Rapid genotyping of methicillin-resistant Staphylococcus aureus (MRSA) using OLigonucleotide microarrays

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1Institute for Medical Microbiology and Hygiene, Faculty of Medicine ‘Carl Gustav Carus’, Technical University of Dresden, Dresden and 2Clondiag Chip Technologies GmbH, Jena, Germany
This study evaluated a DNA oligonucleotide array that recognised 38 different Staphylococcus aureus targets, including all relevant resistance determinants and some toxins and species-specific controls. A new method for labelling sample DNA, based on a linear multiplex amplification that incorporated biotin-labelled dUTP into the amplicon, was established, and allowed detection of hybridisation of the amplicons to the array with an enzymic precipitation reaction. The whole assay was validated by hybridisations with a panel of reference strains and cloned specific PCR products of all targets. To
evaluate performance under routine conditions, the assay was used to test 100 methicillin-resistant S. aureus (MRSA) isolates collected from a university hospital in Saxony, Germany. The results showed a high correlation with conventional susceptibility data. The ermA and ermC macrolide resistance genes were found in 40% and 32% of the isolates, respectively. The most prevalent aminoglycoside resistance gene was aphA3 (57% of the isolates), followed by aacA–aphD (29%) and aadD (29%); tet genes, mupR
and dfrA were rare. There were no isolates with van genes or genes involved in resistance to quinupristin–dalfopristin. Enterotoxins were detected in 27% of the isolates. Genes encoding Panton– Valentine leukocidin, toxic shock syndrome toxin and exfoliative toxins were not found. The DNA array facilitated rapid and reliable detection of resistance determinants and toxins under conditions used in a routine laboratory and has the potential to be used for array-based high-throughput screening.

complete article at http://www.clondiag.com/pub/CMI-2005_Genotyping%20of%20MRSA.pdf

Similar studies are also published at


Microarray studies used for finding genes involved in autoimmune disease

CAMBRIDGE, Mass. (January 21, 2007) — Autoimmune diseases such as type
1 diabetes, lupus and rheumatoid arthritis occur when the immune system
fails to regulate itself. But researchers have not known precisely
where the molecular breakdowns responsible for such failures occur.
Now, a team of scientists from the Whitehead Institute and the
Dana-Farber Cancer Institute have identified a key set of genes that
lie at the core of autoimmune disease, findings that may help
scientists develop new methods for manipulating immune system activity.
“This may shorten the path to new therapies for autoimmune disease,”
says Whitehead Member and MIT professor of biology Richard Young,
senior author on the paper that will appear January 21 online in
Nature. “With this new list of genes, we can now look for possible
therapies with far greater precision.”
The immune system is often described as a kind of military unit, a
defense network that guards the body from invaders. Seen in this way, a
group of white blood cells called T cells are the frontline soldiers of
immune defense, engaging invading pathogens head on.
These T cells are commanded by a second group of cells called
regulatory T cells. Regulatory T cells prevent biological “friendly
fire” by ensuring that the T cells do not attack the body’s own
tissues. Failure of the regulatory T cells to control the frontline
fighters leads to autoimmune disease.
Scientists previously discovered that regulatory T cells are themselves
controlled by a master gene regulator called Foxp3. Master gene
regulators bind to specific genes and control their level of activity,
which in turn affects the behavior of cells. In fact, when Foxp3 stops
functioning, the body can no longer produce working regulatory T cells.
When this happens, the frontline T cells damage multiple organs and
cause symptoms of type 1 diabetes and Crohn’s disease. However, until
now, scientists have barely understood how Foxp3 controls regulatory T
cells because they knew almost nothing about the actual genes under
Foxp3’s purview.
Researchers in Richard Young’s Whitehead lab, working closely with
immunologist Harald von Boehmer of the Dana-Farber Cancer Institute,
used a DNA microarray technology developed by Young to scan the entire
genome of T cells and locate the genes controlled by Foxp3. There were
roughly 30 genes found to be directly controlled by Foxp3 and one,
called Ptpn22, showed a particularly strong affinity.
“This relation was striking because Ptpn22 is strongly associated with
type 1 diabetes, rheumatoid arthritis, lupus and Graves’ disease, but
the gene had not been previously linked to regulatory T-cell function,”
says Alexander Marson, a MD/PhD student in the Young lab and lead
author on the paper. “Discovering this correlation was a big moment for
us. It verified that we were on the right track for identifying
autoimmune related genes.”
The researchers still don’t know exactly how Foxp3 enables regulatory T
cells to prevent autoimmunity. But the list of the genes that Foxp3
targets provides an initial map of the circuitry of these cells, which
is important for understanding how they control a healthy immune
Autoimmune diseases take a tremendous toll on human health, but on a
strictly molecular level, autoimmunity is a black box,” says Young.
“When we discover the molecular mechanisms that drive these conditions,
we can migrate from treating symptoms to developing treatments for the
disease itself.”

Cryptography with DNA binary strands

Biotechnological methods can be used for cryptography. Here two different cryptographic approaches based on DNA binary strands are shown. The rst approach shows how DNA binary strands can be used for steganography to provide rapid encryption and decryption. It is shown that DNA steganogra- phy based on DNA binary strands is secure under the assumption that an interceptor has the same technological capabilities as sender and receiver of encrypted messages. The second approach shown here is based on steganography and a method of graphical subtraction of binary gel-images. It can be used to constitute a molecular checksum and can be combined with the rst approach to support encryption.

full article at http://www.cs.mun.ca/~banzhaf/papers/DNA_Crypt_final.pdf

Microarray for Clinical Diagnostics- For detecting sepsis


Right now there’s no rapid way to diagnose sepsis, a fast-moving blood infection that is a leading cause of death in hospital intensive care units. The illness unleashes a powerful inflammatory response that can quickly overwhelm the body, causing organ failure and death, often within days.

New research now suggests that doctors one day could quickly distinguish sepsis from widespread non-infectious inflammation based on genetic profiles of patients’ blood. Testing this method in mice, researchers at Washington University School of Medicine in St. Louis found the profiles could accurately discriminate between the two conditions 94 percent of the time. The molecular profiles measure differences in patterns of gene expression that are unique to sepsis vs. non-infectious inflammation.

The researchers used microarrays, also called gene chips, to analyze patterns of gene expression in the mice. The same technology is already used by doctors to diagnose breast cancer and predict a patient’s response to various chemotherapy drugs, but this is the first time researchers have attempted to use gene chips to distinguish sepsis from non-infectious inflammation.

Carnegie Mellon U. Transforms DNA Microarrays With Standard Internet Communications Protocol

 Source: Carnegie Mellon University December 2005

A standard Internet protocol that checks errors made during email transmissions has now inspired a revolutionary method to transform DNA microarray analysis, a common technology used to understand gene activation. The new method, which blends experiment and computation, strengthens DNA microarray analysis, according to its Carnegie Mellon University inventor, who has published his findings in the December issue of Nature Biotechnology with collaborators at the Hebrew University in Israel. Ziv Bar Joesph

The innovative method combines a new experimental procedure and a new algorithm to identify gene activation captured by DNA microarray analysis with greater sensitivity and specificity. The work holds great promise for vastly improving research on health and disease, according to Ziv Bar-Joseph, assistant professor of computer science and biological sciences at Carnegie Mellon.

“We are very excited about introducing this versatile, powerful method to the research community because it can be used to study a wide range of complex, dynamic systems more comprehensively,” said Bar-Joseph, who also is a member of the Center for Automated Learning and Discovery at the School of Computer Science. “Such systems under study include stress and drug response, cancer and embryo development.”

DNA microarray analysis — a multimillion-dollar-a-year industry — identifies gene activation in living, complex biological systems. DNA microarrays monitor the behavior of thousands of genes over time by detecting changes in the expression of as many as 30,000 different genes on one small chip. The technique has been used to study some of the most important biological systems, including how cells normally divide (the cell cycle) and immune responses to disease and infection.

“Ultimately, we think that the addition of this method to standard DNA microarray analysis will make it more accurate and cost-effective,” Bar-Joseph added.

“While DNA microarrays are very powerful, they present a sampling problem,” Bar-Joseph said. “DNA microarrays only take static snapshots of gene activity over time. In between these snapshots, genes could be activated and we just don’t see them turning on. Our protocol will offer greater overall sensitivity in detecting the expression of any gene, even if a gene turns on when no microarray sampling takes place.”

Bar-Joseph’s procedure is based on a “check-sum” protocol initially developed to ensure that email messages sent via the Internet don’t become garbled in transmission. In the standard Internet check-sum protocol, bits of information that begin as one value (0 or 1) may inadvertently flip to the opposite value as they move from one computer to the next in the form of an email. This data loss, ascribed to noise in the communication channel, is checked by counting the number of 1’s in the message. If this number is odd, then the last bit is set to 1; otherwise it is set to 0. By comparing the number of 1’s on the sending end with the value of the last bit on the receiving end, the recipient’s computer can determine whether the message was accurately received. If not, the recipient’s computer asks the sender’s computer to forward the message again.

Bar-Joseph’s method carries out a similar analysis of the microarray snapshots by “checking” the sum of a set of DNA microarray data points over time (a time series experiment) against the “summary” of the temporal response. If the two sets of results are equal, then what is captured by the DNA microarray time series is real. If the time series results produce a lower value than the microarray summary, the protocol indicates that the researchers have missed a gene’s activation somewhere in their time series.

Just as important, according to Bar-Joseph, is whether a DNA microarray summary value exceeds its time sequence value. If that’s the case, then researchers have likely identified gene activity that should be attributed to changes taking place during an experiment — adding a chemical or changing the temperature, for instance. This aspect of the method provides scientists with the specificity they need to weed out such introduced gene activation from fundamental gene activation pathways that form the hallmark of processes like cancer or immunity. To prove the effectiveness of this new method, Bar-Joseph studied the human cell division cycle. Considered one of the most important biological systems, the cell cycle plays a major role in cancer. Using their new method, Bar-Joseph and his colleagues identified many new human genes that were not previously found to be participants in this system.

“This new set of gene discoveries opens the way to new and more accurate models of the cell cycle system, which in turn can lead to new targets for cancer drugs,” said Bar-Joseph.

The new method also overcomes synchronization loss, a vexing problem for scientists who study hundreds or thousands of cells over time, according to Bar-Joseph. Large groups of living cells that start out together at the same biological point in time eventually become asynchronized in their activities, he noted.

“You can compare a group of cells starting out in an experiment like a group of marathoners at the starting line. Over time, some marathoners will be far ahead on the track, while others will fall back.” After the race begins, finding one marathoner among the thousands is difficult. Similarly, with asynchronous cells, trying to sort out a single cell response is virtually impossible. But Bar-Joseph has incorporated mathematical tools in his method that can detect genes affected by such asynchrony in a population of cells.

Bioinformatics Techniques for spam detection

Its not a new topic IBM has discovered that it could use many of the pattern detection techniques and analysis used in bioinformatics in other fields as well.

I thought of adding this as bioinformatics is and microarrays are growing in popularity and decided to give few bytes such articles as wel.

Many of these studies are based on the homology detection. Perhaps going forward the techniques used in SNP detection in SNP microarrays might also find use in other fields notably in spam detection and share market analysis or trends analysis

I find some of the presentation onthe web andd from IBm on using the famous Teiresias algorithm, for spam detection

Chung-Kwei applies advanced pattern matching algorithms developed in IBM’s bioinformatics group to spam detection. This new classification algorithm can detect complex patterns in messages that go beyond the simple word or word phrases used in most algorithms.

A technique originally designed to analyse DNA sequences is the latest weapon in the war against spam. An algorithm named Chung-Kwei (after a feng-shui talisman that protects the home against evil spirits) can catch nearly 97 per cent of spam.

Chung-Kwei is based on the Teiresias algorithm, developed by the bioinformatics research group at IBM’s Thomas J Watson Research Center in New York, US. Teiresias was designed to search different DNA and amino acid sequences for recurring patterns, which often indicate genetic structures
that have an important role.

Instead of chains of characters representing DNA sequences, the research group fed the algorithm 65,000 examples of known spam. Each email was treated as a long, DNA-like chain of characters. Teiresias identified six million recurring patterns in this collection, such as “Viagra”.

Each pattern represented a common sequence of letters and numbers that had appeared in more than one unsolicited message. The researchers then ran a collection of known non-spam (dubbed “ham”) through the same process, and removed the patterns that occurred in both groups.

Genuine email Incoming email was given a score based on how many spam patterns it had. A long email that only had a few spammy sentences would get a relatively low score; but one with many patterns spread across the length of the message would score much higher. The Chung-Kwei correctly identified 64,665 of 66,697 test messages as being spam or 96.56 per cent. More importantly, its rate of misidentifying genuine email as spam was just 1 in 6000 messages. Losing a single email in a torrent of spam is a greater failing in a filter than letting the occasional spam email through.

Chung-Kwei deals with common spammer strategies to dodge pattern-recognition schemes, such as replacing the s with a $, as in “increa$e your $ex power” using its built-in tolerance for different, but
functionally equivalent, DNA sequences. Just as in genetic analysis, Teiresias could be taught that CCC and CCU codons both produce the same amino acid, proline, the anti-spam system an be trained to accept $ and s as identical.

IBM intends to include Chung-Kwei in its commercial product, SpamGuru. Justin Mason, who developed SpamAssassin, one of the most popular open-source anti-spam filters, says that Chung-Kwei looks promising.




A little bit of fun- I did a PhD and did NOT go mad by Richard Butterworth from university of Middlesex.

Cartoon of person looking mad

I did a PhD and did NOT go mad

Before reading these wise words advising you how to do a PhD (inspired by three years of the author carefully and diligently banging his head on a table) you are requested to read and digest the following irony…

The only way to find out  how to do a PhD is to do one. Therefore all advice is useless.

To say that I enjoyed doing my PhD would be a lie, not just an ordinary lie mind you. More the sort of lie one would normally associate with Tory party conferences. A big wobbly lie with a dusting of sugar on top. At times I hated my PhD, so why do I have any authority to give advice on doing a PhD? Well, I don’t claim to have any — other than the fact that I completed and passed the thing, so I must have done somthing right.

Read on at http://www.cs.mdx.ac.uk/staffpages/richardb/PhDtalk.html

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