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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Precision Fermentation: Relieving the Crabtree Effect in S. cerevisiae through Genetic Engineering

Nutrient degradation in the current NASA pre-packaged food system poses significant challenges to crew health in missions beyond low earth orbit as certain vitamins and nutrients have been shown to degrade during extended storage, and resupply and fresh food items cannot be readily provided. BioNutrients(BN) uses synthetic biology in an effort to supplement the NASA food system by delivering in situ production of nutrients and therapeutics produced by genetically engineered microbes. One yeast S. cerevisiae strain utilized in BN is genetically modified to produce two types of antioxidants: zeaxanthin and β-carotene. Zeaxanthin is a non-provitamin A carotenoid and has been implicated in eye health and vision performance, and β-carotene is a provitamin A carotenoid. Microbial production of high-value nutrients can aid our understanding and address the issue of nutrient degradation and loss in the NASA food system. Although S. cerevisiae is an excellent candidate for a long shelf life, on-demand nutrient production system, its space applications are limited by the Crabtree effect where respiration is shut down in favor of fermentative metabolism when glucose is present in high concentrations, causing the release of unwanted ethanol. Current and likely future life support systems are negatively impacted by ethanol and ethanol release is strictly limited.To improve feasibility of using S. cerevisiae, we explored ways to reduce the Crabtree effect, thereby limiting its ethanol production. Specifically, we used CRISPR-cas9 to genetically modify various genes in this pathway

E Zaroff↗
NASA NTRSDate not supplied

Developing a Genetic Variant Calling Pipeline for Quantifying the Complex Mutagenic Load Accumulated in BioNutrients-1 Production Pack Samples

Microorganisms hold great promise for on demand production of labile nutrients and pharmaceuticals as well recycling and in situ resource utilization. The utilization of microorganisms for such tasks on space missions is hindered by the limited data on how microbes respond to spaceflight. For example, the genetic stability of microorganisms, and the genomic engineered traits added to deliver desired functions, over long-term storage in the spacecraft environment is poorly understood. The BioNutrients-1 (BN-1) mission conducted a 5-year study of desiccated storage in Low Earth Orbit (LEO) to evaluate the suitability of eight synthetic biology chassis organisms for long-duration space missions. We are employing high-depth, whole genome sequencing (WGS) to determine the mutagenic load that accumulated during long-term storage. Mutation analysis pipelines are well established for homogenous culture grown from a single colony, but the mutational landscape of the BN-1 samples present a unique analysis challenge, as every cell in the BN-1 samples had a unique genetic journey of DNA damage and repair. Consequently, sequence variants are expected at low allele frequency within samples. To address this genetic complexity, we apply two distinct computational approaches to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO. For reference genome free mutation detection, we utilized DiscoSNP++, which is a de Bruijn graph approach. For reference genome-based mutation detection we utilize GATK for Microbes, which is a Bayesian probabilistic approach. We will benchmark these approaches against the mutations originally identified using CRISP, a method optimized for pooled samples. Ultimately, quantifying the mutation load imposed by storage or growth on the ISS will help identify chassis organisms with both high levels of genome stability and viability, which are desirable traits for implementation of bioproduction in long-duration missions.

SNP↗
DOE OSTI2025

Anti-Pdc1p Nanobody as a Genetically Encoded Inhibitor of Ethanol Production Enables Dual Transcriptional and Post-translational Controls of Yeast Fermentations

Microbial fermentation provides a sustainable method of producing valuable chemicals. Adding dynamic control to fermentations can significantly improve titers, but most systems rely on transcriptional controls of metabolic enzymes, leaving existing intracellular enzymes unregulated. This limits the ability of transcriptional controls to switch off metabolic pathways, especially when metabolic enzymes have long half-lives. We developed a two-layer transcriptional/post-translational control system for yeast fermentations. Specifically, the system uses blue light to transcriptionally activate the major pyruvate decarboxylase PDC1, required for cell growth and concomitant ethanol production. Switching to darkness transcriptionally inactivates PDC1 and instead activates the anti-Pdc1p nanobody, NbJRI, to act as a genetically encoded inhibitor of Pdc1p accumulated during the growth phase. This dual transcriptional/post-translational control improves the production of 2,3-BDO and citramalate by up to 100 and 92% compared to using transcriptional controls alone in dynamic two-phase fermentations. Furthermore, this study establishes the NbJRI nanobody as an effective genetically encoded inhibitor of Pdc1p that can enhance the production of pyruvate-derived chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Successful post-translocation reproduction and genetic integration of eastern box turtles

Translocation is a conservation tool increasingly used in the recovery of at-risk species, including turtles, which are one of the world's most imperiled taxa. Post-release monitoring is essential to determine the outcomes of a given intervention and inform future efforts. However, monitoring typically focuses on post-release survival and spatial ecology whereas few studies assess the genetic and demographic outcomes. The eastern box turtle (Terrapene carolina carolina) is in decline throughout its range and is increasingly likely to be subject to translocations, including efforts to repatriate animals confiscated from the illegal wildlife trade. In 2019–2021, we translocated two groups of box turtles to the Savannah River Site in South Carolina, USA, including confiscated turtles (n = 208) and surrendered long-term captive turtles (LTC; n = 35). In 2022, we monitored a subset of confiscated (n = 12), LTC (n = 15), and sympatric resident (n = 8) females for reproductive output and genotyped their offspring and candidate sires to assign parentage. We found that all groups of females produced eggs at a similar rate and produced viable offspring but that the most recently translocated group (LTCs) displayed lower hatching success. Parentage assignment revealed that all groups sired offspring and mated with each other. Furthermore, our results broadly indicate that confiscated and LTC box turtles can successfully reproduce and genetically integrate following their release into wild populations, and that translocation may serve as a valuable tool for local population recovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
DOE OSTI2024

Machine learning-guided discovery of polymer membranes for CO 2 separation with genetic algorithm

Designing polymer membranes with high gas permeability and selectivity is a difficult multi-task constrained problem due to the trade-off between these two properties. In this work, we present a machine learning (ML) driven genetic algorithm to tackle the design problem of polymer membranes for CO 2 separation from N 2 and O 2 . Using literature data of permeability for three gases, we constructed multiple ML models with different fingerprinting featurization schemes to predict gas permeabilities. Then, we employed a genetic algorithm to design new polymers and evaluated their performance using our ML models. We were able to identify new polymer membranes that are promising for both CO 2 /N 2 and CO 2 /O 2 separations. Further, the top discovered polymers are predicted to have high glass transition temperatures. Similarly, the pyridine functionality was found in ≈20% of the predicted polymers. This framework can be used to design polymers for any application involving constrained optimization. Finally, we outlined the challenges and opportunities with using ML guided data-driven inverse design of polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

RT-EZ: A Golden Gate Assembly Toolkit for Streamlined Genetic Engineering of Rhodotorula toruloides

For economic and sustainable biomanufacturing, the oleaginous yeast Rhodotorula toruloides has emerged as a promising platform for producing biofuels, pharmaceuticals, and other valuable chemicals. However, genetic manipulation of R. toruloides has been limited by its high GC content and the lack of a replicating plasmid, necessitating gene integration into the genome of the yeast. To address these challenges, we developed the RT-EZ (R. toruloides Efficient Zipper) toolkit, a versatile tool based on Golden Gate assembly, designed to streamline R. toruloides engineering with improved efficiency and flexibility. The RT-EZ toolkit simplifies vector construction by incorporating new features such as bidirectional promoters and 2A peptides, color-based screening using RFP, and sequences optimized for both Agrobacterium tumefaciens-mediated transformation (ATMT) and easy linearization, enabling straightforward selection and transformation. Notably, the RT-EZ kit can be used to construct an expression cassette with four different genes in one assembly reaction, significantly improving vector construction speed and efficiency. The utility of the RT-EZ toolkit was demonstrated through the successful synthesis of arachidonic acid in R. toruloides by coexpressing fatty acid elongases and desaturases. Furthermore, this result underscores the potential of the RT-EZ toolkit to advance synthetic biology in R. toruloides, providing a streamlined method for addressing genetic engineering challenges in the yeast.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Design of lightweight BCC multi-principal element alloys with enhanced hydrogen storage using a machine learning-driven genetic algorithm

Body-centered cubic (BCC) based multi-principal element alloy (MPEA) hydrides have demonstrated significant potential for compact and efficient hydrogen storage. In this work, we first leverage machine learning (ML) models to predict the hydrogen affinity, storage capacity and phase stability of BCC MPEAs, creating a unique hydrogen-to-metal (H/M) predictor for materials with unprecedented performance. We developed a metaheuristic optimizer high-throughput framework by interfacing ML models with a genetic algorithm for the accelerated search of {Mg, Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Nb, Mo} based lightweight BCC MPEAs with improved hydrogen storage characteristics. We report five new MPEAs with a predicted gravimetric hydrogen storage capacity of around 3.5 wt% or more, including Cr 0.09 Mg 0.73 Ti 0.18 (4.25 wt% H) and Cr 0.21 Nb 0.11 Ti 0.35 V 0.33 (3.5 wt% H). The electronic structure of the top-performing composition, Cr 0.09 Mg 0.73 Ti 0.18 , was analyzed using density functional theory (DFT) to understand the reasons for its improved hydrogen storage properties compared to TiFe (1.90 wt% H), LaNi 5 (1.37 wt% H) or BCC MPEAs like TiVNbCr (3.70 wt% H). Temperature-dependent molecular dynamics (MD) studies were further performed on optimized BCC MPEAs to qualitatively study hydrogen mobility and analyze the effect of different elemental composition on bulk hydrogen diffusion. Our findings demonstrate how a ML assisted genetic algorithm framework can be used for efficient search of stable, lightweight and cost-effective MPEAs while minimizing the need for expensive ab initio calculations.

DFT↗
DOE OSTI2024

A modular GUI-based program for genetic algorithm-based feedback-assisted wavefront shaping

Abstract We have developed a modular graphical user interface (GUI)-based program for use in genetic algorithm-based feedback-assisted wavefront shaping. The program uses a class-based structure to separate out the universal modules (e.g. GUI, multithreading, optimization algorithms) and hardware-specific modules (e.g. code for different SLMs and cameras). This modular design makes the program easily adaptable to a wide range of lab equipment, while providing easy access to a GUI, multithreading, and three optimization algorithms (phase-stepping, simple genetic, and microgenetic).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Complete genomes of Asgard archaea reveal diverse integrated and mobile genetic elements

Asgard archaea are of great interest as the progenitors of Eukaryotes, but little is known about the mobile genetic elements (MGEs) that may shape their ongoing evolution. Here, we describe MGEs that replicate in Atabeyarchaeia, a wetland Asgard archaea lineage represented by two complete genomes. We used soil depth–resolved population metagenomic data sets to track 18 MGEs for which genome structures were defined and precise chromosome integration sites could be identified for confident host linkage. Additionally, we identified a complete 20.67 kbp circular plasmid and two family-level groups of viruses linked to Atabeyarchaeia, via CRISPR spacer targeting. Closely related 40 kbp viruses possess a hypervariable genomic region encoding combinations of specific genes for small cysteine-rich proteins structurally similar to restriction-homing endonucleases. One 10.9 kbp integrative conjugative element (ICE) integrates genomically into theAtabeyarchaeum deiterrae-1chromosome and has a 2.5 kbp circularizable element integrated within it. The 10.9 kbp ICE encodes an expressed Type IIG restriction-modification system with a sequence specificity matching an active methylation motif identified by Pacific Biosciences (PacBio) high-accuracy long-read (HiFi) metagenomic sequencing. Restriction-modification of Atabeyarchaeia differs from that of another coexisting Asgard archaea, Freyarchaeia, which has few identified MGEs but possesses diverse defense mechanisms, including DISARM and Hachiman, not found in Atabeyarchaeia. Overall, defense systems and methylation mechanisms of Asgard archaea likely modulate their interactions with MGEs, and integration/excision and copy number variation of MGEs in turn enable host genetic versatility.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Dual transposon sequencing profiles the genetic interaction landscape in bacteria

Gene redundancy complicates systematic characterization of gene function as single-gene deletions may not produce discernible phenotypes. We report dual transposon sequencing (dual Tn-seq), a platform for assaying the fitness of a comprehensive double mutant pool in parallel. Dual Tn-seq couples random barcode transposon site sequencing with the Cre-lox system, enabling deep sampling of 73% of the 1.3 million possible double gene deletions in Streptococcus pneumoniae. The genetic interactions identified span a wide range of biochemical processes, revealing new factors in presumably well-studied pathways, exemplified by a cytidine triphosphate synthase PyrJ. Moreover, this approach should permit further investigation of growth condition–specific genetic interactions. Because dual Tn-seq does not require the construction of a large array of single mutants, it should be readily adaptable to various microorganisms.

CTP synthesis↗
DOE OSTI2025

Unique genetic signatures in HIV-1 subtype A1 and A1D recombinant envelope glycoprotein distinguish contemporary transmitted/founder viruses from historical strains in East Africa

Introduction: The envelope glycoprotein (Env) of HIV-1 Transmitted/Founder (T/F) viruses in subtypes B and C carries distinct genetic signatures that enhance transmission fitness, augment infectivity and immune evasion. However, there is limited data on such signatures in T/F subtypes A1, D and A1D recombinants that predominate East Africa’s HIV epidemic.Methods: We used phylogenetically corrected approaches to detect distinct genetic signatures by comparing 44 contemporary HIV-1 T/F Envs with 229 historical Envs of the same subtype in East Africa.Results and Discussion: Subtype analysis based on the full-length Env gene of contemporary T/F viruses revealed a high proportion of subtype A1, followed by A1D recombinants, and fewer subtype D. Signature analysis revealed that the contemporary subtype A1 T/Fs were more likely to select distinct amino acids, including M22 in the signal peptide, R82 in gp120, A172 in the V2 loop, E230 in the glycosite 230, K275 in the D loop, Y317 in the V3 loop, K476 and N477 in the CD4 contact site, when compared with the historical Envs (q-value < 0.2). Conversely, the contemporary subtype A1 T/F Envs were less likely to carry the amino acids Q432 in the CD4 contact site, and the L784 signature within the LLP-2 (q-value < 0.2). The A1D recombinant T/Fs were more likely to select the D620 in the C-helix, but under selected the L34 in gp120, P299 in the V3 loop and Y643 in the Heptad repeat-2, compared to the historical Envs (q-value < 0.2). The distinct signature sites reported in this study may contribute to the successful establishment of acute infection as well as the persistence of long-term infection. Therefore, effective therapeutics and vaccines may target these distinct amino acid signatures especially for the East African region as it may be necessary to employ subtype-specific vaccines according to the subtype distribution.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

low-thrust orbit transfer↗
NASA NTRS2012

Genetic Inventory Task Final Report

Contaminant terrestrial microbiota could profoundly impact the scientific integrity of extraterrestrial life-detection experiments. It is therefore important to know what organisms persist on spacecraft surfaces so that their presence can be eliminated or discriminated from authentic extraterrestrial biosignatures. Although there is a growing understanding of the biodiversity associated with spacecraft and cleanroom surfaces, it remains challenging to assess the risk of these microbes confounding life-detection or sample-return experiments. A key challenge is to provide a comprehensive inventory of microbes present on spacecraft surfaces. To assess the phylogenetic breadth of microorganisms on spacecraft and associated surfaces, the Genetic Inventory team used three technologies: conventional cloning techniques, PhyloChip DNA microarrays, and 454 tag-encoded pyrosequencing, together with a methodology to systematically collect, process, and archive nucleic acids. These three analysis methods yielded considerably different results: Traditional approaches provided the least comprehensive assessment of microbial diversity, while PhyloChip and pyrosequencing illuminated more diverse microbial populations. The overall results stress the importance of selecting sample collection and processing approaches based on the desired target and required level of detection. The DNA archive generated in this study can be made available to future researchers as genetic-inventory-oriented technologies further mature.

microbial diversity↗
NASA NTRSDate not supplied

BLOOD-BASED MULTI-SCALE MODEL FOR CANCER RISK FROM GCR IN GENETICALLY DIVERSE POPULATIONS

OBJECTIVES AND METHODS This project addresses the challenge of understanding and predicting individual radiation sensitivity by integrating genetics, demographics and biomarker characteristics across species (mice and humans). We hypothesize that ex vivo DNA repair response to GCR components is a central determinant of cancer risk from space radiation and can serve as a biomarker of radiation risk in combination with genetics. Automated image quantification of 53BP1+ radiation-induced foci (RIF) during the first 4-48 h post-irradiation was performed as a function of dose and LET in non-immortalized primary skin fibroblasts derived from 76 mice across 15 strains (5 inbred reference strains and 10 collaborative-cross strains) exposed to X rays (0.1, 1 and 4 Gy), 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100μm2), as well as in peripheral blood mononuclear cells (PBMCs) from 768 healthy donors (matched ethnicity, 50/50 male/female, 18-70 years old) exposed to gamma rays (0.1 and 1 Gy), 350 MeV/n 28Si, 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100μm2). QUANTIFICATION OF 53BP1+ FOCI IN VITRO AND ASSOCIATIONS TO IN VIVO RADIATION SUSCEPTIBILITY IN 15 MOUSE STRAINS We reported in vitro repair kinetic and repairable fractions of RIF for the 15 mouse strains and introduced a mathematical model for RIF as a function of time, dose and LET. We noted that the metabolic activity of cells modulates the RIF response, and we introduced the open access tool terRIFic (Tool for Enhanced Results of RIF In Cells, https://radbiolab.shinyapps.io/terrific/) to correct for such bias using confluence level. Notably, at 4h post-irradiation, RIF/Gy decreased with dose or LET: as the dose or LET increases, so does the proximity of DNA double-strand-breaks (DSB) and our data suggest that proximal DSBs are brought together inside isolated RIF for repair. The RIF/Gy trend was inverted at 24h, suggesting RIF with high DSB content are more difficult to repair. We showed that in vitro metrics correlate with in vivo measurements in the same 15 mouse strains, such as survival levels of immune cells or spontaneous cancer incidence, suggesting a relationship between the efficiency of DSB repair and cancer risk or radiation toxicity. In addition to the efficiency of repair and persistent RIF, the amount of spontaneous foci before irradiation was also found to be strain dependent. Finally, we performed genome-wide association study in the same 15 mouse strains using all RIF phenotypes measured in vitro, identifying genes of interest and validating RIF as an ideal biomarker for individual radiation sensitivity. BASELINE 53BP1+ FOCI PREDICTS INDIVIDUAL HUMAN RESPONSE TO GCR COMPONENTS Based on the analysis of radiation responses of 576 donor PBMCs (using quantification of 53BP1+ foci, oxidative stress and cell death), we observed a wide variability of subject- and LET-dependent radiation responses, with radiation-induced DNA repair foci increasing with LET, though oxidative stress being notably reduced by high-LET irradiation, potentially due to a switch between hydrogen peroxide and oxygen radical-based mechanisms. We identified a relationship between few spontaneous DNA foci at baseline and increased DNA repair after irradiation, accompanied by an alteration in immunoregulatory cytokine secretion, which might be adapted as biomarkers to predict ionizing radiation sensitivity. Among demographic variables, only latent cytomegalovirus infection and age were predictive of high baseline foci formation. Finally, we have performed low-throughput whole genome sequencing of all samples and are currently in the process of identifying the genes and pathways associated with low and high-LET ionizing radiation sensitivity in humans.

53BP1↗
DOE OSTI2025

Three rate-determining protein roles in photosynthetic O 2 -evolution addressed by time-resolved experiments on genetically modified photosystems

Light-driven water splitting by plants, algae and cyanobacteria is pivotal for global bioenergetics and biomass formation. A manganese cluster bound to the photosystem II proteins catalyzes the complex reaction at high rate, but the rate-determining factors are insufficiently understood. Here we trace the oxygen-evolution transition by time-resolved polarography and infrared spectroscopy for cyanobacterial photosystems genetically modified at two strategic sites, complemented by computational chemistry. Our results highlight three rate-determining roles of the protein environment of the metal cluster: acceleration of proton-coupled electron transfer, acceleration of substrate-water insertion after O 2 -formation, and balancing of rate-determining enthalpic and entropic contributions. Whereas in general the substrate-water insertion step may be unresolvable in time-resolved experiments, here it likely becomes traceable because of deceleration by genetic modification. Our results may stimulate new time-resolved experiments on substrate-water insertion in photosynthesis, clarification of enthalpy-entropy compensation in enzyme catalysis, and knowledge-guided development of inorganic catalyst materials.

Bioenergetics↗
DOE OSTI2025

Translocation mechanism of xeroderma pigmentosum group D protein on single-stranded DNA and genetic disease etiology

Abstract XPD is a key nucleotide excision repair (NER) protein whose function is vital for genome integrity. During NER, XPD serves as a 5′−3′ single-strand DNA translocase that enables lesion scanning and verification in genomic DNA. Yet, its translocation mechanism is incompletely understood. Here we use molecular simulations and chain-of-replicas path optimization methods to model the ATP-driven translocation mechanisms of XPD and its bacterial homolog DinG, revealing all on-path metastable intermediates and corresponding kinetic rates. We identify the XPD(DinG) global domain motions that modulate the strength of DNA association at the opposing ends of the DNA-binding groove. During the ATP hydrolysis cycle, alternating weak and strong interactions at two defined groove constrictions enable DNA reptation and forward displacement of the ATPase. Moreover, we show that DNA- or ATP-binding residues directly involved in translocation are hotspots for genetic disease mutations. Thus, our findings shed light on the etiology of XPD-associated genetic syndromes.

Paul, Tanmoy↗
DOE OSTI2026

Mobile genetic elements shape microbial diversity and functions in thawing permafrost soils

Ecosystems are shaped by communities of microorganisms whose niches and impacts depend on functional profiles influenced by gene gains and losses. Culture-based experiments demonstrate that mobile genetic elements (MGEs) can mediate gene flux, but quantitative understanding of these dynamics in natural systems remains limited. Here we develop and apply a systematic, meta-omic framework to investigate MGEs in a complex natural system using an 8-year soil time series collected at Stordalen Mire, in Sweden’s thawing permafrost margin. In this climate-critical peatland, we identify ~2.1 million MGE recombinases across 89 microbial phyla and assess ecological distributions, affected functions, past mobility and current activity. This revealed an active mobilome that shapes natural genetic diversity via differential impacts on major phyla and affects a wide range of functions, including metabolic genes involved in carbon flux and nutrient cycling. These findings and this analytic framework suggest avenues towards a better understanding of MGE diversity, activity, mobility and impacts across ecosystems.

Biological and medical sciences↗