Monday, July 28, 2014

How sweet it is: metatranscriptomics of the bacterial community within the honey bee gut

My student, Fredrick ("Freddy") Lee, published his first research paper in Environmental Microbiology this month.  Our lab's been focusing a lot recently on the honey bee microbiome and metabolic function of the community in situ.  In this manuscript (titled: Saccharide breakdown and fermentation by the honey bee gut microbiome), we explore the capabilities of the honey bee gut community using RNA sequencing.  We also tested the effect of some commonly used enrichment protocols (the MICROBEnrich kit from life technologies) in the pipeline Freddy used to process the bee samples.  Using three individual bees, he extracted total RNA and also used the aforementioned kit.  We made 100 cycle PE libraries for an Illumina GA IIx.  Here are the highlights:

Major bacterial phyla and classes identified using 16s rRNA gene mining

We used blast to map high quality short reads to our well known honey bee taxonomy and found that, like our previous studies, these transcriptomes are dominated by three major phyla (the Proteos, the Firmicutes, and the Actinobacteria).  The major classes in our dataset were gamma-proteo, Bacilli and Actinobacteria (see Figure below).


If you're interested in "deeper" taxonomic classification, let me point out that short reads are not the best tool for this, as depending on the region they encompass, they can provide taxonomic resolution or not.  Given that, here you can see the effect of using the MICROBEnrich kit on one of our samples (bee #2) - yay - more depth!



Predicted metabolic pathways utilized by the honey bee microbiome in vivo 

Now for the meat of the study: what exactly are those honey bee microbes doing?? The major signal we identified was carbohydrate utilization and within that, the major contributors were our three dominant classes of bacteria.  



As there was already a metagenome published for the honey bee gut, we took advantage of that dataset and used it in combination with the metatranscriptome to put together a genomic + transcriptomic view of metabolism performed by the major bacterial classes.  Interestingly, the majority of the pathways were corroborated by each dataset, even though they were collected from bees in different parts of the country, at different times, and likely different ages.   



Oh, and in case you were wondering how our dataset mapped to the metagenomic one, here's a nice figure.  Essentially, you can find blast hits between our contigs and the published metagenome, but due to variation in microbiome composition and the divergence of these bacterial taxa, we see a very large range of percent identities.


Support for the -omic predictions using community level profiling

One nice aspect to this work is that we went on to validate the -omic data using other kinds of data, in this case, community level profiling.  Freddy went on to use BioLog Ecoplates to find evidence that the honey bee gut community could utilize the substrates predicted based on the metagenomic and metatranscriptomic data.  In this assay, each well of a 96-well plate contains a single substrate.  You essentially inoculate each well with your environmental sample (a mix of microbial members) and look for a color change as the tetrazolium salt in the well is reduced.  Freddy incubated his plates at 37C under anaerobic conditions (we saw no color change for aerobic conditions).  He did this for 10 individual bee guts and saw a surprising amount of variability bee to bee.



It's easy to overinterpret this kind of variability -- keep in mind that the biolog plates rely on viability of the microbe under those conditions, on serial dilutions from the environmental sample (which introduces variability) and just because you don't see a color change, doesn't mean the community isn't capable of that processing.  However, overall, we found that the honey bee microbiome was capable of utilizing almost all the sugars provided, some of the amino acids, and other carbohydrate compounds.  This may not be that surprising to some, as the honey bee diet is composed of sugars (such as those found in nectaries) and pollen.  In future, we hope to identify which bacterial clades are responsible for each of these metabolic capabilities and which products of metabolism each produces.  Understanding how these bacteria interact through co-metabolism of honey bee food will be critical to our understanding of how the microbiome, and dysbiosis of the microbiome, contribute to honey bee health.

<you can check out the official press release here>


Wednesday, April 16, 2014

Wolbachia Variants Induce Differential Protection to Viruses in Drosophila melanogaster

A truly awesome paper on Wolbachia, variation in the pathogen-blocking phenotype, and genetics was published a while back by Luis Teixeira's group.  I've been eager to write a blog post on this particular paper, one of my favorite papers from 2013, so here goes!  
<Follow along with the paper here.>

You are reading this blog, so maybe you don't need convincing that Wolbachia are totally awesome, relevant, and interesting bacteria.  They infect about half of the insect species on the planet and do so by targeting the germ line: that's right folks, these babies come pre-loaded with their bacterial symbiont.  Recently, Wolbachia have become more medically relevant because folks (including Texieira himself with Michael Ashburner) found out that they protect their insect hosts from virus infection -- either by reducing the load that the host carries (resistance) or by preventing disease even if the virus replicates (tolerance).  However, different Wolbachia strains vary in their ability to pathogen block.

In Chrostek et al., (2013), the authors attempt to figure out what's behind this variability -- why do some Wolbachia strains protect their hosts better than others? Is this variability consistent across virus strains?  These are relevant questions to ask because 1) it may reveal the underlying biology of Wolbachia-host interaction and 2) it may lead us to different Wolbachia strains that could be utilized in pathogen blocking. 

The authors start by sequencing some variants of Wolbachia (they also use previously published whole genome data from Casey Bergman's group) and by figuring out if these variants differ in their ability to protect the host from Drosophila C virus.  Just because I'm a big fan of BIG trees - here's the phylogeny resulting from their genomic analysis.
Figure 1

Interestingly, the variants do differ. In Figure 2A (below), you can see that each of the strains provide differing degrees of protection, with the uninfected line (iso) dying quite shortly after infection.  They also repeated this experiment with Flock house virus with similar trends (see Figure 3).

Figure 2 from Chrostek et al., 2013

Next, they look at the density of Wolbachia variants within the same host background.  Interestingly, they found that wMelCS variants exist at MUCH higher titers than wMel variants.  In Figure 4 below, you can see the results of qPCR on Wolbachia genomic DNA from flies that are 3-4 or 6-7 days old.  Strikingly, you see a huge upward trend in wMelCS infection as the flies age (Figure 4D), but no so much for wMel.  Interestingly, some of these wMelCS variants reduce host lifespan!

Figure 4 from Chrostek et al., 2013


So...there must be some difference in these Wolbachia strains.  Chrostek et al were quite careful in their crosses -- they removed confounding variables such as the non-Wolbachia microbiome and  host genetic background.   So, they look at potential genomic differences in these strains -- remember, they sequenced the genomes to characterize their Wolbachia into the different variant clades.  They present a very large table of SNPs in the pairwise comparisons ... some interesting looking genes with ankyrin repeat domains...potentially cool stuff for future work from this group.  

BUT! The rub is that none of these indels are found in wMelPop, the variant that protects BEST against viruses and infects at highest titer (is the most pathogenic to the fly).  So...what else could be different? What is driving the titer differences in these variants?

Could be copy number variation be driving the difference between the strains?  Indeed so! The authors identify a region in wMelPop, containing 8 genes, that is elevated in copy number (between ~2-8x) compared to the other wMel variants. In a brilliant stroke of creativity (or as a result of a potential late night pub crawl), the authors name this region the "Octomom" region (see below):


Figure 7: So called "Octomom" region increase in coverage in the wMelPop genome (A) and qPCR based amplification in wMelPop vs other variants (B)

This region has some interesting genes in it, some of which are phage related, some of which have homologs to mosquitoes! Although we don't know what these genes are doing, these proteins could be of interest for those researchers interested in Wolbachia pathogenesis.  

< Oh, and also, this entire body of work is all in the context of the global replacement of certain Wolbachia strains in Drosophila melanogaster, as it turns out. >

Tuesday, February 11, 2014

Tunes for the post-reviewer blues

Whether you are reading a review of a grant application or that infamous "reviewer #3's" response to your manuscript, rejection can be tough to handle.  Listening to music may be able to help you cope with this stressful event <see research on this phenomenon here>, perhaps enough to get you past this submission cycle with enough cojones for the next.  Here are some suggestions, take 'em or leave 'em:

 Phase 1: Denial and depression - no way, it can't be

 "Better Man" by Pearl Jam.
Can't find a better man. You're stuck in this.

 "Black" by Pearl Jam.
Just so obviously appropriate.  Although maybe love song for NIH/NSF. "Yeah.  I know someday you'll have a beautiful life . I know you'll be a star. In somebody else's sky. But why. Why. Why can't it be. Why can't it be mine."  Why can't that $$ be mine...


"Mr. Self Destruct" by Nine Inch Nails
I bring you the 90's Trent Reznor, sigh, what a dreamboat "I take you where you want to go. I give you all you need to know. I drag you down I use you up Mr. Self-destruct"

"Hurt" by Nine Inch Nails
 Again, Trent Reznor. "I hurt myself today..."doesn't the review process hurt everyone, friends?

"One Step Closer" by Linkin Park
"Everything you say to me takes me one step closer to the edge...and I'm about to break". There's some wisdom in that...develop a thick skin early for the review process.

Phase 2: Anger - what the f**k do they know, anyway?
  
"F**k You" by Cee Lo Green
This song should be a classic. And although this is a song about personal rejection, I think that the chorus itself resonates: "I've got some news for you.  Ooh, I really hate your a** right now"


"The Way I Am" by Eminem
"I am not Mr. Friendly" and "No patience is in me." This how you feel?

"Karma Police" by Radiohead
 "This is what you get when you mess with us".  If only it were an open review...

Phase 3: Empowerment (or self-delusion) -- you ARE the best...you just don't have a grant..or that pub..YET

"Not Afraid" by Eminem
"We'll walk this road together, through the storm, whatever weather, cold or warm. Just letting you know that you're not alone"..that's for damn sure.  With pay lines as meager as they are, you are certainly part of a big club!

"Ella" by Bebe
Obviously, you need to learn Spanish. This song is worth it.  "Hoy vas a descubrir que el mundo es solo para ti, que nadie puede hacerte dano"

"Part of me" by Katy Perry
The pheonix rises from the ashes. "You chewed me up and spit me out, like I was poison in your mouth."

"Firework" by Katy Perry
No one does a fresh start like Katy. "Come on show them what you're worth!"

"I gotta feeling" by Black Eyed Peas
If this song doesn't get you to lift your a** back up and into that bench/desk, nothing will.

Tuesday, January 28, 2014

What's the DEal? Differential Expression using RSEM

We've been looking for ways to analyze transcriptomes correctly, with sufficient power, not too many type I and II errors, and not much fuss.  For those relatively unfamiliar with performing differential expression analyses on RNA-seq data, a great review of the statistical methods employed to analyze these data can be found here.

What it all comes down to is the fundamental problem associated with RNA seq experiments -- the absence of a single transcript could be due to down regulation OR, could be due to the up regulation of ANOTHER gene.  That's right, what you are measuring are RELATIVE expression levels, and given libraries of the same size, you cannot accurately distinguish the first scenario from the second unless you've spiked the libraries with some standards of known quantity (which, interestingly enough, has been done before with success by Mary Ann Moran's group here).

From Mary Ann Moran's paper on the subject, we have this very nice depiction of the problems associated with sampling depth, relative number of reads, etc.:

It is very difficult to distinguish between samples 1 and 2 unless you can take into consideration library size or know, using internal standards how number of reads translates to number of copies.  Even if you use internal standards, it should be noted that RNAs have varying half-lives due to their own specific secondary structures, potential protective modifications, etc. Therefore, there will always be some stochasticity associated with the sampling that will reverberate in your final counts of reads.

I have been playing with the program RSEM to calculate both FPKM (fragments per killobase per million mapped reads, = [# of fragments]/[length of transcript in kilo base]/[million mapped reads]) and TPM (transcripts per million mapped reads) values.  In the RSEM publication, the authors convincingly argue that the TPM metric is a much better way of comparing between libraries -- much better than RPKM (or FPKM) alone.  The reason? Libraries are not all of the same size and it is necessarily the case that an increase in expression of any particular gene in one library will lead to the exclusion of other genes. Also, RSEM uses a statistical model to take into account the uncertainty associated with read mapping - especially in transcriptomics where multiple isoforms exist.  Oooh... also, RSEM doesn't require a reference genome -- awesome!

 RSEM's output provides both FPKM values as well as the TPM values, an estimated fraction of ttranscripts made up by a given gene.  I was curious to know how each of these measures would perform on environmental data -- one would assume that they would be correlated! I used RSEM (-rsem-calculate-expression –calc-ci –paired-end) on a set of illumina libraries and found...



that FPKM and TPM values are amazingly well correlated; within a library, sorting by FPKMs or TPMs will give you the same result.  But, what happens when you compare between libraries? Same answer. At least in the data I used, comparing two libraries using FPKM or TPMs results in the same answer with regards to differential expression. That said, I rest easier knowing that for the TPM values generated RSEM also provides 95% confidence intervals, helping me to better assess statistical differences between libraries.

In all that spare time you have, you can compare Edge-R's gene-specific bayesian modeling  found here to RSEM, the statistical software I'll be exploring today found here.
Oh, and here's another nice review, http://www.biomedcentral.com/content/pdf/gb-2010-11-12-220.pdf

Monday, January 20, 2014

How does an obligately intracellular symbiont maintain genetic diversity? The Wolbachia story

I recently had the pleasure of finally sitting down to read some publications (both open access!) on my favorite bacterium, Wolbachia pipientis.  These recent pubs interested me because they focused on the population genetics of Wolbachia within individual hosts, upon host transfer, and after many generations.  The BIG question that comes out of this body of work, in my mind, is how are low-titer strains in the maternally transmitted population maintained!  (We can discuss ongoing hypotheses at the end of this post)

The first paper I'll tackle (Schneider et al) asks if Wolbachia strains exist as diverse quasi-species within a host and reveals that diversity using host transfer techniques.  In "Uncovering Wolbachia Diversity upon Artificial Host Transfer" by Schneider et al., the authors use the cherry fruit fly Wolbachia (wCer strains) as the inoculum for injection of two new hosts: Drosophila simulans or Ceratitis capitata.  For those unfamiliar with the technique, what it comes down to is harvesting many many embryos from your D. simulans, using differential centrifugation techniques to concentrate the Wolbachia fraction and using that, as you would in microinjection of a construct to make transgenic flies.

The cool thing about this paper is that they see cryptic polymorphisms rise after host transfer.   They looked at 150 generations after microinjection and saw a low titer variant increase in frequency such that it was detectable via PCR.  Now, the data in this paper is entirely PCR based -- they sequenced amplifed fragments and used them to detect SNPs.  That said, if found to be true, it suggests that the host and symbiont evolve really rapidly and that Wolbachia maintains diversity, even under conditions when it should be primarily maternally transmitted (lab stocks).

The second paper I'm highlighting (Symula et al., 2013) investigated the diversity of Wolbachia in tsetse fly populations and correlated Wolbachia haplotypes with specific host mtDNA haplotypes.  Their result = LOTS of Wolbachia diversity and evidence that these infections happened independently, multiple times.  The authors collected tsetse flies across a region in Africa and did an analysis of the Wolbachia MLST genes and groEL - they also looked at host mtDNA haplotypes. Again, they used PCR amplification and sequencing but were VERY conservative in their sequence post-processing (removing all recombinants, for example).  So, the data they present are potentially a lower bound estimate of Wolbachia diversity.  The number of haplotypes found within each host was astounding (see Table 1).  In some cases, 6 different haplotypes found within just 2 hosts!

Mechanisms for maintaining genetic diversity in a maternally transmitted symbiont? 

1) Bend the rules:
During my doctoral work, a lab member discovered that there was cryptic diversity within the maternally transmitted endosymbionts of the deep sea clams. In that work, they discovered that a low frequency (0.02) symbiont haplotype existed in a population of clams that were geographically localized.  It was hypothesized there that the trick to maintaining diversity in this maternally transmitted symbiont was to basically bend the rules: occassionally, transmit your symbiont horizontally.  Since we find evidence of horizontal transmission in Wolbachia, this is one mechanism that genetic diversity could be maintained in the population.

2) Increase mutation rates:
It would be theoretically possible for an endosymbiont to have such rapid rates of mutation that individual populations within a single host would exhibit variability detectable by the methods employed by Schneider et al. and Symula et al.  Evolutionary rates are elevated in endosymbionts, so this is a potential source of new genetic diversity for Wolbachia.

It will be quite interesting to see which (or if both?) of these scenarios play a role in Wolbachia genomic evolution.  These changes in symbiont population dynamics and densities could potentially allow Wolbachia to colonize new hosts, potentially acting as a quasi-species (as seen in virus systems).


Sunday, November 10, 2013

Does your inner scientist wear heels or a beard?

What should a scientist look like?

A pair of articles was published this morning in our local paper (the Herald Times).  In it are details of the many inequities faced by female scientists at Indiana University.  In those articles, some startling statistics were published, including the percentage of female faculty in the sciences at IU (~10%) and directly relevant to me, the inequity in salary (in Biology, the seven female full professors make ~$131,000 while the 22 male full professors make ~161,000).  In addition, there were some poignant anecdotes from female professors: "I never had children" said one, "I or my partner. We never had time we could take out of our careers without feeling like we might lose what we had gained."   It's nigh time to address the root of this insidious problem.  It cannot be fixed by hiring more women -- there are few of us to begin with and additionally, we all agree we want to hire both the best and most diverse faculty (so does Harvard, Stanford, Yale...it's hard to compete for those few).  In addition, this problem cannot be fixed by simply providing maternity leave.  Family leave should be provided to faculty, male or female, but even if it is provided, you need to feel you are supported in taking leave by your department.  In order to overcome the gender gap in the sciences we need to provide work-life friendly policies but ALSO change everyone's perceptions as to what a scientist looks like, does with their spare time, or wears to the lab/office.  That goes for both the folks currently occupying upper ranks in the academy and the would be scientists themselves (altering a young girl's depiction of a scientist to include someone that would look like her).

 I'm a relatively young, hispanic, female scientist.  Barbie "I can be computer engineer" doll aside, I'm certainly not anyone's stereotype of a biologist or computer scientist (indeed, a well meaning, but perhaps socially maladroit coworker once told me that I "don't look like a scientist").  Since so very few women enter science, and computer science in specific, perhaps the view that my clumsy colleague holds is the norm.  In this NYTimes blog post, Catherine Rampell suggests that try as we might to expose young girls with an aptitude for STEM fields to the subject matter, without tech-savy, scientist role models, these budding minds will choose other professions.  I agree whole-heartedly that providing female scientist role models to both male and female students will help to curb our biased stereotypes.  

I'll continue with an anecdote.  My son's 1st grade classroom had an opportunity the other day - a colleague of mine, in Informatics, offered to teach those 6-7yr olds some computing (through the increasingly popular Scratch platform).  After hearing about this computing club, the teacher asked the students to please raise their hand if they were interested in joining.  Not a single girl participated.  Even in 1st grade! Perhaps you don't find that so surprising -- after all, few women enter computer science as an undergraduate major , particularly striking since the gender bias in college is actually inverse (>50% women).   At this point, I should let you in on one important detail: my Informatics colleague is female and own daughter (let's call her Samantha) refused to participate in the computing club.  How could that be? Hasn't Samantha, for her entire life, had her own mother as an example of what a computer scientist is?  The sad truth is that Samantha's peer group had a bigger influence on her decision than the positive exemplar provided by her mother; when queried, Samantha's reason for not joining the coding club was that "none of the other girls raised their hands."  

We must provide both role models and an inclusive environment

There's that old logic puzzle (see http://everything2.com/title/I+can%2527t+operate+on+this+boy%253B+he+is+my+son) about a child and their dad being taken in to the ER and the physician attending proclaiming "I cannot operate on my son" - the punch line being that women can be doctors as well.   It takes folks a sad amount of time to figure this out.  If we continue to imagine the scientist as an old, white, male, that is what will populate our ranks.  Regrettably, my inner scientist sometimes sees this reflection in the mirror - it fills me with all sorts of doubts about whether or not I "belong" in science (Impostor Syndrome).  How can we alter the perceptions of others if we cannot see ourselves as filling these roles? How can we convince young, aspiring scientists to enter the track if they cannot conceive of a multidimensional, multifaceted scientist?

In a topsy-turvey counter example that proves the point, while a faculty member at Wellesley College in the Biological Sciences, I had a laboratory full of bright, female students.  My son, then 3, was asked by one of these students: "what do you want to be when you grow up?"  His response: "I'd like to be a scientist, but I'm not a girl."  Let's show the world that scientists are not who they think we are.

PS - some fun links to continue the discussions below 
http://www.huffingtonpost.com/leeanne-gray-psyd/embracing-femininity-in-t_b_3691572.html
http://www.ncbi.nlm.nih.gov/pubmed/1880753
http://spp.sagepub.com/content/early/2012/03/27/1948550612440735.abstract
http://womeninastronomy.blogspot.com/2012/06/feminine-role-models.html

Tuesday, November 5, 2013

Transcriptional Regulation of Culex pipiens Mosquitoes by Wolbachia influences Cytoplasmic Incompatibility

An intriguing title for an article published in PLoS Pathogens on Halloween.  Needless to say, many of us in the Wolbachia community anxiously await the discovery of the mechanism behind Cytoplasmic Incompatibility.  For those of you who aren't Wolbachia-philes, the short of it is that this bacterium has figured out a nifty way to spread through insect populations. First, it's transmitted via the germ line -- so that means eggs come pre-loaded with Wolbachia from infected mothers.  Second, Wolbachia affect reproduction in a variety of ways but the most common is that infected females can mate with uninfected or infected males.  However, if you are an uninfected female -- if you don't carry the bacterium -- you cannot mate with infected males.  This drops the fecundity of uninfected females and allows Wolbachia, and the host carrying it, to spread.  There are also some interesting incompatibilities that result when hosts are infected with two different Wolbachia -- sometimes the crosses are compatible and sometimes they are not.  This has led the Wolbachia research community to come up with all sorts of complex, mathematical models and explanations for the observed data.

So, what is the molecular mechanism behind these incompatible crosses? How does Wolbachia prevent embryos from hatching when an infected male mates with an uninfected female?  Lots of elegant work has been published by both the Sullivan and Frydman labs suggesting that Wolbachia both associated with cytoskeletal elements (microtubules) and alter the cell cycle progression.  This cell cycle defect has been correlated with cytoplasmic incompatibility.  So, it makes sense to focus on the cell cycle when you're going after CI.

The Pinto et al. paper in PLoS Pathogens this last month takes a candidate gene approach to the CI phenotype.  They start with the Drosophila melanogaster gene grauzone (grau).  This gene is pretty darn important to the fly -- mutants are sterile and lay eggs with aberrant chromosomal segregations which arrest in development at metaphase II.  Pinto et al.  figure out that this gene is over-expressed in Wolbachia-infected mosquitos -- by almost 2 fold at some time points -- compared to uninfected mosquitos (see Figure 1 below from the paper, A,B are females and males, respectively while C,D are their reproductive organs).

Figure 1. Transcription analysis of CPIJ005623 in the Culex pipiens complex.


I think it's neat to find host genes that are differentially expressed when a bacterium invades.  Transcriptomics has been done before in Wolbachia infected cell lines but many of the candidates identified were immunity genes, seemingly irrelevant to the CI phenotype.   Even more interesting is the fact that this gene is important to female reproduction and specifically, meiosis.

Pinto et al. then ask whether by suppressing the grau homolog in mosquitos, they could replicate the CI phenotype.  I guess the idea is that this factor could be the "rescue" factor or the "key" that is produced by the female when crossed with an infected male.  An alternative hypothesis would be that this factor is upregulated in infected insects as a result of Wolbachia increasing the mitotic activity of the germ line cells, a result reported by the Frydman lab a few years ago.  Anyway, they go with hypothesis #1 and use RNAi knockdowns (KD) of this gene to then observe what happens if we cross KDed infected females with infected males?  Interestingly, they observed an increase in the percentage of unhatched embryos, compared to a LacZ control (see Figure 2D below):

Figure 2. Knockdown analysis of CPIJ005623 in C. molestus Italy females.


However, they ddidn't include some important experiments here -- how do we know that this grau knockdown is specific to the CI phentoype? Wouldn't it have been important to cross these KDed infected females with an uninfected male?  What if the observed increase in unhatched embryos is similar?  Since it seems that Wolbachia actually increase the expression of grau, wouldn't you want to take an uninfected female, overexpress grau, and cross her to an infected male?

Moving on, next, they do some interesting crosses. As it turns out, the Wobachia they are working on exists as many different strains in mosquitos, each exhibiting an interesting incompatibilities.  For example, two of their Wolbachia strains, wPel and wItaly, cannot be crossed to each other -- that is, infected females or males from either background do not produce viable embryos (no eggs hatch).  Pinto et al. knockdown grau in wPel infected males and crossed them to wItaly infected females, they also performed a KD in wPel and in wItaly and crossed these two KDed lines.  The results: no difference - no viable embryos.  BUT, they do observe a statistically significant change in the number of embryos reaching stage II and III (which, I forgot to mention, was the opposite phenotype observed for infected KDed females crossed with infected males - Compare Figure 4C below to Figure 2D above).

Figure 4. Knockdown analysis of CPIJ005623 in C. pipiens males.

So it seems like Wolbachia CI-like effects can be modulated by host grau expression (although clearly this isn't the entire "rescue" or the "key" story).

The final part of this paper is a bit disappointing.  As has been done time and time again, the researchers attempt to identify genomic differences in the Wolbachia strains infecting these mosquitos to determine what may be the factor that is changing expression of grau.  Disappointingly, although they identify some regions present in some strains and absent from others, they don't go far enough to establishing mechanism.  In specific, they point out that a transcriptional regulator, which they call wtrM, is present in wPipMol but absent in wPipPel.  What is their evidence that this transcriptional regulator is altering grau expression? Sadly, none. They show it is expressed in ovaries -- note: this is not that surprising since this is where Wolbachia actually hang out.  They go out on a limb and say that wtrM is actually secreted by Wolbachia and modulates host gene expression, so it presumably makes its way to the nucleus and actually binds to host DNA.  No evidence is presented for this presumed activity -- no chromatin immunoprecipitation, no DNA footprinting, not even nuclear localization when expressed in their mosquitos.

wtrM, it turns out, is a pretty well conserved XRE transcriptional regulator  -- that is, a Xenobiotic Response Element.  It's found within the Rickettsiales -- there are even homologs in Anaplasma, Ehrlichia, and Bartonella.  What do XRE's do, you ask? Well, they are known to respond to environmental stimuli in many systems but are probably most famous for their involvement in phage response. It is therefore not so surprising that Pinto et al. find this gene associated with the Wolbachia prophage.  That's not to say that the prophage isn't interesting -- it sure darn is! -- but the connection between this specific XRE element and grau is tenuous, at best.