Author Archives: Reyn Yoshioka

I’ve been searchin’ so long

To find an answer!

We’ve been continuing the search through our DEGs to fill out pathways and to investigate what the diseased stars were up to.  We’re looking at Toll and Complement pathways as components of the sea stars’ immune response.  Further, we’re trying to see what the transcriptome might be telling us about how the signs of the disease.  Echinoderms have mutable collagenous tissues, which they control with their nervous system to become softer or more rigid.  In this way, their nervous system has a very large role in their bodies’ structure and function.  The melted, twisty stars seem to imply a role of both the nervous system and connective tissue.  Disruptions in either could be influencing the changes we see in diseased stars.  It will be interesting to see what else we can see in our transcriptome, but it will likely take much more research to be sure what’s going on.

It’s taking its Toll


There’s been so much going on that I haven’t posted in TWO DAYS!  I hope I don’t flunk…

On Friday, we entered the lab to find that (YES!) our slides from our ISH treatment developed!  Unfortunately, it wasn’t too clear what was what on the slides, but Sarah et al are working on figuring that out.  Also, A HUGE shout-out to Monica and Ruth who successfully created a ISH probe for our putative sea star pathogen.  It’ll be really exciting to see what comes out of them in the coming week!

On Saturday, we really began the festivities with the EIMD Research Coordination Network (which we simply call RCN for brevity).  It was a great day full of talks about the research and approaches towards marine disease being done by some very incredible researchers.  I’m particularly excited to see the products of the working groups, who will be looking at forecasting disease outbreaks (cleverly named the “outbreak breakout”) and setting up diagnostic and policy pipelines to assist with future disease work.

All through this we’ve been working on our sea star transcriptome to figure out what the stars are doing as they’re getting ill.  Preliminarily, we’re seeing a huge immune response in our differentially expressed genes and enriched processes.  Also, we have an inkling that our transcriptome might be telling us quite a bit about the pathology of the disease: the twisty and melty nature of the disease might be due to changes in neurological and connective tissue function.  Of course it’s only speculation at this point, but it’s an exciting avenue to investigate.

Until tomorrow!

Wash & Buff(er)

Today was not much of a departure from yesterday.  After we left our slides hybridize overnight, we began this morning by washing them in a series of SSC (saline sodium citrate).  We then incubated the slides in blocking buffer, which prevents the antibody from targeting anything else other than our probe.  Next, we incubated the slides with our antibody that targets the DIG on our probes.  After letting them go for two hours, we added nitro blue tetrazolium and 5-Bromo-4-chloro-3-indolyl phosphate (or BCIP).  These are catalyzed by the phosphotase on the antibody to form a blue dye, which allows us to see where our probes hybridized.  Unfortunately, we didn’t see any development yet but we’re leaving it overnight to see if it works!

We also got our game plan down for tackling the sea star transcriptome!  We’re aiming to investigate different enriched processes and the differentially expressed genes in them to see whats up with the sick stars!

In Da Hood


A lot of our work today was in the hood for 1) sterility, and 2) safety.

Today we’re moving forward with the coral ISH probes we made yesterday.  After the sea star probes didn’t work out yesterday (twice), we decided to save those for another time.  Monica and Ruth are currently running PCR again with our two new primers to verify that there’s nothing wrong with the PCR in itself.

As for the ISH with the coral bacterial probes, we need to go through several steps to prepare histology slides for hybridization:

  1. Select slides!
  2. De-paraffinize slides with safeclear (a low-toxicity alternative to xylene)
  3. Rehydrate slide tissues with a descending series of ethanol: 100% EtOH (x2), 95%, 80% 70%, 50%
  4. Rinse in sterile H2O and equilibrate in Tris buffer
  5. Incubate in Proteinase K/Tris buffer (opens up tissues to allow our probe to bind to DNA)
  6. Incubate in prehybridization buffer (makes DNA more accepting of annealing to probes)

And then we’ll see tomorrow if they worked!


Today we continued to work on our ISH of both coral and sea star pathogens.  After seeing the banding we got from the PCRs we did yesterday, we selected the primers and samples (templates) that gave us the best bands for the next step.

We did another round of PCRs today to create the probes we’re using for the ISH.  For ISH, we used DNTPs labeled with DIG (Digoxigenin) to build probes of our PCR products.

After this, Sarah and Monica ran the gels to see that the DIG was incorporated into our products.  Sadly, the sea star probes didn’t work out (twice!), but the coral ones are promising!  We’ll be moving forward with those tomorrow.

Priming the ISH

I am really lacking creativity at the moment so I apologize for the pathetic title.

Today we had a full day planning our ISHs.  The first step was making sure that our primers worked, which was especially important considering we had several new primer sets to test out for a variety of DNA:  Three primers for our putative pathogen for Sea Star Wasting Disease and a suite more to try and figure out possible Rickettsia-Like Organisms (RLOs) in coral.  We tested this by running a large number of PCRs – five different cycles – to double check that our primers amplified our target DNA.  If they didn’t then we wouldn’t be able to move further in our ISHs, as there wouldn’t be the necessary hybridization.

While just running PCR might sound like an easy task, the planning, sheer number of reactions, and the multitude of people in the lab area made it take much longer than we had anticipated.  Fortunately, we were able to get it all done by dinner, after which Sarah and Ruth graciously started our electrophoresis gels.

Though I have yet to see the bands on the gel, preliminary reports (read: conversation in the hallway) from Sarah and Ruth give promising results!  After we see what happened tomorrow morning we should have a good idea of how we’re moving forward.

Until tomorrow!

GOing GOing GOne…

To be honest I’m not entirely sure what happened today.

We began our day in the computer lab looking at our transcriptome using Weighted Correlation Network Analysis (WGCNA), an R package that looks at patterns of co-expression to see possible groupings of similarly behaving genes.

The R script we used looks as follows:


# The following setting is important, do not omit.
options(stringsAsFactors = FALSE);
#Read in the female liver data set
SSData = read.csv("Phel_rnaseq_normalized_expression.csv");
# Take a quick look at what is in the data set:

datExpr0 =


names(datExpr0) = SSData$Contig;
rownames(datExpr0) = names(SSData)




gsg = goodSamplesGenes(datExpr0, verbose = 3);

powers = c(c(1:10), seq(from = 12, to=20, by=2))
# Call the network topology analysis function
sft = pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)
# Plot the results:
sizeGrWindow(9, 5)
par(mfrow = c(1,2));
cex1 = 0.9;
# Scale-free topology fit index as a function of the soft-thresholding power
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
main = paste("Scale independence"));
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
# this line corresponds to using an R^2 cut-off of h
# Mean connectivity as a function of the soft-thresholding power
plot(sft$fitIndices[,1], sft$fitIndices[,5],
xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")

net = blockwiseModules(datExpr, power = 10,
TOMType = "unsigned", minModuleSize = 9,
reassignThreshold = 0, mergeCutHeight = 0.25,
numericLabels = TRUE, pamRespectsDendro = FALSE,
saveTOMs = TRUE,
saveTOMFileBase = "femaleMouseTOM",
verbose = 3)

sizeGrWindow(12, 9)
# Convert labels to colors for plotting
mergedColors = labels2colors(net$colors)
# Plot the dendrogram and the module colors underneath
plotDendroAndColors(net$dendrograms[[1]], mergedColors[net$blockGenes[[1]]],
"Module colors",
dendroLabels = FALSE, hang = 0.03,
addGuide = TRUE, guideHang = 0.05)

This gives us a dendrogram of our transcriptome, grouping our genes into modules.


Try as I might, today’s attempts to do more with IPython were a bit pathetic…