Tracking functional bacterial biomarkers in response to a gradient of contaminant exposure within a river continuum

Journal Pre-proof Tracking functional bacterial biomarkers in response to a gradient of contaminant exposure within a river continuum T. Reid, I.G. Droppo, C.G. Weisener PII:

S0043-1354(19)30941-8

DOI:

https://doi.org/10.1016/j.watres.2019.115167

Reference:

WR 115167

To appear in:

Water Research

Received Date: 19 June 2019 Revised Date:

11 September 2019

Accepted Date: 8 October 2019

Please cite this article as: Reid, T., Droppo, I.G., Weisener, C.G., Tracking functional bacterial biomarkers in response to a gradient of contaminant exposure within a river continuum, Water Research (2019), doi: https://doi.org/10.1016/j.watres.2019.115167. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

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Tracking Functional Bacterial Biomarkers in Response to a Gradient of Contaminant

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Exposure within a River Continuum

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T. Reid*a, I.G. Droppoa, C.G. Weisenerb

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a

Environment and Climate Change Canada 867 Lakeshore Rd. Burlington, Ontario, Canada, L7S 1A1 b

Great Lakes Institute for Environmental Research, University of Windsor 401 Sunset Ave, Windsor, Ontario, Canada, N9B 3P4

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*Corresponding Author: [email protected] (previously at Great Lakes Institute for Environmental Research, University of Windsor)

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ABSTRACT

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Within all aquatic environments, aside from the physical dispersal of dissolved and/or particulate

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phase contaminants, alteration from both biological and chemical processes are shown to change

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the chemistry of the parent compounds. Often these alterations can lead to secondary influences

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because of cooperative microbial processes (i.e. coupled respiratory pathways and/or energy and

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biodegradation cycles), complicating our understanding of the biological impact that these

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mobile compounds impose on ecosystem health. The McMurray Formation (MF) (the formation

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constituting the minable bituminous oil sands) is a natural, ongoing source of hydrocarbon-

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bound sediments to river ecosystems in the region (via terrestrial and aquatic erosion), providing

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a natural “mesocosm” to track and characterize the effects of these compounds on regional

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aquatic primary productivity. Here we characterize the natural, in-situ microbial response to

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increasing hydrocarbon exposure along a river continuum in the downstream direction. Using the

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Steepbank River (STB), suspended and bed sediment samples were collected at 3 sites from

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upstream to downstream, as the water flows into and through the MF. Samples were then

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analyzed for the active, in-situ gene expression of the microbial communities. Results from both

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suspended and bed sediments show clear and significant shifts in the microbial metabolic

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processes within each respective compartment, in response to the elevated polycyclic aromatic

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compound (PAC) concentrations. Specific genes likely responsible for hydrocarbon breakdown

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(Alkane Monooxygenase, Benzoyl-CoA Reductase etc.) experience elevated expression levels,

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while certain energy metabolism genes (nitrogen, sulfur, methane) reveal fundamental shifts in

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their pathway specificity, indicating an adaptation response in their basic energy metabolism.

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Expression from suspended sediments reveal subtle yet delayed metabolic response further

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downstream compared to bed sediments, indicative of the erosion and transport dynamics within 2

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a lotic system. These results provide insight into the use of novel clusters of gene biomarkers to

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track the active, in-situ microbial response of both emerging and legacy contaminants. Such

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information will be important in determining the best management strategies for the monitoring

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and assessment of aquatic health in both natural and contaminated ecosystems.

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GRAPHICAL ABSTRACT

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1.0 Introduction

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While water quality and ecological changes within the lower Athabasca River (LAR) are

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often perceived to be resultant from industrial oil sand deposit exploitation, the differentiation of

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industrial versus natural contributions of PACs is a complex and, yet, unresolved issue.

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Additionally, there has yet to be definitive studies identifying fundamental biogeochemical shifts

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attributed to this natural PAC signature. No proven fingerprinting method to achieve the above

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has been proven or accredited to date (Culp et al., 2018). Further, Culp et al. (2018) have

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suggested that while there is significant data on particulate atmospheric deposition, there is

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limited information (Birks et al. 2017) or model that can project the proportion of surface wash

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off that enters a river course. As such, Droppo et al. (2018) suggests that most of the PACs that

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are transported within the rivers of the LAR are derived from hillslope and channel erosion of the

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McMurray formation (MF) (the geologic formation that defines the minable oil sands deposit),

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and not from direct or atmospheric anthropogenic inputs.

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Regardless of PAC source, a substantive knowledge gap in relation to how the microbial

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consortium, 1) is influenced by the high hydrocarbon content of the sediments (suspended and

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bed), and 2) on how the microbes influence the contaminant speciation of the PACs. Both of

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these synonymous biochemical processes will inevitably alter the biogeochemical footprints with

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concomitant impacts on the natural ecology. While most studies of contaminants and microbial

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impacts in rivers have been studied only on the bed sediment community structure (e.g. biofilms

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– Yergeau et al. 2012), few studies have characterized the dynamics of bitumen containing

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suspended sediment (SS) within rivers of the LAR. Droppo et al. (2018) did observe temporal

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and spatial trends in SS loads and PAC signatures with distance downstream for the Ells and

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Steepbank (STB) rivers using passive SS samplers. They however did not investigate any

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microbial influence relative to these spatial and temporal trends. Recent studies have begun to

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unravel the natural microbial structure within bed sediments in the region (Reid et al., 2019; Reid

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et al., 2018; Yergeau et al., 2012), though there remains a lack of understanding with respect to

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the true microbial function within the entire region, including for both SS and bed sediments.

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Further, there are, to our knowledge, no studies that provide potential gene biomarkers which

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reveal statistically differing expression patterns as a direct result to increasing hydrocarbon

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exposure of the MF. Tracking contaminants is clearly not a new area of research. For years,

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scientists have studied contaminant sites such as those of the Exxon Valdez oil spill and the

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Deep-Water Horizon disaster. In most studies tracking contaminant distribution, conventional

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geochemical testing in association with taxonomic surveys, only provides researchers a glimpse

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into the effects these contaminants have on the natural ecology. What has been missing is how

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the primary producers, such as the diverse microbial communities found in all lakes, rivers and

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streams, deal with the stressors resulting from the exposure to various compounds. Shifts in the

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functional attributes of microbial communities are direct and primary biological responses, that

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can induce ecosystem-wide alteration. What is too often the case is that contaminant sites are

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only studied after contamination has occurred, leaving a knowledge gap as to the true baseline

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character of the ecosystem prior to xenobiotic exposure.

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This study uses metatranscriptomics analyses to determine the efficiency of shotgun gene

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expression surveys for tracking the microbial response to increasing hydrocarbon exposure in SS

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and bed sediments through the river continuum of the Steepbank River (STB) of the LAR

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(Alberta, Canada). A control location upstream of the natural hydrocarbon deposit (MF) provide

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our reference site within the same river continuum (Clearwater Formation (CF). This research

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aims to provide 1) insight into the natural genetic response of microbes to increasing

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hydrocarbon exposure, and 2) provide potential gene biomarkers that reveal statistically differing

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expression patterns as a direct result to increasing hydrocarbon exposure. This has strong

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implications worldwide, where gene expression surveys of aquatic microbial communities could

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be ideal tools in tracking contaminant exposure as an alternative or augmentation to conventional

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geochemical fingerprinting practices.

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2.0 Methods

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2.1 Sampling Site Descriptions

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Samples were collected on the Steepbank River (STB) at three sites representing a

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gradient of PAC concentrations from upstream to downstream (Figure 1) in October 2017. The

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middle to lower reaches of STB cuts through the MF, the ore deposit constituting the Athabasca

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Oil Sands. The upstream site was located within the (CF) and as such contained marginal to no

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bitumen. Sample sites from upstream to downstream are therefore designated CF-Low, MF-Med,

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and MF-High. All samples are presumed to represent the natural PAC characteristics of the STB

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given there are legislated no direct discharges from industrial properties. If there were an input, it

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can only be from atmospheric deposition which will be highly diluted by the volume of water in

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the rivers and the primary source of particle load being from the channel itself (Droppo et al.

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2018).

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The STB flows from the east, with an elevation change from 625 to 320 m at the

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confluence with the Athabasca River (Figure 1). MF is composed of minable bitumen-rich

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deposits combined with fine- to coarse-grained sand interbedded with lesser amounts of silt and

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clay (Musial et al. 2012; Haug et al. 2014). STB has a mean annual flow of 4.8 m3 s-1 and is

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considered a groundwater dominated river with an average of 50 % of flow derived from

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groundwater (Gibson and Birks, 2016). 7

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2.2 Sediment Collection

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Sediments were collected from both the river beds and suspended particulates. Bed

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sediments consisting of the interface down to approximately 1 cm depth were collected in

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triplicate into sterile 5 ml cryotubes, and flash frozen in liquid nitrogen in the field for nucleic

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acid extractions. Bulk sediments were collected into 500 ml HDPE Nalgene Bottles with no

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headspace and kept frozen until analysis.

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Suspended sediments were collected by a portable continuous-flow centrifugation (Alfa-

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Laval model WSB 103B). Water was pumped (4 L min-1) to the centrifuge by a 5C-MD March

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submersible pump located in the middle of the river at mid depth. Recovery of SS was greater

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than 90% (% recovery = outflow TSS/inflow TSS). Sediment collected in the centrifuge bowl

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was immediately removed and placed in 5 ml cryotubes and flash frozen as above. Total PAC

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data from each sampling location was obtained from the Regional Aquatics Monitoring Program

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(RAMP) database (RAMP, 2015).

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2.3 SEM Analysis

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Bulk bed sediments were analyzed by Scanning Electron Microscopy (SEM), to

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understand particle distribution and evidence for hydrocarbon signatures. Specifically, the

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Environmental Scanning Electron Microscope (FEI Quanta200F, Eindhoven, Netherlands) was

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used at the Great Lakes Institute for Environmental Research (GLIER), University of Windsor

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(Windsor, ON, Canada). Here, analyses were performed at high vacuum (20 kV) with a

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theoretical spot size of 2.6 nm. Grain inspections and size distributions were performed using

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both secondary electron (SE) and backscattered electron (BSE) detectors. Elemental composition

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analysis was performed with the EDAX® SiLi detector (EDAX, Mahwah, New Jersey, USA).

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2.4 DNA/RNA Extractions & Library Preparation

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Total environmental DNA extractions were performed according to manufacturer

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protocols, in duplicate using the MoBio Powersoil Total DNA Isolation Kits (now Qiagen

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DNeasy Powersoil Kit). DNA was amplified and purified according to Reid et al. (Reid et al.

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2016), to specifically explore the V5/V6 region of the bacterial 16S rRNA gene. Briefly, the

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V5/V6 region was amplified using dual polymerase chain reactions (PCR), followed by a second

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barcoding PCR to uniquely identify samples for pooling and sequencing on the Ion Torrent PGM

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platform (Life Technologies, Carlsbad, California) at the Great Lakes Institute for Environmental

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Research at the University of Windsor (Windsor, Ontario, Canada). Pooled DNA libraries were

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checked for quality assurances and concentration on the Agilent 2100 Bioanalyzer prior to

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sequencing.

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Total environmental RNA extractions were performed using the MoBio Powersoil Total

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RNA Isolation Kits (MoBio Laboratories; Carlsbad, California) per slightly modified protocols.

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Initial sediment quantity for extraction was bumped to 5 g to increase overall RNA yield. All

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reagents and samples were kept on ice throughout extraction to maintain RNA integrity and

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minimize degradation throughout the extraction. Quality and concentrations were analyzed on

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the Agilent 2100 Bioanalyzer, where concentrations exceeded 100 ng/µL, and quality scores

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exceeded RIN # of 7.5. Samples of sufficient quality and concentration were sent in duplicate to

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Genome Quebec Innovation Center at McGill University in Montreal, Quebec, Canada. There,

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samples were again checked to ensure they met quality control standards, and rRNA-depletion

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(Illumina Ribo-Zero rRNA Removal Kit, bacterial and yeast) was performed to enhance the

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mRNA concentration. Samples were then sequenced on the Illumina HiSeq 4000 Nextgen

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sequencer.

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2.5 16S rRNA Community Structure Analysis

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Microbial taxonomy was analyzed to augment the metatranscriptomics (functional)

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dataset, providing information as to which microbes were likely associated with observed gene

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expression. Raw sequence files were processed through the MacQiime (v.1.9.1) pipeline

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(www.qiime.org), an open-source bioinformatics pipeline for 16S rRNA amplicon sequences

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(Caporaso et al., 2010). Sequence files were filtered and sequences with a Phred score below 20

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were omitted. Sequences were then clustered into operational taxonomic units (OTUs), omitting

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singletons and doubletons, at a similarity threshold of 97% using the uclust algorithm. Chimeras

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were removed using the usearch61 algorithm with MacQiime. Taxonomy was assigned to

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representative OTU sequences for each cluster, using the default GreenGenes database, at a 90%

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confidence threshold (DeSantis et al., 2006). Relative abundances (% of reads per sample) were

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calculated to gain an understanding of the dominant taxa between sample sites.

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2.6 Metatranscriptomics Data Processing and Differential Expression Analysis

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All raw metatranscriptomics sequence data was processed through the MG-RAST

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(MetaGenomics Rapid Annotation using Subsystem Technology) pipeline (Meyer et al., 2008).

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This automated pipeline was designed for the analysis of high-throughput sequencing datasets,

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proving both functional and phylogenetic summaries. Raw paired-end sequence files were

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submitted, where pairing, quality filtering, and the annotation of functional genes was performed.

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A Phred quality score of >30 was applied to submitted raw sequence files. Transcripts were

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annotated to the KO (Kegg Orthology) database. Visualizations for gene expression correlations

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was then performed using the START app, a web-based RNAseq analysis and visualization

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resource (Nelson et al., 2017). Raw expression values were normalized to counts per million

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(CPM) using the limma package (Linear Models for Microarray and RNA-seq Data) of

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R/Bioconductor, within the START application. Gene expression boxplots are expressed as

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logCPM values, and p-values are provided where statistical significance is noted. Mean-variance

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modelling at the observation level (voom) was utilized for the logCPM determination.

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Expression heatmaps were created through Heatmapper, a free online server for the creation of

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heatmaps and other visualization tools (Babicki et al., 2016).

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3.0 Results and Discussion

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3.1 Physicochemical Characteristics of Suspended and Bed Sediments

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Spatial trends of PAC concentrations in the suspended and bed sediments of the STB,

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were measured at the 3 sample sites (Supplementary Table S1). PAC concentrations indicate a

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generally increasing load of PAC moving downstream from CL-Low (upstream) to MF-High

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(downstream). Overall, there is a significant increase in total PAC as exposure increases through

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the MF (Figure 2), with concentrations totaling 34.8, 118.3 and 425.5 ng/L at CF-Low, MF-Med

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and MF-High respectively. These findings correlate with those of Droppo et al., (2018), who also

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observed increases in PAC loadings in the SS moving through the formation in the STB. The

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highest concentration PACs were alkylated compounds, specifically C2, C3, C4-

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Dibenzothiphenes, C2, C3, C4-Phenanthrenes/Anthracenes, C2-Fluoranthenes/Pyrenes and C4-

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Napthalenes. These alkyl homologs are indicative of the MF and have been used as indicators of

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the bitumen deposit in various other studies, as they can reveal minute variations within the

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deposit (Droppo et al., 2018).

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Scanning Electron Microscopy also revealed similar trends of increasing hydrocarbon

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concentrations within the bed sediment. Figure 3 shows the interstitial hydrocarbon content of

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the bed sediments, where increasing carbon content correlates to the observed bituminous

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compounds within the interstitial sediment grains. RAMAN spectroscopy specifically confirmed 11

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the bitumen content within MF-High, revealing peaks suggestive of raw bitumen (results not

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presented). The presence of bitumen in the interstitial pore spaces of several prepared SEM

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slides portrays the truly ubiquitous presence of the PACs in the sediments of the MF.

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Additionally, visual analysis on the SEM appeared to show a decreasing trend in particle size

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distribution from CF-Low to MF-High. Sediment particles appeared to be well sorted upstream

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of the MF, though became more varied as exposure to the MF increased.

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It is interesting to note that CF-Low does in fact contain some measurable PACs despite

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being outside of the McMurray Formation (though no bitumen was observable under the SEM).

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However, this could be attributed to natural PACs resulting from combustion of organics such as

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wood or grasses, or perhaps some other anthropogenic influence (e.g., atmospheric deposition).

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This is especially relevant in this region, where a large forest fire swept through a year earlier

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and would have inevitably introduced a PAC signature to the natural landscape. However, this

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type of PAC is indicative of pyrogenic origin, and thus is chemically different than the PAC

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signature of the MF. Droppo et al., 2018 indicated that there was a correlation between

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wood/grass/coal burning and low flow events, which may be what we are observing here.

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Nonetheless, there was no visible evidence of bitumen derived from the MF within the sediments

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observed under the SEM. These chemical characterizations of increasing PAC load within the SS

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and the associated microscopy results reveal considerable hydrocarbon content, that is not only

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highly concentrated in the bed sediments but is readily eroded and transported through the water

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column and downstream. These hydrocarbon signatures in both the water and sediment

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compartment provides an ideal opportunity to characterize how the microbial community is

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dealing with such a unique hydrodynamic sediment regime under varying PAC exposures.

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3.2 Microbial Community Structure & Functional Dynamics

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3.2.1 Sequencing Summaries and Statistics

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The metataxonomic analysis consisted of a total of 12 single-end sequence files,

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averaging 88,995 raw sequences per sample. Clustering resulted in a total of 19,925 unique

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OTUs at a 97% similarity threshold. The metatranscriptomics dataset also consisted of 12 paired-

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end sequence files, with over 39 million reads per sample, averaging 46,549,109 sequences each,

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with Phred quality scores all >30. Read annotation resulted in reads mapped to basic cellular

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processes, genetic information processing and environmental information processing, alongside

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extensive coverage of metabolic pathways indicative of energy metabolism of interest herein (i.e.

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nitrogen, sulfur, methane cycling). Sequence files for both 16S rRNA and metatranscriptomics

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are submitted to the NCBI Sequence Read Archive (SRA) under the project number

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PRJNA564313.

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3.2.2 Microbial Community Structure (16S rRNA Gene Amplicon)

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Metataxonomic analyses were performed to understand how the exposure of MF

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hydrocarbons may have altered the taxonomic distribution at each site. Figures 4 & 5 clearly

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illustrate a shift in the microbial structure between the SS and bed sediments. Most prominent is

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the dominance of Proteobacteria in both the SS and bed sediments, constituting approximately

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61, 59 and 66 % of the community within the SS and only about 41, 43, and 47 % in the bed

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sediments of CF-Low, MF-Med and MF-High respectively. In general, SS is more abundant in

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Actinobacteria, Cyanobacteria and Proteobacteria compared to the bed at each respective site.

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Bed sediments showed similar trends in community structure as previous studies, with more

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abundant Acidobacteria, Bacteroidetes, Chlorobi, Chloroflexi, Firmicutes, Plantomycetes,

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Spirochaetes and Verrumicrobia, representing a diverse microbial assemblage, capable of

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thriving in this unique environment (Yergeau et al., 2012). Acidobacteria, though remaining

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poorly understood, are a diverse range of organisms ubiquitous in soils and sediments (Naether

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et al., 2012). Of note is the family Holophagaceae, containing genera capable of aromatic

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compound degradation, and Geothrix, and anaerobic chemoorganotroph generally utilizing

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Fe(III) as its electron acceptor. Bacteroidetes also comprise a diverse range of organisms and

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was second in abundance overall behind the Proteobacteria. One of the most dominant families

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of Bacteroidetes was Cytophagaceae. Within this family, a novel genus Dyadobacter is known to

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degrade xenobiotic compounds in aromatic hydrocarbon contaminated soils (Willumsen et al.,

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2005). Dyadobacter was most observed here to be most abundant in the SS at MF-Med, though

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these were at relatively low abundances overall. Still, the overall high abundance of

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Cytophagaceae suggests that there may be other genera of similar metabolic capabilities

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responsible for degradation of similarly complex carbon compounds. Chlorobi contains

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organisms that are both anaerobic photoautotrophs alongside obligate heterotrophs, and was also

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found in man-made reservoirs in the region (Reid et al. 2019). It’s presence in the bed sediments

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indicates a response to sulfide production deeper within the sediments, perhaps also acting as a

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detoxifier in its oxidation of sulfides. Chloroflexi are also anoxygenic phototrophic bacteria,

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though some are also able to utilize halogenated compounds as electron acceptors in their

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respiratory process. Notable taxonomic families observed include Anaerolinaceae and

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Dehalococcoidaceae. Anaerolinaceae has been found to closely affiliate with methanogenic

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activity, with the ability to activate the breakdown of long-chain n-alkanes (Liang et al., 2016).

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Dehalococcoidaceae is an organohalide-respiring organism, known to utilize H2 as its electron

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donor. This is significant as it may suggest a means by which to reduce the build-up of H2

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because of methanogenic biodegradation processes, allowing for these processes to proceed.

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Clostridium spp. within the Firmicutes phylum has been shown to also closely associate with

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methanogens, and has been shown to be highly abundant in oil cultures and toluene-degrading

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methanogenic cultures (Fowler et al., 2016). However, it was also noted that they may only be

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involved in the subsequent breakdown of hydrocarbons after initial activation has taken place

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(Fowler et al., 2016). The remaining phyla observed, Plantomycetes, Spirochaetes and

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Verrumicrobia all represent diverse groups of organisms, many of which are commonly found in

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both soils and marine and freshwater environments worldwide.

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It is interesting to note that there was an increased abundance of the Proteobacteria and

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Cyanobacteria within the SS. Within the Cyanobacteria phylum, particularly dominant was the

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family of Nostocaceae, known for its nitrogen-fixation abilities. Reid et al., 2019 also observed

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the presence of these organisms in deeper freshwater reservoirs, suggesting that there is a

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significant phototrophic influence on the biogeochemistry of this region. The presence of

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Nostocaceae could be suggestive of a syntrophic cooperation with aquatic plants, who would

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benefit from the production of ammonia. Additionally, the production of ammonia could be

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utilized by other chemoheterotrophs in the breakdown of organics such as the PACs of the MF.

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The increased abundance of Cyanobacteria is expected in the SS, where ample sunlight is present

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alongside ample nutrients derived from some of the breakdown processes within the water

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column or bed sediments. The increased abundance of Proteobacteria however was somewhat

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unexpected,

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Betaproteobacteria was the most dominant class overall, second only to the Alphaproteobacteria

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(Figure 5). The prominence of these classes of organisms could be linked to their highly diverse

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metabolic abilities, making them particularly efficient in adapting to the hydrocarbon gradient of

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the MF (Dworkin et al., 2006). The Betaproteobacteria family of Comamonadaceae was most

where

Betaproteobacteria

were

largely

responsible

for

this

increase.

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dominant in all the SS samples. Comamonadaceae, is an extremely diverse family of organisms,

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harboring genera capable of

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Comamonadaceae and other families of Betaproteobacteria to degrade a variety of petroleum

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contaminants makes the presence of this organism extremely relevant in the context of this

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unique hydrocarbon gradient present within the STB (Mukherjee et al., 2017; Zedelius et al.,

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2011). Additionally, Betaproteobacteria contain organisms with the ability to oxidize ammonia

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(Stein and Klotz, 2016), part of the nitrification pathway, potentially resulting from PAC

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breakdown within the STB. Perhaps the greatest noted increase from CF-Low to MF-Med was in

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the abundance of Spirobacillales, an order of Deltaproteobacteria. Spirobacillales is a known

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degrader of toxins produced by certain cyanobacteria (Lezcano et al., 2017), providing a

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potentially interesting correlation to the depletion of Cyanobacteria noted here also . This may be

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evidence towards the notion of PAC inhibition of photosynthetic activity as noted in other

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studies (Marwood et al., 1999). However, this heightened abundance could just be attributed to

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the increased abundance of complex organics, which could also be utilized by Spirobacillales

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(Lezcano et al., 2017).

wide-ranging phenotypic abilities. Noted abilities of

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Moving downstream from the CF to MF, there are clearly changes in the microbial

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community structure, though subtle from upper taxonomic classifications (aside from the

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Betaproteobacteria). Principal Coordinate Analyses (PCoA) of all operational taxonomic units

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(OTUs) was implemented to better understand community structure variation between sample

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sites. Initially evident is a clear separation of SS from the bed sediments (Figure 6). Coordinate 1

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represents over 50 % of the variation, while coordinate 2 represents approximately 21 %. CF-

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Low SS and bed sediments appear to be the most unique of all the other sites, and is the only site

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showing relatively similar SS and bed sediment communities. This is likely attributed to its

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upstream situation above the MF, where it has not yet been influenced by the significant

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hydrocarbon signature of MF. The divergent nature of both the SS and bed sediments from the

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CF-Low “control” site, indicates increasing differences in the community structure not only

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between sites, but also within site SS and bed samples. MF-Med SS and bed sediments are most

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divergent from the CF-Low sites, perhaps representative of its sudden hydrocarbon signature

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moving into the MF. It is interesting that MF-High is taxonomically more closely related to CF-

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Low, especially given its geographically the most distant sampling site and exhibits the highest

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PAC signature overall. However, what this may be suggesting is that by MF-High, given enough

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exposure time within the MF, the microbial community has adapted or equilibrated to the altered

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environmental stimuli of the MF. The microbial community at MF-Med may be under a drastic

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state of flux or adaptation, which is perhaps inducing rapid alteration to the DNA signature,

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resulting in the heightened variation at the OTU level. Nonetheless, this data suggests that the

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use of PCoA and similarity indices (Bray-Curtis in this case) do reveal shifts in the community

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composition because of hydrocarbon exposure, though not necessarily indicative of the stepwise

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increase of PACs observed down the river continuum.

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3.2.3 Suspended Sediment Microbial Function

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The microbial gene expression associated with the suspended sediment (SS) observed

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through the STB river continuum reveals significant shifts in the metabolic pathways in response

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to the incremental increases in PAC content moving downstream from CF-Low through to MF-

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High (Figures 7 & 8). These metabolic shifts were observed despite extremely low turbidity

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levels (< 5 NTU) and cold water temperatures (~10 oC) at the time of sampling. In higher

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turbidity instances and warmer summer temperatures, one could reasonably expect an even

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greater biotic signature to be ascertained through these metatranscriptomics approaches. In the

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SS samples, there is a significant suppression of several energy metabolism genes, including

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nitrogen fixation, nitrite reductase and assimilatory sulfate reductase. Additionally, there is

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significant suppression of photosynthetic genes responsible for both the coloring of

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cyanobacteria (cpcEF genes for Phycocyanobilin lyase (logFC > -2.06; p<0.05)) and the

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photosystem I and II systems (psa and psb) found in phototrophic organisms (LogFC < -1;

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p<0.05). This suggests a fundamental shift in natural metabolic character of the microbes,

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including the phototrophic communities residing within the water column. This suppression of

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genes specific to cyanobacteria and their photosynthetic functions correlate to the decrease in

389

cyanobacterial abundance noted within the 16S rRNA amplicon data, alongside other studies

390

which revealed photosynthetic inhibition due to PAC presence (Marwood et al., 1999). However,

391

there is a significant increase in the expression of bchQR genes, responsible for the

392

photosynthetic function of green sulfur bacteria (anoxygenic phototrophs). Relying on sulfides as

393

an electron source, this may indicate a shift towards a more sulfide-rich system moving towards

394

MF-High. Dissimilatory sulfate reductase genes (dsrAB; aprAB) and methanogenesis genes (mcr

395

and mtr) showed relatively consistent expression within the SS through to MF-High. However,

396

methane oxidation transcripts (mxaF, methanol dehydrogenase) show signficantly increasing

397

expression (logFC = 0.774; p<0.05) moving downstream, with some of the overall highest

398

expression noted in the SS. Further, the expression of amoAB (ammonia monooxygenase)

399

showed a gradual increase in expression moving downstream as well, previously shown to be

400

correlated to hydrocarbon exposure and metabolism (Dolan et al., 2010; Urakawa et al., 2019).

401

This too may correlate to the highly abundant Betaproteobacteria, some of which harbor these

402

same genes for ammonia oxidation. These iterative expression changes correlating to the

403

stepwise increase in hydrocarbon content indicates that even the microbial community within the

18

404

SS in the water column (which likely has lower cellular density than in the bed) is experiencing

405

significant alteration to their upstream metabolic signature which is in fact measureable utilizing

406

these shotgun RNAseq protocols. The increased expression of methanotrophic genes (i.e.

407

methane oxidation) indicate that perhaps the microbial population is adapting to the changes in

408

hydrocarbon content, whereby enhanced methanogenic activity from below (see 3.2.4) is being

409

at least partially offset by the methanotrophs in the overlying water. Further, the fact that many

410

of the significantly differentially expressed genes belong to the broader categories of

411

environmental information processing and cellular processes indicates that the microbial

412

communities travelling on or with the SS are experiencing dramatic changes in their core cellular

413

functionality, not just their energy metabolism alone.

414

Aside from energy metabolism pathways, dominant trends where also observed in the

415

expression of known biodegradation genes within the SS. There were significantly increasing

416

trends from CF-Low through MF-Med to MF-High of several known hydrocarbon degradation

417

genes, including (but not imited to) alkane monooxygenase (alkB), benzoyl-CoA reductase

418

(badDEFG), nitronate monooxygenase (NMO), and hydroxybenzoate monooxygenase (pobA)

419

(logFC > 0.15; p<0.05). There was also a spike in the expression of Benzylsuccinate-CoA (bbsB)

420

at both MF-Med and MF-High compared to CF-Low (logFC > 2), though this was not

421

significantly different (p = 0.268). The expression of alkB may correlate to the highly abundant

422

Alpha-, Beta- and Gammaproteobacteria classes, alongside Actinobacteria, who all contain

423

closely related homolog genes for alkane-degradation. The magnitude of these fold changes are

424

low, though this is somewhat expected given that what we are observing are small communities

425

of microbes in transit within the water column. There is likely little in the way of redundancy in

426

their functional abilities beyond necessary cell survival, so even small changes observed here

19

427

may be relatively drastic in terms of their overall functional characteristic. The signficant

428

increase in gene expressions noted here indicate that the microbial community suspended in the

429

water column are directly responding to the hydrocarbon influence of the MF altering their

430

natural energy metabolisms to adjust towards a seemingly increased biodegradation capacity

431

within the MF. This abundance and specificity of the significantly different transcripts within SS,

432

are potential targets from which to evaluate genes for the purpose of hydrocarbon compound

433

tracking in other freshwater environments.

434

3.2.4 Bed Sediment Microbial Function

435

Similar to the SS analyses, bed sediment community functions shifted significantly in

436

response to the MFs hydrocarbon influence. The absolute magnitude of expressions from within

437

the bed sediments was generally significantly higher than that of the SS (aside from

438

methanotrophy), likely related to the increased cell density within the sediments. The above is

439

also evidenced in the magnitude of change exhibited by the genes mentioned previously. Here,

440

similar trends in the expression of specific energy metabolism pathways were observed, though

441

almost all indicate suppression. Dissimilatory and assimilatory nitrate reductase and assimilatory

442

sulfate reductase both showed signs of suppression in both MF-Med and MF-High. Within MF-

443

High however, dissimilatory nitrate reductase expression was considerably higher than CF-Low.

444

This would suggest a shift in the general nitrogen metabolism moving into the MF, noted by the

445

increased dissimilatory nitrate reduction genes. Ammonium Monooxygenase (amoABC),

446

dissimilatory sulfate reductase (dsrAB and aprAB) and methanogenesis (mcr and mtr) genes saw

447

sharp increases in expression at MF-Med, followed by a slight decrease in the MF-High location.

448

These coinsiding increases in dissimilatory processes, may be evidence of energy conservation

449

strategies in response to an overall shift towards biodegradation or perhaps a response to the

20

450

increased nutrient availability associated with the PACs present. The differential expression

451

analyses between MF-Med and CF-Low, shows several mtr and mcr subunits as the most highly

452

overexpressed in MF-Med (logFC > 2; p<0.05), clearly a response to the increase PAC exposure

453

also observed in previous studies within the MF (Reid et al., 2019, 2018).

454

Known and identified biodegradation genes within bed sediments also exhibit a clear

455

response to MF exposure, here showing a generally increasing expression moving from Clear

456

water formation (upstream) to McMurray Formation (downstream). Alkane monooxygenase

457

(alkB),

458

benzoylsuccinyl-CoA dehydrogenase (bbsBD) all show increasing expression moving

459

downstream towards MF-High. Specifically, significant overexpression of alkB, badDEFG, and

460

bbsBD was observed between MF-High and CF-Low (logFC > 1; p<0.05). These elevated

461

expression levels further support a specific genetic response to increasing exposure to elevated

462

hydrocarbon levels, indicating their potential as PAC biomarkers further downstream. It is

463

interesting to note that some of these biodegradation expression signatures appeared to be

464

expressed earlier in the bed sediments than in the SS, again possibly due to of the lag time

465

needed for sediments to become suspended in the water column (from surface washoff or

466

channel erosion).

467 468

3.2.5 Identifying Contaminant Gradients and their impacts on Microbial Metabolic Expression

benzoyl-CoA

reductase

(badDEFG),

nitronate

monooxygenase

(NMO),

and

469

The breadth of data obtained through shotgun metatranscriptomics surveys is immense,

470

making it difficult to effectively, efficiently and accurately correlate trends in gene expression

471

patterns. However, by making observations of clusters of closely co-expressing genes, one can

472

overcome variation potentially attributed to the expression of single genes in-situ, and make

21

473

more robust inferences based on redundant genetic response. By observing these metabolic

474

signatures via expression heatmap (Figure 7) and computing average linkage clustering between

475

certain genes, it becomes evident that there are contrasting functional characteristics between SS

476

and bed sediments, alongside those between the CF and MF. CF-Low bed sediments more

477

closely associate with the SS of all other sites compared to the other bed sites. This is likely

478

attributed to the fact that this site acts as a control and is the only to be outside of the MF

479

influence. This also suggests that the presence of the MF hydrocarbons alone causes a dramatic

480

increase in expressional diversity.

481

The dramatic shifts in the metabolic signatures of both the SS and bed sediments are

482

observed in the differential expression scatterplots from the differential expression analysis

483

(Supplementary Figure S1). Here, shifts in specific gene expression depending on sample

484

location are evident in the amount of deviation from the overall expressional mean. Bed

485

sediments in general, as mentioned previously, generally indicate higher expression magnitudes

486

compared to the SS. Although, there are clearly specific genes and clusters of genes that reveal

487

increased expression at CF-Low and in the SS. The suppression of the sir gene for assimilatory

488

sulfate reduction alongside an increase in the genes for dissimilatory sulfate reduction (dsrAB;

489

aprAB) indicates a shift from energy consuming to energy producing activity (similar to the

490

noted shift in nitrogen metabolism). This shift of assimilating these sulfur compounds towards

491

the production of sulfides, could be the energy source for the anoxygenic phototrophs mentioned

492

earlier. This noted abundance of phototrophic sulfur bacteria and sulfur cycling genes here,

493

alongside adjacent freshwater reservoirs in the region, provides insight into their apparent

494

significance in the region (Reid et al. 2019). It has been observed that anoxygenic phototrophs

495

will dominate in oligotrophic systems and exceeds primary production rates of oxygenic

22

496

phototrophs by a large margin. The dense microbial mats that likely inhabit these sediments,

497

containing micro-redox zones of both sulfate and sulfide has been suggested to act as an almost

498

closed system, limiting the reduction of oxygen. In some systems, the oxidation of sulfides

499

within these bed sediments has been up to 100 %, resulting from the dense communities residing

500

within layered biofilms (Dworkin et al., 2006). This therefore explains the relatively little sulfur

501

metabolism observed within the water column, compared to the bed sediments.

502

The increased expression of both methanogenic and methane-oxidizing transcripts

503

suggests a shift towards a biodegradation dominated metabolic signature of these sites. What is

504

interesting is the closely co-expressed nature of several of the methanogenic transcripts (mcr and

505

mtr) with biodegradation genes (benB; bbsBD). In fact, the most significantly over-expressed

506

genes at MF-Med was the mcrCD and mtrC genes, both responsible for different segments of the

507

larger methanogenic pathway. The mcr gene and its various subunits are well known

508

methanogenic markers, and are known indicators of biodegradation activity (McKay et al. 2017;

509

Reid et al. 2018, 2019; Vigneron et al. 2017). The mtr gene, a membrane bound

510

methyltransferase, is also responsible for methane production, though is perhaps the less studied

511

of the two. Responsible for the extrusion of a sodium ion from the cell, mtr subunits provide the

512

necessary cellular chemiosmotic gradients for final steps of the methanogenic pathway. This is

513

also an energy conservation mechanism, perhaps allowing for the continuation of these processes

514

under potentially limited thermodynamic constraints. The dependence on this cellular sodium

515

pump could also suggest a broader salinity impact on the expression of the methanogenic

516

pathways observed here. Other studies have indicated the impacts of NaCl on methanogenesis,

517

though in different environmental contexts (Patricia J. et al., 2007). If valid, a genetic response

518

related to salinity gradients in the environment could be an ideal marker for industrial oil sands

23

519

inputs, given their highly saline nature (Volik et al., 2017; Warren et al., 2016). Though all

520

methanogenic pathways lead to CH4, the mechanism and phylogenetic diversity of the organisms

521

that lead to this point are quite diverse. This provides an interesting future direction from which

522

to move towards an increased specificity of methanogenic biomarkers depending on the

523

compound of interest. This too could be extended to other gene groups and subunits, where

524

specific environmental stimuli could induce slightly altered expressional patterns potentially

525

useful for source tracking research.

526

Tracking other nutrient gradients (i.e. carbon, nitrates and phosphorus) would likely

527

induce a similar alteration to the characteristics of the in-situ microbial metabolism as observed

528

here. Shifts in basic physico-chemical parameters such as DO, salinity and conductivity can and

529

will induce a genetic response that could be elucidated through these same shotgun gene

530

expression surveys. The induced response from any environmental stimuli causes what is in

531

essence a cooperative response by groups or communities of microbes, and never isolated to

532

single microbial taxa in-situ. Integrating genomics techniques such as these, where clusters of

533

co-expressed genes can be traced alongside conventional chemical tracer approaches is the next

534

step in characterizing the biological impact of compounds as they move through our surrounding

535

environments. The ability to genetically determine how target substances effect the natural

536

microbiology of a system provides researchers with a more holistic understanding of the bio-

537

transformations and/or biodegradation trends associated with the given compound of interest.

538

4.4 Conclusions

539

A major challenge facing mining companies and environmental scientists globally is

540

trying to evaluate the extent to which anthropogenic practices are affecting the surrounding

541

natural environment. This paper provides insights for intriguing avenues from which to begin 24

542

unravelling these complex problems. This is the first study to explicitly characterize the natural

543

microbial community function in reference to a true baseline, reference-site, upstream of the

544

McMurray Formation and its ubiquitous hydrocarbon-rich composition. Additionally, these are

545

the first results to identify microbial gene expression trends in a compound tracking context,

546

whereby functional trends are directly correlated to the environmental hydrocarbon gradient

547

measured in both the SS and bed sediments. These results, along with the PAC characteristics,

548

provides powerful insight into not only the McMurray Formation’s impact on both the SS and

549

bed sediment biogeochemistry, but also on the prospect of microbial functional biomarkers for

550

tracking hydrocarbon exposures in other dynamic aquatic ecosystems.

551

Results clearly show that the complexities of specific gene subunits can and do provide

552

significant insight into the fundamental metabolic preferences within a given ecosystem. There

553

are fundamental differences in expression pathways between SS and bed sediments, and as such,

554

future studies characterizing river continuums should be aware of these differences in designing

555

their research approach. Alterations are clearly observed for not only genes responsible for

556

metabolizing these hydrocarbons (alkB, badB, bbsBD etc.), but also the fundamental energy

557

metabolism processes governing this ecosystem’s microbial communities. Shifts from

558

assimilatory to dissimilatory reduction processes give interesting insight into fundamental shifts

559

in the flow of energy through these systems and reveal how widely ecosystems can be impacted

560

biologically.

561

These advancements in contaminant biogeochemical studies, exemplified here, provide

562

avenues from which to explore bioassay surveys, isolating specific gene clusters for targeting

563

and tracking specific compound exposures. There is a need to further our understanding of how

564

entire microbial communities behave in the presence of contaminants, moving beyond single 25

565

microbe studies to understand entire community dynamics. The very nature of microbial

566

plasticity allows them to be extremely effective and efficient in their metabolic capabilities when

567

in the presence of other microbes and any given environmental stimuli. By harnessing and

568

unravelling their complex genetic response, the novel tools exemplified here have great potential

569

moving forward for unravelling the movement of compounds through hydrodynamic

570

environments worldwide.

571 572 573 574 575 576 577

ACKNOWLEDGEMENTS

578 579 580 581

This project was funded in part by NSERC Discovery Grant 860006. The authors would also like to thank Environment and Climate Change Canada for their partial funding, logistical and field sampling support. They would also like to thank Genome Quebec for their support in RNAseq using their Illumina HiSeq 4000 platform.

582 583

AUTHOR CONTRIBUTIONS STATEMENT

584 585

T.R, I.G.D, and C.G.W all contributed to the sample collection, writing and editing of the manuscript. All figures were produced by T.R. All authors reviewed the manuscript.

586 587

COMPETING INTERESTS

588

The authors declare no competing interests.

589

26

590

FIGURES

591

27

592 593 594 595

Figure 1: Map of Fort McMurray Region, bedrock formations constituting the bitumen deposit (McMurray Formation) and sampling sites along the Steepbank River

28

Total Polycyclic Aromatic Compound Concentrations 450 400 350

Total PAC (ng/L)

300 250 200 150 100 50 0 CF-Low

MF-Med

MF-High

Location (Upstream to Downstream)

596 597 598

Figure 2: Total PAC loads in the SS of the STB River at each respective sampling site (RAMP, 2015)

599

29

CF-Low

MF-Med

Carbon

MF-High

Carbon

600 601 602 603 604 605

Figure 3: Scanning Electron Microscope images revealing hydrocarbon content in bed sediments of CF-Low (Left), MF-Med (Middle), and MF-High (Right). Associated EDAX analysis histograms reveal correspondingly high peaks of carbon indicative of a hydrocarbon signature. Note, the interstitial black seen in CF-Low (left) is the carbon backing tape on the SEM stub.

30

606 Actinobacter

Cyanobacteria

Verrucomicrobia Unassigned Acidobacteria

Bacteroidetes Firmicutes

607 608 609

Figure 4: Phylum level normalized abundances (DESeq2) for SS (left) and bed sediments (right). Inner data series represents CF-Low; Middle series MF-Med; Outer series MF-High.

610

31

Normalized Abundance

Classes of Proteobacteria 0.6 0.5 0.4 0.3 0.2 0.1 0

CF(SS)-Low

MF(SS)-Med

MF(SS)-High

CF(BED)-Low

MF(BED)-Med

MF(BED)-High

611 612

Figure 5: Distribution of Proteobacteria classes within the SS and bed sediments of the STB

613

32

614 615 616

Figure 6: PCoA of Bray-Curtis similarity index of OTUs from both the bed and SS of CF-Low, MF-Med and MF-High

617

33

618 619 620 621

Figure 7: Expression heatmap with dendrograms from both genes and sample locations. Average linkage clustering was performed to identify clusters of genes that closely relate in terms of their expressional shifts down the STB River.

34

622

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Tracking Functional Bacterial Biomarkers in Response to a Gradient of Contaminant Exposure within a River Continuum

HIGHLIGHTS • • • •

Using metatranscriptomics revealed sensitive biomarkers for in-situ compound tracking Suspended sediments and bed sediments reveal clear responses to increasing hydrocarbons Clusters of co-expressing genes provide novel biomarkers to fingerprint exposure Co-expression of methanogenic and biodegradation genes in both suspended and bed sediment

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: