Generic analysis for depression therapy

The failure of drugs such as SSRIs, used to treat depression, can be a result of genetic variations in patients. Variations within the gene that encodes the CYP2C19 enzyme results in extreme differences in the levels of escitalopram achieved. Prescribing the dose of escitalopram based on a patient's specific genetic constitution would greatly improve therapeutic outcomes.

Pharmaceutical treatment of depression commonly makes use of selective serotonin reuptake inhibitors (SSRIs) of which escitalopram is the most frequently administered clinically. However, escitalopram therapy is currently limited by the fact that some patients do not respond well to the drug, while others develop adverse reactions requiring discontinuation of treatment.

In order to individualise drug therapy, researchers are attempting to establish genetic biomarkers that can predict an individual's response to drugs. In a recent study, it was discovered that variation in the gene encoding the enzyme responsible for escitalopram metabolism (CYP2C19) is very important in this respect.

 Individuals with a variant of the gene promoting increased enzyme expression had blood levels of escitalopram too low to impact the depression symptoms, whereas patients with a defective CYP2C19 gene reached drug levels which were too high. Overall, one third of the study participants achieved escitalopram blood levels that were either too high or too low. The researchers found that 30 per cent of the patients carrying gene variants causing excessive or inadequate enzyme levels switched to other drugs within one year, in contrast with only 10 to 12 per cent of patients carrying the common gene.

The study shows that genotyping of CYP2C19 could be of considerable clinical value in individualising doses of escitalopram so that a better all-round antidepressive effect could be achieved for the patients. CYP2C19 is involved in the metabolism of many different SSRIs, the finding is also applicable to other types of antidepressants.
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The secrets of Ebola

In a comprehensive and complex molecular study of blood samples from Ebola patients in Sierra Leone. Conducting a sweeping analysis of everything from enzymes to lipids to immune-system-associated molecules, the teamfound 11 biomarkers that distinguish fatal infections from nonfatal ones and two that, when screened for early symptom onset, accurately predict which patients are likely to die.

Health workers collected blood samples from patients after they were diagnosed with Ebola and at multiple points thereafter.They obtained 29 blood samples from 11 patients who ultimately survived and nine blood samples from nine patients who died from the virus. For comparison, the research team also obtained blood samples from 10 healthy volunteers with no exposure to Ebola virus.
"Our team studied thousands of molecular clues in each of these samples, sifting through extensive data on the activity of genes, proteins and other molecules to identify those of most interest.

The team found that survivors had higher levels of some immune-related molecules, and lower levels of others compared to those who died. Plasma cytokines, which are involved in immunity and stress response, were higher in the blood of people who perished. Fatal cases had unique metabolic responses compared to survivors, higher levels of virus, changes to plasma lipids involved in
processes like blood coagulation, and more pronounced activation of some types of immune cells.

Pancreatic enzymes also leaked into the blood of patients who died, suggesting that damage from these enzymes contributes to the tissue damage characteristic of fatal Ebola virus disease. The study showed that levels of two biomarkers, known as L-threonine (an amino acid) and vitamin D binding protein, may accurately predict which patients live and which die. Both were present at lower levels at the time of admission in the patients who ultimately perished.

When Ebola virus leads to death, experts believe it is because of overwhelming viral replication. Symptoms of infection include severe hemorrhaging, vomiting and diarrhea, fever and more. In the current Ebola study, the team found that many of the molecular signals present in the
blood of sick, infected patients overlap with sepsis, a condition in which the body in response to infection by bacteria or other pathogens mounts a damaging inflammatory reaction.
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Lung cancer genetic biomarkers

Single-nucleotide polymorphisms SNPs are variations in human DNA that determine human susceptibility to developing some diseases. Using the largest genome-wide SNP-smoking interaction analysis reported for lung cancer, researchers identified three novel SNPs. The results from their study reinforce that gene-smoking interactions play important roles in the etiology of lung cancer and responsible for part of the missing heritability of this disease.

Environmental and genetic risk factors contribute to development of lung cancer. Tobacco smoking is the most well-known environmental risk factor associated with lung cancer. They conducted a study to display that gene-smoking interactions play important roles in the etiology of lung cancer.

In their study, three novel SNPs (single-nucleotide polymorphisms), or variations in our DNA that underlie human susceptibility to developing disease, were identified in the interaction analysis, including two SNPs for non-small cell lung cancer risk and one SNP for squamous cell lung cancer risk. The three identified novel SNPs provide potential candidate biomarkers for lung cancer risk screening and intervention.

The genotype and phenotype data used in this analysis came from OncoArray Consortium. Genome-wide interaction scanning remains a challenge as most genome-wide association studies are designed for main effect association analysis and have limited power for interaction analysis.

The three SNPs, identified in the team's study, stratify lung cancer risk by smoking behavior. These three SNPs can be potential biomarkers used to improve the precision to which researchers can categorize an individual's risk of lung cancer disease by smoking behavior, which are helpful for individualized prognosis and prediction of treatment plan.
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The use of metabolism to subtype hepatoblastoma tumors

Hepatoblastoma is a rare pediatric liver cancer, usually diagnosed in the first three years of life. There are many subtypes of hepatoblastoma, the two major ones are fetal and embryonal.
Scientists have identified new biomarkers that could accurately classify the two main subtypes of hepatoblastoma, a children liver cancer.

Different types of hepatoblastoma use different nutrients to grow. Some use glucose or fatty acids. The genetics of hepatoblastoma involves frequent mutations in the gene CTNNB1. This gene produces the protein beta-catenin, which is involved in cell-cell adhesion and gene transcription. Because of its dual function, mutations of the CTNNB1 gene can cause hepatoblastoma cancer.

Beta-catenin is a component of a signaling pathway known as Wnt/beta-catenin, which is responsible for regulating the expression of multiple genes. Many components of the Wnt/beta-catenin pathway are affected and overactive in various tumors.

Researchers examined the relationship between the CTNNB1 gene and the cell's metabolism, they discovered that beta-catenin, as part of the Wnt signaling pathway directly regulates the expression of a gene that produces a glucose transporter protein. GLUT3

They used RNA sequencing to identify molecular and metabolic features that are specific to hepatoblastoma. This approach revealed that several enzymes involved in the metabolism of glucose are overexpressed in embryonal hepatoblastoma cells as compared to fetal hepatoblastoma cells.

Embryonal hepatoblastoma cells show high levels of glucose uptake, they also discovered that these cells are very sensitive to the perturbation of an enzyme involved in the cell's use of glucose. They immunohistochemistry of the three metabolic biomarkers to distinguish embryonal from fetal components out of a large panel of human hepatoblastoma biopsies.

The study shows that the Wnt/beta-catenin pathway is important for reprograming the energy management of tumor cells. It also provides a new
metabolic classification of human hepatoblastoma that can help oncologists develop novel diagnostic methods and treatments.
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The 16 genetic markers associated with short lifespan

How long we will live is encoded in our genome. Researchers have identified 16 genetic markers associated with a short lifespan. This reveals computational framework to uncover the genetics of our time of death and disease. It explained why some people live longer than others. 

 About 30 percent of the variation in human lifespan comes down to our genome. Changes in particular locations in our DNA sequence, such as single-nucleotide polymorphisms (SNPs), could be responsible for some of the reasons for our longevity.

Changes in the DNA is linked to age-related diseases. About 1 in 10 people carry some configurations of these markers that shorten their life by over a year compared with the population average. Those who inherited a lifespan-shortening version of one of these SNPs may die earlier than their mate. 

The researchers explore how the DNA changes affected lifespan in a holistic way. They discovered that most SNPs had an effect on lifespan by impacting more than a single disease or risk factor, for example through being more addicted to smoking as well as through being predisposed to schizophrenia.

The discovered SNPs and gene expression data, allowed the researchers to identify that lower brain expression of three genes neighbouring the SNPs- RBM6, SULT1A1 and CHRNA5, involved in nicotine dependence was linked to increased lifespan. These three genes could therefore act as biomarkers for surviving beyond 100 years. 

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