DOI: 10.5061/dryad.cjsxksnd0
title: An evolution-based framework for describing human gut bacteria

description: The human gut microbiome contains many bacterial strains of the same
 species (‘strain-level variants’). Describing strains in a
 biologically meaningful manner rather than purely
 taxonomic objects is an important goal but challenging due to the
 complexity of strain-level variation. Here, we measured patterns
 of co-evolution across &gt;7,000 strains spanning the bacterial
 tree-of-life. Using these patterns as a prior for studying
 hundreds of gut commensal strains that we isolated, sequenced,
 and metabolically profiled revealed widespread structure beneath the
 phylogenetic level of species. Defining strains by their
 co-evolutionary signatures enabled predicting their metabolic
 phenotypes and engineering consortia from strain genome content alone.
 Our findings demonstrate a biologically relevant organization to
 strain-level variation and motivate a new schema for describing
 bacterial strains based on their evolutionary history.
description: Materials and Methods (futher details and metadata in
 associated article's supplementary material) Creating a bank of
 commensal human gut microbiome strains Fecal samples were obtained from 28
 human donors that fell within the age range of 18 to 63 with a median age
 of 35. Donors were selected as those with no antibiotic use in the past
 year, no known history of diabetes, colitis, autoimmune disease, cancer,
 pneumonia, dysentery, or cellulitis at time of consent. Institutions that
 approved protocols of fecal sample collection were Memorial Sloan
 Kettering (MSK) and the University of Chicago. Fresh fecal samples were
 immediately reduced in an anaerobic chamber upon collection and diluted
 and cultured on various growth media. Agar media types vary, but include
 any of following: Columbia Blood Agar, Brain Heart Infusion +Yeast, Brain
 Heart Infusion + Mucin, Brain Heart Infusion + Yeast + Acetate or
 N-Acetylglucosamine, reinforced Clostridial Agar, Peptone Yeast Glucose,
 Yeast Casitone Fatty Acids, Defined media M5. Colonies were selected and
 grown to be sufficiently turbid, 20% glycerol/PBS stocks were created and
 stored in a -80C freezer.Colonies were selected for whole-genome based on
 pyro-sequencing of the 16S region which provides a rough estimate of genus
 level designation. For each donor, only colonies that had a sequence
 identity threshold of less than 99% from CD-Hit (v. 4.8.1) were selected
 for whole-genome sequencing (1). Bacterial genomic DNA was extracted using
 QIAamp DNA Mini Kit (QIAGEN) according to manufacturer’s manual. The
 purified DNA was quantified using a Qubit 2.0 fluorometer. 1000ng of each
 sample was prepared for sequencing using the QIAseq FX DNA Library Kit
 (QIAGEN). The protocol was carried out for a targeted fragment size of
 550bp. Sequencing was performed on the MiSeq or NextSeq platform
 (Illumina) with a paired- end (PE) kit in pools designed to provide 1-3
 million PE reads per sample with read length of 250 or 150 bp. Adapters
 were trimmed off with Trimmomatic with following parameters: the leading
 and trailing 3 bp of the sequences were trimmed off, quality was
 controlled by a sliding window of 4, with an average quality score of 15
 (default parameters of Trimmomatic). Moreover, any read that was less than
 50 bp long after trimming and quality control were discarded. The
 remaining high-quality reads were assembled into contigs using SPAdes
 (v3.14.0)(2).Taxonomic classification of the assembled contigs was
 performed with the following methods: (a) Kraken2 (v2.1.1); (b)
 full/partial length 16S rRNA gene from each isolated colony’s assembled
 contigs is extracted and input into BLASTn (v2.10.1+) to query against
 NCBI’s RNA RefSeq database (3, 4). Top five hits for each query are
 manually curated to determine an isolate’s identity, with identity and
 coverage cutoff both at 95%; (c) GTDB-Tk (v1.5.1) (5). Final taxonomy is
 determined by the consensus of the three methods. Any colony that did not
 match initial pyro-sequencing taxonomy or lacked consensus are excluded
 from the commensal strain bank.  
 Annotating each strain in the commensal strain bank by its
 orthologous gene group (OGG) content For individual isolates, the genome
 assemblies were annotated using Prokka (v1.12) producing a fasta file of
 all coding regions from the assembled genome translated to the amino- acid
 protein sequences (10). This fasta file was then input to eggNOG mapper
 (v2.0.1b) to annotate each protein sequence against the eggNOG database
 (v5.0) of orthologous gene- groups (OGGs) at the level of Bacteria (‘@2’)
 (11, 12). Each isolate was then aligned based on this common set of OGG
 features, where isolates correspond to each row and OGGs correspond to
 each column and each entry holds the number of protein sequences
 that matched to that OGG. This OGG alignment of 669 isolates forms the CSB
 OGG matrix (Fig. 1C). Each isolate was annotated across 11,248 total OGGs
 of which 5,449 OGGs have greater than zero variance; annotations of
 isolated were done with 16S-BLASTn, GTDB, and final NCBI taxonomic
 designations at the level of Phylum through Species.
description: # An evolution-based framework for describing human gut bacteria
 Extended Data for "An
 evolution-based framework for describing human gut bacteria". ##
 Description of the data and file structure **Data: xslx file**
 ·     **Panel A:** Data matrix of Orthologous Gene Groups (OGGs)
 annotations for each of the 669 strains in the Commensal Strain Bank.
 First column holds the unique ID for each strain. Subsequent columns hold
 the data for each OGG as listed in first row. Each value denotes the
 number of sequences from the strains coding regions that are mapped to
 that OGG (see methods). ·     **Panel B:** Projections of each of 669
 strains onto the principal components describing co-evolutionary variation
 in UniProt (see methods). First column is the strain id, subsequent
 columns are for each of the 7047 principal components with each value
 denoting the contribution of strain *i* onto principal component *k.*
 ·      **Panel C:** Spectral tree of all 669 strains from commensal strain
 bank in Newick format. **Table: S3 (CSV file)** Patristic distances
 between each strain in the UniProt Spectral Tree. Patristic distance is a
 unitless distance metric calculated as the sum of branch lengths between
 two leaves of a phylogenetic tree. Columns: * strain_1 (uniprot id for
 strain 1) * strain_2 (uniprot id for strain 2) * patristic_distance
 (patristic distance between strain 1 and strain 2 in the UniProt spectral
 tree) ## Code/Software