Being the cause for significant amount of morbidities and mortalities, parasitic diseases remain the major challenge for the healthcare community due to the limitations associated with the current chemotherapeutics. Drug discovery/invention can be achieved by collaborative efforts of biotechnologists and pharmacists for identifying potential candidates and successfully turn them into medicine for improving the healthcare system. Although molecular medicine for disease intervention is still in its infancy, however, significant research works and successful trials in short span of time have made it broadly accepted among the scientific community. This chapter identifies different molecular medicine approaches for dealing with parasites that have been coming up on the horizon with the new technological advances in bioinformatics and in the field of omics. With the better understanding of the genomics, molecular medicine field has not only raised hopes to deal with parasitic infections but also accelerated the development of personalized medicine. This will provide a targeted approach for identifying the druggable targets and their pathophysiological importance for disease intervention.
Landscape genetics combines population genetics and landscape ecology to understand processes that shape the distribution and organization of human, animal, or plant populations. This field of genetics emerged from the availability of several studies with classical molecular markers, such as isozymes, RAPD, AFLP, and microsatellites. Population genetic studies enabled the detection of population structure with those markers, but a more comprehensive analysis of natural populations was only possible with the development of statistical methods that combined both molecular data and environmental variables. Ultimately, the rapid development of sequencing technologies allowed studies at the genomic level, augmenting the resolution of association with environment factors. This chapter outlines basic concepts in landscape genetics, the main statistical methods used so far, and the perspectives of this field of knowledge into strategies for conservation of natural populations of plant and animal species. Moreover, we briefly describe the application of the field to understand historical human migration processes as well as how some diseases are spread throughout the world.
In recent years, next-generation sequencing (NGS) platforms that facilitate generation of a vast amount of genomic variation data have become widely used for diagnostic purposes in medicine. However, identifying the potential effects of the variations and their association with a particular disease phenotype is the main challenge in this field. Several strategies are used to discover the causative mutations among hundreds of variants of uncertain significance. Incorporating information from healthy population databases, other organisms’ databases, and computational prediction tools are evolution-based strategies that give valuable insight to interpret the variant pathogenicity. In this chapter, we first provide an overview of NGS analysis workflow. Then, we review how evolutionary principles can be integrated into the prioritization schemes of analyzed variants. Finally, we present an example of a real-life case where the use of evolutionary genetics information facilitated the discovery of disease-causing variants in medical genomics.
Molecular medicine describes molecular structures and mechanisms and this chapter focuses on molecular and genetics errors of diseases. Diseases can be classified into deficiency diseases, hereditary diseases, infectious diseases and physiological diseases and to get a glimpse of the mechanisms the chapter covers the most common disease of each class.
A progress in treatment of hematological cancers was achieved. Unfortunately, some youngsters, because of rare genetic alterations that are not easy to detect, as well as heavily pretreated old patients, because of coexisting diseases that lead to changes in patient metabolism, do not respond to therapy. Moreover, sometimes familiar diversities and alterations on genetic or epigenetic level that could be transferred on diversities in metabolism or cell signaling might be a reason why patients do not respond to therapy. Interestingly, for older patients a resistance to therapy could also occur as a reason of drug cross-reactivity. For designing of effective anticancer therapy for patient with chronic lymphocytic leukemia before drug administration, patient’s leukemic cell response to anticancer drug(s) should be checked. Moreover, for patient response to treatment, also drugs prescribed previously by other medical doctors or even patients’ diet could be important for achieving therapeutic success of therapy. Therefore it is important to choose the effective drugs before their administration to patient that will improve treatment efficacy and exclude resistance to therapy. It must be stated that the special attention for personalized therapy tests should be focused on patients previously resistant to therapy, more sensitive to drugs or heavily pretreated.
Retroelements have been considered as “Junk” DNA although the encyclopedia of DNA elements (ENCODE) project has demonstrated that most of the genome is functional. Since the contribution of LINE1 (L1) and human endogenous retrovirus (HERV) has been suspected to cause human cancers, their regulations and putative molecular functions have been investigated in diverse types of cancer. Their diagnostic, prognostic, and therapeutic potentials have been incessantly proposed using cancer associated or specific properties, such as hypomethylation, increased transcripts, and reverse transcriptase, as well as cancer-associated antigens. This chapter presents the current knowledge on retroelements in various aspects during tumorigenesis and their clinical usage in many cancer studies.
Establishing whole exome sequencing (WES) in an accredited clinical diagnostic space is challenging. The validation (as opposed to verification) of an approach that will lead to clinical reports requires adhering to international guidelines and recommendations and developing a robust analytical pipeline that can scale due to the increasing clinical demand for comprehensive gene screening. This chapter will present a step-wise approach to WES validation that any laboratory can follow. The focus will be on highlighting the pivotal technical issues that must be addressed in validating WES and the analytical tools and QC metrics that must be considered before implementing WES in a clinical environment.
Parkinson disease is a complex disease that has multiple genetic and environmental factors. To achieve the early diagnosis and to be able to modify the disease progression, efforts are being made to identify individuals at risk. About 20 year ago, an evidence of major prevalence of Parkinsonism in patients with Gaucher Disease reported by studies worldwide led to the putative involvement of the GBA gene. Nowadays, the link from a rare disease with a common disease is well known and it is confirmed that mutations in the GBA gene are the most important genetic risk factor. Apart from rare mutations, genetic association studied appointed common variants in genes well associated with familial cases as LRRK2 and SNCA may also contribute to the increased risk for sporadic cases. Other common variants in the MAPT gene were also reported. At least, genetic studies have been observed an excessive burden of relevant variants in genes with lysosomal function. Thus, a synergistic action of variants in genes that codifies proteins involved with the lysosome may be a mean of modulating the risk. In this chapter, we review the most robust genetic risk factor and the relevance of lysosomal function for Parkinson disease.
Hybridization is the formation of hybrid nucleic acid molecules with complementary nucleotide sequences in DNA:DNA, DNA:RNA, or RNA:RNA forms. In situ hybridization is a highly sensitive technique that allows detection and localization of specific DNA or RNA molecules in morphologically preserved isolated cells, histological tissue sections, or chromosome preparations. In situ hybridization has broad range of applications and has been used to (a) localize viral infection, (b) identify sites of gene expression, (c) analyze mRNA transcription and tissue distribution, and (d) map gene sequences in chromosomes. There are several advantages of the use of in situ hybridization including the fact that it can be applied to archival materials and frozen tissues and can be combined with immunohistochemistry to detect protein as well as mRNA of interest or phenotype of cells expressing the target genome, detecting more than one nucleic acid sequences using different labeling methods.
Monoclonal antibodies (mabs) are powerful tools for the quantification, detection, and targeting of specific molecules. Allergen-specific mabs are important for the quantification of major allergens in allergen preparations used for allergen-specific immunotherapy and allergy diagnosis. Indeed, progress in the understanding of the mechanisms of the immunological responses underlying allergic disease would not have been possible without the use of mabs. Quantification assays are also important in the assessment of environmental allergen exposure and monitoring of avoidance procedures.Mabs against human IgE provide the basis for various test systems for the detection of specific and nonspecific IgE. Mabs raised against IgE or defined cytokines or cytokine receptors have potential as neutralizing reagents in vivo for the treatment of allergic diseases.Allergen-specific mabs are also valuable tools for the localization of allergens within their source material and the characterization of allergens derived from natural sources and by recombinant technologies. Furthermore they are often used for the isolation of allergens from complex extracts by affinity chromatography.The procedure described in this chapter has been used successfully to produce mabs against numerous allergens from house dust mites, insect venoms, cat, hens egg white, tree-, grass-, and herb pollens, and fungi, with the ultimate aim of obtaining matched antibody pairs to establish two-site binding assays for the quantification of major allergens. The method has also been used successfully to generate mabs against human IgE.
In recent years, allergy research has focused on the causes and mechanisms of allergy. In parallel, there is also an impetus to try to understand mechanisms of natural tolerance and immunotherapy wher
Bacterial signal transduction systems can be used as drug targets. The signal transduction targets fall into two groups--sensor kinases and response regulators. Previously reported studies describe hits that were thought to inactivate sensor kinases but on closer examination were found to act elsewhere instead; a possible reason for this is that full-length sensor kinases are integral membrane proteins whose activity might reflect interaction with the cell membrane or with membrane components. We describe a model system that instead is based on the interaction between a test compound and a response regulator in a homogeneous phase reaction. In this system, response regulator-DNA complex formation and its inhibition by a test compound are measured by fluorescence polarization. The model system should be readily adaptable to drug discovery based on other bacterial two-component s transduction systems.
Identification of pollen is like entering a world of great variation in size, shape, and structure. To obtain a correct result, a good microscope, basic information on pollen grain morphology and a reference sample of the plant to be identified are needed. Purity determination of pollen can be performed by particle count or by volumetric analysis. In our experience, particle counting is the better and most reproducible method and is not greatly influenced by interindividual variation. In this chapter, we have described the detailed procedure to obtain satisfactory results for identification and determination of pollen purity.
DNA sequencing is increasingly used in a range of medical activities involving DNA diagnostics and research. This is the result of improving technology and cheaper costs. Paradoxically, a greater demand for DNA sequencing has placed additional work on the laboratory because sequencing profiles must be checked visually despite the availability of informatics-based tools in interpreting DNA sequence traces. In this environment it is essential to have more sophisticated software that will allow the sites of known and unknown DNA variants to be quickly identified, as well as providing an objective assessment of quality for the DNA sequence generated. This chapter describes the Applied Biosystems SeqScape software program (version 2.5) and how it has assisted in the interpretation of DNA sequencing in a DNA diagnostic laboratory.
Whole-genome association studies of complex diseases hold great promise to identify systematically genetic loci that influence one's risk of developing these diseases. However, the polygenic nature of the complex diseases and genetic interactions among the genes pose significant challenge in both experimental design and data analysis. High-density genotype data make it possible to identify most of the genetic loci that may be involved in the etiology. On the other hand, utilizing large number of statistic tests could lead to false positives if the tests are not adequately adjusted. In this paper, we discuss a two-stage method that sequentially applies a generalized linear model (GLM) and principal components analysis (PCA) to identify genetic loci that jointly determine the likelihood of developing disease. The method was applied to a pilot case-control study of esophageal squamous cell carcinoma (ESCC) that included 50 ESCC patients and 50 neighborhood-matched controls. Genotype data were determined by using the Affymetrix 10K SNP chip. We will discuss some of the special considerations that are important to the proper interpretation of whole genome-wide association studies, which include multiple comparisons, epistatic interaction among multiple genetic loci, and generalization of predictive models.
Mast cells generate mediators of inflammation which are stored in granules and secreted on activation either by allergen crosslinking of membrane-bound IgE or through other stimuli. Most methods for mast cell identification rely on the histochemical detection of constituents of the secretory granules. Although staining for mast cells with histochemical stains can be rapid and relatively inexpensive, it is not always possible to distinguish reliably between mast cells and basophils in tissues. A further problem with the staining of mast cells with commonly used basic dyes is that the reagents employed to fix the tissues can influence the results, leading to confusion regarding the numbers of mast cells present in various tissues. Recognition that there is considerable heterogeneity between mast cell populations in the degree to which staining properties are lost with formalin fixation has led to mast cell subsets being defined on this basis.The development and application of procedures for identifying mast cell proteases has led to important advances in our understanding of the role of mast cells and in the nature of heterogeneity in man. The techniques described here should allow the reliable detection of mast cells and mast cell subsets in a range of tissues and cell preparations. There will be a continuing need for validation, for consideration of potential sources of error, and for the development of new and more reliable techniques for mast cell identification.
Complex diseases and traits are influenced by a combination of genetic and environmental risk factors, some of which may be known, and many of which are unknown. It is possible to estimate the relative importance of the influence of genes and environment on a trait by studying correlations in the trait in related individuals. Known risk factors can be measured and included in the statistical models to understand disease etiology better. The joint effect of specific genes and environmental exposures can be estimated by measuring these in individuals, not necessarily related, with and without the disease of interest or with a range of trait values. These methods are illustrated by considering two example analyses in detail. The first is an analysis of a study of adolescent twins, quantifying the effect of genes and environment, including measured sun exposure, on the density of nevi. The second is an analysis of a case-control study, examining the joint effect of the GSTT1 gene and vegetable intake on risk of colorectal cancer.
