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Aim. To study the clinical and histological profile of lung tissue in patients with persistent pulmonary disease, respiratory symptoms and CT findings after SARS-CoV-2 infection. Materials and methods. The study included 15 patients (7 females and 8 males) with a mean age of 57.7 years. All patients underwent laboratory tests, chest computed tomography, echocardiography, and pulmonary function tests. Pulmonary tissue and bronchoalveolar lavage samples were obtained by fibrobronchoscopy, transbronchial forceps (2 patients), and lung cryobiopsy (11 patients); open biopsy was performed in 2 patients. Cellular composition, herpesvirus DNA, SARS-CoV-2, Mycobacterium tuberculosis complex, galactomannan optical density index, and bacterial and fungal microflora growth were determined in bronchoalveolar lavage. SARS-CoV-2 was also identified in samples from the nasal mucosa, throat and feces using a polymerase chain reaction. Results. The results showed no true pulmonary fibrosis in patients recovered from SARS-CoV-2 infection with persistent respiratory symptoms, functional impairment, and CT findings after SARS-CoV-2 infection. The observed changes comply with the current and/or resolving infection and inflammatory process. Conclusion. Thus, no true pulmonary fibrosis was found in patients after SARS-CoV-2 infection with persistent respiratory symptoms, functional impairment, and CT findings. The observed changes comply with the current and/or resolving infection and inflammatory process.

The chickpea plays a significant role in global agriculture and occupies an increasing share in the human diet. The main aim of the research was to develop a model for the prediction of two chickpea productivity traits in the available dataset. Genomic data for accessions were encoded in Artificial Image Objects, and a model for the thousand-seed weight (TSW) and number of seeds per plant (SNpP) prediction was constructed using a Convolutional Neural Network, dictionary learning and sparse coding for feature extraction, and extreme gradient boosting for regression. The model was capable of predicting both traits with an acceptable accuracy of 84–85%. The most important factors for model solution were identified using the dense regression attention maps method. The SNPs important for the SNpP and TSW traits were found in 34 and 49 genes, respectively. Genomic prediction with a constructed model can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired phenotype.

To assess the genomic diversity of Fusarium oxysporum f. sp. lini strains and compile a comprehensive gene repertoire, we constructed a pangenome using 13 isolates from four different clonal lineages, each exhibiting distinct levels of virulence. Syntenic analyses of two selected genomes revealed significant chromosomal rearrangements unique to each genome. A comprehensive examination of both core and accessory pangenome content and diversity points at an open genome state. Additionally, Gene Ontology (GO) enrichment analysis indicated that non-core pangenome genes are associated with pathogen recognition and immune signaling. Furthermore, the Folini pansecterome, encompassing secreted proteins critical for fungal pathogenicity, primarily consists of three functional classes: effector proteins, CAZYmes, and proteases. These three classes account for approximately 3.5% of the pangenome. Each functional class within the pansecterome was meticulously annotated and characterized with respect to pangenome category distribution, PFAM domain frequency, and strain virulence assessment. This analysis revealed that highly virulent isolates have specific types of PFAM domains that are exclusive to them. Upon examining the repertoire of SIX genes known for virulence in other formae speciales, it was found that all isolates had a similar gene content except for two, which lacked SIX genes entirely.

Chickpea is a staple crop for many nations worldwide. Modeling genotype-by-environment interactions and assessing the genotype’s ability to contribute adaptive alleles are crucial for chickpea breeding. In this study, we evaluated 12 agronomically important traits of 159 accessions from the N.I. Vavilov All Russian Institute for Plant Genetic Resources collection. These included 145 landraces and 13 cultivars grown in different climatic conditions in Kuban (45°18′ N and 40°52′ E) in both 2016 and 2022, as well as in Astrakhan (46°06′ N and 48°04′ E) in 2022. Using the IIIVmrMLM model in multi-environmental mode, we identified 161 quantitative trait nucleotides (QTNs) with stable genetic effects across different environments. Furthermore, we have observed 254 QTN-by-environment interactions with distinct environment-specific effects. Notably, five of these interactions manifested large effects, with R2 values exceeding 10%, while the highest R2 value for stable QTNs was 4.7%. Within the protein-coding genes and their 1 Kb flanking regions, we have discerned 22 QTNs and 45 QTN-by-environment interactions, most likely tagging the candidate causal genes. The landraces obtained from the N.I Vavilov All Russian Institute for Plant Genetic Resources collection exhibit numerous favorable alleles at quantitative trait nucleotide loci, showing stable effects in the Kuban and Astrakhan regions. Additionally, they possessed a significantly higher number of Kuban-specific favorable alleles of the QTN-by-environment interaction loci compared to the Astrakhan-specific ones. The environment-specific alleles found at the QTN-by-environment interaction loci have the potential to enhance chickpea adaptation to specific climatic conditions.

While the domestication process has been investigated in many crops, the detailed route of cultivation range expansion and factors governing this process received relatively little attention. Here, using mungbean (Vigna radiata var. radiata) as a test case, we investigated the genomes of more than 1000 accessions to illustrate climatic adaptation’s role in dictating the unique routes of cultivation range expansion. Despite the geographical proximity between South and Central Asia, genetic evidence suggests mungbean cultivation first spread from South Asia to Southeast, East and finally reached Central Asia. Combining evidence from demographic inference, climatic niche modeling, plant morphology, and records from ancient Chinese sources, we showed that the specific route was shaped by the unique combinations of climatic constraints and farmer practices across Asia, which imposed divergent selection favoring higher yield in the south but short-season and more drought-tolerant accessions in the north. Our results suggest that mungbean did not radiate from the domestication center as expected purely under human activity, but instead, the spread of mungbean cultivation is highly constrained by climatic adaptation, echoing the idea that human commensals are more difficult to spread through the south-north axis of continents.

Human pluripotent stem cells have the potential for unlimited proliferation and controlled differentiation into various somatic cells, making them a unique tool for regenerative and personalized medicine. Determining the best clone selection is a challenging problem in this field and requires new sensing instruments and methods able to automatically assess the state of a growing colony (‘phenotype’) and make decisions about its destiny. One possible solution for such label-free, non-invasive assessment is to make phase-contrast images and/or videos of growing stem cell colonies, process the morphological parameters (‘morphological portrait’, or signal), link this information to the colony phenotype, and initiate an automated protocol for the colony selection. As a step in implementing this strategy, we used machine learning methods to find an effective model for classifying the human pluripotent stem cell colonies of three lines according to their morphological phenotype (‘good’ or ‘bad’), using morphological parameters from the previously published data as predictors. We found that the model using cellular morphological parameters as predictors and artificial neural networks as the classification method produced the best average accuracy of phenotype prediction (67%). When morphological parameters of colonies were used as predictors, logistic regression was the most effective classification method (75% average accuracy). Combining the morphological parameters of cells and colonies resulted in the most effective model, with a 99% average accuracy of phenotype prediction. Random forest was the most efficient classification method for the combined data. We applied feature selection methods and showed that different morphological parameters were important for phenotype recognition via either cellular or colonial parameters. Our results indicate a necessity for retaining both cellular and colonial morphological information for predicting the phenotype and provide an optimal choice for the machine learning method. The classification models reported in this study could be used as a basis for developing and/or improving automated solutions to control the quality of human pluripotent stem cells for medical purposes.

Abstract Background Mungbean (Vigna radiata (L.) R. Wilczek, or green gram) is important tropical and sub-tropical legume and a rich source of dietary protein and micronutrients. In this study we employ GWAS to examine the genetic basis of variation in several important traits in mungbean, using the mini-core collection established by the World Vegetable Center, which includes 296 accessions that represent the major market classes. This collection has been grown in a common field plot in southern European part of Russia in 2018. Results We used 5041 SNPs in 293 accessions that passed strict filtering for genetic diversity, linkage disequilibrium, population structure and GWAS analysis. Polymorphisms were distributed among all chromosomes, but with variable density. Linkage disequilibrium decayed in approximately 105 kb. Four distinct subgroups were identified within 293 accessions with 70% of accessions attributed to one of the four populations. By performing GWAS on the mini-core collection we have found several loci significantly associated with two important agronomical traits. Four SNPs associated with possibility of maturation in Kuban territory of Southern Russia in 2018 were identified within a region of strong linkage which contains genes encoding zinc finger A20 and an AN1 domain stress-associated protein. Conclusions The core collection of mungbean established by the World Vegetable Center is a valuable resource for mungbean breeding. The collection has been grown in southern European part of Russia in 2018 under incidental stresses caused by abnormally hot weather and different photoperiod. We have found several loci significantly associated with color of hypocotyl and possibility of maturation under these stressful conditions. SNPs associated with possibility of maturation localize to a region on chromosome 2 with strong linkage, in which genes encoding zinc finger A20 and AN1 domain stress associated protein (SAP) are located. Phenotyping of WorldVeg collection for maturation traits in temperate climatic locations is important as phenology remains a critical breeding target for mungbean. As demand rises for mungbean, production in temperate regions with shorter growing seasons becomes crucial to keep up with needs. Uncovering SNPs for phenology traits will speed breeding efforts.

Abstract Background Phenology data collected recently for about 300 accessions of Vigna radiata (mungbean) is an invaluable resource for investigation of impacts of climatic factors on plant development. Results We developed a new mathematical model that describes the dynamic control of time to flowering by daily values of maximal and minimal temperature, precipitation, day length and solar radiation. We obtained model parameters by adaptation to the available experimental data. The models were validated by cross-validation and used to demonstrate that the phenology of adaptive traits, like flowering time, is strongly predicted not only by local environmental factors but also by plant geographic origin and genotype. Conclusions Of local environmental factors maximal temperature appeared to be the most critical factor determining how faithfully the model describes the data. The models were applied to forecast time to flowering of accessions grown in Taiwan in future years 2020-2030.

A collection of flax accessions maintained by the Russian Federal Research Center for Bast Fiber Crops was characterized to evaluate its resistance to Fusarium wilt. 297 samples representing different morphotypes and selection status were infected with highly virulent MI39 strain of Fusarium oxysporum f. sp. lini. Evaluation of disease symptoms was performed at the full emergence stage in a greenhouse. The experiment lasted for 3 successive years. The disease severity index (DSI) record was obtained for every genotype in each year of the experiment. The data set was produced in a framework of a project focused on both a) deciphering the mechanisms of plant immunity, and b) development of flax cultivars resistant to Fusarium wilt. The data is available via Figshare repository.

Genetic diversity in a breeding program is essential to overcome modern-day environmental challenges faced by humanity and produce robust, resilient crop cultivars with improved agronomic characteristics, as well as to trace crop domestication history. Flax (Linum usitatissimum), one of the first crops domesticated by mankind, has been traditionally cultivated for fiber as well as for medicinal purposes and as a nutritional product. The origins of fiber flax are hidden in the mists of time and can be hypothetically traced back to either the Indo-Afghan region or Fertile Crescent. To shed new light on fiber flax genetic diversity and breeding history, in this study, we presented a comprehensive analysis of the core collection of flax (306 accessions) of different morphotypes and geographic origins maintained by the Russian Federal Research Center for Bast Fiber Crops. We observed significant population differentiation between oilseed and fiber morphotypes, as well as mapped genomic regions affected by recent breeding efforts. We also sought to unravel the origins of kryazhs, Russian heritage landraces, and their genetic relatedness to modern fiber flax cultivars. For the first time, our results provide strong genetic evidence in favor of the hypothesis on kryazh’s mixed origin from both the Indo-Afghan diversity center and Fertile Crescent. Finally, we showed predominant contribution from Russian landraces and kryazhs into the ancestry of modern fiber flax varieties. Taken together, these findings may have practical implications on the development of new improved flax varieties with desirable traits that give farmers greater choice in crop management and meet the aspirations of breeders.

Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. In this work, a new approach is proposed in which the SNP markers influencing time to flowering in mung bean are selected as important features in a random forest model. The genotypic and weather data are encoded in artificial image objects, and a model for flowering time prediction is constructed as a convolutional neural network. The model uses weather data for only a limited time period of 5 days before and 20 days after planting and is capable of predicting the time to flowering with high accuracy. The most important factors for model solution were identified using saliency maps and a Score-CAM method. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time.

Abstract Background Accurate prediction of crop flowering time is required for reaching maximal farm efficiency. Several models developed to accomplish this goal are based on deep knowledge of plant phenology, requiring large investment for every individual crop or new variety. Mathematical modeling can be used to make better use of more shallow data and to extract information from it with higher efficiency. Cultivars of chickpea, Cicer arietanum, are currently being improved by introgressing wild C. reticulatum biodiversity with very different flowering time requirements. More understanding is required for how flowering time will depend on environmental conditions in these cultivars developed by introgression of wild alleles. Results We built a novel model for flowering time of wild chickpeas collected at 21 different sites in Turkey and grown in 4 distinct environmental conditions over several different years and seasons. We propose a general approach, in which the analytic forms of dependence of flowering time on climatic parameters, their regression coefficients, and a set of predictors are inferred automatically by stochastic minimization of the deviation of the model output from data. By using a combination of Grammatical Evolution and Differential Evolution Entirely Parallel method, we have identified a model that reflects the influence of effects of day length, temperature, humidity and precipitation and has a coefficient of determination of R 2=0.97. Conclusions We used our model to test two important hypotheses. We propose that chickpea phenology may be strongly predicted by accession geographic origin, as well as local environmental conditions at the site of growth. Indeed, the site of origin-by-growth environment interaction accounts for about 14.7% of variation in time period from sowing to flowering. Secondly, as the adaptation to specific environments is blueprinted in genomes, the effects of genes on flowering time may be conditioned on environmental factors. Genotype-by-environment interaction accounts for about 17.2% of overall variation in flowering time. We also identified several genomic markers associated with different reactions to climatic factor changes. Our methodology is general and can be further applied to extend existing crop models, especially when phenological information is limited.

A collection of flax accessions from Russian Federal Research Center for Bast Fiber Crops was characterised to evaluate its phenotypic diversity. 406 samples representing different morphotypes were selected for thorough quantitative assessment of various agronomic traits. We measured height, length of technical part of the stem, technical part weight, inflorescence length, number of bolls and seeds per plant, 1000 seed weight, the diameter of the stem, the number of internodes and finally, distance between internodes. The fiber quality was estimated by calculating stem slenderness, stem taperingness and elementary fiber length. The dataset was produced in a framework of a project focused on characterization of diversity of flax genotypes and phenotypes, as well as on identification of genomic regions associated with various traits, it is hosted on Figshare.

The solution of the so-called mixed-integer optimization problem is an important challenge for modern life sciences. A wide range of methods has been developed for its solution, including metaheuristics approaches. Here, a modification is proposed of the differential evolution entirely parallel (DEEP) method introduced recently that was successfully applied to mixed-integer optimization problems. The triangulation recombination rule was implemented and the recombination coefficients were included in the evolution process in order to increase the robustness of the optimization. The deduplication step included in the procedure ensures the uniqueness of individual integer-valued parameters in the solution vectors. The developed algorithms were implemented in the DEEP software package and applied to three bioinformatic problems. The application of the method to the optimization of predictors set in the genomic selection model in wheat resulted in dimensionality reduction such that the phenotype can be predicted with acceptable accuracy using a selected subset of SNP markers. The method was also successfully used to optimize the training set of samples for such a genomic selection model. According to the obtained results, the developed algorithm was capable of constructing a non-linear phenomenological regression model of gene expression in developing a Drosophila eye with almost the same average accuracy but significantly less standard deviation than the linear models obtained earlier.

Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. A new approach is proposed that uses Approximate Bayesian Computation with Differential Evolution to construct a pool of models for flowering time. The functions for daily progression of the plant from planting to flowering are obtained in analytic form and depend on daily values of climatic factors and genetic information. The resulting pool of models demonstrated high accuracy on the dataset. Day length, solar radiation and temperature had a large impact on the model accuracy, while the impact of precipitation was comparatively small and the impact of maximal temperature has the maximal variation. The model pool was used to investigate the behavior of accessions from the dataset in case of temperature increase by 0.05–6.00°. The time to flowering changed differently for different accessions. The Pearson correlation coefficient between the SNP value and the change in time to flowering revealed weak but significant association of SNP7 with behavior of the accessions in warming climate conditions. The same SNP was found to have a considerable influence on model prediction with a permutation test. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time.

Transposons are genomic elements that can relocate within a host genome using a ‘cut’- or ‘copy-and-paste’ mechanism. They make up a significant part of many genomes, serve as a driving force for genome evolution, and are linked with Mendelian diseases and cancers. Interactions between two specific retrotransposon types, autonomous (e.g., LINE1/L1) and nonautonomous (e.g., Alu), may lead to fluctuations in the number of these transposons in the genome over multiple cell generations. We developed and examined a simple model of retrotransposon dynamics under conditions where transposon replication machinery competed for cellular resources: namely, free ribosomes and available energy (i.e., ATP molecules). Such competition is likely to occur in stress conditions that a malfunctioning cell may experience as a result of a malignant transformation. The modeling revealed that the number of actively replicating LINE1 and Alu elements in a cell decreases with the increasing competition for resources; however, stochastic effects interfere with this simple trend. We stochastically simulated the transposon dynamics in a cell population and showed that the population splits into pools with drastically different transposon behaviors. The early extinction of active Alu elements resulted in a larger number of LINE1 copies occurring in the first pool, as there was no competition between the two types of transposons in this pool. In the other pool, the competition process remained and the number of L1 copies was kept small. As the level of available resources reached a critical value, both types of dynamics demonstrated an increase in noise levels, and both the period and the amplitude of predator–prey oscillations rose in one of the cell pools. We hypothesized that the presented dynamical effects associated with the impact of the competition for cellular resources inflicted on the dynamics of retrotransposable elements could be used as a characteristic feature to assess a cell state, or to control the transposon activity.

Accurate prediction of flowering time helps breeders to develop new varieties that can achieve maximal efficiency in a changing climate. A methodology was developed for the construction of a simulation model for flowering time in which a function for daily progression of the plant from one to the next phenological phase is obtained in analytic form by stochastic minimization. The resulting model demonstrated high accuracy on the recently assembled data set of wild chickpeas. The inclusion of genotype-by-climatic factors interactions accounted to 77% of accuracy in terms of root mean square error. It was found that the impact of minimal temperature is positively correlated with the longitude at primary collection sites, while the impact of day length is negatively correlated. It was interpreted as adaptation of accessions from highlands to lower temperatures and those from lower elevation river valleys to shorter days. We used bootstrap resampling to construct an ensemble of models, taking into account the influence of genotype-by-climatic factors interactions and applied it to forecast the time to flowering for the years 2021–2099, using generated daily weather in Turkey, and for different climate change scenarios. Although there are common trends in the forecasts, some genotypes and SNP groups have distinct trajectories.

Summary. Differential Evolution Entirely Parallel (DEEP) package is a software for finding unknown real and integer parameters in dynamical models of biological processes by minimizing one or even several objective functions that measure the deviation of model solution from data. Numerical solutions provided by the most efficient global optimization methods are often problem-specific and cannot be easily adapted to other tasks. In contrast, DEEP allows a user to describe both mathematical model and objective function in any programming language, such as R, Octave or Python and others. Being implemented in C, DEEP demonstrates as good performance as the top three methods from CEC-2014 (Competition on evolutionary computation) benchmark and was successfully applied to several biological problems. Availability. DEEP method is an open source and free software distributed under the terms of GPL licence version 3. The sources are available at http://deepmethod.sourceforge.net/ and binary packages for Fedora GNU/Linux are provided for RPM package manager at https://build.opensuse.org/project/repositories/home:mackoel:compbio.

The segmentation gene network in Drosophila embryo solves the fundamental problem of embryonic patterning: how to establish a periodic pattern of gene expression, which determines both the positions and the identities of body segments. The gap gene network constitutes the first zygotic regulatory tier in this process. Here we have applied the systems-level approach to investigate the regulatory effect of gap gene Kruppel (Kr) on segmentation gene expression. We acquired a large dataset on the expression of gap genes in Kr null mutants and demonstrated that the expression levels of these genes are significantly reduced in the second half of cycle 14A. To explain this novel biological result we applied the gene circuit method which extracts regulatory information from spatial gene expression data. Previous attempts to use this formalism to correctly and quantitatively reproduce gap gene expression in mutants for a trunk gap gene failed, therefore here we constructed a revised model and showed that it correctly reproduces the expression patterns of gap genes in Kr null mutants. We found that the remarkable alteration of gap gene expression patterns in Kr mutants can be explained by the dynamic decrease of activating effect of Cad on a target gene and exclusion of Kr gene from the complex network of gap gene interactions, that makes it possible for other interactions, in particular, between hb and gt, to come into effect. The successful modeling of the quantitative aspects of gap gene expression in mutant for the trunk gap gene Kr is a significant achievement of this work. This result also clearly indicates that the oversimplified representation of transcriptional regulation in the previous models is one of the reasons for unsuccessful attempts of mutant simulations.

Developing embryos exhibit a robust capability to reduce phenotypic variations that occur naturally or as a result of experimental manipulation. This reduction in variation occurs by an epigenetic mechanism called canalization, a phenomenon which has resisted understanding because of a lack of necessary molecular data and of appropriate gene regulation models. In recent years, quantitative gene expression data have become available for the segment determination process in the Drosophila blastoderm, revealing a specific instance of canalization. These data show that the variation of the zygotic segmentation gene expression patterns is markedly reduced compared to earlier levels by the time gastrulation begins, and this variation is significantly lower than the variation of the maternal protein gradient Bicoid. We used a predictive dynamical model of gene regulation to study the effect of Bicoid variation on the downstream gap genes. The model correctly predicts the reduced variation of the gap gene expression patterns and allows the characterization of the canalizing mechanism. We show that the canalization is the result of specific regulatory interactions among the zygotic gap genes. We demonstrate the validity of this explanation by showing that variation is increased in embryos mutant for two gap genes, Krüppel and knirps, disproving competing proposals that canalization is due to an undiscovered morphogen, or that it does not take place at all. In an accompanying article in PLoS Computational Biology (doi:10.1371/journal.pcbi.1000303), we show that cross regulation between the gap genes causes their expression to approach dynamical attractors, reducing initial variation and providing a robust output. These results demonstrate that the Bicoid gradient is not sufficient to produce gap gene borders having the low variance observed, and instead this low variance is generated by gap gene cross regulation. More generally, we show that the complex multigenic phenomenon of canalization can be understood at a quantitative and predictive level by the application of a precise dynamical model.

The variation in the expression patterns of the gap genes in the blastoderm of the fruit fly Drosophila melanogaster reduces over time as a result of cross regulation between these genes, a fact that we have demonstrated in an accompanying article in PLoS Biology (see Manu et al., doi:10.1371/journal.pbio.1000049). This biologically essential process is an example of the phenomenon known as canalization. It has been suggested that the developmental trajectory of a wild-type organism is inherently stable, and that canalization is a manifestation of this property. Although the role of gap genes in the canalization process was established by correctly predicting the response of the system to particular perturbations, the stability of the developmental trajectory remains to be investigated. For many years, it has been speculated that stability against perturbations during development can be described by dynamical systems having attracting sets that drive reductions of volume in phase space. In this paper, we show that both the reduction in variability of gap gene expression as well as shifts in the position of posterior gap gene domains are the result of the actions of attractors in the gap gene dynamical system. Two biologically distinct dynamical regions exist in the early embryo, separated by a bifurcation at 53% egg length. In the anterior region, reduction in variation occurs because of stability induced by point attractors, while in the posterior, the stability of the developmental trajectory arises from a one-dimensional attracting manifold. This manifold also controls a previously characterized anterior shift of posterior region gap domains. Our analysis shows that the complex phenomena of canalization and pattern formation in the Drosophila blastoderm can be understood in terms of the qualitative features of the dynamical system. The result confirms the idea that attractors are important for developmental stability and shows a richer variety of dynamical attractors in developmental systems than has been previously recognized.