Why would already abundant ‘natural killer’ cells proliferate even further after subduing an infection? It’s been a biological mystery for 30 years. But now Brown University scientists have an answer: After proliferation, the cells switch from marshaling the immune response to calming it down. The findings illuminate the functions of a critical immune system cell important for early defense against disease induced by viral infection.
Archive for the ‘Interesting Studies’ Category
A grandfather clock is, on its surface, a simple yet elegant machine. Tall and stately, its job is to steadily tick away the time. But a look inside reveals a much more intricate dance of parts, from precisely-fitted gears to cable-embraced pulleys and bobbing levers.
Like exploring the inner workings of a clock, a team of University of Wisconsin-Madison researchers is digging into the inner workings of the tiny cellular machines called spliceosomes, which help make all of the proteins our bodies need to function. In a recent study published in the journal Nature Structural and Molecular Biology, UW-Madison’s David Brow, Samuel Butcher and colleagues have captured images of this machine, revealing details never seen before.
In their study, they reveal parts of the spliceosome — built from RNA and protein — at a greater resolution than has ever been achieved, gaining valuable insight into how the complex works and also how old its parts may be.
By better understanding the normal processes that make our cells tick, this information could some day act as a blueprint for when things go wrong. Cells are the basic units of all the tissues in our bodies, from our hearts to our brains to our skin and lungs.
It may also help other scientists studying similar cellular machinery and, moreover, it provides a glimpse back in evolutionary time, showing a closer link between proteins and RNA, DNA’s older cousin, than was once believed.
“It gives us a much better idea of how RNA and proteins interact than ever before,” says Brow, a UW-Madison professor of biomolecular chemistry.
The spliceosome is composed of six complexes that work together to edit the raw messages that come from genes, cutting out (hence, splicing) unneeded parts of the message. Ultimately, these messages are translated into proteins, which do the work of cells. The team created crystals of a part of the spliceosome called U6, made of RNA and two proteins, including one called Prp24.
Crystals are packed forms of a structure that allow scientists to capture three-dimensional images of the atoms and molecules within it. The crystals were so complete, and the resolution of the images so high, the scientists were able to see crucial details that otherwise would have been missed.
The team found that in U6, the Prp24 protein and RNA — like two partners holding hands — are intimately linked together in a type of molecular symbiosis. The structure yields clues about the relationship and the relative ages of RNA and proteins, once thought to be much wider apart on an evolutionary time scale.
“What’s so cool is the degree of co-evolution of RNA and protein,” Brow says. “It’s obvious RNA and protein had to be pretty close friends already to evolve like this.”
The images revealed that a part of Prp24 dives through a small loop in the U6 RNA, a finding that represents a major milestone on Brow and Butcher’s quest to determine how U6′s protein and RNA work together. It also confirms other findings Brow has made over the last two decades.
“No one has ever seen that before and the only way it can happen is for the RNA to open up, allow the protein to pass through, and then close again,” says Butcher, a UW-Madison professor of biochemistry.
Ultimately, Butcher says they want to understand what the entire spliceosome looks like, how the machines get built in cells and how they work.
While this is the first protein-RNA link like this seen, Brow doesn’t believe it is unique. Once more complete, high-resolution images are captured of other RNA-protein machines and their components, he thinks these connections will appear more commonly.
He hopes the findings mark a transition in the journey to understand these cellular workhorses.
“It’s exciting studying these machines,” he says. “There are only three big RNA machines. Ours evolved 2 billion years ago. But once it’s figured out, it’s done.”
The U6 crystal structure was imaged using the U.S. Department of Energy Office of Science’s Advanced Photon Source at Argonne National Laboratory. The work was funded by a joint grant from the National Institutes of Health shared by Brow and Butcher.
Thanks to University of Wisconsin-Madison for contributing this story.
A new approach to studying microbes in the wild will allow scientists to sequence the genomes of individual species from complex mixtures. It marks a big advance for understanding the enormous diversity of microbial communities —including the human microbiome. The work is described in an article published May 22 in Early Online form in the journal G3: Genes|Genomes|Genetics, published by the Genetics Society of America.
“This new method will allow us to discover many currently unknown microbial species that can’t be grown in the lab, while simultaneously assembling their genome sequences,” says co-author Maitreya Dunham, a biologist at the University of Washington’s Department of Genome Sciences.
Microbial communities, whether sampled from the ocean floor or a human mouth, are made up of many different species living together. Standard methods for sequencing these communities combine the information from all the different types of microbes in the sample. The result is a hodgepodge of genes that is challenging to analyze, and unknown species in the sample are difficult to discover.
“Our approach tells us which sequence fragments in a mixed sample came from the same genome, allowing us to construct whole genome sequences for individual species in the mix,” says co-author Jay Shendure, also of the University of Washington’s Department of Genome Sciences.
The key advance was to combine standard approaches with a method that maps out which fragments of sequence were once near each other inside a cell. The cells in the sample are first treated with a chemical that links together DNA strands that are in close proximity. Only strands that are inside the same cell will be close enough to link. The DNA is then chopped into bits, and the linked portions are isolated and sequenced.
“This elegant method enables the study of microbes in the environment,” says Brenda Andrews, editor-in-chief of the journal G3: Genes|Genomes|Genetics. Andrews is alsoDirector of the Donnelly Centre and the Charles H. Best Chair of Medical Research at the University of Toronto. “It will open many windows into an otherwise invisible world.”
At a time when personal microbiome sequencing is becoming extremely popular, this method breaks important ground in helping researchers to build a complete picture of the genomic content of complex mixtures of microorganisms. This complete picture will be crucial for understanding the impact of varying microbiome populations and the relevance of particular microorganisms for individual health.
CITATION: Species-Level Deconvolution of Metagenome Assemblies with Hi-C-Based Contact Probability Maps Joshua N. Burton, Ivan Liachko, Maitreya J. Dunham, and Jay Shendure. G3: Genes|Genomes|Genetics g3.114.011825; Early Online May 22, 2014, doi:10.1534/g3.114.011825; PMID 24855317.
Thanks to the Genetics Society of America for contributing this story.
Cells are the basic structural units of all life. They were first observed more than 400 years ago after the invention of the microscope. One of the most studied cells in science is E. coli, a sausage-shaped bacterium that can cause food poisoning.
In fact, cells resemble sausages insofar as both consist of outer envelopes stuffed with an inner mass. For decades biologists have believed that the growth of this inner mass, pressing on the outer membrane, is what caused cell walls to grow.
However, using new techniques to isolate and visualize cells in different environments, the Stanford team proved that cell wall growth occurred regardless of the pressures exerted on the cell – whether from inside or out.
Here it is critical to understand that, unlike a sausage, the outer envelope of a cell is alive, dynamic and porous. It is designed to allow water to seep in or out. This is important because cells live in fluids and hence are subject to the pressure of osmosis.
Osmosis relates to the amount of solid materials dissolved in a liquid solution. Stirring sugar into coffee, for instance, increases its osmotic pressure. The more sugar you stir in, the higher the osmotic pressure of the solution.
Life is based on water, so cells have an internal osmotic pressure. When a cell enters a solution with a higher osmotic pressure – such as a sugary liquid – its porous membrane tries to protect the cell by letting water out. This causes the cell membrane to shrivel up, compacting the cell to withstand the pressure from without. Put the same cell back into a normal solution, and the porous cell wall allows water to seep back in, causing the cell to swell to its former size.
Biologists have long supposed that this same pressure dynamic retarded cell wall growth. It made sense given the prevailing wisdom – if cell wall growth were indeed driven by expansion from inside the cell, and outward pressure forced the cell to contract, how could the outer cell wall continue to grow?
In fact, the Stanford team initially designed its experiment to measure precisely how much osmotic pressure slowed cell wall growth in E. coli.
They used microfluidic devices to trap the bacterial cells in tiny chambers. This allowed them to bathe the confined cells, first in highly concentrated sugars (high osmotic pressure), then in normal solutions (low osmotic pressure), while recording precise images of cell contraction or expansion.
Initially, the results seemed to confirm the prevailing wisdom: cells bathed in a sugar solution appeared to grow more slowly.
But whenever the researchers “shocked” the cells by flushing out the sugars and bathing the cells in normal solution they were surprised to see that the cells expanded rapidly – in a matter of seconds — to a size roughly equivalent to cells growing at full speed in normal solutions. Click here to see the video.
“The cells just didn’t seem to care that they had been subjected to frequent and large (osmotic) insults in the chamber,” Huang said.
The Stanford researchers came to realize that the cell walls had continued to grow in the sugar solution just as fast as in the normal solution – but the extra mass was shriveled like a raisin. When the cell re-entered the normal solution and water seeped back in through the porous membrane, the now-turgid cell smoothed out like a grape, and all the non-apparent growth became visible.
To follow up this surprising finding, Rojas is in Bangladesh, extending the investigations to study how bacterial pathogens such as Vibrio cholerae respond to rapidly changing fluid environments and how to use this knowledge to fight this scourge.
Thanks to Stanford School of Engineering for contributing this story.
Developing and testing a new anti-cancer drug can cost billions of dollars and take many years of research. Finding an effective anti-cancer medication from the pool of drugs already approved for the treatment of other medical conditions could cut a considerable amount of time and money from the process.
Now, using a novel bioinformatics approach, a team led by investigators at Beth Israel Deaconess Medical Center (BIDMC) has found that the approved antimicrobial drug pentamidine may help in the treatment of patients with advanced kidney cancer. Described online in the journal Molecular Cancer Therapeutics, the discovery reveals how linking cancer gene expression patterns with drug activity might help advance cancer care.
“The strategy of repurposing drugs that are currently being used for other indications is of significant interest to the medical community as well as the pharmaceutical and biotech industries,” says senior author Towia Libermann, PhD, Director of the Genomics, Proteomics, Bioinformatics and Systems Biology Center at BIDMC and Associate Professor of Medicine at Harvard Medical School. “Our results demonstrate that bioinformatics approaches involving the analysis and matching of cancer and drug gene signatures can indeed help us identify new candidate cancer therapeutics.”
Renal cell cancer consists of multiple subtypes that are likely caused by different genetic mutations. Over the years, Libermann has been working to identify new disease markers and therapeutic targets through gene expression signatures of renal cell cancer that distinguish these different cancer subtypes from each other, as well as from healthy individuals. In this new paper, he and his colleagues were looking for drugs that might be effective against clear cell renal cancer, the most common and highly malignant subtype of kidney cancer. Although patients with early stage disease can often be successfully treated through surgery, up to 30 percent of patients with renal cell cancer present with advanced stages of disease at the time of their diagnosis.
To pursue this search, they made use of the Connectivity Map (C-MAP) database, a collection of gene expression data from human cancer cells treated with hundreds of small molecule drugs.
“C-MAP uses pattern-matching algorithms to enable investigators to make connections between drugs, genes and diseases through common, but inverse, changes in gene expression,” says Libermann. “It provided us with an exciting opportunity to use our renal cell cancer gene signatures and a new bioinformatics strategy to match kidney cancer gene expression profiles from individual patients with gene expression changes inducted by various commonly used drugs.”
After identifying drugs that may reverse the gene expression changes associated with renal cell cancer, the investigators used assays to measure the effect of the selected drugs on cells. This led to the identification of a small number of FDA-approved drugs that induced cell death in multiple kidney cancer cell lines. The investigators then tested three of these drugs in an animal model of renal cell cancer and demonstrated that the antimicrobial agent pentamidine (primarily used for the treatment of pneumonia) reduced tumor growth and enhanced survival. Gene expression experiments using microarrays also identified the genes in renal cell cancer that were counteracted by pentamidine.
“One of the main challenges in treating cancer is the identification of the right drug for the right individual,” explains first author Luiz Fernando Zerbini, PhD, of the International Center for Genetic Engineering and Biotechnology in Cape Town, South Africa, adding that this bioinformatics approach could be a particularly valuable lower-cost model in developing countries.
The authors say their next step will be to evaluate the potential of pentamidine in combination with the current standard-of-care therapies to treat kidney cancer. “Since the drugs we are evaluating are already FDA-approved, successful studies in preclinical animal models may enable us to rapidly move these drugs into clinical trials,” adds Libermann.
Thanks to Beth Israel Deaconess Medical Center for contributing this story.