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Productive turnover and generations in the fruit fly

Productive turnover and generations in the fruit fly


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I was reading about the Lenski experiments on the evolution of E. coli bacterium and Dr. Elders's experiments on the evolution of the guppy. These two experiments absolutely fascinated me, and seemed to happen relatively quickly by evolutionary standards. It was mentioned in the book that the fruit fly's productive turnover time is especially short.

I am extremely interested and would very much like to conduct an experiment into the evolution of the fruit fly, in much the same way as the Lenski and Elder experiments when I'm older.

Just so I can get a good idea, as I can find nothing on the internet, how long would it take for the fruit fly to get through, say, 10 generations?

Also, what would be the best way to go about beginning an experiment like this? Would there be certain people to contact? Would you need certain qualifications?


What do you mean by you couldn't find anything on the internet? Drosophila generation time is explained here?

Your other question is a bit off-topic here but I'll give all advice I have heard myself:

You can leave out a banana skin and catch some fruit flies, then do the experiment in your kitchen :) Joke aside, unless you perform the experiment independently it will probably take a long time and it's well possible that you won't be able to do it at all (unless you become some famous scientist). I'm not familiar with the experiments you mention, but if you require equipment and/or funding, you will need a qualification and/or a good explanation why people should give you money or let you use their equipment for this experiment.

I don't know what stage of education you are at, but it sounds like you are still at school ("when I'm older")? Most countries have some schemes where students (below uni) can apply for young researcher kind of things, so you could try and google that. Apart from that, the best bet is probably to try and get into a uni with good facilities and research programmes for their students. What you study shouldn't matter so much as long as it's science.

In response to Marta's commment: if you understand the experiment and you think you can gather everything they used at home, nothing speaks against just doing it on your own. Just make sure you don't let those flies swarm your house ;)


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Key gene behind hallmark of Lou Gehrig’s disease identified

Stanford researchers identified a gene crucial to the formation of toxic proteins in amyotrophic lateral sclerosis and showed how it could inform potential therapies for the disease.

Aaron Gitler and his collaborators used experiments in yeast, in fruit flies and in cells derived from people with ALS to identify a gene linked with the formation of toxic protein clumps that are a hallmark of the disease.
Paul Sakuma

Inside the brains of patients with amyotrophic lateral sclerosis, a debilitating neurodegenerative disease, is a telltale sign that marks almost every case: clumps of toxic proteins.

Now, researchers from the Stanford University School of Medicine and their collaborators have pinpointed a key gene behind the formation of one type of these neuron-damaging aggregates. They’ve also shown how inhibiting the gene’s function curbs production of the harmful protein.

“We know that these protein-rich aggregates are a clear hallmark of ALS,” said Aaron Gitler, PhD, professor of genetics. “But this finding allows us a deeper look into how those aggregates are made, and potentially how we can hinder that process.”

The gene, RPS25, codes for a piece of cellular machinery necessary for creating the protein-based gunk that amasses in some forms of ALS and damages healthy neurons. When the gene’s activity was experimentally depleted — in yeast, in neurons derived from patients with ALS and in fruit flies — Gitler and his team saw levels of the lethal protein drop by about 50 percent across the board.

The team also tested the function of RPS25 in human cells modeling Huntington’s disease and spinocerebellar ataxia, two other neurodegenerative illnesses that have protein-aggregate “hallmarks” similar to ALS, said Shizuka Yamada, a graduate student in Gitler’s lab. There, too, inhibiting the gene helped tamp down the levels of bad protein.

It’s still early days, Yamada said, but hampering the RPS25 gene seems like a promising target for reducing the destructive proteins seen in ALS and even extending life span, as was seen in the fruit fly model of ALS with low activity levels of the gene.

A paper detailing the results of the research was published July 29 in Nature Neuroscience. Gitler, who holds the Stanford Medicine Basic Science Professorship, is the senior author. Yamada is the lead author.

An alternate route

Also known as Lou Gehrig’s disease, ALS is a condition that kills off motor neurons, which are crucial to all physical tasks, from brushing one’s hair to breathing. The root cause behind every case is not always the same there’s a slew of genetic factors that play into the onset of ALS. Yet one gene is often the culprit. In ALS, it harbors a string of DNA that erroneously repeats itself.

It’s these DNA repeats that are transformed into the harmful proteins that build up in the brain. As the proteins amass, they interfere with healthy neurons, blocking the cells’ ability to function normally.

Outside of their toxic properties, what’s notable about the protein aggregates is that they aren’t made like other proteins found in the body, Yamada said. “These repeats actually shouldn’t be made into proteins at all,” she said. “They come from DNA that isn’t supposed to code for anything, and yet somehow the proteins come to be anyway.”

During run-of-the-mill protein formation, the ribosome, a sort of molecular machine that resides in the cell, processes messenger RNA, which contains genetic code based on DNA, and turns it into the raw materials of a protein. That process is called translation, and it’s initiated by a code in the mRNA that shows the ribosome where to start translating. The ALS-associated DNA repeats don’t have that start code, unlike normal mRNA.

“So regular translation doesn’t work with the repeats,” Yamada said. But it turns out there’s a molecular workaround: an unconventional translation process called repeat-associated non-AUG translation, or RAN translation, that turns the ALS repeats into destructive protein bodies.

Putting the brakes on RPS25

The exact mechanism of RAN translation and its role in human biology is not clear, but scientists do know that it still depends on the ribosome. To better understand the process, Gitler and Yamada turned to yeast, a simple organism that still has the major proteins and pathways of human cells. One at a time, the researchers decreased the function of individual yeast genes and monitored the fungus’ RAN function. When subdued, several genes swayed RAN function, but one in particular, RPS25, stood out. With the gene hindered, production of the toxic protein fell by 50 percent.

The researchers also saw a 50 percent dip in the toxic protein when they tested how neurons derived from patients with ALS fared without RPS25.

“We were really excited to see the decrease in repeat proteins carry over into human cells,” Yamada said. “It’s always pretty cool when yeast biology can directly inform human biology.” Because these cells came from patients who suffer from ALS, the research offered a reliable glimpse into how the neurons of individuals with ALS would respond to lower levels of RPS25, she said.

“Through genomic analyses, we could see that the ALS-associated repeats were still there the sequences hadn’t changed,” Yamada said. “What was changing was the output of the ribosome the repeats weren’t being made into toxic proteins nearly as often.”

Slashing a part of the cell’s protein-making machine might sound risky, but it turns out a defunct RPS25 gene doesn’t spoil normal protein production. Yet the researchers also showed that an inactive RPS25 gene affects more than just the ALS repeats the dysfunctional gene similarly stunted erroneous protein production in cellular models of Huntington’s disease and spinocerebellar ataxia, two neurodegenerative illnesses that have hallmark protein aggregates similar to ALS.

Moving toward more complexity

Finally, the researchers turned to fruit fly models of ALS to investigate how depleting RPS25 affected the insect overall. Not only did they see a similar decrease in toxic protein levels, they also saw an increased life span in the flies that lacked fully functional RPS25. Flies that harbored both the ALS mutation and a working RPS25 gene died by day 29, on average, while those that had the ALS mutation and lower amounts of RPS25 lived on average for 38 days. A healthy fruit fly lives about 50 days on average.

The findings are intriguing, Yamada said, but before the scientists can begin to pursue RPS25 as a drug target, the team has a couple boxes to tick off. The team now is investigating how a more complex animal model — like a mouse — would fair without RPS25.

“With the fruit flies, we tampered with the gene we didn’t remove it completely,” Yamada said. “Whether an animal can survive without the gene entirely is a big part of our next step.”

Furthermore, Yamada said, she and Gitler are still after a clearer picture of RAN translation in humans, overall. “Does it only occur under neurogenerative conditions? Or is there a broader role for it in healthy individuals?” she said. “We don’t know the answer to those questions yet, and it will be crucial to figure out before pursuing RPS25 as a therapeutic target.”

Other Stanford co-authors of the study are graduate students Naomi Genuth and Nicholas Kramer postdoctoral scholar Rosslyn Grosely, PhD research technician Lisa Nakayama high school student Shirleen Fang research assistant Tai Dinger Maria Barna, PhD, assistant professor of genetics and of developmental biology and Joseph Puglisi, PhD, professor of structural biology.

Researchers from the Mayo Clinic, the University College London and the University of Southern California also contributed to the research.

Gitler is a member of Stanford Bio-X and the Wu Tsai Neurosciences Institute at Stanford.

The work was funded by the National Institutes of Health (grants R35NS097263, AI099506, AG064690, R35NS097273, P01NS099114, 2T32HG000044 and R01NS097850), the U.S. Department of Defense, the Muscular Dystrophy Association, the European Research Council and Alzheimer’s Research UK.

Stanford’s departments of Genetics, of Biology, of Developmental Biology and of Structural Biology also supported the work.


Productive turnover and generations in the fruit fly - Biology

Drosophila Melanogaster

Drosophila was first used as a model organism by Thomas Morgan in the early 1900s. He used the Drosophila to study genetics and showed that genes were arranged on chromosomes in a linear array.

Since then our knowledge of the Drosophila, and its usefulness as a model organism has increased dramatically as new techniques have been developed.

The recent sequencing of the genome of both the Fruit Fly and the Human has shown the vast similarities between the two genomes and highlights the conservation that occurs through evolution. Of the 298 genes found to be involved in human disease, so far 177 of them have also been found in the Drosophila.

"We are much more like flies in our development than you might think" - Lewis Wolpert

Advantages of the Fruit Fly as a model organism:

  • Short life cycle – develops into adult fly 9 days after fertilisation.
  • Genome sequenced – 13600 protein coding genes have been predicted from this sequence.
  • Cheap and easy to look after and reproduce.
  •  Mutant flies easily crossed, and the results have been shown to be transferable to h umans.
  • Large batches of embryos.
  • Detailed cytological and genetic map.

Disadvantages of the Fruit Fly as a model organism:

  • Small embryo.
  • Non-mammalian invertebrate model, so some discoveries cannot be directly transferred across to apply to the human system. 

What is the fly used as a model organism for?

Classic Experiment:

Maternal mutations:  Bicoid mutant – missing head end.

This therefore allowed scientists to conclude that these genes were involved in segmentation.

Drosophila are great models for studying development:

Axis formation, through studying maternal genes, gap genes, pair rule genes and segment polarity genes. Also a great organism for studying the Hox genes – expressed along AP axis in the same order that they occur in the genome.

Leg and wing formation – Imaginal discs. Experiments showed that the patterning mechanisms used to develop the drosophila leg is broadly conserved in vertebrates, using vertebrate gene homologs.

Drosophila in Medical Research:

Aniridia: Pax 6 or eyeless mutations were studied in Drosophila. Mutations in this gene are responsible for the human condition Aniridia. Experiments mis-expressing human Pax 6 in flies Showed that Pax 6 is necessary and sufficient to form an eye.

Alzheimers: Drosophila have recently been used for models in Alzheimers disease because they have highly developed muscles and nerves in contrast to their simple brains. 

References:

Principles of Development 3rd edition - Lewis Wolpert

Drosophila Image courtesy of flickr under the creative commons license.


Fruit Flies First Began Feeding on Our Fresh Produce About 10,000 Years Ago

Humanity has had a few long time companions over the millennia, including dogs, lice and the plague. Among the most annoying, however, is the common fruit fly, Drosophila melanogaster, the tiny little red-eyed insect that tends to spoil fresh fruit. Though the little buggers seem to have followed humans all across the world and into the laboratory, their exact origin story was unknown.

According to Nell Greenfieldboyce at NPR, a new study presents an answer. Researchers understood that the flies likely started out somewhere in Africa, but they have never been found living in the wild. During a recent survey of fruit fly genetics with sub-Saharan ancestry, it was found that the most diverse set of fruit fly genes come from Zambia and Zimbabwe, suggesting that the wild ancestors of the flies might originate in the forests of south-central Africa.

But Marcus Stensmyer from the University of Lund in Sweden and co-author of the study Current Biology tells Greenfieldboyce that expeditions to find the flies in the area struck out. Then he and his team began to think perhaps unlike in our kitchens, where the flies lay their eggs on all types of overripe or rotting fruit and vegetables, the flies were picky eaters in the wild, attracted to one type of fruit. The team took a look at the wild fruits available in the region and decided that the marula, a sweet plum-sized fruit, most closely resembled the fruits that flies tend to prefer in the kitchen.

The team set out fruit fly traps near marula trees in Matobo National Park in Zimbabwe and, lo and behold, they caught loads of wild fruit flies going after the rotting fruit. They also found the flies were particularly attracted to ethyl isovalerate, a compound found in the fruit. When researchers set out rotting oranges near the marula fruit, the flies still chose the marula, though they chose oranges spiked with ethyl isovalerate equally.

“They are drawn to particular aromatic substances from marula that activate receptors on the antennae. When these are activated, it’s a sign that it’s a good place to lay eggs,” Stensmyr says in a press release.

The association with marula fruit also helps researchers understand how fruit flies ended up in our kitchens. According to the study, archeologists have found that ancient San tribes native to the area have relied on marula fruit for thousands of years. In one cave, they found 24 million walnut-sized 8,000- to 12,000-year-old marula pits discarded by generations of humans snacking on the fruit. The scent of all that luscious overripe fruit likely attracted lots of flies. The team even tested whether the flies would enter the dark caves, finding that, indeed they would take the risk for a little taste of the marula sweetness.

Over time the people and flies forged their lasting bond in these caves. “The fly has developed into a generalist that eats and breeds in all sorts of fruit,” Stensmyr says in the release. “But originally it was a real specialist that only lived where there was marula fruit.”

While some might wish the San had kept the flies out of their caves, so that they would have never ended up in our households, that’s not the case with scientists. Common fruit flies are an animal model in genetics research and they have contributed to five Nobel prize winning studies. Fruit flies have led to the understanding of thousands of genes that are also found in humans. Which, if you think about it, is worth a little spoiled fruit.

About Jason Daley

Jason Daley is a Madison, Wisconsin-based writer specializing in natural history, science, travel, and the environment. His work has appeared in Discover, Popular Science, Outside, Men’s Journal, and other magazines.


Insect Declines in the Anthropocene

David L. Wagner
Vol. 65, 2020

Abstract

Insect declines are being reported worldwide for flying, ground, and aquatic lineages. Most reports come from western and northern Europe, where the insect fauna is well-studied and there are considerable demographic data for many taxonomically disparate . Read More

Figure 1: Location of 73 insect decline reports by taxon or group, adapted from Sánchez-Bayo & Wyckhuys (156). Each square represents a single study, with the base of each stacked bar positioned over .

Figure 2: Population trends for insects tracked by the International Union for Conservation of Nature (IUCN) and UK insects from Dirzo et al. (34). (a) Trend data for IUCN-listed Coleoptera (Col), Hym.

Figure 3: Reversal of fortunes. An important aspect of recent decline reports is evidence of steep population declines in formerly abundant species. (a) The Rocky Mountain locust (Melanoplus spretus)—.


2. Description of the model

Odor distribution and the insects’ responses are by definition spatial processes. Therefore, a spatio-temporal model is the most appropriate approach to gain insight into the effect of infochemicals on population dynamics.

2.1. Dispersal of fruit flies and population dynamics

The reproductive life of female fruit flies generally starts with searching for a suitable resource. When they find a resource they settle to feed, mate, and oviposit. Thereafter, they leave the resource to search for another suitable resource. To model these different activities, we divided the adult population density P into three activity states: a searching state S (with fly density P S), in which individuals fly in the air and use infochemicals that are present in the air to find a suitable resource, the moment they find a resource and land they come into a settled state R (with fly density P R), in which individuals spend time on a resource, and a moving state M (with fly density P M), in which individuals actively fly away from the resource. In our model, the total adult population remains constant within a generation there is no adult immigration, emigration, or mortality. Furthermore, we only modeled the adult females. In Drosophila melanogaster, adult males produce the aggregation pheromone (cis-vaccenyl acetate) and transfer it to females during mating (Bartelt et al., 1985). Recently mated females then emit the aggregation pheromone. The amount of aggregation pheromone emitted by males is very small compared to the amount that the females emit. In addition, Bartelt et al. (1985) showed that both sexes respond similarly to the aggregation pheromone. We, therefore, assumed that the distribution of females gives a good representation of the distribution of the whole fruit fly population and that the adult female population dynamics could be modeled satisfactorily without considering the adult males.

2.1.1. Dispersal of fruit flies

The dispersal of searching fruit flies (S) is random in the absence of infochemicals. In the presence of infochemicals, however, the movement of searching fruit flies is usually directed toward the source of the odor. We assume that D. melanogaster only uses a concentration gradient to find the odor source and that dispersal can be modeled with a two-dimensional chemotaxis model for the redistribution of flies.

Powell et al. (1998) gave a general format for chemotactic movement in biology. We used this format to model the population responses toward a concentration gradient of the food odor (F) and aggregation pheromone (A)

where P S is the density of the searching Drosophila population, ν is the attraction constant of the infochemicals, f is the effective sensory index of D. melanogaster with respect to F and A (see below), and D P is the dispersal constant of the searching population.

2.1.1.1. Sensory index

Bartelt et al. (1985) showed two important features concerning the responses of D. melanogaster toward food odors (blend of fermentation products and yeast odor) and its aggregation pheromone: (1) the aggregation pheromone is only attractive when food odors are present (2) D. melanogaster is about four times more attracted to the combination of food odors and its aggregation pheromone than to food odors alone. A description of the response of D. melanogaster to infochemicals that is consistent with these findings is:

, where F and A are the food odors and aggregation pheromone, respectively F 0 and A 0 are the corresponding half saturation values, and η represents the attraction ratio of food odor in combination with aggregation pheromones (F + A) relative to the attraction to food odor alone (F).

2.1.1.2. Leaving the resource

While the movement of searching fruit flies is directed by infochemicals, the movement away from the resource by the moving fruit fly population (M) in the model is not considered to be affected by the presence of food odors and aggregation pheromone. The movement of the moving population is described by ring-random dispersal, where fruit flies first actively fly away from the resource and then distribute randomly (see (9)). We chose this type of dispersal to achieve that a large part of the moving population actually does leave the resource. Without first flying away from the resource, the largest part of the fruit flies would remain on the resource.

2.1.2. Population dynamics

2.1.2.1. Within-generation dynamics

The total adult population is held constant within a generation, and there is no migration over the boundaries of the spatial domain (reflecting boundary conditions). However, the distribution of individuals over the three activity states does change over time (Fig. ​ (Fig.1). 1 ). When a searching individual finds a resource (R) it settles on the resource with settlement probability λ (min 𢄡 ). Settled individuals leave the resource at a constant rate, the patch leaving probability, α 1 (min 𢄡 ). A moving individual that has left the resource starts searching again with a probability α 2 (min 𢄡 ). The distribution of the resources (i.e., yeast-infected apples) and local within-generation population dynamics in each point in space (x, y) (see Section 2.4) are described by the following equations

Schematic representation of the population dynamics. The total population is divided into three activity states, S the searching part of the population, R the settled part of the population, M the moving part of the population, with λ, α 1 and α 2 the transition rates. The dashed block represents a resource.

From hereon, we refer to yeast-infected apples simply as apples. Values for the parameters used are given in Table ​ Table1. 1 . For more details on how these values are arrived at, we refer to the companion paper (de Gee et al., 2008).

Table 1

The model parameters involved in the short time dynamics and their values. For dimensionless parameters, the “–” sign is used.

NameDescriptionValueUnits
D PDispersion coefficient of at random moving fruit flies0.058m 2 min 𢄡
α 1Rate of settled fruit flies leaving the resource0.002min −
α 2Rate of moving fruit flies that start searching for resources0.5min
λSettlement rate of searching fruit flies0.25min
ρVelocity of movement away from the resource1m min 𢄡
F 0Saturation parameter for food odors10ng m −
A 0Saturation parameter for aggregation pheromones0.04ng m 𢄢
D IDispersion rate of infochemicals1m 2 min 𢄡
μ(720)Loss rate of infochemicals in a 12 hours period (measured from the moment of production)0.025min 𢄡
μ(5)Loss rate of infochemicals in a 5 minutes period (measured from the moment of production)0.171min 𢄡
θ FFood odor production by the resource2ng apple 𢄡 min 𢄡
θ AAggregation pheromone production by settled fruit flies0.83ng fly 𢄡 min 𢄡
ωEvaporation rate of liquid aggregation pheromone4.10 𢄤 min
νAttraction towards infochemicals5D P
κRelative strength of movement towards infochemicals compared to random dispersal5
ηAttraction ratio of food odor together with aggregation pheromones relative to the attraction to food odor alone2.51
ξFecundity of the settled population0.0083min 𢄡
ϕSex ratio of the larvae (fraction of females)0.5
L ANumber of larvae per apple at which 50% survives the Allee effect25
L CNumber of larvae per apple at which 50% survives competition250
c ASlope sigmoid survival curve modeling the Allee effect0.088
c CSlope sigmoid survival curve modeling the competition0.044
2.1.2.2. Between-generation dynamics: reproduction

Adult females that have settled on a resource (R) deposit ξ eggs per minute on average. The cumulative number of eggs (L) on each resource item after 3 days (in generation n) determines whether larvae develop successfully into adults (4). The percentage of the larvae on one substrate that survives depends on the number of larvae present, survival is best for intermediate numbers of larvae. When there are only a small number of larvae on an apple, a fraction dies due to the Allee effect, while mortality due to competition plays a role when there are many larvae present. Of the surviving larvae, a fraction ϕ is female and these constitute the next female adult generation.

The number of larvae becoming adult females in the next generation P(x, y, n + 1) depends on the survival probabilities for the Allee effect (s A(L)) and for competition (s C(L)) in the following way

, where P(x, y, n + 1) denotes the newly emerged adult females that start searching immediately (S). The graphs of these functions are sigmoid curves with values between 0 and 1. The functions s A(L) and s C(L) are increasing and decreasing, respectively, the parameters c A and c C affect the slope of this increase or decrease and L A and L C are the number of larvae at which the survival rate is 50%.

2.2. Infochemical distribution

D. melanogaster responds to food odors (F) and its aggregation pheromone (A). In this model, we assume that there is no wind and that these odors thus diffuse randomly, with an equal probability to go in all directions. Odor diffusion is a much faster process than the dispersal of adult fruit flies. In addition, odor diffusion is a 3-dimensional process, while we model in two dimensions. We, therefore, introduced a loss term to represent odor matter that gets out of reach of the searching population by diffusion in the vertical direction (see also our companion paper by de Gee et al., 2008). Aggregation pheromone is not excreted in a gaseous form, but as a fluid accompanying the eggs. We, therefore, divide the aggregation pheromones in two phases: a liquid form on the resource that slowly evaporates (A R), and a gaseous form (A) that is detectable for the searching fruit flies in the air. The distribution of food odors and aggregation pheromone can thus be modeled by

, respectively, where D I is the diffusion constant of the infochemicals, μ is the average loss rate of the infochemicals in the z-direction, the value of this loss rate is dependent on the average time used. For more details on odor loss, we refer to the companion paper (de Gee et al., 2008). As dispersal and loss are mainly driven by atmospheric turbulence, these parameters have the same value for both food odors and aggregation pheromone. Furthermore, θ F and θ A are the production rates of the food odor by the resource (R) and the aggregation pheromone by the settled population (P R), respectively, and ω is the odor release rate by evaporation.

2.3. Integro-difference equation (IDE) approach

The model derived contains spatial dispersion of fruit flies and odors. Therefore, the system of ordinary differential equations that result after spatial discretization is stiff. This means that it contains a range of different timescales while we are interested in the process at the slowest timescale, the fastest time scale may control the numerical stability of explicit methods for solving this system of ordinary differential equations. In our case, this situation is aggravated by the fact that the odor distribution is a much faster process than the dispersal of fruit flies. Therefore, simple explicit integration methods are unsuitable because of a small step size, while on the other hand, the nonlinearity of the model impedes the use of implicit integration methods. For this reason, we chose the integro-difference approach (as in Neubert et al., 1995 Powell et al., 1998 and Etienne et al., 2002), which treats the dispersion as a separate process that can be solved analytically. This approach is effective because it allows us to take large time steps in accordance to the slow process, without running into any stability problems. In this approach, dispersal and population dynamics (e.g., reproduction) are treated as two distinct phases we model odor and fruit fly dispersal separate from adult population dynamics.

The dispersal of the population is calculated by taking the convolution product of the population density and the dispersal probability function. We take dispersal to be described by one of the following two-dimensional probability density functions or dispersal kernels (8) and (9). Random dispersal, used for modeling the dispersal of the searching population and for odor diffusion, is described by

, where Δt is the time step taken and D is the dispersal constant of the fruit flies (D P) or the diffusion constant of the infochemicals (D I). Ring random dispersal, used to model the moving population, is described by

, where ρ is the velocity of the displacement away from the resource.

The random dispersal kernel above, models the diffusion of the odor that is already present in the system. During each time step, odor is produced by the resources and released into the air. The dispersal of the produced odor is calculated by taking the convolution product of the odor produced per minute and a dispersal probability function for a continuously producing source. The distribution of the produced odor is described by (10),

, where Ei is the exponential integral.

The infochemicals direct the movement of the searching population toward the odor source. The spatial distribution of searching fruit flies, resulting from (1), can according to Powell et al. (1998), be approximated in discrete time by

, where K RD is the random dispersal kernel for the population of fruit flies and N a normalization constant. The “*” denotes the convolution operator over all spatial coordinates, i.e.,

. In (11), chemotaxis is modeled as a diffusion process. Diffusion is the movement of materials from an area with a high density to an area with a low density until equilibrium is reached. We can model movement toward the attractive source by artificially reducing the population density, with a stronger reduction for a more attractive spot. As diffusion occurs from high densities to low densities, the reduced population diffuses toward the attractive source, because the population density is𠅊rtificially—low at and around the source.

Equation (11) is best interpreted when it is read from the right to the left. It describes how the population at time t is first multiplied by a factor that contains the sensory index of the species and the attraction ratio κ (= ν/D P). This multiplication amounts to rescaling the population density. It strongly decreases the population density at points with a high odor concentration (combination of food odor and aggregation pheromone), while the density remains approximately the same where odor concentration is low. The dispersal is now directed toward the points with a low—rescaled—population density. After dispersal, the rescaled population is scaled back with the inverse of the above mentioned factor. This results in a strong increase of the population density in points with a high odor concentration. In this way, dispersal with a bias directed toward the infochemicals is modeled. However, this dispersal is not completely mass-conservative when using numerical approximations. Therefore, the dispersal step is finalized by normalization. To this end, we multiply the resulting density after chemotactically driven diffusion by such a number N that the total number of searching fruit flies is preserved.

2.3.1. Attractiveness to infochemicals

The dimensionless ratio κ = ν/D P is a measure for the relative strength of the chemical attraction (ν) as compared to the random dispersal (D P). If there is no chemical attraction, then the movement is at random and κ = 0. On the other hand, a strong influence of the chemical attraction in comparison to the random dispersion corresponds to high values of κ. In that case, the movement is directed toward the odor source.

2.4. Simulation

We considered a square spatial domain with reflecting boundary conditions for the population of fruit flies, and absorbing boundaries for the infochemicals. The reflecting boundary conditions for the flies represent a closed system (they cannot escape). On the other hand, the infochemicals can freely pass through the boundaries, never to come back: this is modeled by the absorbing boundary condition. We ran simulations for one generation, consisting of 3 dispersal days (short term population dynamics) and 10 generations of 3 dispersal days (long term population dynamics). Because evaporation is temperature dependent, odor evaporation during the night is much smaller than during the day. Also, the activity of yeasts, the main producers of the attractive fermenting fruit smell, is temperature dependent. We, therefore, assume that during the night no odor is produced. Furthermore, we assume that fruit flies do not reproduce or disperse during the night. Therefore, we modeled 12 hours per day. We discretized each dispersal day in steps of 5 minutes of dispersal by adult females followed by population dynamics (i.e., by settlement on resource, reproduction, patch leaving, or start of searching behavior) (Fig. ​ (Fig.2 2 ).

Flow chart of the processes in the model. In our model, the time step, Δt, is 5 min. We simulated 1 generation in the short term simulation and 10 generations in the long term simulation. Each generation consisted of 3 days.

2.4.1. Short term (one generation) simulation

To study the basic distribution patterns of fruit flies in a two-dimensional environment where infochemicals are present or absent, we ran three simulations, one 𠇌ontrol” simulation where no odors were present, one simulation where only food odors were present “F”, and a simulation where both food odors and aggregation pheromone were present “F + A”. We simulated the dispersal of fruit flies on a spatial domain of 30 m × 30 m. It is divided into 256 × 256 cells with a diameter of 0.117 m. The factor 256 is not essential for the design of the experiments, nor does it influence the results essentially however, it enhances the efficiency of the numerical computations because powers of 2 allow use of the fast Fourier transform for the convolutions.

2.4.1.1. Initial distribution of resources

We are interested in spatial differences due to aggregation. We, therefore, divided the domain in four quadrants. Each quadrant contained 9 resource patches of 1 m 2 clustered in one block (of 5 m × 5 m), with an interpatch distance of 1 m (Fig. ​ (Fig.3a). 3a ). The blocks were situated 6.5 m from the boundaries of the domain. The distance between two adjacent blocks was 7 m. The initial resource density was 5 apples m 𢄢 , evenly distributed over the block (like apple-sauce).

The set-up of the domain with spatial dimensions of the (a) short term simulation, size 30 m × 30 m, with 4 blocks of 9 clustered resources with resource density of 5 apples m 𢄢 , and the (b) long term simulation, size 90 m × 90 m, with 36 blocks containing randomly distributed apples with resource density of 5 apples m 𢄢 . The release point of the initial population is denoted with ×.

2.4.1.2. Initial adult distribution

To study whether aggregation occurs at resources with a higher initial density, we unevenly divided 800 adults (P 0) over the four quadrants. We situated 500 females in the lower left quadrant and 100 females in each of the other three quadrants. The fruit flies were released near the center of the domain. The release points were situated 2.5 m from the nearest resource. The distance between the release points in the center was 3.5 m.

2.4.1.3. Larval survival

At the end of the generation, the larval survival is calculated. We assessed the number of larvae that successfully developed into an adult female. In addition, to determine the costs and benefits of the use of infochemicals, we also assessed the number of larvae that did not survive due to the Allee effect or due to competition, and calculated mortality rates for both effects separately.

2.4.1.4. Statistics

To calculate the degree of aggregation of the population, we use k a measure of the amount of clumping, given by

, where μ is the mean and σ 2 the variance of the negative binomial distribution (Southwood and Henderson, 2000). The smaller the value of k, the greater the extent of aggregation, whereas for k → ∞(i.e., in practice k × 8), the distribution approaches a Poisson distribution, i.e., is virtually random. The value of k is influenced by the size of the sampling unit. In our model, we use same-sized units. Thus, we are able to use this measure to compare the degree of aggregation for the different treatments of availability of infochemicals.

To test the effects of infochemical use on settlement and on larval survival, we used the G-independence test on the number of settled and moving fruit flies for each treatment or on the number of larvae that survived or died for each treatment (Sokal and Rohlf, 1981).

The short term simulation is also used for a sensitivity analysis. For more details on the sensitivity analysis, we refer to our companion paper (de Gee et al., 2008).

2.4.2. Long term (ten generations) simulation

To study the long term population dynamics, we modeled a fruit fly population in an unpredictable heterogeneous environment. D. melanogaster cannot survive the winter in the natural climate of the Netherlands. Therefore, we simulated only one breeding season, consisting of 10 discrete generations. We model discrete nonoverlapping generations of 3 days each. These days consist of 12 dispersal hours, divided in time steps of 5 minutes (Fig. ​ (Fig.2). 2 ). The larval development was lumped, and computed at the end of the generation. For the long term simulation, we considered a domain of 90 m × 90 m, divided into 512 × 512 square cells, each with a diameter of 0.176 m. We ran three simulations, one 𠇌ontrol” simulation where no odors were present, one simulation where only food odors were present “F”, and a simulation where both food odors and aggregation pheromone were present “F + A”.

2.4.2.1. Initial adult and resource distribution

We introduced 2,000 fruit flies (P 0) in one single cell in the center of the domain. This domain contains 36 resource blocks of 5 m × 5 m (Fig. ​ (Fig.3b). 3b ). The resource blocks were situated 17.5 m from the boundaries of the domain. The distance between two adjacent blocks was 5 m. The initial resource density was 1 apple m 𢄢 . To study at which spatial scale effects take place, we looked at the population dynamics at a large spatial scale, i.e., the four quadrants of the domain (each containing 9 resource blocks), at an intermediate spatial scale, i.e., the resource blocks and 2.5 meter around the blocks, and at a small spatial scale, i.e. individual apples. For the simulation at the largest spatial scale, we used two additional resource densities, 5 and 10 apples m 𢄢 to study whether the spatial dynamics of the fruit fly population depends on resource availability.

To mimic the natural situation, the apples were placed randomly each generation, with each grid cell in the block containing either one apple or no apple (3a). Outside the resource blocks, the cells were empty. The total amount of apples per quadrant of the domain was fixed (for example, when the initial resource density is 1 apple m 𢄢 , a quadrant contains 9 (blocks) × 25 (m 2 ) × 1 (apple m 𢄢 ) = 225 apples), but as they were placed randomly over the resource blocks, there were differences in the amount of apples per resource block.

We ran the simulations at the largest spatial scale three times, with a different starting point of the random number generator, to verify the consistency of the results.

2.4.2.2. Statistics

We used linear mixed models to test the effect of the use of infochemicals on the mortality due to the Allee effect (%), mortality due to competition (%), and larval survival (%) in the first five generations (during the population expansion). This method is especially suitable for data, where the measurements are correlated in time. In the model, we took “generation” as repeated measurement, and “treatment”, “generation”, and “treatment × generation” as fixed effects. We tested the model for four different covariance structures, compound symmetry (CS), first-order autoregressive (AR(1)), heterogeneous first-order autoregressive (ARH(1)), and an unstructured covariance matrix (UN). The unstructured covariance matrix was the best model for the data (it had the lowest AIC). For these statistics, we used SAS 9.1.

2.5. Parameter values

We used the parameter values as given in Table ​ Table1. 1 . For more details on how these values are arrived at, we refer to the companion paper (de Gee et al., 2008).


The Embryo Project Encyclopedia

Hermann Joseph Muller conducted three experiments in 1926 and 1927 that demonstrated that exposure to x-rays, a form of high-energy radiation, can cause genetic mutations, changes to an organism's genome, particularly in egg and sperm cells. In his experiments, Muller exposed fruit flies (Drosophila) to x-rays, mated the flies, and observed the number of mutations in the offspring. In 1927, Muller described the results of his experiments in "Artificial Transmutation of the Gene" and "The Problem of Genic Modification". His discovery indicated the causes of mutation and for that research he later received the Nobel Prize in Physiology or Medicine in 1946. Muller's experiments with x-rays established that x-rays mutated genes and that egg and sperm cells are especially susceptible to such genetic mutations.

Muller studied genetic mutations and the structure of chromosomes in fruit flies during the early twentieth century. From 1910 to 1915, Muller worked with Thomas Hunt Morgan, a scientist at Columbia University in New York City, New York, who researched the role chromosomes play in heredity. Chromosomes are structures that consist of DNA, the genetic material of the cell, and are found within a cell's nucleus. While working in Morgan's fly lab, Muller helped discover a class of genes called marker genes, also called genetic markers, that enabled scientists to identify specific places in the genome, even after making changes to particular chromosomes or genes. Muller used genetic markers in his later x-ray experiments that identified mutations in the genome. In the 1920s, Muller studied the role of temperature as a possible mutagen, or cause of genetic mutations. He showed that high temperatures had the capability to mutate genes. Through his work studying the effects of temperature on genetic mutations, Muller developed a method to quantify the number and frequency of mutations which he used in his later experiments with radiation.

In the early 1900s, professionals used x-rays, a form of high-intensity radiation, in the medical, dental, and industrial fields, though they knew little about the long-term effects of exposure to radiation. Many researchers studied how x-rays affected living cells. In 1907, physician Charles Russell Bardeen showed that toad eggs fertilized with sperm he exposed to x-rays resulted in embryos with developmental abnormalities that prevented toad larvae from developing into tadpoles. Experiments like Bardeen's supported Muller's hypothesis that mutations involved changes to individual genes. Muller hypothesized that he could induce genetic mutations using x-rays. Muller performed a series of three experiments in 1926 and 1927 exploring the role of x-rays as a mutagen.

Muller began his first experiment testing x-rays as a mutagen in 1926 while at the University of Texas in Austin, Texas. In his first experiment, Muller bred flies whose genomes contained particular genetic markers on the X-chromosome, which enabled him to identify mutations. In normal flies, female flies have two X-chromosomes. Male flies, however, have only one X-chromosome and one Y chromosome which does not contain those particular genetic markers. Muller used male flies that contained the X-linked gene, meaning it was located on the X-chromosome, for bobbed bristles (bb) as a genetic marker. The bb gene, led to offspring with noticeable differences in the shape of the flies' sensory bristles compared to normal flies. Muller also used female flies that were homozygous for the X-linked gene sc v f, meaning that both X-chromosomes contained the sc v f gene. The sc v f gene led to offspring with a difference in eye color and distinguishably different sensory bristles. Before mating the male and female flies, Muller exposed the flies to x-ray radiation in an attempt to induce genetic mutations in them. Following the x-ray treatment, he mated the flies to produce offspring consisting of heterozygous females, meaning that each female offspring had one X-chromosome with the bb gene and one with the sc v f gene, and male offspring had the sc v f gene. The bb genetic markers were recessive, so the heterozygous females displayed the physical traits associated with the sc v f gene, but still carried the bb gene and, any mutations induced by the radiation in that chromosome.

To determine whether genetic mutations were induced in a parent exposed to radiation, Muller mated the heterozygous female offspring, which had both bb and sc v f genes, with their sc v f brothers. The male offspring of that cross (grandsons of the irradiated male or female parent) revealed whether or not the x-rays had induced any mutations. Rather than looking for abnormal body parts, Muller determined whether or not mutations had occurred by studying lethal mutations, types of mutations that cause the offspring to die before being born. To identify the lethal mutations, Muller observed the ratio of bb male and sc v f male offspring. If the offspring lacked bb males and only sc v f males were present, Muller reasoned that exposure to radiation induced lethal mutations in the bb genes of male grandparents. Alternatively, if only bb males were present, he reasoned that exposure to radiation induced a lethal mutation in the sc v f female grandparent.

Muller created over 1,000 cultures of first generation flies whose parents had been subjected to x-rays and a similar amount of control cultures, cultures in which the flies' parents were not subjected to the radiation. By comparing the results of his x-ray experiments with control cultures, Muller confirmed that x-rays caused the genetic mutations and that the mutations did not spontaneously arise, or arise from normal cell function.

After examining the cultures, Muller observed that there were a high number of lethal mutations in the offspring of the x-ray treated flies (88 lethal mutations in 758 cultures). The control group showed a lower frequency of the lethal mutations (1 lethal mutation in 947 cultures). Muller concluded that the x-ray exposure caused the lethal mutations in the offspring of the x-ray treated flies. He also found that mutations occurred in both the male and female flies when exposed to x-rays, indicating that both sexes were vulnerable to radiation-induced mutations.

Building on the results from his first experiment, Muller conducted a second. In the second experiment, Muller used a different type of genetic marker. He used a group of X-linked genes called ClB genes which were lethal to male fruit flies. Through controlled mating, Muller bred a generation of fruit flies in which the male offspring could inherit one of only two kinds of X-chromosomes: ones that had the lethal ClB gene, or ones that had been exposed to radiation in earlier generations. If parent flies radiated in earlier generations had developed lethal mutations in the x chromosomes of their gametes, then mating the files would fail to produce any living male offspring. By counting the number of male and female offspring, Muller inferred the results of mutations caused by radiation. He determined that x-ray exposure caused 150 times more flies to die through a lethal mutation compared to the spontaneous rate of mutations that occurred in the control group.

Muller then developed a third experiment in the spring of 1927 that specifically looked for visible mutations in the offspring of radiation exposed fruit flies. Muller used females with two X-chromosomes fused together as well as a single, separate Y-chromosome to mate with males with the bb gene that he had also exposed to x-rays. Mating females with fused X-chromosomes leads to an unusual mode of inheritance. Unlike typical sex inheritance, where males inherit their X-chromosome from their mother and their Y-chromosome from their father, mating a female with fused X-chromosomes leads the opposite. Instead, males inherit their X-chromosomes from their father and their Y-chromosome from their mother. Consequently, any non-lethal mutation induced in a male parent can be identified in their sons because their sons inherit their fathers' X-chromosome. By that method, Muller noted that many visible mutations caused by radiation were the same as visible mutations that occurred spontaneously, such as white eyes, small wings, and bobbed bristles. Furthermore, he found that the mutations occurred more frequently in flies exposed to radiation than they did in the untreated flies.

In his 1927 article "Artificial Transmutation of the Gene," Muller published his conclusions that exposure to x-rays could cause genetic mutations. However, Muller failed to include complete data and methods of his prior experiments. According to Elof Carlson, a student of Muller, the paper grabbed the attention of geneticists, but many questioned Muller's findings because of the missing data. Later that year Muller released the paper "The Problem of Genic Modification" at a genetics conference in Berlin, Germany. His second paper detailed his 1926 and 1927 experiments and provided clarifications that "Artificial Transmutation of the Gene" lacked. A year after the conference, other scientists confirmed Muller's claims that x-rays led to mutations in both plant and animal chromosomes.

Muller's discovery that x-rays caused genes to mutate had many different implications in various fields. In the field of radiology, Muller's work illuminated previously undocumented dangers of x-rays. His findings showed that x-rays could be particularly harmful to humans in their reproductive years. Radiologists began taking precautions to shield patients from radiation that could cause genetic mutations to sperm and egg cells, possibly affecting later embryo development. In the field of genetics, Muller's work showed that environmental factors like radiation affected heritable characteristics. Furthermore, his discovery enabled scientists to directly induce genetic mutations instead of waiting for mutations to occur spontaneously. For his experiments on the mutagenic effects of x-rays, Muller received the Nobel Prize in Physiology or Medicine in 1946.


Effects of Cellular Environment on Stem Cell Differentiation Discussed at Stubenbord Visiting Lectureship

Drosophila melanogaster, or the common fruit fly, is an important model organism for scientific research such as stem cell therapeutics.

Drosophila melanogaster , more often known as the fruit fly, has long been used as a model organism for both genetics and developmental biology. However, a recent lecture by Dr. Allan Spradling, the William D. Stubenbord Visiting Professor at Weill Cornell and director of the Department of Embryology at the Carnegie Institute of Washington, made the case that Drosophila can be a model for the complex and emerging science of stem cell therapeutics.

"For several reasons, you can hardly imagine how well-suited a fruit fly is for studying stem cell biology," Dr. Spradling said. "The Drosophila stem cell system is much like the more complicated vertebrate system, but stripped down just to the essentials."

Dr. Spradling has used Drosophila in a number of stem-cell-related projects, including the identification of regulatory cells that govern stem-cell replacement and differentiation in vivo, the mechanisms by which cells can "de-differentiate" back into stem cells, and the identification of stem cells in the Drosophila intestine.

In 2000, Dr. Spradling began using Drosophila to revisit the role of "niches"—the microenvironments surrounding stem cells that regulate their differentiation—because the relatively straightforward production of Drosophila embryonic germline stem cells could be studied in vivo, unlike their mammalian counterparts, which had only been studied in culture to that point. Additionally, because of a very clear lineage property in the production of stem cells within the Drosophila ovary, the cells could be marked genetically, making them distinguishable from one another as either differentiated daughter cells or persistent stem cells.

"Our first surprise was that stem cells are not stable nor are they all that permanent," Dr. Spradling said. "There is a turnover, or replacement, and somatic stem cell replacement is occurring from a distant source." That source was often, although not necessarily, neighboring stem cell "daughters," leading Dr. Spradling and his team to propose that cell "de-differentiation"—the reversion of a specialized cell back into a stem cell—could be closely studied in Drosophila .

Although stem cell "de-differentiation" was known to occur in natural systems in general, and in the human liver specifically, the process had never been closely studied in the laboratory, because mammalian stem cell organization, behavior and regulation had not been characterized well enough at the cellular level.

Dr. Spradling and his laboratory blasted Drosophila larva with a regulatory protein that induces stem cell differentiation in the ovary. As the protein disappeared, they were able to closely monitor the "de-differentiation" process, which occurred uniformly without cell loss and without affecting the insects' fertility as adults.

While a great deal of research, media and political attention has focused on the process of differentiating a stem cells into target tissues (and perhaps one day, complete organs), Dr. Spradling believes the reverse process may be a quicker route to stem cell therapies, because stem cell behavior is so heavily affected by its environment. By studying the "de-differentiation" of specialized tissue cells back into stem cells, scientists may be able to eventually reverse-engineer stem cell therapies.

"Stem cell biology is relevant because, to some extent, all cells respond to their environment," Dr. Spradling said. "These are very dynamic systems, and there is a great deal of environmental influence. We need to understand why these cells make the decisions they make."

This year's William D. Stubenbold Visiting Professor Lecture was presented by the Department of Cell and Development Biology and sponsored by the Louis Calder Foundation. The Stubenbord Fund was established by the Louis Calder Foundation in memory of Louis Calder Sr. and in recognition of the outstanding professional services and long friendship of the late William D. Stubenbord for them and members of their families.


Lords of the Fly : Drosophila Genetics and the Experimental Life

The common fruit fly, Drosophila, has long been one of the most productive of all laboratory animals. From 1910 to 1940, the center of Drosophila culture in America was the school of Thomas Hunt Morgan and his students Alfred Sturtevant and Calvin Bridges. They first created "standard" flies through inbreeding and by organizing a network for exchanging stocks of flies that spread their practices around the world.

In Lords of the Fly, Robert E. Kohler argues that fly laboratories are a special kind of ecological niche in which the wild fruit fly is transformed into an artificial animal with a distinctive natural history. He shows that the fly was essentially a laboratory tool whose startling productivity opened many new lines of genetic research. Kohler also explores the moral economy of the "Drosophilists": the rules for regulating access to research tools, allocating credit for achievements, and transferring authority from one generation of scientists to the next.

By closely examining the Drosophilists' culture and customs, Kohler reveals essential features of how experimental scientists do their work.

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Review: Lords of the Fly: Drosophila Genetics and the Experimental Life

Scientists began using Drosophila melanogaster as a model organism around 1910, long before the advent of modern molecular biology techniques. My big question was "HOW on Earth were they able?" and . Читать весь отзыв


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