Metabolic composition is known to exert influence on several important agronomic traits, and metabolomics, which represents the chemical composition in a cell, has long been recognized as a powerful tool for bridging phenotype-genotype interactions. In this work, sixteen truly representative sugarcane Brazilian varieties were selected to explore the metabolic networks in buds and culms, the tissues involved in the vegetative propagation of this species. Due to the fact that bud sprouting is a key trait determining crop establishment in the field, the sprouting potential among the genotypes was evaluated. The use of partial least square discriminant analysis indicated only mild differences on bud outgrowth potential under controlled environmental conditions. However, primary metabolite profiling provided information on the variability of metabolic features even under a narrow genetic background, typical for modern sugarcane cultivars. Metabolite-metabolite correlations within and between tissues revealed more complex patterns for culms in relation to buds, and enabled the recognition of key metabolites (e.g., sucrose, putrescine, glutamate, serine, and myo-inositol) affecting sprouting ability. Finally, those results were associated with the genetic background of each cultivar, showing that metabolites can be potentially used as indicators for the genetic background.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6027322PMC
http://dx.doi.org/10.3389/fpls.2018.00857DOI Listing

Publication Analysis

Top Keywords

genetic background
12
bud outgrowth
8
metabolite profiles
4
sugarcane
4
profiles sugarcane
4
sugarcane culm
4
culm reveal
4
reveal relationship
4
relationship metabolism
4
metabolism axillary
4

Similar Publications

Background: Punctal agenesis (PA) is a rare congenital anomaly that can occur in isolation or as part of an underlying syndrome. The benefit of genetic assessment in individuals with PA and clinical features that should prompt molecular workup has not been systematically studied. The aim of this study was to identify ocular and extraocular features associated with PA and determine its association with an underlying syndrome.

View Article and Find Full Text PDF

Background: Wildlife conservation and management aims to restore population declines, it is the vulnerable or endangered populations who require the greatest conservation efforts. In this context, non-invasive sampling has been evaluated as an option for reporting prey/predator impact. Galemys pyrenaicus is currently threatened throughout its range, and cohabits with Nemys anomalus, in Extremadura (Spain).

View Article and Find Full Text PDF

Background: Candidate Phyla Radiation (CPR) is a large monophyletic group encompassing about 25% of bacterial diversity. Among CPR, "Candidatus Saccharibacteria" is one of the most clinically relevant phyla. Indeed, it is enriched in the oral microbiota of subjects suffering from immune-mediated disorders and it has been found to have immunomodulatory activities.

View Article and Find Full Text PDF

Background: Current clinical sequencing methods cannot effectively detect DNA methylation and allele-specific variation to provide parent-of-origin information from the proband alone. Parent-of-origin effects can lead to differential disease and the inability to assign this in de novo cases limits prognostication in the majority of affected individuals with retinoblastoma, a hereditary cancer with suspected parent-of-origin effects.

Methods: To directly assign parent-of-origin in retinoblastoma patients, genomic DNA was extracted from blood samples for sequencing using a programmable, targeted single-molecule long-read DNA genomic and epigenomic approach.

View Article and Find Full Text PDF

Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.

J Am Med Inform Assoc

December 2024

Statistical Modeling, Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riβ 88400, Germany.

Background: Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.

Methods: To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!