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TbasCO: trait-based comparative ‘omics identifies ecosystem-level and niche-differentiating adaptations of an engineered microbiome

November 2022
ISME Communications 2(1)

DOI:10.1038/s43705-022-00189-2

License
CC BY 4.0

Authors:

Daniel R Noguera

University of Wisconsin–Madison

Katherine Mcmahon

University of Wisconsin–Madison

Show all 7 authorsHide

A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes are not linked to traits with complete fidelity, nor do they capture important attributes, such as the timing of gene expression or coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by statistically defining a trait attribute as a shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while many genomes containing high-affinity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under phosphorus starvation), we identified another attribute shared by many genomes where transcription was highest under high phosphorus conditions. Taken together, we provide a novel framework for unravelling the functional dynamics of uncultivated microorganisms by assigning trait-attributes through genome-resolved time-series metatranscriptomics.

Genome quality statistics and relative abundance calculations for all 66 EBPR SBR MAGs.

…

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ARTICLE OPEN

TbasCO: trait-based comparative ‘omics identiﬁes

ecosystem-level and niche-differentiating adaptations

of an engineered microbiome

E. A. McDaniel

1,2,8

✉, J. J. M. van Steenbrugge

3,4,5,8

✉, D. R. Noguera

, K. D. McMahon

1,6

, J. M. Raaijmakers

4,7

, M. H. Medema

3,7

and

B. O. Oyserman

3,4

✉

A grand challenge in microbial ecology is disentangling the traits of individual populations within complex communities. Various

cultivation-independent approaches have been used to infer traits based on the presence of marker genes. However, marker genes

are not linked to traits with complete ﬁdelity, nor do they capture important attributes, such as the timing of gene expression or

coordination among traits. To address this, we present an approach for assessing the trait landscape of microbial communities by

statistically deﬁning a trait attribute as a shared transcriptional pattern across multiple organisms. Leveraging the KEGG pathway

database as a trait library and the Enhanced Biological Phosphorus Removal (EBPR) model microbial ecosystem, we demonstrate

that a majority (65%) of traits present in 10 or more genomes have niche-differentiating expression attributes. For example, while

many genomes containing high-afﬁnity phosphorus transporter pstABCS display a canonical attribute (e.g. up-regulation under

phosphorus starvation), we identiﬁed another attribute shared by many genomes where transcription was highest under high

phosphorus conditions. Taken together, we provide a novel framework for unravelling the functional dynamics of uncultivated

microorganisms by assigning trait-attributes through genome-resolved time-series metatranscriptomics.

ISME Communications; https://doi.org/10.1038/s43705-022-00189-2

INTRODUCTION

A longstanding cornerstone of deterministic ecological theory is

that the environment selects for traits. Traits may be deﬁned as

any physiological, morphological, or genomic signature that

affects the ﬁtness or function of an individual [1]. Trait-based

approaches have become indispensable in macroecological

systems to describe ﬁtness trade-offs and the effects of

biodiversity on ecosystem functioning [2–5]. Recently, trait-based

frameworks have been proposed as an alternative to taxonomy-

based methods for describing microbial ecosystem processes

[6,7]. Connecting microbial traits and their phylogenetic distribu-

tions to ecosystem-level functions can provide powerful insights

into the ecological and evolutionary dynamics underpinning

community assembly, microbial biogeography, and organismal

responses to changes in the environment [8–10]. Additionally,

pinpointing the organismal distribution of traits and the ecological

selective pressures that enrich them may be leveraged to

reproducibly and rationally engineer stable, functionally redun-

dant ecosystems [11–15]. However, applying trait-based

approaches to microbial communities is challenging due to the

difﬁculty in identifying and measuring relevant ecological traits for

a given ecosystem [16].

High-throughput sequencing technologies and multi-omics

techniques are now routinely used to describe the diversity,

activity, and functional potential of uncultivated microbial

lineages [17–21]. Improvements in bioinformatics algorithms,

and in particular metagenomic binning methods, have allowed

for genome-resolved investigations of microbial communities

rather than gene-based analyses of assembled contigs [22]. These

(meta) genomes are subsequently leveraged to detect the

presence of key genes or pathways and predict speciﬁc traits of

the whole community [19,23]. Integrating metatranscriptomics

data addresses a key limitation, as expression patterns better

reﬂect the actual functional dynamics of a trait compared to gene

presence alone. Here, we present TbasCO, a software package and

statistical framework for Trait-based Comparative ‘Omics to

identify expression attributes. We adopt the terminology attribute

as a hierarchically structured feature of a trait and assert that

statistically similar transcriptional patterns of traits across multiple

organisms be treated as attributes (Fig. 1). This new terminology

addresses two key semantic challenges. First, by extending upon

the current usage of the term “trait”for the presence and absence

of pathways to the corresponding transcriptional patterns.

Second, it addresses a limitation of the terminology of “co-

Received: 20 May 2022 Revised: 29 September 2022 Accepted: 10 October 2022

Department of Bacteriology, University of Wisconsin—Madison, Madison, WI, USA.

Microbiology Doctoral Training Program, University of Wisconsin—Madison, Madison, WI,

USA.

Bioinformatics Group, Wageningen University and Research, Wageningen, The Netherlands.

Microbial Ecology, Netherlands Institute of Ecological Research, Wageningen,

The Netherlands.

Laboratory of Nematology, Wageningen University, Wageningen, The Netherlands.

Department of Civil and Environmental Engineering, University of

Wisconsin—Madison, Madison, WI, USA.

Institute of Biology, Leiden University, Leiden, Netherlands.

These authors contributed equally: E. A. McDaniel, J. J. M. van Steenbrugge.

✉email: elizabethmcd93@gmail.com; jorisvansteebrugge@gmail.com; BenOyserman@gmail.com

www.nature.com/ismecomms

1234567890();,:

expression”, which becomes biologically inaccurate when compar-

ing across independent populations of organisms within a

community. In this manner, the identiﬁcation of expression-

based attributes provides a high-throughput and intuitive frame-

work for extending trait-based methods to time-series expression

patterns in microbial communities. We implement this trait-based

approach to classify transcriptional attributes in a microbial

community performing Enhanced Biological Phosphorus Removal

(EBPR), a globally important biotechnological process implemen-

ted in numerous wastewater treatment plants (WWTPs).

The fundamental feature of the engineered EBPR ecosystem is

the decoupled and cyclic availability of an external carbon source

and terminal electron acceptor. This cycling is often referred to as

“feast-famine”conditions and provides a strong selective pressure

for traits such as polymer cycling. Accumulation of intracellular

polyphosphate through cyclic anaerobic-aerobic conditions ulti-

mately results in net phosphorus removal and accomplishes the

EBPR process [24,25]. One of the most well-studied polypho-

sphate accumulating organisms (PAOs) belongs to the unculti-

vated bacterial lineage ‘Candidatus Accumulibacter phosphatis’

(hereby referred to as Accumulibacter) [24,26]. Numerous

genome-resolved ‘omics methods have been used to investigate

the physiology and regulation of this model PAO enriched in

engineered lab-scale enrichment bioreactor systems [27–35].

However, novel and putative PAOs have been discovered that

remove phosphorus without exhibiting the hallmark traits of

Accumulibacter [36–41]. Additionally, although these lab-scale

systems are designed to speciﬁcally enrich for Accumulibacter, a

diverse bacterial community persists in these environments [27],

and their ecological roles have largely remained unexplored. As a

result, the general adaptations of microbial lineages inhabiting the

EBPR community are not well understood. Using genome-resolved

metagenomics and metatranscriptomics, we assembled 66

species-representative genomes spanning several signiﬁcant EBPR

lineages and identiﬁed the distribution of expression-based

attributes. We show that while some expression attributes are

distributed in few genomes, many are redundant and shared

across many lineages. Furthermore, we ﬁnd that a majority of core

traits (as deﬁned by the presence of marker genes) have multiple

attributes, suggesting that identifying niche-differentiating

expression attributes may be used to reveal a large hidden

metabolic versatility when investigating genomic data alone.

MATERIALS AND METHODS

Metagenomic assembly, annotation, and metatranscriptomic

mapping

Three metagenomes sampled from an EBPR bioreactor in May of 2013 with

linked time-series metatranscriptomics data were sequenced [42]. Samples

were collected and DNA extracted according to the Supplemental

Methods. Metagenomic samples were processed and assembled into 66

species-representative bins as described in detail in the Supplemental

Methods. All bins are greater than 75% complete and contain less than

10% contamination, with a large majority (44/66) >95% complete and <5%

redundant as calculated by CheckM [43] and are all described in Table 1.

Each bin was functionally annotated using the KEGG database through

an HMM-based approach under KEGG release 93.0 using the command-

Fig. 1 Overview of trait-based comparative transcriptomics approach In genome-resolved metagenomics approaches, representative MAGs

are assembled from a microbial community of interest, and the presence and/or absence of key metabolic pathways are used to make

inferences of metabolic potential and ecosystem processes. However, metagenomic data alone can only assess the metabolic potential of a

given pathway, and do not provide other biologically relevant information such as the timing or induction of these traits. Using time-series

metatranscriptomics, we developed a trait-based comparative ‘omics (TbasCO) pipeline that statistically assesses the inter-organismal

differences in gene expression pattern of a given trait to cluster into trait attributes. As expression patterns are determined by the time-points

assessed in an experiment, it is important to design the sampling regime to capture relevant ecophysiological changes within the ecosystem.

E.A. McDaniel et al.

ISME Communications

Table 1. Genome quality statistics and relative abundance calculations for all 66 EBPR SBR MAGs.

Code Genbank

accession

Classiﬁcation Completeness Contamination Size

(Mbp)

Contigs GC Abundance

2013-5-13

Abundance

2013-5-23

Abundance

2013-5-28

Total

Transcriptional

Reads Mapped

Total

rRNAs

Total

tRNAs

AUS1 GCA_020161845.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Actinomycetales;f__

Dermatophilaceae;g__

Austwickia;s__

99.45 5.01 4.39 82 71.2 0.261 0.720 0.124 255331 3 61

PHYC1 GCA_020161815.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Actinomycetales;f__

Dermatophilaceae;g__

Phycicoccus;s__

98.02 0.54 3.06 34 71 1.355 3.007 0.341 332509 1 49

PHYC2 GCA_020161155.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Actinomycetales;f__

Dermatophilaceae;g__

Phycicoccus;s__

Phycicoccus

95.82 1.89 3.20 111 69.2 0.047 0.174 0.112 152031 1 52

TET1 GCA_020160805.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Actinomycetales;f__

Dermatophilaceae;g__

Tetrasphaera_A;s__

98.42 0.54 3.75 57 67.9 0.446 0.436 0.507 1378316 2 47

TET2 GCA_020160795.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Actinomycetales;f__

Dermatophilaceae;g__

Tetrasphaera_A;s__

Tetrasphaera_A

98.92 0.05 3.96 66 69.3 0.803 0.236 1.244 2538782 1 76

LEU1 GCA_020161315.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Actinomycetales;f__

Microbacteriaceae;g__

Leucobacter;s__

96.06 2.05 3.01 74 63.5 0.272 0.083 0.093 99061 3 47

LEU2 GCA_020161175.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Actinomycetales;f__

Microbacteriaceae;g__

Leucobacter;s__

Leucobacter

83.22 1.48 2.31 140 64.8 0.065 0.101 0.092 22050 2 44

SAL1 GCA_020160915.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Actinomycetales;f__

Microbacteriaceae;g__

Salinibacterium;s__

97.81 0 2.93 8 67.2 0.335 0.142 0.559 178111 2 45

NANO1 GCA_020161245.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Nanopelagicales;f__;g__;s__

99.14 3.68 4.29 95 72.7 0.106 0.047 0.172 64510 1 52

PROP1 GCA_020161795.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Propionibacteriales;f__

Propionibacteriaceae;g__;s__

91.04 0.91 3.47 67 69.3 0.063 0.108 0.206 100351 0 60

PROP2 GCA_020161755.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Propionibacteriales;f__

Propionibacteriaceae;g__

Propionicimonas;s__

93.63 3.02 4.08 61 70.7 0.094 0.046 0.413 130384 3 52

E.A. McDaniel et al.

ISME Communications

Table 1. continued

Code Genbank

accession

Classiﬁcation Completeness Contamination Size

(Mbp)

Contigs GC Abundance

2013-5-13

Abundance

2013-5-23

Abundance

2013-5-28

Total

Transcriptional

Reads Mapped

Total

rRNAs

Total

tRNAs

PROP3 GCA_020161015.1 d__Bacteria;p__

Actinobacteriota;c__

Actinobacteria;o__

Propionibacteriales;f__

Propionibacteriaceae;g__

Propionicimonas;s__

94.14 3.15 3.67 65 71.6 0.074 0.176 0.249 96105 0 51

FIMBRI1 GCA_020161505.1 d__Bacteria;p__

Armatimonadota;c__

Fimbriimonadia;o__

Fimbriimonadales;f__

Fimbriimonadaceae;g__

Uphvl-Ar1;s__

96.55 0 3.14 38 58.8 0.068 0.234 0.009 27830 3 48

BAC1 GCA_020161835.1 d__Bacteria;p__

Bacteroidota;c__

;o__;f__;g__;s__

94.52 0 4.40 36 41.6 0.345 0.024 0.003 32140 4 42

BAC2 GCA_020162035.1 d__Bacteria;p__

Bacteroidota;c__

Bacteroidia;o__

AKYH767;f__

b-17BO;g__;s__

99.05 0.48 3.17 31 29.6 0.757 0.010 0.015 46346 3 32

CHIT1 GCA_020161435.1 d__Bacteria;p__

Bacteroidota;c__

Bacteroidia;o__

Chitinophagales;f__

Chitinophagaceae;g__;s__

99.01 0 4.19 10 46.3 0.183 0.174 3.613 3141341 0 34

CHIT2 GCA_020161535.1 d__Bacteria;p__

Bacteroidota;c__

Bacteroidia;o__

Chitinophagales;f__

Chitinophagaceae;g__

Flavihumibacter;s__

100 1.23 4.03 23 48.2 0.195 0.383 0.033 24003 3 40

SAP1 GCA_020160935.1 d__Bacteria;p__

Bacteroidota;c__

Bacteroidia;o__

Chitinophagales;f__

Saprospiraceae;g__;s__

96.53 0.99 5.84 51 50.3 0.226 0.007 0.128 702648 3 36

SAP2 GCA_020160855.1 d__Bacteria;p__

Bacteroidota;c__

Bacteroidia;o__

Chitinophagales;f__

Saprospiraceae;g__

OLB8;s__

97.52 0.5 3.73 65 37.2 0.290 0.167 0.016 10636 3 34

LEAD1 GCA_020161355.1 d__Bacteria;p__

Bacteroidota;c__

Bacteroidia;o__

Cytophagales;f__

Spirosomaceae;g__

Leadbetterella;s__

99.11 0.6 4.81 17 37.7 0.136 0.002 0.858 1017458 2 36

RUN1 GCA_020161055.1 d__Bacteria;p__

Bacteroidota;c__

Bacteroidia;o__

Cytophagales;f__

Spirosomaceae;g__

Runella;s__Runella

100 0 7.44 60 44.4 0.124 1.088 1.749 10725342 2 40

FLAVO1 GCA_020161455.1 d__Bacteria;p__

Bacteroidota;c__

Bacteroidia;o__

Flavobacteriales;f__

Flavobacteriaceae;g__

Flavobacterium;s__

99.29 0.35 3.08 18 32.5 0.030 0.002 0.742 3002991 3 36

CHRYS1 GCA_020161485.1 d__Bacteria;p__

Bacteroidota;c__

Bacteroidia;o__

Flavobacteriales;f__

Weeksellaceae;g__

Chryseobacterium_A;s__

Chryseobacterium_A

100 0.25 2.57 11 36.7 0.107 3.917 0.358 209940 2 35

E.A. McDaniel et al.

ISME Communications

Table 1. continued

Code Genbank

accession

Classiﬁcation Completeness Contamination Size

(Mbp)

Contigs GC Abundance

2013-5-13

Abundance

2013-5-23

Abundance

2013-5-28

Total

Transcriptional

Reads Mapped

Total

rRNAs

Total

tRNAs

BAC3 GCA_020162015.1 d__Bacteria;p__

Bacteroidota;c__

Bacteroidia;o__

NS11–12g;f__

UKL13-3;g__B1;s__

100 0 3.74 45 41.1 0.445 0.892 0.693 9991372 0 34

IGNAVI1 GCA_020161395.1 d__Bacteria;p__

Bacteroidota;c__

Ignavibacteria;o__

Ignavibacteriales;f__

Ignavibacteriaceae_A;g__

UTCHB3;s__

97.27 0.55 4.07 21 42.2 0.163 0.635 0.025 58496 3 44

RTHERM1 GCA_020160835.1 d__Bacteria;p__

Bacteroidota;c__

Rhodothermia;o__

Rhodothermales;f__;g__;s__

98.36 1.38 3.25 36 67 0.328 0.050 0.060 116472 3 52

ANAER1 GCA_020161935.1 d__Bacteria;p__

Chloroﬂexota;c__

Anaerolineae;o__

SBR1031;f__A4b;g__;s__

98.17 0 7.64 32 54.2 0.375 0.190 0.153 910673 4 48

HERP1 GCA_020161265.1 d__Bacteria;p__

Chloroﬂexota;c__

Chloroﬂexia;o__

Chloroﬂexales;f__

Herpetosiphonaceae;g__

Herpetosiphon;s__

99.09 0.91 6.04 13 50.2 0.774 0.025 0.008 7917 0 55

OBS1 GCA_020161235.1 d__Bacteria;p__

Cyanobacteria;c__

Vampirovibrionia;o__

Obscuribacterales;f__

Obscuribacteraceae;g__

Obscuribacter;s__

Obscuribacter

98.28 0.94 5.09 17 49.2 6.272 0.681 0.197 1713299 6 42

FUSI1 GCA_020161295.1 d__Bacteria;p__

Firmicutes_A;c__

Clostridia;o__

Peptostreptococcales;f__

Fusibacteraceae;g__

UBA5201;s__

96.5 1.75 3.08 41 42.8 0.001 0.580 0.001 11649 3 57

GEMMA1 GCA_020161135.1 d__Bacteria;p__

Gemmatimonadota;c__

Gemmatimonadetes;o__

Gemmatimonadales;f__

Gemmatimonadaceae;g__

;s__

98.35 3.3 4.55 8 70.1 0.004 0.031 0.494 2624259 3 55

SACCH1 GCA_020160975.1 d__Bacteria;p__

Patescibacteria;c__

Saccharimonadia;o__

Saccharimonadales;f__

Saccharimonadaceae;g__

Saccharimonas;s__

84.48 0 0.97 1 49.6 0.637 1.437 0.035 65079 3 43

ALPHA1 GCA_020161965.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

;f__;g__;s__

82.43 2.65 3.94 581 64.6 0.015 0.165 0.007 1283274 3 39

CAED1 GCA_020161545.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Caedimonadales;f__

UBA1908;g__;s__

86.36 1.1 1.88 96 52.8 0.034 0.201 0.002 41264 3 35

BREV1 GCA_020161595.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Caulobacterales;f__

Caulobacteraceae;g__

Brevundimonas;s__

Brevundimonas

97.51 3.41 3.07 155 67.2 0.011 0.254 0.004 27852 2 45

E.A. McDaniel et al.

ISME Communications

Table 1. continued

Code Genbank

accession

Classiﬁcation Completeness Contamination Size

(Mbp)

Contigs GC Abundance

2013-5-13

Abundance

2013-5-23

Abundance

2013-5-28

Total

Transcriptional

Reads Mapped

Total

rRNAs

Total

tRNAs

CAULO1 GCA_020161365.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Caulobacterales;f__

Caulobacteraceae;g__

Caulobacter;s__

100 0 4.43 25 66.9 0.048 0.093 0.589 4627825 3 55

HYPHO1 GCA_020161405.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Caulobacterales;f__

Hyphomonadaceae;g__

UBA1942;s__

98.43 0.32 2.98 6 39.4 0.844 0.006 2.208 4138107 0 33

REYR1 GCA_020160955.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Reyranellales;f__

Reyranellaceae;g__

Reyranella;s__

89.96 7.34 5.08 210 70 0.057 0.090 0.238 224063 3 53

REYR2 GCA_020160995.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Reyranellales;f__

Reyranellaceae;g__

Reyranella;s__Reyranella

91.04 6.01 5.71 258 65.3 0.074 0.102 0.134 62018 1 53

ANDERS1 GCA_020161855.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhizobiales;f__

Anderseniellaceae;g__

PALSA-927;s__

97.64 0.4 3.36 19 61.6 0.187 0.175 0.029 25238 2 46

BEIJ1 GCA_020161915.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhizobiales;f__

Beijerinckiaceae;g__

Bosea;s__

81.6 8.48 4.44 777 66.3 0.156 0.319 0.423 338238 0 43

BEIJ2 GCA_020161975.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhizobiales;f__

Beijerinkiaceae_A;g__

;s__

81.18 5.25 3.99 465 62.5 0.042 0.157 0.018 28432 0 42

BEIJ3 GCA_020161475.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhizobiales;f__

Beijerinkiaceae_A;g__

PAR1;s__

76.21 1.72 3.08 320 63.3 0.017 1.744 0.099 77102 0 41

BEIJ4 GCA_020161575.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhizobiales;f__

Beijerinkiaceae_A;g__

PAR1;s__

97.89 0 3.19 17 63.2 0.176 0.538 0.014 26820 0 47

PHREA1 GCA_020161695.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhizobiales;f__

Phreatobacteraceae;g__

Phreatobacter;s__

98.35 3.96 4.69 38 67.7 0.022 0.273 0.103 133243 1 50

RHIZO1 GCA_020161035.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhizobiales;f__

Rhizobiaceae;g__

Aminobacter;s__

Aminobacter

94.26 5.5 5.50 80 63.8 0.136 0.095 0.095 219213 3 48

E.A. McDaniel et al.

ISME Communications

Table 1. continued

Code Genbank

accession

Classiﬁcation Completeness Contamination Size

(Mbp)

Contigs GC Abundance

2013-5-13

Abundance

2013-5-23

Abundance

2013-5-28

Total

Transcriptional

Reads Mapped

Total

rRNAs

Total

tRNAs

RHIZO2 GCA_020161665.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhizobiales;f__

Rhizobiaceae;g__

QFOR01;s__

88.41 2.12 3.39 43 60.6 0.035 0.335 0.003 24536 0 47

RHIZO3 GCA_020161625.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhizobiales;f__

Rhizobiaceae;g__

Shinella;s__Shinella

78.53 6.03 6.98 935 63.6 0.010 0.169 0.037 149921 0 48

RHODO1 GCA_020161655.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhodobacterales;f__

Rhodobacteraceae;g__

Deﬂuviimonas;s__

100 0.35 4.08 24 65.5 0.321 0.141 0.848 3645270 0 44

RHODO2 GCA_020161615.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhodobacterales;f__

Rhodobacteraceae;g__

Pararhodobacter;s__

99.09 1.19 4.87 26 67.9 0.084 0.534 0.009 25807 1 49

RHODO3 GCA_020160875.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rhodospirillales_C;f__

Rhodospirillaceae_A;g__

;s__

91.2 2.27 3.76 236 62.2 0.153 0.046 0.185 153017 1 39

RICK1 GCA_020160775.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Rickettsiales;f__

Rickettsiaceae;g__

GCA-2402195;s__

75.59 1.58 1.18 82 34.5 0.085 0.075 0.052 17671 2 26

SPHING1 GCA_020160755.1 d__Bacteria;p__

Proteobacteria;c__

Alphaproteobacteria;o__

Sphingomonadales;f__

Sphingomonadaceae;g__

Sphingopyxis;s__

99.98 1.56 4.31 20 65.1 0.026 0.014 0.607 600695 3 47

ALIC1 GCA_020161945.1 d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Burkholderiales;f__

Burkholderiaceae;g__

Alicycliphilus;s__

99.64 1.04 3.83 33 66.3 0.166 2.959 0.738 770970 1 48

OTTO1 GCA_020161215.1 d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Burkholderiales;f__

Burkholderiaceae;g__

Ottowia;s__

93.66 5.56 4.52 250 67.1 0.011 0.276 0.004 26717 1 46

OTTO2 GCA_020161715.1 d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Burkholderiales;f__

Burkholderiaceae;g__

Ottowia;s__Ottowia

99.26 0.62 3.40 35 69.1 0.372 4.140 0.424 121379 1 50

RAM1 GCA_020161775.1 d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Burkholderiales;f__

Burkholderiaceae;g__

Ramlibacter;s__

99.84 0.06 4.36 32 66.1 0.778 0.536 1.814 1832037 1 45

E.A. McDaniel et al.

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Table 1. continued

Code Genbank

accession

Classiﬁcation Completeness Contamination Size

(Mbp)

Contigs GC Abundance

2013-5-13

Abundance

2013-5-23

Abundance

2013-5-28

Total

Transcriptional

Reads Mapped

Total

rRNAs

Total

tRNAs

RUBRI1 GCA_020161065.1 d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Burkholderiales;f__

Burkholderiaceae;g__

Rubrivivax;s__

99.52 0.05 6.29 41 71.2 0.236 0.347 0.306 1259737 1 73

VITREO1 GCA_020161145.1 d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Burkholderiales;f__

Burkholderiaceae;g__

Vitreoscilla_A;s__

100 0.7 3.51 13 68.9 0.397 4.498 0.530 382529 1 46

CAPIA NA d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Burkholderiales;f__

Rhodocyclaceae;g__

Accumulibacter;s__

Accumulibacter

100 0.03 4.59 61 63.8 18.797 10.533 0.106 2411395 0 46

CAPIIA NA d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Burkholderiales;f__

Rhodocyclaceae;g__

Accumulibacter;s__

Accumulibacter

99.84 0.24 4.64 81 64.3 33.479 26.824 49.334 102762132 0 44

ZOO1 GCA_020161115.1 d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Burkholderiales;f__

Rhodocyclaceae;g__

Zoogloea;s__

91.62 3.51 4.99 501 65.7 0.090 0.026 0.106 913411 4 59

LEG1 GCA_020161725.1 d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Legionellales;f__

Legionellaceae;g__;s__

92.74 1.07 2.58 182 36.1 0.094 0.126 0.006 19591 1 27

LUTEI1 GCA_020161335.1 d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Xanthomonadales;f__

Xanthomonadaceae;g__

Luteimonas;s__

96.89 0.71 3.56 252 69.9 0.002 0.309 0.011 49418 1 39

PSEUDO1 GCA_020160895.1 d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Xanthomonadales;f__

Xanthomonadaceae;g__

Pseudoxanthomonas_A;s__

99.95 0.89 3.67 28 67.8 0.416 0.730 3.125 3964795 2 50

PSEUDO2 GCA_020161075.1 d__Bacteria;p__

Proteobacteria;c__

Gammaproteobacteria;o__

Xanthomonadales;f__

Xanthomonadaceae;g__

Pseudoxanthomonas;s__

99.02 0 2.99 6 69.6 1.750 6.111 1.228 515369 3 52

Genome code names match names used in all ﬁgures and within the text. Classiﬁcations were assigned using the GTDB-tk [87] and conﬁrmed by comparing against select publicly available references and a

subset of HQ MAGs from Singleton et al. 2021 [40]. Completeness and redundancy estimates and GC content were calculated by CheckM [43]. tRNA and rRNA predictions were performed with Barrnap as

part of the Prokka software [88]. Relative abundance estimates reﬂect the proportion of reads mapped to the genome in that sample divided by the total number of reads mapped to all genomes as

performed with SingleM. Table available at https://ﬁgshare.com/articles/dataset/EBPR_SBR_MAGs_Metadata/13063874.

E.A. McDaniel et al.

ISME Communications

line KofamKOALA pipeline [44,45], selecting annotations that were

signiﬁcant hits above the speciﬁc HMM threshold. This resulted in 117,657

total annotations with 5,228 unique annotations. We used a metatran-

scriptomic dataset of six timepoints collected over a single EBPR cycle from

Oyserman et al. 2016 [42], with three timepoints from the anaerobic phase

and three from the aerobic phase. Raw reads were quality ﬁltered using

BBtools suite v38.07 [46] and ribosomal rRNA was removed from each

sample using SortMeRNA [47]. Reads from each sample were mapped

against the concatenated set of open reading frames from all 66 bins using

kallisto v0.44.0 and parsed using the R package tximport [48,49].

TbasCO method implementation

The TbasCO package identiﬁes expression-based attributes of predeﬁned

traits using time-series (meta)transcriptomics data (Fig. 1). As expression

patterns are determined by the time-points assessed in an experiment, it is

important to design the sampling regime to capture relevant ecophysio-

logical changes within the ecosystem. In general, traits are deﬁned by the

presence of a pathway or other collection of genes from an externally

provided database. A weighted distance metric between expression

patterns for all genes that deﬁne a trait is calculated, and statistically

signiﬁcant similarity is determined based on the background distribution

of a trait of equal size. Thereby, two or more organisms with a statistically

similar expression pattern for a trait share an attribute. As the expression

proﬁles of genes within a trait are compared across genomes indepen-

dently, co-expression of genes within a genome is not a pre-requisite for

identifying an attribute.

Input and preprocessing. The input that is accepted by TbasCO is a table

of RNAseq counts in csv format. Each row is treated as gene that has

columns for the gene/locus name, counts per sample, the genome the

gene belongs to, and the KEGG Orthology (KO) identiﬁer. The RNAseq

counts table may be provided pre-normalized or can be normalized by the

program. The default normalization method is designed to minimize

compositional bias in the differential abundance and activity of constituent

populations in metatranscriptomics studies. RNA expression counts are

therefore normalized relative to each genomic bin separately for each

sample [42]. After normalization, a pruning step is introduced to ﬁlter

genes that have zero counts or a mean absolute deviation of less than one

across all time points. To make inter-organismal comparisons of the

relative contribution of a gene to total measured organismal RNA, an

additional statistic is calculated ranking the expression counts from each

sample from highest to lowest. The ranks for each sample are then

normalized by dividing them by the maximum rank value in that sample.

This normalization is applied to make ranks comparable between

organisms with different genome sizes.

To assess the statistical signiﬁcance of the calculated distances between

the expression patterns of all genes within a trait, random background

distributions are created for (1) individual genes and (2) traits of N genes.

For individual genes, three different distributions were calculated, based

on the distances between randomly sampled open reading frames,

randomly sampled genes with an annotation (but not necessarily the same

annotation), and randomly sampled genes with the same annotation. The

background distribution for a trait of N genes is based on the distances

between randomly composed sets of genes. For each gene pair, two

distances metrics are calculated, the Pearson Correlation (PC) and the

Normalized Rank Euclidean Distance (NRED). In practice, it is often found

that a certain annotation is assigned to multiple genes in the same

genome. If this occurs, there is an option to use either a random selection,

or the highest scoring pair. In the latter case, a correction for multiple

testing is implemented. This process is repeated N-times, where N equals

the number of genes in any given trait. The background distribution for

traits is determined by ﬁrst randomly sampling two genomes, identifying

the overlap in annotations, and ﬁnally artiﬁcially deﬁning a trait containing

N annotations. For each annotation in the trait, the distances are calculated

between genome A and genome B, as described in the previous section.

As modules vary in size, this process is repeated for traits of different sizes.

Identifying attributes. TbasCO provides both a cluster-based and pair-wise

approach to identify attributes. In both methods, the distance between

expression patterns of a trait between two genomes is ﬁrst calculated

based on a composite Z score of the PC and NRED for each gene

composing the trait. In the cluster-based analysis, the distances are

subsequently clustered using the Louvain clustering algorithm to identify

trait attributes. To determine if an attribute is signiﬁcantly similar or not, a

one-sided T-test between the attribute and the random background

distribution of traits is conducted. This is done for both cluster-based and

model-based comparisons. Many traits are complex and represented in

databases such as KEGG by numerous alternative routes. To deal with this

complexity, each pathway is expanded into all possible alternative routes.

Due to the extremely high number of alternative routes for some traits,

attributes are pruned based on a strict requirement of 100% completion.

Distance calculations. To determine the similarity in expression patterns

between genes, two dissimilarity metrics are calculated: the PC between

RNAseq counts across samples, and the NRED, where ranks are a measure

of relative abundance of RNA in each sample, normalized the abundance

of RNA in the corresponding genome. These distance scores are converted

to Z scores using a background distribution of distances between

randomly sampled genes as previously described. To determine statisti-

cally signiﬁcant similarities in expression patterns of a trait, a composite

score is calculated. For each of these genes the PC and NRED are calculated

and transformed to Z scores and combined as (−1*PC +NRED). The

distance of the trait between two genomes is deﬁned as the average of

these composite distance scores. If traits being compared do not have

100% overlap in gene content, then the dissimilarity score is normalized by

the Jaccard distance between gene content of the trait.

PC þNREDðÞ1dJðÞ

Statistical assessment of trait attributes. In both model-based and pair-

wise approaches, the distance is ﬁrst calculated between expression

patterns of a trait between two genomes based on the composite Z score

of the PC and NRED for each gene composing the trait. In the clustering-

based analysis, the distances are subsequently clustered using the Louvain

clustering algorithm to identify trait-attributes. To determine if attributes

are signiﬁcantly similar, a one-sided T-test is conducted between the

attribute and a background distribution of randomly sampled traits with

the same number of genes. To derive the random background

distributions, multiple distributions are calculated ranging in gene

numbers from the smallest trait to the largest trait in the dataset as

described previously. For each background distribution, N (default: 10,000)

traits are randomly composed. The distances between these artiﬁcial traits

are calculated in the same way as for the actual traits. In addition to a

statistical pruning step, the attributes are pruned based on a strict

requirement of 100% completion of each module. A benchmarking

analysis to examine the effects of different parameters, including the

presence of zero counts, was conducted to determine their inﬂuence on

the number of attributes identiﬁed and may be found in the

supplementary materials (Supplementary Table 1, Supplementary

Figs. 2–4).

RESULTS AND DISCUSSION

Reconstructing a diverse EBPR SBR community

To explore trait-based transcriptional dynamics of a semi-complex

microbial community, we applied genome-resolved metage-

nomics and metatranscriptomics to an EBPR sequencing-batch

reactor (SBR) ecosystem (Fig. 2). We previously performed a

metatranscriptomics time-series experiment over the course of a

normally operating EBPR cycle to investigate the regulatory

controls of Accumulibacter gene expression [42]. In this experi-

ment, six samples were collected for RNA sequencing: three from

the anaerobic phase and three from the aerobic phase (Fig. 2A).

Additionally, three metagenomes were collected from the same

month of the metatranscriptomic experiment, including a sample

from the same date of the experiment. We reassembled

contemporary Accumulibacter clade IIA and IA genomes that

were previously assembled from the same bioreactor system

[27,28]. The genomes of Accumulibacter clades IA and IIA are

similar by approximately 85% average-nucleotide identity [28,31],

which is well below the common species-resolved cutoff of 95%,

and these groups have recently been designated as separate

species (Candidatus Accumulibacter regalis and Candidatus

Accumulibacter phosphatis, respectively) [35]. However, we

maintain references to the Accumulibacter clade nomenclature

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based on polyphosphate kinase (ppk1) sequence identity through-

out the manuscript (CAPIA and CAPIIA) [31,50,51]. During the

experiment, the bioreactor was highly enriched in Accumulibacter

clade IIA, accounting for approximately 50% of the mapped

metagenomic reads and the highest transcriptional counts (Fig. 2B,

C) [42]. Whereas Accumulibacter clade IA exhibited low abun-

dance patterns but was within the top 10 genomes with the

highest total transcriptional counts (Fig. 2C).

Although this bioreactor system was highly enriched in

Accumulibacter, a diverse bacterial community persisted and

was active in this ecosystem (Fig. 2B, C). We reconstructed

representative population genomes of the microbial community

of the SBR system, resulting in 64 metagenome-assembled

genomes (MAGs) of the (non-Accumulibacter) bacterial commu-

nity. Interestingly, we recovered genomes of experimentally

veriﬁed and putative PAOs previously not detected in these

bioreactors, including two Tetrasphaera spp. (TET1 and TET2)

‘Candidatus Obscuribacter phosphatis’(OBS1), and Gemmatimona-

detes (GEMMA1). Pure cultures of Tetrasphaera have been

experimentally shown to cycle polyphosphate without incorpor-

ating PHA [37], deviating from the hallmark Accumulibacter PAO

model. The ﬁrst cultured representative of the Gemmatimonadetes

phylum Gemmatimonas aurantiaca was isolated from an SBR

simulating EBPR and was shown to accumulate polyphosphate

through Neisser and DAPI staining [52]. Additionally, Ca.

Obscuribacter phosphatis has been hypothesized to cycle phos-

phorus based on the presence of genes for phosphorus transport,

polyphosphate incorporation, and potential for both anaerobic

and aerobic respiration [38], and was enriched in a photobior-

eactor EBPR system [53]. Both Tetrasphaera spp. TET1 and TET2,

OBS1, and GEMMA1 groups exhibit higher relative abundance

patterns than CAPIA but have similar relative transcriptional levels

(Fig. 2B, C, Table 1).

Numerous SBR MAGs among the Actinobacteria and Proteobac-

teria contain the high-afﬁnity phosphorus transporter pstABCS

system, polyphosphate kinase ppk1, and the low-afﬁnity pit

phosphorus transporter (Supplementary Fig. 5). Additionally,

select MAGs within the Alphaproteobacteria,Betaproteobacteria,

and Gammaproteobacteria contain all required subunits for

polyhydroxyalkanoate synthesis (Supplementary Fig. 5). Other

abundant and transcriptionally active groups in the SBR ecosys-

tem that are not predicted to be PAOs are members of the

Fig. 2 Genome-resolved metatranscriptomics approach of an EBPR system. Application of a genome-resolved metatranscriptomics

approach to a lab-scale sequencing batch reactor (SBR) designed to enrich for Accumulibacter. ASchematic of the main cycle parameters and

analyte dynamics of an SBR simulating EBPR. Six samples were taken for RNA sequencing within the cycle at time-points denoted by arrows.

BPhylogenetic identity and abundance patterns of 66 assembled MAGs from the EBPR system. The phylogenetic tree was constructed from

concatenated markers contained in the GTDB-tk with muscle, calculated with RAxML, and visualized in iTOL. A phylogenetic tree of all 66

MAGs with reference genomes and high-quality genomes from Singleton et al. constructed with concatenated markers from GTDB-tk are

provided in Supplementary Fig. 1. Sizes of circles represent relative abundance patterns calculated from metagenomic reads obtained from a

sample collected the same day as the metatranscriptomic experiment was performed, and are not to scale. CTranscriptional patterns of each

MAG in the anaerobic and aerobic phases of the EBPR cycle. RNA-seq reads from each time-point were competitively mapped to all 66

assembled MAGs and counts normalized by transcripts per million (TPM). Total counts in the anaerobic and aerobic phases for each genome

were averaged separately and plotted on a log scale. Order of MAGs from left to right mirrors the order of MAGs in the phylogenetic tree in

Bfrom the top of the circle going clockwise.

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Bacteroidetes such as CHIT1 within the Chitinophagaceae, and

Cytophagales members Runella sp. RUN1 and Leadbetterella sp.

LEAD1 (Fig. 2B, C, Table 1). Interestingly, an uncharacterized group

within the Bacteroidetes, represented by BAC1, contributed the

third most to the pool of transcripts (Fig. 2C), and did not show

phylogenetic similarity to MAGs assembled from Danish full-scale

wastewater treatment systems [40] (Supplementary Fig. 1). Other

groups from which we assembled MAGs for that do not exhibit

clear roles in EBPR systems were Chloroﬂexi ANAER1 and HERP1

MAGs, Armatimonadetes FIMBRI1, Firmicutes FUSI1, and Patesci-

bacteria SACCH1. Members of the Chloroﬂexi are ﬁlamentous

bacteria that have been associated with bulking and foaming

events in full-scale WWTPs [54–56], but also aid in forming the

scaffolding around ﬂoc aggregates and degrade complex poly-

mers [56–58]. The Patescibacteria (formerly TM7) are widespread

but low abundant members of natural and engineered ecosys-

tems, have reduced genome sizes, and may contribute to

ﬁlamentous bulking in activated sludge [22,59]. To summarize,

lab-scale SBRs designed to enrich for Accumulibacter contain

diverse bacterial microorganisms [27,32], but their ecological

functions and putative interactions remain to be fully understood

in the context of the EBPR ecosystem.

Identifying expression-based trait attributes among the EBPR

SBR community with TbasCO

Current metatranscriptomics analyses often employ either a gene-

centric [31,60–62] or genome-centric approach [42,63–65]. In

both approaches, highly, differentially, or co-expressed genes are

identiﬁed and tested for enrichment of speciﬁc functions.

Enrichment- or annotation-based approaches are employed in

numerous metatranscriptomics tools such as MG-RAST, MetaTrans,

SAMSA2, COMAN, IMP, and Anvi’o[66–71]. Here, we expand on

the use of molecular markers as traits by deﬁning expression

attributes by leveraging a priori knowledge from predeﬁned trait

libraries, such as the KEGG database [72], to statistically assess

inter-species expression patterns of genes that together form a

trait (Fig. 1). First, our results showed that there is statistically

signiﬁcant transcriptional conservation of genes at the community

level; genes that share an annotation were signiﬁcantly more

similar than expected using two different distance metrics (NRED:

pvalue <2.2e–16, PC: pvalue <2.2e–16). Extending this statistical

analysis to the trait level, we identiﬁed 1674 attributes distributed

across the 66 genomes. On average, we identiﬁed 9.12 genomes

per attribute (SD -5.22), with a minimum of 3 genomes and a

maximum of 35 (Fig. 3B). Based on these statistics, we deﬁned

redundant attributes as those two standard deviations above the

mean (19 genomes). With this cutoff applied, we identiﬁed 79

redundant trait attributes mostly belonging to pathways among

carbohydrate metabolism, purine metabolism, and fatty acid

metabolism categories (Table 2). Of 290 traits, we identiﬁed 97

traits with two or more attributes identiﬁed (33%). Of these, traits

in 10 or more genomes were twice as likely to have two or more

attributes (65%), suggesting that divergent expression patterns for

a trait are common, and may represent a niche-differentiating

feature (Fig. 3A). Henceforth, when multiple attributes are

identiﬁed for a trait, we refer to these as niche-differentiating

attributes.

From the ecosystem perspective, a clear phylogenetic signal is

observed in the distribution of attributes, as genomes cluster

together by shared trait attributes by phylum with some

exceptions, such as genomes belonging to the Bacteroidetes,

Actinobacteria, and Proteobacteria clustering together, respectively

(Fig. 3C). For simplicity, we ﬁltered the network to only include

nodes with more than 5 connections. Highly redundant trait

attributes belonged to modules in the lipid metabolism, energy

metabolism, and nucleotide metabolism KEGG functional cate-

gories. In contrast, more specialized trait attributes on the

periphery of the network or amongst group-speciﬁc clusters such

as within the Actinobacteria or subsets of the Proteobacteria

belonged to amino acid metabolism, biosynthesis of terpenoids

and polyketides, metabolism of cofactors and vitamins, and

carbohydrate metabolism KEGG modules. Pathways of note that

showed a high level of redundancy include the TCA cycle,

isoleucine biosynthesis, acyl-CoA synthesis, threonine biosynth-

esis, and cytochrome c oxidase activity (Table 2). Large pathways

with hundreds of possible routes such as glycolysis, the TCA cycle,

gluconeogenesis, and the pentose phosphate pathway are not

included in the main network and are displayed as individual

networks (Supplementary Fig. 6).

We next explored the distribution of non-redundant attributes

(e.g. 3–18 genomes) (Fig. 3B). A total of 796 trait attributes with

low redundancy were identiﬁed belonging to pathways involved

in carbohydrate cofactor and vitamin metabolism including

glycolysis, gluconeogenesis, parts of the TCA cycle, tetrahydrofo-

late biosynthesis, tryptophan biosynthesis, and the pentose

phosphate pathway (Table 3). Different sets of low redundancy

trait attributes were identiﬁed within respective phyla (Supple-

mentary Fig. 7). Between genomes belonging to the Actinobac-

teria,Alphaproteobacteria, Bacteroidetes, Betaproteobacteria, and

Gammaproteobacteria, low redundancy attributes (belonging to

less than half of the total genomes within the phylum) include

carbohydrate metabolism, amino acid metabolism and metabo-

lism of cofactors and vitamins (Supplementary Fig. 7). Redundant

trait attributes within individual phyla belong to core energy

metabolism pathways, fatty acid biosynthesis, and carbohydrate

metabolism. However, even within individual phyla, non-

redundant attributes include different amino acids and cofactors

(Extended Table 1 - available on Figshare https://ﬁgshare.com/

articles/dataset/Lineage-Speciﬁc_Core_and_Niche_Differentiating

_Traits/15001200).

As noted previously, one of the most striking ﬁndings is that a

majority, 65% of traits present in 10 or more genomes have multiple

expression attributes. Thus, it seems that while the presence of

marker genes suggests many organisms share a particular trait, the

presence of niche-differentiating expression proﬁles suggest an

alternative story, that there is a level of hidden metabolic diversity.

For example, central carbon metabolism and energy pathways such

as the TCA cycle, glycolysis, gluconeogenesis, and the pentose

phosphate pathway are oftentimes considered core traits when

only analyzing the presence and/or absence of individual markers

belonging to these pathways. Among over 1000 high-quality MAGs

assembled from full-scale Danish WWTPs, the TCA cycle and

pentose phosphate pathway are highly represented among the

abundant microorganisms, with glycolysis less so [40]. Whereas the

TCA cycle and pentose phosphate pathway are present among a

high number of genomes in the EBPR SBR community, different

routes or parts of these pathways have niche-differentiating

distributions (Supplementary Fig. 6, Tables 2and 3). These ﬁner-

scale differences in expression of “core”traits may explain the

persistence of a diverse community when solely fed acetate, as

different lineages could employ similar carbon utilization pathways

differently or in more versatile ways. Another salient aspect of this

analysis is the astonishingly high number of possible routes within

individual pathways here represented by their Disjunctive Normal

Forms. For example, accounting for all alternative routes and

enzymes, the glycolysis pathway has 100 s of possible routes.

Layering upon this many expression attributes reveals a large

hidden metabolic versatility.

Dimensionality of the high-afﬁnity phosphorus transporter

system PstABCS

The EBPR ecosystem is characterized by its highly dynamic

phosphorus cycles. To explore how different lineages respond to

ﬂuctuating phosphorus concentrations, we examined the

expression-based attributes for the KEGG module of the high-

afﬁnity phosphorus transporter pstABCS (Fig. 4). The pstABCS

E.A. McDaniel et al.

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system is an ABC-type transporter that strongly binds phosphate

with high afﬁnity under phosphorus-limiting conditions, and

therefore we expected that the highest expression levels would be

at the end of the aerobic cycle [73]. In contrast, we found that

pstABCS expression was characterized by two different trait

attributes. In the ﬁrst attribute shared by 14 community members,

all pstABCS components displayed the highest activity towards the

end of the aerobic cycle, when phosphorus concentrations were

depleted (Fig. 4, Attribute 1). Conversely, 11 community members

displayed an alternate attribute where the highest activity of

pstABCS was at the transition from anaerobic to aerobic phases

when phosphorus concentrations are highest (Fig. 4, Attribute 2).

Interestingly, the two Accumulibacter clades IA and IIA are split

amongst these separate pstABCS attributes. These results are in

agreement with previous results showing that Accumulibacter

clade IIC has a canonical pstABCS expression pattern (as in Fig. 4,

Attribute 1), whereas the Accumulibacter clade IA has a non-

canonical expression (as in Fig. 4, Attribute 2) [31]. By assigning

trait attributes, we can extend these ﬁndings beyond Accumuli-

bacter to other community members in the SBR ecosystem

suggesting that there are conserved ecological pressures driving

niche differentiating expression patterns in pstABCS within the

EBPR community.

Distribution and expression of truncated denitriﬁcation steps

among EPBR community members

Denitriﬁcation gene induction is an important ecosystem property

linked to the redox status of an environment. In EBPR

Fig. 3 Clustering and distribution of trait attributes across EBPR SBR community members. Using the TbasCO method, we identiﬁed

expression-based trait attributes from predeﬁned trait modules in the KEGG library and explored the distribution of these trait attributes

across community members. ADistribution of trait-attributes among sets of genomes. Bars represent the number of trait-attributes present in

a set number of genomes and colored by KEGG module category. Among a total of 35 genomes, trait attributes present between 3 and 18

genomes are designated as niche differentiating, whereas trait attributes present in 19 or greater genomes are designated as core trait

attributes. Inset ﬁgure demonstrates the maximum number of attributes for the maximum number of genomes. BCytoscape network

showing the connectedness of genomes to trait attributes. The network was ﬁltered to only include nodes with more than 5 connections,

therefore ﬁltering out both genomes with few trait attributes and trait attributes connected to less than 5 genomes. Genomes are represented

as squares colored by phylum, and trait attributes are represented as circles colored by KEGG category. The size of both the squares and circles

represents the number of connections to that genome or trait attribute, respectively.

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Table 2. KEGG pathways for core trait-attributes present in greater than 19 genomes.

Module description Number of attributes

Citrate cycle, second carbon oxidation, 2-oxoglutarate => oxaloacetate [PATH:map00020 map01200

map01100]

Citrate cycle (TCA cycle, Krebs cycle) [PATH:map00020 map01200 map01100] 10

Shikimate pathway, phosphoenolpyruvate +erythrose-4P => chorismate [PATH:map00400 map01230

map01100 map01110]

Fatty acid biosynthesis, initiation [PATH:map00061 map01212 map01100] 7

Glycolysis, core module involving three-carbon compounds [PATH:map00010 map01200 map01230

map01100]

Adenine ribonucleotide biosynthesis, IMP => ADP,ATP [PATH:map00230 map01100] 4

Guanine ribonucleotide biosynthesis IMP => GDP,GTP [PATH:map00230 map01100] 4

Inosine monophosphate biosynthesis, PRPP +glutamine => IMP [PATH:map00230 map01100] 4

Isoleucine biosynthesis, threonine => 2-oxobutanoate => isoleucine [PATH:map00290 map01230

map01100]

NADH:quinone oxidoreductase, prokaryotes [PATH:map00190] 3

beta-Oxidation, acyl-CoA synthesis [PATH:map00061 map00071 map01212 map01100] 2

F-type ATPase, prokaryotes and chloroplasts [PATH:map00190 map00195] 2

Valine/isoleucine biosynthesis, pyruvate => valine / 2-oxobutanoate => isoleucine [PATH:map00290

map00770 map01210 map01230 map01100 map01110]

CAM (Crassulacean acid metabolism), dark [PATH:map00620 map00710 map01200 map01100

map01120]

Cytochrome c oxidase, cbb3-type [PATH:map00190] 1

Cytochrome c oxidase, prokaryotes [PATH:map00190] 1

dTDP-L-rhamnose biosynthesis [PATH:map00521 map00523 map01100 map01130] 1

Leucine biosynthesis, 2-oxoisovalerate => 2-oxoisocaproate [PATH:map00290 map01210 map01230

map01100 map01110]

Phosphatidylethanolamine (PE) biosynthesis, PA =>PS=> PE [PATH:map00564 map01100] 1

PRPP biosynthesis, ribose 5 P => PRPP [PATH:map00030 map00230 map01200 map01230 map01100] 1

Pyruvate oxidation, pyruvate => acetyl-CoA [PATH:map00010 map00020 map00620 map01200

map01100]

Semi-phosphorylative Entner-Doudoroff pathway, gluconate => glycerate-3P [PATH:map00030

map01200 map01100 map01120]

Threonine biosynthesis, aspartate => homoserine => threonine [PATH:map00260 map01230

map01100 map01110]

Table 3. KEGG Pathways for differentiating trait-attributes present between 3 and 18 genomes.

Module_description Number of attributes

Glycolysis (Embden-Meyerhof pathway), glucose => pyruvate [PATH:map00010 map01200

map01100]

279

Citrate cycle (TCA cycle, Krebs cycle) [PATH:map00020 map01200 map01100] 208

Gluconeogenesis, oxaloacetate => fructose-6P [PATH:map00010 map00020 map01100] 76

Inosine monophosphate biosynthesis, PRPP +glutamine => IMP [PATH:map00230 map01100] 45

Citrate cycle, second carbon oxidation, 2-oxoglutarate => oxaloacetate [PATH:map00020

map01200 map01100]

Heme biosynthesis, plants and bacteria, glutamate => heme [PATH:map00860 map01100

map01110]

Tetrahydrofolate biosynthesis, GTP => THF [PATH:map00790 map00670 map01100] 25

Tryptophan biosynthesis, chorismate => tryptophan [PATH:map00400 map01230 map01100

map01110]

Ornithine biosynthesis, glutamate => ornithine [PATH:map00220 map01210 map01230 map01100] 24

Histidine biosynthesis, PRPP => histidine [PATH:map00340 map01230 map01100 map01110] 17

Pentose phosphate pathway (Pentose phosphate cycle) [PATH:map00030 map01200 map01100

map01120]

Lysine biosynthesis, succinyl-DAP pathway, aspar tate => lysine [PATH:map00300 map01230

map01100]

Uridine monophosphate biosynthesis, glutamine (+PRPP) => UMP [PATH:map00240 map01100] 11

E.A. McDaniel et al.

ISME Communications

communities, we ﬁnd many genomes with diverse and incom-

plete denitriﬁcation pathways, distributed across many lineages

denitriﬁcation steps expected in denitrifying systems (Fig. 5).

Among all 66 MAGs, we did not identify any single MAG with a

complete denitriﬁcation pathway consisting of the genetic

repertoire necessary to fully reduce nitrate to nitrogen gas

(Supplementary Fig. 5). Instead, we identiﬁed multiple groups of

organisms with truncated denitriﬁcation pathways, with steps

distributed among cohorts of community members (Fig. 5).

For the ﬁrst steps of reducing nitrate to nitrite, we examined

expression attributes of the napAB and narGH pathways (Fig. 5B,

C). For the narGH pathway, two attributes were identiﬁed (Fig. 5B).

The ﬁrst narGH attribute was characterized by high expression in

the anaerobic phase, with decreasing transcript levels by the

second time point of the anaerobic phase. Genomes containing

this attribute included the experimentally veriﬁed and putative

PAOs Tetrasphaera (TET1 and TET2) and Ca. Obscuribacter (OBS1),

respectively. The second attribute was exhibited among members

of the Actinobacteria (PROP2, PHYC2, PROP3, and NANO1),

Proteobacteria (BEIJ4), and Bacteroidetes (BAC1). The attribute

identiﬁed for napAB was also more highly expressed anaerobically

and included CAPIA, CAPIIA, ALIC1, REYR2, RUBRI1, and BEIJ3.

Interestingly, this napAB attribute had expression patterns that

quickly decreased in the ﬁrst aerobic time point, suggesting a

tighter regulation than Attribute 1 for narGH. Together, this

suggests that the regulation of denitriﬁcation within the EBPR

ecosystem is a niche-differentiating feature whereby the induction

of denitriﬁcation pathways occurs either anaerobically or only

after anaerobic carbon contact.

A smaller cohort contained the genetic repertoire to reduce

nitrite to nitrogen gas and exhibited hallmark anaerobic-aerobic

expression patterns (Fig. 5E) These members within the Proteo-

bacteria (OTTO2, BEIJ3, VITREO1, and ZOO1) contained the nirS

nitrite reductase, the norBC nitric oxide reductase, and nosZ, and

showed highest expression of these subunits towards the

beginning of the anaerobic cycle, slowly decreasing over the

aerobic period to their lowest in the end of the aerobic cycle.

Although BEIJ2 was lacking the norBC system, it contained the nirS

nitrite reductase and nosZ subunit, and exhibited similar expres-

sion patterns to others in this cohort. Other Proteobacteria

Fig. 4 Trait attributes of the high-afﬁnity phosphorus transporter system pstABCS.Using the TbasCO method, two trait attributes of the

high-afﬁnity phosphorus transporter system pstABCS were identiﬁed. The pstABCS system consists of a phosphate-binding protein and ABC-

type transporter, and the corresponding KEGG orthologs for each subunit are shown. Timepoints 1–3 refer to the three anaerobic phase

timepoints, and timepoints 4–6 refer to the three anaerobic phase timepoints (Fig. 1). Expression values are log-transformed based on setting

the lowest expression value within each genome across the time-series to 0 for each subunit. Speciﬁc subunits for some genomes in both

attributes are missing to the high cutoff thresholds for annotations. However we kept genomes with 2/4 subunits to show similarities in

expression proﬁles. The ﬁrst pstABCS trait-attribute includes microbial lineages that exhibited the highest expression of all subunits towards

the end of the aerobic cycle, when phosphate concentrations are expected to be lowest. This includes microbial lineages within the

Actinobacteria, Proteobacteria, Gemmatimonadetes, and Chloroﬂexi. The second pstABCS trait-attribute includes lineages that exhibited highest

expression of all subunits upon the switch from anaerobic to aerobic phases, or when phosphate concentrations are expected to be the

highest. This includes lineages within the Actinobacteria and Proteobacteria.

E.A. McDaniel et al.

ISME Communications

Fig. 5 Expression dynamics of distributed denitriﬁcation routes. Expression of denitriﬁcation traits distributed among community members

in the EBPR SBR ecosystem. Timepoints 1–3 correspond to the anaerobic phase and timepoints 4–6 correspond to the aerobic phase as

referenced in Fig. 1.AComplete denitriﬁcation pathway and associated genetic repertoire with each sequential step. BTrait attributes of

expression dynamics for community members with the narGH nitrate reductase system. This trait was the only denitriﬁcation trait identiﬁed

with more than one attribute. CExpression dynamics of the napAB nitrate reductase system. DExpression dynamics of the norBC nitrous oxide

reductase system. EExpression of all steps of denitriﬁcation starting at nitrite reduction. FExpression of the most complete denitriﬁcation

route among three community members, with the norC subunit for nitrous oxide reduction missing. Note that OTTO1 only contains nirS but is

included in this trait attribute because the expression dynamics are similar to that of the other three genomes for this subunit.

E.A. McDaniel et al.

ISME Communications

lineages only contained the norBC subunits but were expressed in

similar fashions (RHODO2, FLAVO1, RHIZO1, and LEAD1) (Fig. 5D).

Accumulibacter clades IA and IIA as well as ALIC1 were the only

lineages with near-complete denitriﬁcation pathways. These

lineages contained the napAB nitrate reductase system as

mentioned above, the nirS nitrite reductase, norB (missing a

conﬁdent hit for the norC subunit), and nosZ. These three lineages

also exhibited hallmark upregulation of all steps in the anaerobic

phase, with decreased activity after aerobic contact (Fig. 5F).

Interestingly, Accumulibacter clade IA exhibited a higher level

of transcripts associated denitriﬁcation steps when expression

levels were normalized relative to clade IIA, supporting the

hypothesis that denitriﬁcation is a niche-differentiating feature

among clades [28,31,74], and possibly a strain-speciﬁc trait since

denitriﬁcation traits cannot be predicted based on ppk1 clade

designations [32]. For example, independent observations in

differences among denitriﬁcation activities among strains within

Accumulibacter clade IC are inconsistent [34,75]. Within the same

bioreactor environment, coexisting Accumulibacter clades differ

between denitriﬁcation abilities and expression proﬁles [31–33].

Truncated denitriﬁcation pathways have also been previously

shown to be distributed among community members, with the

complete denitriﬁcation genetic repertoire only present in few

members [32,33], which could be due to extensive horizontal

Fig. 6 Biosynthetic potential compared to expression of amino acid and vitamin synthesis pathways for top 15 expressed MAGs.

Biosynthetic potential and expression patterns of amino acid and vitamin pathways were analyzed for the top 15 genomes with the highest

transcriptional counts (Table 1). AFor a pathway to be considered present for downstream analysis in the TbasCO pipeline, 80% of the

pathway had to be present in a genome. Thus, we used this cutoff criterion to discern whether a speciﬁc pathway was present or absent in a

genome (with the expectation of methionine, as all genomes did not contain at least 80% of the subunits in the KEGG methionine synthase

pathway, we inferred the presence of the methionine synthase as presence of this pathway). Orange colored boxes for cofactor biosynthesis

pathways represents the presence of that pathway, whereas grey infers absence. For amino acid biosynthetic pathways, amino acids are listed

by their side chain groups –charged, polar, hydrophobic, and other. Blue colored boxes for amino acid biosynthesis pathways represents the

presence of that pathway, whereas grey infers absence. BMini-networks of vitamin co-factors. Squares are genomes with the colors matching

the color bar in A. Nodes are attributes, where the colored nodes for the tetrahydrofolate attributes represent the different routes. CMini-

networks of amino acid biosynthesis pathways split by type. Colors of nodes for each amino acid represent the different routes for that

pathway. Squares represent genomes with colors matching the color bar in A.

E.A. McDaniel et al.

ISME Communications

gene transfer of genes comprising denitriﬁcation steps [32,76].

Although this experiment was not conducted under denitrifying

conditions, our approach could be applied to denitrifying EBPR

systems to further understand the distribution of denitriﬁcation

traits among community members and how to selectively enrich

for diverse DPAOs.

Biosynthetic potential and expression dynamics of amino acid

and vitamin synthesis pathways

Although SBRs are designed to enrich for Accumulibacter by

providing acetate as the sole carbon source, a diverse bacterial

community persists in these setups [27,32]. One hypothesis for

the persistence of these bacterial community members may be

cooperative interactions due to underlying auxotrophies of amino

acid and vitamin biosynthetic pathways in Accumulibacter. Amino

acids and vitamin cofactors are metabolically expensive to

synthesize, and widespread auxotrophies have been widely

documented among microbial communities [77,78]. Speciﬁcally,

auxotrophies of vitamin cofactors have been shown to fuel

bacterial and cross-kingdom interactions with de novo synthesi-

zers [79,80]. To explore this hypothesis in the EPBR SBR

community, we analyzed the presence of amino acid and vitamin

biosynthetic pathways and their expression patterns among the

top 15 genomes based on transcript abundance (Fig. 6).

Within Accumulibacter, there are a few key vitamin cofactor and

amino acid auxotrophies that could fuel potential interactions

with other community members. Both Accumulibacter clade

genomes are missing the riboﬂavin pathway for FAD cofactor

synthesis, as well as known pathways for serine and aspartic acid

(Fig. 6A). The biosynthetic pathway for aspartic acid is distributed

among members of the Bacteroidetes and Proteobacteria, whereas

only TET2 contains the pathway for serine synthesis (Fig. 5A). The

lack of serine biosynthesis pathways in Accumulibacter and other

genomes seems striking given that serine is one of the least

metabolically costly amino acids to synthesize [81]. Interestingly,

Accumulibacter clade IIA (strain CAPIIA) does not contain the

biosynthetic machinery for thiamine and pantothenate synthesis,

whereas clade IA (strain CAPIA) does (Fig. 6A). Only the CAULO1,

HYPHO1, and PSEUDO1 genomes within the Proteobacteria can

synthesize thiamine, whereas several other members can synthe-

size pantothenate (Fig. 6A). The absence of the pantothenate

biosynthetic pathway in Accumulibacter CAP IIA is particularly

interesting given that coenzyme A is essential for polyhydroxyalk-

anote biosynthesis, which fuels the rapid and extensive polymer

cycling PAO phenotype of Accumulibacter [24].

In addition to other community members potentially support-

ing the growth of Accumulibacter due to underlying auxotrophies,

the reciprocal logic may be possible as well. Both Accumulibacter

clades contain the pathways for synthesizing tyrosine and

phenylalanine, which are missing in a majority of the top 15

active non-Accumulibacter bacterial genomes (Fig. 6A). Only two

other members within the Proteobacteria can synthesize tyrosine

and phenylalanine, where RAM1 can synthesize both and

PSEUDO1 only phenylalanine. Interestingly, phenylalanine and

tyrosine are the second and third most metabolically expensive

amino acids to synthesize, respectively, with tryptophan being the

most costly [81]. Additionally, a few highly active non-

Accumulibacter bacterial community members lack the biosyn-

thetic machinery for several vitamin cofactors and amino acids,

such as FLAVO1 and BAC3 within the Bacteroidetes and the

putative PAO Ca. Obscuribacter phosphatis OBS1 (Fig. 6A).

Particularly, RAM1 within the Proteobacteria is missing the

biosynthetic machinery for all vitamin cofactors but can synthesize

most amino acids including the most metabolically expensive as

mentioned above.

We next analyzed the distribution of trait-attributes of vitamin

and amino acid pathways among these genomes to understand

how these biosynthetic pathways are expressed similarly or

differently in the EBPR SBR ecosystem (Fig. 6B, C). Members of

the Proteobacteria containing thiamine and cobalamin biosyn-

thetic pathways all express these traits similarly (Fig. 6B). However,

the pantothenate synthesis pathway contains two trait-attributes

and is expressed differently among two cohorts. In the ﬁrst

attribute, RUN1, TET1, CAULO1, CAPIA, and PSEUDO1 express the

pantothenate pathway similarly. However, OBS1 and TET2 express

the pantothenate pathway differently (Fig. 6B). Because tetrahy-

drofolate can be synthesized through different metabolic routes,

we analyzed the differences in trait attribute expression for all

routes in genomes that contained sufﬁcient coverage of this trait.

Bacteroidetes and Proteobacteria members mostly cluster together

among tetrahydrofolate attributes, whereas the TET1 and TET2

genomes are differentiated (Fig. 6B).

Expression of various groups of amino acids show more

differentiated expression patterns for genomes with these path-

ways. Several amino acids also contain different metabolic routes

for biosynthesis, and we analyzed all trait attributes for each

amino acid for all routes grouped by type (Fig. 6C). For the

charged amino acids arginine, histidine, and lysine, Proteobacteria

and Bacteroidetes members cluster within their phylogenetic

groups, respectively, with lysine and histidine expressed differ-

ently among these groups (Fig. 6C). In contrast, arginine is

expressed similarly among all Proteobacteria genomes. Among the

polar charged amino acids, TET2 is the only genome among the

top 15 genomes that contains the pathway to synthesize serine

(Fig. 6A). Several groups contain the pathway for threonine

synthesis, and expression of different threonine routes are

differentiated among the Proteobacteria, Bacteroidetes, and Tetra-

sphaera spp., though they mostly cluster phylogenetically (Fig. 6C).

Notably, the expression patterns for the cysteine and proline

biosynthetic pathways do not cluster phylogenetically, such as

both Tetrasphaera genomes expressing the proline pathway more

similarly to other Proteobacteria and Bacteroidetes (Fig. 6C). The

few lineages that can synthesize tyrosine and phenylalanine

(CAPIA, CAPIIA, RAM1, PSEUDO1) show different expression

patterns. These results show that beyond the presence or absence

of key vitamin cofactor and amino acid biosynthetic pathways,

EBPR SBR organisms also display coherent and differentiated

expression patterns for these traits, of which the functional

consequences remain to be further understood.

CONCLUSIONS AND FUTURE PERSPECTIVES

In this work, we applied a novel trait-based ‘omics pipeline to a

semi-complex, engineered bioreactor microbial community to

explore ecosystem-level and niche-differentiating traits. Through

recovering 66 MAGs from the EBPR SBR community and using a

time-series metatranscriptomics experiment, we were able to extend

functional predictions such as identifying multiple attributes of high-

afﬁnity phosphate transporters beyond hypotheses made from traits

alone. We extended this framework to other signiﬁcant traits that

are distributed among community members such as denitriﬁcation

and amino acid metabolism. Speciﬁcally, we demonstrate that traits

with similar expression proﬁles may be clustered into attributes

providing a new layer to trait-based approaches.

We believe that identifying expression-based attributes will be a

powerful tool to explore microbial traits in natural, engineered, and

host-associated microbiomes. Outside of activated sludge systems,

trait-based approaches could illuminate how similar secondary

metabolite clusters are expressed among different species in a

community [82,83], how auxotrophies for amino acid and vitamin

cofactors govern interactions [84], how rhizosphere microorganisms

respond to day-night cycles, and identify putative traits that

universally exhibit ecosystem-level or niche-differentiating patterns

across ecosystems [19,23]. Importantly, our trait-based approach

can be used to screen for expected expression patterns of a key trait

compared to a model organism, and then prioritize speciﬁc

E.A. McDaniel et al.

ISME Communications

microbial lineages for downstream experimental veriﬁcation with

techniques such as Raman-FISH [85,86].

DATA AVAILABILITY

All supplementary ﬁles including functional annotations and transcriptome count ﬁles

are available at https://ﬁgshare.com/projects/EBPR_Trait-Based_Comparative_Omics/

90437. All 64 genomes have been deposited in NCBI at Bioproject PRJNA714686.The

remainingtwo reassembled Accumulibacter genomes have not been deposited in NCBI

to not confuse between the original CAPIA and CAPIIA assemblies [27,28]. These

contemporary assemblies are available at the Figshare repository. The three

metagenomes and six metatranscriptomes used in this study are available on the

JGI/IMG at accession co des 3300026302, 3300026286, 3300009517, and 33000 02341-46 ,

respectively. All code for performing metagenomic assembly, binning, and annotation

can be found at https://github.com/elizabethmcd/EBPR-MAGs. The TbasCO method has

been implemented as a reproducible R package and can be accessed at https://

github.com/Jorisvansteenbrugge/TbasCO.

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ACKNOWLEDGEMENTS

We thank Caitlin Singleton for providing early access to high-quality genomes from a

full-scale WWTP to compare our MAGs against. Metagenomic and metatranscrip-

tomic sequencing was provided through a Joint Genome Institute Community

Science Proposal (Proposal ID 873). This work was supported by funding from the

National Science Foundation (MCB-1518130) to K.D.M and D.R.N. Funding was

provided to E.A.M. by a fellowship through the Department of Bacteriology at the

University of Wisconsin –Madison. Funding for B.O.O was in part provided by the

Technology Foundation of the Dutch National Science Foundation (NWO-TTW). This

research was performed in part using the Wisconsin Energy Institute computing

cluster, which is supported by the Great Lakes Bioenergy Research Center as a part of

the U.S. Department of Energy Ofﬁce of Science (DE-SC0018409).

AUTHOR CONTRIBUTIONS

EAM and JJMVS contributed equally to this work. EAM performed metagenomic

assembly and genome curation, metatranscriptomic and functional analysis, and

software testing. JJMVS and BOO designed the TbasCO analysis software and JJMVS

performed benchmarking testing. DRN, KDM, JMR, and MHM provided critical

feedback on the manuscript. EAM, JJMVS, and BOO performed analyses, interpreta-

tion, and wrote the manuscript with input from all coauthors.

COMPETING INTERESTS

The authors declare no competing interests.

E.A. McDaniel et al.

ISME Communications

ADDITIONAL INFORMATION

Supplementary information The online version contains supplementary material

available at https://doi.org/10.1038/s43705-022-00189-2.

Correspondence and requests for materials should be addressed to E. A. McDaniel,

J. J. M. van Steenbrugge or B. O. Oyserman.

Reprints and permission information is available at http://www.nature.com/

reprints

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E.A. McDaniel et al.

ISME Communications

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Abstract and Figures