PanTools v3: functional annotation, classification and phylogenomics.

SUMMARY: The ever-increasing number of sequenced genomes necessitates the development of pangenomic approaches for comparative genomics. Introduced in 2016, PanTools is a platform that allows pangenome construction, homology grouping and pangenomic read mapping. The use of graph database technology makes PanTools versatile, applicable from small viral genomes like SARS-CoV-2 up to large plant or animal genomes like tomato or human. Here, we present our third major update to PanTools that enables the integration of functional annotations and provides both gene-level analyses and phylogenetics. AVAILABILITY AND IMPLEMENTATION: PanTools is implemented in Java 8 and released under the GNU GPLv3 license. Software and documentation are available at https://git.wur.nl/bioinformatics/pantools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Pectobacterium pangenome, with a focus on Pectobacterium brasiliense, shows a robust core and extensive exchange of genes from a shared gene pool.

BACKGROUND: Bacterial plant pathogens of the Pectobacterium genus are responsible for a wide spectrum of diseases in plants, including important crops such as potato, tomato, lettuce, and banana. Investigation of the genetic diversity underlying virulence and host specificity can be performed at genome level by using a comprehensive comparative approach called pangenomics. A pangenomic approach, using newly developed functionalities in PanTools, was applied to analyze the complex phylogeny of the Pectobacterium genus. We specifically used the pangenome to investigate genetic differences between virulent and avirulent strains of P. brasiliense, a potato blackleg causing species dominantly present in Western Europe. RESULTS: Here we generated a multilevel pangenome for Pectobacterium, comprising 197 strains across 19 species, including type strains, with a focus on P. brasiliense. The extensive phylogenetic analysis of the Pectobacterium genus showed robust distinct clades, with most detail provided by 452,388 parsimony-informative single-nucleotide polymorphisms identified in single-copy orthologs. The average Pectobacterium genome consists of 47% core genes, 1% unique genes, and 52% accessory genes. Using the pangenome, we zoomed in on differences between virulent and avirulent P. brasiliense strains and identified 86 genes associated to virulent strains. We found that the organization of genes is highly structured and linked with gene conservation, function, and transcriptional orientation. CONCLUSION: The pangenome analysis demonstrates that evolution in Pectobacteria is a highly dynamic process, including gene acquisitions partly in clusters, genome rearrangements, and loss of genes. Pectobacterium species are typically not characterized by a set of species-specific genes, but instead present themselves using new gene combinations from the shared gene pool. A multilevel pangenomic approach, fusing DNA, protein, biological function, taxonomic group, and phenotypes, facilitates studies in a flexible taxonomic context.

Meiotic recombination profiling of interspecific hybrid F1 tomato pollen by linked read sequencing.

Genome wide screening of pooled pollen samples from a single interspecific F1 hybrid obtained from a cross between tomato, Solanum lycopersicum and its wild relative, Solanum pimpinellifolium using linked read sequencing of the haploid nuclei, allowed profiling of the crossover (CO) and gene conversion (GC) landscape. We observed a striking overlap between cold regions of CO in the male gametes and our previously established F6 recombinant inbred lines (RILs) population. COs were overrepresented in non-coding regions in the gene promoter and 5'UTR regions of genes. Poly-A/T and AT rich motifs were found enriched in 1 kb promoter regions flanking the CO sites. Non-crossover associated allelic and ectopic GCs were detected in most chromosomes, confirming that besides CO, GC represents also a source for genetic diversity and genome plasticity in tomato. Furthermore, we identified processed break junctions pointing at the involvement of both homology directed and non-homology directed repair pathways, suggesting a recombination machinery in tomato that is more complex than currently anticipated.

PERGOLA: fast and deterministic linkage mapping of polyploids.

BACKGROUND: A large share of agriculturally and horticulturally important plant species are polyploid. Linkage maps are used to locate associations between genes and traits by breeders and geneticists. Linkage map creation for polyploid species is not supported by standard tools. We want to overcome this limitation and validate our results with simulation studies. RESULTS: We developed PERGOLA, a deterministic and heuristic method that addresses this problem. We show that it creates correct linkage groups, marker orders and distances for simulated and real datasets. We compare it to existing tools and demonstrate that it overcomes limitations in ploidy and outperforms them in computational time and mapping accuracy. We represent linkage maps as dendrograms and show that this has advantages in the comparison of different maps. CONCLUSIONS: PERGOLA can be used successfully to calculate linkage maps for diploid and polyploid species and outperforms existing tools.

Advantages of continuous genotype values over genotype classes for GWAS in higher polyploids: a comparative study in hexaploid chrysanthemum.

BACKGROUND: Association studies are an essential part of modern plant breeding, but are limited for polyploid crops. The increased number of possible genotype classes complicates the differentiation between them. Available methods are limited with respect to the ploidy level or data producing technologies. While genotype classification is an established noise reduction step in diploids, it gains complexity with increasing ploidy levels. Eventually, the errors produced by misclassifications exceed the benefits of genotype classes. Alternatively, continuous genotype values can be used for association analysis in higher polyploids. We associated continuous genotypes to three different traits and compared the results to the output of the genotype caller SuperMASSA. Linear, Bayesian and partial least squares regression were applied, to determine if the use of continuous genotypes is limited to a specific method. A disease, a flowering and a growth trait with h (2) of 0.51, 0.78 and 0.91 were associated with a hexaploid chrysanthemum genotypes. The data set consisted of 55,825 probes and 228 samples. RESULTS: We were able to detect associating probes using continuous genotypes for multiple traits, using different regression methods. The identified probe sets were overlapping, but not identical between the methods. Baysian regression was the most restrictive method, resulting in ten probes for one trait and none for the others. Linear and partial least squares regression led to numerous associating probes. Association based on genotype classes resulted in similar values, but missed several significant probes. A simulation study was used to successfully validate the number of associating markers. CONCLUSIONS: Association of various phenotypic traits with continuous genotypes is successful with both uni- and multivariate regression methods. Genotype calling does not improve the association and shows no advantages in this study. Instead, use of continuous genotypes simplifies the analysis, saves computational time and results more potential markers.

Integrating gene expression and GO classification for PCA by preclustering.

BACKGROUND: Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per group can then be used to identify interesting GO categories in relation to the experimental settings. However, the expression profiles present in GO classes are often heterogeneous, i.e., there are several different expression profiles within one class. As a result, important experimental findings can be obscured because the summarizing profile does not seem to be of interest. We propose to tackle this problem by finding homogeneous subclasses within GO categories: preclustering. RESULTS: Two microarray datasets are analyzed. First, a selection of genes from a well-known Saccharomyces cerevisiae dataset is used. The GO class "cell wall organization and biogenesis" is shown as a specific example. After preclustering, this term can be associated with different phases in the cell cycle, where it could not be associated with a specific phase previously. Second, a dataset of differentiation of human Mesenchymal Stem Cells (MSC) into osteoblasts is used. For this dataset results are shown in which the GO term "skeletal development" is a specific example of a heterogeneous GO class for which better associations can be made after preclustering. The Intra Cluster Correlation (ICC), a measure of cluster tightness, is applied to identify relevant clusters. CONCLUSIONS: We show that this method leads to an improved interpretability of results in Principal Component Analysis.

Osteo-transcriptomics of human mesenchymal stem cells: accelerated gene expression and osteoblast differentiation induced by vitamin D reveals c-MYC as an enhancer of BMP2-induced osteogenesis.

Bone marrow-derived human mesenchymal stem cells (hMSCs) have the in vitro capacity to differentiate into osteoblasts, chondrocytes or adipocytes, depending on the applied stimulus. In order to identify novel regulators of osteogenesis in hMSCs, osteo-transcriptomics was performed whereby differentiation induced by dexamethasone (DEX), DEX+ bone morphogenetic protein 2 (BMP2), and DEX+ Vitamin D(3) (1,25(OH)(2)D(3)) was studied over a course of 12 days. Microarray analysis revealed that 2095 genes were significantly regulated by DEX+ 1,25(OH)(2)D(3), of which 961 showed accelerated expression kinetics compared to treatment by DEX alone. The majority of these genes were accelerated 24-48 h after onset of osteogenic treatment. Gene ontology (GO) analysis of these 1,25(OH)(2)D(3)-accelerated genes indicated their involvement in biological processes related to cellular differentiation and cell cycle regulation. When compared to cells treated with DEX or DEX+BMP2, treatment with DEX+ 1,25(OH)(2)D(3) clearly accelerated osteoprogenitor commitment and osteoblast maturation, as measured by alkaline phosphatase (ALP) activity and calcification of the matrix. Cell cycle progression, as observed after initial growth arrest, was not significantly accelerated by 1,25(OH)(2)D(3) and was not required for onset and progression of osteogenesis. However, expression of c-Myc was accelerated by 1,25(OH)(2)D(3), and binding sites for c-MYC were enriched in promoters of genes accelerated by 1,25(OH)(2)D(3). Lentiviral overexpression of c-MYC strongly promoted DEX+ BMP2-induced osteoblast differentiation and matrix maturation. In conclusion, our studies show for the first time that 1,25(OH)(2)D(3) strongly accelerates expression of genes involved in differentiation of hMSCs and, moreover, identify c-MYC as a novel regulator of osteogenesis.

Genome-wide mRNA expression analysis of hepatic adaptation to high-fat diets reveals switch from an inflammatory to steatotic transcriptional program.

BACKGROUND: Excessive exposure to dietary fats is an important factor in the initiation of obesity and metabolic syndrome associated pathologies. The cellular processes associated with the onset and progression of diet-induced metabolic syndrome are insufficiently understood. PRINCIPAL FINDINGS: To identify the mechanisms underlying the pathological changes associated with short and long-term exposure to excess dietary fat, hepatic gene expression of ApoE3Leiden mice fed chow and two types of high-fat (HF) diets was monitored using microarrays during a 16-week period. A functional characterization of 1663 HF-responsive genes reveals perturbations in lipid, cholesterol and oxidative metabolism, immune and inflammatory responses and stress-related pathways. The major changes in gene expression take place during the early (day 3) and late (week 12) phases of HF feeding. This is also associated with characteristic opposite regulation of many HF-affected pathways between these two phases. The most prominent switch occurs in the expression of inflammatory/immune pathways (early activation, late repression) and lipogenic/adipogenic pathways (early repression, late activation). Transcriptional network analysis identifies NF-kappaB, NEMO, Akt, PPARgamma and SREBP1 as the key controllers of these processes and suggests that direct regulatory interactions between these factors may govern the transition from early (stressed, inflammatory) to late (pathological, steatotic) hepatic adaptation to HF feeding. This transition observed by hepatic gene expression analysis is confirmed by expression of inflammatory proteins in plasma and the late increase in hepatic triglyceride content. In addition, the genes most predictive of fat accumulation in liver during 16-week high-fat feeding period are uncovered by regression analysis of hepatic gene expression and triglyceride levels. CONCLUSIONS: The transition from an inflammatory to a steatotic transcriptional program, possibly driven by the reciprocal activation of NF-kappaB and PPARgamma regulators, emerges as the principal signature of the hepatic adaptation to excess dietary fat. These findings may be of essential interest for devising new strategies aiming to prevent the progression of high-fat diet induced pathologies.