I'm elated to release four @metasub papers today, with new species, data interfaces, & online resources. The first is in @CellCellPress, "A global metagenomic map of urban microbiomes and antimicrobial resistance" led by @dcdanko @Daniela_Bezdan cell.com/cell/fulltext/…" a 🧵 /1
Our metagenome atlas spans ~5,000 samples from mass-transit systems in 60 cities over 3 years, showing thousands of species, antimicrobial resistance (AMR) markers & novel biology everywhere: 10,928 new viruses, 1,302 new bacteria, 2 new archaea, and 838,532 new CRISPR arrays /2
We found a set of 31 "core urban" microbes, present in 97% of all swabs, which included known commensals like C. acnes - most common on doorknobs and touchscreen buttons- but also several water associated microbes that were especially abundant in coastal cities /3
About 40% of all the DNA found in the city represents novel fragments of DNA that have never been seen before; this is true even when we tried other databases of sequence data and shows how much is left to discover /4
@gxr and @akkah21 created a global atlas of k-mers to enable searching across all @metasub data around the world, as well as all other indexed DNA NGS sets, which can help for tracking associations between known, novel, and emerging organisms: metagraph.ethz.ch/search /5
We can use these indices to compare not only of all DNA found on Earth, but someday also compare this to DNA/RNA that might be found on Mars or other planets; our metagenomics framework has already been used as a pilot framework for planetary protection microbiomejournal.biomedcentral.com/articles/10.11… /6
There are distinct microbial signatures for each of the world's cities, which enable almost 90% accuracy to predict where you are from, based on your metagenome; this gets better and better with more samples and will only improve in the future as sequencing continues /7
This has broad implications for a person's "reasonable expectation of privacy" and GINA laws, since you carry signatures that are beyond just your human DNA and which can be combined with other forensics data, as @EranElhaik and I have recently shown /8 link.springer.com/epdf/10.1186/s…
We also found that genes for antimicrobial resistance (AMR) were evident across many surfaces, also in a city-specific fashion, but that these AMR genes were at levels below what one might find in a gut microbiome or soil sample, which is good news for straphangers /9
There is some evidence that the proportion of AMR genes in a given city are actually a mirror of the reported usage of antibiotics for the population, based on W.H.O. data, across plasmid and chromosome-derived sequences: /10
To browse all these AMR genes across the world, we worked with @johnbrownstein and his team, who overlaid our AMR genes data with his resistance map, which you can see here resistanceopen.org/results/ /11
then, with @omarabudayyeh and @jgooten, we mined the metagenome-assembled genomes (MAGs) to find >800K new CRISPR arrays, which showed the ongoing dynamics of bacteria battling their ever-evolving viruses and some unknown CRISPR types /12
We also used the MAGs to find >4.2 million new peptides (all at least 500 amino acids long) that represent novel biosynthetic gene clusters (BGCs), which we are now following up on with @donia_lab and Arbor Bio. Tokyo had the greatest bang for the buck in terms of new BGCs /13
Then Marius Dybwad and Patrick Lee lead the matching collections of the air microbiome from some of the same cities at the same time, which also mapped microbes and AMR genes around the world: microbiomejournal.biomedcentral.com/articles/10.11… /14
Each city's air also had a unique microbial profile, with two skin commensals (C. acnes and M. luteus) making up nearly 50% of the public transit air microbiome species across the six tested cities, all with their own genetic variants and thus their own unique clades /15
The source of the DNA was different in each city, with most being from the "core urban" microbiome of 31 species, human skin, and soil, but occasionally some evidence of animal or human feces, or wastewater; make sure you wash your hands! /16
The air also carries a rich source of new BGCs, including proteins associated with the synthesis of terpenes, bacteriocins, polyketides (polyketide synthases), and those that encode non-ribosomal peptide synthetases (NRPSs) and NRPS-like proteins /17
Finally, @cemmeydan and @dcdanko built an interactive map for the species across the world's cities and their metadata, plus annotation from The Microbe Directory led by @asierra93 @krista_ryon ncbi.nlm.nih.gov/pmc/articles/P…. Here is the link: metasub.org/map/ /18
Every time you look at a subway railing, know that it contains both the map of the antibiotic resistance and a possible source of NEW antibiotics, plus phages and CRISPR arrays that could be used in diagnostics or therapeutics, just like a rainforest under your fingers 🌿👈 /19
@leungmarcus did phenomenal work across all these studies

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More from @mason_lab

22 May 20
Announcing the "COVIDgenes" data portal: covidgenes.weill.cornell.edu, an open site for all to mine the RNA-seq expression results from our @WeillCornell|@nyphospital COVID-19 patient data. This site (v1.0) hosts various visualization, statistics, & normalization options including: /1
Single-gene plots for expression changes with infection status, comparison to other respiratory infections’ expression changes, absolute expression or z-score, & a differentially expressed gene (DEG) q-value sorting tab based on a variety of patient group comparisons /2
Multi-gene heatmaps of expression changes with infection, including auto-updating custom-entry fields and sample annotations of infection status /3
Read 4 tweets
22 Apr 20
Our new @biorxivpreprint is up! "Host, Viral, and Environmental Transcriptome Profiles of SARS-CoV-2." COVID-19 patients (n=338) examined w/ qRT-PCR, RT-LAMP, & total RNA-seq, host responses (e.g. ACE2) & new diagnostic methods. biorxiv.org/content/10.110… Some highlights: /1
Viral evolution maps can be built directly from the total RNA-seq data, with likely origin of NYC viruses from Europe, and less commonly from Asia, plus we think an NYC-specific clade and some novel mutations, also now posted in @nextstrain and @GISAID of course. /2
Host microbiome data shows why COVID-negative patients likely came in when they were sick. We see other coronaviruses (229E, HKU1, NL63, OC43, some rhinoviruses, and various bacterial infections), as well as NP swab microbiome profiles that show immune perturbations /3
Read 7 tweets

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