Genomic Mutants

Mutation, an alteration in the genetic material (the genome) of a cell of a living organism or of a virus that is more or less permanent and that can be transmitted to the cell's or the virus's descendants. (The genomes of organisms are all composed of DNA, whereas viral genomes can be of DNA or RNA; see heredity: The physical basis of heredity.) Mutation in the DNA of a body cell of a multicellular organism (somatic mutation) may be transmitted to descendant cells by DNA replication and hence result in a sector or patch of cells having abnormal function, an example being cancer. Mutations in egg or sperm cells (germinal mutations) may result in an individual offspring all of whose cells carry the mutation, which often confers some serious malfunction, as in the case of a human genetic disease such as cystic fibrosis. Mutations result either from accidents during the normal chemical transactions of DNA, often during replication, or from exposure to high-energy electromagnetic radiation (e.g., ultraviolet light or X-rays) or particle radiation or to highly reactive chemicals in the environment. Because mutations are random changes, they are expected to be mostly deleterious, but some may be beneficial in certain environments. In general, mutation is the main source of genetic variation, which is the raw material for evolution by natural selection.

15.5 Effect of NPs on Genomics

Genomics deals with changes at gene or DNA level in any organism. It plays an important role in the study of stress physiology for understanding the mechanisms of toxicity through high-throughput methods such as cDNA microarrays or quantitative real-time polymerase chain reaction (Xu et al., 2011). As discussed earlier, NPs negatively affect plants, and Table 15.2 has a brief account of nanotoxicity at the genomics level in plants. Gene expression analyses are performed to test the changes at the genetic level for any morphological and/or physiological pathways and modes of action of any compound (Ankley et al., 2006; Dietz and Herth, 2011). Gene expression analysis helps to identify sensitive or tolerant genes for particular stresses, which can help in the development of transgenic plants for particular stresses (Merrick and Bruno, 2004; Thomas et al., 2011). NPs showed changes in gene expression at very low dose, which may help to investigate the cellular impact of chronic toxicity associated with NPs (Poma and Di Giorgio, 2008; Poynton and Vulpe, 2009). Therefore genomics analysis is very useful for the correlation of any morphological/physiological changes to the genetic level of the plants and also provides the mechanism of toxicity of NPs. Thus genomics analysis of NP-mediated toxicity is associated with disruption of the basic processes of electron transport chain signaling, which ultimately leads to the impairment of the cell cycle of the organism, as shown in Fig. 15.3.

Table 15.2. Genome Analysis of Nanoparticle-Mediated Toxicity

NanomaterialSize/DoseModel SystemEffect on the Cellular SystemReferencesAg NPs60 nmVicia fabaImpairment in mitosis causing chromosomal aberration and micronucleus induction, which caused genotoxicityPatlolla et al. (2012)Ag NPs10 nmTriticum aestivumEnhancement in oxidative stress that resulted in accumulation of oxidized glutathione (GSSG), and metallothionein encoding gene expression was enhancedDimkpa et al. (2013)Ag NPs20 nmArabidopsisEnhancement in the expression of genes associated with sulfur assimilation, glutathione S-transferase, and glutathione reductaseNair and Chung (2014a)Ag NPs20 nmArabidopsisVariable expression of genes; some were upregulated, while others were downregulated; downregulation of DNA mismatch repair protein (MSH), while upregulation of proliferating cell nuclear antigen (PCNA)Nair and Chung (2014b)Ag NPs20 nmArabidopsisUp- and downregulation of genes associated with protein family domain (PFAM) and interpro proteinKohan-Baghkheirati and Geisler-Lee (2015)Ag NPs45–47 nmArabidopsisAccumulation of Cu/Zn superoxide dismutase (CSD2), cell-division-cycle kinase 2 (CDC2), protochlorophyllide oxidoreductase (POR), and fructose-1, 6-bisphosphate aldolase (FBA). Enhancement in expression of genes associated with indoleacetic acid protein 8 (IAA8), 9-cis-epoxycarotenoid dioxygenase (NCED3), and dehydration-responsive RD22, while decline in expression of aminocyclopropane-1-carboxylic acid (ACC)-derivatives, ACC synthase 7 (ACS7), and ACC oxidase 2 (ACO2)Syu et al. (2014)Ag NPs, ZnO, and TiO2–MedicagoInduced shift in expression profiles of genes associated with biological pathways such as nitrogen metabolism, nodulation, metal homeostasis, and stress responsesChen et al. (2015)Ag–silica hybrid complex30 nmArabidopsisUpregulation of pathogenesis related (PR) genes, which is implicated in systemic acquired resistance (SAR)Chu et al. (2012)Al2O3Not specifiedNicotiana tabacumEnhancement in the expression of miRNA involved in mediating stress responsesBurklew et al. (2012)CuONot specifiedBrassica junceaUpregulation in genes associated with CuZn superoxide dismutase (CuZnSOD), while no significant change in expression of catalase (CAT) and ascorbate peroxidase (APX) genesNair and Chung (2015)Multiwalled carbon nanotubes (MWCNTs)20 nmNicotiana tabacumIncrement in expression of genes associated with aquaporin, cell division, and cell wall extensionKhodakovskaya et al. (2012)MWCNTs15–40 nmGlycine max, Hordeum vulgare, Zea maysIncrease in expression of gene encoding water channel proteinsLahiani et al. (2013)Single-walled carbon nanotubes (SWCNTs)1–2 nmArabidopsis, Oryza sativaEnhanced cell aggregation and chromatin condensation, negative impact on protoplast leading to oxidative stressShen et al. (2010)SWCNTs1–2 nmZ. maysVariable expression of genes, such as genes associated with increase in seminal root;epigenetic modification enzymes were enhanced, while those of root hair were reducedYan et al. (2013)SWCNTs50–100 nmHordeum vulgare, Z. mays, O. sativa, G. max, Panicum virgatum, Solanum lycopersicum, N. tabacumGenes associated with stress responses, cellular responses, and metabolic processes were variably expressedLahiani et al. (2015)TiO225 nmNicotiana tabacumExpression profiles of miRNA involved in regulating important genes related to tolerance were affectedFrazier et al. (2014)TiO2Not specifiedArabidopsisDifferential expression of genes involved in DNA metabolism, hormone metabolism, tetrapyrrole synthesis, and photosynthesisTumburu et al. (2015)Graphene oxide (GO)40–50 nmArabidopsisAltered expression of genes involved in development of abiotic stress tolerance and induction in oxidative stressWang et al. (2014)CeO2, In2O310–30 nm

20–70 nmArabidopsisAlteration in expression of major stress-responsive genes, i.e., glutathione biosynthetic gene and sulfur assimilationMa et al. (2013)ZnO20 nmArabidopsisDifferential expression of genes, such as genes associated with stress, were upregulated, while those associated with cell organization and biogenesis and DNA/RNA metabolism were downregulatedLanda et al. (2015)ZnO, CeO27–8 nmG. maxDamage to DNA and mutation, expression of new bandsLópez-Moreno et al. (2010)ZnO

TiO2<100 nm

<150 nmA. thalianaUpregulation in genes associated with response under biotic and abiotic stress and downregulation in genes involved in translation, nucleosome assembly, and processes involved with microtubule and cell organization and biogenesisLanda et al (2012)Ag

TiO210–80 nm

10–40 nmA. thalianaRepression in transcriptional factors related to pathogenesis and phosphate starvationGarcía-Sánchez et al. (2015)Ag20 nmA. thalianaGenes related to response to oxidative stress, namely, vacuolar cation/proton exchanger, superoxide dismutase, cytochrome P450-dependent oxidase, and peroxidase, were upregulated, while those related with pathogenesis and hormonal stimuli were downregulatedKaveh et al. (2013)Ag

TiO2

ZnO

Quantum dots20 nm

5 nm

20 nm

6–10 nmChlamydomonas reinhardtiiDecline in expression of genes associated with photosynthesis and enhancement in transcripts encoding cell wall and flagella componentsSimon et al. (2013)

Figure 15.3. Cellular uptake and cyto-/genotoxicity mediated by nanoparticles.

Modified from Magdolenova, Z., Collins, A., Kumar, A., Dhawan, A., Ston, V., Dusinska., M., 2013. Mechanisms of genotoxicity. A review of in vitro and in vivo studies with engineered nanoparticles. Nanotoxicology, 1–46. http://dx.doi.org/10.3109/17435390.2013.773464.

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Issues, Challenges, Scientific Bottlenecks and Perspectives

Denis Faure, Dominique Joly, in Insight on Environmental Genomics, 2016

1.2.9 Genomic observatories

Genomics observatories (GO) are first rate research facilities that produce genomic-level biodiversity observations that are contextualized, localized in territories and in compliance with international data acquisition standards. There are currently 15 of them. They represent marine and continental ecosystems for which genomic data acquisition is a long-term activity. These facilities aim to quantify the biotic interactions of ecosystems and to build models of biodiversity to predict the quality and distribution of ecosystem services. They are spread all around the globe: two in the Asia-Pacific area, including a French one in Polynesia - http://usr3278.univ-perp.fr/moorea/?lang=en, eight in Europe including the Rothamsted site used by the TerraGenome program and two French marine stations in Roscoff and Banyuls, involved in the aforementioned Oceanomics and Idealg programs - two in the Arctic and Antarctic Polar zones as well as three in the USA. They form a network that represents the "pulse of the planet" and whose main goal is to promote sustainable development through a better understanding of the interactions between humans and their environment. The approach consists of applying cutting-edge genomics technologies to monitor the stream of genetic variations in human and natural ecosystems. Genetic data is systematically related to biophysical and socio-economical data (metadata), which enables the integration of all the information into predictive models. Such models aim at mapping the quality and distribution of biodiversity as well as the ecosystem services it provides, according to various scenarios of future change and human activity. These observatories also play a major role in the promotion of training, technical support, resources and guidelines in the form of a learning portal. This internet resource is available for new sites or organizations that wish to perfom genomic observation and especially for new facilities from developing countries where the levels of biodiversity vulnerability are often high.

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Genetics is Involved in Everything, but not Everything is Genetic

R.G. Ramos, K. Olden, in Encyclopedia of Environmental Health, 2011

Initiatives to Study the Role of Genomics in Human Disease

Genomics is defined as 'the study of the complete genetic material, including both the structure and function of genes, of an organism.' In 1990, the NIH, in collaboration with the Department of Energy and international partners, sought to sequence the human genome hence known as the HGP. The goal of the HGP was to provide new knowledge research tools that would further the understanding of the genetic contribution to human disease. In 1998, the National Institute of Environmental Health Sciences (NIEHS), one of the institutes within the NIH, established the Environmental Genome Project (EGP). The EGP's primary mission was to investigate the role of genetic polymorphisms in susceptibility to human illness induced by environmental exposures. Such studies will improve the understanding of the interaction between genetic susceptibility and environmental exposures in human disease incidence and prevalence. This project supported research activities at both the NIH and the university level. In 2005, using the EGP as a model, the US Department of Health & Human Services launched the Genes, Environment and Health Initiative (GEI). The GEI is a collaborative effort between the NIEHS and the National Human Genome Research Institute (NHGRI), another institute at the NIH. This initiative consists of two components: the Exposure Biology Program and the Genetics Program. The Exposure Biology Program is currently led by the NIEHS and focuses on developing tools capable of measuring biomarkers that would delineate the relationship between the disease phenotype and exposure to chemicals, poor diet, reduced/no physical activity, psychosocial stress, and addictive substances. The Genetics Program, led by the NHGRI, will further the development of scientific tools (i.e., bioinformatics, high-throughput sequencing, and so forth) that will permit the analysis of the genetic variation between individuals with specific phenotypes. In December 2005, the Cancer Genome Atlas pilot project was announced. This project, being led by the National Cancer Institute (NCI), seeks to improve the understanding of the molecular mechanisms involved in cancer, including development and metastasis, and reflects the NIH roadmap that prioritizes translational research. Ultimately, the knowledge gained from the Cancer Genome Atlas pilot project will translate into new strategies for the prevention, diagnosis, and treatment of cancer.

At the public health level, the Centers for Disease Control and Prevention (CDC) recently celebrated the 10th anniversary of the Public Health Genomics at the CDC. The office is now known as the National Office for Public Health Genomics (NOPHG), and its primary mission is to 'integrate genomics into public health research, policy, and programs.' For this, the NOPHG has prioritized integrating genomics into national CDC surveys, promoting increased utilization of family history in assessing the risk of disease, and developing evidenced-based recommendations for the use of genetic tests to improve population health. At the National Institute of Occupational Safety and Hazards (NIOSH), the CDC's branch for occupational health research, the individual variability in worker response to occupational hazards is now an emerging and exciting field of gene–environment research. Thus, it is becoming obvious that the powerful tool of genomics has redefined the public health approach to the study, prevention, and control of environmentally and occupationally related diseases. Furthermore, the need for public health professionals has resulted in the establishment of public health genomic and human genetic programs at graduate schools of public health in the United States.

On the global front, the International HapMap Project and the Public Population Project in Genomics (P3G) are initiatives that promote the establishment of an international consortium. The former seeks to identify the distribution of individual haplotypes (alterations in DNA expression) within and between populations, so that other researchers can link the risk of specific diseases to a specific genotype. The latter will allow the biomedical research community to unravel the complex genetic and environmental interactions responsible for most common diseases and thus deliver improved disease prevention and treatment.

Several examples of gene–environment interactions have been reported recently. In the next section, examples of diseases that are known to have a significant contribution from both genetics and the environment in their development will be discussed. It is clear from the examples cited that the effects that mutated genes can have on the phenotype of the organism can vary in both severity and time of onset, suggesting that genes are not the sole determinants and that nongenetic factors also play a role in the expression of the phenotype. Irrespective of the disease, it is obvious that the reduction of human suffering and the improvement in quality of life will continue to be primary beneficiaries of gene–environment research.

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Drinking Water Treatment and Distribution

Charles P. Gerba, Ian L. Pepper, in Environmental Microbiology (Third Edition), 2015

28.3.3 Microbial Community Structure

Genomic-based molecular methods have greatly increased our ability to understand microbial community dynamics in drinking water distribution systems. Recent studies indicate that these communities are complex and influenced by the source water (ground vs. surface), chemical properties of the water, treatment and type of disinfectant residual. Studies have shown Alphaproteobacteria, Betaproteobacteria or Gammaproteobacteria are in predominance (Hwang et al., 2012). The abundance of different groups of bacteria has been found to vary between distribution systems that have a free chlorine residual and those that use chloramines (Gomez-Alvarez et al., 2012). Such changes in community structure can be significant to protection of community health as it was found that disinfection type can cause changes in abundance of opportunistic pathogens (Tables 28.7 and 28.8).

Table 28.7. Distribution of Opportunistic Pathogens in Distributions with Different Disinfection Residuals

OrganismPercent of Total inFree ChlorineChloramineMycobacterium1.2919.65Legionella0.310.09Amoeba0.03>0.0001

Table 28.8. Distribution of Members of Bacteria Domain Determined via Taxonomic Identifications of Annotated Proteins at the Class Level

DomainFree Chlorine %aChloramine %Actinobacteria6.227.8Cytophaga02.3Flavobacteria02.3Sphingobacteria02.0Chlamydiae0.40.1Chlorobia1.40Chloroflexi1.30Gloeobacteria1.30Cyanobacteria9.00Bacilli4.10Clostridia5.60Planctomycetacia1.30Alphaproteobacteria35.122.5Betaproteobacteria6.224.1Deltaproteobacteria11.910.5Gammaproteobacteria0.50.1Other classes representing <1%15.48.4Total100100

aEach number in brackets=% total sequences in each group.

Source: Gomez-Alvarez et al. (2012).

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Technological Revolutions: Possibilities and Limitations

Denis Faure, Dominique Joly, in Insight on Environmental Genomics, 2016

2.2 Genomic DNA sequencing from a single cell: "single cell genomics"

Genomic DNA sequencing from a single cell, "single cell genomics" (SCG), is an innovative use of NGS.

SCG is achieved through several steps:

cell isolation by dilution, micromanipulation or automated sorting by flow cytometry;

lysis of cells;

WGA (Whole Genome Amplification)-based amplification of DNA, mostly using the DNA polymerases Phi29 or Bst with the "multiple displacement amplification" (MDA) method;

NGS sequencing. This original method still suffers from some defects and biases. One of them is linked to difficulties in the lysis, i.e. in the disintegration of the cell wall of organisms. Another is the biases induced by the MDA amplification, as it generates DNA chimeras. Some of these biases can be reduced by decontaminating the reactives, miniaturizing and, therefore, reducing reaction volumes from nanoliters (10− 9 liters) to picoliters (10− 12 liters).

Completion rates of the resulting genomes are variable (0 to 100%) and depend on many factors such as the amount of available DNA after the cell anlysis, the contamination by other DNA sequences and the genome complexity. The SCG approach is expected to enable the identification of rare or still unknown organisms from one single cell. This would give access to non-cultivable organisms, to the characterization of infra-specific biodiversity to document genomic variability or the inter-specific variability within communities of organisms and even to the study of their evolution and adaptation by detecting mutations within populations of organisms. Other fields of research that the SCG approach opens up include studies on epigenomes, embryology, organogenesis and neurobiology as well as medical and clinical studies on humans, related for example to research on cancer processes [WAN 15].

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Public driven and public perceptible innovation of environmental sector

Attila Gere, ... Howard Moskowitz, in Innovation Strategies in Environmental Science, 2020

8 Conclusions

Mind genomics and text mining are complementary tools. Text mining looks at the way people emit information when communicating. Text mining is the big data of human behavior. To understand behavior, there must be rules created in an ad hoc way. Only then do the data make sense. Text mining provides a great deal of information, but it requires heavy-duty computation to extract the patterns. Metaphorically, text minding can be seen as a concentrated effort with a sea of information, increasing the concentration and usability of information so that the originally dilute input is more structured and usable, with patterns waiting to emerge. However, the patterns must be realized by the researcher who makes the interpretation.

Mind genomics can be likened to working with highly concentrated materials, in which the objective is to run simple experiments to extract vital information. One does not need to pore through masses of data. Rather, as few as 50 respondents in a rapid, 4-h effort, from start to finish, allows the research to understand the mind-sets, and in turn to create a PVI. What mind genomics lacks in scope in terms of materials, it more than compensates for in terms of insight, data usability, archival information, and future applications beyond the scientific experiment itself.

Mind genomics and text mining work on different inputs with different analytic tools and produce outputs substantially different in nature and scope. They are complementary to each other, rather than competitors. They should be considered companions that help each other to achieve the task of understanding, predicting, and using. We have shown the beginning of this, using text mining, based on millions of data points, to inform the inputs of mind genomics, the experiment.

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Foreword

Farooq Azam, in Biogeochemistry of Marine Dissolved Organic Matter (Second Edition), 2015

Genomic predictions have provided powerful constraints. They tell us the molecular interactions among DOM molecules and microbes that are possible. However, predicting the biogeochemical dynamics of the complex DOM pool also requires ecophysiological and biochemical studies of DOM-bacteria interactions to determine: what DOM transformations do take place, at what rates, by what biochemical mechanisms, subject to what regulatory forces and in what ecosystem context. This is indeed a tall order; but the problem is critical to solve because of the central role of DOM-bacteria interactions in predicting the carbon cycle of the future ocean. Ocean acidification and warming are likely to affect the nature and rates of microbial production and transformation of DOM with potential influence on the carbon balance between the ocean and the atmosphere. Understanding what renders some of the DOM semi-labile or refractory will also require such mechanistic studies. This important research on dissolved phase carbon cycling and sequestration requires new methods, model systems, and concepts (e.g., Microbial Carbon Pump; Jiao et al., 2010) addressing in situ dynamics and interactions among microbes and (DOM) molecules.

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Pyrethroids

M.K. Ross, in Encyclopedia of Environmental Health, 2011

Modern 'Omic' Approaches to Study Pyrethroids

Genomic and toxicogenomic studies on pyrethroids have focused on the underlying metabolic differences between insecticide-resistant and insecticide -sensitive strains of mosquitoes (A. gambiae). Few 'omic' studies have focused on the effects of pyrethroids in mammalian species, although some have examined the neurotoxicological impact of these compounds. For example, the type II pyrethroid cyfluthrin was shown to both up- and downregulate several genes in primary human fetal astrocytes. The affected genes were shown to encode chaperones, transcription factors, transporters, and signal transducers. A recent study indicated that pyrethroids induced changes in gene expression in the rat frontal cortex that influence branching morphogenesis, suggesting that these compounds may act as developmental neurotoxicants that affect normal neuronal morphology. In other studies, exposure of mice to pyrethroids (deltamethrin and permethrin) elevated the amount of dopamine transporter (DAT) in the brain. It has been suggested that increased levels of DAT may contribute to Parkinson disease due to the upregulation of a protein that acts as a gateway for toxicants into dopaminergic neurons. This finding may have significance in terms of the hypothesis that pesticides contribute to Parkinson disease in humans.

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Functional Genomics and Molecular Analysis of a Subtropical Harmful Algal Bloom Species, Karenia brevis

T.I. McLean, M. Pirooznia, in Encyclopedia of Environmental Health, 2011

Functional genomics is an attempt not only to identify the complement of genes that a particular organism contains in its genome, but also to understand how the expression of those genes are regulated and how the gene products interact to produce the biology associated with an organism. Genomics-based studies initially focused on biomedically important model organisms, but now they are being applied to a myriad of organisms for a multitude of reasons. The study of dinoflagellates is now benefiting from these techniques and analyses. This article describes some new findings surrounding the molecular nature of a particular dinoflagellate, Karenia brevis, which is endemic to the Gulf of Mexico and causes near-yearly harmful algal blooms to one or more coastlines within the Gulf. Four independent expressed sequence tag (EST) libraries have been constructed for this dinoflagellate creating a large genomics resource for investigating facets of this organism's biology such as why and how it forms blooms, how it produces toxins, how it regulates its growth, and how it interacts with its environment. Some preliminary work is highlighted that shows how the organism may be sensing its environment and regulating its gene expression and growth accordingly.

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Pharmaceuticals

Bryan G. Reuben, in Encyclopedia of Physical Science and Technology (Third Edition), 2003

VIII.B Genomics and Proteomics

Genomics is the branch of biotechnology dealing with the structure of DNA. It is possible today to sequence the units in the DNA of different organisms, and that of the TB microorganism, among others, has already been fully identified. The human genome was elucidated in 2000. So far, about 100 genes involved with disease have been identified, most notably those responsible for Huntingdon's chorea and sickle cell anemia. It was estimated that, by 2000, the pharmaceutical industry would have invested nearly $5 billion in genomics.

Knowledge of the DNA sequence enables one to predict the protein that a cell might synthesize. It should produce an abundance of well-defined disease-related targets for drug therapy. It was thought at one time that, since genes encoded for proteins, a cell's protein state would in effect be a projection of its genetic state, but this hope proved unfounded. Correlation between the gene structure and protein production is low, showing that that the presence and abundance of specific proteins cannot be predicted from the presence of the genes that encode them.

The fact that a gene's structure did not predict protein production was a setback to the biotechnology industry. The new field of proteomics developed, and was concerned with protein-level analysis. This included the liberalization of protein within the cell, the other proteins with which it gave short- or long-lived complexes, and modifications such as phosphorylation and glycosylation, which are central to the function of the protein but which are not coded genetically. There promises to be an explosion of knowledge of the protein level on the same scale as has been seen on the genetic level.

Some of these applications are intellectually and commercially exciting. For example, tamoxifen (110) is the most widely prescribed anticancer drug with 2.8 million prescriptions per year. Meanwhile, only 40% of women with breast cancer respond to it. Presumably, the other women have an enzyme that destroys it before it reaches the tumor. If this enzyme could be blocked, tamoxifen could be more widely used. Alternatively, it might be possible to find which people did not produce enzymes to metabolize certain drug products and would therefore suffer adverse reactions when exposed to them. If that section of the population could be excluded, certain drugs not permitted at present could become usable.

There are many obstacles to the third chemotherapeutic revolution. Innovation is not a linear process, and the science simply may not work. It may work but take too long and be too expensive for the people who fund it. Political pressure on the drug companies to set lower prices may mean that resources are not available. On almost any scenario, there is a mismatch between what the drug companies are hoping to deliver and what the paymasters–governments, insurance companies and so on—are prepared to pay for. How this onflict will be resolved is still in doubt

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Medical and Health Genomics

2016, Pages 1-13

Chapter 1 - The Human Genome

Author links open overlay panelD.Kumar12

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https://doi.org/10.1016/B978-0-12-420196-5.00001-0Get rights and content

Abstract

New discoveries and innovations in biological and life sciences during the five decades before the 21st century have centered on genetics and genomics. It took just over 50 years after the unraveling of the structure of the molecule of nucleic acids, the key unit of the biological life, for scientists to embark on sequencing the major entire genetic constitution or genome of many single-cell and large mammalian creatures including man. The word genome includes gene and -ome, implying complete knowledge of all genes and related elements in any single organism. Inevitably, this led to enthusiastic expansion of the whole science and thence to the emergence of genomics. The suffix -omic, derived from the ancient Greek, refers to in-depth knowledge. Not surprisingly, genomics was followed by a plethora of related -omics; for example, proteomics, metabolomics, transcriptomics, and so on. Currently, we have over 30 such disciplines with the -omics suffix. Developments and advances in genetics have led to a better understanding of genomic variation, the principles governing heredity and the familial transmission of physical characteristics and diseases, in-depth understanding of the pathophysiology of diseases, the development of new methods of clinical and laboratory diagnosis, and innovative approaches to making early diagnoses (eg, prenatal diagnoses and newborn screening) and offering reproductive choices, including preimplantation genetic diagnoses. All these developments are now accepted within the broad fields of human genetics, medical genetics, clinical genetics, genetic medicine, and the new emerging field of genomic medicine. Not surprisingly, the field remains wide open, encompassing the massive field of human genomics, broadly focusing on medical and health genomics. This chapter leads the book, providing the basic factual information for grasping the concepts of human heredity, genes, genetics, and genomics. It is expected that the reader will proceed to subsequent chapters better equipped with the introduction to genetic/genome sciences as applied to humans, specifically genetic diseases, genetics, and genomics in medicine; public health; and specific issues related to society, ethics, and law.

Keywords

Bioinformatics

DNA

Gene

Genome

Genomic variation

Genomics

Heredity

Mitochondrial genome

Nuclear genome

Nucleic acids

Proteomics

RNA

Transcriptomics

What You Can Expect to Know

Human genome sequencing is one of the greatest endeavors of biology. Because of the efforts of publically funded human genome projects, the sequence is freely available to the public, which has contributed to significant discoveries worldwide. This chapter concentrates on understanding the architecture of the human genome in light of human genome sequencing, and how this knowledge has revolutionized the study of biology and medicine. The chapter briefly describes the human genome projects carried out by a publicly funded international consortium and a private company. The major part of the chapter elucidates various interesting findings of the human genome, and the outcomes and implications of this information. The Human Genome Project also contributed to advancement of new sequencing technologies, making personalized genome sequencing and individualized drug therapy a certainty in the near future.

ennifer K. Sehn, in Clinical Genomics, 2015

Exome and genome sequencing are often applied to the study of cancer as a discovery tool in the investigative setting. Because of limitations related to result interpretation and technical factors like depth of coverage and sensitivity, these broad approaches have not been widely adopted in clinical cancer testing. In this chapter, issues related to variant interpretation in clinical cancer testing will be addressed. Specifically, the limited utility of sequencing genes without established clinical significance will be discussed. The advantages and limitations of exome and genome sequencing in cancer from a technical perspective, including depth of coverage and variant detection, also will be considered.

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3.5 Return of the Results for Pediatric Patients

Genomic sequencing is being rapidly introduced into pediatric clinical practice (Wade et al., 2013). The results of sequencing are distinctive not only for their complexity but also for their interpretation challenges they pose for physicians, parents, and patients. Genomic sequencing analysis can be grouped based on its clinical purposes. They are performed for disease diagnosis, risk assessment for future health, combined diagnosis and risk assessment, reproductive risk assessment, identification of variants of unknown clinical significance, and pharmacogenomics.

Acknowledged that ethical oversight is essential in returning results from pediatric genomic studies, the Gene Partnership Informed Cohort Oversight Board (ICOB) was formed in 2009. The Board defined its guiding principles based on respect for the developing autonomy of pediatric participants and parental decision-making authority, when returning the results, based on their preferences and on the principle not to harm the participants (Holm et al., 2014). ICOB took in considerations the severity of the disease, age of onset, medical and nonmedical actionability, reproductive implications, and issues like stigmatization, personal/family issues, and protection of future autonomy. For the participant preferences, the age of the child was considered in developing specific guidelines (Holm et al., 2014).

Therefore, an ethical framework comprising three core concepts of pediatric ethics, the best interests of the child, parental surrogate decision-making, and pediatric assent was recently proposed to guide pediatricians in introducing genomic sequencing into pediatric clinical practice in an ethical way (McCullough et al., 2015).

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Bacterial Whole-Genome Determination and Applications

Yongqun He, in Molecular Medical Microbiology (Second Edition), 2015

Concluding Remarks

Genome sequencing has become an increasingly important tool in basic research, translational vaccine and drug development, and clinical diagnosis and investigation. We are coming to an era when sequencing cost and high-throughput data generation are no longer limiting factors. While currently a big core facility is still needed to operate the procedures of whole-genome sequencing, it is expected that the continuous technological improvements may eventually allow the routine usage of bench-top sequencing instruments in regular laboratories. Whole-genome sequencing is increasingly used to address various questions in microbiology and replace many old technologies, such as genotyping, diagnosis, environmental and microbiome profiling, and mutation and evolutionary studies. Whole-genome sequencing is changing how molecular medical microbiology works.

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Constitutional Disorders

Benjamin D. Solomon, in Clinical Genomics, 2015

Due to relatively recent technological breakthroughs, genomic sequencing, including (whole) exome and (whole) genome sequencing, are becoming more frequently used tools in the general practice of clinical medicine. The study of and the care for patients with constitutional disorders has been especially heavily impacted by the recent revolution in genomic sequencing methodologies. Genomic sequencing now allows the discovery of many new etiologies of genomic disease and the more rapid identification of causes in individuals affected by diverse genetic conditions. This chapter will discuss clinical applications of genomic sequencing as they apply to constitutional disorders. These discussions will include general techniques and strategies aimed at maximizing yield from genomic data sets, including potential pitfalls that frequently arise. This chapter will additionally describe many of the positive effects, as well as some of the more challenging consequences of the rapidly increasing prevalence of genomic sequencing.

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Globins and Other Nitric Oxide-Reactive Proteins, Part A

Enrico Negrisolo, ... Cinzia Verde, in Methods in Enzymology, 2008

Abstract

Sequencing genomes of model organisms is a great challenge for biological sciences. In the past decade, scientists have developed a large number of methods to align and compare sequenced genomes. The analysis of a given sequence provides much information on the genome structure but to a lesser extent on the function. Comparative genomics are a useful tool for functional and evolutionary annotation of genomes. In principle, comparison of genomic sequences may allow for identification of the evolutionary selection (negative or positive) that the functional sequences have been subjected to over time. Positively selected genome regions are the most important ones for evolution, because most changes are adaptive and often induce biological differences in organisms. The draft genomes of five fish species have recently become available. We herewith review and discuss some new insights into comparative genomics in fish globin genes. Special attention will be given to a complementary methodological approach to comparative genomics, fluorescence in situ hybridization (FISH). Internet resources for analyzing sequence alignments and annotations and new bioinformatic tools to address critical problems are thoroughly discussed.

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Whole Genome Sequencing in the Molecular Pathology Laboratory

G.T. Haskell, J.S. Berg, in Diagnostic Molecular Pathology, 2017

Incidental Findings and the Ethical, Legal, and Social Implications of CGS

CGS will inevitably uncover incidental findings unrelated to the patient's primary condition. A small number of these variants will be pathogenic for relatively penetrant conditions, for which medical interventions exist. The ACMG has issued guidelines regarding the return of incidental findings [18], and the current consensus is that pathogenic variants in a list of 56 medically actionable genes should be reported if the patient chooses to receive them. Studies are currently looking at how often these secondary findings are encountered, how best to report them, as well as understanding how people differ in their preference to find out about different categories of secondary results. Although the finding of a mutation that highly predisposes to disease in an asymptomatic individual does not provide a medical diagnosis, it can still produce a great deal of anxiety and worry. For many, CGS will be viewed as the opening of Pandora's box. Parents may learn things about their children or about their ex-spouse. Individuals may learn about predispositions to develop disease that they cannot do anything about. These issues warrant a better understanding of the ethical, legal, and social aspects of applying genomic testing clinically.

Given how impactful comprehensive genomic testing information is, genetic counselors must include in their discussions the potential return of secondary findings during the enrollment and return of results process. People are not uniform in terms of what they want—some people may want to learn all of their results at the same time, but there will always be some people who do not want to know everything [19]. This may be particularly true for conditions that are severe and we cannot do anything about, such as Huntington's, but is also highly influenced by people's personal histories. Thus, patient preferences should be considered when developing guidelines regarding how consenters, laboratories, and reporting clinicians handle WGS test results. This awareness has been reflected in part by the recent update of the ACMG's guidelines on incidental findings to include an opt-out for receipt of incidental findings. It will be particularly important for practitioners to be aware of the wishes and privacy of minors—will we allow their parents to receive WGS results that are entered into their medical record? Will parents know of their children's carrier status? If this sort of information is going to be clinically utilized, protections on health information must be sought in parallel, and this is currently an area of legislation [20].

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The Filamentous Fungus Sordaria macrospora as a Genetic Model to Study Fruiting Body Development

Ines Teichert, ... Ulrich Kück, in Advances in Genetics, 2014

5.2.1 NOX Complexes in Fungi

Genome sequencing of filamentous fungi showed early on that they have subfamilies of NOX enzymes sharing similarity to their mammalian homologs. NOX1 (NoxA) and NOX2 (NoxB) are homologs of the mammalian catalytic subunit gp91phox. The third NOX enzyme, NOX3 (NoxC), has only been detected in some fungi such as F. graminearum or Podospora anserina (Brun, Malagnac, Bidard, Lalucque, & Silar, 2009; Van Thuat, Schafer, & Bormann, 2012). NOR1 (NoxR) is a regulatory subunit homologous to the mammalian p67phox.

Another component of the NOX complex is a homolog of the small guanosine triphosphatase (GTPase) RAC. Quite recently, further candidates were suggested to be regulatory proteins of the multisubunit NOX complex. The tetraspanin PLS1 and a protein termed NoxD from Botrytis cinerea seem to be functionally and structurally related to the mammalian p22phox (P. Tudzynski, personal communication) (Siegmund, Heller, van Kan, & Tudzynski, 2013). Sequence analysis of NoxD found it to be homologous to the endoplasmic reticulum (ER)-localized protein PRO41 (Nowrousian, Frank, et al., 2007). Figure 4.3 depicts the supposed composition of the two NOX complexes in S. macrospora. It remains to be elucidated how activation and regulation of fungal NOX enzymes occurs.

Figure 4.3. Two NOX complexes in Sordaria macrospora. The functional analysis of NOX1, NOX2, and NOR1 was described for S. macrospora (see Section 5.2.3). The composition of both complexes was suggested from work with other fungi, as described in the text.

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Bioinformatics

J. Frellsen, ... A. Krogh, in Comprehensive Biomedical Physics, 2014

6.03.1.1.2 Types of experiments

Genome sequencing was the first application of the new HTS technologies, and it was first applied to bacteria and later to eukaryotes; now it is a commonplace practice, with many more genome projects planned (Genome 10K Community of Scientists, 2009). With costs decreasing, the re-sequencing of individual genomes is becoming a regular tool in medical research for identifying genetic variations as causes of disease and between populations of individuals (1000 Genomes Project Consortium et al., 2010).

Besides genome sequencing, several other types of experiments have been devised to study the processing and regulation of DNA or RNA at various stages in their life cycle. HTS methods have revolutionized the understanding of RNA, both messenger-RNA and noncoding RNA, by making the parallel sequencing of whole cellular RNA possible (Ozsolak and Milos, 2011). RNA-seq is now widely used to analyze (differential) gene expression levels and gene isoforms, and also to study non-protein-coding transcripts.

The regulation of gene expression is largely dependent on the binding of proteins to DNA (e.g., transcription factors and polymerases) and RNA (e.g., proteins involved in the microRNA pathway). Thus, the identification of DNA/RNA binding sites of these proteins is a major determinant for understanding the regulation of individual genes or pathways. Experiments such as CHiP-Seq for DNA (Furey, 2012; Robertson et al., 2007) and CLIP-Seq for RNA (Hafner et al., 2010; Zhang and Darnell, 2011) enrich and sequence the DNA/RNA fragments bound by specific proteins, which allows for determining the specific locations of protein binding. Epigenetic silencing is caused by methylation of certain nucleotides in the genome. A precise genome-wide identification of methylated nucleotides became feasible only with HTS based protocols, such as MethylC-seq (Lister et al., 2009) or bisulfite sequencing (Meissner et al., 2005). Furthermore, methylation sites in RNAs can be identified by MeRIP-Seq (Meyer et al., 2012) or m6a-seq (Dominissini et al., 2012) experiments.

For experimental determination of RNA secondary structures, experiments using Frag-Seq (Underwood et al., 2010), SHAPE-seq (Lucks et al., 2011), or PARS (Kertesz et al., 2010) apply structure-aware enzymes to cleave the RNA sequences at either structured or unstructured regions. Sequencing of the RNA fragments then allows structural profiling of the cell's RNA molecules at single-nucleotide resolution.

The review by Soon et al. (2013) gives an overview of several other experiments based on HTS.

Common to all these types of experiments is the fact that the genomic origin of the sequencing reads that are produced by the HTS machines needs to be determined by comparing the DNA sequence of each read to a reference genome sequence. This problem is called mapping of the reads to the reference sequence, which is usually the full genome or transcriptome of the organism.

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Can Early Clinical Trials Help Deliver More Precise Cancer Care?

Joline S.J. Lim, ... Johann S. De Bono, in Novel Designs of Early Phase Trials for Cancer Therapeutics, 2018

9.4.1 Genomics

Genomic sequencing for molecular profiling frequently employs NGS techniques whereby massively parallel DNA sequencing is carried out, then subsequently aligned to reference genomes during data analysis to obtain whole sequences of genes of interest [19]. Targeted sequencing techniques, where a panel of specific genes, either preselected or custom designed, is used are by far most commonly employed for molecular profiling currently (Table 9.2). This method allows for high throughput, deep coverage, of such genes at a relatively affordable price, an important criterion for implementation of molecular profiling more broadly. Nonetheless, while targeted sequencing is able to detected specific genetic mutations with good sensitivity and specificity, detection of genomic rearrangements like fusions or copy number changes is more challenging to detect [20]. With increased economic efficiency in sequencing, the use of whole-exome sequencing (WES) and whole-genome sequencing (WGS) have been gaining momentum in such molecular profiling. Comparing WES and hotspot mutation analysis in a cohort of lung cancer sufferers showed that WES identified actionable genomic alterations in 65% of patients thought to be negative for driver mutations [21]. While the ability to obtain more information from an individual tumor sample is attractive, this increased information can come at a price of either increased costs or decreased depth or coverage, limiting the confidence in confirming that the identified genetic aberrations are a true biological finding instead of artifact. Additionally, larger datasets from WES or WGS require greater bioinformatic resources, resulting not only in longer turnaround time, but also challenges in accurate interpretation of the clinical significance of the molecular findings [22].

Table 9.2. Selected Technology Platforms for Precision Medicine

StrategyMethodAdvantageLimitationsGenomicsWhole-exome sequencing–

Allows simultaneous detection of sequences

–

Allows assessment of copy number changes

–

Use of FFPE samples to give reasonable data

–

Need for bioinformatics support

–

Results limited to protein-coding regions of genome

Whole-genome sequencing–

Allows study of genomic rearrangements

–

Allows assessment of noncoding regions

–

Longer readout time required due to large size of data generated

–

Increased cost

TranscriptomicsMicroarrays–

Specific assay developed allow for quick read out of transcription levels of genes of interest

–

lower cost

–

Limited to smaller number of genes of interests

–

Higher background noise

–

Larger amounts of RNA required

RNA sequencing–

Transcriptome size smaller than genome size, allowing for a shorter readout time

–

Allows detection of RNA splice variant

–

Unable to study untranscribed regions

–

Need for bioinformatics

Genome Sequences

A genome sequence underpins systems biology studies; it is now required for metabolic engineering and is able to be rapidly attained with recent advances in next-generation DNA sequencing technologies. The ATCC 27405 type strain was the first to have its genome sequence determined for this species, and the classical Sanger method of DNA sequencing was used by the U.S. Joint Genome Institute (GenBank accession number CP000568). Professor J.H. David Wu (University of Rochester) and Dr. Michael E. Himmel (National Renewable Energy Laboratory) submitted the proposal to generate the ATCC 27405 genome sequence. Professor Wu's laboratory supplied DNA for the ATCC 27405 genome project and the first draft sequence was available to the public in November 2003; however, repetitive sequences made closing this genome difficult and the genome sequence was not finished until February 2007. The Glimmer49 and Critica50 gene prediction algorithms were originally used and combined to predict gene models, which was followed by a round of manual curation. More recently, an improved gene prediction algorithm was applied to the ATCC 27405 genome and its annotation was updated (GenBank accession number CP000568.1).51 A comprehensive comparison of different annotation versions can be found at http://genome.ornl.gov/microbial/cthe/. As algorithms continue to improve and novel features such as small regulatory RNAs are discovered and identified, it is likely there will be refinements to genomes.

Since the first C. thermocellum genome sequence was generated, there has been a revolution in DNA sequencing technologies.52 Twenty genome sequences for Clostridia species across multiple genera were recently determined,53 two of which were for C. thermocellum strains JW20 (4150) and LQRI (DSM 2360). Finished and draft genomes have been described for C. thermocellum strains DSM 1313,54 YS and derivative strain AD2,55 and strain BC1.23 The genome sequence for strain ATCC 27405 has been used to design oligonucleotides for strain DSM 1313, indicating that they are closely related,56 which was confirmed by subsequent in silico genome comparisons.4 C. thermocellum DSM 1313 is the background strain for one recently developed genetic system (see below). A summary of several key genome features is provided for wild-type strains for which the genome sequences are available (Table 1). Although there are strain-level differences in gene content for encoding transposes and restriction systems,4 many of the differences in genome sizes and the number of predicted genes likely reflect differences in sequencing technologies, assembly methods, and gene prediction algorithms. Longer read technologies continue to develop, and we expect that such approaches will be useful to improve genome assemblies,57 which will facilitate comparative genomic studies. Future comparative genomic studies may permit more refined bioinformatics predictions for genes, operons, and cis-regulatory motifs and insights into phenotypic differences reported for strain BC1 or others such as hypercellulase production.58

Table 1. Summary statistics for wild-type C. thermocellum genome sequences

StrainStatusGenome Size (bp)% G + CTotal GenesProtein Coding GenesrRNA OperonsRef.aATCC 27405Finished3,84,3301393,3353,236451DSM 1313Finished3,56,1619393,1023,031429YSDraft3,84,3301393,0813,026155JW20 (DSM 4150)Draft3,32,1980393,0272,9793b53LQRI (DSM 2360)Draft3,45,4608393,1473,091153BC1Draft3,45,4918393,1593,095423

aWith the exception of strain BC1, data were obtained from the Integrated Microbial Genomes database on September 7, 2013.bThree 16S rDNA genes were identified for strain JW20, but only single copies of the 5S and 23S genes were noted.

These C. thermocellum genome sequences have been leveraged to produce a genome-scale metabolic model.59 This model consisted of 577 reactions, 525 intracellular metabolites, and 432 genes. In addition to providing a tool to predict modifications that could improve fuel production, it also highlighted gaps in metabolic pathways. Because these missing reactions are part of essential metabolic pathways, they either represent incorrectly annotated genes or situations in which C. thermocellum uses an unusual pathway. Future studies will be needed to resolve this question. The Roberts model was further updated using RNAseq data, further improving this tool.60

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Metabolic Engineering – Applications, Methods, and Challenges

Shang-Tian Yang, ... Yali Zhang, in Bioprocessing for Value-Added Products from Renewable Resources, 2007

4.5 Omics and high-throughput tools

The genome sequences of many microorganisms, including E. coli, S. cerevisiae, C. glutamicum, Bacillus subtilis, Lactococcus lactis, and C. acetobutylicum, have been completed and many more are in progress. These genome sequences are available for functional genomics and metabolic engineering research. Besides genomic sequence information, transcriptomic, proteomic and metabolomic data can be generated at an everincreasing rate from high-throughput technologies, such as DNA sequencers, microarrays (gene chips), two-dimensional gel electrophoresis combined with tandem mass spectrometry, and isotopic label distributions probing metabolic phenotypes (see Table 4). Genome libraries can be analyzed to identify the genetic basis of relevant phenotypes [161, 196]. DNA microarrays can be used to efficiently analyze gene disruption/molecularly barcoded mutant libraries to identify genes essential to a particular phenotype [197, 198]. Transcriptional profiling or the analysis of genome-wide gene expression (transcriptome) can differentiate between genes with altered expression levels, either as the result or cause of the phenotype of interest [199, 200]. Proteomics enables quantitative profiling of cellular proteins using two-dimensional gel electrophoresis or chromatography coupled with mass spectrometry [201, 202]. High-throughput quantification of metabolites (metabolome) by sophisticated NMR, gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and MALDI-TOF MS enables the comparative analysis of metabolite profiles under genetic and environmental perturbations [203, 204]. Intracellular fluxes can then be obtained by metabolite balancing using computational methods, such as MFA and FBA, and isotopomer experiments using 13C-labelled substrates [171–173]. These fluxomic data allow us to have a better understanding of physiological states and phenotypic behaviors and the relationship between genes and their functions, also referred to as phenomics [205].

Table 4. Omic data and high-throughput experimental tools useful to metabolic engineering

OmicsDataToolsReferencesGenomicsthe DNA sequence of the genomeDNA sequencerTranscriptomicsthe abundance of all mRNA's of a genomecDNA microarray[196]Proteomicsthe presence or absence of all proteins of the genome2D electrophoresis Mass spectrometry Protein microarray[201, 202]Metabolomicsintracellular concentrations of metabolitesGC-MS, LC-MS NMR[203, 204]Fluxomicsthe steady-state rates at which extracellular metabolites are producedFlux & isotopomer balance[171–173]

In the post-genomic era, the vast genome sequence information can be used to manipulate the metabolism of the organism, resulting in more efficient production strains [206, 207]. Through comparative analysis of wild-type and recombinant strains, genomics can be used to identify gene targets [208], and transcriptomics have been used to optimize fermentation conditions [209] and understand regulatory mechanisms [199, 210]. Today, functional genomics, which combines transcriptomic, proteomic, and metabolomic data, can provide insights on cellular metabolism that are difficult to obtain with traditional approaches. Based on results from functional genomic studies, new metabolic pathways that are expressed under different conditions or stress can be identified, and new strategies for rationally engineering metabolic pathways and cellular properties can be developed [160–162].

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The isolation and improvement of industrially important microorganisms

Peter F. Stanbury, ... Stephen J. Hall, in Principles of Fermentation Technology (Third Edition), 2017

Application of genomics

Genomics uses the tools of gene sequencing and bioinformatics to study the biology of an organism at the chromosomal level. The knowledge obtained from a whole genome sequence enables the development of a raft of new information on the functioning of an organism both at, and below, the level of the genome itself. Comparison of the sequence with gene databases enables the prediction of the role of each gene and the protein each produces. Thus, the development of information systems, and the means to interrogate them, has been as crucial to the success of genome investigation as has been the sequencing science. Three major DNA databases have been established: GenBank (USA), EMBL-BANK (Europe), and DDBJ (Japan) that receive information from laboratories, and share it with each other, on a daily basis. The information stored in these data depositories is available to the public and thus enables laboratories all over the world to benefit from, and contribute to, the development of the subject. The searching of both DNA and protein databases for matching sequences is enabled by a number of algorithms such as BLAST (Basic Local Allignment Search Tool). Thus, the term in silico has been added to in vivo and in vitro, describing a new era of biological exploration.

The complete genome sequence of C. glutamicum ATCC 13032 was first elucidated in 2001 by the Japanese company Kyowa Hakko Kogyo Co., Ltd. (Nakagawa et al., 2001) and deposited in the public database (GenBank NC_003450). Kyowa's competitor, Ajinomoto, sequenced the genome of a closely related species, Corynebacterium efficiens, in 2002 (Fudou et al., 2002) and deposited it in 2003 (GenBank database, NC_004369). Quite independently, Kalinowski et al., 2003 published the sequence of C. glutamicum ATCC 13032 in 2003 and in 2007 the sequence of C. glutamicum strain R was published by Yukawa, Omumasaba, Nonaka, Kos, and Okai (2007). Ohnishi et al. (2002) was the first to apply the knowledge of C. glutamicum's genome sequence in an attempt to produce a "minimum mutation strain." Amino acid producing strains that have been developed by mutation and selection have proved to be highly successful commercial organisms. However, the selection of desirable traits using, for example, analog resistance, does not prevent the coselection of other mutations that negatively affect strain performance. Thus, strains that have undergone multiple mutation/selection procedures may have accumulated a range of undesirable mutations resulting in their being less vigorous, slower growing, and less resistant to stressful conditions. Also, the presence of background mutations may confuse the interpretation of the mechanism of over production that may, in fact, not be due to a "selected" mutation, thus making further logical, directed strain improvement problematic. As discussed earlier, protoplast fusion was used in an attempt to remove deleterious markers by generating recombinants between high producing strains (that lacked vigor) and wild types that grew well but did not over produce. Ohnishi et al.'s more direct strategy was to compare key gene sequences of a high lysine producing strain of C. glutamicum (B6) with that of the fully sequenced wild type to identify any mutated genes that could be responsible for over production. The influence of the mutated genes on lysine production could then be assessed by their sequential introduction into the wild-type by allelic replacement (Fig. 3.31). Ohnishi et al. focused their initial attention on 16 genes of the terminal lysine pathway (Fig. 3.32) and, knowing the sequence of the wild-type, were able to prepare PCR primers based on the nucleotide sequences flanking each intact gene. The PCR products derived from the high producer were then sequenced and compared with the wild type genes. Each of the following five genes were shown to contain a point mutation:

Figure 3.31. Ohnishi et al.'s Strategy for the Development of a Minimal Mutation L-Lysine Producing Strain of Corynebacterium glutamicum by the Sequential Addition to the Wild-Type of Mutations Identified From the Production Strain (Ohnishi et al., 2002)

Figure 3.32. L-Lysine Biosynthetic Pathway in C. glutamicum

Enzymes encoded by genes: asd, aspartate semialdehyde dehydrogenase; aspC, aspartate aminotransferase; dapA, dihydrodipicolinate synthase; dapB, dihydrodipicolinate reductase; dapC, succinyl-L-diaminopimelate aminotransferase; dapD, tetrahydodip-icolinate succinylase; dapE, succinyl-L-diaminopimelate desuccinylase; dapF, diaminopimelate epimerase; ddh, diaminopimelate dehydrogenase; hom, homoserine dehydrogenase; lysA, diaminopimelate decarboxylase; lysC, aspartokinase; lysE, lysine exporter; lysG, lysine exporter regulator; ppc, phosphenolpyruvate carboxylase; pyc, pyruvate carboxylase.

Modified from Ohnishi et al. (2002). Further details are given in Fig. 3.37 and Fig. 3.38.

hom—coding for homoserine dehydrogenase

lysC—coding for aspartokinase

dapE—coding for succinyl-L-diaminopimelate desuccinylase

dapF—coding for diaminopimelate epimerase

pyc—pyruvate carboxylase

The two dap mutations (E and F) were considered negligible because they resulted in neither amino acid substitution nor change to a rare codon. It can be seen from Fig. 3.16 that the control of the aspartate family of amino acids in C. glutamicum is achieved by the concerted inhibition of aspartokinase by lysine and threonine and the inhibition of homoserine dehydrogenase by threonine. The mutant alleles of lysC and hom (designated lysC311 and hom59 respectively) were introduced individually into the wild type strain by allelic replacement. The presence of lysC311 gave the phenotype of resistance to the lysine analog S-(2-aminoethyl)-L-cysteine (AEC) and hom59 resulted in a partial requirement for homoserine, observations commensurate with the history of the original producer strain (B6). Analog resistance of aspartokinase may be expected to release feedback inhibition; and partial auxotrophy for homoserine would result in depleted threonine, thereby lifting inhibition of homoserine dehydrogenase. The lysC311 single mutant produced 50 g dm−3 lysine; the hom59 mutant produced 10 g dm−3 lysine whereas a wild type background transformed with both mutations resulted in a synergistic production of 75 g dm−3 lysine. Crucially, this reconstructed strain resembled the wild-type in its high growth rate and rate of glucose consumption, indicating that the background of deleterious mutations introduced by the many rounds of mutation and selection had been circumvented.

The final mutation revealed in this work was that of pyc coding for pyruvate carboxylase (pyc458), an anaplerotic enzyme fixing carbon dioxide in the synthesis of oxaloacetate, the immediate precursor of the aspartate family. Previous work on the lysine fermentation had concentrated on the terminal pathway and had not addressed the supply of precursors. The further incorporation of pyc458 along with lysC311 and hom59 into the wild-type resulted in a strain (designated AHP-3) producing 80 g dm−3 lysine and, importantly, the highest production rate of 3.0 g dm−3 h−1 reported at that time; the high production rate being due to the high growth rate of the strain. Pyruvate carboxylase had not been a target in the strain improvement process used in the development of strain B6 and no selection mechanism existed for its isolation. Thus, the mutation had been coselected along with selectable markers during the process, illustrating that the undefined background of the industrial strain included both desirable and undesirable lesions. It may be recalled from our earlier discussion that the three key focal points for yield improvement are—control of the terminal pathway, provision of precursors, and the provision of NADPH. Thus, Ohnishi, Katahira, Mitsuhashi, Kakita, and Ikeda (2005) turned their attention to the supply of NADPH by investigating the genes associated with the pentose phosphate pathway, the major source of NADPH. Again, the gene sequence of the wild-type was used to prepare PCR primers based on the nucleotide sequences flanking each intact gene of the pentose phosphate pathway. Following comparison of the sequences of the PCR products with the wild-type, a point mutation was identified in the gnd gene, coding for 6-phosphogluconate dehydrogenase. Using the same allelic replacement methodology described earlier the mutated allele was added to the manipulated wild-type containing pyc458, lysC311, and hom59. The yield of this strain improved by 15% and again retained the vitality of the wild-type such that the fermentation was completed in 30 h, compared with 50 for the industrial B6 producer. Thus, this mutation had also been coselected and its addition to the other three mutations in a background free of undesirable lesions led to the development of a high-producing vigorous strain.

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Integrated Production of Butanol from Glycerol

Keerthi P. Venkataramanan, Carmen Scholz, in Biorefineries, 2014

11.2.1 Improving Product Yield and Productivity

The lack of a genome sequence for C. pasteurianum has limited the engineering of this microorganism using the tools developed for Clostridium species and Gram-positive bacteria in general. A recently released draft genome sequence offers clear insights into the metabolic organization and capabilities of the organism, however [19]. The use of Pacific Biosciences RS II technology to identify host R-M (restriction-methylation) systems can lead to the development of genetic tools to facilitate the study and generation of genetically intractable strains of this bacterium [20,21], and one study has reported the successful use of chemical mutagens, such as N-methyl-N-nitro-N-nitosoguanidine (NTG), to generate a mutant strain of C. pasteurianum capable of enhanced butanol production and greater selectivity for butanol over other coproducts [16]. Generated by chemical mutagenesis, a mutant strain of C. pasteurianum ATCC 6013 produced butanol with a yield of 0.43 g per gram of glycerol consumed, leading to an approximately 50% increase in butanol formation compared to the production of the wild strain. Jensen et al. (2012) demonstrated another method of chemical mutagenesis using ethyl methyl sulfonate (EMS), a chemical mutagen that has been shown to be very effective in producing mutant strains [17,18]. A mutant strain using EMS mutation was able to tolerate crude glycerol at a very high concentration of 205 g/L. The generation of these mutant strains eliminates the need to cleanse crude glycerol of its inhibitory compounds, mainly free fatty acids in the form of soap. The mutant MN06 strain generated using EMS was also capable of growing in crude glycerol at a high rate of glycerol utilization (7.59 g/L h), while also producing more butanol and 1,3-PDO than the same strain grown on technical grade glycerol. The continuous fermentation of glycerol offers numerous advantages over batch fermentation in terms of maintaining process parameters at a level that maximizes production of the desired product. The butanol formation is a phase-dependent process, indicating a strong correlation between productivity and the pH of the medium. In a batch culture, the cells have to undergo acidogenesis, which results in the reduction of pH, a condition favorable for solvent formation. Maintaining an acidic pH results in solventogenic fermentation of the medium into predominantly butanol, leading to higher total butanol productivity.

Researchers have also studied fermentations that use a secondary carbon at a minimal concentration to increase butanol titers. Specifically, simple sugars (glucose, xylose, and arabinose) and acids (lactate) have been employed as the secondary carbon source. The application of glucose in this manner has been found to increase butanol productivity and titer leading to an increased yield of butanol from glycerol [22]. Equally, the addition of thin stillage and lactate into the glycerol fermentation broth has yielded similar results, enhancing butanol production by C. pasteurianum [23].

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RADIONUCLIDE IMAGING

LORAINE V. UPHAM, DAVID F. ENGLERT, in Handbook of Radioactivity Analysis (Second Edition), 2003

4. DNA Microarray Applications

The emergence of whole genome sequence data has brought about gene array technology for differential gene expression, mutation screening, sequence analysis, and drug target identification. The commercial availability of mouse, human, and rat genome sequences preprinted on nylon membranes provide a convenient way to conduct gene array assays using radiolabeled sequences and a storage phosphor system. Storage phosphor imaging technology provides the long linear dynamic range and accuracy required for detection of subtle changes in a large range of gene expression levels that can occur within a given experiment. The following are two examples of the use of storage phosphor screen imaging for radiolabeled gene array samples.

Gene arrays can be used to analyze the effects of drug treatments at the molecular level. Atlas Rat Toxicology II arrays (Clontech, Palo Alto, CA) are filters containing rat liver total RNA. Filters containing 465 unique cDNA fragments in duplicate were hybridized with radiolabeled cDNA reverse transcribed from RNAs isolated from Rats exposed to Fenofibrate drug treatment for 10 days. Gene expression profiles from control and treated animals were analyzed to look for clues to the changes that may be a result of drug treatment and potentially cause adverse effects in humans (Jiao and Zhao, 2002). Filters were exposed 18–24 h on SR screens and scanned with the Cyclone. Images are overlaid in QuantArray software to determine which genes are up or down regulated with drug treatment. Figure 13.13a, b are the images obtained by this method. Figures 13.14a,b show the scatter plot display of quantified spots and results of one spot as analyzed by QuantArray (Upham and Fox, 2001).

FIGURE 13.13. Cyclone images of rat liver total RNA hybridized with control (a) and Fenofibrate treated (b) rat liver total RNA reverse transcribed into cDNA and radiolabeled with 33P-dATP.

FIGURE 13.14. (a) Scatterplot display of comparison of control and treated filters based on data from Cyclone; (b) Representation of specific spots as selected from Scatterplot.

Another application of the use of quantitative gene array analysis is in research on effects of the environment on human gene expression. For example, it is well documented that exposure to sun causes or results in an increase in actinic keratosis and eventually squamous cell carcinoma (Hodges and Smoller, 2002). Researchers at University of New Mexico collect punch biopsies from patients diagnosed with squamous cell carcinoma (SCC). Four samples are collected from each patient including tissue from (a) the SSC, (b) an actinic keratosis, a precursor lesion of SCC, (c) adjacent sun exposed normal skin, and (d) unexposed skin from the buttocks. Total RNA is isolated from each sample, reversed transcribed into cDNA and labeled with α-33P-dATP, and hybridized to an ID1001 DermArray Filter containing 5000 human genes from Invitrogen/Research Genetics (Carlsbad, CA). After washing, membranes are exposed to SR screens for 24 hours, to bring out low expressors, and scanned on the Cyclone system. The intensities of the spots correspond to the relative abundance of various transcripts at the time that the RNA was harvested. By comparing multiple filters, differences in gene expression profiles between each of the states, such as tumor versus adjacent normal skin and unexposed versus exposed skin can be observed. Figure 13.15 are quantitative images of these high density gene array filters.

FIGURE 13.15. Gene expression arrays probed with mRNA isolated from normal and tumor tissue.

(Courtesy of Dr. Bryan E. Alexander, University of New Mexico.)

Additional references in literature describe the use of storage phosphor screen imaging for receptor binding assays (Chan et al., 1991); Southern and northern blot analysis (Muller and Gebel, 1994; Robben et al., 2002) western blot analysis (Taylor et al., 1992; Shelton et al., 1994); Gel shift assays (Zinck et al., 1993; Olivas and Maher, 1995); BioChip Imaging (Schena, 2000) and Microarray assays (Popovici et al., 2000), other related isotopes (Gonzalez et al., 2002), and double label autoradiography (Pickett, et al., 1992).

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Conclusions and Future Trends

Eleni I. Georga, ... Stelios K. Tigas, in Personalized Predictive Modeling in Type 1 Diabetes, 2018

10.2 Toward Precision Diabetes Medicine

Knowledge of the human genome sequence has contributed significantly to our understanding of the pathogenesis and the underlying molecular mechanisms of both types 1 and 2 diabetes [13–16]. The HLA class II alleles account almost for 50% of the genetic susceptibility to Type 1 diabetes, whereas GWAS explain a lower proportion of type 2 diabetes heritability. Meyer et al. find no apparent way for precision medicine to permeate insulin therapy of type 1 diabetes and they locate its role mainly in (1) identifying effective preventive interventions targeting genetically susceptible individuals (characterized as primary prevention) and (2) understanding the immune mediators and propagators of β-cell destruction in individuals with islet autoimmunity (characterized as secondary prevention) [13]. To this end, the Environmental Determinants of Diabetes in the Young (TEDDY) multicenter study has already provided significant insight into the genetic-environmental associations triggering the development of islet autoimmunity or promoting type 1 diabetes progression in genetically at risk children (≤5 years) [17–19]. Recently, TEDDY study provided evidence of clear differences in the initiation of autoimmunity (insulin autoantibodies, GAD antibodies) according to genetic factors (e.g., presence of SNPs rs689 [INS], rs2476601 [PTPN22], rs2292239 [ERBB3], rs3184504 [SH2B3], rs3757247 [BACH2]), and environmental exposures (i.e., sex, family history, HLA, country, probiotics at age 28 days, weight at age 12 months) in infants with HLA-DR high-risk genotypes followed-up until 6 years of age [18]. On the other hand, Meyer et al. postulate that type 2 diabetes pharmacological interventions are more likely to benefit from the inclusion of precise knowledge on an individual's genotype and on predictive biomarkers of secondary complications; however, they acknowledge the lack of robust scientific evidence at present which would guide drug regulation. As a first step toward type 2 diabetes therapy individualization, the GRADE comparative effectiveness long-term study of commonly used glycemia-lowering medications (i.e., sulfonylurea, DPP-4 inhibitor, GLP-1 receptor agonist, insulin) when combined with metformin, having enrolled ~5000 participants, assesses the differences in study outcomes by race/ethnicity, sex, age, diabetes duration, weight, body mass index, HbA1c, and measures of insulin sensitivity, insulin secretion, and the glucose disposal index [20].

The NIH Precision Medicine Initiative in tandem with other linked precision medicine activities (e.g., the National Heart, Lung and Blood Institute Trans-Omics for Precision Medicine [TOPMed] Program), supporting the collection of longitudinal multivariate data (genome, proteome metabolome, microbiome, exposome, and phenome) from large population cohorts, will enable the development of systems biology approaches to elucidating the underlying pathophysiological mechanisms of diabetes onset and progression and the identification of new biomarkers of diabetes-related vascular complications [3,15,21–23]. Data mining of daily longitudinal self-monitoring data (e.g., continuous glucose monitoring, physical activity, stress) along with EHR data is an additional valuable asset, which has the potential to explain both the short-term and long-term glycemic status of an individual and facilitate the evaluation of the glycemic effectiveness of a specific intervention [24–28]. In addition, the consequent finer stratification of people with type 1 or type 2 diabetes per se, possibly defining new diabetes subtypes, could provide opportunities for more effective personalized therapeutic schemes as well as for new hypotheses about disease pathogenesis and medical care which could be tested at different stages of disease progression [3,15,21–23]. A paradigm is the Integrated Human Microbiome Project (iHMP) aiming at identifying physiological changes in microbiome–host omics temporal profiles during healthy and stress conditions [4]. The diabetes-associated iHMP exemplar substudy tests the individual effect of stress [i.e., medical illness, physical injury/pain, major or minor operation, major life changes (birth, death, divorce, marriage, and change of home or job)] on the human microbiome, metabolome, and epigenome, as well as its common effect on the host and the microbiome based on 3-year longitudinal observation of ~60 individuals at risk for developing type 2 diabetes. Multiomic analysis, including whole metagenome shotgun and meta-transcriptome sequencing, host whole genome/transcriptome sequencing, cytokine and autoantibody profiles, metabolome profiles, and standard clinical lab tests and surveys of behavioral and psychosocial information (e.g., physical activity, food intake, stress), lay the foundation for analyzing the biological properties of the human microbiome and host during the onset and progression of type 2 diabetes.

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Recent Advancements on the Role of Biologically Active Secondary Metabolites from Aspergillus

Shafiquzzaman Siddiquee, in New and Future Developments in Microbial Biotechnology and Bioengineering, 2018

4.2.19 Aflatrem

The availability of A. flavus genome sequence data, the tremorgenic indolediterpene aflatrem (1) (Fig. 4.22) had used degenerate primers for conserved domains of geranylgeranyl synthases to clone a GGPP synthase gene (atmG) and used chromosome walking to identify a cluster containing two additional secondary metabolite genes (atmC and atmM) (Zhang et al., 2004). Penicillium paxilli generated a structurally similar indole-diterpene, paxilline (2) (Fig. 4.22). A plasmid containing a copy of atmM was introduced into a strain of P. paxilli missing the ortholog paxM, rescuing paxilline production and implicating atmM (and the clustered genes) in aflatrem biosynthesis. As a result of the whole genome sequencing of A. flavus, four additional candidate aflatrem genes were located on another chromosome, based on their homology to paxilline genes (Nicholson et al., 2009). The monooxygenase gene atmP was introduced into a P. paxilli paxP mutant, which resulted predominantly in the synthesis of paxilline.

Figure 4.22. Structures of aflatrem (1) and paxilline (2).

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Proteomic Techniques for Functional Identification of Bacterial Adhesins

Elisabet Carlsohn, Carol L. Nilsson, in Lectins, 2007

5 Proteomic Analysis of H. pylori

Since the completion of the genome sequence of the two H. pylori strains 26695 and J99 in 1997 and 1999, respectively [72, 73], a large number of proteomic analyses have been applied to this pathogen. This has made the H. pylori proteome one of the best characterized microbial proteomes.

The first proteomic investigation of H. pylori aimed towards identification of diagnostic and vaccine candidates [91]. By use of 2D-GE and MALDI-TOF analysis, McAtee and coauthors identified twenty proteins including urease, flagellin, and AlpA, which were found to be reactive with sera from H. pylori infected patients. Two years later, the group of Jungblut et al. presented a comparative proteome analysis of three different H. pylori strains. They used MALDI-TOF MS to identify 126 proteins from strain 26695. Several virulence factors, including urease and HpaA, were detected, but no OMPs were identified. Their main finding was the high proteomic variability between the strains, which most likely depends on shifts in the amino acid composition of certain proteins [92].

In 2002, Sarabath et al. published a new proteomic approach in which intact H. pylori cells were biotinylated followed by affinity purification of membrane proteins using streptavidin. Several virulence factors, including two OMPs (HefA and HP1564) were found among the eighteen identified proteins [93]. The same year, Jungblut and coworkers used immunoproteomics for identification of H. pylori antigens. They reported that a number of antigens, including some surface proteins, were recognized differently by sera from patients with different clinical outcomes, and thereby demonstrated the potential to use certain proteins as candidate indicators for clinical manifestations [94]. This group also published a study in which they performed proteomic analysis for characterization of the H. pylori secretome [95].

Later, Hynes et al. used a protein chip technology for the comparison of OMP profiles between H. pylori strains and found alterations in the protein profile between culture collection strains and clinical isolates with low numbers of passages [96]. In 2004, Lee et al. presented a proteomic analysis of a ferric uptake regulator H. pylori mutant [97] and Baik et al. used subcellular fractionation in combination with 2D-GE analysis to identify sixteen OMPs expressed by H. pylori strain 26695. Four OMPs (Omp11, Omp14, Omp20 and Omp21) were found to be immunoreactive [98]. Recently, a subproteomic study resulted in identification of numerous virulence factors including some OMPs [99]. These authors are now aiming towards the establishment of a dynamic 2D-GE reference database with multiple subproteomes of H. pylori.

Standard proteomic approaches can be useful for mapping protein expression, but cannot be used easily to assign protein functions to their identity. For adhesins, knowledge of a receptor saccharide should be possible to use for the functional identification of the microbial protein, provided that the genome sequence of the microbe is available, using a proteomics approach combined with affinity tagging.

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Identification of Genetic Targets to Improve Lignocellulosic Hydrocarbon Production in Trichoderma reesei Using Public Genomic and Transcriptomic Datasets

Shihui Yang, ... Min Zhang, in Direct Microbial Conversion of Biomass to Advanced Biofuels, 2015

Trichoderma reesei Protein Function Annotation and Pathway Reconstruction

Although the 34-Mb genome sequence of T. reesei has been reported and annotated,24 the annotation has not been systematically conducted since its first release to reflect the recent exponential explosion of the genomic information. To identify the proteins related to hydrocarbon (e.g., terpenoid and fatty acid) biosynthesis, metabolism, and regulation, the protein sequences of T. reesei has been extracted and reannotated functionally. In brief, 9143 protein sequences containing all manually curated and automatically annotated models chosen from the filtered model sets representing the best gene model of each locus (TreeseiV2_FrozenGeneCatalog20081022.proteins.fasta) were downloaded from the JGI website (http://genome.jgi-psf.org/Trire2/Trire2.download.ftp.html) and imported into CLC Genomics Workbench (V7.0) as the reference protein sequences for Blast search. In addition, the protein sequences were also imported into Blast2GO for the functional annotation and CAZYmes Analysis Toolkit (CAT) for analysis and annotation of CAZYmes (Carbohydrate Active enZYmes),59,60 which was then compared to a recent reannotated CAZy genes of T. reesei.22 The KEGG pathways were extracted from annotation result, as was the information of KOG, enzyme code, and the reaction substrate(s) and product(s). The potential homologous gene(s) in T. reesei were identified by reiterated BlastP searches. The information of protein product and conserved domains were examined, and the pathway was reconstructed with the enzyme and pathway information from literature search (Figure 1).

Figure 1. Flowchart of pathway reconstruction and omics data integration for this study.

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Plant Metabolic Engineering

Neelam S. Sangwan, ... Rajender S. Sangwan, in Omics Technologies and Bio-Engineering, 2018

9.3.3.3.3 Genome-Scale Model-Based Analysis

These are the models based on genome sequences with stoichiometric reactions. These global metabolic pathway models are used for understanding metabolism and predicting phenotypes, identifying essential genes, determination of targets for metabolic engineering. Though many genome-scale models of microorganisms are available, these models are still limited in case of plant owing to compartmentation within the cell with distinct tissues and organs (Poolman et al., 2009; Grafahrend-Belau et al., 2009; Hay and Schwender, 2011; Saha et al., 2011). Nevertheless, it is very important to test the model experimentally and validate it for its promising application in plant metabolic engineering. Some examples may be studied regarding the application of microbial genome-scale models in metabolic design and it was achieved by the application of OptForce a computational, multilevel optimization procedure which predicted the complete set of metabolic modifications (knockout, upregulate, downregulate) in E. coli leading to the overproduction of the target chemicals (acetyl CoA and malonyl CoA) approximately four times more than wild type. An integrated flux technology is thus capable of providing more specific targets quantitatively (Xu et al., 2011). Experimentally, assignment of only 13% of plant genes is achieved with computational assignment of a few genes and rest being still unknown (Collakova et al., 2012).

A gene mutation is a permanent alteration in the DNA sequence that makes up a gene, such that the sequence differs from what is found in most people. Mutations range in size; they can affect anywhere from a single DNA building block (base pair) to a large segment of a chromosome that includes multiple genes.

Gene mutations can be classified in two major ways:

Hereditary mutations are inherited from a parent and are present throughout a person's life in virtually every cell in the body. These mutations are also called germline mutations because they are present in the parent's egg or sperm cells, which are also called germ cells. When an egg and a sperm cell unite, the resulting fertilized egg cell receives DNA from both parents. If this DNA has a mutation, the child that grows from the fertilized egg will have the mutation in each of his or her cells.

Acquired (or somatic) mutations occur at some time during a person's life and are present only in certain cells, not in every cell in the body. These changes can be caused by environmental factors such as ultraviolet radiation from the sun, or can occur if an error is made as DNA copies itself during cell division. Acquired mutations in somatic cells (cells other than sperm and egg cells) cannot be passed to the next generation.

Genetic changes that are described as de novo (new) mutations can be either hereditary or somatic. In some cases, the mutation occurs in a person's egg or sperm cell but is not present in any of the person's other cells. In other cases, the mutation occurs in the fertilized egg shortly after the egg and sperm cells unite. (It is often impossible to tell exactly when a de novo mutation happened.) As the fertilized egg divides, each resulting cell in the growing embryo will have the mutation. De novo mutations may explain genetic disorders in which an affected child has a mutation in every cell in the body but the parents do not, and there is no family history of the disorder.

Somatic mutations that happen in a single cell early in embryonic development can lead to a situation called mosaicism. These genetic changes are not present in a parent's egg or sperm cells, or in the fertilized egg, but happen a bit later when the embryo includes several cells. As all the cells divide during growth and development, cells that arise from the cell with the altered gene will have the mutation, while other cells will not. Depending on the mutation and how many cells are affected, mosaicism may or may not cause health problems.

Most disease-causing gene mutations are uncommon in the general population. However, other genetic changes occur more frequently. Genetic alterations that occur in more than 1 percent of the population are called polymorphisms. They are common enough to be considered a normal variation in the DNA. Polymorphisms are responsible for many of the normal differences between people such as eye color, hair color, and blood type. Although many polymorphisms have no negative effects on a person's health, some of these variations may influence the risk of developing certain disorders.

Gene mutations can be classified in two major ways:

In biology, a mutation is an alteration in the nucleotide sequence of the genome of an organism, virus, or extrachromosomal DNA. Viral genomes contain either DNA or RNA.

Genotypes are defined by sets of parameters of equations formalizing morphogenetic (organ appearance rate, dimensioning at initiation time, expansion, and tillering), physiological processes (light interception and conversion efficiencies) and their regulation by plant state variables defined as the ratio between plant supply and demand for water and carbohydrates that result from genotypic characteristics and environmental conditions.

Genotype–Phenotype Correlations

Genotype–phenotype correlations have been difficult to establish due to the complexity of both the phenotype and the gene. Complete gene deletions, consisting of about 1.5 Mb, including the NF1 gene and multiple contiguous genes, are associated with a severe presentation, including dysmorphic facial features, mental retardation, cardiac anomalies, a large neurofibroma burden, and a substantially higher lifetime risk of developing MPNST. Another distinct phenotype, familial spinal neurofibromatosis, characterized by café-au-lait spots, paucity of dermal neurofibromas, and numerous dumbbell-shaped neurofibromas along the spinal roots, may also be associated with a distinct mutational spectrum, with enrichment for amino acid substitutions or stop mutations near the 3′ end of the gene. A three base deletion in exon 17 has been associated with lack of development of neurofibromas, as has amino acid substation at arginine codon 1809. It is possible that other genotype–phenotype correlations will emerge as a larger number of carefully phenotyped patients are studied.

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