Concepts In Bacterial Virulence (Contributions to Microbiology)
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These are locations where the host cells are in direct contact with the external environment. Shown are different portals of entry where pathogens can gain access into the body.
With the exception of the placenta, many of these locations are directly exposed to the external environment. Mucosal surfaces are the most important portals of entry for microbes; these include the mucous membranes of the respiratory tract, the gastrointestinal tract, and the genitourinary tract.
Although most mucosal surfaces are in the interior of the body, some are contiguous with the external skin at various body openings, including the eyes, nose, mouth, urethra, and anus. Most pathogens are suited to a particular portal of entry. The respiratory and gastrointestinal tracts are particularly vulnerable portals of entry because particles that include microorganisms are constantly inhaled or ingested, respectively.
Pathogens can also enter through a breach in the protective barriers of the skin and mucous membranes. Pathogens that enter the body in this way are said to enter by the parenteral route. For example, the skin is a good natural barrier to pathogens, but breaks in the skin e. In pregnant women, the placenta normally prevents microorganisms from passing from the mother to the fetus. However, a few pathogens are capable of crossing the blood-placental barrier. The gram-positive bacterium Listeria monocytogenes , which causes the foodborne disease listeriosis, is one example that poses a serious risk to the fetus and can sometimes lead to spontaneous abortion.
Other pathogens that can pass the placental barrier to infect the fetus are known collectively by the acronym TORCH Table 3. Transmission of infectious diseases from mother to baby is also a concern at the time of birth when the baby passes through the birth canal. Babies whose mothers have active chlamydia or gonorrhea infections may be exposed to the causative pathogens in the vagina, which can result in eye infections that lead to blindness. Fifth disease erythema infectiosum Treponema pallidum bacterium.
Upon learning that Pankaj became sick the day after the party, the physician orders a blood test to check for pathogens associated with foodborne diseases. There he is to receive additional intravenous antibiotic therapy and fluids. Following the initial exposure, the pathogen adheres at the portal of entry. The term adhesion refers to the capability of pathogenic microbes to attach to the cells of the body using adhesion factors , and different pathogens use various mechanisms to adhere to the cells of host tissues.
Glycocalyx produced by bacteria in a biofilm allows the cells to adhere to host tissues and to medical devices such as the catheter surface shown here. Molecules either proteins or carbohydrates called adhesins are found on the surface of certain pathogens and bind to specific receptors glycoproteins on host cells. Adhesins are present on the fimbriae and flagella of bacteria, the cilia of protozoa, and the capsids or membranes of viruses. Protozoans can also use hooks and barbs for adhesion; spike proteins on viruses also enhance viral adhesion.
Biofilm growth can also act as an adhesion factor. A biofilm is a community of bacteria that produce a glycocalyx, known as extrapolymeric substance EPS , that allows the biofilm to attach to a surface. Persistent Pseudomonas aeruginosa infections are common in patients suffering from cystic fibrosis, burn wounds, and middle-ear infections otitis media because P.
The EPS allows the bacteria to adhere to the host cells and makes it harder for the host to physically remove the pathogen. The EPS not only allows for attachment but provides protection against the immune system and antibiotic treatments, preventing antibiotics from reaching the bacterial cells within the biofilm. In addition, not all bacteria in a biofilm are rapidly growing; some are in stationary phase. Since antibiotics are most effective against rapidly growing bacteria, portions of bacteria in a biofilm are protected against antibiotics.
Once adhesion is successful, invasion can proceed. Invasion involves the dissemination of a pathogen throughout local tissues or the body. Pathogens may produce exoenzymes or toxins, which serve as virulence factors that allow them to colonize and damage host tissues as they spread deeper into the body. Pathogens may also produce virulence factors that protect them against immune system defenses. Some are obligate intracellular pathogens meaning they can only reproduce inside of host cells and others are facultative intracellular pathogens meaning they can reproduce either inside or outside of host cells.
By entering the host cells, intracellular pathogens are able to evade some mechanisms of the immune system while also exploiting the nutrients in the host cell. Entry to a cell can occur by endocytosis. For most kinds of host cells, pathogens use one of two different mechanisms for endocytosis and entry. One mechanism relies on effector proteins secreted by the pathogen; these effector proteins trigger entry into the host cell. This is the method that Salmonella and Shigella use when invading intestinal epithelial cells. When these pathogens come in contact with epithelial cells in the intestine, they secrete effector molecules that cause protrusions of membrane ruffles that bring the bacterial cell in.
This process is called membrane ruffling. The second mechanism relies on surface proteins expressed on the pathogen that bind to receptors on the host cell, resulting in entry. For example, Yersinia pseudotuberculosis produces a surface protein known as invasin that binds to beta-1 integrins expressed on the surface of host cells. Some host cells, such as white blood cells and other phagocytes of the immune system, actively endocytose pathogens in a process called phagocytosis.
Although phagocytosis allows the pathogen to gain entry to the host cell, in most cases, the host cell kills and degrades the pathogen by using digestive enzymes. Normally, when a pathogen is ingested by a phagocyte, it is enclosed within a phagosome in the cytoplasm; the phagosome fuses with a lysosome to form a phagolysosome, where digestive enzymes kill the pathogen see Pathogen Recognition and Phagocytosis. However, some intracellular pathogens have the ability to survive and multiply within phagocytes. Bacteria such as Mycobacterium tuberculosis , Legionella pneumophila , and Salmonella species use a slightly different mechanism to evade being digested by the phagocyte.
These bacteria prevent the fusion of the phagosome with the lysosome, thus remaining alive and dividing within the phagosome. Following invasion, successful multiplication of the pathogen leads to infection. Infections can be described as local, focal, or systemic, depending on the extent of the infection.
A local infection is confined to a small area of the body, typically near the portal of entry. For example, a hair follicle infected by Staphylococcus aureus infection may result in a boil around the site of infection, but the bacterium is largely contained to this small location. Other examples of local infections that involve more extensive tissue involvement include urinary tract infections confined to the bladder or pneumonia confined to the lungs.
In a focal infection , a localized pathogen, or the toxins it produces, can spread to a secondary location. For example, a dental hygienist nicking the gum with a sharp tool can lead to a local infection in the gum by Streptococcus bacteria of the normal oral microbiota. These Streptococcus spp. When an infection becomes disseminated throughout the body, we call it a systemic infection.
How Pathogens Cause Disease | Microbiology
For example, infection by the varicella-zoster virus typically gains entry through a mucous membrane of the upper respiratory system. It then spreads throughout the body, resulting in the classic red skin lesions associated with chickenpox. Since these lesions are not sites of initial infection, they are signs of a systemic infection.
Sometimes a primary infection , the initial infection caused by one pathogen, can lead to a secondary infection by another pathogen. For example, the immune system of a patient with a primary infection by HIV becomes compromised, making the patient more susceptible to secondary diseases like oral thrush and others caused by opportunistic pathogens. Similarly, a primary infection by Influenzavirus damages and decreases the defense mechanisms of the lungs, making patients more susceptible to a secondary pneumonia by a bacterial pathogen like Haemophilus influenzae or Streptococcus pneumoniae.
Some secondary infections can even develop as a result of treatment for a primary infection. Antibiotic therapy targeting the primary pathogen can cause collateral damage to the normal microbiota, creating an opening for opportunistic pathogens. Anita, a year-old mother of three, goes to an urgent care center complaining of pelvic pressure, frequent and painful urination, abdominal cramps, and occasional blood-tinged urine.
Suspecting a urinary tract infection UTI , the physician requests a urine sample and sends it to the lab for a urinalysis. Since it will take approximately 24 hours to get the results of the culturing, the physician immediately starts Anita on the antibiotic ciprofloxacin. The next day, the microbiology lab confirms the presence of E. After taking her antibiotics for 1 week, Anita returns to the clinic complaining that the prescription is not working.
Although the painful urination has subsided, she is now experiencing vaginal itching, burning, and discharge. After a brief examination, the physician explains to Anita that the antibiotics were likely successful in killing the E. The new symptoms that Anita has reported are consistent with a secondary yeast infection by Candida albicans , an opportunistic fungus that normally resides in the vagina but is inhibited by the bacteria that normally reside in the same environment. To confirm this diagnosis, a microscope slide of a direct vaginal smear is prepared from the discharge to check for the presence of yeast.
A sample of the discharge accompanies this slide to the microbiology lab to determine if there has been an increase in the population of yeast causing vaginitis. After the microbiology lab confirms the diagnosis, the physician prescribes an antifungal drug for Anita to use to eliminate her secondary yeast infection. As with portals of entry, many pathogens are adapted to use a particular portal of exit.
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Similar to portals of entry, the most common portals of exit include the skin and the respiratory, urogenital, and gastrointestinal tracts. Coughing and sneezing can expel pathogens from the respiratory tract. A single sneeze can send thousands of virus particles into the air. Secretions and excretions can transport pathogens out of other portals of exit. Feces, urine, semen, vaginal secretions, tears, sweat, and shed skin cells can all serve as vehicles for a pathogen to leave the body.
Pathogens that rely on insect vectors for transmission exit the body in the blood extracted by a biting insect. Similarly, some pathogens exit the body in blood extracted by needles. Pathogens leave the body of an infected host through various portals of exit to infect new hosts. Which pathogen is most virulent? Skip to main content. Microbial Mechanisms of Pathogenicity. Search for:. The suspected pathogen can be isolated and grown in pure culture. A healthy test subject infected with the suspected pathogen must develop the same signs and symptoms of disease as seen in postulate 1.
The pathogen must be re-isolated from the new host and must be identical to the pathogen from postulate 2. Which is more closely related to the severity of a disease? Think about It Explain the difference between a primary pathogen and an opportunistic pathogen. Describe some conditions under which an opportunistic infection can occur.
What portal of entry did the bacteria use to cause this infection? A Secondary Yeast Infection Anita, a year-old mother of three, goes to an urgent care center complaining of pelvic pressure, frequent and painful urination, abdominal cramps, and occasional blood-tinged urine. The functional validity of this approach was also strengthened by, for instance, the ability to recover 6 out of 10 known peptidoglycan genes with the PP of penicillin binding protein gene, pbp1A. These results support our original hypothesis that a group of virulence genes with closely-related mechanisms can be widely distributed across bacterial genomes.
Thus, the concept of virulence gene-infectious disease relationship may be modified from one that involves a simple association between a gene and a pathogen trait, where virulence is related to the presence or absence of incriminated genes, to a complex repertoire of widely distributed genes that confer specific survival advantage on the pathogen. The good prediction results from our rediscovery experiments imply that there are specific combination patterns of virulence genes in bacterial pathogens.
The existence of such patterns is conceivable, because the co-occurrence of virulence genes is a fundamental requirement for pathogen function and interaction with the host at the cellular level . However, the interpretation and comprehension of these implicit patterns is challenging. Bowers et al. The integration of PP-based gene prioritization methods with other data sources should be explored. For example, mapping PP signatures to gene ontology and annotation databases, to decipher the underlying meaning of these highly-conserved profiles, can be of value.
There are several points to note in the selection of training data and algorithms. First, we based our de novo predictions on the individual categories of virulence function as opposed to a training set consisting of all known virulence factors. Although novel genes may be revealed by training the ICGP models with the aggregated training set, the categorized approach can be justified because results are likely to be skewed towards gene functions presented with higher proportion in the training set see Text S1.
It is also evident that training sets with higher functional consistency at molecular level have better cross-validation results. For example, the category of neu cluster is more consistent over the broader category of immune system evasins. One disadvantage of using a discriminative model is that the classifier outputs do not generally correspond to a true probability distribution of gene-function relationships.
Although attempts were made to rectify the probability estimates for models such as SVM i. This may also explain the disparity of good rediscovery performances achieved by most algorithms Table 2 and poor agreements between individual gene rankings Text S2. Thirdly, our approach only aims to recover the genes having similar phylogenetic profile to the known virulence factors. In cases where no virulence genes are known, alternative methods would need to be sought for the gene prioritization task. In conclusion, we have performed a computational genome-wide prioritization for discovering potential virulence genes in S.
Our comparative genomic approach requires fewer genomes of the target virulence species for hypothesizing potential virulence genes. A number of plausible molecular mechanisms have been revealed, some of which have been documented in other bacterial pathogens. Furthermore, we have significantly extended the number of potential bacterial gene targets for drug and vaccine design by identifying highly-ranked yet uncharacterized candidate genes which may have roles in GBS virulence. This approach can also be applicable to the discovery of virulence genes in other bacterial pathogens.
The phylogenetic profiles of the whole genome of three strains of S. The presence of a potential homologous gene was determined at the critical E-value of Dataset S1. For each known GBS virulence factor, a further literature search was performed and the location of associated genes identified and labeled in the reference genomes see Text S3 for more details. The criteria for grouping of the known virulence factors into 15 functional categories were: discriminable by BLAST and a distinguishable biological mechanism in GBS pathogenesis.
Descriptive analysis of the PPs revealed that of 6, genes 8.
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Four hundred and seventy seven genes 7. The inclusion of multiple genomes per species may have introduced redundancy, as all NCBI genomes were used as the reference panel. However, it has been previously shown that redundancy did not result in performance penalties in machine learning-based gene prioritization methods  and hence a more inclusive approach was adopted in the selection of reference genomes. Four machine learning algorithms were applied to each of the functional categories of known GBS genes.
Algorithm selection was based on performance in our previous work . The output of each classifier was used for the basis for gene ranking. Logistic models were fitted to estimate the posterior probabilities of both SVM algorithms. For each functional GBS gene category containing n virulence genes, a n -fold cross-validation was performed, with the remaining candidate genes assigned a negative class. Rediscovery performance was measured by AUC for each combination of algorithm and gene category. All genes in NEM genome were used for cross-validation in the first rediscovery experiment, and all genes from the 3 reference genomes were applied in the second experiment.
For each functional category, all of known virulence genes were labeled as positive gene examples in the training set. To reduce the oversampling of negative classes, only a subset of the remaining unlabeled genes were labeled as negative examples in the training set. Predictions were made on the remaining one-quarter of the unknown genes and scores from each run were obtained for each gene to be predicted.
The above procedure was repeated for runs to improve coverage. Scores from each run were averaged by arithmetic means which formed the basis of ranking. This procedure is detailed in Text S3. To increase the likelihood of identifying true virulence genes, we aggregated ranks produced by 4 machine learning algorithms into a final rank by using the following voting function: where g is a candidate gene, is the final aggregated score of gene , is number of ranks , X is an uniform random variable, and is the rank fraction position of the rank, starting from 1, divided by the total number of genes in the entire list of rank.
Because homologous including both orthologs and closely-related paralogs genes would appear multiple times in close proximity in a prioritized rank due to high degrees of similarities in PPs, the genes from each resultant rank were collated into homolog clusters to ease the interpretation of results. The complete list of homolog clusters is shown in Table S2. Top genes of each virulence function category prioritized by inductive CGP. List of homolog clusters in the three S. Prioritization of candidate virulence genes in the GBS genomes by using all known virulence factors as training set.
Correlations between prioritized gene lists produced by different machine learning algorithms. The section on pneumococcal virulence gene discovery was previously presented at the Genetics and Genomics of Infectious Diseases conference in Singapore, March poster. Performed the experiments: FL. Analyzed the data: FL RL. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract The phylogenetic profile of a gene is a reflection of its evolutionary history and can be defined as the differential presence or absence of a gene in a set of reference genomes.
Introduction Virulence - the ability of a pathogen to damage a host and evade host immune defenses - arises from a range of complex host-pathogen interactions and can be expressed as the pathogen's toxicity, invasiveness, colonization, and ability to be transmitted to another host  , .
Download: PPT. Results GBS genes contributing to virulence through molecular mechanisms similar to those of genes of other bacterial species can be identified using a PP-based model We tested the hypothesis that PP can predict whether a GBS gene is associated with virulence. Table 1. List of known GBS virulence genes with systematic gene names in three published reference genomes.
Table 2. Table 3. The performance of inductive CGP algorithms in the rediscovery of known virulence genes in all 3 GBS reference genomes. De novo discovery of S. Table 4. List of genes encoding hypothetical proteins and their putative biological significance. Corroborated discovery of virulence genes using functionally unrelated virulence genes as a training set Because bacterial pathogenesis is mediated by a variety of distinct molecular mechanisms, a good gene prioritization model would be expected to identify different classes of virulence genes from which the predictive model can be built.
Figure 3. Number of other gene categories discoverable at a certain rank position. Highly-ranked genes are not linked with the genes in training sets To demonstrate that the virulence genes predicted by ICGP do not merely discover neighboring genes, highly-ranked genes in the NEM genome were plotted on the chromosome map Figure 5. Figure 5. Positions of the training set in red and top genes in blue in each of the 15 virulence gene categories in S.
The rediscovery of virulence genes in Streptococcus pneumoniae To demonstrate that our approach is generalizable to other species, the rediscovery experiments were replicated on 6, genes in 3 published S. Discussion This paper demonstrates a new approach to discover potential virulence genes in bacterial genomes. Materials and Methods Data sources The phylogenetic profiles of the whole genome of three strains of S.
Machine learning algorithms Four machine learning algorithms were applied to each of the functional categories of known GBS genes. Rediscovery of the training genes For each functional GBS gene category containing n virulence genes, a n -fold cross-validation was performed, with the remaining candidate genes assigned a negative class.
Sub-sampling of negative examples in the de novo discovery of GBS virulence genes For each functional category, all of known virulence genes were labeled as positive gene examples in the training set. Combining the ranks from multiple models To increase the likelihood of identifying true virulence genes, we aggregated ranks produced by 4 machine learning algorithms into a final rank by using the following voting function: where g is a candidate gene, is the final aggregated score of gene , is number of ranks , X is an uniform random variable, and is the rank fraction position of the rank, starting from 1, divided by the total number of genes in the entire list of rank.
Clustering of homologous genes Because homologous including both orthologs and closely-related paralogs genes would appear multiple times in close proximity in a prioritized rank due to high degrees of similarities in PPs, the genes from each resultant rank were collated into homolog clusters to ease the interpretation of results.
Virulence Factors for Adhesion
Supporting Information. Dataset S1. Table S1. Table S2. Text S1. Text S2. Text S3. Additional materials and methods. Acknowledgments The section on pneumococcal virulence gene discovery was previously presented at the Genetics and Genomics of Infectious Diseases conference in Singapore, March poster. References 1. Casadevall A, Pirofski L Host-pathogen interactions: the attributes of virulence. J Infect Dis — View Article Google Scholar 2. Casadevall A, Pirofski L Host-pathogen interactions: redefining the basic concepts of virulence and pathogenicity.
Infect Immun — View Article Google Scholar 3. Falkow S Molecular Koch's postulates applied to bacterial pathogenicity—a personal recollection 15 years later. Nat Rev Microbiol 2: 67— View Article Google Scholar 4. Fredericks D, Relman D Sequence-based identification of microbial pathogens: a reconsideration of Koch's postulates.
Clin Microbiol Rev 9: 18— View Article Google Scholar 5. Cell — View Article Google Scholar 6. View Article Google Scholar 7. View Article Google Scholar 8. Microbiology — View Article Google Scholar 9. View Article Google Scholar Bioinformatics 20 Suppl 1 : i— BMC Bioinformatics 6: Vert J A tree kernel to analyse phylogenetic profiles. Bioinformatics 18 Suppl 1 : S— BMC Bioinformatics 7: Lin F, Coiera E, Lan R, Sintchenko V In silico prioritisation of candidate genes for prokaryotic gene function discovery: an application of phylogenetic profiles.