Experimental evidence that root-associated fungi improve plant growth at high altitude.

Unravelling how species communities change along environmental gradients requires a dual understanding: the direct responses of the species to their abiotic surroundings and the indirect variation of these responses through biotic interactions. Here, we focus on the interactive relationships between plants and their symbiotic root-associated fungi (RAF) along stressful abiotic gradients. We investigate whether variations in RAF community composition along altitudinal gradients influence plant growth at high altitudes, where both plants and fungi face harsher abiotic conditions. We established a translocation experiment between pairs of Bistorta vivipara populations across altitudinal gradients. To separate the impact of shifting fungal communities from the overall influence of changing abiotic conditions, we used a root barrier to prevent new colonization by RAF following translocation. To characterize the RAF communities, we applied DNA barcoding to the root samples. Through the utilization of joint species distribution modelling, we assessed the relationship between changes in plant functional traits resulting from experimental treatments and the corresponding changes in the RAF communities. Our findings indicate that RAF communities influence plant responses to stressful abiotic conditions. Plants translocated from low to high altitudes grew more when they were able to associate with the resident high-altitude RAF compared to those plants that were not allowed to associate with the resident RAF. We conclude that interactions with RAF impact how plants respond to stressful abiotic conditions. Our results provide experimental support that interactions with RAF improve plant stress tolerance to altitudinal stressors such as colder temperatures and less nutrient availability.

Phylogenomics and genetic analysis of solvent-producing Clostridium species.

The genus Clostridium is a large and diverse group within the Bacillota (formerly Firmicutes), whose members can encode useful complex traits such as solvent production, gas-fermentation, and lignocellulose breakdown. We describe 270 genome sequences of solventogenic clostridia from a comprehensive industrial strain collection assembled by Professor David Jones that includes 194 C. beijerinckii, 57 C. saccharobutylicum, 4 C. saccharoperbutylacetonicum, 5 C. butyricum, 7 C. acetobutylicum, and 3 C. tetanomorphum genomes. We report methods, analyses and characterization for phylogeny, key attributes, core biosynthetic genes, secondary metabolites, plasmids, prophage/CRISPR diversity, cellulosomes and quorum sensing for the 6 species. The expanded genomic data described here will facilitate engineering of solvent-producing clostridia as well as non-model microorganisms with innately desirable traits. Sequences could be applied in conventional platform biocatalysts such as yeast or Escherichia coli for enhanced chemical production. Recently, gene sequences from this collection were used to engineer Clostridium autoethanogenum, a gas-fermenting autotrophic acetogen, for continuous acetone or isopropanol production, as well as butanol, butanoic acid, hexanol and hexanoic acid production.

Parasympathetic neurons derived from human pluripotent stem cells model human diseases and development.

Autonomic parasympathetic neurons (parasymNs) control unconscious body responses, including "rest-and-digest." ParasymN innervation is important for organ development, and parasymN dysfunction is a hallmark of autonomic neuropathy. However, parasymN function and dysfunction in humans are vastly understudied due to the lack of a model system. Human pluripotent stem cell (hPSC)-derived neurons can fill this void as a versatile platform. Here, we developed a differentiation paradigm detailing the derivation of functional human parasymNs from Schwann cell progenitors. We employ these neurons (1) to assess human autonomic nervous system (ANS) development, (2) to model neuropathy in the genetic disorder familial dysautonomia (FD), (3) to show parasymN dysfunction during SARS-CoV-2 infection, (4) to model the autoimmune disease Sjögren's syndrome (SS), and (5) to show that parasymNs innervate white adipocytes (WATs) during development and promote WAT maturation. Our model system could become instrumental for future disease modeling and drug discovery studies, as well as for human developmental studies.

Whole community shotgun metagenomes of two biological soil crust types from the Mojave Desert.

We present six whole community shotgun metagenomic sequencing data sets of two types of biological soil crusts sampled at the ecotone of the Mojave Desert and Colorado Desert in California. These data will help us understand the diversity and function of biocrust microbial communities, which are essential for desert ecosystems.

Metatranscriptomes of two biological soil crust types from the Mojave desert in response to wetting.

We present eight metatranscriptomic datasets of light algal and cyanolichen biological soil crusts from the Mojave Desert in response to wetting. These data will help us understand gene expression patterns in desert biocrust microbial communities after they have been reactivated by the addition of water.

IMG/PR: a database of plasmids from genomes and metagenomes with rich annotations and metadata.

Plasmids are mobile genetic elements found in many clades of Archaea and Bacteria. They drive horizontal gene transfer, impacting ecological and evolutionary processes within microbial communities, and hold substantial importance in human health and biotechnology. To support plasmid research and provide scientists with data of an unprecedented diversity of plasmid sequences, we introduce the IMG/PR database, a new resource encompassing 699 973 plasmid sequences derived from genomes, metagenomes and metatranscriptomes. IMG/PR is the first database to provide data of plasmid that were systematically identified from diverse microbiome samples. IMG/PR plasmids are associated with rich metadata that includes geographical and ecosystem information, host taxonomy, similarity to other plasmids, functional annotation, presence of genes involved in conjugation and antibiotic resistance. The database offers diverse methods for exploring its extensive plasmid collection, enabling users to navigate plasmids through metadata-centric queries, plasmid comparisons and BLAST searches. The web interface for IMG/PR is accessible at https://img.jgi.doe.gov/pr. Plasmid metadata and sequences can be downloaded from https://genome.jgi.doe.gov/portal/IMG_PR.

Morphology and Phase Compositions of FePt and CoPt Nanoparticles Enriched with Noble Metal.

The article reveals for the first time the features of nanoparticle morphology, phase compositions, and their changes when heating FePt and CoPt nanoalloys. Nanoparticles were obtained by co-reduction of precursor solution mixtures with hydrazine hydrate. The features were found by a complex of methods of X-ray diffraction (in situ XRD and X-ray scattering), TEM HR, and cyclic voltammetry. In addition, adsorbometry results were obtained, and the stability of different nanocluster structures was calculated by the molecular dynamics method. There were only FCC solid solutions in the X-ray patterns of the FePt and CoPt nanoalloys. According to XRD, in the case of nanoparticle synthesis with Fe and Co content less than 10 at. %, the composition of solid solutions was close to or practically equal to the composition of the as-synthesized nanoparticles quantified by inductively coupled plasma optical emission spectrometry. For systems synthesis with Fe and Co content greater than the above, the solubility limits (SLs) of Fe and Co in Pt were set 11.4 ± 0.7 at. % and 17.5 ± 0.6 at. %, respectively. Therefore, there were non-registered XRD extra-phases (XRNDPh-1) in the systems when CFe,Co ≥ SL. This statement was supported by the results of TEM HR and X-ray scattering: the smallest nanocrystals (1-2 nm) and amorphous particles were found, which qualitatively agreed with the sorbometry and SAXS results. Molecular dynamics calculations of stability for FePt and CoPt alloys claimed the structures of the most stable phase corresponded to phase diagrams (A1 and L12). Specific peculiarities of the morphology and compositions of the solid solutions of nanoalloys were established: structural blockiness (domain) and composition heterogeneity, namely, platinum enrichment of internal (deep) layers and homogenization of the nanoalloy compositions at relatively low temperatures (130-200 °C). The suggested model of the formation of nanoalloys during the synthesis, qualitatively, was compliant with the results of electrochemical deposition of FePt films on the surface of various electrodes. When nanocrystals of solid solutions (C(Fe, Co) < SL) were heated above specific temperatures, there were phase transformations with the formation of two-phase regions, with solid solutions enriched with platinum or iron (non-registered XRD phase XRNDPh-2). The newly formed phase was most likely intermetallic compounds, FePt3, CoPt3. As a result of the study, the model was developed, taking into account the nanoscale of the particles: XRDPh (A1, FeaPt1-a) → XRDPh (A1, Fem×a-xPtm-m×a+x) + XRNDPh-2 (Fen×a+yPtn-n×a-y) (here, m + n = 1, m ≤ 1, n ≤ 1).

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning.

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

Trends in Outcome of Hematopoietic Stem Cell Transplantation: 5000 Transplantations and 30 Years of Single-Center Experience.

In this single-center analysis, we evaluated the trends in 5185 hematopoietic cell transplantations performed between 1990 and 2022. The study group comprised 3237 allogeneic (alloHCT) and 1948 autologous (autoHCT) hematopoietic cell transplantations. In the multivariate analysis, there was an improvement in event-free-survival (EFS) after autoHCT (HR 0.6, 95% CI 0.4-0.7, p < 0.0001) due to reduced cumulative incidence of relapse in the last five years (56% in 2010-2014 vs. 38% in 2015-2022). An improvement in EFS after alloHCT over time was observed (HR 0.33, 95% CI 0.23-0.48, p < 0.0001), which was due to reduced non-relapse mortality. No difference in cumulative relapse incidence was observed over the last decade for allografted patients. Survival after autoHCT improved in Hodgkin's disease (HR 0.1, 95% CI 0.1-0.3), multiple myeloma (HR 0.4, 95% CI 0.2-0.7) and solid tumors (HR 0.2, 95% CI 0.2-0.4), while after alloHCT, improvement was observed in acute myeloid leukemia (HR 0.3, 95% CI 0.1-0.5), acute lymphoblastic leukemia (HR 0.2, 95% CI 0.1-0.5), Hodgkin's disease (HR 0.1, 95% CI 0.0-0.4), non-Hodgkin's lymphomas and chronic lymphocytic leukemia (HR 0.2, 95% CI 0.0-0.6), inborn diseases (HR 0.2, 95% CI 0.2-0.4) and acquired aplastic anemia with matched related donors and matched unrelated donors (HR 0.3, 95% CI 0.2-0.8).

Unraveling the functional dark matter through global metagenomics.

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.

Genome sequences of key bacterial symbionts of entomopathogenic nematodes: Xenorhabdus cabanillasii DSM17905, Xenorhabdus ehlersii DSM16337, Xenorhabdus japonica DSM16522, Xenorhabdus koppenhoeferii DSM18168, and Xenorhabdus mauleonii DSM17908.

Xenorhabdus species are bacterial symbionts of entomopathogenic Steinernema nematodes, in which they produce diverse secondary metabolites implicated in pathogenesis. To expand resources for natural product prospecting and exploration of host-symbiont-pathogen relationships, the genomes of Xenorhabdus cabanillasi, Xenorhabdus ehlersii, Xenorhabdus japonica, Xenorhabdus koppenhoeferii, and Xenorhabdus mauleonii were analyzed.

Prediction of Distant Metastases in Patients with Kidney Cancer Based on Gene Expression and Methylation Analysis.

Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive histological type of cancer in this location. Distant metastases are present in approximately 30% of patients at the time of first examination. Therefore, the ability to predict the occurrence of metastases in patients at early stages of the disease is an urgent task aimed at personalized treatment. Samples of tumor and paired histologically normal kidney tissue from patients with metastatic and non-metastatic ccRCC were studied. Gene expression was analyzed using real-time PCR. The level of gene methylation was evaluated using bisulfite conversion followed by quantitative methylation-specific PCR. Two groups of genes were analyzed in this study. The first group includes genes whose expression is significantly reduced during metastasis: CA9, NDUFA4L2, EGLN3, and BHLHE41 (p < 0.001, ROC analysis). The second group includes microRNA genes: MIR125B-1, MIR137, MIR375, MIR193A, and MIR34B/C, whose increased methylation levels are associated with the development of distant metastases (p = 0.002 to <0.001, ROC analysis). Based on the data obtained, a combined panel of genes was formed to identify patients whose tumors have a high metastatic potential. The panel can estimate the probability of metastasis with an accuracy of up to 92%.

Out of thin air: surveying tropical bat roosts through air sampling of eDNA.

Understanding roosting behaviour is essential to bat conservation and biomonitoring, often providing the most accurate methods of assessing bat population size and health. However, roosts can be challenging to survey, e.g., physically impossible to access or presenting risks for researchers. Disturbance during monitoring can also disrupt natural bat behaviour and present material risks to the population such as disrupting hibernation cycles. One solution to this is the use of non-invasive monitoring approaches. Environmental (e)DNA has proven especially effective at detecting rare and elusive species particularly in hard-to-reach locations. It has recently been demonstrated that eDNA from vertebrates is carried in air. When collected in semi-confined spaces, this airborne eDNA can provide remarkably accurate profiles of biodiversity, even in complex tropical communities. In this study, we deploy novel airborne eDNA collection for the first time in a natural setting and use this approach to survey difficult to access potential roosts in the neotropics. Using airborne eDNA, we confirmed the presence of bats in nine out of 12 roosts. The identified species matched previous records of roost use obtained from photographic and live capture methods, thus demonstrating the utility of this approach. We also detected the presence of the white-winged vampire bat (Diaemus youngi) which had never been confirmed in the area but was long suspected based on range maps. In addition to the bats, we detected several non-bat vertebrates, including the big-eared climbing rat (Ototylomys phyllotis), which has previously been observed in and around bat roosts in our study area. We also detected eDNA from other local species known to be in the vicinity. Using airborne eDNA to detect new roosts and monitor known populations, particularly when species turnover is rapid, could maximize efficiency for surveyors while minimizing disturbance to the animals. This study presents the first applied use of airborne eDNA collection for ecological analysis moving beyond proof of concept to demonstrate a clear utility for this technology in the wild.

Discovery of a novel filamentous prophage in the genome of the Mimosa pudica microsymbiont Cupriavidus taiwanensis STM 6018.

Integrated virus genomes (prophages) are commonly found in sequenced bacterial genomes but have rarely been described in detail for rhizobial genomes. Cupriavidus taiwanensis STM 6018 is a rhizobial Betaproteobacteria strain that was isolated in 2006 from a root nodule of a Mimosa pudica host in French Guiana, South America. Here we describe features of the genome of STM 6018, focusing on the characterization of two different types of prophages that have been identified in its genome. The draft genome of STM 6018 is 6,553,639 bp, and consists of 80 scaffolds, containing 5,864 protein-coding genes and 61 RNA genes. STM 6018 contains all the nodulation and nitrogen fixation gene clusters common to symbiotic Cupriavidus species; sharing >99.97% bp identity homology to the nod/nif/noeM gene clusters from C. taiwanensis LMG19424T and "Cupriavidus neocalidonicus" STM 6070. The STM 6018 genome contains the genomes of two prophages: one complete Mu-like capsular phage and one filamentous phage, which integrates into a putative dif site. This is the first characterization of a filamentous phage found within the genome of a rhizobial strain. Further examination of sequenced rhizobial genomes identified filamentous prophage sequences in several Beta-rhizobial strains but not in any Alphaproteobacterial rhizobia.

IMG/VR v4: an expanded database of uncultivated virus genomes within a framework of extensive functional, taxonomic, and ecological metadata.

Viruses are widely recognized as critical members of all microbiomes. Metagenomics enables large-scale exploration of the global virosphere, progressively revealing the extensive genomic diversity of viruses on Earth and highlighting the myriad of ways by which viruses impact biological processes. IMG/VR provides access to the largest collection of viral sequences obtained from (meta)genomes, along with functional annotation and rich metadata. A web interface enables users to efficiently browse and search viruses based on genome features and/or sequence similarity. Here, we present the fourth version of IMG/VR, composed of >15 million virus genomes and genome fragments, a ≈6-fold increase in size compared to the previous version. These clustered into 8.7 million viral operational taxonomic units, including 231 408 with at least one high-quality representative. Viral sequences in IMG/VR are now systematically identified from genomes, metagenomes, and metatranscriptomes using a new detection approach (geNomad), and IMG standard annotation are complemented with genome quality estimation using CheckV, taxonomic classification reflecting the latest taxonomic standards, and microbial host taxonomy prediction. IMG/VR v4 is available at https://img.jgi.doe.gov/vr, and the underlying data are available to download at https://genome.jgi.doe.gov/portal/IMG_VR.

The IMG/M data management and analysis system v.7: content updates and new features.

The Integrated Microbial Genomes & Microbiomes system (IMG/M: https://img.jgi.doe.gov/m/) at the Department of Energy (DOE) Joint Genome Institute (JGI) continues to provide support for users to perform comparative analysis of isolate and single cell genomes, metagenomes, and metatranscriptomes. In addition to datasets produced by the JGI, IMG v.7 also includes datasets imported from public sources such as NCBI Genbank, SRA, and the DOE National Microbiome Data Collaborative (NMDC), or submitted by external users. In the past couple years, we have continued our effort to help the user community by improving the annotation pipeline, upgrading the contents with new reference database versions, and adding new analysis functionalities such as advanced scaffold search, Average Nucleotide Identity (ANI) for high-quality metagenome bins, new cassette search, improved gene neighborhood display, and improvements to metatranscriptome data display and analysis. We also extended the collaboration and integration efforts with other DOE-funded projects such as NMDC and DOE Biology Knowledgebase (KBase).

High-Quality Draft Genome Sequence of Fischerella thermalis JSC-11, a Siderophilic Cyanobacterium with Bioremediation Potential.

Here, we report the draft genome sequence of the siderophilic cyanobacterium Fischerella thermalis JSC-11, which was isolated from an iron-depositing hot spring. JSC-11 has bioremediation potential because it is capable of both extracellular absorption and intracellular mineralization of colloidal iron. This genomic information will facilitate the exploration of JSC-11 for bioremediation.

Multiomics in the central Arctic Ocean for benchmarking biodiversity change.

Multiomics approaches need to be applied in the central Arctic Ocean to benchmark biodiversity change and to identify novel species and their genes. As part of MOSAiC, EcoOmics will therefore be essential for conservation and sustainable bioprospecting in one of the least explored ecosystems on Earth.