Bioinformatics Approach that Identifies Therapies for Chronic Inflammatory Diseases

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.


A Brain Model That Explores the Onset Patterns of Epileptic Seizure

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.


Microbes in Human Body: Association Between Abundance of Microbiota Species and Accessibility of Its Resources

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.