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Direct observation of evolution in response to natural environmental change can resolve fundamental questions about adaptation, including its pace, temporal dynamics, and underlying phenotypic and genomic architecture. We tracked the evolution of fitness-associated phenotypes and allele frequencies genome-wide in 10 replicate field populations of Drosophila melanogaster over 10 generations from summer to late fall. Adaptation was evident over each sampling interval (one to four generations), with exceptionally rapid phenotypic adaptation and large allele frequency shifts at many independent loci. The direction and basis of the adaptive response shifted repeatedly over time, consistent with the action of strong and rapidly fluctuating selection. Overall, we found clear phenotypic and genomic evidence of adaptive tracking occurring contemporaneously with environmental change, thus demonstrating the temporally dynamic nature of adaptation.

Evolve-and-resequence (E+R) experiments leverage next-generation sequencing technology to track the allele frequency dynamics of populations as they evolve. While previous work has shown that adaptive alleles can be detected by comparing frequency trajectories from many replicate populations, this power comes at the expense of high-coverage (>100x) sequencing of many pooled samples, which can be cost-prohibitive. Here, we show that accurate estimates of allele frequencies can be achieved with very shallow sequencing depths (

Population genomic data has revealed patterns of genetic variation associated with adaptation in many taxa. Yet understanding the adaptive process that drives such patterns is challenging; it requires disentangling the ecological agents of selection, determining the relevant timescales over which evolution occurs, and elucidating the genetic architecture of adaptation. Doing so for the adaptation of hosts to their microbiome is of particular interest with growing recognition of the importance and complexity of host-microbe interactions. Here, we track the pace and genomic architecture of adaptation to an experimental microbiome manipulation in replicate populations of Drosophila melanogaster in field mesocosms. Shifts in microbiome composition altered population dynamics and led to divergence between treatments in allele frequencies, with regions showing strong divergence found on all chromosomes. Moreover, at divergent loci previously associated with adaptation across natural populations, we found that the more common allele in fly populations experimentally enriched for a certain microbial group was also more common in natural populations with high relative abundance of that microbial group. These results suggest that microbiomes may be an agent of selection that shapes the pattern and process of adaptation and, more broadly, that variation in a single ecological factor within a complex environment can drive rapid, polygenic adaptation over short timescales.

Seasonality is a critically important aspect of environmental variability, and strongly shapes all aspects of life for organisms living in highly seasonal environments. Seasonality has played a key role in generating biodiversity, and has driven the evolution of extreme physiological adaptations and behaviors such as migration and hibernation. Fluctuating selection pressures on survival and fecundity between summer and winter provide a complex selective landscape, which can be met by a combination of three outcomes of adaptive evolution: genetic polymorphism, phenotypic plasticity, and bet-hedging. Here, we have identified four important research questions with the goal of advancing our understanding of evolutionary impacts of seasonality. First, we ask how characteristics of environments and species will determine which adaptive response occurs. Relevant characteristics include costs and limits of plasticity, predictability, and reliability of cues, and grain of environmental variation relative to generation time. A second important question is how phenological shifts will amplify or ameliorate selection on physiological hardiness. Shifts in phenology can preserve the thermal niche despite shifts in climate, but may fail to completely conserve the niche or may even expose life stages to conditions that cause mortality. Considering distinct environmental sensitivities of life history stages will be key to refining models that forecast susceptibility to climate change. Third, we must identify critical physiological phenotypes that underlie seasonal adaptation and work toward understanding the genetic architectures of these responses. These architectures are key for predicting evolutionary responses. Pleiotropic genes that regulate multiple responses to changing seasons may facilitate coordination among functionally related traits, or conversely may constrain the expression of optimal phenotypes. Finally, we must advance our understanding of how changes in seasonal fluctuations are impacting ecological interaction networks. We should move beyond simple dyadic interactions, such as predator prey dynamics, and understand how these interactions scale up to affect ecological interaction networks. As global climate change alters many aspects of seasonal variability, including extreme events and changes in mean conditions, organisms must respond appropriately or go extinct. The outcome of adaptation to seasonality will determine responses to climate change.

The congenital sideroblastic anemias (CSAs) can be caused by primary defects in mitochondrial iron-sulfur (Fe-S) cluster biogenesis. HSCB (heat shock cognate B), which encodes a mitochondrial cochaperone, also known as HSC20 (heat shock cognate protein 20), is the partner of mitochondrial heat shock protein A9 (HSPA9). Together with glutaredoxin 5 (GLRX5), HSCB and HSPA9 facilitate the transfer of nascent 2-iron, 2-sulfur clusters to recipient mitochondrial proteins. Mutations in both HSPA9 and GLRX5 have previously been associated with CSA. Therefore, we hypothesized that mutations in HSCB could also cause CSA. We screened patients with genetically undefined CSA and identified a frameshift mutation and a rare promoter variant in HSCB in a female patient with non-syndromic CSA. We found that HSCB expression was decreased in patient-derived fibroblasts and K562 erythroleukemia cells engineered to have the patient-specific promoter variant. Furthermore, gene knockdown and deletion experiments performed in K562 cells, zebrafish, and mice demonstrate that loss of HSCB results in impaired Fe-S cluster biogenesis, a defect in RBC hemoglobinization, and the development of siderocytes and more broadly perturbs hematopoiesis in vivo. These results further affirm the involvement of Fe-S cluster biogenesis in erythropoiesis and hematopoiesis and define HSCB as a CSA gene., Introduction The congenital sideroblastic anemias (CSAs) are a group of uncommon inherited blood disorders characterized by abnormal iron accumulation in the mitochondria of erythroid cells. As the normal location of [...]

Mice have been essential for distinguishing the role of hepcidin in iron homeostasis. Currently, investigators monitor levels of murine hepatic hepcidin-1 mRNA as a surrogate marker for the bioactive hepcidin protein itself. Here, we describe and validate a competitive, enzyme-linked immunosorbent assay that quantifies hepcidin-1 in mouse serum and urine. The assay exhibits a biologically relevant lower limit of detection, high precision, and excellent linearity and recovery. We also demonstrate correlation between serum and urine hepcidin-1 values and validate the competitive enzyme-linked immunosorbent assay by analyzing plasma hepcidin response of mice to physiological challenges, including iron deficiency, iron overload, acute blood loss, and inflammation. Furthermore, we analyze multiple murine genetic models of iron dysregulation, including β-thalassemia intermedia (Hbb(th3/+)), hereditary hemochromatosis (Hfe(-/-), Hjv(-/-), and Tfr2(Y245X/Y245X)), hypotransferrinemia (Trf(hpx/hpx)), heterozygous transferrin receptor 1 deficiency (Tfrc(+/-)) and iron refractory iron deficiency anemia (Tmprss6(-/-) and Tmprss6(hem8/hem8)). Novel compound iron metabolism mutants were also phenotypically characterized here for the first time. We demonstrate that serum hepcidin concentrations correlate with liver hepcidin mRNA expression, transferrin saturation and non-heme liver iron. In some circumstances, serum hepcidin-1 more accurately predicts iron parameters than hepcidin mRNA, and distinguishes smaller, statistically significant differences between experimental groups.