there are many other bacteria that may influence the course of the disease.
The New Age of Genomics
Tremendous breakthroughs in DNA-sequencing technologies and bioinformatics now allow scientists to rapidly sequence, analyze, and study entire bacterial genomes (also known as genomics). There are now more than 13,000 complete genome sequences for bacteria, about half of which are from medically relevant bacteria, and there are over 175,000 incomplete or ongoing bacterial sequencing projects. These numbers and the pace of discovery are staggering considering that only a handful of complete bacterial genomes were available in the year 2000. At one time, it would have made sense to provide a list of available genome sequences, but additions to this list are coming so fast that the best solution is to provide the address of a website that keeps track of genomes that have been or are being sequenced: https://gold.jgi.doe.gov/.
Once a genome sequence becomes available, scientists examine the open reading frames (orfs) (i.e., the putative genes) one by one to try to assign each gene a name and function. In some cases, this process of genome annotation is easy, because the gene and its expressed protein product have already been characterized. In other cases, tentative identification of a gene is made on the basis of similarities to known genes or proteins present in other organisms that have been deposited into public repositories for DNA and protein sequences—DNA databases and protein databases, respectively. These automated assignments are useful but should be treated with some degree of caution, as many are based on relatively poor sequence matches. The best way to approach DNA sequence data is to realize that the function of a gene based on its sequence similarity to known genes is only a hypothesis that needs to be confirmed by more rigorous testing. A sobering fact is that even in the case of well-studied bacteria, such as E. coli and Salmonella, at least one-third of the genes in their genomes have no similarity to any known genes. A challenging job for future scientists is to determine the biological role of these genes of unknown function.
The way in which DNA sequence information can reveal surprising things about an organism is illustrated by the genome sequence of Borrelia burgdorferi, the spirochete that causes Lyme disease. Scientists noted that no genes corresponding to the usual iron-containing proteins normally found in bacteria were present in the genome of this organism. This suggested a radical hypothesis: that B. burgdorferi copes with the problem of low iron concentrations in the mammalian host by not using iron at all. Instead, its proteins use other metals that are abundant in humans, such as manganese. Scientists who were trained in an era in which every article on iron utilization by bacteria started by describing that all bacteria require iron were startled by this suggestion. Biochemical analyses confirmed, however, that indeed B. burgdorferi apparently does live without the need for iron, thus solving one problem most other pathogens have to confront: how to obtain iron in a host whose iron sequestration mechanisms keep the supply of available iron very low. As can be seen from this example, genome sequences not only provide valuable insights into unique bacterial metabolic processes, but also are excellent hypothesis-generating tools for understanding virulence mechanisms.
Along with the availability of complete genome sequences has come new technology enabling the high-throughput sequencing of an organism’s total RNA (RNA-seq, also called whole transcriptome shotgun sequencing). This technology has provided the means for scientists to measure the expression of thousands of genes in a single experiment. If the number of bacteria is high enough in a body site of a colonized or infected animal, RNA isolated from bacteria growing under these in vivo conditions can be obtained and analyzed by RNA-seq to assess the expression profile (i.e., the transcriptome) of different genes in the animal. Comparison of this expression profile with that obtained from bacteria grown outside of the host body has led, in turn, to the identification of genes that are only expressed during an infection and that might contribute to virulence in the host.
Another form of genomic analysis being used to detect and identify unknown pathogenic bacteria takes advantage of the fact that ribosomal RNA (rRNA) genes contain highly conserved regions of sequence separated by more variable regions. PCR primers that target conserved regions of the rRNA genes are used to amplify these genes from genomic DNA extracted from tissue suspected to contain an infectious organism. The PCR-amplified DNA is called an amplicon. Of course, if there are no bacteria present or if the level of bacterial DNA is too low, no PCR amplicon will be obtained; however, if an amplicon is obtained, its sequence can be determined and compared to the thousands of rRNA gene sequences now available in the DNA databases.
The variable regions of the 16S rRNA gene are particularly valuable in helping determine what known microbe is most similar to the one found in the diseased tissue. The next step is to establish whether the amplified DNA that comes from that organism is present in all cases in which there are similar symptoms of disease. The first unknown organism to be identified in this way was the bacterium that causes a rare intestinal disease called Whipple’s disease (Tropheryma whipplei). Currently, this approach is being used in an attempt to identify bacterial pathogen(s) thought to be responsible for other diseases with unknown causes (i.e., etiology), such as bacterial vaginosis, atherosclerosis, and inflammatory bowel disease.
Insights into Pathogen Evolution
A current preoccupation of many infectious disease researchers interested in deciphering the root causes of pathogen evolution and the dynamics of epidemics is to combine epidemiological and evolutionary knowledge about pathogen virulence gleaned from genomics. Application of mathematical modeling is then used to gain better understanding of pathogen physiology, ecology, and disease transmission. In formulating these models, the two most common assumptions are that pathogens evolve in response to selective pressures placed on them by their environment, namely the host—and, as we are beginning to learn, also the external environment—and that virulence (i.e., the deleterious effects of the pathogen on its host) is directly proportional to its ability to be transmitted.
Recent studies along these lines have provided new insights into the potential effects of imperfect vaccines and the reemergence of certain diseases once thought eradicated (or at least well-controlled) through vaccination, such as whooping cough. It has been a generally accepted premise that infection with one strain of a bacterial pathogen will significantly reduce the susceptibility of the host to subsequent infections with other related bacterial strains due to the host’s acquired immunity. However, these recent studies now indicate that the selective pressure of immunization may also be driving the evolution of pathogens like Bordetella pertussis, the bacterium responsible for whooping cough. Genomic sequence comparisons indicate that Bordetella strains, against which most of the current vaccines were designed and used extensively in developed countries, have now been replaced in the population by novel variant strains lacking key components previously recognized by the immune system to help clear the pathogen from the body. Experiments in mice further suggest that the existing vaccines are less protective against some of these new variants.
Modeling the Host-Pathogen Interaction in Experimental Animals
Studies of disease-causing bacteria growing under laboratory conditions need to be supplemented by studies in animal models. The most familiar type of infectious disease model is the laboratory rodent. Inbred strains of mice and rats are widely used as models for the infection process. The availability of animals with known genetic mutations in their defense systems has increased the utility of these rodent models. Numerous examples of the use of these models will be seen in later chapters of this book, including discussions of when it is appropriate to use animals in experimental schemes to study infectious diseases and the ethics of using vertebrate animals in experiments.
A new wrinkle on the animal model story is the increasing range of “animals” used, from the nematode Caenorhabditis elegans to the fruit fly Drosophila melanogaster and the zebra fish Danio rerio. Certainly, one motivation for using such models is the fact that the complex body of regulations and restrictions that has built up around the use of laboratory rodents and other warm-blooded animals is so complicated and expensive that only the best-funded laboratories can use them. However, more compelling reasons for the use of these new models are that so much is known about their genetics and that they are much more easily and rapidly genetically manipulated than mammals.
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