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Abstract The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology. 

Abstract BackgroundStudying science identity has been useful for understanding students’ continuation in science-related education and career paths. Yet knowledge and theory related to science identity among students on the path to becoming a professional science researcher, such as students engaged in research at the undergraduate, postbaccalaureate, and graduate level, is still developing. It is not yet clear from existing science identity theory how particular science contexts, such as research training experiences, influence students’ science identities. Here we leverage existing science identity and professional identity theories to investigate how research training shapes science identity. We conducted a qualitative investigation of 30 early career researchers—undergraduates, postbaccalaureates, and doctoral students in a variety of natural science fields—to characterize how they recognized themselves as science researchers. ResultsEarly career researchers (ECRs) recognized themselves as either science students or science researchers, which they distinguished from being a career researcher. ECRs made judgments, which we refer to as “science identity assessments”, in the context of interconnected work-learning and identity-learning cycles. Work-learning cycles referred to ECRs’ conceptions of the work they did in their research training experience. ECRs weighed the extent to which they perceived the work they did in their research training to show authenticity, offer room for autonomy, and afford opportunities for epistemic involvement. Identity-learning cycles encompassed ECRs’ conceptions of science researchers. ECRs considered the roles they fill in their research training experiences and if these roles aligned with their perceptions of the tasks and traits of perceived researchers. ECRs’ identity-learning cycles were further shaped by recognition from others. ECRs spoke of how recognition from others embedded within their research training experiences and from others removed from their research training experiences influenced how they see themselves as science researchers. ConclusionsWe synthesized our findings to form a revised conceptual model of science researcher identity, which offers enhanced theoretical precision to study science identity in the future. We hypothesize relationships among constructs related to science identity and professional identity development that can be tested in further research. Our results also offer practical implications to foster the science researcher identity of ECRs. 

Abstract The remarkable pace of genomic data generation is rapidly transforming our understanding of life at the micron scale. Yet this data stream also creates challenges for team science. A single microbe can have multiple versions of genome architecture, functional gene annotations, and gene identifiers; additionally, the lack of mechanisms for collating and preserving advances in this knowledge raises barriers to community coalescence around shared datasets. “Digital Microbes” are frameworks for interoperable and reproducible collaborative science through open source, community-curated data packages built on a (pan)genomic foundation. Housed within an integrative software environment, Digital Microbes ensure real-time alignment of research efforts for collaborative teams and facilitate novel scientific insights as new layers of data are added. Here we describe two Digital Microbes: 1) the heterotrophic marine bacteriumRuegeria pomeroyiDSS-3 with > 100 transcriptomic datasets from lab and field studies, and 2) the pangenome of the cosmopolitan marine heterotrophAlteromonascontaining 339 genomes. Examples demonstrate how an integrated framework collating public (pan)genome-informed data can generate novel and reproducible findings. 

Abstract Marine biogeochemical cycles are built on interactions between surface ocean microbes, particularly those connecting phytoplankton primary producers to heterotrophic bacteria. Details of these associations are not well understood, especially in the case of direct influences of bacteria on phytoplankton physiology. Here we catalogue how the presence of three marine bacteria (Ruegeria pomeroyiDSS‐3,Stenotrophomonassp. SKA14 andPolaribacter dokdonensisMED152) individually and uniquely impact gene expression of the picoeukaryotic algaMicromonas commodaRCC 299. We find a dramatic transcriptomic remodelling byM. commodaafter 8 h in co‐culture, followed by an increase in cell numbers by 56 h compared with the axenic cultures. Some aspects of the algal transcriptomic response are conserved across all three bacterial co‐cultures, including an unexpected reduction in relative expression of photosynthesis and carbon fixation pathways. Expression differences restricted to a single bacterium are also observed, with the FlavobacteriiaP. dokdonensisuniquely eliciting changes in relative expression of algal genes involved in biotin biosynthesis and the acquisition and assimilation of nitrogen. This study reveals thatM. commodahas rapid and extensive responses to heterotrophic bacteria in ways that are generalizable, as well as in a taxon specific manner, with implications for the diversity of phytoplankton‐bacteria interactions ongoing in the surface ocean. 

ABSTRACT The euphotic zone of the surface ocean contains distinct physical-chemical regimes that vary in light and nutrient concentrations as an inverse function of depth. The most numerous phytoplankter of the mid- and low-latitude ocean is the picocyanobacteriumProchlorococcus, which consists of ecologically distinct subpopulations (i.e., “ecotypes”). Ecotypes have different temperature, light, and nutrient optima and display distinct relative abundances along gradients of these niche dimensions. As a primary producer,Prochlorococcusfixes and releases organic carbon to neighboring microbes as part of the microbial loop. However, little is known about the specific moleculesProchlorococcusaccumulates and releases or how these processes vary among its ecotypes. Here, we characterize the metabolite diversity ofProchlorococcusby profiling three ecologically distinct cultured strains: MIT9301, representing a high-light-adapted ecotype dominating shallow tropical and sub-tropical waters; MIT0801, representing a low-light-adapted ecotype found throughout the euphotic zone; and MIT9313, representing a low-light-adapted ecotype relatively most abundant at the base of the euphotic zone. In both intracellular and extracellular metabolite profiles, we observe striking differences across strains in the accumulation and release of molecules, such as the DNA methylating agent S-adenosyl-methionine (intracellular) and the branched-chain amino acids (intracellular) and their precursors (extracellular). While some differences reflect variable genome content across the strains, others likely reflect variable regulation of conserved pathways. In the extracellular profiles, we identify molecules such as pantothenic acid and aromatic amino acids that may serve as currencies inProchlorococcus’ interactions with neighboring microbes and, therefore, merit further investigation. IMPORTANCEApproximately half of the annual carbon fixation on Earth occurs in the surface ocean through the photosynthetic activities of phytoplankton such as the ubiquitous picocyanobacteriumProchlorococcus. Ecologically distinct subpopulations (or ecotypes) ofProchlorococcusare central conduits of organic substrates into the ocean microbiome, thus playing important roles in surface ocean production. We measured the chemical profile of three cultured ecotype strains, observing striking differences among them that have implications for the likely chemical impact ofProchlorococcussubpopulations on their surroundings in the wild. Subpopulations differ in abundance along gradients of temperature, light, and nutrient concentrations, suggesting that these chemical differences could affect carbon cycling in different ocean strata and should be considered in models ofProchlorococcusphysiology and marine carbon dynamics. 

Abstract This study characterized ocean biological carbon pump metrics in the second iteration of the REgional Carbon Cycle Assessment and Processes (RECCAP2) project. The analysis here focused on comparisons of global and biome‐scale regional patterns in particulate organic carbon (POC) production and sinking flux from the RECCAP2 ocean biogeochemical model ensemble against observational products derived from satellite remote sensing, sediment traps, and geochemical methods. There was generally good model‐data agreement in mean large‐scale spatial patterns, but with substantial spread across the model ensemble and observational products. The global‐integrated, model ensemble‐mean export production, taken as the sinking POC flux at 100 m (6.08 ± 1.17 Pg C yr⁻¹), and export ratio defined as sinking flux divided by net primary production (0.154 ± 0.026) both fell at the lower end of observational estimates. Comparison with observational constraints also suggested that the model ensemble may have underestimated regional biological CO₂drawdown and air‐sea CO₂flux in high productivity regions. Reasonable model‐data agreement was found for global‐integrated, ensemble‐mean sinking POC flux into the deep ocean at 1,000 m (0.65 ± 0.24 Pg C yr⁻¹) and the transfer efficiency defined as flux at 1,000 m divided by flux at 100 m (0.122 ± 0.041), with both variables exhibiting considerable regional variability. The RECCAP2 analysis presents standard ocean biological carbon pump metrics for assessing biogeochemical model skill, metrics that are crucial for further modeling efforts to resolve remaining uncertainties involving system‐level interactions between ocean physics and biogeochemistry. 

Abstract Viruses impact microbial systems through killing hosts, horizontal gene transfer, and altering cellular metabolism, consequently impacting nutrient cycles. A virus-infected cell, a “virocell,” is distinct from its uninfected sister cell as the virus commandeers cellular machinery to produce viruses rather than replicate cells. Problematically, virocell responses to the nutrient-limited conditions that abound in nature are poorly understood. Here we used a systems biology approach to investigate virocell metabolic reprogramming under nutrient limitation. Using transcriptomics, proteomics, lipidomics, and endo- and exo-metabolomics, we assessed how low phosphate (low-P) conditions impacted virocells of a marine Pseudoalteromonas host when independently infected by two unrelated phages (HP1 and HS2). With the combined stresses of infection and nutrient limitation, a set of nested responses were observed. First, low-P imposed common cellular responses on all cells (virocells and uninfected cells), including activating the canonical P-stress response, and decreasing transcription, translation, and extracellular organic matter consumption. Second, low-P imposed infection-specific responses (for both virocells), including enhancing nitrogen assimilation and fatty acid degradation, and decreasing extracellular lipid relative abundance. Third, low-P suggested virocell-specific strategies. Specifically, HS2-virocells regulated gene expression by increasing transcription and ribosomal protein production, whereas HP1-virocells accumulated host proteins, decreased extracellular peptide relative abundance, and invested in broader energy and resource acquisition. These results suggest that although environmental conditions shape metabolism in common ways regardless of infection, virocell-specific strategies exist to support viral replication during nutrient limitation, and a framework now exists for identifying metabolic strategies of nutrient-limited virocells in nature. 

Abstract Microbial associations that result in phytoplankton mortality are important for carbon transport in the ocean. This includes parasitism, which in microbial food webs is dominated by the marine alveolate group, Syndiniales. Parasites are expected to contribute to carbon recycling via host lysis; however, knowledge on host dynamics and correlation to carbon export remain unclear and limit the inclusion of parasitism in biogeochemical models. We analyzed a 4-year 18S rRNA gene metabarcoding dataset (2016–19), performing network analysis for 12 discrete depths (1–1000 m) to determine Syndiniales–host associations in the seasonally oligotrophic Sargasso Sea. Analogous water column and sediment trap data were included to define environmental drivers of Syndiniales and their correlation with particulate carbon flux (150 m). Syndiniales accounted for 48–74% of network edges, most often associated with Dinophyceae and Arthropoda (mainly copepods) at the surface and Rhizaria (Polycystinea, Acantharea, and RAD-B) in the aphotic zone. Syndiniales were the only eukaryote group to be significantly (and negatively) correlated with particulate carbon flux, indicating their contribution to flux attenuation via remineralization. Examination of Syndiniales amplicons revealed a range of depth patterns, including specific ecological niches and vertical connection among a subset (19%) of the community, the latter implying sinking of parasites (infected hosts or spores) on particles. Our findings elevate the critical role of Syndiniales in marine microbial systems and reveal their potential use as biomarkers for carbon export. 

ABSTRACT Microbiology conferences can be powerful places to build collaborations and exchange ideas, but for queer and transgender (trans) scientists, they can also become sources of alienation and isolation. Many conference organizers would like to create welcoming and inclusive events but feel ill-equipped to make this vision a reality, and a historical lack of representation of queer and trans folks in microbiology means we rarely occupy these key leadership roles ourselves. Looking more broadly, queer and trans scientists are systematically marginalized across scientific fields, leading to disparities in career outcomes, professional networks, and opportunities, as well as the loss of unique scientific perspectives at all levels. For queer and trans folks with multiple, intersecting, marginalized identities, these barriers often become even more severe. Here, we draw from our experiences as early-career microbiologists to provide concrete, practical advice to help conference organizers across research communities design inclusive, safe, and welcoming conferences, where queer and trans scientists can flourish. 

ABSTRACT Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available athttps://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCEMapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.