Potato virus Y, an important viral pathogen of potato, has several genetic variants and geographic distributions which could be affected by environmental factors, aphid vectors, and reservoir plants. PVY is transmitted to virus-free potato plants by aphids and passed on to the next vegetative generations through tubers, but the effects of tuber transmission in PVY is largely unknown. By using high-throughput sequencing, we investigated PVY populations transmitted to potato plants by aphids in different climate zones of Russia, namely the Moscow and Astrakhan regions. We analyzed sprouts from the tubers produced by field-infected plants to investigate the impact of tuber transmission on PVY genetics. We found a significantly higher diversity of PVY isolates in the Astrakhan region, where winters are shorter and milder and summers are warmer compared to the Moscow region. While five PVY types, NTNa, NTNb, N:O, N-Wi, and SYR-I, were present in both regions, SYRI-II, SYRI-III, and 261-4 were only found in the Astrakhan region. All these recombinants were composed of the genome sections derived from PVY types O and N, but no full-length sequences of such types were present. The composition of the PVY variants in the tuber sprouts was not always the same as in their parental plants, suggesting that tuber transmission impacts PVY genetics.

Potato virus Y (PVY; genus Potyvirus, family Potyviridae) is one of the most devastating and economically important potato pathogens. Members of the Potyviridae family demonstrate high recombination rates. In nature, five major parental variants of PVY were identified with at least 35 recombinants. In this study, we report two novel PVY recombinants discovered in the sprouts of potato tubers produced in field conditions in the Astrakhan region of Russia in 2021 using high-throughput sequencing and de novo genome assembly. These recombinants, which were named Ast-A-I and Ast-A-II, have previously unknown arrangements of genome sections derived from PVY^O and PVY^N, with a novel recombination junction at position 7,850 nt of the PVY genome in the middle part of the RNA-dependent RNA polymerase (NIb) gene, with PVY^O type sequence at the 5′ end relative to this junction and PVY^N type at the 3′ end. Other recombinant junctions in the novel PVY variants were previously found in PVY^NTNa and PVY^NTNb. This includes those at positions 2,390 and 9,200 (PVY^N at the 5′ end and PVYIS° at the 3′ end), which were present in both novel recombinants, and at position 500 (PVY^O at the 5′ end and PVY^N at the 3′ end), which present in Ast-A-II. The presence of PVY variants with a novel recombinant point is verified by Sanger sequencing. Phylogenetic analysis of genomic RNA showed that the sequences of these recombinants form a separate branch that do not cluster with previously described PVY strains.

DNA motif discovery and, particularly, computational modeling of transcription factor binding motifs, has been a mecca of algorithmic bioinformatics for several decades. Here, we report the results of the largest open community challenge in Inferring BInding Specificities (IBIS), where participants all over the world were invited to construct binding specificity models from multi-assay experimental data for poorly studied human transcription factors. The submissions were rigorously tested against a rich held-out dataset. Benchmarking demonstrated a consistent advantage of properly designed deep learning models over traditional positional weight matrices and other machine learning methods. Yet, the positional weight matrices displayed a surprisingly strong performance out of the box, being only slightly behind the best deep learning models. A post-challenge assessment of a selection of other deep learning methods further solidified this finding. IBIS highlights the power of benchmarking in finding adequate DNA motif representations, emphasizes the pros and cons of various machine learning methods applied to DNA motif modeling, and establishes a rich dataset, benchmarking protocols, and computational framework for a fair cross-platform evaluation of future models of transcription factor binding motifs in DNA sequences.

A DNA sequence pattern, or “motif”, is an essential representation of DNA-binding specificity of a transcription factor (TF). Any particular motif model has potential flaws due to shortcomings of the underlying experimental data and computational motif discovery algorithm. As a part of the Codebook/GRECO-BIT initiative, here we evaluated at large scale the cross-platform recognition performance of positional weight matrices (PWMs), which remain popular motif models in many practical applications. We applied ten different DNA motif discovery tools to generate PWMs from the “Codebook” data comprised of 4,237 experiments from five different platforms profiling the DNA-binding specificity of 394 human proteins, focusing on understudied transcription factors of different structural families. For many of the proteins, there was no prior knowledge of a genuine motif. By benchmarking-supported human curation, we constructed an approved subset of experiments comprising about 30% of all experiments and 50% of tested TFs which displayed consistent motifs across platforms and replicates. We present the Codebook Motif Explorer ( https://mex.autosome.org ), a detailed online catalog of DNA motifs, including the top-ranked PWMs, and the underlying source and benchmarking data. We demonstrate that in the case of high-quality experimental data, most of the popular motif discovery tools detect valid motifs and generate PWMs, which perform well both on genomic and synthetic data. Yet, for each of the algorithms, there were problematic combinations of proteins and platforms, and the basic motif properties such as nucleotide composition and information content offered little help in detecting such pitfalls. By combining multiple PMWs in decision trees, we demonstrate how our setup can be readily adapted to train and test binding specificity models more complex than PWMs. Overall, our study provides a rich motif catalog as a solid baseline for advanced models and highlights the power of the multi-platform multi-tool approach for reliable mapping of DNA binding specificities. Abstract Figure: Graphical Abstract

Bicuspid aortic valve, a prevalent congenital malformation, predisposes individuals to severe complications. Although the condition exhibits substantial heritability, known protein-coding and common regulatory mutations explain a minority of cases. To assess the contribution of rare regulatory variants, here we integrate high-resolution three-dimensional genome organization profiling with matched whole-genome sequencing from eight individuals with bicuspid aortic valves and eight with standard tricuspid aortic valves. In bicuspid aortic valve patients, mutation-driven chromatin rewiring affected 1.8-fold more valve development genes than in healthy individuals. Genome-wide in silico analyses show that rare regulatory mutations disrupt the transcriptomes of mesenchymal cell populations necessary for endocardial cushion formation. We identify 198 candidate genes associated with bicuspid aortic valve, revealing pronounced heterogeneity and complex interplay between coding and regulatory mutations. Collectively, our findings establish rare regulatory mutations as contributors to the heritability of bicuspid aortic valve and underscore the need to elucidate their mechanistic roles in disease pathogenesis.

Plant rhabdoviruses cause considerable economic losses and are a threat to the agriculture of Solanaceae plants. Two novel virus isolates belonging to the family Rhabdoviridae are identified by high-throughput sequencing (HTS) in Russian eggplant cultivars grown in the Volga river delta region for the first time. The phylogenetic inference of L protein (polymerase) shows that these virus isolates belong to Alphanucleorhabdovirus physostegia (Physostegia chlorotic mottle virus—PhCMoV), and their minus-sense RNA genomes have the typical gene order 3′-nucleocapsid (N)—X protein (X)—phosphoprotein (P)—Y protein (Y)—matrix protein (M)—glycoprotein (G)—polymerase (L)-5′ observed in some plant-infecting alphanucleorhabdoviruses. One of the PhCMoV isolates from the eggplant cultivar Almaz is genetically very similar to the Russian PhCMoV isolate from tomato and grouped in a subclade together with four isolates from Belgium, Germany, the Netherlands, and France. However, another eggplant-infecting isolate from the Russian cultivar Boggart is the most divergent compared with the other 45 virus genomes of European PhCMoV isolates. Thus, our comparative analysis reveals that two virus isolates from Russia may either share a close evolutionary relationship with European isolates or significantly diverge from all known virus isolates. The potential to use the protein sequence comparative analysis of accessory polypeptides, along with the early developed strategy of the nucleotide sequence comparison of the RNA genomes, is shown.