The Body’s Ecosystem | The Scientist Magazine®
Bacteria and humans have many important relationships. Bacteria make our lives Insects are the most common vectors of human diseases. Humans and bacteria have a curious relationship. What it does: This is one of the most common microbes found on the human skin and nose. Altogether, the members of the human body's microbial ecosystem make up anywhere Association “animal parasites of the mouth and their relation to dental disease. The most common bacteria found in healthy lungs are.
While pathogens and nonpathogens had comparable similarity to the human proteome, pathogens causing chronic infections were found to be more similar to the human proteome than those causing acute infections. While the relationship between bacteria and human has been studied from many angles, their proteomic similarity still needs to be examined in more detail.
This paper sheds further light on this relationship, particularly with respect to immunity and pathogenicity. Introduction Microorganisms and their hosts have complex relationships that are still not completely understood. Given the ramifications for health and disease, the microbe-human relationship is of particular interest.
Recently, there has been increasing interest in the level of protein sequence overlap between microbes and human. According to this view, self-tolerance is achieved by deletion or anergy of immature self-reactive B- and T-cells—a process called negative selection. Negative selection suggests that the immune system would be unlikely to recognize foreign peptide segments that are sufficiently similar to self-peptides, as B- and T-cells specific for these peptides would be deleted or rendered anergic.
This leads to the hypothesis that similarity to the host proteome may play an important role in determining whether a potential protein antigen will be recognized by the immune system—or more specifically, that a peptide segment that is found rarely or never in the host proteome is more likely to be immunogenic than a peptide segment that is found many times in the host proteome.
The similarity hypothesis predicts that epitopes will be less similar to the host proteome than will non-epitopes. This prediction has been examined in a number of experimental studies see  and references therein.
The Body’s Ecosystem
For instance, Rolland and colleagues found an inverse relationship between the immunogenicity of HIV-derived peptides and their similarity to the human proteome and Amela and co-authors discovered that B-cell epitopes derived from pathogens have lower sequence similarity to the human proteome than would be expected by chance . They found that the degree of overlap between the human proteome and the proteomes of viruses infecting humans was significantly higher than expected.
More recently, it was discovered that viruses infecting humans have fewer peptides likely to be recognized by the human immune system than do viruses infecting non-human hosts . The same authors also found that proteins expressed early in the viral life cycle contain fewer epitopes than proteins expressed later, perhaps giving the virus time to replicate before a viable immune response can be initiated.
Comparing the Similarity of Different Groups of Bacteria to the Human Proteome
Combined, these observations suggest that viruses experience selective pressure to eliminate immunogenic epitopes from the proteins that they express. There are two primary differences between this study and the ones cited in the previous paragraph.
First, bacteria are examined rather than viruses. Second, instead of focusing exclusively on immunogenicity and pathogenicity, we conduct a broad examination of bacterial properties and determine if they have a relationship with bacteria-human proteome similarity.
The specific aspects of bacteria-human similarity that are examined in this study are described below.
First, we report a general characterization of bacteria-human similarity among all bacteria whose genome sequences were available at the time of this study. Next, three predictions stemming from the similarity hypothesis are examined. First, we predict that the proteomes of pathogenic bacteria are more similar to the human proteome than those of nonpathogenic bacteria, since pathogenic bacteria may experience evolutionary pressure to modify their proteins so that they become more similar to the host i.
Second, for analogous reasons we predict that proteins that are accessible to the immune system are more similar to the human proteome in pathogens than in nonpathogens.
Third, we predict that the proteomes of bacteria causing chronic infections are more similar to the human proteome than those of bacteria causing acute infections, as the former group seems better able to resist immune responses.
Methods Comparing the Similarity of Bacterial Proteomes to the Human Proteome Most of the analyses in this paper compare the similarity to the human proteome of different bacterial proteomes or sets of proteomes.
The different sets of proteomes and proteins used are described in subsequent sections. The technique used to compare the similarity of bacterial proteomes to the human proteome has been described previously but is reiterated here.
As peptide segments of length five 5-mers have been described as fundamental units for immunological recognition and protein-protein interactions to measure bacteria-human similarity we determined the percentage of 5-mers in different bacterial proteomes that are found a certain number of times in the human proteome.
To do this, each protein from each bacterial proteome was decomposed into all possible 5-mers; for example, a protein of amino acids was decomposed into 5-mers. It was then determined how often each 5-mer was found in the human proteome.
If the same 5-mer was found more than once in a given bacterial proteome, then each instance was counted. For example, if a given 5-mer say, ACDEF was found ten times in a particular bacterial proteome, and that 5-mer was found zero times in the human proteome, then this increased by ten the number of 5-mers in that bacterial proteome that were found zero times in the human proteome.
This would have the same effect as if ten distinct 5-mers were each found one time in the bacterial proteome, and each of these ten 5-mers were found zero times in the human proteome. Because there is no standard definition of a rare 5-mer, we used three possible definitions: Whenever a statistical test was performed, it was done for each of these definitions. However, some analyses not involving statistical tests were done only for the zero-times definition.
In a previous paper examining the level of self-similarity of human proto-oncoproteins to the human proteome we also performed the above procedure using 6-mer and 7-mer peptides, and found that the results were uniformly consistent with those obtained when using 5-mers.
Comparing the Similarity of Different Groups of Bacteria to the Human Proteome
That is, if one set of proteins contained more rare 5-mers than another set, then it also contained more rare 6-mers and 7-mers.
The methods to perform such analysis can be either supervised database with known sequences or unsupervised direct search for contig groups in the collected data. However, both methods require a kind of metric to define a score for the similarity between a specific contig and the group in which it must be put, and algorithms to convert the similarities into allocations in the groups.
The computational challenges for this type of analysis are greater than for single genomes, due the fact that usually metagenomes assemblers have poorer quality, and many recovered genes are non-complete or fragmented. After the gene identification step, the data can be used to carry out a functional annotation by means of multiple alignment of the target genes against orthologs databases.
The genetic region is characterized by a highly variable region which can confer detailed identification; it is delimited by conserved regions, which function as binding sites for primers used in PCR. The technique is fast and not so expensive and enables to obtain a low-resolution classification of a microbial sample; it is optimal for samples that may be contaminated by host DNA.
Primer affinity varies among all DNA sequences, which may result in biases during the amplification reaction; indeed, low-abundance samples are susceptible to overamplification errors, since the other contaminating microorganisms result to be over-represented in case of increasing the PCR cycles. Therefore, the optimization of primer selection can help to decrease such errors, although it requires complete knowledge of the microorganisms present in the sample, and their relative abundances.
The first thing to do in a marker gene amplicon analysis is to remove sequencing errors; a lot of sequencing platforms are very reliable, but most of the apparent sequence diversity is still due to errors during the sequencing process. To reduce this phenomenon a first approach is to cluster sequences into Operational taxonomic unit OTUs: Another approach is Oligotypingwhich includes position-specific information from 16s rRNA sequencing to detect small nucleotide variations and from discriminating between closely related distinct taxa.
These methods give as an output a table of DNA sequences and counts of the different sequences per sample rather than OTU. Other popular analysis packages provide support for taxonomic classification using exact matches to reference databases and should provide greater specificity, but poor sensitivity.
Unclassified microorganism should be further checked for organelle sequences. Phylogenetic comparative methods PCS are based on the comparison of multiple traits among microorganisms; the principle is: Ancestral state reconstruction is used in microbiome studies to impute trait values for taxa whose traits are unknown.
Phylogenetic variables are chosen by researchers according to the type of study: All this methods are negatively affected by horizontal gene trasmission HGTsince it can generate errors and lead to the correlation of distant species. There are different ways to reduce the negative impact of HGT: Skin and vaginal sites showed smaller diversity than the mouth and gut, these showing the greatest richness. The bacterial makeup for a given site on a body varies from person to person, not only in type, but also in abundance.
Bacteria of the same species found throughout the mouth are of multiple subtypes, preferring to inhabit distinctly different locations in the mouth. Even the enterotypes in the human gut, previously thought to be well understood, are from a broad spectrum of communities with blurred taxon boundaries.
Firmicutes and Bacteroidetes dominate but there are also ProteobacteriaVerrumicrobiaActinobacteriaFusobacteria and Cyanobacteria.