Connecting the Human Variome Project to nutrigenomics

Nutrigenomics is the science of analyzing and understanding gene–nutrient interactions, which because of the genetic heterogeneity, varying degrees of interaction among gene products, and the environmental diversity is a complex science. Although much knowledge of human diversity has been accumulated, estimates suggest that ~90% of genetic variation has not yet been characterized. Identification of the DNA sequence variants that contribute to nutrition-related disease risk is essential for developing a better understanding of the complex causes of disease in humans, including nutrition-related disease. The Human Variome Project (HVP; http://www.humanvariomeproject.org/) is an international effort to systematically identify genes, their mutations, and their variants associated with phenotypic variability and indications of human disease or phenotype. Since nutrigenomic research uses genetic information in the design and analysis of experiments, the HVP is an essential collaborator for ongoing studies of gene–nutrient interactions. With the advent of next generation sequencing methodologies and the understanding of the undiscovered variation in human genomes, the nutrigenomic community will be generating novel sequence data and results. The guidelines and practices of the HVP can guide and harmonize these efforts.     

Conditional Random Fields for Fast, Large-Scale Genome-Wide Association Studies

Understanding the role of genetic variation in human diseases remains an important problem to be solved in genomics. An important component of such variation consist of variations at single sites in DNA, or single nucleotide polymorphisms (SNPs). Typically, the problem of associating particular SNPs to phenotypes has been confounded by hidden factors such as the presence of population structure, family structure or cryptic relatedness in the sample of individuals being analyzed. Such confounding factors lead to a large number of spurious associations and missed associations. Various statistical methods have been proposed to account for such confounding factors such as linear mixed-effect models (LMMs) or methods that adjust data based on a principal components analysis (PCA), but these methods either suffer from low power or cease to be tractable for larger numbers of individuals in the sample. Here we present a statistical model for conducting genome-wide association studies (GWAS) that accounts for such confounding factors. Our method scales in runtime quadratic in the number of individuals being studied with only a modest loss in statistical power as compared to LMM-based and PCA-based methods when testing on synthetic data that was generated from a generalized LMM. Applying our method to both real and synthetic human genotype/phenotype data, we demonstrate the ability of our model to correct for confounding factors while requiring significantly less runtime relative to LMMs. We have implemented methods for fitting these models, which are available at http://www.microsoft.com/science.     

Proteomics applied to pediatric medicine: opportunities and challenges

Introduction: Care in pediatrics often refers to treatments directed to adults. However, childhood is a specific life period, with molecular pathways connected to development and thereby it requires distinctive considerations and special treatments under disease. Proteomics can help to elucidate the molecular mechanisms underlying the human development and disease onset in pediatric age and this review is devoted to underline the results recently obtained in the field.

Areas covered: The contribution of proteomics to the characterization of physiological modifications occurring during human development is presented. The proteomic studies carried out to elucidate the molecular mechanisms underlying different pediatric pathologies and to discover new markers for early diagnosis and prognosis of disease, comprising genetic and systemic pathologies, sepsis and pediatric oncology are thereafter reported. The investigations concerning milk composition in human and farm mammals are also presented. Finally, the chances offered by the integration of different -omic platforms are discussed.

Expert commentary: The growing utilization of holistic technologies such as proteomics, metabolomics and microbiomics will allow, in the near future, to define at the molecular level the complexity of human development and related diseases, with great benefit for future generations.     

Global views of proteasome-mediated degradation by mass spectrometry

Introduction: Degradation of proteins by cellular proteasomes is critical for the fidelity of protein homeostasis and proper cell function. Indeed, perturbations in proteasome function, as well as the degradation of specific substrates, are associated with a variety of human diseases. Yet, monitoring and analyzing protein degradation in a high throughput manner in physiology and pathology remains limited.

Areas covered: Here we discuss several of the recently developed mass spectrometry-based methods for studying proteasome-mediated cellular degradation and discuss their advantages and limitations. We highlight Mass Spectrometry Analysis of Proteolytic Peptides (MAPP), a method designed to purify and identify proteasome-cleaved cellular proteins as a novel approach in molecular and clinical profiling of human disease.

Expert opinion: The recent improvement of proteomics technologies now offers an unprecedented ability to study disease in clinical settings. Expanding clinical studies to include the degradation landscape will provide a new resolution to complement the cellular proteome. In turn, this holds promise to provide both new disease targets and novel peptide biomarkers which will further enhance personalized proteomics.     

Molecular Data and the Dynamic Nature of Polyploidy

During the past decade, molecular techniques have provided a wealth of data that have facilitated the resolution of several controversial questions in polyploid evolution. Herein we have focused on several of these issues: (1) the frequency of recurrent formation of polyploid species; (2) the genetic consequences of multiple polyploidizations within a species; (3) the prevalence and genetic attributes of autopolyploids; and (4) the genetic changes that occur in polyploid genomes following their formation.

Molecular data provide a more dynamic picture of polyploid evolution than has been traditionally espoused. Numerous studies have demonstrated multiple origins of both allopolyploids and autopolyploids. In several polyploid species studied in detail, multiple origins were found to be frequent on a local geographic scale, as well as during a short span of time. Molecular data strongly suggest that recurrent formation of polyploid species is the rule, rather than the exception. In addition, molecular data indicate that recurrent formation of polyploids has important genetic consequences, introducing considerable genetic variation from diploid progenitors into polyploid derivatives.

Molecular data also suggest a much more important role for natural autopolyploids than has been historically envisioned. In contrast to the longstanding view of autopolyploidy as being rare, molecular data continue to reveal steadily increasing numbers of well-documented autoploids having tetrasomic or higher-level polysomic inheritance. Although autopolyploidy undoubtedly occurs much less frequently than allopolyploidy in natural populations, it nonetheless has been a significant evolutionary mechanism. Molecular data also provide compelling genetic evidence that contradicts the traditional view of autopolyploidy as being maladaptive. Electrophoretic studies have revealed three important attributes of autopolyploids compared to their diploid progenitors: (1) enzyme multiplicity, (2) increased heterozygosity, and (3) increased allelic diversity. Genetic variability is, in fact, typically substantially higher in autopoloids than in their diploid progenitors. These genetic attributes of autopolyploids are due to polysomic inheritance and provide strong genetic arguments for the potential success of autopolyploids in nature.

In addition to providing numerous important insights into the formation of polyploids and the immediate genetic consequences of polyploidy, molecular data also have been used to study the subsequent evolution of polyploid genomes. Common hypotheses on the subsequent evolution of polyploid genomes include (1) gene silencing, eventually leading to extensively diploidized polyploid genomes; (2) gene diversification, resulting in regulatory or functional divergence of duplicate genes; and (3) genome diversification, resulting in chromosomal repatterning. Compelling, but limited, genetic evidence for all of these factors has been obtained in molecular analyses of polyploid species. The occurrence of these processes in polyploid genomes indicates that polyploid genomes are plastic and susceptible to evolutionary change.

In summary, molecular data continue to demonstrate that polyploidization and the subsequent evolution of polyploid genomes are very dynamic processes.     

The Priority position paper: Protecting Europe's food chain from prions

Bovine spongiform encephalopathy (BSE) created a global European crisis in the 1980s and 90s, with very serious health and economic implications. Classical BSE now appears to be under control, to a great extent as a result of a global research effort that identified the sources of prions in meat and bone meal (MBM) and developed new animal-testing tools that guided policy. Priority (www.prionpriority.eu) was a European Union (EU) Framework Program 7 (FP7)-funded project through which 21 European research institutions and small and medium enterprises (SMEs) joined efforts between 2009 and 2014, to conduct coordinated basic and applied research on prions and prion diseases. At the end of the project, the Priority consortium drafted a position paper (www.prionpriority.eu/Priority position paper) with its main conclusions. In the present opinion paper, we summarize these conclusions.

With respect to the issue of re-introducing ruminant protein into the feed-chain, our opinion is that sustaining an absolute ban on feeding ruminant protein to ruminants is essential. In particular, the spread and impact of non-classical forms of scrapie and BSE in ruminants is not fully understood and the risks cannot be estimated. Atypical prion agents will probably continue to represent the dominant form of prion diseases in the near future in Europe. Atypical L-type BSE has clear zoonotic potential, as demonstrated in experimental models. Similarly, there are now data indicating that the atypical scrapie agent can cross various species barriers. More epidemiological data from large cohorts are necessary to reach any conclusion on the impact of its transmissibility on public health. Re-evaluations of safety precautions may become necessary depending on the outcome of these studies.

Intensified searching for molecular determinants of the species barrier is recommended, since this barrier is key for important policy areas and risk assessment. Understanding the structural basis for strains and the basis for adaptation of a strain to a new host will require continued fundamental research, also needed to understand mechanisms of prion transmission, replication and how they cause nervous system dysfunction and death. Early detection of prion infection, ideally at a preclinical stage, also remains crucial for development of effective treatment strategies.     

The use of animal models in the study of complex disease: all else is never equal or why do so many human studies fail to replicate animal findings?

The study of the genetics of complex human disease has met with limited success. Many findings with candidate genes fail to replicate despite seemingly overwhelming physiological data implicating the genes. In contrast, animal model studies of the same genes and disease models usually have more consistent results. We propose that one important reason for this is the ability to control genetic background in animal studies. The fact that controlling genetic background can produce more consistent results suggests that the failure to replicate human findings in the same diseases is due to variation in interacting genes. Hence, the contrasting nature of the findings from the different study designs indicates the importance of non-additive genetic effects on human disease. We discuss these issues and some methodological approaches that can detect multilocus effects, using hypertension as a model disease. This article contains supplementary material, which may be viewed at the BioEssays website at http://www.interscience.wiley.com/jpages/0265-9247/suppmat/index.html.     

The potential impact of recent insights into proteomic changes associated with glaucoma

Introduction: Glaucoma, a major ocular neuropathy, is still far from being understood on a molecular scale. Proteomic workflows revealed glaucoma associated alterations in different eye components. By using state-of-the-art mass spectrometric (MS) based discovery approaches large proteome datasets providing important information about glaucoma related proteins and pathways could be generated. Corresponding proteomic information could be retrieved from various ocular sample species derived from glaucoma experimental models or from original human material (e.g. optic nerve head or aqueous humor). However, particular eye tissues with the potential for understanding the disease’s molecular pathomechanism remains underrepresented.

Areas covered: The present review provides an overview of the analysis depth achieved for the glaucomatous eye proteome. With respect to different eye regions and biofluids, proteomics related literature was found using PubMed, Scholar and UniProtKB. Thereby, the review explores the potential of clinical proteomics for glaucoma research.

Expert commentary: Proteomics will provide important contributions to understanding the molecular processes associated with glaucoma. Sensitive discovery and targeted MS approaches will assist understanding of the molecular interplay of different eye components and biofluids in glaucoma. Proteomic results will drive the comprehension of glaucoma, allowing a more stringent disease hypothesis within the coming years.     

Understanding the molecular consequences of inherited muscular dystrophies: advancements through proteomic experimentation

Introduction: Proteomic techniques offer insights into the molecular perturbations occurring in muscular-dystrophies (MD). Revisiting published datasets can highlight conserved downstream molecular alterations, which may be worth re-assessing to determine whether their experimental manipulation is capable of modulating disease severity.

Areas covered: Here, we review the MD literature, highlighting conserved molecular insights warranting mechanistic investigation for therapeutic potential. We also describe a workflow currently proving effective for efficient identification of biomarkers & therapeutic targets in other neurodegenerative conditions, upon which future MD proteomic investigations could be modelled.

Expert commentary: Studying disease models can be useful for identifying biomarkers and model specific degenerative cascades, but rarely offer translatable mechanistic insights into disease pathology. Conversely, direct analysis of human samples undergoing degeneration presents challenges derived from complex chronic degenerative molecular processes. This requires a carefully planed & reproducible experimental paradigm accounting for patient selection through to grouping by disease severity and ending with proteomic data filtering and processing.     

Genetic profiling of the critically endangered palm species Medemia argun using newly developed chloroplast DNA markers

Background: Medemia argun is a rare wild palm tree species. Its global existence is assumed to include the main population of about 1000 trees in the Nubian Desert of Sudan and some scattered individuals in southern Egypt. The species had previously been assumed to be extinct, but then reported to be extant about 20 years ago.

Aims: To assess genetic variation and explore population genetic structure of M. argun, through development and analysis of microsatellite markers.

Methods: The genome sequence mining approach was applied in order to identify microsatellites in the chloroplast genome of Bismarckia nobilis, a species closely related of M. argun. A set of 49 markers were designed, and their characteristics are now provided. Seven chloroplast DNA markers were developed for use in the genetic characterisation of this threatened species.

Results: Five markers were found polymorphic in M. argun, which enabled the assessment of the genetic diversity of the species. Significant genetic differentiation was observed among generations and collection sites, accompanied by low genetic variation. The seven markers developed were polymorphic among the wild relatives Hyphaene thebaica and Borassus aethiopum.

Conclusions: This is the first study to report molecular markers for M. argun. Our results suggest that the genetic consequences of population fragmentation in M. argun are beginning to be evident.     

Transposon tagging in maize

Through recent government- and industry-sponsored efforts, several forward and reverse genetic screening programs have emerged over the past few years to aid in the genetic dissection of gene function in maize. Despite a US maize crop valued at $18.4 billion last year (http://www.ncga.com/03world/main/US_crop_value_2000.html) and rich genetic history, maize has taken a back seat to Arabidopsis thaliana as the model genetic system for plants over the past decade. With a fully sequenced genome, short generation time and small size, studies of Arabidopsis have provided plant scientists with a molecular framework for hormonal, developmental and environmental signaling pathways in plants. As investigations into Arabidopsis continue, our capacity to engineer biochemical pathways and alter plant physiological responses will become increasingly sophisticated. Nevertheless, approximately 130 million years have passed since monocot and higher eudicot lineages diverged. Thus, our ability to engineer agronomically important monocot grasses such as maize, rice and wheat will become increasingly limited by our lack of understanding of the physiological and morphological differences that have evolved in the monocots and higher eudicots. The sophisticated transposon collections now being generated for maize are but one of several recent projects (http://www.nsf.gov/bio/pubs/awards/genome01.htm) to provide grass researchers with essential tools for genome analysis. Because grain crops are such a closely related group, it is hoped that many of the findings made in one grass will be directly applicable to understanding the biology of another. The goal of this review is to highlight the recent developments in maize transposon-based gene characterization programs and provide a critical examination of the advantages and disadvantages each system offers. Electronic Publication     

Recent insights into human bronchial proteomics – how are we progressing and what is next?

Introduction: The human respiratory system is highly prone to diseases and complications. Many lung diseases, including lung cancer (LC), tuberculosis (TB), and chronic obstructive pulmonary disease (COPD) have been among the most common causes of death worldwide. Cystic fibrosis (CF), the most common genetic disease in Caucasians, has adverse impacts on the lungs. Bronchial proteomics plays a significant role in understanding the underlying mechanisms and pathogenicity of lung diseases and provides insights for biomarker and therapeutic target discoveries.

Areas covered: We overview the recent achievements and discoveries in human bronchial proteomics by outlining how some of the different proteomic techniques/strategies are developed and applied in LC, TB, COPD, and CF. Also, the future roles of bronchial proteomics in predictive proteomics and precision medicine are discussed.

Expert commentary: Much progress has been made in bronchial proteomics. Owing to the advances in proteomics, we now have better ability to isolate proteins from desired cellular compartments, greater protein separation methods, more powerful protein detection technologies, and more sophisticated bioinformatic techniques. These all contributed to our further understanding of lung diseases and for biomarker and therapeutic target discoveries.     

Evaluation of genetic diversity and population structure of Macqueen’s Bustard Chlamydotis macqueenii in Iran

Capsule: There is low genetic diversity in the Macqueen’s Bustard Chlamydotis macqueenii in Iran.

Aims: To investigate the genetic diversity and population structure of Macqueen’s Bustard in Iran, using two mitochondrial DNA loci.

Methods: Molecular diversity of the mitochondrial cytochrome oxidase c subunit I (COI) gene and part of the mitochondrial control region D-loop (in total 1183 base pairs) were analysed from 26 individual Macqueen’s Bustards from three regions of Iran.

Results: There was little variation in nucleotides and haplotypes in the populations for genes of both CR and COI. The population had free breeding and gene flow between the three study regions in Iran: Petregan, Ferdows and Yazd.

Conclusion: The use of molecular and genetic studies is essential to strengthen the protection of genetic diversity of the Macqueen’s Bustard.     

Genetic and environmental variation of seed weight in Trichloris species (Chloridoideae,Poaceae) and its association with seedling stress tolerance

Background: Seed weight is a key fitness-related trait associated with plant adaptation and is commonly targeted in plant breeding.

Aims: We evaluated seed weight variation within and between Trichloris crinita and Trichloris pluriflora across their geographical ranges in Argentina.

Methods: Genetic variation in seed weight was evaluated through a common garden experiment. To examine the possible role of such variation in local adaptation, we compared the seed weight of plants of populations raised in the common garden with seed weight variation and ecogeographical variables across their original habitats. We also evaluated experimentally the effects of seed weight variation upon osmotic stress tolerance at germination.

Results: Variation in seed weight existed in both species. Such variation had a genetic basis in T. crinita related to several ecogeographical variables. Larger seeds of T. crinita were associated with more stressful environments and produced larger seedlings under both osmotic stress and non-stress conditions.

Conclusions: Our results suggest that seed weight variation in T. crinita is likely adaptive, with large seed having an advantage during early developmental stages, particularly under stressful conditions. Such knowledge should prove helpful in selecting the most suitable populations for restoration and plant breeding.     

The genome revolution and its role in understanding complex diseases

The completion of the human genome sequence in 2003 clearly marked the beginning of a new era for biomedical research. It spurred technological progress that was unprecedented in the life sciences, including the development of high-throughput technologies to detect genetic variation and gene expression. The study of genetics has become “big data science”. One of the current goals of genetic research is to use genomic information to further our understanding of common complex diseases. An essential first step made towards this goal was by the identification of thousands of single nucleotide polymorphisms showing robust association with hundreds of different traits and diseases. As insight into common genetic variation has expanded enormously and the technology to identify more rare variation has become available, we can utilize these advances to gain a better understanding of disease etiology. This will lead to developments in personalized medicine and P4 healthcare. Here, we review some of the historical events and perspectives before and after the completion of the human genome sequence. We also describe the success of large-scale genetic association studies and how these are expected to yield more insight into complex disorders. We show how we can now combine gene-oriented research and systems-based approaches to develop more complex models to help explain the etiology of common diseases. This article is part of a Special Issue entitled: From Genome to Function.