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The head smut (Tilletia maclaganii) is a significant pathogen of the bioenergy crop switchgrass. T. maclaganii typically is more prevalent in older stands of switchgrass and can contribute to significant biomass loss. Here, we outline the methods for the sequencing, assembly, and annotation of the first reference genome for Tilletia maclaganii.
Decades of successes in statistical genetics have revealed the molecular underpinnings of traits as they vary across individuals of a given species. But standard methods in the field cannot be applied to divergences between reproductively isolated taxa. Genome-wide reciprocal hemizygosity mapping (RH-seq), a mutagenesis screen in an interspecies hybrid background, holds promise as a method to accelerate the progress of interspecies genetics research. Here, we describe an improvement to RH-seq in which mutants harbor barcodes for cheap and straightforward sequencing after selection in a condition of interest. As a proof of concept for the new tool, we carried out genetic dissection of the difference in thermotolerance between two reproductively isolated budding yeast species. Experimental screening identified dozens of candidate loci at which variation between the species contributed to the thermotolerance trait. Hits were enriched for mitosis genes and other housekeeping factors, and among them were multiple loci with robust sequence signatures of positive selection. Together, these results shed new light on the mechanisms by which evolution solved the problems of cell survival and division at high temperature in the yeast clade, and they illustrate the power of the barcoded RH-seq approach.
High-throughput PCR screening is vital in synthetic biology and metabolic engineering as it allows researchers to rapidly analyze and detect numerous targeted genetic mutation in the genome. Current challenges for high-throughput PCR screening in synthetic biology include efficiently preparing genomic DNA, optimizing protocols for diverse sample types, managing contamination risks, and effectively analyzing the large volumes of data generated while ensuring consistent and accurate results. In this study, we present the development of a High Throughput Genome Releaser (HTGR), an innovative device addressing common challenges in screening PCR. This genome DNA releaser is designed based on a squash method for rapid, cost-effective, and efficient DNA release, optimized for subsequent PCR reactions. After experimenting with various synthetic materials, we selected a plastic that closely replicates the smooth surface and compression properties of microscope slides, ensuring reliable performance. We engineered a device featuring a 96-Well Plate and a shear applicator, operable both manually and automatically, and compatible with standard liquid-handling robot platform. This compatibility enhances ease of use in high-throughput PCR workflows. Additionally, we developed software to support its automatic functions. Our results demonstrated that the specially engineered 96-Well Plate and HTGR can effectively squash fungal spores , which release enough genome DNA for PCR screening. The genome releaser facilitates the preparation of PCR-amplifiable genomic DNA substrate from 96 samples within minutes, eliminates the need for extraction buffers, and is adaptable to a wide range of microorganisms and cells, which could significantly advance biomanufacturing processes.
Background Horizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, and are often classified by the content of their cargo genes or their means of integration and mobility. While various computational methods have been devised to detect different types of GIs, no single method is capable of detecting all types. Results We propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools. Conclusions We used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was re-trained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.
Summary The native, perennial shrub American hazelnut ( Corylus americana ) is cultivated in the Midwestern United States for its significant ecological benefits, as well as its high‐value nut crop. Implementation of modern breeding methods and quantitative genetic analyses of C. americana requires high‐quality reference genomes, a resource that is currently lacking. We therefore developed the first chromosome‐scale assemblies for this species using the accessions ‘Rush’ and ‘Winkler’. Genomes were assembled using HiFi PacBio reads and Arima Hi‐C data, and Oxford Nanopore reads and a high‐density genetic map were used to perform error correction. N50 scores are 31.9 Mb and 35.3 Mb, with 90.2% and 97.1% of the total genome assembled into the 11 pseudomolecules, for ‘Rush’ and ‘Winkler’, respectively. Gene prediction was performed using custom RNAseq libraries and protein homology data. ‘Rush’ has a BUSCO score of 99.0 for its assembly and 99.0 for its annotation, while ‘Winkler’ had corresponding scores of 96.9 and 96.5, indicating high‐quality assemblies. These two independent assemblies enable unbiased assessment of structural variation within C. americana , as well as patterns of syntenic relationships across the Corylus genus. Furthermore, we identified high‐density SNP marker sets from genotyping‐by‐sequencing data using 1343 C. americana , C. avellana and C. americana × C. avellana hybrids, in order to assess population structure in natural and breeding populations. Finally, the transcriptomes of these assemblies, as well as several other recently published Corylus genomes, were utilized to perform phylogenetic analysis of sporophytic self‐incompatibility (SSI) in hazelnut, providing evidence of unique molecular pathways governing self‐incompatibility in Corylus .
iPHoP is a bioinformatic tools that uses a set of approaches to predict the potential host of novel bacteriophages (viruses infecting bacteria) that are only known by their genome sequence, and not cultivated in the laboratory. Existing technologies typically rely on a single method, and the main advantage of iPHoP is its ability to integrate the results from multiple methods into a single prediction. This is of interest for microbial ecology researchers, as they often analyze novel bacteriophage genomes that they were able to assemble from metagenomes, but they don't know which bacteria these phages infect.
NASA is committed to developing new materials and manufacturing methods that can enable new missions with ever increasing mission demands. Typically, the development and certification of new materials and manufacturing methods in the aerospace industry has required more than 20 years of development time with a costly testing and certification program. To reduce the cost and time to mature these emerging technologies, NASA is developing computational materials tools to improve understanding of the material and guide the certification process.
The importance of virus disease outbreaks and its prevention is of growing public concern but our understanding of virus transmission routes is limited by adequate sampling strategies. While conventional swabbing methods provide merely a microbial snapshot, an ideal sampling strategy would allow reliable collection of viral genomic data over longer time periods. This study has evaluated a new, paper-based sticker approach for collection of reliable viral genomic data over longer time periods up to 14 days and after implementation of different hygiene measures. In contrast to swabbing methods, which sample viral load present on a surface at a given time, the paper-based stickers are attached to the surface area of interest and collect viruses that would have otherwise been transferred onto that surface. The major advantage of one-side adhesive stickers is that they are permanently attachable to a variety of surfaces. Initial results demonstrate that stickers permit stable recovery characteristics, even at low virus titers. Stickers also allow reliable virus detection after implementation of routine hygiene measures and over longer periods up to 14 days. Overall, results for this new sticker approach for virus genomic data collection are encouraging, but further studies are required to confirm anticipated benefits over a range of virus types.
Hybridization brings together chromosome sets from two or more distinct progenitor species. Genome duplication associated with hybridization, or allopolyploidy, allows these chromosome sets to persist as distinct subgenomes during subsequent meioses. Here, we present a general method for identifying the subgenomes of a polyploid based on shared ancestry as revealed by the genomic distribution of repetitive elements that were active in the progenitors. This subgenome-enriched transposable element signal is intrinsic to the polyploid, allowing broader applicability than other approaches that depend on the availability of sequenced diploid relatives. We develop the statistical basis of the method, demonstrate its applicability in the well-studied cases of tobacco, cotton, and Brassica napus, and apply it to several cases: allotetraploid cyprinids, allohexaploid false flax, and allooctoploid strawberry. These analyses provide insight into the origins of these polyploids, revise the subgenome identities of strawberry, and provide perspective on subgenome dominance in higher polyploids.
Understanding evolutionary genomic and population processes within a species range is key to anticipating the extinction of plant species before it is too late. However, most models of biodiversity risk under global change do not account for the genetic variation and local adaptation of different populations. Population diversity is critical to understanding extinction because different populations may be more or less susceptible to global change and, if lost, would reduce the total diversity within a species. Here, two new modeling frameworks advance our understanding of extinction from a population and evolutionary angle: Rapid climate change-driven disruptions in population adaptation are predicted from associations between genomes and local climates. Furthermore, losses of population diversity from global land-use transformations are estimated by scaling relationships of species' genomic diversity with habitat area. Overall, these global eco-evolutionary methods advance the predictability – and possibly the preventability – of the ongoing extinction of plant species.
Abstract There are a staggering number of publicly available bacterial genome sequences (at writing, 2.0 million assemblies in NCBI's GenBank alone), and the deposition rate continues to increase. This wealth of data begs for phylogenetic analyses to place these sequences within an evolutionary context. A phylogenetic placement not only aids in taxonomic classification but informs the evolution of novel phenotypes, targets of selection, and horizontal gene transfer. Building trees from multi-gene codon alignments is a laborious task that requires bioinformatic expertise, rigorous curation of orthologs, and heavy computation. Compounding the problem is the lack of tools that can streamline these processes for building trees from large-scale genomic data. Here we present OrthoPhyl, which takes bacterial genome assemblies and reconstructs trees from whole genome codon alignments. The analysis pipeline can analyze an arbitrarily large number of input genomes (>1200 tested here) by identifying a diversity-spanning subset of assemblies and using these genomes to build gene models to infer orthologs in the full dataset. To illustrate the versatility of OrthoPhyl, we show three use cases: E. coli/Shigella, Brucella/Ochrobactrum and the order Rickettsiales. We compare trees generated with OrthoPhyl to trees generated with kSNP3 and GToTree along with published trees using alternative methods. We show that OrthoPhyl trees are consistent with other methods while incorporating more data, allowing for greater numbers of input genomes, and more flexibility of analysis.
Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Further, advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.
Abstract Aims Antimicrobial resistance in Pseudomonas aeruginosa represents a major global challenge in public and veterinary health, particularly from a One Health perspective. This study aimed to investigate antimicrobial resistance, the presence of virulence genes, and the genetic diversity of P. aeruginosa isolates from diverse sources. Methods and results The study utilized antimicrobial susceptibility testing, genomic analysis for resistance and virulence genes, and multilocus sequence typing to characterize a total of 737 P. aeruginosa isolates that were collected from humans, domestic animals, and aquatic environments in Northern Portugal. Antimicrobial resistance profiles were analyzed, and genomic approaches were employed to detect resistance and virulence genes. The study found a high prevalence of multidrug-resistant isolates, including high-risk clones such as ST244 and ST446, particularly in hospital sources and wastewater treatment plants. Key genes associated with resistance and virulence, including efflux pumps (e.g. MexA and MexB) and secretion systems (T3SS and T6SS), were identified. Conclusions This work highlights the intricate dynamics of multidrug-resistant P. aeruginosa across interconnected ecosystems in Northern Portugal. It underscores the importance of genomic studies in revealing the mechanisms of resistance and virulence, contributing to the broader understanding of resistance dynamics and informing future mitigation strategies.
Bayesian Metabolic Inference is a platform to integrate genome-scale multi-omics data into an iterative method to obtain optimized recommendations for optimal target compound production. This method uses a variational inference approach to approximate new posterior data to analyze in order to obtain useful correlations and control coefficients.
Among the rare earth elements (REEs), a crucial group of metals for high-technologies. Gadolinium (Gd) is the only REE intentionally injected to human patients. The use of Gd-based contrasting agents for magnetic resonance imaging (MRI) is the primary route for Gd direct exposure and accumulation in humans. Consequently, aquatic environments are increasingly exposed to Gd due to its excretion through the urinary tract of patients following an MRI examination. The increasing number of reports mentioning Gd toxicity, notably originating from medical applications of Gd, necessitates an improved risk–benefit assessment of Gd utilizations. To go beyond toxicological studies, unravelling the mechanistic impact of Gd on humans and the ecosystem requires the use of genome-wide approaches. We used functional deletomics, a robust method relying on the screening of a knock-out mutant library of Saccharomyces cerevisiae exposed to toxic concentrations of Gd. The analysis of Gd-resistant and -sensitive mutants highlighted the cell wall, endosomes and the vacuolar compartment as cellular hotspots involved in the Gd response. Furthermore, we identified endocytosis and vesicular trafficking pathways (ESCRT) as well as sphingolipids homeostasis as playing pivotal roles mediating Gd toxicity. Finally, tens of yeast genes with human orthologs linked to renal dysfunction were identified as Gd-responsive. Therefore, the molecular and cellular pathways involved in Gd toxicity and detoxification uncovered in this study underline the pleotropic consequences of the increasing exposure to this strategic metal.
Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.
Gene recognition is essential to understanding existing and future DNA sequence data. CRITICA (Coding Region Identification Tool Invoking Comparative Analysis) is a suite of programs for identifying likely protein-coding sequences in DNA by combining comparative analysis of DNA sequences with more common noncomparative methods. In the comparative component of the analysis, regions of DNA are aligned with related sequences from the DNA databases; if the translation of the aligned sequences has greater amino acid identity than expected for the observed percentage nucleotide identity, this is interpreted as evidence for coding. CRITICA also incorporates noncomparative information derived from the relative frequencies of hexanucleotides in coding frames versus other contexts (i.e., dicodon bias). The dicodon usage information is derived by iterative analysis of the data, such that CRITICA is not dependent on the existence or accuracy of coding sequence annotations in the databases. This independence makes the method particularly well suited for the analysis of novel genomes. CRITICA was tested by analyzing the available Salmonella typhimurium DNA sequences. Its predictions were compared with the DNA sequence annotations and with the predictions of GenMark. CRITICA proved to be more accurate than GenMark, and moreover, many of its predictions that would seem to be errors instead reflect problems in the sequence databases. The source code of CRITICA is freely available by anonymous FTP (rdp.life.uiuc.edu in/pub/critica) and on the World Wide Web (http:/(/)rdpwww.life.uiuc.edu).
As molecular phylogeny increasingly shapes our understanding of organismal relationships, no molecule has been applied to more questions than have ribosomal RNAs. We review this role of the rRNAs and some of the insights that have been gained from them. We also offer some of the practical considerations in extracting the phylogenetic information from the sequences. Finally, we stress the importance of comparing results from multiple molecules, both as a method for testing the overall reliability of the organismal phylogeny and as a method for more broadly exploring the history of the genome.