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Lipidomics in Human Cancer and Malnutrition
Published in Qiu-Xing Jiang, New Techniques for Studying Biomembranes, 2020
Iqbal Mahmud, Timothy J. Garrett
The entire spectrum of lipid molecular species in any biological system, tissue, cell or fluid is called the lipidome, and the profiling/mapping of the lipidome is called lipidomics.1,2 Lipidomics not only involves the full characterization of lipid molecular species, but also explains biological roles with respect to expression/regulation of genes/proteins involved in lipid metabolism and function.3 Lipidomics can be either untargeted, in which case global lipid profiling is usually performed on complex biological mixtures, or targeted, in which lipids of interest are already known and the instrument is set to analyze only those of interest.4,5 Lipids are a collection of an extremely heterogeneous group of molecules and have been divided into eight categories (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, and polyketides) where ketoacyl or isoprene subunits are the two common building blocks6–9 (Figure 2.1).
Functional Omics and Big Data Analysis in Microalgae
Published in Gokare A. Ravishankar, Ranga Rao Ambati, Handbook of Algal Technologies and Phytochemicals, 2019
Chetan Paliwal, Tonmoy Ghosh, Asha A. Nesamma, Pavan P. Jutur
Lipidomics can be used to quantify different lipid classes along with their molecular species (Brügger 2014). A lipidome gives insights into lipid remodeling during altered environmental conditions such as nitrogen starvation. A study on Chlorella sp. (Trebouxiophyceae) and Nannochloropsis sp. (Eustigmatophyceae) has shown that in nitrogen depletion, phosphoglycerolipids tend to increase while long-chain fatty acids in TAGs were broken down (Martin et al. 2014). Another study to assess the variation in lipidomes due to heat stress on C. reinhardtii found that at 42°C, cells produce higher polyunsaturated TAGs and diacylglycerols (DAGs), while major chloroplastic monogalactosyldiacyl glycerol sn1-18:3/sn2-16:4 was decreased, triggering an increase in accumulation of DAG sn1-18:3/sn2-16:4 and TAG sn1-18:3/sn2-16:4/sn3-18:3 (Légeret et al. 2016). The study also revealed that TAGs are converted from DAGs via direct conversion from monogalactosyldiacylglycerols (MGDG). The study also finds that the third fatty acid of a TAG is generally originated from a phosphatidyl ethanolamine or a diacylglyceryl-O-4′- (N, N, N, -trimethyl)-homoserine betaine.
Precision medicine in multiple sclerosis
Published in Debmalya Barh, Precision Medicine in Cancers and Non-Communicable Diseases, 2018
Omics technologies in MS (Table 15.9) include the following techniques (Katsavos and Anagnostouli, 2013): Genomics is used for whole DNA sequencing.Transcriptomics is used for studying RNA sequences. It is commonly exemplified by microarrays and next-generation sequencing.Proteomics is used for investigating distribution of specific proteins.Lipidomics is concerned with recognition of cellular lipid pathways.Metabolomics can be utilized for gaining insight into metabolic pathways implicated in MS.Epigenomics is focused on epigenetic modifications that bring MS susceptibility.
Screening of radiation gastrointestinal injury biomarkers in rat plasma by high-coverage targeted lipidomics
Published in Biomarkers, 2022
Cong Xi, Hua Zhao, Hai-Xiang Liu, Jia-Qi Xiang, Xue Lu, Tian-Jing Cai, Shuang Li, Ling Gao, Xue-Lei Tian, Ke-Hui Liu, Mei Tian, Qing-Jie Liu
Lipidomics, a subdiscipline of metabolomics, provides a promising avenue to expand potential biomarkers of radiation-induced GI injury. Lipidomics has emerged in molecular biology and disease research for the crucial role of lipids in cell, tissue, and organ physiology (Han et al.2003, 2005, Wenk 2005, Han 2007, 2016, 2017, Shevchenko et al.2010). Lipidomics with in-depth mining is considered a critical member of the multiple omics family and a lipid-specific tool to discover biomarkers and understand disease-associated lipid metabolism (Zhang et al.2018, 2020). In recent years, great efforts and improvements have been made to delineate lipid signatures of radiation-induced GI injury in murine (Ghosh et al.2013, Jones et al.2019) and non-human primates models (Kumar et al.2020) by lipidomics. Lipids including fatty acids, glycerophospholipids, and sphingolipids were reported to be perturbed when comparing sham or pre- and post-IR GI tissues at relatively high doses. Targeted lipidomic assay in previous research only included glycerophospholipids and sphingolipids, which were narrow in coverage scope. Up to now, little information is known about the linear dose-response relationship of potential lipid biomarkers. Classification performance of the potential biomarkers has not been evaluated so far.
Standardizing and increasing the utility of lipidomics: a look to the next decade
Published in Expert Review of Proteomics, 2020
Yuqin Wang, Eylan Yutuc, William J Griffiths
At the beginning of the third decade of the twenty first century lipidomics is at a crossroads. Will it continue straight on, largely as the preserve of scientists in academia, or will it branch toward clinical science or perhaps toward the agricultural sector? If lipidomics is to have a meaningful impact in society beyond academia there are a number of aspects that the community will need to agree on and adopt [1–3]. In this perspective we give our views on the key areas that these might be. The authors will focus this article on biomedical lipidomics Table 1 but lipidomics is likely to have equally important impacts in the agriculture and food, cosmetics and perfumery industries. The article will be biased toward mass spectrometry (MS)-based lipidomics, which is the research interest of the authors.
A conjunctive lipidomic approach reveals plasma ethanolamine plasmalogens and fatty acids as early diagnostic biomarkers for colorectal cancer patients
Published in Expert Review of Proteomics, 2020
Tong Liu, Zhirong Tan, Jing Yu, Feng Peng, Jiwei Guo, Wenhui Meng, Yao Chen, Tai Rao, Zhaoqian Liu, Jingbo Peng
Colorectal cancer (CRC) is one of the major causes of human morbidity and mortality worldwide, with incidence rates steadily rising in young people under 50 years, according to the latest data [5,6]. Early-stage CRC is often misdiagnosed as cholelithiasis or chronic appendicitis in young patients, and the exact etiology remains unclear. Thus, it is essential to diagnose CRC at an early stage. The primary detection method for CRC includes, computed tomographic (CT) colonography, fecal occult blood testing, colonoscopy, and fecal immunochemistry testing. Detection of CRC using biochemistry tests has the disadvantage of low sensitivity and accuracy. Besides, as a gold standard for early detection, colonoscopy is invasive and expensive and may lead to potential risks of complications. Thus, new screening methods that are highly specific, sensitive, and noninvasive are critically needed for early diagnosis of CRC. Lipidomics, together with metabolomics, provides information on altered lipid metabolism under various pathological conditions, such as obesity, diabetes, and various cancers. Among all the altered metabolic processes, lipid dysregulation is recognized as an essential factor to support the occurrence in and subsequent deterioration of CRC patients [7–9]. Establishing a link between the CRC and lipidome will not only provide a deeper insight into its pathogenesis but also help in identifying novel targets of lipid metabolism for CRC diagnosis. Thus, our aim is to construct a plasma-based lipid biomarker series using a combined assay suitable for early diagnosis of CRC.