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CRISPR: The Biggest Revolution in Gene Editing - The Next Generation

2020-08-13T12:36:36.944Z

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What is CRISPR

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) technology is a simple but powerful tool for genome editing. This tool enables life science researchers to easily edit DNA sequences and modify gene function. It has many potential applications include correcting genetic defects, treating and preventing the spread of diseases and improving crops.

By delivering the CRISPR enzyme Cas9 nuclease coupled with synthetic guide RNA (gRNA) into a cell, the cell's genome can be cut at a desired location, that allows existing genes to be removed or add new ones.

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Applications

CRISPR Gene Editing

CRISPR technology has been applied in the food and farming industries to engineer probiotic cultures and to immunize industrial cultures (for yogurt, for instance) versus infections. It is also being used in crops to enhance yield, drought tolerance and nutritional homes.

CRISPR as Diagnostic Tool

CRISPR associated nucleases have shown to be useful as a tool for molecular testing due to their ability to specifically target nucleic acid sequences in a high background of non-target sequences. In 2016, the Cas9 nuclease was used to deplete unwanted nucleotide sequences in next-generation sequencing libraries while requiring only 250 pg of initial RNA input. Beginning in 2017, CRISPR associated nucleases were also used for direct diagnostic testing, down to single molecule sensitivity.

The Future of CRISPR

Of course, any new technology takes some time to understand and perfect. It will be important to verify that a particular guide RNA is specific for its target gene, so that the CRISPR system does not mistakenly attack other genes. It will also be important to find a way to deliver CRISPR therapies into the body before they can become widely used in medicine. Although a lot remains to be discovered, there is no doubt that CRISPR has become a valuable tool in research. In fact, there is enough excitement in the field to warrant the launch of several Biotech start-ups that hope to use CRISPR-inspired technology to treat human diseases.

Metagenomics - Sequencing Booms

2020-08-06T13:45:28.351Z

The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics

Metagenomics can be defined as the study of the metagenome, which is collective genome of microorganisms. The microorganisms are collected from an environmental samples and the studies are conducted to provide information on the microbial diversity and ecology of a specific environment.

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Bioinformatics

The data generated by metagenomics experiments are both enormous and inherently noisy, containing fragmented data representing as many as 10,000 species. The sequencing of the cow rumen metagenome generated 279 gigabases, or 279 billion base pairs of nucleotide sequence data, while the human gut microbiome gene catalog identified 3.3 million genes assembled from 567.7 gigabases of sequence data.

Collecting, curating, and extracting useful biological information from datasets of this size represent significant computational challenges for researchers.

Antibiotics

Antibiotics and enzymes are among the discoveries from metagenomics. The discovery of streptomycin, turbomycin sprang from basic studies of the soil microbiome. Functional metagenomics serves to find novel antibiotics or novel antibiotic resistance genes, and descriptive metagenomics serves to analyze changes in the composition of the microbiome and to track the presence and abundance of known antibiotic resistance genes in different environments.

Sequence pre-filtering

The first step of metagenomic data analysis requires the execution of certain pre-filtering steps, including the removal of redundant, low-quality sequences and sequences of probable eukaryotic origin (especially in metagenomes of human origin). The methods available for the removal of contaminating eukaryotic genomic DNA sequences include Eu-Detect and DeConseq.

Biotechnology

Microbial communities produce a vast array of biologically active chemicals that are used in competition and communication. Many of the drugs in use today were originally uncovered in microbes; recent progress in mining the rich genetic resource of non-culturable microbes has led to the discovery of new genes, enzymes, and natural products. The application of metagenomics has allowed the development of commodity and fine chemicals, agrochemicals and pharmaceuticals where the benefit of enzyme-catalyzed chiral synthesis is increasingly recognized.

Bioinformatics: The Biological Data Science

2020-07-03T05:14:42.267Z

Bioinformatics is an amalgamation of biology and information technology, which uses software to link biological data with techniques for information storage, distribution and analysis to support multiple areas of scientific research, including biomedicine.

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Bioinformatics includes biological studies that use computer programming as part of their methodology, as well as a specific analysis "pipelines" that are repeatedly used, particularly in the field of genomics. Common uses of bioinformatics include the identification of candidates genes and single nucleotide polymorphisms (SNPs).

Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences, called proteomics.

DNA Sequencing

Before sequences can be analyzed they have to be obtained from the data storage bank example the Genbank. DNA sequencing is still a non-trivial problem as the raw data may be noisy or afflicted by weak signals. Algorithms have been developed for base calling for the various experimental approaches to DNA sequencing.

Genome annotation

In the context of genomics, annotation is the process of marking the genes and other biological features in a DNA sequence. This process needs to be automated because most genomes are too large to annotate by hand, not to mention the desire to annotate as many genomes as possible, as the rate of sequencing has ceased to pose a bottleneck. Annotation is made possible by the fact that genes have recognisable start and stop regions, although the exact sequence found in these regions can vary between genes.

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.

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.

Gene and Protein Expression

The expression of many genes can be determined by measuring mRNA levels with multiple techniques including microarrays, expressed cDNA sequence tag (EST) sequencing, serial analysis of gene expression (SAGE) tag sequencing, massively parallel signature sequencing (MPSS), RNA-Seq, also known as "Whole Transcriptome Shotgun Sequencing" (WTSS), or various applications of multiplexed in-situ hybridization.

All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing statistical tools to separate signal from noise in high-throughput gene expression studies. Such studies are often used to determine the genes implicated in a disorder: one might compare microarray data from cancerous epithelial cells to data from non-cancerous cells to determine the transcripts that are up-regulated and down-regulated in a particular population of cancer cells.

Epigenetics – It’s not Just Genes that Make us

2020-07-01T14:55:53.617Z

The study of heritable changes in gene expression that do not involve changes to the underlying DNA sequence is known as epigenetics.

A single or multiple change in phenotype without a changing the genotype which results affects the cells that can read the genes.

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One example of an epigenetic change in eukaryotic biology is the process of cellular differentiation. During morphogenesis, totipotent stem cells become the various pluripotent cell lines of the embryo, which in turn become fully differentiated cells.

In other words, as a single fertilized egg cell – the zygote – continues to divide, the resulting daughter cells change into all the different cell types in an organism, including neurons, muscle cells, epithelium, endothelium of blood vessels, etc., by activating some genes while inhibiting the expression of others.

MicroRNAs (miRNAs) are members of non-coding RNAs that range in size from 17 to 25 nucleotides. miRNAs regulate a large variety of biological functions in plants and animals.

So far, in 2013, about 2000 miRNAs have been discovered in humans and these can be found online in a miRNA database. Each miRNA expressed in a cell may target about 100 to 200 messenger RNAs(mRNAs) that it downregulates. Most of the downregulation of mRNAs occurs by causing the decay of the targeted mRNA, while some downregulation occurs at the level of translation into protein.

Developmental epigenetics can be divided into predetermined and probabilistic epigenesis. Predetermined epigenesis is a unidirectional movement from structural development in DNA to the functional maturation of the protein. "Predetermined" here means that development is scripted and predictable. Probabilistic epigenesis on the other hand is a bidirectional structure-function development with experiences and external molding development.[91]

Medicine

Epigenetics has many and varied potential medical applications. In 2008, the National Institutes of Health announced that $190 million had been earmarked for epigenetics research over the next five years.

In announcing the funding, government officials noted that epigenetics has the potential to explain mechanisms of aging, human development, and the origins of cancer, heart disease, mental illness, as well as several other conditions. Some investigators, like Randy Jirtle, Ph.D., of Duke University Medical Center, think epigenetics may ultimately turn out to have a greater role in disease than genetics.

Cancer

A variety of epigenetic mechanisms can be perturbed in different types of cancer. Epigenetic alterations of DNA repair genes or cell cycle control genes are very frequent in sporadic (non-germ line) cancers, being significantly more common than germ line (familial) mutations in these sporadic cancers.

Epigenetic alterations are important in cellular transformation to cancer, and their manipulation holds great promise for cancer prevention, detection, and therapy. Several medications which have epigenetic impact are used in several of these diseases. These aspects of epigenetics are addressed in cancer epigenetics.

The Global Epigenetics Market is expected to reach US$ 2,611.57 Mn in 2025 from US$ 991.45 in 2017 and estimated to grow with a CAGR of 13.6% from 2018-2025.

The prominent players operating in the market of epigenetics across the globe includes Merck KGaA, Thermo Fisher Scientific, Inc., Abcam plc, Agilent Technologies, Active Motif, QIAGEN, Bio-Rad Laboratories, Inc., PerkinElmer Inc., New England Biolabs (NEB), and Illumina, Inc. among others.