Publisher Health

Researchers led by Yasuhiro Murakawa at the RIKEN Center for Integrative Medical Sciences (IMS) and Kyoto University in Japan and IFOM ETS in Italy have discovered several rare types of helper T cells that are associated with immune disorders such as multiple sclerosis, rheumatoid arthritis, and even asthma. Published July 4 in Science, the discoveries were made possible by a newly developed technology they call ReapTEC, which identified genetic enhancers in rare T cell subtypes that are linked to specific immune disorders. The new T cell atlas is publicly available and should help in the development of new drug therapies for immune-mediated diseases.Helper T cells are kind of white blood cell that make up a large part of the immune system. They recognize pathogens and regulate the immune response. Many immune-mediated disease are caused by abnormal T cell function. In autoimmune diseases like multiple sclerosis, they mistakenly attack parts of the body as if they were pathogens. In the case of allergies, T cells overreact to harmless substances in the environment like pollen. We know of several common T cells, but recent studies have shown that rare and specialized types of T cells exist, and they might be related to immune-mediated diseases.Within all cells, including T cells, there are regions of DNA called “enhancers”. This DNA does not code for proteins. Instead, it codes for small pieces of RNA, and enhances the expression of other genes. Variations in T cell enhancer DNA therefore lead to differences in gene expression, and this can affect how T cells function. Some enhancers are bidirectional, which means that both strands of the DNA are used as templates for enhancer RNA. The researchers from several different laboratories at RIKEN IMS, as well as colleagues at other institutes, teamed up to develop the new ReapTEC technology and look for connections between bidirectional T cell enhancers and immune diseases.After analyzing about a million human T cells, they found several groups of rare T cell types, accounting for less than 5% of the total. Applying ReapTEC to these cells identified almost 63,000 active bidirectional enhancers. To figure out if any of these enhancers are related to immune diseases, they turned to genome-wide association studies (GWAS), which have reported numerous genetic variants, called single-nucleotide polymorphisms, that are related to various immune diseases.When the researchers combined the GWAS data with the results of their ReapTEC analysis, they found that genetic variants for immune-mediated diseases were often located within the bidirectional enhancer DNA of the rare T cells that they had identified. In contrast, genetic variants for neurological diseases did not show a similar pattern, meaning that the bidirectional enhancers in these rare T cells are related specifically to immune-mediated diseases.Going even deeper into the data, the researchers were able to show that individual enhancers in certain rare T cells are related to specific immune diseases. Overall, among the 63,000 bidirectional enhancers, they were able to identify 606 that included single-nucleotide polymorphisms related to 18 immune-mediated diseases. Lastly, the researchers were able to identify some of the genes that are the targets of these disease-related enhancers. For example, when they activated an enhancer that contained a genetic variant related to inflammatory bowel disease, the resulting enhancer RNA triggered upregulation of the IL7R gene.“In the short-term, we have developed a new genomics method that can be used by researchers around the world,” says Murakawa. “Using this method, we discovered new types of helper T cells as well as genes related to immune disorders. We hope that this knowledge will lead to a better understanding of the genetic mechanisms underlying human immune-mediated diseases.”In the long-term, the researchers believe follow-up experiments will be able to identify new molecules that can be used to treat immune-mediated diseases.

Pseudomonas aeruginosa — an environmental bacteria that can cause devastating multidrug-resistant infections, particularly in people with underlying lung conditions — evolved rapidly and then spread globally over the last 200 years, probably driven by changes in human behaviour, a new study has found.
P. aeruginosa is responsible for over 500,000 deaths per year around the world, of which over 300,000 are associated with antimicrobial resistance (AMR). People with conditions such as COPD (smoking-related lung damage), cystic fibrosis (CF), and non-CF bronchiectasis, are particularly susceptible.
How P. aeruginosa evolved from an environmental organism into a specialised human pathogen was not previously known. To investigate this, an international team led by scientists at the University of Cambridge examined DNA data from almost 10,000 samples taken from infected individuals, animals, and environments around the world. Their results are published today in Science
By mapping the data, the team was able to create phylogenetic trees — ‘family trees’ — that show how the bacteria from the samples are related to each other. Remarkably, they found that almost seven in ten infections are caused by just 21 genetic clones, or ‘branches’ of the family tree, that have rapidly evolved (by acquiring new genes from neighbouring bacteria) and then spread globally over the last 200 years. This spread occurred most likely as a result of people beginning to live in densely-populated areas, where air pollution made our lungs more susceptible to infection and where there were more opportunities for infections to spread.
These epidemic clones have an intrinsic preference for infecting particular types of patients, with some favouring CF patients and other non-CF individuals. It turns out that the bacteria can exploit a previously unknown immune defect in people with CF, allowing them to survive within macrophages. Macrophages are cells that ‘eat’ invading organisms, breaking them down and preventing the infection from spreading. But a previously-unknown flaw in the immune systems of CF patients means that once the macrophage ‘swallows’ P. aeruginosa, it is unable to get rid of it.
Having infected the lungs, these bacteria then evolve in different ways to become even more specialised for a particular lung environment. The result is that certain clones can be transmitted within CF patients and other clones within non-CF patients, but almost never between CF and non-CF patient groups.
Professor Andres Floto, Director of the UK Cystic Fibrosis Innovation Hub at the University of Cambridge and Royal Papworth Hospital NHS Foundation Trust, and senior author of the study said: “Our research on Pseudomonas has taught us new things about the biology of cystic fibrosis and revealed important ways we might be able to improve immunity against invading bacteria in this and potentially other conditions.

“From a clinical perspective, this study has revealed important information about Pseudomonas. The focus has always been on how easily this infection can spread between CF patients, but we’ve shown that it can spread with worrying ease between other patients, too. This has very important consequences for infection control in hospitals, where it’s not uncommon for an infected individual to be on an open ward with someone potentially very vulnerable.
“We are incredibly lucky at Royal Papworth Hospital where we have single rooms and have developed and evaluated a new air-handling system to reduce the amount of airborne bacteria and protect all patients.”
Dr Aaron Weimann from the Victor Phillip Dahdaleh Heart & Lung Research Institute at the University of Cambridge, and first author on the study, said: “It’s remarkable to see the speed with which these bacteria evolve and can become epidemic and how they can specialise for a particular lung environment. We really need systematic, pro-active screening of all at risk patient groups to detect and hopefully prevent the emergence of more epidemic clones.”
The research was funded by Wellcome and the UK Cystic Fibrosis Trust.

18 hours agoGetty ImagesThere have been 11 cases of measles recorded in Northern Ireland in the past 6 months.

In September 2023, Simon Boas was diagnosed with throat cancer. Aged just 46, he was told the disease was terminal, and that it would ultimately take his life.

7 hours agoBy 

16 minutes agoGetty ImagesHello there election enthusiasts!

32 minutes agoBy 

Reducing consumption of processed meat by around one-third could prevent more than 350,000 cases of diabetes in the US over 10 years, a study suggests.
Cutting US adults’ processed meat intake by 30 per cent — the equivalent of around 10 slices of bacon a week — would also lead to tens of thousands of fewer cases of cardiovascular disease and colorectal cancer, researchers say.
A team from the University of Edinburgh’s Global Academy of Agriculture and Food Systems together with the University of North Carolina, Chapel Hill, has developed a simulation tool to estimate the health impacts of reducing consumption of processed meat and unprocessed red meat.
While many studies have identified links between high levels of processed meat consumption and chronic disease, few have evaluated the impact on multiple health outcomes. Some previous research also suggests unprocessed red meat may contribute to chronic disease risk but evidence is still limited.
The researchers used data from a Centers for Disease Control and Prevention (CDC) national health survey to create a simulated, representative sample of the US adult population — a so-called microsimulation.
Their microsimulation is the first to estimate the effects of reducing processed meat and unprocessed red meat consumption — from between 5 and 100 per cent — on multiple health outcomes in the US.
The team estimated how changes in meat consumption affect adults’ risk of diabetes, cardiovascular disease, colorectal cancer and death. The effects were evaluated in the overall population and separately based on age, sex, household income and ethnicity.

As well as preventing more than 350,000 cases of diabetes, cutting processed meat intake by 30 per cent would lead to 92,500 fewer cardiovascular disease cases and 53,300 fewer colorectal cancer cases over a decade, researchers say.
In this scenario, white males and those with an annual household income between $25,000 and $55,000 were found to experience the greatest health benefits.
Researchers also analysed the impacts of reducing unprocessed red meat intake alone and cutting consumption of both processed meat and unprocessed red meat.
Reducing consumption of both by 30 per cent resulted in 1,073,400 fewer diabetes cases, 382,400 fewer cardiovascular disease cases and 84,400 fewer colorectal cancer cases.
Cutting unprocessed red meat intake alone by 30 per cent — which would mean eating around one less quarter-pound beef burger a week — resulted in more than 732,000 fewer diabetes cases. It also led to 291,500 fewer cardiovascular disease cases and 32,200 fewer colorectal cancer cases.
The finding that more disease cases were prevented by reducing unprocessed red meat compared to processed meat is partly due to the average daily intake of unprocessed red meat being higher than processed meat, at 47g a day versus 29g a day, respectively.
As less is known about the effect of eating unprocessed red meat on chronic disease risk, the team says these estimates should be interpreted with caution and that more research is needed.
The study, published in The Lancet Planetary Health journal, was funded by The Wellcome Trust.
Professor Lindsay Jaacks, Personal Chair of Global Health and Nutrition at the University of Edinburgh, and one of the authors of the study, said “Cutting consumption of meat has been recommended by national and international organisations to reduce greenhouse gas emissions, including the Climate Change Committee here in the UK and the United Nations Intergovernmental Panel on Climate Change or IPCC. Our research finds that these changes in diets could also have significant health benefits in the US, and so this is a clear win-win for people and planet.”

Drosophila — commonly known as fruit flies — are a valuable model for human heart pathophysiology, including cardiac aging and cardiomyopathy. However, a choke point in evaluating fruit fly hearts is the need for human intervention to measure the heart at moments of its largest expansion or its greatest contraction, measurements that allow calculations of cardiac dynamics.
Researchers at the University of Alabama at Birmingham now show a way to significantly cut the time needed for that analysis while utilizing more of the heart region, using deep learning and high-speed video microscopy for each heartbeat in the fly.
“Our machine learning method is not just fast; it minimizes human error because you don’t have to manually mark each heart wall under systolic and diastolic conditions,” said Girish Melkani, Ph.D., associate professor in the UAB Department of Pathology, Division of Molecular and Cellular Pathology. “Furthermore, you can run the analyses of several hundred hearts and look at the analyses when done for all the hearts.”
This can expand the ability to test how different environmental or genetic factors affect heart aging or pathology. Melkani envisions using deep learning-assisted studies to explore cardiac mutation models and other small animal models, such as zebrafish and mice. “Additionally, our techniques could be adapted for human heart models, providing valuable insights into cardiac health and disease. Incorporating uncertainty quantification methods could further enhance the reliability of our analyses. Moreover, the machine learning approach can predict cardiac aging with high accuracy.”
The fruit fly model has already been tremendously powerful for understanding the pathophysiological bases for several human cardiovascular diseases, Melkani says. Cardiovascular disease continues to be one of the leading causes of death and disability in the United States.
Melkani and UAB colleagues assessed their trained model on heart performance both in fruit fly cardiac aging and in a fruit fly model of dilated cardiomyopathy caused by the knockdown of a pivotal TCA cycle enzyme, oxoglutarate dehydrogenase. These automated assessments were then validated against existing experimental datasets. For example, for aging of fruit flies at one week versus five weeks of age, which is about halfway through a fruit fly’s life span, the UAB team used 54 hearts for model training and then validated their measurements against an experimental aging model with 177 hearts. Their trained model was able to reconstruct expected trends in cardiac parameters with aging.
Melkani says his team’s model can be applied to readily available consumer hardware, and his team’s code can provide calculated statistics including diastolic and systolic diameters/intervals, fractional shortening, ejection fraction, heart period/rate, and quantified heartbeat arrhythmicity.
“To our knowledge, this innovative platform for deep learning-assisted segmentation is the first of its kind to be applied to standard high-resolution high-speed optical microscopy of Drosophila hearts while also quantifying all relevant parameters,” Melkani said.
“By automating the process and providing detailed cardiac statistics, we pave the way for more accurate, efficient and comprehensive studies of heart function in Drosophila. This method holds tremendous potential — not only for understanding aging and disease in fruit flies — but also for translating these insights into human cardiovascular research.”

Childhood home temperature and community connectedness can help predict how U.S. residents set their thermostats, offering new ways to encourage energy conservation and combat climate change, according to a study published July 3 in the open-access journal PLOS Climate by Dritjon Gruda from the National University of Ireland Maynooth and Paul Hanges from the University of Maryland.
Half of U.S. households’ annual electricity use goes to heating and cooling, but less than half of homeowners tweak their thermostats to save energy throughout the day. Reducing residential energy consumption (which occupies 21% of the U.S. energy pie chart) is therefore a promising strategy for curbing national energy consumption and burning fewer fossil fuels. But first, scientists seek a reliable way to provide evidence for policy makers to incentivize at-home energy conservation.
To investigate why U.S. adults heat and cool their homes the way they do, Gruda and Hanges surveyed 2,128 participants, who reported the average winter thermostat settings in their current and childhood homes. They also rated their emotional connectedness to their current communities, a metric referred to as “community fit.” The researchers controlled for age, gender, and household income, and participants were representative of the U.S. population.
Results indicated that participants’ childhood home temperatures positively predicted their current home temperatures. For example: of people who live in cold-winter locales like New York, those raised in warmer homes tend to dial up their thermostats higher than those reared in cooler abodes.
Additionally, the researchers observed that individuals with a strong sense of community belonging were more likely to align their home temperature settings with others in their community. For example: a New Yorker with strong community ties may keep their indoor climate cooler, while someone less enamored with the Big Apple may turn up the heat.
These results suggest that “policymakers may need to pivot towards campaigns that deeply resonate with the unique identity and values of individual communities” to encourage energy conservation, the researchers state.
The authors acknowledge that community connectedness may not translate directly to energy savings, especially in affluent communities, and encourage longitudinal studies to address household income and other contributing factors.
Summarizing, the authors add: “Community fit determines how low you set your thermostat during winter, and how much heating energy you save.”