Category Archives: Genetics

What is psoriasis and how do you get it?

Psoriasis is a skin condition characterized by itchy scales, inflammation, and redness. It usually occurs on the scalp, knees, elbows, hands, and feet.

According to one study, about 7.4 million people in the United States were living with psoriasis in 2013.

Psoriasis is an autoimmune disease. Immune cells in your blood mistakenly recognize newly produced skin cells as foreign invaders and attack them. This can cause the overproduction of new skin cells beneath the surface of your skin.

These new cells migrate to the surface and force out existing skin cells. That causes the scales, itching, and inflammation of psoriasis.

Genetics almost certainly plays a role. Read on to learn more about the role of genetics in the development of psoriasis.

Psoriasis usually appears between the ages of 15 and 35, according to the National Psoriasis Foundation (NPF). However, it may occur at any age. For example, about 20,000 children under the age of 10 are diagnosed with psoriasis every year.

Psoriasis can occur in people with no family history of the disease. Having a family member with the disease increases your risk.

Scientists working on the genetic causes of psoriasis start by assuming that the condition results from a problem with the immune system. Research on psoriatic skin shows that it contains large numbers of immune cells that produce inflammatory molecules known as cytokines.

Psoriatic skin also contains gene mutations known as alleles.

Early research in the 1980s led to the belief that one specific allele might be responsible for passing on the disease through families.

Researchers later discovered that the presence of this allele, HLA-Cw6, wasnt sufficient to cause a person to develop the disease. More recent studies show that more research is still needed to better understand the relationship between HLA-Cw6 and psoriasis.

Use of more advanced techniques has led to the identification of about 25 different regions in human genetic material (the genome) that may be associated with psoriasis.

As a result, genetic studies can now give us an indication of a persons risk of developing psoriasis. The link between the genes that are associated with psoriasis and the condition itself isnt yet fully understood.

Psoriasis involves an interaction between your immune system and your skin. That means its hard to know whats the cause and whats the effect.

The new findings in genetic research have provided important insights, but we still dont clearly understand what causes a psoriasis outbreak. The precise method by which psoriasis is passed from parent to child is also not fully understood.

Most people with psoriasis have periodic outbreaks or flare-ups followed by periods of remission. About 30 percent of people with psoriasis also experience inflammation of the joints that resembles arthritis. This is called psoriatic arthritis.

Environmental factors that may trigger a psoriasis onset or flare-up include:

Injury or trauma to a portion of your skin may sometimes become the site of a psoriasis flare-up. Infection may also be a trigger. The NPF notes that infection, especially strep throat in young people, is reported as a trigger for psoriasis onset.

Some diseases are more likely in people with psoriasis than in the general population. In one study of women with psoriasis, about 10 percent of the participants had also developed an inflammatory bowel disease like Crohns disease or ulcerative colitis.

People with psoriasis have an increased incidence of:

Gene therapy isnt currently available as a treatment, but theres an expansion of research into the genetic causes of psoriasis. In one of the many promising discoveries, researchers found a rare gene mutation thats linked to psoriasis.

The gene mutation is known as CARD14. When exposed to an environmental trigger, such as an infection, this mutation produces plaque psoriasis. Plaque psoriasis is the most common form of the disease. This discovery helped establish the connection of the CARD14 mutation to psoriasis.

These same researchers also found the CARD14 mutation present in two large families that had many family members with plaque psoriasis and psoriatic arthritis.

This is one of a number of recent discoveries that hold promise that some form of gene therapy may one day be able to help people living with psoriasis or psoriatic arthritis.

For mild to moderate cases, dermatologists usually recommend topical treatments such as creams or ointments. These can include:

If you have a more severe case of psoriasis, your doctor may prescribe phototherapy and more advanced systemic or biologic medications, taken orally or by injection.

Researchers have established a link between psoriasis and genetics. Having a family history of the condition also increases your risk. More research is needed to fully understand the inheritance of psoriasis.

Read this article in Spanish.

See original here:
Psoriasis: Whats the Genetic Link? - Healthline

Defined, toned abs commonly called a six-pack are an often sought-after goal in the gym. But not all toned abs look the same. Some people sport a four-pack, while others may have an eight-pack.

Lets take a look at the difference between ab types as well as the diet, exercise, and lifestyle tips that can help you achieve the strongest abs your genetics will allow.

The difference between ab types lies in the structure of your abdominal muscles.

Your abdomen contains four muscle groups. To get toned abs, youll need to do exercises that strengthen all four muscle groups. These muscle groups are:

Once toned, the rectus abdominis becomes your four-, six-, or eight-pack. It comprises two connected muscle bands that run parallel to each other, down either side of the abdomen.

The linea alba is the fibrous band that separates the rectus abdominis. It forms the line that runs down the middle of the abdomen.

The rectus abdominis also helps:

The transverse abdominis is located deep within the abdomen. It extends from the front of your abdomen to the sides of your body. It helps provide stability and strength to your entire core, back, and pelvis.

If your transverse abdominis isnt being worked, your rectus abdominis wont become defined.

The internal and external obliques help control the twisting and turning movements of your body. Along with the transverse abdominis, they provide a stabilizing girdle for your back and pelvis.

The external obliques are a large muscle group located at the sides of the rectus abdominis. The internal obliques are located just underneath, inside your hip joints. Working your obliques adds definition and tone to your abs.

Being able to achieve a 10-pack is possible for some people.

You need to be born with a rectus abdominis that contains five bands of connective tissue running horizontally across it. You also need to regularly work out these muscles and follow a healthy diet.

Of course, what you eat and how you exercise also play large roles in how your abs ultimately look.

The rectus abdominis muscle has bands of connective tissue (fascia) crossing it horizontally. These bands give the appearance of multiple packs stacked on top of each other on either side of your abdomen.

Youre born with a set number of these connective tissue bands. You cant build additional ones. Your genetics also determine their symmetry, length, and size.

A person with an eight-pack has four bands. A person with a six-pack has three bands. A person with a four-pack has two bands.

Many peoples rectus abdominis has three intersections. This means that if most people worked at it, they could achieve a six-pack.

But just because you have more or less doesnt mean youre stronger or weaker. Its just your genes.

Some of the fittest people around cant achieve six- or eight-pack abs. One of these people is Arnold Schwarzenegger, who, even during his bodybuilding days, sported a four-pack.

Of course, what you eat and how you exercise also play large roles in how your abs ultimately look.

Both sexes have a genetic predetermination for the number of packs they can achieve. However, women require more body fat than men. This essential body fat is needed for:

Because of this, it may be more difficult for women to lose enough abdominal fat to define their abs while staying healthy. Having too little body fat for your body type can lead to various complications in women, like:

Men have around 61 percent more muscle mass than women due to their higher testosterone levels. Men require less body fat for optimum health, too. So, they can more readily lose enough fat to show their toned rectus abdominis muscles underneath.

While your genetics help determine how your abs look, you can still build a strong core. A strong core protects your back and spine, preventing injury.

These exercises can help strengthen your abs and build muscle mass. If you want to have visible abs, youll have to spend time toning them at least every other day and follow a healthy diet.

This highly effective exercise works your entire core, as well as your glutes and hamstrings. It also improves balance and stability.

You can also try harder modifications, like side planks and knee touches.

The dead bug works your obliques, rectus abdominis, and transverse abdominis muscles. It also improves core stability and helps correct excessive anterior pelvic tilt.

If your lower back doesnt touch the floor, roll up a small towel and place it in the small of your back to stay stable during the exercise. This isnt an easier or modified version, and it wont diminish the exercises intensity. Itll protect your lower back from injury.

Looking for a challenge? Check out these dead bug variations.

This exercise focuses directly and intensely on the rectus abdominis muscle. Its excellent for balance and full-body stability. Its also effective whether its done quickly or slowly.

For many people, getting sculpted abs requires time and dedication. These tips can help you get started.

Cardio exercise has been linked to reductions in belly fat. Less belly fat will help make your abs more visible. Cardio examples include:

Try to build cardio into your day-to-day life. Walk or ride a bike instead of driving. Take a run or swim before or after work. Hate running? Here are nine cardio alternatives to try.

Aim for a minimum of 20 to 40 minutes of cardio at least four times a week.

Exercises that require you to move your body against resistance help build muscle strength, tone, and endurance.

Exercise machines and enhancements, such as weights and body bands, all provide resistance. So do many water exercises.

HIIT refers to short, one- to two-minute bursts of high-intensity cardio followed by a rest period of equal time. To be effective, each burst of cardio must be done at your very top capacity.

Because your body is working at its highest capacity, HIIT sessions burn lots of calories both during workouts and for several hours afterward.

A high-protein diet will help you build and repair muscle. Itll also help you feel fuller longer. Opt for lean protein sources, such as:

Your ability to achieve a visible pack of abs whether a four-, six-, or eight-pack is largely determined by genetics.

However, healthy lifestyle choices, like losing belly fat and exercising, can provide anyone with a fit and toned abdomen. A strong core also helps with overall strength and balance.

Original post:
Healthline: Medical information and health advice you can trust.

Deep Dive Ties Together Dog Genetics, Brain Physiology and Behavior to Explain Why Collies Are Different from Terriers  Scientific American

More:
Deep Dive Ties Together Dog Genetics, Brain Physiology and Behavior to Explain Why Collies Are Different from Terriers - Scientific American

Donate

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

Advertisement

YesNo

Advertisement

Advertisement

Original post:
Genetics - breast cancer

JK Agri Genetics reports standalone net loss of Rs 15.77 crore in the September 2022 quarter  Business Standard

Read more:
JK Agri Genetics reports standalone net loss of Rs 15.77 crore in the September 2022 quarter - Business Standard

BERKELEY, CA Amid much speculation and research about how our genetics affect the way we age, a University of California, Berkeley, study now shows that individual differences in our DNA matter less as we get older and become prone to diseases of aging, such as diabetes and cancer.

In a study of the relative effects of genetics, aging and the environment on how some 20,000 human genes are expressed, the researchers found that aging and environment are far more important than genetic variation in affecting the expression profiles of many of our genes as we get older. The level at which genes are expressed that is, ratcheted up or down in activity determines everything from our hormone levels and metabolism to the mobilization of enzymes that repair the body.

How do your genetics what you got from your sperm donor and your egg donor and your evolutionary history influence who you are, your phenotype, such as your height, your weight, whether or not you have heart disease? said Peter Sudmant, UC Berkeley assistant professor of integrative biology and a member of the campuss Center for Computational Biology. Theres been a huge amount of work done in human genetics to understand how genes are turned on and off by human genetic variation. Our project came about by asking, How is that influenced by an individuals age? And the first result we found was that your genetics actually matter less the older you get.

In other words, while our individual genetic makeup can help predict gene expression when we are younger, it is less useful in predicting which genes are ramped up or down when were older in this study, older than 55 years. Identical twins, for example, have the same set of genes, but as they age, their gene expression profiles diverge, meaning that twins can age much differently from each other.

The findings have implications for efforts to correlate diseases of aging with genetic variation in humans, Sudmant said. Such studies should perhaps focus less on genetic variants that impact gene expression when pursuing drug targets.

Almost all human common diseases are diseases of aging: Alzheimers, cancers, heart disease, diabetes. All of these diseases increase their prevalence with age, he said. Massive amounts of public resources have gone into identifying genetic variants that predispose you to these diseases. What our study is showing is that, well, actually, as you get older, genes kind of matter less for your gene expression. And so, perhaps, we need to be mindful of that when were trying to identify the causes of these diseases of aging.

Sudmant and his colleagues reported their results this week in the journal Nature Communications.

The findings are in line with Medawars hypothesis: Genes that are turned on when we are young are more constrained by evolution because they are critical to making sure we survive to reproduce, while genes expressed after we reach reproductive age are under less evolutionary pressure. So, one would expect a lot more variation in how genes are expressed later in life.

Were all aging in different ways, Sudmant said. While young individuals are closer together in terms of gene expression patterns, older individuals are further apart. Its like a drift through time as gene expression patterns become more and more erratic.

This study is the first to look at both aging and gene expression across such a wide variety of tissues and individuals, Sudmant said. He and his colleagues built a statistical model to assess the relative roles of genetics and aging in 27 different human tissues from nearly 1,000 individuals and found that the impact of aging varies widely more than twentyfold among tissues.

Across all the tissues in your body, genetics matters about the same amount. It doesnt seem like it plays more of a role in one tissue or another tissue, he said. But aging is vastly different between different tissues. In your blood, colon, arteries, esophagus, fat tissue, age plays a much stronger role than your genetics in driving your gene expression patterns.

Sudmant and colleagues also found that Medawars hypothesis does not hold true for all tissues. Surprisingly, in five types of tissues, evolutionary important genes were expressed at higher levels in older individuals.

From an evolutionary perspective, it is counterintuitive that these genes should be getting turned on, until you take a close look at these tissues, Sudmant said. These five tissues happen to be the ones that constantly turn over throughout our lifespan and also produce the most cancers. Every time these tissues replace themselves, they risk creating a genetic mutation that can lead to disease.

I guess this tells us a little bit about the limits of evolution, he said. Your blood, for instance, always has to proliferate for you to live, and so these super-conserved, very important genes have to be turned on late in life. This is problematic because it means that those genes are going to be susceptible to getting somatic mutations and getting turned on forever in a bad, cancerous way. So, it kind of gives us a little bit of a perspective on what the limitations of living are like. It puts bounds on our ability to keep living.

Sudmant noted that the study indirectly indicates the effect on aging of ones environment, which is the impact of everything other than age and genetics: the air we breathe, the water we drink, the food we eat, but also our levels of physical exercise. Environment amounts to up to a third of gene expression changes with age.

Sudmant is conducting similar analyses of the expressed genes in several other organisms bats and mice to see how they differ and whether the differences are related to these animals different lifespans.

UC Berkeley graduate students Ryo Yamamoto and Ryan Chung are co-first authors of the paper. Other co-authors are Juan Manuel Vazquez, Huanjie Sheng, Philippa Steinberg and Nilah Ioannidis. The work was supported by the National Institute of General Medical Sciences (R35GM142916) of the National Institutes of Health.

This press release was produced by UC Berkeley News. The views expressed here are the authors own.

Link:
Age Vs. Genetics: Which Is More Important For How You Age? - Patch

Research Nester

Key Companies Covered in the Global Animal Genetics Market Research Report by Research Nester are Hendrix Genetics BV, Genus plc, Animal Genetics Inc., Crv Holding B.V., Vetgen LLC, Neogen Corporation, Topigs Norsvin, Zoetis Inc., GROUPE GRIMAUD LA CORBIERE, Royal Agrifirm Group, and other key market players.

New York, Oct. 12, 2022 (GLOBE NEWSWIRE) -- Research Nester has published a detailed market report on Global Animal Genetics Market for the forecast period, i.e. 2023 2033 which includes the following factors:

Market growth over the forecast period

Detailed regional synopsis

Market segmentation

Growth drivers

Challenges

Key market players and their detailed profiling

Global Animal Genetics Market Size:

The global animal genetics market is estimated to garner a revenue of ~USD 10067 Million by the end of 2033 by growing at a CAGR of ~6% over the forecast period. Additionally, the market generated a revenue of ~USD 5407.0 Million in the year 2022. The soaring animal livestock population around the world is the main factor predicted to contribute to the market's expansion over the ensuing years. It has been noted that the number of cattle increased from over 900 million in 2018 to approximately 1000 million in 2022.

Get a Sample PDF Brochure: https://www.researchnester.com/sample-request-4390

Further, the trends in the global animal genetics market including the explosive rise of animal genetic companies, the rising number of malnourished people worldwide, and the considerable increase in beef consumption are anticipated to favorably impact the market's growth over the forecast period. For instance, the production of beef and veal was estimated to be around 55 million metric tons in 2022 across the globe. In addition to this, animal genetics, or the study of genes and how they affect animals, is an important aspect of the growth of livestock. For better production of goods obtained from animals, including eggs, meat, milk, fiber, and others, animal genetics becomes crucial. As a result, each of these variables is anticipated to contribute to the market's expansion throughout the upcoming years.

Story continues

Global Animal Genetics Market: Key Takeaways

North America region gains the largest portion of the revenue

Porcine segment to dominate the revenue graph

Genetic trait sub-segment remains prominent in the genetic testing segment

Growing Prevalence of Animal Infectious Diseases and Higher Inclination Toward Pet Ownership to Boost Market Growth

Understanding the source of some diseases is very useful in developing a better treatment. Animals also transmit a wide range of illnesses, including hookworms, ringworms, salmonella, parrot fever, Lyme disease, and others. Therefore, understanding animal genetics is helpful to prevent such severe medical diseases. Further, the growing prevalence of these animal infectious diseases across the globe is estimated to boost market growth. Based on the report released by the Centers for Disease Control and Prevention, it was stated that 6 out of 10 infectious diseases are spread from animals to people. Furthermore, nearly 450,000 illnesses are caused by animals annually.

For more information in the analysis of this report, visit: https://www.researchnester.com/reports/animal-genetics-market/4390

The growth of the market can be attributed to the increase in the population of pets all around the globe. In 2021-22, almost 65% of the households in the USA were noticed to own a pet summing up to nearly 85 million families. Further, the global market is further expected to be propelled by other factors, mainly, the rapid urbanization & increasing disposable incomes, the growing number of pet owners, and an upsurge in demand for veterinary services over the forecast period.

Global Animal Genetics Market: Regional Overview

The global animal genetics market is segmented into five major regions including North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa region.

Rising Production and Consumption of Meat to Drive Growth in the North America Region

As of 2022, the market in the North American region has the largest share of 36.26%, and it is estimated to garner a significant revenue over the forecast period on account of rising demand for premium breeds as well as increased consumption and production of different types of meat, primarily pork. As of 2021, approximately 25,000 million pounds of pork were produced in the U.S., up from almost 24,000 million pounds in 2018. Further, the growing awareness among people regarding protein intake and a healthy lifestyle is estimated to boost the market growth in the region.

Get a Sample PDF of Animal Genetics Market Report@ https://www.researchnester.com/sample-request-4390

Rising Pet Population to Drive Growth in the Europe Region

On the other hand, the market in Europe region is estimated to gain noteworthy market share over the forecast period owing to increasing pet population in the region. According to one of the surveys, in Europe nowadays, 38% of households have a pet. 88 million households, in total. Further, the easy availability of better medical facilities, backed by the favorable medical policies for veterinary treatment in the region are estimated to elevate the market growth.

The study further incorporates Y-O-Y growth, demand & supply and forecast future opportunity in:

North America (U.S., Canada)

Europe (U.K., Germany, France, Italy, Spain, Hungary, Belgium, Netherlands & Luxembourg, NORDIC [Finland, Sweden, Norway, Denmark], Poland, Turkey, Russia, Rest of Europe)

Latin America (Brazil, Mexico, Argentina, Rest of Latin America)

Asia-Pacific (China, India, Japan, South Korea, Indonesia, Singapore, Malaysia, Australia, New Zealand, Rest of Asia-Pacific)

Middle East and Africa (Israel, GCC [Saudi Arabia, UAE, Bahrain, Kuwait, Qatar, Oman], North Africa, South Africa, Rest of Middle East and Africa).

Global Animal Genetics Market, Segmentation by Animal Type

Canine

Bovine

Porcine

Poultry

Others

The porcine segment has the biggest market share of these, accounting for 31.15% in 2022. The increasing number and demand for pigs around the world can be attributed to the segment's rise during the forecast period. For instance, China had the most pigs with over 400 million heads, whereas in the United States, it was anticipated that there would be close to 70 million heads in 2022. In addition to this, the growing liking of pork consumption around the world, backed by rising food industry across the globe is estimated to boost the segment growth.

For more insights on the market share of various regions: https://www.researchnester.com/sample-request-4390

Global Animal Genetics Market, Segmentation by Genetic Testing

Among these, the genetic trait segment is estimated to hold significant market share over the forecast period. This segment of genetic testing looks for changes, sometimes called mutations or variants, in the DNA. Genetic trait testing is useful in many areas of medicine and can change medical care for animals as it can test the disease in the initial stage. Moreover, the rising rate of infection and diseases among and due to animals is estimated to boost the segment growth. As per the World Health Organization, globally, it is estimated that zoonoses cause about a billion cases of disease and millions of fatalities each year. Zoonoses account for over 60% of new infectious illnesses that are reported globally.

Global Animal Genetics Market, Segmentation by Genetic Material

Few of the well-known market leaders in the global animal genetics market that are profiled by Research Nester are Hendrix Genetics BV, Genus plc, Animal Genetics Inc., Crv Holding B.V., Vetgen LLC, Neogen Corporation, Topigs Norsvin, Zoetis Inc., GROUPE GRIMAUD LA CORBIERE, Royal Agrifirm Group, and others.

Do You Have Any Query Or Specific Requirement? Ask to Our Expert: https://www.researchnester.com/ask-the-analyst/rep-id-4390

Recent Developments in the Global Animal Genetics Market

In June 2022, Hendrix Genetics BV will work with CSIRO, Australia's national science organization. This partnership has been established to carry out research on cutting-edge sex-sorting technologies for the egg-laying sector.

In October 2020, Tropic Biosciences, a pioneering agricultural-biotechnology business, and Genus Plc, a global leader in animal genetic improvement, have established a partnership to investigate the use of Tropic's Gene Editing induced Gene Silencing technology in porcine and bovine genetics.

Explore Our Recent Related Reports:

Enzyme Replacement Therapy Market Analysis by Product (Agalsidase Beta, Imiglucerase, Velaglucerase Alfa, Idursulfase, Galsulfase, and Laronidase); by Disease (Gaucher Disease, Fabry Disease, Pompe Disease, Mucopolysaccharidosis, and Exocrine Pancreatic Insufficiency [EPI]); and End User (Hospitals, and Infusion Centers & Home Healthcare Settings) Global Supply & Demand Analysis & Opportunity Outlook 2023-2033Infusion Pump Market Analysis by Pump Type (Ambulatory, Enteral, Implantable, Insulin, Volumetric, PCA, and Others); by Type (Stationary and Portable Pumps); by Technology (Specialty and Tradition); by End-User (Home Care Facility, Research Centers, Hospitals, and Others); and by Application (Oncology, Hematology, Analgesia, Neonatology, Gastroenterology, and Others) Global Supply & Demand Analysis & Opportunity Outlook 2023-2033Adhesive Bandages Market Analysis by Product (Medicated Bandages, and Non-Medicated Bandages); by Material (Plastic, Woven Fabric, Latex Strip, and Others); by Indication (Wound Management, Edema Control & Pain Management, Orthopedic Support, Sports & Athletic Wraps, and Others); and by Distribution Channel (Hospitals & Clinics, Retail Stores, E- Commerce, and Others) Global Supply & Demand Analysis & Opportunity Outlook 2023-2033Chronic Wound Care Market Analysis by Type (Pressure Ulcers, Diabetic Ulcers, Venous Leg Ulcers, and Others); by Product (Wound Care Devices, Advanced Wound Dressings, Active Therapy, and Others); and by End-User (Hospitals, Wound Care Centers, Ambulatory Centers, Homecare Settings, and Others) Global Supply & Demand Analysis & Opportunity Outlook 2023-2033Scleral Lens Market Analysis by Type (Mini-Scleral Lenses, Semi-Scleral Lenses, and Full Scleral Lenses); by Application (Ocular Surface Disease, Irregular Cornea, Refractive Error, and Others); and by Distribution Channel (Hospitals, Eye Clinics, and Others) Global Supply & Demand Analysis & Opportunity Outlook 2023-2033

About Research Nester

Research Nester is a one-stop service provider with a client base in more than 50 countries, leading in strategic market research and consulting with an unbiased and unparalleled approach towards helping global industrial players, conglomerates and executives for their future investment while avoiding forthcoming uncertainties. With an out-of-the-box mindset to produce statistical and analytical market research reports, we provide strategic consulting so that our clients can make wise business decisions with clarity while strategizing and planning for their forthcoming needs and succeed in achieving their future endeavors. We believe every business can expand to its new horizon, provided a right guidance at a right time is available through strategic minds.

Contact for more Info:AJ DanielEmail:info@researchnester.comU.S. Phone: +1 646 586 9123U.K. Phone: +44 203 608 5919

For More Update Follow: -LinkedIn|Twitter|Facebook|Xing

See original here:
Global Animal Genetics Market is estimated to garner a revenue of ~USD 10067 Million by the end of 2033; Growing Prevalence of Animal Infectious...

Untargeted plasma metabolites in Dutch cohorts

In this study, we examined plasma metabolomes in 1,679 fasting plasma samples from 1,368 individuals from two LLD5 sub-cohorts (LLD1 and LLD2) and the GoNL6 cohort (Extended Data Fig. 1 and Supplementary Table 1). The LLD1 cohort was the discovery cohort, with information about genetics, diet and the gut microbiome available for 1,054 participants. Moreover, 311 LLD1 subjects were followed up 4years later (LLD1 follow-up). We also included two independent replication cohorts: 237 LLD2 participants for whom we had genetic and dietary data and 77 GoNL participants for whom only genetic data were available (Extended Data Fig. 1 and Supplementary Table 1). Untargeted metabolomics profiling was done using flow-injection time-of-flight mass spectrometry (FI-MS)10,11, which yielded plasma levels of 1,183 metabolites (Supplementary Table 2). These metabolites covered a wide range of lipids, organic acids, phenylpropanoids, benzenoids and other metabolites (Extended Data Fig. 2a). As we observed weak (absolute rSpearman<0.2) correlations among the 1,183 metabolites (Extended Data Fig. 2b), data reduction was not required and, consequently, all metabolites were subjected to subsequent analyses. We validated the identification and quantification of some metabolites (for example, bile acids, creatinine, lactate, phenylalanine and isoleucine) by comparing their abundance levels from FI-MS with those previously determined by liquid chromatography with tandem mass spectrometry (LC-MS/MS)12 or NMR13 (rSpearman>0.62; Extended Data Fig. 2c,d).

To compare the relative importance of diet, genetics and the gut microbiome in explaining inter-individual plasma metabolome variability, we calculated the proportion of variance explained by these three factors for the whole plasma metabolome profile and for the individual metabolites separately. We have detailed information on 78 dietary habits (Supplementary Table 3), 5.3million human genetic variants and the abundances of 156 species and 343 MetaCyc pathways for each individual of the LLD1 cohort. Diet, genetics and the gut microbiome could explain 9.3, 3.3 and 12.8%, respectively, of inter-individual variations in the whole plasma metabolome, without adjusting for covariates (see the Methods section Distance matrix-based variance estimation; false discovery rate (FDR)<0.05; Fig. 1a and Supplementary Table 4), whereas intrinsic factors (age, sex and body mass index (BMI)) and smoking collectively explained 4.9% of the variance. Together, these factors explain 25.1% of the variance in the plasma metabolome (Fig. 1a).

a, Inter-individual variation in the whole plasma metabolome explained by the indicated factors, estimated using the PERMANOVA method. All, all of the indicated factors combined; smk, smoking status. b, Venn diagram indicating the number of metabolites whose inter-individual variation was significantly explained by diet, genetics or the gut microbiome, as estimated using the linear regression method (FDRF-test<0.05). c, Inter-individual variations in metabolites explained by diet, genetics or the gut microbiome, as estimated using the linear regression method (the lasso regression method was applied for feature selection) with a significant estimated adjusted r2>5% (FDRF-test<0.05). The blue bars represent dietary contributions to metabolite variations, the yellow bars indicate genetic contributions and the orange bars indicate microbial contributions. The other colors indicate the metabolic categories of metabolites (see legend). The yaxis indicates the proportion of variation explained. TMAO, trimethylamine N-oxide.

Next, we tested for pairwise associations between each metabolite and the dietary variables, genetic variants and microbial taxa. We observed 2,854 associations with dietary habits (Supplementary Table 5), 48 associations with 40 unique genetic variants (metabolite quantitative trait loci (mQTLs); Supplementary Table 6), 1,373 associations with gut bacterial species (Supplementary Table 7) and 2,839 associations with bacterial MetaCyc pathways (Supplementary Table 8) (see the Methods sections Associations with dietary habits, QTL mapping and Microbiome-wide associations). In total, 769 metabolites were significantly associated with at least one factor (Fig. 1b and Supplementary Tables 58). We then performed interaction analysis to assess the role of dietmicrobiome, geneticsmicrobiome and dietgenetics interactions in regulating the human metabolome using an interaction term in the linear model (see the Methods section Interaction analysis). Among these, 185 metabolites were associated with multiple factors and seven were affected by either geneticsmicrobiome, geneticsdiet or dietmicrobiome interactions (Supplementary Table 9).

As interactions were limited, we further assessed the proportion of variance of each metabolite that was explained by these factors using an additive model with the least absolute shrinkage and selection operator (lasso) method (see the Methods section Estimating the variance of individual metabolites). In general, the inter-individual variations in 733 metabolites could be explained by at least one of the three factors (FDRF-test<0.05; Supplementary Table 10). In detail, dietary habits contributed 0.435% of the variance in 684 metabolites; microbial abundances contributed 0.725% of the variance in 193 metabolites; and genetic variants contributed 328% of the variance in 44 metabolites (adjusted r2; FDRF-test<0.05; Supplementary Table 10). We also estimated the explained variance of metabolites using Elastic Net14, which is designed for highly correlated features, and found that the estimated explained variances were comparable between linear regression and the Elastic Net regression (Supplementary Fig. 1).

We further compared the variance explained by each type of factor (diet, genetics or the microbiome) and assigned the dominant factor for each metabolite if one factor explained more variance than the other two. Inter-individual variations in 610 metabolites were mostly explained by diet, 85 were explained by the gut microbiome and 38 were explained by genetics (Supplementary Table 10). Hereafter, we refer to these as diet-dominant, microbiome-dominant and genetics-dominant metabolites, respectively. The dominant factors of metabolites highlight their origin. For instance, ten out of the 21 diet-dominant metabolites for which diet explained >20% of the variance (FDRF-test<0.05; Supplementary Table 10) were food components based on their annotation in the Human Metabolome Database (HMDB)15. Similarly, of the 85 microbiome-dominant metabolites, 23 were annotated in the HMDB as microbiome-related metabolites (including 15 uremic toxins). Furthermore, out of the 38 genetics-dominant metabolites, ten were lipid species and eight were amino acids. Taken together, our analysis highlights that one factoreither dietary, genetic or microbialcan have a dominant effect over the other two in explaining the variances of plasma metabolites, with diet or the microbiome being particularly dominant. However, we also found that the variances in 185 metabolites were significantly attributable to more than one factor (Supplementary Table 10), including six metabolites associated with both genetics and the microbiome and 153 metabolites associated with both diet and the microbiome. For example, genetics and the microbiome explained 4 and 5%, respectively, of the variance in plasma 5-carboxy--chromanol (Fig. 1c)a dehydrogenated carboxylate product of 5-hydroxy--tocopherol16 that may reduce cancer and cardiovascular risk17. Another example is hippuric acida uremic toxin that can be produced by bacterial conversion of dietary proteins18, with 13% of its variance explained by diet and 13% explained by the microbiome (Fig. 1c).

Temporal changes in plasma metabolites can reflect changes in an individuals diet, gut microbiome and health status. When assessing the plasma metabolome in the 311 LLD1 follow-up samples, we indeed observed a significant shift in the plasma metabolome, with a significant difference in the second principal component (PPC1 paired Wilcoxon=0.1 and PPC2 paired Wilcoxon=1.3105; Fig. 2a). Baseline genetics, diet and microbiome, together with age, sex and BMI, could explain 59.4% of the variance in the follow-up plasma metabolome (PPERMANOVA=0.004) (Supplementary Fig. 2). We also observed that temporal stability can vary substantially between different metabolites (see the Methods section Temporal consistency of individual metabolites; Supplementary Table 11). Previously, we had assessed the changes in the gut microbiome in the LLD1 follow-up cohort and linked these to changes in the plasma metabolome7. Here, we further checked the temporal variability of the plasma metabolome and assessed the stability of diet-, microbiome- and genetics-dominant metabolites over time. Interestingly, the temporal correlation of the microbiome-dominant metabolites was similar to that of the genetics-dominant metabolites (PWilcoxon=0.51; Fig. 2b), whereas the temporal correlation between diet-dominant metabolites was significantly lower than between microbiome- and genetics-dominant metabolites (PWilcoxon<3.4105; Fig. 2b). However, the dominant dietary, microbial and genetic factors identified at baseline also explained similar variance in metabolic levels in the follow-up samples (Extended Data Fig. 3 and Supplementary Table 10). Our data also revealed a positive correlation between stability and the amount of variance that could be explained: the more variance explained, the more stable a metabolite is over time (Fig. 2c). For a few metabolites, we could not replicate the variance explained at baseline at the second time point, and these metabolites also showed weak or no correlation in their abundances between the two time points. For example, N-acetylgalactosamine showed very weak correlation between the two time points (r=0.13; P=0.02), and its genetic association was not replicated at the second time point.

a, Principal component analysis of metabolite levels at two time points (Euclidean dissimilarity). The green dots indicate baseline samples and the orange dots indicate follow-up samples (n=311 biologically independent samples). The KruskalWallis test (two sided) was used to check differences between baseline and follow-up. b, Temporal stability of metabolites stratified by the dominantly associated factor for each metabolite. The Wilcoxon test (two sided) was used to check the differences between groups. Each dot represents one metabolite. The yaxis indicates the Spearman correlation coefficient of abundances of each metabolite between two time points (n=311 biologically independent samples). In a and b, the box plots show the median and first and third quartiles (25th and 75th percentiles) of the first and second principal components (a) or correlation coefficients (b); the upper and lower whiskers extend to the largest and smallest value no further than 1.5 the interquartile range (IQR), respectively; and outliers are plotted individually. c, Correlation between metabolite stability and the metabolite variance explained by diet (left), genetics (middle) and the microbiome (right). The xaxis indicates the inter-individual variation explained by each factor and the yaxis indicates the Spearman correlation coefficient (two sided) of abundances of each metabolite between the two time points. The dashed white lines show the best fit and the gray shading represents the 95% confidence interval (CI) (n=311 biologically independent samples).

Having established the variances in metabolites explained by diet, genetics and the gut microbiome and the dominant factors that explained most of this variance, we focused on detailing specific associations and on the potential implications of our findings for assessing diet quality and improving our understanding of the genetic risk of complex diseases and the interaction and causality relationships among diet, the microbiome, genetics and metabolism.

We observed 2,854 significant associations (FDRSpearman<0.05) between 74 dietary factors and 726 metabolites (Fig. 3a and Supplementary Table 5; see the Methods section Lifelines diet quality score prediction). Associations with food-specific metabolites can, in theory, be used to verify food questionnaire data. For instance, the strongest association we observed was between quinic acid levels and coffee intake (rSpearman=0.54; P=1.61080; Fig. 3b). Quinic acid is found in a wide variety of different plants but has a particularly high concentration in coffee. Another example is 2,6-dimethoxy-4-propylphenol, which was strongly associated with fish intake (rSpearman=0.53; P=1.51076; Fig. 3c). This association is expected as this compound is particularly present in smoked fish according to HMDB annotation15. In addition, we also detected associations between dietary factors and metabolic biomarkers of some diseases. For example, 1-methylhistidine is a biomarker for cardiometabolic diseases including heart failure19 that is enriched in meat, and we observed significant associations between 1-methylhistidine and meat (rSpearman=0.12; P=7.2105) and fish intake (rSpearman=0.11; P=3.1104) as well as a lower level of 1-methylhistidine in vegetarians (rSpearman=0.15; P=9.7107; Fig. 3d).

a, Summary of the associations between diet and metabolites. The bars represent dietary habits, with the bar order sorted by the number of significant associations. Association directions are colored differently: orange indicates a positive association, whereas blue indicates a negative association. The length of each bar indicates the number of significant associations at FDR<0.05 (Spearman; two sided). b, Association between plasma quinic acid levels and coffee intake. The x and yaxes indicate residuals of coffee intake and the metabolic abundance after correcting for covariates, respectively (n=1,054 biologically independent samples). c, Association between plasma 2,6-dimethoxy-4-propylphenol levels and fish intake frequency (n=1,054 biologically independent samples). The x and yaxes refer to residuals of fish intake and metabolic abundance after correcting for covariates, respectively. d, Differential plasma levels of 1-methylhistidine between vegetarians and non-vegetarians (n=1,054 biologically independent samples). The yaxis indicates normalized residuals of metabolic abundance. The Pvalue from the Wilcoxon test (two sided) is shown. The box plots show the median and first and third quartiles (25th and 75th percentiles) of the metabolite levels. The upper and lower whiskers extend to the largest and smallest value no further than 1.5 the IQR, respectively. Outliers are plotted individually. e, Association between the diet quality score predicted by the plasma metabolome (yaxis) and the diet quality score assessed by the FFQ (xaxis) (n=237 biologically independent samples). In b, c and e, each gray dot represents one sample, the dark gray dashed line shows the linear regression line and the gray shading represents the 95% CI. In b and c, the association strength was assessed using Spearman correlation (two sided; the correlation coefficient and Pvalue are reported) and in e, the prediction performance was assessed with linear regression (F-test; two sided; the adjusted r2 value and Pvalue are reported).

Given the relationship between diet, metabolism and human health, we wondered whether the plasma metabolome could predict diet quality. For each of the Lifelines participants, we constructed a Lifelines Diet Score based on food frequency questionnaire (FFQ) data that reflected the relative diet quality based on dietdisease relationships8. To build a metabolic model to predict an individuals diet quality, we used LLD1 as the training set and LLD2 as the validation set. The resulting metabolic model included 76 metabolites, 51 of which were dominantly associated with diet. The diet score predicted by metabolites showed a significant association with the real diet score assessed by the FFQ in the validation set (r2adjusted=0.27; PF-test=3.5105; Fig. 3e). We also tested four other dietary scores (the Alternate Mediterranean Diet Score20, Healthy Eating Index (HEI)21, Protein Score22 and Modified Mediterranean Diet Score23) and found that the HEI predicted by plasma metabolites was also significantly associated with the FFQ-based HEI (r2adjusted=0.23; PF-test=6.5105; Supplementary Table 12).

Genetic associations of plasma metabolites may provide functional insights into the etiologies of complex diseases. After correcting for the first two genetic principal components, age, sex, BMI, smoking, 78 dietary habits, 40 diseases and 44 medications, QTL mapping in LLD1 identified 48 study-wide, independent genetic associations between 44 metabolites and 40 single-nucleotide polymorphisms (SNPs) (PSpearman<4.21011; clumping r2=0.05; clumping window=500kilobases (kb); Fig. 4a and Supplementary Table 6). All 48 genetic associations were replicated in either LLD1 follow-up or the two independent replication datasets (LLD2 and GoNL; Supplementary Fig. 3 and Supplementary Table 6). We also assessed the impact of physical activity, as assessed by questionnaires24, on the genetics association of metabolism, but found its influence to be negligible (Supplementary Fig. 4). Functional mapping and annotation (FUMA) of genome-wide association studies (GWAS)25 analysis revealed that the identified mQTLs were enriched in genes expressed in the liver and kidney (Extended Data Fig. 4) and related to metabolic phenotypes (Supplementary Table 6).

a, Manhattan plot showing 48 independent mQTLs identified linking 44 metabolites and 40 genetic variants with P<4.21011 (Spearman; two sided). Representative genes for the SNPs with significant mQTLs are labeled. b, Association between a tag SNP (rs1495741) of the NAT2 gene and plasma AFMU levels. c, Association between a SNP (rs13100173) within the HYAL3 gene and plasma levels of N-acetylgalactosamine-4-sulfate. d, Association between a tag SNP (rs17789626) of the SCLT1 gene and plasma mizoribine levels. e, Differences in coffee intake between participants with different genotypes at rs1495741. f, Correlations between coffee intake and AFMU in participants with different genotypes at rs1495741. g, Differences in bacterial fatty acid -oxidation pathway abundance in participants with different genotypes at rs67981690. h, Correlations between bacterial fatty acid -oxidation pathway abundance and 5-carboxy--chromanol in participants with different genotypes at rs67981690. In be and g, the xaxis indicates the genotype of the corresponding SNP and the yaxis indicates normalized residuals of the corresponding metabolic abundance (n=927 biologically independent samples). Each dot represents one sample. The box plots show the median and first and third quartiles (25th and 75th percentiles) of the metabolite levels. The upper and lower whiskers extend to the largest and smallest value no further than 1.5 the IQR, respectively. Outliers are plotted individually. The association strength is shown by the Spearman correlation coefficient and corresponding Pvalue (two sided). In f and h, the xaxis indicates the normalized abundance of coffee intake (f) or the bacterial fatty acid -oxidation pathway (h) and the yaxis indicates the normalized residuals of the corresponding metabolic abundance. Each dot represents one sample (n=927 biologically independent samples). The lines indicate linear regressions for each genotype group separately. Areas with light gray shading indicate the 95% CI of the linear regression lines. The association strength per genotype is shown by the Spearman correlation and the corresponding Pvalue (two sided).

The strongest association we found was between the caffeine metabolite 5-acetylamino-6-formylamino-3-methyluracil (AFMU) and SNP rs1495741 near the N-acetyltransferase 2 (NAT2) gene (rSpearman=0.52; P=1.71066; Fig. 4b), which showed strong linkage disequilibrium (r2=0.98) with a SNP, rs35246381, that was recently reported to be associated with urinary AFMU26. AFMU is a direct product of NAT2 activity and has been associated with bladder cancer risk27. Interestingly, the plasma level of AFMU was associated not only with coffee intake (rSpearman=0.29; P=9.21022; Supplementary Table 5) and the genotype of rs1495741, but also with their interactions (Supplementary Table 9). Individuals with a homologous AA genotype had a similar level of coffee intake, but their correlation between coffee intake and plasma AFMU level was significantly lower compared with individuals with GG and GA genotypes (Fig. 4e,f).

Pleotropic mQTL effects were also observed at several loci, including SLCO1B1, FADS2, KLKB1 and PYROXD2 (Supplementary Table 6). For example, three associations (related to three metabolites, two of them lipids) were observed for two SNPs (rs67981690 and rs4149067; linkage disequilibrium r2=0.72 in Northern Europeans from Utah) in SLCO1B1, which encodes the solute carrier organic anion transporter family member 1B1. Expression of the SLCO1B1 protein is specific to the liver, where this transporter is involved in the transport of various endogenous compounds and drugs, including statins28, from blood into the liver. The SLCO1B1 locus has also been linked to plasma levels of fatty acids and to statin-induced myopathy29. Furthermore, we detected a geneticsmicrobiome interaction between rs67981690 and microbial fatty acid oxidation pathways in regulating plasma levels of 5-carboxy--chromanol (P=1.5103), where the association of the bacterial fatty acid oxidation pathway with plasma levels of 5-carboxy--chromanol was dependent on the genotype of rs67981690 (Fig. 4g,h).

To identify novel mQTLs, we performed a systematic search of all published mQTL studies from 2008 onwards (Supplementary Table 13). This approach identified three novel mQTLs in our datasets (Supplementary Table 13) that were either not located close to previously reported mQTLs (distance>1,000kb) or not in linkage disequilibrium (r2<0.05). The first two novel SNPsrs13100173 at HYAL3 and rs11741352 at ARSBwere associated with N-acetylgalactosamine-4-sulfate (Fig. 4c,d), which is associated with mucopolysaccharidosis30. Interestingly, N-acetylgalactosamine-4-sulfate can bind to HYAL proteins (HYAL1, HYAL2, HYAL3 and HYAL4), suggesting that mQTLs can also pinpoint potential metaboliteprotein interactions. The third novel mQTL was rs17789626 at SCLT1, which was associated with mizoribinea compound used to treat nephrotic syndrome31.

We established 4,212 associations between 208 metabolites and 314 microbial factors (114 species and 200 MetaCyc pathways) (FDRLLD1<0.05; PLLD1 follow-up<0.05; Supplementary Tables 7 and 8). Interestingly, many of the metabolites that were associated with microbial species and MetaCyc pathways are also known to be gut microbiome related based on their HMDB annotations15. For instance, we observed 919 associations with 25 uremic toxins, 142 associations with thiamine (vitamin B1) and 117 associations with five phytoestrogens (FDR<0.05; Supplementary Tables 7 and 8). Uremic toxins and thiamine have been shown to be related to various diseases, including chronic kidney disease and cardiovascular diseases32,33. Phytoestrogens are a class of plant-derived polyphenolic compounds that can be transformed by gut microbiota into metabolites that promote the hosts metabolism and immune system33,34.

To assess whether gut microbiome composition causally contributes to plasma metabolite levels, we carried out bi-directional MR analyses (see the Methods section Bi-directional MR analysis). Here, we focused on the 37 microbial features that were associated with at least three independent genetic variants at P<1105 and with 45 metabolites (Supplementary Table 14). At FDR<0.05 (corresponding to P=2103 obtained from the inverse variance weighted (IVW) test)35, we observed four potential causal relationships at baseline that could also be found in the follow-up in the microbiomes to metabolites direction (Fig. 5ad and Supplementary Tables 15 and 16) but not in the opposite direction (Supplementary Table 17), and these outcomes were maintained following weighted median testing (P<0.03; Supplementary Fig. 5). To ensure that the data followed MR assumptions, we performed several sensitivity analyses, including checking for horizontal pleiotropy (MR-Egger36 intercept P>0.05; Supplementary Table 15) and heterogeneity (Cochrans Q test P>0.05; Supplementary Table 15) and leave-one-out analysis (Extended Data Fig. 5). We did not use causal estimates derived using the MR-Egger method to filter the results, as its power to detect causality is known to be low36. These sensitivity checks further confirmed the reliability of these four MR causal estimates.

a, Analysis of the association between adenosylcobalamin biosynthesis pathway abundance and 5-hydroxytryptophol levels. b, Glycogen biosynthesis pathway abundance versus 5-sulfo-1,3-benzenedicarboxylic acid levels. c, E. rectale abundance versus hydrogen sulfite levels. d, Veillonella parvula abundance versus 2,3-dehydrosilybin levels. In the top panels of ad, the xaxis shows the SNP exposure effect, and the yaxis shows the SNP outcome effect and each dot represents a SNP. Error bars represent the s.e. of each effect size. The bottom panels of ad, show the MR effect size (center dot) and 95% CI for the baseline (blue) and follow-up (green) datasets of the LLD1 cohort, estimated with the IVW MR approach (two sided) (n=927 biologically independent samples at baseline and n=311 biologically independent samples at follow-up).

We further found that increased abundance of microbial adenosylcobalamin biosynthesis (coenzyme B12) was associated with reduced plasma levels of 5-hydroxytryptophol (Fig. 5a)a uremic toxin related to Parkinsons disease37. We also found that plasma hydrogen sulfite levels were related to Eubacterium rectale (Fig. 5c)a core gut commensal species38 that is highly prevalent (presence rate=97%) and abundant (mean abundance=8.5%) in both our cohorts and in other populations39,40,41. As a strict anaerobe, E. rectale promotes the hosts intestinal health by producing butyrate and other short-chain fatty acids from non-digestible fibers42, and a reduced abundance of this species has been observed in subjects with inflammatory bowel disease39,43 and colorectal cancer44 compared with healthy controls. As a toxin, hydrogen sulfite interferes with the nervous system, cardiovascular functions, inflammatory processes and the gastrointestinal and renal system45. Our results thus reveal a potential new beneficial effect of E. rectale.

To further investigate the metabolic potential of individual bacterial species, we applied newly developed pipelines to identify microbial primary metabolic gene clusters (gutSMASH pathways)46 and microbial genomic structural variants (SVs)47. These two tools profile microbial genomic entities that are implicated in metabolic functions. By associating 1,183 metabolites with 3,075 gutSMASH pathways and 6,044 SVs (1,782 variable SVs (vSVs) and 4,262 deletion SVs (dSVs); see Methods), we observed 23,662 associations with gutSMASH pathways and 790 associations with bacterial SVs (FDRLLD1<0.05; PLLD1 follow-up<0.05; Supplementary Tables 1820). These associations connect the genetically encoded functions of microbes with metabolites, thereby providing putative mechanistic information underlying the functional output of the gut microbiome. In one example, we observed that the microbial uremic toxin biosynthesis pathways, including the glycine cleavage pathway (in Olsenella and Clostridium species) and the hydroxybenzoate-to-phenol pathway (in Clostridium species) responsible for hippuric acid and phenol sulfate biosynthesis, were associated with the hippuric acid (Olsenella species: rSpearman=0.15; P=9.3107; Clostridium species: rSpearman=0.18; P=5.9109) and phenol sulfate (rSpearman=0.17; P=4.2108; Extended Data Fig. 6a) levels measured in plasma, respectively (FDRLLD1<0.05 and PLLD1 follow-up<0.05; Extended Data Fig. 6b).

Next, we carried out a mediation analysis to investigate the links between diet, the microbiome and metabolites. For 675 microbial features that were associated with both dietary habits and metabolites (FDR<0.05), we applied bi-directional mediation analysis to evaluate the effects of microbiome and metabolites for diet (see the Methods section Bi-directional mediation analysis). This approach established 146 mediation linkages: 133 for the dietary impact on the microbiome through metabolites and 13 for the dietary impact on metabolites through the microbiome (FDRmediation<0.05 and Pinverse-mediation>0.05; Fig. 6a,b and Supplementary Table 21). Most of these linkages were related to the impact of coffee and alcohol on microbial metabolic functionalities (Fig. 6a).

a, Parallel coordinates chart showing the 133 mediation effects of plasma metabolites that were significant at FDR<0.05. Shown are dietary habits (left), plasma metabolites (middle) and microbial factors (right). The curved lines connecting the panels indicate the mediation effects, with colors corresponding to different metabolites. freq., frequency; PFOR, pyruvate:ferredoxin oxidoreductase; OD, oxidative decarboxylation; HGD, 2-hydroxyglutaryl-CoA dehydratase; TPP, thiamine pyrophosphate. b, Parallel coordinates chart showing the 13 mediation effects of the microbiome that were significant at FDR<0.05. Shown are dietary habits (left), microbial factors (middle) and plasma metabolites (right). For the microbial factors column, number ranges represent the genomic location of microbial structure variations (SVs) in kilobyte unit, and colons represent the detailed annotation of certain gutSMASH pathway. c, Analysis of the effect of coffee intake on the abundance of M. smithii as mediated by hippuric acid. d, Analysis of the effect of beer intake on the C. methylpentosum Rnf complex pathway as mediated by hulupinic acid. e, Analysis of the effect of fruit intake on urolithin B in plasma as mediated by a vSV in Ruminococcus species (300305kb). In ce, the gray lines indicate the associations between the two factors, with corresponding Spearman coefficients and Pvalues (two sided). Direct mediation is shown by a red arrow and reverse mediation is shown by a blue arrow. Corresponding Pvalues from mediation analysis (two sided) are shown. inv., inverse; mdei., mediation.

Coffee contains various phenolic compounds that can be converted to hippuric acid by colonic microflora48. Hippuric acid is an acyl glycine that is associated with phenylketonuria, propionic acidemia and tyrosinemia49. We observed that hippuric acid can mediate the impact of drinking coffee on Methanobrevibacter smithii abundance (Pmediation=2.21016; Fig. 6c). We also observed that hulupinic acid, which is commonly detected in alcoholic drinks, can mediate the impact of beer consumption on the Clostridium methylpentosum ferredoxin:NAD+ oxidoreductase (Rnf) complex (Pmediation=2.21016; Fig. 6d)an important membrane protein in driving the ATP synthesis essential for all bacterial metabolic activities50.

Of the dietary impacts on metabolites through the microbiome (Fig. 6b and Supplementary Table 21), one interesting example is a Ruminococcus species vSV (300305kb) that encodes an ATPase responsible for transmembrane transport of various substrates51. This Ruminococcus species vSV mediated the effect of fruit consumption on plasma levels of urolithin B (Pmediation=2.21016; Fig. 6e). Urolithin B is a gut microbiota metabolite that protects against myocardial ischemia/reperfusion injury via the p62/Keap1/Nrf2 signaling pathway52. Taken together, our data provide potential mechanistic underpinnings for dietmetabolite and dietmicrobiome relationships.

Continue reading here:
Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome - Nature.com

Note: All in-person attendees and media must be fully vaccinated. Prior to arriving on-site, please upload your vaccination record to the ASHG/Safe Expo Portal to ensure timely access to the event.

ROCKVILLE, MD--Thousands of human genomics and genetics researchers, clinicians, counselors, public health experts and others will attend the annual meeting of the American Society of Human Genetics (ASHG) in Los Angeles, California, October 25-29. Journalists covering ASHG 2022, the world's largest and most influential human genetics and genomics meeting, will have access to thousands of scientific papers and oral presentations, workshops, and collaborative events. The annual meeting fosters discussion about individual research and the big picture of cutting-edge science across the field. It is a remarkable opportunity to meet sources, chart trends and uncover story ideas.

This years meeting will offer in-person opportunities for networking, sharing the latest scientific findings with nearly 400 live presentations, more than 2,500 published posters, and over 200 exhibitors. A post-meeting virtual program will capture highlights, provide additional learning, and feature live networking for those unable to attend in person.

ASHG is thrilled to bring together attendees in person for the first time in three years to present, learn and discuss the most recent findings in human genetics and genomics in the worlds largest venue for geneticists on earth, said ASHG President Charles Rotimi, PhD. The breadth of science being presented at this years meeting reflects the expansive reach of genomics in all areas of research and its role to help promote health and prevent disease.

This years program features exciting sessions highlighting many breakthroughs in research progress and ongoing field dialogue on emerging issues that can realize benefits of this research for science, health, and society. Learn more in the online planner.

COVID-19 in the Post-Pandemic Era: Long COVID, Vaccine Response, and Beyond This event will share information about the contributions of human genetic variation to susceptibility to COVID and risk of long COVID as well as response to vaccines.

Tuesday, October 25, from 4:30 p.m. 6:00 p.m.

Presidential Symposium on H3Africa and the African Genomics Ecosystem This event featuring former NIH Director, Francis Collins, MD will highlight Africa, a profoundly dynamic and diverse continent, and its major advances, new directions and goals, emerging scientific leadership, exciting investment in technology infrastructure, and more. How can and will genomics in Africa spread its wings and what areas are most exciting?Thursday, October 27, 8:30 a.m.10:00 a.m.

Upset the Set Up: Moving from Community Engagement to Community Empowerment The overarching objectives of this session are to: (1) examine ongoing efforts that break the mold of transactional community engaged research; and (2) explore remaining needs for community empowered research in genetics and genomics. It does so by bringing together diverse stakeholders in the field to consider the need to transition from community engagement to community empowerment.

Friday, October 28, from 8:30 a.m.- 10:00 a.m.

Research presented at the annual meeting will also cover:

In addition, ASHG will hold a special media availability session with geneticists from ASHGs Public Education and Awareness Committee on Wednesday, October 26 from 9:45-10:15 a.m., exclusively for registered media. During this discussion, presenters will highlight new initiatives; findings related to basic, translational, and clinical genetics; therapeutics and drug discovery; population genetics and evolution; and more. Media can register for credentials here.

* * *

About the American Society of Human Genetics (ASHG)

Founded in 1948, the American Society of Human Genetics is the primary professional membership organization for human genetics specialists worldwide. Its community of nearly 8,000 members include researchers, academicians, clinicians, laboratory practice professionals, genetic counselors, nurses, and others with an interest in human genetics. The Society serves scientists, health professionals, and the public by providing forums to: (1) share research results through theASHG Annual Meetingand inThe American Journal of Human GeneticsandHuman Genetics and Genomics Advances; (2) advance genetic research by advocating for research support; (3) educate current and future genetics professionals, health care providers, advocates, policymakers, educators, students, and the public about all aspects of human genetics; and (4) promote genetic services and support responsible social and scientific policies. For more information, visit:http://www.ashg.org.

6120 Executive Blvd, Suite 500 | Rockville, MD 20852 | 301.634.7300 |society@ashg.org|www.ashg.orgConnect with ASHG onTwitter(@GeneticsSociety) |Facebook|LinkedIn

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Link:
ASHG 2022 in Los Angeles brings together researchers from around the world to advance discoveries in genetics, genomics research - EurekAlert

Some Fulgent Genetics, Inc. (NASDAQ:FLGT) shareholders are probably rather concerned to see the share price fall 33% over the last three months. But that does not change the realty that the stock's performance has been terrific, over five years. In that time, the share price has soared some 833% higher! So it might be that some shareholders are taking profits after good performance. But the real question is whether the business fundamentals can improve over the long term. Unfortunately not all shareholders will have held it for the long term, so spare a thought for those caught in the 55% decline over the last twelve months. We love happy stories like this one. The company should be really proud of that performance!

So let's assess the underlying fundamentals over the last 5 years and see if they've moved in lock-step with shareholder returns.

View our latest analysis for Fulgent Genetics

To quote Buffett, 'Ships will sail around the world but the Flat Earth Society will flourish. There will continue to be wide discrepancies between price and value in the marketplace...' One way to examine how market sentiment has changed over time is to look at the interaction between a company's share price and its earnings per share (EPS).

During the five years of share price growth, Fulgent Genetics moved from a loss to profitability. That kind of transition can be an inflection point that justifies a strong share price gain, just as we have seen here.

You can see how EPS has changed over time in the image below (click on the chart to see the exact values).

earnings-per-share-growth

We know that Fulgent Genetics has improved its bottom line over the last three years, but what does the future have in store? This free interactive report on Fulgent Genetics' balance sheet strength is a great place to start, if you want to investigate the stock further.

While the broader market lost about 21% in the twelve months, Fulgent Genetics shareholders did even worse, losing 55%. However, it could simply be that the share price has been impacted by broader market jitters. It might be worth keeping an eye on the fundamentals, in case there's a good opportunity. On the bright side, long term shareholders have made money, with a gain of 56% per year over half a decade. If the fundamental data continues to indicate long term sustainable growth, the current sell-off could be an opportunity worth considering. I find it very interesting to look at share price over the long term as a proxy for business performance. But to truly gain insight, we need to consider other information, too. Case in point: We've spotted 3 warning signs for Fulgent Genetics you should be aware of, and 1 of them makes us a bit uncomfortable.

Story continues

For those who like to find winning investments this free list of growing companies with recent insider purchasing, could be just the ticket.

Please note, the market returns quoted in this article reflect the market weighted average returns of stocks that currently trade on US exchanges.

Have feedback on this article? Concerned about the content? Get in touch with us directly. Alternatively, email editorial-team (at) simplywallst.com.

This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned.

Join A Paid User Research SessionYoull receive a US$30 Amazon Gift card for 1 hour of your time while helping us build better investing tools for the individual investors like yourself. Sign up here

See the original post:
Those who invested in Fulgent Genetics (NASDAQ:FLGT) five years ago are up 833% - Yahoo Finance