If you’re still skeptical about barnacles, you must be a robot. C’mon, look at that thing! If ninjas lived in the deep sea, they would use these barnacles as their weapon. Case in point:

Barnacles live at hydrothermal vents all over the world, sometimes packed as densely as 1500 individuals per square meter. Currently, there are 13 described barnacles species across 4 taxonomic families. But morphology isn’t great at distinguishing species, so in recent years researchers have needed to rely on DNA sequences to untangle the relationships between deep-sea barnacle species.

In honor or Darwin’s birthday today (collectively known as #DarwinDay), the most appropriate marine biology homage is story about the evolution of deep-sea barnacles. Herrera et al. (2015) have a recent, really fantastic paper, in Molecular Ecology, where they used fancy genomics tools to ask:

• Do vent barnacles have a single evolutionary origin (e.g. did they all evolve from a common ancestor)?
• When and where did vent barnacles first evolve?
• Historically, how did vent barnacles spread (radiate) across the deep-sea?

Herrera et al. collected 94 barnacle specimens from 18 hydrothermal vents worldwide (it’s really hard to do deep-sea biology, so this is actually LOT of barnacles painstakingly collected with robotic claws). Next, they sequenced three genes from each individual (the mitochondrial cytochrome c oxidase 1 gene, the nuclear 28S rRNA gene, and the nuclear Histone H3 gene), and additionally got crazy amounts of whole-genome data from each barnacle using a technique called Restriction-site-associated DNA sequencing (RAD-seq).

<cue elevator music and montage of genomic data analysis, where a hacker-looking scientist sits in a dark room, furiously typing code and downing shots of espresso. Finally he/she builds evolutionary trees, glorious trees.>

The results of this study showed that, contrary to prior hypotheses, barnacles have colonized deep-sea hydrothermal vents at least twice in the course of their evolutionary history. This can be seen by the two distinct clades (red and yellow) recovered in Herrera et al.’s phylogenetic Tree O’Barnacles:

The largest group of vent barnacles (Clade A, the red clade above) seems to have originated in the Western Pacific Ocean and then moved east, colonizing “the Eastern Pacific, the Atlantic sector of the Southern Ocean and the Indian Ocean during the late Miocene to early Pliocene” (Herrera et al. 2015, using ancestral state reconstruction to analyze phylogenetic patterns). Once barnacles had adopted the hydrothermal vent lifestyle, it looks like they moved east. Based on molecular clock estimates using DNA sequences, the timing of their dispersal is concordant with geologic events such as the opening of the Drake Passage (41 million years ago).

Barnacle DNA also indicates that hydrothermal vent species arose fairly recently (well, in geologic time), emerging after a deep-sea mass extinction event during the Cretaceous– Paleogene period boundary. That boundary–65 million years ago–should be familiar. Deep-sea barnacles started their ascension as the dinosaurs were on their last breath.

We’ve only just begun dipping our toes into the world of deep-sea genomics. Given the time-machine-like powers of DNA sequences, and the fact that hydrothermal vents are essentially “islands” in the deep sea (thus giving us the perfect system to test some big evolutionary theories), the next few years should produce some really exciting deep-sea discoveries. Forget hoverboards: if Darwin came Back to the Future I’m sure he’d much rather have genomics.

Reference:

Herrera S, Watanabe H, Shank TM (2015) Evolutionary and biogeographical patterns of barnacles from deep-sea hydrothermal vents. Molecular Ecology, 24:673-689.

The post Deep-Sea Barnacle Genomics. Because, #DarwinDay first appeared on Deep Sea News.

Galindo LA, Puillandre N, Strong EE, Bouchet P (2014) Using microwaves to prepare gastropods for DNA barcoding. Molecular Ecology Resources, 14(4): 700-705.

This paper is so simple, yet so epic in so many ways:

We have experimented with a method traditionally used to clean shells that involves placing the living gastropods in a microwave (MW) oven; the electromagnetic radiation very quickly heats both the animal and the water trapped inside the shell, resulting in separation of the muscles that anchor the animal to the shell. Done properly, the body can be removed intact from the shell and the shell voucher is preserved undamaged.

To reiterate: these researchers put snails in the microwave and got a paper out of it. Now of course, this is actually a brilliant method – the scientists stumbled across this quick fix because they need to preserve BOTH the shell and DNA from their specimens. With such thick shells, preservatives can’t get into the tissue very easily, and other methods (boiling the snails alive! or using chemical relaxants to pull out the muscle) are time consuming, sloooowwwwww, and downright dangerous:

To some extent, [these methods] can also represent a hazard (electrical drill and boiling water) on an unstable research vessel at sea.

Microwaves can zap lots of animals quickly and keep all their DNA intact!

This paper also wins for the most unnecessary use of acronyms, shortening the terms for microwaves (MW) and microwave ovens (MWO). So in everyday life I guess we can now further reduce MWs to a hand signal, and just say that we’re going to heat up our coffee in the “Muah”.

The post Putting snails in the microwave…for science! first appeared on Deep Sea News.

Maybe if Red Bull funded marine research, we could send a skydiving human icebreaker, whose parachute doubles as an otter trawl and niskin bottle, to crash through polar seas in the winter and collect scientific samples. (Felix Baumgartner, CALL ME!)

No, marine research is funded by government agencies – they hold the dolla$ for the ships. Winter sampling-by-stuntman would be a little too risky for their tastes, and so research in polar regions by default has to happen in the summer (one of the reasons the Antarctic program was almost screwed by the government shutdown). My point? Our knowledge of polar regions is almost exclusively based on research done during ONE season. But according to a recent study, that seasonal bias is really messing with our understanding of biology.

In a badass new paper, Ladau et al. (2013) looked at diversity in bacterial communities around the globe, comparing patterns across seasons, and at the equator versus the poles. Although we generally think of the tropics as “biodiversity hotspots” for larger organisms, bacteria swimming in surface ocean waters are way to hipster to follow such mainstream diversity patterns.

Because scientists always sample high latitudes during summer months, previous data seemed to give the appearance of higher bacterial species diversity in tropical waters. But this isn’t actually true! The bacteria all go party at high latitudes in the WINTER – you know, that time of year when there are NO SCIENTISTS doing any sampling. Bacterial diversity shows huge, seasonal winter peaks (in December at temperate and high latitudes in the Northern Hemisphere, and in June at temperate latitudes in the Southern Hemisphere), making tropical biodiversity look  pretty LAME in comparison.

Ladau et al. used a modeling approach to compensate for the persistent sampling bias in time and location – they were able to extrapolate the predicted species distributions of bacteria based on real environmental datasets (rRNA genes sequences). They pummeled the data from every possible angle – changing models, changing parameters, subsampling their data, using other datasets, and even looking at error rates. Nothing could mess with their results – the predicted biodiversity patterns stood up to every kind of test.

Why does bacterial diversity show seasonal peaks during the wintertime in both hemispheres? We’re still not sure, but it might be due to vertical mixing which brings nutrients (and species?) to the surface. Another theory is that bacteria could migrate across latitudes. Models are no substitute old fashioned fieldwork, but they’re important for letting us look at biology from a different perspective. In this case, it shows we need to collect way more wintertime samples.

Reference:

Ladau J, Sharpton TJ, Finucane MM, Jospin G, Kembel SW, Dwyer JOA, et al. (2013) Global marine bacterial diversity peaks at high latitudes in winter. The ISME Journal, 7:1669–77.

The post Hipster bacteria hate the tropics (it’s too mainstream) first appeared on Deep Sea News.

Microbe species don’t fuck around. They’re everywhere. You just have to sequence lots of DNA to find them all.

Except…some deep sea species were *only* found in the Deep Sea…

For example, the marine cold seep biome contributed OTUs [Operational Taxonomic Units, a.k.a putative “species” defined solely from DNA] from the Halanaerobiaceae family. This family includes anaerobic, halophylic species, which have been found to be highly abundant in hypersaline brine pools such as those associated with cold seeps (19); this comparison suggests that a number of Halanaerobiaceae OTUs in the cold seep biome were not detected in the L4 [English Channel] site.

…the marine hydrothermal vent samples contributed members of the Campylobacterales not detected in the L4-DeepSeq [English Channel] sample. Campylobacterales is an order within the e-proteobacteria that includes both free-living and host-associated chemolithotrophs, such as those associated with tube-worms surrounding hydrothermal vents (22).

This study was only looking at bacteria and archaea – no DNA from multicelled microbes – and I’m not sure how intensively the deep sea ICoMM samples were sequenced. But I’m becoming more and more convinced that the Deep Sea is an untapped Candy Cane forest of genomes. So much endemic DNA for us to frolic and play with!

We won’t find these genomes roving around at the surface – marine biologists need to focus more on genomic technologies to sequence the deep.

Reference: Gibbons SM, Caporaso JG, Pirrung M, Field D, Knight R, Gilbert JA. (2013) Evidence for a persistent microbial seed bank throughout the global ocean. Proceedings of the National Academy of Sciences, USA, 110(12):4651–5.

The post Endemic Genomes? Reason #1 to sequence the Deep Sea first appeared on Deep Sea News.

Ken described the project (funded by the US National Science foundation) in a recent e-mail:

We left Punta Arenas Chile Jan 1st, 2013 and arrive into McMurdo Station, Antarctica Feb 7th. The purpose of this cruise is to study genetic patterns of biodiversity in the Bellingshausen, Amundsen and Ross Seas. These are some of the most remote waters on the planet. Given the rapidly changing environment in this region due to climate change, we also want to establish an understanding of where different species currently occur.

You can follow their cruise on Twitter (@Icy_Inverts_AU and  @CMU_Antarctica), and find more information at the websites listed below. Just remember guys, Cabin Fever and/or extended periods of sleeplessness DO NOT MIX WELL with Tweeting.

Blog/web pages:

Icy Inverts 2013 – Shipboard Blog

Icy Inverts 2013 – Project portal at Auburn University

Biology in Antarctica – Project portal at Central Michigan University

YouTube video describing the project:

Auburn University – Icy Inverts 2013 – sorry DSN readers, I couldn’t embed the video here because of the privacy settings :(

The post “Icy Inverts” 2013 Cruise – Scientific Adventures in Antarctic Waters first appeared on Deep Sea News.

Only…I don’t work with actual animals. I work with DNA sequences. I spent my PhD sitting under the microscope, where I vowed never again! Now I work with gigabyte-sized text files listing millions of gene sequences.

However! That will not stop be from pontificating on the Top 10 scariest species that I encounter in my daily life as a (computational) marine biologist. Be warned…you might not be able to sleep tonight.

5. “Environmental” species

Stop the presses. Big news. There are species. And they live in the environment. WHOA! This is news to me. I had no idea – someone needs to call the president! “Environmental species” are petrifying because you have no idea what they are. I mean I could have just sequenced Godzilla from my deep-sea mud (maybe I sampled next to his undersea lair?).

For anyone that works with DNA, it is common knowledge that database sequences are 100% trustworthy and researchers make every effort to put informative names on any data they submit. No scientist would be so cavalier as to dump thousands of unnamed sequences into GenBank. And GenBank certainly would never get clogged up with said type of sequences. So you see, I am still left scratching my head at my “environmental species” result.

4. “No Match” Species

These species have no good match to ANY DNA sequence listed in public databases. You should be terrified. What if we live in the Matrix, and DNA from these species has been conveniently erased from the reality construct? Or what if its Aliens?! The government might not want me to find a match for these bits of DNA.

You all know that we’ve done an amazing job counting and describing all the species that existed ever, so much so that scientists have now extensively characterized pretty much all the biodiversity on earth. The appearance of new species is practically unheard of. We’ve named all the worms and bacteria, and the thought of a new mammal species makes me roll over with laughter. Given our current state, I am left shaking in terror at my enigmatic “no match” DNA results.

3. Homo sapiens

Holy Crap, look at all that human DNA! There’s no ambiguity about what species I found, because this sequence has a 100% match to Homo sapiens. And it isn’t just one sequence. There are hundreds! This is frightening. What if my marine mud sample was collected on the EXACT SPOT where Jimmy Hoffa’s body is buried. All that human DNA I found could be evidence in a murder scene.

At least I can be sure that this is not routine contamination from the lab, because that never happens and all my data is 100% most certainly trustworthy. Should I call the police?

2. Command line error species

OMG I tried to process data and I discovered something I’ve NEVER SEEN BEFORE. And then I searched Google! The only person that has ever seen this before is Joe Programmer who asked about the SAME THING on a biology newsgroup back in 1996. But this thing I found is unknown in the scientific literature. Its like I stumbled upon a mythical creature that had been lurking amongst us all along. Soon the world will build statues!

I pressed a button and did everything the software tutorial said, so these results must be right. I’m the best computer user in the world! More so, all computer programs automatically know how to process our DNA data – because the biology is so simple!

My new species is scary because I don’t know if people will understand. What if they react badly to the news that I’ve just described use of uninitialized value $ncbi_name in concatenation (.) or string from the deep sea?

1. Tyrannosaurus rex

You heard me. Dinosaurs lurk amongst us. How do I know? Because the DNA TOLD ME SO. When processing my deep sea data, I found a sequence whose best match was to the T. rex sequence listed in GenBank. Either there are dinosaurs walking the earth (whereby I collected my sample in a fresh T. rex footprint), or T. rex DNA is so indestructible that it survived floating around in the dirt for millions of years. Either way I’m shaking in my boots.

There’s no way that that the information I got from GenBank is wrong. Because who would ever confuse DNA from common soil bacteria with DNA from a T-Rex fossil?! And there are three T. Rex protein sequences in the database. No one would mess that up multiple times. Which means I have to go find an underground bunker, because I have a sinking feeling that Jurassic Park was actually a documentary…

The post Top 5 scariest species…from, er, DNA? first appeared on Deep Sea News.

Descriptions of each melody from the Argonne Lab Website:

Blues for Elle: This composition highlights seasonal patterns in marine physical parameters at the L4 Station. The chords are generated from seasonal changes in photosynthetically active radiation. The melody of each measure is comprised of eight notes, each mapped to a physical environmental parameter, in the following order: temperature, soluble reactive phosphate, nitrate, nitrite, saline, silicate, and chlorophyll A concentrations.

Bloom: Some marine microbial taxa are most often present in the L4 Station community at very low abundance, but occasionally become highly dominant community members. To link these microbial blooms to relevant physical parameters, the chords in this composition are derived from changes in chlorophyll A concentrations and salinity. The melody for each measure is derived from the relative abundances of typically rare taxa that were observed to occasionally bloom to higher abundance in the following order: Cyanobacteria, Vibrionales, Opitulates, Pseudomondales, Rhizobiales, Bacillales, Oceanospirallales, and Sphingomonadales.

Far and Wide: Microbial species of the Order Rickettsiales, such as the highly abundant, free-living planktonic species Pelagibacter ubique, are typically, highly abundant taxa in L4 Station data. Its relative abundance in the microbial community at L4 Station follows a distinctive seasonal pattern. In this composition, there are two chords per measure, generated from photosynthetically active radiation measurements and temperature. The melody of each measure is six notes that describe the relative abundance of the Order Rickettsiales. The first note of each measure is from the relative abundance at a time point. The next five notes of a measure follow one of the following patterns: a continuous rise in pitch, a continuous drop in pitch, a rise then drop in pitch, or a drop then rise in pitch. These patterns are matched to the relative abundance of Rickettsiales at the given time point, relative to the previous and subsequent time points. The pattern of notes in a measure is mapped to the relative abundance of Rickettsiales with fewer rests per measure indicating higher abundance. For time points at which Rickettsiales was the most abundant microbial taxa, the corresponding measure is highlighted with a cymbal crash.

Fifty Degrees North, Four Degrees West: All of the data in this composition derives from twelve observed time points collected at monthly intervals at the L4 Station during 2007. The composition is composed of seven choruses. Each chorus has the same chord progression of 12 measures each in which chords are derived from monthly measures of temperature and chlorophyll A concentrations. The first and last chorus melodies are environmental parameter data as in ‘Blues for Elle’. The melody in each of the second through sixth chorus is generated from the relative abundances of one of the five most common microbial taxa: Rickettsiales, Rhodobacteriales, Flavobacteriales, Cyanobactera, and Pseudomondales. A different ‘instrument’ is used to represent each microbial taxon. Melodies for microbial taxa were generated as in ‘Far and Wide’.

The post TGIF: Some Friday jazz, courtesy of marine microbes first appeared on Deep Sea News.

But when I read Dr. M’s new (P ***ing NAS) paper last week, it hit me: I really should care. We all should. Its not about forcing to ourselves to waste time reading about topics or environments that aren’t relevant. All knowledge is essential–especially diverse knowledge, especially in today’s changing landscape of science.

How can energy limitation in the deep-sea be relevant to a parent trying to keep his/her kitchen germ-free? It’s all relevant, because in each case we’re trying to understand how complex communities function and change over time – regardless of whether a given ecosystem resides in the built environment or a natural setting.

Biology is heading towards integrated data – I study microbial genomics, but I should also be thinking about metabolism and temperature effects on the species I search for in environmental data. For us genomicists we’re so used to dealing with so little information. We just get (rather large) computer files listing lots and lots of DNA. The challenge for us is to relate those A’s, T’s, C’s and G’s back to something meaningful. But of course we don’t just want to look at DNA – we want a holistic understanding of ecosystem function. I want to know how one piece of DNA relates to a body size, how that body size relates to the type of food that pariticular species eats, and how all that interacts on a grand scale in an ecoysytem.

Often I feel like us genomicists have our hands tied – but we have a powerful tool in our pocket (that’s what she said) that can set us free in ways that no one else can experience. DNA is objective in ways that other types of data aren’t–taxonomy is subjective and decisions vary depending on the expert identifying a specimen. What if we could use parameters like temperature, depth and type of food input to predict what species will be there? Of course, that’s a lifelong obsession for researchers like Jack Gilbert (@gilbertjacka) and colleagues, and we’re steadily making progress towards these modeling goals. Some of the new microbial model papers are pretty badass.

To summarize the ongoing transformation in biology, I’ll bestow some eloquent foresight from Poole et al. 2012:

As researchers seek to go beyond function and understand the effects of global environmental changes on ecosystems [6], metagenomics will be essential. It has already helped to unlock the mechanisms for climate–carbon-cycle feedbacks [7] and, for simple microbial ecosystems, has illuminated the probable metabolic basis for key community interactions [8]. These examples underscore two crucial points. First, genomic knowledge is increasing the understanding of how simple organisms interact with their multicellular counterparts in an ecosystem context [9]. Second, the ability to zoom in on the functional roles of species within an ecological community [3] will make metagenomics indispensable for the future study of whole-ecosystem functioning.

And this data revolution isn’t simply relegated to basic research. In terms ecotoxicology and ecosystem monitoring, Van Aggelen et al 2010 note that:

Omic and bioinformatic tools offer sub­stantial promise for discovery of gene, pro­tein, and/or metabolite alterations indicative of the mode of action (MOA) of chemicals and improved understanding of mechanisms in prospective studies (Ankley et al. 2006). Knowing the MOA can reduce uncertain­ ties in chemical risk assessments, providing, for example, a basis for extrapolating effects across species (Benson and Di Giulio 2007).

Ideally, omics data would reflect both the MOA and deleterious outcome(s). To achieve this, the cascade of pathways asso­ciated with toxicity must be defined, from a molecular initiating event (e.g., receptor bind­ing) through subsequent biological alterations (reflected by omic and cellular changes) that culminate in a deleterious outcome (NRC 2007).

Thus, although gene expression is affected by many environmental factors, a subset of genes with altered expression can inform on stress responses.

The biology landscape (and earth’s climate) are changing and science must adapt. Scientific infrastructure and even researcher mindsets must change in order to accomodate a new order of thinking.

There is also a need to build capacity within academia, the private sector, and gov­ernment agencies to implement omic tools and to evaluate omics data, particularly with respect to biological and ecological significance. These institutions will require resources, support, and targeted training to bring scientists and deci­sion makers within their organizations to a point where these tools can be used effectively in regulatory decision making, especially in risk assessment. (Van Aggelen et al 2010)

The deep sea may face a perpetual energy recession, but in terms of scientific data we’re about to experience one hell of an economic boom.

References:

McClain CR, Allen AP, Tittensor DP, Rex MA. (2012) Energetics of life on the deep seafloor. Proc Natl Acad Sci USA. Advance Access

Poole AM, Stouffer DB, Tylianakis JM. (2012) “Ecosystomics”: ecology by sequencer. Trends in Ecology & Evolution, 27(6):309–10.

Van Aggelen G, Ankley GT, Baldwin WS, Bearden DW, Benson WH, Chipman JK, et al. (2010) Integrating omic technologies into aquatic ecological risk assessment and environmental monitoring: hurdles, achievements, and future outlook. Environ. Health Perspect. p. 1–5.

The post Capitalizing on recessions with economic booms of data first appeared on Deep Sea News.

This was devastating news for me. Evan was a bright kid–the kind of student everyone hopes to find in their lab. Even though he was only in high school, I’d suggest journal articles for him to read (often talking about some pretty complex computational biology). He’d come back, having read the articles front to back, asking a slew of deeply insightful questions. I remember giving him about 5 minutes of training for our lab’s PCR protocols, and then he smoothly took over and carried out the reactions like a pro. It was like mentoring a grad student or postdoc.

Evan was working with some of our high-throughput sequence data generated from deep sea sediments (the basis of our recent Molecular Ecology paper). As I was analyzing millions of DNA sequences and preparing our manuscript, I was interested in the overarching narrative: global patterns in biodiversity, identifying cosmopolitan species that might be present in both the Pacific and Atlantic Deep-sea. But with millions of DNA sequences you can literally ask millions of questions–each sequence has its own story.

With the broad focus of my work, things can get pretty frustrating: After processing and analyzing SO MANY sequences, our main result is basically a pie chart. Granted, a pie chart that no one has ever seen or even been able to generate before, but still a pie chart. I guess its kind of like baking up a new type of pie; pretty delicious, unless you’re really sick of eating pie.

So we end up slapping informative-ish names on most things. It’s a worm! This one is not a worm! This other thing is definitely an amoeba! These labels can be pretty informative…sometimes. Unfortunately there’s also always a huge (unnerving) chunk of DNA sequences that leave us shrugging our shoulders. To match DNA sequences with a species name, or even broadly lump it into a taxonomic group (and by proxy, gain an understanding of the morphology and ecological role that organism might fill in an environment), the most common approach is to compare sequence similarities between known and unknown sequences. You’re lining up two sequences, letter by letter, to see what bases match and what bases don’t. We can paste an unidentified environmental sequence into the BLAST (Basic Local Alignment Search Tool) tool at NCBI (the government-run public repository for biological sequences) and get a result that looks something like this:

Because the sequences seem to match well–the alignment is perfect–we can be pretty confident that our DNA sequence is a close relative. With this sequence, we have a 100% match to a Polychaete worm species, Aurospio dibranchiata. Unfortunately, with unknown environments we more often get results that look like this:

The percent of nucleotides that line up with the database sequence isn’t as high as we’d like (only 89%), so even though the the two sequences seem similar, there are a lot of non-matching positions in the alignment, and the BLAST scores aren’t high enough for us to automatically accept the taxonomy (for good matches, we basically copy-and-paste the taxonomy from the database sequence onto our unknown sequence). In our high-throuhput data analysis, we tend to use a 90% sequence similarity score as a minimum cutoff for accepting the taxonomy of the top-scoring BLAST result; the second sequence in particular doesn’t meet that cutoff–so, we label this as having “no match”. Before you call the Nobel Prize committee, let me just clarify: it’s not that we found some weird crazy new species in our data. This result just means that our unnamed environmental sequences doesn’t have a good match in public databases–probably because no one has generated a DNA sequence from a species that is closely related to this unidentified deep-sea creature. If we look closer, we’d find that most of these sequences seem to fit within known groups of organisms, albeit representing divergent (previously undiscovered) branches on the Tree of Life. The taxonomy from the “badly matched” sequence seems to indicate that the DNA came from a nematode, but the statistical score is too low for us to be confident about this assignment.

In another scenario, it’s quite common to have uninformative taxonomic assignments tacked on to your query sequences, because researchers who deposit data into public sequence databases like NCBI don’t always put useful labels on their DNA. Someone, somewhere might have sequenced the SAME species–they found a DNA sequence that looks exactly like your sequence, with the BLAST sequence similarity of 100%–but if the researcher didn’t label the sequence with any useful taxonomy (like “nematode” or “paramecium”), then we can’t link an informative name with the DNA. This situation is frustratingly ubiquitous–there are far too many datasets where everything is labelled as “unclassified environmental sequence”. Researchers who went into an environment and sequenced a bunch of DNA, but weren’t interested in (or didn’t have the capacity for) attaching taxonomic names to the sequences they generated. An example from our dataset, where the label of “uncultured eukaryote” is absolutely useless:

So all of this brings me back to Evan. For his summer lab project, we wanted to delve deeper into the world of enigmatic deep-sea sequences that were labelled as “no match” or “unclassified environmental sequence”. Since high-throughput sequencing technologies only return short DNA barcodes (100 to 400 bases long), we thought that perhaps we could generate gene-length sequences (~1000 bases long) that would be more informative for studying molecular evolution, and inferring the relationship between these divergent sequences and known taxa in public databases. We could take this approach because our baseline dataset was generated LIKE A BOSS. In our deep-sea study, we simultaneously amplified two different DNA barcodes from our environmental pool, located at either end of the 18S rRNA gene:

Evan was pulling out sequences from both ends of the gene labelled as “no match” or “environmental sequence”, and trying to match up sequences exhibiting similar overall proportions (=relative abundances) at both loci. He’d look a bit deeper into the BLAST results (pouring through a list of ~50 close relatives in the database, instead of just looking at the top-scoring hit) and postulate the Phylum each sequence belonged to. We then paired up “matching” sequences from either end of the gene, hoping that similar taxonomy and abundances meant we had generated two DNA barcodes from a single species. The next step was to design sets of PCR primers that were highly specific to these environmental sequences; using customized primers designed to bind either end of the gene, we wanted to amplify the 18S rRNA gene from *one* species, starting from the complex genomic mixture of deep-sea environmental DNA. This was a crazy idea. We had no idea if it would work. We had no idea if we were even matching up sequences from the same species.

Evan got reeeeeeealllllyyyy good at doing PCR. Since we were taking a shot in the dark, we wanted to exhaust all our options before giving up completely. Every forward primer had to be tested with every reverse primer (and vice versa) to make sure we hadn’t mis-matched sequences from the “same” species. And of course designing PCR primers is notoriously difficult anyways, so even if we were matching up things correctly our awesome idea might be complete rubbish anyway, for chemical and logistical reasons. Evan became a PCR reaction mixologist and a connoisseur of agarose gels.

The moral of this story: sometimes the ambitious project you give your undergrad or high school student is miraculously successful. We got positive results. Full-length 18S rRNA sequences. AND we could use these to build a badass tree. So now, drumroll please…here’s some unpublished data for you guys. The blue sequences in the figure below are our reference sequences, pulled down from public databases. The red sequences are the full-length 18S rRNA genes Evan skillfully managed to amplify from our messy pool of deep-sea DNA.

So what does this mean, biologically? Well for one, an extremely talented student got some serious training in cutting-edge research. Secondly, it seems we were able to isolate DNA from a really cool group of species from deep-sea mud, identifying a divergent clade that has never before been sequenced.

Once we placed our gene sequences onto the Tree of Life, we could see that our red sequences (labelled “Contig”) fell within a group of eukaryotes known as the Labyrinthulids. This is a supercool group of protists known as slime nets (people used to think these things were fungi)–the main cell body glides along the sticky, filamentous strings as it uses these extensions to slurp up nutrients.

During this project, Evan learned the importance of tree-based (phylogenetic) approaches, and the pitfalls of taxonomic assignments that rely on BLAST-based sequence similarity alone. As the field of high-throughput sequencing progresses, the outlook is slowly getting better for putting names on millions of environmental DNA sequences. We’re now moving towards (more robust) phylogenetic approaches to assign taxonomy to unknown sequences, where short sequences reads are placed onto a reference guide tree constructed out of full-length gene sequences. Short reads are “tested” and scored at every position in the tree, and inserted into the tree at the best-scoring position. Little by little, tree-based methods such as these are slowly reducing our reliance on BLAST comparisons and their inherent limitations.

Hearing the news about Evan has made me think a lot about science. The beach where Evan took his final swim was probably littered with some of the very enigmatic taxa that he had been seeking in our datasets. This post is a tribute to Evan: his research in New Hampshire, his memory, and his unrealized potential. I’m so grateful for the opportunity to work with such a talented young man, and extremely saddened by his loss. My thoughts go out to his family, friends, and fellow students. Rest in peace, Evan.

The post Beaches, Trees, and Mysterious Species : A tribute to Evan first appeared on Deep Sea News.

My opinions are similarly skewed across the Tree of Life. We all know I love nematodes. Protists are really weird, and therefore really cool. Algae can hold my interest. But I really can’t stand plants. I feel so guilty about this, because I’ve learned so many awesome things about plants over the years. Every time I learn a new fact, I reevaluate my reasons for shunning leafy species and commit to do some more reading. And then I get bored. I guess its kind of like reading unabridged Victor Hugo–you know you really should sit down and read classics like Les Miserables and The Hunchback of Notre Dame, but trying to persevere through Hugo’s hundred-page architectural descriptions is just so…tedious.

At my inaugural Eisen lab meeting in UC Davis last month, I had an epiphany: I FINALLY found a plant I liked

Seagrasses are unabashedly awesome.

These marine species are kind of like the nematodes of the plant kingdom: they might not be the most well known group, but they can be used as model organisms to investigate the Big Questions in biology and evolution.

Seagrasses are also the whales of the plant kingdom – extant aquatic species came from terrestrial ancestors that, over time, walked back into the sea (since before that, we think all life originally spawned from a primordial ocean). Like nematodes, it wasn’t just one hipster seagrass group that decided continental life was “too mainstream”. Re-entering marine habitats happened independently in at least three separate branches on the seagrass tree of life.

Moving from the land to the sea doesn’t just happen. If it did, we’d all be Aquaman. But nope, Aquaman’s abilities stem from the fact that he is some weird genetic mutant (Sidenote: The whole story of Aquaman presents some serious ethical concerns, since he appears to be the by-product human experimentation from his own FATHER: “.. By training and a hundred scientific secrets, I became what you see — a human being who lives and thrives under the water“. I don’t even want to know what kind of lab chemicals he was cajoled into ingesting).

In seagrasses, gene changes must also have occurred for these plans to live in marine environments. Think about it – even if you kept your head above water (breathing oxygen), you couldn’t live life with your whole body submerged in the ocean. Your skin cells are dependent on exposure to air, and I imagine that briny bath would get pretty painful after a while.

Water bends light. And seagrasses are one plant group that needs a lot of light. Species must deal with the lower intensity of underwater light, as well as the shift in proportions of different wavelengths that penetrate the ocean surface.

The sea is also salty. At the level of cells and tissues, a huge array of basic molecular processes are controlled by the flow of sodium and potassium ions across membranes. Certain ions like potassium are also a critical ingredient for enzymatic reactions governing protein synthesis and ribosome function. For non-adapted species, seawater can be a poisonous broth, fatally disrupting these basic cellular processes. Marine species must possess specific adaptations to grow and thrive amongst high environmental levels of salt.

Surprise #3, the ocean has waves. Flimsy grasses must be able to hold their ground. Tides and currents also impact reproduction (you don’t want all your gametes to float away) and photosynthesis (a reduced availability of carbon dioxoide).

So life in the sea required seagrasses to address some serious issues that would otherwise be very detrimental to essential biological processes. Remember that three different groups of seagrasses did this on three separate occasions – for us modern day scientists, this has provided a seriously elegant look into the exact genetic modifications that are critical for adapting to life in the ocean.

Strikingly, despite their independent evolutionary routes, seagrasses from the three different lineages have evolved many similar morphologies, life history strategies, and breeding systems [3,18]. This indicates that the aquatic habitat imposes novel selection forces that can lead to parallel evolution. [Wissler et al. 2011]

Which is pretty awesome.

In a recent study, Wisslet et. al (2011) wanted to take things to a whole other level and look for candidate genes that helped seagrasses survive life in a marine environment. They focused on identifying adaptive mutations in conserved (orthologous) protein-coding genes also present within the genomes of their terrestrial plant relatives. [Although it was not applied in this study, environmental adaptation can also be studied in the context of changes to gene expression, e.g. the same set of genes used in different ways across tissues and life stages]

Reverting to a marine life showed a noticeable impact on 51 seagrass genes. Note that these kind of comparative studies are restricted to genes that share a common ancestry; in this case, the seagrass study only looked at 189 gene clusters, equivalent to glimpsing at only ~1% of an entire genome.

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Now the results of this study aren’t particularly surprising–the pathways for marine adaptation are pretty logical–but this study perfectly showcases the POWER OF GENOMICS (please invoke Darth Vader voice there).

…photosynthesis, a few metabolic pathways, and ribosomes have strongly diverged after the split of the common ancestor of seagrasses from terrestrial monocots. Further studies will need to address the following questions: (1) How seagrasses have acquired osmoregulatory capacity to tolerate high salinities, (2) how CO2 is fixated, (3) how their photosynthetic apparatus has evolved for under water light harvesting, and (4) under what conditions anaerobiosis takes place. [Wissler et al. 2011]

The (comparatively) harsh marine environment, coupled with the action of natural selection, has caused seagrass genomes to diverge noticeably from their terrestrial ancestors. By promoting the survival of individuals that thrive in salt water, natural selection has, over time, tinkered with the cellular machinery for nutrient production and ribosome function. Additional tweaking has allowed seagrasses to deal with the different availability of gasses in the environment–for example, low oxygen availability in marine sediments (e.g. that disgusting pond muck smell if you start digging in an estuary) means that seagrasses can uniquely switch to fermentation instead of aerobic respiration if needed.

Seagrasses’ ancestors may have been a sputtering old tractor when they first tried to live in the sea, but modern-day species have evolved into a fine-tuned Lotus Exige. A beautiful, glorious machine that was contstructed on three separate occasions.

Reference: 
Wissler et al. (2011) Back to the sea twice: identifying candidate plant genes for molecular evolution to marine life. BMC Evolutionary Biology 11:8 http://www.biomedcentral.com/1471-2148/11/8

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