Greek scientists have come up with a new bioinformatics tool that identifies potential therapies for chronic inflammatory diseases. The researchers used this approach to identify and confirm therapeutic potential of two molecules to target a protein known as Tumor Necrosis Factor (TNF) that is active in rheumatoid arthritis, multiple sclerosis and other diseases.
TNF is not only a key protein in almost all inflammatory processes but also have negative effects in chronic inflammatory diseases. For long, drug companies have been trying to develop anti-TNF treatments that target the protein, blocking TNF function. However existing therapies can be lethal and cause negative side effects. In addition, not all patients respond well to approve anti-TNF therapies.
Recently, the scientists developed a bioinformatics approach to virtually screen nearly 15,000 small molecules whose activities are not known. Concentrating on the protein chemical structures and compound, this new method identified all molecules that could interrupt Tumor Necrosis Factor and its receptor interaction.
Since both Tumor Necrosis Factor shares structural characteristics with RANK, another protein that is also involved in inflammatory processes, the researchers identified compounds that target pro-inflammatory proteins using their virtual screen tool. The scientists identified two small molecules (T8 and T23) that could interact with both TNF and RANKL.
According to a study in PLOS Computational Biology, different characteristics of brain tissue that surround the origin site of seizure’s may determine which of two major patterns of brain activity are seen before an epileptic seizure begin.
At the start of an epileptic seizure, the brain’s electrical activity follows either a “high amplitude slow” pattern or a “low amplitude fast” pattern. After surgical treatment, patients who have seizures that come after the high amplitude slow pattern are more likely to experience continuing seizures. However, the mechanisms that underlie these differing onset patterns are not clear.
To understand the onset of the patterns, a team of researchers from Newcastle University, U.K. used a previously made computer model that simulated brain activity at the onset of a seizure. The output of the model suggested that the initial seizure may be determined by characteristics of the neighbouring “healthy” brain tissue and not by brain tissue at the spot where the seizure originates.
The simulation suggested that the high amplitude slow pattern happens when the surrounding tissue of the brain is characterized by higher excitability; meaning that the brain cell strongly responds to stimulation and reacts immediately to seizure initiation. In the meantime, the low amplitude fast pattern occurs when surrounding brain tissue has lower excitability. Consequently, seizure activity penetrates them slowly.
The study shows why different treatment outcomes are associated with the different onset patterns. Surgically removing seizure-stimulating brain tissue may be enough to stop seizure activity in neighbouring low-excitability tissue. High-excitability tissue, however, may still be triggered by alternative trigger spots after surgery.
Each human house thousands of various types of microbes called the microbiota. In other terms, microbiotas are microbes in the human body that are involved in biological systems of the human body. For example, natural procedures help determine novel group of species in every individual
According to a research that was published in Science Daily, microbial species in the human body vary from individual to individual and this variation is as result of the variation in resources that are accessible to the microbes in the body.
Conducted by Charles Fisher and his colleagues from the Ecole Normale Superieure in Paris, the study examines how the microbes in the human body are impacted by the resource availability in the human body. The researchers developed a scientific mathematical model that shows the relationship between the abundance of different microbiota species and the resource accessibility they use inside the human body.
The analysis of the research on the microbes within the human body also showed that the species that are closely related in terms of taxonomy share the common resources. The research was focused on the microbiota from people who were healthy. The study can further assist scientists to examine the role of shared resources in controlling diseases that are related to the microbiota within the human body.
Researchers have identified two molecules that could treat inflammatory disease. Referred to as T23 and T8, these molecules inhibit the function of the protein known as tumour necrosis factor, which is involved in inflammatory diseases such as multiple sclerosis, psoriasis, Crohn’s disease, rheumatoid arthritis, and more. According to a paper that was published in PLOS Computational Biology, the scientists identified the molecules using a drug screening method they developed.
Led by Georgia Melagraki, the researchers from Greece and Cyprus came up with a new computer-based drug screening platform that aided them to discover better tumour necrosis factor inhibitor drugs. The platform integrates proprietary molecular characteristics shared between tumour necrosis factor and another protein known as RANK, which also plays a role in chronic inflammatory diseases.
The scientists developed the platform based on several advanced computational tools. Then, the platform was used to screen almost 15, 0000 molecules with unidentified activity. After that, they predicted the interactions of the molecules with RANKL proteins and tumour necrosis factor; particularly, how the molecules might interrupt the protein-protein interactions leading to activation of these vital proteins. Out of thousands of candidates, the experiment identified nine potential molecules.
To further evaluate the potential of the molecules, the researchers studied how the nine molecules interacted with RANKL and tumour necrosis factor in real-world laboratory experiments. T23 and T8 were identified as strong tumour necrosis factor inhibitor.
It is not easy to find a bioinformatics book that provides all the information in computing, biology, and mathematics fields. The few books available are very expensive. Books on computational biology can be grouped into books of general interest, those for biologists interested in bioinformatics and those best suited to individuals from the mathematical or computational background.
If you are interested in learning human genome, look for Matt Ridley’s “Genome”. The book offers an interesting introduction to issues raised by the revolution of bioinformatics. If you’re non-scientists, go for James Watson’s “The Double Helix“. The book enables the reader to understand the structure of the DNA. A broad introduction to key computational ideas applied in bioinformatics is provided in “Bioinformatics Algorithms: An Active Learning Approach” by Pavel Pevzner and Phillip Compeau.
If you are interested in mathematical/ Computational aspects, Michael Waterman’s “Introduction to Computational Biology“ is the best book for you. The book describes the computational the structure of biological data, especially from chromosomes and sequences. The text exposes the reader to structure of biological data and describes how to treat associated combinatorial and statistical problems.
One outstanding general book for biologists is “Bioinformatics” by David W. Mount. Although the book is quite expensive, it is one of the best books if you want to study bioinformatics.
Autism spectrum disorder affects nearly 1.5% of children. Unfortunately, the diagnosis of the disorder is difficult and relies on multidisciplinary medics. Although past research has shown distinct metabolic processes in children who suffer from the disorder, they have not previously been looked at in diagnosis.
Scientists Daniel Howsmon, Juergen Hahn and colleagues have successfully developed an accurate diagnostic technique for children centred on blood sampling. The technique identifies constituents in the blood produced by metabolic processes: the transulfuration (TS) pathways and the folate-dependent one-carbon (FOCM) metabolism. Both processes are altered in children suffering from autism.
The researchers compared blood sample from neurotypical children and children with autism, all between three and ten years old. Advanced statistical analysis tools enabled the scientist to accurately classify 97.1% neurotypical children and 97.6% of the children with autism based exclusively on their blood biomarkers.
While further study is required to confirm the results and to look at any effect of medications on the biomarkers’ blood concentration, this research is an indication that in the future, there might be a simple and accurate method to detect autism in children
A computer-based simulation has shown that Tourette Tics may arise from interactions between many brain parts, rather than one malfunctioning part, according to a study that was published in PLOS Computational Biology.
The symptoms of Tourette syndrome include involuntary motor tics, such as sniffing, clapping, or eye blinking. Traditionally, those tics were related to dysfunction of a brain part referred to as the basal ganglia. However, recent studies of human, monkey and rat brains show that thalamus, cortex, and cerebellum may be involved, too.
Led by Daniele Caligiore of the National Research Council, Italy, researchers have developed a computer-based brain simulation that triggers motor tics in Tourette syndrome. The model imitates neutral activity that was related to tics in the monkey study, which suggested that tics also involve signalling between basal ganglia, cerebellum, and cortex.
The researchers tweaked the model to reproduce the results of the study of the monkey brain. Consequently, they were able to understand how the brain produces tics. The model shows that abnormal activity of dopamine in the basal ganglia and activity in the thalamo-cortical system work together to generate a tic. The model also suggests that the cerebellum- basal ganglia link discovered in the study of monkey may enable the cerebellum to influence the production of tic.
For a long time, genomics have been a “read-only” science. However, researchers have developed a tool that can easily and quickly delete DNA in living cells. Published in PLOS Computational Biology, this software will boost scientists’ effort to understand the vast areas of non-coding DNA. Consequently, this may lead to discovery of new genes that cause disease and potential new drugs.
CRISPR-Cas9 is an innovative method that is used to edit genomes. The aim of most studies that use this technique is to silence protein-coding genes (the most-studied area of our genome). It is important, however, to know that 99 percent of DNA does not take part in encoding protein. The “non-coding DNA”, also known as “Dark Matter”, is known to be important for understanding aspects of human biology such as evolution and disease.
Recently, the Johnson lab developed a tool called “DECKO”. The tool can be used to delete a desired part of non-coding DNA. One unique advantage of the tool is that it uses binary individual sgRNAs, working like two scissors that cut out a section of DNA. The method was extensively adopted, but since there was no software to design sgRNAs pairs that are needed, designing deletion tests was slow.
In response to this, Carlos Pulido and his colleagues developed software known as CRISPETa that is used to design CRISPR deletion experiments. CRISPETa receives instructions from the user who tells it the part they want to delete. Then, the software sends a set of optimised sgRNAs pairs that are directly used by researchers. CRISPETa designs effectively delete desired targets in animal cells.
In computational biology, a sequence alignment is a method of arranging the sequences of DNA, protein, or RNA to identify areas of similarity that may be as a result of revolutionary, functional, or structural relationships between the sequences. Typically, aligned sequences of amino acids or nucleotide residues are represented as rows within a matrix. Between the residues, gaps are inserted so that similar or identical characters are arranged in successive columns.
In an alignment, if two sequences share a common ancestor, disparities are construed as point mutations and gaps are interpreted as indels (deletion or insertion mutations) introduced in both or one lineages in the period since they separated from one another. In protein sequence alignment, the degree of resemblance between amino acids that occupy a specific position in the sequence is construed as a rough indication of how conserved a given region is among lineages.
The presence of only conservative substitutions or the absence of substitutions in a given region of sequence shows that that region has functional and structural importance. Although RNA or DNA nucleotides base are more analogous to each other compared to amino acids, base pair conservation can suggest a similar structural or functional role.
According to a study published in PLOS Computational Biology, a new mathematical model may help explain what brought the evolution of big brains in human and other animals. Animals with large brains also have a high cognitive ability, but the factors that cause the evolution of large brains remain unclear. Researchers have hypothesized social, cultural, and ecological factors could each play a role. Although these hypotheses have not been mathematically formalized, doing so would enable researchers to refine their tests.
To address the issue, a mathematical model was developed by Mauricio González-Forero of University of Lausanne and colleagues. The model predicts how big the human brain should be at various stages of a person’s life, depending on several evolutionary scenarios.
One assumption of the model is that the brain uses energy on cognitive skills that enable a person to extract energy from food, which in turn enables the brain to develop. Given natural selection, the model predicts the amount of energy that is used to support brain development at different ages under diverse biological settings.
The scientists used the model to test a scenario in which cultural factors and social interactions are excluded, showing the impact of ecological factors alone. In the setting, a human hunts or gathers food alone. However, it may be helped by its mother when it is still young.
Under those conditions, the hypothetical brain of human being grew as large as early humans brains are believed to have grown. In addition, the slow growth rate matched the modern human brains. The results run counter to a common believe that cultural and social influences are needed to achieve these growth rates and sizes.