Truncated transcription factor-like proteins called microProteins (miPs) can modulate transcription factor activities, thereby increasing transcriptional regulatory complexity. To understand their prevalence, evolution, and function, we predicted over 400 genes that encode putative miPs from Arabidopsis (Arabidopsis thaliana) using a bioinformatics pipeline and validated two novel miPs involved in flowering time and response to abiotic and biotic stress. We provide an evolutionary perspective for a class of miPs targeting homeodomain transcription factors in plants and metazoans. We identify domain loss as one mechanism of miP evolution and suggest the possible roles of miPs on the evolution of their target transcription factors. Overall, we reveal a prominent layer of transcriptional regulation by miPs, show pervasiveness of such proteins both within and across genomes, and provide a framework for studying their function and evolution.

All plants synthesize basic metabolites needed for survival (primary metabolism), but different taxa produce distinct metabolites that are specialized for specific environmental interactions (specialized metabolism). Because evolutionary pressures on primary and specialized metabolism differ, we investigated differences in the emergence and maintenance of these processes across 16 species encompassing major plant lineages from algae to angiosperms. We found that, relative to their primary metabolic counterparts, genes coding for specialized metabolic functions have proliferated to a much greater degree and by different mechanisms and display lineage-specific patterns of physical clustering within the genome and coexpression. These properties illustrate the differential evolution of specialized metabolism in plants, and collectively they provide unique signatures for the potential discovery of novel specialized metabolic processes.

Cellular membranes act as signaling platforms and control solute transport. Membrane receptors, transporters, and enzymes communicate with intracellular processes through protein-protein interactions. Using a split-ubiquitin yeast two-hybrid screen that covers a test-space of 6.4 x 10(6) pairs, we identified 12,102 membrane/signaling protein interactions from Arabidopsis. Besides confirmation of expected interactions such as heterotrimeric G protein subunit interactions and aquaporin oligomerization, >99% of the interactions were previously unknown. Interactions were confirmed at a rate of 32% in orthogonal in planta split-green flourescent protein interaction assays, which was statistically indistinguishable from the confirmation rate for known interactions collected from literature (38%). Regulatory associations in membrane protein trafficking, turnover, and phosphorylation include regulation of potassium channel activity through abscisic acid signaling, transporter activity by a WNK kinase, and a brassinolide receptor kinase by trafficking-related proteins. These examples underscore the utility of the membrane/signaling protein interaction network for gene discovery and hypothesis generation in plants and other organisms.

Plant biology is becoming a data-driven science. High-throughput technologies generate data quickly from molecular to ecosystem levels. Statistical and computational approaches enable describing and interpreting data quantitatively. We highlight the purpose, common problems, and general principles in data analysis. We use RNA sequencing (RNAseq) analysis to illustrate the rationale behind some of the choices made in statistical data analysis. Finally, we provide a list of free online resources that emphasize intuition behind quantitative data analysis.

Background: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO- based similarity because of the limited proportion of genes that are annotated to GO in most organisms. Results: We introduce a novel approach called NETSIM (network- based similarity measure) that incorporates information from gene co- function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. Conclusions: Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome- specific information. NETSIM incorporates both GO annotations and gene co- function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon- specific manner become measurable when GO annotations are limited. Supplementary information and software are available at http://www.msu.edu/similar to jinchen/NETSIM.

An emerging concept in transcriptional regulation is that a class of truncated transcription factors (TFs), called microProteins (miPs), engages in protein-protein interactions with TF complexes and provides feedback controls. A handful of miP examples have been described in the literature but the extent of their prevalence is unclear. Here we present an algorithm that predicts miPs and their target TFs from a sequenced genome. The algorithm is called miP prediction program (miP3), which is implemented in Python. The software will help shed light on the prevalence, biological roles, and evolution of miPs. Moreover, miP3 can be used to predict other types of miP-like proteins that may have evolved from other functional classes such as kinases and receptors. The program is freely available and can be applied to any sequenced genome.

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis ( Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.

In January 2014, an international meeting sponsored by the International Life Sciences Institute/Health and Environmental Sciences Institute and the Canadian Food Inspection Agency titled "Genetic Basis of Unintended Effects in Modified Plants" was held in Ottawa, Canada, bringing together over 75 scientists from academia, government, and the agro-biotech industry. The objectives of the meeting were to explore current knowledge and identify areas requiring further study on unintended effects in plants and to discuss how this information can inform and improve genetically modified (GM) crop risk assessments. The meeting featured presentations on the molecular basis of plant genome variability in general, unintended changes at the molecular and phenotypic levels, and the development and use of hypothesis-driven evaluations of unintended effects in assessing conventional and GM crops. The development and role of emerging "omics" technologies in the assessment of unintended effects was also discussed. Several themes recurred in a number of talks; for example, a common observation was that no system for genetic modification, including conventional methods of plant breeding, is without unintended effects. Another common observation was that "unintended" does not necessarily mean "harmful". This paper summarizes key points from the information presented at the meeting to provide readers with current viewpoints on these topics.

A report on the 10th plant genome meeting entitled 'Plant genomes and biotechnology: from genes to networks', held at Cold Spring Harbor Laboratory, 2-5 December, 2015.

Complex traits such as crop performance and human diseases are controlled by multiple genetic loci, many of which have small effects and often go undetected by traditional quantitative trait locus (QTL) mapping. Recently, bulked segregant analysis with large F2 pools and genome-level markers (named extreme-QTL or X-QTL mapping) has been used to identify many QTL. To estimate parameters impacting QTL detection for X-QTL mapping, we simulated the effects of population size, marker density, and sequencing depth of markers on QTL detectability for traits with differing heritabilities. These simulations indicate that a high (>90%) chance of detecting QTL with at least 5% effect requires 5000X sequencing depth for a trait with heritability of 0.4-0.7. For most eukaryotic organisms, whole-genome sequencing at this depth is not economically feasible. Therefore, we tested and confirmed the feasibility of applying deep sequencing of target-enriched markers for X-QTL mapping. We used two traits in Arabidopsis thaliana with different heritabilities: seed size (H-2 = 0.61) and seedling greening in response to salt (H-2 = 0.94). We used a modified G test to identify QTL regions and developed a model-based statistical framework to resolve individual peaks by incorporating recombination rates. Multiple QTL were identified for both traits, including previously undiscovered QTL. We call our method target-enriched X-QTL (TEX-QTL) mapping; this mapping approach is not limited by the genome size or the availability of recombinant inbred populations and should be applicable to many organisms and traits.