Bioenergetic Effects of Polycyclic Aromatic Hydrocarbon Resistance Manifest Later in Life in Offspring of <i>Fundulus heteroclitus</i> from the Elizabeth River

Shifts in key physiological processes can confer resistance to chemical pollutants. However, these adaptations may come with certain trade-offs, such as altered energy metabolic processes, as evident in Atlantic killifish (Fundulus heteroclitus) in Virginia’s Elizabeth River (ER) that have evolved resistance to polycyclic aromatic hydrocarbons (PAHs). We seek to understand the bioenergetic costs of PAH resistance among subpopulations of Atlantic killifish with differing contamination levels in order to examine how these changes manifest across multiple life stages and how these costs might be exacerbated by additional stressors. Bioenergetics data revealed differences in metabolic rates between offspring of PAH-resistant fish and reference fish were absent or minimal in both the embryo and larval stages but pronounced at the juvenile life stage, suggesting that bioenergetic changes in pollution-adapted killifish manifest later in life. We also provide evidence that killifish from remediated sites are more sensitive to PAH exposure than killifish from nonremediated sites, suggesting loss of PAH tolerance following relaxed selection. Collectively, our data suggest that the fitness consequences associated with evolved resistance to anthropogenic stressors may manifest differently over time and depend on the magnitude of the selection pressure. This information can be valuable in effective risk and remediation assessments as well as in broadening our understanding of species responses to environmental change

The pillars of transdisciplinary Ph.D. training in the sustainability sciences.

The pillars of transdisciplinary Ph.D. training in the sustainability sciences.</p

High-Throughput Tissue Bioenergetics Analysis Reveals Identical Metabolic Allometric Scaling for Teleost Hearts and Whole Organisms

<div><p>Organismal metabolic rate, a fundamental metric in biology, demonstrates an allometric scaling relationship with body size. Fractal-like vascular distribution networks of biological systems are proposed to underlie metabolic rate allometric scaling laws from individual organisms to cells, mitochondria, and enzymes. Tissue-specific metabolic scaling is notably absent from this paradigm. In the current study, metabolic scaling relationships of hearts and brains with body size were examined by improving on a high-throughput whole-organ oxygen consumption rate (OCR) analysis method in five biomedically and environmentally relevant teleost model species. Tissue-specific metabolic scaling was compared with organismal routine metabolism (RMO₂), which was measured using whole organismal respirometry. Basal heart OCR and organismal RMO₂ scaled identically with body mass in a species-specific fashion across all five species tested. However, organismal maximum metabolic rates (MMO₂) and pharmacologically-induced maximum cardiac metabolic rates in zebrafish <i>Danio rerio</i> did not show a similar relationship with body mass. Brain metabolic rates did not scale with body size. The identical allometric scaling of heart and organismal metabolic rates with body size suggests that hearts, the power generator of an organism’s vascular distribution network, might be crucial in determining teleost metabolic rate scaling under routine conditions. Furthermore, these findings indicate the possibility of measuring heart OCR utilizing the high-throughput approach presented here as a proxy for organismal metabolic rate—a useful metric in characterizing organismal fitness. In addition to heart and brain OCR, the current approach was also used to measure whole liver OCR, partition cardiac mitochondrial bioenergetic parameters using pharmacological agents, and estimate heart and brain glycolytic rates. This high-throughput whole-organ bioenergetic analysis method has important applications in toxicology, evolutionary physiology, and biomedical sciences, particularly in the context of investigating pathogenesis of mitochondrial diseases.</p></div

Heart metabolic scaling relationship with heart size.

<p>Log of basal oxygen consumption rates (OCR) for isolated hearts is plotted against log of isolated heart mass for (a) all five species <i>(n = 130)</i>, (b) <i>Danio rerio (n = 48)</i> and (c) <i>Fundulus heteroclitus (n = 28)</i>. Non-linear regressions were conducted across each data set based on least-squares test. <i>b</i> is the scaling exponent. Standard error, 95% confidence interval and <i>R</i>^<i>2</i> values and the scaling exponent <i>b</i> for individual species are included in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137710#pone.0137710.s008" target="_blank">S5 Table</a>.</p

Metabolic profiles of <i>Danio rerio</i> heart and brain tissues.

<p>Oxygen consumption rate (OCR) calculated per gram of tissues is plotted against extracellular acidification rates (ECAR) <i>(n = 5)</i>. Measurements recorded in un-buffered solution are shown in square symbols and triangles represent values post-injection of buffering components of the Ringer’s solution. Values are expressed as means ± S.E.M.</p

Slopes (exponent <i>b</i>) and <i>R</i>^<i>2</i> values for log oxygen consumption rates of whole organisms, hearts and brains <i>vs</i> log body mass for all five species combined and for each individual species¹.

<p>¹See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137710#pone.0137710.s006" target="_blank">S3 Table</a> for Y intercept values, 95% confidence intervals and sample sizes.</p><p>^aExponent <i>b</i> for hearts or brains are statistically similar (<i>P>0</i>.<i>05</i>) to that of whole organisms (<i>F (DFn</i>, <i>DFd</i>) and <i>P</i> values are in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137710#pone.0137710.s007" target="_blank">S4 Table</a>).</p><p>Slopes (exponent <i>b</i>) and <i>R</i>^<i>2</i> values for log oxygen consumption rates of whole organisms, hearts and brains <i>vs</i> log body mass for all five species combined and for each individual species<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137710#t002fn001" target="_blank">¹</a>.</p

Slopes and Y intercepts for log₁₀ oxygen consumption rates for hearts plotted against log₁₀ heart mass for all five species and for each <i>Danio rerio and Fundulus heteroclitus</i>.

<p>^aTotal number of fish tested is obtained by combining number of samples in analyzed and outliers rows.</p><p>Slopes and Y intercepts for log₁₀ oxygen consumption rates for hearts plotted against log₁₀ heart mass for all five species and for each <i>Danio rerio and Fundulus heteroclitus</i>.</p

Brain metabolic scaling relationship with size.

<p>Log of basal oxygen consumption rate (OCR) for isolated brains is plotted against (a) log of isolated brain mass and (b) log of body size for all five species <i>(n = 130)</i>. Non-linear straight line and segmental regressions were conducted across each data set based on least-squares test. Filled symbols represent outliers determined by the linear regression model.</p

Metabolic partitioning of <i>Danio rerio</i> and <i>Fundulus heteroclitus</i> heart tissue oxygen consumption rate (OCR).

<p>(a) Conceptual diagram depicting use of FCCP and antimycin A + rotenone and the metabolic parameters calculated from changes in oxygen consumption. (b) OCR following exposure to FCCP and antimycin A + rotenone (Ant + Rot). (c) Total OCR due to mitochondrial respiration, total mitochondrial capacity and mitochondrial reserve capacity calculated based on FCCP and antimycin A + rotenone. ‘a’ denotes statistical significance compared to <i>D</i>. <i>rerio</i> basal heart OCR and ‘b’ denotes statistical significance compared to <i>F</i>. <i>heteroclitus</i> basal heart OCR. Statistical significance between <i>D</i>. <i>rerio</i> and <i>F</i>. <i>heteroclitus</i> for a given measurement is denoted by * (Two-Way ANOVA followed by Tukey’s post-hoc test to correct for multiple comparisons; <i>P<0</i>.<i>05</i>). Values are expressed as means ± S.E.M.</p

sj-docx-1-cjk-10.1177_20543581231199013 – Supplemental material for Prevalence of Chronic Kidney Disease of Uncertain Etiology Within Selected Farming Communities in Rural Sri Lanka

Supplemental material, sj-docx-1-cjk-10.1177_20543581231199013 for Prevalence of Chronic Kidney Disease of Uncertain Etiology Within Selected Farming Communities in Rural Sri Lanka by E. M. D. V. Ekanayake, P. Mangala C. S. De Silva, T. D. K. S. C. Gunasekara, W. A. K. G. Thakshila, S. D. Gunarathna, R. A. I. Pinipa, Sudheera Jayasinghe, E. P. S. Chandana, E. S. Wijewickrama and Nishad Jayasundara in Canadian Journal of Kidney Health and Disease</p