Monthly Archives: November 2022

The Puzzle of Puffy Snout

Puffy snout syndrome: though it has a cute-sounding name, this debilitating condition causes masses on the face of Scombridae fish (a group of fish that includes mackerel and tuna.) Fish afflicted with puffy snout syndrome (PSS) develop excessive collagenous tumor-like growths around the eyes, snout, and mouth. This ultimately leads to visual impairment, difficulty feeding, and eventual death. PSS is surprisingly confined to just fish raised in captivity – those in aquaculture farms or aquariums, for example. Unfortunately, when PSS is identified in aquaculture, the only option is to cull the entire tank — no treatments or cures currently exist.

*Left: a mackerel with puffy snout syndrome. Collagenous growths cover the snout and eye. Right: a healthy mackerel. Photos Emily Miller*

PSS was first identified in the 1950s, in a fish research center in Honolulu, Hawaii. Since then, there have only been 9 publications in the scientific literature documenting the condition and possible causes, although the fish community has come to the conclusion that PSS is likely a transmittable condition with an infectious agent as the cause. But despite this conclusion, there’s been no success so far in identifying such a cause – tests for parasites, bacterial growth, and viruses have come up empty-handed. That was until a 2021 paper, using high-resolution electron microscopy, found evidence of viral particles in facial tissues taken from Pacific mackerel. Suddenly, there was a lead: could PSS be caused by a virus that we just don’t have a test for yet?

*Electron microscopy images showing viral-like particles (red arrows) in facial tissue from Pacific mackerel (Miller et al 2022).*

Putting Together the Pieces

To investigate this hypothesis, this week’s guest Savanah Leidholt (a co-author of the 2021 microscopy study) is using an approach for viral detection known as metatranscriptomics. Leidholt, a fourth year PhD candidate in the Microbiology department, sees this complex approach as a sort of puzzle: “Your sample of RNA has, say, 10 giant jigsaw puzzles in it. But the individual puzzles might not be complete, and the pieces might fit into multiple places, so your job is to reassemble the pieces into the puzzles in a way that gives you a better picture of your story.”

*Savanah Leidholt, PhD candidate in Rebecca Vega-Thurber’s lab, is looking for evidence of viruses in the tissues of fish with puffy snout syndrome.*

RNA, or ribonucleic acid, is a nucleic acid similar to DNA found in all living organisms, But where DNA is like a blueprint – providing the code that makes you, you; RNA is more like the assembly manual. When a gene is expressed (meaning the corresponding protein is manufactured), the double-stranded DNA is unwound and the information is transcribed into a molecule called messenger RNA. This single-stranded mRNA is now a copy of the gene that can be translated into protein. The process of writing an mRNA copy of the DNA blueprint is called transcription, and these mRNA molecules are the target of this metatranscriptomics approach, with the prefix “meta” meaning all of the RNA in a sample (both the fish RNA and the potential viral RNA, in this case) and the suffix “omics” just referring to the fact that this approach happens on a large scale (ALL of the RNA, not just a single gene, is sequenced here!) When mRNA is sequenced in this manner, the researchers can then conclude that the gene it corresponds to was being expressed in the fish at the time the sample was collected.

*The process of transcription: making messenger RNA from DNA. Image from* *Nature Education.*

So far, Leidholt has identified some specific genes in fish that tend to be much more abundant in fish from captive settings versus those found in the wild. Could these genes be related to why PSS is only seen in fish in captivity? It’s likely – the genes identified are immune markers, and the upregulation of immune markers is well-known to be associated with chronic stress. Think about a college student during finals week – stress is high after a long semester, maybe they’ve been studying until late in the night and not eating or sleeping well, consuming more alcohol than is recommended. And then suddenly, on the day of the test, they’re stuck in bed with the flu or a cold. The same thing can happen to fish (well, maybe not the part where they take a test!,) especially in captivity – Pacific mackerel, tuna, and other scombrid species susceptible to PSS are fairly large, sometimes swimming hundreds of miles in a single day in the ocean. But in captivity, they are often in very small tanks, constantly swimming in constrained circles. They’re not exposed to the same diversity of other fish, plankton, prey, and landscape as they would be in the wild. “Captivity is a great place to be if you’re a pathogen, but not great if you’re a fish”, says Leidholt.

The results of Leidholt’s study are an exciting step forward in the field of PSS research, as one of the biggest challenges currently facing aquaculture farms and aquariums is that there is no way to screen for PSS in healthy fish before symptoms begin to show. Finding these marker genes that appear in fish that could later on develop PSS means that in the future a test could be developed. If vulnerable fish could be identified and removed from the population before they begin to show symptoms and spread the condition, then it would mean fish farmers no longer have to cull the entire tank when PSS is noticed.

The elusive virus

One of the challenges that remains is going beyond the identification of genes in the fish and beginning to identify viruses in the samples. Viruses, which are small entities made up of a DNA or RNA core and a protective protein coating, are thought to be the most abundant biological entities on the planet Earth – and the smallest in terms of size. They usually get a bit of a bad reputation due to their association with diseases in humans and other animals, but there are also viruses that play important positive roles in their ecosystems – bacteriophages, for example, are viruses that infect bacteria. In humans, bacteriophages can attack and invade pathogenic or antibiotic-resistance bacteria like E. coli or S. aureus (for more information on phages and how they are actually studied as a potential therapy for infections, check out this November 2021 interview with Miriam Lipton!) Across the entire planet there are estimated to be between 10^7 to 10^9 distinct viral species – that’s between 10 million and 10 billion different species. And fish are thought to host more viruses than any other vertebrate species. Because of technological advancements, these viral species have only really been identified very recently, and identification still poses a significant challenge.

As a group, viruses are very diverse, so one of the challenges is finding a reliable way to identify them in a given sample. For bacteria, researchers can use a marker gene called the 16S rRNA gene – this gene is found in every single bacterial cell, making it universal, but it also has a region of variability. This region of variability allows for identification of different strains of bacteria. “Nothing like 16S exists for viruses,” Leidholt says. “Intense sequencing methods have to be used to capture them in a given sample.” The metatranscriptomic methods that Leidholt is using should allow her to capture elusive viruses by taking a scorched earth approach – targeting and sequencing any little bit of RNA in the sample at all, and trying to match up that RNA to a virus.

To learn more about Savanah’s research on puffy snout syndrome, her journey to Oregon State, and the amazing outreach she’s doing with high school students in the Microbiology Department, tune in to Inspiration Dissemination on Sunday, November 20th at 7 PM Pacific!

Lean, Mean, Bioinformatics Machine

Machines take me by surprise with great frequency. – Alan Turing

This week we have a PhD student from the College of Engineering and advised by Dr. Maude David in Microbiology, Nima Azbijari, to discuss how he uses machine learning to better understand biology. Before we dig in to the research, let’s dig into what exactly machine learning is, and how it differs from artificial intelligence (AI). Both AI and machine learning learn patterns from data they are fed, but the difference is that AI is typically developed to be interacted with and make decisions in real time. If you’ve ever lost a game of chess to a computer, that was AI playing against you. But don’t worry, even the world’s champion at an even more complex game, Go, was beaten by AI. AI utilizes machine learning, but not all machine learning is AI. Kind of like how a square is a rectangle, but not all rectangles are squares. The goal of machine learning is to use data to improve at tasks using data it is fed.

So how exactly does a machine, one of the least biological things on this planet, help us understand biology?

Ten years ago it was big news that a computer was able to recognize images of cats, but now photo recognition is quite common. Similarly, Nima uses machine learning with large sets of genomic (genes/DNA), proteomic (proteins), and even gut microbiomic data (symbiotic microbes in the digestive track) to then see if the computer can predict varying patient outcomes. By using computational power, larger data sets and the relationships between the varying kinds of data can be analyzed more quickly. This is great for both understanding the biological world in which we live, and also for the potential future of patient care.

How exactly do you teach an old machine a new trick?

First, it’s important to note that he’s using a machine, not magic, and it can be massively time consuming (even for a computer) to do any kind of analysis on every element of a massive set. Potentially millions of computations, or even more. So to isolate only the data that matters, Nima uses graph neural networks to extrapolate the important pieces. Imagine if you had a data set about your home, and you counted both the number of windows and the number of blinds and found that they were the same. Then you might conclude that you only need to count windows, and that counting blinds doesn’t tell you anything new. The same idea works with reducing data into only the components that add meaning.

The phrase ‘neural network’ can invoke imagery of a massive computer-brain made of wires, but what does this neural network look like, exactly? The 1999 movie The Matrix borrowed its name from a mathematical object which contains columns and rows of data, much like the iconic green columns of data from the movie posters. These matrices are useful for storing and computing data sets since they can be arranged much like an excel sheet, with columns for each patient and rows for each type of recorded data. He (or the computer?) can then work with that matrix to develop this neural network graph. Then, the neural network determines which data is relevant and can also illustrate connections between the different pieces of data. Much like how you might be connected to friends, coworkers, and family on a social network, except in this case, each profile is a compound or molecule and the connections can be any kind of relationship, such as a common reaction between the pair. However, unlike a social network, no one cares how many degrees from Kevin Bacon they are. The goal here isn’t to connect one molecule to another but to instead identify unknown relationships. Perhaps that makes it more like 23 and Me than Facebook.

TLDR

Nima is using machine learning to discover previously unknown relationships between various kinds of human biological data such as genes and the gut microbiome. Now, that’s a machine you don’t need to rage against.

Excited to learn more about machine learning?
Us too. Be sure to listen live on Sunday November 13th at 7PM on 88.7FM, or download the podcast if you missed it. And if you want to stay up to date on Nima’s research, you can follow them on Twitter.

Heat, Hatchlings, and Sea Turtle Survival

A team of researchers makes its way across the beach on this dark night, lighting their way only with starlight and moonlight. It’s high tide on this small island off the coast of Brazil, and the kind of night when green sea turtles love to come ashore to nest. The turtles fall into a trance-like state after wandering around for hours and finally building their nests, and this is when the team approaches. They take a skin sample, place a temperature logger to measure the nest temperature, and tag the turtle with a nail polish marking for future identification. One member of the team is Vic Quennessen (she/they), the subject of our next episode. Vic is a PhD student in the Department of Fisheries, Wildlife, and Conservation Sciences. Quennessen is a computational researcher on the project but helping out on nights like these is part of the job. Vic’s team collaborates with Projeto TAMAR, a Brazilian nonprofit organization that works to preserve and conserve these endangered animals throughout Brazil since the 1980s.

Vic Quennessen releases their first hatchling!

Sea turtles have no sex chromosomes, and their sex is instead determined by the environmental temperature during incubation. Eggs subjected to higher temperatures are more likely to produce female hatchlings. The point at which the sex ratio of eggs approaches 50/50 is around 29 degrees Celsius, but at just one degree higher, some clutches of eggs produce as high as 90% female hatchlings. As temperatures rise due to climate change, this has resulted in a worrying oversupply of female hatchlings.

Sea turtles are difficult to study due to their long and mysterious life cycles. It is believed that they reach reproductive maturity after around twenty-five years, but only females are readily observed because they return to land to build their nests and lay eggs. In contrast, the males stay out at sea for their entire lives. This complicates any effort to ascertain the true population structure. Sea turtles also live a long time, so there is a lag between changes in the hatchling population and the overall population. Finally, hatchlings lack external reproductive organs or other visible sexual characteristics, so the sex ratios must be estimated using temperature as a surrogate.

Vic has always loved the ocean, and they came to OSU looking to help conserve resources that are threatened, such as fish stocks or sea turtles. While attending UMass Dartmouth for their undergraduate degree, they double majored in computational mathematics and marine biology. Initially these felt like separate interests, until a professor suggested that she apply to a NOAA workshop on marine resources and population dynamics. Here she learned that mathematical methods could be a part of rigorous modeling efforts in population biology. After a gap year dedicated to science education, Vic made her way to Oregon State for a Masters in Fisheries Science. Her advisor, Prof. Will White, persuaded her to stay on for a PhD with an opportunity to study her beloved sea turtles.

Sea turtles visit the beaches of more than eighty countries, but Vic’s fieldwork focuses on a population that nests on a small Brazilian island.

Quennessen’s research seeks to predict how the green sea turtle population will be affected by their looming sex imbalance. Vic uses data collected from over 3000 hatchlings per season, including nest temperature readings as well as the numbers of nesting females, hatchlings, and captured males. They build a mathematical model to explore possible scenarios for the “mating function”, the unknown relationship between the sex ratio and reproductive success. On the one hand it is easy to imagine that such a mismatch could reduce the number of mating pairs and lead to a rapid population decline. On the other, it is not well understood how many breeding males are required to sustain the population, and adaptations in mating behavior could slow the decline in population long enough for the more optimistic climate mitigation scenarios to take effect. In any case, it will take a lot of international cooperation to conserve these ancient marine creatures – green sea turtles nest on the shores of over 80 countries. Vic’s hope is that a mathematical exploration of this question could help reveal the chances of survival for the green sea turtles and possibly inform these conservation efforts.

To learn more about Vic’s research and their other interests, including science education and working with CGE, the graduate student union at OSU, tune in Sunday, Nov 6th at 7pm PST on KBVR 88.7 FM or online!

Missed the show? Don’t worry, you can download this episode via your podcast player of choice here.