Lipidomics-Paving the Road towards Better Insight and Precision Medicine in Rare Metabolic Diseases

Multi-omics profiling in spinal muscular atrophy (SMA): investigating lipid and metabolic alterations through longitudinal CSF analysis of Nusinersen-treated patients

Spinal muscular atrophy (SMA) is a rare neuromuscular disease caused by biallelic mutations in the SMN1 gene, leading to progressive muscle weakness due to degeneration of the anterior horn cells. Since 2017, SMA patients can be treated with the anti-sense oligonucleotide Nusinersen, which promotes alternative splicing of the SMN2 gene, by regular intrathecal injections. In this prospective study, we applied metabolomic, lipidomic, and proteomic analysis to examine sequential CSF samples from 13 SMA patients and controls. This multi-omic approach identified over 800 proteins and 400 small molecules including lipids. Multivariate analysis of multi-omic data successfully discriminated between the CSF derived from SMA patients and control subjects. Lipidomic analysis revealed increased levels of cholesteryl esters and lyso-phospholipids, along with reduced levels of cholesterol and phospholipids in the CSF of SMA patients as compared to healthy controls. These data, combined with results from functional assays, led us to conclude that SMA patients exhibit altered levels and function of high-density-lipoprotein (HDL)-like particles in the CSF. Notably, Nusinersen therapy was observed to reverse disease-specific profile changes toward a physiological state, potentially explicable by restoring HDL function.

Research on Lipidomic Profiling and Biomarker Identification for Osteonecrosis of the Femoral Head

Objectives: Abnormal lipid metabolism is increasingly recognized as a contributing factor to the development of osteonecrosis of the femoral head (ONFH). This study aimed to explore the lipidomic profiles of ONFH patients, focusing on distinguishing between traumatic ONFH (TONFH) and non-traumatic ONFH (NONFH) subtypes and identifying potential biomarkers for diagnosis and understanding pathogenesis. Methods: Plasma samples were collected from 92 ONFH patients (divided into TONFH and NONFH subtypes) and 33 healthy normal control (NC) participants. Lipidomic profiling was performed using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Data analysis incorporated a machine learning-based feature selection method, least absolute shrinkage and selection operator (LASSO) regression, to identify significant lipid biomarkers. Results: Distinct lipidomic signatures were observed in both TONFH and NONFH groups compared to the NC group. LASSO regression identified 11 common lipid biomarkers that signify shared metabolic disruptions in both ONFH subtypes, several of which exhibited strong diagnostic performance with areas under the curve (AUCs) > 0.7. Additionally, subtype-specific lipid markers unique to TONFH and NONFH were identified, providing insights into the differential pathophysiological mechanisms underlying these subtypes. Conclusions: This study highlights the importance of lipidomic profiling in understanding ONFH-associated metabolic disorders and demonstrates the utility of machine learning approaches, such as LASSO regression, in high-dimensional data analysis. These findings not only improve disease characterization but also facilitate the discovery of diagnostic and mechanistic biomarkers, paving the way for more personalized therapeutic strategies in ONFH.

Preliminary investigations of plasma lipidome and selenium levels in adults with treated hypothyroidism and in healthy individuals without selenium deficiency

The present preliminary study aimed to provide a targeted lipidomic analysis of Hashimoto (HT) and non-HT patients with well-controlled hypothyroidism as well as in healthy adults, and is the first to demonstrate the association of several components of the human lipidome with hypothyroidism in relation to the total plasma selenium content. All the patients and age-, sex-, and BMI-matched healthy controls met the very strict qualification criteria. Se levels were analyzed by ICP-MS, and lipidome studies were conducted using TQ-LC/MS. The 40 acylcarnitines, 90 glycerophospholipids, and 15 sphingomyelins were identified and quantified. PCaaC26:0 and PCaaC40:1 were negatively correlated with Se concentrations. Other lipids that were negatively correlated with Se concentrations but did not present significant differences between the three groups in the Kruskal-Wallis ANOVA test were PCaaC32:0, PCaeC30:0, PCaeC36:5, SMC18:0, and SM C18:1. In the multiple linear regression analyses, Se levels showed negative relationship, whereas different phosphatidylcholines: PCaaC24:0, PCaaC26:0, PCaeC30:1, PCaeC34:0, PCaeC36:4, PCaeC42:0 were positively associated with the presence of (H). Different lipidome components were identified in healthy and hypothyroid patients regardless of the cause of that condition. Studies on larger populations are needed to determine cause-and-effect relations and the potential mechanisms underlying these associations.

Application of Human Plasma Targeted Lipidomics and Analysis of Toxic Elements to Capture the Metabolic Complexities of Hypothyroidism

BACKGROUND

Hypothyroidism (HT) affects millions worldwide and can lead to various lipid disorders. The metabolic complexity and the influence of toxic elements in autoimmune and non-autoimmune HT subtypes are not fully understood. This study aimed to investigate the relationships between plasma lipidome, toxic elements, and clinical classifications of HT in unexposed individuals.

METHODS

Samples were collected from 120 adults assigned to a study group with Hashimoto's disease and non-autoimmune HT, and a healthy control group. Quantification of 145 pre-defined lipids was performed by using triple quadrupole tandem mass spectrometry (TQ MS/MS) in multiple reactions monitoring (MRM) mode via positive electrospray ionization (ESI). Levels of toxic elements were determined using inductively coupled plasma mass spectrometry (ICP-MS).

RESULTS

Significant associations between altered levels of several components of the plasma lipidome and Al, Cd, Ni, As, and Pb with HT were found. We show metabolic differences in lysophosphatidylcholines (LPC) and phosphatidylcholines (PC) between HT and controls, with distinct predicted activation patterns for lysolecithin acyltransferase and phospholipase A2.

CONCLUSIONS

There are significant changes in the lipidome profiles of healthy subjects compared to euthyroid HT patients treated with L-thyroxine, which are related to the type of hypothyroidism and non-occupational exposure to toxic elements.

The Utility of Lipidomic Analysis in Colorectal Cancer Diagnosis and Prognosis-A Systematic Review of Recent Literature

Colorectal cancer (CRC) is among the most prevalent and lethal malignancies. Lipidomic investigations have revealed numerous disruptions in lipid profiles across various cancers. Studies on CRC exhibit potential for identifying novel diagnostic or prognostic indicators through lipidomic signatures. This review examines recent literature regarding lipidomic markers for CRC. PubMed database was searched for eligible articles concerning lipidomic biomarkers of CRC. After selection, 36 articles were included in the review. Several studies endeavor to establish sets of lipid biomarkers that demonstrate promising potential to diagnose CRC based on blood samples. Phosphatidylcholine, phosphatidylethanolamine, ceramides, and triacylglycerols (TAGs) appear to offer the highest diagnostic accuracy. In tissues, lysophospholipids, ceramides, and TAGs were among the most altered lipids, while unsaturated fatty acids also emerged as potential biomarkers. In-depth analysis requires both cell culture and animal studies. CRC involves multiple lipid metabolism alterations. Although numerous lipid species have been suggested as potential diagnostic markers, the establishment of standardized methods and the conduct of large-scale studies are necessary to facilitate their clinical application.

The expanding diagnostic toolbox for rare genetic diseases

Genomic technologies, such as targeted, exome and short-read genome sequencing approaches, have revolutionized the care of patients with rare genetic diseases. However, more than half of patients remain without a diagnosis. Emerging approaches from research-based settings such as long-read genome sequencing and optical genome mapping hold promise for improving the identification of disease-causal genetic variants. In addition, new omic technologies that measure the transcriptome, epigenome, proteome or metabolome are showing great potential for variant interpretation. As genetic testing options rapidly expand, the clinical community needs to be mindful of their individual strengths and limitations, as well as remaining challenges, to select the appropriate diagnostic test, correctly interpret results and drive innovation to address insufficiencies. If used effectively - through truly integrative multi-omics approaches and data sharing - the resulting large quantities of data from these established and emerging technologies will greatly improve the interpretative power of genetic and genomic diagnostics for rare diseases.

Tracing the lipidome in inborn errors of metabolism

Inborn errors of metabolism (IEM) represent a heterogeneous group of more than 1800 rare disorders, many of which are causing significant childhood morbidity and mortality. More than 100 IEM are linked to dyslipidaemia, but yet our knowledge in connecting genetic information with lipidomic data is limited. Stable isotope tracing studies of the lipid metabolism (STL) provide insights on the dynamic of cellular lipid processes and could thereby facilitate the delineation of underlying metabolic (patho)mechanisms. This mini-review focuses on principles as well as technical limitations of STL and describes potential clinical applications by discussing recently published STL focusing on IEM.

Advancing Precision Medicine in South Tyrol, Italy: A Public Health Development Proposal for a Bilingual, Autonomous Province

This paper presents a comprehensive development plan for advancing precision medicine in the autonomous province of South Tyrol, Italy, a region characterized by its bilingual population and unique healthcare challenges. This study highlights the need to address the shortage of healthcare professionals proficient in language for person-centered medicine, the lag in healthcare sector digitalization, and the absence of a local medical university, all within the context of an initiated pharmacogenomics program and a population-based precision medicine study known as the "Cooperative Health Research in South Tyrol" (CHRIS) study. The key strategies for addressing these challenges and integrating CHRIS study findings into a broader precision medicine development plan are discussed, including workforce development and training, investment in digital infrastructure, enhanced data management and analytic capabilities, collaboration with external academic and research institutions, education and capacity building, securing funding and resources, and promoting a patient-centered approach. This study emphasizes the potential benefits of implementing such a comprehensive development plan, including improved early detection, personal ized treatment, and prevention of chronic diseases, ultimately leading to better healthcare outcomes and overall well-being in the South Tyrolean population.

Perturbations in fatty acid metabolism and collagen production infer pathogenicity of a novel MBTPS2 variant in Osteogenesis imperfecta

Osteogenesis imperfecta (OI) is a heritable and chronically debilitating skeletal dysplasia. Patients with OI typically present with reduced bone mass, tendency for recurrent fractures, short stature and bowing deformities of the long bones. Mutations causative of OI have been identified in over 20 genes involved in collagen folding, posttranslational modification and processing, and in bone mineralization and osteoblast development. In 2016, we described the first X-linked recessive form of OI caused by MBTPS2 missense variants in patients with moderate to severe phenotypes. MBTPS2 encodes site-2 protease, a Golgi transmembrane protein that activates membrane-tethered transcription factors. These transcription factors regulate genes involved in lipid metabolism, bone and cartilage development, and ER stress response. The interpretation of genetic variants in MBTPS2 is complicated by the gene's pleiotropic properties; MBTPS2 variants can also cause the dermatological conditions Ichthyosis Follicularis, Atrichia and Photophobia (IFAP), Keratosis Follicularis Spinulosa Decalvans (KFSD) and Olmsted syndrome (OS) without skeletal abnormalities typical of OI. Using control and patient-derived fibroblasts, we previously identified gene expression signatures that distinguish MBTPS2-OI from MBTPS2-IFAP/KFSD and observed stronger suppression of genes involved in fatty acid metabolism in MBTPS2-OI than in MBTPS2-IFAP/KFSD; this was coupled with alterations in the relative abundance of fatty acids in MBTPS2-OI. Furthermore, we observed a reduction in collagen deposition in the extracellular matrix by MBTPS2-OI fibroblasts. Here, we extrapolate our observations in the molecular signature unique to MBTPS2-OI to infer the pathogenicity of a novel MBTPS2 c.516A>C (p.Glu172Asp) variant of unknown significance in a male proband. The pregnancy was terminated at gestational week 21 after ultrasound scans showed bowing of femurs and tibiae and shortening of long bones particularly of the lower extremity; these were further confirmed by autopsy. By performing transcriptional analyses, gas chromatography-tandem mass spectrometry-based quantification of fatty acids and immunocytochemistry on fibroblasts derived from the umbilical cord of the proband, we observed perturbations in fatty acid metabolism and collagen production similar to what we previously described in MBTPS2-OI. These findings support pathogenicity of the MBTPS2 variant p.Glu172Asp as OI-causative and highlights the value of extrapolating molecular signatures identified in multiomics studies to characterize novel genetic variants.