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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.

How to connect your github account to a local machine or an HPC cluster (example at Puhti CSC)

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.

Oceanographic research cruises produce abundant data, using a wide range of methods and equipment; very often through large collaborative efforts. These research endeavors span a broad array of dis...

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/.

A plethora of tools exist for identifying phage sequences in bacterial genomes, single cell amplified genomes, and host-associated and environmental metagenomes. Yet because the genetics of phages and their hosts are closely intertwined, distinguishing viral from bacterial signal remains an ongoing challenge. Further the size, quantity and fragmentary nature of modern ‘omics datasets ushers in a new set of computational challenges. Here, we detail the promises and pitfalls of using currently available gene-centric or k-mer based tools for identifying prophage sequences in genomes and prophage and viral contigs in metagenomes. Each of these methods offers a unique piece of the puzzle to elucidating the intriguing signatures of phage-host coevolution.

Disturbance of glutathione (GSH) metabolism is a hallmark of numerous diseases, yet GSH functions are poorly understood. One key to this question is to consider its functional compartmentation. GSH is present in the endoplasmic reticulum (ER), where it competes with substrates for oxidation by the oxidative folding machinery, composed in eukaryotes of the thiol oxidase Ero1 and proteins from the disulfide isomerase family (protein disulfide isomerase). Yet, whether GSH is required for proper ER oxidative protein folding is a highly debated question. Recent Advances: Oxidative protein folding has been thoroughly dissected over the past decades, and its actors and their mode of action elucidated. Genetically encoded GSH probes have recently provided an access to subcellular redox metabolism, including the ER.Of the few often-contradictory models of the role of GSH in the ER, the most popular suggest it serves as reducing power. Yet, as a reductant, GSH also activates Ero1, which questions how GSH can nevertheless support protein reduction. Hence, whether GSH operates in the ER as a reductant, an oxidant, or just as a "blank" compound mirroring ER/periplasm redox activity is a highly debated question, which is further stimulated by the puzzling occurrence of GSH in the Escherichia coli periplasmic "secretory" compartment, aside from the Dsb thiol-reducing and oxidase pathways.Addressing the mechanisms controlling GSH traffic in and out of the ER/periplasm and its recycling will help address GSH function in secretion. In addition, as thioredoxin reductase was recently implicated in ER oxidative protein folding, the relative contribution of each of these two reducing pathways should now be addressed. Antioxid. Redox Signal. 27, 1178-1199.

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A wealth of viral data sits untapped in publicly available metagenomic data sets when it might be extracted to create a usable index for the virological research community. We hypothesized that work of this complexity and scale could be done in a hackathon setting. Ten teams comprised of over 40 participants from six countries, assembled to create a crowd-sourced set of analysis and processing pipelines for a complex biological data set in a three-day event on the San Diego State University campus starting 9 January 2019. Prior to the hackathon, 141,676 metagenomic data sets from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) were pre-assembled into contiguous assemblies (contigs) by NCBI staff. During the hackathon, a subset consisting of 2953 SRA data sets (approximately 55 million contigs) was selected, which were further filtered for a minimal length of 1 kb. This resulted in 4.2 million (Mio) contigs, which were aligned using BLAST against all known virus genomes, phylogenetically clustered and assigned metadata. Out of the 4.2 Mio contigs, 360,000 contigs were labeled with domains and an additional subset containing 4400 contigs was screened for virus or virus-like genes. The work yielded valuable insights into both SRA data and the cloud infrastructure required to support such efforts, revealing analysis bottlenecks and possible workarounds thereof. Mainly: (i) Conservative assemblies of SRA data improves initial analysis steps, (ii) existing bioinformatic software with weak multithreading/multicore support can be elevated by wrapper scripts to use all cores within a computing node, (iii) redesigning existing bioinformatic algorithms for a cloud infrastructure to facilitate its use for a wider audience, and (iv) a cloud infrastructure allows a diverse group of researchers to collaborate effectively. The scientific findings will be extended during a follow-up event. Here, we present the applied workflows, initial results, and lessons learned from the hackathon.

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Background Scientists have amassed a wealth of microbiome datasets, making it possible to study microbes in biotic and abiotic systems on a population or planetary scale; however, this potential has not been fully realized given that the tools, datasets, and computation are available in diverse repositories and locations. To address this challenge, we developed iMicrobe.us, a community-driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with those from the broader community. Findings The iMicrobe platform brings together analysis tools and microbiome datasets by leveraging National Science Foundation–supported cyberinfrastructure and computing resources from CyVerse, Agave, and XSEDE. The primary purpose of iMicrobe is to provide users with a freely available, web-based platform to (1) maintain and share project data, metadata, and analysis products, (2) search for related public datasets, and (3) use and publish bioinformatics tools that run on highly scalable computing resources. Analysis tools are implemented in containers that encapsulate complex software dependencies and run on freely available XSEDE resources via the Agave API, which can retrieve datasets from the CyVerse Data Store or any web-accessible location (e.g., FTP, HTTP). Conclusions iMicrobe promotes data integration, sharing, and community-driven tool development by making open source data and tools accessible to the research community in a web-based platform.

BackgroundScientists have amassed a wealth of microbiome datasets, making it possible to study microbes in biotic and abiotic systems on a population or planetary scale; however, this potential has not been fully realized given that the tools, datasets, and computation are available in diverse repositories and locations. To address this challenge, we developed iMicrobe.us, a community-driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with those from the broader community.FindingsThe iMicrobe platform brings together analysis tools and microbiome datasets by leveraging National Science Foundation–supported cyberinfrastructure and computing resources from CyVerse, Agave, and XSEDE. The primary purpose of iMicrobe is to provide users with a freely available, web-based platform to (1) maintain and share project data, metadata, and analysis products, (2) search for related public datasets, and (3) use and publish bioinformatics tools that run on highly scalable computing resources. Analysis tools are implemented in containers that encapsulate complex software dependencies and run on freely available XSEDE resources via the Agave API, which can retrieve datasets from the CyVerse Data Store or any web-accessible location (e.g., FTP, HTTP).ConclusionsiMicrobe promotes data integration, sharing, and community-driven tool development by making open source data and tools accessible to the research community in a web-based platform.

Infections are a serious health concern worldwide, particularly in vulnerable populations such as the immunocompromised, elderly, and young. Advances in metagenomic sequencing availability, speed, and decreased cost offer the opportunity to supplement or even replace culture-based identification of pathogens with DNA sequence-based diagnostics. Adopting metagenomic analysis for clinical use requires that all aspects of the workflow are optimized and tested, including data analysis and computational time and resources. We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. Binary mixtures of bacteria showed all three reliably identified organisms down to 1% relative abundance, while only the relative abundance estimates of Centrifuge and CLARK were accurate. All three classifiers identified the organisms present in their default databases from a mock bacterial community of 20 organisms, but only Centrifuge had no false positives. In addition, Centrifuge required far less computational resources and time for analysis. Centrifuge analysis of metagenomes obtained from samples of VAP, infected DFUs, and FN showed Centrifuge identified pathogenic bacteria and one virus that were corroborated by culture or a clinical PCR assay. Importantly, in both diabetic foot ulcer patients, metagenomic sequencing identified pathogens 4–6 weeks before culture. Finally, we show that Centrifuge results were minimally affected by elimination of time-consuming read quality control and host screening steps., Author summary Currently, the gold standard for identifying pathogens that are causing infection is to attempt growth in culture followed by identification based on physical characteristics such as shape and metabolic profile. However, many organisms do not grow in culture or are overgrown by faster growing organisms that out-compete them. Another method to identify pathogens in infections is to sequence the DNA in the samples and use that DNA sequence to identify the pathogens present—a process called metagenomic sequencing. Analyzing clinical metagenomic data can be difficult given the amount of data generated, high levels of human DNA contamination and a lack of well-defined bioinformatics methods. In this study, three leading software tools were compared for identification and quantitation of microbes in metagenomic data. One tool, called Centrifuge, reliably identified microbes present at just 1% relative abundance while requiring less computer time and resources than the others to which it was compared. Moreover, we found that Centrifuge results changed minimally when time-consuming quality control and host-screening steps were eliminated. We also examined Centrifuge’s performance in real-word clinical data sets showing that Centrifuge identified the same pathogens as culture.

How to run Prodigal version 2.6.3 (Hyatt et al. 2010)through theiMicrobeplaform. Prodigalis a protein-coding gene prediction software tool for bacterial and archaeal genomes.Prodigal runs smoothly on finished genomes, draft genomes, and metagenomes. Please note that Prodigal has not been tested on viruses, and that Prodigal contains no special rules or routines to handle viral genomes. More informations aboutProdigal can be found here : https://github.com/hyattpd/Prodigal/wiki

The transcriptome of Frankia alni strain ACN14a was compared between in vitro ammonium-replete (N-replete) and ammonium-free dinitrogen-fixing (N-fixing) conditions using DNA arrays. A Welch-test (p \\textless 0.05) revealed significant upregulation of 252 genes under N-fixing vs. N-replete (fold-change (FC) >= 2), as well as significant downregulation of 48 other genes (FC

Summary In the endoplasmic reticulum (ER), Ero1 catalyzes disulfide bond formation and promotes glutathione (GSH) oxidation to GSSG. Since GSSG cannot be reduced in the ER, maintenance of the ER glutathione redox state and levels likely depends on ER glutathione import and GSSG export. We used quantitative GSH and GSSG biosensors to monitor glutathione import into the ER of yeast cells. We found that glutathione enters the ER by facilitated diffusion through the Sec61 protein-conducting channel, while oxidized Bip (Kar2) inhibits transport. Increased ER glutathione import triggers H 2 O 2 -dependent Bip oxidation through Ero1 reductive activation, which inhibits glutathione import in a negative regulatory loop. During ER stress, transport is activated by UPR-dependent Ero1 induction, and cytosolic glutathione levels increase. Thus, the ER redox poise is tuned by reciprocal control of glutathione import and Ero1 activation. The ER protein-conducting channel is permeable to small molecules, provided the driving force of a concentration gradient.

We present the genome of a cyanosiphovirus (KBS2A) that infects a marine Synechococcus sp. (strain WH7803). Unique to this genome, relative to other sequenced cyanosiphoviruses, is the absence of elements associated with integration into the host chromosome, suggesting this virus may not be able to establish a lysogenic relationship.

The distribution of cyanomyoviruses was estimated using a quantitative PCR (qPCR) approach that targeted the g20 gene as a proxy for phage. Samples were collected spatially during a3000 km transect through the Sargasso Sea and temporally during a gyre-constrained phytoplankton bloom within the southern Pacific Ocean. Cyanomyovirus abundances were lower in the Sargasso Sea than in the southern Pacific Ocean, ranging from 2.75 × 10(3) to 5.15 × 10(4) mL(-1) and correlating with the abundance of their potential hosts (Prochlorococcus and Synechococcus). Cyanomyovirus abundance in the southern Pacific Ocean (east of New Zealand) followed Synechococcus host populations in the system: this included a decrease in g20 gene copies (from 4.3 × 10(5) to 9.6 × 10(3) mL(-1) ) following the demise of a Synechococcus bloom. When compared with direct counts of viruses, observations suggest that the cyanomyoviruses comprised 0.5 to25% of the total virus community. We estimated daily lysis rates of 0.2-46% of the standing stock of Synechococcus in the Pacific Ocean compared with c.1.0% in the Sargasso Sea. In total, our observations confirm this family of viruses is abundant in marine systems and that they are an important source of cyanobacterial mortality.