Academic Resolutions 2017

2016 was a big year. I defended my PhD in May, and the final months of becoming a doctor where as painful as everyone warned they would be. I said goodbye to my remaining Ithaca friends and moved west to Boulder. Big things. I continue to process what it means to be an early career woman scientist in a post-truth era. Meanwhile, I attempt to be productive as a postdoc and work to advance my career, whatever that even means. Let’s all say an expletive filled goodbye to 2016, and welcome 2017 with resolve. I’m generally not a fan of New Year’s resolutions, but a few 2017 academic resolutions feel particularly appropriate. Drum roll, please.

  • #250 papers in 2017, because #365papers is probably pushing it. Idea came from this post on academic resolutions by @JacquelynGill from last year. I’ll be keeping track of my progress here.
  • 20 blogs posts in 2017. You’ve heard it before, but now it’s in writing.
  • EBIO Diversity Discussion Group. Myself and members of CU faculty, postdocs, and grad students are organizing a diversity in STEM discussion group for the Spring 2017 semester. The goals of this group are to educate ourselves on best practices for promoting diversity and inclusion in STEM, to document resources, and to ultimately improve our personal practices to create a sustainable diverse and inclusive community. I’m really excited to be a part of this.
  • Super Secret Awesome Project. I’m collaborating with a group of badass feminist women scientist BFFs on a scicomm project. I can’t wait to share it! Launching mid-January.

Peace out 2016, let’s do this!



I’m excited to attend my first ISME conference in lovely Montreal! I’m presenting a poster in the Evolution session on some of my PhD work. It will be up Monday and Tuesday August 22nd-23rd: poster 325A “Gene content and diversification of secondary metabolite biosynthetic gene clusters coincides with divergence of terrestrial Streptomyces populations.”

Here, I want to delve more into the Background and Methods sections. Ok. I am working with two recently diverged, species-like populations of Streptomyces. For this poster, I focused on the evolutionary dynamics of secondary metabolite biosynthetic gene clusters (SMGC). There are a gazillion SMGCs that encode a massive diversity of natural products (e.g. antibiotics). Most of these originate in soil-dwelling Actinobacteria, including Streptomyces. However, we don’t have a solid understanding of what sorts of evolutionary and ecological processes generate this immense diversity of SMGCs. So I used a comparative population approach to evaluate the forces driving diversification of SMGCs.

The web-server antiSMASH is a great tool for exploring SMGCs in your genomes. antiSMASH uses a handful of algorithms to identify SMGCs of different types/classes and also provides information on any homology to the MIBiG database. I uploaded each of my 24 RAST-annotated genomes to this server which identified 28-47 SMGC per genome and a total of 22 different cluster classes. (Many of these clusters were identified as hybrids). Ouph, stacked bar chart. So many gene clusters!

Each bar along the x-axis is an individual genome. Hybrid gene clusters are counted multiple times to account for each class.

Next order of business was to identify clusters that are shared between genomes, conserved within members of the sample population, conserved within all genomes, etc. Here is where I ran into some problems. Very few of these SMGCs show high homology to the database. For many, only a few of the genes within a cluster show database homology. Remember, these are gene clusters comprised of many biosynthetic and regulatory genes. antiSMASH tells us the percentage of genes within a cluster that show homology to the database. Each genome was run independently, so it’s likely that “unknown” clusters are shared between genomes. For this next part, Chuck Pepe-Ranney helped me out significantly. We decided to define SMGCs based on gene content. Thus, we no longer need to depend on database homology.

  1. First, I made a master multi-fasta file containing all of the genes that make up all of the SMGCs from all of the genomes.
  2. I called orfs using Prodigal.
  3. Then I used parasail to identify pairs of orfs that are orthologous.
  4. From here, I generated an “OTU” table where columns are orthologous gene groups, and each row is a single SMGC from a single genome.
  5. From the OTU table, I made a binary/jaccard distance matrix.
    1. The working cutoff I’m using based on this presence/absence gene content is a dissimilarity of ≤0.4.
  6.   Finally, the R package igraph was used to visualize these clusters and to define cluster membership.

## network is a table with 3 columns: cluster 1, cluster 2, dissimilarity

snetwork = network %>% filter(value <= 0.4)

g =, directed = F)

## Calculate a layout   

layout = igraph::layout_with_fr(g)  

## Extract layout coordinates

gpoints = data.frame("biocluster" = igraph::V(g)$name, "x" = layout[,1], "y" = layout[,2])


igraph network clustering SMGCs based on presence/absence gene content. Each circle is a single SMGC from a single genome (colored by population). Circles that are close together have similar genes.

Now, I have a useful network of shared SMGCs for comparing intra- and inter-population patterns of diversification! If you don’t get a chance to see it in person, you can catch my poster here as well.

Fall Fitness Challenge

I have been thinking about fitness recently. Like how to improve my pitiful 9.5 minute mile time (ba-dum-che). Kidding! I have been thinking about how (evolutionary) fitness relates to selection and adaptation and my Streptomyces. I am interested in the evolutionary processes driving diversification between two recently diverged, but distinct, Streptomyces populations. If I can identity different fitness patterns in my isolates under laboratory growth conditions, I hope to identify possible advantages this provides in their natural habitat. Then I can extrapolate and infer potential ecological adaptations driving (or consequence of) the divergence between these populations.

Fitness is a fundamental concept in evolution. In population biology, fitness (often noted ‘w’) is the reproductive success of a genotype in a given environment. In other words, fitness refers to the genetic contribution of an individual to subsequent generations. There are generally two measurements of fitness, absolute fitness and relative fitness. Absolute fitness is the ratio of a given genotype (number of progeny or alleles) in the subsequent generation under different environmental conditions. Relative fitness is a means to normalize between different genotypes and is the number of progeny (or alleles) of one genotype with relation to another genotype in the subsequent generation under the same environmental conditions. The more fit genotype outcompetes the less fit genotype, thus contributing more genetic material to future generations: differential fitness between genotypes is necessary for natural selection to occur.

The reproductive success of an organism can be difficult to quantify, so biologists often use components of fitness as a proxy measurement (e.g. pollen production or size). Microbial systems can be ideal for exploring selection and fitness dynamics since population sizes are so vast, generation times so short, and it’s so easy manipulate growth conditions. For microbes, maximum growth rate of a pure culture is often used as a proxy for absolute fitness. Relative fitness can be assessed by competing two microbial genotypes against each other and measuring the relative contribution of each genotype to the next generation. The latter approach is exemplified by Richard Lenski’s E. coli Long-term Experimental Evolution (LTEE) project. This is brilliant work and should be required reading for both microbiologists and evolutionary biologists.

Streptomyces, life cycle involves the formation of branching mycelia and aerial hyphae.
The life cycle of Streptomyces involves the formation of branching mycelia and aerial hyphae containing spores.                           

Maximum growth rate in microbes (like E. coli) is usually determined by measuring optical density (OD) of liquid cultures. The problem for me is Streptomyces don’t like to grow in liquid media. The cells clump into a flocculent culture. This is partially because Streptomyces don’t follow the canonical bacterial life style, and instead practice a filamentous life style forming branch-like substrate mycelia that elongate from the tips. While there is a substantial body of literature detailing different techniques for measuring growth rate of Streptomyces (like this), I’m not actually interested in growth rate so much as using growth as a proxy for fitness. Wait, what about counting spores? Have you ever tried to count Streptomyces spores?!?!? Instead of going down this painful rabbit hole, I have  explored two approaches for quantifying absolute fitness proxies for Strepotmyces 1) biomass production and 2) growth rate on solid media.

Filters + Biomass #prettyinpurple
Filters + Biomass #prettyinpurple

Biomass Production For this approach, spores are plated onto solid media overlaid with a 0.22 µM filter (made of material Streptomyces can’t eat). Cultures are grown under the desired
condition (e.g. antibiotics, nutrient limitation, iron starvation) and after incubation, filters are oven dried for a few days. Biomass can be determined by subtracting the weight of the dry filter+biomass minus the weight of the. Absolute fitness is inferred from the ratio of an isolate’s biomass production under a control to experimental condition.

Streptomyces cultures at the end of the experiment.
Streptomyces cultures at the end of the experiment.

Growth Rate  For this approach , spores are inoculated into culture plate wells containing solid media. Cultures are again grown under the desired condition. Growth on solid media is measured with a plate reader (ABS 450 nm). Despite heterogeneity in pigmentation and colony morphology, rate of growth seems to fit a linear approximation. Absolute fitness is inferred from the ratio of an isolate’s growth rate under a control to experimental condition.

Each color represents the growth curve of a single Stireptomyces isolate. Lines are linear fit.
Each color represents the growth curve of a single Streptomyces isolate. Lines are linear fit~growth rate.

I think both of these approaches are practical, informative, and provide insight into a complex and extremely important parameter in population biology. #thinkingaboutfitness

Back to the basics: Streptomyces soil enrichments

Time flies when you are getting your PhD. Time also flies when you promise your brand new blog that you will brilliantly expound on lofty themes of evolution and ecology by diving into a massive body of literature (centuries worth?!?!?) in the coming weeks. Sigh. Rookie mistake. As per suggestion of my friend and colleague @chuck_pr (you should follow him and check out his blog), I’ve  instead decided to change focus and share some insider tips about my intimate encounters with Streptomyces over the last five years.

Let’s go back to the very beginning. So you want to isolate Streptomyces? You’re in luck because they are easy to come by and relatively easy to culture. For my research in the Buckley lab, we are interested in patterns of biogeography driven by dispersal limitations, so we aimed to minimize environmental variables by sampling predominately neutral-slightly acidic grassland soils. However, Streptomyces are ubiquitous in soil habitats (forests, beaches, etc.) and are also found in aquatic systems. While any enrichment culture-based strategy is unable to capture the true picture of standing diversity, we found our method recovers an appreciable amount of Streptomyces (known) taxonomic diversity. Here’s a rundown of our protocol:

  1. Air dry soil for 2-3 days. This will eliminate some non-spore forming microbes. Streptomyces spores are desiccant resistant but are not endospores, so don’t heat shock.
  2. Dilute a small amount of air dried soil with sterile PBS (e.g. 50 mg soil in 5 ml PBS) in a conical tube and shake it vigorously for 2-3 minutes to suspend the spores.
  3. Plate out 50-100 µl of the spore suspension. Depending on the sample you can expect anywhere from 10 to more than 100 Streptomyces colonies to form, so scale up accordingly.
  4. We use arginine-glycerol-salt (AGS) agar media (pH8.7). It’s a classic for selective isolation of aerobic Actinomycetes like Streptomyces (El-Nakeeb and Lechevalier, 1963). 
  5. Fungal contamination and overgrowth can be a problem. Anti-fungals cycloheximide (300 mg/L) and Rose Bengal (30 mg/L) are helpful. Rose Bengal makes your plates an awesome hot pink color.

    Soil enrichment on AGS media with Rose Bengal, representative Streptomyces colonies circles in bright green.
  6. Incubate for 1-2 weeks at room temperature. I like to do this in a drawer or tupperware with some wet paper towels to keep things from drying out.
  7. Streptomyces are easy to spot for isolation. They are the sporulated colonies, generally small and circular and come in a range of colors from white to grey to yellow to green. They often produce pigments and can grow above the agar in various morphologies.
  8. Pick colonies of interest with a sterile toothpick and transfer onto new AGS plates (no need for anti-fungals at this point). I skip streaking for isolated colonies and instead do a “wedge” streak to make wheel plates (see photo).

    Wheel plate of Streptomyces isolates from soil.
    Wheel plate of Streptomyces isolates from soil.
  9. You may need to streak for purity a few times. It’s common for pure cultures to exhibit some heterogeneity in growth patterns, so don’t let this alarm you.
  10. Plates keep for months at 4˚C, but for longer storage you will want to make glycerol spore stocks for your -80˚C (stayed tuned for that post).

That’s all for now!

Of Microbes and Maps

Biogeography is the study of the spatial and temporal distribution of organisms. Anyone who has ever had a visit to the zoo or natural history museum has taken a crash course in biogeography. Jaguars hangout in Central and South American rain forests, and cheetahs cruise African savannas. Badass dinosaurs roamed the earth millions of years ago. The field of biogeography is centuries old, and its hypotheses and theories are foundational to the contemporary fields of ecology, evolution, zoology, botany, geology, and geography.

While patterns of biogeography are governed by a multitude of variables and forces, we can boil it down to a few key ecological and evolutionary processes. Rates of dispersal, colonization, and diversification (i.e. speciation and extinction) ultimately create these patterns. The big question is, are there universal rules that dictate the formation and maintenance of biogeography across all domains of life? Sometimes it seems that microbes dance to the beat of their own drum when it comes to ecological and evolutionary theory. But maybe we need to develop the appropriate tools and approaches to ask the right questions.

For microbial biogeographers, the standing hypothesis for nearly a century has been the frequently cited, but often mistranslated, Bass Becking hypothesis, “Everything is everywhere, but the environment selects.” In other words, microbes are ubiquitously dispersed and filtered across habitats. The patterns of biogeography we observe result from the environment selecting a specific community from a global microbial pool that is locally adapted to particular conditions. In support of this is a microbe’s small size, large population, and ability to withstand unfavorable conditions through dormancy strategies. An alternative hypothesis is that patterns of biogeography arise due to dispersal limitation and subsequent diversification processes, like genetic drift and local adaptation. How do we test for dispersal limitation? Well, it requires a serious consideration of scale.

We’ve found microbial life practically everywhere we’ve looked. We know that coarse environmental variables like pH influence microbial community structure (for more info, check out the publications of the Fierer and Knight labs). However, these surveys are generally conducted at broad phylogenetic scales, using 16S rRNA gene sequences to bin microbes into units of diversity or species (the canonical unit being ≥97% 16S rRNA identity), across environmental gradients. Diversification of 16S rRNA genes takes millions of years, approximately 1% divergence/50 million years! To put that into perspective, the last common ancestor of all primates is estimated at 81.7 Mya. That is a lot of time for dispersal and diversification to create patterns of biogeography in monkeys, apes, and humans. When we assess microbial biogeography using a 97% 16S rRNA identity cutoff, we are obscuring the genetic and ecological diversity accumulated over 100 million years of evolution.

Microbes are everywhere, but when we increase our taxonomic resolution, do we see limits to dispersal? To rigorously test if dispersal limitation can influence patterns of microbial biogeography, we need to narrow our phylogenetic focus. We need to study populations of closely related microbes sampled across appropriate environmental, spatial, and temporal scales. Easier said than done. This is the approach I am using for my research on Streptomyces biogeography. In the coming weeks I’d like to expand this discussion on the processes creating patterns of microbial biogeography by diving into the literature. For an excellent review on the current status of microbial biogeography research, check out this article by Hanson and others. That’s all for now, cheers!


Microbial Valentine

Hi there! Welcome to my blog (with the creative witty title to be announced soon). I’m motivated to write more about science, and here is where I’m going to do it. So check back often to find my thoughts and ideas about my research, peer-reviewed articles, and science related pop culture topics. That’s all for now, but before you go, a microbial valentine for you.

Serratia is red,

S. coelicolor is blue,

If I had pure cultures,

I’d streak them for you!