Genome-wide Molecular Data Research Articles

Unveiling a potential threat to forest ecosystems: molecular diagnosis of Alliaria petiolata, a newly introduced alien plant in Korea.

Identifying stages of a species invasion in a new habitat (i.e., colonization, establishment, and landscape spread) and their primary determinants in biological invasion warrants attention, as it provides vital insights for preventing non-native species from becoming pervasive invaders. However, delineating invasion stages and their associated factors can pose significant challenges due to the ambiguous distinctions between these stages. Alliaria petiolata, one of the most noxious weeds in woodland habitats, has recently been introduced to Korea and observed in a few distant locations. Although the plant's spread has been relatively slow thus far, rapid spread is highly likely in the future, given the high invasive potential reported elsewhere. We indirectly diagnose the current status of A. petiolata invasion in Korea through the assessment of genetic diversity and phylogenetic inferences using genome-wide molecular markers and cytological data. We analyzed 86 individual samples collected from two native and six introduced populations, employing 1,172 SNPs. Our analysis estimated within- and among-population genetic diversity and included two clustering analyses. Furthermore, we investigated potential gene flow and reticulation events among the sampled populations. Our data unraveled that Korean garlic mustard exhibits a hexaploid ploidy level with two distinct chromosome numbers, 2n = 36 and 42. The extent of genetic diversity measured in Korean populations was comparable to that of native populations. Using genome-wide SNP data, we identified three distinct clusters with minor gene flow, while failing to detect indications of reticulation among Korean populations. Based on the multifaceted analyses, our study provides valuable insights into the colonization process and stressed the importance of closely monitoring A. petiolata populations in Korea.

Low depth sequencing reveals the critically endangered Batagur kachuga (Red-crowned roofed turtle) mitochondrial genome and its evolutionary implications

The Batagur kachuga (B. kachuga), commonly known as the Red-crowned roofed turtle, is a critically endangered species native to India and its neighboring countries like Bangladesh, and Nepal. The present study is the first report of the complete mitochondrial genome of B. kachuga (16,517 bp) construed via the next-generation sequencing (NGS) approach from eggshell DNA. There are 22 transfer RNAs (tRNAs), 2 ribosomal RNAs (rRNAs), 13 protein-coding genes (PCGs), and one putative control region (CR/D-loop) in the mitogenome. The CR region from the current study reveals conserved TAS, CD, and CSB domains and two AT-rich tandem repeat regions. Most genes are encoded in the heavy strand except the NADH dehydrogenase subunit 6 (ND6) gene and seven tRNA genes. Most PCGs start with the initiation codon ATG, except the COI (Cytochrome Oxidase Subunit-I) gene, which starts with the GTG codon. The present investigation also predicts the distinctive cloverleaf structures of tRNAs except for tRNA-Ser1 and tRNA-Ser-2, which lack a DHU arm. The comparative analysis of Ka/Ks with other 33 species from Order Testudines, in relation to B. kachuga, revealed negative selection in most PCGs, indicating a process of preservation and purification that aids in eliminating undesirable or detrimental substitutes. Phylogenetic analysis of this species has been analysed using the complete mitogenome of 33 turtle species. The maximum likelihood phylogenetic tree strongly supports each family in different clades and also reveals a close relationship between the Pangashura and Batagur genera. Our study suggests the generation of genome-wide molecular data, in terms of mitogenomes, SNPs, and SSRs, is needed to improve the understanding of this species and their phylogenetics and evolutionary relationships, which will help to improve the conservation efforts of this species.

A revised phylogeny of Boletaceae using whole genome sequences

ABSTRACT The porcini mushroom family Boletaceae is a diverse, widespread group of ectomycorrhizal (ECM) mushroom-forming fungi that so far has eluded intrafamilial phylogenetic resolution based on morphology and multilocus data sets. In this study, we present a genome-wide molecular data set of 1764 single-copy gene families from a global sampling of 418 Boletaceae specimens. The resulting phylogenetic analysis has strong statistical support for most branches of the tree, including the first statistically robust backbone. The enigmatic Phylloboletellus chloephorus from non-ECM Argentinian subtropical forests was recovered as a new subfamily sister to the core Boletaceae. Time-calibrated branch lengths estimate that the family first arose in the early to mid-Cretaceous and underwent a rapid radiation in the Eocene, possibly when the ECM nutritional mode arose with the emergence and diversification of ECM angiosperms. Biogeographic reconstructions reveal a complex history of vicariance and episodic long-distance dispersal correlated with historical geologic events, including Gondwanan origins and inferred vicariance associated with its disarticulation. Together, this study represents the most comprehensively sampled, data-rich molecular phylogeny of the Boletaceae to date, establishing a foundation for future robust inferences of biogeography in the group.

Genomic selection for meat quality traits in Pekin duck

Genomic selection uses genome-wide molecular marker data to predict an animal's genetic value in the breeding program. This study's objective was to present heritability estimates and accuracy of genomic prediction using different methods for meat quality traits in Pekin duck. There were two kinds of ducks in the genomic selection training population: 639 fat-type ducks and 540 lean-type ducks. A single-trait animal model was used to estimate heritability and adjust the phenotype. GBLUP and BayesR methods were performed to estimate the SNP effects. The accuracy of genomic prediction was calculated using 5-fold cross-validation. The accuracy varied from 0.235 to 0.501 with the lowest accuracy estimated for traits associated with abdominal fat weight in the combined population and the most remarkable accuracy observed for abdominal fat percentage traits in the lean-type duck population. Overall, BayesR can achieve the highest prediction accuracy, while the combined population strategy could be used to increase the accuracy of prediction only when the two populations have the same breeding aim for a certain trait.

Evaluation of Genomic Prediction for Fusarium Head Blight Resistance with a Multi-Parental Population.

Simple SummaryGenomic selection is a promising approach to select superior wheat lines with better resistance to Fusarium head blight. The accuracy of genomic selection is determined by many factors. In this study, we found a training population with large size, genomic selection models incorporating biological information, and multi-environment modelling led to considerably better predictabilities. A training population designed by the coefficient of determination (CDmean) could increase accuracy of prediction. Relatedness between training population (TP) and testing population is the key for accuracies of genomic selection across populations. Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat. In this study, we performed a comprehensive evaluation of factors that affect GS accuracies with a multi-parental population designed for FHB resistance. We found larger sample sizes could get better accuracies. Training population designed by CDmean based optimization algorithms significantly increased accuracies than random sampling approach, while mean of predictor error variance (PEVmean) had the poorest performance. Different genomic selection models performed similarly for accuracies. Including prior known large effect quantitative trait loci (QTL) as fixed effect into the GS model considerably improved the predictability. Multi-traits models had almost no effects, while the multi-environment model outperformed the single environment model for prediction across different environments. By comparing within and across family prediction, better accuracies were obtained with the training population more closely related to the testing population. However, achieving good accuracies for GS prediction across populations is still a challenging issue for GS application.

Abstract 2085: The evolutionary trajectory of epigenomics in adult glioma

Abstract Background: Glioma is the most common malignant tumor of the central nervous system, often behaving very aggressively. Recently, The Cancer Genome Atlas (TCGA) and others have shown that epigenomic alterations in primary glioma tumors have prognostic and predictive roles, but there is a gap in knowledge of the molecular alterations after glioma treatment. In order to fill this gap, the Glioma Longitudinal AnalySiS (GLASS) Consortium, a multi-national collaboration from 13 institutions, is investigating genome-wide molecular data from primary and recurrent matched pairs. The current data freeze has DNA methylation data for 266 primary-recurrent pairs fromIllumina 450K and EPIC array platforms. Methods: We hypothesize that there will be evidence of aggressivity in the DNA methylation profiles of recurrent tumors relative to their matched primary, and we explored this through tumor subtyping, patterns of differentially methylated CpGs (DMPs), and epigenomic aging. Results: The subtype classification in the primary tumors was as follows for IDH wildtype tumors - 43.8%Classical, 47.3% Mesenchymal, 8.7% PA-like, none LGm6-GBM and for IDH mutant tumors 19.1% Codel, 79.4% G-CIMP-high, 1.3% G-CIMP-low. We observed that among IDH wildtype tumors 29.8% changed subtype, 47.1% of which shifted to the more aggressive Mesenchymal-like subtype. In IDH mutant tumors, 26.0% changed at recurrence, of which 57.9% shifted to the aggressive G-CIMP-low subtype. Patterns of DMPs in IDH mutant-code tumors (15 pairs) showed a loss of methylation upon recurrence, with 651 DMPs identified (paired Wilcoxon test, FDR &lt0.05). In unsupervised clustering, recurrent Codel tumors thus move away from IDH mutant tumors and align more closely to IDH wildtype tumors. We do not see the same increases in hypomethylation upon recurrence among IDHwt tumors, with only 17 DMPs at FDR &lt0.05 in the classical subtype (24 pairs). It has been shown that biological age estimates of the tumor using DNA methylation can predict aggressivity of certain tumor types. We examined epigenetic aging changes between primary and recurrent tumors, relative to the patients chronological age, with Horvath (tissue-based) and epiTOC (mitotic-based) DNA methylation clocks. Both clocks showed shifts in age acceleration, that is increased biological age in relation to chronological age. For the Horvath clock, we found increased age acceleration in Classical-like tumors (primary=39.5±8.9, recurrent 41.7± 19.7) and Codel tumors (primary= 57.0±22.3, recurrent= 64.2±31.8). Conclusions: Collectively, we observed glioma tumor epigenetic changes from the primary to recurrent state and these differences tend to reflect a shift to a more aggressive phenotype. Future work will explore the relationship of these findings with clinical treatments received between primary and recurrent states. Citation Format: Indrani Datta, Tathi Malta, Thais Sabedot, Ruicong She, The GLASS Consortium, Antonio Iavarone, Houtan Noushmehr, Laila M. Poisson. The evolutionary trajectory of epigenomics in adult glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2085.

The Spatial Signature of Introgression After a Biological Invasion With Hybridization

The accumulation of genome-wide molecular data has emphasized the important role of hybridization in the evolution of many organisms, which may carry introgressed genomic segments resulting from past admixture events with other taxa. Despite a number of examples of hybridization occurring during biological invasions, the resulting spatial patterns of genomic introgression remain poorly understood. Preliminary simulation studies have suggested a heterogeneous spatial level of introgression for invasive taxa after range expansion. We investigated in detail the robustness of this pattern and its persistence over time for both invasive and local organisms. Using spatially explicit simulations, we explored the spatial distribution of introgression across the area of colonization of an invasive taxon hybridizing with a local taxon. The general pattern for neutral loci supported by our results is an increasing introgression of local genes into the invasive taxon with the increase in the distance from the source of the invasion and a decreasing introgression of invasive genes into the local taxon. However, we also show there is some variation in this general trend depending on the scenario investigated. Spatial heterogeneity of introgression within a given taxon is thus an expected neutral pattern in structured populations after a biological invasion with a low to moderate amount of hybridization. We further show that this pattern is consistent with published empirical observations. Using additional simulations, we argue that the spatial pattern of Neanderthal introgression in modern humans, which has been documented to be higher in Asia than in Europe, can be explained by a model of hybridization with Neanderthals in Eurasia during the range expansion of modern humans from Africa. Our results support the view that weak hybridization during range expansion may explain spatially heterogeneous introgression patterns without the need to invoke selection.

Shared molecular genetic risk of alcohol dependence and posttraumatic stress disorder (PTSD).

Alcohol use disorder (AUD) and posttraumatic stress disorder (PTSD) frequently co-occur, highlighting the importance of understanding the etiology of these comorbid conditions. Although AUD and PTSD are moderately heritable with modest overlap in genetic risk as estimated from family studies, there has been a paucity of work using molecular genetic data to estimate shared genetic effects on these conditions. This study used large-scale genomewide molecular data to examine shared genetic risk for AUD, specifically alcohol dependence (AD), and PTSD through cross-trait linkage disequilibrium (LD) score regression (LDSC; also known as LDSR). Summary statistics came from the Psychiatric Genomics Consortium (PGC) PTSD Workgroup Freeze 2 European ancestry (EA) participants (N = 174,659) and AD summary statistics in EA participants (N = 38,686) came from the PGC Substance Use Disorders (SUD) Workgroup. We performed LDSC to estimate genetic correlation between AD and PTSD using HapMap3 variants and LD scores from the 1000 Genomes project. A moderate, significant correlation was observed between AD and PTSD (rg = .35, p = .02), with sex differences identified through stratified analyses. Our results are the first to demonstrate evidence of a shared molecular genetic etiology for AD and PTSD. Further research is needed to better understand possible sex differences in shared heritability and extend these results to additional populations. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A Comparative Phylogeographic Approach to Facilitate Recovery of an Imperiled Freshwater Mussel (Bivalvia: Unionida: Potamilus inflatus)

North American freshwaters are among the world’s most threatened ecosystems, and freshwater mussels are among the most imperiled inhabiting these systems. A critical aspect of conservation biology is delineating patterns of genetic diversity, which can be difficult when a taxon has been extirpated from a significant portion of its historical range. In such cases, evaluating conservation and recovery options may benefit by using surrogate species as proxies when assessing overall patterns of genetic diversity. Here, we integrate the premise of surrogate species into a comparative phylogeographic framework to hypothesize genetic relationships between extant and extirpated populations of Potamilus inflatus by characterizing genetic structure in co-distributed congeners with similar life histories and dispersal capabilities. Our mitochondrial and nuclear sequence data exhibited variable patterns of genetic divergence between Potamilus spp. native to the Mobile and Pascagoula + Pearl + Pontchartrain (PPP) provinces. However, hierarchical Approximate Bayesian Computation indicated that the diversification between Mobile and PPP clades was synchronous and represents a genetic signature of a common history of vicariance. Recent fluctuations in sea-level appear to have caused Potamilus spp. in the PPP to form a single genetic cluster, providing justification for using individuals from the Amite River as a source of brood stock to re-establish extirpated populations of P. inflatus. Future studies utilizing eDNA and genome-wide molecular data are essential to better understand the distribution of P. inflatus and establish robust recovery plans. Given the imperilment status of freshwater mussels globally, our study represents a useful methodology for predicting relationships among extant and extirpated populations of imperiled species.

Modeling Cellular Response in Large-Scale Radiogenomic Databases to Advance Precision Radiotherapy.

Radiotherapy is integral to the care of a majority of patients with cancer. Despite differences in tumor responses to radiation (radioresponse), dose prescriptions are not currently tailored to individual patients. Recent large-scale cancer cell line databases hold the promise of unravelling the complex molecular arrangements underlying cellular response to radiation, which is critical for novel predictive biomarker discovery. Here, we present RadioGx, a computational platform for integrative analyses of radioresponse using radiogenomic databases. We fit the dose-response data within RadioGx to the linear-quadratic model. The imputed survival across a range of dose levels (AUC) was a robust radioresponse indicator that correlated with biological processes known to underpin the cellular response to radiation. Using AUC as a metric for further investigations, we found that radiation sensitivity was significantly associated with disruptive mutations in genes related to nonhomologous end joining. Next, by simulating the effects of different oxygen levels, we identified putative genes that may influence radioresponse specifically under hypoxic conditions. Furthermore, using transcriptomic data, we found evidence for tissue-specific determinants of radioresponse, suggesting that tumor type could influence the validity of putative predictive biomarkers of radioresponse. Finally, integrating radioresponse with drug response data, we found that drug classes impacting the cytoskeleton, DNA replication, and mitosis display similar therapeutic effects to ionizing radiation on cancer cell lines. In summary, RadioGx provides a unique computational toolbox for hypothesis generation to advance preclinical research for radiation oncology and precision medicine. SIGNIFICANCE: The RadioGx computational platform enables integrative analyses of cellular response to radiation with drug responses and genome-wide molecular data. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/79/24/6227/F1.large.jpg.See related commentary by Spratt and Speers, p. 6076.

Morphology and molecules reveal two new species of Porites (Scleractinia, Poritidae) from the Red Sea and the Gulf of Aden

Two new reef coral species, Porites farasani sp. nov. and Porites hadramauti sp. nov. (Scleractinia, Poritidae), are described from the Red Sea and the Gulf of Aden. Porites farasani sp. nov. only occurs in the Farasan Islands in the southern Red Sea, while P. hadramauti sp. nov. has been collected in the Yemen Hadramaut region in the Gulf of Aden. Both species presented striking in situ differences with respect to other Porites species, and were characterized by small encrusting colonies and unusual polyp colouration. In order to test the genetic distinctiveness of P. farasani sp. nov. and P. hadramauti sp. nov. between each other and with respect to other representatives in the genus Porites, we investigated their evolutionary relationships with eight other morphological species of Porites occurring in the Red Sea and in the Gulf of Aden. Two DNA loci, the mitochondrial putative control region and the nuclear ribosomal ITS region, were sequenced, and three species delimitation approaches based on barcoding threshold (Automated Barcoding Gap Discovery) and coalescence theory (Poisson-Tree process, Generalized Mixed Yule Coalescent) were applied. Phylogenetic and species delimitation analyses were overall concordant, resolving P. farasani sp. nov. and P. hadramauti sp. nov. as two divergent but closely related lineages. Of the other morphologically defined Porites species, three were genetically differentiated (P. rus, P. columnaris and P. fontanesii), but five were genetically indistinguishable. The discovery of two regional endemics confirms the importance of the Red Sea and the Gulf of Aden as regions of high biodiversity and suggests the need for an integration of genome-wide molecular data with the re-evaluation of skeletal structures in the systematics of Porites.

Deep fusion of contextual and object-based representations for delineation of multiple nuclear phenotypes.

Nuclear delineation and phenotypic profiling are important steps in the automated analysis of histology sections. However, these are challenging problems due to (i) technical variations (e.g. fixation, staining) that originate as a result of sample preparation; (ii) biological heterogeneity (e.g. vesicular versus high chromatin phenotypes, nuclear atypia) and (iii) overlapping nuclei. This Application-Note couples contextual information about the cellular organization with the individual signature of nuclei to improve performance. As a result, routine delineation of nuclei in H&E stained histology sections is enabled for either computer-aided pathology or integration with genome-wide molecular data. The method has been evaluated on two independent datasets. One dataset originates from our lab and includes H&E stained sections of brain and breast samples. The second dataset is publicly available through IEEE with a focus on gland-based tissue architecture. We report an approximate AJI of 0.592 and an F1-score 0.93 on both datasets. The code-base, modified dataset and results are publicly available. Supplementary data are available at Bioinformatics online.

Disturbed Placental Imprinting in Preeclampsia Leads to Altered Expression of DLX5, a Human-Specific Early Trophoblast Marker.

Preeclampsia is a complex and common human-specific pregnancy syndrome associated with placental pathology. The human specificity provides both intellectual and methodological challenges, lacking a robust model system. Given the role of imprinted genes in human placentation and the vulnerability of imprinted genes to loss of imprinting changes, there has been extensive speculation, but no robust evidence, that imprinted genes are involved in preeclampsia. Our study aims to investigate whether disturbed imprinting contributes to preeclampsia. We first aimed to confirm that preeclampsia is a disease of the placenta by generating and analyzing genome-wide molecular data on well-characterized patient material. We performed high-throughput transcriptome analyses of multiple placenta samples from healthy controls and patients with preeclampsia. Next, we identified differentially expressed genes in preeclamptic placentas and intersected them with the list of human imprinted genes. We used bioinformatics/statistical analyses to confirm association between imprinting and preeclampsia and to predict biological processes affected in preeclampsia. Validation included epigenetic and cellular assays. In terms of human specificity, we established an in vitro invasion-differentiation trophoblast model. Our comparative phylogenetic analysis involved single-cell transcriptome data of human, macaque, and mouse preimplantation embryogenesis. We found disturbed placental imprinting in preeclampsia and revealed potential candidates, including GATA3 and DLX5, with poorly explored imprinted status and no prior association with preeclampsia. As a result of loss of imprinting, DLX5 was upregulated in 69% of preeclamptic placentas. Levels of DLX5 correlated with classic preeclampsia markers. DLX5 is expressed in human but not in murine trophoblast. The DLX5high phenotype resulted in reduced proliferation, increased metabolism, and endoplasmic reticulum stress-response activation in trophoblasts in vitro. The transcriptional profile of such cells mimics the transcriptome of preeclamptic placentas. Pan-mammalian comparative analysis identified DLX5 as part of the human-specific regulatory network of trophoblast differentiation. Our analysis provides evidence of a true association among disturbed imprinting, gene expression, and preeclampsia. As a result of disturbed imprinting, the upregulated DLX5 affects trophoblast proliferation. Our in vitro model might fill a vital niche in preeclampsia research. Human-specific regulatory circuitry of DLX5 might help explain certain aspects of preeclampsia.

Demography and genome divergence of lake and stream populations of an East African cichlid fish.

Disentangling the processes and mechanisms underlying adaptive diversification is facilitated by the comparative study of replicate population pairs that have diverged along a similar environmental gradient. Such a setting is realized in a cichlid fish from southern Lake Tanganyika, Astatotilapia burtoni, which occurs within the lake proper as well as in various affluent rivers. Previously, we demonstrated that independent lake and stream populations show similar adaptations to the two habitat regimes. However, little is known about the evolutionary and demographic history of the A.burtoni populations in question and the patterns of genome divergence among them. Here, we apply restriction site-associated DNA sequencing (RADseq) to examine the evolutionary history, the population structure and genomic differentiation of lake and stream populations in A.burtoni. A phylogenetic reconstruction based on genome-wide molecular data largely resolved the evolutionary relationships among populations, allowing us to re-evaluate the independence of replicate lake-stream population clusters. Further, we detected a strong pattern of isolation by distance, with baseline genomic divergence increasing with geographic distance and decreasing with the level of gene flow between lake and stream populations. Genome divergence patterns were heterogeneous and inconsistent among lake-stream population clusters, which is explained by differences in divergence times, levels of gene flow and local selection regimes. In line with the latter, we only detected consistent outlier loci when the most divergent lake-stream population pair was excluded. Several of the thus identified candidate genes have inferred functions in immune and neuronal systems and show differences in gene expression between lake and stream populations.

Computational models for predicting drug responses in cancer research.

The computational prediction of drug responses based on the analysis of multiple types of genome-wide molecular data is vital for accomplishing the promise of precision medicine in oncology. This will benefit cancer patients by matching their tumor characteristics to the most effective therapy available. As larger and more diverse layers of patient-related data become available, further demands for new bioinformatics approaches and expertise will arise. This article reviews key strategies, resources and techniques for the prediction of drug sensitivity in cell lines and patient-derived samples. It discusses major advances and challenges associated with the different model development steps. This review highlights major trends in this area, and will assist researchers in the assessment of recent progress and in the selection of approaches to emerging applications in oncology.

Inferring Population Size History from Large Samples of Genome-Wide Molecular Data - An Approximate Bayesian Computation Approach.

Inferring the ancestral dynamics of effective population size is a long-standing question in population genetics, which can now be tackled much more accurately thanks to the massive genomic data available in many species. Several promising methods that take advantage of whole-genome sequences have been recently developed in this context. However, they can only be applied to rather small samples, which limits their ability to estimate recent population size history. Besides, they can be very sensitive to sequencing or phasing errors. Here we introduce a new approximate Bayesian computation approach named PopSizeABC that allows estimating the evolution of the effective population size through time, using a large sample of complete genomes. This sample is summarized using the folded allele frequency spectrum and the average zygotic linkage disequilibrium at different bins of physical distance, two classes of statistics that are widely used in population genetics and can be easily computed from unphased and unpolarized SNP data. Our approach provides accurate estimations of past population sizes, from the very first generations before present back to the expected time to the most recent common ancestor of the sample, as shown by simulations under a wide range of demographic scenarios. When applied to samples of 15 or 25 complete genomes in four cattle breeds (Angus, Fleckvieh, Holstein and Jersey), PopSizeABC revealed a series of population declines, related to historical events such as domestication or modern breed creation. We further highlight that our approach is robust to sequencing errors, provided summary statistics are computed from SNPs with common alleles.

In Silico Modeling of Oncogenic Drivers in Waldenstrom Macroglobulinemia to Assess Additional Therapeutic Targets within the BCR Signaling Pathway Identifies MEK1/2 As a Target: Potential Therapeutic Role of Binimetinib

Background: The observation that B-cell receptor (BCR) signaling play a critical role in the survival of malignant Waldenstrom macroglobulinemia (WM) cells is highlighted by clinical efficacy of the BTK-inhibitor, ibrutinib in WM patients. BCR signaling leads to downstream activation of several intermediary components whose activity results in increased cell proliferation. Although disruption of this axis at the proximal end (BCR) has proved successful in WM, the exact role and impact of targeting the distal intermediaries remains unknown, particularly in aggressive forms of the disease. This is important as induction of resistance to ibrutinib is clearly documented which can either be mediated through mutational changes in BTK or other members within the BCR signalosome. Using a novel simulation-based approach, we reverse-engineered the WM cell molecular architecture to uncover the role of oncogenic intermediaries distal to the BCR complex in advanced-stage WM and conducted a virtual drug-screen targeting these pathways with in vitro validation.Methods: The human WM cell line, RPCI-WM1, was used in simulation and validation experiments. RPCI-WM1 was established from a highly drug-refractory advanced disease stage patient refractory to both rituximab and bortezomib. Publically available as well as proprietary genomic and cytogenetic data was utilized for the creation of the RPCI-WM1 patient avatar, which through simulation identified the salient and prominently dysregulated cellular pathways. Importantly, illustrating these pathways highlights common convergence points on increased proliferation and viability. These convergence points were then directly and indirectly targeted by simulated testing of a library of FDA approved drugs and those impacting these dysregulated pathways were shortlisted. A standardized library of equations models all the biological reactions such as enzymatic reactions, allosteric binding and protein modulation by phosphorylation, de-phosphorylation, ubiquitination, acetylation, prenylation and others. A library of FDA approved drug agents (n~150) and those in clinical study has been developed and was simulated individually and in combination on the RPCI-WM1 (advanced stage WM patient) avatar.Results: MYD88L265P mutation, absence of CXCR4 mutations and additional chromosomal aberrations (derivatives, translocations, deletions and amplifications of chromosomes 3, 6, 9, 13, 18 and 19), which are notable features of RPCI-WM1 cells were configured and modeled in silico. The RPCI-WM1 patient avatar was predicted to have increased IRAK1/4 engagement due to MYD88 mutation and high copy number (CN) of IL18. Downregulation of DUSP1 through MYD88-mediated signaling was noted to result in high ERK activity. Increase gene copy number of both FOS and ETS1, which are downstream of ERK, were noted. Notably, FOS is a key regulator of the AP1 complex, whose activity is regulated upstream by ERK. The transcription factor, ETS1 was also predicted as over expressed and ERK-mediated phosphorylation of ETS1 regulates the activity of ETS1. The RPCI-WM1 patient avatar model also indicated high AKT due to indirect convergence of multiple aberrations. Drug screening revealed sensitivity to the MEK inhibitor, binimetinib. Although no copy number variation in MEK-ERK pathway genes were detected, per simulation, MYD88 mutation through inactivation of DUSP1 activated ERK and downstream associated survival pathways. The simulation predictions were experimentally validated. As predicted, binimetinib significantly inhibited proliferation and viability of RPCI-WM1 cells (IC50 <100nM).Conclusions: Our study demonstrates the functional role and impact of MEK1/2 - an oncogenic component distal to the BCR, and whose activity can be targeted with binimetinib to elicit a lethal effect in advanced-stage WM cells. We also show the utility of a novel technology, which is capable of integrating genome-wide molecular data points to emulate the salient genomic drivers of a given tumor cell and test its sensitivity to numerous drugs in a high-throughput manner for truly personalized therapy. DisclosuresVali:Cellworks Group, Inc.: Employment, Equity Ownership. Kumar:Cellworks Group, Inc.: Employment. Singh:Cellworks Group, Inc.: Employment. Usmani:3Cellworks Research India Limited: Employment. Grover:3Cellworks Research India Limited: Employment. Abbasi:Cellworks Group, Inc.: Employment, Equity Ownership.

Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy.

Pea is an important food and feed crop and a valuable component of low-input farming systems. Improving resistance to biotic and abiotic stresses is a major breeding target to enhance yield potential and regularity. Genomic selection (GS) has lately emerged as a promising technique to increase the accuracy and gain of marker-based selection. It uses genome-wide molecular marker data to predict the breeding values of candidate lines to selection. A collection of 339 genetic resource accessions (CRB339) was subjected to high-density genotyping using the GenoPea 13.2K SNP Array. Genomic prediction accuracy was evaluated for thousand seed weight (TSW), the number of seeds per plant (NSeed), and the date of flowering (BegFlo). Mean cross-environment prediction accuracies reached 0.83 for TSW, 0.68 for NSeed, and 0.65 for BegFlo. For each trait, the statistical method, the marker density, and/or the training population size and composition used for prediction were varied to investigate their effects on prediction accuracy: the effect was large for the size and composition of the training population but limited for the statistical method and marker density. Maximizing the relatedness between individuals in the training and test sets, through the CDmean-based method, significantly improved prediction accuracies. A cross-population cross-validation experiment was further conducted using the CRB339 collection as a training population set and nine recombinant inbred lines populations as test set. Prediction quality was high with mean Q2 of 0.44 for TSW and 0.59 for BegFlo. Results are discussed in the light of current efforts to develop GS strategies in pea.

Thousands of RAD-seq Loci Fully Resolve the Phylogeny of the Highly Disjunct Arctic-Alpine Genus Diapensia (Diapensiaceae)

Restriction-site associated DNA sequencing (RAD-seq) has recently become an important method to generate genome-wide molecular data for species delimitation, phylogeography, and population genetic studies. However, very few empirical studies have so far tested its applicability in phylogenetic reconstruction. The alpine-arctic genus Diapensia was selected to study the origin of the disjunction between the Arctic and the Himalayan-Hengduan Mountains (HHM). However, a previous phylogenetic analysis based on one nuclear and four plastid DNA regions failed to resolve the oldest divergences in Diapensia as well as the relationship between the two HHM species. Here we reconstruct a fully resolved phylogeny of Diapensia and address the conflict between the currently accepted taxonomy and the gene trees in the HHM species using RAD-seq. Based on a data set containing 2,650 loci selected to maximize the number of parsimony informative sites and allowing for a high level of missing data (51%), the phylogeny of Diapensia was fully resolved and each of the four species was reciprocally monophyletic. Whereas the arctic D. lapponica was inferred as sister to the HHM clade in the previous study, the RAD-seq data resolved the two arctic species as sisters to the HHM clade. Similar relationships were inferred from a differently filtered data set with far fewer loci (114) and less missing data (21%), but with lower support and with one of the two HHM species as non-monophyletic. Bayesian concordance analysis and Patterson’s D-statistic tests suggested that admixture has occurred between the two HHM species.

Predicting genetic variance in bi-parental breeding populations is more accurate when explicitly modeling the segregation of informative genomewide markers

Robust predictions of genetic variances for important traits would facilitate greater genetic gains in plant breeding. Previous attempts to predict the genetic variance ( $$\sigma_{\text{G}}^{2}$$ ) of traits in bi-parental breeding populations were inconsistent and context specific. The weakness of methods that consider the phenotypic distance, genetic distance, and relationship-based distance of pairs of parents, which we collectively term historical methods, stems from the fact that they do not explicitly model the segregation of the underlying genetic effects for a trait within a population. To address this issue, we propose the use of three modern methods made possible by the commonplace use of genomewide molecular marker data and genomic selection in modern breeding programs. These modern methods utilize both phenotypic and genotypic records to, in varying degrees, explicitly model the segregation of informative genomewide markers to predict $$\sigma_{\text{G}}^{2}$$ in bi-parental breeding populations. In this study, we evaluate the accuracy of historical and modern methods to predict $$\sigma_{\text{G}}^{2}$$ using 40 field-tested bi-parental barley breeding populations evaluated during 2003–2010 for Fusarium head blight severity. In general, the modern methods predicted the field-based estimates of $$\sigma_{\text{G}}^{2}$$ more accurately than the historical methods. Specifically, the modern method that most explicitly models the segregation of informative genomewide markers, called ‘PopVar,’ was the most accurate $$\sigma_{\text{G}}^{2}$$ prediction method.