 Promoters Using a Genomically-Encoded Multiplexed Reporter Assay in E. coli

Promoters are the key drivers of gene expression and are largely responsible for the regulation of cellular responses to time and environment. In E. coli, decades of studies have revealed most, if not all, of the sequence elements necessary to encode promoter function. Despite our knowledge of these motifs, it is still not possible to predict the strength and regulation of a promoter from primary sequence alone. Here we develop a novel multiplexed assay to study promoter function in E. coli by building a site-specific genomic recombination-mediated cassette exchange (RMCE) system that allows for the facile construction and testing of large libraries of genetic designs integrated into precise genomic locations. We build and test a library of 10,898 {sigma}70 promoter variants consisting of all combinations of a set of eight -35 elements, eight -10 elements, three UP elements, eight spacers, and eight backgrounds. We find that the -35 and -10 sequence elements can explain approximately 74% of the variance in promoter strength within our dataset using a simple log-linear statistical model. Simple neural network models explain greater than 95% of the variance in our dataset by capturing nonlinear interactions with the spacer, background, and UP elements.

molecular biology↗