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Mitchell, J. D.

Publications and source records attributed to Mitchell, J. D..

5 recordsLinked to original sources

Non-convergent aridity adaptation despite pervasive linked selection in Eucalyptus

Whether independent lineages evolve similar genetic solutions when faced with the same environmental pressure is central to understanding how repeatable and predictable adaptation is. Answering this question is increasingly urgent as climate change intensifies drought and aridity worldwide, a shared pressure to which many species must independently adapt. Here we characterised genomic adaptation in Eucalyptus across three independent species pairs, each comprising two closely related lineages that have diverged from wetter into drier environments. Across all pairs we found concordant genome-wide landscapes of diversity and divergence, and strong evidence for pervasive linked selection. Because linked selection acts most strongly in the same conserved features of the genome, this shared architecture could concentrate differentiation in the same regions across lineages, creating an appearance of convergent adaptation. Despite this, the genomic regions associated with the transition to drier environments were largely non-convergent, with almost no sharing of outlier loci among pairs, demonstrating that each lineage adapted through a largely independent genetic route. Some convergence was instead evident at the level of biological function, indicating that lineages reached similar functional outcomes using different genes. We further identified large genomic islands of differentiation, which were dominated by the signature of linked selection rather than elevated divergence, though several harboured candidate genes within the drought and abscisic acid regulatory networks. Together, our results indicate that adaptation to aridity in Eucalyptus is complex and polygenic, and largely unpredictable at the level of individual loci.

evolutionary biology↗

Identification Drug Targets for Oxaliplatin-Induced Cardiotoxicity without affecting cancer treatment through Inter Variability Cross-Correlation Analysis (IVCCA)

The successful treatment of side effects of chemotherapy faces two major limitations: the need to avoid interfering with pathways essential for the cancer-destroying effects of the chemotherapy drug, and the need to avoid helping tumor progression through cancer promoting cellular pathways. To address these questions and identify new pathways and targets that satisfy these limitations, we have developed the bioinformatics tool Inter Variability Cross-Correlation Analysis (IVCCA). This tool calculates the cross-correlation of differentially expressed genes, analyzes their clusters, and compares them across a vast number of known pathways to identify the most relevant target(s). To demonstrate the utility of IVCCA, we applied this platform to RNA-seq data obtained from the hearts of the animal models with oxaliplatin-induced CTX. RNA-seq of the heart tissue from oxaliplatin treated mice identified 1744 differentially expressed genes with False Discovery Rate (FDR) less than 0.05 and fold change above 1.5 across nine samples. We compared the results against traditional gene enrichment analysis methods, revealing that IVCCA identified additional pathways potentially involved in CTX beyond those detected by conventional approaches. The newly identified pathways such as energy metabolism and several others represent promising target for therapeutic intervention against CTX, while preserving the efficacy of the chemotherapy treatment and avoiding tumor proliferation. Targeting these pathways is expected to mitigate the damaging effects of chemotherapy on cardiac tissues and improve patient outcomes by reducing the incidence of heart failure and other cardiovascular complications, ultimately enabling patients to complete their full course of chemotherapy with improved quality of life and survival rates.

bioinformatics↗

Oxaliplatin-induced cardiotoxicity in mice is connected to the changes in energy metabolism in the heart tissue

Oxaliplatin is a platinum-based alkylating chemotherapeutic agent used for cancer treatment. At high cumulative dosage, the negative effect of oxaliplatin on the heart becomes evident and is linked to a growing number of clinical reports. The aim of this study was to determine how chronic oxaliplatin treatment causes the changes in energy-related metabolic activity in the heart that leads to cardiotoxicity and heart damage in mice. C57BL/6 male mice were treated with a human equivalent dosage of intraperitoneal oxaliplatin (0 and 10 mg/kg) once a week for eight weeks. During the treatment, mice were followed for physiological parameters, ECG, histology and RNA sequencing of the heart. We identified that oxaliplatin induces strong changes in the heart and affects the hearts energy-related metabolic profile. Histological post-mortem evaluation identified focal myocardial necrosis infiltrated with a small number of associated neutrophils. Accumulated doses of oxaliplatin led to significant changes in gene expression related to energy related metabolic pathways including fatty acid (FA) oxidation, amino acid metabolism, glycolysis, electron transport chain, and NAD synthesis pathway. At high accumulative doses of oxaliplatin, the heart shifts its metabolism from FAs to glycolysis and increases lactate production. It also leads to strong overexpression of genes in NAD synthesis pathways such as Nmrk2. Changes in gene expression associated with energy metabolic pathways can be used to develop diagnostic methods to detect oxaliplatin-induced cardiotoxicity early on as well as therapy to compensate for the energy deficit in the heart to prevent heart damage. Significance StatementThis study uncovers the detrimental impact of chronic oxaliplatin treatment on heart metabolism in mice, linking high accumulative dosages to cardiotoxicity and heart damage. By identifying significant changes in gene expression related to energy metabolic pathways, the findings pave the way for the development of diagnostic methods to detect oxaliplatin-induced cardiotoxicity at an early stage. Furthermore, these insights may inform the creation of therapies that compensate for the energy deficit in the heart, ultimately preventing heart damage and improving patient outcomes in cancer treatment.

pharmacology and toxicology↗

MSCquartets 1.0: Quartet methods for species trees and networks under the multispecies coalescent model in R

MSCquartets is an R package for species tree hypothesis testing, inference of species trees, and inference of species networks under the Multispecies Coalescent model of incomplete lineage sorting. Input for these analyses are collections of metric or topological locus trees which are then summarized by the quartets displayed on them. Results of hypothesis tests at user-supplied levels are displayed in a simplex plot by color-coded points. The package includes the QDC and WQDC algorithms for topological and metric species tree inference, and the NANUQ algorithm for level-1 topological species network inference, all of which give statistically consistent estimators under the model.

bioinformatics↗

Gene tree discord, simplex plots, and statistical tests under the coalescent

AO_SCPLOWBSTRACTC_SCPLOWA simple graphical device, the simplex plot of quartet concordance factors, is introduced to aid in the exploration of a collection of gene trees on a common set of taxa. A single plot summarizes all gene tree discord, and allows for visual comparison to the expected discord from the multispecies coalescent model (MSC) of incomplete lineage sorting on a species tree. A formal statistical procedure is described that can quantify the deviation from expectation for each subset of four taxa, suggesting when the data is not in accord with the MSC, and thus either gene tree inference error is substantial or a more complex model such as that on a network may be required. If the collection of gene trees appears to be in accord with the MSC, the plots may reveal when substantial incomplete lineage sorting is present and coalescent based species tree inference is preferred over concatenation approaches. Applications to both simulated and empirical multilocus data sets illustrate the insights provided.

evolutionary biology↗