LATEST RESEARCH

Parkinson’s disease (PD) is a progressive neurodegenerative disease, causing loss of motor and nonmotor function. Diagnosis is based on clinical symptoms that do not develop until late in the disease progression, at which point the majority of the patients’ dopaminergic neurons are already destroyed. While many PD cases are idiopathic, hereditable genetic risks have been identified, including mutations in the gene for LRRK2, a multidomain kinase with roles in autophagy, mitochondrial function, transcription, molecular structural integrity, the endo-lysosomal system, and the immune response. A definitive PD diagnosis can only be made post-mortem, and no noninvasive or blood-based disease biomarkers are currently available. Alterations in metabolites have been identified in PD patients, suggesting that metabolomics may hold promise for PD diagnostic tools. In this study, we sought to identify metabolic markers of PD in plasma. Using a 1H-13C heteronuclear single quantum coherence spectroscopy (HSQC) NMR spectroscopy metabolomics platform coupled with machine learning (ML), we measured plasma metabolites from approximately age/sex-matched PD patients with G2019S LRRK2 mutations and non-PD controls. Based on the differential level of known and unknown metabolites, we were able to build a ML model and develop a Biomarker of Response (BoR) score, which classified male LRRK2 PD patients with 79.7% accuracy, 81.3% sensitivity, and 78.6% specificity. The high accuracy of the BoR score suggests that the metabolomics/ML workflow described here could be further utilized in the development of a confirmatory diagnostic for PD in larger patient cohorts. A diagnostic assay for PD will aid clinicians and their patients to quickly move toward a definitive diagnosis, and ultimately empower future clinical trials and treatment options.

Gadolinium is a paramagnetic relaxation enhancement (PRE) agent that accelerates the relaxation of metabolite nuclei. In this study, we noted the ability of gadolinium to improve the sensitivity of two-dimensional, non-uniform sampled NMR spectral data collected from metabolomics samples. In time-equivalent experiments, the addition of gadolinium increased the mean signal intensity measurement and the signal-to-noise ratio for metabolite resonances in both standard and plasma samples. Gadolinium led to highly linear intensity measurements that correlated with metabolite concentrations. In the presence of gadolinium, we were able to detect a broad array of metabolites with a lower limit of detection and quantification in the low micromolar range. We also observed an increase in the repeatability of intensity measurements upon the addition of gadolinium. The results of this study suggest that the addition of a gadolinium-based PRE agent to metabolite samples can improve NMR-based metabolomics.

Breast cancer is the most common cancer in women worldwide and despite improved treatment strategies, it persists as the second leading cause of death of women globally. Overall prognosis drops drastically once the cancer has metastasized, which is also associated with resistance to therapy. The evolution from a localized breast cancer to metastatic disease is complex and multifactorial. Metabolic reprogramming is a pre-requisite for this transition. In this graphical review, we provide an overview of altered metabolic pathways observed in metastatic breast cancer (mBC) and detail how metabolite biomarkers could serve as a novel class of precision medicine tools to improve the diagnosis, monitoring, and treatment of mBC.

The presentation, led by Olaris Metabolite Scientist Dr. Chandrashekhar Honrao, highlights the use of Olaris’ BoR platform to identify metabolic signatures correlated with overall survival and to predict response or resistance to treatment in patients with advanced GIST.  “Targeted treatments have revolutionized cancer care, especially for GIST. However, due to patient heterogeneity it remains challenging to know who will benefit from one therapy over another. This study is particularly exciting as we were able to demonstrate and validate in an independent set of patient samples that metabolite biomarkers either before treatment or during treatment can identify patients with favorable clinical outcomes” said Dr. Elizabeth O’Day, CEO and Founder of Olaris.

Dr. Elizabeth O'Day and Leslie Hoyt's paper on discusses the opportunity for NUS NMR metabolite in-vitro diagnostics- key tools that can accelerate the goals of precision medicine. 

The ease of sample preparation, quantification of metabolite levels for overlapping peaks often found in patient samples, and reproducibility afforded by NMR can be used to develop metabolite based diagnostics with NUS. This may serve to reduce the long acquisition time associated with NMR.  

Olaris' paper was recently published in the journal Metabolites. The paper demonstrates the reproducibility of non-uniform sampling 2D NMR in metabolomics. 

Olaris' will be presenting a poster and giving a talk at the ENC! The Experimental NMR Conference features innovative research in NMR spectroscopy and Olaris is very excited to share our recent studies on the power of NUS 13C 1H HSQC NMR. See the poster below. 

Olaris’ metabolite profiling platform and machine learning algorithms have the potential to accurately indicate whether HER2-positive metastatic breast cancer patients will respond to trastuzumab therapy.

Research Dr. Zhang contributed to was published in the Journal of Proteome Research. They developed a method to better identify lipid molecules through 2D NMR and COLMAR Lipids Web Server. 

Olaris is thrilled to present at the SABCS! We are excited to learn from the world's top breast cancer oncologists and share the results of our recent study, "Pretreatment serum metabolome predicts PFS in first-line trastuzumab-treated metastatic breast cancer." Learn more about our findings here. 

"Accurate and Efficient Determination of Unknown Metabolites in Metabolomics by NMR-Based Molecular Motif Identification" discusses the new SUMMIT method to identify chemical classes of metabolites, a critical tool in understanding patient health and disease. Read the paper here!

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