21  Appendix

This is a parking place for content that should be incorporated into other parts of the book

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

Mentions uBiome’s Melissa Agnello for processing some samples through uBiome’s lab.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease

Xin Zhou, Xiaotao Shen, Jethro S. Johnson, Daniel J. Spakowicz, Melissa Agnello, Wenyu Zhou, Monica Avina, Alexander Honkala, Faye Chleilat, Shirley Jingyi Chen, Kexin Cha, Shana Leopold, Chenchen Zhu, Lei Chen, Lin Lyu, Daniel Hornburg, Si Wu, Xinyue Zhang, Chao Jiang, Liuyiqi Jiang, Lihua Jiang, Ruiqi Jian, Andrew W. Brooks, Meng Wang, Kévin Contrepois, Peng Gao, Sophia Miryam Schüssler-Fiorenza Rose, Thi Dong Binh Tran, Hoan Nguyen, Alessandra Celli, Bo-Young Hong, Eddy J. Bautista, Yair Dorsett, Paula Kavathas, Yanjiao Zhou, Erica Sodergren, George M. Weinstock, Michael P. Snyder bioRxiv 2024.02.01.577565; doi: https://doi.org/10.1101/2024.02.01.57756

21.1 Beyond Microbes

‘Obelisks’: Entirely New Class of Life Has Been Found in The Human Digestive System Stanford University biologist Ivan Zheludev searched millions of published genome and identified at least 30K different Obelisks that appeared in about 10% of the samples.

It’s RNA with only 1000 nucleotides.

Viroid-like colonists of human microbiomes Ivan N. Zheludev, Robert C. Edgar, Maria Jose Lopez-Galiano, Marcos de la Peña, Artem Babaian, Ami S. Bhatt, Andrew Z. Fire bioRxiv 2024.01.20.576352; doi: https://doi.org/10.1101/2024.01.20.576352

21.2 Diversity and Immunity

21.3 Statistically Modeling for Health Status

Zhu, J., Xie, H., Yang, Z. et al. Statistical modeling of gut microbiota for personalized health status monitoring. Microbiome 11, 184 (2023). https://doi.org/10.1186/s40168-023-01614-x

Statistical modeling of gut microbiota for personalized health status monitoring

We systematically developed a statistical monitoring diagram for personalized health status prediction and analysis. Our framework comprises three elements: (1) a statistical monitoring model was established, the health index was constructed, and the health boundary was defined; (2) healthy patterns were identified among healthy people and analyzed using contrast learning; (3) the contribution of each bacterium to the health index of the diseased population was analyzed. Furthermore, we investigated disease proximity using the contribution spectrum and discovered multiple multi-disease-related targets.

via Ken Lassessen

21.4 Eye Disease and Gut Microbiome

Some inherited eye diseases, including blindness may be caused by gut bacteria that

RB1 gene is key to controlling the integrity of the lower gastrointestinal tract, the first ever such observation. There, it combats pathogens and harmful bacteria by regulating what passes between the contents of the gut and the rest of the body.

The team found that when the gene has a particular mutation, dampening its expression (reducing its effect), these barriers in both the retina and the gut can be breached, enabling bacteria in the gut to move through the body and into the eye, leading to lesions in the retina that cause sight loss.

The research was conducted on mice by Professor Richard Lee (UCL Institute of Ophthalmology and Moorfields Eye Hospital NHS Foundation Trust).

https://www.cell.com/cell/abstract/S0092-8674(24)00108-9

21.5 Skin Microbiome and Attractiveness to Mosquitoes

Skin microbiome alters attractiveness to Anopheles mosquitoes > Staphylococcus 2 ASVs are four times as abundant in the highly-attractive compared to poorly-attractive group.

via Axios

21.6 Ticks and Alpha-Gal

see Ticks, Alpha-Gal, Neu5gc and more

The CDC warns about

Alpha-gal syndrome (AGS) is a serious, potentially life-threatening allergic condition. AGS is also called alpha-gal allergy, red meat allergy, or tick bite meat allergy.

does it have an association with microbes?

Figure 2: IgM and IgG antibodies are generated by continuous stimulation by the intestinal microbiome and probably also by food

21.7 Gut-Brain

Science Magazine

a new study reveals the gut has a much more direct connection to the brain through a neural circuit that allows it to transmit signals in mere seconds.

21.8 Paleo Humans

Natural products from reconstructed bacterial genomes of the Middle and Upper Paleolithic from a German team that > Now, a new study from an interdisciplinary team has taken important steps to understanding stone age bacteria by sequencing genomes recovered from ancient dental calculus. The hardened tartar preserved bacterial fragments on the teeth of 12 Neanderthals and 34 humans that had lived anywhere from 102,000 to 150 years ago. Formed from plaque, this calculus fossilized during these humans’ lifetime, trapping genetic fragments inside.

Discover Magazine on Bacterial DNA from ancient humans

21.9 Microbiome uniqueness

See https://pubmed.ncbi.nlm.nih.gov/30150716/

A Chinese study that found microbiome patterns that predict health in one province don’t work in another.

He et al. (2018)

He Y, Wu W, Zheng HM, Li P, McDonald D, Sheng HF, Chen MX, Chen ZH, Ji GY, Zheng ZD, Mujagond P, Chen XJ, Rong ZH, Chen P, Lyu LY, Wang X, Wu CB, Yu N, Xu YJ, Yin J, Raes J, Knight R, Ma WJ, Zhou HW. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat Med. 2018 Oct;24(10):1532-1535. doi: 10.1038/s41591-018-0164-x. Epub 2018 Aug 27. Erratum in: Nat Med. 2018 Sep 24;: PMID: 30150716.


https://www.nature.com/articles/s42255-021-00348-0

see evernote

Wilmanski et al. (2021)

21.10 Methods

Greengenes2 unifies microbial data in a single reference tree

Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce Greengenes2, a reference tree that unifies genomic and 16S rRNA databases in a consistent, integrated resource. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy and phenotype effect size when analyzed with the same tree.

21.11 Mapping the Capacity of a Single Subject’s Microbiome to Metabolize Hundreds of Drugs

57 drugs that are transformed by the microbiome, with lots of variance from person to person.

https://www.cell.com/cell/fulltext/S0092-8674(20)30563-8#secsectitle0035

Javdan et al. (2020)