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Protein and gene modification has uses in health, research, industry, and agriculture and may be employed on various plants, animals, and microbes. Bacteria, the first organism to be genetically modified, can have new genes that code for medications or enzymes that digest food and other substrates added into their DNA.
Plants have been genetically engineered to provide insect protection, herbicide resistance, viral resistance, improved nutrition, tolerance to environmental stresses, and the creation of edible vaccines.
The method of producing changes in gene expression and the production of new genes, known as genetic manipulation, has proven to be a crucial tool in modern genetic research. Current approaches allow for the precise removal of target gene expression, tissue-specific stimulation of reporter gene expression, cellular gene overexpression, and more.
The direct alteration of an organism’s genes through biotechnology is known as genetic manipulation. It is a collection of methods used to alter the genetic makeup of cells, including gene transfer inside and beyond species borders, to create enhanced or unique creatures.
Gene manipulation is known as genetic engineering. New DNA is created by extracting and duplicating the genetic material of interest using recombinant DNA technologies or artificially synthesizing the DNA.
Paul Berg discovered the first recombinant DNA molecule in 1972 by mixing DNA from the monkey virus SV40 with DNA from the lambda virus. In addition to adding genes, the method used is to delete or “knock out” genes. The new DNA was introduced at random or specifically targeted to a specific genome region.
Recombinant DNA comprises molecules of DNA from two distinct species that are put into a host organism to create novel genetic combinations useful in research, medicine, agriculture, and industry. Because the gene is the focal point of all genetics, the primary purpose of laboratory geneticists is to extract, define, and modify genes.
Recombinant DNA technology produces artificial DNA to manufacture the desired output. This involves several stages, instruments, and other particular procedures. Let’s discuss each stage in further detail.
Recombinant DNA (rDNA) is the generic term for a fragment of DNA that has been generated by combining at least two strands.
They are DNA molecules created through laboratory methods of genetic recombination (such as molecular cloning) to combine genetic material from numerous sources, resulting in sequences not found in the genome.
Polymerase chain reaction (PCR) and molecular cloning are the two most used techniques to make recombinant DNA. There are two major distinctions between the techniques. One difference is that molecular cloning includes DNA replication within a living cell, whereas PCR replicates DNA in a test tube devoid of living cells. Another distinction is that cloning requires cutting and pasting DNA sequences, whereas PCR amplifies by replicating an existing sequence.
A cloning vector is a DNA molecule that replicates within a live cell. A cloning vector is required for the creation of recombinant DNA. Vectors are generally derived from plasmids or viruses and represent relatively small segments of DNA that contain the necessary genetic signals for replication and additional elements for ease of inserting foreign DNA. Identifying cells that contain recombinant DNA is also required.
The host organism determines the vector used for molecular cloning, the DNA size to be cloned, and whether or not the foreign DNA is to be expressed. Several procedures, such as restriction enzyme/ligase cloning or Gibson assembly, can be used to join the DNA segments.
Cloning any DNA fragment follows a seven-step process in typical cloning protocols:
After creating a recombinant molecule, the following step is to introduce it into an appropriate host. Several techniques for introducing recombinant vectors vary depending on multiple aspects such as vector type and host cell.
The following are some regularly utilized processes:
Once a recombinant DNA molecule has been delivered into appropriate host cells, it is critical to separate the cells with the rDNA from the original host cells that have not picked up the DNA.
The selection methods are based on the expression or non-expression of certain traits like antibiotic resistance, the expression of an enzyme such as -galactosidase or a protein such as GFP (Green Fluorescent Protein), and the dependence or independence of a nutritional requirement such as the amino acid leucine. For example, if the host E. coli cells have taken up the plasmid pBR322, these cells will thrive on a medium containing the antibiotics ampicillin or tetracycline, but normal E. coli cells will be destroyed by the antibiotics.
As previously noted, the easiest strategy for selecting transformants is based on the presence of antibiotic resistance genes on the plasmid or phage-based vectors. However, the transformants may have the vector but not the foreign DNA. While the technique for creating a recombinant vector is efficient, it is not completely failsafe! If, on the other hand, a vector contains two antibiotic-resistant genes, such as pBR322, and the insert is contained in the tetracycline-resistant gene, the ampicillin-resistant gene will be normally expressed, allowing the transformed cells to grow on ampicillin containing medium. However, the cells will be tetracycline sensitive due to an insertional inactivation phenomenon (insert in tetracycline gene).
A technique known as replica plating is used to select a sensitive or negative feature of rDNA.
Blue-white selection is another effective way of screening for the presence of recombinant plasmids. This approach is based on the insertional inactivation of the vector’s lac Z gene (e.g., pUC 19). This gene produces the enzyme -galactosidase, which may convert X-Gal’s colorless substrate into a blue product.
The procedures outlined above are used to select E. coli recombinants. Other host cell recombinants are created in various ways, but the basics remain the same. Furthermore, approaches for identifying recombinant proteins from colonies must be applied where applicable. When amplifying the insert DNA is the primary goal, plasmids are separated from host cells after substantial numbers of the latter are grown, and the insert is cut off the plasmid using the same restriction enzyme and recovered after electrophoresis.
Kary Mullis invented the PCR (Polymerase Chain Reaction) technology in the 1980s. PCR relies on the capacity of DNA polymerase to manufacture a new strand of DNA that is complementary to the provided template strand. Because DNA polymerase can only add a nucleotide to an already formed 3′-OH group, it requires a primer to which it may add the initial nucleotide. This criterion allows the researcher to define a specific section of the template sequence that he or she wishes to amplify. The exact sequence will be amassed in billions of copies after the PCR operation (amplicons).
DNA Template – The sample DNA containing the target sequence is one of the PCR DNA template components. High temperatures are given to the initial double-stranded DNA molecule at the start of the process to separate the strands from each other.
DNA polymerase – DNA polymerase is an enzyme that creates new strands of DNA that complement the target sequence. Taq DNA polymerase (from Thermus aquaticus) is the earliest and most extensively utilized of these enzymes, but PfuDNA polymerase (from Pyrococcus furiosus) is widely employed due to its superior fidelity when replicating DNA.
Primers – PCR primers are single-stranded DNA fragments that are typically approximately 20 nucleotides in length. In each PCR reaction, two primers are required, and they are intended to surround the target area (the region that should be copied).
Denaturation (96°C): Strongly heat the process to separate or denature the DNA strands. This creates a single-stranded template for the following step.
Annealing (55 – 65°C) so that the primers may bind to their corresponding sequences on the single-stranded template DNA.
The extension (72 °C): Increase the reaction temperatures so that Taq polymerase can stretch the primers and synthesize new DNA strands.
In a normal PCR reaction, this cycle is repeated 25 – 35 times, which takes 22 – 44 hours depending on the length of the DNA are being copied. If the reaction is efficient (effective), the target region can grow from one or a few copies to billions.
Identifying the nucleic acid sequence – the order of nucleotides in DNA – is known as DNA sequencing. It refers to any method or technology for determining the order of the four bases: adenine, guanine, cytosine, and thymine. Rapid DNA sequencing tools have significantly advanced biological and medical research and discoveries.
The nucleotide sequence is the basic level of understanding of a gene or genome. The blueprint holds the instructions for making an organism, and without it, no knowledge of genetic function or evolution would be complete.
The work of Frederick Sanger, who by 1955 had determined the sequence of all the amino acids in insulin, established the groundwork for protein sequencing.
In 1977, Allan Maxam and Walter Gilbert presented a technique for DNA sequencing based on chemical alteration of DNA and subsequent breakage at particular bases. This technology, also known as chemical sequencing, allows for the use of pure quantities of double-stranded DNA without additional cloning. The use of radioactive labeling in this approach and its technical difficulty hindered widespread adoption once the Sanger methods had been refined.
The chain-termination technique, developed by Frederick Sanger and colleagues in 1977, immediately became the method of choice due to its relative ease and durability. The chain-terminator approach used fewer dangerous chemicals and radiation than the Maxam and Gilbert procedures when it was initially developed. The Sanger approach was soon mechanized and used in the first generation of DNA sequencers due to its relative simplicity.
Individual genes, bigger genetic areas (i.e., clusters of genes or operons), whole chromosomes, or entire genomes of any organism may all be sequenced using DNA sequencing. DNA sequencing is also the most effective method for sequencing RNA or proteins indirectly (via their open reading frames). DNA sequencing has become an important tool in many fields of biology and other disciplines, including medicine, forensics, and archaeology.
The purposeful change of a protein’s amino acid sequence is called protein engineering. A protein folds from higher energy unfolded state to a lower energy folded state thermodynamically. Covalent modification of polypeptides occurs during or after ribosome production, giving birth to the terms co-translational and post-translational modification.
Protein engineering may be divided into rational protein design and directed evolution. These approaches aren’t mutually incompatible; researchers frequently use both. Protein engineering’s abilities may be substantially extended in the future as a more precise understanding of protein structure and function and breakthroughs in high-throughput screening become available. Even strange amino acids may eventually be incorporated, thanks to emerging approaches like extended genetic code, which allow for the encoding of novel amino acids in genetic code.
Understanding the links between protein structure and function is still a major emphasis in structural biology, having significant implications in domains as diverse as molecular biology, genetics, biochemistry, protein engineering, and bioinformatics.
The structure of a protein dictates how it interacts with other molecules, which defines how it functions. Proteins fold into distinct shapes based on the amino acid sequence in the polypeptide chain.
When a protein’s particular 3D structure is broken, it is said to be denatured, and the protein loses its action. Denatured enzymes, for example, lose their catalytic activity, denatured antibodies lose their ability to bind antigen, and disrupted subunits are unable to form multimers to execute their tasks. In other words, protein function is directly dependent on the protein structure, which cannot be carried out if the structure is disrupted.
The main amino acid sequences and their related tertiary structures provide particularly helpful structural information for understanding the structure-function paradigm.
Several recent advances in tertiary structural analysis of the “protein universe” have supplied critical criteria for determining the spectrum of family folds that exist and some of the evolutionary ties that link them.
The sequencing databases, which currently include the sequences of the whole genomes of multiple bacteria, an archaeon, and a microbial eukaryote, are far larger than the tertiary structure database. Shortly, many more genomes will be solved.
Computational tools to solve the “structure-function” conundrum may now assist serious attempts to explore the underlying links between protein structure and function using a large amount of protein sequence and structural data.
Proteins are large, complex molecular entities that are produced as a result of biological processes. Their size, molecular structure, and physicochemical characteristics are all different. Protein analysis and characterization can be accomplished by separation and identification.
Proteins are usually never entirely pure and homogeneous molecules throughout the manufacturing process but instead exhibit some structural variability. This indicates that there are always three components to a protein’s characterization:
Separation of protein – Electrophoresis, in which proteins are separated by size or mass, and isoelectric focusing are the most common separation methods. This is done either independently or in combination with 2D electrophoresis.
Identification – Mass spectrometry identifies compounds by ionizing them and determining their mass to charge ratios. Protein fractionators, GC/MS, LC/MS, CE/MS, and time of flight systems are all examples of mass spectrometry equipment. Amino acid sequencing by Edman degradation, crystal imaging, and surface plasmon resonance can also be used for identification. Because there are many alternatives, laboratories will often use various protein analysis and characterization methodologies.
One of the most significant aspects of recombinant protein production is protein identification and characterization. Characterization of proteins is a difficult task.
Protein therapeutics are often heterogeneous mixes of nearly comparable molecular weights and charged isoforms produced from live cells, with a complicated pattern of contaminants from the manufacturing process.
Furthermore, recombinant proteins are complicated, containing a variety of post-translational changes, a highly specialized three-dimensional structure, and the ability to aggregate, adsorb, and truncate. The amino acid sequence, molecular weight, charge variations, glycosylation, aggregation level, and oxidation level are key aspects of a protein drug’s full characterization.
Proteins can be categorized into the following groups from a commercial standpoint:
Blood Products and Vaccines – For decades, blood and plasma proteins have been commercially accessible. While some of these are still made from blood donated by human donors, others are now made using recombinant DNA technology. For example, Factor VIII is used to treat Haemophilia A, Factor IX is used to treat Haemophilia B, and Hepatitis B vaccination is used to prevent hepatitis.
Therapeutic antibodies and enzymes – Polyclonal antibodies have been utilized for medicinal purposes for over a century. Monoclonal antibody preparations and antibody fragments made by recombinant DNA technology have lately been discovered to be therapeutically useful.
Analytical application – Hexokinase for a quantitative estimate of glucose in serum, uricase for uric acid levels in serum, horseradish peroxide and alkaline phosphates in ELISA, and other enzymes and antibodies have found a variety of analytical uses in the diagnosis of illnesses.
Functional non-catalytic proteins – Non-catalytic proteins with features including emulsification, gelation, water binding, whipping, and foaming are examples of functional non-catalytic proteins.
Therapeutic hormones and growth factors – For decades, various hormone formulations have been employed in therapeutic settings. Though insulin was previously made from the pancreas of cows and pigs, the ability to genetically transfer human insulin genes into bacteria and change amino acid residues (protein engineering) has aided the production of modified versions such as humulin.
Regulatory factors – Several additional regulatory variables that did not meet the traditional definition of a hormone were uncovered. Originally, they were called cytokines. Interferons, interleukins, tumor necrosis factors, and colony-stimulating factors are among them. Interferon-alpha, beta, and gamma are interferon family members, which include INF alpha, beta, and gamma. Interferon-alpha is used to treat Hepatitis C, beta is used to treat Multiple Sclerosis, and gamma is used to treat Chronic Granulomatous Disease.
Nutraceutical proteins – Nutraceutical is a term used to describe a product that combines nutrition with medications. Several dietary proteins have been discovered to have therapeutic properties. Whey protein concentrates, lactose-free milk (for lactose-intolerant infants), and baby meal formulations are just a few examples.
Industrial enzymes – Proteolytic enzymes account for an annual market of Rs for industrial enzymes. 8000 crores. They’re used in the beverage, detergent, bread, confectionery industries, and cheese-making, leather processing, and meat processing. The soap business uses alcalde, the beverage industry uses papain, the candy sector uses glucose isomerize, and the cheese industry uses chymosin.
Protein design is the process of creating new protein molecules to create new activities, behaviors, or functions while also improving our understanding of how proteins work.
Proteins can be created from the ground up or by calculating a known protein structure and sequence variations. Rational protein design methods anticipate how proteins will fold into specified shapes based on their sequences.
Experiments using peptide synthesis, site-directed mutagenesis, or artificial gene synthesis can subsequently be used to confirm the anticipated sequences.
Computer simulations of the molecular factors that drive proteins in vivo are used in protein design programs. Protein design models simplify these forces to make the task tractable.
The protein’s target structure (or structures) is known in protein design. However, to maximize the number of sequences that may be designed for that structure while minimizing the possibility of a sequence folding to a different shape, a rational protein design technique must model some flexibility on the target structure. If the surrounding side-chains are not permitted to repack, a rational design method would expect relatively few mutants to fold to the desired structure in a protein redesign of one minor amino acid (such as alanine) in the densely packed core of a protein.
Protein design aims to find a protein sequence that folds into a certain structure. As a result, a protein design algorithm must examine all of each sequence’s conformations about the target fold and rank sequences based on the lowest-energy conformation of each, as defined by the protein design energy function. The goal fold, sequence space, structural flexibility, and energy function are common inputs to the protein design algorithm, and the output is one or more sequences expected to fold stably to the target structure.
Genomics – The structure, function, evolution, mapping, and editing of genomes are all studied in genomics. A genome is an organism’s whole collection of DNA, containing all of its genes and its three-dimensional hierarchical structure.
Proteomics – Proteomics is the study of proteins on a vast scale. Proteins provide a variety of tasks in living organisms. An organism’s or system’s proteome is the total collection of proteins it produces or modifies. Proteomics allows an ever-increasing number of proteins to be identified.
Bioinformatics -The use of computing and analysis tools to acquire and interpret biological data is characterized as bioinformatics. In modern biology and medicine, bioinformatics is critical for data management.
The process of discovering the sections of genomic DNA that encode genes is known as gene prediction or gene discovery. This comprises both protein-coding and RNA-coding genes and the prediction of additional functional components like regulatory regions. Once a species’ genome has been sequenced, discovering genes is one of the first and most crucial stages in comprehending it.
Gene prediction is a critical subject in computational biology, and there are a variety of algorithms that predict genes using known genes as a training set. This becomes a restriction since most of the knowledge needed to make these predictions comes from experimentally discovered genes.
Even though the genes are in the genome, counting them is challenging. Due to the occurrence of overlapping genes and splice variants, determining which regions of the DNA should be considered the same or numerous separate genes is challenging. Nonetheless, we can quantify the number of genes in an organism for practical reasons (allowing for some “experimental error”).
As the name indicates, the gene counting approach simply counts the number of copies of a gene present in an individual and hence a population/study group. A diploid creature, such as humans, counts genes by first assigning a genotype to each sample.
The amount of reads that align to a gene in RNA-Seq is used to determine its abundance level. We must now count the number of reads that map to RNA units of interest to get gene/exon/transcript counts once the reads have been mapped to our reference.
The human genome project has frequently highlighted two intriguing questions: whose genome is being sequenced and how similar are two persons’ genomes. According to reports, several anonymous samples were obtained and shared for human genome research.
We may speak in terms of the consensus human genome since each person’s genome is thought to be 99.8% similar to everyone else’s. SNPs are DNA sequence variants that arise when a single base (A, C, G, or T) is changed, resulting in different bases at various places in different people.
Comparative genomics is based on the similarity of related genomes. If two organisms have a recent common ancestor, the differences in their genomes arose from the genomes of their forefathers. The greater the genetic similarity between two creatures, the more similar their genomes are.
If they have a tight association, their genomes will have a linear behavior (synteny), which means that part or all of their genetic sequences will be preserved. Thus, genome sequences may be utilized to detect gene function by examining their homology (sequence similarity) to genes of known function.
Orthologous sequences are related sequences in distinct species: a gene exists in the original species, the species has been split into two species, and the genes in the new species are orthologous to the original species’ sequence.
Gene cloning (gene duplication) separates paralogous sequences: when a gene in the genome is replicated, the copy of the two sequences is paralogous to the original gene. Orthologous pairs (orthologs) are a pair of orthologous sequences, whereas collateral pairings are a pair of paralogous sequences (paralogs). Orthologous pairings normally have the same or comparable functions, whereas collateral pairs do not always. The sequences in collateral pairs tend to develop into having diverse purposes.
SNPs (single nucleotide polymorphisms) are a form of polymorphism that involves a single base pair variation. In the genome, single nucleotide polymorphisms, or SNPs (pronounced “snips”) are being studied to see how they relate to illness, treatment response, and other characteristics.
Each SNP is a change in a single nucleotide, which is a DNA-building component. In a particular length of DNA, an SNP could replace the nucleotide cytosine (C) with the nucleotide thymine (T). SNPs are found naturally in everyone’s DNA. On average, they occur once per 1,000 nucleotides, implying that a person’s genome has 4 to 5 million SNPs.
Scientists have discovered more than 100 million SNPs in populations worldwide, and these differences may be unique or common in many people. These changes are most typically discovered in the DNA between genes. They can serve as biological markers, allowing scientists to identify genes linked to illness. SNPs that occur within a gene or near a gene’s regulatory region may directly impact illness by changing the gene’s function.
Comparative genomics is a branch of biology that compares the genetic characteristics of various animals. The DNA sequence, genes, gene order, regulatory sequences, and other genomic structural markers are examples of genomic characteristics. Whole or substantial portions of genomes arising from genome projects are compared in this field of genomics to investigate basic biological similarities and differences and evolutionary links between organisms. Comparative genomics’ main idea is that similar traits of two species are frequently encoded within their evolutionarily conserved DNA.
Comparative genomics also brings up new research opportunities in other fields. Sequenced genomes have increased as DNA sequencing technology has become more widely available. The utility of comparative genomic inference has increased as the pool of accessible genetic data has risen.
A recent primate study provides one example of this enhanced potency. Researchers have obtained knowledge regarding genetic diversity, differential gene expression, and evolutionary dynamics in primates using comparative genomic methodologies that were previously inaccessible due to a lack of data and tools.
The study of how genes and intergenic areas of the genome contribute to various biological processes is known as functional genomics. A researcher in this discipline analyses genes or regions on a “genome-wide” scale with the hopes of narrowing down a list of possible genes or regions to investigate further.
Functional genomics aims to figure out how the different parts of a biological system interact to create a certain phenotype. The dynamic expression of gene products in a given environment, such as during a developmental stage or an illness, is the subject of functional genomics.
Functional genomics focuses on the dynamic features of genomic information, such as gene transcription, translation, gene control, and protein-protein interactions, rather than the static aspects, such as DNA sequence or structures.
Transcriptomics, proteomics, and metabolomics all describe a biological system’s transcripts, proteins, and metabolites. Combining these data should result in a full description of the biological system under investigation.
Function-related features of the genome, such as mutation and polymorphism and the assessment of molecular activity, are all covered by functional genomics.
Multiplex methods are often used in functional genomics to determine the amount of many or all gene products such as mRNAs or proteins in a biological sample. A more targeted functional genomics method may employ sequencing as a readout of activity to examine the function of all variations of one gene and quantify the impact of mutations. These measuring modalities work together to quantify numerous biological processes and better understand gene and protein activities and interactions.
Proteomics is the study of proteomes on a vast scale. A proteome is a collection of proteins made by a living creature, system, or biological milieu. We can talk about a species’ proteome (Homo sapiens) or an organ’s proteome (for example, the liver). The proteome is dynamic, varying from cell to cell and changing throughout time.
The proteome reflects the underlying transcriptome to some extent. However, in addition to the expression level of the relevant gene, many other variables influence protein activity (which is generally measured by the response rate of the processes in which the protein is engaged).
With the discovery of 2D protein electrophoresis in 1975, the first investigations that meet the label of “proteomic” research were done.
Proteomic research gives a global perspective of the mechanisms at the protein level that underpin healthy and pathological cellular functions. Each proteomic research focuses on one or more of the following aspects of a target organism’s proteome at a time, gradually building on previous information.
Proteomics is the next phase in studying biological systems after genomes and transcriptomics. Because an organism’s genome is more or less constant, proteomes vary from cell to cell, and throughout time, it is more difficult than genomics. Various cell types express different genes. Therefore even the most basic collection of proteins generated by a cell must be recognized.
RNA analysis was previously used to investigate this phenomenon, but it was discovered to have no link with protein content. It is now well understood that mRNA is not always translated into protein and that the amount of protein generated for a given amount of mRNA depends on the gene from which it is transcribed and the physiological condition of the cell.
Bioinformatics research and applications require databases. Several databases cover diverse sorts of information, such as DNA and protein sequences, molecular structures, phenotypes, and biodiversity. Databases may contain empirical data (data gained directly from experiments), projected data (data obtained by analysis), or both. They might be unique to a particular organism, route, or chemical. Alternatively, they can combine data from a variety of other databases. These databases differ in terms of format, access mechanism, and whether or not they are open to the public.
The following are some of the most regularly used databases. Please see the link at the beginning of the subsection for a fuller list.
The National Center for Biotechnology Information (NCBI) was founded at the National Institutes of Health in 1988 to develop molecular biology information systems. NCBI provides data retrieval technologies and computing resources for the study of GenBank data and the diversity of other biological data made available through NCBI and maintaining the GenBank nucleic acid sequence database.
Bioinformatics software tools span from simple command-line tools to more complicated graphical applications and independent web services, all of which are accessible from bioinformatics enterprises or governmental organizations.
Bioconductor, BioPerl, Biopython, BioJava, BioJS, BioRuby, Bioclipse, EMBOSS, .NET Bio, Orange with its bioinformatics add-on, Apache Taverna, UGENE, and GenoCAD are among the open-source software packages available. Since 2000, the non-profit Open Bioinformatics Foundation has funded the annual Bioinformatics Open Source Conference (BOSC) to continue this heritage and generate new possibilities.
Using the MediaWiki engine with the WikiOpener extension is another way to create public bioinformatics databases. This technology allows all specialists in the field to access and update the database.
Manually analyzing DNA sequences has long been problematic due to the expanding volume of data. Computer algorithms like BLAST are commonly used to search sequences from approximately 260,000 species, including over 190 billion nucleotides, as of 2008.
Various bioinformatics tools may be used to perform a variety of analyses. These are some of them: