Figure 3B demonstrate how common allergies develops. Intensive oral hygiene causes the biofilm to become vulnerable and collapse under the assault of antibiotics (Abeles et al., 2016). This process suppress oral probiotics. Species of bacteria which are less sensitive to antibiotics take over the ecospace of the previous probiotics. The immune system, without enough pacifying messengers from probiotics, then becomes hostile to allergens. Therefore, I propose a new name, oral probiotic deficiency, for the condition that oral probiotics reduce to a level where they cannot produce enough SCFA, the peaceful message to the immune system. The consequences of oral probiotic deficiency may include allergy and autoimmune conditions.
The hypersensitive immune system may cause other inflammations in the airway such as periodontitis, rhinosinusitis, and tonsillitis by attacking commensal bacteria (Figure 3B). Therefore, this theory can explain previously observed association between oral “infectious” diseases and common allergies (Arbes and Matsui, 2011). In fact, taking the prebiotic composition relieved the allergic family member’s inflammation when they had a sore throat and enlarged tonsils.
Figure 3C illustrates how the immune system regains its strength under pathogenic infection. First, the invading pathogen can directly out-compete probiotics that produce pacifying metabolites. Second, severe infection induces pyrogens that raise body temperature to inhibit or kill many common, temperature-sensitive bacterial species in the oral microbiota, including oral species such as Veillonella (Carlier, 2015). Suppression of probiotics will likely cease or significantly reduce the production of responsible metabolites and remove restraints on, or brake of, the immune system. Third, the pathogen cause damages to normal tissue and release toxins that stimulate the immune system directly, which like accelerator to the immune system. The immune system, with release of the brake pedal and pressing down of gasoline pedal, will then regain its full strength to fight pathogens. This mechanism could explain how fever increases immunity and helps the body fight diseases, even cancers (Atkinson, 1979).
Theoretically, too many immune pacifying metabolites from overgrowing probiotics may dampen the local immune system too much to fend off even the normal commensal bacteria and lead to chronic or acute infection which would reshape the whole community structure (Figure 3D). Study on people under normal life conditions will help to verify that theory, though. The TNT hypothesis would benefit from further testing from many different perspectives: infection, allergy, and autoimmunity.
TNT has the following propositions. First, the triggers can be removed and reapplied relatively easily and quickly. The epidemic of common allergies, the disappearance of oral biofilm under moderate fever, and the quick allergy-relieving effect of the prebiotic compound support this proposition.
Second, the gut has a less significant role in common allergies. Results in this study indicate the interactions between probiotics and the immune system come primarily at the local level. Long distance interaction or circulation of immune cells likely plays a secondary role.
Third, immune system programming is less important in this situation. Theoretically, the effects of the negative trigger should not last long after removing it as SCFA can be absorbed and metabolized by many different cells in human body. Otherwise, the power of the immune system cannot be released in time to protect the host. Practically, this study showed that allergies started before two years of age can be reversed with the prebiotic mix. The concept of a critical time for immune system development should be re-evaluated to determine what is programmed and what is impacted if critical time is missed.
This is all good news for many who have allergies, autoimmune diseases, inflammations, and other conditions (including cancer) and who would benefit from releasing the power of the immune system or suppressing it. Developing a method to fine-tune the strength of immune system temporally and spatially under these conditions would improve people’s health significantly. For example, one may make the immune system more active by suppressing probiotics in the airway and gut with physical and chemical (include antibiotics) means and by adding immune stimulants (such as vaccine). This may help to control chronic infection and cancers.
The theory also suggest the first critical component of a microbiota associated with a host should be the ones that can communicate with host to make a peace agreement. Bacteria in this role should first not cause immediate harm to the host and second are able to send pacifying signal to the host immune system. In human, this communication is likely achieved through bacteria producing short chain fatty acid. They are Streptococcus and Veillonella in the mouth and airway, fiber digesting bacteria in the gut, C. Akney on the skin. The second component of healthy microbiota should be enough diversity to occupy all nutrient space with members not harmful or even better benefit to the host. The last components are guest members dropped in accidentally.
This hypothesis suggests possible solutions to many practical issues. It explains why it is beneficial for parents to transfer their microbiota to their children by confinement of mother and newborn at the beginning of life, and fever should be kept if it is not too high. This theory also suggests what comprises healthy microbiota and whether it is possible to reshape microbiota for optimal immune status in other situations.
Ethics approval and consent to participate: All Participates consented to participate.
Consent for publication: N/A
Availability of data and material: The sequence data is available upon request.
Competing interests: CSH is the owner of Knoze Jr Corp. that holds granted/pending patents covering the oral microbiota inducing method described here.
Author’s contributions: CSH is the sole author and responsible for the content of the manuscript.
Acknowledgements: I thank my family for giving their saliva samples. This study is impossible without their contribution. I am grateful for language editing by Shena Han and James Kennedy III. I thank the following people for reading and commenting the manuscript: Armand Dichosa, Joe Alcock.
Materials and Methods
1 Sample collection
Family members provided fully informed consent to participate the study by providing samples and related information. Saliva samples were collected by directly expectorating into 15 ml tube and immediately frozen for storage.
2 Extraction of genome DNA
Total genome DNA from samples was extracted using Nucleo spin DNA stool kit (cat No. 740472.50) according to manufacturer’s instructions. DNA concentration and purity was monitored on 1% agarose gels. According to the concentration, DNA was diluted to 1ng/μL using sterile water.
3 Amplicon Generation
16S rRNA genes of 16SV4 region were amplified used specific primer(16S V4: 515F-806R) with the barcode. All PCR reactions were carried out with Phusion® High-Fidelity PCR Master Mix (New England Biolabs).
4 PCR Products quantification and qualification
Mix same volume of 1X loading buffer (contained SYB green) with PCR products and operate electrophoresis on 2% agarose gel for detection. Samples with bright main strip between 400-450bp were chosen for further experiments.
5 PCR Products Mixing and Purification
PCR products was mixed in equidensity ratios. Then, mixture PCR products was purified with Qiagen Gel Extraction Kit (Qiagen, Germany).
6 Library preparation and sequencing
Sequencing libraries were generated using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) following manufacturer’s recommendations and index codes were added. The library quality was assessed on the Qubit@ 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina HiSeq 2500 platform (Illumina, USA) and 250 bp paired-end reads were generated.
1 Paired-end reads assembly and quality control
1.1 Data split: Paired-end reads was assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence.
1.2 Sequence assembly: Paired-end reads were merged using FLASH (V1.2.7) (Magoc and Salzberg, 2011), a very fast and accurate analysis tool, which was designed to merge paired-end reads when at least some of the reads overlap the read generated from the opposite end of the same DNA fragment, and the splicing sequences were called raw tags.
1.3 Data Filtration: Quality filtering on the raw tags were performed under specific filtering conditions to obtain the high-quality clean tags according to the Qiime (V1.7.0) (Kuczynski et al., 2011) quality controlled process.
1.4 Chimera removal: The tags were compared with the reference database(Gold database using UCHIME algorithm (Edgar et al., 2011) to detect chimera sequences, and then the chimera sequences were removed. Then the Effective Tags finally obtained.
2 OTU cluster and Species annotation
2.1 OTU Production: Sequences analysis were performed by Uparse software (Uparse v7.0.1001) (Edgar, 2013)Sequences with ≥97% similarity were assigned to the same OTUs. Representative sequence for each OTU was screened for further annotation.
2.2 Species annotation: For each representative sequence, the GreenGene Database (McDonald et al., 2012) was used based on RDP classifier(Version 2.2) (Wang et al., 2007) algorithm to annotate taxonomic information.
2.3 Phylogenetic relationship Construction: In order to study phylogenetic relationship of different OTUs, and the difference of the dominant species in different samples(groups), multiple sequence alignment were conducted using the PyNAST software(Version 1.2) (Caporaso et al., 2010) against the “Core Set” dataset in the GreenGene database.
2.4 Data Normalization: OTUs abundance information were normalized using a standard of sequence number corresponding to the sample with the least sequences. Subsequent analysis of alpha diversity and beta diversity were all performed basing on this output normalized data.
3 Alpha Diversity
Alpha diversity is applied in analyzing complexity of species diversity for a sample through6 indices, including Observed-species, Chao1, Shannon, Simpson, ACE, Good-coverage. All this indices in our samples were calculated with QIIME(Version 1.7.0) (Kuczynski et al., 2011) and displayed with R software(Version 2.15.3).
Two indices were selected to identify Community richness:
Chao – the Chao1 estimator (Chao, 1984);
ACE – the ACE estimator (Chao et al., 1992) ;
Two indices were used to identify Community diversity:
Shannon – the Shannon index (Seaby R. M. & Henderson, 2006);
Simpson – the Simpson index (Harper, 1999);
One indices to characterized Sequencing depth:
Coverage – the Good’s coverage (Seaby R. M. & Henderson, 2006)
4 Beta Diversity
Beta diversity analysis was used to evaluate differences of samples in species complexity, Beta diversity on both weighted and unweighted unifrac were calculated by QIIME software (Version 1.7.0) (Kuczynski et al., 2011).
Non-metric multidimensional scaling(NMDS) was performed to get principal coordinates and visualize from complex, multidimensional data. A distance matrix of weighted or unweighted unifrac among samples obtained before was transformed to a new set of orthogonal axes, by which the maximum variation factor is demonstrated by first principal coordinate, and the second maximum one by the second principal coordinate, and so on. NMDS analysis was displayed by WGCNA package, stat packages and ggplot2 package in R software(Version 2.15.3).
Unweighted Pair-group Method with Arithmetic Means(UPGMA) Clustering was performed as a type of hierarchical clustering method to interpret the distance matrix using average linkage and was conducted by QIIME software (Version 1.7.0) (Kuczynski et al., 2011).
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