skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • Stochastic variation in the initial phase of bacterial infection predicts the probability of survival in D. melanogaster
Title: Stochastic variation in the initial phase of bacterial infection predicts the probability of survival in D. melanogaster
Sick individuals do not all respond to an infection in the same way. One individual may experience mild symptoms and recover easily, while another may suffer devastating illness or even death. A number of factors are often assumed to account for these differences, including the sex, age and genes of the individuals, and differences in the environments the individuals have been exposed to. However, random variations in how an individual’s immune system interacts with the infection could also play an important role in recovery. Duneau et al. have now studied how genetically identical fruit flies who were raised in the same environment respond to different bacterial infections. This enabled them to develop a mathematical model that describes how a bacterial infection develops in an individual. In an initial phase, the bacteria proliferate freely. If the immune defenses activate in time to control the infection, the number of bacteria in the fly decreases to a constant level and the infection enters a long-term, or chronic, phase. The sooner this happens the more likely it is that the fly will survive. If the immune control happens too late, the infection enters a terminal phase and the fly will die once the number of bacteria increases to a certain level. The model therefore reveals that the precise time at which the immune system takes control of the bacterial population – termed the “Time to Control” – determines the outcome of the infection. Duneau et al. confirmed this by injecting bacteria into identical flies. A small variation in the Time to Control was sometimes the difference between a fly living or dying. Understanding what controls this apparently random variation is key to understanding infection and potentially developing more efficient treatments for a wide range of diseases – not just those caused by bacteria, but also those caused by viruses and parasites, like HIV and malaria.  more » « less
Award ID(s):
1354421
PAR ID:
10057330
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
eLife
Volume:
6
ISSN:
2050-084X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, eLife)
    null (Ed.)
    The human immune system develops a memory of pathogens that it encounters over its lifetime, allowing it to respond quickly to future infections. It does this partly through T cells, white blood cells that can recognize different pathogens. During an infection, the T cells that recognize the specific pathogen attacking the body will divide until a large number of clones of these T cells is available to help in the fight. After the infection clears, the immune system ‘keeps’ some of these cells so it can recognize the pathogen in the future, and respond quicker to an infection. Over the course of their lives, people will be infected by many different pathogens, leading to a wide variety of T cells that each respond to one of these pathogens. However, it is not well understood how various infections throughout the human lifespan shape the overall population of different T cells. Gaimann et al. used mathematical modelling to study how the composition of the immune system changes in people of different ages. Different populations of T cells – each specialized against a specific antigen – had been previously identified through genetic sequencing. Gaimann et al. analyzed their dynamics to show that many of the largest populations originate around birth, during the formation of the immune system. These findings suggest a potential mechanism for how exposure to pathogens in infancy can influence the immune system much later in life. The results may also explain variations in how people respond to infections and in their risk of developing autoimmune conditions. This understanding could help develop new treatments or interventions to guide the immune system as it develops. 
    more » « less
  2. ; ; ; (, eLife)
    Many behaviors that we perform everyday, including something as familiar as making a peanut-butter sandwich, consist of a sequence of recognizable acts. These acts may include, for example, holding a knife and opening a jar. Yet often neither the sequence nor the individual acts are always performed in the exact same way. For example, there are many ways to hold a knife and there are many ways to open a jar, meaning neither of these actions could be called “stereotyped”. A lack of stereotypy makes it difficult for a computer to automatically recognize the individual acts in a sequence. This same problem would apply to other common behaviors, such as walking around somewhere you have not visited before. While we easily recognize it when we see it, walking is not a stereotyped behavior. It consists of a series of movements that differ between individuals, and even in the same individual at different times. So how can someone automatically recognize the individual acts in a non-stereotyped behavior like walking? To begin to find out, Tao et al. developed a mathematical model that can recognize the walking behavior of a fruit fly. Existing recordings of fruit flies walking were analyzed using a type of mathematical model called a Hierarchical Hidden Markov Model (often shortened to HHMM). Such models assume that there are hidden states that influence the behaviors we can see. For example, someone’s chances of going skiing (an observable behavior) depend on whether or not it is winter (a hidden state). The HHMM revealed that the seemingly random wanderings of a fly consist of ten types of movement. These include the “meander”, the “stop-and-walk”, as well as right turns and left turns. Exposing the flies to a pleasant odor – in this case, apple cider vinegar – altered how the flies walked by changing the time they spent performing each of the different types of movement. All flies in the dataset used the same ten movements, but in different proportions. This means that each fly showed an individual pattern of movement. In fact, the differences between flies are so great that Tao et al. argue that there is no such thing as an average walk for a fruit fly. The model represents a complete description of how fruit flies walk. It thus provides clues to the processes that transform an animal’s sensory experiences into behavior. But it also has potential clinical applications. Similar models for human behaviors could help reveal behaviors that are abnormal because of disease. Normal behaviors also show variability, and some diseases increase or decrease this variability. By making it easier to detect these changes, mathematical models could support earlier diagnosis of medical conditions. 
    more » « less
  3. ; ; ; ; (, eLife)
    From birds that preen their feathers to dogs that lick their fur, many animals groom themselves. They do so to stay clean, but routine grooming also has a range of other uses, such as social communication or controlling body temperature. Despite its importance, grooming remains poorly understood; it is especially unclear how this behavior is regulated. Fruit flies could be a good model to study grooming because they are often used in laboratories to look into the genetic and brain mechanisms that control behavior. Flies clean themselves by sweeping their legs over their wings and body, but little is known about how the insects groom ‘naturally’ over long periods of time. This is partly because scientists have had to recognize and classify grooming behavior by eye, which is highly time-consuming. Here, Qiao, Li et al. have created a system to automatically detect grooming behavior in fruit flies over time. First, a camera records the movement of an individual insect. A computer then analyzes the images and picks out general features of the fly’s movement that can help work out what the insect is doing. For example, if a fly is moving its limbs, but not the main part of its body, it is probably grooming itself. Qiao, Li et al. then borrowed an algorithm from an area of computer science known as ‘machine learning’ to teach the computer how to classify each fly’s behavior automatically. The new system successfully recognized grooming behavior in over 90% of cases, and it revealed that fruit flies spend about 13% of their waking life grooming. It also showed that grooming seems to be controlled by two potentially independent internal programs. One program is tied to the internal body clock of the fly, and regulates when the insect grooms during the day. The other commands how long the fly cleans itself, and balances the amount of time spent on grooming with other behaviors. Cleaning oneself is not just important for animals to stay disease-free: it also reflects the general health state of an individual. For example, a loss of grooming is associated with sickness, old age, and, in humans, with mental illness. If scientists can understand how grooming is controlled at the brain and molecular levels, this may give an insight into how these mechanisms relate to diseases. The system created by Qiao, Li et al. could help to make such studies possible. 
    more » « less
  4. ; ; ; ; ; ; (, Microbial Ecology)
    Bats harbor diverse intracellular Bartonella bacteria, but there is limited understanding of the factors that influence transmission over time. Investigation of Bartonella dynamics in bats could reveal general factors that control transmission of multiple bat-borne pathogens, including viruses. We used molecular methods to detect Bartonella DNA in paired bat (Pteropus medius) blood and bat flies in the family Nycteribiidae collected from a roost in Faridpur, Bangladesh between September 2020 and January 2021. We detected high prevalence of Bartonella DNA in bat blood (35/55, 64%) and bat flies (59/60, 98%), with sequences grouping into three phylogenetic clades. Prevalence in bat blood increased over the study period (33% to 90%), reflecting an influx of juvenile bats in the population and an increase in the prevalence of bat flies. Discordance between infection status and the clade/genotype of detected Bartonella was also observed in pairs of bats and their flies, providing evidence that bat flies take blood meals from multiple bat hosts. This evidence of bat fly transfer between hosts and the changes in Bartonella prevalence during a period of increasing nycteribiid density support the role of bat flies as vectors of bartonellae. The study provides novel information on comparative prevalence and genetic diversity of Bartonella in pteropodid bats and their ectoparasites, as well as demographic factors that affect Bartonella transmission and potentially other bat-borne pathogens. 
    more » « less
  5. ; ; ; ; (, eLife)
    Flocks of birds, schools of fish and herds of animals are all good examples of collective migration, where individuals co-ordinate their behavior to improve survival. This process also happens on a cellular level; for example, when bacteria consume a nutrient in their surroundings, they will collectively move to an area with a higher concentration of food via a process known as chemotaxis. Several studies have examined how disturbing collective migration can cause populations to fall apart. However, little is known about how groups withstand these interferences. To investigate, Bhattacharjee, Amchin, Alert et al. studied bacteria called Escherichia coli as they moved through a gel towards nutrients. The E. coli were injected into the gel using a three-dimensional printer, which deposited the bacteria into a wiggly shape that forces the cells apart, making it harder for them to move as a collective group. However, as the bacteria migrated through the gel, they smoothed out the line and gradually made it straighter so they could continue to travel together over longer distances. Computer simulations revealed that this smoothing process is achieved by differences in how the cells respond to local nutrient levels based on their position. Bacteria towards the front of the group are exposed to more nutrients, causing them to become oversaturated and respond less effectively to the nutrient gradient. As a result, they move more slowly, allowing the cells behind them to eventually catch-up. These findings reveal a general mechanism in which limitations in how individuals sense and respond to an external signal (in this case local nutrient concentrations) allows them to continue migrating together. This mechanism may apply to other systems that migrate via chemotaxis, as well as groups whose movement is directed by different external factors, such as temperature and light intensity. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email