Genome-wide association study and network analysis of in vitro transformation in Populus trichocarpa support key roles of diverse phytohormone pathways and cross talk.

Wide variation in amenability to transformation and regeneration (TR) among many plant species and genotypes presents a challenge to the use of genetic engineering in research and breeding. To help understand the causes of this variation, we performed association mapping and network analysis using a population of 1204 wild trees of Populus trichocarpa (black cottonwood). To enable precise and high-throughput phenotyping of callus and shoot TR, we developed a computer vision system that cross-referenced complementary red, green, and blue (RGB) and fluorescent-hyperspectral images. We performed association mapping using single-marker and combined variant methods, followed by statistical tests for epistasis and integration of published multi-omic datasets to identify likely regulatory hubs. We report 409 candidate genes implicated by associations within 5 kb of coding sequences, and epistasis tests implicated 81 of these candidate genes as regulators of one another. Gene ontology terms related to protein-protein interactions and transcriptional regulation are overrepresented, among others. In addition to auxin and cytokinin pathways long established as critical to TR, our results highlight the importance of stress and wounding pathways. Potential regulatory hubs of signaling within and across these pathways include GROWTH REGULATORY FACTOR 1 (GRF1), PHOSPHATIDYLINOSITOL 4-KINASE ß1 (PI-4Kß1), and OBF-BINDING PROTEIN 1 (OBP1).

The probability of edge existence due to node degree: a baseline for network-based predictions.

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections using network permutation to generate features that depend only on degree. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Researchers seeking to predict new or missing edges in biological networks should use our permutation approach to obtain a baseline for performance that may be nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).

GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa.

Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.

GWAS identifies candidate genes controlling adventitious rooting in Populus trichocarpa.

Adventitious rooting (AR) is critical to the propagation, breeding, and genetic engineering of trees. The capacity for plants to undergo this process is highly heritable and of a polygenic nature; however, the basis of its genetic variation is largely uncharacterized. To identify genetic regulators of AR, we performed a genome-wide association study (GWAS) using 1148 genotypes of Populus trichocarpa. GWASs are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale; to help overcome this limitation, we developed a computer vision system to measure an array of traits related to adventitious root development in poplar, including temporal measures of lateral and basal root length and area. GWAS was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics and to gain statistical power. These analyses yielded a total of 277 unique associations, suggesting that genes that control rooting include regulators of hormone signaling, cell division and structure, reactive oxygen species signaling, and other processes with known roles in root development. Numerous genes with uncharacterized functions and/or cryptic roles were also identified. These candidates provide targets for functional analysis, including physiological and epistatic analyses, to better characterize the complex polygenic regulation of AR.

Knockout of floral and meiosis genes using CRISPR/Cas9 produces male-sterility in Eucalyptus without impacts on vegetative growth.

Eucalyptus spp. are widely cultivated for the production of pulp, energy, essential oils, and as ornamentals. However, their dispersal from plantings, especially when grown as an exotic, can cause ecological disruptions. To provide new tools for prevention of sexual dispersal by pollen as well as to induce male-sterility for hybrid breeding, we studied the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-mediated knockout of three floral genes in both FT-expressing (early-flowering) and non-FT genotypes. We report male-sterile phenotypes resulting from knockout of the homologs of all three genes, including one involved in meiosis and two regulating early stages of pollen development. The targeted genes were Eucalyptus homologs of REC8 (EREC8), TAPETAL DEVELOPMENT AND FUNCTION 1 (ETDF1), and HECATE3 (EHEC3-like). The erec8 knockouts yielded abnormal pollen grains and a predominance of inviable pollen, whereas the etdf1 and ehec3-like knockouts produced virtually no pollen. In addition to male-sterility, both erec8 and ehec3-like knockouts may provide complete sterility because the failure of erec8 to undergo meiosis is expected to be independent of sex, and ehec3-like knockouts produce flowers with shortened styles and no visible stigmas. When comparing knockouts to controls in wild-type (non-early-flowering) backgrounds, we did not find visible morphological or statistical differences in vegetative traits, including average single-leaf mass, stem volume, density of oil glands, or chlorophyll in leaves. Loss-of-function mutations in any of these three genes show promise as a means of inducing male- or complete sterility without impacting vegetative development.

Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease.

Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aß) deposits in the brain and to study Alzheimer's disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; ß = 0.35, SE = 0.01, P = 6.2 × 10-311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; ß = 0.07, SE = 0.01, P = 9.2 × 10-09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; ß = 0.1, SE = 0.02, P = 2.4 × 10-10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; ß = 0.16, SE = 0.03, P = 1.1 × 10-09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, ß = 0.79, SE = 0.14, P = 1.4 × 10-08, MAF = 0.006, sex-interaction P = 9.8 × 10-07) and chr11p.15.2 (rs192346166, ß = 0.94, SE = 0.17, P = 3.7 × 10-08, MAF = 0.004, sex-interaction P = 1.3 × 10-03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.

Hetnet connectivity search provides rapid insights into how two biomedical entities are related.

Hetnets, short for "heterogeneous networks", contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes - including genes, diseases, drugs, pathways, and anatomical structures - with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the GJA1 gene might be involved in insomnia. We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any two nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). We find that predictions are broadly similar to those from previously described supervised approaches for certain node type pairs. Scoring of individual paths is based on the most specific paths of a given type. Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. We implemented the method on Hetionet and provide an online interface at https://het.io/search . We provide an open source implementation of these methods in our new Python package named hetmatpy .

The probability of edge existence due to node degree: a baseline for network-based predictions.

Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Degree's predictive performance diminishes when the networks used for training and testing-despite measuring the same biological relationships-were generated using distinct techniques and hence have large differences in degree distribution. We introduce the permutation-derived edge prior as the probability that an edge exists based only on degree. The edge prior shows excellent discrimination and calibration for 20 biomedical networks (16 bipartite, 3 undirected, 1 directed), with AUROCs frequently exceeding 0.85. Researchers seeking to predict new or missing edges in biological networks should use the edge prior as a baseline to identify the fraction of performance that is nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).

The genetic regulation of protein expression in cerebrospinal fluid.

Studies of the genetic regulation of cerebrospinal fluid (CSF) proteins may reveal pathways for treatment of neurological diseases. 398 proteins in CSF were measured in 1,591 participants from the BioFINDER study. Protein quantitative trait loci (pQTL) were identified as associations between genetic variants and proteins, with 176 pQTLs for 145 CSF proteins (P < 1.25 × 10-10 , 117 cis-pQTLs and 59 trans-pQTLs). Ventricular volume (measured with brain magnetic resonance imaging) was a confounder for several pQTLs. pQTLs for CSF and plasma proteins were overall correlated, but CSF-specific pQTLs were also observed. Mendelian randomization analyses suggested causal roles for several proteins, for example, ApoE, CD33, and GRN in Alzheimer's disease, MMP-10 in preclinical Alzheimer's disease, SIGLEC9 in amyotrophic lateral sclerosis, and CD38, GPNMB, and ADAM15 in Parkinson's disease. CSF levels of GRN, MMP-10, and GPNMB were altered in Alzheimer's disease, preclinical Alzheimer's disease, and Parkinson's disease, respectively. These findings point to pathways to be explored for novel therapies. The novel finding that ventricular volume confounded pQTLs has implications for design of future studies of the genetic regulation of the CSF proteome.

Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator.

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

Hetnet connectivity search provides rapid insights into how biomedical entities are related.

BACKGROUND: Hetnets, short for "heterogeneous networks," contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet, connects 11 types of nodes-including genes, diseases, drugs, pathways, and anatomical structures-with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious about not only how metformin is related to breast cancer but also how a given gene might be involved in insomnia. FINDINGS: We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any 2 nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. CONCLUSION: We implemented the method on Hetionet and provide an online interface at https://het.io/search. We provide an open-source implementation of these methods in our new Python package named hetmatpy.

Overexpression of SHORT VEGETATIVE PHASE-LIKE (SVL) in Populus delays onset and reduces abundance of flowering in field-grown trees.

The spread of transgenes and exotic germplasm from planted crops into wild or feral species is a difficult problem for public and regulatory acceptance of genetically engineered plants, particularly for wind-pollinated trees such as poplar. We report that overexpression of a poplar homolog of the floral repressor SHORT VEGETATIVE PHASE-LIKE (SVL), a homolog of the Arabidopsis MADS-box repressor SHORT VEGETATIVE PHASE (SVP), delayed the onset of flowering several years in three genotypes of field-grown transgenic poplars. Higher expression of SVL correlated with a delay in flowering onset and lower floral abundance, and did not cause morphologically obvious or statistically significant effects on leaf characteristics, tree form, or stem volume. Overexpression effects on reproductive and vegetative phenology in spring was modest and genotype-specific. Our results suggest that use of SVL and related floral repressors can be useful tools to enable a high level of containment for vegetatively propagated short-rotation woody energy or pulp crops.

Lymphangiogenic therapy prevents cardiac dysfunction by ameliorating inflammation and hypertension.

The lymphatic vasculature is involved in the pathogenesis of acute cardiac injuries, but little is known about its role in chronic cardiac dysfunction. Here, we demonstrate that angiotensin II infusion induced cardiac inflammation and fibrosis at 1 week and caused cardiac dysfunction and impaired lymphatic transport at 6 weeks in mice, while co-administration of VEGFCc156s improved these parameters. To identify novel mechanisms underlying this protection, RNA sequencing analysis in distinct cell populations revealed that VEGFCc156s specifically modulated angiotensin II-induced inflammatory responses in cardiac and peripheral lymphatic endothelial cells. Furthermore, telemetry studies showed that while angiotensin II increased blood pressure acutely in all animals, VEGFCc156s-treated animals displayed a delayed systemic reduction in blood pressure independent of alterations in angiotensin II-mediated aortic stiffness. Overall, these results demonstrate that VEGFCc156s had a multifaceted therapeutic effect to prevent angiotensin II-induced cardiac dysfunction by improving cardiac lymphatic function, alleviating fibrosis and inflammation, and ameliorating hypertension.

Multiplex proteomics identifies novel CSF and plasma biomarkers of early Alzheimer's disease.

To date, the development of disease-modifying therapies for Alzheimer's disease (AD) has largely focused on the removal of amyloid beta Aß fragments from the CNS. Proteomic profiling of patient fluids may help identify novel therapeutic targets and biomarkers associated with AD pathology. Here, we applied the Olink™ ProSeek immunoassay to measure 270 CSF and plasma proteins across 415 Aß- negative cognitively normal individuals (Aß- CN), 142 Aß-positive CN (Aß+ CN), 50 Aß- mild cognitive impairment (MCI) patients, 75 Aß+ MCI patients, and 161 Aß+ AD patients from the Swedish BioFINDER study. A validation cohort included 59 Aß- CN, 23 Aß- + CN, 44 Aß- MCI and 53 Aß+ MCI. To compare protein concentrations in patients versus controls, we applied multiple linear regressions adjusting for age, gender, medications, smoking and mean subject-level protein concentration, and corrected findings for false discovery rate (FDR, q < 0.05). We identified, and replicated, altered levels of ten CSF proteins in Aß+ individuals, including CHIT1, SMOC2, MMP-10, LDLR, CD200, EIF4EBP1, ALCAM, RGMB, tPA and STAMBP (- 0.14 < d < 1.16; q < 0.05). We also identified and replicated alterations of six plasma proteins in Aß+ individuals OSM, MMP-9, HAGH, CD200, AXIN1, and uPA (- 0.77 < d < 1.28; q < 0.05). Multiple analytes associated with cognitive performance and cortical thickness (q < 0.05). Plasma biomarkers could distinguish AD dementia (AUC = 0.94, 95% CI = 0.87-0.98) and prodromal AD (AUC = 0.78, 95% CI = 0.68-0.87) from CN. These findings reemphasize the contributions of immune markers, phospholipids, angiogenic proteins and other biomarkers downstream of, and potentially orthogonal to, Aß- and tau in AD, and identify candidate biomarkers for earlier detection of neurodegeneration.

Corrigendum: Opportunities for Innovation in Genetic Transformation of Forest Trees.

[This corrects the article DOI: 10.3389/fpls.2018.01443.].

Opportunities for Innovation in Genetic Transformation of Forest Trees.

The incorporation of DNA into plant genomes followed by regeneration of non-chimeric stable plants (transformation) remains a major challenge for most plant species. Forest trees are particularly difficult as a result of their biochemistry, aging, desire for clonal fidelity, delayed reproduction, and high diversity. We review two complementary approaches to transformation that appear to hold promise for forest trees.

GWAS of self-reported mosquito bite size, itch intensity and attractiveness to mosquitoes implicates immune-related predisposition loci.

Understanding the interaction between humans and mosquitoes is a critical area of study due to the phenomenal burdens on public health from mosquito-transmitted diseases. In this study, we conducted the first genome-wide association studies (GWAS) of self-reported mosquito bite reaction size (n = 84,724), itchiness caused by bites (n = 69,057), and perceived attractiveness to mosquitoes (n = 16,576). In total, 15 independent significant (P < 5×10-8) associations were identified. These loci were enriched for immunity-related genes that are involved in multiple cytokine signalling pathways. We also detected suggestive enrichment of these loci in enhancer regions that are active in stimulated T-cells, as well as within loci previously identified as controlling central memory T-cell levels. Egger regression analysis between the traits suggests that perception of itchiness and attractiveness to mosquitoes is driven, at least in part, by the genetic determinants of bite reaction size.Our findings illustrate the complex genetic and immunological landscapes underpinning human interactions with mosquitoes.

Genome-wide RNAi screen reveals ALK1 mediates LDL uptake and transcytosis in endothelial cells.

In humans and animals lacking functional LDL receptor (LDLR), LDL from plasma still readily traverses the endothelium. To identify the pathways of LDL uptake, a genome-wide RNAi screen was performed in endothelial cells and cross-referenced with GWAS-data sets. Here we show that the activin-like kinase 1 (ALK1) mediates LDL uptake into endothelial cells. ALK1 binds LDL with lower affinity than LDLR and saturates only at hypercholesterolemic concentrations. ALK1 mediates uptake of LDL into endothelial cells via an unusual endocytic pathway that diverts the ligand from lysosomal degradation and promotes LDL transcytosis. The endothelium-specific genetic ablation of Alk1 in Ldlr-KO animals leads to less LDL uptake into the aortic endothelium, showing its physiological role in endothelial lipoprotein metabolism. In summary, identification of pathways mediating LDLR-independent uptake of LDL may provide unique opportunities to block the initiation of LDL accumulation in the vessel wall or augment hepatic LDLR-dependent clearance of LDL.

Identification of 15 genetic loci associated with risk of major depression in individuals of European descent.

Despite strong evidence supporting the heritability of major depressive disorder (MDD), previous genome-wide studies were unable to identify risk loci among individuals of European descent. We used self-report data from 75,607 individuals reporting clinical diagnosis of depression and 231,747 individuals reporting no history of depression through 23andMe and carried out meta-analysis of these results with published MDD genome-wide association study results. We identified five independent variants from four regions associated with self-report of clinical diagnosis or treatment for depression. Loci with a P value <1.0 × 10(-5) in the meta-analysis were further analyzed in a replication data set (45,773 cases and 106,354 controls) from 23andMe. A total of 17 independent SNPs from 15 regions reached genome-wide significance after joint analysis over all three data sets. Some of these loci were also implicated in genome-wide association studies of related psychiatric traits. These studies provide evidence for large-scale consumer genomic data as a powerful and efficient complement to data collected from traditional means of ascertainment for neuropsychiatric disease genomics.

The 4p16.3 Parkinson Disease Risk Locus Is Associated with GAK Expression and Genes Involved with the Synaptic Vesicle Membrane.

Genome-wide association studies (GWAS) have identified the GAK/DGKQ/IDUA region on 4p16.3 among the top three risk loci for Parkinson's disease (PD), but the specific gene and risk mechanism are unclear. Here, we report transcripts containing the 3' clathrin-binding domain of GAK identified by RNA deep-sequencing in post-mortem human brain tissue as having increased expression in PD. Furthermore, carriers of 4p16.3 PD GWAS risk SNPs show decreased expression of one of these transcripts, GAK25 (Gencode Transcript 009), which correlates with the expression of genes functioning in the synaptic vesicle membrane. Together, these findings provide strong evidence for GAK clathrin-binding- and J-domain transcripts' influence on PD pathogenicity, and for a role for GAK in regulating synaptic function in PD.