Health Science Reviews

by Casey Valentine

Macular dystrophies (MDs) are acknowledged as inherited retinal disorders which cause loss of vision due to the macula’s deterioration (1). Macular dystrophies cause irregularities that damage the macula and therefore affect the central vision (1). A common form of macular dystrophy is Stargardt disease which results from a mutation in the ABCA4 protein and is autosomal recessive. (2).

ABCA4 is the ATP-binding cassette transporter gene and is crucial for transporting vitamin A derivatives out of the visual cycle. Too much vitamin A will cause a toxic build-up and damage to the eye which resultsin blindness. ABCA4 is recognised as the most common cause of retinal degeneration in Mendelian inheritance.

A study performed by A. Auricchio et al. entitled “Gene Therapy of ABCA4-Associated Diseases” aimed to reveal the best treatment options for those affected by ABCA4-associated diseases (3). The study revealed that direct gene replacement therapy was the most promising treatment option as all ABCA-4 associated diseases are autosomal recessive and therefore the addition of a functional gene would re-establish visual function.

The main strategies considered for the transport of the genetic material included viral vectors and non-viral vectors. As the ABCA4 gene has a large sequence of 6.8kb, there was a challenge in finding the most appropriate vector as it would require a transport vector that has a large cargo capacity and effective photoreceptor (PR) capability (3).

The best nonviral strategy described included a polylysine-based compacted DNA nanoparticle (NP) CK30-NP, which showed improved effectiveness in ocular gene transfer. This method is beneficial as it allows the vector to enter the nucleus of cells and has the capacity for plasmids up to 20kb’s in length. (3) Moreover, a test performed on a homozygous null mutation of ABCA4 in a mouse model of Stargardt disease, indicated that 8 months after an injection of CK30-NP, there was improved recovery of dark adaptation and reduced lipofusion accumulation (3).

The viral vectors examined in this study were based on adeno-associated viruses (AAV) and lentiviral vectors. Dual AAV strategies including trans-splicing, overlapping and hybrid dual-vector strategies were investigated. It was indicated that dual AAV trans-splicing and the hybrid F1 phage genome (AK) vectors showed promising results in mouse models with Stargardt disease. This method allowed the vectors to carry and transport the ABCA4 protein to the photoreceptor cells. This indicated a favourable strategy for the treatment of ABCA4-related disorders. Many trials were thus performed using this model which proved that retinal therapy using the dual AAV model is safe and effective for treatment in ABCA4. The other viral vector considered was lentiviral vectors. Lentiviral vectors were considered beneficial as a vector for ABCA4 as it has the capacity to carry large expression cassettes as that of the ABCA4 protein. Lentiviral vectors are also able to transport genes steadily into its target genome. Lentiviral vectors however did not show much improvement for the treatment of ABCA4 in rodent models which caused this method to be less reliable. However, studies done in non-human primates, such as macaques, showed better improvement. The studies performed in non-human primates indicated that was an improvement in the affected photoreceptors. Although this is promising for possible lentiviral vector usage in humans, more research would need to be done to ensure its safety and efficacy.

This study showed that although extensive research is being done to find the best treatment for ABCA4-related disorders, more still needs to be investigated before a definite decision can be made. It is important to continue research in this area especially as ABCA4 disorders are the most common retinal disorder of mendelian inheritance.

References

1. Rahman N, Georgiou M, Khan KN, Michaelides M. Macular dystrophies: clinical and imaging
   features, molecular genetics and therapeutic options. Br J Ophthalmol. 2020;104:451–60.
2. Roberts LJ, Nossek CA, Greenberg LJ, Ramesar RS. Stargardt macular dystrophy: common
   ABCA4 mutations in South Africa–establishment of a rapid genetic test and relating risk to
   patients. Mol Vis [Internet]. 2012 Feb 1 [cited 2022 May 11];18:280–9. Available from:
   https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22328824/?tool=EBI
3. Auricchio A, Trapani I, Allikmets R. Gene Therapy of ABCA4-Associated Diseases. Cold Spring
   Harb Perspect Med [Internet]. 2015 May 1 [cited 2022 Sep 16];5(5). Available from:
   /pmc/articles/PMC4448589/

by Sethu Poswa

Let us set the scene, an individual has just been diagnosed with depression, making them part of the 280 million other people who suffer with it worldwide. Should they decide to go on treatment, the next step would involve the healthcare professional selecting the appropriate treatment for them, which should be easy right? Wrong. Often times patients are subjected to clinical trials which is based trial and error to find the appropriate treatment. The problem with this method is its inefficiency, in that treatment only starts to work after 4-6 weeks of the trial and during that time period, there is no reliable way of predicting whether the patient will respond to the treatment or whether they will experience drug-induced adverse events, starting the entire process from scratch until appropriate treatment is determined. The danger with this is that recovery is delayed, and the patient may prematurely stop taking medication. Fortunately, pharmacogenetics provides a potential tool in successfully predicting treatment response. 

With National Mental Health Awareness Month approaching soon in October, it is only appropriate that we discuss the steps being taken by science to improve the clinical outcome of patients suffering from depression. Pharmacogenetics studies how an individual’s genetic make-up affects their response to drugs (in this case, SSRIs) and aims to improve disease outcome while preventing the occurrence of drug-related adverse events such as suicide attempts. The most commonly prescribed class of antidepressants are the selective serotonin reuptake inhibitors (SSRIs). This is because SSRIs such as citalopram, fluoxetine, fluvoxamine, paroxetine and sertraline display efficacy and are generally tolerable. Although SSRIs are commonly prescribed, there has been variable responses to them, with only about 33% of people on treatment experiencing an effective response to SSRIs. It is estimated that genetic factors account for approximately 42% of the variability in response to SSRIs, which is why pharmacogenetic studies mainly analyse the genes involved in the metabolism of SSRIs.

SSRIs work by reducing the reuptake of the neurotransmitter serotonin by the presynaptic neurons and it does so by inhibiting the serotonin transporter (SERT). This results in serotonin remaining in the synapse for an extended period of time so that it can act even more on the postsynaptic serotonin receptors. In the past, it was hypothesised that depression was caused by lower levels of serotonin in the body, but modern scientific literature rejects that hypothesis, although it is interesting that literature has observed that people whose serotonin levels have been increased by SSRIs showed improvement with regards to experiencing symptoms. It is also worth mentioning that serotonin plays a role in mood regulation so that feelings of anxiety and depression are reduced within an individual.

SSRIs are mainly metabolised by the enzymes cytochrome P450 2D6 (CYP2D6) and CYP2C19 and is transported by P-glycoprotein (P-gp). CYPD2D6, CYP2C19 and P-gp are encoded by the CYP2D6 gene, the CYP2C19 gene and the ATP Binding Cassette B1 (ABCB1) gene, respectively. Genes have a reference nucleotide sequence and differences from those reference sequences among individuals are referred to as polymorphisms. Polymorphisms exist in different forms such as insertions/deletions, length variation, single nucleotide polymorphisms (SNPs), etc. with some polymorphisms being beneficial while others have detrimental consequences. All of the different polymorphisms of a particular gene forms different versions of the same gene namely, alleles.

Polymorphisms typically alter the structure of the protein for which it encodes, which results in altered protein function. A polymorphism can either affect enzyme activity and/or expression. Polymorphisms in the highly polymorphic CYP2D6 and CYP2C19 genes can determine an individual’s ability to metabolise SSRIs. An individual can be a poor (PM), intermediate (IM), extensive (EM) or ultrarapid metabolizer (UM). Literature has reported that UMs relates to the number of copies of the CYP2D6 gene that a person possesses while PMs are associated with the possession of alleles that are known to correspond with decreased or deficient CYP2D6 activity. UMs are going to display no response to a standard dose of SSRIs since UMs display a high enzyme activity and so UMs will have low concentrations of the drug and its active metabolites, meaning they will experience no effect from the SSRI. On the other hand, UMs are also at risk of SSRI toxicity since active metabolites can accumulate in the body, leading to adverse drug reactions (ADRs) such as the development hypertension or anxiety. PMs are will inefficiently convert the parent drug to its active metabolite, and they will therefore not respond to treatment and are at risk of the toxic accumulation of the parent drug in the body as well experiencing more side effects such as nausea, diarrhoea, etc. 

Literature has reported that IMs for CYP2D6 have shown better response to antidepressants, while UMs have been associated with a higher risk of not responding to treatment and higher suicide cases. PMs and IMs of CYP2D6 or CYP2C19 were reported to experience more severe side effects and side effects occurred the most in individuals with these metaboliser statuses. PMs of CYP2D6 and CYP2C19 have also been linked to having higher plasma concentrations of the parent drug. 

The same pharmacogenetic principles can be applied to the ABCB1 gene, which encodes for P-gp. P-gp is a transporter protein that limits drug intake of certain drugs into the brain by active transport and therefore plays a role in regulating the availability of SSRIs at the brain, which is the action of site of SSRIs. A polymorphism in the ABCB1 gene could result in increased/decreased P-gp expression or increased/decreased functioning. This means that either more or less SSRIs will be removed from the brain, and this will affect the treatment outcome. Resistance to SSRIs is hypothesised to be linked to P-gp hyperactivity, by removing a large enough concentration to have no effect on the patient. Other polymorphisms in ABCB1 have also been linked to treatment response as well as a decreased/increased occurrence of side effects, depending on whether the SSRI is a substrate of P-gp, which includes fluoxetine, citalopram, sertraline fluvoxamine and paroxetine.

With pharmacogenetics being a relatively new field in science, there is still a lot more knowledge to harvest, for example, the physiological role of several genes are unknown as well as the mode of action of a high proportion of drugs, including antidepressants. Of course, pharmacogenetics cannot be the only tool used to determine what antidepressant would be safe and affective for an individual to use., as there are other factors to take into consideration as well as epigenetics. For example, gene and environment interactions have to be taken into consideration, as well as drug-drug interactions, because the patient could also be on medication that is metabolised by CYP2D6 or CYP2C19, for example and they could potentially be potent inhibitors of those enzymes and affect the efficiency with which the SSRI is metabolised. Believe it or not, but an individual’s ethnicity will also affect their metaboliser status since the alleles that determine metaboliser status as well as P-gp functioning are distributed differently, depending on what population the person is part of, for example, approximately 5-10% of people who are of European descent are PMs of CYP2D6 and it is rarer in people of African and Asian descent (approximately 3%) in non-European populations.

The field of pharmacogenetics is providing valuable information that is sure to become even more valuable in the future as technologies develop and more is known about how xenobiotics interact with biological systems. This information will help improve clinical outcomes in an efficient and less intrusive manner.

References

Bertilsson, I., Dahl, M. and Tybring, G., 1997. Pharmacogenetics of antidepressants: clinical aspects. Acta Psychiatrica Scandinavica, 96(s391), pp.14-21.

De Vane, C.L., 1999. Metabolism and Pharmacokinetics of Selective Serotonin Reuptake Inhibitors. Cellular and Molecular Neurobiology, 19.

Fabbri, C., Di Girolamo, G. and Serretti, A., 2013. Pharmacogenetics of antidepressant drugs: An update after almost 20 years of research. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 162(6), pp.487-520.

Fabbri, C., Minarini, A., Niitsu, T. and Serretti, A., 2014. Understanding the pharmacogenetics of selective serotonin reuptake inhibitors. Expert Opinion on Drug Metabolism & Toxicology, 10(8), pp.1093-1118.

Hirsch, M. and Birnbaum, R., 2022. Selective serotonin reuptake inhibitors: Pharmacology, administration, and side effects. Medi Media, [online] Available at: <https://www.medilib.ir/uptodate/show/14675&gt;

 Porcelli S., Drago A., Fabbri C., Gibiino S., Calati R., Serretti A., 2011. J Psychiatry Neurosci 2011;36(2):87-113, DOI 10.1503/jpn.100059

Ramsey, L., Bishop, J. and Strawn, J., 2019. Pharmacogenetics of treating pediatric anxiety and depression. Pharmacogenomics, 20(12), pp.867-870.

Rybakowski J.K., Serretti A. (eds.), Genetic Influences on Response to Drug Treatment for Major Psychiatric Disorders, DOI 10.1007/978-3-319-27040-1_3

Zobel, A. and Maier, W., 2010. Pharmacogenetics of antidepressive treatment. European Archives of Psychiatry and Clinical Neuroscience, 260(5), pp.407-417.

by Precious Kunyenje

Data visualization is a graphical representation of information and data by using visual elements like charts, graphs, and maps. It makes data easily accessible, provides an understanding of trends, outliers, and patterns in data, and makes it easier to share information.

Different data visualization tools and software are available for use by biologists. They are used to analyse and present biological data in visual formats.

The commonly used data visualization software by biologists includes SPSS, STATA, Excel, RStudio, and Python. Other data visualization software used by biologists include Graphpad Prism, Eviews, NVivo, and ATLAS just to mention a few.

Most data visualization tools and software come with resources on how to use them for analysis and data presentation. These are useful and a more convenient way to familiarize yourself with the tools and software on your own. The following are links to the resources for the frequently used data visualization tools and software.

1. SPSS
   SPSS is a software package for editing, analysing, and visualising data. It is used for
   statistical analysis of all sorts of data including from scientific research and provides
   the results in visual formats. To know more about SPSS, use the following link:
   https://www.spss-tutorials.com/basics/.

2. STATA
   STATA is a statistical software package for data manipulation, visualisation, statistics,
   and automated reporting. It is used by researchers in many fields including
   biomedicine, epidemiology, and science. It is an excellent alternative to SPSS, shares a
   lot of features, and has other additional tools giving it an advantage.
   For an easy guide and introduction to using STATA, use the following link:
   https://data.princeton.edu/stata. It is an introductory tutorial and will get you to know
   STATA in no time.

3. RStudio
   RStudio is an open-source integrated development environment that facilitates
   statistical modelling as well as graphical capabilities for R. It is integrated with a lot of
   statistical and analytical packages for managing biological data.
   It is an excellent data visualization software for biological scientists. The link below will
   provide guidance to understanding RStudio and R programming: https://dataflair.training/blogs/rstudio-tutorial/.

4. NVivo
   NVivo is a good data analysis and visualization software for qualitative research. It
   helps qualitative researchers organise, analyse and find insights in unstructured or
   qualitative data like interviews, open-ended survey responses, and journal articles.
   NVivo handles virtually any data, including Word documents, PDFs, pictures, database
   tables, spreadsheets, audio files, and videos. You can display connections, ideas, and
   findings using a range of visualization tools such as charts, maps, and models, and you
   can easily view the live data behind them. To learn more about NVivo, use the following
   link: https://tc.instructure.com/courses/7395.

Conclusion
There are many software packages that can be utilised by biologists for data analysis and visualisation. It is important for biologists to know the type of data they are using and their intended outcome. This helps make better decisions on which software package to use for data analysis and visualisation. All software packages cannot be
exhausted in this single blog, there are many amazing data visualisation software with cool features, all to be explored!

References

• Data Flair (2022). RStudio Tutorial – A Complete Guide for Novice Learners! https://data-flair.training/blogs/rstudio-tutorial/
• Rodriguez G. (2022). Stata Tutorial. https://data.princeton.edu/stata
• SPSS Tutorials (2022). SPSS Beginners Tutorial. https://www.spss-tutorials.com/basics/
• Tableau (2022). What Is Data Visualization? Definition, Examples, And Learning Resources. https://www.tableau.com/learn/articles/data-visualization
• Teachers College, Columbia University (2022). Tutorial: NVivo. https://tc.instructure.com/courses/7395
• Wikipedia (2022). Stata. https://en.wikipedia.org/wiki/Stata

by Siphenathi Ntoba

Do you know about your blood pressure levels? Many were murdered obliviously by this ‘silent killer’ disease. Hypertension (HTN) is a multifactorial (involves genetic and nongenetic factors) condition, characterized by persistent elevated blood pressure (BP) against blood vessels. It is a risk factor for heart disease. HTN grade1 (Systolic BP: 130–139 mmHg and Diastolic BP:80–89 mmHg), and HTN grade2 ≥ 140/90 mmHg (SBP/DBP).

Globally, 1.39 billion adult people are hypertensive and 10.4 million deaths worldwide. HTN has remarkable increased prevalence in Sub-Saharan Africa resulting to a rise of premature deaths. In South Africa, HTN has drastically enhanced as a great burden with 27-58% of prevalence.

The writer seeks to provide knowledge and awareness of the risk factors of ‘silent killer’ (no obvious symptoms)- hypertension. HTN is influenced by genetics, sociodemographic, and lifestyle behaviors. Physical inactivity and unbalanced food have great influence on HTN development and progress. Body mass index (BMI) is known to be the number one prompting factor of HTN which is associated with type 2 diabetes mellitus development (T2DM– 2xmore risking factor for HTN). It was reported that most rural people of Mthatha were unaware of their T2DM due to their unavailability for diagnosis hence they were victims thereof.

Most studies revealed that blood pressure (BP) is poorly controlled due to unknown HTN status, hence untreated regardless available resources for such responsibility. South Africa found it challenging to manage overburdening of HTN and its complication while experiencing poverty, increase in unemployment, socio-economic inequality, and its apartheid history. Cultural observance and masculine stigma have caused men to be victims of HTN.

Therefore, HTN prevalence has drastically increased due to the mention risk especially BMI and unbalanced diet. The best way to prevent and treat HTN requires an individual diagnosis and adherence to medication when applicable and being physical active.

Reference

Sharma, J. R., Mabhida, S. E., Myers, B., Apalata, T., Nicol, E., Benjeddou, M., Muller, C., & Johnson, R. (2021). Prevalence of Hypertension and Its Associated Risk Factors in a Rural Black Population of Mthatha Town, South Africa. International journal of environmental research and public health, 18(3), 1215. https://doi.org/10.3390/ijerph18031215