Novel lipidomes profile and clinical phenotype identified in pneumoconiosis patients.

BACKGROUND: Pneumoconiosis is a group of occupational lung diseases caused by the inhalation of mineral dust in the lungs, leading to lung dysfunction. Patients with pneumoconiosis are usually accompanied by weight loss, which suggests a lipid metabolism disorder. Recent progress in lipidomics uncovered detailed lipid profiles that play important roles in respiratory diseases, such as asthma, lung cancer and lung injury. The purpose of this study was to shed light on the different expression of lipidome between pneumoconiosis and healthy, hoping to bring new ideas for the diagnosis and treatment of pneumoconiosis. METHODOLOGY: This non-matching case-control study was performed among 96 subjects (48 outpatients with male pneumoconiosis and 48 healthy volunteers), data of clinical phenotypes were recorded, and plasma biochemistry (lipidomic profiles) was tested for both pneumoconiosis patients and healthy controls. A total of 426 species in 11 lipid classes were analyzed by high-performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (HPLC-QqQ-MS) for the cases and controls. We also analyzed the correlation of lipid profiles with clinical phenomes from pneumoconiosis patients by expression quantitative trait locus (eQTL) model to evaluate trans-nodules between lipidomic profiles and clinical phenomes. All visually re-checked data were analyzed using appropriate statistical tools (t-test or one-way ANOVA test) on SPSS. RESULTS: Compared with healthy people, 26 significantly increased (> 1.5-fold) and 30 decreased lipid elements (< 2/threefold) in patients with pneumoconiosis were identified (P values all < 0.05). The majority of those elevated lipid elements were phosphatidylethanolamines (PEs), and the minority were free fatty acids (FFAs), while phosphatidylcholines (PCs) and lysophosphatidylcholines (lysoPCs) declined in pneumoconiosis. Clinical trans-omics analyses demonstrated that phenomes in pneumoconiosis connections with multiple lipids, which showed that pH, lung function, mediastinal lymph node calcification, and complication were highly correlated with lipid elements. Furthermore, up-regulated PE was corresponded to pH, smoking history and mediastinal lymph node calcification. PC was corresponded to dust exposure history, BMI and mediastinal lymph node calcification. CONCLUSION: We found altered lipid panels between male pneumoconiosis patients and healthy people by qualitatively and quantitatively measured plasma lipidomic profiles. The trans-omic analysis between clinical phenomes and lipidomes might have the potential to uncover the heterogeneity of lipid metabolism of pneumoconiosis patients and to screen out clinically significant phenome-based lipid panels.

Trans-omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer.

Lung cancer has high mortality, often accompanied with systemic metabolic disorders. The present study aimed at defining values of trans-nodules cross-clinical phenomic and lipidomic network layers in patients with adenocarcinoma (ADC), squamous cell carcinomas, or small cell lung cancer (SCLC). We measured plasma lipidomic profiles of lung cancer patients and found that altered lipid panels and concentrations varied among lung cancer subtypes, genders, ages, stages, metastatic status, nutritional status, and clinical phenome severity. It was shown that phosphatidylethanolamine elements (36:2, 18:0/18:2, and 18:1/18:1) were SCLC specific, whereas lysophosphatidylcholine (20:1 and 22:0 sn-position-1) and phosphatidylcholine (19:0/19:0 and 19:0/21:2) were ADC specific. There were statistically more lipids declined in male, <60 ages, late stage, metastasis, or body mass index < 22 . Clinical trans-omics analyses demonstrated that one phenome in lung cancer subtypes might be generated from multiple metabolic pathways and metabolites, whereas a metabolic pathway and metabolite could contribute to different phenomes among subtypes, although those needed to be furthermore confirmed by bigger studies including larger population of patients in multicenters. Thus, our data suggested that trans-omic profiles between clinical phenomes and lipidomes might have the value to uncover the heterogeneity of lipid metabolism among lung cancer subtypes and to screen out phenome-based lipid panels as subtype-specific biomarkers.

Ancestral transcriptome inference based on RNA-Seq and ChIP-seq data.

With the help of high-throughput NGS (next-generation sequencing) technologies, ancestral transcriptome reconstruction is helpful to understand the complexity of transcriptional regulatory systems that underlies the evolution of multiple cellular metazoans with sophisticated functions and distinctive morphologies. To this end, we report a new method of ancestral state inference. The new method used Ornstein-Uhlenbeck (OU) model, which is more biologically realistic, to replace the Brownian motion (BM) model and is suitable for multi-transcriptome data. Implemented in the free R package, AnceTran is specially designed for RNA-seq and ChIP-seq data, which is feasible. It should be noticed that our work will be integrated to a unified, statistically-sound phylogenetic framework to study the evolution of many other molecular phenomes such as proteomics, chromatin accessibility, methylation status, and metabolomics. We exemplify our method by a case study, using the ChIP-seq binding data of three liver-specific transcription factors and the RNA-seq liver expression data in four closely related mice species, and some technical issues are discussed.

Values of integration between lipidomics and clinical phenomes in patients with acute lung infection, pulmonary embolism, or acute exacerbation of chronic pulmonary diseases: a preliminary study.

BACKGROUND: The morbidity and mortality of patients with critical illnesses remain high in pulmonary critical care units and a poorly understood correlation between alterations of lipid elements and clinical phenomes remain unelucidated. METHODS: In the present study, we investigated plasma lipidomic profiles of 30 patients with severe acute pneumonia (SAP), acute pulmonary embolism (APE), and acute exacerbation of chronic pulmonary diseases (AECOPD) or 15 healthy with the aim to compare disease specificity of lipidomic patterns. We defined the specificity of lipidomic profiles in SAP by comparing it to both APE and AECOPD. Analysis of the correlation between altered lipid elements and clinical phenotypes using the lipid-QTL model was then carried out. RESULTS: We integrated lipidomic profiles with clinical phenomes measured by score values from the digital evaluation score system and found phenome-associated lipid elements to identify disease-specific lipidomic profiling. The present study demonstrates that lipidomic profiles of patients with acute lung diseases are different from healthy lungs, and there are also disease-specific portions of lipidomics among SAP, APE, or AECOPD. The comprehensive profiles of clinical phenomes or lipidomics are valuable in describing the disease specificity of patient phenomes and lipid elements. The combination of clinical phenomes with lipidomic profiles provides more detailed disease-specific information on panels of lipid elements When compared to the use of each separately. CONCLUSIONS: Integrating biological functions with disease specificity, we believe that clinical lipidomics may create a new alternative way to understand lipid-associated mechanisms of critical illnesses and develop a new category of disease-specific biomarkers and therapeutic targets.

Clinical lipidomics: a new way to diagnose human diseases.

Lipidomics is a measurement of a large scale of lipid species to understand roles of their carbon atoms, dual bonds, or isomerism in the lipid molecule. Clinical lipidomics was recently defined "as a new integrative biomedicine to discover the correlation and regulation between a large scale of lipid elements measured and analyzed in liquid biopsies from patients with those patient phenomes and clinical phenotypes". The first step to translate lipidomics into clinical lipidomics is to settle a number of standard operation procedures and protocols of lipidomics performance and measurement. Clinical lipidomics is the part of clinical trans-omics which was coined as a new emerging scientific discipline where clinical phenomes are integrated with molecular multiomics. We believe it is the time to translate lipid science and lipidomics into clinical application and to understand the importance of clinical lipidomics as one of the most helpful approaches during the design and decision-making of therapeutic strategies for individuals. We emphasize here that clinical lipidomics should be merged with clinical phenomes, e.g. patient signs and symptoms, biomedical analyses, pathology, images, and responses to therapies, although it is difficult to integrate and fuse the information of clinical lipidomics with clinical phenomes. It will be a great achievement if we can draw the networks of lipidomic species fused with networks of genes and proteins to describe the molecular mechanisms of the disease in multi-dimensions.