Category Archives: Protein Folding

A breakthrough on protein folding could unlock new possibilities into disease understanding and drug discovery, among other fields.(Photo: DeepMind)

Anew discovery about "protein folding" could unlock a world of possibilities into the understanding ofeverything from diseases to drugs, researchers say.

The breakthrough that is sending ripples of excitement throughthe science and medical communities this week deals with theshapestiny proteins in our bodies essential to all life fold into.

The so-called "protein-folding problem" has puzzled scientists for five decades, and the discovery this week from the London-based artificial intelligence lab DeepMind has been heralded as a major milestone.

"This computational work represents a stunning advance on the protein-folding problem, a 50-year old grand challenge in biology," said Venki Ramakrishnan, president of the U.K.'s Royal Society. "It has occurred decades before many people in the field would have predicted. It will be exciting to see the many ways in which it will fundamentally change biological research.

'The Ultimate': Astronomers want to put a huge telescope on the moon to study the Big Bang

Proteins are essential to life, supporting practically all of its functions, according to DeepMind, which is owned by Google. They are large, complex molecules, made up of chains of amino acids, and what a protein does largely depends on its unique 3D structure.

The ability to predict protein structures accurately enables a better understanding of what they do and how they work.

This isn't your typical space rock: There's a metal asteroid out there worth $10,000 quadrillion

When proteins are translated from their DNA codes, they quickly transform from a non-functional, unfolded state into their folded, functional state. Problems in folding can lead to diseases such asAlzheimer's and Parkinson's.

The companys breakthrough essentially means that it figured out how to use artificial intelligence to deliver relatively quick answers to questions about protein structure and function that would take many months or years to solve using currently available methods, according to STAT News.

Lunar discovery: Water discovered on sunlit part of the moon for the first time, NASA says

DeepMinds program, called AlphaFold, outperformed about 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction, according to the journal Nature.

We have been stuck on this one problem how do proteins fold up for nearly 50 years," said University of Maryland professor John Moult, co-founder and chair of CASP. "To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts wondering if wed ever get there, is a very special moment.

Researchers from DeepMind plan to publish their results in a peer-reviewed journal in the near future.

Read or Share this story: https://www.usatoday.com/story/news/nation/2020/12/03/protein-folding-discovery-major-breakthrough-deepmind/3809693001/

Read more here:
'Stunning advance' on 'protein folding': A 50-year-old science problem solved and that could mean big things - USA TODAY

Your Colleagues

UBNOW STAFF

Published November 30, 2020

Four UB professors have been elected fellows of the American Association for the Advancement of Science (AAAS), the world's largest general scientific society and publisher of the journal Science.

The honor is bestowed on AAAS members by their peers for their scientifically or socially distinguished efforts to advance science applications. The UB faculty members were among 489 members to receive the prestigious distinction this year.

The new UB fellows include:

AAAS fellows will be recognized in the journal Science on Nov. 27. An induction ceremony will be held during the virtual AAAS Fellows Forum on Feb. 13.

Eaton Lattman (biological sciences)

Lattman was honored for his distinguished contributions in scholarship, education and leadership in the fields of molecular biophysics and structural biology.

A prolific researcher in crystallography and biophysics, Lattman has focused on protein folding and on development and improvement of methods in protein crystallography. He has pioneered the emerging field of using X-ray free electron lasers to study biological and nonbiological processes.

Lattman spent nearly his entire academic career at Johns Hopkins University, as professor of biophysics in both the School of Medicine and the Krieger School of Arts and Sciences, where he also served as dean of research and graduate education. He played a key role in establishing the Hopkins Institute for Biophysical Research.

In 2008, Lattman came to Buffalo to serve as chief executive officer at Hauptman-Woodward Medical Research Institute. He joined the UB Department of Structural Biology in 2009.

In 2013, he was instrumental in the awarding of a $25 million U.S. National Science Foundation grant to UB and its partners to establish BioXFEL, an X-ray laser science center, to transform the field of structural biology. It was UBs first NSF Science and Technology Center Grant. Lattman was named director and led the national consortium until 2017. Under his direction, the consortium made significant progress in refining X-ray laser techniques to study biological processes and innovating new approaches to use these methods to advance materials science and other nonbiological disciplines as well. He continues to serve as a member of the BioXFEL steering committee.

Xiufeng Liu (education)

Liu was recognized for his distinguished contributions to the fields of science education research, and communicating and interpreting science to the public.

Liu is renowned for his scholarship on measuring and evaluating student achievement in science, technology, engineering and math (STEM). He served as the inaugural director of UBs Center for Educational Innovation, with a mission to improve university teaching, learning and assessment.

He also strives to increase scientific literacy among members of the public, and inspired a program at UB called Science and the Public that prepares museum curators, zoo directors, pharmacists and other informal science educators to teach science to a general audience, including by engaging in activities and debates related to science.

Liu has received more than $18 million in research funding, and published more than 100 academic articles and 10 books. He received a doctorate in science education from the University of British Columbia and a masters degree in chemical education from East China Normal University.

Janet Morrow (chemistry)

Morrow was honored for her distinguished contributions to the field of inorganic complexes and their biomedical applications, particularly for magnetic resonance imaging contrast agents and for nucleic acid modifications.

Morrow is an expert in bioinorganic chemistry, with a wide range of innovations and publications in the field. The central theme of her research is the synthesis of inorganic complexes for biomedical diagnostics, sensing or catalytic applications. Focus areas include research and development of novel MRI contrast agents, yeast cell labeling with metal complex probes to track infections, and bimodal imaging agents. Morrow is also an inventor and entrepreneur, having co-founded Ferric Contrast, a startup that is developing iron-containing MRI contrast agents.

She is a recipient of the Jacob F. Schoellkopf Medal presented by the Western New York section of the American Chemical Society, the UB Exceptional Scholar Award for Sustained Achievement, the National Science Foundation Award for Special Creativity and the Alfred P. Sloan Research Fellowship. Morrow holds a doctorate in chemistry from the University of North Carolina at Chapel Hill and a bachelors degree in chemistry from the University of California, Santa Barbara.

Stefan Ruhl (dentistry and oral health sciences)

Ruhl was recognized for his distinguished contributions to the field of oral biology, particularly for work on glycan-mediated microbial adhesion in the oral cavity.

Ruhl is an internationally renowned expert on saliva, oral bacteria and the oral microbiome. His research attempts to unravel the roles that saliva and microorganisms play in health, including in adhesion to the teeth and surfaces of the mouth, defense against pathogens and colonization of the oral cavity. He investigates the molecular mechanisms of microbial binding to glycans, a common but little understood class of biomolecules that help bacteria attach to host surfaces, including those in the mouth. The goal of his lab is to harness tools that ultimately help scientists examine how the microorganisms bind to glycans in the mouth to form dental biofilms more commonly known as plaque increasing the risk for cavities and periodontal disease.

He was among the first researchers to catalogue the human salivary proteome, which is the entirety of proteins present in saliva and in salivary gland ductal secretions. Ruhl has led or participated in recent studies that have identified how saliva is made, tracing each salivary protein back to its source. He also discovered that 2 million years of eating meat and cooked food has led humans to develop a saliva that is now starkly different from that of chimpanzees and gorillas, our closest genetic relatives. This seminal discovery has resulted in collaborative projects exploring saliva to understand the factors that helped shape human evolution and, in particular, the evolution of the human mouth. These evolutionary projects identified a starch-digesting enzyme called amylase in the saliva of dogs and various other starch-consuming mammals, and through analysis of a salivary mucin protein found genetic evidence that humans may have mated with a ghost species of archaic humans.

Ruhl received the 2020 Distinguished Scientist Award in Salivary Research and the 2014 Salivary Researcher of the Year award from the International Association for Dental Research, as well as the UB Exceptional Scholar Award for Sustained Achievement. He holds a doctor of dental surgery degree and a doctoral degree in immunology from Georg-August University of Gttingen.

Here is the original post:
Lattman, Liu, Morrow and Ruhl elected AAAS fellows - UB Now: News and views for UB faculty and staff - University at Buffalo Reporter

An artificial intelligence company that gained fame for designing computer systems that could beat humans at games has now made a huge advancement in biological science.

The company, DeepMind, which is owned by the same parent company as Google, has created an AI system that can rapidly and accurately predict how proteins fold to get their 3D shapes, a surprisingly complex problem that has plagued researchers for decades, according to The New York Times.

Figuring out a protein's structure can require years or even decades of laborious experimentation, and current computer simulations of protein folding fall short on accuracy. But DeepMind's system, known as AlphaFold, required only a few hours to accurately predict a protein's structure, the Times reported.

Related: Why does artificial intelligence scare us so much?

Proteins are large molecules that are essential for life. They are made up of a string of chemical compounds known as amino acids. These "strings" fold in intricate ways to create unique structures that determine what the protein can do. (For example, the "spike" protein on the new coronavirus allows the virus to bind to and invade human cells.)

Nearly 50 years ago, scientists hypothesized that you could predict a protein's structure knowing just its sequence of amino acids. But solving this "protein folding problem" has proved enormously challenging because there are a mind-boggling number of ways in which the same protein could theoretically fold to take on a 3D structure, according to a statement from DeepMind.

Twenty-five years ago, scientists created an international competition to compare various methods of predicting protein structure something of a "protein olympics," known as CASP, which stands for Critical Assessment of Protein Structure Prediction, according to The Guardian.

In this year's challenge, AlphaFold's performance was head and shoulders above its competitors'. It achieved a level of accuracy that researchers were not expecting to see for years.

"This computational work represents a stunning advance on the protein-folding problem, a 50-year-old grand challenge in biology," Venki Ramakrishnan, president of the Royal Society in the United Kingdom, who was not involved with the work, said in a statement. "It has occurred decades before many people in the field would have predicted. It will be exciting to see the many ways in which it will fundamentally change biological research."

For the competition, teams are given the amino acid sequences of about 100 proteins, the structures of which are known but have not been published, according to Nature News. The predictions are given a score from zero to 100, with 90 considered on par with the accuracy of experimental methods.

AlphaFold trained itself to recognize the relationship between the amino acid sequence and protein structure using existing databases. Then, it used a neural network a computer algorithm modeled on the way the human brain processes information to iteratively improve its prediction of the unpublished protein structures.

Overall, AlphaFold had a median score of 92.5. That's up from a score of less than 60 that the system achieved in its first CASP competition in 2018.

The system isn't perfect in particular, AlphaFold did not perform well in modeling groups of proteins that interact with each other, Nature News reported.

But the advance is a game-changer.

"I think it's fair to say this will be very disruptive to the protein-structure-prediction field. I suspect many will leave the field as the core problem has arguably been solved," Mohammed AlQuraishi, a computational biologist at Columbia University told Nature News. "It's a breakthrough of the first order, certainly one of the most significant scientific results of my lifetime."

DeepMind previously made headlines when it created an AI program, known as AlphaGo, that beat humans at the ancient game of Go.

Researchers hope AlphaFold can have many real-world applications. For example, it could help identify the structures of proteins involved in certain diseases and accelerate drug development.

DeepMind is currently working on a peer-reviewed paper on its work on AlphaFold, the Times reported.

Originally published on Live Science.

The rest is here:
AI system solves 50-year-old protein folding problem in hours - Livescience.com

Sifting through the trillions of molecules out there that might have powerful medicinal effects is a daunting task, but the solution biotech has found is to work smarter, not harder. Genesis Therapeutics has a new simulation approach and cross-disciplinary team that has clearly made an impression: the company just raised a $52 million A round.

Genesis competed in the Startup Battlefield at Disrupt last year, impressing judges with its potential, and obviously others saw it as well in particular Rock Springs Capital, which led the round.

Over the last few years many companies have been formed in the drug discovery space, powered by increased computing and simulation power that lets them determine the potential of molecules in treating certain diseases. At least thats the theory. The reality is a bit messier, and while these companies can narrow the search, they cant just say here, a cure for Parkinsons.

Founder Evan Feinberg got into the field when an illness he inherited made traditional lab work, as an intern at a big pharma company, difficult for him. The computational side of the field, however, was more accessible and ended up absorbing him entirely.

He had dabbled in the area before and arrived at what he feels is a breakthrough in how molecules are represented digitally. Machine learning has, of course, accelerated work in many fields, biochemistry among them, but he felt that the potential of the technology had not been tapped.

I think initially the attempts were to kind of cut and paste deep learning techniques, and represent molecules a lot like images, and classify them like youd say, this is a cat picture or this is not a cat picture, he explained in an interview. We represent the molecules more naturally: as graphs. A set of nodes or vertices, those are atoms, and things that connect them, those are bonds. But were representing them not just as bond or no bond, but with multiple contact types between atoms, spatial distances, more complex features.

The resulting representation is richer and more complex, a more complete picture of a molecule than youd get from its chemical formula or a stick diagram showing the different structures and bonds. Because in the world of biochemistry, nothing is as simple as a diagram. Every molecule exists as a complicated, shifting 3D shape or conformation where important aspects like the distance between two carbon formations or bonding sites is subject to many factors. Genesis attempts to model as many of those factors as it can.

Step one is the representation, he said, but the logical next step is, how does one leverage that representation to learn a function that takes an input and outputs a number, like binding affinity or solubility, or a vector that predicts multiple properties at once?

Thats the work theyve focused on as a company not just creating a better model molecule, but being able to put a theoretical molecule into simulation and say, it will do this, it wont do this, it has this quality but not that one.

Some of this work may be done in partnerships, such as the one Genesis has struck up with Genentech, but the teams could very well find drug candidates independent of those, and for that reason the company is also establishing an internal development process.

The $52 million infusion ought to do a lot to push that forward, Feinberg wrote in an email:

These funds allow us to execute on a number of critical objectives, most importantly further pioneering AI technologies for drug development and advancing our therapeutics pipeline. We will be hiring more top notch AI researchers, software engineers, medicinal chemists and biotech talent, as well as building our own research labs.

Other companies are doing simulations as well and barking up the same tree, but Feinberg says Genesis has at least two legs up on them, despite the competition raising hundreds of millions and existing for years.

Were the only company in the space thats working at the intersection of modern deep neural network approaches and biophysical simulation conformational change of ligands and proteins, he said. And were bringing this super technical platform to experts who have taken FDA-approved drugs to market. Weve seen tremendous value creation just from that the chemists inform the AI too.

The recent breakthrough of AlphaFold, which is performing the complex task of simulation protein folding far faster than any previous system, is as exciting to Feinberg as to everyone else in the field.

As scientists, we are incredibly excited by recent progress in protein structure prediction. It is an important basic science advance that will ultimately have important downstream benefits to the development of novel therapeutics, he wrote. Since our Dynamic PotentialNet technology is unique in how it leverages 3D structural information of proteins, computational protein folding similar to recent progress in cryo-EM is a nice complementary tailwind for the Genesis AI Platform. We applaud all efforts to make protein structure more accessible such that therapeutics can be more easily developed for patients of all conditions.

Also participating in the funding round were T. Rowe Price Associates, Andreessen Horowitz (who led the seed round), Menlo Ventures and Radical Ventures.

Read more from the original source:
Genesis Therapeutics raises $52M A round for its AI-focused drug discovery mission - TechCrunch

Takeaways

A deep learning software program from Google-owned lab DeepMind showed great progress in solving one of biologys greatest challenges understanding protein folding.

Protein folding is the process by which a protein takes its shape from a string of building blocks to its final three-dimensional structure, which determines its function.

By better predicting how proteins take their structure, or fold, scientists can more quickly develop drugs that, for example, block the action of crucial viral proteins.

Solving what biologists call the protein-folding problem is a big deal. Proteins are the workhorses of cells and are present in all living organisms. They are made up of long chains of amino acids and are vital for the structure of cells and communication between them as well as regulating all of the chemistry in the body.

This week, the Google-owned artificial intelligence company DeepMind demonstrated a deep-learning program called AlphaFold2, which experts are calling a breakthrough toward solving the grand challenge of protein folding.

Proteins are long chains of amino acids linked together like beads on a string. But for a protein to do its job in the cell, it must fold a process of twisting and bending that transforms the molecule into a complex three-dimensional structure that can interact with its target in the cell. If the folding is disrupted, then the protein wont form the correct shape and it wont be able to perform its job inside the body. This can lead to disease as is the case in a common disease like Alzheimers, and rare ones like cystic fibrosis.

Deep learning is a computational technique that uses the often hidden information contained in vast datasets to solve questions of interest. Its been used widely in fields such as games, speech and voice recognition, autonomous cars, science and medicine.

I believe that tools like AlphaFold2 will help scientists to design new types of proteins, ones that may, for example, help break down plastics and fight future viral pandemics and disease.

I am a computational chemist and author of the book The State of Science. My students and I study the structure and properties of fluorescent proteins using protein-folding computer programs based on classical physics.

After decades of study by thousands of research groups, these protein-folding prediction programs are very good at calculating structural changes that occur when we make small alterations to known molecules.

But they havent adequately managed to predict how proteins fold from scratch. Before deep learning came along, the protein-folding problem seemed impossibly hard, and it seemed poised to frustrate computational chemists for many decades to come.

The sequence of the amino acids which is encoded in DNA defines the proteins 3D shape. The shape determines its function. If the structure of the protein changes, it is unable to perform its function. Correctly predicting protein folds based on the amino acid sequence could revolutionize drug design, and explain the causes of new and old diseases.

All proteins with the same sequence of amino acid building blocks fold into the same three-dimensional form, which optimizes the interactions between the amino acids. They do this within milliseconds, although they have an astronomical number of possible configurations available to them about 10 to the power of 300. This massive number is what makes it hard to predict how a protein folds even when scientists know the full sequence of amino acids that go into making it. Previously predicting the structure of protein from the amino acid sequence was impossible. Protein structures were experimentally determined, a time-consuming and expensive endeavor.

Once researchers can better predict how proteins fold, theyll be able to better understand how cells function and how misfolded proteins cause disease. Better protein prediction tools will also help us design drugs that can target a particular topological region of a protein where chemical reactions take place.

The success of DeepMinds protein-folding prediction program, called AlphaFold, is not unexpected. Other deep-learning programs written by DeepMind have demolished the worlds best chess, Go and poker players.

In 2016 Stockfish-8, an open-source chess engine, was the worlds computer chess champion. It evaluated 70 million chess positions per second and had centuries of accumulated human chess strategies and decades of computer experience to draw upon. It played efficiently and brutally, mercilessly beating all its human challengers without an ounce of finesse. Enter deep learning.

On Dec. 7, 2017, Googles deep-learning chess program AlphaZero thrashed Stockfish-8. The chess engines played 100 games, with AlphaZero winning 28 and tying 72. It didnt lose a single game. AlphaZero did only 80,000 calculations per second, as opposed to Stockfish-8s 70 million calculations, and it took just four hours to learn chess from scratch by playing against itself a few million times and optimizing its neural networks as it learned from its experience.

AlphaZero didnt learn anything from humans or chess games played by humans. It taught itself and, in the process, derived strategies never seen before. In a commentary in Science magazine, former world chess champion Garry Kasparov wrote that by learning from playing itself, AlphaZero developed strategies that reflect the truth of chess rather than reflecting the priorities and prejudices of the programmers. Its the embodiment of the clich work smarter, not harder.

Every two years, the worlds top computational chemists test the abilities of their programs to predict the folding of proteins and compete in the Critical Assessment of Structure Prediction (CASP) competition.

In the competition, teams are given the linear sequence of amino acids for about 100 proteins for which the 3D shape is known but hasnt yet been published; they then have to compute how these sequences would fold. In 2018 AlphaFold, the deep-learning rookie at the competition, beat all the traditional programs but barely.

Two years later, on Monday, it was announced that Alphafold2 had won the 2020 competition by a healthy margin. It whipped its competitors, and its predictions were comparable to the existing experimental results determined through gold standard techniques like X-ray diffraction crystallography and cryo-electron microscopy. Soon I expect AlphaFold2 and its progeny will be the methods of choice to determine protein structures before resorting to experimental techniques that require painstaking, laborious work on expensive instrumentation.

One of the reasons for AlphaFold2s success is that it could use the Protein Database, which has over 170,000 experimentally determined 3D structures, to train itself to calculate the correctly folded structures of proteins.

The potential impact of AlphaFold can be appreciated if one compares the number of all published protein structures approximately 170,000 with the 180 million DNA and protein sequences deposited in the Universal Protein Database. AlphaFold will help us sort through treasure troves of DNA sequences hunting for new proteins with unique structures and functions.

As with the chess and Go programs AlphaZero and AlphaGo we dont exactly know what the AlphaFold2 algorithm is doing and why it uses certain correlations, but we do know that it works.

Besides helping us predict the structures of important proteins, understanding AlphaFolds thinking will also help us gain new insights into the mechanism of protein folding.

[Deep knowledge, daily. Sign up for The Conversations newsletter.]

One of the most common fears expressed about AI is that it will lead to large-scale unemployment. AlphaFold still has a significant way to go before it can consistently and successfully predict protein folding.

However, once it has matured and the program can simulate protein folding, computational chemists will be integrally involved in improving the programs, trying to understand the underlying correlations used, and applying the program to solve important problems such as the protein misfolding associated with many diseases such as Alzheimers, Parkinsons, cystic fibrosis and Huntingtons disease.

AlphaFold and its offspring will certainly change the way computational chemists work, but it wont make them redundant. Other areas wont be as fortunate. In the past robots were able to replace humans doing manual labor; with AI, our cognitive skills are also being challenged.

Original post:
AI makes huge progress predicting how proteins fold one of biology's greatest challenges promising rapid drug development - The Conversation US

Princess Margaret scientists have revealed how stem cells are able to generate new blood cells throughout our life by looking at vast, uncharted regions of our genetic material that hold important clues to subtle biological changes in these cells.

The finding, obtained from studying normal blood, can be used to enhance methods for stem cell transplantation, and may also shed light into processes that occur in cancer cells that allow them to survive chemotherapy and relapse into cancer growth many years after treatment.

Using state-of-the art sequencing technology to perform genome-wide profiling of the epigenetic landscape of human stem cells, the research revealed important information about how genes are regulated through the three-dimensional folding of chromatin.

Chromatin is composed of DNA and proteins, the latter which package DNA into compact structures, and is found in the nucleus of cells. Changes in chromatin structure are linked to DNA replication, repair and gene expression (turning genes on or off).

The research by Princess Margaret Cancer Centre Senior Scientists Drs. Mathieu Lupien and John Dick is published in Cell Stem Cell, Wednesday, November 25, 2020.

"We don't have a comprehensive view of what makes a stem cell function in a specific way or what makes it tick," says Dr. Dick, who is also a Professor in the Department of Molecular Genetics, University of Toronto.

"Stem cells are normally dormant but they need to occasionally become activated to keep the blood system going. Understanding this transition into activation is key to be able to harness the power of stem cells for therapy, but also to understand how malignant cells change this balance.

"Stem cells are powerful, potent and rare. But it's a knife's edge as to whether they get activated to replenish new blood cells on demand, or go rogue to divide rapidly and develop mutations, or lie dormant quietly, in a pristine state."

Understanding what turns that knife's edge into these various stem cell states has perplexed scientists for decades. Now, with this research, we have a better understanding of what defines a stem cell and makes it function in a particular way.

"We are exploring uncharted territory," says Dr. Mathieu Lupien, who is also an Associate Professor in the Department of Medical Biophysics, University of Toronto. "We had to look into the origami of the genome of cells to understand why some can self-renew throughout our life while others lose that ability. We had to look beyond what genetics alone can tell us."

In this research, scientists focused on the often overlooked noncoding regions of the genome: vast stretches of DNA that are free of genes (i.e. that do not code for proteins), but nonetheless harbour important regulatory elements that determine if genes are turned on or off.

Hidden amongst this noncoding DNA - which comprise about 98% of the genome - are crucial elements that not only control the activity of thousands of genes, but also play a role in many diseases.

The researchers examined two distinct human hematopoietic stem cells or immature cells that go through several steps in order to develop into different types of blood cells, such as white or red blood cells, or platelets.

They looked at long-term hematopoietic stem cells (HSCs) and short-term HSCs found in the bone marrow of humans. The researchers wanted to map out the cellular machinery involved in the "dormancy" state of long-term cells, with their continuous self-renewing ability, as compared to the more primed, activated and "ready-to-go" short-term cells which can transition quickly into various blood cells.

The researchers found differences in the three-dimensional chromatin structures between the two stem cell types, which is significant since the ways in which chromatin is arranged or folded and looped impacts how genes and other parts of our genome are expressed and regulated.

Using state-of-the-art 3D mapping techniques, the scientists were able to analyze and link the long-term stem cell types with the activity of the chromatin folding protein CTCF and its ability to regulate the expression of 300 genes to control long-term, self-renewal.

"Until now, we have not had a comprehensive view of what makes a stem cell function in a particular way," says Dr. Dick, adding that the 300 genes represent what scientists now think is the "essence" of a long-term stem cell.

He adds that long-term dormant cells are a "protection" against malignancy, because they can survive for long periods and evade treatment, potentially causing relapse many years later.

However, a short-term stem cell that is poised to become active, dividing and reproducing more quickly than a long-term one, can gather up many more mutations, and sometimes these can progress to blood cancers, he adds.

"This research gives us insight into aspects of how cancer starts and how some cancer cells can retain stem-cell like properties that allow them to survive long-term," says Dr. Dick.

He adds that a deeper understanding of stem cells can also help with stem cells transplants for the treatment of blood cancers in the future, by potentially stimulating and growing these cells ex vivo (out of the body) for improved transplantation.

Read the original here:
Mapping out the mystery of blood stem cells - Science Codex