Accurate Phenotyping Research Articles

P1307 Variant in Complement Factor B is associated with perianal disease in Ulcerative Colitis

Abstract Background Perianal disease is an increasingly recognised complication of ulcerative colitis (UC) (1). Variant rs4151651 in complement factor B (CFB) is associated with severe phenotype UC and, more recently, implicated in the pathogenesis of perianal Crohn’s disease (CD) (2, 3). In our previous work, we have shown the minor allele frequency (MAF) for the rs4151651 variant to be 0.03 for non-IBD affected control patients, 0.05 in patients with mild UC, and 0.13 in patients with severe phenotype UC (3). Here, we aim to determine the MAF for the rs4151651 variant in UC patients with perianal disease. Methods Ulcerative colitis patients with perianal disease were identified from our extensive inflammatory bowel disease (IBD) biobank. Perianal disease was defined as perianal abscesses or fistulae (haemorrhoids and anal fissures were excluded). Appropriate consent was obtained from all participants (HREC/14/QRBW/323). Demographic and phenotypic data were collected, and UC diagnoses were confirmed by expert IBD pathologists through histological review. To further confirm accurate phenotyping of our UC cohort, and to distinguish it from CD, we also present MAF data from the NOD2 gene, where available. Genotyping was either previously performed by genome-wide association study (GWAS) analysis or performed using the rs4151651 single nucleotide polymorphism (SNP). Results Forty-one patients were identified. Baseline demographics and clinical characteristics are presented in Table 1. Phenotypically and histologically all patients had disease consistent with UC. The MAFs for the NOD2 variants rs2066844, rs2066845 and rs2066847 were 0.04, 0.01 and 0.01 respectively. Eight patients had both a perianal abscess and a fistula, 1 patient had a perianal abscess only, and 33 patients had perianal fistulae only. Twenty-four patients in our cohort had a prior colectomy. The MAF for the rs4151651 variant in the 41 UC patients with perianal disease was 0.28. In the 21 patients who had extensive disease (E3), the MAF was 0.29. In the 24 patients who had a prior colectomy, the MAF was 0.30 (Figure 1). Conclusion The rs4151651 variant on the CFB gene is associated with perianal disease in UC patients. These findings align with prior studies linking the rs4151651 variant to a more severe UC phenotype. This SNP could be a valuable biomarker in predicting perianal complications for patients with severe UC undergoing colectomy and considering reconstructive (pouch) surgery.

Large-scale audiometric phenotyping identifies distinct genes and pathways involved in hearing loss subtypes.

Age-related hearing loss affects one-third of the population over 65 years. However, the diverse pathologies underlying these heterogenous phenotypes complicate genetic studies. To overcome challenges associated with accurate phenotyping for older adults with hearing loss, we applied computational phenotyping approaches based on audiometrically measured hearing loss. This novel phenotyping strategy uncovered distinct genetic variants associated with sensory and metabolic hearing loss. Sex-stratified analyses of these sexually dimorphic hearing loss phenotypes revealed a novel locus of relevance to sensory hearing loss in males, but not females. Enrichment analyses revealed that genes involved in frontotemporal dementia were implicated in metabolic hearing loss, while genes relating to sensory processing of sound by hair cells were implicated in sensory hearing loss. Our study has enhanced our understanding of these two distinct hearing loss phenotypes, representing the first step in the development of more precise treatments for these pathologically distinct hearing loss phenotypes.

Isolation of colony-forming cells from human blood and evaluation of population purity by flow cytometry

Endothelial colony-forming cells (ECFCs) represent a rare population of circulating endothelial progenitor cells (EPCs), which is of interest for tissue engineering, cell therapy, and studying diseorders associated with endothelial dysfunction. Advances in this field imply isolation and pure culturing of endothelial colony-forming cells, which may be obtained from the mononuclear fraction of human peripheral blood. Flow cytometry may provide rather accurate phenotyping and checking purity of the cell cultures. The aim of this study was to obtain a population of colony-forming endothelial cells evaluated by flow cytometry in order to confirm the phenotype and assess the purity of the cell populations. Heparinized donor blood from peripheral vein was used to obtain ECFCs. The mononuclear cell fraction was isolated on a Histopaque 1077 density gradient. The obtained cells were cultured in complete nutrient medium EGM-2 MV with 5% FBS. On the 11th, 15th and 18th days of culture, the cells were detached with trypsin. Some cell aliquots were taken for flow cytometry, and the remaining cells were transferred to a plate for further cultivation until 70% confluency was reached. The phenotype of colonies obtained was evaluated by flow cytometry. Sample preparation of whole peripheral blood, mononuclear fraction and cell culture was performed using two panels of fluorochrome-labeled monoclonal antibodies. Colony growth was visually evaluated by phase-contrast microscopy. Statistical processing of the obtained results was performed using the applied program package “Statistica 6.0”. Up to 9 days, the cell colonies were not detectable visually. During further cultivation, the proliferating colonies of the “cobblestone” type were formed. Two populations were detected by flow cytometry: CD45+ and CD45-. In the course of our study, it was revealed that, with increasing cultivation terms, a decrease of CD45+ population was observed, along with progressive increase of CD45- cells, which may be associated with the gradual displacement of hematopoiesis cells by endothelial colony-forming cells. As a result of our study, the detected CD45+ population had a phenotype of hematopoietic lineage cells, whereas the CD45- population exhibited endothelial phenotype: CD31+CD309+vWF+CD146+. By 18 days, the purity of culture reached 97.6%.

The increasing role of the allergist in the management of infusion reactions at the Oncology Infusion Center.

Hypersensitivity reactions to chemotherapy disrupt treatment schedules and compromise patient outcomes. Rapid Drug Desensitization (RDD) enables patients to tolerate future treatments after an allergy workup. However, Same-Day Desensitization (SDD) is a novel approach that capitalizes on RDD to allow the continuation of chemotherapy on the same day as the index reaction, preventing treatment delays. To evaluate the safety and efficacy of SDD in managing hypersensitivity reactions during chemotherapy and emphasize the essential role of allergists in the Oncology Infusion Center (OIC) for accurate drugs hypersensitivity reactions (DHRs) phenotyping and management. This retrospective cohort included patients experiencing DHRs during chemotherapy. Under allergist supervision, SDD was performed once the index reaction was controlled. At a later date, clinical phenotypes and endotypes of DHRs were assessed through clinical history, skin tests, serum biomarkers (including tryptase and IL-6 levels), and drug provocation testing (DPT) to reach an accurate diagnosis. SDD was successful in 35 cases, even for patients with severe initial reactions. Only 14% experienced breakthrough reactions, all mild. Same-day assessment by allergists ensured a 92% correlation between initial and final diagnoses, optimizing DHR management. Early engagement with Allergy allowed 86% of reactive patients to continue treatment through RDD or after ruling out an allergy. SDD is a safe and effective procedure that ensures that patients don't miss their oncology treatment on the day of a reaction. The presence of an allergist in the OIC is crucial for rapid access to accurate DHR phenotyping and optimal management, supporting personalized precision medicine in oncology.

ASSESSING DYNAMIC AFFECT RECOGNITION FOR DISTINGUISHING FRONTOTEMPORAL DEMENTIA SUBTYPES

AbstractBackgroundRecent international work suggests that more precise subtyping within frontotemporal dementia (FTD) syndromes leads to better prediction of pathology, supporting individualized disease‐specific treatments. Recent studies emphasize that identification of one such subtype, semantic behavioral variant FTD (sbvFTD), relies in part on measuring emotion recognition abilities.MethodIn order to evaluate the effectiveness of current tools, we compared the brief video‐based Dynamic Affect Recognition Test (DART) against the TASIT Emotion Evaluation (EET) and Comprehensive Affect Testing System Affect Matching tests. 105 individuals in the very early stages (CDR≤1) of FTD (61 behavioral variant FTD [bvFTD], 25 semantic variant primary progressive aphasia [svPPA], 19 sbvFTD), and 71 healthy older participants underwent emotion testing and structural MRI.ResultTests using dynamic (video) stimuli outperformed static face stimuli for distinguishing sbvFTD from bvFTD, but the DART outperformed the EET distinguishing sbvFTD from svPPA (Figure 1). Structural VBM analysis of DART performance showed predominant contribution of right‐sided structures involved in semantics and attention (e.g. temporal pole, insula), though in sbvFTD, who had worse DART scores than all other syndromes (p<0.05), DART performance also corresponded with temporo‐orbitofrontal structures mediating hedonic evaluation.ConclusionOur findings indicate that emotion reading tests using dynamic stimuli are best for differentiating among FTD syndromes. Also, unique profiles of emotion reading deficits emerging from different syndromes correspond to distinct patterns of damage to emotion, semantics, attention, and reward‐related brain networks. Emotion identification deficits are a core feature of FTD, therefore careful selection of tests that reflect the key underlying neural circuits will result in more accurate phenotyping.

Nucleotide Transformer: building and evaluating robust foundation models for human genomics.

The prediction of molecular phenotypes from DNA sequences remains a longstanding challenge in genomics, often driven by limited annotated data and the inability to transfer learnings between tasks. Here, we present an extensive study of foundation models pre-trained on DNA sequences, named Nucleotide Transformer, ranging from 50 million up to 2.5 billion parameters and integrating information from 3,202 human genomes and 850 genomes from diverse species. These transformer models yield context-specific representations of nucleotide sequences, which allow for accurate predictions even in low-data settings. We show that the developed models can be fine-tuned at low cost to solve a variety of genomics applications. Despite no supervision, the models learned to focus attention on key genomic elements and can be used to improve the prioritization of genetic variants. The training and application of foundational models in genomics provides a widely applicable approach for accurate molecular phenotype prediction from DNA sequence.

MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.

Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, such as the classification of cell phenotypes, the existing machine learning models often suffer from low adaptability and transferability. In addition, SCMS studies of rare cells can be restricted by limited number of cell samples. To overcome these limitations, we performed SCMS analyses of melanoma cancer cell lines with two phenotypes (i.e., primary and metastatic cells). We then developed a meta-learning-based model, MetaPhenotype, that can be trained using a small amount of SCMS data to accurately classify cells into primary or metastatic phenotypes. Our results show that compared with standard transfer learning models, MetaPhenotype can rapidly predict and achieve a high accuracy of over 90% with fewer new training samples. Overall, our work opens the possibility of accurate cell phenotype classification based on fewer SCMS samples, thus lowering the demand for sample acquisition.

Integrating All-rounder TiO2 Accelerated Electrochemiluminescence with Dual-Quenching PDA@COF Probes for Sensitive Quantification and Protein Profiling of Tumorous Exosomes.

Exosomes have been perceived as promising biomarkers for noninvasive cancer diagnosis and treatment monitoring. However, the sensitive and accurate quantification and phenotyping of exosomes remains challenging. Herein, a versatile electrochemiluminescence (ECL) aptasensor was proposed for the sensitive analysis of tumorous exosomes. Specifically, a ternary nanohybrid (Ru-HAuTiO2), by covalently linking ECL luminophore Ru(dcbpy)32+ with gold nanoparticles (AuNPs)-decorated hollow urchin-like TiO2 (HTiO2), was ingeniously designed as a highly luminescent and self-enhanced ECL nanoemitter. Notably, the porous HTiO2 played an "all-rounder" role, including the carrier for ECL luminophores and AuNPs, coreaction accelerator, and specific exosome capturing scaffold through Ti-phosphate coordination interaction. On the other hand, a polydopamine modified covalent organic framework (PDA@COF) was employed as a quencher to remarkably attenuate the ECL of Ru-HAuTiO2 through a dual-quenching mechanism, and further labeled with a specific aptamer (Apt) of exosomal surface protein. Based on forming a Ru-HAuTiO2/exosome/Apt-PDA@COF sandwich structure on the electrode, a "signal on-off" ECL platform for tumorous exosomes was constructed, realizing sensitive detection within the range of 3.1 × 103 particles/mL to 1 × 108 particles/mL and a low limit of detection of 1.41 × 103 particles/mL, achieving phenotypic profiling of surface proteins on different tumorous exosomes. This work provides a promising alternative method for the detection and analysis of exosomes.

Towards automated phenotype definition extraction using large language models

Electronic phenotyping involves a detailed analysis of both structured and unstructured data, employing rule-based methods, machine learning, natural language processing, and hybrid approaches. Currently, the development of accurate phenotype definitions demands extensive literature reviews and clinical experts, rendering the process time-consuming and inherently unscalable. Large language models offer a promising avenue for automating phenotype definition extraction but come with significant drawbacks, including reliability issues, the tendency to generate non-factual data (“hallucinations”), misleading results, and potential harm. To address these challenges, our study embarked on two key objectives: (1) defining a standard evaluation set to ensure large language models outputs are both useful and reliable and (2) evaluating various prompting approaches to extract phenotype definitions from large language models, assessing them with our established evaluation task. Our findings reveal promising results that still require human evaluation and validation for this task. However, enhanced phenotype extraction is possible, reducing the amount of time spent in literature review and evaluation.

Key Imaging Factors for Transcatheter Management of Tricuspid Regurgitation: Device and Patient Selection

The growing awareness of tricuspid regurgitation (TR) and the fast-expanding array of devices aiming to percutaneously repair or replace the tricuspid valve have underscored the central role of multi-modality imaging in comprehensively assessing the anatomical and functional characteristics of TR. Accurate phenotyping of TR, the right heart, and pulmonary vasculature via echocardiography, computed tomography, and, occasionally, cardiovascular magnetic resonance and right heart catheterization is deemed crucial in choosing the most suitable treatment strategy for each patient and achieving procedural success. In the first part of the present review, key imaging factors for patient selection will be discussed. In the ensuing sections, an overview of the most commonly used, commercially available systems for transcatheter repair/replacement will be presented, along with their respective selection criteria and information on intraprocedural imaging guidance; these are edge-to-edge repair, orthotopic and heterotopic replacement, and valve-in-valve procedures.

Clinico-Radiological and Genotypic Spectrum of Nuclear Mitochondriopathies.

Mitochondrial disorders are a diverse group of diseases caused by mutations in genes encoded by either nuclear or mitochondrial DNA. In a group of patients with nuclear mitochondriopathies, the authors analysed the clinico-radiological and genotypic spectrum. The study included 25 patients with a genetic diagnosis of nuclear mitochondrial cytopathy who were seen over a 5 y period. There were 25 patients in the study cohort (Mean age of presentation- 14 mo). Biallelic mutations involving nuclear mitochondrial genes were identified in all 25 of them. In 13 and 9 patients, respectively, respiratory chain defects (complex I and complex IV) and mitochondrial DNA depletion syndromes were identified. Twelve novel variants were identified. Interestingly, NDUFV1 with a south Indian founder variant c.1156C > T (p.Arg386Cys) was the commonest variant. Accurate phenotyping combined with next generation sequencing aids in the precise diagnosis of mitochondrial nuclear gene defects and provides the opportunity for appropriate counseling.

Integrating deep phenotyping with genetic analysis: a comprehensive workflow for diagnosis and management of rare bone diseases.

Phenotypes play a fundamental role in medical genetics, serving as external manifestations of underlying genotypes. Deep phenotyping, a cornerstone of precision medicine, involves precise multi-system phenotype assessments, facilitating disease subtyping and genetic understanding. Despite their significance, the field lacks standardized protocols for accurate phenotype evaluation, hindering clinical comprehension and research comparability. We present a comprehensive workflow of deep phenotyping for rare bone diseases from the Genetics Clinic of Skeletal Deformity at Peking Union Medical College Hospital. Our workflow integrates referral, informed consent, and detailed phenotype evaluation through HPO standards, capturing nuanced phenotypic characteristics using clinical examinations, questionnaires, and multimedia documentation. Genetic testing and counseling follow, based on deep phenotyping results, ensuring personalized interventions. Multidisciplinary team consultations facilitate comprehensive patient care and clinical guideline development. Regular follow-up visits emphasize dynamic phenotype reassessment, ensuring treatment strategies remain responsive to evolving patient needs. In conclusion, this study highlights the importance of deep phenotyping in rare bone diseases, offering a standardized framework for phenotype evaluation, genetic analysis, and multidisciplinary intervention. By enhancing clinical care and research outcomes, this approach contributes to the advancement of precision medicine in the field of medical genetics.

Tuning a bioengineered hydrogel for studying astrocyte reactivity in glioblastoma

Astrocytes play many essential roles in the central nervous system (CNS) and are altered significantly in disease. These reactive astrocytes contribute to neuroinflammation and disease progression in many pathologies, including glioblastoma (GB), an aggressive form of brain cancer. Current in vitro platforms do not allow for accurate modeling of reactive astrocytes. In this study, we sought to engineer a simple bioengineered hydrogel platform that would support the growth of primary human astrocytes and allow for accurate analysis of various reactive states. After validating this platform using morphological analysis and qPCR, we then used the platform to begin investigating how astrocytes respond to GB derived extracellular vesicles (EVs) and soluble factors (SF). These studies reveal that EVs and SFs induce distinct astrocytic states. In future studies, this platform can be used to study how astrocytes transform the tumor microenvironment in GB and other diseases of the CNS. Statement of significanceRecent work has shown that astrocytes help maintain brain homeostasis and may contribute to disease progression in diseases such as glioblastoma (GB), a deadly primary brain cancer. In vitro models allow researchers to study basic mechanisms of astrocyte biology in healthy and diseased conditions, however current in vitro systems do not accurately mimic the native brain microenvironment. In this study, we show that our hydrogel system supports primary human astrocyte culture with an accurate phenotype and allows us to study how astrocytes change in response to a variety of inflammatory signals in GB. This platform could be used further investigate astrocyte behavior and possible therapeutics that target reactive astrocytes in GB and other brain diseases.

Working with Miraculous Mice: Mus musculus as a Model Organism.

The laboratory mouse has been described as a "miracle" model organism, providing a window by which we may gain an understanding of ourselves. Since the first recorded mouse experiment in 1664, the mouse has become the most used animal model in biomedical research. Mice are ideally suited as a model organism because of their small size, short gestation period, large litter size, and genetic similarity to humans. This article provides a broad overview of the laboratory mouse as a model organism and is intended for undergraduates and those new to working with mice. We delve into the history of the laboratory mouse and outline important terminology to accurately describe research mice. The types of laboratory mice available to researchers are reviewed, including outbred stocks, inbred strains, immunocompromised mice, and genetically engineered mice. The critical role mice have played in advancing knowledge in the areas of oncology, immunology, and pharmacology is highlighted by examining the significant contribution of mice to Nobel Prize winning research. International mouse mutagenesis programs and accurate phenotyping of mouse models are outlined. We also explain important considerations for working with mice, including animal ethics; the welfare principles of replacement, refinement, and reduction; and the choice of mouse model in experimental design. Finally, we present practical advice for maintaining a mouse colony, which involves adequate training of staff, the logistics of mouse housing, monitoring colony health, and breeding strategies. Useful resources for working with mice are also listed. The aim of this overview is to equip the reader with a broad appreciation of the enormous potential and some of the complexities of working with the laboratory mouse in a quest to improve human health. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC.

Identification of Anomalies in Lung and Colon Cancer Using Computer Vision-Based Swin Transformer with Ensemble Model on Histopathological Images.

Lung and colon cancer (LCC) is a dominant life-threatening disease that needs timely attention and precise diagnosis for efficient treatment. The conventional diagnostic techniques for LCC regularly encounter constraints in terms of efficiency and accuracy, thus causing challenges in primary recognition and treatment. Early diagnosis of the disease can immensely reduce the probability of death. In medical practice, the histopathological study of the tissue samples generally uses a classical model. Still, the automated devices that exploit artificial intelligence (AI) techniques produce efficient results in disease diagnosis. In histopathology, both machine learning (ML) and deep learning (DL) approaches can be deployed owing to their latent ability in analyzing and predicting physically accurate molecular phenotypes and microsatellite uncertainty. In this background, this study presents a novel technique called Lung and Colon Cancer using a Swin Transformer with an Ensemble Model on the Histopathological Images (LCCST-EMHI). The proposed LCCST-EMHI method focuses on designing a DL model for the diagnosis and classification of the LCC using histopathological images (HI). In order to achieve this, the LCCST-EMHI model utilizes the bilateral filtering (BF) technique to get rid of the noise. Further, the Swin Transformer (ST) model is also employed for the purpose of feature extraction. For the LCC detection and classification process, an ensemble deep learning classifier is used with three techniques: bidirectional long short-term memory with multi-head attention (BiLSTM-MHA), Double Deep Q-Network (DDQN), and sparse stacked autoencoder (SSAE). Eventually, the hyperparameter selection of the three DL models can be implemented utilizing the walrus optimization algorithm (WaOA) method. In order to illustrate the promising performance of the LCCST-EMHI approach, an extensive range of simulation analyses was conducted on a benchmark dataset. The experimentation results demonstrated the promising performance of the LCCST-EMHI approach over other recent methods.

Converging Pathways: A Review of Pulmonary Hypertension in Interstitial Lung Disease.

Pulmonary hypertension (PH) in interstitial lung disease (ILD) is relatively common, affecting up to 50% of patients with idiopathic pulmonary fibrosis (IPF). It occurs more frequently in advanced fibrotic ILD, although it may also complicate milder disease and carries significant clinical implications in terms of morbidity and mortality. Key pathological processes driving ILD-PH include hypoxic pulmonary vasoconstriction and pulmonary vascular remodelling. While current understanding of the complex cell signalling pathways and molecular mechanisms underlying ILD-PH remains incomplete, there is evidence for an interplay between the disease pathogenesis of fibrotic ILD and PH, with interest in the role of the pulmonary endothelium in driving pulmonary fibrogenesis more recently. This review examines key clinical trials in ILD-PH therapeutics, including recent research showing promise for the treatment of both ILD-PH and the underlying pulmonary fibrotic process, further supporting the hypothesis of interrelated pathogenesis. Other important management considerations are discussed, including the value of accurate phenotyping in ILD-PH and the success of the "pulmonary vascular" phenotype. This article highlights the close and interconnected nature of fibrotic ILD and PH disease pathogenesis, a perspective likely to improve our understanding and therapeutic approach to this complex condition in the future.

Genetics of female and male infertility.

Infertility is defined as the inability to conceive within one year of unprotected intercourse, and the causes are equally distributed between both sexes. Genetics play a crucial role in couple infertility and respective diagnostic testing should follow available guidelines. Appropriate tiered genetic analyses require comprehensive physical examination of both partners in an infertile couple. A wide range of chromosomal and monogenic variants can be the underlying genetic cause of infertility in both women and men. Accurate clinical phenotyping, together with identification of the genetic origin, helps to recommend the proper treatment and to counsel couples on the success rates and potential risks for offspring.

Automated phenotyping empowered by deep learning for genomic prediction of body size in the tiger pufferfish, Takifugu rubripes

Although genomic selection (GS) is accelerating the genetic improvement of economically important traits in aquaculture species, the accurate phenotyping of thousands of individuals remains a bottleneck in GS programs. Computer vision (CV) technologies with the assistance of deep learning may provide a cost-efficient solution for automated phenotyping of fish body size. In this study, we designed a CV-based automated phenotyping system based on the deep learning model Mask R-CNN (region-based convolutional neural networks) and tested its ability to predict standard length (SL) of tiger pufferfish (Takifugu rubripes) fed low fishmeal (LFM) diet. Simultaneously, we investigated the impact of the automated phenotyping on genetic parameter estimation and genomic prediction for growth-related traits. We raised a test population (n = 1001) that was fed the LFM diet from three months old until harvest at 22 months. Manual phenotyping of the fish was performed at 10-, 14-, 17-, and 22-months-old. At the last two measurement times, images of each fish were collected to train Mask R-CNN for fish detection and pixel-level segmentation of the fish body excluding the tail fin. An automated phenotyping pipeline combining the trained Mask R-CNN and standard CV algorithms were designed to estimate SL. We found a high correlation (0.965–0.971) and low relative difference (0.015–0.027) between the CV-derived and manually measured SL, indicating high precision of our CV system. A high correlation (0.912–0.956) was also observed between CV-derived body area and manual body weight. The heritability estimations and genomic predictions on manual SLs were conducted using a Genomic Best Linear Unbiased Prediction (GBLUP) model using a total of 16,471 single nucleotide polymorphism (SNP). Estimated heritability at the four sampling points ranged from 0.573 to 0.622, and the predictive ability of genomic prediction for harvest size was 0.627. These values are comparable to those reported in previous studies collected from the population fed a normal fishmeal diet. Our heritability estimation and genomic prediction derived from automated SL measurements differed negligibly to those based on manual measurements. This finding indicates the utility of our CV-based automated phenotyping system for GS breeding programs.

The role of ascorbate redox turnover in iron toxicity tolerance

Iron (Fe) toxicity is a major abiotic stress in lowland rice production. Breeding tolerant varieties has proven challenging due to the complex genetic architecture of Fe toxicity tolerance and the strong genotype-by-environment interactions. Additionally, conventional methods for phenotyping visible stress symptoms are often inaccurate, inconsistent, and lack reproducibility.In our previous work, we identified that ascorbate redox regulation, mediated by the activities of dehydroascorbate reductase (DHAR) and ascorbate oxidase (AO), contributed to high tolerance in an indica rice genotype across various environments. To explore whether this mechanism is common among other rice genotypes, we selected ten genotypes with contrasting stress symptoms under Fe-toxic conditions to examine the roles of DHAR and AO in regulating Fe toxicity tolerance. Additionally, we aimed to develop objective and accurate image-based phenotyping methods to replace the traditional leaf bronzing scoring method.Among the ten genotypes we tested, we found significant positive correlations between DHAR activity and stress symptoms in plants grown under both Fe toxicity and control conditions, suggesting a general link between ascorbate redox regulation and Fe toxicity tolerance. Using RGB signals from leaf images of plants exposed to 1000 mg/L Fe2+, we evaluated 36 different color indices to quantify stress symptoms. We identified the normalized green‒red difference index as most significant in quantifying stress symptoms under Fe toxicity conditions.Our findings suggest that DHAR activity could be potentially employed as a biomarker in the screening of rice germplasms and breeding tolerant cultivars to Fe toxicity.

Multi-Omics Profiles of Chronic Low Back Pain and Fibromyalgia - Study Protocol.

Chronic low back pain (CLBP) and fibromyalgia (FM) are leading causes of suffering, disability, and social costs. Current pharmacological treatments do not target molecular mechanisms driving CLBP and FM, and no validated biomarkers are available, hampering the development of effective therapeutics. Omics research has the potential to substantially advance our ability to develop mechanism-specific therapeutics by identifying pathways involved in the pathophysiology of CLBP and FM, and facilitate the development of diagnostic, predictive, and prognostic biomarkers. We will conduct a blood and urine multi-omics study in comprehensively phenotyped and clinically characterized patients with CLBP and FM. Our aims are to identify molecular pathways potentially involved in the pathophysiology of CLBP and FM that would shift the focus of research to the development of target-specific therapeutics, and identify candidate diagnostic, predictive, and prognostic biomarkers. We are conducting a prospective cohort study of adults ≥18 years of age with CLBP (n=100) and FM (n=100), and pain-free controls (n=200). Phenotyping measures include demographics, medication use, pain-related clinical characteristics, physical function, neuropathiccomponents (quantitative sensory tests and DN4 questionnaire), pain facilitation (temporal summation), and psychosocial function as moderator. Blood and urine samples are collected to analyze metabolomics, lipidomics and proteomics. We will integrate the overall omics data to identify common mechanisms and pathways, and associate multi-omics profiles to pain-related clinical characteristics, physical function, indicators of neuropathic pain, and pain facilitation, with psychosocial variables as moderators. Our study addresses the need for a better understanding of the molecular mechanisms underlying chronic low back pain and fibromyalgia. Using a multi-omics approach, we hope to identify converging evidence for potential targets of future therapeutic developments, as well as promising candidate biomarkers for further investigation by biomarker validation studies. We believe that accurate patient phenotyping will be essential for the discovery process, as both conditions are characterized by high heterogeneity and complexity, likely rendering molecular mechanisms phenotype specific.