Biology subjects

Clum, A.

Publications and source records attributed to Clum, A..

9 recordsLinked to original sources

bioRxiv2025

Tetranucleotide frequencies differentiate genomic boundaries and metabolic strategies across environmental microbiomes

Microbiomes are constrained by physicochemical conditions, nutrient regimes, and community interactions across diverse environments, yet genomic signatures of this adaptation remain unclear. Metagenome sequencing is a powerful technique to analyze genomic content in the context of natural environments, establishing concepts of microbial ecological trends. Here, we developed a data discovery tool - a tetranucleotide-informed metagenome stability diagram - that is publicly available in the Integrated Microbial Genomes and Microbiomes (IMG/M) platform for metagenome-ecosystem analyses. We analyzed the tetranucleotide frequencies from quality-filtered and unassembled sequence data of over 12,000 metagenomes to assess ecosystem-specific microbial community composition and function. We found that tetranucleotide frequencies can differentiate communities across various natural environments, and that specific functional and metabolic trends can be observed in this structuring. Our tool places metagenomes sampled from diverse environments into clusters and along gradients of tetranucleotide frequency similarity, suggesting microbiome community compositions specific to gradient conditions. Within the resulting metagenome clusters, we identify protein-coding gene identifiers that are most differentiated between ecosystem classifications. We plan for annual updates to the metagenome stability diagram in IMG/M with new data, allowing for refinement of the ecosystem classifications delineated here. This framework has the potential to inform future studies on microbiome engineering, bioremediation, and the prediction of microbial community responses to environmental change. ImportanceMicrobes adapt to diverse environments influenced by factors like temperature, acidity, and nutrient availability. We developed a new tool to analyze and visualize the genetic makeup of over 12,000 microbial communities, revealing patterns linked to specific functions and metabolic processes. This tool groups similar microbial communities and identifies characteristic genes within environments. By continually updating this tool, we aim to advance our understanding of microbial ecology, enabling applications like microbial engineering, bioremediation, and predicting responses to environmental change.

ecology↗

bioRxiv2022

A standardized quantitative analysis strategy for stable isotope probing metagenomics

Stable isotope probing (SIP) facilitates culture-independent identification of active microbial populations within complex ecosystems through isotopic enrichment of nucleic acids. Many SIP studies rely on 16S rRNA sequences to identify active taxa but connecting these sequences to specific bacterial genomes is often challenging. Here, we describe a standardized laboratory and analysis framework to quantify isotopic enrichment on a per-genome basis using shotgun metagenomics instead of 16S rRNA sequencing. To develop this framework, we explored various sample processing and analysis approaches using a designed microbiome where the identity of labeled genomes, and their level of isotopic enrichment, were experimentally controlled. With this ground truth dataset, we empirically assessed the accuracy of different analytic models for identifying active taxa, and examined how sequencing depth impacts the detection of isotopically labeled genomes. We also demonstrate that using synthetic DNA internal standards to measure absolute genome abundances in SIP density fractions improves estimates of isotopic enrichment. In addition, our study illustrates the utility of internal standards to reveal anomalies in sample handling that could negatively impact SIP metagenomic analyses if left undetected. Finally, we present SIPmg, an R package to facilitate the estimation of absolute abundances and perform statistical analyses for identifying labeled genomes within SIP metagenomic data. This experimentally validated analysis framework strengthens the foundation of DNA-SIP metagenomics as a tool for accurately measuring the in situ activity of environmental microbial populations and assessing their genomic potential. ImportanceAnswering the question of who is eating what? within complex microbial communities is paramount for our ability to model, predict, and modulate microbiomes for improved human and planetary health. This question is often pursued using stable isotope probing to track the incorporation of labeled compounds into cellular DNA during microbial growth. However, with traditional stable isotope methods, it is challenging to establish links between an active microorganisms taxonomic identity and genome composition, while providing quantitative estimates of the microorganisms isotope incorporation rate. Here, we report an experimental and analytical workflow that lays the foundation for improved detection of metabolically active microorganisms and better quantitative estimates of genome-resolved isotope incorporation, which can be used to further refine ecosystem-scale models for carbon and nutrient fluxes within microbiomes.

microbiology↗

bioRxiv2021

Genetic determinants of endophytism in the Arabidopsis root mycobiome

Roots of Arabidopsis thaliana do not engage in symbiotic associations with mycorrhizal fungi but host taxonomically diverse fungal communities that influence health and disease states. We sequenced the genomes of 41 fungal isolates representative of the A. thaliana root mycobiota for comparative analysis with 79 other plant-associated fungi. We report that root mycobiota members evolved from ancestors with diverse lifestyles and retained large repertoires of plant cell wall-degrading enzymes (PCWDEs) and effector-like small secreted proteins. We identified a set of 84 gene families predicting best endophytism, including families encoding PCWDEs acting on xylan (GH10) and cellulose (AA9). These genes also belong to a core transcriptional response induced by phylogenetically-distant mycobiota members in A. thaliana roots. Recolonization experiments with individual fungi indicated that strains with detrimental effects in mono-association with the host not only colonize roots more aggressively than those with beneficial activities but also dominate in natural root samples. We identified and validated the pectin degrading enzyme family PL1_7 as a key component linking aggressiveness of endophytic colonization to plant health.

microbiology↗

bioRxiv2021

Deeplasmid: Deep learning accurately separates plasmids from bacterial chromosomes

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC-ROC of over 93%, and it was much more precise than the state-of-the-art methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 Kbp long plasmid, demonstrating Deeplasmids ability to detect novel plasmids. AvailabilityThe software is available with a BSD license: deeplasmid.sourceforge.io. A Docker container is available on DockerHub under: billandreo/deeplasmid. Contactwandreopoulos@lbl.gov alevy@mail.huji.ac.il

bioinformatics↗

bioRxiv2021

Metagenomes from the Loxahatchee wildlife refuge in the Florida Everglades

The Florida Everglades ecosystem represents a significant wetlands area and serves as a terrestrial carbon reservoir mediated in large part by microorganisms. Shotgun metagenome sequencing provides a snapshot of microbial diversity and the frequency of metabolic and functional gene content. Here, we present an analysis of 20 sediment samples collected from the Arthur R. Marshall Loxahatchee National Wildlife Refuge to characterize the taxonomic and functional potential of the microbial and viral communities, and reconstructed metagenome-assembled genomes. A total of 122 medium-quality and 6 high-quality MAGs are reported, three of which likely represent a novel species within the class Dehalococcoidia. The most abundant phyla of bacteria and archaea were Proteobacteria and Euryarchaeota, respectively. Caudovirales was the most abundant viral order. Significant differences in taxonomic composition and diversity were observed among collection sites. Additionally, water samples were analyzed for pH, total nitrogen, total organic carbon, elements (P, K, Mg, Fe, Mn, Pb, Ca, S), chloride, electric conductivity, orthophosphate, nitrate, and ammonia, while the sediment samples were analyzed for carbon, nitrogen, and pH. Differences in measured aquatic and sediment analytes revealed significant correlations with numerous phyla. Significant correlations were observed between estimated gene frequencies of both aquatic and sediment analytes, most notably between kup/kdpB and dsrA/cysC with potassium and sulfur, respectively, as well as phoD/phnX and cysC with pH. Together, these data provide an important view into the functional and metabolic potential encoded within the sediment microbial communities in the Florida Everglades.

microbiology↗

bioRxiv2021

Long-read metagenomics of soil communities reveals phylum-specific secondary metabolite dynamics

Microbial biosynthetic gene clusters (BGCs) encoding secondary metabolites are thought to impact a plethora of biologically mediated environmental processes, yet their discovery and functional characterization in natural microbiomes remains challenging. Here we describe deep long-read sequencing and assembly of metagenomes from biological soil crusts, a group of soil communities that are rich in BGCs. Taking advantage of the unusually long assemblies produced by this approach, we recovered nearly 3,000 BGCs for analysis, including 695 novel, full-length BGCs. Functional exploration through metatranscriptome analysis of a 3-day wetting experiment uncovered phylum-specific BGC expression upon activation from dormancy, elucidating distinct roles and complex phylogenetic and temporal dynamics in wetting processes. For example, a pronounced increase in BGC transcription occurs at night in cyanobacteria but not in other phyla, implicating BGCs in nutrient scavenging roles and niche competition. Taken together, our results demonstrate that long-read metagenomic sequencing combined with metatranscriptomic analysis provides a direct view into the functional dynamics of BGCs in environmental processes and suggests a central role of secondary metabolites in maintaining phylogenetically conserved niches within biocrusts.

genomics↗

bioRxiv2020

The DOE JGI Metagenome Workflow

The DOE JGI Metagenome Workflow performs metagenome data processing, including assembly, structural, functional, and taxonomic annotation, and binning of metagenomic datasets that are subsequently included into the Integrated Microbial Genomes and Microbiomes (IMG/M) comparative analysis system (I. Chen, K. Chu, K. Palaniappan, M. Pillay, A. Ratner, J. Huang, M. Huntemann, N. Varghese, J. White, R. Seshadri, et al, Nucleic Acids Rsearch, 2019) and provided for download via the Joint Genome Institute (JGI) Data Portal (https://genome.jgi.doe.gov/portal/). This workflow scales to run on thousands of metagenome samples per year, which can vary by the complexity of microbial communities and sequencing depth. Here we describe the different tools, databases, and parameters used at different steps of the workflow, to help with interpretation of metagenome data available in IMG and to enable researchers to apply this workflow to their own data. We use 20 publicly available sediment metagenomes to illustrate the computing requirements for the different steps and highlight the typical results of data processing. The workflow modules for read filtering and metagenome assembly are available as a Workflow Description Language (WDL) file (https://code.jgi.doe.gov/BFoster/jgi_meta_wdl.git). The workflow modules for annotation and binning are provided as a service to the user community at https://img.jgi.doe.gov/submit and require filling out the project and associated metadata descriptions in Genomes OnLine Database (GOLD) (S. Mukherjee, D. Stamatis, J. Bertsch, G. Ovchinnikova, H. Katta, A. Mojica, I Chen, and N. Kyrpides, and T. Reddy, Nucleic Acids Research, 2018). IMPORTANCEThe DOE JGI Metagenome Workflow is designed for processing metagenomic datasets starting from Illumina fastq files. It performs data pre-processing, error correction, assembly, structural and functional annotation, and binning. The results of processing are provided in several standard formats, such as fasta and gff and can be used for subsequent integration into the Integrated Microbial Genome (IMG) system where they can be compared to a comprehensive set of publicly available metagenomes. As of 7/30/2020 7,155 JGI metagenomes have been processed by the JGI Metagenome Workflow.

bioinformatics↗

bioRxiv2020

Isolation of potential plant growth-promoting bacteria from nodules of legumes grown in arid Botswana soil

As the world population increases, improvements in crop growth and yield will be needed to meet rising food demands, especially in countries that have not developed agricultural practices optimized for their own soils and crops. In many African countries, farmers improve agricultural productivity by applying synthetic fertilizers and pesticides to crops, but their continued use over the years has had serious environmental consequences including air and water pollution as well as loss of soil fertility. To reduce the overuse of synthetic amendments, we are developing inocula for crops that are based on indigenous soil microbes, especially those that enhance plant growth and improve agricultural productivity in a sustainable manner. We first isolated environmental DNA from soil samples collected from an agricultural region to study the composition of the soil microbiomes and then used Vigna unguiculata (cowpea), an important legume crop in Botswana and other legumes as "trap" plants using the collected soil to induce nitrogen-fixing nodule formation. We have identified drought-tolerant bacteria from Botswana soils that stimulate plant growth; many are species of Bacillus and Paenibacillus. In contrast, the cowpea nodule microbiomes from plants grown in these soils house mainly rhizobia particularly Bradyrhizobium, but also Methylobacterium spp. Hence, the nodule microbiome is much more limited in non-rhizobial diversity compared to the soil microbiome, but also contains a number of potential pathogenic bacteria.

microbiology↗

bioRxiv2019

Impact of soil salinity on the cowpea nodule-microbiome and the isolation of halotolerant PGPR strains to promote plant growth under salinity stress

Four soil samples (SS-1--SS-4) isolated from semi-arid soils in Punjab, Pakistan were used as inocula for cowpea (Vigna unguiculata L.) grown under salinity stress to analyze the composition of bacteria in the rhizosphere and within nodules through cultivation-dependent and cultivation-independent methods. Two cowpea varieties, 603 and the salt-tolerant CB 46, were each inoculated with four different native soil samples, and data showed that plants inoculated with soil samples SS-2 and SS-4 grew better than plants inoculated with soil samples SS-1 and SS-3. Bacteria were isolated from both soils and nodules, and 34 of the 51 original isolates tested positive for PGPR traits in plate assays with many exhibiting multiple plant growth-promoting properties. A number of isolates were positive for all PGPR traits tested. For the microbiome studies, environmental DNA (eDNA) was isolated from SS-1 and SS-4, which represented the extremes of the Pakistan soils to which the plants responded, and by 16S rRNA gene sequencing analysis were found to consist mainly of Actinobacteria, Firmicutes, and Proteobacteria. However, sequencing analysis of eDNA isolated from cowpea nodules established by the trap plants grown in the four Pakistan soils indicated that the nodule microbiome consisted almost exclusively of Proteobacterial sequences, particularly Bradyrhizobium. Yet, many other bacteria including Rhizobium, Mesorhizobium, Pseudomonas, as well as Paenibacillus, Bacillus as well as non-proteobacterial genera were isolated from the nodules of soil-inoculated cowpea plants. This discrepancy between the bacteria isolated from cowpea nodules (Proteobacteria and non-Proteobacteria) versus those detected in the nodule microbiome (Proteobacteria) needs further study.

plant biology↗