Sinusitis, ear infections, and pneumonias are common examples of secondary infections. For example, a runny nose that persists beyond days may be a sinus infection that would be best treated with an antibiotic. Ear pain and new onset fever after several days of a runny nose is probably an ear infection. Depending on your child's age, these infections may or may not require an antibiotic. Pneumonia may be detected by persistent cough, stomach ache, or difficulty breathing.
Your physician may diagnose pneumonia by physical exam or may request a chest x-ray. Other bacterial illnesses that we are concerned about include urinary tract infections UTIs , which can be hard to detect and can cause kidney damage if they are untreated. If your child has a fever without a great source of infection, your doctor will likely want to check the urine. UTIs are more common in little girls and in baby boys under one year of age who are not circumcised.
More serious concerns are bacterial illnesses like sepsis bacteria in the blood and bacterial meningitis bacterial infection in the lining of the brain and spinal cord. We become concerned about meningitis in older children with a stiff neck or changes in mental status. Babies are less likely to be able to show us these symptoms, and we are more likely to do more tests on them to make sure these infections are not part of the illness.
Remember that many of the vaccines that your child receives in the first years are meant to prevent these serious bacterial infections. Tests that are frequently performed to help us with the diagnosis of a bacterial infection include a complete blood count and cultures of fluid that we are concerned about. This may include a blood culture, urine culture, or spinal culture which requires a spinal tap. Whether the infection turns out to be caused by virus or bacteria, you should watch your child for any of the following signs and bring them to medical attention if they develop:.
Children who are around other children will have more frequent infections. But remember most children these days thanks to vaccines that prevent most serious secondary bacterial infections will have viruses that take supportive care only.
Therefore, the population growth rate, b , can be expressed as a product of the Arrhenius factor corresponding to the metabolic free-energy barrier, H 12 , and the folded fraction of properly functioning RDPs:.
Because a protein's folding free energy is a function of temperature, it will change when temperature is perturbed. The temperature dependence of protein stability is given by a classical expression 24—26 :. The last term in Eq. Our concern is how protein stability changes with temperature upon temperature change from T R to T.
We have divided the following discussion into three parts. First, we present results from the numerical analysis of the thermal adaptation process, and we discuss thermal adaptation behavior for bacteria and viruses with semiconservative and conservative replication, respectively.
In the second section, we develop a semianalytical model of thermal adaptation and discuss in quantitative terms the relationship between various parameters that are relevant for thermal adaptation and thermal response curves. In addition, we estimate an optimal growth temperature associated with each species. In the last section, we compare experimental results with model predictions.
We find that our model can provide a good explanation for thermal adaptation of bacteria, using only two independent parameters for each bacterial species. We prepared the system such that an organism initially has a probability of 0. Therefore, we define 10 time steps as an initial generation time in our simulation. This is nonetheless an approximate time, since, due to the diverse nature of the population, some organisms may evolve to replicate much faster than others.
At each time step, an organism can replicate with a probability determined by the genotype-dependent replication rate, as given by Eq. An organism is eliminated as soon as a lethal mutation occurs that confers a folding-free-energy value greater than zero on any of its proteins. Upon replication, mutations may happen in a descendant organism. A mutation in our model represents the change in stability of a mutated protein in the daughter organism compared with the parent organism see details in the Supporting Material.
Here, we assume that a mutated protein folds into the same structure as the wild-type protein, as indeed has been observed in many protein engineering experiments We ran many series of independent simulations to eliminate the effect of genetic drift due to a relatively small population size in simulations.
During the numerical simulation, we let organisms evolve in a stable environment for around 20, generations, and studied population dynamics and evolution of protein stabilities in a range of parameters. Parameter b o establishes the correspondence between real time and time step in the simulation. We studied evolution and adaptation for both conservative and semiconservative replication processes.
In semiconservatively replicating species DNA-based organisms that do not have a methylation mechanism to distinguish between parent and newly synthesized strands , mutations can occur in both descendant copies. Conservative replication, on the other hand, occurs in single-strand RNA viruses, and in bacteria that have methylation mechanisms to discriminate between parent and daughter strands.
For conservative replication, one copy or strand retains the same genome sequence as previous generations, whereas the daughter copy or strand may acquire mutations. Bacteria species usually have a much lower mutation rate than RNA virus species 30—32 , which leads to a considerable difference in their thermal responses, as well as their thermal adaptation dynamics, as will be shown below. These values give thermal response predictions that agree well with experimental observations of thermal adaptation of mesophiles.
Meanwhile, we chose the bacterium species to have a realistic mutation rate of 0. After evolving in a steady thermal environment for 20, generations, the distribution of protein stabilities within a population reaches equilibrium.
When temperature decreased, fitness of both semiconservatively and conservatively duplicated species slowly declined, whereas a temperature increase caused a sharper drop of fitness, especially for viral species Fig. When temperature increases above the evolutionary temperature at which the species have been cultivated, the fraction of folded form for some proteins—the least stable ones—significantly decreases, thereby decreasing the genome replication rate very rapidly.
This is especially pronounced for viral strains, which have a larger proportion of less stable proteins due to their elevated mutation rate see Fig. Thermal response of fitness and protein stability distributions for a model bacterium species black lines and a model RNA virus species red lines gray in print see text for details. A Fitness response to temperature variation.
In addition to instantaneous thermal response, we also studied long-time thermal adaptation of bacteria and viruses after they had adapted to a new environment for a period of time.
Here, we use 10, generations, the same as the experimental timescale for bacterial evolution 7. For RNA viruses, because of their high mutation rates, we set the adaptation time at generations.
We then measured relative to the wild-type species fitness as a function of the temperature change. We can see from Fig. To better understand the distribution of protein stabilities within each strain, we studied denaturation temperatures of all proteins for each strain. As noted in the Model section, as temperature increases, some of the proteins in the organism will become unstable and get denatured.
Here, we define the lethal denaturation temperature LDT for an organism as the temperature above the evolutionary temperature at which the least stable protein in this organism becomes denatured, i. A plot of the distribution of the organismal LDTs over all organisms in a population for each strain can be seen in Fig. It is clear from Fig. From Fig. Nonetheless, we note that the distribution of LDT reflects the effect of temperature only approximately.
In reality, some proteins can function at temperatures higher than this theoretical denaturation temperature, albeit with dramatically reduced functional copy numbers. Distribution of the LDT for wild-type solid lines and high-temperature-evolved dashed lines bacteria black lines and viruses red lines gray in print.
The results for all strains are listed in Table 1. From this analysis, we can see that although the LDT for bacteria cultured at the elevated temperature has improved significantly Fig. This observation follows from the nature of the processes of mutation and selection, which occur during thermal adaptation.
On the one hand, selection pressure introduced by increasing the environmental temperature would eliminate organisms that contain very unstable proteins, so that the LDT of the bacteria strain is significantly enhanced. On the other hand, the low mutation rate of the bacteria strain, as well as limited evolutionary time, gives the cultured strain only a limited opportunity to adapt to the new environment.
In Fig. According to Zeldovich et al. Here, C 0 is the normalization constant for the probability distribution. D , h , and L are parameters obtained from the distribution of energetic effects of the protein point mutations.
Although the distribution of stabilities given by the analytical expression Eq. From Eq. Given this information, it is convenient to take the mean-field approximation for the organismal birth rate and consider the ensemble average over all organisms in a species.
Therefore, the logarithmic population growth rate can be expressed as. Using Eqs. Here, C 1 — C 4 are various constants see the Supporting Material for their derivation. See the Supporting Material for the derivation and analysis. Analysis of Eqs. From the analytical expressions of C 1 … C 4 , we can write that.
We plotted the fitness change versus temperature for bacteria having different RDG numbers Fig. We note that at a constant metabolic free-energy barrier and environmental temperature, increasing the number of RDGs leads to a lower OGT. According to Eq. This can also be seen in Fig. S1 of the Supporting Material , although the effect is relatively weak. Several experiments were carried out to study thermal response and adaptation behavior of bacteria and viruses 1,2, Ratkowski et al.
Here, we analyzed thermal response curves for these 35 mesophilic strains using our model. Since we have limited information about the evolutionary temperature for each bacterial strain, we used the OGT as a proxy for evolutionary temperature, motivated by observations and our results showing that the two are not too different for mesophiles.
We evaluate growth rate as a function of temperature for each bacterial strain from Eq. This number is also consistent with a recent estimate by Forster and Church 34 of a minimal gene set, albeit somewhat smaller.
Here, we were able to obtain a relatively good fit for almost all of the datasets, and several examples comparing the experimental data with our theoretical predictions are shown in Fig. Comparison between experimental thermal response curves and predictions from the semianalytical model. Here, we show results for 3 of 35 species studied by Ratkowsky and co-authors Thermal adaptation in viruses and bacteria has been studied extensively in the past, and a number of qualitative features of thermal response and adaptation have been found to be common to most studied species and strains.
In particular, the following observations have been made. Our model, although quite minimalistic, explains all these findings, providing a unified picture of physical mechanisms of thermal adaptation. The key premise of the theory is that to function, proteins must be stable, and that one of the key determinants of the rate of growth i. The protein stability factor affects replication rate through modulation of the fraction of correctly folded proteins, as suggested by Eq.
Nevertheless, the dependence of replication rate on the copy number of folded RDPs given by Eq. We believe that the key novel aspect of our model is that it explicitly takes into account and derives a broad distribution of protein stabilities in the genome of a bacterial or viral species, in contrast to earlier studies in which it was assumed that stability of a single protein determines the growth rate of bacteria or that all proteins in an organism have the same stability Although the study of Ratkowsky and co-authors 12 was successful in fitting thermal response curves for many bacterial strains, such fitting was achieved at the expense of a large number of fitting parameters five for each species to describe the thermodynamics of the single RDP.
A broad distribution of protein stabilities within a species is a key factor determining a prokaryotic thermal response. The analytical approximation and simulations show that deviation of the OGT from evolutionary temperature is small, in agreement with experimental observations.
Travisano and Lenski 5 systematically studied the thermal response curves of E. Our theory provides the physical rationale for this observation. Indeed, broad equilibrium distribution of stabilities of RDPs see Fig.
It is the drop in the copy number of these folded RDPs that brings about an immediate loss of fitness upon an increase of temperature above the evolutionary temperature. Thermal adaptation experiments showed that E.
Then, according to the analysis of Eq. The fact that the OGT is especially close to evolutionary temperature for the equilibrated species, points to an interesting evolutionary observation. National Science Foundation propels the nation forward by advancing fundamental research in all fields of science and engineering. NSF supports research and people by providing facilities, instruments and funding to support their ingenuity and sustain the U.
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