Only a few #Shigella_bacteria are sufficient for the development of #gastroenteritis, whereas the development of #cholera requires millions of #Vibrio_cholerae bacteria. The question has remained- ‘Why does the #infective_dose (disease-causing dosage) differ so much among bacteria?’
Now #mathematicians have sought out an answer to this black box. They say that the difference might be due to how bacteria attack their hosts. Shigella bacteria have a local activity as they directly inject toxic proteins into host cells, whereas, cholera bacteria launch a distant attack by secreting the #cholera_toxin.
#Mathematical_models support the hypothesis that the #scale_of_pathogenetic_mechanisms underlies the differences in infective doses among various bacteria. They also predict that the mechanism of #pathogenesis influences the spread of #infection in the host. The models demonstrate that there exists a #threshold_for_infective_doses.
Bacteria that launch an attack using diffusible toxins suffer from a shortcoming, i.e. when the bacterial load (number of bacteria) is less- the toxin spreads away rapidly, leaving the bacteria defenceless and vulnerable to #immune_attack. So these bacteria require help from other bacteria. Only when there are sufficient bacteria (high bacterial load or threshold) producing adequate amount of toxin, are they secured against attack by the immune system. Bacteria producing #locally_acting_toxins, require a lower threshold, i.e., since the toxin doesn't diffuse far-off, even a few bacteria can come together to protect themselves from immune attack.
Therefore, when bacteria release toxins that act locally, a lower bacterial threshold is required to instigate an infection. However, when bacteria produce long-acting toxins, a huge #bacterial_load is required to initiate an infection. Nonetheless, while #local_pathogenetic_mechanisms have a need of smaller infective doses, #pathogens producing long acting toxins have a tendency to spread faster and might even harm the host to a greater extent.Tags: Bacterial Infections, bacterial pathogenesis, disease prevention, Epidemiology, health, host-pathogen interactions, Immunology, Infectious Disease, Mathematical Modelling, Microbiology, public health, public services, toxin-dependent bacterial infections