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How your gut influences your mood and behaviour Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The dopamine connection 11/07/25, 10:02 Last updated: Published: 25/03/24, 12:01 How your gut influences your mood and behaviour Introduction to dopamine Dopamine is a neurotransmitter derived from an amino acid called phenylalanine, which must be obtained through the diet, through foods such as fish, meat, dairy and more. Dopamine is produced and released by dopaminergic neurons in the central nervous system and can be found in different brain regions. The neurotransmitter acts via two mechanisms: wiring transmission and volume transmission. In wiring transmission, dopamine is released to the synaptic cleft and acts on postsynaptic dopamine receptors. In volume transmission, extracellular dopamine arrives at neurons other than postsynaptic ones. Through methods such as diffusion, dopamine then reaches receptors in other neurons that are not in direct contact with the cell that has released the neurotransmitter. In both mechanisms, dopamine binds to the receptors, transmitting signals between neurons and affecting mood and behaviour. The link between dopamine and gut health Dopamine has been known to result in positive emotions, including pleasure, satisfaction and motivation, which can be influenced by gut health. Therefore, what you eat and other factors, including motivation, could impact your mood and behaviour. This was proven by a study (Hamamah et al., 2022), which looked at the bidirectional gut-brain connection. The study found that gut microbiota was important in maintaining the concentrations of dopamine via the gut-brain connection, also known as the gut microbiota-brain axis or vagal gut-to-brain axis. This is the communication pathway between the gut microbiota and the brain facilitated by the vagus nerve, and it is important in the neuronal reward pathway, which regulates motivational and emotional states. Activating the vagal gut-to-brain axis, which leads to dopamine release, suggests that modulating dopamine levels could be a potential treatment approach for dopamine-related disorders. Some examples of gut microbiota include Prevotella, Bacteroides, Lactobacillus, Bifidobacterium, Clostridium, Enterococcus, and Ruminococcus , and they can affect dopamine by modulating dopaminergic activity. These gut microbiota are able to produce neurotransmitters, including dopamine, and their functions and bioavailability in the central nervous system and periphery are influenced by the gut-brain axis. Gut dysbiosis is the disturbance of the healthy intestinal flora, and it can lead to dopamine-related disorders, including Parkinson's disease, ADHD, depression, anxiety, and autism. Gut microbes that produce butyrate, a short-chain fatty acid, positively impact dopamine and contribute to reducing symptoms and effects seen in neurodegenerative disorders. Dopamine as a treatment It is important to understand the link between dopamine and gut health, as this could provide information about new therapeutic targets and improve current methods that have been used to prevent and restore deficiencies in dopamine function in different disorders. Most cells in the immune system contain dopamine receptors, allowing processes such as antigen presentation, T-cell activation, and inflammation to be regulated. Further research into this could open up a new possibility for dopamine to be used as a medication to treat diseases by changing the activity of dopamine receptors. Therefore, dopamine is important in various physiological processes, both in the central nervous and immune systems. For example, studies have shown that schizophrenia can be treated with antipsychotic medications which target dopamine neurotransmission. In addition, schizophrenia has also been treated by targeting the dysregulation (decreasing the amount) of dopamine transmission. Studies have shown promising results regarding dopamine being used as a form of treatment. Nevertheless, further research is needed to understand the interactions between dopamine, motivation and gut health and explore how this knowledge can be used to create medications to treat conditions. Conclusion The bidirectional gut-brain connection shows the importance of gut microbiota in controlling dopamine levels. This connection influences mood and behaviour but also has the potential to lead to new and innovative dopamine-targeted treatments being developed (for conditions including dopamine-related disorders). For example, scientists could target and manipulate dopamine receptors in the immune system to regulate the above mentioned processes: antigen presentation, T-cell activation, and inflammation. While current research has shown some promising results, further investigations are needed to better comprehend the connection between gut health and dopamine levels. Nevertheless, through consistent studies, scientists can gain a deeper understanding of this mechanism to see how changes in gut microbiota could affect dopamine regulation and influence mood and behaviour. Written by Naoshin Haque Related articles: the gut microbiome / Crohn's disease / Microbes in charge Project Gallery

An introductory and comprehensive review of complex diseases and their environmental influences. Using schizophrenia as an example, we are interested in exploring one of the biggest questions that underlie complex diseases. Go Back Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The environment on complex diseases: schizophrenia Last updated: 18/11/24 Published: 08/05/23 An introductory and comprehensive review of complex diseases and their environmental influences. Using schizophrenia as an example, we are interested in exploring one of the biggest questions that underlie complex diseases. Introduction: Not Exactly a Yes or No Question Many things in science revolve around questions. It is remarkable to find the number of questions left for scientists to answer or those that will remain unanswered. Indeed, one of the most daunting tasks for any scientist would be to see through every detail of a piece of information, even if everyone has seen it, but with different sets of lenses and asking different sets of questions. After all, “why did the apple fall from its tree?”. However, asking questions is one thing. Finding answers and, more importantly, the evidence or proof that supports them does not always yield conclusive results. Nevertheless, perhaps some findings may shine a new light on a previously unanswered question. We can categorise the study of genetics into two questions: “What happens if everything goes well?” and “What happens if it goes wrong?”. Whilst there are virtually limitless potential causes of any genetic disease, most genetic diseases are known to be heritable. A mutation in one gene that causes a disease can be inherited from the parents to their offspring. Often, genetic diseases are associated with a fault in one gene, known as a single-gene disorder, with notorious names including Huntington’s disease, cystic fibrosis, sickle cell anaemia, and familial hypercholesterolaemia. These diseases have different mechanisms, and the causes are also diverse. But all these diseases have one thing in common: they are all caused by a mutation or fault in one gene, and inheriting any specific genes may lead to disease development. In other words, “either you have it, or you do not”. The role of DNA and mutations in complex diseases. Image/ craiyon.com Multifactorial or complex diseases are a classification geneticists give to diseases caused by factors, faults or mutations in more than one gene. In other words, a polygenic disease. As a result, the research, diagnosis, and identification of complex diseases may not always produce a clear “black-and-white” conclusion. Furthermore, complex diseases make up most non-infectious diseases known. The diseases associated with leading causes of mortality are, in their respective ways, complex. Household names include heart diseases, Alzheimer’s and dementia, cancer, diabetes, and stroke. All of these diseases may employ many mechanisms of action, involving multiple risk factors instead of direct cause and effect, using environmental and genetic interactions or factors to their advantage, and in contrast to single-gene disorders, do not always follow clear or specific patterns of inheritance and always involve more than one problematic genes before the complete symptoms manifest. For these reasons, complex diseases are infamously more common and even more challenging to study and treat than many other non-infectious diseases. No longer the easy “yes or no” question. The Complex Disease Conundrum: Schizophrenia Here we look at the case of a particularly infamous and, arguably, notorious complex disease, schizophrenia (SCZ). SCZ is a severely debilitating and chronic neurodevelopmental disorder that affects around 1% of the world’s population. Like many other complex diseases, SCZ is highly polygenic. The NHS characterise SCZ as a “disease that tends to run in families, but no single gene is known to be directly responsible…having these genes does not necessarily mean one will develop SCZ”. As previously mentioned, many intricate factors are at play behind complex diseases. In contrast, there is neither a single known cause for SCZ nor a cure. Additionally, despite its discovery a century ago, SCZ is arguably not well understood, giving a clue to the sophisticated mechanisms that underlie SCZ. To further illustrate how such complexities may pose a challenge to future medical treatments, we shall consider a conundrum that diseases like SCZ may impose. The highly elaborate nature of complex diseases means that it is impossible to predict disease outcomes or inheritance with absolute certainty nor rule out potential specific causes of diseases. One of the most crucial aspects of research on complex diseases is their genetic architecture, just as a house is arguably only as good as its blueprint. Therefore, a fundamental understanding of the genes behind diseases can lead to a better knowledge of diseases’ pathogenesis, epidemiology, and potential drug target, and hopefully, one day bridge our current healthcare with predictive and personalised medicine. However, as mentioned by the NHS, one of the intricacies behind SCZ is that possessing variants of diseased genes does not translate to certainty in disease development or symptom manifestation. Our conundrum, and perhaps the biggest question on complex diseases like SCZ is: “Why, even when an individual possesses characteristic genes of a complex disease, they may not necessarily exhibit symptoms or have the disease?”. The enigma surrounding complex diseases lies in the elegant interactions between our genes, the blueprint of life, and “everything else”. Understanding the interplay of factors behind complex diseases may finally explain many of the intricacies behind diseases like SCZ. Genes and Environment: an Obvious Interaction? The gene-environment important implications on complex disease development were demonstrated using twin studies. A twin study, as its name suggests, is the study of twins by their similarities, differences, and many other traits that twins may exhibit to provide clues to the influences of genetic and external factors. Monozygotic (MZ) twins each share the same genome and, therefore, are genetically identical. Therefore, if one twin shows a phenotype, the other twin would theoretically also have said genes and should exhibit the corresponding trait. Experimentally, we calculate the concordance rate, which means the probability of both twins expressing a phenotype or characteristic, given that one twin has said characteristic. Furthermore, the heritability score may be mathematically approximated using MZ concordance and the concordance between dizygotic twins (twins that share around half a genome). These studies are and have been particularly useful in demonstrating the exact implications genetic factors have on phenotypes and how the expression of traits may have been influenced by confounding factors. In the case of SCZ, scientists have seen, over decades, a relatively low concordance rate but high heritability score. A recent study (published in 2018) through the Danish SCZ research cohort involved the analysis of around 31,500 twins born between the years 1951 and 2000, where researchers reported a concordance rate of 33% and estimated heritability score of 79%, with other older studies reporting a concordance rate up to and around 50%. The percentages suggest that SCZ is likely to be passed down. In other words, a genetically identical twin only has approximately 1 in 2 risks of also developing symptoms of SCZ if its opposite twin also displays SCZ. The scientists concluded that although genetic predisposition significantly affects one’s susceptibility or vulnerability against SCZ, it is not the single cause of SCZ. Demographically, there have been studies that directly link environmental risks to SCZ. Some risk factors, such as famines and malnutrition, are more evident than others. However, some studies also associate higher SCZ risk among highly industrialised countries and first or second-generation migrants. For instance, few studies point out an increased risk of SCZ within ethnic minorities and Afro-Caribbean immigrants in the United Kingdom. Hypotheses that may explain such data include stress during migration, potential maternal malnutrition, and even exposure to diseases. With this example, hopefully, we all may appreciate how the aetiology of SCZ and other complex diseases are confounded by environmental factors. In addition, how such factors may profoundly influence an individual’s genome. SCZ is a clear example of how genetic predisposition, the presence of essential gene variants characteristic of a disease, may act as a blueprint to a terrible disease waiting to be “built” by certain factors as if they promote such development. It is remarkable how genetic elements and their interactions with many other factors may contribute almost collectively to disease pathogenesis. We can reflect this to a famous quote amongst clinical geneticists: “genetics loads the gun, and environment pulls the trigger.” Carrying high-risk genes may increase the susceptibility to a complex disease, and an environment that promotes such disease may tip the balance in favour of the disease. However, finding and understanding the “blueprints” of SCZ, what executes this “blueprint”, and how it works is still an area of ongoing research. Furthermore, how the interplay between genetics and external factors can lead to profound effects like disease outcomes is still a relatively new subject. The Epigenome: the Environment’s Playground To review, it is clear that genes are crucial in complex disease aetiology. In the case of SCZ, high-risk genes and variances are highly attributed to disease onset and pathogenesis. However, we also see with twin studies that genetics alone cannot explain the high degree of differences between twins, particularly when referring to SCZ concordance between identical twins. In other words, external factors are at play, influencing one’s susceptibility and predisposition to SCZ. These differences can be explained by the effects epigenetics have on our genome. Epigenetic mechanisms regulate gene expression by modifying the genome. In short, on top of the DNA double strands, the genome consists of additional proteins, factors, and even chemical compounds that all aid the genetic functions our body heavily relies on. The key to epigenetics lies in these external factors’ ability to regulate gene expression, where some factors may promote gene expression whilst others may prevent it. Epigenetic changes alter gene functions as they can turn gene expression “on” and “off”. Furthermore, many researchers have also shown how epigenetic changes may accumulate and be inherited somatically with cell division and even passed down through generations. Therefore, epigenetic changes may occur without the need to change any of the DNA codes, yet, they may cause a profound effect by controlling gene expression throughout many levels of the living system. These underlying mechanisms are crucial for the environment’s effect on complex diseases. Some external factors may directly cause variances or even damage to the genome (e.g. UV, ionising radiation), and other sources may indirectly change gene expression by manipulating epigenetic changes. The exact molecular genetics behind epigenetic mechanisms are elaborate. However, we can generally find three common epigenetic mechanisms: DNA Methylation, Histone Modification, and Non-coding RNA. Although each method works differently, they achieve a common goal of promoting or silencing gene expression. All of these are done by the many molecular components of epigenetics, altering the genome without editing the gene sequence. We refer to the epigenome, which translates to “above the genome”, the genome itself and all the epigenetic modifiers that regulates gene expression on many levels. Environmental factors and exposure may influence epigenetic mechanisms, affecting gene expression in the cell or throughout the body, sometimes permanently. Therefore, it is clear how the epigenome may change throughout life as different individuals are exposed to numerous environmental factors. Furthermore, each individual may also have a unique epigenome. Depending on which tissues or cells are affected by these mechanisms, tissues or cells may even have a distinct epigenome, unlike the genome, which is theoretically identical in all cells. One example of this is the potential effects of DNA methylation on schizophrenia epidemiology. DNA methylation can silence genes via the enzymes DNA methyltransferases (DNMT), a family of enzymes capable of catalysing the addition of methyl groups directly into the DNA. The DNMT enzymes may methylate specific nucleotides on the gene, which usually would silence said gene. Many researchers have found that the dysregulation of DNA methylation may increase the risk towards the aetiology of numerous early onset neuro-developmental disorders. However, SCZ later-onset development also points towards the influence of environmental risk factors that target DNA methylation mechanisms. Studies show links between famines and SCZ increased prevalence, as the DNMT enzymes heavily rely on nutrients to supply essential amino acids. Malnutrition is thought to play a considerable role in DNA methylation changes and, therefore, the risk of SCZ. Small Piece of a Changing Puzzle Hopefully, we can see a bigger picture of the highly intricate foundation beneath complex diseases. Bear in mind that SCZ is only one of many complex diseases known. SCZ is ultimately not a pristine and impartial model to study complex disorders. For instance, concordance rates of complex diseases change depending on their genetic background. In addition, they may involve different mutations, variance, or dysregulation of differing pathways and epigenetic mechanisms. After all, complex diseases are complex. Finally, this article aimed to give a rundown of the epigenetics behind complex diseases like SCZ. However, it is only a snapshot compared to the larger world of the epigenome. Furthermore, some questions remain unanswered: the genetic background and architecture of complex diseases, and ways to study, diagnose, and treat complex diseases. This Scientia article is one of the articles in Scientia on the theme of complex disease science and genetics. Hopefully, this introductory article is an insight and can be used to reflect upon, especially when tackling more complicated subjects of complex diseases and precision medicine. Written by Stephanus Steven Related articles: Schizophrenia, Inflammation, and Accelerated Ageing / An Introduction to Epigenetics

When we think of bacteria, we tend to focus on their pathogenicity and ability to cause diseases such as tuberculosis, which infects around one-quarter of the world’s population. However, whilst bacteria do have the potential to become parasitic, if the trillions of bacterial cells that make up the human microbiome ceased to exist, human health would experience a rapid decline. Go back Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Why bacteria are essential for human survival Last updated: 13/11/24 Published: 13/04/23 When we think of bacteria, we tend to focus on their pathogenicity and ability to cause diseases such as tuberculosis, which infects around one-quarter of the world’s population. However, whilst bacteria do have the potential to become parasitic, if the trillions of bacterial cells that make up the human microbiome ceased to exist, human health would experience a rapid decline. One reason for this is due to the critical role bacteria play in inducing the immune system against pathogenic threats. Upon viral infection, the interferon (IFN) defence system is initiated where the synthesis of antiviral cytokines is upregulated. Evidence suggests bacteria in the gut are capable of modulating the IFN system. They work by inducing macrophages and plasmacytoid dendritic cells to express type 1 IFN, which in turn primes natural killer cells and prepares cytotoxic CD8+ T cells for action. Erttmann et al (2022) demonstrate that a depletion of the gut microbiota diminishes the cell signalling pathways modulated by these commensal bacteria. This causes a reduction in type 1 IFN production, and thus an impairment in the activation of NK and CD8+ T cells. As a result, the body becomes more susceptible to attack by viral infections and less able to defend itself. This highlights just how vital the role bacteria in our microbiome play in providing us with innate immunity against viral pathogens and protecting our health. This also brings attention to our use of antibiotics, and the potential negative effects they may have on the commensal bacteria residing in our body. Erttmann et al (2022) further showed that mice treated with a variety of antibiotics exhibited a major reduction in gut microbiota diversity, thus severely comprising their ability to fight off viral infections. Research like this is important in informing doctors to be sensible in their administration of antibiotics, as well as informing patients to not self-medicate and unnecessarily ingest antibiotics. Ultimately, the commensal bacteria living in our bodies play essential roles in protecting human health, and it is, therefore, vital we take the necessary steps to also protect these remarkable microorganisms in return. Written by Bisma Butt Related article: The rising threat of antibiotic resistance REFERENCES Erttmann, S.F., Swacha, P., Aung, K.M., Brindefalk, B., Jiang, H., Härtlova, A., Uhlin, B.E., Wai, S.N. and Gekara, N.O., 2022. The gut microbiota prime systemic antiviral immunity via the cGAS-STING-IFN-I axis. Immunity, 55(5), pp.847-861. Ganal, S.C., Sanos, S.L., Kallfass, C., Oberle, K., Johner, C., Kirschning, C., Lienenklaus, S., Weiss, S., Staeheli, P., Aichele, P. and Diefenbach, A., 2012. Priming of natural killer cells by nonmucosal mononuclear phagocytes requires instructive signals from commensal microbiota. Immunity, 37(1), pp.171-186.

Antifreeze proteins and frozen foods Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link The Survival Secrets of the Arctic Springtail 04/07/25, 12:59 Last updated: Published: 21/09/24, 16:09 Antifreeze proteins and frozen foods Introduction Approximately 450 million years ago, during the Ordovician period, the Earth was characterised by a hot and humid globe. The sea was teeming with life, with early squids, eel-like fish, and sea worms hunting smaller animals. However, there was no sign of movement above ground as the animals had not yet crawled ashore. This period of warmth created ideal living conditions for wildlife, but it was about to change dramatically. Shortly after, the land masses began to freeze, and an ice cap started to spread. The once warm and accommodating waters turned cold and inhospitable, leading to the second-worst mass extinction in the history of the planet. Many species succumbed to the harsh conditions, but one animal survived - the springtail. The springtail, a small insect-like animal, had developed a special strategy to combat the cold. Its cells started producing proteins that could protect them from freezing. This discovery challenges the previous belief that animals did not develop antifreeze proteins until much later. Research from Aarhus University has shown that the springtail might have been the first animal to develop such proteins. Applications in the Food Industry Since then, scientists have found antifreeze proteins in various animals, plants, and microorganisms. These proteins have also found applications in different industries. One of the industries utilising antifreeze proteins is the food industry, especially in producing frozen foods. Frozen foods often suffer from changes in taste and texture due to the formation of ice crystals. However, by incorporating antifreeze proteins, these undesirable effects can be prevented. Industrial yeast cell cultures have been engineered to produce antifreeze proteins derived from fish genes. These proteins can then be added to different foods, including ice cream, to improve texture and prevent the formation of ice crystals. Exploring Arctic Fish Aside from their contribution to the food industry, springtails have also fascinated scientists due to their ability to survive in extremely cold regions. The discovery of antifreeze proteins explained how arctic fish could swim in icy seawater. The proteins prevent ice from forming in the cells and blood of the fish, allowing them to survive in freezing conditions. Martin Holmstrup, a researcher at Aarhus University, oversees colonies of springtails in his laboratory. These small animals require minimal space and can be easily maintained in Petri dishes with a base of moist plaster and a feed of dry yeast. Researchers have determined that springtails developed these proteins long before other animals by studying the DNA sequences responsible for building antifreeze proteins. The discovery of antifreeze proteins in springtails opens up possibilities for various applications, including in the food industry. These proteins have been found to prevent ice crystal formation, which can affect the taste and texture of frozen foods. The genes responsible for their production have been copied into industrial yeast cell cultures to utilise these proteins. This allows the yeast to produce the antifreeze proteins, which can then be added to different foods. One example is the use of these proteins in ice cream, where they help create a delightful texture and allow the ice cream to be thawed and refrozen without compromising its quality. Conclusion The discovery of antifreeze proteins in springtails has revolutionised various industries, particularly the food industry. These proteins have been found to prevent ice crystal formation, improving the taste and texture of frozen foods. Incorporating antifreeze proteins derived from fish genes into yeast cell cultures can produce and add these proteins to different foods, such as ice cream, ensuring a delightful texture and the ability to thaw and refreeze without compromising quality. This remarkable adaptation of springtails has provided insight into their survival in extremely cold regions and opened up possibilities for further applications of antifreeze proteins in various fields. Written by Sara Maria Majernikova Related articles: p53 protein / Zinc finger proteins / Emperor penguins, kings of ice Project Gallery

Arginine, the key to metabolic reprogramming in liver cancer Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Linking arginine and tumour growth: a breakthrough in cancer research Last updated: 20/02/25, 15:29 Published: 27/02/25, 08:00 Arginine, the key to metabolic reprogramming in liver cancer Unpicking the secrets of tumour growth: arginine, the key to metabolic reprogramming in liver cancer. We will look at how unleashing the power of arginine and elevating levels of this amino acid drive metabolic reprogramming and fuel tumour growth. Introduction In recent years, the field of cancer research has made significant progress in unravelling the complexities of this devastating disease. Scientists at the University of Basel have made a groundbreaking discovery regarding the role of the amino acid arginine in promoting tumour growth. Their findings shed light on the mechanisms underlying metabolic reprogramming in cancer cells and present new avenues for improving liver cancer treatment. Elevated levels of arginine: a surprising revelation An intriguing aspect of the study conducted by the researchers is the observation that tumour cells accumulate high levels of arginine despite producing less or none of this amino acid. Through careful analysis of liver tumour samples from both mice and patients, the team discovered that the tumour cells achieve this accumulation by increasing the uptake of arginine and suppressing its consumption. The role of arginine in tumorigenicity Upon further investigation, the scientists at the University of Basel found that high concentrations of arginine bind to a specific factor, triggering metabolic reprogramming in the tumour cells. This reprogramming, in turn, promotes tumour growth by regulating the expression of metabolic genes. The tumour cells revert to an undifferentiated embryonic cell state, enabling them to divide indefinitely. Immune system escape: a beneficial effect for tumour cells Another fascinating discovery made by the researchers is the role of arginine in aiding tumour cells in evading the immune system. Immune cells rely on arginine to function properly. By depleting arginine in the tumour environment, the tumour cells can escape immune surveillance. This finding opens up new possibilities for targeted therapies. Targeting the arginine-binding factor: a novel approach Instead of depleting arginine levels overall, which can have unwanted side effects, the scientists propose targeting the specific arginine-binding factor responsible for promoting metabolic reprogramming. By inducing the degradation of this factor, the researchers were able to prevent metabolic reprogramming in liver tumours. This approach offers a promising alternative to liver cancer treatment. Metabolic changes as biomarkers for early cancer detection Furthermore, the study suggests that metabolic changes, such as increased arginine levels, may serve as biomarkers for the early detection of cancer. Early detection is crucial for successful cancer treatment and patient survival. This finding provides hope for the development of non-invasive diagnostic methods that can detect elevated arginine levels. By measuring arginine levels in patients, these diagnostic methods can potentially identify liver cancer at an early stage. By identifying the elevated levels of arginine in liver tumour cells, these diagnostic methods could potentially use metabolic changes, such as increased arginine levels, as biomarkers for detecting cancer. Therefore, this would be crucial for successful cancer treatment and patient survival, as early detection allows for prompt intervention and improved outcomes. Conclusion The discovery of the role of arginine in driving metabolic reprogramming and promoting tumour growth opens up new avenues for liver cancer treatment. Additionally, the elevated levels of arginine observed in liver cancer patients suggest the potential for using arginine as a biomarker for non-invasive cancer detection. Further research is needed to explore the full potential of arginine as a diagnostic marker and to develop targeted therapies that exploit the metabolic vulnerabilities of cancer cells. With continued advancements in our understanding of cancer metabolism and the role of arginine in tumour growth, further research is needed to explore the full potential of arginine as a diagnostic marker and to develop targeted therapies that exploit the metabolic vulnerabilities of cancer cells. By studying the specific arginine-binding factor and its role in promoting metabolic reprogramming, scientists may be able to develop new treatments that selectively target tumour cells while minimising harm to immune cells that rely on arginine. Additionally, investigating the metabolic changes associated with increased arginine levels may lead to new biomarker designs for early cancer detection, which is crucial for successful treatment and patient survival. Written by Sara Maria Majernikova Related articles: Immune signals and metastasis / Cancer research treatment / Prostatate cancer treatment REFERENCE MOSSMANN, D., MÜLLER, C., PARK, S., RYBACK, B., COLOMBI, M., RITTER, N., WEISSENBERGE, D., DAZERT, E., COTO-LLERENA, M., NUCIFORO, S., BLUKACZ, L., ERCAN, C., JIMENEZ, V., PISCUOGLIO, S., BOSCH, F., TERRACCIANO, L. M., SAUER, U., HEIM, M. H. & HALL, M. N. Arginine reprograms metabolism in liver cancer via RBM39. Cell . DOI: https://doi.org/10.1016/j.cell.2023.09.011 Project Gallery

Their hidden power in AI and machine learning Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Latent space transformations 21/08/25, 15:53 Last updated: Published: 19/09/23, 16:42 Their hidden power in AI and machine learning Getting machines to understand the information we want to give it is quite the task. Especially, given the level of complexity of the information we give it. For example, when trying to process an image for classification algorithms, how does the algorithm recognise the paws of a dog or the curvature of a boat? We need to simplify the information for simpler processing and manipulation. Similar to how you would take summarised notes in a lecture instead of copying everything. While information is lost, the key features are kept. That is where the term “ latent space ” comes in. What are latent spaces? In the realm of mathematics, various types of spaces play crucial roles. One such space is the linear space, which encompasses the number line—a fundamental construct. Then there's Euclidean space, a broader category that encompasses 2D, 3D, and higher-dimensional spaces. However, as the number of dimensions increases, the mathematical intricacies become exceedingly complex, often pushing the limits of computational feasibility. In a latent space transformation, we essentially reduce the dimensions of the space in which the data exists and create an abstract representation of the key features in a lower dimension space. This has a host of benefits with the main one being a reduction in the compute power needed to process the data. It’s an example of data compression and a direct instance of dimension reduction with neither being new concepts. Example: auto-encoders Auto-encoders are a type of neural network. They consist of an encoder-to-decoder architecture (see image with caption). The transformation allows us to process and store the input data more efficiently. In addition, once trained, auto-encoders can sample data from the latent space to generate new data points also called data generation of a synthetic nature. Other applications of latent space Now that we can store our information more effectively for computers to understand, there are a host of applications for the technique you might want to be aware of: - Natural Language Processing: Latent space models have been used in natural language processing for tasks such as text classification, sentiment analysis, and machine translation. - Audio Processing: Latent space models have been used for music analysis, speech recognition, and audio processing. - Computer Vision: This we have partially discussed already. - Anomaly Detection: Latent space models can be used to recognise security failures in cybersecurity, or potentially fraud in the financial system. The applications of data reduction would be endless but those are just few applications in technology right now. Written by Temi Abbass Related articles: Markov chains / Evolution of AI / Study on brain metastasis Project Gallery

Unraveling the mysteries of protein-DNA interactions Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Zinc fingers in action 14/07/25, 15:21 Last updated: Published: 07/01/24, 14:22 Unraveling the mysteries of protein-DNA interactions Zinc-finger proteins are one of the most prevalent proteins used in DNA-binding motifs in biological processes. They are common as eukaryotic transcriptional factors. As they are structurally diverse, they interact in cellular processes like RNA packaging, DNA recognition, and transcriptional activation. Cys2His2 zinc- finger proteins are significant in cellular processes because of their short helical structure. The motif forms from a few amino acid sequences that contain cysteine and histidine residues that coordinate to a zinc ion. The zinc ions are crucial in stabilising the protein during folding. They also hold the α-helix and β-sheetstructures in place. The protein’s stability comes from the weak hydrophobic core and zinc coordination created by chelating. The zinc-finger/DNA complex is formed from the fingers interacting with up to four bases. The zinc finger DNA complex was first discovered from the transcription factor TFIIIA. The transcription factor had a ninefold pattern containing hydrophobic residues, histidine, and cysteine. The zinc finger motif was then concluded to consist of thirty amino acids and have a DNA binding domain with a zinc ion. This was confirmed by an extended x-ray absorption fine structure analysis. It was concluded that the contacts between the DNA strand and α helix occur due to hydrogen bonding and Van der Waals interactions. From these studies, the structures of zinc finger domains play vital roles in many processes other than DNA binding. Their tertiary structure allows the proteins to act as DNA-binding motifs. The alpha helix functions as the protein recognition component by inserting the protein into the main groove of DNA. Immobilizing zinc-finger proteins on a polymer chip can be used as an example to identify infections in the human body. This section provides a summary of the many kinds of DNA recognition and the generic protein-folding principles. Firstly, a specific binding site probe is needed to identify the DNA sequence region. This allows the identification of specific base pairs in the sequence. The hydrogen bonds between the amino acids in the zinc-finger proteins and DNA bases allow the zinc- finger proteins to bind to non-specific backbone phosphates. The non-specific backbone phosphates are formed from the interactions in the major and minor grooves of the DNA. The zinc-finger DNA interactions contribute substantially to hydrogen bonding and overall binding energy. To conclude, zinc fingers are very common structural motifs that are used as model systems to investigate how these proteins can recognise DNA sequences. This research has been involved in developing important therapeutic tools. Their unique structure allows them to be heavily involved in DNA binding, most commonly the Cys2His2 fingers. These binding interactions can be further explored to understand how certain target genes are bound to or how inhibitors can show the pharmacological properties of the zinc finger proteins. Written by Anam Ahmed Related articles: p53 protein / Anti-freeze proteins Project Gallery

How it's used Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link AI in medicinal chemistry 08/07/25, 16:18 Last updated: Published: 07/07/23, 20:47 How it's used We are always surrounded by medicine, whether this be through, for example, the cabinet in your house containing prescription drugs or walking by a pharmacy during the day. It is no secret that medical drugs are essential - they both mitigate the symptoms of disease and even prevent further future illness. However, whilst ingesting a tablet is easy for most, it seems to be that we can sometimes forget the vigorous amount of scientific research that goes into successfully synthesising a new drug, i.e. the core of medicinal chemistry. This process typically takes up to an astounding 10 years or more, but with new artificial intelligence (AI) emerging it is thought to be that this number will lower. What exactly is artificial intelligence? It can broadly be defined as the ability to produce human intelligence through the use of machinery such as computers or software. Based on this, one may question why AI is needed if we can just simply communicate ideas through writing, speaking and so on. The answer is increased efficiency – one example of man made neurones is discussed on the website Interesting Engineering, which are able to produce impulses up to one billion times per second. Fascinatingly, this is quicker than humans, so it could also be argued that AI is actually better than us! There are many phases of the drug development process, from early pre-clinical research to post-market surveillance. When a drug is administered, the body uses enzymes such as mainly those from the CYP family to break the compound down into smaller structures, through a process known as metabolism. Drug metabolism can create toxic molecules that are able to covalently bind to proteins in the body causing serious illness, but also molecules that can be harmlessly excreted through faeces or urine. Of course, chemists can look for sites of metabolism by studying the angles and positions of atoms, however AI is able to do this much quicker and with higher accuracy. SuperCYPsPred is an example of a free online web application that can predict if a drug may be a CYP enzyme inhibitor in pre-clinical drug discovery, as the software is able to identify five of such inhibitors. Through this, we can understand how a drug’s metabolic pathway may differ and investigate further early on, allowing scientists to make structural changes before proceeding onto the next phase of development. Through this, millions of pounds can be saved from marketing an unsuccessful drug as well as decrease the chances of causing injury to the public. AI is also able to use machine learning (ML) to carry out tasks. ML is when machinery processes a large data set and identifies complex patterns to problem solve. From this then comes deep learning (DL), which allows this ML to be applied in different fields. For example, DeepCE is a “novel deep learning computer model” that helps predict changes in gene expression with certain drugs. It is able to do this by using the following two sources: DrugBank which contains data for 11,000 safely approved drugs and the L1000 dataset that has information on over 1 million perturbed organ tissue gene expressions. From this, researchers were able to obtain 10 drug candidates for the treatment of COVID-19 infection, in which 2 have been successfully marketed. Based on the above, it is clear that AI holds a lot of power in speeding up the drug discovery and development process. With the technology sector advancing in general as well, we are looking at a future where AI will become even more dominant in the pharmaceutical research industry. Whilst AI can predict several drug properties, it is also important to remember that we physically cannot predict every single thing out there – we can only try our best, which AI is aiding. Written by Harsimran Kaur Related articles: AI in drug discovery / A breakthrough procedure in efficient drug discovery / Role of chemistry in medicine Project Gallery

Molecular gastronomy Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link Exploring food at a molecular level 09/07/25, 14:07 Last updated: Published: 13/05/24, 14:46 Molecular gastronomy Imagine taking a bite of your favourite dish, not just savouring the flavours, but peering into the very essence of its existence. That's the realm of molecular gastronomy, a fascinating exploration of food through the lens of science. This article takes you on a journey at the microscopic level of what fuels the human body. The foundation of all food lies in macromolecules, large molecules formed from the intricate assembly of smaller ones. Carbohydrates, proteins, and lipids are the main players, each with unique structures and roles. Carbohydrates: These sugary giants, like starches and sugars, provide our bodies with energy. Imagine them as long chains of sugar molecules linked together, like beads on a necklace. Proteins: The workhorses of the cellular world, proteins are responsible for countless functions. They're built from amino acids, each with a distinct side chain, creating a diverse and essential cast of characters. Lipids: Fats and oils, these slippery molecules store energy and form cell membranes. Think of them as greasy chains with attached rings, like chubby tadpoles swimming in oil. The symphony of cooking and the final dance Applying heat, pressure, and chemical reactions, chefs become culinary alchemists at the molecular level. Water, the universal solvent, facilitates the movement and interaction of these molecules. As we cook, proteins unfold and rearrange, starches break into sugars, and fats melt and release flavours. Maillard Reaction: This browning phenomenon, responsible for the delicious crust and crunch on your food, arises from the dance between sugars and amino acids. Imagine them waltzing and exchanging partners, creating new flavorful molecules that paint your food with golden hues. Emulsification: Oil and water don't mix, but lecithin, a molecule found in egg yolks, acts as a matchmaker. It bridges the gap between these unlikely partners, allowing for the creation of creamy sauces and fluffy cakes. Think of lecithin as a tiny cupid, shooting arrows of attraction between oil and water droplets. Saponification: Techniques like spherification use alginate and calcium to create edible spheres filled with liquid, transforming into playful pearls that burst with flavor in your mouth. A world of potential Understanding food at the molecular level unlocks a treasure trove of possibilities. It can help us create healthier, more sustainable food choices, develop personalized nutrition plans, and even combat food-borne illnesses. By peering into the microscopic world of our meals, we gain a deeper appreciation for the magic that happens on our plates, bite after delicious bite. So next time you savor a meal, remember the intricate dance of molecules that brought it to life. From the building blocks of carbohydrates to the symphony of cooking, food is a story written in the language of chemistry, waiting to be deciphered and enjoyed. Written by Navnidhi Sharma Related articles: Emotional chemistry on a molecular level / Food prices and malnutrition / Vitamins References and further readings: Chapter 2: Protein structure . (2019, July 10). Chemistry. https://wou.edu/chemistry/courses/online-chemistry-textbooks/ch450-and-ch451-biochemistry-d efining-life-at-the-molecular-level/chapter-2-protein-structure/ Gan, J., Siegel, J. B., & German, J. B. (2019). Molecular annotation of food - Towards personalized diet and precision health. Trends in Food Science & Technology , 91 , 675–680. https://doi.org/10.1016/j.tifs.2019.07.016 Grant, P. (2020, August 4). Sugar, fiber, starch: What’s A carbohydrate? — Pamela Grant, L.Ac , NTP. Pamela Grant, L.Ac , NTP . https://pamela-grant.com/blog-ss/sugar-fiber-starch Helmenstine, A. (2022, October 25). Examples of carbohydrates . Science Notes and Projects. https://sciencenotes.org/examples-of-carbohydrates/ Project Gallery

The maths behind trading Facebook X (Twitter) WhatsApp LinkedIn Pinterest Copy link A comprehensive guide to the Relative Strength Index (RSI) 08/07/25, 14:37 Last updated: Published: 27/12/23, 11:02 The maths behind trading In this piece, we will delve into the essential concepts surrounding the Relative Strength Index (RSI). The RSI serves as a gauge for assessing the strength of price momentum and offers insights into whether a particular stock is in an overbought or oversold condition. Throughout this exploration, we will demystify the underlying calculations of RSI, explore its significance in evaluating market momentum, and unveil its practical applications for traders. From discerning opportune moments to buy or sell based on RSI values to identifying potential shifts in market trends, we will unravel the mathematical intricacies that underpin this critical trading indicator. Please note that none of the below content should be used as financial advice, but for educational purposes only. This article does not recommend that investors base their decisions on technical analysis alone. As indicated in the name, RSI measures the strength of a stock's momentum and can be used to show when a stock can be considered over- or under-bought, allowing us to make a more informed decision as to whether we should enter a position or hold off until a bit longer. It’s all very well and good to know that ‘you should buy when RSI is under 30 and sell when RSI is over 70' , but in this article, I will attempt to explain why this is the case and what RSI is really measuring. The calculations The relative strength index is an index of the relative strength of momentum in a market. This means that its values range from 0 to 100 and are simply a normalised relative strength. But what is the relative strength of momentum? Initial Average Gain = Sum of gains over the past 14 days / 14 Initial Average Loss = Sum of losses over the past 14 days / 14 Relative strength is the ratio of higher closes to lower closes. Over a fixed period of usually 14 days (but sometimes 21), we measure how much the price of the stock has increased in each trading day and find the mean average between them. We then repeat and do the same to find the average loss. The subsequent average gains and losses can then be calculated: Average Gain = [(Previous Avg. Gain * 13) + Current Day's Gain] / 14 Average Loss = [(Previous Avg. Loss * 13) + Current Day's Loss] / 14 With this, we can now calculate relative strength! Therefore, if our stock gained more than it lost in the past 14 days, then our RS value would be >1. On the other hand, if we lost more than we gained, then our RS value would be <1. Relative strength tells us whether buyers or sellers are in control of the price. If buyers were in control, then the average gain would be greater than the average loss, so the relative strength would be greater than 1. In a bearish market, if this begins to happen, we can say that there is an increase in buyers’ momentum; the momentum is strengthening. We can normalise relative strength into an index using the following equation: Relative Strength= Average Gain / Average Loss Traders then use the RSI in combination with other techniques to assess whether to buy or sell. When a market is ranging, which means that price is bouncing between support and resistance (has the same highs and lows for a period), we can use the RSI to see when we may be entering a trend. When the RSI is reaching 70, it is an indication that the price is being overbought, and in a ranging market, there is likely to be a correction and the price will fall so that the RSI stays at around 50. The opposite is likely to happen when the RSI dips to 30. Price action is deemed to be extreme, and a correction is likely. It should, however, be noted that this type of behaviour is only likely in assets presenting mean-reversion characteristics. In a trending market, RSI can be used to indicate a possible change in momentum. If prices are falling and the RSI reaches a low and then, a few days later, it reaches a higher low (therefore, the low is not as low as the first), it indicates a possible change in momentum; we say there is a bullish divergence. Divergences are rare when a stock is in a long-term trend but is nonetheless a powerful indicator. In conclusion, the relative strength index aims to describe changes in momentum in price action through analysing and comparing previous day's highs and lows. From this, a value is generated, and at the extremes, a change in momentum may take place. RSI is not supposed to be predictive but is very helpful in confirming trends indicated by other techniques. Written by George Chant Project Gallery