Understudied Organisms Research Articles

EBryoSoil: a citizen science application to monitor changes in soil ecosystems.

Biological soil covers (BSCs) play a pivotal role in ecosystem functioning by enhancing soil stability, mediating nutrient cycling, and influencing soil hydrology. Recognized as ecosystem engineers, they can physically modify, maintain, or create habitats, facilitating plant community development. Through these intricate interactions, BSCs contribute significantly to ecological processes, highlighting their importance in the overall health and functionality of the ecosystems of the Iberian Peninsula. Here we present the results obtained from the contributions of the citizen scientists uploaded from November 2019 to January 2021 with eBryoSoil, an app that allows citizens to participate in mapping the BSC communities across the Iberian Peninsula. Here, we emphasize the importance of habitats and consequently, their interaction with climatic variables for the persistence of BSCs (lichens and bryophytes). Conservation efforts targeted at preserving diverse habitats are essential to ensure the continued presence of lichen and bryophyte communities. Despite challenges posed by the SARS-CoV-2 outbreak, this citizen science project demonstrated success in utilizing a specifically tailored app to gather valuable information on BSC communities, providing insights into their vulnerability to climate change. This program serves as an illustrative example of how citizen science can effectively identify and study vulnerable habitats, offering a blueprint for future studies focused on understudied organisms.

Framework for exploring the sensory repertoire of the human gut microbiota.

Bacteria sense changes in their environment and transduce signals to adjust their cellular functions accordingly. For this purpose, bacteria employ various sensors feeding into multiple signal transduction pathways. Signal recognition by bacterial sensors is studied mainly in a few model organisms, but advances in genome sequencing and analysis offer new ways of exploring the sensory repertoire of many understudied organisms. The human gut is a natural target of this line of study: it is a nutrient-rich and dynamic environment and is home to thousands of bacterial species whose activities impact human health. Many gut commensals are also poorly studied compared to model organisms and are mainly known through their genome sequences. To begin exploring the signals human gut commensals sense and respond to, we have designed a framework that enables the identification of sensory domains, prediction of signals that they recognize, and experimental verification of these predictions. We validate this framework's functionality by systematically identifying amino acid sensors in selected bacterial genomes and metagenomes, characterizing their amino acid binding properties, and demonstrating their signal transduction potential.IMPORTANCESignal transduction is a central process governing how bacteria sense and respond to their environment. The human gut is a complex environment with many living organisms and fluctuating streams of nutrients. One gut inhabitant, Escherichia coli, is a model organism for studying signal transduction. However, E. coli is not representative of most gut microbes, and signaling pathways in the thousands of other organisms comprising the human gut microbiota remain poorly understood. This work provides a foundation for how to explore signals recognized by these organisms.

DRANetSplicer: A Splice Site Prediction Model Based on Deep Residual Attention Networks.

The precise identification of splice sites is essential for unraveling the structure and function of genes, constituting a pivotal step in the gene annotation process. In this study, we developed a novel deep learning model, DRANetSplicer, that integrates residual learning and attention mechanisms for enhanced accuracy in capturing the intricate features of splice sites. We constructed multiple datasets using the most recent versions of genomic data from three different organisms, Oryza sativa japonica, Arabidopsis thaliana and Homo sapiens. This approach allows us to train models with a richer set of high-quality data. DRANetSplicer outperformed benchmark methods on donor and acceptor splice site datasets, achieving an average accuracy of (96.57%, 95.82%) across the three organisms. Comparative analyses with benchmark methods, including SpliceFinder, Splice2Deep, Deep Splicer, EnsembleSplice, and DNABERT, revealed DRANetSplicer's superior predictive performance, resulting in at least a (4.2%, 11.6%) relative reduction in average error rate. We utilized the DRANetSplicer model trained on O. sativa japonica data to predict splice sites in A. thaliana, achieving accuracies for donor and acceptor sites of (94.89%, 94.25%). These results indicate that DRANetSplicer possesses excellent cross-organism predictive capabilities, with its performance in cross-organism predictions even surpassing that of benchmark methods in non-cross-organism predictions. Cross-organism validation showcased DRANetSplicer's excellence in predicting splice sites across similar organisms, supporting its applicability in gene annotation for understudied organisms. We employed multiple methods to visualize the decision-making process of the model. The visualization results indicate that DRANetSplicer can learn and interpret well-known biological features, further validating its overall performance. Our study systematically examined and confirmed the predictive ability of DRANetSplicer from various levels and perspectives, indicating that its practical application in gene annotation is justified.

'Bingo'-a large language model- and graph neural network-based workflow for the prediction of essential genes from protein data.

The identification and characterization of essential genes are central to our understanding of the core biological functions in eukaryotic organisms, and has important implications for the treatment of diseases caused by, for example, cancers and pathogens. Given the major constraints in testing the functions of genes of many organisms in the laboratory, due to the absence of in vitro cultures and/or gene perturbation assays for most metazoan species, there has been a need to develop in silico tools for the accurate prediction or inference of essential genes to underpin systems biological investigations. Major advances in machine learning approaches provide unprecedented opportunities to overcome these limitations and accelerate the discovery of essential genes on a genome-wide scale. Here, we developed and evaluated a large language model- and graph neural network (LLM-GNN)-based approach, called 'Bingo', to predict essential protein-coding genes in the metazoan model organisms Caenorhabditis elegans and Drosophila melanogaster as well as in Mus musculus and Homo sapiens (a HepG2 cell line) by integrating LLM and GNNs with adversarial training. Bingo predicts essential genes under two 'zero-shot' scenarios with transfer learning, showing promise to compensate for a lack of high-quality genomic and proteomic data for non-model organisms. In addition, the attention mechanisms and GNNExplainer were employed to manifest the functional sites and structural domain with most contribution to essentiality. In conclusion, Bingo provides the prospect of being able to accurately infer the essential genes of little- or under-studied organisms of interest, and provides a biological explanation for gene essentiality.

The importance of taxonomy for determining species distribution: a case study using the disjunct lichen Brodoa oroarctica

Species-focused conservation requires a thorough understanding of species’ distributions. Delineating a species’ distribution requires taxonomic knowledge and adequate occurrence data. For plants and fungi, herbaria represent a valuable source of large-scale occurrence data. Advances in digital technology mean that data from many herbarium collections worldwide are now easily accessible. However, species concepts can change over time requiring herbarium records to be re-examined and databases updated, which does not always occur synchronously across all collections. Therefore, non-critical use of these data can promote inaccuracies in understanding species distributions. Taxonomic revisions are common in understudied organisms, such as lichens. Here, we illustrate how changing taxonomy and non-critical acceptance of online data affects our understanding of disjunct distributions, using the lichen Brodoa oroarctica (Krog) Goward as an example. Defining the distribution of the arctic lichen B. oroarctica is confounded by changing taxonomy and uncertainty of herbarium records that pre-date taxonomic revisions. We review the distribution of this species in the literature and in aggregate occurrence databases, and verify herbarium specimens that represent disjunct occurrences in eastern North America to present an updated account of its distribution and frequency in eastern North America. We show that knowledge of changing species taxonomy is essential to depicting accurate species distributions.

The spatial patterns of community composition, their environmental drivers and their spatial scale dependence vary markedly between fungal ecological guilds

AbstractAimHow community composition varies in space and what governs the variation has been extensively investigated in macroorganisms. However, we have only limited knowledge of microorganisms, especially fungi, despite their ecological and economic significance. Based on previous research, we define and test a series of hypotheses regarding the composition of fungal communities, their most influential drivers and their spatial scale dependence.LocationCzech Republic.Time periodPresent.Taxa studiedFungi.MethodsWe analysed the distance decay relationships, community composition and its drivers (physical distance, litter and soil chemistry, tree composition and climate) in fungi using multivariate analyses. We compared the results across three fungal ecological guilds (ectomycorrhizal fungi, saprotrophs and yeasts), two forest microhabitats (litter and bulk soil) and six spatial scales (from 5 m to 80 km) that comprehensively cover the Czech Republic.ResultsWe found that, similar to macroorganisms, the ectomycorrhizal fungi and saprotrophs showed marked distance–decay relationships, and their community composition was driven mainly by vegetation and dispersal at local scales but, at regional scales, by environmental effects. In contrast, the third fungal guild, the unicellular yeasts, showed little distance decay, suggesting extraordinary spatial homogeneity, as often seen in microorganisms, such as bacteria.Main conclusionsOur results underscore the remarkable variation in the community ecology of fungi, which seems to range well‐known patterns both from the macro‐ and the microworld. Knowledge of these patterns advances our understanding of the ecology of fungi, rather understudied organisms of significant ecological and economic importance, which our findings identify as a potentially suitable model for bridging the gaps between the biogeography of micro‐ and macroorganisms.

MEM-FET: Essential protein prediction using membership feature and machine learning approach.

Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab. The results of observation in wet lab always ensure high reliability and accuracy in the biological ground. Essential protein prediction using computational approaches is an alternative choice in research. It proves its significance rapidly in day-to-day life as well as reduces the experimental cost of wet lab effectively. Existing computational methods were implemented using Protein interaction networks (PPIN), Sequence, Gene Expression Dataset (GED), Gene Ontology (GO), Orthologous groups, and Subcellular localized datasets. Machine learning has diverse categories of features that enable to model and predict essential macromolecules of understudied organisms. A novel methodology MEM-FET (membership feature) is predicted based on features, that is, edge clustering coefficient, Average clustering coefficient, subcellular localization, and Gene Ontology within a compartment of common neighbors. The accuracy (ACC) values of the predicted true positive (TP) essential proteins are 0.79, 0.74, 0.78, and 0.71 for YHQ, YMIPS, YDIP, and YMBD datasets. An enriched set of essential proteins are also predicted using the MEM-FET algorithm. Ensemble ML also validated the proposed model with an accuracy of 60%. It has been predicted that MEM-FET algorithms outperform other existing algorithms with an ACC value of 80% for the yeast dataset.

MP01-07 A NEXT-GENERATION SEQUENCING ANALYSIS OF THE SEMEN MICROBIOME IN MEN WITH NON-OBSTRUCTIVE AZOOSPERMIA

You have accessJournal of UrologyCME1 Apr 2023MP01-07 A NEXT-GENERATION SEQUENCING ANALYSIS OF THE SEMEN MICROBIOME IN MEN WITH NON-OBSTRUCTIVE AZOOSPERMIA Katherine Campbell, Farah Rahman, Camila Suarez, Aaron Miller, Emad Ibrahim, Scott D. Lundy, and Ranjith Ramasamy Katherine CampbellKatherine Campbell More articles by this author , Farah RahmanFarah Rahman More articles by this author , Camila SuarezCamila Suarez More articles by this author , Aaron MillerAaron Miller More articles by this author , Emad IbrahimEmad Ibrahim More articles by this author , Scott D. LundyScott D. Lundy More articles by this author , and Ranjith RamasamyRanjith Ramasamy More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003212.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Detection of a microbiome profile coupled with advanced genetic sequencing has opened a new research frontier. We hypothesized that the semen microbiome profile in men with non-obstructive azoospermia (NOA) would differ from that of fertile controls (FC). METHODS: NOA was diagnosed using semen analysis and clinical phenotype (small testis volume, FSH >10 IU/mL). FC were identified as men undergoing vasectomy after fathering at least one child. Semen samples were collected and underwent Next Generation Sequencing (NGS) including quantitative PCR and 16s rRNA V1/V2 region comprehensive sequencing using Illumina MiSeq technology. Data was processes and analyzed in dada2. Alpha diversity was analyzed via Margalef. Beta diversity was analyzed via Bray-Curtis difference. Differential abundance was evaluated with ANCOMBC procedure. RESULTS: After rarefaction analysis, 33 samples were included (18 NOA and 15 FC) determined samples had sufficient sequencing depth to capture microbiome diversity. Alpha diversity was lower in the NOA men but did not reach statistical significance. Beta diversity showed a difference between the NOA and fertile groups primarily driven by differences in ethnicity. At the phylum level, NOA patients demonstrated lower levels of Proteobacteria and Firmicutes and higher levels of Actinobacteriota compared to the FC group consistent with prior published work. At the genus level, the known uropathogens Ureaplasma (1.3% vs 0.16%) and Eschrichia/Shigella (2.26% vs 0.42%) were higher in the NOA group compared to fertile controls. Other notable differences were Raoultella (39-fold higher in Fertile – 2% vs 0.09%) and Sneathia (9.5-fold higher in Fertile – 1% vs. 0.1%). Differential abundance analysis largely confirmed these findings. CONCLUSIONS: Compared to fertile controls, men with NOA have a less diverse seminal microbiome enriched for known pathogens and several understudied organisms. This work supports the presence of a testicular microbiome, which may become dysbiotic with infertility and could be leveraged as a biomarker to guide treatment decision making in the future. Source of Funding: MicrogenDX © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e4 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Katherine Campbell More articles by this author Farah Rahman More articles by this author Camila Suarez More articles by this author Aaron Miller More articles by this author Emad Ibrahim More articles by this author Scott D. Lundy More articles by this author Ranjith Ramasamy More articles by this author Expand All Advertisement PDF downloadLoading ...

Unsupervised domain adaptation methods for cross-species transfer of regulatory code signals.

Due to advances in NGS technologies whole-genome maps of various functional genomic elements were generated for a dozen of species, however experiments are still expensive and are not available for many species of interest. Deep learning methods became the state-of-the-art computational methods to analyze the available data, but the focus is often only on the species studied. Here we take advantage of the progresses in Transfer Learning in the area of Unsupervised Domain Adaption (UDA) and tested nine UDA methods for prediction of regulatory code signals for genomes of other species. We tested each deep learning implementation by training the model on experimental data from one species, then refined the model using the genome sequence of the target species for which we wanted to make predictions. Among nine tested domain adaptation architectures non-adversarial methods Minimum Class Confusion (MCC) and Deep Adaptation Network (DAN) significantly outperformed others. Conditional Domain Adversarial Network (CDAN) appeared as the third best architecture. Here we provide an empirical assessment of each approach using real world data. The different approaches were tested on ChIP-seq data for transcription factor binding sites and histone marks on human and mouse genomes, but is generalizable to any cross-species transfer of interest. We tested the efficiency of each method using species where experimental data was available for both. The results allows us to assess how well each implementation will work for species for which only limited experimental data is available and will inform the design of future experiments in these understudied organisms. Overall, our results proved the validity of UDA methods for generation of missing experimental data for histone marks and transcription factor binding sites in various genomes and highlights how robust the various approaches are to data that is incomplete, noisy and susceptible to analytic bias.

Fantastic Flatworms and Where to Find Them: Insights into Intertidal Polyclad Flatworm Distribution in Southeastern Australian Boulder Beaches

There is a rapid and extensive decline of our marine biodiversity due to human impacts. However, our ability to understand the extent of these effects is hindered by our lack of knowledge of the occurrence and ecology of some species groups. One such group of understudied organisms are marine flatworms of the order Polycladida, a conspicuous component of southeastern Australia’s marine ecosystems that has received little attention over the years. Intertidal boulder beaches support a diverse range of polyclad flatworms in other countries, but the role of these environments in maintaining biodiversity is not well understood. In this study, we identified hotspots of flatworm occurrence by assessing the diversity and overall abundance of flatworms at boulder beaches along the southeast Australian coast. Bottle and Glass, Sydney Harbour, was found to be the most diverse site for flatworms. We also identified a higher occurrence of flatworms under large boulders and less exposed beaches and noted an increased presence of flatworms at higher latitudes. Probable influences on these patterns such as the requirement for shelter and protection are discussed. This study contributes to our knowledge of Australia’s coastal biodiversity and can be used to assist in the management and conservation of our marine environments.

SEQ2MGS: an effective tool for generating realistic artificial metagenomes from the existing sequencing data.

Assessment of bioinformatics tools for the metagenomics analysis from the whole genome sequencing data requires realistic benchmark sets. We developed an effective and simple generator of artificial metagenomes from real sequencing experiments. The tool (SEQ2MGS) analyzes the input FASTQ files, precomputes genomic content, and blends shotgun reads from different sequenced isolates, or spike isolate(s) in real metagenome, in desired proportions. SEQ2MGS eliminates the need for simulation of sequencing platform variations, reads distributions, presence of plasmids, viruses, and contamination. The tool is especially useful for a quick generation of multiple complex samples that include new or understudied organisms, even without assembled genomes. For illustration, we first demonstrated the ease of SEQ2MGS use for the simulation of altered Schaedler flora (ASF) in comparison with de novo metagenomics generators Grinder and CAMISIM. Next, we emulated the emergence of a pathogen in the human gut microbiome and observed that Kraken, Centrifuge, and MetaPhlAn, while correctly identified Klebsiella pneumoniae, produced inconsistent results for the rest of real metagenome. Finally, using the MG-RAST platform, we affirmed that SEQ2MGS properly transfers genomic information from an isolate into the simulated metagenome by the correct identification of antimicrobial resistance genes anticipated to appear compared to the original metagenome.

Environmental DNA analysis confirms extant populations of the cryptic Irwin\u2019s turtle within its historical range

BackgroundApproximately 50% of freshwater turtles worldwide are currently threatened by habitat loss, rural development and altered stream flows. Paradoxically, reptiles are understudied organisms, with many species lacking basic geographic distribution and abundance data. The iconic Irwin’s turtle, Elseya irwini, belongs to a unique group of Australian endemic freshwater turtles capable of cloacal respiration. Water resource development, increased presence of saltwater crocodiles and its cryptic behaviour, have made sampling for Irwin’s turtle in parts of its range problematic, resulting in no confirmed detections across much of its known range for > 25 years. Here, we used environmental DNA (eDNA) analysis for E. irwini detection along its historical and contemporary distribution in the Burdekin, Bowen and Broken River catchments and tributaries. Five replicate water samples were collected at 37 sites across those three river catchments. Environmental DNA was extracted using a glycogen-aided precipitation method and screened for the presence of E. irwini through an eDNA assay targeting a 127 base pair-long fragment of the NADH dehydrogenase 4 (ND4) mitochondrial gene.ResultsElseya irwini eDNA was detected at sites within its historic distribution in the lower Burdekin River, where the species had not been formally recorded for > 25 years, indicating the species still inhabits the lower Burdekin area. We also found higher levels of E. iriwni eDNA within its contemporary distribution in the Bowen and Broken Rivers, matching the prevailing scientific view that these areas host larger populations of E. irwini.ConclusionsThis study constitutes the first scientific evidence of E. irwini presence in the lower Burdekin since the original type specimens were collected as part of its formal description, shortly after the construction of the Burdekin Falls Dam. From the higher percentage of positive detections in the upper reaches of the Broken River (Urannah Creek), we conclude that this area constitutes the core habitat area for the species. Our field protocol comprises a user-friendly, time-effective sampling method. Finally, due to safety risks associated with traditional turtle sampling methods in the Burdekin River (e.g., estuarine crocodiles) we propose eDNA sampling as the most pragmatic detection method available for E. irwini.

Population connectivity of aquatic insects in a dam‐regulated, desert river

AbstractHumans have exaggerated natural habitat fragmentation, negatively impacting species dispersal and reducing population connectivity. Habitat fragmentation can be especially detrimental in freshwater populations, whose dispersal is already constrained by the river network structure. Aquatic insects, for instance, are generally limited to two primary modes of dispersal: downstream drift in the aquatic juvenile life stages and flight during the terrestrial winged adult stage. Yet the impacts of large hydropower dams can make rivers uninhabitable for incoming (drifting) juvenile insects, with remaining refugia found only in tributaries. The ability of adult aquatic insects to traverse such river stretches in search of suitable tributary habitat likely depends on factors such as species‐specific dispersal ability and distance between tributaries. To explore the intersection of natural and human‐induced habitat fragmentation on aquatic insect dispersal ability, we quantified population genetics of three taxa with varying dispersal abilities, a caddisfly (Hydropsychidae, Hydropsyche oslari), a mayfly (Baetidae: Fallceon quilleri), and a water strider (Veliidae: Rhagovelia distincta), throughout tributaries of the Colorado River in the Grand Canyon, Arizona, USA. Using 2bRAD reduced genome sequencing and landscape genetics analyses, we revealed a strong pattern of isolation by distance among mayfly populations. This contrasts with caddisfly and water strider populations, which were largely panmictic. Analysis of thousands of informative single nucleotide polymorphisms showed that realized dispersal ability may not be accurately predicted by species traits for these widespread species. Principal components analysis revealed a strong division between caddisfly populations upstream and downstream of Havasu Creek (279 km through the 390 km study reach), suggesting that the geography of the Grand Canyon imposes a dispersal barrier for this species. Our use of genetic tools in the Grand Canyon to understand population structure has enabled us to elucidate dispersal barriers for aquatic insects. Ultimately, these data may be useful in informing effective conservation management plans for understudied organisms of conservation interest.

A proteomic analysis of the effect of ocean acidification on the haemocyte proteome of the South African abalone Haliotis midae

As a result of increasing CO2 emissions and the prevalence of global climate change, ocean acidification (OA) is becoming more pervasive, affecting many trophic levels, particularly those that rely on succinctly balanced ocean chemistry. This ultimately threatens community structures, as well as the future sustainability of the fishing/aquaculture industry. Understanding the molecular stress response of key organisms will aid in predicting their future survivability under changing environmental conditions. This study sought to elucidate the molecular stress response of the South African abalone, Haliotis midae, an understudied organism with high economic value, utilising a high throughput iTRAQ-based proteomics methodology. Adult abalone were exposed to control (pH 7.9) and experimental (pH 7.5) conditions for 12, 72 and 168 h, following which protein was isolated from sampled haemocytes and subsequently processed. iTRAQ-labelled peptides were analysed using mass spectrometry, while an array of bioinformatics tools was utilised for analysing the proteomic data. COG analysis identified “Cytoskeleton”, “Translation, ribosomal structure and biogenesis”, “Post-translational modification, protein turnover, chaperones”, and “Intracellular trafficking, secretion and vesicular transport” to be the most enriched functional classes, while statistical analysis identified a total of 33 up-regulated and 23 down-regulated effectors of OA stress in abalone. Several of the up-regulated proteins that were identified function in central metabolism (ENO1, PGK, DUOX1, GPD2), the stress/immune response (CAMKI, HSPA5/GRP78, MAPKI), and cytoskeleton, protein sorting and signal transduction (IQGAP1, MYO9B, TLN1, RDX, TCP-1/CCT, SNX6, CHMP1a, VPS13a). Protein-protein interactions were predicted using STRING DB, Cytoscape and Ingenuity Pathway Analysis, providing a model of the effects of OA on the H. midae haemocyte proteome. The data indicated that H. midae underwent a metabolic shift under OA conditions to utilize more energy-efficient mechanisms of ATP generation, while attempts at restoring haemocyte stabilisation and homeostasis were reflected by up-regulation of oxidative stress and cytoskeletal proteins. Our results support other molluscan studies that report a complex array of overlapping functions of both the stress and immune response systems. This interplay of the mounted stress and immune response is maintained and observed through the up-regulation of proteins involved in protein synthesis and turnover, as well as intracellular signalling and transport. The data presented in this study highlight the value of employing sensitive and robust -omics technologies for assessing the effects of changing environmental conditions on marine organisms.

Diet Diversity in Carnivorous Terebrid Snails Is Tied to the Presence and Absence of a Venom Gland

Predator-prey interactions are thought to play a driving role in animal evolution, especially for groups that have developed venom as their predatory strategy. However, how the diet of venomous animals influences the composition of venom arsenals remains uncertain. Two prevailing hypotheses to explain the relationship between diet and venom composition focus on prey preference and the types of compounds in venom, and a positive correlation between dietary breadth and the number of compounds in venom. Here, we examined venom complexity, phylogenetic relationship, collection depth, and biogeography of the Terebridae (auger snails) to determine if repeated innovations in terebrid foregut anatomy and venom composition correspond to diet variation. We performed the first molecular study of the diet of terebrid marine snails by metabarcoding the gut content of 71 terebrid specimens from 17 species. Our results suggest that the presence or absence of a venom gland is strongly correlated with dietary breadth. Specifically, terebrid species without a venom gland displayed greater diversity in their diet. Additionally, we propose a revision of the definition of venom complexity in conoidean snails to more accurately capture the breadth of ecological influences. These findings suggest that prey diet is an important factor in terebrid venom evolution and diversification and further investigations of other understudied organisms, like terebrids, are needed to develop robust hypotheses in this area.

The Switchgrass Microbiome: A Review of Structure, Function, and Taxonomic Distribution

Switchgrass (Panicum virgatum L.) has been championed as a promising bioenergy crop due to its high productivity across a wide environmental range. The switchgrass microbiome—including bacteria, archaea, fungi, and other microbiota inhabiting soil and plant tissues—can influence plant function substantially. We conducted a review of the literature investigating switchgrass microbiome structure, key functional roles, and taxa isolated from field-grown plants. Although site conditions and plant compartment (i.e., location within shoots, roots, or root-influenced soil) appear to be the strongest drivers of switchgrass microbiome structure, the microbiome is also shaped by climate, season, and host genotype. Studies comparing across plant species show that the switchgrass microbiome is more similar to the microbiomes of other perennial plants than to the microbiomes of annual plants. Members of the switchgrass microbiome confer several benefits to their host. Most notably, mycorrhizal fungi can increase plant biomass many-fold, associative nitrogen-fixing bacteria can provide a substantial portion of the plant’s nitrogen demand, and fungal endophytes can improve plant tolerance to drought. Although the fungi and bacteria cultured from switchgrass represent only a portion of the microbiome, these serve as a valuable resource for researchers interested in investigating functional outcomes of the switchgrass microbiome. We highlight areas where additional research is necessary for a more comprehensive understanding of switchgrass microbiome structure, function, and potential to enhance sustainable bioenergy production. Key gaps include the role of understudied organisms (e.g., viruses, microeukaryotes, and nonmycorrhizal fungi), multitrophic relationships, mechanisms underpinning switchgrass–microbiome interactions, and field-scale validation of experimental findings.

Hidden Diversity in the Mountain Chorus Frog (Pseudacris brachyphona) and the Diagnosis of a New Species of Chorus Frog in the Southeastern United States

A major driver generating amphibian diversity in the Appalachian Mountains is the complex paleogeography of the zone. Although the Appalachian Mountains have been widely studied, much of its amphibian phylogeography remains poorly known. The Mountain Chorus Frog (Pseudacris brachyphona) is one example of an understudied organism due to its elusiveness, patchy distribution, and short breeding seasons. Phylogenetic studies have suggested the existence of divergent lineages within P. brachyphona; however, insufficient sampling and the lack of diagnostic morphological traits have prevented further assessment of their taxonomic status. Using a genome-wide nuclear data set obtained via anchored hybrid enrichment, acoustic data, and ecological modeling, we tested for the existence of cryptic species within P. brachyphona. Our species tree estimation supports previous mitochondrial-based phylogenetic hypotheses that separate P. brachyphona into the Northern and Southern clades. Population genetic clustering also shows a sharp genetic break, which is concordant with these clades. Admixture was observed between the Northern clade and another chorus frog species (P. feriarum). Analysis of advertisement calls shows a divergent, faster pulse rate, and higher dominant frequency call for the Southern clade in comparison to the Northern clade and other trilling chorus frogs. Furthermore, species distribution models showed that habitat suitability for the Southern clade included pine-dominated and drier areas compared to the Northern clade. In light of the genetic, acoustic, and ecological divergence between the clades, we propose to elevate the taxonomic status of the Southern clade and name this new species the Collinses' Mountain Chorus Frog (P. collinsorum, new species). The new species ranges across northern Alabama, and extends into Georgia, eastern Mississippi, and southern Tennessee. Pseudacris collinsorum, new species, is geographically separated from its sister species, P. brachyphona, by the Tennessee River and surrounding Blue Ridge Escarpment. This study highlights the need for genus-wide, population-level genetic assessments and integrative data sets to uncover diversity within anurans.

The Destructive Fungal Pathogen Botrytis cinerea-Insights from Genes Studied with Mutant Analysis.

Botrytis cinerea is one of the most destructive fungal pathogens affecting numerous plant hosts, including many important crop species. As a molecularly under-studied organism, its genome was only sequenced at the beginning of this century and it was recently updated with improved gene annotation and completeness. In this review, we summarize key molecular studies on B. cinerea developmental and pathogenesis processes, specifically on genes studied comprehensively with mutant analysis. Analyses of these studies have unveiled key genes in the biological processes of this pathogen, including hyphal growth, sclerotial formation, conidiation, pathogenicity and melanization. In addition, our synthesis has uncovered gaps in the present knowledge regarding development and virulence mechanisms. We hope this review will serve to enhance the knowledge of the biological mechanisms behind this notorious fungal pathogen.

The human microbiome and genitourinary malignancies.

The human microbiome contains a vast network of understudied organisms that have an intimate role in our health and wellness. These microbiomes differ greatly between individuals, creating what may be thought of as a unique and dynamic microbial signature. Microbes have been shown to have various roles in metabolism, local and systemic inflammation, as well as immunity. Recent findings have confirmed the importance of both the gut and urinary microbiomes in genitourinary malignancies. Numerous studies have identified differences in microbial signatures between healthy patients and those with urologic malignancies. The microbiomes have been shown to contain microbes that may contribute to the etiology of disease state as well as yield information in regard to a person’s health and their responsiveness to certain drugs such as immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs). Less well understood are the effects of antibiotics on oncologic outcomes in such treatment courses. This review will explore our current understanding and advancements in the field of microbiome research and discuss its intimate association with genitourinary diseases including bladder cancer, prostate cancer, and kidney cancer. With a better understanding of the association between the microbiome and genitourinary malignancy, further investigation may produce reliable predictors of disease, prognostic indicators as well as therapeutic targets.

Genome skimming is a low-cost and robust strategy to assemble complete mitochondrial genomes from ethanol preserved specimens in biodiversity studies.

Global loss of biodiversity is an ongoing process that concerns both local and global authorities. Studies of biodiversity mainly involve traditional methods using morphological characters and molecular protocols. However, conventional methods are a time consuming and resource demanding task. The development of high-throughput sequencing (HTS) techniques has reshaped the way we explore biodiversity and opened a path to new questions and novel empirical approaches. With the emergence of HTS, sequencing the complete mitochondrial genome became more accessible, and the number of genome sequences published has increased exponentially during the last decades. Despite the current state of knowledge about the potential of mitogenomics in phylogenetics, this is still a relatively under-explored area for a multitude of taxonomic groups, especially for those without commercial relevance, non-models organisms and with preserved DNA. Here we take the first step to assemble and annotate the genomes from HTS data using a new protocol of genome skimming which will offer an opportunity to extend the field of mitogenomics to under-studied organisms. We extracted genomic DNA from specimens preserved in ethanol. We used Nextera XT DNA to prepare indexed paired-end libraries since it is a powerful tool for working with diverse samples, requiring a low amount of input DNA. We sequenced the samples in two different Illumina platform (MiSeq or NextSeq 550). We trimmed raw reads, filtered and had their quality tested accordingly. We performed the assembly using a baiting and iterative mapping strategy, and the annotated the putative mitochondrion through a semi-automatic procedure. We applied the contiguity index to access the completeness of each new mitogenome. Our results reveal the efficiency of the proposed method to recover the whole mitogenomes of preserved DNA from non-model organisms even if there are gene rearrangement in the specimens. Our findings suggest the potential of combining the adequate platform and library to the genome skimming as an innovative approach, which opens a new range of possibilities of its use to obtain molecular data from organisms with different levels of preservation.