Cellular Senior Moments

English

 

(l-r) Dr. Zohar Mukamel, Zohar Shipony and Prof. Amos Tanay
 
Aged cells, just like aged humans, have memory issues, according to a new study at the Weizmann Institute of Science. Insights gained by the study of a particular form of cellular memory may shed new light on the development of cancer and on the use of stem cells in therapy.

Our cells have a great deal to remember. Their genetic memory has long been known to be remarkably accurate: The rate of mistakes, or mutations, in the DNA during cell division is as low as one in millions or even billions of genetic letters, so that all the cells in our body – which have originated from a single cell, the fertilized egg – have the same genes. But as reported recently in Nature, Weizmann Institute scientists have now found that the cells’ epigenetic memory, the one not encoded in their DNA, is fraught with a surprisingly high rate of mistakes.


The scientists have focused on a particular epigenetic mechanism in which a chemical tag, a methyl group, is attached to the DNA. The activity of the cell’s genes is in part dictated by the distribution of these tags, the DNA methylation pattern. The methyl tags help regulate the turning of genes on and off, which, among other things, determines the cell’s identity – whether it will become part of the skin, the kidneys, the brain or some other tissue.
 

Selection of cells by CellCelector, a technology used in the research on cellular epigenetic memory: The machine identifies a single cell (circled in red) in a cell culture and removes it for study
 
Using innovative techniques they developed for mapping memory patterns, which combine massive DNA sequencing with mathematical algorithms, the scientists found that when the methylation pattern is passed on to daughter cells in the same organism during cell division, mistakes occur in as often as one in 200 - 1000 tags. The mistakes accumulate with each division, so that the methylation of an old cell – one that has been produced by a large number of divisions – can be completely garbled. In this sense, the passing on of epigenetic memory resembles the game of Chinese whispers, or broken telephone.
 
This revelation suggests that mistakes in epigenetic memory may make older cells more liable to undergo malignant transformation. Just as the build-up of minerals prevents older people from making full use of their joints, so the accumulation of epigenetic mistakes can prevent an older cell from responding properly to various signals, among them the signal to self-destruct when the cell is about to lose its normal growth control and turn cancerous.
 
A colony of human skin cells (circled in red) is picked out by the CellCelector
 
The “Chinese whispers” discovery may prove useful in fighting cancer. By studying the pattern of epigenetic mistakes in tumor cells, it might be possible to chart their “family trees,” that is, to trace their origins to a particular site in the body. This, in turn, can affect treatment decisions. In addition, epigenetic mistakes may serve as markers helping to distinguish cancerous cells – which undergo so many divisions they accumulate these mistakes in huge numbers – from cells that are old but healthy.

Moreover, the study suggests that even in the same tissue, individual cells – for example, certain cells of the spleen or liver – differ from one another in their methylation patterns. Such findings need to be taken into consideration in the development of diagnostic assays or drugs, because this variability may affect the effectiveness of a medication.

The Weizmann researchers have also found that in contrast to mature cells, embryonic stem cells have an excellent epigenetic memory. For instance, when stem cells are maintained in their immature cell state for a while, they preserve a stable methylation pattern despite numerous divisions. How do these cells remember so well who they are?

The surprising answer is that they do not listen to their ancestors: The researchers have shown that unlike what happens in mature cells, epigenetic memory in embryonic stem cells is not maintained through the transmission of stored information to the newly divided cells. Rather, the pattern of methyl tags is erased and created anew continuously during and between cell divisions. Thanks to this dynamic mechanism, an accumulation of epigenetic mistakes is avoided.

This discovery explains what happens when mature cells are genetically reprogrammed into stem cells. Scientists who discovered the possibility of such cellular reprogramming – which promises to ensure an unlimited supply of stem cells for therapy – were awarded the 2012 Nobel Prize in medicine; but until now it was unclear how a mature cell could be suddenly induced to “remember” the methylation pattern it had as a stem cell. The Weizmann study has resolved the mystery: When a mature cell is reprogrammed, machinery capable of erasing and rewriting methyl tags is turned on, so that the epigenetic memory state of an embryonic stem cell is rapidly reconstituted.

The study was performed by Prof. Amos Tanay of the Computer Science and Applied Mathematics, and Biological Regulation Departments and his research team, including lead authors Zohar Shipony and Dr. Zohar Mukamel; together with Netta Mendelson Cohen, Gilad Landan and Elad Chomsky; Weizmann researcher Dr. Nir Friedman and Dr. Shlomit Reich-Zeliger from his group in the Immunology Department; and Drs. Yael Chagit Fried and Elena Ainbinder of the Department of Biological Services.
 
Prof. Amos Tanay’s research is supported by the Helen and Martin Kimmel Award for Innovative Investigation; Pascal and Ilana Mantoux, Israel/France; the Wolfson Family Charitable Trust; the Rachel and Shaul Peles Fund for Hormone Research; Moise and Carol-Ann Emquies, Santa Monica, CA; and the estate of Evelyn Wellner.
 
Dr. Nir Friedman’s research is supported by the Nella and Leon Benoziyo Center for Neurological Diseases; the Clore Center for Biological Physics; the  Henry Chanoch Krenter Institute for Biomedical Imaging and Genomics; the Victor Pastor Fund for Cellular Disease Research; the Abraham and Sonia Rochlin Foundation; the Adelis Foundation; the Norman E. Alexander Family Foundation; the Jeanne and Joseph Nissim Foundation for Life Sciences Research; the Crown Endowment Fund for Immunological Research; the estate of John Hunter; and the estate of Suzy Knoll. Dr. Friedman is the incumbent of the Pauline Recanati Career Development Chair.

 
(l-r) Dr. Zohar Mukamel, Zohar Shipony and Prof. Amos Tanay
Math & Computer Science
English
  • Aging
  • Algorithm
  • Amos Tanay
  • Biological Regulation
  • Cancer
  • Computer Science and Applied Mathematics
  • Epigenetics
  • Human stem cells
  • Immunology
  • Nir Friedman

The Path to Personalized Prognosis

English

Doctors seeking to prescribe personalized therapy for a cancer patient face a great deal of uncertainty. How fast is the tumor growing? How aggressively should the cancer be treated? And which treatment approach is likely to be most effective?


Answers to these questions remain elusive, even though in the past decade or so scientists have accumulated mounds of data about the human genome in health and disease. In fact, this embarrassment of riches is itself part of the problem. Cancer is a disease of faulty genes, but since there are more than 20,000 human genes, a physician trying to define a particular tumor in terms of its genetic defects confronts a daunting challenge.

“Deregulation scores” of hundreds of brain tumor patients: Each row corresponds to a pathway (that is, a biochemical process), and each column to a sample taken from a patient. Every colored spot stands for a number – the value of the “deregulation score” of the corresponding pathway, as determined for a particular patient. Dark blue stands for the activity of the pathway in normal brain tissue, whereas dark red indicates a high level of deviation from normal behavior. The clearly distinct group of normal brain samples appears as the dark blue vertical stripe, TgS7, in the middle. The TgS15 stripe corresponds to the newly identified subgroup of patients with longer survival prospects
 
 
 
 
 
 
 
 
 
 
 
 
 
Weizmann Institute scientists propose an original and relatively simple method for making sense of the vast amounts of cancer-related genomic data. Prof. Eytan Domany and Dr. Yotam Drier of the Physics of Complex Systems Department have developed an algorithm called Pathifier that can help medical researchers and practicing physicians assess the prognosis of a given tumor based on its genetic profile. As reported in the Proceedings of the National Academy of Sciences (PNAS), the scientists, together with postdoctoral fellow Dr. Michal Sheffer, have already used Pathifier to identify previously unknown cancer subtypes that differ in their prognosis.

Pathifier, as its name suggests, analyzes pathways, the biochemical processes making up the life of the cell, each involving 20 to 30 genes. About 500 such pathways are known to science. In any given cancer, at least a few of them are abnormal. For example, if some of the pathways that govern growth are defective, the cell keeps dividing uncontrollably, leading to the formation and growth of a tumor. The extent of deregulation of different pathways varies from cancer to cancer and from patient to patient.
 
By comparing genomic data from cancerous and healthy cells, Pathifier assigns a “deregulation score” to each pathway. The set of these scores makes up the profile of the tumor.  The researchers believe it can help evaluate the cancer’s aggressiveness, assess its chances of responding to a particular therapy and perhaps even identify key biochemical processes that in the future may serve as targets for therapy.
 

pathways

 
Because the new method focuses on entire pathways, it gives a more accurate picture of the cancer’s properties, as opposed to monitoring individual genes. “It’s like analyzing what’s wrong with a car by observing the performance of its engine, brakes, steering and other systems, rather than dismantling it and looking at all the individual nuts and bolts,” Domany says. Moreover, the method is particularly reliable because it is based on the analysis of large sets of genomic data from hundreds of patients. Yet it is manageable because it focuses only on essential data, rather than trying to encompass the entirety of genomic details.

In the new study, the researchers applied Pathifier to a malignant brain tumor called glioblastoma. It was known from past genetic analyses that patients with a certain type of this tumor survive longer than others, but on the basis of the tumor profiles, the algorithm has allowed scientists to identify a smaller subtype of patients who are the truly longer survivors, as opposed to the rest of the patients with the same tumor type, who are not.  Furthermore, Pathifier has enabled the scientists to identify three pathways whose levels of deregulation are strongly indicative of the survival prospects of colon cancer patients.

Currently, Pathifier can be used as a research tool, offering medical researchers a reliable way of processing cancer-related genomic data. In the future, the algorithm can point to relevant biomarkers – that is, measurements of the levels of certain chemicals that are indicative of the activation of key pathways – that could help practicing physicians choose appropriate treatments for their patients. The physicians could rely on such biomarkers to evaluate a tumor’s deregulation profile.
 
Prof. Eytan Domany's research is supported by the Kahn Family Research Center for Systems Biology of the Human Cell, which he heads; the Mario Negri Institute for Pharmacological Research -Weizmann Institute of Science Exchange Program, the Leir Charitable Foundations; Mordechai Segal, Israel; and the
Louis and Fannie Tolz Collaborative Research Project.  Prof. Domany is the incumbent of the Henry J. Leir Professorial Chair.
 


 
 
“Deregulation scores” of hundreds of brain tumor patients: Each row corresponds to a pathway (that is, a biochemical process), and each column to a sample taken from a patient. Every colored spot stands for a number – the value of the “deregulation score” of the corresponding pathway, as determined for a particular patient. Dark blue stands for the activity of the pathway in normal brain tissue, whereas dark red indicates a high level of deviation from normal behavior. The clearly distinct group of normal b
Math & Computer Science
English
  • Algorithm
  • Cancer
  • Cancer genetics
  • Eytan Domany
  • Molecular pathways
  • Personalized medicine
  • Physics of Complex Systems

Noncoding Sequences Get Equal Listing

English
New and improved GeneCards, over 100,000 separate entries: The orange arc shows GeneCards before the addition of the ncRNAs in the study, the blue those added in the study (with an overlap of around 14,000 entries)
 
Nowadays, to the familiar mRNA that ferries the genetic instructions for protein production out of the cell nucleus, we can now add thousands of microRNAs, long non-coding RNA, piRNA, antisense RNA and more. In fact, of the roughly 80% of the DNA in the human genome that is estimated to be copied out (transcribed) into RNA sequences, only around 2% gets translated into proteins. Though we still don’t know exactly how much of the rest is functional, it is already clear that a better understanding of the various kinds of non-protein-coding RNA sequences (ncRNAs) and the roles they play will have important consequences for research on health and disease. If the diverse and still growing collection of RNAs is bewildering, the various attempts to catalog them have created even more bafflement.
 
That is why the group of Prof. Doron Lancet of the Molecular Genetics Department decided to take on the challenge of fully incorporating these novel RNAs into GeneCards, their user-friendly, searchable, unified database of human genes. Initiated by Lancet and his team in 1996, GeneCards has become one of the world’s most popular genomic research tools. But until recently this database focused mainly on the 20,000-odd protein-encoding genes, while a handful of ncRNA genes were scantly represented. Their intent was to significantly enhance the representation of ncRNAs within the GeneCards framework – an improvement that could provide the scientific and medical community with an authoritative, fully annotated compendium of these varied, versatile and vital cellular components.
 
.
 
Prof. Doron Lancet
 
The lead figure in this project was Dr. Frida Belinky, a postdoctoral fellow with Lancet, head of the Institute’s Crown Human Genome Center. The work was done in close collaboration with other members of the GeneCards development team headed by Marilyn Safran.

Belinky started out with 15 different ncRNA gene databases and developed computerized integration methods for bringing them together into a single one. Among other things, the sorting and assembling process involved finding genes that overlapped by more than 70% – suggesting they were the same gene – and separating sequences that apparently have some function from those that do not seem to be of use. For example, one gene group, called piRNAs, that was thought to contain over 30,000 genes, was eventually narrowed down to a mere 20,000.

By the time they had finished the project, they had expanded the GeneCards ncRNA content from about 15,000 to some 80,000 distinct genes. In addition to the sequences and their placement in the genome, the database contains information on where these genes are expressed and which other species contain their similes – highly useful features for unraveling their function. Since the team’s paper detailing the creation of this database appeared in January’s Bioinformatics, it has garnered considerable interest among researchers in various fields in the life sciences. Cancer researchers, for instance, can use it to find ncRNAs that may be active in initiating or promoting tumor growth. Many rare diseases are also thought to be tied to faulty ncRNAs, and the extended database could help researchers identify the sequences involved.

 

 
Dr. Frida Belinky
 
“This ‘grand unification’ of ncRNA genes will enable scientists to make new discoveries on biological and disease-related roles for genes belonging to this newly opened vista of the human genome,” says Lancet.


GeneCards and the Human Genome


When Interface magazine first reported on GeneCards in 1998, the web-based database included a mere 7,000 genes and averaged 22,000 hits a month. By the end of the Human Genome Project in 2003, GeneCards contained web cards for all of the roughly 20,000 well-documented human protein-coding genes, plus about the same number of predicted or suspected genes. In the decade since, a great deal of research has focused on the 97% of the genome that does not direct protein production, spearheaded by the world-wide ENCODE project. Views have come around from seeing it as “junk DNA” to realizing that ncRNA genes encompass a complex network of activities that complements and regulates that of the coding genes. This has led to the intense proliferation of knowledge in the new realm of ncRNA studies – and has necessitated the relevant, unified view now provided by GeneCards. There are now over 12 million page visits to the GeneCards site a year, and its users obtain what may prove to be the most updated, inclusive ncRNA view available.

The GeneCards project has a research grant from LifeMap Sciences, Inc., a subsidiary of the California-based biotech firm BioTime, Inc. LifeMap holds an exclusive worldwide license for GeneCards from Yeda Research and Development, Ltd., Weizmann’s technology transfer arm. LifeMap also recently helped Lancet’s lab establish MalaCards, a companion database of human diseases.
 
Prof. Doron Lancet's research is supported by the Crown Human Genome Center, which he heads; the Dr. Dvora and Haim Teitelbaum Endowment Fund; the Nella and Leon Benoziyo Center for Neurological Diseases; and the estate of Nathan Baltor. Prof. Lancet is the incumbent of the Ralph D. and Lois R. Silver Professorial Chair of Human Genomics.


 
 
 
Dr. Frida Belinky
Math & Computer Science
English
  • Doron Lancet
  • Gene code
  • MicroRNA›miRNA
  • Molecular Genetics
  • RNA

An Atlas for Cells

English

 

Michal Breker and Dr. Maya Schuldiner
 
Where do proteins go when they move around the cell? Interested scientists will now be able to look up the answer. Dr. Maya Schuldiner and graduate student Michal Breker of the Molecular Genetics Department recently produced a comprehensive atlas of changes in yeast proteins’ localization and abundance under stress that presents a wealth of new information. This atlas, which they are making available online, is likely to become an important tool for the many scientists who use this model research organism to investigate the workings of living cells.

Schuldiner and Breker began with a different kind of reference work: a strain library. Strain libraries hold sets of cells with specific genetic alterations. Bakers’ yeast cells contain some 6000 different genes, each encoding a protein. In each “volume” of their library, one “bookmark” has been created – a gene has been modified such that a particular protein is tagged with a fluorescent “highlighter” molecule that glows under a special microscope. In Schuldiner’s lab, using a state-of-the-art, automated microscopy system, researchers can examine an entire strain library at once. Other types of libraries exist, as well, including those in which each gene, in turn, has been removed. Libraries can even be mixed and matched to create new combinations. A robotic search then scans the entire set to find out which proteins are active in any given experimental situation.  
 
 
This unique set-up enabled Schuldiner and Breker to track protein movement – one of the “big” questions in cell biology. Knowing where proteins go, says Schuldiner, can help to answer a number of important research questions: “How many of the cells’ proteins are mobile? Under what conditions? How does the cell use this protein mobility to remain healthy and divide under a variety of different conditions?”

Subjecting the yeast cell libraries to various conditions and putting them through the robotic system, the researchers were able to trace the movements of each protein in the yeast cell. The result: a complete, detailed map describing protein routes, as well as a record of the amounts of each protein produced in the different situations.

A bird's-eye view of the data presents a picture of constant bustle in the cells. At any one time, hundreds of proteins are in transit. But the numbers the researchers collected on protein amounts held some real surprises. In much of today’s research, protein levels are inferred from experiments that actually check the production of messenger RNA (the instructions sent to the cell’s protein factories) – something like using building plans rather than actual buildings to map a town, when those plans can be shelved or reused for the next housing development. In tracing the proteins themselves, Schuldiner and Breker revealed that messenger RNA and true protein quantities don’t always match. Schuldiner: “Many beautiful works have already shown that protein production is regulated in many different ways after the RNA leaves the nucleus. Our findings hint that some of the later stages may be more significant than we thought in determining protein levels.”
Yeast cell atlas: image by Michal Breker
 
The online atlas is named Loqate (LOcalzation and Quantization Atlas of the yeast proteome). Schuldiner: “The atlas can be used by those seeking answers to such specific questions as: Which proteins are involved in a particular cellular activity, and when and where do they act? In addition, those who want to integrate different kinds of information to attain a more comprehensive picture of the cell’s life will find the atlas an indispensible aid.”

 

Checking all possibilities


The latest research methods have advanced molecular biology beyond the standard ABCs of research – formulating a hypothesis and then testing it through experiments. Using fast, powerful, completely automated equipment, much of it assembled according to Schuldiner’s specifications, she and her research team can now check all of the possibilities and extract the significant data. “With these tools, our research can be totally unbiased,” she says. “If we once started with an educated guess – say, ‘A affects B’ – and then tried to confirm our conjecture experimentally, we can now ask: ‘Which proteins are involved in B’s activity?’ In this way, we might find that G, L and M also act on B. And if a doctoral student formerly spent the whole of his or her studies checking that hypothesis, he or she can now get answers in a matter of weeks.”

 
Dr. Maya Schuldiner's research is supported by the European Research Council; the Berlin Family Foundation; James and Ilene Nathan, Beverly Hills, CA; the Minna James Heineman Stiftung; the Enoch Foundation; Roberto and Renata Ruhman, Brazil; Karen Siem, UK; and the Kahn Family Research Center for Systems Biology of the Human Cell.


 

 
 
Yeast cell atlas: image by Michal Breker
Math & Computer Science
English
  • Cell
  • Maya Schuldiner
  • Microscopy
  • Molecular Genetics
  • Protein

Making Sense of a Complex Situation

English
Dr. Jasmin Fisher
              

 

 
“The world of biology is incredibly complex,” says Dr. Jasmin Fisher, who holds appointments at Microsoft Research, in Cambridge, UK, and Cambridge University. “The hand-drawn diagrams of biological processes I first learned to produce are static illustrations; they often don’t provide much insight into the highly dynamic interactions that occur. That is why I opened myself to the world of computation.” Today, Fisher is considered a world leader in a rapidly growing field of biological research in which computational models are the impetus for new discoveries.

Fisher came to the Weizmann Institute for her doctoral studies after completing a B.Sc. and M.Sc. in biology at Ben-Gurion University of the Negev. It was at Ben-Gurion, she says, that her adviser, Prof. Shai Silberberg, imparted the basics of science that she still applies every day: “Be a perfectionist in your work and learn to ask the right questions.”

At the Weizmann Institute, Fisher conducted her Ph.D. research in the neurobiology group of Prof. Michal Schwartz, investigating the dialog between the immune system and the central nervous system. Among other things, research in the group was changing ideas about the protective role that certain immune cells play in the brain and nervous system. In Schwartz, Fisher found an insistence on thoroughness that fit in with her own inclination, as well as a role model of an accomplished woman scientist.

Along the way, Fisher was coming to understand that the processing power of computers was needed to help life science researchers deal with the complexity of even the simplest living systems. So she stayed on at the Institute to conduct postdoctoral research with Prof. David Harel of the Computer Science and Applied Mathematics Department. In the 1980s, Harel had developed a visual programming language called Statecharts to deal with specifying and developing such complex systems as avionics. But he and others soon realized that biological systems could be described in much the same way as air-flight systems, and began applying tools like Statecharts to cells and organisms. “Because Statecharts is a visual language, it is easy for a non-programmer to use. Because it is state-based, it is an intuitive tool for describing biological mechanisms,” says Fisher. She joined the effort under way in Harel’s group to use computers to map out developmental processes in the nematode C. elegans, a model lab organism.

Her research took her next to Switzerland, to further her training in computational methods in the group of Prof. Thomas Henzinger at the Ecole Polytechnique Federale de Lausanne. There, she invented the term “executable biology” to describe the kinds of models that simulate biological processes, and she and Henzinger argued that such computer-based research could not only help scientists make sense of complexity, it would bring a precise, formal, quantifiable approach to the life sciences. “A computerized model begins with a hypothesis. When you organize the experimental results in the same formal language as the model, you can compare the two and immediately see the gaps. These gaps then enable you to refine your model and design new experiments,” she says.

As Fisher was finishing her work with Henzinger, Microsoft Research in Cambridge was adding biology to its list of computational research subjects. Fisher was invited to join the Programming Principles and Tools group there, and was later offered a position as a research group head at Cambridge University.
 
Nowadays, Fisher engages in long-term collaborations with experimental groups to continue investigating cell decision-making processes. In one of those collaborations, she works on understanding how blood cell development goes awry in leukemia. In another, she investigates cell signaling in C. elegans – research that has implications for understanding cancer.  This signaling has been preserved throughout evolution, up to and including humans. Recent findings in her group demonstrated the benefits of repeated cycles of computational modeling, prediction and experimentation. They revealed a crucial molecular mechanism by which developing cells synchronize their cell cycles and then break that synchrony to continue developing toward individual cell fates.

Eventually, says Fisher, her group and others working in the field will produce a broad, common platform for computational biology, and its tools will become standard in the life sciences – akin to the use of microscopes and DNA sequencing equipment today.

Though her rise in the field has taken her out of the country for over a decade, Fisher, a seventh-generation Israeli, admits it has been hard to be away. “The Weizmann Institute still feels like home to me,” she says. Conducting research there has always been a source of pride for me.”
Dr. Jasmin Fisher
Math & Computer Science
English
  • Computer Science and Applied Mathematics
  • David Harel
  • Made at the Institute
  • Michal Schwartz
  • Neurobiology

A Proofreader for DNA

English

The device library consists of (1) an input module containing many different variants of the same gene (green) and (2) a selection module (blue) integrated within an Amp resistance gene (gray). The selection module contains a loop on its coding strand which frame-shifts (dark gray) and stops the translation (red stop codon) of the Amp gene

 

A synthetic device made of biological molecules can be programmed to search for and identify exact DNA sequences inside living cells – and reject any of those sequences that contain errors, however tiny. The Weizmann Institute researchers who invented the device believe that the concept behind it may lead to the development of new, highly sensitive diagnostic techniques, as well as enhanced methods for creating interfaces between natural and synthetic biological molecules.


Dr. Tuval Ben Yehezkel and Tamir Biezuner in the lab of Prof. Ehud Shapiro of the Biological Chemistry, and Computer Science and Applied Mathematics Departments created numerous copies of the DNA-based devices – each containing an identical, preprogrammed genetic sequence hooked up to a different target gene – and inserted them into bacterial cells. Inside the cells, the synthetic infiltrators, like tiny moles, recruited the cell’s “proofreaders”: internal repair mechanisms that normally, during cell replication, check for mismatches between the genetic “letters” A,T,G and C in a new DNA strand and those in the parallel sequence of the double strand. Normally, these proofreaders would snip out any offending letters on one strand and call on the paired strand of DNA to try substituting the information. The synthetic device, however, picked up on the error-correction activity and co-opted this mechanism, using it instead to reprogram itself to destroy the cell. Thus only cells containing the complete, correct sequence remained at the end of the process.
 
 
(l-r) Dr. Tuval Ben Yehezkel, Prof. Ehud Shapiro and Tamir Biezuner
 
In the experiment, the devices were programmed to preserve a DNA sequence that does not confer any selective advantage in E. coli cells. This ability to work with any DNA segment, regardless of its use in the cell, highlights its advantage over other methods: The other methods often rely on a gene’s functionality to select for it using classical Darwinian selection, or are liable to miss tiny errors in the sequence. In the future, slightly more sophisticated versions of the device could be used by researchers working with artificial genetic sequences to ensure their accuracy. The researchers think that future devices based on their concept could be employed in medicine to seek out hard-to-detect genes – for example harmful mutations in fetal cells in the mother’s blood or cancer-causing mutations that appear in just a few cells.

The findings, which appeared recently in PLoS One, incorporate aspects of two fairly new fields of research: synthetic biology and biological computing. They have demonstrated how a man-made genetic sequence can be inserted into a living cell and interface there with the cell’s natural mechanisms – a feat that has been a major, ongoing challenge for synthetic biologists. And with the help of those mechanisms, this synthetic construct performs like a simple computing device, in which DNA inputs – sequences that may or may not contain errors – are processed to arrive at an output – in this case the preservation or elimination of those sequences.  Shapiro says: “Future work in this direction may bring about the integration of synthetic DNA devices into ever more complex cellular environments. This, in turn, could lead to a wide variety of applications.”
 
Prof. Ehud Shapiro’s research is supported by the Paul Sparr Foundation; and the European Research Council. Prof. Shapiro is the incumbent of the Harry Weinrebe Professorial Chair of Computer Science and Biology.
 
(l-r) Dr. Tuval Ben Yehezkel, Prof. Ehud Shapiro and Tamir Biezuner
Math & Computer Science
English
  • Biological Chemistry
  • Computer Science and Applied Mathematics
  • DNA computer
  • DNA damage and repair›DNA repair
  • Ehud Shapiro

A Fragile Equilibrium

English
 
 
Prof. Vered Rom-Kedar
 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A baby with dangerously low levels of certain white blood cells recovers, while an adult with nearly twice the levels succumbs to bacterial infection. A new mathematical model proposes a possible explanation for such medical mysteries, shedding light on those appearing after chemotherapy, and even suggesting how their occurrence could be reduced.
 
This study, reported recently in the Journal of Clinical Investigation, has shown that to properly assess the risk of infection, it is essential to evaluate not only the quantity of immune cells but also their quality, which varies from one person to another. This research may therefore lead to more personalized chemotherapy: Better precautions might be taken to prevent infection in high-risk patients, whereas those at a low risk could be spared unnecessary preventive treatments. The new model was developed by Weizmann Institute mathematicians in collaboration with physicians from the Meir Medical Center in Kfar Saba and from the Hoffmann-La Roche research center in Basel, Switzerland.
 
The model reveals how the immune system functions under conditions of neutropenia – a dangerously low level of white blood cells, mainly neutrophils. In this condition, which often emerges after chemotherapy or bone marrow transplant but can also be present at birth, severe infections can develop if the immune system fails to perform the crucial function of devouring and destroying bacteria.
 
Rom-Kedar screen
 
The tug of war between the blood cells and the bacteria cannot be explained away by the simple bacteria-to-cell ratio, nor by a threshold that the blood cell count must exceed. Rather, the model shows that when neutrophil counts are low, the patient’s immune system enters a fragile equilibrium – described in mathematical terms as “bistability” – that can easily be disrupted, with dramatic consequences, by even minute changes in bacterial concentration or in the number of neutrophils. Other factors that can dramatically affect this equilibrium include the effectiveness of the neutrophil functioning and the tissues’ permeability to bacteria, which can increase due to cancer therapy.

Thus, according to the model, in healthy people, the fact that the effectiveness of neutrophils varies from one person to another usually has no significance. By contrast, in patients with neutropenia, this individual variability can make a difference between life and death. For example, after chemotherapy, some cancer patients contract life-threatening infections even when they are maintained in isolation under sterile conditions. It turns out that if the neutrophils of these patients are "weak," even the smallest number of bacteria, such as those present in the gut, can tilt the fragile immune balance in favor of the bacteria.
 
 
The study also explains why certain patients following chemotherapy or a bone marrow transplant may develop an acute infection even when their neutrophil levels have returned to relatively normal levels. The chemotherapy lowers the neutrophil levels and function, in addition to making the tissues of these patients more penetrable to bacteria. As a result, in some patients the bacterial concentrations might increase so quickly that by the time the neutrophil counts rise back to “normal,” the rapidly multiplying bacteria have already gained a head start, so that the neutrophil recovery is insufficient for overcoming the severe infection. This scenario may eventually also shed light on the rare cases in which acute bacterial infections develop in individuals with normal immunological function. The model suggests that in such cases, a high growth rate of unusually virulent bacteria may have overcome even the appropriate quantitative and qualitative neutrophil response.
 
Neutrophil levels of three hypothetical patients with neutropenia. The patient with the "strong" neutrophils (P1) can overcome infections if treated with supportive medications. In contrast, the patient with the "weak" neutrophils (P4) cannot overcome even the minute bacteria load that comes from the gut. Source: the Journal of Clinical Investigation
 
A potential solution to two previously unsolved cases has already emerged from the model. A newborn baby treated at Meir Medical Center recovered from neutropenia even though his absolute neutrophil count (ANC) had fallen as low as 200 neutrophils per microliter of blood, whereas an adult whose ANC stood at 380 after chemotherapy died of infection. The model has suggested how various clinical parameters, such as the poor quality of the neutrophils, could have led to the death of the adult despite his higher ANC.

In addition, the model might help researchers understand the mechanism behind the development of severe recurrent infections in some patients. For example, of one thousand patients referred to Meir Medical Center because of such infections, diagnosis could be established in only one-third of the cases. Weizmann Institute mathematical modeling showed that at least some of the unexplained cases might have resulted from a combination of several mild defects, including variation in the function of neutrophils and other immune cells.
 
 
This research was based upon the blood analysis of four healthy volunteers. To use the model in the clinic, such analysis will have to be applied to large populations. “Our mathematical model has revealed previously unknown mechanisms responsible for the variability in the vulnerability to infections of neutropenia patients,” says research leader Prof. Vered Rom-Kedar of the Weizmann Institute’s Computer Science and Applied Mathematics Department.

The study was performed by researchers with an unusual combination of backgrounds. The mathematician, Rom-Kedar, specializes in the investigation of dynamic systems. The first author, electrical engineer Dr. Roy Malka, conducted this research as part of his Ph.D. studies in mathematics at Weizmann; he is now engaged in postdoctoral research on related subjects at Harvard Medical School. The idea for the project was first proposed by Dr. Eliezer Shochat, a senior oncologist who also has a Ph.D. in applied mathematics from Weizmann and now works in a research group at Hoffman-La Roche in Basel, Switzerland. The study was performed in collaboration with the Meir Medical Center team: Prof. Baruch Wolach, M.D., Head of the Laboratory for Leukocyte Function and Chair of Pediatric Immunology at Tel Aviv University’s Sackler Faculty of Medicine; and laboratory manager Ronit Gavrieli, M.Sc., who performed the experiments.
 
Says Prof. Wolach: “Our study suggests that to achieve optimal results – in applying chemotherapy and/or in treating patients with innate neutrophil dysfunction – it is of value to periodically assess the patient’s neutrophils as well as the bacterial concentration. Such assessments will help reduce the morbidity and mortality, as well as the cost, associated with unnecessary hospitalizations and the administration of expensive medications. Moreover, by cutting down on the use of antibiotics, these assessments can help prevent the rise in antibiotic resistance.”

Prof. Vered Rom Kedar heads the Moross Research School of Mathematics and Computer Science; her research is supported by the Yeda-Sela Center for Basic Research. Prof. Rom Kedar is the incumbent of the Estrin Family Chair of Computer Science and Applied Mathematics.

 
 
 
Prof. Vered Rom-Kedar
Math & Computer Science
English
  • Bacterial diseases
  • Cancer therapy›Chemotherapy
  • Computer Science and Applied Mathematics
  • Mathematical model
  • Vered Rom-Kedar
  • White blood cell

Cancer at a Breaking Point

English
 
 
Drs. Yotam Drier (left) and Gad Getz at the Broad Institute

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Like police detectives using DNA fingerprinting in their effort to eradicate crime, cancer researchers are building a DNA profile of malignancy in an attempt to eradicate cancer. One of their greatest challenges is that they are not dealing with a single criminal: There are at least 200 forms of cancer, and many more subtypes. The goal is to “fingerprint” each one of these subtypes so that, ultimately, people with cancer can be treated with genetically matched personalized therapies.

Important strides in bringing this vision to reality have emerged recently from a collaboration between the Weizmann Institute of Science, on the one hand, and the Eli and Edythe L. Broad Institute of Harvard University and the Massachusetts Institute of Technology, on the other. Both sides of this collaboration are linked to the laboratory of Prof. Eytan Domany in Weizmann’s Physics of Complex Systems Department: Some three years ago, Domany’s former Ph.D. student Dr. Gad Getz, today Director of Cancer Genome Computational Analysis at the Broad Institute, joined forces with Dr. Yotam Drier, then Domany’s student, at Weizmann.
 
 
A cancer genome is the full set of DNA in the tumor’s cells, each tumor type being characterized by its own genomic abnormalities. Getz’s team at the Broad Institute – numbering some 30 biologists, biochemists, physicists and software engineers – is participating in the Cancer Genome Atlas, a collaborative effort led by the U.S. National Institutes of Health, as well as in other studies aimed at the same goal: deciphering the genomes of all major tumor types.

These ambitious projects have been made possible by the relatively recent advent of high-throughput sequencing, in which numerous DNA segments are “read out” in parallel. By dramatically speeding up the sequencing process, this technology has led to an equally dramatic drop in costs: from $30,000 per million DNA bases in 1999 to a mere 10 cents in 2011. As a result, scientists can now rapidly sequence hundreds of whole tumor genomes, each genome comprising billions of DNA base pairs. But the sequencing alone is not enough: making sense of these mounds of genomic data is no less of a challenge.
 
DNA structural rearrangements and copy number alterations detected in a colorectal tumors displayed as a CIRCOS plot. Source: Nature Genetics
 
Getz’s group at Broad is developing computational tools for analyzing such data. When Getz, on a visit to the Weizmann Institute in December 2008, asked his former thesis adviser Domany if someone in his team would be interested in taking part in this work, Domany suggested Yotam Drier. Getz and Drier's similar backgrounds promised a fruitful collaboration: Both had done their army service through the prestigious Talpiot program, during which each obtained a university degree in math and other exact sciences, Getz majoring in physics, Drier in computer science.

Indeed, Drier soon developed BreakPointer, a computer algorithm that scans the whole human genome for a hallmark of cancer: faulty DNA repair resulting in structural rearrangements that differ from the normal DNA sequence. It was the first algorithm to detect the exact break points in DNA at which such rearrangements occur. “This tool is now a major part of our effort to map out all the genes and other phenomena that contribute to cancer,” says Getz.
 
 
Incorporated into all cancer genome analyses at the Broad Institute, BreakPointer has since helped scientists make a number of significant discoveries, including the uncovering of the crucial cancer-related defects after which the algorithm was named: DNA rearrangement break points.  As reported in Nature, BreakPointer has helped reveal a previously unknown pattern of chromosomal rearrangements in prostate cancer: complex chains of rearrangements that occur within or adjacent to known cancer genes. Moreover, the scientists discovered a relationship between the location of break points and the condition of chromatin, a major component in the protective packaging of chromosomes, suggesting that genomic rearrangements may be related not only to genes but to epigenetic factors – that is, factors not directly encoded in the genome. 
 
In a study of colorectal cancer reported in Nature Genetics, the scientists used BreakPointer to discover 11 DNA rearrangements leading to abnormally fused genes. Among these was the first “recurrent” gene – so called because it recurs in different tumors – to be described in colorectal cancer.  Abnormally fused genes produce abnormal proteins, which may serve as potential drug targets – not only because they are required for the cancer to thrive, but also because these proteins don’t exist in healthy cells – which means that the drug can be specifically directed at the tumor cells, sparing healthy tissues.
Prof. Eytan Domany
 
"BreakPointer identifies the precise location of break points in the cancer genome by looking for 'suspicious' sequences and comparing them to relevant areas in a reference genome,” says Drier, who now conducts postdoctoral research at Harvard Medical School and the Broad Institute. “When we applied the algorithm, we found correlations between DNA rearrangements and other features of the genome, such as point mutations and the condition of the chromatin.” Such correlations and their biological significance in different cancers are to be described in an upcoming issue of Genome Research.

The mapping of all the major cancer genomes will enable researchers to better understand the molecular processes that drive cancer, facilitating the search for drugs tailored to each tumor’s unique genetic defects. Such targeted therapies already exist for a limited number of cancers, and their number is constantly growing. One day in the future, doctors will be routinely mapping the genome of the cancer of each individual patient and prescribing genetically tailored drugs, maximizing the effectiveness of treatment and minimizing its side effects.  
 
Prof. Eytan Domany's research is supported by the Kahn Family Research Center for Systems Biology of the Human Cell, which he heads; the Leir Charitable Foundations; and Mordechai Segal, Israel. Prof. Domany is the incumbent of the Henry J. Leir Professorial Chair.
 
 
 
 
Drs. Yotam Drier (left) and Gad Getz at the Broad Institute
Math & Computer Science
English
  • Algorithm
  • Cancer
  • Eytan Domany
  • Mathematical model
  • Personalized medicine
  • Physics of Complex Systems

Weizmann Institute’s Mathematical Model May Lead to Safer Chemotherapy

English

Cancer chemotherapy can be a life-saver, but it is fraught with severe side effects, among them an increased risk of infection. Until now, the major criterion for assessing this risk has been the blood cell count: if the number of white blood cells falls below a critical threshold, the risk of infection is thought to be high. A new model built by Weizmann Institute mathematicians in collaboration with physicians from the Meir Medical Center in Kfar Saba and from the Hoffmann-La Roche research center in Basel, Switzerland, suggests that for proper risk assessment, it is essential to evaluate not only the quantity of these blood cells, but also their quality, which varies from one person to another.

 
This research may represent an important step in the emerging field of personalized medicine, leading to a more individualized approach to chemotherapy. In particular, better precautions might need to be taken to prevent infection in high-risk patients whereas those at a low risk could be spared unnecessary preventive treatments.

 
cell and numbers. Image: Thinkstock
 

 

The study, recently published in the Journal of Clinical Investigation, brought together the expertise of researchers from such diverse disciplines as applied mathematics, electrical engineering, oncology, immunology and pediatrics.

The new model reveals how the immune system functions under conditions of neutropenia – dangerously low levels of white blood cells, mainly neutrophils. In this condition, which often emerges after chemotherapy or bone marrow transplant, severe infections can develop if the immune system fails to perform the crucial function of devouring and destroying bacteria. “Our mathematical model has revealed previously unknown mechanisms responsible for the variability in the vulnerability of neutropenia patients to infections,” says research leader Prof. Vered Rom-Kedar of the Weizmann Institute’s Computer Science and Applied Mathematics Department.
 
 
 
The model suggests that in neutropenia, the tug of war between the blood cells and the bacteria cannot be explained away by the simple bacteria-to-cell ratio, nor by the threshold that the blood cell count must exceed. Rather, when neutrophil counts are low, the patient’s immune system enters a fragile equilibrium – described as “bistability” in mathematical terms – which can easily be disrupted, with dramatic consequences, by even minute changes in bacterial concentration or neutrophil numbers. Other factors that can radically affect this equilibrium include the effectiveness of the neutrophil functioning and the permeability of tissues to bacteria, which can increase due to cancer therapy.
 
Thus according to the model, in healthy people, the fact that the effectiveness of neutrophils varies from one person to another usually has no significant consequences. In contrast, in patients with neutropenia, this individual variability can make a difference between life and death. This conclusion is drawn from the study based upon the blood analysis of four healthy volunteers. To use the model in the clinic, such analysis should be applied to large populations.

The model has already offered a plausible explanation for a number of medical mysteries. It helps explain, for example, why after chemotherapy, some cancer patients contract life-threatening infections even when in isolation under sterile conditions: If the neutrophils of these patients are “weak,” even the smallest numbers of bacteria, for example, those present in the gut, can tilt the fragile immune balance in favor of the bacteria.
 
Neutrophil levels of three hypothetical patients with neutropenia. The patient with the "strong" neutrophils (P1) can overcome infections if treated with supportive medications. In contrast, the patient with the "weak" neutrophils (P4) cannot overcome even the minute bacteria load that comes from the gut. The Journal of Clinical Investigation

 
The study also explains why certain patients, following chemotherapy or a bone marrow transplant, may develop acute infections even if their neutrophil levels have returned to relatively normal levels. The chemotherapy lowers both neutrophil levels and function, making the tissues of these patients more penetrable to bacteria. The model suggests that as a result, in some patients the bacterial concentrations might increase so quickly that by the time the neutrophil counts rise back to “normal,” the rapidly multiplying bacteria have already gained a head start, so that the neutrophil recovery is insufficient for overcoming the infection. This scenario may eventually also shed light on the rare cases in which acute bacterial infections develop in individuals with normal immunological function. The model suggests that in such cases, a high growth rate of unusually virulent bacteria could overcome the appropriate quantitative and qualitative neutrophil response.

Certain puzzling medical cases could be clarified thanks to the model. For example,  a newborn baby treated at Meir Medical Center recovered from neutropenia even though his absolute neutrophil count (ANC) had fallen as low as 200 neutrophils per microliter of blood, whereas an adult whose ANC stood at 380 after chemotherapy died of infection. The model has suggested how such clinical parameters as the poor quality of the neutrophils might have lead to the death of the adult. In addition, the model could help doctors to understand the mechanism behind the development of severe recurrent infections in some patients. Of one thousand patients referred to Meir Medical Center because of such infections, diagnosis could be established in only one-third of the cases. Weizmann Institute’s mathematical modeling suggests that at least some of the unresolved cases may have resulted from a combination of several mild defects, including variation in the function of neutrophils and other immune cells.

The study was performed by researchers with an unusual combination of backgrounds. Lead author, Weizmann Institute’s mathematician Prof. Vered Rom-Kedar, specializes in the investigation of dynamic systems. The first author, Dr. Roy Malka, an electrical engineer who has become an applied mathematician, conducted this research as part of his Ph.D. studies at Weizmann; he is now conducting postdoctoral research on related subjects at Harvard Medical School. The idea for the project was first proposed by Dr. Eliezer Shochat, a senior oncologist who also has a Ph.D. in applied mathematics from Weizmann and now works in a research group at Hoffman-La Roche in Basel, Switzerland. The study was performed in collaboration with the Meir Medical Center team: Prof. Baruch Wolach, M.D., Head of the Laboratory for Leukocyte Function and Chair of Pediatric Immunology at Tel Aviv University’s Sackler Faculty of Medicine; and laboratory manager Ronit Gavrieli, M.Sc., who performed the experiments.

Says Wolach: “Our study suggests that to achieve optimal results in applying chemotherapy, and/or in patients with innate neutrophil dysfunction, it is of value to assess the patient’s neutrophils periodically, as well as the bacterial concentration. Such assessments will help reduce the morbidity and the mortality, as well as the cost, associated with unnecessary hospitalizations and the administration of expensive medications. Moreover, by cutting down on the use of antibiotics, these assessments can help in preventing the rise in antibiotic resistance.”
 
Prof. Vered Rom-Kedar heads the Moross Research School of Mathematics and Computer Science; and her research is supported by the Yeda-Sela Center for Basic Research. Prof. Rom-Kedar is the incumbent of the Estrin Family Chair of Computer Science and Applied Mathematics.
 
 
 
cell and numbers. Image: Thinkstock
Math & Computer Science
English
  • Bacterial diseases
  • Cancer therapy›Chemotherapy
  • Computer Science and Applied Mathematics
  • Mathematical model
  • Vered Rom-Kedar
  • White blood cell

Why Chemotherapy Fails

English

 

 
The fight against cancer is not won in a single battle: Long after a cancer has been beaten into remission, it can return. The reason for this is under debate, and much is unclear. New research led by Weizmann Institute scientists shows that, at least for one type of blood cancer, the source of cancer recurrence is in a set of cells that do not proliferate as quickly as regular cancer cells, and thus able to survive chemotherapy. The findings, which appeared today in the journal Blood, have some important implications for the future of the war on cancer.

Cancer involves a breakdown in the mechanism that regulates the pace of cell division. When this happens, cells divide rapidly, leading to unchecked growth that overruns the body. The most common chemotherapy drugs are those which specifically attack cells that are undergoing rapid division, and these, indeed, often destroy all the cancer and cure the patient.

But there are also quite a few leukemia patients who go through chemotherapy only to have the cancer return. Why does this happen? Several explanations have been proposed. One is that the chemotherapy does not kill every last cancer cell, leaving a few to continue dividing out of control until the disease returns in full force. A second explanation proposes that chemotherapy does get all the regular cancer cells, but there is another type of cancer cell that hides in the body. As opposed to the rapidly dividing majority of cancer cells, these undergo slow division, enabling them to evade the chemotherapy drugs. These insidious cells can give rise to new rapidly-dividing cancer cells, which is why they are known as “cancer stem cells.”

Which explanation is correct? The debate is an important one because, if the first explanation holds true, improving upon the existing treatments might help, while the second implies that a completely different approach to treatment will be needed to root out the slowly dividing cancer stem cells.

To attempt to resolve that debate, the team of Prof. Ehud Shapiro of the Weizmann Institute’s Biological Chemistry, and Applied Mathematics and Computer Sciences Departments, worked with scientists and physicians from Rambam Medical Center and the Technion, both in Haifa. They used a method of reconstructing cell lineage trees that has been developed over the past few years in Shapiro’s lab. This method is based on the fact that the genetic material in different body cells accumulates unique mutations during cell division, and these mutations are passed on to daughter cells during cell division. By comparing mutations, one can map out cells’ detailed family trees, and thus determine how far back they share a common ancestor. The end product of this analysis is a tree that reconstructs the genealogy of the cells from their earliest forebears at the base of the tree to the youngest cells at the tips of the branches.

To reconstruct the cancer cell lineage tree, the team used two sets of blood samples: the first taken from leukemia patients right after the disease was diagnosed, and the second from those patients who had undergone chemotherapy and in whom the cancer had returned. The researchers could then trace the relationships of the recurring cancer cells back to see if they descended from the original cancer cells. The lineage tree showed that, at least in some of the patients, the source of the renewed cancer was not in the rapidly proliferating cancer cells, but rather in cells that were close to the root of the tree. These cells had only divided a few times. In other words, the cancer arose from cells that divide very slowly, making them resistant to the attacks of chemotherapy drugs.
 

Leukemia cells. Image: Wikimedia commons, NIH    

 

Shapiro: “We know that in many cases, chemotherapy alone is not able to cure leukemia. Our results suggest that to completely eliminate it, we must look for a treatment that will not only eliminate the rapidly dividing cells, but also target the cancer stem cells that are resistant to conventional treatment.”


Participating in the research were Dr. Liran Shlush of the Technion’s Rappaport Faculty of Medicine and Rambam Medical Center; Noa Chapal-Ilani and Dr. Rivka Adar of the Weizmann Institute; Profs. Karl Skorecki and Jacob Rowe of the Technion; Dr. Tsila Zuckerman of Rambam and the Technion; Prof. Clara D. Bloomfield of Ohio State University; and additional investigators.
 

Prof. Ehud Shapiro’s research is supported by the Paul Sparr Foundation; Miel de Botton, UK. The Carolito Stiftung; and the European Research Council. Prof. Shapiro is the incumbent of the Harry Weinrebe Professorial Chair of Computer Science and Biology. His research is also supported by an ERC Advanced Grant.

 
Leukemia cells. Image: Wikimedia commons, NIH
Math & Computer Science
English
  • Applied Mathematics and Computer Science
  • Biological Chemistry
  • Cancer
  • Cancer cell survival
  • Ehud Shapiro

Pages