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DNA/RNA-stable isotope probing (SIP) is a powerful tool to link in situ microbial activity to sequencing data. Every SIP dataset captures distinct information about microbial community metabolism, process rates, and population dynamics, offering valuable insights for a wide range of research questions. Data reuse maximizes the information derived from the labor and resource-intensive SIP approaches. Yet, a review of publicly available SIP sequencing metadata showed that critical information necessary for reproducibility and reuse was often missing. Here, we outline the Minimum Information for any Stable Isotope Probing Sequence (MISIP) according to the Minimum Information for any (x) Sequence (MIxS) framework and include examples of MISIP reporting for common SIP experiments. Our objectives are to expand the capacity of MIxS to accommodate SIP-specific metadata and guide SIP users in metadata collection when planning and reporting an experiment. The MISIP standard requires 5 metadata fields-isotope, isotopolog, isotopolog label, labeling approach, and gradient position-and recommends several fields that represent best practices in acquiring and reporting SIP sequencing data (e.g., gradient density and nucleic acid amount). The standard is intended to be used in concert with other MIxS checklists to comprehensively describe the origin of sequence data, such as for marker genes (MISIP-MIMARKS) or metagenomes (MISIP-MIMS), in combination with metadata required by an environmental extension (e.g., soil). The adoption of the proposed data standard will improve the reuse of any sequence derived from a SIP experiment and, by extension, deepen understanding of in situ biogeochemical processes and microbial ecology.

Predicting elemental cycles and maintaining water quality under increasing anthropogenic influence requires understanding the spatial drivers of river microbiomes. However, the unifying microbial processes governing river biogeochemistry are hindered by a lack of genome-resolved functional insights and sampling across multiple rivers. Here we employed a community science effort to accelerate the sampling, sequencing, and genome-resolved analyses of river microbiomes to create the Genome Resolved Open Watersheds database (GROWdb). This resource profiled the identity, distribution, function, and expression of thousands of microbial genomes across rivers covering 90% of United States watersheds. Specifically, GROWdb encompasses 1,469 microbial species from 27 phyla, including novel lineages from 10 families and 128 genera, and defines the core river microbiome for the first time at genome level. GROWdb analyses coupled to extensive geospatial information revealed local and regional drivers of microbial community structuring, while also presenting a myriad of foundational hypotheses about ecosystem function. Building upon the previously conceived River Continuum Concept 1 , we layer on microbial functional trait expression, which suggests the structure and function of river microbiomes is predictable. We make GROWdb available through various collaborative cyberinfrastructures 2, 3 so that it can be widely accessed across disciplines for watershed predictive modeling and microbiome-based management practices.

Abstract: DNA/RNA-stable isotope probing (SIP) is a powerful tool to linkin situmicrobial activity to sequencing data. Every SIP dataset captures distinct information about microbial community metabolism, kinetics, and population dynamics, offering novel insights according to diverse research questions. Data re-use maximizes the information available from the time and resource intensive SIP experimental approach. Yet, a review of publicly available SIP sequencing metadata reveals that critical information necessary for reproducibility and reuse is often missing. Here, we outline the Minimum Information for any Stable Isotope Probing Sequence (MISIP) according to the Minimum Information for any (x) Sequence (MIxS) data standard framework and include examples of MISIP reporting for common SIP approaches. Our objectives are to expand the capacity of MIxS to accommodate SIP-specific metadata and guide SIP users in metadata collection when planning and reporting an experiment. The MISIP standard requires five metadata fields: isotope, isotopolog, isotopolog label and approach, and gradient position, and recommends several fields that represent best practices in acquiring and reporting SIP sequencing data (ex.gradient density and nucleic acid amount). The standard is intended to be used in concert with other MIxS checklists to comprehensively describe the origin of sequence data, such as for marker genes (MISIP-MIMARKS) or metagenomes (MISIP-MIMS), in combination with metadata required by an environmental extension (e.g., soil). The adoption of the proposed data standard will assure the reproducibility and reuse of any sequence derived from a SIP experiment and, by extension, deepen understanding ofin situbiogeochemical processes and microbial ecology.

Microbial genome annotation is the process of identifying structural and functional elements in DNA sequences and subsequently attaching biological information to those elements. DRAM is a tool developed to annotate bacterial, archaeal, and viral genomes derived from pure cultures or metagenomes. DRAM goes beyond traditional annotation tools by distilling multiple gene annotations to genome level summaries of functional potential. Despite these benefits, a downside of DRAM is the requirement of large computational resources, which limits its accessibility. Further, it did not integrate with downstream metabolic modeling tools that require genome annotation. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated with the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point-and-click interface, as well as generate genome-scale metabolic models from DRAM annotations.Availability and implementationFor kb_DRAM users, the kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. For kb_DRAM users, a tutorial workflow with all documentation is available at https://narrative.kbase.us/narrative/129480. For kb_DRAM developers, software is available at https://github.com/shafferm/kb_DRAM.

ABSTRACTObjective SARS-CoV-2 infection during pregnancy has been linked with an increased risk of hypertensive disorders of pregnancy (HDP). The aim of this study was to examine how both trimester and severity of SARS-CoV-2 infection impact HDP.Methods We conducted a cohort study of SARS-CoV-2-infected individuals during pregnancy (n = 205) and examined the association between trimester and severity of infection with incidence of HDP using modified Poisson regression models to calculate risk ratios (RR) and 95% confidence intervals (CI). We stratified the analysis of trimester by severity to understand the role of timing of infection among those with similar symptomatology and also examined timing of infection as a continuous variable.Results Compared to a reference cohort from 2018, SARS-CoV-2 infection did not largely increase the risk of HDP (RR: 1.17; CI:0.90, 1.51), but a non-statistically significant higher risk of preeclampsia was observed (RR: 1.33; CI:0.89, 1.98), in our small sample. Among the SARS-CoV-2 cohort, severity was linked with risk of HDP, with infections requiring hospitalization increasing the risk of HDP compared to asymptomatic/mild infections. Trimester of infection was not associated with risk of HDP, but a slight decline in the risk of HDP was observed with later gestational week of infection. Among patients with asymptomatic or mild symptoms, SARS-CoV-2 in the first trimester conferred a higher risk of HDP compared to the third trimester (RR: 1.70; CI:0.77, 3.77), although estimates were imprecise.Conclusion SARS-CoV-2 infection in early pregnancy may increase the risk of HDP compared to infection later in pregnancy.

In the version of this article initially published, the affiliation for Qizhi Zhang and Chris S. Henry was incorrectly shown as the University of Tennessee instead of Argonne National Laboratory. The affiliations have been corrected in the HTML and PDF versions of the article.

This article is composed of three independent commentaries of how “Integrated, Coordinated, Open, and Networked” (ICON), as well as “Findable, Accessible, Interoperable, and Reusable” (FAIR; under the umbrella of “Open” from ICON), principles are currently embedded in ocean sciences, and what are the opportunities and challenges by adopting them. Each commentary focuses on a different perspective as follows: (a) field sampling and experimentation to remote-sensing and autonomous observations; (b) global collaboration, technology transfer and application, reproducibility, and data sharing and infrastructure; (c) increasing diversity and broadening participation in ocean sciences. Overall, there is a consensus that ocean sciences is well-advanced in adopting many of the Integrated, Coordinated, Open, and Networked-Findable, Accessible, Interoperable, and Reusable (ICON-FAIR) principles regarding (a and b), although there are still plenty of opportunities to develop further by, for instance, making broader use of data-of-opportunity and citizen science, improving efforts toward standardized data organization and avoiding data fragmentation, and increasing training activities to implement good practices. On the other hand, fostering diversity, and broadening participation in ocean sciences is still in its infancy. Actions are needed to overcome under-representation of women and other marginalized groups, create opportunities for early-career scientists, and build bridges to support scientists from low-income countries. We recognize that adopting ICON-FAIR principles comes with high costs and efforts, but it has strong potential to develop ocean sciences in many aspects.

Over the last 25 years, biology has entered the genomic era and is becoming a science of 'big data'. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3-4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

The National Microbiome Data Collaborative (NMDC) Data Portal (https://data.microbiomedata.org) supports microbiome multi-omics data exploration and access through an integrated, distributed data framework aligned with the FAIR (Findable, Accessible, Interoperable and Reusable) data principles (1). The NMDC Data Portal currently hosts 10.2 terabytes of multi-omics microbiome data, spanning five data types (metagenomes, metatranscriptomes, metaproteomes, metabolomes, and natural organic matter characterizations), generated at two Department of Energy User Facilities, the Joint Genome Institute (JGI) at Lawrence Berkeley National Laboratory (LBNL) and the Environmental Molecular Systems Laboratory (EMSL) at Pacific Northwest National Laboratory (PNNL). A flexible data schema (https://github.com/microbiomedata/nmdc-schema) leveraging community-driven standards underpins how data is managed and integrated. Annotated multi-omic data products are produced by the NMDC workflows and linked through common biosamples to enable search capabilities based on environmental context, instrumentation, and functional attributes. As a pilot system, the NMDC Data Portal offers download capabilities and several search components, including interactive geographic visualization of samples; environmental classification distribution visualized through an interactive Sankey diagram; time-series slider to select longitudinal samples of interest; and an upset plot displaying the number of multi-omics data generated from the same biosample within a study.

Microbes drive myriad ecosystem processes, but under strong influence from viruses. Because studying viruses in complex systems requires different tools than those for microbes, they remain underexplored. To combat this, we previously aggregated double-stranded DNA (dsDNA) virus analysis capabilities and resources into 'iVirus' on the CyVerse collaborative cyberinfrastructure. Here we substantially expand iVirus's functionality and accessibility, to iVirus 2.0, as follows. First, core iVirus apps were integrated into the Department of Energy's Systems Biology KnowledgeBase (KBase) to provide an additional analytical platform. Second, at CyVerse, 20 software tools (apps) were upgraded or added as new tools and capabilities. Third, nearly 20-fold more sequence reads were aggregated to capture new data and environments. Finally, documentation, as "live" protocols, was updated to maximize user interaction with and contribution to infrastructure development. Together, iVirus 2.0 serves as a uniquely central and accessible analytical platform for studying how viruses, particularly dsDNA viruses, impact diverse microbial ecosystems.

Volume 6, no. 1, e01194-20, 2021, https://doi.org/10.1128/mSystems.01194-20. The article byline and affiliation line should read as given in this correction.

The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth's continents and oceans, including metagenomes from human and animal hosts, engineered environments, and natural and agricultural soils, to capture extant microbial, metabolic and functional potential. This comprehensive catalog includes 52,515 metagenome-assembled genomes representing 12,556 novel candidate species-level operational taxonomic units spanning 135 phyla. The catalog expands the known phylogenetic diversity of bacteria and archaea by 44% and is broadly available for streamlined comparative analyses, interactive exploration, metabolic modeling and bulk download. We demonstrate the utility of this collection for understanding secondary-metabolite biosynthetic potential and for resolving thousands of new host linkages to uncultivated viruses. This resource underscores the value of genome-centric approaches for revealing genomic properties of uncultivated microorganisms that affect ecosystem processes.

In the version of this article initially published, four people were missing from the alphabetical list of IMG/M Data Consortium members: Lauren V. Alteio of the Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria; Jeffrey L. Blanchard of the Biology Department, University of Massachusetts Amherst, Amherst, MA, USA; Kristen M. DeAngelis of the Department of Microbiology, University of Massachusetts Amherst, Amherst, MA, USA; and William Rodriguez-Reillo of the Research Computing Division, Harvard Medical School, Boston, MA, USA. The error has been corrected in the PDF and HTML versions of the article.

This paper was originally published under standard Springer Nature copyright (© The Author(s), under exclusive licence to Springer Nature America, Inc.). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International license. The error has been corrected in the print, HTML and PDF versions of the article.

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical. Important contributions have been made in the development of community-driven metadata standards; however, these standards have not been uniformly embraced by the microbiome research community. To understand how these standards are being adopted, or the barriers to adoption, across research domains, institutions, and funding agencies, the National Microbiome Data Collaborative (NMDC) hosted a workshop in October 2019. This report provides a summary of discussions that took place throughout the workshop, as well as outcomes of the working groups initiated at the workshop.

In the Abstract, the url https://modelseed.org has been replaced with https://modelseed.org/biochem.

Over the past year, biology educators and staff at the U.S. Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free web-based platform where anyone can conduct sophisticated and reproducible bioinformatic analyses via a graphical user interface. Here, we demonstrate the utility of KBase as a platform for bioinformatics education, and present a set of modular, adaptable, and customizable instructional units for teaching concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. Each module contains teaching resources, publicly available data, analysis tools, and Markdown capability, enabling instructors to modify the lesson as appropriate for their specific course. We present initial student survey data on the effectiveness of using KBase for teaching bioinformatic concepts, provide an example case study, and detail the utility of the platform from an instructor’s perspective. Even as in-person teaching returns, KBase will continue to work with instructors, supporting the development of new active learning curriculum modules. For anyone utilizing the platform, the growing KBase Educators Organization provides an educators network, accompanied by community-sourced guidelines, instructional templates, and peer support, for instructors wishing to use KBase within a classroom at any educational level–whether virtual or in-person.

Opioid use disorder (OUD) in pregnant women has increased significantly in recent years. Maintaining these women on sublingual (SL) buprenorphine (BUP) is an evidence-based practice but BUP-SL is associated with several disadvantages that an extended-release (XR) BUP formulation could eliminate. The National Drug Abuse Treatment Clinical Trials Network (CTN) is conducting an intent-to-treat, two-arm, open-label, pragmatic randomized controlled trial, Medication treatment for Opioid-dependent expectant Mothers (MOMs), to compare mother and infant outcomes of pregnant women with OUD treated with BUP-XR, relative to BUP-SL. A second aim is to determine the relative economic value of utilizing BUP-XR. Approximately 300 pregnant women with an estimated gestational age (EGA) of 6-30 weeks, recruited from 12 sites, will be randomized in a 1:1 ratio to BUP-XR or BUP-SL, balancing on site, EGA, and BUP-SL status (taking/not taking) at the time of randomization. Participants will be provided with study medication and attend weekly medication visits through 12 months postpartum. Participants will be invited to participate in two sub-studies to evaluate the: 1) mechanisms by which BUP-XR may improve mother and infant outcomes; and 2) effects of prenatal exposure to BUP-XR versus BUP-SL on infant neurodevelopment. This paper describes the key design decisions for the main trial made during protocol development. This Investigational New Drug (IND) trial uniquely uses pragmatic features where feasible in order to maximize external validity, hence increasing the potential to inform clinical practice guidelines and address multiple knowledge gaps for treatment of this patient population.

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Amid the current US opioid crisis, hepatitis C virus (HCV) infection rates continue to rise in young adults, including among pregnant women, yet few studies describe linkage to care and treatment in pregnant or postpartum women with HCV infection. We used electronic health record data to estimate HCV treatment rates for postpartum women before (January 2014‐September 2016) and during (October 2016‐March 2018) implementation of a maternal–infant HCV linkage program in combination with a multidisciplinary clinic to colocate mother and infant care. Using Poisson regression models, we compared HCV treatment initiation rates, adjusting for demographics, substance use, and treatment. From January 2014 through March 2018, 343 women who were HCV seropositive delivered at our institution. Of these, 95% completed HCV nucleic acid testing and 255 women had chronic HCV infection. Mean age was 30 years, 96% were publicly insured, and 94% had documented substance use. HCV treatment initiation increased from 28/164 (17.1%) women with chronic HCV infection in the preintervention period to 16/66 (24.2%) with the linkage‐only intervention and 13/25 (52.0%) with the linkage intervention and colocated care. Adjusted analyses demonstrated that women delivering during the intervention period initiated HCV treatment at 2.40 times (95% confidence interval [CI], 1.10‐5.25; linkage only) and 3.36 times (95% CI, 1.57‐7.17; linkage and colocated care) the rate of women delivering preintervention. Women on buprenorphine had higher HCV treatment initiation rates compared with those on methadone (rate ratio, 2.10; 95% CI, 1.05‐4.21). Conclusion: HCV linkage to care and treatment rates improved in the setting of mother–infant linkage and colocated care interventions. Perinatal care may represent a critical venue to identify, link, and treat women for HCV infection to improve their own health and prevent transmission to subsequent pregnancies.

Abstract This article is composed of three independent commentaries of how “Integrated, Coordinated, Open, and Networked” (ICON), as well as “Findable, Accessible, Interoperable, and Reusable” (FAIR; under the umbrella of “Open” from ICON), principles are currently embedded in ocean sciences, and what are the opportunities and challenges by adopting them. Each commentary focuses on a different perspective as follows: (a) field sampling and experimentation to remote‐sensing and autonomous observations; (b) global collaboration, technology transfer and application, reproducibility, and data sharing and infrastructure; (c) increasing diversity and broadening participation in ocean sciences. Overall, there is a consensus that ocean sciences is well‐advanced in adopting many of the Integrated, Coordinated, Open, and Networked‐Findable, Accessible, Interoperable, and Reusable (ICON‐FAIR) principles regarding (a and b), although there are still plenty of opportunities to develop further by, for instance, making broader use of data‐of‐opportunity and citizen science, improving efforts toward standardized data organization and avoiding data fragmentation, and increasing training activities to implement good practices. On the other hand, fostering diversity, and broadening participation in ocean sciences is still in its infancy. Actions are needed to overcome under‐representation of women and other marginalized groups, create opportunities for early‐career scientists, and build bridges to support scientists from low‐income countries. We recognize that adopting ICON‐FAIR principles comes with high costs and efforts, but it has strong potential to develop ocean sciences in many aspects.