Biomechanical phenotyping pipeline for stalk lodging resistance in maize.

Stalk lodging (structural failure crops prior to harvest) significantly reduces annual yields of vital grain crops. The lack of standardized, high throughput phenotyping methods capable of quantifying biomechanical plant traits prevents comprehensive understanding of the genetic architecture of stalk lodging resistance. A phenotyping pipeline developed to enable higher throughput biomechanical measurements of plant traits related to stalk lodging is presented. The methods were developed using principles from the fields of engineering mechanics and metrology and they enable retention of plant-specific data instead of averaging data across plots as is typical in most phenotyping studies. This pipeline was specifically designed to be implemented in large experimental studies and has been used to phenotype over 40,000 maize stalks. The pipeline includes both lab- and field-based phenotyping methodologies and enables the collection of metadata. Best practices learned by implementing this pipeline over the past three years are presented. The specific instruments (including model numbers and manufacturers) that work well for these methods are presented, however comparable instruments may be used in conjunction with these methods as seen fit.•Efficient methods to measure biomechanical traits and record metadata related to stalk lodging.•Can be used in studies with large sample sizes (i.e., > 1,000).

Methods for Optimization of Protein Extraction and Proteogenomic Mapping in Sweet Potato.

The complexity in chemical composition alongside the genomic complexity of crop plants poses significant challenges for the characterization of their proteomes. This chapter provides specific methods that can be used for the extraction and identification of proteins from sweet potato, and a proteogenomic method for the subsequent peptide mapping on the haplotype-derived sweet potato genome assembly. We outline two basic methods for extracting proteins expressed in root and leaf tissues for the label-free quantitative proteomics-one phenol-based procedure and one polyethylene glycol (PEG) 4000-based fractionation method-and discuss strategies for the organ-specific protein extraction and increased recovery of low-abundance proteins. Next, we describe computational methods for improved proteome annotation of sweet potato based on aggregated genomics and transcriptomics resources available in our and public databases. Lastly, we describe an easily customizable proteogenomics approach for mapping sweet potato peptides back to their genome location and exemplify its use in improving genome annotations using a mass spectrometry data set.

Proteomics and Proteogenomics Analysis of Sweetpotato ( Ipomoea batatas) Leaf and Root.

Two complementary protein extraction methodologies coupled with an automated proteomic platform were employed to analyze tissue-specific proteomes and characterize biological and metabolic processes in sweetpotato. A total of 74 255 peptides corresponding to 4321 nonredundant proteins were successfully identified. Data were compared to predicted protein accessions for Ipomoea species and mapped on the sweetpotato transcriptome and haplotype-resolved genome. The two methodologies exhibited differences in the number and class of the unique proteins extracted. Overall, 39 916 peptides mapped to 3143 unique proteins in leaves, and 34 339 peptides mapped to 2928 unique proteins in roots. Primary metabolism and protein translation processes were enriched in leaves, whereas genetic pathways associated with protein folding, transport, sorting, as well as pathways in the primary carbohydrate metabolism were enriched in storage roots. A proteogenomics analysis successfully mapped 90.4% of the total uniquely identified peptides against the sweetpotato transcriptome and genome, predicted 741 new protein-coding genes, and specified 2056 loci where gene annotations can be further improved. The proteogenomics results provide evidence for the translation of new open reading frames (ORFs), alternative ORFs, exon extensions, and intronic ORF sequences. Data are available via ProteomeXchange with identifier PXD012999.