Characterization of Proteome Variation During Modern Maize Breeding

Transcriptional and translational landscape fine-tune genome annotation and explores translation control in cotton

INTRODUCTION

The unavailability of intergenic region annotation in whole genome sequencing and pan-genomics hinders efforts to enhance crop improvement.

OBJECTIVES

Despite advances in research, the impact of post-transcriptional regulation on fiber development and translatome profiling at different stages of fiber growth in cotton (G. hirsutum) remains unexplored.

METHODS

We utilized a combination of reference-guided de novo transcriptome assembly and ribosome profiling techniques to uncover the hidden mechanisms of translational control in eight distinct tissues of upland cotton.

RESULTS

Our study identified P-site distribution at three-nucleotide periodicity and dominant ribosome footprint at 27 nucleotides. Specifically, we have detected 1,589 small open reading frames (sORFs), including 1,376 upstream ORFs (uORFs) and 213 downstream ORFs (dORFs), as well as 552 long non-coding RNAs (lncRNAs) with potential coding functions, which fine-tune the annotation of the cotton genome. Further, we have identified novel genes and lncRNAs with strong translation efficiency (TE), while sORFs were found to affect mRNA transcription levels during fiber elongation. The reliability of these findings was confirmed by the high consistency in correlation and synergetic fold change between RNA-sequencing (RNA-seq) and Ribosome-sequencing (Ribo-seq) analyses. Additionally, integrated omics analysis of the normal fiber ZM24 and short fiber pag1 cotton mutant revealed several differentially expressed genes (DEGs), and fiber-specific expressed (high/low) genes associated with sORFs (uORFs and dORFs). These findings were further supported by the overexpression and knockdown of GhKCS6, a gene associated with sORFs in cotton, and demonstrated the potential regulation of the mechanism governing fiber elongation on both the transcriptional and post-transcriptional levels.

CONCLUSION

Reference-guided transcriptome assembly and the identification of novel transcripts fine-tune the annotation of the cotton genome and predicted the landscape of fiber development. Our approach provided a high-throughput method, based on multi-omics, for discovering unannotated ORFs, hidden translational control, and complex regulatory mechanisms in crop plants.

Effects of Climate Change and Drought Tolerance on Maize Growth

Climate change is affecting all regions of the world with different climates, and the scale of damage is increasing due to the occurrence of various natural disasters. In particular, maize production is highly affected by abnormal climate events such as heat waves and droughts. Increasing temperatures can accelerate growth and shorten the growing season, potentially reducing productivity. Additionally, enhanced temperatures during the ripening period can accelerate the process, reducing crop yields. In addition, drought stress due to water deficit can greatly affect seedling formation, early plant growth, photosynthesis, reproductive growth, and yield, so proper water management is critical to maize growth. Maize, in particular, is tall and broad-leaved, so extreme drought stress at planting can cause leaves to curl and stunt growth. It is important to understand that severe drought can have a detrimental effect on the growth and reproduction of maize. In addition, high temperatures caused by drought stress can inhibit the induction of flowering in male flowers and cause factors that interfere with pollen development. It is therefore important to increase the productivity of all food crops, including maize, while maintaining them in the face of persistent drought caused by climate change. This requires a strategy to develop genetically modified crops and drought-tolerant maize that can effectively respond to climate change. The aim of this paper is to investigate the effects of climate change and drought tolerance on maize growth. We also reviewed molecular breeding techniques to develop drought-tolerant maize varieties in response to climate change.

Drought stress in maize: stress perception to molecular response and strategies for its improvement

Given the future demand for food crops, increasing crop productivity in drought-prone rainfed areas has become essential. Drought-tolerant varieties are warranted to solve this problem in major crops, with drought tolerance as a high-priority trait for future research. Maize is one such crop affected by drought stress, which limits production, resulting in substantial economic losses. It became a more serious issue due to global climate change. The most drought sensitive among all stages of maize is the reproductive stages and the most important for overall maize production. The exact molecular basis of reproductive drought sensitivity remains unclear due to genes' complex regulation of drought stress. Understanding the molecular biology and signaling of the unexplored area of reproductive drought tolerance will provide an opportunity to develop climate-smart drought-tolerant next-generation maize cultivars. In recent decades, significant progress has been made in maize to understand the drought tolerance mechanism. However, improving maize drought tolerance through breeding is ineffective due to the complex nature and multigenic control of drought traits. With the help of advanced breeding techniques, molecular genetics, and a precision genome editing approach like CRISPR-Cas, candidate genes for drought-tolerant maize can be identified and targeted. This review summarizes the effects of drought stress on each growth stage of maize, potential genes, and transcription factors that determine drought tolerance. In addition, we discussed drought stress sensing, its molecular mechanisms, different approaches to developing drought-resistant maize varieties, and how molecular breeding and genome editing will help with the current unpredictable climate change.

Mapping of QTLs for morphophysiological and yield traits under water-deficit stress and well-watered conditions in maize

Maize productivity is significantly impacted by drought; therefore, improvement of drought tolerance is a critical goal in maize breeding. To achieve this, a better understanding of the genetic basis of drought tolerance is necessary. Our study aimed to identify genomic regions associated with drought tolerance-related traits by phenotyping a mapping population of recombinant inbred lines (RILs) for two seasons under well-watered (WW) and water-deficit (WD) conditions. We also used single nucleotide polymorphism (SNP) genotyping through genotyping-by-sequencing to map these regions and attempted to identify candidate genes responsible for the observed phenotypic variation. Phenotyping of the RILs population revealed significant variability in most of the traits, with normal frequency distributions, indicating their polygenic nature. We generated a linkage map using 1,241 polymorphic SNPs distributed over 10 chromosomes (chrs), covering a total genetic distance of 5,471.55 cM. We identified 27 quantitative trait loci (QTLs) associated with various morphophysiological and yield-related traits, with 13 QTLs identified under WW conditions and 12 under WD conditions. We found one common major QTL (qCW2-1) for cob weight and a minor QTL (qCH1-1) for cob height that were consistently identified under both water regimes. We also detected one major and one minor QTL for the Normalized Difference Vegetation Index (NDVI) trait under WD conditions on chr 2, bin 2.10. Furthermore, we identified one major QTL (qCH1-2) and one minor QTL (qCH1-1) on chr 1 that were located at different genomic positions to those identified in earlier studies. We found co-localized QTLs for stomatal conductance and grain yield on chr 6 (qg_s6-2 and qGY6-1), while co-localized QTLs for stomatal conductance and transpiration rate were identified on chr 7 (qg_s7-1 and qTR7-1). We also attempted to identify the candidate genes responsible for the observed phenotypic variation; our analysis revealed that the major candidate genes associated with QTLs detected under water deficit conditions were related to growth and development, senescence, abscisic acid (ABA) signaling, signal transduction, and transporter activity in stress tolerance. The QTL regions identified in this study may be useful in designing markers that can be utilized in marker-assisted selection breeding. In addition, the putative candidate genes can be isolated and functionally characterized so that their role in imparting drought tolerance can be more fully understood.

Evolution of enzyme levels in metabolic pathways: A theoretical approach. Part 2

Metabolism is essential for cell function and adaptation. Because of their central role in metabolism, kinetic parameters and enzyme concentrations are under constant selective pressure to adapt the fluxes of the metabolic networks to the needs of the organism. In line with various studies dealing with enzyme evolution, we recently developed a model of the evolution of enzyme concentrations under selection for increased flux, considered as a proxy for fitness (Coton et al., 2022). With this model, taking into account two realistic cellular constraints, competition for resources and co-regulation, we determined the evolutionary equilibria and range of neutral variations of enzyme concentrations. In this article, we expanded this model by considering that the enzymes in a pathway can belong to different co-regulation groups. We determined the equilibria and showed that the constraints modify the adaptive landscape by limiting the number of independent dimensions. We also showed that any trade-off between enzyme concentrations is sufficient to limit the flux and relax selection for increasing the concentration of other enzymes. Even though this model is based on simplifying assumptions, the complexity of the relationship between enzyme concentrations prevents the formal analysis of the range of neutral variation of enzyme concentrations. However, we could show that selection for maximizing the flux results in selective neutrality for all enzymes regardless the constraints applied, giving generality to the prediction of Hartl et al. (1985).

Multiomic Approaches Reveal Hormonal Modulation and Nitrogen Uptake and Assimilation in the Initial Growth of Maize Inoculated with Herbaspirillum seropedicae

Herbaspirillum seropedicae is an endophytic bacterium that can fix nitrogen and synthesize phytohormones, which can lead to a plant growth-promoting effect when used as a microbial inoculant. Studies focused on mechanisms of action are crucial for a better understanding of the bacteria-plant interaction and optimization of plant growth-promoting response. This work aims to understand the underlined mechanisms responsible for the early stimulatory growth effects of H. seropedicae inoculation in maize. To perform these studies, we combined transcriptomic and proteomic approaches with physiological analysis. The results obtained eight days after inoculation (d.a.i) showed increased root biomass (233 and 253%) and shoot biomass (249 and 264%), respectively, for the fresh and dry mass of maize-inoculated seedlings and increased green content and development. Omics data analysis, before a positive biostimulation phenotype (5 d.a.i.) revealed that inoculation increases N-uptake and N-assimilation machinery through differentially expressed nitrate transporters and amino acid pathways, as well carbon/nitrogen metabolism integration by the tricarboxylic acid cycle and the polyamine pathway. Additionally, phytohormone levels of root and shoot tissues increased in bacterium-inoculated-maize plants, leading to feedback regulation by the ubiquitin-proteasome system. The early biostimulatory effect of H. seropedicae partially results from hormonal modulation coupled with efficient nutrient uptake-assimilation and a boost in primary anabolic metabolism of carbon-nitrogen integrative pathways.

Multi-omics revolution to promote plant breeding efficiency

Crop production is the primary goal of agricultural activities, which is always taken into consideration. However, global agricultural systems are coming under increasing pressure from the rising food demand of the rapidly growing world population and changing climate. To address these issues, improving high-yield and climate-resilient related-traits in crop breeding is an effective strategy. In recent years, advances in omics techniques, including genomics, transcriptomics, proteomics, and metabolomics, paved the way for accelerating plant/crop breeding to cope with the changing climate and enhance food production. Optimized omics and phenotypic plasticity platform integration, exploited by evolving machine learning algorithms will aid in the development of biological interpretations for complex crop traits. The precise and progressive assembly of desire alleles using precise genome editing approaches and enhanced breeding strategies would enable future crops to excel in combating the changing climates. Furthermore, plant breeding and genetic engineering ensures an exclusive approach to developing nutrient sufficient and climate-resilient crops, the productivity of which can sustainably and adequately meet the world's food, nutrition, and energy needs. This review provides an overview of how the integration of omics approaches could be exploited to select crop varieties with desired traits.

A pan-Zea genome map for enhancing maize improvement

Background

Maize (Zea mays L.) is at the vanguard facing the upcoming breeding challenges. However, both a super pan-genome for the Zea genus and a comprehensive genetic variation map for maize breeding are still lacking.

Results

Here, we construct an approximately 6.71-Gb pan-Zea genome that contains around 4.57-Gb non-B73 reference sequences from fragmented de novo assemblies of 721 pan-Zea individuals. We annotate a total of 58,944 pan-Zea genes and find around 44.34% of them are dispensable in the pan-Zea population. Moreover, 255,821 common structural variations are identified and genotyped in a maize association mapping panel. Further analyses reveal gene presence/absence variants and their potential roles during domestication of maize. Combining genetic analyses with multi-omics data, we demonstrate how structural variants are associated with complex agronomic traits.

Conclusions

Our results highlight the underexplored role of the pan-Zea genome and structural variations to further understand domestication of maize and explore their potential utilization in crop improvement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02742-7.

Plant proteostasis: a proven and promising target for crop improvement

The Green Revolution of the 1960s accomplished dramatic increases in crop yields through genetic improvement, chemical fertilisers, irrigation, and mechanisation. However, the current trajectory of population growth, against a backdrop of climate change and geopolitical unrest, predicts that agricultural production will be insufficient to ensure global food security in the next three decades. Improvements to crops that go beyond incremental gains are urgently needed. Plant biology has also undergone a revolution in recent years, through the development and application of powerful technologies including genome sequencing, a pantheon of 'omics techniques, precise genome editing, and step changes in structural biology and microscopy. Proteostasis - the collective processes that control the protein complement of the cell, comprising synthesis, modification, localisation, and degradation - is a field that has benefitted from these advances. This special issue presents a selection of the latest research in this vibrant field, with a particular focus on protein degradation. In the current article, we highlight the diverse and widespread contributions of plant proteostasis to agronomic traits, suggest opportunities and strategies to manipulate different elements of proteostatic mechanisms for crop improvement, and discuss the challenges involved in bringing these ideas into practice.

Variation in upstream open reading frames contributes to allelic diversity in maize protein abundance

SignificanceProteins are the machinery which execute essential cellular functions. However, measuring their abundance within an organism can be difficult and resource-intensive. Cells use a variety of mechanisms to control protein synthesis from mRNA, including short open reading frames (uORFs) that lie upstream of the main coding sequence. Ribosomes can preferentially translate uORFs instead of the main coding sequence, leading to reduced translation of the main protein. In this study, we show that uORF sequence variation between individuals can lead to different rates of protein translation and thus variable protein abundances. We also demonstrate that natural variation in uORFs occurs frequently and can be linked to whole-plant phenotypes, indicating that uORF sequence variation likely contributes to plant adaptation.

To the proteome and beyond: advances in single-cell omics profiling for plant systems

Recent advances in single-cell proteomics for animal systems could be adapted for plants to increase our understanding of plant development, response to stimuli, and cell-to-cell signaling.

Evolution of enzyme levels in metabolic pathways: A theoretical approach

The central role of metabolism in cell functioning and adaptation has given rise to countless studies on the evolution of enzyme-coding genes and network topology. However, very few studies have addressed the question of how enzyme concentrations change in response to positive selective pressure on the flux, considered a proxy of fitness. In particular, the way cellular constraints, such as resource limitations and co-regulation, affect the adaptive landscape of a pathway under selection has never been analyzed theoretically. To fill this gap, we developed a model of the evolution of enzyme concentrations that combines metabolic control theory and an adaptive dynamics approach, and integrates possible dependencies between enzyme concentrations. We determined the evolutionary equilibria of enzyme concentrations and their range of neutral variation, and showed that they differ with the properties of the enzymes, the constraints applied to the system and the initial enzyme concentrations. Simulations of long-term evolution confirmed all analytical and numerical predictions, even though we relaxed the simplifying assumptions used in the analytical treatment.

Proteome-wide systems genetics identifies UFMylation as a regulator of skeletal muscle function

Improving muscle function has great potential to improve the quality of life. To identify novel regulators of skeletal muscle metabolism and function, we performed a proteomic analysis of gastrocnemius muscle from 73 genetically distinct inbred mouse strains, and integrated the data with previously acquired genomics and >300 molecular/phenotypic traits via quantitative trait loci mapping and correlation network analysis. These data identified thousands of associations between protein abundance and phenotypes and can be accessed online (https://muscle.coffeeprot.com/) to identify regulators of muscle function. We used this resource to prioritize targets for a functional genomic screen in human bioengineered skeletal muscle. This identified several negative regulators of muscle function including UFC1, an E2 ligase for protein UFMylation. We show UFMylation is up-regulated in a mouse model of amyotrophic lateral sclerosis, a disease that involves muscle atrophy. Furthermore, in vivo knockdown of UFMylation increased contraction force, implicating its role as a negative regulator of skeletal muscle function.

A natural single-nucleotide polymorphism variant in sulfite reductase influences sulfur assimilation in maize

Plants absorb sulfur from the environment and assimilate it into suitable forms for the biosynthesis of a broad range of molecules. Although the biochemical pathway of sulfur assimilation is known, how genetic differences contribute to natural variation in sulfur assimilation remains poorly understood. Here, using a genome-wide association study, we uncovered a single-nucleotide polymorphism (SNP) variant in the sulfite reductase (SiR) gene that was significantly associated with SiR protein abundance in a maize natural association population. We also demonstrated that the synonymous C to G base change at SNP69 may repress translational activity by altering messenger RNA secondary structure, which leads to reduction in ZmSiR protein abundance and sulfur assimilation activity. Population genetic analyses showed that the SNP69C allele was likely a variant occurring after the initial maize domestication and accumulated with the spread of maize cultivation from tropical to temperate regions. This study provides the first evidence that genetic polymorphisms in the exon of ZmSiR could influence the protein abundance through a posttranscriptional mechanism and in part contribute to natural variation in sulfur assimilation. These findings provide a prospective target to improve maize varieties with proper sulfur nutrient levels assisted by molecular breeding and engineering.

Large-scale translatome profiling annotates the functional genome and reveals the key role of genic 3' untranslated regions in translatomic variation in plants

The translatome, a profile of the translational status of genetic information within cells, provides a new perspective on gene expression. Although many plant genomes have been sequenced, comprehensive translatomic annotations are not available for plants due to a lack of efficient translatome profiling techniques. Here, we developed a new technique termed 3' ribosome-profiling sequencing (3'Ribo-seq) for reliable, robust translatomic profiling. 3'Ribo-seq combines polysome profiling and 3' selection with a barcoding and pooling strategy. Systematic translatome profiling of different tissues of Arabidopsis, rice, and maize using conventional ribosome profiling (Ribo-seq) and 3'Ribo-seq revealed many novel translational genomic loci, thereby complementing functional genome annotation in plants. Using the low-cost, efficient 3'Ribo-seq technique and genome-wide association mapping of translatome expression (eGWAS), we performed a population-level dissection of the translatomes of 159 diverse maize inbred lines and identified 1,777 translational expression quantitative trait loci (eQTLs). Notably, local eQTLs are significantly enriched in the 3' untranslated regions of genes. Detailed eQTL analysis suggested that sequence variation around the polyadenylation (polyA) signal motif plays a key role in translatomic variation. Our study provides a comprehensive translatome annotation of plant functional genomes and introduces 3'Ribo-seq, which paves the way for deep translatomic analysis at the population level.

Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits

KEY MESSAGE

Integrating genomics technologies and breeding methods to tweak core parameters of the breeder's equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security. Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers' fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder's equation. A prerequisite to enhancing genetic variance (σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder's equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.

Genetic dissection of maize drought tolerance for trait improvement

Maize is one of the most important crops, but its production is threatened by drought stress worldwide. Thus, increased drought tolerance has been a major goal of maize breeding. Conventional breeding strategies have led to significantly increase of maize yields; however, these strategies often fail to meet the need for drought stress tolerance enhancement. Here, we focus on progress related to the genetic dissection of drought tolerance in maize at different developmental stages achieved through linkage mapping and association mapping. Moreover, recent molecular breeding systems, including transgenic, genome-wide marker-assisted selection, and genome editing technologies, have provided a more direct, efficient, and accurate approach for trait improvement. We also provide perspectives on future directions regarding multi-omics studies and maize improvement. Overall, the application of acquired knowledge will facilitate maize breeding to meet the challenges.

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

Temporal Regulation of the Metabolome and Proteome in Photosynthetic and Photorespiratory Pathways Contributes to Maize Heterosis

Heterosis or hybrid vigor is widespread in plants and animals. Although the molecular basis for heterosis has been extensively studied, metabolic and proteomic contributions to heterosis remain elusive. Here we report an integrative analysis of time-series metabolome and proteome data in maize (Zea mays) hybrids and their inbred parents. Many maize metabolites and proteins are diurnally regulated, and many of these show nonadditive abundance in the hybrids, including key enzymes and metabolites involved in carbon assimilation. Compared with robust trait heterosis, metabolic heterosis is relatively mild. Interestingly, most amino acids display negative mid-parent heterosis (MPH), i.e., having lower values than the average of the parents, while sugars, alcohols, and nucleoside metabolites show positive MPH. From the network perspective, metabolites in the photosynthetic pathway show positive MPH, whereas metabolites in the photorespiratory pathway show negative MPH, which corresponds to nonadditive protein abundance and enzyme activities of key enzymes in the respective pathways in the hybrids. Moreover, diurnally expressed proteins that are upregulated in the hybrids are enriched in photosynthesis-related gene-ontology terms. Hybrids may more effectively remove toxic metabolites generated during photorespiration, and thus maintain higher photosynthetic efficiency. These metabolic and proteomic resources provide unique insight into heterosis and its utilization for high yielding maize and other crop plants.

A systems genetics approach reveals environment-dependent associations between SNPs, protein coexpression, and drought-related traits in maize

The effect of drought on maize yield is of particular concern in the context of climate change and human population growth. However, the complexity of drought-response mechanisms makes the design of new drought-tolerant varieties a difficult task that would greatly benefit from a better understanding of the genotype–phenotype relationship. To provide novel insight into this relationship, we applied a systems genetics approach integrating high-throughput phenotypic, proteomic, and genomic data acquired from 254 maize hybrids grown under two watering conditions. Using association genetics and protein coexpression analysis, we detected more than 22,000 pQTLs across the two conditions and confidently identified 15 loci with potential pleiotropic effects on the proteome. We showed that even mild water deficit induced a profound remodeling of the proteome, which affected the structure of the protein coexpression network, and a reprogramming of the genetic control of the abundance of many proteins, including those involved in stress response. Colocalizations between pQTLs and QTLs for ecophysiological traits, found mostly in the water deficit condition, indicated that this reprogramming may also affect the phenotypic level. Finally, we identified several candidate genes that are potentially responsible for both the coexpression of stress response proteins and the variations of ecophysiological traits under water deficit. Taken together, our findings provide novel insights into the molecular mechanisms of drought tolerance and suggest some pathways for further research and breeding.

5Gs for crop genetic improvement

Here we propose a 5G breeding approach for bringing much-needed disruptive changes to crop improvement. These 5Gs are Genome assembly, Germplasm characterization, Gene function identification, Genomic breeding (GB), and Gene editing (GE). In our view, it is important to have genome assemblies available for each crop and a deep collection of germplasm characterized at sequencing and agronomic levels for identification of marker-trait associations and superior haplotypes. Systems biology and sequencing-based mapping approaches can be used to identify genes involved in pathways leading to the expression of a trait, thereby providing diagnostic markers for target traits. These genes, markers, haplotypes, and genome-wide sequencing data may be utilized in GB and GE methodologies in combination with a rapid cycle breeding strategy.

Genomic, Transcriptomic and Epigenomic Tools to Study the Domestication of Plants and Animals: A Field Guide for Beginners

In the last decade, genomics and the related fields of transcriptomics and epigenomics have revolutionized the study of the domestication process in plants and animals, leading to new discoveries and new unresolved questions. Given that some domesticated taxa have been more studied than others, the extent of genomic data can range from vast to nonexistent, depending on the domesticated taxon of interest. This review is meant as a rough guide for students and academics that want to start a domestication research project using modern genomic tools, as well as for researchers already conducting domestication studies that are interested in following a genomic approach and looking for alternate strategies (cheaper or more efficient) and future directions. We summarize the theoretical and technical background needed to carry out domestication genomics, starting from the acquisition of a reference genome and genome assembly, to the sampling design for population genomics, paleogenomics, transcriptomics, epigenomics and experimental validation of domestication-related genes. We also describe some examples of the aforementioned approaches and the relevant discoveries they made to understand the domestication of the studied taxa.

Dark response genes: a group of endogenous pendulum/timing players in maize?

MAIN CONCLUSION

Maize has a set of dark response genes, expression of which is influenced by multiple factor and varies with maize inbred lines but without germplasm specificity. The response to photoperiod is a common biological issue across the species kingdoms. Dark is as important as light in photoperiod. However, further in-depth understanding of responses of maize (Zea mays) to light and dark transition under photoperiod is hindered due to the lack of understanding of dark response genes. With multiple public "-omic" datasets of temperate and tropical/subtropical maize, 16 maize dark response genes, ZmDRGs, were found and had rhythmic expression under dark and light-dark cycle. ZmDRGs 6-8 were tandemly duplicated. ZmDRGs 2, 13, and 14 had a chromosomal collinearity with other maize genes. ZmDRGs 1-11 and 13-16 had copy-number variations. ZmDRGs 2, 9, and 16 showed 5'-end sequence deletion mutations. Some ZmDRGs had chromatin interactions and underwent DNA methylation and/or m6A mRNA methylation. Chromosomal histones associated with 15 ZmDRGs were methylated and acetylated. ZmDRGs 1, 2, 4, 9, and 13 involved photoperiodic phenotypes. ZmDRG16 was within flowering-related QTLs. ZmDRGs 1, 3, and 6-11 were present in cis-acting expression QTLs (eQTLs). ZmDRGs 1, 4, 6-9, 11, 12, and 14-16 showed co-expression with other maize genes. Some of ZmDRG-encoded ZmDRGs showed obvious differences in abundance and phosphorylation.

CONCLUSION

Sixteen ZmDRGs 1-16 are associated with the dark response of maize. In the process of post-domestication and/or breeding, the ZmDRGs undergo the changes without germplasm specificity, including epigenetic modifications, gene copy numbers, chromatin interactions, and deletion mutations. In addition to effects by these factors, ZmDRG expression is influenced by promoter elements, cis-acting eQTLs, and co-expression networks.

Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.

Exploiting Interdata Relationships in Next-generation Proteomics Analysis

Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called "integromics." We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets.

Meeting the challenge of developing food crops with improved nutritional quality and food safety: leveraging proteomics and related omics techniques

Eliminating malnutrition remains an imminent priority in our efforts to achieve food security and providing adequate calories, proteins, and micronutrients to the growing world population. Malnutrition may be attributed to socio-economic factors (poverty and limited accessibility to nutritional food), dietary preferences, inherent nutrient profiles of traditional food crops, and to a combination of all such factors. Modern advancements in "omics" technology have made it possible to reliably predict, diagnose, and suggest ways to remedy the low protein content and bioavailability of key micronutrients in food crops. In this review, we briefly describe how proteomics techniques can potentially be used for improving the nutrient profile of major crops, through high throughput multiplexed assays. Food safety is another important issue where proteomics and related platforms can offer solution for absolute quantitation of food allergens and mycotoxins present in the plant-based food. The purpose of the present review is to discuss the proteomic-based strategies in food crops to meet the challenges of overcoming malnutrition throughout the world.