The rapid evolution of mass spectrometry (MS)-based lipidomics has enabled the simultaneous measurement of numerous lipid classes. With lipidomics datasets becoming increasingly available, lipidomic-focused software tools are required to facilitate data analysis as well as mining of public datasets, integrating lipidomics-unique molecular information, such as lipid class, chain length and unsaturation. To address this need, we developed lipidr, an open-source R/Bioconductor package for data mining and analysis of lipidomics datasets. lipidr implements a comprehensive lipidomic-focused analysis workflow for targeted and untargeted lipidomics. lipidr imports numerical matrices, Skyline exports and Metabolomics Workbench files directly into R, automatically inferring lipid class and chain information from lipid names. Through integration with the Metabolomics Workbench API, users can search, download and reanalyze public lipidomics datasets seamlessly. lipidr allows thorough data inspection, normalization, uni-and multivariate analyses, displaying results as interactive visualizations.To enable interpretation of lipid class, chain length and total unsaturation data, we also developed and implemented a novel Lipid Set Enrichment Analysis. A companion online guide with two live example datasets is presented at https://www.lipidr.org/.We expect that the ease of use and innovative features of lipidr allow the lipidomics research community to gain novel detailed insights from lipidomics data.

Research in natural products has always enhanced drug discovery by providing new and unique chemical compounds. However, recently, drug discovery from natural products is slowed down by the increasing chance of re-isolating known compounds. Rapid identification of previously isolated compounds in an automated manner, called dereplication, steers researchers toward novel findings, thereby reducing the time and effort for identifying new drug leads. Dereplication identifies compounds by comparing processed experimental data with those of known compounds, and so, diverse computational resources such as databases and tools to process and compare compound data are necessary. Automating the dereplication process through the integration of computational resources has always been an aspired goal of natural product researchers. To increase the utilization of current computational resources for natural products, we first provide an overview of the dereplication process, and then list useful resources, categorizing into databases, methods and software tools and further explaining them from a dereplication perspective. Finally, we discuss the current challenges to automating dereplication and proposed solutions.

Cellular membranes feature dynamic submicrometer-scale lateral domains termed lipid rafts, membrane rafts or glycosphingolipid-enriched microdomains (GEM). Numerous proteomics studies have been conducted on the lipid raft proteome, however, interpretation of individual studies is limited by potential undefined contaminant proteins. To enable integrated analyses, we previously developed RaftProt (http://lipid-raft-database.di.uq.edu.au/), a searchable database of mammalian lipid raft-associated proteins. Despite being a highly used resource, further developments in annotation and utilities were required. Here, we present RaftProt V2 (http://raftprot.org), an improved update of RaftProt. Besides the addition of new datasets and re-mapping of all entries to both UniProt and UniRef IDs, we have implemented a stringent annotation based on experimental evidence level to assist in identification of possible contaminant proteins. RaftProt V2 allows for simultaneous search of multiple proteins/experiments at the cell/tissue type and UniRef/Gene level, where correlations, interactions or overlaps can be investigated. The web-interface has been completely re-designed to enable interactive data and subset selection, correlation analysis and network visualization. Overall, RaftProt aims to advance our understanding of lipid raft function through integrative analysis of datasets collected from diverse tissue and conditions. Database URL: http://raftprot.org.

Esophageal adenocarcinoma (EAC) incidence has been rapidly increasing, potentially associated with the prevalence of the risk factors gastroesophageal reflux disease (GERD), obesity, high-fat diet (HFD), and the precursor condition Barrett’s esophagus (BE). EAC development occurs over several years, with stepwise changes of the squamous esophageal epithelium, through cardiac metaplasia, to BE, and then EAC. To establish the roles of GERD and HFD in initiating BE, we developed a dietary intervention model in C57/BL6 mice using experimental HFD and GERD (0.2% deoxycholic acid, DCA, in drinking water), and then analyzed the gastroesophageal junction tissue lipidome and microbiome to reveal potential mechanisms. Chronic (9 months) HFD alone induced esophageal inflammation and metaplasia, the first steps in BE/EAC pathogenesis. While 0.2% deoxycholic acid (DCA) alone had no effect on esophageal morphology, it synergized with HFD to increase inflammation severity and metaplasia length, potentially via increased microbiome diversity. Furthermore, we identify a tissue lipid signature for inflammation and metaplasia, which is characterized by elevated very-long-chain ceramides and reduced lysophospholipids. In summary, we report a non-transgenic mouse model, and a tissue lipid signature for early BE. Validation of the lipid signature in human patient cohorts could pave the way for specific dietary strategies to reduce the risk of BE in high-risk individuals.

Advances in mass spectrometry enabled high throughput profiling of lipids but differential analysis and biological interpretation of lipidomics datasets remains challenging. To overcome this barrier, we present LipidSuite, an end-to-end differential lipidomics data analysis server. LipidSuite offers a step-by-step workflow for preprocessing, exploration, differential analysis and enrichment analysis of untargeted and targeted lipidomics. Three lipidomics data formats are accepted for upload: mwTab file from Metabolomics Workbench, Skyline CSV Export, and a numerical matrix. Experimental variables to be used in analysis are uploaded in a separate file. Conventional lipid names are automatically parsed to enable lipid class and chain length analyses. Users can interactively explore data, choose subsets based on sample types or lipid classes or characteristics, and conduct univariate, multivariate and unsupervised analyses. For complex experimental designs and clinical cohorts, LipidSuite offers confounding variables adjustment. Finally, data tables and plots can be both interactively viewed or downloaded for publication or reports. Overall, we anticipate this free, user-friendly webserver to facilitate differential lipidomics data analysis and re-analysis, and fully harness biological interpretation from lipidomics datasets. LipidSuite is freely available at http://suite.lipidr.org.

Cholangiocarcinoma (CCA) is a major health problem in northeastern Thailand. The majority of CCA cases are clinically silent and difficult to detect at an early stage. Although abdominal ultrasonography (US) can detect premalignant periductal fibrosis (PDF), this method is not suitable for screening populations in remote areas. With the goal of developing a blood test for detecting CCA in the at-risk population, we carried out serum protein biomarker discovery and qualification. Label-free shotgun proteomics was performed on depleted serum samples from 30 participants (n = 10 for US-normal, US-PDF, and CCA groups). Of 40 protein candidates selected using multiple reaction monitoring on 90 additional serum samples (n = 30 per group), 11 discriminatory proteins were obtained using supervised multivariate statistical analysis. We further evaluated 3 candidates using ELISA and immunohistochemistry (IHC). S100A9, thioredoxin (TRX), and cadherin-related family member 2 (CDHR2) were significantly different between CCA and normal, and CCA and PDF groups when measured in an additional 247 serum samples (P < 0.0001). By IHC, TRX and CDHR2 were detected in the cytoplasm and nucleus of CCA and inflammatory cells. S100A9 was detected in the infiltrating tumor stroma immune cells. Proteomics discovery and qualification in depleted sera revealed promising biomarker candidates for CCA diagnosis.

Proximal tubular epithelial cells (PTEC) are central players in inflammatory kidney diseases. However, the complex signalling mechanism/s via which polarized PTEC mediate disease progression are poorly understood. Small extracellular vesicles (sEV), including exosomes, are recognized as fundamental components of cellular communication and signalling courtesy of their molecular cargo (lipids, microRNA, proteins). In this study, we examined the molecular content and function of sEV secreted from the apical versus basolateral surfaces of polarized human primary PTEC under inflammatory diseased conditions. PTEC were cultured under normal and inflammatory conditions on Transwell inserts to enable separate collection and isolation of apical/basolateral sEV. Significantly increased numbers of apical and basolateral sEV were secreted under inflammatory conditions compared with equivalent normal conditions. Multi‐omics analysis revealed distinct molecular profiles (lipids, microRNA, proteins) between inflammatory and normal conditions for both apical and basolateral sEV. Biological pathway analyses of significantly differentially expressed molecules associated apical inflammatory sEV with processes of cell survival and immunological disease, while basolateral inflammatory sEV were linked to pathways of immune cell trafficking and cell‐to‐cell signalling. In line with this mechanistic concept, functional assays demonstrated significantly increased production of chemokines (monocyte chemoattractant protein‐1, interleukin‐8) and immuno‐regulatory cytokine interleukin‐10 by peripheral blood mononuclear cells activated with basolateral sEV derived from inflammatory PTEC. We propose that the distinct molecular composition of sEV released from the apical versus basolateral membranes of human inflammatory PTEC may reflect specialized functional roles, with basolateral‐derived sEV pivotal in modulating tubulointerstitial inflammatory responses observed in many immune‐mediated kidney diseases. These findings provide a rationale to further evaluate these sEV‐mediated inflammatory pathways as targets for biomarker and therapeutic development.

Actinic keratosis (AK) is a premalignant lesion, common on severely photodamaged skin, that can progress over time to cutaneous squamous cell carcinoma (SCC). A high bacterial load of Staphylococcus aureus is associated with AK and SCC, but it is unknown whether this has a direct impact on skin cancer development. To determine whether S. aureus can have cancer-promoting effects on skin cells, we performed RNA sequencing and shotgun proteomics on primary human keratinocytes after challenge with sterile culture supernatant (‘secretome’) from four S. aureus clinical strains isolated from AK and SCC. Secretomes of two of the S. aureus strains induced keratinocytes to overexpress biomarkers associated with skin carcinogenesis and upregulated the expression of enzymes linked to reduced skin barrier function. Further, these strains induced oxidative stress markers and all secretomes downregulated DNA repair mechanisms. Subsequent experiments on an expanded set of lesion-associated S. aureus strains confirmed that exposure to their secretomes led to increased oxidative stress and DNA damage in primary human keratinocytes. A significant correlation between the concentration of S. aureus phenol soluble modulin toxins in secretome and the secretome-induced level of oxidative stress and genotoxicity in keratinocytes was observed. Taken together, these data demonstrate that secreted compounds from lesion-associated clinical isolates of S. aureus can have cancer-promoting effects in keratinocytes that may be relevant to skin oncogenesis.