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Metabolomics: The Future is Here

2021-03-08T11:38:38.062Z

Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates and products of cell metabolism. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles.

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Key applications

Toxicity assessment/toxicology by metabolic profiling (especially of urine or blood plasma samples) detects the physiological changes caused by toxic insult of a chemical (or mixture of chemicals). In many cases, the observed changes can be related to specific syndromes, e.g. a specific lesion in liver or kidney. This is of particular relevance to pharmaceutical companies wanting to test the toxicity of potential drug candidates: if a compound can be eliminated before it reaches clinical trials on the grounds of adverse toxicity, it saves the enormous expense of the trials.

For functional genomics, metabolomics can be an excellent tool for determining the phenotype caused by a genetic manipulation, such as gene deletion or insertion. Sometimes this can be a sufficient goal in itself—for instance, to detect any phenotypic changes in a genetically modified plant intended for human or animal consumption. More exciting is the prospect of predicting the function of unknown genes by comparison with the metabolic perturbations caused by deletion/insertion of known genes. Such advances are most likely to come from model organisms such as Saccharomyces cerevisiae and Arabidopsis thaliana. The Cravatt laboratory at The Scripps Research Institute has recently applied this technology to mammalian systems, identifying the N-acyltaurines as previously uncharacterized endogenous substrates for the enzyme fatty acid amide hydrolase (FAAH) and the monoalkylglycerol ethers (MAGEs) as endogenous substrates for the uncharacterized hydrolase KIAA1363.

Metabologenomics is a novel approach to integrate metabolomics and genomics data by correlating microbial-exported metabolites with predicted biosynthetic genes. This bioinformatics-based pairing method enables natural product discovery at a larger-scale by refining non-targeted metabolomic analyses to identify small molecules with related biosynthesis and to focus on those that may not have previously well known structures.

Fluxomics is a further development of metabolomics. The disadvantage of metabolomics is that it only provides the user with steady-state level information, while fluxomics determines the reaction rates of metabolic reactions and can trace metabolites in a biological system over time.

Nutrigenomics is a generalised term which links genomics, transcriptomics, proteomics and metabolomics to human nutrition. In general a metabolome in a given body fluid is influenced by endogenous factors such as age, sex, body composition and genetics as well as underlying pathologies. The large bowel microflora are also a very significant potential confounder of metabolic profiles and could be classified as either an endogenous or exogenous factor. The main exogenous factors are diet and drugs. Diet can then be broken down to nutrients and non-nutrients. Metabolomics is one means to determine a biological endpoint, or metabolic fingerprint, which reflects the balance of all these forces on an individual's metabolism.

Epigenomics - Future Medicine

2021-02-26T13:02:34.973Z

Epigenomics is the study of the complete set of epigenetic modifications on the genetic material of a cell, known as the epigenome. The field is analogous to genomics and proteomics, which are the study of the genome and proteome of a cell.

Epigenetic modifications are reversible modifications on a cell's DNA or histones that affect gene expression without altering the DNA sequence. Epigenomic maintenance is a continuous process and plays an important role in stability of eukaryotic genomes by taking part in crucial biological mechanisms like DNA repair.

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Plant flavones are said to be inhibiting epigenomic marks that cause cancers. Two of the most characterized epigenetic modifications are DNA methylation and histone modification. Epigenetic modifications play an important role in gene expression and regulation, and are involved in numerous cellular processes such as in differentiation/development and tumorigenesis. The study of epigenetics on a global level has been made possible only recently through the adaptation of genomic high-throughput assays.

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DNA methylation

The first epigenetic modification to be characterized in depth was DNA methylation. As its name implies, DNA methylation is the process by which a methyl group is added to DNA. The enzymes responsible for catalyzing this reaction are the DNA methyltransferases (DNMTs). While DNA methylation is stable and heritable, it can be reversed by an antagonistic group of enzymes known as DNA de-methylases. In eukaryotes, methylation is most commonly found on the carbon 5 position of cytosine residues (5mC) adjacent to guanine, termed CpG dinucleotides.

DNA methylation patterns vary greatly between species and even within the same organism. The usage of methylation among animals is quite different; with vertebrates exhibiting the highest levels of 5mC and invertebrates more moderate levels of 5mC. Some organisms like Caenorhabditis elegans have not been demonstrated to have 5mC nor a conventional DNA methyltransferase; this would suggest that other mechanisms other than DNA methylation are also involved.

Within an organism, DNA methylation levels can also vary throughout development and by region. For example, in mouse primordial germ cells, a genome wide de-methylation even occurs; by implantation stage, methylation levels return to their previous somatic values. When DNA methylation occurs at promoter regions, the sites of transcription initiation, it has the effect of repressing gene expression. This is in contrast to unmethylated promoter regions which are associated with actively expressed genes.

Genome Editing & Applications: Are We Ready for the Next Frontier?

2021-02-15T13:18:39.389Z

Genome editing, or genome engineering, or gene editing, is a type of genetic engineering in which DNA is inserted, deleted, modified or replaced in the genome of a living organism. Unlike early genetic engineering techniques that randomly inserts genetic material into a host genome, genome editing targets the insertions to site specific locations.

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Genetic engineering as method of introducing new genetic elements into organisms has been around since the 1970s. One drawback of this technology has been the random nature with which the DNA is inserted into the hosts genome, which can impair or alter other genes within the organism.

Although, several methods have been discovered which target the inserted genes to specific sites within an organism genome. It has also enabled the editing of specific sequences within a genome as well as reduced off target effects. This could be used for research purposes, by targeting mutations to specific genes, and in gene therapy. By inserting a functional gene into an organism and targeting it to replace the defective one it could be possible to cure certain genetic diseases.

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Applications

As of 2012 efficient genome editing had been developed for a wide range of experimental systems ranging from plants to animals, often beyond clinical interest, and was becoming a standard experimental strategy in research labs.

The recent generation of rat, zebrafish, maize and tobacco ZFN-mediated mutants and the improvements in TALEN-based approaches testify to the significance of the methods, and the list is expanding rapidly.

Genome editing with engineered nucleases will likely contribute to many fields of life sciences from studying gene functions in plants and animals to gene therapy in humans.

For instance, the field of synthetic biology which aims to engineer cells and organisms to perform novel functions, is likely to benefit from the ability of engineered nuclease to add or remove genomic elements and therefore create complex systems. In addition, gene functions can be studied using stem cells with engineered nucleases.

Listed below are some specific tasks this method can carry out:

Targeted gene mutation
Gene therapy
Creating chromosome rearrangement
Study gene function with stem cells
Transgenic animals
Endogenous gene labeling
Targeted transgene addition

Translational Bioinformatics: Data Science and Knowledge Representation in Healthcare

2021-02-05T13:05:42.162Z

Healthcare Informatics on High

Translational Bioinformatics (TBI) is a relatively new field that surfaced in the year of 2000 when human genome sequence was released. The commonly used definition of TBI is lengthy and could be found on the AMIA website.

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In simpler terms, TBI could be defined as a collection of colossal amounts of health related data (biomedical and genomic) and translation of the data into individually tailored clinical entities. Today, TBI field is categorized into four major themes that are briefly described below:

Clinical big data is a collection of electronic health records that are used for innovations. The evidence-based approach that is currently practiced in medicine is suggested to be merged with the practice-based medicine to achieve better outcomes for patients.

As CEO of California-based cognitive computing firm Apixio, Darren Schutle, explains that the care can be better fitted to the patient if the data could be collected from various medical records, merged, and analyzed. Further, the combination of similar profiles can serve as a basis for personalized medicine pointing to what works and what does not for certain condition.

Genomic data are used to identify the genes involvement in unknown or rare conditions/syndromes. Currently, the most vigorous area of using genomics is oncology. The identification of genomic sequencing of cancer may define reasons of drug(s) sensitivity and resistance during oncological treatment processes.

Repurposing of the drug is an appealing idea that allows the pharmaceutical companies to sell an already approved drug to treat a different condition/disease that the drug was not initially approved for by the FDA. The observation of “molecular signatures in disease and compare those to signatures observed in cells” points to the possibility of a drug ability to cure and/or relieve symptoms of a disease.

In the US, several companies offer direct-to-consumer (DTC) genetic testing. The company that performs the majority of testing is called 23andMe. Utilizing genetic testing in health care raises many ethical, legal and social concerns; one of the main questions is whether the health care providers are ready to include patient-supplied genomic information while providing care that is unbiased (despite the intimate genomic knowledge) and a high quality. The documented examples of incorporating such information into a health care delivery showed both positive and negative impacts on the overall health care related outcomes.

Bioinformatics: Medicine, Science and The Future

2021-02-03T13:23:34.851Z

Genetics of Disease

With the advent of next-generation sequencing we are obtaining enough sequence data to map the genes of complex diseases infertility, breast cancer or Alzheimer's disease. Genome-wide association studies are a useful approach to pinpoint the mutations responsible for such complex diseases.

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Through these studies, thousands of DNA variants have been identified that are associated with similar diseases and traits. Furthermore, the possibility for genes to be used at prognosis, diagnosis or treatment is one of the most essential applications. Many studies are discussing both the promising ways to choose the genes to be used and the problems and pitfalls of using genes to predict disease presence or prognosis.

source: freepik

Analysis of Mutations in Cancer

In cancer, the genomes of affected cells are rearranged in complex or even unpredictable ways. Massive sequencing efforts are used to identify previously unknown point mutations in a variety of genes in cancer. Bioinformaticians continue to produce specialized automated systems to manage the sheer volume of sequence data produced, and they create new algorithms and software to compare the sequencing results to the growing collection of human genome sequences and germline polymorphisms.

New physical detection technologies are employed, such as oligonucleotide microarrays to identify chromosomal gains and losses (called comparative genomic hybridization), and single-nucleotide polymorphism arrays to detect known point mutations. These detection methods simultaneously measure several hundred thousand sites throughout the genome, and when used in high-throughput to measure thousands of samples, generate terabytes of data per experiment. Again the massive amounts and new types of data generate new opportunities for bioinformaticians. The data is often found to contain considerable variability, or noise, and thus Hidden Markov model and change-point analysis methods are being developed to infer real copy number changes.

Two important principles can be used in the analysis of cancer genomes bioinformatically pertaining to the identification of mutations in the exome. First, cancer is a disease of accumulated somatic mutations in genes. Second cancer contains driver mutations which need to be distinguished from passengers.

With the breakthroughs that this next-generation sequencing technology is providing to the field of Bioinformatics, cancer genomics could drastically change. These new methods and software allow bioinformaticians to sequence many cancer genomes quickly and affordably. This could create a more flexible process for classifying types of cancer by analysis of cancer driven mutations in the genome. Furthermore, tracking of patients while the disease progresses may be possible in the future with the sequence of cancer samples.

Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.

CRISPR - What is Cas9 ?

2021-02-02T12:32:08.288Z

CRISPR is a class of DNA sequences detected in the genomes of prokaryotic organisms, such as bacteria and archaea. These sequences are obtained from DNA fragments of bacteriophages that had earlier infected the prokaryote. They are used to identify and suppress DNA from similar bacteriophages through subsequent.

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Cas9

Researchers studied a simpler CRISPR system from Streptococcus pyogenes that relies on the protein Cas9. The Cas9 endonuclease is a four-component system that includes two small crRNA molecules and trans-activating CRISPR RNA (tracrRNA). Jennifer Doudna and Emmanuelle Charpentier re-engineered the Cas9 endonuclease into a more manageable two-component system by fusing the two RNA molecules into a "single-guide RNA" that, when combined with Cas9, could find and cut the DNA target specified by the guide RNA.

This contribution was so significant that it was recognized by the Nobel Prize in Chemistry in 2020. By manipulating the nucleotide sequence of the guide RNA, the artificial Cas9 system could be programmed to target any DNA sequence for cleavage. Another group of collaborators comprising Virginijus Šikšnys together with Gasiūnas, Barrangou and Horvath showed that Cas9 from the S. thermophilus CRISPR system can also be reprogrammed to target a site of their choosing by changing the sequence of its crRNA. These advances fueled efforts to edit genomes with the modified CRISPR-Cas9 system.

Groups led by Feng Zhang and George Church simultaneously published descriptions of genome editing in human cell cultures using CRISPR-Cas9 for the first time. It has since been used in a wide range of organisms, including baker's yeast (Saccharomyces cerevisiae), the opportunistic pathogen Candida albicans, zebrafish (Danio rerio), fruit flies (Drosophila melanogaster), ants (Harpegnathos saltator[50] and Ooceraea biroi), mosquitoes (Aedes aegypti), nematodes (Caenorhabditis elegans), plants, mice, monkeys and human embryos.

CRISPR has been modified to make programmable transcription factors that allow scientists to target and activate or silence specific genes.

Bioinformatics Tools for Proteomics Research

2021-01-27T13:12:53.116Z

Much proteomics data is collected with the help of high throughput technologies such as mass spectrometry and microarray. It would often take weeks or months to analyze the data and perform comparisons by hand.

For this reason, biologists and chemists are collaborating with computer scientists and mathematicians to create programs and pipeline to computationally analyze the protein data. Using bioinformatics techniques, researchers are capable of faster analysis and data storage. A good place to find lists of current programs and databases is on the ExPASy bioinformatics resource portal. The applications of bioinformatics-based proteomics includes medicine, disease diagnosis, biomarker identification, and many more.

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source: freepik

Protein identification

Mass spectrometry and microarray produce peptide fragmentation information but do not give identification of specific proteins present in the original sample. Due to the lack of specific protein identification, past researchers were forced to decipher the peptide fragments themselves.

However, there are currently programs available for protein identification. These programs take the peptide sequences output from mass spectrometry and microarray and return information about matching or similar proteins. This is done through algorithms implemented by the program which perform alignments with proteins from known databases such as UniProt and PROSITE to predict what proteins are in the sample with a degree of certainty.

Protein Structure

The biomolecular structure forms the 3D configuration of the protein. Understanding the protein's structure aids in identification of the protein's interactions and function. It used to be that the 3D structure of proteins could only be determined using X-ray crystallography and NMR spectroscopy.

As of 2017, Cryo-electron microscopy is a leading technique, solving difficulties with crystallization (in X-ray crystallography) and conformational ambiguity (in NMR); resolution was 2.2Å as of 2015. Now, through bioinformatics, there are computer programs that can in some cases predict and model the structure of proteins.

These programs use the chemical properties of amino acids and structural properties of known proteins to predict the 3D model of sample proteins. This also allows scientists to model protein interactions on a larger scale. In addition, biomedical engineers are developing methods to factor in the flexibility of protein structures to make comparisons and predictions.

Post-Translational Modifications

Most programs available for protein analysis are not written for proteins that have undergone post-translational modifications. Some programs will accept post-translational modifications to aid in protein identification but then ignore the modification during further protein analysis. It is important to account for these modifications since they can affect the protein's structure.

In turn, computational analysis of post-translational modifications has gained the attention of the scientific community. The current post-translational modification programs are only predictive. Chemists, biologists and computer scientists are working together to create and introduce new pipelines that allow for analysis of post-translational modifications that have been experimentally identified for their effect on the protein's structure and function.

Computational methods in studying protein biomarkers

One example of the use of bioinformatics and the use of computational methods is the study of protein biomarkers. Computational predictive models have shown that extensive and diverse feto-maternal protein trafficking occurs during pregnancy and can be readily detected non-invasively in maternal whole blood.

This computational approach circumvented a major limitation, the abundance of maternal proteins interfering with the detection of fetal proteins, to fetal proteomic analysis of maternal blood. Computational models can use fetal gene transcripts previously identified in maternal whole blood to create a comprehensive proteomic network of the term neonate. Such work shows that the fetal proteins detected in pregnant woman’s blood originate from a diverse group of tissues and organs from the developing fetus. The proteomic networks contain many biomarkers that are proxies for development and illustrate the potential clinical application of this technology as a way to monitor normal and abnormal fetal development.

An information theoretic framework has also been introduced for biomarker discovery, integrating biofluid and tissue information. This new approach takes advantage of functional synergy between certain biofluids and tissues with the potential for clinically significant findings not possible if tissues and biofluids were considered individually. By conceptualizing tissue-biofluid as information channels, significant biofluid proxies can be identified and then used for guided development of clinical diagnostics. Candidate biomarkers are then predicted based on information transfer criteria across the tissue-biofluid channels. Significant biofluid-tissue relationships can be used to prioritize clinical validation of biomarkers.

Frontiers in Genetics | Epigenomics

2021-01-25T12:05:28.117Z

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The study of epigenetics on a global level has been made possible only recently through the adaptation of genomic high-throughput assays.

source: freepik

Relation to Other Genomic Fields

Epigenomics shares many commonalities with other genomics fields, in both methodology and in its abstract purpose. Epigenomics seeks to identify and characterize epigenetic modifications on a global level, similar to the study of the complete set of DNA in genomics or the complete set of proteins in a cell in proteomics.

The logic behind performing epigenetic analysis on a global level is that inferences can be made about epigenetic modifications, which might not otherwise be possible through analysis of specific loci. As in the other genomics fields, epigenomics relies heavily on bioinformatics, which combines the disciplines of biology, mathematics and computer science.

However while epigenetic modifications had been known and studied for decades, it is through these advancements in bioinformatics technology that have allowed analyses on a global scale. Many current techniques still draw on older methods, often adapting them to genomic assays as is described in the next section.

Entering the Modern Era of Gene Therapy

2021-01-22T12:54:28.097Z

The ideal gene therapy practice is that which replaces the defective gene with a normal allele at its natural location. This is advantageous over a virally delivered gene as there is no need to include the full coding sequences and regulatory sequences when only a small proportions of the gene needs to be altered as is often the case. The expression of the partially replaced genes is also more consistent with normal cell biology than full genes that are carried by viral vectors.

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The first clinical use of TALEN-based genome editing was in the treatment of CD19+ acute lymphoblastic leukemia in an 11-month old child in 2015. Modified donor T cells were engineered to attack the leukemia cells, to be resistant to Alemtuzumab, and to evade detection by the host immune system after introduction.

Extensive research has been done in cells and animals using CRISPR-Cas9 to attempt to correct genetic mutations which cause genetic diseases such as Down syndrome, spina bifida, anencephaly, and Turner and Klinefelter syndromes.

source: freepik

In February 2019, medical scientists working with Sangamo Therapeutics, headquartered in Richmond, California, announced the first ever "in body" human gene editing therapy to permanently alter DNA - in a patient with Hunter Syndrome. Clinical trials by Sangamo involving gene editing using Zinc Finger Nuclease (ZFN) are ongoing.

Vectors

Viruses

In order to replicate, viruses introduce their genetic material into the host cell, tricking the host's cellular machinery into using it as blueprints for viral proteins. Retroviruses go a stage further by having their genetic material copied into the genome of the host cell. Scientists exploit this by substituting a virus's genetic material with therapeutic DNA.

A number of viruses have been used for human gene therapy, including retroviruses, adenoviruses, herpes simplex, vaccinia, and adeno-associated virus. Like the genetic material (DNA or RNA) in viruses, therapeutic DNA can be designed to simply serve as a temporary blueprint that is degraded naturally or (at least theoretically) to enter the host's genome, becoming a permanent part of the host's DNA in infected cells.

Non-viral

Non-viral methods present certain advantages over viral methods, such as large scale production and low host immunogenicity. However, non-viral methods initially produced lower levels of transfection and gene expression, and thus lower therapeutic efficacy. Newer technologies offer promise of solving these problems, with the advent of increased cell-specific targeting and subcellular trafficking control.

Methods for non-viral gene therapy include the injection of naked DNA, electroporation, the gene gun, sonoporation, magnetofection, the use of oligonucleotides, lipoplexes, dendrimers, and inorganic nanoparticles.

Microbiology: The Microbial Sciences

2021-01-21T12:59:52.302Z

Microbiology is the study of microorganisms, those being unicellular (single cell), multicellular (cell colony), or acellular (lacking cells). Microbiology encompasses numerous sub-disciplines including virology, bacteriology, protistology, mycology, immunology and parasitology.

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Eukaryotic microorganisms possess membrane-bound organelles and include fungi and protists, whereas prokaryotic organisms-all of which are microorganisms are conventionally classified as lacking membrane-bound organelles and include Bacteria and Archaea.

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Microbiologists traditionally relied on culture, staining, and microscopy. However, less than 1% of the microorganisms present in common environments can be cultured in isolation using current means. Microbiologists often rely on molecular biology tools such as DNA sequence based identification, for example the 16S rRNA gene sequence used for bacteria identification.

Viruses have been variably classified as organisms, as they have been considered either as very simple microorganisms or very complex molecules. Prions, never considered as microorganisms, have been investigated by virologists, however, as the clinical effects traced to them were originally presumed due to chronic viral infections, and virologists took search—discovering "infectious proteins".

The existence of microorganisms was predicted many centuries before they were first observed, for example by the Jains in India and by Marcus Terentius Varro in ancient Rome. The first recorded microscope observation was of the fruiting bodies of moulds, by Robert Hooke in 1666, but the Jesuit priest Athanasius Kircher was likely the first to see microbes, which he mentioned observing in milk and putrid material in 1658.

Antonie van Leeuwenhoek is considered a father of microbiology as he observed and experimented with microscopic organisms in the 1670s, using simple microscopes of his own design. Scientific microbiology developed in the 19th century through the work of Louis Pasteur and in medical microbiology Robert Koch.

Branches

The branches of microbiology can be classified into applied sciences, or divided according to taxonomy, as is the case with bacteriology, mycology, protozoology, virology and phycology.

There is considerable overlap between the specific branches of microbiology with each other and with other disciplines, and certain aspects of these branches can extend beyond the traditional scope of microbiology A pure research branch of microbiology is termed cellular microbiology.