The preeminent belief has been that the difference between species lies in their DNA—the number of genes an organism has, the function of those genes and when and where those genes are expressed. As it turns out, the answer is not quite so simple.
“There’s very high conservation of the total number of protein coding genes across different vertebrate species,” says Serge Gueroussov, a PhD student in Dr. Benjamin Blencowe’s lab at the University of Toronto. “When [researchers] compared gene expression across different organs in different species, there was also a lot of conservation. It suggests that organisms don’t differ so much in the genes they have and the extent to which they express [those genes].”
In other words, while we may look drastically different from a frog or a chicken, our repertoire of genes and when and where we express those genes are actually pretty similar. So where is the variation coming from?
How did our Solar System evolve to its current state?
That’s a difficult question to answer. Since the formation of the Solar System roughly 4.6 billion years ago, it has been in a state of constant change and evolution. Moons formed and planets shifted. Studying the evolution of the Solar System has been tricky because, well, we weren’t around to see it happen. While unearthing skeletons and imprints have helped us understand the evolution of plants and animals, similar records are hard to come by for the Solar System. Now, an international team of researchers has discovered a disc-shaped region of debris that can help shed light on how our Solar System evolved.
The newly discovered ring of debris is similar to the Kuiper Belt, a region of our Solar System located just beyond Neptune’s orbit. It contains a number of dwarf planets, including Pluto, as well as many leftover remnants from when planets were formed in the early Solar System. “If we understand the evolution and composition of the Kuiper Belt, that gives us good clues to understanding the earlier stages of the Solar System’s evolution,” says Dr. Thayne Currie, the lead author of the study. “You can almost think of it like a fossil record of the Solar System.” Continue reading →
What scares you? As a kid, I hid behind couch cushions while watching Jurassic Park and could never finish a Goosebumps book. Nowadays, I am terrified of the growing epidemic of antimicrobial resistance. And I’m not the only one. Last year, as part of a five-year strategy to combat drug resistance, British Prime Minister David Cameron commissioned a review to examine the economic and health costs of antimicrobial resistance. In their first report published last December, the panel predicted that left unchecked, antimicrobial resistance will lead an extra 100 million deaths by 2050 and cost the world economy up to $100 trillion USD.
Efforts to halt the spread of antimicrobial resistance have focused on removing antibiotics from animal feed and curtailing the overzealous and oftentimes unnecessary use of antibiotics in humans. Another strategy to prevent resistance from developing is combination therapy, when two or more drugs with unique modes of action are taken together to treat an infection. In a paper published this week in the Proceedings of the National Academy of Sciences, a team of mathematicians and biologists led by Dr. Pleuni Pennings at San Francisco State University examined how differences in drug penetrance can impact the effectiveness of combination therapy and subsequent emergence of multidrug resistance.
Combination therapy reduces the risk of drug resistance because in theory, the pathogen needs to acquire multiple mutations at the same time to withstand the assault of multiple drugs. In reality, combination therapies fail to stem the development of resistance for a number of reasons. For example, some patients are started on a single drug first before a second drug is added. This type of treatment regiment facilitates resistance development because bacteria can acquire singular mutations in a stepwise fashion. Another reason is that different drugs have different staying power, which means that even though you may be taking both drugs at the same time, one could pass through your body much faster than the other. This creates periods of “effective monotherapy” where resistance can develop easily to the single long-lived drug. While a lot of attention has been paid to how drugs with different half-lives impact resistance, not a lot is known about how the spatial distribution of drugs influence the evolution of multidrug resistance. That’s where this paper comes in. Continue reading →