SNPeffect: identifying functional roles of SNPs using metabolic networks

A brief clinical genetics review: stepwise diagnostic processes of a monogenic disorder-hypertriglyceridemia

The completion of the Human Genome Project and tremendous advances in automated high-throughput genetic analysis technologies have enabled explosive progress in the field of genetics, which resulted in countless discoveries of novel genes and pathways. Many phenotype- or disease-associated single nucleotide polymorphisms (SNPs) with a high statistical significance have been identified through numerous genome-wide association studies (GWAS), and various polygenic risk scoring (PRS) schemes have been proposed to identify individuals with a high risk for a certain trait or disorder. Meanwhile, medical education in genetics has lagged far behind, leaving many physicians and healthcare providers unprepared in the genomic era. Thus, there is an urgent need to educate physicians and healthcare providers with basic knowledge and skills in genetics. To facilitate this, some basic terminologies and concepts are discussed in this review. In addition, some important considerations in delineating and incorporating clinical genetic testing in the diagnosis and management of a monogenic disorder are illustrated in a stepwise fashion. Furthermore, the effects of disease-associated SNPs represented by a PRS scheme clearly demonstrated that even the phenotypes of a monogenic disorder due to the same pathogenic variant in family members are modulated by the polygenic background. In human genetics, despite these explosive advancements, we are still far from clearly deciphering the interplay of gene variants to effect unique characteristics in an individual. In addition, sophisticated genome or gene directed therapies are being investigated for numerous disorders. Therefore, evolution in the field of genetics is likely to continue into the foreseeable future. In the meantime, much emphasis should be placed on educating physicians and healthcare professionals to be well-versed and skillful in the clinical use of genetics so that they can fully embrace the new era of precision medicine.

Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective

Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed.

Deep learning the cis-regulatory code for gene expression in selected model plants

Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predicting gene expression profiles from gene flanking regions of the plant species Arabidopsis thaliana, Solanum lycopersicum, Sorghum bicolor, and Zea mays. With over 80% accuracy, our models enabled predictive feature selection, highlighting e.g. the significant role of UTR regions in determining gene expression levels. The models demonstrated remarkable cross-species performance, effectively identifying both conserved and species-specific regulatory sequence features and their predictive power for gene expression. We illustrated the application of our approach by revealing causal links between genetic variation and gene expression changes across fourteen tomato genomes. Lastly, our models efficiently predicted genotype-specific expression of key functional gene groups, exemplified by underscoring known phenotypic and metabolic differences between Solanum lycopersicum and its wild, drought-resistant relative, Solanum pennellii.

Genomic approaches to enhance adaptive plasticity to cope with soil constraints amidst climate change in wheat

Climate change is varying the availability of resources, soil physicochemical properties, and rainfall events, which collectively determines soil physical and chemical properties. Soil constraints-acidity (pH < 6), salinity (pH ≤ 8.5), sodicity, and dispersion (pH > 8.5)-are major causes of wheat yield loss in arid and semiarid cropping systems. To cope with changing environments, plants employ adaptive strategies such as phenotypic plasticity, a key multifaceted trait, to promote shifts in phenotypes. Adaptive strategies for constrained soils are complex, determined by key functional traits and genotype × environment × management interactions. The understanding of the molecular basis of stress tolerance is particularly challenging for plasticity traits. Advances in sequencing and high-throughput genomics technologies have identified functional alleles in gene-rich regions, haplotypes, candidate genes, mechanisms, and in silico gene expression profiles at various growth developmental stages. Our review focuses on favorable alleles for enhanced gene expression, quantitative trait loci, and epigenetic regulation of plant responses to soil constraints, including heavy metal stress and nutrient limitations. A strategy is then described for quantitative traits in wheat by investigating significant alleles and functional characterization of variants, followed by gene validation using advanced genomic tools, and marker development for molecular breeding and genome editing. Moreover, the review highlights the progress of gene editing in wheat, multiplex gene editing, and novel alleles for smart control of gene expression. Application of these advanced genomic technologies to enhance plasticity traits along with soil management practices will be an effective tool to build yield, stability, and sustainability on constrained soils in the face of climate change.

The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber

Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.

Exome-based new allele-specific PCR markers and transferability for sodicity tolerance in bread wheat (Triticum aestivum L.)

Targeted exome-based genotype by sequencing (t-GBS), a sequencing technology that tags SNPs and haplotypes in gene-rich regions was used in previous genome-wide association studies (GWAS) for sodicity tolerance in bread wheat. Thirty-nine novel SNPs including 18 haplotypes for yield and yield-components were identified. The present study aimed at developing SNP-derived markers by precisely locating new SNPs on ~180 bp allelic sequence of t-GBS, marker validation, and SNP functional characterization based on its exonic location. We identified unknown locations of significant SNPs/haplotypes by aligning allelic sequences on to IWGSC RefSeqv1.0 on respective chromosomes. Eighteen out of the target 39 SNP locations fulfilled the criteria for producing PCR markers, among which only eight produced polymorphic signals. These eight markers associated with yield, plants m-2, heads m-2, and harvest index, including a pleiotropic marker for yield, harvest index, and grains/head were validated for its amplification efficiency and phenotypic effects in focused identification germplasm strategy (FIGS) wheat set and a doubled haploid (DH) population (Scepter/IG107116). The phenotypic variation explained by these markers are in the range of 4.1-37.6 in the FIGS population. High throughput PCR-based genotyping using new markers and association with phenotypes in FIGS wheat set and DH population validated the effect of functional SNP on closely associated genes-calcineurin B-like- and dirigent protein, basic helix-loop-helix (BHLH-), plant homeodomain (PHD-) and helix-turn-helix myeloblastosis (HTH myb) type -transcription factor. Further, genome-wide SNP annotation using SnpEff tool confirmed that these SNPs are in gene regulatory regions (upstream, 3'-UTR, and intron) modifying gene expression and protein-coding. This integrated approach of marker design for t-GBS alleles, SNP functional annotation, and high-throughput genotyping of functional SNP offers translation solutions across crops and complex traits in crop improvement programs.

Genome resequencing and transcriptome profiling reveal molecular evidence of tolerance to water deficit in barley

INTRODUCTION

Frequent climate change-induced drought events are detrimental environmental stresses affecting global crop production and ecosystem health. Several efforts have facilitated crop breeding for resilient varieties to counteract stress. However, progress is hampered due to the complexity of drought tolerance; a greater variety of novel genes are required across varying environments. Tibetan annual wild barley is a unique and precious germplasm that is well adapted to abiotic stress and can provide elite genes for crop improvement in drought tolerance.

OBJECTIVES

To identify the genetic basis and unique mechanisms for drought tolerance in Tibetan wild barley.

METHODS

Whole genome resequencing and comparative RNA-seq approaches were performed to identify candidate genes associated with drought tolerance via investigating the genetic diversity and transcriptional variation between cultivated and Tibetan wild barley. Bioinformatics, population genetics, and gene silencing were conducted to obtain insights into ecological adaptation in barley and functions of key genes.

RESULTS

Over 20 million genetic variants and a total of 15,361 significantly affected genes were identified in our dataset. Combined genomic, transcriptomic, evolutionary, and experimental analyses revealed 26 water deficit resilience-associated genes in the drought-tolerant wild barley XZ5 with unique genetic variants and expression patterns. Functional prediction revealed Tibetan wild barley employs effective regulators to activate various responsive pathways with novel genes, such as Zinc-Induced Facilitator-Like 2 (HvZIFL2) and Peroxidase 11 (HvPOD11), to adapt to water deficit conditions. Gene silencing and drought tolerance evaluation in a natural barley population demonstrated that HvZIFL2 and HvPOD11 positively regulate drought tolerance in barley.

CONCLUSION

Our findings reveal functional genes that have been selected across barley's complex history of domestication to thrive in water deficit environments. This will be useful for molecular breeding and provide new insights into drought-tolerance mechanisms in wild relatives of major cereal crops.

Toward mechanistic modeling and rational engineering of plant respiration

Plant respiration not only provides energy to support all cellular processes, including biomass production, but also plays a major role in the global carbon cycle. Therefore, modulation of plant respiration can be used to both increase the plant yield and mitigate the effects of global climate change. Mechanistic modeling of plant respiration at sufficient biochemical detail can provide key insights for rational engineering of this process. Yet, despite its importance, plant respiration has attracted considerably less modeling effort in comparison to photosynthesis. In this update review, we highlight the advances made in modeling of plant respiration, emphasizing the gradual but important change from phenomenological to models based on first principles. We also provide a detailed account of the existing resources that can contribute to resolving the challenges in modeling plant respiration. These resources point at tangible improvements in the representation of cellular processes that contribute to CO2 evolution and consideration of kinetic properties of underlying enzymes to facilitate mechanistic modeling. The update review emphasizes the need to couple biochemical models of respiration with models of acclimation and adaptation of respiration for their effective usage in guiding breeding efforts and improving terrestrial biosphere models tailored to future climate scenarios.

Genome-scale modeling of the primary-specialized metabolism interface

Environmental challenges and development require plants to reallocate resources between primary and specialized metabolites to survive. Genome-scale metabolic models, which map carbon flux through metabolic pathways, are a valuable tool in the study of tradeoffs that arise at this interface. Due to annotation gaps, models that characterize all the enzymatic steps in individual specialized pathways and their linkages to each other and to central carbon metabolism are difficult to construct. Recent studies have successfully curated subsystems of specialized metabolism and characterized the interfaces where flux is diverted to the precursors of glucosinolates, terpenes, and anthocyanins. Although advances in metabolite profiling can help to constrain models at this interface, quantitative analysis remains challenging because of the different timescales on which specialized metabolites from constitutive and reactive pathways accumulate.

Characterization of effects of genetic variants via genome-scale metabolic modelling

Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.

The chromosome-level reference genome assembly for Panax notoginseng and insights into ginsenoside biosynthesis

Panax notoginseng, a perennial herb of the genus Panax in the family Araliaceae, has played an important role in clinical treatment in China for thousands of years because of its extensive pharmacological effects. Here, we report a high-quality reference genome of P. notoginseng, with a genome size up to 2.66 Gb and a contig N50 of 1.12 Mb, produced with third-generation PacBio sequencing technology. This is the first chromosome-level genome assembly for the genus Panax. Through genome evolution analysis, we explored phylogenetic and whole-genome duplication events and examined their impact on saponin biosynthesis. We performed a detailed transcriptional analysis of P. notoginseng and explored gene-level mechanisms that regulate the formation of characteristic tubercles. Next, we studied the biosynthesis and regulation of saponins at temporal and spatial levels. We combined multi-omics data to identify genes that encode key enzymes in the P. notoginseng terpenoid biosynthetic pathway. Finally, we identified five glycosyltransferase genes whose products catalyzed the formation of different ginsenosides in P. notoginseng. The genetic information obtained in this study provides a resource for further exploration of the growth characteristics, cultivation, breeding, and saponin biosynthesis of P. notoginseng.

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.