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At least 127 records · Page 7

Comparative physiological and genomic characterization of a novel Nitrobacter vulgaris strain from a nitrate-contaminated subsurface

Nitrite-oxidizing bacteria (NOB) represent a crucial node in the global nitrogen cycle. By catalyzing the second step of nitrification—the oxidation of nitrite to nitrate to generate energy for growth—NOB activity controls the fate of nitrite (NO 2 - ) in aerobic environments. Despite thriving in diverse environments, including soils, freshwater, marine ecosystems, subsurface habitats, and water treatment systems, organisms capable of nitrite oxidation are confined to Nitrobacter, Nitrospira, Nitrospina, Nitrotoga, and a few other specific lineages. The genus Nitrobacter, recognized for its facultative heterotrophic metabolism, is often associated with high-nitrogen environments. Here, we report the physiological characterization of a novel strain, Nitrobacter vulgaris strain MLSD-S22, isolated from a nitrate- and heavy-metal-contaminated subsurface. Growth inhibition experiments revealed that strain MLSD-S22 and the N. vulgaris type strain Z exhibited similar sensitivities to nitrite and nitrate, with nitrite being the most inhibitory. Microrespirometry demonstrated that the two N. vulgaris strains and Nitrobacter winogradskyi Nb-255 possessed higher affinities for nitrite and oxygen than previously reported for Nitrobacter, suggesting potential to compete in low-substrate environments. Long-read DNA sequencing provided a complete genome for strain MLSD-S22, revealing two plasmids and an intact nitrous oxide (N 2 O) reduction operon—an unexpected feature for Nitrobacter. While N 2 O reduction activity was not observed under the tested conditions, this discovery raises questions about the contribution of Nitrobacter NOB to the N 2 O sink. These findings broaden the physiological and genomic diversity of Nitrobacter, offering new insights into their adaptation strategies and providing a framework for future evaluation of their potential roles in nitrogen loss.

Nitrobacter↗

Community Structure of Arbuscular Mycorrhizal Fungi in Soils of Switchgrass Harvested for Bioenergy

We assessed the different species of beneficial fungi living in agricultural fields of switchgrass, a large grass grown for biofuels, using high-resolution DNA sequencing. Contrary to our expectations, the fungi were not greatly affected by fertilization. However, we found a positive relationship between plant productivity and the number of families of beneficial fungi at one site. Furthermore, we sequenced many species that could not be identified with existing reference databases. One group of fungi was highlighted in an earlier study for being widely distributed but of unknown taxonomy. We discovered that this group belonged to a family called Pervetustaceae , which may benefit switchgrass in stressful environments. To produce higher-yielding switchgrass in a more sustainable manner, it could help to study these undescribed fungi and the ways in which they may contribute to greater switchgrass yield in the absence of fertilization.

59 BASIC BIOLOGICAL SCIENCES↗

Synthetic overlapping genes stabilize genetic systems

Overlapping genes—wherein two different proteins are translated from alternative reading frames of the same DNA sequence—provide a means to stabilize an engineered gene by directly linking its evolutionary fate with that of an overlapping gene. However, creating overlapping gene pairs is challenging, as it requires redesigning both protein products to accommodate overlap constraints. Here, we present a new “overlapping, alternate-frame insertion” (OAFI) method for creating synthetic overlapping genes by inserting an “inner” gene, encoded in an alternate frame, into a flexible region of an “outer” gene. Using OAFI, we create new overlapping gene pairs of genetic reporters and bacterial toxins within an antibiotic resistance gene. We show that both the inner and outer genes retain function despite redesign, with translation of the inner gene influenced by its overlap position in the outer gene. Importantly, we show that, despite these inner gene sequences not contributing to outer gene function, selection for the outer gene alters the permitted inactivating mutations in the inner gene, and that overlapping toxins can restrict horizontal gene transfer of the antibiotic resistance gene. Overall, OAFI offers a versatile tool for synthetic biology, expanding the applications of overlapping genes in gene stabilization and biocontainment.

Biological and medical sciences↗

Geochemistry and Multiomics Data Differentiate Streams in Pennsylvania Based on Unconventional Oil and Gas Activity

Unconventional oil and gas (UOG) extraction is increasing exponentially around the world, as new technological advances have provided cost-effective methods to extract hard-to-reach hydrocarbons. While UOG has increased the energy output of some countries, past research indicates potential impacts in nearby stream ecosystems as measured by geochemical and microbial markers. Here, we utilized a robust data set that combines 16S rRNA gene amplicon sequencing (DNA), metatranscriptomics (RNA), geochemistry, and trace element analyses to establish the impact of UOG activity in 21 sites in northern Pennsylvania. These data were also used to design predictive machine learning models to determine the UOG impact on streams. We identified multiple biomarkers of UOG activity and contributors of antimicrobial resistance within the order Burkholderiales. Furthermore, we identified expressed antimicrobial resistance genes, land coverage, geochemistry, and specific microbes as strong predictors of UOG status. Of the predictive models constructed (n = 30), 15 had accuracies higher than expected by chance and area under the curve values above 0.70. The supervised random forest models with the highest accuracy were constructed with 16S rRNA gene profiles, metatranscriptomics active microbial composition, metatranscriptomics active antimicrobial resistance genes, land coverage, and geochemistry (n = 23). The models identified the most important features within those data sets for classifying UOG status. These findings identified specific shifts in gene presence and expression, as well as geochemical measures, that can be used to build robust models to identify impacts of UOG development.

16S rRNA↗

BigDNA

SAND2020-13696 M BigDNA takes a user input fast DNA sequence and outputs overlapping long PCR primers for Gibson Assembly. The software can design primers that remove gene products, insert new gene products, or completely circularize long DNAs. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Porter, Kelley↗

Auto_PDI

Protein-DNA Interaction Workflow (PDI Workflow), a pipeline that focuses on generating high-quality docking and molecular dynamics simulations for Protein-DNA complexes. This allows us to take DNA sequences with unknown tertiary structures, accurately predict their structure, dock them with the desired target protein, and then simulate their interactions using molecular dynamics simulations

Kumar, Neeraj↗

Inventory of Composable Elements (ICE) v6.0.0

The Inventory of Composable Elements (ICE) is an open source registry software platform for managing information about biological parts. It is capable of recording information about plasmids, microbial host strains and seeds, as well as DNA parts. Includes features such as DNA sequence visualization, editing and annotation, auto-aligning sequencing trace files against reference templates, SBOL XML/RDF support, and web-of-registries functionality. The web of registries functionality provides strong support for distributed interconnected use and enables sharing and transfer of biological parts across various independent ICE instances. ICE adopts modern software development principles, leveraging component-base frameworks, offering a REST API for convenient third-party integration and emphasizing scalability, security, and service integrations for dynamic content availability. The source code is hosted at https://github.com/JBEI/ice. A public instance is available at public-registry.jbei.org, where users can try out features, upload parts or simply use it for their projects.

Plahar, Hector↗

germs-lab/LAMPS-miscanthus-microbiome

Data and analysis to accompany our submitted paper characterizing the LAMPS Miscanthus Microbiome comparing 16S rRNA gene DNA sequencing of Miscanthus in a staggered-start experiment. Authors for this analysis include Fernando Igne Rocha, Lanying Ma, and Adina Howe.

Igne Rocha, Fernando↗

GenomeFace v1.0

GenomeFace is meta-genome binning software. Metagenomic binning, the process of grouping DNA sequences into taxonomic units, is critical for understanding the functions, interactions, and evolutionary dynamics of microbial communities. We propose a deep learning approach to binning using two neural networks, one based on composition and another on environmental abundance, dynamically weighting the contribution of each based on characteristics of the input data. Trained on over 43,000 prokaryotic genomes, our network for composition-based binning is inspired by metric learning techniques used for facial recognition. Using a task-specific, multi-GPU accelerated algorithm to cluster the embeddings produced by our network, our binner leverages marker genes observed to be universally present in nearly all taxa to grade and select optimal clusters of sequences from a hierarchy of candidates. We evaluate our approach on four simulated datasets with known ground truth. Our linear time integration of marker genes recovers more near complete genomes than state of the art but computationally infeasible solutions using them, while being over an order of magnitude faster. Finally, we demonstrate the scalability and acuity of our approach by testing it on three of the largest metagenome assemblies ever performed. Compared to other binners, we produced 47%-183% more near complete genomes. From these datasets, we find over the genomes of over 3000 new candidate species which have never been previously cataloged, representing a potential 4% expansion of the known bacterial tree of life.

Lettich, Richard [Lawrence Berkeley National Labor↗

Genomic Language model for Annotation of Repetitive Elements (GLARE) v1.0

GLARE (Genomic Language model for Annotation of Repetitive Elements) is a tool that classifies transposable elements (TEs)—the mobile, repetitive DNA sequences that make up large fractions of eukaryotic genomes. GLARE fine-tunes the NTv3-650M genomic language model on a harmonized collection of curated TE sequences from the PanTEon and Repbase reference databases, assigning each input sequence to one of 11 orders and 32 superfamilies in a Wicker-compatible taxonomy. Features. From nucleotide FASTA input, GLARE outputs per-sequence predictions, class summaries, composition figures, and an annotated FASTA. It provides calibrated confidence scores with optional abstention and runs on CPU or GPU. Uses. GLARE serves as a classification component in genome-annotation pipelines, downstream of TE discovery, supporting genome annotation and comparative and evolutionary genomics. Advantages. GLARE is the first repeat-element classifier to leverage a pretrained genomic language model. Combined with multi-database training, this approach outperformed all nine classifiers in the PanTEon benchmark, generalized better to unseen taxonomic clades, and remained robust to sequence orientation—a common failure mode of existing tools.

Bruna, Tomas [Lawrence Berkeley National Laborator↗

Nanobodies as potential tools for microbiological testing of live biotherapeutic products

Nanobodies are highly specific binding domains derived from naturally occurring single chain camelid antibodies. Live biotherapeutic products (LBPs) are biological products containing preparations of live organisms, such as Lactobacillus, that are intended for use as drugs, i.e. to address a specific disease or condition. Demonstrating potency of multi-strain LBPs can be challenging. The approach investigated here is to use strain-specific nanobody reagents in LBP potency assays. Llamas were immunized with radiation-killed Lactobacillus jensenii or L. crispatus whole cell preparations. A nanobody phage-display library was constructed and panned against bacterial preparations to identify nanobodies specific for each species. Nanobody-encoding DNA sequences were subcloned and the nanobodies were expressed, purified, and characterized. Colony immunoblots and flow cytometry showed that binding by Lj75 and Lj94 nanobodies were limited to a subset of L. jensenii strains while binding by Lc38 and Lc58 nanobodies were limited to L. crispatus strains. Mass spectrometry was used to demonstrate that Lj75 specifically bound a peptidase of L. jensenii, and that Lc58 bound an S-layer protein of L. crispatus. The utility of fluorescent nanobodies in evaluating multi-strain LBP potency assays was assessed by evaluating a L. crispatus and L. jensenii mixture by fluorescence microscopy, flow cytometry, and colony immunoblots. Our results showed that the fluorescent nanobody labelling enabled differentiation and quantitation of the strains in mixture by these methods. Development of these nanobody reagents represents a potential advance in LBP testing, informing the advancement of future LBP potency assays and, thereby, facilitation of clinical investigation of LBPs.

60 APPLIED LIFE SCIENCES↗

Bacterial diversity dynamics in microbial consortia selected for lignin utilization

Lignin is nature’s largest source of phenolic compounds. Its recalcitrance to enzymatic conversion is still a limiting step to increase the value of lignin. Although bacteria are able to degrade lignin in nature, most studies have focused on lignin degradation by fungi. To understand which bacteria are able to use lignin as the sole carbon source, natural selection over time was used to obtain enriched microbial consortia over a 12-week period. The source of microorganisms to establish these microbial consortia were commercial and backyard compost soils. Cultivation occurred at two different temperatures, 30°C and 37°C, in defined culture media containing either Kraft lignin or alkaline-extracted lignin as carbon source. iTag DNA sequencing of bacterial 16S rDNA gene was performed for each of the consortia at six timepoints (passages). The initial bacterial richness and diversity of backyard compost soil consortia was greater than that of commercial soil consortia, and both parameters decreased after the enrichment protocol, corroborating that selection was occurring. Bacterial consortia composition tended to stabilize from the fourth passage on. After the enrichment protocol, Firmicutes phylum bacteria were predominant when lignin extracted by alkaline method was used as a carbon source, whereas Proteobacteria were predominant when Kraft lignin was used. Bray-Curtis dissimilarity calculations at genus level, visualized using NMDS plots, showed that the type of lignin used as a carbon source contributed more to differentiate the bacterial consortia than the variable temperature. The main known bacterial genera selected to use lignin as a carbon source were Altererythrobacter , Aminobacter , Bacillus , Burkholderia , Lysinibacillus , Microvirga , Mycobacterium , Ochrobactrum , Paenibacillus , Pseudomonas , Pseudoxanthomonas , Rhizobiales and Sphingobium . These selected bacterial genera can be of particular interest for studying lignin degradation and utilization, as well as for lignin-related biotechnology applications.

59 BASIC BIOLOGICAL SCIENCES↗

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor’s sensitivity (EC 50 ) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC 50 . Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

59 BASIC BIOLOGICAL SCIENCES↗

The genotype-phenotype landscape of an allosteric protein

Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype-phenotype relationships remains elusive. Here, we report the large-scale measurement of the genotype-phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli , LacI. Using a method that combines long-read and short-read DNA sequencing, we quantitatively measure the dose-response curves for nearly 10 5 variants of the LacI genetic sensor. The resulting data provide a quantitative map of the effect of amino acid substitutions on LacI allostery and reveal systematic sequence-structure-function relationships. We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural-network models, but unpredictable changes also occur. For example, we were surprised to discover a new band-stop phenotype that challenges conventional models of allostery and that emerges from combinations of nearly silent amino acid substitutions.

59 BASIC BIOLOGICAL SCIENCES↗

Soil microbiome resilience to short-term (30 days, 90 days) and long-term (1000 days) drought

This dataset contains data used for the paper "Drought duration does not impact soil microbiome resilience". The Related References will be updated with a full citation when available. Increasing global droughts exert large but poorly understood effects on the microbial communities and ecology of soil. Microbial communities generally show resilience and return to pre-drought conditions when short-term droughted soils are rewet; soils exposed to long-term drought, however, often show a lag upon rewetting, after which microbial communities may or may not return to their pre-stressed conditions. Though short-term droughts have been widely studied, long-term drought manipulation experiments remain rare, especially those that compare microbial response to short-term and long-term drought in tandem. We conducted a 1000-day drought simulation in controlled laboratory conditions with soil cores collected from a tidal freshwater ecosystem in Washington state, USA, and subsequently exposed them to rewetting for two weeks. We also included short-term (30-day and 90-day) drought and rewet treatments to directly compare microbial community and organic matter responses across drought durations. We found distinct microbial taxa belonging to Firmicutes and Actinobacteria enriched after the 1000-day drought, but not after the short-term droughts. While we hypothesized that the microbial community would recover from a short-term drought after rewetting to resemble pre-drought conditions, our results revealed community dissimilarities between rewet and pre-drought conditions across all drought durations. These findings suggest unique microbial life history strategies within certain microbial phyla that make them successful colonizers during an extended drought period, and the influence of environmental and physiological context on microbial responses to rewetting. The 16SrRNA gene amplicon dataset contains processed DNA sequences in the form of an ASV table with raw unrarefied read counts and representative sequences in .fasta format as described in the ESS-DIVE amplicon sequence reporting format (https://ess-dive.gitbook.io/amplicon-sequencing-reporting-format/instructions). The Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) dataset consists of processed files containing presence absence data of molecular formulae and molecular characterization of FTICR resolved peaks. The Nuclear Magnetic Resonance (NMR) dataset contains files relevant to NMR spectra and peaks. A sample key file and a sample metadata file is included for the FTICR/NMR and 16S dataset respectively.

1000-day drought↗

Unbalancing Symbiotic Nitrogen Fixation: Can We Make Effectiveness More Effective?

One of the most critical uses of carbon fuels is in generating nitrogen fertilizer. Atmospheric dinitrogen is too stable to be used directly by plants, so plants need other chemical forms of nitrogen. Nitrogen fertilizer production uses ~4% of the world’s natural gas. Making N fertilizers leverages methane’s energy content by enabling additional energy to be captured into food and fiber through increased photosynthesis. Manufacturing fertilizer is essential—without it there would only be enough combined nitrogen to feed ~3 billion of the world’s ~7 billion people. How to alter this addiction is not obvious, but the best chance to secure substantial and sustainable amounts of future N might be through symbiotic nitrogen fixation (SNF). SNF describes mutualistic interactions where nitrogen-fixing bacteria provide fixed nitrogen to their plant hosts, freeing them from the need for nitrogen fertilizers. SNF already occurs on a large scale; "biological nitrogen” contributes about 5 Tg N/yr to American agriculture, a quarter of the nitrogen needed. Improving SNF and replacing inorganic fertilizers are both important goals in establishing sustainable and more energy-efficient farming. SNF has been used in agriculture for millennia, largely by using legumes like beans or alfalfa in mixed cropping systems. Legume SNF occurs in root nodules, organs that develop after a growing root is infected by bacteria called rhizobia. In the plant cells of a legume nodule, specialized forms of rhizobia fix nitrogen and can produce enough ammonia to supply the growing plant. The exchange of photosynthetically derived plant carbon compounds for nitrogen reduced by the bacteria is the engine that drives the symbiosis. Important details about the organization of development and metabolism in SNF remain to be determined, such as how plant and bacterial metabolism are coordinated as nitrogen is being fixed and what governs the overall level of nitrogen fixation. Most bacterial mutations that affect SNF completely block fixation (e.g. are Fix – ), providing a limited window into the feedback interactions that must be part of the process. We have investigated an unusual mutant of Sinorhizobium meliloti, a symbiotic partner of alfalfa, that fixes dinitrogen at a normal rate (i.e., it is Fix + ) but is not effective in supporting plant growth (i.e., it is ineffective or Eff – ). We have shown that the mutant has a specific mutation in the glnD gene, which codes for a protein that regulates the bacterial response to nitrogen stress, among other key pathways. A Fix + Eff – phenotype contains an unavoidable puzzle—how is it possible for the bacteria to fix nitrogen at a normal rate and without benefiting the nitrogen starved plant host? After eliminating other possibilities, we proposed that the bacteria synthesize a nitrogen-containing compound that the plant can’t use so that acquiring this compound does not relieve plant nitrogen stress. Extracts of nodules made by glnD mutant strains have high concentrations of pyruvate canaline oxime (PCO), a derivative of the plant defensive compound canaline, a toxic analog of ornithine, an amino acid. Many legumes produce canaline and the related arginine mimic canavanine. For reasons we do not yet understand, the mutation in glnD appears to trigger significant overproduction of canaline in a defense response that leads to substantial synthesis of PCO. We expected the bacteria to be able to use PCO, but we have not been able to grow S. meliloti on PCO under several conditions. PCO thus appears to be a metabolic dead end for both the plant and bacteria. We also examined bacterial catabolic pathways that degrade arginine and canavanine and showed that mutation of these did not alter the symbiosis in an obvious way. In related experiments to examine the metabolic development of root nodules, we carried out an ambitious experiment to simultaneously measure metabolites and protein changes during nodule maturation. For these experiments we used a related symbiotic interaction between S. medicae and Medicago truncatula. M. truncatula has a simpler genome than alfalfa so we could identify plant proteins based on predictions from the DNA sequence. Nodules formed on Medicago grow linearly, with the least mature tissues at the tip. Because development of nitrogen fixation is accompanied by the production of the pigmented plant oxygen carrier protein leghemoglobin, we were able to locate and manually separate the tip, transitional, and mature nitrogen-fixing regions from each other. Advances in proteomic and metabolomic techniques allowed us to analyze these tissues from a pool of as few as 10 nodules. We observed correlated transitions of various enzymes in the plant and bacterial proteomes from those characteristic of free-living metabolism to the microaerobic metabolism of the nitrogen-fixing tissues. The metabolome could not be separated into plant and bacterial domains but was consistent with this overall transition. A subsequent paper examined proteolysis in nodule bacteria. Protein turnover in mature regions of the nodule is significant. We were able to show complexes between various proteins and important proteases, using precipitation capture techniques to isolate the proteases and sensitive proteomic analysis to reveal the associated proteins.

09 BIOMASS FUELS↗

Bubble lifetimes in DNA gene promoters and their mutations affecting transcription

Relative lifetimes of inherent double stranded DNA openings with lengths up to ten base pairs are presented for different gene promoters and corresponding mutants that either increase or decrease transcriptional activity, in the framework of the Peyrard-Bishop-Dauxois model. Extensive microcanonical simulations are used, with energies corresponding to physiological temperature. The bubble lifetime profiles along the DNA sequences demonstrate a significant reduction of the average lifetime at the mutation sites when the mutated promoter decreases transcription, while a corresponding enhancement of the bubble lifetime is observed in the case of mutations leading to increased transcription. The relative difference of bubble lifetimes between the mutated and the wild type promoters at the position of mutation varies from 20% to more than 30% as the bubble length is decreasing.

59 BASIC BIOLOGICAL SCIENCES↗

Physical, resource supply, and biological controls on nutrient processing along the river continuum

Nutrient impairment has led to damages to US surface and groundwater systems in excess of 100 billion dollars per year. Therefore, there is a strong need to develop methods to predict the transport, uptake, and export of nutrients along fluvial networks. We present results that are based on a data-driven mechanistic understanding of three factors that largely control nutrient uptake and export: 1) interactions between transport-related processes (mass transfer to metabolically active zones), 2) resource supply dynamics (nutrient concentration, stoichiometric constraints, etc.), and 3) biological controls (microbial community structure and function). Our results were generated from column experiments conducted along the Jemez River-Rio Grande continuum, which spans four orders of magnitude in mean annual discharge, more than 2000 m in altitude, and more than 500 km of stream longitude. Two resource supply injections were performed on each of the columns, i.e., a nitrate only addition, followed by a stoichiometrically ‘balanced’ 106Carbon:16Nitrogen:1Phosphorus addition. We quantified NO3-N uptake kinetics while constraining three variables: stream order, sediment type and type of injection (N vs stoichiometrically ‘balanced’ C:N:P). Following the laboratory nutrient uptake experiments, the columns were destructively sampled and the contents were homogenized to collect subsamples for DNA sequencing. Amplicon analysis was carried out as described by the Earth Microbiome Protocol for 16s and ITS sequencing.

54 ENVIRONMENTAL SCIENCES↗