Bioactivity-Guided Isolation and Antihypertensive Activity of Citrullus colocynthis Polyphenols in Rats with Genetic Model of Hypertension.

Background and Objectives: Citrullus colocynthis belongs to the Cucurbitaceae family and is a wild medicinal plant used in folk literature to treat various diseases. The purpose of the current study was to explore the antihypertensive and antioxidant potentials of Citrullus colocynthis (CC) polyphenol-rich fractions using a spontaneous hypertensive rat (SHR) model. Materials and Methods: The concentrated aqueous ethanol extract of CC fruit was successively fractioned using solvents of increasing polarity, i.e., hexane, chloroform, ethyl acetate and n-butanol. The obtained extracts were analyzed for total phenolic content (TPC), total flavonoid content (TFC) and total flavonol content (TOF). Moreover, the CC extracts were further evaluated for radical scavenging capacity using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assays and antioxidant activity using inhibition of linoleic acid peroxidation and determination of reducing potential protocols. The phytochemical components were characterized by HPLC-MWD-ESI-MS in positive ionization mode. Results: The results showed that ethyl acetate fraction (EAF) exhibited a higher content of phenolic compounds in term of TPC (289 mg/g), TFC (7.6 mg/g) and TOF (35.7 mg/g). EAF showed higher antioxidant and DPPH and ABTS scavenging activities with SC50 values of 6.2 and 79.5 µg/mL, respectively. LCMS analysis revealed that twenty polyphenol compounds were identified in the EAF, including phenolic acids and flavonoids, mainly myricetin and quercetin derivatives. The in vivo antihypertensive activity of EAF of CC on SHR revealed that it significantly decreased the mean arterial pressure (MAP), systolic blood pressure (SBP), diastolic blood pressures (DBP) and pulse pressure (PP) as compared to normal and hypertensive control groups. Moreover, EAF of CC significantly reduced the oxidative stress in the animals in a dose-dependent manner by normalizing the levels of superoxide dismutase (SOD), malondialdehyde (MDA), reduced glutathione (GSH), nitric oxide (NOx) and total antioxidant capacity (TAC). Furthermore, the treatment groups, especially the 500 mg of EAF per kg body weight (EA-500) group, significantly (p ≤ 0.05) improved the electrocardiogram (ECG) pattern and pulse wave velocity (PWV). Conclusion: It was concluded that the EAF of CC is a rich source of polyphenols and showed the best antioxidant activity and antihypertensive potential in SHR.

Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments.

It is of paramount importance in plant breeding to have methods dealing with large numbers of predictor variables and few sample observations, as well as efficient methods for dealing with high correlation in predictors and measured traits. This paper explores in terms of prediction performance the partial least squares (PLS) method under single-trait (ST) and multi-trait (MT) prediction of potato traits. The first prediction was for tested lines in tested environments under a five-fold cross-validation (5FCV) strategy and the second prediction was for tested lines in untested environments (herein denoted as leave one environment out cross validation, LOEO). There was a good performance in terms of predictions (with accuracy mostly > 0.5 for Pearson's correlation) the accuracy of 5FCV was better than LOEO. Hence, we have empirical evidence that the ST and MT PLS framework is a very valuable tool for prediction in the context of potato breeding data.

Neuroprotective potential of plant derived parenchymal stem cells extract on environmental and genetic models of Parkinson disease through attenuation of oxidative stress and neuroinflammation.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by both motor and non-motor features. The current treatment regimen for PD are dopamine enhancers which have been reported to worsen the disease prognosis after long term treatment, thus, the need for better treatment options. This study sought to investigate the protective action of Double Stem Cell® (DSC), a blend of stem cells extracts from Swiss apples (Malus Domestica) and Burgundy grapes (Vitis vinifera) on 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced Parkinsonism in mice and genetic model of PD in Drosophila melanogaster. Male albino mice were pretreated with MPTP (4 × 20 mg/kg, i.p., two hourly in 8 h), twelve hours before administration of DSC (8, 40, or 200 mg/kg, p.o.). Thereafter, behavioural, biochemical and immunohistochemical assays were carried out. The impact of vehicle or DSC supplementation on α-synuclein aggregation was evaluated in Drosophila melanogaster using the UAS-Gal4 system, female DDC-Gal4 flies were crossed with male UAS-α-synuclein, the progenies were examined for fecundity, locomotion, memory, and lifespan. MPTP-induced motor deficits in open field test (OFT), working memory impairment (Y-maze test (YMT)) and muscle incoordination (rotarod test) were ameliorated by DSC (8, 40 or 200 mg/kg) through dose-dependent and significant improvements in motor, cognitive and motor coordination. Moreso, MPTP exposure caused significant increase in lipid peroxidation and decrease in antioxidant enzymes activities (glutathione, catalase and superoxide dismutase) in the midbrain which were attenuated by DSC. MPTP-induced expression of microglia (iba-1), astrocytes (glia fibrillary acidic protein; GFAP) as well as degeneration of dopamine neurons (tyrosine hydroxylase positive neurons) in the substantia nigra (SN) were reversed by DSC. Supplementation of flies feed with graded concentration of DSC (0.8, 4 or 20 mg/ml) did not affect fecundity but improved climbing activity and lifespan. Findings from this study showed that Double Stem Cell improved motor and cognitive functions in both mice and Drosophila through attenuation of neurotoxin-induced oxidative stress and neuroinflammation.

Neuroprotective Effects of Lycium Barbarum Fruit Extract on Pink1B9Drosophila Melanogaster Genetic Model of Parkinson's Disease.

Lycium barbarum (LB) is a famous traditional Chinese medicinal plant as well as food supplement possessing various pharmacological functions such as anti-aging and antioxidant effects. The Parkinson's disease (PD)-related kinase Pink1 plays vital role in maintaining the neuron cell homeostasis, having been recognized as a potential target for the development of anti-PD drugs. In this work, the neuroprotective effects of methanol extract of LB fruit (LBFE) were investigated using a Drosophila PD model (PINK1B9) and a human neuroblastoma SH-SY5Y cell line. We found that when LBFE was supplied to the PINK1B9 flies at 6, 12, and 18 days of age, it raised the ATP and dopamine levels at all ages, extended life span, improved motor behavior, and rescued olfactory deficits of the PINK1B9 flies. In addition, histopathological examinations indicated that muscle atrophy in thoraces of the mutant flies was significantly repaired. Finally, LBFE was able to rescue the SH-SY5Y cells against MPP+-induced neurotoxicity. This work reports for the first time the anti-PD potential of L. barbarum fruit extract in PINK1 mutant fruit flies, presenting a new viewpoint for studing the mechanism of action of LBFE.

A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids.

Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.

I-circular codes.

A message such as mRNA, which consists of continuous characters without separators (such as commas or spaces), can easily be decoded incorrectly if it is read in the wrong reading frame. One construct to theoretically avoid these reading frame errors is the class of block codes. However, the first hypothesis of Watson and Crick (1953) that block codes are used as a tool to avoid reading frame errors in coding sequences already failed because the four periodical codons AAA, CCC, GGG and UUU seem to play an important role in protein coding sequences. Even the class of circular codes later discovered by Arquès and Michel (1996) in coding sequences cannot contain a periodic codon. However, by incorporating the interpretation of the message into the robustness of the reading frame, the extension of circular codes to include periodic codons is theoretically possible. In this work, we introduce the new class of I-circular codes. Unlike circular codes, these codes allow frame shifts, but only if the decoded interpretation of the message is identical to the intended interpretation. In the following, the formal definition of I-circular codes is introduced and the maximum and the maximal size of I-circular codes are given based on the standard genetic code table. These numbers are calculated using a new graph-theoretic approach derived from the classical one for the class of circular codes. Furthermore, we show that all 216 maximum self-complementary C3-codes (see Fimmel et al., 2015) can be extended to larger I-circular codes. We present the increased code coverage of the 216 newly constructed I-circular codes based on the human coding sequences in chromosome 1. In the last section of this paper, we use the polarity of amino acids as an interpretation table to construct I-circular codes. In an optimization process, two maximum I-circular codes of length 30 are found.

flopp: Extremely Fast Long-Read Polyploid Haplotype Phasing by Uniform Tree Partitioning.

Resolving haplotypes in polyploid genomes using phase information from sequencing reads is an important and challenging problem. We introduce two new mathematical formulations of polyploid haplotype phasing: (1) the min-sum max tree partition problem, which is a more flexible graphical metric compared with the standard minimum error correction (MEC) model in the polyploid setting, and (2) the uniform probabilistic error minimization model, which is a probabilistic analogue of the MEC model. We incorporate both formulations into a long-read based polyploid haplotype phasing method called flopp. We show that flopp compares favorably with state-of-the-art algorithms-up to 30 times faster with 2 times fewer switch errors on 6 × ploidy simulated data. Further, we show using real nanopore data that flopp can quickly reveal reasonable haplotype structures from the autotetraploid Solanum tuberosum (potato).

Perinatal supplementation with omega-3 fatty acids corrects the aberrant social and cognitive traits observed in a genetic model of autism based on FMR1 deletion in rats.

Background and objective: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder for which no treatments exist. Fragile X syndrome (FXS) is the most common form of inherited mental retardation and the most frequent monogenic cause of ASD. Given the lack of pharmacological treatments for ASD, increasing interest is devoted to non-pharmacological approaches, including dietary interventions. Omega-3 polyunsaturated fatty acids (PUFAs) are critical for neurobehavioraldevelopment. This study had two aims: 1. To validatethe recently developed Fmr1-Δexon 8 rat model of FXS; 2. To assess the impact of omega-3 PUFAs dietary supplementation during pregnancy and lactation on the altered behavior displayed by Fmr1-Δexon 8 rats.Methods: Female Fmr1-Δexon 8 and wild-type Sprague-Dawley rats were fed with either an omega-3 PUFAs enriched diet or with an isocaloric control diet during pregnancy and lactation. Behavioral experiments were carried out on the infant (Postnatal days (PNDs) 9 and 13), juvenile (PND 35) and adult (PND 90) male offspring.Results: Fmr1-Δexon 8 pups showed hypolocomotion, reduced ultrasonic vocalizations (USVs) emission and impaired social discrimination compared to wild-type controls. Juvenile and adult Fmr1-Δexon 8 rats showed deficits in the social and cognitive domains, that were counteracted by perinatal omega-3 PUFAs supplementation.Conclusion: Our results support the validity of the Fmr1-Δexon 8 rat model to mimic key autistic-like features and support an important role of omega-3 PUFAs during of neurodevelopment. Although the mechanisms underlying the beneficial effects of omega-3 PUFAs supplementation in ASD needs to be clarified, this dietary intervention holds promise to mitigate core and comorbid autistic features.

The power of genomic estimated breeding values for selection when using a finite population size in genetic improvement of tetraploid potato.

Potato breeding relies heavily on visual phenotypic scoring for clonal selection. Obtaining robust phenotypic data can be labor intensive and expensive, especially in the early cycles of a potato breeding program where the number of genotypes is very large. We have investigated the power of genomic estimated breeding values (GEBVs) for selection from a limited population size in potato breeding. We collected genotypic data from 669 tetraploid potato clones from all cycles of a potato breeding program, as well as phenotypic data for eight important breeding traits. The genotypes were partitioned into a training and a test population distinguished by cycle of selection in the breeding program. GEBVs for seven traits were predicted for individuals from the first stage of the breeding program (T1) which had not undergone any selection, or individuals selected at least once in the field (T2). An additional approach in which GEBVs were predicted within and across full-sib families from unselected material (T1) was tested for four breeding traits. GEBVs were obtained by using a Bayesian Ridge Regression model estimating single marker effects and phenotypic data from individuals at later stages of selection of the breeding program. Our results suggest that, for most traits included in this study, information from individuals from later stages of selection cannot be utilized to make selections based on GEBVs in earlier clonal generations. Predictions of GEBVs across full-sib families yielded similarly low prediction accuracies as across generations. The most promising approach for selection using GEBVs was found to be making predictions within full-sib families.

QTL mapping in outbred tetraploid (and diploid) diallel populations.

Over the last decade, multiparental populations have become a mainstay of genetics research in diploid species. Our goal was to extend this paradigm to autotetraploids by developing software for quantitative trait locus (QTL) mapping in connected F1 populations derived from a set of shared parents. For QTL discovery, phenotypes are regressed on the dosage of parental haplotypes to estimate additive effects. Statistical properties of the model were explored by simulating half-diallel diploid and tetraploid populations with different population sizes and numbers of parents. Across scenarios, the number of progeny per parental haplotype (pph) largely determined the statistical power for QTL detection and accuracy of the estimated haplotype effects. Multiallelic QTL with heritability 0.2 were detected with 90% probability at 25 pph and genome-wide significance level 0.05, and the additive haplotype effects were estimated with over 90% accuracy. Following QTL discovery, the software enables a comparison of models with multiple QTL and nonadditive effects. To illustrate, we analyzed potato tuber shape in a half-diallel population with three tetraploid parents. A well-known QTL on chromosome 10 was detected, for which the inclusion of digenic dominance lowered the Deviance Information Criterion (DIC) by 17 points compared to the additive model. The final model also contained a minor QTL on chromosome 1, but higher-order dominance and epistatic effects were excluded based on the DIC. In terms of practical impacts, the software is already being used to select offspring based on the effect and dosage of particular haplotypes in breeding programs.

Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO.

Identifying individuals who are at high risk of cancer due to inherited germline mutations is critical for effective implementation of personalized prevention strategies. Most existing models focus on a few specific syndromes; however, recent evidence from multi-gene panel testing shows that many syndromes are overlapping, motivating the development of models that incorporate family history on several cancers and predict mutations for a comprehensive panel of genes.We present PanelPRO, a new, open-source R package providing a fast, flexible back-end for multi-gene, multi-cancer risk modeling with pedigree data. It includes a customizable database with default parameter values estimated from published studies and allows users to select any combinations of genes and cancers for their models, including well-established single syndrome BayesMendel models (BRCAPRO and MMRPRO). This leads to more accurate risk predictions and ultimately has a high impact on prevention strategies for cancer and clinical decision making. The package is available for download for research purposes at https://projects.iq.harvard.edu/bayesmendel/panelpro.

Genetic mutations that increase cancer risk can be passed down from parents to their children, which can affect families across many generations. In these families, multiple members may be affected by different types of cancer, and these cancers often develop at an early age. Unaffected family members are often referred to genetic counselling, where they can explore their own risk of cancer. Clinicians and genetic counselors can provide recommendations to minimize cancer risk and inform personal choices on how to manage that risk, such as opting for preventative surgeries or participating in regular screening. In genetic counselling sessions, highly trained clinicians and specialists use software that takes an individual's family history of cancer and uses it to estimate their individual risk of carrying certain genetic mutations. These estimates can in turn help to predict their future risk of cancer. Many existing software packages are limited to estimating risks based on mutations in well-known cancer-related genes, such as BRCA1 and BRCA2 in breast and ovarian cancer. However, emerging evidence suggests that many of the genes associated with cancer risk work as part of a complex and overlapping network. Since current risk-profiling software packages are only designed to consider such genes in isolation, they cannot generate the most robust, accurate or comprehensive cancer risk profiles. To address this challenge, Lee, Liang et al. have developed a new risk-profiling software that can integrate a large number of gene mutations and a wide range of potential cancer types to provide more accurate estimates of individual cancer risk. This software, called PanelPRO, uses evidence identified from extensive literature reviews to model the complex interplay between genes and cancer risk. The software not only calculates risks based on known genes, but also allows other developers to integrate new cancer-related genes that may be identified in the future. Importantly, the software is compatible with genetic counselling applications, since it returns answers within seconds when reasonable family and gene database sizes are used. PanelPRO is a new, modern, flexible and efficient software package that provides an important advance towards modelling the vast genetic and biological complexity that contributes to inherited cancer risk. This software is designed to provide a more accurate and comprehensive estimate of cancer risk for individuals with family histories of cancer. As an open-source software, it is freely available for research purposes, and can be licensed by software companies and healthcare organizations to integrate electronic patient records and rapidly identify at-risk individuals across larger patient groups. Ultimately, this software has the potential to improve cancer prevention strategies and optimize the personalized decision-making processes around cancer risk.

Realistic genetic architecture enables organismal adaptation as predicted under the folk definition of inclusive fitness.

A fundamental task of evolutionary biology is to explain the pervasive impression of organismal design in nature, including traits benefiting kin. Inclusive fitness is considered by many to be a crucial piece in this puzzle, despite ongoing discussion about its scope and limitations. Here, we use individual-based simulations to study what quantity (if any) individual organisms become adapted to maximize when genetic architectures are more or less suitable for the presumed main driver of biological adaptation, namely cumulative multi-locus evolution. As an expository device, we focus on a hypothetical situation called Charlesworth's paradox, in which altruism is seemingly predicted to evolve, yet altruists immediately perish along with their altruistic genes. Our results support a recently proposed re-definition of inclusive fitness, which is concerned with the adaptive design of whole organisms as shaped by multi-locus evolution, rather than with selection for any focal gene. They also illustrate how our conceptual understanding of adaptation at the phenotypic level should inform our choice of genetic assumptions in abstract simplified models.

Chronic cannabidiol (CBD) administration induces anticonvulsant and antiepileptogenic effects in a genetic model of epilepsy.

Cannabidiol (CBD) is a marijuana compound implicated in epilepsy treatment in animal models and pharmacoresistant patients. However, little is known about chronic CBD administration's effects in chronic models of seizures, especially regarding its potential antiepileptogenic effects. In the present study, we combined a genetic model of epilepsy (the Wistar Audiogenic Rat strain - WARs), a chronic protocol of seizures (the audiogenic kindling - AuK), quantitative and sequential behavioral analysis (neuroethology), and microscopy imaging to analyze the effects of chronic CBD administration in a genetic model of epilepsy. The acute audiogenic seizure is characterized by tonic-clonic seizures and intense brainstem activity. However, during the AuK WARs can develop limbic seizures associated with the recruitment of forebrain and limbic structures. Here, chronic CBD administration, twice a day, attenuated brainstem, tonic-clonic seizures, prevented limbic recruitment, and suppressed limbic (kindled) seizures, suggesting CBD antiepileptogenic effects. Additionally, CBD prevented chronic neuronal hyperactivity, suppressing FosB immunostaining in the brainstem (inferior colliculus and periaqueductal gray matter) and forebrain (basolateral amygdala nucleus and piriform cortex), structures associated with tonic-clonic and limbic seizures, respectively. Chronic seizures increased cannabinoid receptors type 1 (CB1R) immunostaining in the hippocampus and the BLA, while CBD administration prevented changes in CB1R expression induced by the AuK. The neuroethological analysis provided details about CBD's protective effects against brainstem and limbic seizures associated with FosB expression. Our results strongly suggest chronic CBD anticonvulsant and antiepileptogenic effects associated with reduced chronic neuronal activity and modulation of CB1R expression. We also support the chronic use of CBD for epilepsies treatments.

The effect of GABAergic neurotransmission on the seizure-related activity of the laterodorsal thalamic nuclei and the somatosensory cortex in a genetic model of absence epilepsy.

The present study aimed to investigate the alterations of the GABAergic system in the laterodorsal nucleus (LDN) of the thalamus and the somatosensory cortex (SC) in an experimental model of absence seizure. The effects of pharmacological manipulation of both GABAA and GABAB receptor subunits in the LDN on the generation of spike-wave discharges (SWD) were evaluated. The experiments were carried out in four groups of both WAG/Rij and Wistar rats with 2 and 6 months of age. The expressions of various GABA receptor subunits were studied in the LDN and SC. Furthermore, recordings of unit activity from the LDN and electrocorticography were simultaneously monitored before, during, and after the application of GABAA and GABAB antagonists in the LDN. The generation of SWD in the older WAG/Rij rats was associated with significant alterations in the expression of GABAARα1, GABAARß3, and GABABR2 subunits in the LDN as well as GABAARα1, GABAARß3, GABAARγ2, and GABABR2 subunits in the SC. Furthermore, the occurrence of SWD was associated with a significant reduction of gene expression of GABAARα1 and increase of GABAARß3 in the LDN as well as reduction of GABAARα1, GABAARß3, GABAARγ2, and GABABR2 in the SC. The microionthophoretic application of the GABAA antagonist bicuculline resulted in a significant increase in the population firing rate of LDN neurons as well as the mean number and duration of SWD. The application of the GABAB antagonist CGP35348 significantly increased the population firing rate of LDN neurons but decreased the mean number of SWD. Our data indicate the regulatory effect of the GABAergic system of the LDN and SC in absence seizures.

Innovative approach to identify multigenomic and environmental interactions associated with birth defects in family-based hybrid designs.

Genes, including those with transgenerational effects, work in concert with behavioral, environmental, and social factors via complex biological networks to determine human health. Understanding complex relationships between causal factors underlying human health is an essential step towards deciphering biological mechanisms. We propose a new analytical framework to investigate the interactions between maternal and offspring genetic variants or their surrogate single nucleotide polymorphisms (SNPs) and environmental factors using family-based hybrid study design. The proposed approach can analyze diverse genetic and environmental factors and accommodate samples from a variety of family units, including case/control-parental triads, and case/control-parental dyads, while minimizing potential bias introduced by population admixture. Comprehensive simulations demonstrated that our innovative approach outperformed the log-linear approach, the best available method for case-control family data. The proposed approach had greater statistical power and was capable to unbiasedly estimate the maternal and child genetic effects and the effects of environmental factors, while controlling the Type I error rate against population stratification. Using our newly developed approach, we analyzed the associations between maternal and fetal SNPs and obstructive and conotruncal heart defects, with adjustment for demographic and lifestyle factors and dietary supplements. Fourteen and 11 fetal SNPs were associated with obstructive and conotruncal heart defects, respectively. Twenty-seven and 17 maternal SNPs were associated with obstructive and conotruncal heart defects, respectively. In addition, maternal body mass index was a significant risk factor for obstructive defects. The proposed approach is a powerful tool for interrogating the etiological mechanism underlying complex traits.

MiR-CLIP reveals iso-miR selective regulation in the miR-124 targetome.

Many microRNAs regulate gene expression via atypical mechanisms, which are difficult to discern using native cross-linking methods. To ascertain the scope of non-canonical miRNA targeting, methods are needed that identify all targets of a given miRNA. We designed a new class of miR-CLIP probe, whereby psoralen is conjugated to the 3p arm of a pre-microRNA to capture targetomes of miR-124 and miR-132 in HEK293T cells. Processing of pre-miR-124 yields miR-124 and a 5'-extended isoform, iso-miR-124. Using miR-CLIP, we identified overlapping targetomes from both isoforms. From a set of 16 targets, 13 were differently inhibited at mRNA/protein levels by the isoforms. Moreover, delivery of pre-miR-124 into cells repressed these targets more strongly than individual treatments with miR-124 and iso-miR-124, suggesting that isomirs from one pre-miRNA may function synergistically. By mining the miR-CLIP targetome, we identified nine G-bulged target-sites that are regulated at the protein level by miR-124 but not isomiR-124. Using structural data, we propose a model involving AGO2 helix-7 that suggests why only miR-124 can engage these sites. In summary, access to the miR-124 targetome via miR-CLIP revealed for the first time how heterogeneous processing of miRNAs combined with non-canonical targeting mechanisms expand the regulatory range of a miRNA.

A tetrasomic inheritance model and likelihood-based method for mapping quantitative trait loci in autotetraploid species.

Dissecting the genetic architecture of quantitative traits in autotetraploid species is a methodologically challenging task, but a pivotally important goal for breeding globally important food crops, including potato and blueberry, and ornamental species such as rose. Mapping quantitative trait loci (QTLs) is now a routine practice in diploid species but is far less advanced in autotetraploids, largely due to a lack of analytical methods that account for the complexities of tetrasomic inheritance. We present a novel likelihood-based method for QTL mapping in outbred segregating populations of autotetraploid species. The method accounts properly for sophisticated features of gene segregation and recombination in an autotetraploid meiosis. It may model and analyse molecular marker data with or without allele dosage information, such as that from microarray or sequencing experiments. The method developed outperforms existing bivalent-based methods, which may fail to model and analyse the full spectrum of experimental data, in the statistical power of QTL detection, and accuracy of QTL location, as demonstrated by an intensive simulation study and analysis of data sets collected from a segregating population of potato (Solanum tuberosum). The study enables QTL mapping analysis to be conducted in autotetraploid species under a rigorous tetrasomic inheritance model.

Identification of determinants of pollen donor fecundity using the hierarchical neighborhood model.

Individual differences in male reproductive success drive genetic drift and natural selection, altering genetic variation and phenotypic trait distributions in future generations. Therefore, identifying the determinants of reproductive success is important for understanding the ecology and evolution of plants. Here, based on the spatially explicit mating model (the neighborhood model), we develop a hierarchical probability model that links co-dominant genotypes of offspring and candidate parents with phenotypic determinants of male reproductive success. The model accounts for pollen dispersal, genotyping errors as well as individual variation in selfing, pollen immigration, and differentiation of immigrant pollen pools. Unlike the classic neighborhood model approach, our approach is specially designed to account for excessive variation (overdispersion) in male fecundity. We implemented a Bayesian estimation method (the Windows computer program available at: https://www.ukw.edu.pl/pracownicy/plik/igor_chybicki/1806/) that, among others, allows for selecting phenotypic variables important for male fecundity and assessing the fraction of variance in fecundity (R2 ) explained by selected variables. Simulations showed that our method outperforms both the classic neighborhood model and the two-step approach, where fecundities and the effects of phenotypic variables are estimated separately. The analysis of two data examples showed that in wind-pollinated trees, male fecundity depends on both the amount of produced pollen and the ability to pollen spread. However, despite that the tree size was positively correlated with male fecundity, it explained only a fraction of the total variance in fecundity, indicating the presence of additional factors. Finally, case studies highlighted the importance of accounting for pollen dispersal in the estimation of fecundity determinants.

Haplotype threading: accurate polyploid phasing from long reads.

Resolving genomes at haplotype level is crucial for understanding the evolutionary history of polyploid species and for designing advanced breeding strategies. Polyploid phasing still presents considerable challenges, especially in regions of collapsing haplotypes.We present WHATSHAP POLYPHASE, a novel two-stage approach that addresses these challenges by (i) clustering reads and (ii) threading the haplotypes through the clusters. Our method outperforms the state-of-the-art in terms of phasing quality. Using a real tetraploid potato dataset, we demonstrate how to assemble local genomic regions of interest at the haplotype level. Our algorithm is implemented as part of the widely used open source tool WhatsHap.

Integrating binding and expression data to predict transcription factors combined function.

BACKGROUND: Transcription factor binding to the regulatory region of a gene induces or represses its gene expression. Transcription factors share their binding sites with other factors, co-factors and/or DNA-binding proteins. These proteins form complexes which bind to the DNA as one-units. The binding of two factors to a shared site does not always lead to a functional interaction. RESULTS: We propose a method to predict the combined functions of two factors using comparable binding and expression data (target). We based this method on binding and expression target analysis (BETA), which we re-implemented in R and extended for this purpose. target ranks the factor's targets by importance and predicts the dominant type of interaction between two transcription factors. We applied the method to simulated and real datasets of transcription factor-binding sites and gene expression under perturbation of factors. We found that Yin Yang 1 transcription factor (YY1) and YY2 have antagonistic and independent regulatory targets in HeLa cells, but they may cooperate on a few shared targets. CONCLUSION: We developed an R package and a web application to integrate binding (ChIP-seq) and expression (microarrays or RNA-seq) data to determine the cooperative or competitive combined function of two transcription factors.