Genomic signatures of natural selection at phenology-related genes in a widely distributed tree species Fagus sylvatica L

Rolling down that mountain: microgeographical adaptive divergence during a fast population expansion along a steep environmental gradient in European beech

Forest tree populations harbour high genetic diversity thanks to large effective population sizes and strong gene flow, allowing them to diversify through adaptation to local environmental pressures within dispersal distance. Many tree populations also experienced historical demographic fluctuations, including spatial population contraction or expansions at various temporal scales, which may constrain their ability to adapt to environmental variations. Our aim is to investigate how recent contraction and expansion events interfere with local adaptation, by studying patterns of adaptive divergence between closely related stands undergoing environmentally contrasted conditions, and having or not recently expanded. To investigate genome-wide signatures of local adaptation while accounting for demography, we analysed divergence in a European beech population by testing pairwise differentiation among four tree stands at ~35k Single Nucleotide Polymorphisms from ~9k genomic regions. We applied three divergence outlier search methods resting on different assumptions and targeting either single SNPs or contiguous genomic regions, while accounting for the effect of population size variations on genetic divergence. We found 27 signals of selective signatures in 19 target regions. Putatively adaptive divergence involved all stand pairs. We retrieved signals both when comparing old-growth stands and recently colonised areas and when comparing stands within the old-growth area. Therefore, adaptive divergence processes have taken place both over short time spans, under strong environmental contrasts, and over short ecological gradients, in populations that have been stable in the long term. This suggests that standing genetic variation supports local, microgeographic divergence processes, which can maintain genetic diversity at the landscape level.

A genome-wide genetic association study reveals SNPs significantly associated with environmental variables and specific leaf area in European beech

European beech is negatively affected by climate change and a further growth decline is predicted for large parts of its distribution range. Despite the importance of this species, little is known about its genetic adaptation and especially the genetic basis of its physiological traits. Here, we used genotyping by sequencing to identify SNPs in 43 German European beech populations growing under different environmental conditions. In total, 28 of these populations were located along a precipitation and temperature gradient in northern Germany, and single tree-based hydraulic and morphological traits were available. We obtained a set of 13,493 high-quality SNPs that were used for environmental and SNP-trait association analysis. In total, 22 SNPs were identified that were significantly associated with environmental variables or specific leaf area (SLA). Several SNPs were located in genes related to stress response. The majority of the significant SNPs were located in non-coding (intergenic and intronic) regions. These may be in linkage disequilibrium with the causative coding or regulatory regions. Our study gives insights into the genetic basis of abiotic adaptation in European beech, and provides genetic resources that can be used in future studies on this species. Besides clear patterns of local adaptation to environmental conditions of the investigated populations, the analyzed morphological and hydraulic traits explained most of the explainable genetic variation. Thus, they could successfully be altered in tree breeding programs, which may help to increase the adaptation of European beech to changing environmental conditions in the future.

Leaf gene expression trajectories during the growing season are consistent between sites and years in American beech

Transcriptomics provides a versatile tool for ecological monitoring. Here, through genome-guided profiling of transcripts mapping to 33 042 gene models, expression differences can be discerned among multi-year and seasonal leaf samples collected from American beech trees at two latitudinally separated sites. Despite a bottleneck due to post-Columbian deforestation, the single nucleotide polymorphism-based population genetic background analysis has yielded sufficient variation to account for differences between populations and among individuals. Our expression analyses during spring-summer and summer-autumn transitions for two consecutive years involved 4197 differentially expressed protein coding genes. Using Populus orthologues we reconstructed a protein-protein interactome representing leaf physiological states of trees during the seasonal transitions. Gene set enrichment analysis revealed gene ontology terms that highlight molecular functions and biological processes possibly influenced by abiotic forcings such as recovery from drought and response to excess precipitation. Further, based on 324 co-regulated transcripts, we focused on a subset of GO terms that could be putatively attributed to late spring phenological shifts. Our conservative results indicate that extended transcriptome-based monitoring of forests can capture diverse ranges of responses including air quality, chronic disease, as well as herbivore outbreaks that require activation and/or downregulation of genes collectively tuning reaction norms maintaining the survival of long living trees such as the American beech.

Signatures of local adaptation to current and future climate in phenology-related genes in natural populations of Quercus robur

BACKGROUND

Local adaptation is a key evolutionary process that enhances the growth of plants in their native habitat compared to non-native habitats, resulting in patterns of adaptive genetic variation across the entire geographic range of the species. The study of population adaptation to local environments and predicting their response to future climate change is important because of climate change.

RESULTS

Here, we explored the genetic diversity of candidate genes associated with bud burst in pedunculate oak individuals sampled from 6 populations in Poland. Single nucleotide polymorphism (SNP) diversity was assessed in 720 candidate genes using the sequence capture technique, yielding 18,799 SNPs. Using landscape genomic approaches, we identified 8 FST outliers and 781 unique SNPs in 389 genes associated with geography, climate, and phenotypic variables (individual/family spring and autumn phenology, family diameter at breast height (DBH), height, and survival) that are potentially involved in local adaptation. Then, using a nonlinear multivariate model, Gradient Forests, we identified vulnerable areas of the pedunculate oak distribution in Poland that are at risk from climate change.

CONCLUSIONS

The model revealed that pedunculate oak populations in the eastern part of the analyzed geographical region are the most sensitive to climate change. Our results might offer an initial evaluation of a potential management strategy for preserving the genetic diversity of pedunculate oak.

Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital-acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation-maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU.

First genome-wide data from Italian European beech (Fagus sylvatica L.): Strong and ancient differentiation between Alps and Apennines

The European beech (Fagus sylvatica L.) is one of the most widespread forest trees in Europe whose distribution and intraspecific diversity has been largely shaped by repeated glacial cycles. Previous studies, mainly based on palaeobotanical evidence and a limited set of chloroplast and nuclear genetic markers, highlighted a complex phylogeographic scenario, with southern and western Europe characterized by a rather heterogeneous genetic structure, as a result of recolonization from different glacial refugia. Despite its ecological and economic importance, the genome of this broad-leaved tree has only recently been assembled, and its intra-species genomic diversity is still largely unexplored. Here, we performed whole-genome resequencing of nine Italian beech individuals sampled from two stands located in the Alpine and Apennine mountain ranges. We investigated patterns of genetic diversity at chloroplast, mitochondrial and nuclear genomes and we used chloroplast genomes to reconstruct a temporally-resolved phylogeny. Results allowed us to test European beech differentiation on a whole-genome level and to accurately date their divergence time. Our results showed comparable, relatively high levels of genomic diversity in the two populations and highlighted a clear differentiation at chloroplast, mitochondrial and nuclear genomes. The molecular clock analysis indicated an ancient split between the Alpine and Apennine populations, occurred between the Günz and the Riss glaciations (approximately 660 kyrs ago), suggesting a long history of separation for the two gene pools. This information has important conservation implications in the context of adaptation to ongoing climate changes.