Associations between picocyanobacterial ecotypes and cyanophage host genes across ocean basins and depth

Background Cyanophages, viruses that infect cyanobacteria, are globally abundant in the ocean’s euphotic zone and are a potentially important cause of mortality for marine picocyanobacteria. Viral host genes are thought to increase viral fitness by either increasing numbers of genes for synthesizing nucleotides for virus replication, or by mitigating direct stresses imposed by the environment. The encoding of host genes in viral genomes through horizontal gene transfer is a form of evolution that links viruses, hosts, and the environment. We previously examined depth profiles of the proportion of cyanophage containing various host genes in the Eastern Tropical North Pacific Oxygen Deficient Zone (ODZ) and at the subtropical North Atlantic (BATS). However, cyanophage host genes have not been previously examined in environmental depth profiles across the oceans. Methodology We examined geographical and depth distributions of picocyanobacterial ecotypes, cyanophage, and their viral-host genes across ocean basins including the North Atlantic, Mediterranean Sea, North Pacific, South Pacific, and Eastern Tropical North and South Pacific ODZs using phylogenetic metagenomic read placement. We determined the proportion of myo and podo-cyanophage containing a range of host genes by comparing to cyanophage single copy core gene terminase (terL). With this large dataset (22 stations), network analysis identified statistical links between 12 of the 14 cyanophage host genes examined here with their picocyanobacteria host ecotypes. Results Picyanobacterial ecotypes, and the composition and proportion of cyanophage host genes, shifted dramatically and predictably with depth. For most of the cyanophage host genes examined here, we found that the composition of host ecotypes predicted the proportion of viral host genes harbored by the cyanophage community. Terminase is too conserved to illuminate the myo-cyanophage community structure. Cyanophage cobS was present in almost all myo-cyanophage and did not vary in proportion with depth. We used the composition of cobS phylotypes to track changes in myo-cyanophage composition. Conclusions Picocyanobacteria ecotypes shift with changes in light, temperature, and oxygen and many common cyanophage host genes shift concomitantly. However, cyanophage phosphate transporter gene pstS appeared to instead vary with ocean basin and was most abundant in low phosphate regions. Abundances of cyanophage host genes related to nutrient acquisition may diverge from host ecotype constraints as the same host can live in varying nutrient concentrations. Myo-cyanophage community in the anoxic ODZ had reduced diversity. By comparison to the oxic ocean, we can see which cyanophage host genes are especially abundant (nirA, nirC, and purS) or not abundant (myo psbA) in ODZs, highlighting both the stability of conditions in the ODZ and the importance of nitrite as an N source to ODZ endemic LLV Prochlorococcus

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

© The Author(s), 2011. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in BMC Systems Biology 5 Suppl 2 (2011): S15, doi:10.1186/1752-0509-5-S2-S15.The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsaThis research is partially supported by the National Science Foundation (NSF) DMS-1043075 and OCE 1136818

Physician Characteristics Associated With Ordering 4 Low-Value Screening Tests in Primary Care

Importance: Efforts to reduce low-value tests and treatments in primary care are often ineffective. These efforts typically target physicians broadly, most of whom order low-value care infrequently. Objectives: To measure physician-level use rates of 4 low-value screening tests in primary care to investigate the presence and characteristics of primary care physicians who frequently order low-value care. Design, Setting, and Participants: A retrospective cohort study was conducted using administrative health care claims collected between April 1, 2012, and March 31, 2016, in Ontario, Canada. This study measured use of 4 low-value screening tests-repeated dual-energy x-ray absorptiometry (DXA) scans, electrocardiograms (ECGs), Papanicolaou (Pap) tests, and chest radiographs (CXRs)-among low-risk outpatients rostered to a common cohort of primary care physicians. Exposures: Physician sex, years since medical school graduation, and primary care model. Main Outcomes and Measures: This study measured the number of tests to which a given physician ranked in the top quintile by ordering rate. The resulting cross-test score (range, 0-4) reflects a physician's propensity to order low-value care across screening tests. Physicians were then dichotomized into infrequent or isolated frequent users (score, 0 or 1, respectively) or generalized frequent users for 2 or more tests (score, ≥2). Results: The final sample consisted of 2394 primary care physicians (mean [SD] age, 51.3 [10.0] years; 50.2% female), who were predominantly Canadian medical school graduates (1701 [71.1%]), far removed from medical school graduation (median, 25.3 years; interquartile range, 17.3-32.3 years), and reimbursed via fee-for-service in a family health group (1130 [47.2%]), far removed from medical school graduation (median, 25.3 years; interquartile range, 17.3-32.3 years), and reimbursed via fee-for-service in a family health group (1130 [47.2%). They ordered 302 509 low-value screening tests (74 167 DXA scans, 179 855 ECGs, 19 906 Pap tests, and 28 581 CXRs) after 3 428 557 ordering opportunities. Within the cohort, generalized frequent users represented 18.4% (441 of 2394) of physicians but ordered 39.2% (118 665 of 302 509) of all low-value screening tests. Physicians who were male (odds ratio, 1.29; 95% CI, 1.01-1.64), further removed from medical school graduation (odds ratio, 1.03; 95% CI, 1.02-1.04), or in an enhanced fee-for-service payment model (family health group) vs a capitated payment model (family health team) (odds ratio, 2.04; 95% CI, 1.42-2.94) had increased odds of being generalized frequent users. Conclusions and Relevance: This study identified a group of primary care physicians who frequently ordered low-value screening tests. Tailoring future interventions to these generalized frequent users might be an effective approach to reducing low-value care

Accurate Genome Relative Abundance Estimation Based on Shotgun Metagenomic Reads

Accurate estimation of microbial community composition based on metagenomic sequencing data is fundamental for subsequent metagenomics analysis. Prevalent estimation methods are mainly based on directly summarizing alignment results or its variants; often result in biased and/or unstable estimates. We have developed a unified probabilistic framework (named GRAMMy) by explicitly modeling read assignment ambiguities, genome size biases and read distributions along the genomes. Maximum likelihood method is employed to compute Genome Relative Abundance of microbial communities using the Mixture Model theory (GRAMMy). GRAMMy has been demonstrated to give estimates that are accurate and robust across both simulated and real read benchmark datasets. We applied GRAMMy to a collection of 34 metagenomic read sets from four metagenomics projects and identified 99 frequent species (minimally 0.5% abundant in at least 50% of the data- sets) in the human gut samples. Our results show substantial improvements over previous studies, such as adjusting the over-estimated abundance for Bacteroides species for human gut samples, by providing a new reference-based strategy for metagenomic sample comparisons. GRAMMy can be used flexibly with many read assignment tools (mapping, alignment or composition-based) even with low-sensitivity mapping results from huge short-read datasets. It will be increasingly useful as an accurate and robust tool for abundance estimation with the growing size of read sets and the expanding database of reference genomes

The Bioinformatics Virtual Coordination Network: An open-source and interactive learning environment

Lockdowns and “stay-at-home” orders, starting in March 2020, shuttered bench and field dependent research across the world as a consequence of the global COVID-19 pandemic. The pandemic continues to have an impact on research progress and career development, especially for graduate students and early career researchers, as strict social distance limitations stifle ongoing research and impede in-person educational programs. The goal of the Bioinformatics Virtual Coordination Network (BVCN) was to reduce some of these impacts by helping research biologists learn new skills and initiate computational projects as alternative ways to carry out their research. The BVCN was founded in April 2020, at the peak of initial shutdowns, by an international group of early-career microbiology researchers with expertise in bioinformatics and computational biology. The BVCN instructors identified several foundational bioinformatic topics and organized hands-on tutorials through cloud-based platforms that had minimal hardware requirements (in order to maximize accessibility) such as RStudio Cloud and MyBinder. The major topics included the Unix terminal interface, R and Python programming languages, amplicon analysis, metagenomics, functional protein annotation, transcriptome analysis, network science, and population genetics and comparative genomics. The BVCN was structured as an open-access resource with a central hub providing access to all lesson content and hands-on tutorials (https://biovcnet.github.io/). As laboratories reopened and participants returned to previous commitments, the BVCN evolved: while the platform continues to enable “a la carte” lessons for learning computational skills, new and ongoing collaborative projects were initiated among instructors and participants, including a virtual, open-access bioinformatics conference in June 2021. In this manuscript we discuss the history, successes, and challenges of the BVCN initiative, highlighting how the lessons learned and strategies implemented may be applicable to the development and planning of future courses, workshops, and training programs

CCAAT/Enhancer Binding Protein alpha uses distinct domains to prolong pituitary cells in the Growth 1 and DNA Synthesis phases of the cell cycle

BACKGROUND: A number of transcription factors coordinate differentiation by simultaneously regulating gene expression and cell proliferation. CCAAT/enhancer binding protein alpha (C/EBPα) is a basic/leucine zipper transcription factor that integrates transcription with proliferation to regulate the differentiation of tissues involved in energy balance. In the pituitary, C/EBPα regulates the transcription of a key metabolic regulator, growth hormone. RESULTS: We examined the consequences of C/EBPα expression on proliferation of the transformed, mouse GHFT1-5 pituitary progenitor cell line. In contrast to mature pituitary cells, GHFT1-5 cells do not contain C/EBPα. Ectopic expression of C/EBPα in the progenitor cells resulted in prolongation of both growth 1 (G1) and the DNA synthesis (S) phases of the cell cycle. Transcription activation domain 1 and 2 of C/EBPα were required for prolongation of G1, but not of S. Some transcriptionally inactive derivatives of C/EBPα remained competent for G1 and S phase prolongation. C/EBPα deleted of its leucine zipper dimerization functions was as effective as full-length C/EBPα in prolonging G1 and S. CONCLUSION: We found that C/EBPα utilizes mechanistically distinct activities to prolong the cell cycle in G1 and S in pituitary progenitor cells. G1 and S phase prolongation did not require that C/EBPα remained transcriptionally active or retained the ability to dimerize via the leucine zipper. G1, but not S, arrest required a domain overlapping with C/EBPα transcription activation functions 1 and 2. Separation of mechanisms governing proliferation and transcription permits C/EBPα to regulate gene expression independently of its effects on proliferation

Explore mediated co-varying dynamics in microbial community using integrated local similarity and liquid association analysis

Abstract Background Discovering the key microbial species and environmental factors of microbial community and characterizing their relationships with other members are critical to ecosystem studies. The microbial co-occurrence patterns across a variety of environmental settings have been extensively characterized. However, previous studies were limited by their restriction toward pairwise relationships, while there was ample evidence of third-party mediated co-occurrence in microbial communities. Methods We implemented and applied the triplet-based liquid association analysis in combination with the local similarity analysis procedure to microbial ecology data. We developed an intuitive scheme to visualize those complex triplet associations along with pairwise correlations. Using a time series from the marine microbial ecosystem as example, we identified pairs of operational taxonomic units (OTUs) where the strength of their associations appeared to relate to the values of a third “mediator” variable. These “mediator” variables appear to modulate the associations between pairs of bacteria. Results Using this analysis, we were able to assess the OTUs’ ability to regulate its functional partners in the community, typically not manifested in the pairwise correlation patterns. For example, we identified Flavobacteria as a multifaceted player in the marine microbial ecosystem, and its clades were involved in mediating other OTU pairs. By contrast, SAR11 clades were not active mediators of the community, despite being abundant and highly correlated with other OTUs. Our results suggested that Flavobacteria are more likely to respond to situations where particles and unusual sources of dissolved organic material are prevalent, such as after a plankton bloom. On the other hand, SAR11s are oligotrophic chemoheterotrophs with inflexible metabolisms, and their relationships with other organisms may be less governed by environmental or biological factors. Conclusions By integrating liquid association with local similarity analysis to explore the mediated co-varying dynamics, we presented a novel perspective and a useful toolkit to analyze and interpret time series data from microbial community. Our augmented association network analysis is thus more representative of the true underlying dynamic structure of the microbial community. The analytic software in this study was implemented as new functionalities of the ELSA (Extended local similarity analysis) tool, which is available for free download (http://bitbucket.org/charade/elsa)

VERTICAL AND SEASONAL PATTERNS CONTROL BACTERIOPLANKTON COMMUNITIES AT TWO HORIZONTALLY COHERENT COASTAL UPWELLING SITES OFF GALICIA (NW SPAIN)

Analysis of seasonal patterns of marine bacterial community structure along horizontal and vertical spatial scales can help to predict long-term responses to climate change. Several recent studies have shown predictable seasonal reoccurrence of bacterial assemblages. However, only a few have assessed temporal variability over both horizontal and vertical spatial scales. Here we simultaneously studied the bacterial community structure at two different locations and depths in shelf waters of a coastal upwelling system during an annual cycle. The most noticeable biogeographic patterns observed were seasonality, horizontal homogeneity and spatial synchrony in bacterial diversity and community structure related with regional upwelling-downwelling dynamics. Water column mixing eventually disrupted bacterial community structure vertical heterogeneity. Our results are consistent with previous temporal studies of marine bacterioplankton in other temperate regions, and also suggest a marked influence of regional factors on the bacterial communities inhabiting this coastal upwelling system. Bacterial-mediated carbon fluxes in this productive region appear to be mainly controlled by community structure dynamics in surface waters, and local environmental factors at the base of the euphotic zone