Bioinformatics Proteomics Omics Gene Expression Protein Synthesis
MixOmics: An omics integration "Swiss army knife"
The study of genomics involves examining an organism’s entire genetic makeup, including its DNA sequence, genes, and mechanisms that control gene expression. Proteomics and metabolomics, two other “omics” methods, concentrate on the study of proteins and metabolites, respectively. Researchers can gain new insights into how an organism functions and how it reacts to its surroundings by combining the analysis of these many forms of data. For instance, by fusing genomics data with proteomics and metabolomics data, researchers can acquire a fuller knowledge of an organism’s gene expression, protein synthesis, and metabolic processes, as well as how these processes interact to produce health or malfunction to produce disease. This information can offer insightful information for a variety of purposes, such as medication research, disease diagnostics, and environmental monitoring. Searching for patterns or linkages between various data sets is the process of finding correlations between linked datasets. This can offer insightful information about the underlying biological mechanisms and operations of an organism. For instance, a strong positive correlation between two datasets indicates that they are related and that changes in one dataset may be related to changes in the other. Researchers can gain a better understanding of the mechanisms underlying biological processes and how they are controlled by finding these relationships. This can be helpful for a number of applications, including determining prospective targets for medical intervention or forecasting the effects of new medications.
I looked through the literature to find any methods that could combine different -omics datasets. There are numerous possibilities, as there are for each assignment in bioinformatics. The “mixOmics” package (available for download and installation from Bioconductor) is one of the best tools currently available for performing this type of integration analysis, in my opinion, when taking into account factors like ease of installation, documentation quality, a strong user community, user support, and published analyses.
The mixOmics strategy
Several multivariate techniques for integrating numerous datasets are included in the mixOmics package. In this problem space, where there are many more attributes than samples, multivariate analysis is ideally suited. The analysis makes it simpler for a human analyst to recognize patterns and understand correlations by lowering the dimension of the data. “Partial least squares” is one of the most popular algorithm categories in mixOmics for this task. A mathematical tool called partial least squares (PLS) is used to examine connections between two or more datasets. Similar to PCA analysis, but with a focus on maximizing correlation/covariance across latent variables, it works by discovering the underlying patterns and correlations in the data and using this knowledge to create a set of “composite” variables that reflect the most crucial elements of the data.
The links between the datasets can then be inferred or predicted using these composite (latent) variables. For instance, the PLS approach can be used to determine the precise features of one dataset that are most highly linked with the other and then create composite variables based on these features if it is known that the two datasets are related in some way. PLS can be used to predict relationships between the dependent and independent variables since it is more resistant to highly correlated features than PCA. By incorporating a “feature-selection” option dubbed “sparse PLS” or simply “sPLS” that employs “lasso” penalization to remove unneeded features from the final model to improve interpretation and also minimize computing time, mixOmics advances the PLS approach. The regularization term, which increases the complexity of the ordinary least squares regression model, is how lasso regression operates. The “lasso” regularization term effectively removes the less significant predictors from the model by forcing their coefficients to be zero.
As a result, the model is made simpler, easier to understand, and more capable of producing precise predictions. For datasets with many factors, lasso regression is especially helpful since it can help to pinpoint the most crucial predictors and lessen the chance of the model becoming overfit.
Poultry Plants: Reducing Salmonella And Campylobacter Consumer Risk
Poultry Plants: Reducing Salmonella And Campylobacter Consumer Risk
The draft compliance guideline’s sections on microbiological testing will be briefly discussed in this post, and issues with Salmonella and Campylobacter will be highlighted.
Chicken and Pathogens
According to the Centers for Disease Control and Prevention (CDC), non-typhoidal Salmonella is responsible for roughly 1.2 million foodborne infections in the United States each year, and acute Salmonellosis is thought to be responsible for 450 fatalities. The typical symptoms of a Salmonella infection include nausea, vomiting, fever, diarrhea, and abdominal cramps. The majority of the Salmonella bacterium is found in animals. Many animals’ digestive tracts, including chickens, may naturally have salmonella. It frequently resides in their typical flora. Salmonella is a bacteria that can be found in feces, thus chickens can get it from the ground or possibly contaminated feed.
Several poultry factories keep barns covered so no wild birds come into touch with the hens to limit the likelihood that the animals would contract Salmonella from the environment. Yet, there is a possibility that bacteria from the intestines could contaminate the finished product during slaughter and processing. Around two million people are affected by Campylobacter each year in the United States, making it one of the most prevalent causes of diarrheal sickness. During two to five days of exposure, campylobacteriosis, an infectious condition brought on by this bacteria, manifests as fever, cramps, diarrhea, and stomach pain. The ideal growth range for Campylobacter jejuni is between 37 to 42 °C, or almost the same as a bird’s body temperature. This delicate bacteria may be carried by birds without making them sick, but raw or undercooked fowl can spread the infection.
Proposed Compliance Guideline for FSIS
The draught advice document, now in its fourth revision, aims to help poultry facilities manage Salmonella and Campylobacter. When developing food safety systems, FSIS says that it’s crucial to take both pathogens into account for removal. The draft guideline specifically advises that food laboratories use the results of microbiological testing to track how well their Hazard Analysis Critical Control Points (HACCP) system is working. The proposed guideline’s main goal is to make poultry facilities’ management procedures better. For instance, federally inspected facilities are required to execute a hazard analysis that keeps an eye out for potential threats to food safety before, during, and after entrance. To eliminate or lessen these risks, a HACCP strategy is implemented. Protocols including environmental monitoring, water testing, and verification may be used in addition to using the results of microbiological tests.
Poultry facilities may carry out microbiological testing themselves or have a third-party laboratory do so for a variety of reasons, such as:
- Assisting with judgements including a hazard analysis
- Assessing the success of a sanitation program
- Meeting customer standards
- Adhering to legal requirements.
- Supporting the ongoing verification of a HACCP plan.
The FSIS claims that it is “very simple to identify the segment of the process where control has been lost” by doing microbiological studies at various points within a process. The draught guideline strongly advises employing statistical process control to monitor and evaluate the data gathered from continuous HACCP verification (SPC). Lower statistical control limits may misrepresent the existence of process control problems, and higher limits may fail to detect potential process flaws, according to the draught guideline. The draught guideline strongly advises employing statistical process control to monitor and evaluate the data gathered from continuous HACCP verification (SPC). Lower statistical control limits may misrepresent the existence of process control problems, and higher limits may fail to detect potential process flaws, according to the draught guideline.
FSIS advises establishments to refer to the Establishment Guidelines for the Selection of a Commercial or Private Microbiological Testing Laboratory to learn more about the relevant testing requirements when choosing a microbiological testing laboratory. The components of a well-designed microbiological sampling program, according to FSIS, include:
- intended use of testing programs
- organisms that will be tested
- products that will be tested
- sample collection methods and locations
- checks to ensure sample integrity
- methods for analyzing samples
- laboratories performing the analyses
- techniques for evaluating test results
- actions taken in response to test results.
Only pathogen testing, according to FSIS, can reliably confirm that infections are managed to safe levels in finished goods. Indicator organisms can show that a situation has been under control, and routine pathogen testing can confirm that the establishment is bringing pathogen levels down to acceptable levels, the agency adds.
Establishments should direct testing laboratories to any relevant testing methodologies, including those outlined in this compliance guidance, according to FSIS advice. Establishments that “…choose a laboratory that does not utilize acceptable testing methodologies of effective Quality Control/Quality Assurance (QC/QA) practices may not acquire dependable or meaningful testing findings,” according to the government, risk not receiving accurate or useful test results. Despite the fact that this version of the compliance guideline is marked as a draught, FSIS suggests that its suggestions may be applied to the agency’s current decision-making procedures.
Scientists use centrifuge to discover a hormone
Scientists use centrifuge to discover a hormone
Discovering compounds with therapeutic potential that were previously hidden by highly numerous proteins is the goal of a new technique for isolating extracellular fluid.
Muscles are not isolated entities; in order for the body to operate as a whole, they must interact with one another and the brain. However, it has been challenging to pinpoint precisely which molecules are engaged in this communication since they are frequently overshadowed by other, more prevalent biomolecules. According to a study published Friday (January 20) in Cell Metabolism, a team of researchers has now discovered a quicker and simpler method to identify these molecules. Researchers may be able to identify proteins that can be utilized in treatments and better understand how muscles communicate using the new approach.
The work, in the opinion of molecular physiologist Christopher Newgard of Duke University Medical Center, is “remarkably fundamental.” “It almost seems too simple compared to other approaches that tried to do this in the past.”
Bruce Spiegelman, a cell biologist at Harvard’s Dana-Farber Cancer Institute and the paper’s lead author, has been studying the hormones that muscles and fat release during exercise and how those hormones affect the body as a whole for more than ten years. The hormones he was looking for were, however, overshadowed by other, more dominant proteins when he employed mass spectrometry to analyze entire muscle and fat tissues, such as albumin (which is a plentiful component of blood). Spiegelman reasoned that by separating this extracellular fluid, he would be able to investigate the hormones more precisely and possibly discover new ones. Spiegelman was aware that after being secreted, these hormones accumulate in the extracellular fluid surrounding tissues.
Spiegelman decided to attempt a similar technique to isolate molecules from extracellular fluid after observing Harvard colleagues utilize centrifuges at extremely low speeds to separate out tiny metabolites. He set up an experiment by adding some muscle tissue to a centrifuge. The centrifuge needed to spin quickly enough to separate the fluid from the muscle tissue without becoming too rapid. This proved to be more challenging than anticipated. Spiegelman and the rest of the crew eventually settled on a centrifugal force that was around 600 times that of gravity. They were able to separate a pinkish-yellow liquid from the muscle at this pace. The researchers passed the fluid through gel electrophoresis and then analyzed it using mass spectrometry after centrifuging it once more to get rid of as much of the leftover albumin and immunoglobulins as they could.
It didn’t appear like muscle or blood, according to the computer analysis of the mass spec, says Spiegelman. We were hoping for something different, and that’s what we got.
The researchers then began the challenge of looking for new biomolecules with presumptive extracellular fluid in hand. After some research, they identified a hormone produced by muscles and fat that they dubbed prosaposin. They discovered that the newly discovered protein aids in mice’s thermogenesis, suggesting that it may be effective in therapies for obesity since it might help burn fat. Spiegelman finds it “very intriguing” that “we uncovered a bona fide neurotrophic factor coming out of the muscle.” “And there was no other way we could have done it.”
Spiegelman plans to use this approach to carry out additional studies into the protein composition of muscle and fat tissues in order to advance knowledge of the challenges with intercellular communication that are associated with neurodegenerative illnesses and malignancies. Newgard does issue a warning that there is still some work to be done. A few proteins typically seen in plasma were discovered in the centrifuged extracellular fluid, as was indicated in the research, which suggests that plasma may be leaking into the purportedly separated fluid. However, he is optimistic that the work will be useful to other researchers in the future.
There are still some unanswered questions, he argues. However, I would describe it as a pretty daring first effort.
DEMO
rDNA Technology
The method of synthesizing artificial DNA by fusing DNA from multiple sources with various genetic components is known as recombinant DNA technology.
Recombinant DNA technologies are referred to as genetic engineering.
Recombinant DNA technology was created as a result of the discovery of restriction enzymes by Swiss scientist Werner Arber in 1968.
Splicing the target gene into the host’s DNA is more difficult than it seems. It involves selecting the best vector to incorporate the desired gene into and producing recombinant DNA after selecting the right gene to be injected into the host.
Therefore, the recombinant DNA must be administered to the host. It must then be maintained in the host and passed on to the offspring. Procedure using recombinant DNA technology. In order to generate the intended result, recombinant DNA technology uses a number of processes that are kept in a specific order.
First step, Isolate the genetic material
The first and most crucial step in the recombinant DNA technology procedure is to isolate the desired DNA in its pure state, which is uncontaminated by extraneous macromolecules.
Second step, Slashing the gene at the points of recognition
The choice of where to insert the desired gene into the vector genome depends on the restriction enzymes. These procedures go under the term of restriction enzyme digestion.
Third step, Increasing the gene copies using the Polymerase Chain Reaction (PCR)
A single copy of DNA is multiplied into hundreds to millions of copies after the desired gene has been cut using restriction enzymes.
Fourth step, DNA molecule ligation
In this stage of ligation, a cut segment of DNA and the vector are joined together using the DNA ligase enzyme.
Fifth step, Inserting Recombinant DNA Into the Host
In this stage, the recombinant DNA is introduced into a recipient host cell. This process is known as transformation. Under ideal circumstances, after being incorporated into the host cell, recombinant DNA replicates and is expressed as the produced protein. This can be done in a variety of methods, as previously mentioned in Tools of recombinant DNA technology. The effectively transformed cells or organisms transfer the recombinant gene to the progeny.
Application of recombinant DNA technology
- DNA technology can also be used to detect HIV in an individual.
- Hereditary illnesses are caused by genetic defects, which can be fixed by gene therapy.
- Recombinant technology is used in clinical diagnosis; ELISA is one example.
- Recombinant DNA technology is frequently used in agriculture to develop genetically modified species like Flavr Savr tomatoes, protein-rich golden rice, Bt-cotton to protect the plant against ball worms, and many others.
- The pharmaceutical sector uses recombinant DNA technology to create insulin.