Assessment of Bacterial Inoculant Delivery Methods for Cereal Crops

Despite growing evidence that plant growth-promoting bacteria can be used to improve crop vigor, a comparison of the different methods of delivery to determine which is optimal has not been published. An optimal inoculation method ensures that the inoculant colonizes the host plant so that its potential for plant growth-promotion is fully evaluated. The objective of this study was to compare the efficacy of three seed coating methods, seedling priming, and soil drench for delivering three bacterial inoculants to the sorghum rhizosphere and root endosphere. The methods were compared across multiple time points under axenic conditions and colonization efficiency was determined by quantitative polymerase chain reaction (qPCR). Two seed coating methods were also assessed in the field to test the reproducibility of the greenhouse results under non-sterile conditions. In the greenhouse seed coating methods were more successful in delivering the Gram-positive inoculant (Terrabacter sp.) while better colonization from the Gram-negative bacteria (Chitinophaga pinensis and Caulobacter rhizosphaerae) was observed with seedling priming and soil drench. This suggested that Gram-positive bacteria may be more suitable for the seed coating methods possibly because of their thick peptidoglycan cell wall. We also demonstrated that prolonged seed coating for 12 h could effectively enhance the colonization of C. pinensis, an endophytic bacterium, but not the rhizosphere colonizing C. rhizosphaerae. In the field only a small amount of inoculant was detected in the rhizosphere. This comparison demonstrates the importance of using the appropriate inoculation method for testing different types of bacteria for their plant growth-promotion potential

Soil depth and geographic distance modulate bacterial β-diversity in deep soil profiles throughout the U.S. Corn Belt

Understanding how microbial communities are shaped across spatial dimensions is of fundamental importance in microbial ecology. However, most studies on soil biogeography have focused on the topsoil microbiome, while the factors driving the subsoil microbiome distribution are largely unknown. Here we used 16S rRNA amplicon sequencing to analyse the factors underlying the bacterial β-diversity along vertical (0–240 cm of soil depth) and horizontal spatial dimensions (~500,000 km2) in the U.S. Corn Belt. With these data we tested whether the horizontal or vertical spatial variation had stronger impacts on the taxonomic (Bray-Curtis) and phylogenetic (weighted Unifrac) β-diversity. Additionally, we assessed whether the distance-decay (horizontal dimension) was greater in the topsoil (0–30 cm) or subsoil (in each 30 cm layer from 30–240 cm) using Mantel tests. The influence of geographic distance versus edaphic variables on the bacterial communities from the different soil layers was also compared. Results indicated that the phylogenetic β-diversity was impacted more by soil depth, while the taxonomic β-diversity changed more between geographic locations. The distance-decay was lower in the topsoil than in all subsoil layers analysed. Moreover, some subsoil layers were influenced more by geographic distance than any edaphic variable, including pH. Although different factors affected the topsoil and subsoil biogeography, niche-based models explained the community assembly of all soil layers. This comprehensive study contributed to elucidating important aspects of soil bacterial biogeography including the major impact of soil depth on the phylogenetic β-diversity, and the greater influence of geographic distance on subsoil than on topsoil bacterial communities in agroecosystems

Rhythm's role in genitive and dative construction choice in English

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Soil depth and geographic distance modulate bacterial β-diversity in deep soil profiles throughout the U.S. Corn Belt

Understanding how microbial communities are shaped across spatial dimensions is of fundamental importance in microbial ecology. However, most studies on soil biogeography have focused on the topsoil microbiome, while the factors driving the subsoil microbiome distribution are largely unknown. Here we used 16S rRNA amplicon sequencing to analyse the factors underlying the bacterial β-diversity along vertical (0–240 cm of soil depth) and horizontal spatial dimensions (~500,000 km2) in the U.S. Corn Belt. With these data we tested whether the horizontal or vertical spatial variation had stronger impacts on the taxonomic (Bray-Curtis) and phylogenetic (weighted Unifrac) β-diversity. Additionally, we assessed whether the distance-decay (horizontal dimension) was greater in the topsoil (0–30 cm) or subsoil (in each 30 cm layer from 30–240 cm) using Mantel tests. The influence of geographic distance versus edaphic variables on the bacterial communities from the different soil layers was also compared. Results indicated that the phylogenetic β-diversity was impacted more by soil depth, while the taxonomic β-diversity changed more between geographic locations. The distance-decay was lower in the topsoil than in all subsoil layers analysed. Moreover, some subsoil layers were influenced more by geographic distance than any edaphic variable, including pH. Although different factors affected the topsoil and subsoil biogeography, niche-based models explained the community assembly of all soil layers. This comprehensive study contributed to elucidating important aspects of soil bacterial biogeography including the major impact of soil depth on the phylogenetic β-diversity, and the greater influence of geographic distance on subsoil than on topsoil bacterial communities in agroecosystems.This article is published as Lopes, Lucas Dantas, Stephanie L. Futrell, Emily E. Wright, Gerasimos J. Danalatos, Michael J. Castellano, Tony J. Vyn, Sotirios V. Archontoulis, and Daniel P. Schachtman. "Soil depth and geographic distance modulate bacterial β‐diversity in deep soil profiles throughout the US Corn Belt." Molecular Ecology (2023). doi:10.1111/mec.16945. Posted with permission.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes

Sequencing-Based Bin Map Construction of a Tomato Mapping Population, Facilitating High-Resolution Quantitative Trait Loci Detection

Genotyping-by-sequencing (GBS) was employed to construct a highly saturated genetic linkage map of a tomato ( L.) recombinant inbred line (RIL) population, derived from a cross between cultivar NC EBR-1 and the wild tomato L. accession LA2093. A pipeline was developed to convert single nucleotide polymorphism (SNP) data into genomic bins, which could be used for fine mapping of quantitative trait loci (QTL) and identification of candidate genes. The pipeline, implemented in a python script named SNPbinner, adopts a hidden Markov model approach for calculation of recombination breakpoints followed by genomic bins construction. The total length of the newly developed high-resolution genetic map was 1.2-fold larger than previously estimated based on restriction fragment length polymorphism (RFLP) and polymerase chain reaction (PCR)–based markers. The map was used to verify and refine QTL previously identified for two fruit quality traits in the RIL population, fruit weight (FW) and fruit lycopene content (LYC). Two well-described FW QTL ( and ) were localized precisely at their known underlying causative genes, and the QTL intervals were decreased by two- to tenfold. A major QTL for LYC content () was verified at high resolution and its underlying causative gene was determined to be ζ (). The RIL population, the high resolution genetic map, and the easy-to-use genotyping pipeline, SNPbinner, are made publicly available

The Kinase Chemogenomic Set (KCGS): an open science resource for kinase vulnerability identification

We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS), current Version 1.0, is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens