Biology subjects

Wijesinghe, D.

Publications and source records attributed to Wijesinghe, D..

3 recordsLinked to original sources

bioRxiv2025

Lifespan Trajectories of the Brains Functional Complexity Characterized by Multiscale Sample Entropy

Resting state functional magnetic resonance imaging (rs-fMRI) is a widely used imaging modality that can capture spontaneous neural activity of the brain. The human brain is a complex system, and emerging evidence suggests that the complexity of neural activity may serve as an index of the brains capacity of information processing. In this study we used multiscale sample entropy (MSE) to analyze the complexity of rs-fMRI timeseries of 504 healthy subjects between the age of 6 to 85 years. We constructed the global and regional trajectories of brains functional complexity over the lifespan and analyzed its correlation with executive function. We observed a nonlinear trajectory of fMRI-complexity over the lifespan with a peak age occurring at 23 (95% CI 21.27, 26.38 years) years of age. Males reach the peak age of complexity at 26 years (95% CI 19.95, 33.14 years) whereas females at 23 (95% CI 20.16, 29.18 years). We found significant correlations between complexity and Number-Letter Switching of Trail Making Test in parietal and medial temporal lobes while Inhibition and Inhibition/Switching of Color Word Interference Test also revealed a significant negative correlation with rs-fMRI complexity in lateral and medial frontal cortex. These results help to understand behavior of fMRI-complexity with aging and reveals association with executive function of the brain. As a non-invasive biomarker, fMRI-complexity could provide novel approach to understand information processing capacity in the brain and deficits thereof in illness.

neuroscience↗

bioRxiv2019

Metabolic Modulation Predicts Heart Failure Tests Performance

Cardiopulmonary testing (CPET) and biomarkers such as NT-proBNP, Galectin-3, and C-reactive protein (CRP) have shown great promise for characterizing the heart failure (HF) phenotypes. However, the underlying metabolic changes that cause, predict and accompany changes in these parameters are not well known. In depth knowledge of these metabolic changes has the ability to provide new insights towards the clinical management of HF as well as providing better biomarkers for the assessment of disease progression and measurement of success from clinical interventions. To address this knowledge gap, we undertook a deep metabolomic and lipidomic phenotyping of a cohort of HF patients and utilized Multiple Regression Analysis (MRA) to identify associations between the patients plasma metabolome and the lipidome to parameters of CPET and the above mentioned HF biomarkers (HFBio). A total of 49 subjects were submitted to CPET and HFBio evaluation with deep metabolomic and lipidomic profiling of the plasma. Stepwise MRA and Standard Least Squares methods were used to determine models of best fit. Metabolic Pathway Analysis was utilized to detect what metabolites were over-represented and significantly enriched, and Metabolite Set Enrichment Analysis was used to explore pre-existing biological knowledge from studies of human metabolism. The study cohort was predominantly males of African American decent, presenting a high frequency of diabetes, hyperlipidemia, and hypertension with a low mean left ventricular ejection fraction and a high left ventricular end-diastolic and end-systolic volume. VE/VCO2 and Peak VO2 (CV=0.07 and 0.08, respectively) showed the best metabolic predictive model for test performance, while CRP and NT-ProBNP (CV=0.31 and 0.29, respectively) were the least fitted models. Aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP) were revealed to be the most relevant pathways. Principal related diseases were brain dysfunction and enzyme deficiencies associated with lactic acidosis. Considering the modulations in HF poor test performance, our results indicate an overall profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction. Overall, the insights resulting from this studys MRA coincides with what has previously been discussed in parts in existing literature thereby confirming the validity of our findings while at the same time thoroughly characterizing the metabolic underpinning of CPET and HFBio results.

biochemistry↗

bioRxiv2018

Determining the Quantitative Principles of T Cell Response to Antigenic Disparity: The Case of Stem Cell Transplant Donor-Recipient Exome Wide Mismatching and HLA Bound Alloreactive Peptides

Alloreactivity compromising clinical outcomes in stem cell transplantation is observed despite HLA matching of donors and recipients. This has its origin in the variation between the exomes of the two, which provides the basis for minor histocompatibility antigens (mHA). The mHA presented on the HLA class I and II molecules and the ensuing T cell response to these antigens results in graft versus host disease. In this paper, results of a whole exome sequencing study are presented, with resulting alloreactive polymorphic peptides and their HLA class I and HLA class II (DRB1) binding affinity quantified. Large libraries of potentially alloreactive recipient peptides binding both sets of molecules were identified, with HLA-DRB1 presenting an order of magnitude greater number of peptides. These results are used to develop a quantitative framework to understand the immunobiology of transplantation. A tensor-based approach is used to derive the equations needed to determine the alloreactive donor T cell response from the mHA-HLA binding affinity and protein expression data. This approach may be used in future studies to simulate the magnitude of expected donor T cell response and risk for alloreactive complications in HLA matched or mismatched hematopoietic cell and solid organ transplantation.

immunology↗