Biology subjects

Gaul, D. A.

Publications and source records attributed to Gaul, D. A..

5 recordsLinked to original sources

bioRxiv2024

Ultrahigh Resolution Mass Spectrometry Imaging Maps Brain Lipidome Alterations Associated with Mild Traumatic Brain Injury

Traumatic brain injury (TBI) is a global public health problem with 50-60 million incidents per year, most of which are considered mild (mTBI) and many of these repetitive (rmTBI). Despite their massive implications, the pathologies of mTBI and rmTBI are not fully understood, with a paucity of information on brain lipid dysregulation following mild injury event(s). To gain more insight on mTBI and rmTBI pathology, a non-targeted spatial lipidomics workflow utilizing ultrahigh resolution mass spectrometry imaging was developed to map brain region-specific lipid alterations in rats following injury. Discriminant multivariate models were created for regions of interest including the hippocampus, cortex, and corpus callosum to pinpoint lipid species that differentiated between injured and sham animals. A multivariate model focused on the hippocampus region differentiated injured brain tissues with an area under the curve of 0.994 using only four lipid species. Lipid classes that were consistently discriminant included polyunsaturated fatty acid-containing phosphatidylcholines (PC), lysophosphatidylcholines (LPC), LPC-plasmalogens (LPC-P) and PC potassium adducts. Many of the polyunsaturated fatty acid-containing PC and LPC-P selected have never been previously reported as altered in mTBI. The observed lipid alterations indicate that neuroinflammation, oxidative stress and disrupted sodium-potassium pumps are important pathologies that could serve to explain cognitive deficits associated with rmTBI. Therapeutics which target or attenuate these pathologies may be beneficial to limit persistent damage following a mild brain injury event.

neuroscience↗

bioRxiv2023

Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women

Distinguishing ovarian cancer (OC) from other gynecological malignancies remains a critical unmet medical need with significant implications for patient survival. However, non-specific symptoms along with our lack of understanding of OC pathogenesis hinder its diagnosis, preventing many women from receiving appropriate medical assistance. Accumulating evidence suggests a link between OC and deregulated lipid metabolism. Most studies, however, are limited by small sample size, particularly for early-stage cases. Furthermore, racial/ethnic differences in OC survival and incidence have been reported, yet most of the studies consist largely of non-Hispanic white women or women with European ancestry. Studies of more diverse racial/ethnic populations are needed to make OC diagnosis and prevention more inclusive. Here, we profiled the serum lipidome of 208 OC, including 93 patients with early-stage OC, and 117 non-OC (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning approaches. Results show that lipidome alterations unique to OC were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in OC in comparison to other gynecological diseases. Machine learning methods selected a panel of 17 lipids that discriminated OC from non-OC cases with an AUC of 0.85 for an independent test set. This study provides a systemic analysis of lipidome alterations in human OC, specifically in Korean women, emphasizing the potential of circulating lipids in distinguishing OC from non-OC conditions.

cancer biology↗

bioRxiv2023

Sexual dimorphism and the multi-omic response to exercise training in rat subcutaneous white adipose tissue

Subcutaneous white adipose tissue (scWAT) is a dynamic storage and secretory organ that regulates systemic homeostasis, yet the impact of endurance exercise training and sex on its molecular landscape has not been fully established. Utilizing an integrative multi-omics approach with data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we identified profound sexual dimorphism in the dynamic response of rat scWAT to endurance exercise training. Despite similar cardiorespiratory improvements, only male rats reduced whole-body adiposity, scWAT adipocyte size, and total scWAT triglyceride abundance with training. Multi-omic analyses of adipose tissue integrated with phenotypic measures identified sex-specific training responses including enrichment of mTOR signaling in females, while males displayed enhanced mitochondrial ribosome biogenesis and oxidative metabolism. Overall, this study reinforces our understanding that sex impacts scWAT biology and provides a rich resource to interrogate responses of scWAT to endurance training.

molecular biology↗

bioRxiv2023

The unknown lipids project: harmonized methods improve compound identification and data reproducibility in an inter-laboratory untargeted lipidomics study

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

systems biology↗

bioRxiv2023

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages, and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer. TeaserTime-resolved lipidome remodeling in an ovarian cancer model is studied through lipidomics and machine learning.

cancer biology↗