Publisher Health

Writing in the July 12, 2021 online issue of Nature Communications , researchers at University of California San Diego School of Medicine describe a new approach that uses machine learning to hunt for disease targets and then predicts whether a drug is likely to receive FDA approval.
The study findings could measurably change how researchers sift through big data to find meaningful information with significant benefit to patients, the pharmaceutical industry and the nation’s health care systems.
“Academic labs and pharmaceutical and biotech companies have access to unlimited amounts of ‘big data’ and better tools than ever to analyze such data. However, despite these incredible advances in technology, the success rates in drug discovery are lower today than in the 1970s,” said Pradipta Ghosh, MD, senior author of the study and professor in the departments of Medicine and Cellular and Molecular Medicine at UC San Diego School of Medicine.
“This is mostly because drugs that work perfectly in preclinical inbred models, such as laboratory mice, that are genetically or otherwise identical to each other, don’t translate to patients in the clinic, where each individual and their disease is unique. It is this variability in the clinic that is believed to be the Achilles heel for any drug discovery program.”
In the new study, Ghosh and colleagues replaced the first and last steps in preclinical drug discovery with two novel approaches developed within the UC San Diego Institute for Network Medicine (iNetMed), which unites several research disciplines to develop new solutions to advance life sciences and technology and enhance human health.
The researchers used the disease model for inflammatory bowel disease (IBD), which is a complex, multifaceted, relapsing autoimmune disorder characterized by inflammation of the gut lining. Because it impacts all ages and reduces the quality of life in patients, IBD is a priority disease area for drug discovery and is a challenging condition to treat because no two patients behave similarly.

Proteomics produces enormous amounts of data, which can be very complex to analyze and interpret. The free software platform MaxQuant has proven to be invaluable for data analysis of shotgun proteomics over the past decade. Now, Jürgen Cox, group leader at the Max Planck Institute of Biochemistry, and his team present the new version 2.0. It provides an improved computational workflow for data-independent acquisition (DIA) proteomics, called MaxDIA. MaxDIA includes library-based and library-free DIA proteomics and permits highly sensitive and accurate data analysis. Uniting data-dependent and data-independent acquisition into one world, MaxQuant 2.0 is a big step towards improving applications for personalized medicine.
Proteins are essential for our cells to function, yet many questions about their synthesis, abundance, functions, and defects still remain unanswered. High-throughput techniques can help improve our understanding of these molecules. For analysis by liquid chromatography followed by mass spectrometry (MS), proteins are broken down into smaller peptides, in a process referred to as “shotgun proteomics.” The mass-to-charge ratio of these peptides is subsequently determined with a mass spectrometer, resulting in MS spectra. From these spectra, information about the identity of the analyzed proteins can be reconstructed. However, the enormous amount and complexity of data make data analysis and interpretation challenging.
Two ways to analyze proteins with mass spectrometry
Two main methods are used in shotgun proteomics: Data-dependent acquisition (DDA) and data-independent acquisition (DIA). In DDA, the most abundant peptides of a sample are preselected for fragmentation and measurement. This allows to reconstruct the sequences of these few preselected peptides, making analysis simpler and faster. However, this method induces a bias towards highly abundant peptides. DIA, in contrast, is more robust and sensitive. All peptides from a certain mass range are fragmented and measured at once, without preselection by abundance.
As a result, this method generates large amounts of data, and the complexity of the obtained information increases considerably. Up to now, identification of the original proteins was only possible by matching the newly measured spectra against spectra in libraries that comprise previously measured spectra.
Combining DDA and DIA into one world
Jürgen Cox and his team have now developed a software that provides a complete computational workflow for DIA data. It allows, for the first time, to apply algorithms to DDA and DIA data in the same way. Consequently, studies based on either DDA or DIA will now become more easily comparable. MaxDIA analyzes proteomics data with and without spectral libraries. Using machine learning, the software predicts peptide fragmentation and spectral intensities. Hence, it creates precise MS spectral libraries in silico. In this way, MaxDIA includes a library-free discovery mode with reliable control of false positive protein identifications.
Furthermore, the software supports new technologies such as bootstrap DIA, BoxCar DIA and trapped ion mobility spectrometry DIA. What are the next steps? The team is already working on further improving the software. Several extensions are being developed, for instance for improving the analysis of posttranslational modifications and identification of cross-linked peptides.
Enabling researchers to conduct complex proteomics data analysis
MaxDIA is a free software available to scientists all over the world. It is embedded in the established software environment MaxQuant. “We would like to make proteomics data analysis accessible to all researchers,” says Pavel Sinitcyn, first author of the paper that introduces MaxDIA. Thus, at the MaxQuant summer school, Cox and his team offer hands-on training in this software for all interested researchers. They thereby help bridging the gap between wet lab work and complex data analysis.
Sinitcyn states that the aim is to “bring mass spectrometry from the Max Planck Institute of Biochemistry to the clinics.” Instead of measuring only a few proteins, thousands of proteins can now be measured and analyzed. This opens up new possibilities for medical applications, especially in the field of personalized medicine.
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A team led by researchers at Baylor College of Medicine found that a screening method known as untargeted metabolomics profiling can improve the diagnostic rate for inborn errors of metabolism, a group of rare genetic conditions, by about seven-fold when compared to the traditional metabolic screening approach.
The study, published in JAMA Network Open, shows that untargeted metabolomics identifies many more disorders of greater variety as compared to traditional methods, including disorders for which there was not a clinically available biochemical test. The researchers hope that adoption of metabolomics to screen for inborn errors of metabolism will result in a more rapid, more efficient and less expensive diagnostic journey for individuals and families with rare metabolic disorders.
“Currently, newborn screening is conducted in every infant born in the U.S. to check for serious but rare health conditions at birth. Screening includes blood, hearing and heart tests,” said corresponding author Dr. Sarah Elsea, professor of molecular and human genetics at Baylor and senior director of biochemical genetics at Baylor Genetics. “While newborn screening in general has improved in the last 10 years, clinically screening for inborn errors of metabolism has not changed substantially in the last 40 to 50 years.”
Inborn errors of metabolism include conditions that disrupt the normal processes the body uses to transform food into energy and can result in serious conditions. Having an early diagnosis can lead to early treatment, when available. For instance, newborn screening looks for signs of conditions such as phenylketonuria, the body’s inability to break down the amino acid phenylalanine, which results in its accumulation. Buildup of phenylalanine can irreparably harm the nervous system, but early intervention may help manage the condition.
“We developed a clinical test — untargeted metabolomics profiling — that looks at a broader range of metabolic compounds in the blood, therefore screening for many more disorders than the currently used approach,” said Elsea, a member of Baylor’s Dan L Duncan Comprehensive Cancer Center and Center for Drug Discovery. “In the current study, we compared the standard approach and untargeted metabolomics on their effectiveness identifying metabolic conditions.”
The researchers compared the results of applying the two approaches to 4,464 clinical samples received from 1,483 unrelated families. They found that the traditional standard analysis has a positive rate of diagnosis of about 1%. However, using the untargeted metabolomics analysis the researchers were able to confirm a positive rate of diagnosis of 7%.

Researchers from the Buck Institute and Stanford University have created an inflammatory clock of aging (iAge) which measures inflammatory load and predicts multi-morbidity, frailty, immune health, cardiovascular aging and is also associated with exceptional longevity in centenarians. Utilizing deep learning, a form of AI, in studies of the blood immunome of 1001 people, researchers also identified a modifiable chemokine associated with cardiac aging which can be used for early detection of age-related pathology and provides a target for interventions. Results are published in Nature Aging.
“Standard immune metrics which can be used to identify individuals most at risk for developing single or even multiple chronic diseases of aging have been sorely lacking,” said David Furman, PhD, Buck Institute Associate Professor, Director of the 1001 Immunomes Project at Stanford University School of Medicine and senior author of the study. “Bringing biology to our completely unbiased approach allowed us to identify a number of metrics, including a small immune protein which is involved in age-related systemic chronic inflammation and cardiac aging. We now have means of detecting dysfunction and a pathway to intervention before full-blown pathology occurs.”
According to first author Nazish Sayed, MD, PhD, Assistant Professor of Vascular Surgery at Stanford Medicine, the study identified the soluble chemokine CXCL9 as the strongest contributor to iAge. Furman described it as a small immune protein that is usually called into action to attract lymphocytes to the site of an infection. “But in this case we showed that CXCL9 upregulates multiple genes implicated in inflammation and is involved in cellular senescence, vascular aging and adverse cardiac remodeling” adding that silencing CXCL9 reversed loss of function in aging endothelial cells in both humans and mice.
Larger implications for iAge
Results from the initial analysis (which also included information from comprehensive clinical health assessments of 902 individuals) were validated in an independent cohort of centenarians and all-cause mortality in the Framingham Heart Study. Furman says when it comes to health and longevity, the “age” of one’s immune system most certainly trumps the chronological information that can be derived from a driver’s license. “On average, centenarians have an immune age that is 40 years younger than what is considered ‘normal’ and we have one outlier, a super-healthy 105 year-old man (who lives in Italy) who has the immune system of a 25 year old,” he said.
Study results involving cardiac health were also validated in a separate group of 97 extremely healthy adults (age 25 — 90 years of age) recruited from Palo Alto, California. Furman says researchers found a correlation between CXCL9 and results from pulse wave velocity testing, a measure of vascular stiffness. “These people are all healthy according to all available lab tests and clinical assessments, but by using iAge we were able to predict who is likely to suffer from left ventricular hypertrophy (an enlargement and thickening of the walls of the heart’s main pumping chamber) and vascular dysfunction.”
Furman says the tool can be used to track someone’s risk of developing multiple chronic diseases by assessing the cumulative physiological damage to their immune system. For example, age-related frailty can be predicted by comparing biological immune metrics with information about how long it takes someone to stand up from a chair and walk a certain distance as well as their degree of autonomy and independence. “Using iAge it’s possible to predict seven years in advance who is going to become frail,” he said. “That leaves us lots of room for interventions.”
Highlighting the connection between immune health and aging
In 2013 a group of researchers studying aging identified nine “hallmarks” of the aging process. Age-related immune system dysfunction was not part of the mix. “It’s becoming clear that we have to pay more attention to the immune system with age, given that almost every age-related malady has inflammation as part of its etiology,” said Furman. “If you’re chronically inflamed, you will have genomic instability as well as mitochondrial dysfunction and issues with protein stability. Systemic chronic inflammation triggers telomere attrition, as well as epigenetic alterations. It’s clear that all of these nine hallmarks are, by and large, triggered by having systemic chronic inflammation in your body. I think of inflammation as the 10th hallmark”
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A diet rich in fermented foods enhances the diversity of gut microbes and decreases molecular signs of inflammation, according to researchers at the Stanford School of Medicine.
In a clinical trial, 36 healthy adults were randomly assigned to a 10-week diet that included either fermented or high-fiber foods. The two diets resulted in different effects on the gut microbiome and the immune system.
Eating foods such as yogurt, kefir, fermented cottage cheese, kimchi and other fermented vegetables, vegetable brine drinks, and kombucha tea led to an increase in overall microbial diversity, with stronger effects from larger servings. “This is a stunning finding,” said Justin Sonnenburg, PhD, an associate professor of microbiology and immunology. “It provides one of the first examples of how a simple change in diet can reproducibly remodel the microbiota across a cohort of healthy adults.”
In addition, four types of immune cells showed less activation in the fermented-food group. The levels of 19 inflammatory proteins measured in blood samples also decreased. One of these proteins, interleukin 6, has been linked to conditions such as rheumatoid arthritis, Type 2 diabetes and chronic stress.
“Microbiota-targeted diets can change immune status, providing a promising avenue for decreasing inflammation in healthy adults,” said Christopher Gardner, PhD, the Rehnborg Farquhar Professor and director of nutrition studies at the Stanford Prevention Research Center. “This finding was consistent across all participants in the study who were assigned to the higher fermented food group.”
Microbe diversity stable in fiber-rich diet
By contrast, none of these 19 inflammatory proteins decreased in participants assigned to a high-fiber diet rich in legumes, seeds, whole grains, nuts, vegetables and fruits. On average, the diversity of their gut microbes also remained stable. “We expected high fiber to have a more universally beneficial effect and increase microbiota diversity,” said Erica Sonnenburg, PhD, a senior research scientist in basic life sciences, microbiology and immunology. “The data suggest that increased fiber intake alone over a short time period is insufficient to increase microbiota diversity.”

Tel Aviv University’s new and groundbreaking technology inspires hope among people who have lost their sense of touch in the nerves of a limb following amputation or injury. The technology involves a tiny sensor that is implanted in the nerve of the injured limb, for example in the finger, and is connected directly to a healthy nerve. Each time the limb touches an object, the sensor is activated and conducts an electric current to the functioning nerve, which recreates the feeling of touch. The researchers emphasize that this is a tested and safe technology that is suited to the human body and could be implanted anywhere inside of it once clinical trials will be done.
The technology was developed under the leadership of a team of experts from Tel Aviv University: Dr. Ben M. Maoz, Iftach Shlomy, Shay Divald, and Dr. Yael Leichtmann-Bardoogo from the Department of Biomedical Engineering, Fleischman Faculty of Engineering, in collaboration with Keshet Tadmor from the Sagol School of Neuroscience and Dr. Amir Arami from the Sackler School of Medicine and the Microsurgery Unit in the Department of Hand Surgery at Sheba Medical Center. The study was published in the journal ACS Nano.
The researchers say that this unique project began with a meeting between the two Tel Aviv University colleagues — biomedical engineer Dr. Maoz and surgeon Dr. Arami. “We were talking about the challenges we face in our work,” says Dr. Maoz, “and Dr. Arami shared with me the difficulty he experiences in treating people who have lost tactile sensation in one organ or another as a result of injury. It should be understood that this loss of sensation can result from a very wide range of injuries, from minor wounds — like someone chopping a salad and accidentally cutting himself with the knife — to very serious injuries. Even if the wound can be healed and the injured nerve can be sutured, in many cases the sense of touch remains damaged. We decided to tackle this challenge together, and find a solution that will restore tactile sensation to those who have lost it.”
In recent years, the field of neural prostheses has made promising developments to improve the lives of those who have lost sensation in their limbs by implanting sensors in place of the damaged nerves. But the existing technology has a number of significant drawbacks, such as complex manufacturing and use, as well as the need for an external power source, such as a battery. Now, the researchers at Tel Aviv University have used state-of-the-art technology called a triboelectric nanogenerator (TENG) to engineer and test on animal models a tiny sensor that restores tactile sensation via an electric current that comes directly from a healthy nerve and doesn’t require a complex implantation process or charging.
The researchers developed a sensor that can be implanted on a damaged nerve under the tip of the finger; the sensor connects to another nerve that functions properly and restores some of the tactile sensation to the finger. This unique development does not require an external power source such as electricity or batteries. The researchers explain that the sensor actually works on frictional force: whenever the device senses friction, it charges itself.
The device consists of two tiny plates less than half a centimeter by half a centimeter in size. When these plates come into contact with each other, they release an electric charge that is transmitted to the undamaged nerve. When the injured finger touches something, the touch releases tension corresponding to the pressure applied to the device — weak tension for a weak touch and strong tension for a strong touch — just like in a normal sense of touch.
The researchers explain that the device can be implanted anywhere in the body where tactile sensation needs to be restored, and that it actually bypasses the damaged sensory organs. Moreover, the device is made from biocompatible material that is safe for use in the human body, it does not require maintenance, the implantation is simple, and the device itself is not externally visible.
According to Dr. Maoz, after testing the new sensor in the lab (with more than half a million finger taps using the device), the researchers implanted it in the feet of the animal models. The animals walked normally, without having experienced any damage to their motor nerves, and the tests showed that the sensor allowed them to respond to sensory stimuli. “We tested our device on animal models, and the results were very encouraging,” concludes Dr. Maoz. “Next, we want to test the implant on larger models, and at a later stage implant our sensors in the fingers of people who have lost the ability to sense touch. Restoring this ability can significantly improve people’s functioning and quality of life, and more importantly, protect them from danger. People lacking tactile sensation cannot feel if their finger is being crushed, burned or frozen.”
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Boosting the body’s own disease-fighting immune pathway could provide answers in the desperate search for new treatments for tuberculosis.
Tuberculosis still represents an enormous global disease burden and is one of the top 10 causes of death worldwide.
Led by WEHI’s Dr Michael Stutz and Professor Marc Pellegrini and published in Immunity, the study uncovered how cells infected with tuberculosis bacteria can die, and that using new medicines to enhance particular forms of cell death decreased the severity of the disease in a preclinical model.
Fighting antibiotic resistance
Tuberculosis is caused by bacteria that infect the lungs, spreading from person to person through the air. A challenge in the fight against tuberculosis is that the bacteria that cause the disease have developed resistance to most antibiotic treatments, leading to a need for new treatment approaches.
Tuberculosis bacteria grow within immune cells in the lungs. One of the ways that cells protect against these ‘intracellular’ pathogens is to undergo a form of cell death called apoptosis — destroying the cell as well as the microbes within it.

Ancient sediments from caves have already proven to preserve DNA for thousands of years. The amount of recovered sequences from environmental sediments, however, is generally low, which complicates analyses. A study has now successfully retrieved three mammalian environmental genomes from a single soil sample from 25,000 years ago, obtained from the cave of Satsurblia in the Caucasus (Georgia).
The cave of Satsurblia was inhabited by humans in different periods of the Paleolithic: Up to date a single human individual dated from 15,000 years ago has been sequenced from that site. No other human remains have been discovered in the older layers of the cave.
The innovative approach used by the international team led by Prof. Ron Pinhasi and Pere Gelabert with Susanna Sawyer of the University of Vienna in collaboration with Pontus Skoglund and Anders Bergström of the Francis Crick Institute in London permits the identification of DNA in samples of environmental material, by applying extensive sequencing and huge data analysis resources. This technique has allowed the recovery of an environmental human genome from the BIII layer of the cave, which is dated before the Ice Age, about 25,000 years ago.
This new approach has evidenced the feasibility of recovering human environmental genomes in the absence of skeletal remains. The analysis of the genetic material has revealed that the SAT29 human environmental genome represents a human extinct lineage that contributed to the present day West-Eurasian populations. To validate the results, the researchers compared the recovered genome with the genetic sequences obtained from bone remains of the nearby cave of Dzudzuana, obtaining definitive evidence of genetic similarities. This fact validate the results and excludes the possibility of modern contamination of the samples.
Along with the identified human genome, other genomes such as wolf and bison have also been recovered from the environmental samples. The sequences have been used to reconstruct the wolf and bison Caucasian population history and will help better understand the population dynamics of these species.
The team now plans to perform further analyses of soil samples from the cave of Satsurbia with the objective of revealing interactions between extinct fauna and humans and the effect of climatic changes on mammalian populations. The ability to recover DNA from soil samples allows us the reconstruction of the evolution of whole past ecosystems .
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A Sussex team — including university mathematicians — have created a new modelling toolkit which predicts the impact of COVID-19 at a local level with unprecedented accuracy. The details are published in the International Journal of Epidemiology, and are available for other local authorities to use online, just as the UK looks as though it may head into another wave of infections.
The study used the local Sussex hospital and healthcare daily COVID-19 situation reports, including admissions, discharges, bed occupancy and deaths.
Through the pandemic, the newly-published modelling has been used by local NHS and public health services to predict infection levels so that public services can plan when and how to allocate health resources — and it has been conclusively shown to be accurate. The team are now making their modelling available to other local authorities to use via the Halogen toolkit.
Anotida Madzvamuse, professor of mathematical and computational biology within the School of Mathematical and Physical Sciences at the University of Sussex, who led of the study, said:
“We undertook this study as a rapid response to the COVID-19 pandemic. Our objective was to provide support and enhance the capability of local NHS and Public Health teams to accurately predict and forecast the impact of local outbreaks to guide healthcare demand and capacity, policy making, and public health decisions.”
“Working with outstanding mathematicians, Dr James Van Yperen and Dr Eduard Campillo-Funollet, we formulated an epidemiological model and inferred model parameters by fitting the model to local datasets to allow for short, and medium-term predictions and forecasts of the impact of COVID-19 outbreaks.

A new study sheds light on how the specter of dementia and chronic pain reduce people’s desire to live into older ages. Among Norwegians 60 years of age and older the desire to live into advanced ages was significantly reduced by hypothetical adverse life scenarios with the strongest effect caused by dementia and chronic pain, according to research conducted at the Robert N. Butler Columbia Aging Center based at the Columbia Mailman School of Public Health.
The paper is among the first to study Preferred Life Expectancy (PLE) based on hypothetical health and living conditions. The findings are published in the July issue of the journal Age and Ageing.
The research team was led by Vegard Skirbekk, PhD, professor of Population and Family Health, who used data from Norway, because of its relatively high life expectancy at birth. He investigated how six adverse health and living conditions affected PLE after the age of 60 and assessed each by age, sex, education, marital status, cognitive function, self-reported loneliness and chronic pain.
The analysis included data from the population-based NORSE-Oppland County study of health and living conditions based on a representative sample of the population aged 60-69 years, 70-79 years and 80 years and older. The data collection was done in three waves in 2017, 2018 and 2019. A total of 948 individuals participated in the interviews and health examinations.
Skirbekk and colleagues asked the 825 community dwellers aged 60 and older the question, “If you could choose freely, until which age would you wish to live?” The results showed that among Norwegians over 60, the desire to live into advanced ages was significantly reduced by hypothetical adverse life scenarios, such as effects of dementia and chronic pain. Weaker negative PLE effects were found for the prospect of losing one’s spouse or being subject to poverty
According to Skirbekk, “Dementia tops the list of conditions where people would prefer to live shorter lives — which is a particular challenge given the rapid increase in dementia in the years ahead.”
The average Preferred Life Expectancy was 91.4 years of age and there was no difference between men and women, but older participants had higher PLE than younger participants. PLE among singles was not affected by the prospect of feeling lonely. The higher educated had lower PLE for dementia and chronic pain.
“Despite the fact that rising life expectancy to a large extent occurs at later ages, where the experience of loss and disability are widespread, there had been remarkably little scientific evidence on how long individuals would like to live given the impact of such adverse life conditions,’ noted Skirbekk.
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Materials provided by Columbia University’s Mailman School of Public Health. Note: Content may be edited for style and length.