P-Mart: Interactive Analysis of Ion Abundance Global Proteomics Data

PMart Web Application: Marketplace for Interactive Analysis of Panomics Data

PMart is a web-based tool for reproducible quality control, exploratory data analysis, statistical analysis, and interactive visualization of 'omics data, based on the functionality of the pmartR R package. The newly improved user interface supports more 'omics data types, additional statistical capabilities, and enhanced options for creating downloadable graphics. PMart supports the analysis of label-free and isobaric-labeled (e.g., TMT, iTRAQ) proteomics, nuclear magnetic resonance (NMR) and mass-spectrometry (MS)-based metabolomics, MS-based lipidomics, and ribonucleic acid sequencing (RNA-seq) transcriptomics data. At the end of a PMart session, a report is available that summarizes the processing steps performed and includes the pmartR R package functions used to execute the data processing. In addition, built-in safeguards in the backend code prevent users from utilizing methods that are inappropriate based on omics data type. PMart is a user-friendly interface for conducting exploratory data analysis and statistical comparisons of omics data without programming.

Ectomycorrhizal fungi alter soil food webs and the functional potential of bacterial communities

Most of Earth's trees rely on critical soil nutrients that ectomycorrhizal fungi (EcMF) liberate and provide, and all of Earth's land plants associate with bacteria that help them survive in nature. Yet, our understanding of how the presence of EcMF modifies soil bacterial communities, soil food webs, and root chemistry requires direct experimental evidence to comprehend the effects that EcMF may generate in the belowground plant microbiome. To this end, we grew Pinus muricata plants in soils that were either inoculated with EcMF and native forest bacterial communities or only native bacterial communities. We then profiled the soil bacterial communities, applied metabolomics and lipidomics, and linked omics data sets to understand how the presence of EcMF modifies belowground biogeochemistry, bacterial community structure, and their functional potential. We found that the presence of EcMF (i) enriches soil bacteria linked to enhanced plant growth in nature, (ii) alters the quantity and composition of lipid and non-lipid soil metabolites, and (iii) modifies plant root chemistry toward pathogen suppression, enzymatic conservation, and reactive oxygen species scavenging. Using this multi-omic approach, we therefore show that this widespread fungal symbiosis may be a common factor for structuring soil food webs.IMPORTANCEUnderstanding how soil microbes interact with one another and their host plant will help us combat the negative effects that climate change has on terrestrial ecosystems. Unfortunately, we lack a clear understanding of how the presence of ectomycorrhizal fungi (EcMF)-one of the most dominant soil microbial groups on Earth-shapes belowground organic resources and the composition of bacterial communities. To address this knowledge gap, we profiled lipid and non-lipid metabolites in soils and plant roots, characterized soil bacterial communities, and compared soils amended either with or without EcMF. Our results show that the presence of EcMF changes soil organic resource availability, impacts the proliferation of different bacterial communities (in terms of both type and potential function), and primes plant root chemistry for pathogen suppression and energy conservation. Our findings therefore provide much-needed insight into how two of the most dominant soil microbial groups interact with one another and with their host plant.

Deoxyhypusine synthase promotes a pro-inflammatory macrophage phenotype

The metabolic inflammation (meta-inflammation) of obesity is characterized by proinflammatory macrophage infiltration into adipose tissue. Catalysis by deoxyhypusine synthase (DHPS) modifies the translation factor eIF5A to generate a hypusine (Hyp) residue. Hypusinated eIF5A (eIF5A^Hyp) controls the translation of mRNAs involved in inflammation, but its role in meta-inflammation has not been elucidated. Levels of eIF5A^Hyp were found to be increased in adipose tissue macrophages from obese mice and in murine macrophages activated to a proinflammatory M1-like state. Global proteomics and transcriptomics revealed that DHPS deficiency in macrophages altered the abundance of proteins involved in NF-κB signaling, likely through translational control of their respective mRNAs. DHPS deficiency in myeloid cells of obese mice suppressed M1 macrophage accumulation in adipose tissue and improved glucose tolerance. These findings indicate that DHPS promotes the post-transcriptional regulation of a subset of mRNAs governing inflammation and chemotaxis in macrophages and contributes to a proinflammatory M1-like phenotype.

Simple and Efficient Data Analysis Dissemination for Individual Laboratories

Scientific progress comes as we build upon the work of others. Implicit in this advance is that we have access to and can thoroughly examine the work of others. It is important to recognize that our scholarly work as scientists encompasses not only experimental design and data collection but also our analytical methods. Thus when communicating biology experiments, especially those that utilize molecular omics data, the analysis methods that connect raw data to scientific conclusions must be presented with sufficient clarity that others can reproduce our exact work. Although there are many resources for sharing raw data files, there is currently not a widely utilized method for sharing analysis methods. We present a semistructured pattern for sharing analysis methods that is simple and efficient and can be implemented by individual laboratories using existing software. This pattern requires three types of files in a publicly accessible repository, such as GitHub: (1) data files, (2) a universal I/O script that parses all data files, and (3) analysis scripts creating figures and metrics reported in the manuscript. We suggest additional conventions to improve the readability and provide a template repository for the pattern. Sharing our exact analysis methods as software, in addition to their narrative description in a manuscript, will ensure reproducibility and transparency. Importantly, the pattern we present does not require new infrastructure and can be achieved without advanced computing skills.