Antarctic surface oceans are well-studied during summer when irradiance levels are high, sea ice is melting and primary productivity is at a maximum. Coincident with this timing, the bacterioplankton respond with significant increases in secondary productivity. Little is known about bacterioplankton in winter when darkness and sea-ice cover inhibit photoautotrophic primary production. We report here an environmental genomic and small subunit ribosomal RNA (SSU rRNA) analysis of winter and summer Antarctic Peninsula coastal seawater bacterioplankton. Intense inter-seasonal differences were reflected through shifts in community composition and functional capacities encoded in winter and summer environmental genomes with significantly higher phylogenetic and functional diversity in winter. In general, inferred metabolisms of summer bacterioplankton were characterized by chemoheterotrophy, photoheterotrophy and aerobic anoxygenic photosynthesis while the winter community included the capacity for bacterial and archaeal chemolithoautotrophy. Chemolithoautotrophic pathways were dominant in winter and were similar to those recently reported in global ‘dark ocean' mesopelagic waters. If chemolithoautotrophy is widespread in the Southern Ocean in winter, this process may be a previously unaccounted carbon sink and may help account for the unexplained anomalies in surface inorganic nitrogen content.

Marine microorganisms thrive under low levels of nitrogen (N). N cost minimization is a major selective pressure imprinted on open-ocean microorganism genomes. Here we show that amino-acid sequences from the open ocean are reduced in N, but increased in average mass compared with coastal-ocean microorganisms. Nutrient limitation exerts significant pressure on organisms supporting the trade-off between N cost minimization and increased average mass of amino acids that is a function of increased A+T codon usage. N cost minimization, especially of highly expressed proteins, reduces the total cellular N budget by 2.7–10% this minimization in combination with reduction in genome size and cell size is an evolutionary adaptation to nutrient limitation. The biogeochemical and evolutionary precedent for these findings suggests that N limitation is a stronger selective force in the ocean than biosynthetic costs and is an important evolutionary strategy in resource-limited ecosystems.

Background The Human Protein Atlas (HPA) aims to map human proteins via multiple technologies including imaging, proteomics and transcriptomics. Access of the HPA data is mainly via web-based interface allowing views of individual proteins, which may not be optimal for data analysis of a gene set, or automatic retrieval of original images. Results HPAanalyze is an R package for retrieving and performing exploratory analysis of data from HPA. HPAanalyze provides functionality for importing data tables and xml files from HPA, exporting and visualizing data, as well as downloading all staining images of interest. The package is free, open source, and available via Bioconductor and GitHub. We provide examples of the use of HPAanalyze to investigate proteins altered in the deadly brain tumor glioblastoma. For example, we confirm Epidermal Growth Factor Receptor elevation and Phosphatase and Tensin Homolog loss and suggest the importance of the GTP Cyclohydrolase I/Tetrahydrobiopterin pathway. Additionally, we provide an interactive website for non-programmers to explore and visualize data without the use of R. Conclusions HPAanalyze integrates into the R workflow with the tidyverse framework, and it can be used in combination with Bioconductor packages for easy analysis of HPA data.

PurposeResearchers are currently seeking relevant lung cancer biomarkers in order to make informed decisions regarding therapeutic selection for patients in so-called “precision medicine.” However, there are challenges to obtaining adequate lung cancer tissue for molecular analyses. Furthermore, current molecular testing of tumors at the genomic or transcriptomic level are very indirect measures of biological response to a drug, particularly for small molecule inhibitors that target kinases. Kinase activity profiling is therefore theorized to be more reflective of in vivo biology than many current molecular analysis techniques. As a result, this study seeks to prove the feasibility of combining a novel minimally invasive biopsy technique that expands the number of lesions amenable for biopsy with subsequent ex vivo kinase activity analysis.MethodsEight patients with lung lesions of varying location and size were biopsied using the novel electromagnetic navigational bronchoscopy (ENB) technique. Basal kinase activity (kinomic) profiles and ex vivo interrogation of samples in combination with tyrosine kinase inhibitors erlotinib, crizotinib, and lapatinib were performed by PamStation 12 microarray analysis.ResultsKinomic profiling qualitatively identified patient specific kinase activity profiles as well as patient and drug specific changes in kinase activity profiles following exposure to inhibitor. Thus, the study has verified the feasibility of ENB as a method for obtaining tissue in adequate quantities for kinomic analysis and has demonstrated the possible use of this tissue acquisition and analysis technique as a method for future study of lung cancer biomarkers.ConclusionsWe demonstrate the feasibility of using ENB-derived biopsies to perform kinase activity assessment in lung cancer patients.

Kinomics is an emerging field of science that involves the study of global kinase activity. As kinases are essential players in virtually all cellular activities, kinomic testing can directly examine protein function, distinguishing kinomics from more remote, upstream components of the central dogma, such as genomics and transcriptomics. While there exist several different approaches for kinomic research, peptide microarrays are the most widely used and involve kinase activity assessment through measurement of phosphorylation of peptide substrates on the array. Unfortunately, bioinformatic tools for analyzing kinomic data are quite limited necessitating the development of accessible open access software in order to facilitate standardization and dissemination of kinomic data for scientific use. Here, we examine and present tools for data analysis for the popular PamChip® (PamGene International) kinomic peptide microarray. As a result, we propose (1) a procedural optimization of kinetic curve data capture, (2) new methods for background normalization, (3) guidelines for the detection of outliers during parameterization, and (4) a standardized data model to store array data at various analytical points. In order to utilize the new data model, we developed a series of tools to implement the new methods and to visualize the various data models. In the interest of accessibility, we developed this new toolbox as a series of JavaScript procedures that can be utilized as either server side resources (easily packaged as web services) or as client side scripts (web applications running in the browser). The aggregation of these tools within a Kinomics Toolbox provides an extensible web based analytic platform that researchers can engage directly and web programmers can extend. As a proof of concept, we developed three analytical tools, a technical reproducibility visualizer, an ANOVA based detector of differentially phosphorylated peptides, and a heatmap display with hierarchical clustering.