MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest

Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis

The morphology of roots and root systems influences the efficiency by which plants acquire nutrients and water, anchor themselves and provide stability to the surrounding soil. Plant genotype and the biotic and abiotic environment significantly influence root morphology, growth and ultimately crop yield. The challenge for researchers interested in phenotyping root systems is, therefore, not just to measure roots and link their phenotype to the plant genotype, but also to understand how the growth of roots is influenced by their environment. This review discusses progress in quantifying root system parameters (e.g. in terms of size, shape and dynamics) using imaging and image analysis technologies and also discusses their potential for providing a better understanding of root:soil interactions. Significant progress has been made in image acquisition techniques, however trade-offs exist between sample throughput, sample size, image resolution and information gained. All of these factors impact on downstream image analysis processes. While there have been significant advances in computation power, limitations still exist in statistical processes involved in image analysis. Utilizing and combining different imaging systems, integrating measurements and image analysis where possible, and amalgamating data will allow researchers to gain a better understanding of root:soil interactions

Measurement of plant growth in view of an integrative analysis of regulatory networks

As the regulatory networks of growth at the cellular level are elucidated at a fast pace, their complexity is not reduced; on the contrary, the tissue, organ and even whole-plant level affect cell proliferation and expansion by means of development-induced and environment-induced signaling events in growth regulatory processes. Measurement of growth across different levels aids in gaining a mechanistic understanding of growth, and in defining the spatial and temporal resolution of sampling strategies for molecular analyses in the model Arabidopsis thaliana and increasingly also in crop species. The latter claim their place at the forefront of plant research, since global issues and future needs drive the translation from laboratory model-acquired knowledge of growth processes to improvements in crop productivity in field conditions

Towards a methodical framework for comprehensively assessing forest multifunctionality

Funded by Deutsche Forschungsgemeinschaft. Grant Number: DFG FOR 891/1-3 National Natural Science Foundation of China. Grant Numbers: 30710103907, 30930005, 31170457, 31210103910 Swiss National Science Foundation (SNSF) Sino-German Centre for Research Promotion in Beijing. Grant Number: GZ 986Peer reviewedPublisher PD

BACTERIAL INOCULANTS, ENDOPHYTIC BACTERIA AND THEIR INFLUENCE ON \u3cem\u3eNICOTIANA\u3c/em\u3e PHYSIOLOGY, DEVELOPMENT AND MICROBIOME

Soil and root microbial communities have been studied for decades, and the incorporation of high-throughput techniques and analysis has allowed the identification of endophytic/non-culturable organisms. This has helped characterize and establish the core microbiome of many model plant species which include underground and aboveground organs. Unfortunately, the information obtained from some of these model plants is not always transferable to other agronomic species. In this project, we decided to study the microbiome of the Nicotiana genus because of its importance in plant physiological and plant-microbe interactions studies. The data obtained was used as baseline information that allowed us to better understand the effect of microbial inoculums on the assembly of the microbiome of the plant. We analyzed 16s rRNA amplicons to survey the microbiome in different plant organs and rhizosphere from four different species. Bacterial strains evaluated were screened for a consistent reduction or improvement in plant growth. Four bacterial strains were tested and used as seed inoculum (Lf-Lysinobacillus fusisormis, Ms –Micrococcus sp., Bs–Bacillus sp., Bc–Bacillus cereus). Bs and Bc inoculants caused plant growth promotion, and in contrast Ms caused retarded growth, while Lf acted as a neutral or non-inducing phenotype strain. Data supported that microbial inoculum used as seed treatment caused systemic changes in the host plant microbiome. Functionality of the inoculum was studied and the response in plant growth was linked to hormonal changes (evaluated in the plant and in the bacterial strains). Gene expression analysis using a genome-scale approach revealed that genes that could possibly be involved in stress response are down-regulated for Bc and Bs treatments and up-regulated for Ms. Flexibility variability of the inoculum was also evaluated to have a better understanding of the main factors involved in the promotion or suppression of growth, and possibly its effect in following generations. In summary, the findings of this project support that the plant functional microbiome responds to exogenous stimulation from abiotic and biotic factors by adapting endogenous hormone responses