Biology subjects

Cuperlovic-Culf, M.

Publications and source records attributed to Cuperlovic-Culf, M..

12 recordsLinked to original sources

bioRxiv2025

Functional and metabolomic analyses of brown adipose tissue during cold-deacclimation reveal rapid adaptations in N-acetylated amino acid metabolism

Non-shivering thermogenesis (NST) in brown adipose tissue (BAT) is rapidly activated in cold environments and is an important thermoregulatory process. Despite the consensus that BAT is inactive under warm ambient temperatures, few studies have sought to examine the metabolic remodelling that occurs when recovering from the cold and re-acclimating to thermoneutral environments (28-32{degrees}C). To elucidate mitochondrial functional and structural aspects involved in BAT metabolic remodelling during cold deacclimation, we acclimated C57BL/6J mice to the cold (4{degrees}C) for 7 days, and subsequently transferred them to thermoneutrality (30{degrees}C) for 3 h, 12 h, 24 h, or 48 h. Comprehensive metabolic phenotyping analyses demonstrated elevated metabolic rates and high food intake during the cold acclimation period, which immediately decreased by [~]40% upon returning to thermoneutrality. High-resolution respirometry of saponin-permeabilized BAT revealed decreases in mitochondrial leak uncoupling by 24 h of cold deacclimation, which corresponded with gradual declines in mitochondrial protein content and UCP1 gene expression. Decreases in BAT mitochondrial content paralleled declines in protein content, as indicated by decreases in the mtDNA/nDNA ratio and mitochondrial surface area by 48 h of cold deacclimation. Metabolomic analysis of BAT from cold-acclimated mice and from mice deacclimated for 48 h at thermoneutrality revealed major changes in pathways related to amino acid metabolism, the tricarboxylic acid cycle (TCA), glutathione, and purine metabolism. Marked decreases in the abundance of N-acetylated amino acids in cold deacclimated mice corresponded with increased aminoacylase 1 (Acy1) expression. Together, these findings highlight the profound metabolic remodelling in BAT during thermogenesis and deactivation.

physiology↗

bioRxiv2025

Pairwise Causal Discovery in Biochemical Network: A Survey on Directionality Inference within Complex Networks from Stationary Observations

Metabolic networks map complex biochemical reactions within organisms, which is crucial for understanding cellular processes and metabolite flow. This study focuses on inferring the directionality of interactions in metabolomics networks. Given the challenge of using steady-state data, we benchmark various methods, including statistical scores and neural network approaches, on synthetic yet realistic biological models. Our findings highlight the relative success of a few methods in some cases where the interaction mechanism is known, whereas other methods show limited effectiveness.

systems biology↗

bioRxiv2025

Evaluation of network inference algorithms for derivation of metabolic relationships from lipidomic and metabolomic datasets

MotivationVarious methods have been proposed to construct metabolic networks from metabolomic data; however, small sample sizes, multiple confounding factors, the presence of indirect interactions as well as randomness in metabolic processes are of major concern. ResultsIn this study, we benchmark existing algorithms for creating correlation- and regression-based networks of changes in metabolite abundance and we evaluate their performance across different sample sizes of a generative model. Using standard interaction-level tests and network-scale analyses based on centrality scores, we assess how well these methods capture simulated metabolomic networks. Our findings reveal significant challenges in network inference and result interpretation, even when sample sizes are significant and data are the result of computer modeling of metabolic pathways. Despite these limitations, we demonstrate that correlation-based network inference can, to some extent, discriminate between two different metabolic states. This suggests potential utility in distinguishing overarching changes in metabolic processes but not direct pathways in different conditions. AvailabilityAll relevant data is provided at https://github.com/HosnaJabbari/metabolicRelationships Contactjabbari@ualberta.ca Supplementary informationSupplementary data are available at Bioinformatics Advances

bioinformatics↗

bioRxiv2024

Projection Statistics ProST Online statistical assessment of group separation in data projection analysis

MotivationUnsupervised data projection for the determination of trends in the data, visualization of multidimensional data in a reduced dimension space or feature space reduction through combination of data is a major step in data mining. Methods such as Principal Component Analysis or t-Distribution Stochastic Neighbor Embedding are regularly used as one of the first steps in computational biology or omics investigation. However, the significance of the separation of sample groups by these methods generally relies on visual assessment. User-friendly application for different projection methods, each focusing on distinct data properties, are needed as well as a rigorous method for statistical determination of the significance of separation of groups of interest in each dataset. ResultsWe present Projection STatistics (ProST), a user-friendly solution for data projection analysis providing three unsupervised (PCA, t-SNE and UMAP) and one supervised (LDA) approach. For each method we are including a novel statistical investigation of the significance of group separation with Mann-Whitney U-rank or t-test analysis as well as necessary preprocessing steps. ProST provides an unbiased, objective application of the determination of the significance of the separation of measurement groups through either linear or manifold projection analysis with methods ranging from a focus on the separation of points based on major variances or on point proximities based on distance. AvailabilityThe ProST software application is freely available at https://complimet.ca/shiny/ProST/ with source code provided on https://github.com/complimet/prost. Contactdanny.salem@nrc-cnrc.gc.ca or Miroslava.cuperlovic-culf@nrc-cnrc.gc.ca Supplementary informationSupplementary help pages are provided at https://complimet.ca/shiny/ProST/.

bioinformatics↗

bioRxiv2024

OPA1 mediates cardiac function and metabolism: in silico and in vivo evidence

OPA1 is an inner mitochondrial membrane protein that mediates diverse signaling processes. OPA1 is important for cardiac function and protects against cardiac insults such as ischemia reperfusion injury. We sought to further assess OPA1 in human and mouse cardiac pathologies, hypothesizing that OPA1 may also function in a protective manner in chronic heart failure. Bioinformatic analyses of histological and transcript data from the GTEx database indicated that OPA1 expression levels vary in the human heart, where elevated OPA1 transcript levels were correlated with fatty acid, branch chain amino acid and contractile gene signatures. To experimentally assess these correlations, mice with a 1.5-fold whole body OPA1 overexpression (OPA1-OE) were subjected to transverse aortic constriction surgery and displayed improved 2D and 4D cardiac functional parameters compared to WT mice. OPA1-OE mice had no induction of fibrotic transcript markers and displayed sustained transcript levels of fatty acid, branch chain amino acid and contractile markers. Maximal oxidative capacity was sustained in both WT and OPA1-OE cardiac myofibers post-TAC. These results further demonstrate the important role of OPA1 in mediating cardiac function and highlight protective signaling pathways.

biochemistry↗

bioRxiv2024

Imputation for Lipidomics and Metabolomics (ImpLiMet): Online optimization and method selection for missing data imputation

MotivationMissing values are often unavoidable in modern high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. Three missingness patterns have been conceptualized: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Each describes unique dependencies between the missing and observed data. As a result, the optimal imputation method for each dataset depends on the type of data, the cause of the missing data, and the nature of relationships between the missing and observed data. The challenge is to identify the optimal imputation solution for a given dataset. ResultsImpLiMet: is a user-friendly UI-platform that enables users to impute missing data using eight different methods. For the users dataset, ImpLiMet can suggest the optimal imputation solution through a grid search-based investigation of the error rate for imputation across three missingness data simulations. The effect of imputation can be visually assessed by histogram, kurtosis and skewness analyses, as well as principal component analysis (PCA) comparing the impact of the chosen imputation method on the distribution and overall behaviour of the data. Availability and implementationImpLiMet is freely available at https://complimet.ca/shiny/implimet/ with software accessible at https://github.com/complimet/ImpLiMet Contactsteffanyann.bennett@uottawa.ca and miroslava.cuperlovic-culf@nrc-cnrc.gca. Supplementary informationSupplementary data are available at Bioinformatics Advances online.

bioinformatics↗

bioRxiv2023

Multi-variate statistical and machine learning reveals the interplay between sex and age in antibody responses to de novo SARS-CoV-2 infection and vaccination

Prevention of negative COVID19 infection outcomes and infection/vaccine-acquired immunity is associated with the quality of antibody responses, whose variance by age and sex are poorly understood. Integrated, network approaches, identified sex and age effects in antibody responses and neutralization potential of de novo infection and vaccination throughout the Covid-19 pandemic. Cluster analysis found neutralization values followed SARS-CoV-2 specific receptor binding RIgG, spike SIgG and S and RIgA levels based on COVID19 status. Stochastic behavior tests and other analytical methods revealed sex differences only in persons <40y.o. Serum IgA antibody titers correlated with neutralization only in females 40-60y.o. Network analysis found males could improve IgA responses after vaccination dose 2, unlike >60y.o. females. Complex correlation analyses found vaccination induced less antibody isotype switching and neutralization in older persons, especially in females. Sex dependent antibody & neutralization behavior decayed fastest in older males and with vaccination. Such sex and age characterization by machine learning can direct studies integrating cell mediated responses to define yet elusive correlates of protection and inform age and sex precision-focused vaccine design.

immunology↗

bioRxiv2023

Mapping relationships between glutathione and SLC25 transporters in cancers using hybrid machine learning models

BackgroundAlterations of metabolism, including changes in mitochondrial and glutathione (GSH) metabolism, are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH metabolism, which we investigate in the context of cancer. Existing functional annotation approaches from machine (ML) or deep learning (DL) models based only on protein sequences are unable to annotate functions in biological contexts, meaning new approaches must be developed for this task. ResultsWe develop a flexible ML framework for functional annotation from diverse feature data. This hybrid approach leverages cancer cell line multi-omics data and other biological knowledge data as features, to uncover potential genes involved in mGSH metabolism and membrane transport in cancers. This framework achieves an average AUROC across functional annotation tasks of 0.900 and can be effectively applied to annotate a range of biological functions. For our application, classification models predict the known mGSH transporter SLC25A39 but not SLC25A40 as being highly probably related to GSH metabolism in cancers. SLC25A24 and the orphan SLC25A43 are also predicted to be associated with mGSH metabolism by this approach and structural analysis of these proteins reveal similarities in potential substrate binding regions to the binding residues of SLC25A39. ConclusionThese findings have implications for a better understanding of cancer cell metabolism and novel therapeutic targets with respect to GSH metabolism through potential novel functional annotations of genes. The hybrid ML framework proposed here can be applied to other biological function classifications or multi-omics datasets to generate hypotheses in various biological contexts. Code and a tutorial for generating models and predictions in this framework are available at: https://github.com/lkenn012/mGSH_cancerClassifiers.

bioinformatics↗

bioRxiv2022

DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design

Typical drug discovery and development processes are costly, time consuming and often biased by expert opinion. Aptamers are short, single-stranded oligonucleotides (RNA/DNA) that bind to target proteins and other types of biomolecules. Compared with small-molecule drugs, aptamers can bind to their targets with high affinity (binding strength) and specificity (uniquely interacting with the target only). The conventional development process for aptamers utilizes a manual process known as Systematic Evolution of Ligands by Exponential Enrichment (SELEX), which is costly, slow, dependent on library choice and often produces aptamers that are not optimized. To address these challenges, in this research, we create an intelligent approach, named DAPTEV, for generating and evolving aptamer sequences to support aptamer-based drug discovery and development. Using the COVID-19 spike protein as a target, our computational results suggest that DAPTEV is able to produce structurally complex aptamers with strong binding affinities. Author summaryCompared with small-molecule drugs, aptamer drugs are short RNAs/DNAs that can specifically bind to targets with high strength. With the interest of discovering novel aptamer drugs as an alternative to address the long-lasting COVID-19 pandemic, in this research, we developed an artificial intelligence (AI) framework for the in silico design of novel aptamer drugs that can prevent the SARS-CoV-2 virus from entering human cells. Our research is valuable as we explore a novel approach for the treatment of SARS-CoV-2 infection and the AI framework could be applied to address future health crises.

bioinformatics↗

bioRxiv2022

METAbolomics data Balancing with Over-sampling Al-gorithms (META-BOA): an online resource for addressing class imbalance

MotivationClass imbalance, or unequal sample sizes between classes, is an increasing concern in machine learning for metabolomic and lipidomic data mining, which can result in overfitting for the over-represented class. Numerous methods have been developed for handling class imbalance, but they are not readily accessible to users with limited computational experience. Moreover, there is no resource that enables users to easily evaluate the effect of different over-sampling algorithms. ResultsMETAbolomics data Balancing with Over-sampling Algorithms (META-BOA) is a web-based application that enables users to select between four different methods for class balancing, followed by data visualization and classification of the sample to observe the augmentation effects. META-BOA outputs a newly balanced dataset, generating additional samples in the minority class, according to the users choice of Synthetic Minority Over-sampling Technique (SMOTE), Borderline-SMOTE (BSMOTE), Adaptive Synthetic (ADASYN), or Random Over-Sampling Examples (ROSE). META-BOA further displays both principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) visualization of data pre- and post-over-sampling. Random forest classification is utilized to compare sample classification in both the original and balanced datasets, enabling users to select the most appropriate method for their analyses. Availability and implementationMETA-BOA is available at https://complimet.ca/meta-boa. Supplementary InformationSupplementary material is available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

YeastNet: Deep Learning Enabled Accurate Segmentation of Budding Yeast Cells in Bright-field Microscopy

Accurate and efficient segmentation of live-cell images is critical in maximising data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labelling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub. https://github.com/kaernlab/YeastNet

bioinformatics↗

bioRxiv2020

NMR Analysis of the Correlation of Metabolic Changes in Blood and Cerebrospinal Fluid in Alzheimer Model Male and Female Mice

The development of effective therapies as well as early, molecular diagnosis of Alzheimers disease is impeded by the lack of understanding of the underlying pathological mechanisms. Metabolomics studies of body fluids as well as brain tissues have shown major changes in metabolic profiles of Alzheimers patients. However, with analysis performed at the late stages of the disease it is not possible to distinguish causes and consequence. The mouse model APP/PS1 expresses a mutant amyloid precursor protein resulting in early Amyloid {beta} (A{beta}) accumulation as well as many resulting physiological changes including changes in metabolic profile and metabolism. Analysis of metabolic profile of cerebrospinal fluid (CSF) and blood of APP/PS1 mouse model can provide information about metabolic changes in these body fluids caused by A{beta} accumulation. Using our novel method for analysis of correlation and mathematical ranking of significant correlations between metabolites in CSF and blood, we have explored changes in metabolite correlation and connectedness in APP/PS1 and wild type mice. Metabolites concentration and correlation changes in CSF, blood and across the blood brain barrier determined in this work are affected by the production of amyloid plaque. Metabolite changes observed in the APP/PS1 mouse model are the response to the mutation causing plaque formation, not the cause for the plaque suggesting that they are less relevant in the context of early treatment and prevention then the metabolic changes observed only in humans.

neuroscience↗