Health Science Reviews

by Sanele Mdletshe

Introduction
The liver is the most important organ of the body, it performs crucial metabolic functions, and these includes metabolizing toxic substances, producing bile for digestion and maintaining blood sugar levels. It is a very unique organ that is able to repair itself after damage, but this is not the case if the injury has progressed to cirrhosis, which is a chronic liver damage as a results of alcohol abuse or hepatitis. The ultimate treatment for end-stage cirrhosis is liver transplantation, but the shortage of donors remains a major obstacle. According to the global observatory on donation and transplantation, in 2020, the liver was the second most transplanted organ to save people’s lives. GLOBACAN estimates indicate that chronic liver diseases and liver cirrhosis contribute to more than 1 million deaths annually across the world.

In this study, researchers looked at the alternative options for treatment of chronic liver diseases and liver failure. The study was conducted based on the application of the principles of regenerative medicine and tissue engineering. Briefly, the aim was to use induced pluripotent stem cells (iPSCs) to enhance the repair of the liver after cirrhosis, this is due to their pluripotent properties which allows them to differentiate into all cell types including liver cells. iPSCs were generated by transfecting somatic fibroblasts with four transcription factors (OCT4, SOX2, KLF4 and MYC) famously known as “Yamanaka factors”.

Methods

Results

Conclusion/take homes
In this study, authors established a three-step protocol to generate iPSC-derived hepatocytes that can be an alternative treatment for chronic liver diseases and liver failure, as assessed in mice with lethal fulminant hepatic failure. This protocol is very rapid and efficient as it takes only 12 days.

The use of iPSCs holds a great promise in medicine, its advantages includes overcoming ethics against the use of human embryonic stem cells, reduces the risk of immunosuppression as these cells are generated from the patient’s somatic cells.

References
Chen, Y., Tseng, C., Wang, H., Kuo,H., Yang, V.W., and Lee O.K. 2012. Rapid generation of mature hepatocyte-like cellsfrom human induced pluripotent stem cells by an efficient threestep protocol.

by Harry Kim

Mycobacterium Tuberculosis was first discovered in 1882 and it is still the leading cause of death of a single infectious agent. In 2019 alone, TB accounted for 10 million diagnosed cases and 1 to 2 million deaths. So the question arises, is there anything we can do?

The complexity of TB comes from the different levels of drug resistance the bacteria have. There are over 20 anti-TB drugs being used, all with varying resistance in the community. Undertreating leads to higher mortality and overtreating result in more significant side effects (often detrimental to patient’s life). So why after 140 years are we still making this mistake?

Due to the vast number of drugs, it is costly to have resistance testing facilitates for all anti-TB drugs. Often high TB burden countries are also developing countries and can only accommodate for isoniazid and rifampicin testing. So how do test more accurately?

All these problems have one answer: whole genome sequencing.

High throughput can be very expensive. However, in areas where TB is the number one cause of death, it can become an investment to improve the lives of the whole community as correct treatment can decrease mortality rates, decrease spread and improve symptom control.

The research shows 22% of local testing was incorrect according to the WGS testing, with half of these cases being inappropriately treated. It was also shown that 28% of mortalities had incorrect resistance testing. The odd ratio showed that patients are 4 times as likely to die when undertreated instead of receiving appropriate treatment.

The undertreatment of TB due to incorrect resistance testing results seems to be main culprit in this complex disease but whole genome sequencing is the answer to save millions.

Reference

Zürcher K, Reichmuth ML, Balif M, Louiseu C, Borrell S, Reinhard M, et al. Moratlity from drug-resistant tuberculosis in high-burden countries comparing routine drug susceptibility testing with whole-genome sequencing: a multicentre cohort study. Lancet. 2021 July;2:320-9.

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

by Luca Bell

Conventional T cells recognise peptide antigens presented by major histocompatibility complex (MHC) molecules, Human Leukocyte Antigen (HLA) molecules in humans. Unconventional T cell clones bind antigens restricted by MHC related protein 1 (MR1). Such cells, MAIT cells, are documented as recognising metabolic intermediates as antigens presented by MR1.

A group of researchers from around the globe reported a T cell receptor that recognised cancer cells and promoted cell lysis, while not recognising healthy cells. They also sought identify the mechanism of this selection. The T cells reported by the authors showed no difference in targeting engineered cancer cells that lacked peptide presenting molecules. This was similar to MAIT cells.

Peripheral blood mononuclear cells (PBMC) were isolated from blood by centrifuge and then cultured with lung carcinoma cells for two weeks. Primed PBMCs were cloned. These T cells are referred to as MC.7.G5. Embryonic kidney cells from the HEK293T cell line were transduced with a library of single guide RNAs (sgRNA) targeting all protein coding genes in the human genome. These sgRNAs direct the Cas9 nuclease to splice out the gene targeted by the sgRNA. This results in a library of HEK293T cells that each have a gene removed. These cells were incubated along with the MC.7.G5 T cells. HEK293T cells that survived were resistant to lysis by the MC.7.G5 T cells. The HEK293T cells were collected, and their DNA was isolated and sequenced using Illumina sequencing. These reads were compared to a control sample of HEK293T cells that were not incubated with MC.7.G5 T cells. sgRNAs that were enriched compared to the control indicated genes that were spliced out and resulted in increased survival. These genes are thus important for recognition by T cells.

The genes important for T cell recognition were found to encode proteins involved in promoter activation of MR1 and β2M, with which MR1 forms a dimeric antigen-presenting molecule known to activate MAIT cells and other T cells. MR1 being the primary antigen presenting molecule in the targeting of cancer cells by T cells was confirmed by a loss-of-function assay.

MC.7.G5 T cells were shown to decrease the Jurkat leukaemia cell burden in mice by an average of 95% compared to the control, after 18 days.

Benefits of MR1 as an antigen presenting molecule are that, unlike HLA molecules which are highly polymorphic and are present polymorphic peptide antigens, MR1 is monomorphic and presents non-peptide antigens.

In order to test the safety of MC.7.G5 T cells as a treatment, the T cells were incubated with healthy cells of different tissue types that had been stressed or infected with pathogens. MC.7.G5 T cells were found to be inert.

The take home message of this study is that MC.7.G5 T cells are an example of a potential cancer treatment that, unlike chemotherapy for example, does not target healthy cells as an adverse side effect and can be used to treat any human, regardless of their HLA genotype.

Reference

Crowther, M., Dolton, G., Legut, M., Caillaud, M., Lloyd, A., Attaf, M., Galloway, S., Rius, C., Farrell, C., Szomolay, B., Ager, A., Parker, A., Fuller, A., Donia, M., McCluskey, J., Rossjohn, J., Svane, I., Phillips, J. and Sewell, A., 2020. Genome-wide CRISPR–Cas9 screening reveals ubiquitous T cell cancer targeting via the monomorphic MHC class I-related protein MR1. Nature Immunology, 21(2), pp.178-185.

by Jesse Conradie

Fecal microbiota transplantation (FMT) has proven to be an effective treatment in recurrent Clostridioides difficile infections. Long term side effects have not been established to date. The direct link between the gut microbiota and almost 95% of human disease has raised interest in the therapeutic application of FMT for the treatment of autoimmune, metabolic etc., disorders not previously linked with the gut microbiome. However, although FMT is considered safe in the long run, minor short term adverse effects from the procedure include transient diarrhea, abdominal cramps, fever, constipation. Although FMT is considered safe and well-tolerated in high-risk individuals, much research is still required before FMT can be established as an accepted therapy. Special consideration should be given to the current COVID-19 pandemic, as the SARS-COV-2 virus has recently been found to be present in the stool of infected individuals. This raises concerns about whether the oral-fecal transfer of SARS-COV-2 and other pathogenic microorganisms is possible. Therefore, the screening for SARS-COV-2 antigens should be added to the already established screening protocols before FMT treatment, and this would most likely include additional antigens as more pathogenic microbes emerge.

Although many studies have shown that the benefits of FMT outweigh the minor short-term risks, there still needs to be consideration when it comes to patients themselves, as the transplant of fecal matter from donors to recipients is not generally considered favourable. At the current moment, not many individuals outside of the world of science seem to be aware of the benefits of FMT, the future use of this technique in the treatment of many human diseases requires more public engagement and spreading of information regarding its safety and benefits.

References:

Hanssen, N.M.J., de Vos, W.M & Nieuwdorp, M. (2021). Fecal microbiota transplantation in human metabolic diseases: From a murky past to a bright future? Cell Metabolism, 33. https://doi.org/10.1016/j.cmet.2021.05.005

Park, S & Seo, G.S. (2021). Fecal Microbiota Transplantation: Is It Safe? Clin Endosc, 54. https://doi.org/10.5946/ce.2021.072

Bakalchuk, M.M & Besarbab, O.B. (2021). FECAL MICROBIOTES TRANSPLANTATION TECHNOLOGIES: MEDICAL, BIOTECHNOLOGICAL AND REGULATORY ASPECTS. BIOTECHNOLOGIA ACTA, 14 (1). https://doi.org/10.15407/biotech14.01.46

by Nimra Khan

The global disease burden of diabetes is 536.6 million people worldwide. Diabetes can be divided into Type 1 and Type 2 diabetes mellitus. Type 1 diabetes (T1D) is an autoimmune disease that destroys insulin-producing Beta (β) cells in the pancreatic islets (Figure 1). Insulin is a hormone made in your pancreas; a gland located behind your stomach. Insulin allows your body to use glucose for energy. People with T1D rely on daily insulin injections to replace what would have been produced by their β cells. Complications related to diabetes includes an increased risk of cardiovascular disease, kidney failure, heart attack, stroke, lower limb amputation, nerve damage and vision impairment. Current strategy of replacing the damaged β-cell mass involves whole pancreas transplantation. Because of a shortage of organ donors and problems associated with immunosuppressive drugs, transplantation strategies have limited clinical use. Therefore, looking at the possibility of regenerating β-cells in the pancreases is of interest.

Intriguingly, the human epigenetics research team from Monash University in Melbourne, Australia was able to “reawaken” stem-like cells in the pancreas and re-establish insulin expression from those cells using a drug approved by the US FDA. The research team published their findings in the article “Inhibition of pancreatic EZH2 restores progenitor insulin in T1D donor” in Signal Transduction and Targeted Therapy.

Experimental evidence shows inherent plasticity (ability to change function) of pancreatic cells, this fuelled interest in the potential regeneration of β-cells from other pancreatic cells (Figure 1). This potential regeneration of β-cells was previously demonstrated with the discovery of Alpha (α) cell plasticity and the ability of ductal and α-cells to convert into insulin-producing cells in the pancreases. It is also important to understand that the pancreas showed classic silencing of β-cell progenitor (ancestral) genes with barely detectable insulin transcript. This default silencing occurs due to EZH2 gene’s methyltransferase protein activity on H3K27me3. H3K27me3 is an epigenetic modification to the DNA packaging protein called Histone 3, this moditification functions to silence the insulin gene using EZH2 protein.

Therefore, the objective of this study was to investigate the effect of the drug GSK126 on the specific regulation of the H3K27me3 activity by EZH2 protein that is considered responsible for default suppression (transcriptional gene silencing). The overcoming of this suppression is critical to the progenitor cell’s developmental programme by enabling them to differentiate into functioning insulin secreting β-cells.

Isolated cells derived from the pancreas (Figure 2) were stimulated with GSK126 – a specific pharmacological inhibitor of EZH2 gene’s methyltransferase protein. Results showed that the drug effectively reduced h3k27me3 content, thereby influencing beta cell plasticity and promoting insulin gene expression (Figure 3). GSK126 also reinstated Insulin gene expression despite absolute β-cell destruction, dramatically influencing insulin expression in diabetic donor cells with damaged pancreatic islets. Treating the ductal cells with the drug GSK126 caused them to functionally resemble β cells and to produce insulin. These results showed a promising future for T1D treatment.

The main advantage of this method of treatment over beta cell replacement includes rapid insulin restoration requiring several days of drug treatment only as opposed to other protocols that can take several months. This novel research approach would allow insulin-producing β cells that are destroyed in T1D patients to be replaced with new insulin-generating cells. An important step towards developing new therapies.

Concluding remarks and future perspectives

Diabetes affects millions of people worldwide, this research is a starting point towards aiming to improve the quality of life of individuals with diabetes by reducing the need for insulin injections, dietary and activity restrictions, and importantly eliminate the need for pancreas transplants in chronic sufferers. These scientists discovered a way to “awaken” stem cells in the pancreas and harness their ability to express insulin.

Why is this discovery important? Because this provides a potential for treatment whereby using the patient’s own pancreas cells we can turn on genes which promote the production of insulin and will ultimately reduce the dependency on insulin injections (up to 100 per month) and eliminate the need for pancreas transplants. 

References

Al-Hasani, K., Khurana, I., Mariana, L., Loudovaris, T., Maxwell, S., Harikrishnan, K. N., Okabe, J., Cooper, M. E., & El-Osta, A. (2022). Inhibition of pancreatic EZH2 restores progenitor insulin in T1D donor. Signal transduction and targeted therapy, 7(1), 248. https://doi.org/10.1038/s41392-022-01034-7

Healthline. (2019). 10 Facts About Type 2 Diabetes and Insulin: Types, Risks, and More. [online] Available at: https://www.healthline.com/health/type-2-diabetes/insulin-facts-to-know#habits [Accessed 16 Sep. 2022].

by Talya Kebonte

Can you imagine a world where you aren’t able to pick up on the emotions of people around you? What would it be like if you couldn’t sense the sombre mood of a vigil and burst out into belly laughs while everyone around you was mourning? What if every piece of clothing you wore felt like sandpaper against your skin? How lonely would you feel if people labelled you as ‘weird’ and chose to avoid you? These are some of the things that a person with autism experiences.

Autism Spectrum Disorder, commonly referred to as autism, is a lifelong disability that affects one’s ability to communicate with people. It also causes problems in how one senses things [1]. Autism can be diagnosed as early as 18 months but is not usually diagnosed earlier than the age of 3 [2]. The timing of diagnosis is critical for treatment – an infant brain is more able to change according to experience than an adult brain, in other words, it has greater neuroplasticity [3].

To better understand neuroplasticity, picture the brain as a pastel pink ball of soft clay. When you are born it has a distinguishable shape, but it is still mouldable. The older you get, the more the clay hardens until you can’t change its shape anymore. The idea is to diagnose autism ‘while the clay is mouldable’ so that children can receive therapy and avoid social and sensory challenges that would otherwise be lifelong.

A randomised control trial on a group of Australian infants was done to test whether early therapy improved signs of autism. About half of the infants received 10 sessions delivered in their homes by a trained therapist while the other half received placebo care. After two years, it was found that the infants who received therapy had lower chances of having deficits in social-emotional interactions, repetitive movements, and unusual sensory interests compared to those that received placebo care [4].

Though these results are promising, the effects were small in extent. Hence, the clinical significance thereof is uncertain. It should be noted, however, that these small effects were enduring. Perhaps in the future we will have a more robust understanding of how the brain is ‘moulded’ in this condition and tailor more sensitive therapies for infants with autism.

References

[1]       L. Naithani et al., “Early Autism Intervention Components Deliverable by Non-specialists in Low- and Middle-Income Countries: A Scoping Review,” Frontiers in Psychiatry, vol. 13. Frontiers Media S.A., Jun. 29, 2022. doi: 10.3389/fpsyt.2022.914750.

[2]       M. van ’t Hof et al., “Age at autism spectrum disorder diagnosis: A systematic review and meta-analysis from 2012 to 2019,” Autism, vol. 25, no. 4. SAGE Publications Ltd, pp. 862–873, May 01, 2021. doi: 10.1177/1362361320971107.

[3]       B. Kolb and R. Gibb, “Brain plasticity and behaviour in the developing brain.,” J Can Acad Child Adolesc Psychiatry, vol. 20, no. 4, pp. 265–76, Nov. 2011.

[4]       A. J. O. Whitehouse et al., “Effect of Preemptive Intervention on Developmental Outcomes among Infants Showing Early Signs of Autism: A Randomized Clinical Trial of Outcomes to Diagnosis,” JAMA Pediatr, vol. 175, no. 11, Nov. 2021, doi: 10.1001/jamapediatrics.2021.3298.

by Sibongiseni Msipa

Cancer is a creepy and mysterious thing. While we tried to understand it, to get better at killing it, we discovered a biological paradox that remains unsolved to this day: large animals are immune to cancer. Which does not make any sense – the bigger a being, the more cancer it must have. To understand why, we first need to look at the nature of cancer itself.

To understand the nature of cancer we must first understand how cells work. cells are the basic units that make up the human body. they are made from hundreds of millions of parts. Guided by chemical reactions, they build and break down structure, sustain metabolism to gain energy or make copies of themselves. These complex chemical reactions are called pathways. Pathways are biochemical networks upon networks, which intertwined with each other, and function perfectly – until they do not. With these billions of reactions occurring in thousands of networks over many years, the question is not “if” something will go wrong but when? Tiny mistakes accumulate in the DNA of cells until this “perfect” network of systems gets corrupted. To prevent this from happening our cells have kill switches that make them commit suicide when cells are corrupted, however these kill switches are fallible. If they fail a cell can turn cancerous. Given enough time a cell would accrue enough mistakes, slip by unnoticed and begin to make more itself. All animals face this ordeal.

In general, the cells of all animals are the same size. The cells of a mouse are not fewer than yours, it just has fewer cells in total and a shorter lifespan. Fewer cells and a short lifespan mean a lower chance of things going wrong or cell mutating, – or at least it should mean that. Humans live about 50 times longer and have more cells than mice, yet the rate of cancer in humans and mice is the same. Even weirder blue whales have even more cells than humans, but they do not seem to get cancer at all. This is known as Peto’s Paradox.

Peto’s paradox is the baffling realization that larger animals have much less cancer than they should. Evolutionary biologists think that this results from larger animals using protective mechanisms that many smaller animals do not have. To identify how large animals might foster such mechanisms, evolutionary biologists created a theoretical model to simulate which of 100 possible genetic-mutation strategies would become most prevalent over 4,000 generations. The model included two gene types, Tumour suppressor and proto-oncogenes. The latter when mutated is bad news. For example, with the right mutation a cell will lose its ability to kill itself, another mutation and it will develop an ability to hide, another and it will send out a call for resources and multiply quickly. These genes, however, have an antagonist, known as tumour suppressor gene.

Tumour-suppressor genes, repair cellular damage that could otherwise lead to cancer. They found that tumour-suppressor genes and proto-oncogenes react differently along a gradient of body masses. In this model, evolution always favoured tumour-suppressor genes in large animals. Proto-oncogene activation decreased steadily with increasing body mass; the team found. Because of this, whales require more mutation than mice to develop a tumour, they are not immune but more resilient.

Or the solution to this Paradox may be something different; Hyper tumours. Hyper Tumors are named after hyperparasites, the parasites of parasites, hyper tomours are Tumour-within-a-tumour. Unlike normal cells which work together, cancer cells are selfish and work for their own short-term benefit, when successful, these cells form tumours these are huge cancer collectives which are hard to kill.  These cancerous cells require a lot of energy and resources to continue multiplying rapidly. Therefore, the amount of energy needed by these cancer cells becomes its growth limiting factor.  To counter this the body is tricked into building new blood vessels directly to the tumour, to feed the thing killing it. This is where the nature of cancer cells may become its undoing. Because of how unstable they are they continue to mutate and if they do this for a while one of the copies of the copies of the original cancer cell might suddenly think of itself and stop cooperating and just like that the original tumour suddenly becomes an enemy and blood supply is cut off to the original tumour – this starves and kills the original cancer cells. Cancer is killing Cancer. This process can repeat repeatedly, this then prevents cancer from becoming a problem in large animals. It is possible that large animals have more of these hyper tumours than we realize, they might just not become big enough to notice. So, an old blue whale might be filled with tiny cancers and just not care.

There are other proposed solutions to Peto’s Paradox, such as different metabolic rates or different cellular architecture, but right now we just do not know. Figuring out how large animals are so resilient to this deadliest disease; we might open up a path to new therapies and treatments. Cancer has always been a challenge but today we are finally beginning to understand it and by doing so we will finally overcome it.

References

Nagy, D., Victor, M. & Cropper H. (2007) Why don’t all whales have cancer? A novel hypothesis resolving Peto’s paradox. Integrative and Comparative Biology, (47)2, pp 317–328

Gewin, V. (2013) Massive animals may hold secrets of cancer suppression. Nature, https://doi.org/10.1038/nature.2013.12258

by Asande Vilane

Note: this blog is based on a 2018 review by Tiberi et al titled ‘New drugs and perspectives for anti-tuberculosis regimens’. It is third on the reference list and is thus indicated by the [3] in text referencing. Information that has been taken from other sources to provide further background information has been cited accordingly.

Above: the discovery of new anti-TB drugs is necessary to fight the threat of this debilitating disease. Faced with a slowing antibiotic discovery timeline, can scientists innovate new ways of addressing this deadly infection? Image taken from: https://www.technologynetworks.com/drug-discovery/articles/expediting-drug-discovery-using-novel-target-based-approaches-357879

In 2020, 1.5 million people succumbed to tuberculosis (TB)[1]. This debilitating disease, primarily affecting the lungs[2], is commonly treated with a six month regimen consisting of four different drugs, but various factors decrease the efficacy of this approach. In addition to issues with compliance, the treatment is not always well tolerated nor well prescribed, and may have devastating side-effects such as damage to the liver[3]. These issues with treatment adherence, tolerability and sub-optimal drug levels can lead to the development of multi-drug or extremely drug resistant TB (MDR and XDR TB respectively). MDR and XDR TB require more expensive, and increasingly toxic treatments, and have poorer outcomes as compared to drug sensitive TB[3]. Keeping the above in mind, scientists from around the world embarked on a non-systematic literature review to assess progress in the search for new anti-TB treatments – with the intention of assessing and highlighting key advances in the search for new anti-TB treatment approaches.

Discovering and isolating and assessing novel compounds for the treatment of TB is notoriously difficult and time-consuming with a high cost and an even higher rate of failure[3]. This, together with the need of for-profit companies to commercialize drugs for profit has led to research and development in this area slowing down in recent years. To date, only eight new drugs are in the pipeline with two having moved into the latter stages of confirmatory studies. Bearing this in mind, researchers [3] turned their attention to existing drugs used in the treatment of other diseases. This approach is advantageous as these drugs have already been tested and approved for their safety in humans and are manufactured at scale, and thus would be quickly available for treatment use.

They found that the leprosy drug clofazimine as well as the carbapenem class of antibiotics are promising drugs for use against TB[3]. In several studies, clofazimine was shown to be able to kill the TB-causing bacteria in the lungs. These results were further supported by randomized controlled trials (trials which tested the effects of clofazimine vs current interventions in a non-biased manner) as well as a meta-analysis (a systematic analysis of all the research on the topic) which reported an overall success rate of 61%. Additionally, clofazimine’s pharmacokinetic characteristics (the drug’s effect on the body), such as its tissue distribution, intra-cellular distribution and prolonged half-life make it a prime candidate for use as part of second line anti-TB treatment. Despite this, side-effects such as skin discoloration and heart conduction issues may hinder its advance to use for drug-susceptible TB.

Carbapenems (antibiotics typically used to treat a variety of other infections) have also shown promising results when used to treat MDR TB[3]. This has been shown by in-vitro activity, case reports and recently promising results from a phase 2b randomized control early bactericidal activity trial. Together, these results have prompted a whole new look at the management of drug resistant tuberculosis and have inspired new and innovative approaches to drug development in the context of TB.

While TB remains a global threat, there are still some promising leads when it comes to pioneering new approaches to treat this disease. Scientists such as [3] have highlighted that even when the search for new compounds becomes elusive, repurposing existing solutions can still offer some hope – paving the way for new frontiers in our clinical approach to tuberculosis disease.

References

1.         (WHO), W.H.O., Global Tuberculosis Report, W.H.O. (WHO), Editor. 2021.

2.         Pai, M., Behr, M.A., Dowdy, D., Dheda, K., Divangahi, M., Boehme, C.C., Ginsberg, A., Swaminathan, S., Spigelman, M., Getahun, H., Menzies, D., Raviglione, M., Tuberculosis. Nature Reviews: Disease Primers, 2016. 2.

3.         Tiberi, S., Munoz-Torrico, M., Duarte, R., Dalcomo, M., D’Ambrosio, L., Miglioro, G.B., New drugs and perspectives for new anti-tuberculosis regimens. Pulmonology Journal, 2018. 24(2): p. 86-98.

by Alice Piller

Precision medicine is a hot topic amongst the scientific community seeing numerous researchers and practitioners swarm to the field to be at the forefront of this revolutionary concept. Precision medicine is going to change the way we treat diseases by enabling better prevention strategies, hastier diagnoses, quicker recovery, high survival rates, and fewer adverse side-effects. Sounds like a pretty sweet deal worth all the swarming, right? Well, unfortunately precision medicine is not quite a fully-fledged reality yet, but a study on pancreatic cancer [4] conducted by researchers at the University of Cape Town validifies the pursuit of this holy nectareous grail.

While not utilized at its full potential, precision medicine has been put into practice for decades. For example, a person’s blood type is taken into account before receiving a blood transfusion. On a grander scale, precision medicine would see an individual’s genes, environment and lifestyle being considered before administering a treatment. In a few cancer types, genetic markers exist that indicate the cancer’s sensitivity to certain drugs. For example, The KRAS mutation in colon cancer means it will likely respond well to a tyrosine kinase inhibitor drug, and a mutation in ABL1 in chronic myelogenous leukaemia means the cancer is likely going to be resistance to imatinib. [1]

Cancer is currently the second leading cause of death in the world with over 10 million cancer-related deaths occurring in 2019 [2] and a projected 16.4 million deaths in 2040. [3] Current methods for outcome predictions and treatment decisions are largely based on tissues of origin and histological subtyping, leaving vast amounts of informative data unexplored. The researchers at the University of Cape Town lead by Musalula Sinkala, set out to characterise subtypes of pancreatic cancer based on molecular data from cell lines, which could highlight key biological pathways in driving oncogenesis and that could act as potential drug targets.

Sinkala et al. integrated proteomic, transcriptomic, DNA methylation, and miRNA data and identified two distinct pancreatic cancer subtypes, subtype-1 and subtype-2, using a machine learning clustering algorithm. Analysing the attributes of each subtype revealed several critical implications for clinical outcomes, treatment decisions, and drug targets.

Subtype-1 was found to be less aggressive than subtype-2 with a 75% survival rate versus only 35%. A possible reason for this is the increased DNA methylation in genes acting in key pathways including actin cytoskeleton regulation and focal adhesion.

Altered pathways can give great insight into drivers of oncogenesis and potential drug targets. Subtype-1 had hyperactivation and increased protein phosphorylation in the mTOR signaling pathway and displayed evidence of elevated ion channel and secretion pathway activities, whereas subtype-2 displayed hyperactivation and increased protein phosphorylation in cell cycle-associated pathways and elevated peptidase activities. Identification of these altered pathways exposes possible drug targets and, therefore, can help guide treatment decisions. Differential DNA methylation and miRNA signatures were also observed, which could explain the difference in transcriptomic and proteomic profiles of each subtype.

Lastly, the researchers selected a reduced set of 10 mRNA biomarkers and were able to predict drug responses of cell lines, that were not used in training of the model, with considerable accuracy. The reduced set of biomarkers increases this method’s utility in a clinical setting.

Sinkala et al.’s study is a great example of how informative molecular and genomic data can be for improving diagnostic, prognostic and treatment acumen in cancer. The advent of large-scale cell line databases has prompted a shift in the cancer treatment paradigm, away from a “one-size-fits-all” approach to personalised treatment, based on a patient’s unique biological and environmental context. This shift will hopefully see cancer falling from the second leading cause of death to a disease where poor outcomes are a thing of the past.

References

1. Testing.com – What are genetic tests for targeted cancer therapy? [Internet]. [updated 2021 May 13; cited 2022 September 15]. Available from: https://www.testing.com/tests/genetic-tests-targeted-cancer-therapy/.
2. Our World in Data – Causes of Death [Internet]. Hannah Ritchie and Max Roser. [updated 2019 December; cited 2022 September 15]. Available from:  https://ourworldindata.org/causes-of-death.
3. National Cancer Institute – Cancer Statistics [Internet]. [updated 2020 September 25; cited 2022 September 15]. Available from: https://www.cancer.gov/about-cancer/understanding/statistics#:~:text=Cancer%20is%20among%20the%20leading,related%20deaths%20to%2016.4%20million.
4. Sinkala M, Mulder N, Martin D. Machine learning and network analyses reveal disease subtypes of pancreatic cancer and their molecular characteristics. Scientific reports. 2020 Jan 27;10(1):1-4.

by Awakhiwe Makalima

The success of the human race has largely been owed to our continued efforts in creating ways of getting things done quicker and with more ease. From the healthcare industry, to transportation, to energy… numerous innovations have come about all ultimately making it possible to break barriers as we progress towards a better tomorrow. The field of protein studies has been no exception to this.

Over the past decade advances in electron detection systems and image-analysis software have catalyzed a “resolution revolution” in cryo-electron microscopy (cryo-EM), with the number of structures determined to atomic resolution exponentially increasing each year. Since the cryo-EM approach has less restrictions in terms of sample purity, concentration and volume, these atomic structures have even been determined directly from cell extracts. However, this “flexibility” is where the problem often begins.

Imagine you’re working with Mycobacterium tuberculosis, and you’ve put in a lot of work into identifying an important protein complex involved in bacterial survival within macrophages and its structure has yet to be determined. You’ve gone through a cryo-EM workflow, and you’ve used a software like Relion to obtain a near-atomic resolution cryo-EM map. The issue is you now require additional sequence information of your protein otherwise it will be impossible to build an atomic model. At this point the common solutions to your problem would be techniques, such as tandem mass spectrometry and/or bioinformatics which in the case of the former, would not always be easily accessible nor affordable and with latter the results would not always be easy to interpret.

So how would you identify your protein without the hassle of conducting more expensive and time-consuming experiments? Well, Luca Chang and his team have come up with just the right innovative tool and they’ve called it, DeepTracerID.

This server-based approach first requires the user to input a cryo-EM map which is used to generate a 3D model trace by DeepTracer. The user then needs to input an easily attainable AlphaFold2 protein library of the organism of interest after which three different alignment algorithms can be used to align the AlphaFold2 predicted structures to the generated 3D model trace. The aligned predictions are then statistically scored and listed from lowest to highest score. The correct protein of interest being predicted to be among those with the lowest scores.

The simplicity of this approach has the potential to open doors for incredible breakthroughs in the world of molecular imaging. As we continue to try and better understand the mechanisms via which different biochemical processes occur on a molecular level for various applications such as disease control, it is key for there to be good visual information to study. Continuing with the Mycobacterium tuberculosis example from earlier, it is interesting to see that there are multiple studies which have reported proteomic evidence of thousands of exported proteins, hundreds of which are associated with the strategies the bacterial cells use to survive within macrophages and cause disease. A search on protein data banks however gives nearly zero hits on the structures of these exported proteins nor the membrane proteins which could be implicated in their export out of the bacterial cells. Tools such as DeepTracerID could certainly be one of the keys to making it easier to increase the structural information on protein complexes available to us which is crucial to answering a lot of the questions we have in the research world.

Reference

Chang L., Wang F., Connolly K., Meng H., Su Z., Cvirkaite-Krupovic V., Krupovic M., Egelman E.H., Si D. 2022. DeepTracer-ID: De novo protein identification from cryo-EM maps. Biophysical Journal. Volume 121, Issue 15, Pages 2840-2848.