Genome scans for selection and introgression based on k-nearest neighbour techniques

A Multi-Omics, Machine Learning-Aware, Genome-Wide Metabolic Model of Bacillus Subtilis Refines the Gene Expression and Cell Growth Prediction

Given the extensive heterogeneity and variability, understanding cellular functions and regulatory mechanisms through the analysis of multi-omics datasets becomes extremely challenging. Here, a comprehensive modeling framework of multi-omics machine learning and metabolic network models are proposed that covers various cellular biological processes across multiple scales. This model on an extensive normalized compendium of Bacillus subtilis is validated, which encompasses gene expression data from environmental perturbations, transcriptional regulation, signal transduction, protein translation, and growth measurements. Comparison with high-throughput experimental data shows that EM_iBsu1209-ME, constructed on this basis, can accurately predict the expression of 605 genes and the synthesis of 23 metabolites under different conditions. This study paves the way for the construction of comprehensive biological databases and high-performance multi-omics metabolic models to achieve accurate predictive analysis in exploring complex mechanisms of cell genotypes and phenotypes.

Testing for adaptive changes linked to range expansion following a single introduction of the fall webworm

Adaptive evolution following colonization can affect the impact of invasive species. The fall webworm (FWW) invaded China 40 years ago through a single introduction event involving a severe bottleneck and subsequently diverged into two genetic groups. The well-recorded invasion history of FWW, coupled with a clear pattern of genetic divergence, provides an opportunity to investigate whether there is any sign of adaptive evolution following the invasion. Based on genome-wide SNPs, we identified genetically separated western and eastern groups of FWW and correlated spatial variation in SNPs with geographical and climatic factors. Geographical factors explained a similar proportion of the genetic variation across all populations compared with climatic factors. However, when the two population groups were analysed separately, environmental factors explained more variation than geographical factors. SNP outliers in populations of the western group had relatively stronger response to precipitation than temperature-related variables. Functional annotation of SNP outliers identified genes associated with insect cuticle protein potentially related to desiccation adaptation in the western group and genes associated with lipase biosynthesis potentially related to temperature adaptation in the eastern group. Our study suggests that invasive species may maintain the evolutionary potential to adapt to heterogeneous environments despite a single invasion event. The molecular data suggest that quantitative trait comparisons across environments would be worthwhile.

Rapid and Repeated Climate Adaptation Involving Chromosome Inversions following Invasion of an Insect

Following invasion, insects can become adapted to conditions experienced in their invasive range, but there are few studies on the speed of adaptation and its genomic basis. Here, we examine a small insect pest, Thrips palmi, following its contemporary range expansion across a sharp climate gradient from the subtropics to temperate areas. We first found a geographically associated population genetic structure and inferred a stepping-stone dispersal pattern in this pest from the open fields of southern China to greenhouse environments of northern regions, with limited gene flow after colonization. In common garden experiments, both the field and greenhouse groups exhibited clinal patterns in thermal tolerance as measured by critical thermal maximum (CTmax) closely linked with latitude and temperature variables. A selection experiment reinforced the evolutionary potential of CTmax with an estimated h2 of 6.8% for the trait. We identified 3 inversions in the genome that were closely associated with CTmax, accounting for 49.9%, 19.6%, and 8.6% of the variance in CTmax among populations. Other genomic variations in CTmax outside the inversion region were specific to certain populations but functionally conserved. These findings highlight rapid adaptation to CTmax in both open field and greenhouse populations and reiterate the importance of inversions behaving as large-effect alleles in climate adaptation.

A Catalog of Coding Sequence Variations in Salivary Proteins' Genes Occurring during Recent Human Evolution

Saliva houses over 2000 proteins and peptides with poorly clarified functions, including proline-rich proteins, statherin, P-B peptides, histatins, cystatins, and amylases. Their genes are poorly conserved across related species, reflecting an evolutionary adaptation. We searched the nucleotide substitutions fixed in these salivary proteins' gene loci in modern humans compared with ancient hominins. We mapped 3472 sequence variants/nucleotide substitutions in coding, noncoding, and 5'-3' untranslated regions. Despite most of the detected variations being within noncoding regions, the frequency of coding variations was far higher than the general rate found throughout the genome. Among the various missense substitutions, specific substitutions detected in PRB1 and PRB2 genes were responsible for the introduction/abrogation of consensus sequences recognized by convertase enzymes that cleave the protein precursors. Overall, these changes that occurred during the recent human evolution might have generated novel functional features and/or different expression ratios among the various components of the salivary proteome. This may have influenced the homeostasis of the oral cavity environment, possibly conditioning the eating habits of modern humans. However, fixed nucleotide changes in modern humans represented only 7.3% of all the substitutions reported in this study, and no signs of evolutionary pressure or adaptative introgression from archaic hominins were found on the tested genes.

A new hybrid algorithm for three-stage gene selection based on whale optimization

In biomedical data mining, the gene dimension is often much larger than the sample size. To solve this problem, we need to use a feature selection algorithm to select feature gene subsets with a strong correlation with phenotype to ensure the accuracy of subsequent analysis. This paper presents a new three-stage hybrid feature gene selection method, that combines a variance filter, extremely randomized tree, and whale optimization algorithm. First, a variance filter is used to reduce the dimension of the feature gene space, and an extremely randomized tree is used to further reduce the feature gene set. Finally, the whale optimization algorithm is used to select the optimal feature gene subset. We evaluate the proposed method with three different classifiers in seven published gene expression profile datasets and compare it with other advanced feature selection algorithms. The results show that the proposed method has significant advantages in a variety of evaluation indicators.

A novel hybrid algorithm based on Harris Hawks for tumor feature gene selection

Background

Gene expression data are often used to classify cancer genes. In such high-dimensional datasets, however, only a few feature genes are closely related to tumors. Therefore, it is important to accurately select a subset of feature genes with high contributions to cancer classification.

Methods

In this article, a new three-stage hybrid gene selection method is proposed that combines a variance filter, extremely randomized tree and Harris Hawks (VEH). In the first stage, we evaluated each gene in the dataset through the variance filter and selected the feature genes that meet the variance threshold. In the second stage, we use extremely randomized tree to further eliminate irrelevant genes. Finally, we used the Harris Hawks algorithm to select the gene subset from the previous two stages to obtain the optimal feature gene subset.

Results

We evaluated the proposed method using three different classifiers on eight published microarray gene expression datasets. The results showed a 100% classification accuracy for VEH in gastric cancer, acute lymphoblastic leukemia and ovarian cancer, and an average classification accuracy of 95.33% across a variety of other cancers. Compared with other advanced feature selection algorithms, VEH has obvious advantages when measured by many evaluation criteria.

Ghost lineages can invalidate or even reverse findings regarding gene flow

Introgression, endosymbiosis, and gene transfer, i.e., horizontal gene flow (HGF), are primordial sources of innovation in all domains of life. Our knowledge on HGF relies on detection methods that exploit some of its signatures left on extant genomes. One of them is the effect of HGF on branch lengths of constructed phylogenies. This signature has been formalized in statistical tests for HGF detection and used for example to detect massive adaptive gene flows in malaria vectors or to order evolutionary events involved in eukaryogenesis. However, these studies rely on the assumption that ghost lineages (all unsampled extant and extinct taxa) have little influence. We demonstrate here with simulations and data reanalysis that when considering the more realistic condition that unsampled taxa are legion compared to sampled ones, the conclusion of these studies become unfounded or even reversed. This illustrates the necessity to recognize the existence of ghosts in evolutionary studies.

Rapid genetic adaptation to recently colonized environments is driven by genes underlying life history traits

Background

Uncovering the mechanisms underlying rapid genetic adaptation can provide insight into adaptive evolution and shed light on conservation, invasive species control, and natural resource management. However, it can be difficult to experimentally explore rapid adaptation due to the challenges associated with propagating and maintaining species in captive environments for long periods of time. By contrast, many introduced species have experienced strong selection when colonizing environments that differ substantially from their native range and thus provide a “natural experiment” for studying rapid genetic adaptation. One such example occurred when sea lamprey (Petromyzon marinus), native to the northern Atlantic, naturally migrated into Lake Champlain and expanded their range into the Great Lakes via man-made shipping canals.

Results

Utilizing 368,886 genome-wide single nucleotide polymorphisms (SNPs), we calculated genome-wide levels of genetic diversity (i.e., heterozygosity and π) for sea lamprey collected from native (Connecticut River), native but recently colonized (Lake Champlain), and invasive (Lake Michigan) populations, assessed genetic differentiation between all populations, and identified candidate genes that responded to selection imposed by the novel environments. We observed a 14 and 24% reduction in genetic diversity in Lake Michigan and Lake Champlain populations, respectively, compared to individuals from the Connecticut River, suggesting that sea lamprey populations underwent a genetic bottleneck during colonization. Additionally, we identified 121 and 43 outlier genes in comparisons between Lake Michigan and Connecticut River and between Lake Champlain and Connecticut River, respectively. Six outlier genes that contained synonymous SNPs in their coding regions and two genes that contained nonsynonymous SNPs may underlie the rapid evolution of growth (i.e., GHR), reproduction (i.e., PGR, TTC25, STARD10), and bioenergetics (i.e., OXCT1, PYGL, DIN4, SLC25A15).

Conclusions

By identifying the genomic basis of rapid adaptation to novel environments, we demonstrate that populations of invasive species can be a useful study system for understanding adaptive evolution. Furthermore, the reduction in genome-wide levels of genetic diversity associated with colonization coupled with the identification of outlier genes underlying key life history traits known to have changed in invasive sea lamprey populations (e.g., growth, reproduction) illustrate the utility in applying genomic approaches for the successful management of introduced species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07553-x.

Incipient resistance to an effective pesticide results from genetic adaptation and the canalization of gene expression

The resistance of pest species to chemical controls has vast ecological, economic, and societal costs. In most cases, resistance is only detected after spreading throughout an entire population. Detecting resistance in its incipient stages, by comparison, provides time to implement preventative strategies. Incipient resistance can be detected by coupling standard toxicology assays with large-scale gene expression experiments. We apply this approach to a system where an invasive parasite, sea lamprey (Petromyzon marinus), has been treated with the highly effective pesticide 3-trifluoromethyl-4-nitrophenol (TFM) for 60 years. Toxicological experiments revealed that lamprey from treated populations did not have higher survival to TFM exposure than lamprey from untreated populations, demonstrating that full-fledged resistance has not yet evolved. In contrast, we find hundreds of genes differentially expressed in response to TFM in the population with the longest history of exposure, many of which relate to TFM's primary mode of action, the uncoupling of oxidative phosphorylation, and subsequent depletion of ATP. Three genes critical to oxidative phosphorylation, ATP5PB, PLCB1, and NDUFA9, were nearly fixed for alternative alleles in comparisons of SNPs between treated and untreated populations (FST  > 5 SD from the mean). ATP5PB encodes subunit b of ATP synthase and an additional subunit, ATP5F1B, was canalized for high expression in treated populations, but remained plastic in response to TFM treatment in individuals from the untreated population. These combined genomic and transcriptomic results demonstrate that an adaptive, genetic response to TFM is likely driving incipient resistance in a damaging pest species.

Genome scans for selection and introgression based on k-nearest neighbour techniques

In recent years, genome-scan methods have been extensively used to detect local signatures of selection and introgression. Most of these methods are either designed for one or the other case, which may impair the study of combined cases. Here, we introduce a series of versatile genome-scan methods applicable for both cases, the detection of selection and introgression. The proposed approaches are based on nonparametric k-nearest neighbour (kNN) techniques, while incorporating pairwise Fixation Index (F_ST ) and pairwise nucleotide differences (d_xy ) as features. We benchmark our methods using a wide range of simulation scenarios, with varying parameters, such as recombination rates, population background histories, selection strengths, the proportion of introgression and the time of gene flow. We find that kNN-based methods perform remarkably well compared with the state-of-the-art. Finally, we demonstrate how to perform kNN-based genome scans on real-world genomic data using the population genomics R-package popgenome.