Volume 2, Issue 17 December 2005/January 2006
Our Single-Celled Ancestors
Biologist Nicole King studies tiny creatures that may be the closest living relatives to our single-celled ancestors.
Six-hundred million years ago, a pivotal turning point in the history of life occurred. In the ancient sea, multicellular organisms evolved that are now recognized as the world's first animals. But what was the biology of the single-celled organism that made the transition? And how did it become the common progenitor of all animals? To answer these questions, UC Berkeley biologist Nicole King studies tiny creatures called choanoflagellates that may be the closest living relative to our single-celled ancestors.
"These early organisms are not preserved in the fossil record, so we don't know very much about how multicellularity first evolved," says King, a professor in the Departments of Integrative Biology and Molecular and Cell Biology. "But choanoflagellates might provide insight into that transition."
Choanoflagellates are one-celled protozoans that live in fresh water and the ocean. Resembling sperm, the tiny organisms are approximately 10 microns across—nearly 100 would fit on the head of a pin. While choanoflagellates have long been suspected to be relatives of animals, studying their basic biology has historically been difficult. In recent years though, the genomics revolution has spawned techniques that are enabling King and her colleagues to look closely at the cellular secrets inside the organisms.
Propelled by their flagella, choanoflagellates move through water collecting bacteria on a collar of tentacles at the base of the cell body. (photo by Melissa Mott)
"When I first stumbled upon choanoflagellates, I was dumbfounded," says King, who in September received a prestigious MacArthur Foundation "genius award." "As a scientist interested in the cellular bases for animal development, I couldn't believe what a goldmine these organisms might be. Once I realized that we could apply genomic tools to study them, this project really opened up."
King's earliest experiments helped confirm that choanoflagellates are indeed closely related to animals. Next, she and her colleagues surveyed the organism's genes at a high level and quickly discovered that it contains genes that were previously thought to only exist in animals. The big surprise was that two of those genes are actually used by animals to express proteins for cell adhesion and cell communication. In other words, a single-celled animal is making proteins that are seemingly essential only to multicellular animals.
"It's amazing." King says. "We interpret that as evidence that some of the protein machinery for multicellularity actually evolved before the origin of animals, before multicellularity itself. The proteins predated their current function in animals."
According to King, this "a classic example of co-option," an evolutionary process in which an existing biological structure or system is adapted for a new function.
"Right now, we're very interested in understanding how the proteins function in choanoflagellates and to use that as a tool in investigating what they might have been doing in the common ancestor," she says.
Choanoflagellates were first considered to be close relatives of animals in the late nineteenth century. (courtesy the researchers)
As one of only a handful of laboratories around the world studying the choanoflagellates using methods from molecular and cell biology, King's research group is developing most of their techniques from scratch. Meanwhile, they're collaborating with scientists from the Department of Energy's Joint Genome Institute, who are sequencing the whole genome of a choanaoflagellate. Once completed, the code will enable the King and her colleagues to reconstruct the minimal genome of the last common ancestor and seek out the hidden details of its evolutionary history.
"I was surprised to learn that so much of animal biology was in place before the origin of animals," King says. "And I think that's what motivates most scientists--not learning that you were right, but learning that you were wrong."
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Extreme Biomaterials
A fellow of the American Association for the Advancement of Science (AAAS), Douglas Clark is also a scientist with the Lawrence Berkeley National Laboratory's Environmental Energy Technologies Division.
In the deepest oceans, near incredibly hot volcanic vents, a strange, hearty organism survives and thrives. Many scientists study these microorganisms, called extremophiles, for the clues they may hold about the origins of life. UC Berkeley chemical engineer Douglas S. Clark is interested in them for a different reason. The extremophiles contain enzymes that spur biochemical reactions even in the harshest conditions. And much of Clark's research is concerned with novel enzymatic reactions, particularly how they may improve industrial processes and aid in pharmaceutical production.
First identified in the 1970s, extremophiles live in conditions that are either far too hot, cold, acidic, alkaline, or salty for most other organisms. They've been found happily swimming through sewage, petroleum deposits, hot springs, and other seemingly-inhospitable locales. To analyze the extremophiles that call the deep sea thermal vents home, Clark and his colleagues have constructed a laboratory apparatus that mimics the extremophile's natural home. By studying the extremophiles under their preferred conditions, the researchers can begin to identify the genes, proteins, and biological processes that keep the bizarre creatures alive under extreme conditions.
Scanning electron micrograph of cells of Methanocaldococcus jannaschii, a methane-producing extremophile isolated from the vicinity of a deep-sea hydrothermal vent. (courtesy the researchers)
"That knowledge might enable us to genetically engineer more conventional organisms to tolerate a wider range of conditions in industrial practice," Clark says.
Enzymes are used in myriad commercial products and processes, from detergents to DNA fingerprinting to food production. However, all enzymes are sensitive to their environment, requiring careful monitoring and control of temperature, pH levels, or other factors to keep them cranking away. So-called extremozymes that remain productive even in changing environments would be a boon to industry.
For example, many industrial processes require organic solvents to dissolve a substrate or compound in the course of a chemical transformation. While solvents sometimes deactivate enzymes, certain extremozymes could likely withstand such a brutal bath. In addition to characterizing these catalysts, Clark is exploring whether proteins borrowed from the extremozymes might be used to stabilize traditional enzymes in extreme industrial conditions.
The Clark Group has constructed specialized containers like this one that can duplicate the most extreme conditions on Earth known to support life. (courtesy the researchers)
The Clark group is also learning to hijack the extremophiles' cellular machinery for the production of natural compounds like pharmaceuticals. The biochemical pathways that the extremophiles use to form proteins could potentially be transferred to another organism that's "programmed" to produce a certain bio-therapeutic compound. The engineered organism--souped up with the extremophiles' robust protein-folding machinery--would become a much more efficient drug factory.
The researchers have also begun to examine the physiology of extremophiles as a direct source of useful bioproducts. Recently, they discovered a protein in a particular extremophile that forms long, stable filaments. Clark believes these tiny tendrils could possibly be used as nanowires in future integrated circuits or even to pattern other materials on very small scales, a key challenge in nanoengineering.
"Our focus is devising ways to improve the utility of biological systems for practical applications," says Clark, who previously developed a "biochip" for drug toxicity screening that mimics the human liver. "Extremophiles may be a source of new enzymes and proteins that could help us meet that objective."
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Machines That Learn
Computers aren't big thinkers, but they can analyze massive amounts of information much faster than our own brains. That's the idea behind machine learning, a method that enables computer software to recognizes hidden patterns in as slew of data and learn from what if finds. UC Berkeley statistics professor Michael I. Jordan is applying machine learning to myriad applications, from genomics to information retrieval to the development of Internet software that repairs itself.
"Learning is a branch of statistics," says Jordan, who is also a professor in the College of Engineering's Computer Science Division. "Classical statistics generally centered around a person sitting down with some data, applying some procedure, and ending up with a pattern or hypothesis. We want to more thoroughly automate the process so computers can learn from data in a statistical sense."
Michael I. Jordan is a fellow of the American Association for Artificial Intelligence
Drawing from esoteric mathematics, Jordan develops pattern-finding algorithms that compare various sets of historical data and identify commonalities between them. The software then uses those specific instances to generate a predictive model of what's likely to occur in the future.
For example, Jordan and professor Steven Brenner of the Department of Microbial Biology are leading the development of a system to aid biologists in sussing out the function of proteins in plant or animal genomes. Biologists have developed the Gene Ontology, a collection of terms they use to hierarchically organize proteins that have already been characterized. The Gene Ontology has become a lingua franca for the description of biological function, Jordan says.
"A good statistical question is 'given what's currently available in the Gene Ontology and the scientific literature, can I infer the label of an unlabeled protein?'" Jordan says. "In particular, I'd like to take into account the evolutionary history of these proteins and the function of related proteins."
The machine learning technique does just that. First, it identifies all of the known proteins that are nearby the protein in question in the sense that they have similar strings of amino acids. It then builds a phylogenetic tree, a diagram representing the evolutionary relationships among the various proteins. A number of factors are taken into account to place the known proteins in appropriate locations on the tree. Finally, the algorithm propagates information around the tree, labeling the uncharacterized proteins with the terms from the Gene Ontology. Jordan, Brenner, and graduate student Barbara Engelhardt recently tested the system on a known set of proteins and demonstrated accuracies greater than 90 percent.
For Jordan though, a genome is just another set of raw data to be statistically analyzed. The goal of all of his work, he says, "is to create methods that without a huge effort can be turned into analysis engines for different kinds of data."
In another project, he's tailoring the machine learning algorithms to infer meaning from vast bodies of documents so they can be searched and categorized more efficiently and accurately. In this case, pages and pages of, say, technical papers about computer science would be loaded into the system. The system analyzes that data and generates a hierarchical tree where the highest branches represent common words found in the documents like "and," "but," and "the." Somewhere below that level would be more specific words such as "database," "operating system," and "network." As you move down the tree, the terms become more specialized. The result is a method of classifying and clustering documents based on their particular "paths" down the tree.
"So you can pump in a document corpus and it'll figure out the tree, the branches, and the probabilities of all of the words on those branches," Jordan says. "Then when you input a new document, it will predict where it falls on the tree. It's a general purpose corpus analyzer."
The system could be used not only to help readers identify documents that are truly related to one another but might also improve speech recognition software by figuring out the "gist" of what someone is saying. Meanwhile, a group at MIT has adapted the algorithm for a computer vision system that identifies objects in an image.
In the next few months, Jordan will embark on another statistical journey in computer science. He is a founding member of the University's new Reliable, Adaptive and Distributed systems Laboratory (RAD Lab). Funded with $7.5 million from Google, Microsoft, and Sun Microsystems, the laboratory will develop technology that leverages the power of machine learning so that one person can create the next eBay, Amazon, or even Google, by herself. The aim is to enable the quick development of Web services that can adapt to changing numbers of users while self-diagnosing and repairing bugs or other potential problems.
"Systems and software should be much more graceful in their ability to work in the context of the real world and I think statistical methods are the core technology for achieving that," Jordan says.
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