Pinpointing the Pollen: Honeybees and a Host Jumping Virus

Lately I've been revisiting some of my past topics and continuing the story with new research. Such is the case today. A relatively popular post of mine from 2010 called The Buzz on the Bees described a study from that year by Jerry Bromenshenk et al. investigating Colony Collapse Disorder (CCD). CCD describes the mysterious, sudden and serious die-off seen honeybee (Apis mellifera) colonies across the U.S. It is characterized by sudden colony death with a lack of adult bees in front of the die-outs. Honey stores and recent brood rearing are often evidenced, and sometimes the queen and a small number of survivor bees remain. The 2010 study found CCD colonies to contain an iridescent virus (IIV) (Iridoviridae; a DNA virus) that tracks with the microsporidia, Nosema apis and N. ceranae (specifically the latter), when compared to healthy colonies. This and previous scientific studies, using sensitive genome-based and proteomic methods, have also found small RNA bee viruses. These RNA viruses, alone or in conjunction with other pathogens, have frequently been implicated in CCD.

A new study published a couple of days ago in mBio by Ji Lian Li et al. correspondingly takes a look at the role of viruses in CCD. Evidence from previous studies shows that viruses that cause common infections in honeybees also infect other hymenopteran pollinators. A study published by Singh et al. (2010) even showed these viruses to be present and infective in pollen pellets.

I’ll conjecture that if you ask most people you’ll find that they don’t really think of plants as capable of getting viruses. That is until some disease comes and kills all of the fruit trees in their back yards. But plant viruses are like other viruses, obligate intracellular parasites, and they require a way to transmit from one plant to another. As you may have noticed, plants don’t generally get up and move around. This means that their viruses need a vector. Generally, these vectors are herbivorous insects. These insects are carriers, usually by carrying around infected pollen spreading the virus from one plant to another without themselves getting sick. To date, only a few plant viruses are known to also affect their insect vectors.

Li et al.’s new study takes a closer look at the role of pollen in virus transmission in honeybees. Initially, they carried out a study to screen bees and pollen loads of bee colonies for the presence of frequent and rare viruses. This resulted in the chance detection of a plant virus, tobacco ringspot virus (TRSV). This virus is a type species of the genus Nepovirus within the family Secoviridae, and it is known to infect a wide range of herbaceous crops and woody plants. Like other members of this genus, TRSV has a bipartite genome of positive-sense, single-stranded polyadenylated RNA molecules, RNA-1 and RNA-2, encapsidated in separate virions of similar size. For you non-biologists, this basically means that the virus has two genome segments/virus particles and can be directly translated into the desired viral proteins by the host cell. RNA viruses typically mutate very fast and are really good at working around host defenses (HIV and hepatitis are good examples of RNA viruses).

It is known that honeybees transmit TRSV from infected plants to healthy ones, but its presence in the researchers' screens got them to wondering if this plant virus could cause systemic infection in the exposed honeybees. To answer this, they collected adult worker bees, samples of the pollen being processed by a colony, and the ectoparasitic mite Varroa destructor (great name!) within the hive. They assessed 10 colonies for 1 year, classifying them as strong or weak based on the size of adult populations, amount of sealed brood, and presence of food stores. From their samples they purified virus particles from the adult bees and used them for cDNA library construction, virus-specific primer design, total RNA extraction, conventional RT-PCR, in situ hybridization, cDNA sequencing, and a phylogenetic analysis.

The observations of the colonies revealed an increase in bee deaths starting in the autumn and peaking in the winter. The researchers found both TRSV and IAPV (Israeli acute paralysis virus, common in honeybees) to be absent in colonies classified as strong, but both were found in weak colonies. Weak colonies too were found to have more multiple virus infections. These weak colonies were the ones less likely to survive through the cold winter months. Additionally, TRSV of the same strain was detected in the mites of infected colonies suggesting they obtain it from their bee hosts.

These results are the first evidence that honeybees exposed to virus-contaminated pollen can also be infected and that the infection can be systemic and spread throughout their entire body. Any host jumping is not without its challenges. In order for a virus to jump to a new host it must have the opportunity to come into contact with a perspective host, undergo genetic changes so that it may enter a new type of host cell, and gain the ability to spread horizontally between individuals within the new host populations. It seems that TRSV has been successful in overcoming all of these challenges. Its presence in the mites suggests that they could be a vector for the horizontal transmission between colonies. However, food-borne transmission (via pollen) is the most important route for transmission. Their results suggest that TRSV is neurotropic (affecting the nerves) in the honeybees, potentially causing severe functional impairment of nerves and muscles.

Do these results definitively conclude that TRSV is the cause of CCD? Well, no. But this study does add to a growing body of evidence that implicate parasites and pathogens as the key culprits.


Ji Lian Lia, et al. (2014). Systemic Spread and Propagation of a Plant-Pathogenic Virus in European Honeybees, Apis mellifera mBio, 5 (1) DOI: 10.1128/mBio.00898-13


NY Times article: "Bee Deaths May Stem From Virus, Study Says"

Also, check out these links for more information on Colony Collapse Disorder:
Mid-Atlantic Apiculture Research and Extension Consortium (MAAREC)
United States Department of Agriculture: Agricultural Resource Service
United States Department of Agriculture: National Agricultural Library
The Ohio State University's Agriculture Network Information Center's Bees and Pollination Page


(image via Wikipedia)

Scientist Paper Dolls

The lovely people over at Mad Art Lab have put together a host of sciency paper dolls. The best part? They are printable! Here a few. Get more from the link at the bottom of the post.

Jane Goodall

Mad Art Lab's Sandbox of Scientist Paper Dolls

Organisms Do Evolve

A sciency parody of Miley Cyrus's "Wrecking Ball"

You've Got Red On You: Improving Z-Day Models

Yesterday I updated and expanded a long-ago post of mine called "Mmmm...Brains!: Using Mathematics To Save Us On Z-Day."This post summarized a book chapter in 2009 by Philip Munz, Ioan Hudea, Jo Imad, and Robert Smith? called "When Zombies Attack!: Mathematical Modeling of an Outbreak of Zombie Infection." This work was interesting because it combined basic biological assumptions and epidemic modeling with the rise and spread of zombies. Now, Caitlyn Witkowski of Bryant University and Brian Blais of Brown University have written a paper, which was published on the arXiv pre-print server, that extends the Munz et. al. (2009) work and then applies the methods to influenza dynamics.

Let's start with stochastic vs. deterministic models. Stochastic models are all about random variables and chance variations. They estimate probability distributions and outcomes by allowing for random variation over time, and they are good for small populations. Deterministic models assign individuals to subgroups or categories. They work well for large populations, assuming the size of each category can be calculated using only the history used to develop the model. This deterministic type of model is where we'll focus as it is heavily used in modeling disease dynamics. The categories used in a model each represent a specific stage of an epidemic with letters used in equations to represent each. The two types of disease models we'll focus on are the SIR model and the SEIR model. The SIR model is mathematically simpler and follows the flows of people between three states: susceptible (S), infected (I), and recovered/resistant (R). The SEIR model adds a fourth state, exposed (E), representing an infected individuals who are not yet symptomatic or infectious. This addition of a latency period is the primary difference between the models. Additional parameters are then added including the contact rate or transmission parameter (beta), removal of infection rate (alpha), rate from exposed to infected (sigma), rate of infected to recovered/resistant (gamma), and natural mortality rate (mu).

In order to create their model, Witkowski and Blas first needed data. As we currently have no data on actual zombies (that we know of), they gained insight into zombie dynamics by binge watching zombie films and television shows. From this they found that virtually all zombie movies fall into one of two forms that can each be represented by a particular film, either Night of the Living Dead (1968) or Shaun of the Dead (2004).

The Night of the Living Dead (which I'll abbreviate NotLD) category includes the following observations:

1. "Anyone who dies becomes a zombie, regardless of contact with one.
2. Because contact with a zombie is likely to lead to death, the interaction between the two subpopulations of susceptibles and zombies is signi cant.
3. This interaction between susceptibles and zombies results in a temporarily removed subpop- ulation before members of that population become zombies.
4. The only way in which a zombie can be permanently removed is by destroying the brain or burning the body."

The Shaun of the Dead (SotD) category's greatest difference from the NotLD is that contact between a susceptible and a zombie is necessary for the zombie population to grow. Not all susceptibles who die become zombies.

With this knowledge, they used the SIR-type models and applied Bayesian parameter estimations. They then applied Markov Chain Monte Carlo (MCMC) techniques to estimate the posterior probabilities of the parameters. This allowed them to provide both the best estimates and their uncertainty. In both movie categories, they were able to estimate the initial susceptible population by using simple approximation, estimated zombie numbers from scenes with a field of view approximately 50 meter squared area, and estimate overall time values from visual cues (clocks, sun, etc.). They then used the exact same techniques to analyze real-world data on influenza using Google Trend data.

Witkowski and Blas found their models to be a significant improvement over the models in Munz et. al. (2009) in that their model structure had a closer match to the zombie system. They found that when they removed Munz et. al.'s "recycling" parameter (humans in the removed class can resurrect and become a zombie) the stability of the system changed significantly. This showed that the zombies can be completely removed as long as they are removed faster than they are created. They found the rates of infection and removal (beta and alpha, respectively) to be nearly a factor of two smaller for SotD than for NotLD. NotLD also has a higher value for the rate of exposed becoming fully infected than does SotD. NotLD also shows an interesting joint distribution patterns in that higher values of the infection rate (beta) require a higher rate of removal of infection (alpha), and no relationship between the removal rate of infection and the rate of exposed becoming fully infected (sigma).  In both the NotLD and the SotD categories military intervention saves human civilization, the strength of this military attack and time at which it occurs being the two major factors determining the success. A quick, early intervention expectedly annihilates the zombie horde, but a later intervention is essentially a wasted effort. They explored this scenario with SotD. They assumed that the military intervention seen at the end of the film increased the alpha parameter ten-fold which effectively eliminates the zombies, restoring civilization. However, their calculations suggest that if the film had lasted 30 minutes longer, the same intervention strength would have lead to a doomsday scenario. So this model does agree with Munz et. al. that timing is everything.


Caitlyn Witkowski, & Brian Blais (2013). Bayesian Analysis of Epidemics - Zombies, Infuenza, and other Diseases arXiv.org, 1-16
https://arxiv.org/pdf/1311.6376

Learn more about SIR-type disease models:

Murali Haran's presentation slides "An introduction to models for disease dynamics"
Hans Nesse's description and simulator his page "Global Health - SEIR Model"
Jeffrey Moehlis tutorial page "An SEIR model"
Ottar Bjørnstad's 2005 paper "SEIR models"

Some other blog write-ups:

Geekosystem's article "Mathematicians Wrote a Paper on How the Zombie Apocalypse Won’t Kill Us All, Made Us Grateful for Math"
Medium's article "Mathematical Model of Zombie Epidemics Reveals Two Types of Living-Dead Infections"


(image via IMDB)

All the Salamanders

Another video from Tom McFadden. Tom teaches science through music via Science with Tom and The Rhymebosome. This one is all about salamanders in the Great Smoky Mountains, USA.





If you like this then check out these older posts:

Teaching Science Through Rap
Covalent Love
One Bottle at a Time
Blame it on the DNA
It's Too Late to Apoptize
Rappin' Science