Environment dependence of the expression of mutational load and species' range limits.
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ABSTRACT:
SUBMITTER: Perrier A
PROVIDER: S-EPMC9314787 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Using functional traits to explain species' range limits is a promising approach in functional biogeography. It replaces the idiosyncrasy of species-specific climate ranges with a generic trait-based predictive framework. In addition, it has the potential to shed light on specific filter mechanisms creating large-scale vegetation patterns. However, its application to a continental flora, spanning large climate gradients, has been hampered by a lack of trait data. Here, we explore whether five key plant functional traits (seed mass, wood density, specific leaf area (SLA), maximum height, and longevity of a tree)--indicative of life history, mechanical, and physiological adaptations--explain the climate ranges of 250 North American tree species distributed from the boreal to the subtropics. Although the relationship between traits and the median climate across a species range is weak, quantile regressions revealed strong effects on range limits. Wood density and seed mass were strongly related to the lower but not upper temperature range limits of species. Maximum height affects the species range limits in both dry and humid climates, whereas SLA and longevity do not show clear relationships. These results allow the definition and delineation of climatic "no-go areas" for North American tree species based on key traits. As some of these key traits serve as important parameters in recent vegetation models, the implementation of trait-based climatic constraints has the potential to predict both range shifts and ecosystem consequences on a more functional basis. Moreover, for future trait-based vegetation models our results provide a benchmark for model evaluation.
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Project description:BackgroundCarotid body tumor (CBT) is a rare neoplasm arising from paraganglion located near the bifurcation of the carotid artery. There is great intra-tumor heterogeneity, and CBT development could be associated with both germline and somatic allelic variants. Studies on the molecular genetics of CBT are limited, and the molecular mechanisms of its pathogenesis are not fully understood. This work is focused on the estimation of mutational load (ML) in CBT.MethodsUsing the NextSeq 500 platform, we performed exome sequencing of tumors with matched lymph node tissues and peripheral blood obtained from six patients with CBT. To obtain reliable results in tumors with low ML, we developed and successfully applied a complex approach for the analysis of sequencing data. ML was evaluated as the number of somatic variants per megabase (Mb) of the target regions covered by the Illumina TruSeq Exome Library Prep Kit.ResultsThe ML in CBT varied in the range of 0.09-0.28/Mb. Additionally, we identified several pathogenic/likely pathogenic somatic and germline allelic variants across six patients studied (including TP53 variants).ConclusionsUsing the developed approach, we estimated the ML in CBT, which is much lower than in common malignant tumors. Identified variants in known paraganglioma/pheochromocytoma-causative genes and novel genes could be associated with the pathogenesis of CBT. The obtained results expand our knowledge of the mutation process in CBT as well as the biology of tumor development.
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Project description:Gene-environment interactions have the potential to shed light on biological processes leading to disease and to improve the accuracy of epidemiological risk models. However, relatively few such interactions have yet been confirmed. In part this is because genetic markers such as tag SNPs are usually studied, rather than the causal variants themselves. Previous work has shown that this leads to substantial loss of power and increased sample size when gene and environment are independent. However, dependence between gene and environment can arise in several ways including mediation, pleiotropy, and confounding, and several examples of gene-environment interaction under gene-environment dependence have recently been published. Here we show that under gene-environment dependence, a statistical interaction can be present between a marker and environment even if there is no interaction between the causal variant and the environment. We give simple conditions under which there is no marker-environment interaction and note that they do not hold in general when there is gene-environment dependence. Furthermore, the gene-environment dependence applies to the causal variant and cannot be assessed from marker data. Gene-gene interactions are susceptible to the same problem if two causal variants are in linkage disequilibrium. In addition to existing concerns about mechanistic interpretations, we suggest further caution in reporting interactions for genetic markers.
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