Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry

Automatizing phenotype measurement will decisively contribute to increase plant breeding efficiency. Among phenotypes, morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semiautomatically obtained. Yet, fruit morphology evaluation can be enhanced using fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry (Fragaria x ananassa) images. The pipeline segments, classifies, and labels the images and extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominance effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and are moderately heritable. Although we study strawberry images, the algorithm can be applied to other fruits, as shown in the GitHub repository.

Health and longevity studies in C. elegans: the “healthy worm database” reveals strengths, weaknesses and gaps of test compound-based studies

Several biogerontology databases exist that focus on genetic or gene expression data linked to health as well as survival, subsequent to compound treatments or genetic manipulations in animal models. However, none of these has yet collected experimental results of compound-related health changes. Since quality of life is often regarded as more valuable than length of life, we aim to fill this gap with the “Healthy Worm Database” (http://healthy-worm-database.eu). Literature describing health-related compound studies in the aging model Caenorhabditis elegans was screened, and data for 440 compounds collected. The database considers 189 publications describing 89 different phenotypes measured in 2995 different conditions. Besides enabling a targeted search for promising compounds for further investigations, this database also offers insights into the research field of studies on healthy aging based on a frequently used model organism. Some weaknesses of C. elegans-based aging studies, like underrepresented phenotypes, especially concerning cognitive functions, as well as the convenience-based use of young worms as the starting point for compound treatment or phenotype measurement are discussed. In conclusion, the database provides an anchor for the search for compounds affecting health, with a link to public databases, and it further highlights some potential shortcomings in current aging research.

Automatic fruit morphology phenome and genetic analysis: An application in the octoploid strawberry

ABSTRACTAutomatizing phenotype measurement is needed to increase plant breeding efficiency. Morphological traits are relevant in many fruit breeding programs, as appearance influences consumer preference. Often, these traits are manually or semi-automatically obtained. Yet, fruit morphology evaluation can be boosted by resorting to fully automatized procedures and digital images provide a cost-effective opportunity for this purpose. Here, we present an automatized pipeline for comprehensive phenomic and genetic analysis of morphology traits extracted from internal and external strawberry images. The pipeline segments, classifies and labels the images, extracts conformation features, including linear (area, perimeter, height, width, circularity, shape descriptor, ratio between height and width) and multivariate (Fourier Elliptical components and Generalized Procrustes) statistics. Internal color patterns are obtained using an autoencoder to smooth out the image. In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. Bayesian modeling is employed to estimate both additive and dominant effects for all traits. As expected, conformational traits are clearly heritable. Interestingly, dominance variance is higher than the additive component for most of the traits. Overall, we show that fruit shape and color can be quickly and automatically evaluated and is moderately heritable. Although we study the strawberry species, the algorithm can be applied to other fruits, as shown in the GitHub repository https://github.com/lauzingaretti/DeepAFS.

A Low-Cost 3D Phenotype Measurement Method of Leafy Vegetables Using Video Recordings from Smartphones

Leafy vegetables are an essential source of the various nutrients that people need in their daily lives. The quantification of vegetable phenotypes and yield estimation are prerequisites for the selection of genetic varieties and for the improvement of planting methods. The traditional method is manual measurement, which is time-consuming and cumbersome. Therefore, there is a need for efficient and convenient in situ vegetable phenotype identification methods to provide data support for breeding research and for crop yield monitoring, thereby increasing vegetable yield. In this paper, a novel approach was developed for the in-situ determination of the three-dimensional (3D) phenotype of vegetables by recording video clips using smartphones. First, a smartphone was used to record the vegetable from different angles, and then the key frame containing the crop area in the video was obtained using an algorithm based on the vegetation index and scale-invariant feature transform algorithm (SIFT) matching. After obtaining the key frame, a dense point cloud of the vegetables was reconstructed using the Structure from Motion (SfM) method, and then the segmented point cloud and a point cloud skeleton were obtained using the clustering algorithm. Finally, the plant height, leaf number, leaf length, leaf angle, and other phenotypic parameters were obtained through the point cloud and point cloud skeleton. Comparing the obtained phenotypic parameters to the manual measurement results, the root-mean-square error (RMSE) of the plant height, leaf number, leaf length, and leaf angle were 1.82, 1.57, 2.43, and 4.7, respectively. The measurement accuracy of each indicators is greater than 80%. The results show that the proposed method provides a convenient, fast, and low-cost 3D phenotype measurement pipeline. Compared to other methods based on photogrammetry, this method does not need a labor-intensive image-capturing process and can reconstruct a high-quality point cloud model by directly recording videos of crops.

Mass Spectrometry in Precision Medicine: Phenotypic Measurements Alongside Pharmacogenomics

BACKGROUND Precision medicine is becoming a major topic within the medical community and is gaining traction as a standard approach in many disciplines. This approach typically revolves around the use of a patient's genetic makeup to allow the physician to choose the appropriate course of treatment. In many cases the genetic information directs the drug to be used to treat the patient. In other cases the genetic markers associated with enzyme function may inform dosage recommendations. However there is a second way in which precision medicine can be practiced—that is, by therapeutic drug monitoring (TDM). CONTENT A review of the use of mass spectrometry for TDM in the arena of precision medicine is undertaken. Because the measurement of a drug or its metabolites provides the physician with a snapshot of the therapeutic exposure the patient is undergoing, these concentrations can be thought of as an actual phenotype measurement based around the patient's genetics coupled with all of the environmental, pharmacological, and nutritional variables. The outcome of a TDM measurement by mass spectrometry provides the patient's current phenotype vs the potential phenotype imputed by the genetics. SUMMARY The use of mass spectrometry can provide an understanding of how a drug is interacting with the patient, and is orthoganol to the information provided by pharmacogenomic assays. Further, the speed and relatively low expense of drug monitoring by mass spectrometry makes it an ideal test for precision medicine patient management.

Reproductive physiology of a humanized GnRH receptor mouse model: application in evaluation of human-specific analogs

The human GnRH receptor (GNRHR1) has a specific set of properties with physiological and pharmacological influences not appropriately modeled in laboratory animals or cell-based systems. To address this deficiency, we have generated human GNRHR1 knock-in mice and described their reproductive phenotype. Measurement of pituitary GNRHR1 transcripts from homozygous human GNRHR1 knock-in ( ki/ ki) mice revealed a severe reduction (7- to 8-fold) compared with the mouse Gnrhr1 in wild-type mice. 125I-GnRH binding assays on pituitary membrane fractions corroborated reduced human GNRHR1 protein expression in ki/ ki mice, as occurs with transfection of human GNRHR1 in cell lines. Female homozygous knock-in mice displayed normal pubertal onset, indicating that a large reduction in GNRHR1 expression is sufficient for this process. However, ki/ ki females exhibited periods of prolonged estrous and/or metestrous and reduced fertility. No impairment was found in reproductive maturity or adult fertility in male ki/ ki mice. Interestingly, the serum LH response to GnRH challenge was reduced in both knock-in males and females, indicating a reduced GNRHR1 signaling capacity. Small molecules targeting human GPCRs usually have poor activities at homologous rodent receptors, thus limiting their use in preclinical development. Therefore, we tested a human-specific GnRH1 antagonist, NBI-42902, in our mouse model and demonstrated abrogation of a GnRH1-induced serum LH rise in ki/ ki mice and an absence of effect in littermates expressing the wild-type murine receptor. This novel model provides the opportunity to study the human receptor in vivo and for screening the activity of human-specific GnRH analogs.

Case study of a Hungarian breeding program using imported Booroola rams

The first major gene for prolificacy identified in sheep was the Booroola (FecB) gene. Since the recognition of its existence, the Booroola Merino has spread all over the world. In Hungary, a new breed – called Hungarian Prolific Merino – had been established based on the crossing of Hungarian Merino ewes and Booroola Merino rams, and was acknowledged in 1992. The only way to determine the FecB genotypes has been the measurement of the ovulation rate over a long period. In 2001, the Booroola mutation was identified. Mutation on the bone morphogenetic protein receptor – 1B gene was found to be associated with the increased ovulation rate in the Booroola Merino ewes. 138 ewes and 46 rams in the Hungarian Prolific Merino population were tested for this mutation by PCR-RFLP and their FecB genotypes were determined. One copy of the FecB allele increased (P < 0,05) the ovulation rate by 0.89 ova and two copies increased by an average of 2.27 ova. Effectiveness of the FecB genotype estimation based on phenotype measurement was also compared to the results of direct DNA testing, and was found to have up to 80% accuracy.