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Abstract Lichen thalli are formed through the symbiotic association of a filamentous fungus and photosynthetic green alga and/or cyanobacterium. Recent studies have revealed lichens also host highly diverse communities of secondary fungal and bacterial symbionts, yet few studies have examined the viral component within these complex symbioses. Here, we describe viral biodiversity and functions in cyanolichens collected from across North America and Europe. As current machine-learning viral-detection tools are not trained on complex eukaryotic metagenomes, we first developed efficient methods to remove eukaryotic reads prior to viral detection and a custom pipeline to validate viral contigs predicted with three machine-learning methods. Our resulting high-quality viral data illustrate that every cyanolichen thallus contains diverse viruses that are distinct from viruses in other terrestrial ecosystems. In addition to cyanobacteria, predicted viral hosts include other lichen-associated bacterial lineages and algae, although a large fraction of viral contigs had no host prediction. Functional annotation of cyanolichen viral sequences predicts numerous viral-encoded auxiliary metabolic genes (AMGs) involved in amino acid, nucleotide, and carbohydrate metabolism, including AMGs for secondary metabolism (antibiotics and antimicrobials) and fatty acid biosynthesis. Overall, the diversity of cyanolichen AMGs suggests that viruses may alter microbial interactions within these complex symbiotic assemblages. 

Abstract Background Diabetic foot ulcers (DFUs) account for the majority of all limb amputations and hospitalizations due to diabetes complications. With 30 million cases of diabetes in the USA and 500,000 new diagnoses each year, DFUs are a growing health problem. Diabetes patients with limb amputations have high postoperative mortality, a high rate of secondary amputation, prolonged inpatient hospital stays, and a high incidence of re-hospitalization. DFU-associated amputations constitute a significant burden on healthcare resources that cost more than 10 billion dollars per year. Currently, there is no way to identify wounds that will heal versus those that will become severely infected and require amputation. Main body Accurate identification of causative pathogens in diabetic foot ulcers is a critical component of effective treatment. Compared to traditional culture-based methods, advanced sequencing technologies provide more comprehensive and unbiased profiling on wound microbiome with a higher taxonomic resolution, as well as functional annotation such as virulence and antibiotic resistance. In this review, we summarize the latest developments in defining the microbiology of diabetic foot ulcers that have been unveiled by sequencing technologies and discuss both the future promises and current limitations of these approaches. In particular, we highlight the temporal patterns and system dynamics in the diabetic foot microbiome monitored and measured during wound progression and medical intervention, and explore the feasibility of molecular diagnostics in clinics. Conclusion Molecular tests conducted during weekly office visits to clean and examine DFUs would allow clinicians to offer personalized treatment and antibiotic therapy. Personalized wound management could reduce healthcare costs, improve quality of life for patients, and recoup lost productivity that is important not only to the patient, but also to healthcare payers and providers. These efforts could also improve antibiotic stewardship and control the rise of “superbugs” vital to global health. 

Abstract In recent years, large-scale oceanic sequencing efforts have provided a deeper understanding of marine microbial communities and their dynamics. These research endeavors require the acquisition of complex and varied datasets through large, interdisciplinary and collaborative efforts. However, no unifying framework currently exists for the marine science community to integrate sequencing data with physical, geological, and geochemical datasets. Planet Microbe is a web-based platform that enables data discovery from curated historical and on-going oceanographic sequencing efforts. In Planet Microbe, each ‘omics sample is linked with other biological and physiochemical measurements collected for the same water samples or during the same sample collection event, to provide a broader environmental context. This work highlights the need for curated aggregation efforts that can enable new insights into high-quality metagenomic datasets. Planet Microbe is freely accessible from https://www.planetmicrobe.org/. 

Big-data analytics platforms, such as Hadoop, are appealing for scientific computation because they are ubiquitous, well-supported, and well-understood. Unfortunately, load-balancing is a common challenge of implementing large-scale scientific computing applications on these platforms. In this paper we present the design and implementation of Libra, a Hadoop-based tool for comparative metagenomics (comparing samples of genetic material collected from the environment). We describe the computation that Libra performs and how that computation is implemented using Hadoop tasks, including the techniques used by Libra to ensure that the task workloads are balanced despite nonuniform sample sizes and skewed distributions of genetic material in the samples. On a 10-machine Hadoop cluster Libra can analyze the entire Tara Ocean Viromes of ~4.2 billion reads in fewer than 20 hours.