offsetted when considering the impact at the community level. These authors also insist on the fact that it is difficult to conclude on the direct impacts of pesticides on bacterial communities as the impact can be contrasting according to bacterial species, pesticide concentration, time of exposure, etc., probably due to the broad diversity of strategies used by bacteria. Finally, as underlined by Jacobsen and Hjelmso (2014), conclusions on the direct and/or indirect effects induced by the presence of pesticides on microbial diversity are not easy to determine. Indeed, in most cases, the links between phylogeny and functions are not directly established for bacteria, and because in all ecosystems, many groups (such as – according to the ecosystem – bacteria, archaea, fungi, plants, and nematodes) interact within or between each other.

## CONCLUSION

The current study demonstrates that some pesticides and metabolites can have a negative impact on the activity of the denitrifying community in groundwater (decrease of the activity) at environmental concentrations. The impact of metabolites can be higher than that of pesticides (parent molecules) and mainly concerns the first step of the denitrification pathway, very probably because of an inhibitory impact on the nitrate reductase enzyme itself. Finally, the presence of some pesticides and metabolites in groundwater can also impact the global growth of the groundwater microbial community as well as its biodiversity. The study thus emphasizes that pesticides and metabolites affect groundwater communities even at environmental low concentrations (2 µg/L and potentially less), strongly insisting on the need to carry out studies at low pesticide/metabolite concentrations to get a realistic picture on how these molecules can affect subsurface microbial communities and activities.

This study, with a few others, is of particular interest as the side effects of pesticides and metabolites on soil microorganisms already have to be taken into account during European pesticide registration (regulation EC no. 1107/2009). Indeed, approved pesticides shall have no unacceptable effects on the environment, e.g., impact on soil biodiversity and the ecosystem. To date, scientific methods accepted by the authority are available for studying the impact on N cycle in soils (test guideline OCDE 216 "Soil Microorganisms: Nitrogen Transformation Test"), thus

allowing a systematic evaluation for each new approved (or re-approved) active substance. Our results illustrate the importance to also consider more systematically the impacts on subterranean ecosystems in the frame of the European pesticide registration procedure. Finally, the impacts of pesticides and/or metabolites as cocktail will have also to be taken into account in future evaluations of pesticides for their registration.

## DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ebi.ac.uk/ena, PRJEB42682.

## AUTHOR CONTRIBUTIONS

CM, NB, LA, and CJ conceived and designed the research. CM and CJ conducted the experiments, with the technical support of MC, and were actively involved in microbial analyses. NB was involved in sampling site chemical characterization and groundwater sampling as well as chemical analysis. LA was involved in chemical and nitrogen balance analysis. CM wrote and revised the manuscript. NB, LA, and CJ were actively involved in its editing and revision. All authors read, corrected, and approved the manuscript.

## FUNDING

This work was funded by Agence de l'Eau Loire Bretagne (France) and BRGM in the frame of the IPAD project (Decision 2016C.007 du 22/12/2016).

## ACKNOWLEDGMENTS

The authors would like to thank Thibault Conte for chemical analysis management, Chems Mohamad for DNA extraction, and Benoit Henry for gas analysis. The authors also thank the MetaHealth metagenomic-based services (CIRAD, PHIM, Eco&Sols, Montpellier, France) for MiSeq Illumina sequencing.

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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Michel, Baran, André, Charron and Joulian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# Relative Weight of Organic Waste Origin on Compost and Digestate 16S rRNA Gene Bacterial Profilings and Related Functional Inferences

Axel Aigle<fnref n="1" />†, Emilie Bourgeois<fnref n="1" />, Laurence Marjolet<fnref n="1" />, Sabine Houot²,
Dominique Patureau³, Emmanuel Doelsch⁴,⁵, Benoit Cournoyer<fnref n="1" />† and Wessam Galia<fnref n="1" />*‡

OPEN ACCESS

**Edited by:**
Dimitrios Georgios Karpouzas,
University of Thessaly, Greece

**Reviewed by:**
Heribert Insam,
University of Innsbruck, Austria
Panagiotis A. Karas,
University of Thessaly, Greece

*Correspondence:
Wessam Galia
wessam.galia@vetagro-sup.fr

†Present address:
Axel Aigle,
ILM, Villeurbanne, France

‡These authors have contributed
equally to this work

**Specialty section:**
This article was submitted to
Microbiotechnology,
a section of the journal
Frontiers in Microbiology

Received: 11 February 2021

Accepted: 06 April 2021

Published: 14 May 2021

**Citation:**
Aigle A, Bourgeois E, Marjolet L, Houot S, Patureau D, Doelsch E, Cournoyer B and Galia W (2021) Relative Weight of Organic Waste Origin on Compost and Digestate 16S rRNA Gene Bacterial Profilings and Related Functional Inferences. *Front. Microbiol.* 12:667043.
doi: 10.3389/fmicb.2021.667043

Even though organic waste (OW) recycling via anaerobic digestion (AD) and composting are increasingly used, little is known about the impact of OW origin (fecal matters and food and vegetable wastes) on the end products' bacterial contents. The hypothesis of a predictable bacterial community structure in the end products according to the OW origin was tested. Nine OW treatment plants were selected to assess the genetic structure of bacterial communities found in raw OW according to their content in agricultural and urban wastes and to estimate their modifications through AD and composting. Two main bacterial community structures among raw OWs were observed and matched a differentiation according to the occurrences of urban chemical pollutants. Composting led to similar 16S rRNA gene OTU profiles whatever the OW origin. With a significant shift of about 140 genera (representing 50% of the bacteria), composting was confirmed to largely shape bacterial communities toward similar structures. The enriched taxa were found to be involved in detoxification and bioremediation activities. This process was found to be highly selective and favorable for bacterial specialists. Digestates showed that OTU profiles differentiated into two groups according to their relative content in agricultural (manure) and urban wastes (mainly activated sludge). About one third of the bacterial taxa was significantly affected by AD. In digestates of urban OW, this sorting led to an enrichment of 32 out of the 50 impacted genera, while for those produced from agricultural or mixed urban/agricultural OW (called central OW), a decay of 54 genera over 60 was observed. Bacteria from activated sludge appeared more fit for AD than those of other origins. Functional inferences showed AD enriched genera from all origins to share similar functional traits, e.g., chemoheterotrophy and fermentation, while being often taxonomically distinct. The main functional traits among the dominant genera in activated sludge supported a role in

<footnote n="1">¹ Univ Lyon, UMR Ecologie Microbienne (LEM), Université Claude Bernard Lyon 1, CNRS 5557, INRAE 1418, VetAgro Sup, Marcy L'Etoile, France, ² UMR ECOSYS, INRAE, AgroParisTech, Thiverval-Grignon, France, ³ INRAE, Univ Montpellier, LBE, Narbonne, France, ⁴ CIRAD, UPR Recyclage et risque, Montpellier, France, ⁵ Recyclage et Risque, Univ Montpellier, CIRAD, Montpellier, France</footnote>
AD. Raw OW content in activated sludge was found to be a critical factor for predicting digestate bacterial contents. Composting generated highly predictable and specialized community patterns whatever the OW origin. AD and composting bacterial changes were driven by functional traits selected by physicochemical factors such as temperature and chemical pollutants.

**Keywords:** organic waste, anaerobic digestion, composting, 16S rRNA gene meta-barcoding, functional traits

INTRODUCTION

Organic wastes (OWs) have increased 10-fold since the last century and are likely to double by 2025 as a result of the growth of the world population (Hoornweg et al., 2013). There is thus a strong need for sustainable OW management. From a waste management standpoint, aerobic (composting) and anaerobic digestions (AD) are the most obvious operational processes prior to soil applications. These recycling treatments can also be combined by performing a composting of digestates. A major constraint in these OW recycling scenarios is the capacity to assess the related OW health hazards, which are, in fact, dependent on their origin and contents in terms of chemical and microbiological contaminants, and the efficacy of the treatments at reducing these hazards.

AD implies a breakdown of organic materials naturally found in raw OW into biogas (50-75% CH4, 25-50% CO2, and 1-2% H2S, H2, and NH3) and organic residues (called digestate) by a microbial consortium (Atelge et al., 2020). The success of this process relies on the synergies between microorganisms acting in a coordinated succession driven by the environmental changes occurring during the process (Rivière et al., 2009; Yu et al., 2010). To avoid deviation of microbial communities during the AD process, parameters such as volatile fatty acids (VFAs), ammonia and hydrogen sulfide concentrations, acetate:propionate ratios, and pH are regularly monitored (Kayhanian, 1994; Chen et al., 2008; González-Fernández and García-Encina, 2009; Wang et al., 2009; Franke-Whittle et al., 2014; Wagner et al., 2014). The microbial reactions of these processes can be performed by psychrophiles, mesophiles, and thermophiles (Moset et al., 2015; Saady and Massé, 2015; Hupfauf et al., 2018). Mesophilic digestion is considered the most stable AD process (Gou et al., 2014) because diversity of bacterial and archaeal assemblages are greater at mesophilic (35-37°C) temperatures (Levén et al., 2007; Pycke et al., 2011). The initial hydrolysis step of OW is performed by many facultative anaerobes or strictly anaerobic bacteria. This hydrolysis can degrade complex molecules into simple sugar monomers, amino acids, and alcohols. Fermentative bacteria will then convert these monomers into short-chain fatty acids, primary alcohols, as well as H2 and CO2. The production of acetate from the fatty acids will be performed by homoacetogenic bacteria like *Butyribacterium* and *Acetobacterium*, that of H2 will be performed by the acetogenic microbiota such as *Syntrophomonas*, and that of CO2 and H2S will be performed by sulfate-reducing bacteria such as *Desulfovibrio* and *Desulfobacter*. Some of these compounds will then be used by methanogens to generate CH4. Hydrogenotrophic methanogens such as *Methanobacterium* and *Methanogenum* sp. convert H2 and CO2

into CH4, and acetotrophic ones such as *Methanosarcina* will convert acetate into CH4 (Demirel and Scherer, 2008; Lykidis et al., 2011).

In the same way, composting is a natural process characterized by the conversion of OW into by-products that can be used as soil conditioners and/or organic fertilizers (Reinikainen and Herranen, 2001; Ahmad et al., 2007). This process is performed by an aerobic microbial consortium composed of *Proteobacteria*, *Actinobacteria*, and fungi. During the process of composting, microbial community successions are driven by environmental parameters such as moisture, temperature, and the aeration conditions (Blanc et al., 1999; Tian et al., 2013; Zhang et al., 2016). Composting in its initial phase can be carried out by several mesophiles including bacteria, which represent environmental health hazards such as *E. coli*, *Salmonella*, *Klebsiella*, and *Nocardia* (Insam and de Bertoldi, 2007). These organisms can break down soluble organic compounds by exothermic reactions. As the temperature rises above 40°C, these mesophiles will be outcompeted by more thermophilic bacteria such as *Bacillus stearothermophilus* and species from the *Deinococcus/Thermus* group (Insam and de Bertoldi, 2007). These will continue the breakdown processes and generate higher temperatures that will partly sanitize the end products by killing heat-sensitive hazardous microbial agents. A compost pile can reach 65-70°C. As the supply of high-energy compounds will get low, thermostolerant mesophiles will be favored and will perform the last transformation or "stabilization" reactions (Day and Shaw, 2001). The cool zones in the composting systems, particularly with those having no or little mixing, can favor a survival and (re)growth of some bacterial pathogens (Noble and Roberts, 2004).

Beyond the general microbial structures described previously for AD and composting, several questions remain, including how the high inputs of microorganisms and cells from the feeding substrates (OW) under conditions of continuous flow impact the microbial community structure in the end products. On one hand, some studies suggested that the microbial communities of digested OW are unique to a digester because of high differences in substrate and operating conditions (Werner et al., 2011; Sundberg et al., 2013; Zhang et al., 2014; De Vrieze et al., 2016). However, some of these analyses on full-scale AD plants fed with a mixture of substrates (De Vrieze et al., 2016) or with activated sludge (Wang et al., 2018) also indicated that the microbial communities could be stable over time. Nevertheless, in other studies, the microbial communities in AD were found variable over time (among a same digester), even when substrate and operational conditions remained constant (Supaphol et al., 2011; De Vrieze et al., 2013; Williams et al., 2013; Vanwonterghem et al., 2014). On the other hand, during composting, microbial
communities of the raw OW were found to trigger a rise and decrease in temperature (Palaniveloo et al., 2020). These changes are highly selective but their incidence on microbial community patterns of mature compost remain to be evaluated according to the origin of OW. A knowledge gap thus remains about the relative contribution of the raw OW microbiome on the digestate and compost structural and functional microbial community patterns.

Here, we made the hypothesis that the microbiomes found among digesters and composts will be (i) significantly structured by the communities of the raw OW inputs, but that (ii) functional redundancy among these communities would generate distinct community profiles between treatment plants. Similar biodegradation activities between distinct treatment plants would thus be achieved by different microbial organizations because of this functional redundancy. To test these hypotheses, nine full-scale treatment plants located in various rural and urban backgrounds were investigated. These treatment plants were fed with agricultural or urban wastes or a mixture of both. Bacterial genetic structures were inferred from 16S rRNA gene meta-barcoding approaches, and patterns were compared between process types and according to the origin and nature of the raw OW. Functional traits were inferred from the 16S rRNA gene taxonomic allocations and databases recording the metabolic processes of well-defined bacterial taxa (FAPROTAX). The theoretical metabolic pathways of the genera impacted by treatment were further inferred from sequenced genomes using the MACADAM software (Le Boulch et al., 2019).

# MATERIALS AND METHODS

## OW Recycling Sites and Samplings

Nine full-scale OW treatment plants (numbered as Urban-1-2-3, Agri-1-2-3 and Central-1-2-3) were considered in this study (Table 1). The sampling strategy implied the use of at least three independent treatment plants per sample categories. OWs were treated by AD (Urban-1-2, Agri-1-2, and Central-1-2-3) or composting (Urban-1-3 and Agri-1-3). Five of the AD plants applied a post-treatment to the digestate: solid/liquid separation (Central-1-2), solid/liquid separation followed by composting (Urban-1 and Agri-1), or solid/liquid separation followed by solid phase drying (Urban-2). Raw OWs were chosen to represent the most common origins of OW: urban [sewage sludge produced by wastewater treatment plants (WWTP): Urban-1-2-3], agricultural (exclusively or mainly composed of animal manure and/or slurry: Agri-1-2-3), and central (a mix of food industry waste, harvest residues, livestock effluents, and/or sewage sludge: Central-1-2-3) (see Table 1 for details of the OWs compositions). For all sites, samples were taken at each treatment step to capture treatment effects on the microbial community's composition and abundance. Thus, a raw OW sample was taken from each site, resulting in nine samples, respectively, defined as urban (Urban-1-2-3-OW), agricultural (Agri-1-2-3-OW), and central (Central-1-2-3-OW) (Table 1). Digestates were available and sampled for six sites (Urban-1-D, Agri-1-2-D, and Central-1-2-3-D), as well as solid and dried

digestates, respectively, from liquid/solid phase separation, and from solid phase drying (Urban-1-2-SD, Agri-1-SD, Central-1-2-SD, and Urban-2-DD) (Table 1). Liquid digestates were available for four sites (Urban-2-LD, Agri-1-LD, and Central-1-2-LD), and finally, compost was produced and sampled also at four sites (Urban-1-3-C and Agri-1-3-C) (Table 1). At least 1 kg of OW, composts, or digestates was collected per treatment. Samples were coded as indicated in Le Bars et al. (2018). For each sample, certain pollutants and physicochemical parameters were measured (Supplementary Tables 1, 2). These datasets have been presented in Le Bars et al. (2018) and Sertillanges et al. (2020) and further analyzed in order to verify the chemical differentiation of samples according to their origin (urban, agricultural, and central). Total nitrogen (ISO 13878) and total carbon (ISO 10694) were determined by dry combustion with an elemental NC 2100 Soil Analyzer (Thermo Electron Corp.). Inorganic carbon (ISO 10693) was determined with a Bernard Calcimeter by adding HCl and measuring released CO2. Organic carbon ($C_{org}$) was deduced from the difference between total and inorganic carbon. Concentrations of trace elements (TE) were measured using an inductively coupled plasma mass spectrometer (ICP-MS iCAP Q Thermo Fisher Scientific). pH was directly measured on liquid samples, or according to the ISO 10390. More details for those procedures are described in Le Bars et al. (2018). The concentrations of all the elements are expressed on a dry matter (DM) basis. Nonylphenols (NPs) and polycyclic aromatic hydrocarbons (PAHs) were measured in 1 g subsample according to Trably et al. (2004) and Mailler et al. (2017). More details for these monitorings are given in Sertillanges et al. (2020).

## DNA Extractions and MST

DNA was extracted from 0.5 g of solid samples or 500 µl of liquid samples using the FastDNA Spin kit for soil and the FastPrep-24 (MP Biomedicals) following the manufacturer's instructions. All the DNA extractions were triplicated for each sample. DNA was eluted in 50 µl of ultrapure water, and quantity and quality were measured using a nanodrop-One UV-Vis Spectrophotometer (Thermo Fisher Scientific). DNA extracts were visualized after electrophoresis at 6 V/cm using a TBE buffer [89 mM Tris-borate, 89 mM boric acid, and 2 mM EDTA (pH 8.0)] through a 0.8% (w/v) agarose gel, and DNA staining with 0.4 mg ml⁻¹ ethidium bromide. A Gel Doc XR+ System (Bio-Rad, France) was used to observe the stained DNA and confirm their relative quantities and qualities. DNA was kept at -80°C and shipped on ice within 24 h to the DNA sequencing services when appropriate.

MST methods were used to monitor host fecal contaminations by human, bovine, and pig (Supplementary Table 3). These tests were performed on all DNA extracts including all replicates from the same sample. MST qPCR amplifications of general and host-specific *Bacteroides* markers, as well as general 16S rRNA gene qPCR assays, were performed as described in Marti et al. (2017) and Voisin et al. (2020). The human-specific marker (HF183) and the 16S rRNA genes were quantified by the SYBR Green chemistry while the bovine-specific, pig-specific, and the total *Bacteroidetes* markers were quantified using the Taqman chemistry. Six qPCR assays were performed per sample using DNA obtained from three independent extractions
<table><caption>TABLE 1 | Organic waste treatment plants and sample features.</caption><thead><tr><th>Sampling sites</th><th>Reactor size (m³)/temperature for AD</th><th>Samples</th><th>Duration<sup>a</sup>/treatment<sup>b</sup></th><th>Sample code</th><th>Organic waste composition and co-substrate used for composting</th></tr></thead><tbody><tr><td>Urban-1 French wastewater treatment plant and composting platform</td><td>6,200 32-42°C</td><td>Organic waste Digestate Solid digestate Compost<sup>c</sup></td><td>38d/AD 4w/C</td><td>Urban-1-OW Urban-1-D<br>Urban-1-SD Urban-1-C</td><td>Drained activated sludge<br>Co-substrate: green waste</td></tr><tr><td>Urban-2 French wastewater treatment plant</td><td>6,000 32-42°C</td><td>Organic waste Liquid digestate Solid digestate Dried digestate</td><td>34d/AD</td><td>Urban-2-OW Urban-2-LD<br>Urban-2-SD Urban-2-DD</td><td>Drained sludge</td></tr><tr><td>Urban-3 French wastewater treatment plant and composting platform</td><td></td><td>Organic waste Compost<sup>d</sup></td><td>6w/C</td><td>Urban-3-OW Urban-3-C</td><td>Dehydrated activated sludge<br>Co-substrate: green waste</td></tr><tr><td>Agri-1 Anaerobic digestion plant and composting platform (France)</td><td>950 32-42°C</td><td>Organic waste Digestate Liquid digestate Solid digestate Compost<sup>c</sup></td><td>38d/AD 12w/C</td><td>Agri-1-OW Agri-1-D<br>Agri-1-LD Agri-1-SD<br>Agri-1-C</td><td>Porcine slurry (for 6-7 T/day)<br>and grass clippings, food and vegetable wastes, corn silage and straws Co-substrate: none</td></tr><tr><td>Agri-2 Anaerobic digestion plant (France)</td><td>740 45°C</td><td>Organic waste Digestate</td><td>42d/AD</td><td>Agri-2-OW Agri-2-D</td><td>80% of bovine manure and 20% of stercoral material, poultry litter, corn cob, cooking oils, lawn mowing and bran</td></tr><tr><td>Agri-3 Composting platform (France)</td><td></td><td>Manure Compost<sup>d</sup></td><td>12w/C</td><td>Agri-3-OW Agri-3-C</td><td>Raw cattle, sheep and equine manures Co-substrate: none</td></tr><tr><td>Central-1 Anaerobic digestion plant (France)</td><td>1,600 32-42°C</td><td>Organic waste Digestate Liquid digestate Solid digestate</td><td>70d/AD</td><td>Central-1-OW Central-1-D<br>Central-1-LD Central-1-SD</td><td>50% livestock manures (N_manure + animal slurry), 28% wastewater treatment plant sludge, 22% food industry by-products</td></tr><tr><td>Central-2 Anaerobic digestion plant (France)</td><td>6000 32-42°C</td><td>Organic waste Digestate Liquid digestate Solid digestate</td><td>30d/AD</td><td>Central-2-OW Central-2-D<br>Central-2-LD Central-2-SD</td><td>45% pig slurry, 45% wastes from food industries, 10% cereal mixture</td></tr><tr><td>Central-3 Anaerobic digestion plant (France)</td><td>8,000 32-42°C</td><td>Organic waste Digestate</td><td>90d/AD</td><td>Central-3-OW Central-3-D</td><td>50% biowastes (community, catering, mass distribution), 20% dairy cow slurry, 30% wastes from food industries and agricultural by-products</td></tr></tbody></table>

(Supplementary Table 4). Standard curves were generated using 10-fold serial dilutions (ranging from 10⁵ to 10⁰ gene copies) of cloned targeted sequences (Marti et al., 2017). MST qPCR datasets were expressed per copy number of DNA targets per gram of dry sample and normalized by dividing their numbers by the 16S rRNA gene copy numbers per gram of dry sample.

## V5–V6 16S rRNA Gene Sequencing and Reads Processing

The V5–V6 region of the 16S rRNA genes were amplified using the 799F forward (barcode + ACCMGGATTAGATACCCKG) and 1193R reverse (CRTCCMCACCTTCCTC) primers (Chelius and Triplet, 2001; Bodenhausen et al., 2013; Supplementary Table 3) by the Molecular Research DNA lab (MrDNA, Shallowater, United States) and were sequenced by the Illumina MiSeq V3 technology. PCR amplifications were performed on pooled DNA extracts obtained per sample (equimolar mixes

of the triplicates) in order to handle variability between DNA extracts. The HotStarTaq Plus Master Mix Kit (Qiagen, United States) using the following temperature cycles was used to generate the PCR products: 94°C for 3 min, followed by 28 cycles of 94°C for 30 s, 53°C for 40 s, and 72°C for 1 min, with a final elongation step at 72°C for 5 min. PCR products and blank control samples were verified using a 2% agarose gel and following the electrophoretic procedure described above. PCR products obtained from field samples showed sizes around 430 bp but blanks did not show detectable and quantifiable PCR products. Dual-index adapters were ligated to the PCR fragments using the TruSeq® DNA Library Prep Kit, which also involved quality controls of the ligation step (Illumina, Paris, France). Illumina Miseq DNA sequencings of the PCR products were paired-end and set up to obtain around 40K reads per sample.

The generated sequences were processed with the Mothur package (Schloss et al., 2009) according to a standard operation

<footnote><sup>a</sup>Process duration (d = days/w = weeks). <sup>b</sup>Treatments (AD = anaerobic digestion/C = composting). <sup>c</sup>The substrates used for composting were solid digestates from the matching site (Urban-1 or Agri-1). <sup>d</sup>Raw organic wastes were used for the composting at the Urban-3 and Agri-3 sites.</footnote>
protocol (SOP) for MiSeq-based microbial community analysis (Schloss et al., 2009; Kozich et al., 2013), so-called MiSeq SOP, and available at http://www.mothur.org/wiki/MiSeq_SOP. Briefly, primers and barcodes were trimmed according to quality and size filtration, followed by sequence alignment. Size filtration was refined, and unique sequences were extracted. The pre.cluster command was applied in order to mask the remaining sequencing errors as well as the split.abund command where the cutoff was set to 1. Chimera removal was performed using the chimera.uchime command and chimeric sequences were removed. At this stage, sequences were processed following two normalization strategies: (i) subsampling according to the sample containing the lowest number of sequences, and (ii) no subsampling (see statistical analysis for normalization strategy). Finally, for both datasets, the SILVA 16S rRNA Bacterial reference library was used for taxonomic allocation of the OTUs (cutoff 80)<fnref n="1" /> and OTU clustering was performed with a similarity threshold of 97% for subsampled reads (Supplementary Table 5) or using the phylotype command at the genus level, for non-subsampled reads (Supplementary Table 6). The Mothur package was also used to calculate diversity indices (Shannon, Simpson, evenness, and richness) on the 16S rRNA subsampled OTU table (Supplementary Table 7).

## Statistical Analyses

Relations between sample groupings and chemical datasets were tested by principal component analysis using the rda function and plotted using the biplot function; confidence ellipses were drawn to show the variance observed per group. Kruskal-Wallis tests were performed using the kruskal.test function on 16S rRNA diversity indices, MST markers ratio, and physical-chemical data. When significant differences were found (*p* < 0.05), a Dunn test was applied using the kwAllPairsDunnTest function from the PMCMRplus package (Pohlert, 2020) for post hoc analysis.

Hellinger transformation was applied on the 16S rRNA subsampled OTU table using the decostand function (method = "total"), and Bray-Curtis dissimilarities were computed between pairs of samples using the vegdist function ("bray"). NMDS was generated using the metaNMDS function. Ordiellipse option was used to display the variance observed per group, and PERMANOVA tests were performed using the pairwise.adonis and vegdist functions (Bray-Curtis dissimilarities) to confirm the significance of differences between groups.

The non-subsampled 16S rRNA gene reads genus table (Supplementary Table 6) was processed with the DESEQ2 package (Love et al., 2014) in order to reveal genera with significant change in abundance before and after treatment. Counts normalized by the median of ratios method (Anders and Huber, 2010) were recovered and relative abundances were calculated in order to present the percentage of significant differences in abundances at the genus level before and after treatment. Change in abundance has been considered significant if a genus shows a log2 fold change ≤ -2 or ≥ +2 with a minimum Base Mean value of 10 and adjusted *p* value of < 0.05. To facilitate

global comparison with other studies, computations were also performed at the phylum level. All the functions used without package indication were from the vegan package (Oksanen et al., 2019) and all analyses were performed on RStudio (R v3.3.3). The statistical experimental design (Kruskal-Wallis tests on 16S rRNA diversity indices and MST markers, as well as NMDS and DESEQ2 analyses) implied first testing relations according to treatment (AD against composting), regardless of the OW origin and regardless of the post-treatments. Thus, all OW samples were compared to composted samples and digestates. Second, the OW origins were considered in the comparisons to infer their impact on the V5-V6 16S rRNA gene datasets.

The 16S rRNA gene sequences produced in this study are available from webin under the accession number PRJEB40193.

## RESULTS

### General Chemical and Microbiological Features of the Raw OWs, Digestates, and Composts

Principal component analyses were performed on the physicochemical datasets (organic carbon, total nitrogen, C:N ratio, pH, and phosphorus) and pollutants (iron and zinc concentrations, PAHs, NPs, and TE) measured on all samples (Supplementary Tables 1, 2 and Supplementary Figure 1). The first two axes of these PCAs explained a significant part of the variability observed among these datasets (>60%) (Supplementary Figure 1). The origin of the sample was found to explain the chemical dataset (Supplementary Figure 1). The urban samples (treated or not) were significantly differentiated from the agricultural and central (mixed) samples by a set of pollutants including several TE (Ag, Al, As, Cd, Ce, Co, Cr, Cu, Ga, K, Mo, Ni, P, Pb, and Ti), 4-nonylphenol (A2), phenanthrene (H5), anthracene (H6), and total nitrogen concentrations (KW-Dunn's post hoc tests; *p* < 0.05) (Supplementary Figure 2). Similarly, global indicators of the microbiological content of the samples were analyzed by qPCR using microbial source tracking DNA targets (Supplementary Table 4 and Figure 1). These qPCR analyses showed that 16S rRNA gene numbers varied from 4.26 × 10⁵ copies/g sample up to 6.41 × 10¹<fnref n="1" /> copies/g (Supplementary Table 4). Lowest 16S rRNA gene copy numbers/g sample were obtained for digestates. Monitorings of total Bacteroides cell numbers were correlated with the 16S rRNA gene dataset (Kendall's rank correlation tau; *p* < 0.01) but the range of values was lower (2.44 × 10² copies/g sample up to 1.12 × 10⁹ copies/g). The DNA indicators for pig and bovine fecal contaminations were most significant among central and agricultural raw OW (Figure 1 and Supplementary Table 4). These distributions were in agreement with the information given by the operators of the OW treatment sites. The human DNA fecal marker was most often detected among the raw OW but in significantly higher numbers (KW and Dunn's post hoc; *p* < 0.05) among the urban and central groups of samples (Supplementary Table 4 and Figure 1). Digestates and composts showed lower occurrences of these markers but

<footnote n="1">¹https://www.arb-silva.de/documentation/</footnote>
<figure><img src="image_74.png" /><figcaption>**FIGURE 1** | Boxplots showing the relative occurrences of two DNA markers indicative of human and pig fecal contaminations. Datasets are from **Supplementary Table 4**. Numbers of (A) HF183 and (B) pig-2-bac DNA copies per gram dry weight organic wastes, composts, or digestates normalized to 16S rRNA gene copy numbers were presented according to OW origins (agricultural, urban, and central). Boxplots show the minimum, first quartile (Q1), median, third quartile (Q3), and maximum relative abundances observed among the datasets. Outliers or unexpected values are indicated by dots. Samples used in these analyses are those of **Table 1**.</figcaption></figure>

a significant number of HF183 DNA was detected among the urban digestates (Figure 1).

## Bacterial Diversity of Raw OWs, Digestates, and Composts Inferred From V5 to V6 16S rRNA Gene Sequences

About 5 million DNA sequences were obtained for the samples investigated in this work, and 45% were validated as high-quality 16S rRNA gene sequences. The number of high-quality V5–V6 16S rRNA gene sequences per sample ranged from 26,465 to 119,793 (Supplementary Table 7). The number of reads per sample were negatively correlated to the 16S rRNA gene qPCR dataset (Spearman Rank Correlation; rho = -0.45, *p* < 0.05). This highlighted a variability in the dataset that required a normalization of the 16S rRNA gene dataset prior to performing statistical analyses. The global representation of the dataset and the computing of diversity indices were thus performed after a subsampling of the gene reads at the lowest value for the number of reads on a sample, i.e., 26,465 reads. This subsampled dataset led to a grouping of reads into 7755 OTUs, which varied in their distribution from 318 to 1404 per sample (Supplementary Table 5). Accordingly, Shannon diversity, Shannon-evenness, and the Simpson diversity indices were computed (Supplementary Table 7 and Supplementary Figure 3). Significant differences in these indices were observed between those computed for the urban digestates and those of the agricultural or central samples (KW test; *p* < 0.05). More diverse OTU contents and even distributions of the reads between OTUs were observed among the urban digestates than the other ones. However, the initial OTU contents of raw OW were found similar between sites, and diversity indices remained similar between raw OW and digestates of a same site (Supplementary Figure 3).

To better resolve differences in the genetic diversity among samples, Bray–Curtis dissimilarities between OTU profiles of

each pair of samples were computed. NMDS plots were then performed to visualize the differences (Figure 2A). All OTU profiles could be differentiated according to their site category (urban, central, or agricultural) (PERMANOVA *p* < 0.05). A significant effect on the profiles of the OW treatment by composting or AD could also be resolved (PERMANOVA *p* < 0.05). Digestates' OTU profiles could not be differentiated into three categories (PERMANOVA *p* > 0.05) but into two entities with (i) grouping the agricultural/mixed central samples and (ii) the urban ones (PERMANOVA *p* < 0.01) (Figure 2B). A further differentiation of OTU profiles according to post-treatments of the digestates was tested. The OTU patterns of liquid and solid digestates could not be differentiated (Supplementary Figure 4). Composting was found to drive bacterial community changes toward similar structures even though the nature of the OWs was variable (WWTP activated sludge, manure, green wastes, etc.) (PERMANOVA *p* < 0.05) (Figure 2A).

## Incidence of AD or Composting on Bacterial Taxa as Inferred From 16S rRNA Gene Taxonomic Allocations

The chemical and V5–V6 16S rRNA gene OTU pattern analyses indicated in the previous sections showed (i) a significant proximity between the agricultural and central samples (raw OW and digestates), (ii) a significant differentiation of the urban samples (raw OW and digestates) from the agricultural/central samples, and (iii) a significant differentiation of the compost samples from all the other samples. Accordingly, in the following analyses, the datasets were grouped into these latter three entities. Informative groupings of V5–V6 DNA reads matching significant changes (higher or lower) in relative abundances between these entities were analyzed.
<figure><img src="image_75.png" /><figcaption>**FIGURE 2** | Non-metric multidimensional scaling (NMDS) plot of Bray–Curtis dissimilarities computed from the 16S rRNA V5–V6 OTU profiles (subsampled, 97% identity) of the organic samples before (raw OW) and after treatment by anaerobic digestion or composting. (A) NMDS ordinations were built from the full dataset shown in **Table 1**; stress value was 0.1227. (B) NMDS ordinations were built from a dataset limited to raw OW and digestates. The ellipses indicate the variance observed within each group of samples. PERMAMOVA tests are indicated in the text and confirmed the significance (*p* < 0.05) of several groupings.</figcaption></figure>
Variations at the Scale of Phyla

Raw OWs showed a high variability in their OTU contents (Figure 2A). However, these OTUs can be grouped into larger taxonomic entities, which can be conserved between raw OWs, and contribute reproducibly to the buildup of the microbial communities among composts or digestates. These core taxa were found to be distributed among 6 major phyla: *Firmicutes* (31.2%), *Proteobacteria* (26.4%), *Bacteroidetes* (26.2%), *Actinobacteria* (6.1%), *Tenericutes* (4.4%), and *Spirochaetae* (0.7%) but discrepancies were observed in terms of proportion of reads per phylum according to the site category (urban, central, and agricultural) (Supplementary Table 8). Raw urban OW showed lower relative numbers (7.1%) of *Firmicutes* [converted into 2.05 × 10⁹ equivalent 16S rRNA gene copy numbers (16S) per gram of OW] than the raw OWs from the central and agricultural sites (29.9%; equivalent to 1.01 × 10¹⁰ 16S per gram) (Supplementary Table 9 and Figure 3). These urban OWs were found to have higher relative counts of *Proteobacteria* (46.6%;

equivalent to 3.97 × 10¹⁰ 16S per gram) and *Actinobacteria* (11.7%; equivalent to 6.51 × 10⁹ 16S per gram) than the central and agricultural samples (respectively, 29.3%; equivalent to 8.74 × 10⁹ 16S per gram and 2.9%; equivalent to 1.2 × 10⁹ 16S per gram) (Supplementary Table 9 and Figure 3). It is to be noted that 16S rRNA gene reads allocated to the *Nitrospirae*, *Chloroflexi*, and *Acidobacteria* represented more than 1% (equivalent to 7.41 × 10⁹ 16S rRNA gene copies per gram) of the urban OW dataset, but these phyla occurred at a much lower score (equivalent to 4.32 × 10⁶ 16S rRNA gene copies per gram) among the OW of the other sites (Supplementary Table 9 and Figure 3).

As observed among raw OW, *Firmicutes* and *Proteobacteria* were among the most abundant phyla in digestates and composts (Supplementary Table 9 and Figure 3). However, a decrease in the relative abundance of the *Proteobacteria* was observed in all treated samples (Supplementary Table 9 and Figure 3) and was supported by a significant statistical test for the urban

<figure><img src="image_76.png" /><figcaption>**FIGURE 3** | Relative averaged abundances of V5–V6 16S rRNA gene reads allocated to phyla before and after treatment of raw organic wastes (OWs) by anaerobic digestion or composting performed by treatment plants located in rural areas (agricultural/central sites) or urban ones. Only phyla with a relative abundance above 0.5% are displayed (see **Supplementary Table 9** for the complete dataset).</figcaption></figure>
samples (KW *p* ≤ 0.05). These changes in proportion were not
correlated to a significant reduction in bacterial cell numbers
(between 1.77 × 10⁹ and 3.19 × 10¹⁰ 16S rRNA gene copies
per gram of digestates or composts). The observed change
in relative abundance thus implies a significant re-shuffling
of bacterial phyla with some of these taking advantage of
the treatment conditions or becoming negligible. For example,
increases in the relative proportion of *Firmicutes* from the raw
OW to the digestates were observed among both categories
of samples (agricultural/central, and urban). *Firmicutes* showed
the highest occurrence among the central/agricultural digestates
(52.2% of the bacterial taxa). These relative changes matched
an averaged one log increase in *Firmicutes* 16S rRNA gene
copies between the raw OW and the digestates of the urban
sites or composts (**Supplementary Table 9** and **Figure 3**).
Similarly, relative increases in *Actinobacteria*, *Deferribacteres*,
and *Thermotogae* among composts matched significant increases
in 16S rRNA gene copies estimated from the qPCR datasets
(**Supplementary Table 9** and **Figure 3**; KW *p* ≤ 0.05). Similar
trends were also observed between raw OW and urban digestates
for the *Cloacimonetes*, which showed up to a five-log increase
in 16S rRNA gene copies per gram. Significant decreases in the
relative abundance of some phyla were also recorded such as
the raw OW *Fusobacteria*, which became negligible among the
digestates and composts (**Supplementary Table 9** and **Figure 3**;
KW *p* ≤ 0.05).

Variations at the Scale of Genera Among Digestates

To go deeper into the understanding of community changes,
variations of V5–V6 16S rRNA gene reads allocated at the
genus level were investigated. A DESEQ2 analysis was performed
to highlight these changes (Figures 4, 5 and Supplementary
Tables 10, 11). These analyses were performed with the non-
resampled sequenced gene reads dataset (Supplementary Table 6)
because a normalization scheme is directly run by DESEQ2. This
approach revealed that the digestates of the central/agricultural
category of samples led to a significant increase in the relative
proportion of six genera: *Gelria* (*Firmicutes*), *Sporosarcina*
(*Firmicutes*), *Dethiobacter* (*Firmicutes*), and three unclassified
groups belonging to the *Spirochaetales*, *Cloacimonetes*, and
*Firmicutes*. Four (*Gelria*, *Sporosarcina* and two others unclassified
at genus level) of these genera were part of the core bacterial
taxa found among digestates (Figure 4 and Supplementary
Table 10). Four of these were also found to be part of the
*Firmicutes* and confirmed the trends observed at the scale of
phyla, which indicated an increase in *Firmicutes*. These six
genera were two to five times (Log2 fold change) more abundant
in the digestates than the non-treated samples (raw OWs).
They represented from 0.1 to 6.1% (according to the mean
normalized counts) of the total digestate bacterial community
(Supplementary Table 10). Conversion of relative read numbers
into 16S rRNA gene copies from the subsampled contingency
table supported these trends and indicated an abundance of about
3.0 × 10⁷ *Gelria* 16S rRNA gene copies per gram of dry weight
in the raw OW while 5.0 × 10⁸ *Gelria* 16S rRNA gene copies
were estimated in average in the digestates (Supplementary
Table 9). Similarly, *Sporosarcina* and *Dethiobacter* 16S rRNA

gene copies per gram of dry weight indicated a gain of one
log between the raw OW and digestates. Regarding genera that
showed a relative decrease, 54 (of which 15 are parts of the core
bacterial taxa found among digestates) (Supplementary Table 8)
were subjected to a significant decrease after AD of OW of the
agricultural/central sites (Figure 4 and Supplementary Table 10).
This relative reduction varied from 2.6- to 9.9-fold when
compared to raw OW (Figure 4 and Supplementary Table 10).
*Lactobacillus* (4.8–0.1%), *Bacteroides* (4.1–0.4%), *Prevotella* 9
(1.8–0.0%), and *Methanobrevibacter* (0.5–0.1%) were found to
have undergone the most significant reductions (Figure 4 and
Supplementary Table 10). The decrease in bacterial numbers
was confirmed through a conversion of the dataset into 16S
rRNA gene copies. *Lactobacillus* 16S rRNA gene copies went
down by about two logs, and those of *Bacteroides* went down
by about one log (supported by Wilcoxon rank sum tests;
*p* < 0.05). Similar trends were observed for the other genera
(Supplementary Table 9).

Several bacterial genera of the urban OWs were shown
to be significantly enriched in the digestates. A total of 32
genera (of which 25 were part of the core microbiome)
showed a significant increase (Figure 5 and Supplementary
Table 11). These genera were enriched by three to sevenfold
while comparing with the concentrations observed among
OW. These genera represented from 0.03 to 3.6% of the
total digestate microbial community. The most significant
increases in the digestates were observed for V5–V6 16S
rRNA gene reads allocated to the *Smithella* (0.03–3.0%),
part of the *vadinBC27_wastewater-sludge_group* (0.1–2.1%),
*Candidatus_Caldatribacterium* (0.1–1.9%), *Gracilibacter* (0.0–
1.8%), *Proteiniphilum* (0.02–1.6%), *Sedimentibacter* (0.01–1.5%),
*Syntrophorhabdus* (0.01–1.0%), and *Petrimonas* genus (0.01–
0.5%). Conversion of these values into 16S rRNA gene copies
showed the *Smithella* to have undergone an increase from
6.15 × 10⁶ copies per gram of OW to 2.64 × 10⁹ copies
per gram of digestates, and the *Gracilibacter* 16S rRNA
gene copies went up from about 3.0 × 10⁶ to 1.61 × 10⁹
(Supplementary Table 9). Similar trends were observed for
the other above-cited genera (Supplementary Table 9) but
a significant number (n = 18) of genera also underwent a
relative reduction in V5–V6 gene reads from the OW to
the digestates (Figure 5 and Supplementary Table 11). These
decreases were about four- to ninefold in comparison to their
relative numbers among the raw OW samples. Hence, the
*Uliginosibacterium* (1.3–0.1%), *Simiduia* (0.3–0.0%), *Cellvibrio*
(0.1–0.01%), *Fonticella* (0.1–0.0%) *Polyangium* (0.04–0.0%), and
*Clostridium_sensu_stricto_12* (0.03–0.0%) genera showed such a
decrease (Figure 5 and Supplementary Table 11). These shifts
were confirmed while converting these datasets into 16S rRNA
gene copy numbers (Supplementary Table 9).

Global changes in the relative abundance of digestates'
bacterial genera significantly affected by ADs over the dataset
were investigated. DESEQ2 analyses revealed that 30.8 and
35.5% of the total relative abundance of the digestate genera
were significantly affected by the AD treatment, respectively,
at the urban and agricultural/central sites (Supplementary
Tables 10, 11). The relative abundances of 50 and 60
<figure><img src="image_77.png" /></figure>

**FIGURE 4** | Genera with significant changes in relative abundance after anaerobic digestion of raw organic waste samples from agricultural and central mixed origin. Genera with significant ($p_{adj} < 0.05$) decrease in digestates compared to raw OW samples (left part of the plot) are presented according to their initial relative abundance in raw OW. Genera with significant increase in digestates compared to raw OW samples (right part of the plot) are presented according to their relative abundance in digestate. Genera are colored according to the phylum to which they belong. Full mark represents genus present in the core microbiome; empty mark represents genus not present in the core microbiome (raw OW and digestate core microbiomes were, respectively, considered at the **left** and **right** part of the plot). Numbers in brackets represent the sum at the phylum level of relative abundances of all genera with significant change after AD calculated before and after AD (raw OW%/digestate%). Only genera with relative abundance $\ge$ 0.3% and significant change (-2 $\ge$ Log2 FC $\ge$ +2, $p_{adj} < 0.05$, minimum normalized read count = 10) are shown (see **Supplementary Table 10** for the other groups).

<figure><img src="image_78.png" /></figure>

**FIGURE 5** | Genera with significant change in relative abundance after anaerobic digestion of raw organic waste samples from urban origin. Genera with significant ($p_{adj} < 0.05$) decrease in digestates compared to raw OW samples (left part of the plot) are presented according to their initial relative abundance in raw OW. While genera with significant increase in digestates compared to raw OW samples (right part of the plot) are presented according to their relative abundance in digestate. Genera are colored according to the phylum to which they belong. Full mark represents genus present in the core microbiome while empty mark represents genus not present in the core microbiome (raw OW and digestate core microbiomes were, respectively, considered at the **left** and **right** part of the plot). Numbers in brackets represent the sum at the phylum level of relative abundances of all genera with significant change after AD calculated before and after AD (raw OW%/digestate%). Only genera with relative abundance $\ge$ 0.3% and significant change (-2 $\ge$ Log2 FC $\ge$ +2, $p_{adj} < 0.05$, minimum normalized read count = 10) are shown (see **Supplementary Table 11** for the other groups).

genera were significantly impacted by AD in, respectively, the urban and central/agricultural digestates. Despite this similar effect, a remarkable difference depending on OW origins was highlighted. In digestates of urban origin, this sorting mainly involved an enrichment of genera (32 out of the 50 impacted genera) while those produced from agricultural/central OW

led essentially to a reduction of several taxa (54 out of 60 impacted genera) (**Supplementary Tables 10, 11**). These comparisons suggested a full re-shuffling of agricultural/central OW bacterial taxa while those of the urban OW appeared to be well adapted for the living conditions prevailing in the anaerobic digester.
## Variations at the Scale of Genera Among Composts

Composting was found to drive microbial communities toward similar V5–V6 genetic structures whatever the origin of the OW including those that had undergone AD (Figure 2A). These conserved compost microbiomes appeared to have been selected by strong environmental constraints that led to an increase in the relative abundance of, among others, *Actinobacteria*, *Deferribacteres*, *Firmicutes*, and *Thermotogae*. These trends were confirmed while considering the numbers of 16S rRNA gene copies between OW and composts for these phyla. In fact, a significant one log increase was observed for the *Actinobacteria* and *Firmicutes* per gram of composts (Supplementary Table 9). Furthermore, the 16S rRNA gene copies allocated to the *Thermotogae* and *Deferribacteres* went from a negligible score up to several millions. Simultaneously, V5–V6 reads of six phyla (*Nitrospirae*, *Elusimicrobia*, *Acidobacteria*, *Saccharibacteria*, *TM6*, and *GOUTA4*) became negligible and were associated to a two-log reduction in 16S rRNA gene copies (Supplementary Table 9). These shifts among these phyla were confirmed at the scale of genera (Figure 6 and Supplementary Table 12). The compost microbiome was enriched in 36 genera of which only 6 were versatile and not found among all composts (Figure 6 and Supplementary Table 12). Relative distribution patterns of these genera changed by 4.1- to 8.1-Log2 fold when compared to the non-treated OW samples (going from 0.04 to 5.7% of the total compost microbial community). The most significant relative distribution increases were observed for reads allocated to the *Actinomadura*, *Longispora*, *Parapedobacter*, *Saccharomonospora*,

*Brachybacterium*, *Pedobacter*, *Thermobifida*, *Planifilum*, *Oceanobacillus*, *Olivibacter*, and *Paenalcaligenes*. These genera are part of the *Actinobacteria* (50%), *Bacteroidetes*, or *Firmicutes*. In most cases, about two log increases in 16S rRNA gene copy numbers were associated with these changes (all statistical tests were significant; Wilcoxon *p* < 0.05) (Supplementary Table 9). For example, the number of 16S rRNA copies for the *Olivibacter* went up from $2.0 \times 10^8$ to $1.0 \times 10^{10}$. Most impressive was the shift from negligible numbers of *Longispora* among the OW going up to about $2.0 \times 10^9$ 16S rRNA gene copies (Supplementary Table 9). These changes were associated with the relative decreases of V5–V6 16S rRNA gene reads allocated to 105 genera including 29 found in the raw OWs core microbiome (Figure 6 and Supplementary Table 12). This reduction went from 3.8- to 8.1-Log2 fold change compared to OW where these genera originally represented 0.02–4.7% of the total raw OW microbial community (Figure 6 and Supplementary Table 12). Reads from *Tetrasphaera* (1.7–0.04%), *Microthrix* (0.6–0.03%), *Intestinibacter_9* (0.4–0.04%), *Treponema_2* (0.4% to 0.0%), *Proteiniphilum* (0.4–0.04%), *Simplicispira* (0.3–0.0%), *Christensenellaceae_R7_group* (0.3–0.03%), *Lautropia* (0.2–0.0%), *Macellibacteroides* (0.2–0.01%), *BD17_clade* (0.1–0.0%), *Propionivibrio* (0.1–0.0%), *Proteocatella* (0.1–0.0%), and *Streptococcus* (0.1–0.0%) in the OW were the most significantly impacted (Figure 6 and Supplementary Table 12). These decreases among the community were confirmed by an analysis of the equivalent 16S rRNA gene copies for these genera in the OW and composts (Supplementary Table 9). DESEQ2 analyses

<figure><img src="image_79.png" /></figure>
indicated that a re-shuffling of 52.6% of the read distribution patterns occurred during composting of OW. Composting was thus highly selective with a decline of 28.3% of the total relative abundance of the compost's bacterial taxa and an enrichment of 24.4% of the other taxa in the composts (Supplementary Table 12).

## Incidence of OW Digestion Processes on Bacterial Functional Groups

Functional traits selected by AD and composting were inferred from the taxonomic allocations performed at the genus level. At first, the sum of relative abundances of enriched genera potentially covering a functional trait extracted from the FAPROTAX database using MACADAM was calculated (Supplementary Table 13). The contribution of each genus to differences in hypothetical functional profiles is based on relative abundances before and after treatment (Supplementary Table 13). Based on the most abundant taxa in digestates from urban origin, the five main hierarchical functional traits were chemoheterotrophy (6.3%), fermentation (6.2%), human and mammal gut colonizers (each feature at 2.1%), and animal parasites or symbionts (2.1%) (Supplementary Table 13D). Similarly, these functional traits were also associated with the most abundant taxa in digestates from agricultural and central origin (Supplementary Table 13F). In composts, the functional traits of the enriched genera (as suggested from the number of equivalent 16S rRNA gene copies) were related to aliphatic non-methane hydrocarbon degradation, arsenate detoxification, dissimilatory arsenate reduction, hydrocarbon degradation, nitrate and nitrite ammonification, nitrate and nitrite respiration, and oil bioremediation. These taxa had increased by at least 8.5 log after composting of raw OWs (Supplementary Table 13B).

Functional traits related to health hazards were also investigated based on changes in relative abundances of bacterial genera before and after OW treatment (Supplementary Table 13). AD and composting processes contributed significantly at reducing genera potentially harboring infectious agents. The number of 16S rRNA gene copies of genera potentially causing diseases in human, plants, or fishes decreased by 9.3, 7.4, and 7.9 log, respectively, after composting of OW (Supplementary Table 13A). Similar trends could be observed during AD for genera harboring potential human pathogens. The number of 16S rRNA gene copies of genera potentially associated with human pathogens went down by about 7.4 and 1.7 log, respectively, in digestates from urban and agricultural/central categories (Supplementary Tables 13C,E). Interestingly, the number of 16S rRNA gene copies of genera possibly including lung pathogens or plant pathogens went down by about 7.5 log in digestates from the agricultural/central category (Supplementary Table 13E).

The theoretical metabolic pathways of the genera identified in this study were further inferred from sequenced genomes using the MACADAM software. Metabolic pathways (with a PS ≥ 0.5) potentially present in genera significantly impacted by the investigated digestion processes were counted, and

their relative distribution patterns were compared (Table 2 and Supplementary Table 14). Distribution patterns between these functional categories observed among OWs from urban sites and from central/agricultural ones were found positively correlated (Spearman Rank Correlation; rho = 0.69, *p* < 0.05). Such correlations were not observed between the anaerobic and aerobic digestion processes (digestates and composts).

After AD treatments, biosynthesis pathways for cofactors, nucleotides, amino acids, lipids, and aminoacyl-tRNAs charging were more abundant in the enriched genera than in the decaying ones whatever the raw OW origin (Table 2 and Supplementary Table 14). Whereas biosynthesis pathways for secondary metabolites, cell structure, metabolic regulators, and alcohol and hormone biosynthesis were less abundant in the enriched genera than in the decaying ones (Table 2 and Supplementary Table 14). Similarly, degradation pathways for fatty acids and lipids, carbohydrates, secondary metabolites, aromatic compounds, and sulfur-containing compounds metabolism degradation were less abundant in the enriched genera than the decaying ones (Table 2 and Supplementary Table 14). Interestingly, in digestates from agricultural and central origin, nitrogen cycle-related processes (nitrate-reduction, ammonia-assimilation, ammonia-oxidation, denitrification, taurine-degradation, and nitrogen-fixation pathways) were more abundant in the enriched genera than the decaying ones, but the opposite situation occurred among digestates of urban origin (Table 2 and Supplementary Table 14). After composting treatment, excepting the biosynthesis pathways involved in cell structure and the aromatic compounds degradation pathways, the inferred pathways (n = 29) had a similar distribution among the impacted genera (Table 2 and Supplementary Table 14). Cell structure biosynthesis pathways were less abundant in the enriched genera after both types of treatment (AD and composting) than in the decaying ones. Aromatic compounds degradation pathways were more abundant in the enriched genera found among composts but not among those of digestates (Table 2 and Supplementary Table 14).

## DISCUSSION

OW recycling of sewage sludge and livestock effluents represent a major challenge because not only the bioenergy production but also environmental and sanitary issues must be considered. A key criterion in the assessment of the microbiological risks of OW recycling that remain poorly defined is the nature and structure of the various microbiomes getting into these processes and their potential health and ecological hazards. Here, the core and flexible components of the end product microbiomes were inferred, and the likelihood of these at being reproducibly found among highly diverse OW treatment plants was evaluated. Nine full-scale treatment plants distributed over France were selected to obtain a global view of microbiome bacterial taxa in the French OW treatment sector. These inferred bacterial taxa (from metabarcoding 16S rRNA gene datasets) are discussed below according to process type (AD, composting) and OW origins (fecal wastes from WWTP or agricultural sites, and food
products). Key functional traits of the bacterial genera impacted by the digestion processes were investigated to go further with the identification of key structuring bacterial processes.

According to NMDS ordinations of 16S rRNA gene metabarcoding datasets, two main bacterial community structures could be identified and found to match a segregation

<table><caption>**TABLE 2** | Metabolic pathways recorded among the bacterial genera significantly impacted by the anaerobic digestion or composting processes as observed by the MACADAM approach.</caption><thead><tr><th rowspan="3">Main hierarchical<br>groups of<br>pathways</th><th rowspan="3"></th><th colspan="9">Percentage of theoretical metabolic pathways<sup>a</sup> based on available sequenced<br>genomes of isolates correlated to significantly impacted genera<sup>b</sup> in</th></tr><tr><th colspan="3">Digestates from<br>urban origin</th><th colspan="3">Digestates from<br>agricultural/central<br>origin</th><th colspan="3">Compost</th></tr><tr><th>TMP-EG<br>(n = 252)</th><th>TMP-DG<br>(n = 645)</th><th>R</th><th>TMP-EG<br>(n = 321)</th><th>TMP-DG<br>(n = 746)</th><th>R</th><th>TMP-EG<br>(n = 753)</th><th>TMP-DG<br>(n = 800)</th><th>R</th></tr></thead><tbody><tr><td rowspan="11">Biosynthesis<br>pathways</td><td>Cofactor<sup>c</sup></td><td>23.0</td><td>15.0</td><td>1.5</td><td>21.8</td><td>11.7</td><td>1.9</td><td>13.8</td><td>13.5</td><td>1.0</td></tr><tr><td>Amino acid</td><td>13.9</td><td>8.7</td><td>1.6</td><td>12.8</td><td>6.4</td><td>2.0</td><td>8.6</td><td>8.6</td><td>1.0</td></tr><tr><td>Nucleotide</td><td>9.9</td><td>6.1</td><td>1.6</td><td>8.4</td><td>3.6</td><td>2.3</td><td>5.1</td><td>5.3</td><td>1.0</td></tr><tr><td>Lipid</td><td>7.5</td><td>4.3</td><td>1.7</td><td>4.7</td><td>4.0</td><td>1.2</td><td>4.3</td><td>4.5</td><td>1.0</td></tr><tr><td>Carbohydrates</td><td>4.8</td><td>5.1</td><td>0.9</td><td>2.2</td><td>4.8</td><td>0.5</td><td>4.5</td><td>5.4</td><td>0.8</td></tr><tr><td>Aminoacyl-tRNAs-charging</td><td>1.2</td><td>0.5</td><td>2.4</td><td>0.9</td><td>0.1</td><td>9.0</td><td>0.4</td><td>0.4</td><td>1.0</td></tr><tr><td>Secondary metabolite</td><td>1.2</td><td>2.2</td><td>0.5</td><td>0.9</td><td>3.0</td><td>0.3</td><td>2.7</td><td>2.4</td><td>1.1</td></tr><tr><td>Cell structure</td><td>0.8</td><td>1.6</td><td>0.5</td><td>0.6</td><td>1.3</td><td>0.5</td><td>1.1</td><td>1.8</td><td>0.6</td></tr><tr><td>Metabolic regulators</td><td>0.8</td><td>1.4</td><td>0.6</td><td>0.3</td><td>1.6</td><td>0.2</td><td>1.7</td><td>1.6</td><td>1.1</td></tr><tr><td>Aromatic compounds</td><td>0.8</td><td>1.1</td><td>0.7</td><td>1.3</td><td>1.1</td><td>1.2</td><td>1.3</td><td>1.1</td><td>1.2</td></tr><tr><td>Alcohol</td><td>0.4</td><td>1.7</td><td>0.2</td><td>0.6</td><td>1.3</td><td>0.5</td><td>1.2</td><td>1.3</td><td>0.9</td></tr><tr><td>Hormone</td><td>0.0</td><td>0.6</td><td>0</td><td>0.0</td><td>0.4</td><td>0</td><td>0.5</td><td>0.5</td><td>1.0</td></tr><tr><td rowspan="11">Degradation<br>pathways</td><td>Amino acid</td><td>6.4</td><td>7.4</td><td>0.9</td><td>8.1</td><td>8.3</td><td>0.9</td><td>7.6</td><td>7.8</td><td>1.0</td></tr><tr><td>Nucleotide</td><td>4.8</td><td>3.6</td><td>1.3</td><td>4.4</td><td>2.7</td><td>1.6</td><td>2.8</td><td>3.0</td><td>0.9</td></tr><tr><td>Fatty acid and lipid</td><td>0.0</td><td>1.4</td><td>0</td><td>0.6</td><td>1.2</td><td>0.5</td><td>1.2</td><td>1.4</td><td>0.9</td></tr><tr><td>Carbohydrates</td><td>4.8</td><td>7.1</td><td>0.7</td><td>2.5</td><td>7.6</td><td>0.3</td><td>5.4</td><td>6.4</td><td>0.8</td></tr><tr><td>Secondary metabolite</td><td>2.0</td><td>3.3</td><td>0.6</td><td>1.6</td><td>4.2</td><td>0.4</td><td>4.1</td><td>3.3</td><td>1.2</td></tr><tr><td>Aromatic compounds</td><td>0.0</td><td>2.0</td><td>0</td><td>0.9</td><td>7.4</td><td>0.1</td><td>7.7</td><td>5.3</td><td>1.5</td></tr><tr><td>Alcohol</td><td>1.2</td><td>1.7</td><td>0.7</td><td>1.3</td><td>1.3</td><td>1.0</td><td>1.3</td><td>1.5</td><td>0.9</td></tr><tr><td>Carboxylates</td><td>3.2</td><td>5.0</td><td>0.6</td><td>4.1</td><td>5.6</td><td>0.7</td><td>5.1</td><td>5.1</td><td>1.0</td></tr><tr><td>C1 compounds<sup>d</sup></td><td>2.0</td><td>2.3</td><td>0.9</td><td>2.8</td><td>2.7</td><td>1.0</td><td>2.1</td><td>2.4</td><td>0.9</td></tr><tr><td>Non-carbon<br>nutrients<br>metabolism</td><td>1.2</td><td>2.3</td><td>0.5</td><td>2.2</td><td>4.0</td><td>0.6</td><td>2.4</td><td>2.9</td><td>0.8</td></tr><tr><td>Phosphorus<br>compounds</td><td>0.4</td><td>0.8</td><td>0.5</td><td>1.3</td><td>1.2</td><td>1.1</td><td>1.2</td><td>1.0</td><td>1.2</td></tr><tr><td rowspan="5">Energy-<br>metabolism</td><td>Anaerobic respiration</td><td>1.6</td><td>1.9</td><td>0.8</td><td>1.9</td><td>3.2</td><td>0.6</td><td>2.8</td><td>2.9</td><td>1.0</td></tr><tr><td>Aerobic respiration</td><td>1.2</td><td>1.6</td><td>0.8</td><td>2.8</td><td>1.6</td><td>1.8</td><td>1.6</td><td>1.6</td><td>1.0</td></tr><tr><td>Fermentation</td><td>1.2</td><td>4.7</td><td>0.3</td><td>3.1</td><td>3.5</td><td>0.9</td><td>3.3</td><td>3.3</td><td>1.0</td></tr><tr><td>Hydrogen-production</td><td>1.2</td><td>0.8</td><td>1.5</td><td>0.6</td><td>0.7</td><td>0.9</td><td>0.7</td><td>0.6</td><td>1.2</td></tr><tr><td>Hydrogen-oxidation</td><td>0.4</td><td>0.5</td><td>0.8</td><td>0.3</td><td>0.4</td><td>0.8</td><td>0.4</td><td>0.4</td><td>1.0</td></tr><tr><td rowspan="3">Others</td><td>Nitrogen cycling processes<sup>e</sup></td><td>1.6</td><td>3.1</td><td>0.5</td><td>4.1</td><td>2.7</td><td>1.5</td><td>2.8</td><td>2.8</td><td>1.0</td></tr><tr><td>Detoxification<sup>f</sup></td><td>1.2</td><td>1.9</td><td>0.6</td><td>2.2</td><td>2.0</td><td>1.1</td><td>1.9</td><td>1.9</td><td>1.0</td></tr><tr><td>Nucleic acid processing</td><td>1.6</td><td>0.6</td><td>2.7</td><td>0.9</td><td>0.3</td><td>3.0</td><td>0.5</td><td>0.5</td><td>1.0</td></tr></tbody></table>

<footnote><sup>a</sup>A minimum pathway score (PS) and pathway frequency score (PSF) of 0.5 was used for hypothetical metabolic pathways filtering.</footnote>

<footnote><sup>b</sup>To see the list of organisms with the targeted metabolic pathway, see **Supplementary Tables 14A–F**. <sup>c</sup>Cofactor biosynthesis are cobyrate-diamide biosynthesis, vitamin biosynthesis, CoA biosynthesis, reductants, NAD metabolism, porphyrin compounds biosynthesis, quinone biosynthesis, polyprenyl biosynthesis, molybdenum cofactor, lipoate biosynthesis, and tetrapyrrole biosynthesis. <sup>d</sup>C1 compounds are CO₂ fixation, formaldehyde oxidation, methanol oxidation, formaldehyde assimilation, and methane oxidation. <sup>e</sup>Nitrogen cycling processes consist of nitrate reduction, ammonia assimilation, ammonia oxidation, denitrification, taurine degradation, and nitrogen fixation. <sup>f</sup>Detoxification involves acid resistance, arsenate detoxification, methylglyoxal detoxification, antibiotic resistance, and reactive oxygen species degradation. Abbreviations: TMP-EG, theoretical metabolic pathways in the enriched genera after treatments; TMP-DG, theoretical metabolic pathways in the decaying genera; R, relative proportion of pathways per category between the enriched genera and the decaying ones.</footnote>
between bacterial taxa from OW containing agricultural wastes or not (restricted to urban WWTP activated sludge). This segregation was also in line with a differentiation between their contents in micropollutants particularly the TEs. The urban samples (treated or not) had significantly more micropollutants including 14 TEs (Ag, Al, As, Cd, Ce, Co, Cr, Cu, Ga, Mo, Ni, P, Pb, and Ti), 4-nonylphenol, and 2 PAHs (phenanthrene and anthracene) than the agricultural and mixed (agricultural/urban; termed "central" in the text) samples. The 16S rRNA gene microbial community structures of the investigated OW urban samples were in agreement with previous studies (Juretschko et al., 2002; Wong et al., 2005; Hu et al., 2012; Díaz et al., 2018). Inferred 16S rRNA gene taxonomic allocations were predominantly among the *Proteobacteria*, *Bacteroidetes*, *Actinobacteria*, *Firmicutes*, and *Nitrospirae* phyla. *Proteobacteria* were reported to be recurrent among WWTP activated sludge and have been shown to represent almost 50% of the bacterial taxa (Juretschko et al., 2002; Wong et al., 2005; Hu et al., 2012). *Actinobacteria* were also previously mentioned as highly abundant organisms in such wastes and being favored by the high phosphorus content of activated sludge (Wagner et al., 1994). *Nitrospirae* known to be nitrite-oxidizing bacteria were previously considered as common in activated sludge but were not mentioned as being abundant core organisms (Saunders et al., 2016).

After AD of both categories of raw OWs (urban and central/agricultural categories), 16S rRNA gene inferred phyla with similar relative changes were observed. These shifts occurred whatever the raw OW origin. Both categories of digestates (urban and central/agricultural categories) were impacted by selective environmental changes, which led to a decrease in the relative abundance of *Proteobacteria* but an enrichment in *Firmicutes* (which nearly doubled in relative abundance). *Firmicutes* had been reported by several authors as one of the most abundant phyla in AD processes of activated sludge (Hu et al., 2012; Zhang et al., 2012; Abendroth et al., 2015; Díaz et al., 2018). Sundberg et al. (2013) also found that *Firmicutes* were the most prevalent in samples taken from reactors fed with various combinations of wastes (from slaughterhouses, restaurants, households, etc.). In this work, with 52.2% of V5–V6 rRNA gene reads, *Firmicutes* were dominant in the digestates from central (mixed)/agricultural sites. Furthermore, they were enriched by 2.6-fold in digestates from urban sites to reach an 18.4% relative abundance. These results seem to indicate that the AD conditions favored their growth whatever the OW origins. To go deeper into the understanding of these community changes, the impact of AD on the structuration of end product microbiomes was assessed by grouping 16S rRNA gene reads according to their allocations at the genus level, and making comparisons by DESeq2. These analyses revealed that about one third of the total relative abundance (according to the mean normalized counts) of the digestate bacterial taxa were significantly affected by the AD treatment whatever the raw OW origin. The relative abundances of 50 and 60 genera were significantly impacted by AD as observed from, respectively, urban and central/agricultural digestates. However, differences in the shifts of the 16S rRNA gene reads of these taxa were found

related to the origin of the OW. In digestates of urban origin, this sorting mainly implied an increase in the relative abundance of OW urban taxa while those generated from agricultural/central OW led essentially to a reduction in the relative abundance of most of the initial OW taxa. This indicates that the coalescence of bacterial taxa of raw OW with the microbial community of the digesters will be largely dependent on their origin (and nature). In fact, urban OWs (mainly composed of activated sludge) were found to contain better fit communities for the AD process that could interact with the digesters' microbial communities. Bacterial taxa present in agricultural raw OW were outcompeted by the digesters' communities. This could be explained by the pre-treatment conditions performed in municipal wastewater facilities, leading to an increase of specialized bacterial taxa in the activated sludge. These taxa can perform multiple biodegradation processes (Shchegolkova et al., 2016) and can form heterogeneous structures termed flocs (Ju and Zhang, 2015). Functional inferences through FAPROTAX (Louca et al., 2016) were undertaken to highlight their potential contributions to the AD process.

The functional traits of the dominant microbial taxa inferred from the 16S rRNA gene datasets in the AD end products were investigated. Based on FAPROTAX analyses, functional traits such as chemoheterotrophy and fermentation were highly common among the taxa found in digestates of the urban and agricultural/central categories. Chemoheterotrophic bacteria are usually acting as primary decomposers (hydrolysis step), which are responsible for OW recycling in diverse ecosystems (Kämpfer et al., 1993). Fermentation, a widespread anaerobic pathway, is an essential step in AD process where fermentative bacteria convert simple monomers such as simple sugar, amino acids, and alcohols into short-chain fatty acids, primary alcohols, as well as H₂ and CO₂ (Cardinali-Rezende et al., 2009; Bajpai, 2017; Anukam et al., 2019). Interestingly, these functions (chemoheterotrophy and fermentation) were also previously reported by Wei et al. (2018) as the most dominant functions in activated sludge. This elucidates why bacterial genera from activated sludge could directly contribute to the observed diversity in the digestates. This observation is in line with the hypothesis of a selection of the most fit K-strategists (specialists) with ecologically relevant functions during the AD process. Also, these results confirmed that the changes in the AD community composition were driven by functional traits and less constrained by taxonomic relatedness.

To corroborate the above conclusion, closer analyses of the metabolic potential of bacterial communities were performed. Pathways inferred to be present in the enriched genera after treatment against those of the decreased ones were compared according to the origin/natures of the OWs. Interestingly, AD treatment was confirmed to impact similarly numerous metabolic pathways in both categories of OWs (urban and central/agricultural categories). Ten biosynthesis pathways (10/12) involved in the production of secondary metabolites, cell structure, metabolic regulators, biosynthesis, etc., and nine degradation pathways (9/11) (e.g., aromatic compounds and sulfur-containing compounds degradation) were affected similarly in both digestates from urban and agricultural/central categories. Nevertheless, some categories of pathways were
not impacted in the same way according to the OW origin. For example, in digestates from agricultural and central origin, nitrogen cycling processes (e.g., nitrate-reduction, ammonia-assimilation, denitrification, and nitrogen-fixation pathways) were more abundant in the enriched genera than in the decaying ones, but the opposite situation was observed for digestates of urban origin. These pathways (related to nitrogen cycle) have been reported by Wei et al. (2018) as central key functions in activated sludge. Thus, these pathways could be critical in the ecology of activated sludge but less important during AD.

The impact of composting per OW type on the bacterial communities was also investigated. After composting, a NMDS plot grouped together the 16S rRNA community profiles whatever the OW origin. Composting was found to drive bacterial communities toward similar genetic structures in all samples without differentiating OW inputs that had previously undergone AD. This conserved microbiome appeared to have been selected by highly selective constraints leading to a significant decrease in the relative abundance of *Bacteroidetes*, *Chlorobi*, *Chloroflexi*, *Lentisphaerae*, *Planctomycetes*, *Proteobacteria*, *Verrucomicrobia*, and *Spirochaetae* and the loss of six phyla (*Nitrospirae*, *Elusimicrobia*, *Acidobacteria*, *Saccharibacteria*, *TM6*, and *GOUTA4*) from the inputs OW. Other studies reported similar findings (Steger et al., 2007a; Song et al., 2014). In a compost pile, changes in physicochemical factors such as pH, temperature, moisture, and organic carbon were found to be correlated with bacterial growth. Microbial community structures were found to be affected by these evolving parameters (Cahyani et al., 2003; Liang et al., 2003; Tang et al., 2007; Zhang et al., 2011). Among these, temperature was shown to be highly selective (Palmisano and Barlaz, 1996; Steger et al., 2007a). Interestingly, with their large shift in relative abundance (4.3-fold) observed between OW and composts, *Actinobacteria* showed a major re-shuffling with an up to 20% increase. These are clearly key players in the composting process.

To go deeper into the understanding of these effects, changes in the composition of the compost bacterial taxa were investigated at genus level. A total of 141 genera were found to be affected by composting, with 105 genera showing a significant decrease (equivalent to 53.7% of the total relative abundance of the compost's bacterial taxa). Only three of these decaying genera were allocated to the *Actinobacteria*. However, 16 of the 36 enriched genera were part of this phylum. Previous studies have reported the persistence of *Actinobacteria* over the composting process from the thermophilic to the curing stage, regardless of the nature of treated OW (Danon et al., 2008). This bacterial group also constituted a significant part of the microbial community in the late composting stages (Herrmann and Shann, 1997; Tiquia et al., 2002; Hiraishi et al., 2003; Steger et al., 2007b). *Actinobacteria* are Gram-positive bacteria with high G + C contents in DNA, widely distributed in a diversity of ecological environments, including extreme ecosystem (Qin et al., 2011, 2019; Dhakal et al., 2017; Goodfellow et al., 2018). They can perform several important functions. Some *Actinobacteria* can synthesize a wide diversity of secondary

metabolites, which may promote survival in highly selective environments (Fang et al., 2017; Puttaswamygowda et al., 2019; Qin et al., 2019). Several studies have highlighted the importance of temperature as a selective factor of thermo-tolerant *Actinobacteria*. In fact, *Saccharomonospora viridis*, *Thermobifida fusca*, and *Thermobispora bispora* have been reported in the later composting stages. In our study, many genera of thermo-tolerant *Actinobacteria*, e.g., *Thermobifida*, *Actinomadura*, *Saccharomonospora*, *Arthrobacter*, *Thermomonospora*, and *Thermocrispum* were enriched in compost irrespective of the origin of the treated OW. *Actinobacteria* are capable of breaking down a wide range of animal wastes and plant debris particularly complex compounds (e.g., lignocelluloses), playing an important role in the later stages of composting (Herrmann and Shann, 1997; Wang et al., 2016). It is to be noted that thanks to the occurrences of bacterial genera degrading xenobiotics, composting offers the advantage of decreasing the concentration of chemical contaminants (Semple et al., 2001; Ghaly., 2011; Cascant et al., 2016; Ren et al., 2018). In line with these reports, genera of xenobiotic degrading *Actinobacteria*, e.g., *Arthrobacter*, were found to be enriched in the 16S rRNA gene datasets generated from composts DNA. To go deeper, closer analyses of functional potentials per taxa enriched after composting were performed. The functional profiles of the enriched genera suggested that the number of 16S rRNA copies of genera, e.g., *Arthrobacter*, *Alcanivorax*, *Rhodococcus*, *Brevibacterium*, etc., known to consist of many strains having functional traits involved in arsenate detoxification, dissimilatory arsenate reduction, plastic degradation, and oil bioremediation have significantly increased through composting. This further supports the idea that composting is an efficient bioremediation process.

## CONCLUSION

In conclusion, AD and composting induced bacterial changes that were found driven by pH, temperature, but also the C- and energy sources which require specialized metabolic properties. These factors were more selective in composting than AD. Consequently, composting generated highly predictable community patterns whatever the OW origin. In AD, the relative contribution of the raw OW microbiomes on the digestate structural and functional bacterial community patterns was dependent on its origin. Bacterial genera from activated sludge were found to be well-suited for the AD process and contributed directly to the observed diversity observed in the digestates. Content in activated sludge among the OW was thus considered to be a critical factor for predicting the digestate bacterial contents. Our findings provided new insights into our understanding of bacterial coalescence phenomena in aerobic and anaerobic digesters. However, knowledge gap remains regarding potential health hazards associated with the emerging communities observed in the end products. These organic matter biodegradation processes being built from a large panel of exogenous communities, they might offer conditions
favorable for horizontal gene transfer (HGT) events such as the HGT of antibiotic resistance genes (ARG). The next step in these analyses will be to evaluate the frequency of these HGT.

## DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: European Nucleotide Archive (https://www.ebi.ac.uk/research) under the accession number PRJEB40193.

## AUTHOR CONTRIBUTIONS

AA, EB, LM, BC, and WG conceived and designed experiments. AA, EB, LM, SH, DP, ED, BC, and WG performed the experiments. AA, EB, BC, and WG performed the data curation and statistical analyses. AA, BC, and WG wrote the manuscript. All authors read and approved the final manuscript.

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## FUNDING

This study was partly funded by the ANR (Agence Nationale de la Recherche, France) DIGESTATE project ANR-15-CE340003-01, Bacteri'eau project ANR-16-CE04-0014-01, and the "Auvergne-Rhône-Alpes" Region (France) "pack ambition recherche" project ITAS-MZ2M.

## ACKNOWLEDGMENTS

We wish to thank J. P. Delgenes, N. Sertillanges, D. Montenach, A. Savoie (INRAe), M. Monteïls, and C. Chevassut-Rosset (CIRAD) who provided us the samples analyzed in this project.

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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Aigle, Bourgeois, Marjolet, Houot, Patureau, Doelsch, Cournoyer and Galia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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# Biofilm Formation, Production of Matrix Compounds and Biosorption of Copper, Nickel and Lead by Different Bacterial Strains

Md. Manjurul Haque<fnref n="1" />*, Md Khaled Mosharaf<fnref n="1" />, Md. Amdadul Haque², Md. Zahid Hasan Tanvir<fnref n="1" /> and Md. Khairul Alam³

## OPEN ACCESS

**Edited by:**
Mechthild Schmitt-Jansen,
Helmholtz Centre for Environmental
Research (UFZ), Germany

**Reviewed by:**
Zhengbo Yue,
Hefei University of Technology, China
Eldon R. Rene,
IHE Delft Institute for Water
Education, Netherlands

*Correspondence:
Md. Manjurul Haque
haque_bes@bsmrau.edu.bd

**Specialty section:**
This article was submitted to
Microbiotechnology,
a section of the journal
Frontiers in Microbiology

**Received:** 08 October 2020
**Accepted:** 06 May 2021
**Published:** 10 June 2021

**Citation:**
Haque MM, Mosharaf MK, Haque MA, Tanvir MZH and Alam MK (2021) Biofilm Formation, Production of Matrix Compounds and Biosorption of Copper, Nickel and Lead by Different Bacterial Strains. *Front. Microbiol.* 12:615113.
doi: 10.3389/fmicb.2021.615113

Bacterial biofilms play a key role in metal biosorption from wastewater. Recently, *Enterobacter asburiae* ENSD102, *Enterobacter ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *Acinetobacter lwoffii* ENSG302, and *Bacillus thuringiensis* ENSW401 were shown to form air-liquid (AL) and solid-air-liquid (SAL) biofilms in a static condition at 28 and 37°C, respectively. However, how environmental and nutritional conditions affect biofilm formation; production of curli and cellulose; and biosorption of copper (Cu), nickel (Ni), and lead (Pb) by these bacteria have not been studied yet. In this study, *E. asburiae* ENSD102, *E. ludwigii* ENSH201, and *B. thuringiensis* ENSW401 developed the SAL biofilms at pH 8, while *E. asburiae* ENSD102 and *Vitreoscilla* sp. ENSG301 constructed the SAL biofilms at pH 4. However, all these strains produced AL biofilms at pH 7. In high osmolarity and 1/2-strength media, all these bacteria built fragile AL biofilms, while none of these strains generated the biofilms in anaerobic conditions. Congo red binding results showed that both environmental cues and bacterial strains played a vital role in curli and cellulose production. Calcofluor binding and spectrophotometric results revealed that all these bacterial strains produced significantly lesser amounts of cellulose at 37°C, pH 8, and in high osmotic conditions as compared to the regular media, at 28°C, and pH 7. Metal biosorption was drastically reduced in these bacteria at 37°C than at 28°C. Only *Vitreoscilla* sp. ENSG301 and *B. thuringiensis* ENSW401 completely removed (100%) Cu and Ni at an initial concentration of 12.5 mg I⁻<fnref n="1" />, while all these bacteria totally removed (100%) Pb at concentrations of 12.5 and 25 mg I⁻<fnref n="1" /> at pH 7 and 28°C. At an initial concentration of 100 mg I⁻<fnref n="1" />, the removal of Cu (92.5 to 97.8%) and Pb (89.3 to 98.3%) was the highest at pH 6, while it was higher (84.7 to 93.9%) for Ni at pH 7. Fourier transform infrared spectroscopy results showed metal-unloaded biomass biofilms contained amino, hydroxyl, carboxyl, carbonyl, and phosphate groups. The

<footnote n="1">¹ Department of Environmental Science, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh, ² Department of Agro-Processing, Faculty of Agriculture, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh, ³ Soil Science Division, Bangladesh Agricultural Research Institute, Gazipur, Bangladesh</footnote>
peak positions of these groups were shifted responding to Cu, Ni, and Pb, suggesting biosorption of metals. Thus, these bacterial strains could be utilized to remove Cu, Ni, and Pb from aquatic environment.

Keywords: biofilm, curli, cellulose, heavy metal, removal, pH, temperature, metal concentration

INTRODUCTION

Metals at molecular densities greater than 5 g/cm³ are known as heavy metals (Weast, 1984). Heavy metal release from various industries [such as steel, leather, electroplating, mine tailings, paints, wastewater treatment plants, and agricultural operations (fertilizers, pesticides, and irrigations)] is one of the major causes of environmental pollution. Some heavy metals like copper (Cu), zinc (Zn), iron (Fe), cobalt (Co), chromium (Cr), and nickel (Ni) are required for growth and metabolism of organisms when they are present in trace amount, known as trace elements or micronutrients. However, they become toxic when the concentration increases. Conversely, non-essential heavy metals including lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As) are toxic even at very low concentrations. Accumulation of such heavy metals in soils and water bodies poses threat to human health (including potential carcinogenicity), other living organisms, and eventually overall biodiversity (Naser et al., 2014; Ahmed et al., 2016; Alam et al., 2017; Burakov et al., 2018). Various physicochemical technologies such as reverse osmosis, filtration, electro-dialysis, flocculation, ion exchange, activated carbon, and chemical precipitation are being practiced to remove the heavy metals from aqueous systems. However, all these methods have some disadvantages like being expensive, having high energy and reagent requirements, not being appropriate for the removal of low concentrations (1–50 mg l⁻¹) of heavy metals, releasing of chemical sludge, and being less practical under natural environmental conditions (Ahluwalia and Goyal, 2007; Pan et al., 2009; Edwards and Kjellerup, 2013; Redha, 2020). Biosorption is one of the bioremediation technologies and uses fungi, bacteria, algae, and plants to sequester heavy metals (Nies, 1999). In the biosorption process, microbes adsorb metals on the cellular surface through attachment/linkage onto many anionic functional groups (Lo et al., 2014). Indeed, biosorption technique offers several benefits over the physicochemical methods in terms of economic aspects, high metal-binding capacity, eco-friendliness, and regeneration of biosorbents with the possibility of the recovery of metals (Kratochvil and Volesky, 1998; Sag and Kutsal, 2001; He and Chen, 2014; Santhosh et al., 2016). Compared to fungi, algae, and plants, bacterial strains were found effective to remove heavy metals from aquatic environment (Oves et al., 2013; Banerjee et al., 2015; Wei et al., 2016; Yang et al., 2017; El-Naggar et al., 2018). Though free-living bacterial cells have a greater capacity for metal removal from aquatic environment (Malik, 2004; Zeng et al., 2009), their survival is less likely due to decreased protection and the low metabolic activity (von Canstein et al., 2002). Hence, it is urgently needed to find out effective indigenous bacterial biosorbents that can survive

even under toxic environmental conditions along with diverse metabolic states.

Bacterial biofilms are highly structured, surface-associated cells, enclosed in a matrix of self-produced extracellular polymeric substances (EPS) (Costerton et al., 1999). Compared to free-living planktonic counterparts, bacterial biofilms provide numerous benefits, including protection of cells from adverse environmental stresses (e.g., toxic chemicals, pH change, dehydration, and predation), the ability to communicate by expression of quorum-sensing molecules, exchange of genetic material (e.g., horizontal gene transfer) and nutrient availability from waste products, and persistence in different metabolic functions with respect to electron acceptor reduction (Teitzel and Parsek, 2003; Boles et al., 2004; Vu et al., 2009; Haque et al., 2012, 2017; Edwards and Kjellerup, 2013). The bacterial EPS is composed of different high-molecular-weight biopolymers including proteins, cellulose-rich polysaccharides, nucleic acids, and lipids (Flemming and Wingender, 2010; Mosharaf et al., 2018). Bacterial surface appendages including the flagella, pili, and aggregative fimbriae/curli were also reported to stabilize the biofilm matrices (Flemming et al., 2007). Several researchers have shown that negatively charged functional groups/ligands of EPS serve as a trap for heavy metal ions (Sutherland, 1984; Deng et al., 2007; Wei et al., 2016). Enzymatic activities in EPS play a key role in detoxification of heavy metals by transformation and subsequent precipitation in the polymeric mass (Pal and Paul, 2008). Both living and dead biomass biofilms can be applied to remove heavy metals from the wastewater. Among them, living biofilms were found effective to remove heavy metals from both in the continuous treatment effluents (Gadd, 2009) and in the real industrial and municipal effluents (Kotrba et al., 2011). Both biosorption and bioaccumulation simultaneously take place in living bacterial biofilms. Nevertheless, several cellular mechanisms including synthesis of specific enzymes and action of cytoplasmic or membrane proteins were shown to express in the living bacterial biofilms (Kumar et al., 2007). Therefore, instead of free-living planktonic bacteria/dead bacterial biofilms, growing (living) bacterial biofilms have been appreciated for several bacterial-dominated processes and recommended for the removal of heavy metals from the environment (Singh et al., 2006; Pal and Paul, 2008; Edwards and Kjellerup, 2013; Meliani and Bensoltane, 2016; Balan et al., 2020).

*Enterobacter asburiae* ENSD102, *Enterobacter ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, and *Acinetobacter lwoffii* are Gram-negative bacteria that are positive to catalase and acetoin tests but are negative to gelatin liquefaction, methyl red, and indole tests. Some strains of *E. asburiae* degraded polyethylene plastic (Sato et al., 2016) and augmented crop growth (Dolkar et al., 2018; Mahdi et al., 2020). *Vitreoscilla* sp. was reported to synthesize hemoglobin used
in metabolic engineering (Wakabayashi et al., 1986). On the other hand, *Bacillus thuringiensis* is a Gram-positive spore-forming bacterium and well known for the production of insecticidal crystalline (Cry) proteins. Recently, all these bacterial strains were isolated from the wastewater of Bangladesh and reported to form biofilms by expression of curli (a proteinaceous component of the EPS) and nanocellulose fibers (Mosharaf et al., 2018). Important environmental applications of these bacterial strains are summarized in Table 1.

Several studies have shown that environmental conditions affect biofilm formation (Rinaudi et al., 2006; Liang et al., 2010; Haque et al., 2015; Ross et al., 2018) and the expression of curli and cellulose in different bacterial strains (Gerstel and Römling, 2003; Barnhart and Chapman, 2006). Initial metal concentration, temperature, pH, and contact time were shown to affect the biosorption of heavy metals (Oves et al., 2013; Yang et al., 2017; El-Naggar et al., 2018; Redha, 2020). Among the factors, pH plays a key role in the metal speciation, metal solubility, and dissociation of functional groups present in the bacterial cell wall (Esposito et al., 2002). Metal ions in solution undergo hydrolysis as the pH increases. However, the extent of hydrolysis at different pH values differs with each metal, but the usual sequence of hydrolysis is the formation of hydroxylated monomeric species followed by the formation of polymeric species and then the formation of crystalline oxide precipitates after aging (Bacs and Mesmer, 1976). Therefore, adsorption of metals on interfaces is highly pH-dependent. For example, Cu can be present in solution as three different species: Cu²⁺, CuOH⁺, and Cu(OH)₂. Cu²⁺ and CuOH⁺ are more favorable Cu species under lower pH conditions (Yang et al., 2017). Cu, Ni, and Pb are frequently found in industrial wastewater, rivers, sediments, fish, and vegetables in Bangladesh (Naser et al., 2014; Ahmed et al., 2016; Mosharaf et al., 2018; Uddin and Jeong, 2021). Concentrations of these metals were also reported to increase day by day in the environment of Bangladesh (Uddin and Jeong, 2021). Thus, it is urgently needed to study the biosorption of Cu, Ni, and Pb from the environment. How environmental factors affect biofilm formation, the expression of biofilm matrix components (e.g., curli and cellulose), and the biosorption of Cu, Ni, and Pb has never been investigated in *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401. Therefore, it is aimed to quantify the effects of different environmental cues such as temperature, pH, osmolarity, oxygen tension, and nutritional strength on biofilm formation and production of curli and cellulose in these bacterial strains. It is also intended to evaluate these bacterial strains for their efficacies to remove Cu, Ni, and Pb from aqueous solutions in response to initial metal concentration, temperature, and pH. Furthermore, it is aimed to identify the chemical functional groups/ligands present in both metal-unloaded and metal-loaded biomass biofilms produced by these bacterial strains using Fourier transform infrared (FTIR) spectroscopy. The study will contribute toward understanding the mechanisms and potential of these bacterial strains in biosorption of heavy metals from aquatic environment.

# MATERIALS AND METHODS

## Chemicals

Heavy metals {copper sulfate (CuSO₄·5H₂O), nickel sulfate (NiSO₄·6H₂O), and lead nitrate [Pb(NO₃)₂]}), Congo red, and Calcofluor were obtained from Sigma-Aldrich (St. Louis, MO, United States). All other chemicals used were of an analytical grade and were purchased from Wako Pure Chemical Industries, Ltd. (Osaka, Japan), Bio Basic Canada Inc. (Markham, ON, Canada), and HiMedia (Mumbai, India).

## Bacterial Strains and Growth Conditions

*Enterobacter asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 used in this study were recently isolated from wastewaters of Bangladesh and reported to form air-liquid (AL) and solid-air-liquid (SAL) biofilms on salt-optimized broth plus glycerol [((SOBG, pH 7.0) (per liter, 20 g tryptone, 5 g yeast extract, 0.5 g NaCl, 2.4 g MgSO4, 0.186 g KCl, and 2% glycerol] in a static condition at 28 and 37°C, respectively (Mosharaf et al., 2018). In this study, all these bacterial strains were found non-pathogenic to human and animals based on hemolytic test performed using 5% sheep blood (data not shown). These strains were routinely grown in yeast extract peptone (YP) medium (pH 7) at 28°C. An absorption spectrophotometer (Intertech, Inc., Tokyo, Japan) was used to measure the bacterial optical density (OD) at 660 nm.

## Preparation of Inoculum for Different Experiments

In order to prepare inoculum for different studies, a single colony of each bacterium was inoculated in YP broth and incubated at 28°C in a shaking (160 rpm) condition until OD₆₆₀ reached at 0.6–0.8. Then, each bacterial culture was harvested and centrifuged at 10,000 rpm for 5 min. The supernatant was carefully discarded and the pellet was re-suspended in YP broth and used for the experiments.

## Role of Environmental Factors on Biofilm Formation and Expression of Curli and Cellulose

To quantify the effect of pH, pH of the SOBG was adjusted to pH 4, 7, and 8 by adding malic acid or NaOH and autoclaved. For biofilm formation, a 50-µl culture [ca. 10⁵ colony forming unit (CFU) ml⁻¹] of each bacterium was suspended in the glass test tubes (Pyrex, flat bottom) containing 5 ml SOBG at pH 4, 7, or 8. For high osmolarity, SOBG (pH 7) was supplemented with 0.3 M NaCl or 0.3 M D-sorbitol. To examine the role of oxygen tension on biofilm formation, a 50-µl culture of each strain was inoculated in the glass test tubes containing 5 ml SOBG broth and then sealed with paraffin oil (1.5 ml) or without sealing with paraffin oil. The inoculated test tubes were incubated in an incubator or in an anaerobic chamber (Thermo Fisher Scientific, Inc., Portsmouth, NH, United States) at 28°C in a static condition. To study the effect of nutritional strength on biofilm formation, ¹/₂-strength SOBG (pH 7) was used instead of
<table><caption>TABLE 1 | Environmental applications of bacterial strains used in this study.</caption><thead><tr><td>Bacteria</td><td>Important environmental applications</td><td>References</td></tr></thead><tbody><tr><td><i>E. asburiae</i></td><td>Degradation of polythene, detoxification of carcinogenic dyes, removal of heavy metals, solubilization of nutrients, fixation of nitrogen, crop growth promoter, utilization as biofertilizer, and reduction of metal toxicity in crop plants.</td><td>Jetiyanon, 2015; Kang et al., 2015; Paul and Mukherjee, 2016; Ren et al., 2019; Mahdi et al., 2020; Haque et al., 2021a</td></tr><tr><td><i>E. ludwigii</i></td><td>Degradation of dyes, biosorption of heavy metals, functions as nematicides, solubilization of nutrients, reduction of cadmium stress in plants, plant growth enhancer, and prevention of drought and salinity stress in crop plants.</td><td>Shoebitz et al., 2009; Gontia–Mishra et al., 2016; Radwan et al., 2017; Adhikari et al., 2020; Haque et al., 2021b</td></tr><tr><td><i>Vitreoscilla</i> sp.</td><td>Decolorization, degradation, and detoxification of textile dyes; hemoglobin technology used in bio-product synthesis; bioremediation; and enhancement of tolerance of submergence, oxidative, and nitrosative stress in plants.</td><td>Stark et al., 2015; Zelasco et al., 2006; Haque et al., 2021a,b</td></tr><tr><td><i>A. Iwoffii</i></td><td>Detoxification of dyes, biodegradation of diesel, bioremediation of heavy metals, and solubilization of phosphate.</td><td>Sadiq et al., 2013; Mindlin et al., 2016; Imron and Tttah, 2018; Haque et al., 2021a,b</td></tr><tr><td><i>B. thuringiensis</i></td><td>Decolorization of azo dyes; degradation of naproxen, ibuprofen, and chlorpyrifos; removal of heavy metals; used in insect control; plant growth activator; solubilization of nutrients; fixation of nitrogen; and mitigation of drought stress in plants.</td><td>Oves et al., 2013; Armada et al., 2014; Aceves-Diez et al., 2015; de Maagd, 2015; Marchlewicz et al., 2016; Martins et al., 2018; Haque et al., 2021a,b</td></tr></tbody></table>

full strength. In all the cases, AL or SAL biofilm was identified within 72-h incubation as described in Haque et al. (2012) and photographs were taken. All the biofilm formation assays were performed three times.

The role of environmental cues [temperature (28 and 37°C), pH (4, 7, and 8), high osmolarity, and nutritional strength] on the expression of curli and cellulose in different bacterial strains was also studied. Congo red binding assays were performed to detect both curli and cellulose production as described by Haque et al. (2017). Calcofluor binding assays were also done to detect specifically the cellulose production as described by Haque et al. (2017). Moreover, cellulose production by different bacterial strains in response to different environmental cues was quantified by using a spectrophotometer (Ultrospec-3000, Pharmacia Biotech, Cambridge, England) as described by Haque et al. (2017). All these assays were performed twice.

## Determination of Maximum Tolerance Concentration of Cu, Ni, and Pb

The maximum tolerance concentration (MTC) was determined as the method described in Schmidt and Schlegel (1994) with a few modifications. In brief, a single colony of each bacterial strain was inoculated in the glass test tube containing 5 ml YP broth and incubated at 28°C in a shaking condition until optical density (OD<sub>660</sub>) reached at 1.0. Then, 50 µl (ca. 10<sup>7</sup> CFU ml<sup>-1</sup>) culture of each strain was spread onto YP agar plates (three plates/concentration) containing 0 to 1,000 mg l<sup>-1</sup> Cu, Ni, or Pb and incubated at 28°C in a static condition. The MTC was designated as the highest concentration of Cu, Ni, and Pb that allows confluent growth after 72 h of incubation. This experiment was repeated three times.

## Initial Metal Concentration, Temperature, and pH on Biosorption of Cu, Ni, and Pb

To examine the effect of initial heavy metal ion concentration on biosorption, a 50-µl suspension (ca. 10<sup>6</sup> CFU ml<sup>-1</sup>) of each

bacterium was inoculated in 5 ml SOBG (pH 7) supplemented with 12.5, 25, 50, 100, 150, or 200 mg l<sup>-1</sup> Cu, Ni, or Pb. The inoculated test tubes were incubated at 28°C in a static condition. To evaluate the impacts of pH, the pH of the SOBG containing 100 mg l<sup>-1</sup> Cu, Ni, or Pb was adjusted to pH 5, 6, 7, 8, and 9 and then autoclaved. The inoculation was done as described above and then kept at 28°C in a static condition. To quantify the effect of temperature, a 50-µl suspension (ca. 10<sup>6</sup> CFU ml<sup>-1</sup>) of each strain was inoculated in SOBG (pH 7) with 100 mg l<sup>-1</sup> Cu, Ni, or Pb and then incubated at two different temperatures such as 28 and 37°C in a static condition. In all the cases, 1-ml culture was collected from beneath of the biofilms after 72-h incubation and centrifuged at 15,000 rpm for 15 min. Heavy metals such as Cu, Ni, or Pb from the supernatants were determined using an atomic absorption spectrophotometer (VARIAN model AA2407). All these experiments were repeated twice. The percentage of metal removal efficiency (% RE) was calculated using the following equation:

$$ \% \text{ RE} = \frac{(\text{Co} - \text{Cf})}{\text{Co}} \times 100 $$

where Co and Cf are initial and final concentrations (mg l<sup>-1</sup>) of Cu, Ni, or Pb in the solution, respectively.

## Identification of Functional Groups in Metal-Loaded and -Unloaded Biomass Biofilms Using FTIR Spectroscopy

Initially, a 50-µl suspension (ca. 10<sup>6</sup> CFU ml<sup>-1</sup>) of each bacterium was inoculated in glass test tubes containing 5 ml SOBG (pH 7) or 5 ml magnesium-deprived SOBG (pH 7) containing 100 mg l<sup>-1</sup> Cu, Ni, or Pb. Then, the tubes were incubated at 28°C in a static condition. After 72-h incubation, each bacterial biofilm was carefully collected and centrifuged at 14,000 rpm for 10 min. Each pellet was scanned (450 to 4,000 cm<sup>-1</sup>, 16 scans at 4 cm<sup>-1</sup> resolution) at 0.2 cm s<sup>-1</sup> scanning speed using a triglycine sulfate (TGS)
detector. The IR spectra of the metal-unloaded and metal-loaded biofilm matrices were acquired using the PerkinElmer FTIR spectroscopy (Spectrum-2) instrument operated by CPU32M software. PerkinElmer's proprietary software (version 10.05.03) was used to analyze the baseline-subtracted biofilm spectra.

## Experimental Design and Statistical Analysis

All the experiments were performed in a complete randomized design with at least three replications and repeated at least three times unless otherwise stated. Analysis of variance (ANOVA), distribution of data, homogeneity of variance, and mean comparison were analyzed using the R software version 3.3.6. The Fisher's least significant difference test was applied to compare the means.

# RESULTS

## Effect of pH on Biofilm Formation

Information regarding the role of pH on biofilm formation by *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 were not available in the literature. Therefore, these bacterial strains were evaluated for their abilities to form biofilm in response to different pH levels. Initially, growth of these strains was examined in SOBG media at pH 4, 7, and 8 under a shaking condition (160 rpm) at 28°C. All these strains grew rapidly both at pH 7 (Figure 1A) and pH 8 (data not shown). However, the growth rate of these strains was not significantly differed at pH 7 and pH 8 (data not shown). At pH 4, *E. ludwigii* ENSH201, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 were unable to grow (Figure 1B), while *E. asburiae* ENSD102 (OD<sub>660</sub> = 0.81 at 24 h) and *Vitreoscilla* sp. ENSG301 (OD<sub>660</sub> = 0.78 at 24 h) cells grew moderately (Figure 1B). Like growth, biofilm formation was also affected by different pH levels. All these bacterial strains confirmed the development of thick and robust AL biofilms at pH 7 (Figure 1D). *E. asburiae* ENSD102 and *Vitreoscilla* sp. ENSG301 produced a prominent SAL biofilms at pH 4 (Figure 1E). *A. lwoffii* ENSG302 built a strong AL biofilm at pH 8, while *Vitreoscilla* sp. ENSG301 created a weak and fragile AL biofilm at pH 8 (Figure 1F). Conversely, *E. asburiae* ENSD102, *E. ludwigii* ENSH201, and *B. thuringiensis* ENSW401 developed the SAL biofilms at pH 8 (Figure 1F). Thus, these bacterial strains synthesized two types of biofilms, i.e., AL and SAL biofilm, depending on the pH levels.

## The Effect of Osmolarity and Availability of Oxygen on Biofilm Formation

When these bacterial strains were grown in SOBG (pH 7) supplemented with 0.3 M NaCl in a shaking condition at 28°C, the growth rate of *Vitreoscilla* sp. ENS301 and *A. lwoffii* ENSG302 was drastically reduced as compared to that of *E. asburiae* ENSD102, *E. ludwigii* ENSH201, and *B. thuringiensis* ENSW401 (Figure 1C). Similar results were also found when SOBG (pH 7) was supplemented with 0.3 M D-sorbitol (data

not shown). Like bacterial growth, biofilm formation in all these strains was dramatically reduced in high-osmolarity media (Figure 1G) as compared to that of regular SOBG media (Figure 1D). Taken together, these observations suggest that both NaCl and D-sorbitol negatively affect biofilm formation through an osmotic effect.

None of these strains formed the biofilm in the glass test tubes containing 5 ml SOBG broth sealed with paraffin oil after 72-h incubation at 28°C in a static condition (data not shown). Similar results were also found when inoculated tubes were placed in an anaerobic chamber at 28°C (data not shown).

## The Effect of the Nutritional Conditions on Biofilm Formation

Bacterial growth was indistinguishable in 1/2-strength SOBG (data not shown) and full-strength SOBG. However, all these bacterial strains formed a lighter and fragile AL biofilms in 1/2-strength SOBG after 72-h incubation at 28°C in a static condition (Figure 1H). These bacterial strains did not build AL or SAL biofilm in magnesium-deprived SOBG media (Mosharaf et al., 2018). When magnesium-deprived SOBG media was supplemented with 0.009 M Ca²⁺, Cu²⁺, and Zn²⁺, all these strains developed dense and stout AL biofilms (data not shown). Thus, media composition plays an important role in biofilm formation of these bacteria.

## Effect of Initial Cu, Ni, and Pb Concentrations on Biomass Biofilm Formation

Biofilm formation in terms of the production of biomass (wet) biofilms by *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 was also examined in response to different concentrations (12.5, 25, 50, 100, 150, and 200 mg l⁻¹) of Cu, Ni, and Pb. Biomass biofilm production (wet) was not significantly differed in these bacterial strains responding to 12.5 to 200 mg l⁻¹ Cu, Ni, and Pb (Supplementary Figure 1). However, biomass biofilm production (mean) was higher in different concentrations of Cu (11.45 mg l⁻¹) and Ni (11.53 mg l⁻¹) as compared to the varying concentrations of Pb (9.52 mg l⁻¹) in these bacterial strains. The amount of biofilm production by *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 ranged from 11.2 to 11.5, 11.2 to 11.6, 11.3 to 11.6, 11.3 to 11.6, and 11.4 to 11.7 mg ml⁻¹, respectively, in response to 12.5 to 200 mg l⁻¹ Cu (Supplementary Figure 1A). In case of Ni, it varied from 11.4 to 11.7, 11.1 to 11.5, 11.5 to 11.7, 11.4 to 11.6, and 11.5 to 11.8 mg ml⁻¹ in *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401, respectively, responding to 12.5 to 200 mg l⁻¹ (Supplementary Figure 1B). In 12.5 to 200 mg l⁻¹ Pb, production of biomass biofilms by *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii*
<figure><img src="image_81.png" /></figure>

<figure><img src="image_82.png" /></figure>

<figure><img src="image_83.png" /></figure>

<figure><img src="image_84.png" /></figure>

**FIGURE 1** | The Effect of environmental cues on bacterial growth and biofilm formation. Growth of the bacterial strains in SOBG at pH 7 (A), pH 4 (B), and high osmotic condition, i.e., SOBG containing 0.3 M NaCl (C) in a shaking condition. Biofilm formation on SOBG at pH 7 (D), pH 4 (E), pH 8 (F), SOBG containing 0.3 M NaCl (G), and 1/2-strength SOBG (H) by different bacterial strains after 72-h incubation in a static condition. The values are mean, and error bars indicate standard deviations (±) of three independent experiments. SOBG, salt-optimized broth plus glycerol.

ENSG302, and *B. thuringiensis* ENSW401 fluctuated from 9.3 to 9.7, 9.2 to 9.7, 9.5 to 9.9, 9.2 to 9.5, and 9.5 to 9.7 mg ml⁻¹, respectively (Supplementary Figure 1C).

## Congo Red Binding Assays

Numerous studies have shown that biofilm-producing bacteria expressed curli and cellulose – two important components of the biofilm matrices (Römling, 2005; Haque et al., 2009, 2012, 2017; Milanov et al., 2015). Milanov et al. (2015) have demonstrated that bacterial strains producing the red, dry, and rough (rdar) phenotype on Congo red agar plates leads to both curli and cellulose, while only cellulose expresses the pink, dry,

and rough (pdar) phenotype and only curli induces the brown, dry, and rough (bdar) phenotype. When no components are expressed, the phenotype is smooth and white (saw). Bokranz et al. (2005), on the other hand, have reported that bacterial strains generating the red and smooth (ras) and pink and smooth (pas) lead to only cellulose, while brown and smooth (bas) triggers only curli in certain *E. coli* strains. However, the effect of temperature, pH, osmolarity, and nutritional strength on the expression of curli and cellulose has never been investigated in *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 by any other researches. Thus, the phenotypes were examined in
<figure><img src="image_85.png" /><figcaption>**FIGURE 2** | Congo red binding abilities of different bacterial strains in response to pH 7 at 28°C (A), pH 7 at 37°C (B), pH 4 at 28°C (C), pH 8 at 28°C (D), high osmolarity at 28°C (E) and 1/2 strength SOBG, pH 7 at 28°C (F) after 48-h incubation. Photographs represent one of two experiments, which gave similar results.</figcaption></figure>

response to different environmental conditions, and the results are depicted in **Figure 2**.

*Enterobacter asburiae* ENSD102 and *B. thuringiensis* ENSW401 expressed the rdar phenotypes, linked with both curli and cellulose production at both temperatures, i.e., 28 and 37°C (Figures 2A,B), while *Vitreoscilla* sp. ENSG301 triggered the pdar phenotypes, associated with cellulose only at both temperatures (Figures 2A,B). *E. ludwigii* ENSH201 and *A. lwoffii* ENSG302 produced the rdar and pdar phenotypes at 28°C (Figure 2A) and 37°C (Figure 2B), respectively.

When pH of the SOBG Congo red broth was adjusted to 4 with malic acid, the color of the SOBG media was slightly changed after being autoclaved (Figure 2C). At pH 4, *E. asburiae* ENSD102 and *Vitreoscilla* sp. ENSG301 induced the bdar phenotype (Figure 2C), linked with curli production only, while *E. ludwigii* ENSH201 and *A. lwoffii* ENSG302 produced the saw (no expression of cellulose or curli) phenotypes (Figure 2C). At pH 8, *E. asburiae* ENSD102, *E. ludwigii* ENSH201, and *Vitreoscilla* sp. ENSG301 triggered the pas phenotypes (Figure 2D), while *A. lwoffii* ENSG302 produced the rdar phenotype (Figure 2D). Furthermore, the center of

the colonies of *B. thuringiensis* ENSW401 was decorated and pink in color, while the side of the colonies was red at pH 8 (Figure 2D).

Like pH 8, *E. asburiae* ENSD102, *E. ludwigii* ENSH201, and *Vitreoscilla* sp. ENSG301 also constructed the pas phenotypes in high osmotic condition (Figure 2E), while *A. lwoffii* ENSG302 developed the rdar phenotype at the same condition (Figure 2E). In *B. thuringiensis* ENSW401, the side of the colony was red and the center was pink in color but not decorated like pH 8 (Figure 2E). All these bacterial strains developed the rdar phenotypes in 1/2-strength SOBG. Thus, both environmental cues and bacterial strains might be involved in the production of curli and cellulose.

## Calcofluor Binding Assays

The rdar/pdar/pas-expressing bacteria were also shown to bind with Calcofluor, a cellulose-specific dye (Römling, 2005; Steenackers et al., 2012; Haque et al., 2017). The abilities of these bacteria to bind with Calcofluor were also found to be influenced by various environmental factors (Figure 3). All these bacterial strains fluoresced strongly at 28°C and at pH 7 (Figure 3A), while
<figure><img src="image_86.png" /><figcaption>**FIGURE 3** | Calcofluor binding abilities of different bacterial strains in response to pH 7 at 28°C (A), pH 7 at 37°C (B), pH 8 at 28°C (C), high osmolarity at 28°C (D), anaerobic condition (E) and 1/2 strength SOBG at 28°C (F) after 48-h incubation. Photographs represent one of two experiments, which gave similar results.</figcaption></figure>

they fluoresced only slightly at 37°C (Figure 3B) and at pH 7. At pH 8, except A. lwoffii ENSG302, all the other strains fluoresced weakly (Figure 3C). All these bacterial strains also weakly fluoresced in high osmotic condition (Figure 3D). As expected, no fluorescence was observed when these strains were incubated in an anaerobic chamber (Figure 3E). Interestingly, all these strains fluoresced strongly in 1/2-strength SOBG (Figure 3F). Thus, inability/weak/fragile biofilm formation by these strains might be due to no/lesser amounts of cellulose production.

## Spectrophotometric Determination of Cellulose

Differences of Calcofluor binding were shown through the discrepancy of cellulose production in bacteria (Anriany et al., 2006; Haque et al., 2017). Therefore, cellulose production was quantified in different bacterial strains grown in various environmental conditions (Figure 4). In this study, cellulose

production was reduced by 12. 0-, 20. 9-, 17. 7-, 17. 6-, and 2.8-fold in *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401, respectively, at 37°C as compared at 28°C. Cellulose production was also reduced in these strains responding to pH 8 (than at pH 7) and high osmolarity (compared to regular SOBG). Thus, increase of fluorescence in Calcofluor agar plates seemed to have been reflected in the increase of cellulose production in these bacteria.

## Maximum Tolerance Concentration of Cu, Ni, and Pb by Different Bacterial Strains

Before biosorption studies, the MTC of different heavy metals including Cu, Ni, and Pb by *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 was determined. The MTC of Cu, Ni, and
<figure><img src="image_87.png" /><figcaption>**FIGURE 4** | Cellulose production by different biofilm-producing bacterial strains in different environmental conditions. Cellulose was isolated from 250 mg of lyophilized cell mass obtained from each bacterium grown on Calcofluor agar plates after 48-h incubation at 28°C. The amount of cellulose was determined (OD<sub>620</sub>) by addition of anthrone reagent, and Avicel cellulose was used as standard. The values are mean, and error bars indicate standard deviations (±) of two independent experiments.</figcaption></figure>

Pb was remarkably varied in these bacterial strains (Table 2). All these bacterial strains showed the highest (500 to 650 mg l<sup>-1</sup>) MTC in response to Pb. The MTC of Cu ranged from 250 to 600 mg l<sup>-1</sup>. Among the bacterial strains, *B. thuringiensis* ENSW401 exhibited the highest MTC (600 mg l<sup>-1</sup>) in response to Cu followed by *Vitreoscilla* sp. ENSG301 (550 mg l<sup>-1</sup>), *A. lwoffii* ENSG302 (400 mg l<sup>-1</sup>), *E. ludwigii* ENSH201 (300 mg l<sup>-1</sup>), and *E. asburiae* ENSD102 (250 mg l<sup>-1</sup>). However, the MTC of Ni by these bacterial strains was fluctuated from 200 to 300 mg l<sup>-1</sup>. Thus, the MTC might be dependent on both metals and bacterial strains used in the study.

## The Effect of Initial Metal Concentration on the Biosorption of Cu, Ni, and Pb

The physicochemical technologies are not suitable for the removal of low concentrations (1 to 50 mg l<sup>-1</sup>) of heavy metals

from wastewater. Therefore, the effect of initial concentrations of 12.5, 25, 50, 100, 150, and 200 mg l<sup>-1</sup> Cu, Ni, and Pb on metal uptake by *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 was examined. The biosorption rate was not significantly differed in these bacterial strains at initial concentrations of 12.5, 25, and 50 mg l<sup>-1</sup> Cu, Ni, and Pb, respectively (Figure 5). Surprisingly, *Vitreoscilla* sp. ENSG301 and *B. thuringiensis* ENSW401 completely removed (100%) both Cu (Figure 5A) and Ni (Figure 5B) at an initial concentration of 12.5 mg l<sup>-1</sup>. On the other hand, all these bacterial strains completely removed (100%) Pb at initial concentrations of 12.5 and 25 mg l<sup>-1</sup> (Figure 5C). At an initial concentration of 50 mg l<sup>-1</sup>, the removal percentage of Cu ranged from 97.8 to 99.4% by these bacterial strains (Figure 5A). Ni and Pb ranged from 98.9 to 99.8% and 99.1 to 99.8%, respectively, by these bacterial strains (Figure 5C). *Vitreoscilla* sp. ENSG301 sorped the highest percentage (97.1%) of Cu at an initial concentration of 100 mg l<sup>-1</sup>, which was statistically akin with *B. thuringiensis* ENSW401 (96.8%). *E. asburiae* ENSD102, *E. ludwigii* ENSH201, and *A. lwoffii* ENSG302 removed 92.7, 90.2, and 92.5% Cu, respectively, which were incredibly differed from *Vitreoscilla* sp. ENSG301 and *B. thuringiensis* ENSW401 at an initial concentration of 100 mg l<sup>-1</sup> Cu (Figure 5A). The biosorption percentage of Ni by *Vitreoscilla* sp. ENSG301 was also significantly higher (94.2%) at an initial concentration of 100 mg l<sup>-1</sup>, which was statistically alike with *E. asburiae* ENSD102, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 (Figure 5B). On the other hand,

<table><caption>**TABLE 2** | Maximum tolerance concentration (MTC) of heavy metals by different bacterial strains.</caption><thead><tr><th rowspan="2">Bacterial strains</th><th colspan="3">MTC of different heavy metals (mg l<sup>-1</sup>)</th></tr><tr><th>Cu</th><th>Ni</th><th>Pb</th></tr></thead><tbody><tr><td><i>E. asburiae</i> ENSD102</td><td>250</td><td>250</td><td>500</td></tr><tr><td><i>E. ludwigii</i> ENSH201</td><td>300</td><td>200</td><td>500</td></tr><tr><td><i>Vitreoscilla</i> sp. ENSG301</td><td>550</td><td>250</td><td>550</td></tr><tr><td><i>A. lwoffii</i> ENSG302</td><td>400</td><td>200</td><td>550</td></tr><tr><td><i>B. thuringiensis</i> ENSW401</td><td>600</td><td>300</td><td>650</td></tr></tbody></table>
<figure><img src="image_88.png" /></figure>

<figure><img src="image_89.png" /></figure>

<figure><img src="image_90.png" /></figure>

**FIGURE 5** | The effect of initial metal concentration on the biosorption of Cu (A), Ni (B), and Pb (C) by different bacterial strains at pH 7 and 28°C after 72 h. The values are mean, and error bars indicate standard deviations (±) of two independent experiments.

B. thuringiensis ENSW401 removed significantly the maximum Pb (93.5%) followed by *Vitreoscilla* sp. ENSG301 (91.5%), *E. asburiae* ENSD102 (89.8%), *A. lwoffii* ENSG302 (87.6%), and *E. ludwigii* ENSH201 (84.2%) at an initial concentration of

100 mg l⁻¹ (Figure 5C). A significant percentage of Cu (80.7 to 91.2%), Ni (69.8 to 89.6%), and Pb (78.1 to 88.5%) was also removed by these bacterial strains at an initial concentration of 200 mg l⁻¹ (Figure 5).
<figure><img src="image_91.png" /><figcaption>**FIGURE 6** | The effect of different temperatures on the biosorption of Cu, Ni, and Pb by different bacterial strains at an initial concentration of 100 mg l⁻¹ and pH 7 after 72 h. The values are mean, and error bars indicate standard deviations (±) of two independent experiments.</figcaption></figure>

## The Effect of Temperature and pH on the Biosorption of Cu, Ni, and Pb

The biosorption of Cu, Ni, and Pb by different bacterial strains was significantly influenced by temperature as shown in **Figure 6**. Copper biosorption was reduced by 2. 76-, 2. 98-, 1. 80-, 3. 48-, and 2.26-fold in *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401, respectively, at 37°C as compared with 28°C, while Ni biosorption was diminished by 4. 17-, 4. 29-, 7. 85-, 5. 08-, and 5.32-fold in these bacterial strains. Lead biosorption by *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 at 37°C was also decreased by 7. 42-, 6. 68-, 3.75, 3. 43-, and 4.9-fold than at 28°C. Thus, temperature plays a crucial role in the biosorption of Cu, Ni, and Pb.

To study the impact of pH on metal biosorption, an experiment was set up with varying pH (5, 6, 7, 8, and 9) with 100 mg l⁻¹ of Cu, Ni, or Pb (**Figure 7**). The abilities of biosorption for each metal ion by different bacterial strains increased with an increase in pH up to 8, while biosorption capacities of these metal ions decreased at pH 9. The Cu and Pb biosorption was the highest at pH 6, while it was higher for Ni at pH 7 than the sorption at other pH.

## FTIR Analysis

In order to determine the chemical functional groups/ligands responsible for biosorption processes, FTIR analysis was done using metal-loaded (Cu, Ni, and Pb) and metal-unloaded biomass biofilms of *E. asburiae* ENSD102 (**Supplementary Figure 2**), *E. ludwigii* ENSH201 (**Supplementary Figure 3**),

*Vitreoscilla* sp. ENSG301 (**Figure 8A**), *A. lwoffii* ENSG302 (**Supplementary Figure 4**), and *B. thuringiensis* ENSW401 (**Figure 8B**). In the present study, several functional groups/ligands including -OH, -NH, -CH, C = O, COO⁻, and P-O were found in different wave numbers (cm⁻¹) in metal-unloaded biomass biofilms of different bacterial strains as shown in **Figures 8A,B** and **Supplementary Figures 2-4**. Overall, the peak position was shifted in metal-loaded biomass biofilms in response to 100 mg l⁻¹ Cu, Ni, and Pb as compared to that of metal-unloaded biomass biofilms.

The IR spectra of metal-unloaded biomass biofilm of *E. asburiae* ENSD102 exhibited C = O of amide groups at 1,641.90 cm⁻¹ and were shifted at 1,645.90, 1,636.51 and 1,635.85 cm⁻¹ in response to Cu, Ni, and Pb, respectively (**Supplementary Figure 2**), while COO⁻ of the carboxylate groups appeared at 1,544.10 cm⁻¹ in metal-unloaded biomass biofilm of *E. asburiae* ENSD102 and was changed to 1,544.0, 1,545.21, and 1,545.78 cm⁻¹. Moreover, phosphate groups and P-O of the (C-PO₂⁻³) moiety at 1,045 cm⁻¹ in metal-unloaded biomass biofilm of *E. asburiae* ENSD102 were shifted to 1,049, 1,083.27, and 1,045.84 cm⁻¹ in response to Cu, Ni, and Pb, respectively. Thus, Cu, Ni, and Pb could be sorbed by carbonyl, carboxyl, and phosphate groups of *E. asburiae* ENSD102.

The spectra of the metal-unloaded biomass of *E. ludwigii* ENSH201 exhibited 1,445.90 cm⁻¹ for C = O of amide group and were moved to 1,635.98, 1,635.72, and 1,635.99 cm⁻¹, respectively, responding to Cu, Ni, and Pb, respectively (**Supplementary Figure 3**), while 1,544.77 cm⁻¹ for COO⁻ of carboxylate group was displaced to 1,544.20, 1,544.36, and 1,546.70 cm⁻¹, respectively (**Supplementary Figure 2**).
<figure><img src="image_92.png" /><figcaption>**FIGURE 7** | The effect of pH on the biosorption of Cu (A), Ni (B), and Pb (C) by different bacterial strains at an initial concentration of 100 mg l⁻¹ for each metal at 28°C after 72 h. The values are mean, and error bars indicate standard deviations (±) of two independent experiments.</figcaption></figure>

Furthermore, phosphate group and P–O of the (C–PO₂⁻³) moiety at 1,045 cm⁻¹ in metal-unloaded biomass biofilm were changed to 1,080.50 and 1,047.67 cm⁻¹, respectively, challenging to Ni and Pb, respectively. However, phosphate group and P–O of the (C–PO₂⁻³) moiety at 1,045 cm⁻¹ in metal-unloaded and Cu-loaded biomass biofilm were not changed (Supplementary Figure 2). Thus, phosphate groups and P–O of the (C–PO₂⁻³) moiety of the biomass biofilm of *E. ludwigii* ENSH201 might not be involved in the removal of Cu.

Similarly, shifting of band of metal-unloaded biofilm biomass of *Vitreoscilla* sp. ENSG301 showed a stretched band appearing at 1,645.16 cm⁻¹ for C = O of amide groups and was moved to 1,635.28, 1,629.09, and 1,636.07 cm⁻¹, respectively, in response to Cu, Ni, and Pb, respectively (Figure 7A), while COO⁻ of the carboxylate groups displayed at 1,545.64 cm⁻¹ was changed to 1,545.71, 1,542.37, and 1,545.71 cm⁻¹, respectively. Moreover, band positioning at 1,064.70 cm⁻¹ was shifted to 1,056.84, 1,053.50, and 1,053.24 cm⁻¹ in response to Cu, Ni, and Pb, respectively (Figure 8A). Thus, carbonyl, carboxyl, and phosphate groups of *Vitreoscilla* sp. ENSG301 could sorp Cu, Ni, and Pb.

Compared to the metal-unloaded biomass biofilm of *A. lwoffii* ENSG302, C = O group was remarkably shifted to 10.92, 10.90, and 9.40 cm⁻¹ in response to Cu, Ni, and Pb, respectively (Supplementary Figure 3). However, COO⁻ group was moved to 0.66, 1.11, and 0.91 cm⁻¹, respectively, responding to Cu, Ni, and Pb, respectively. Band positioning of phosphate groups and P–O of the (C–PO₂⁻³) moiety were incredibly changed to 30.38, 28.54, and 5.49 cm⁻¹, respectively, in response to Cu, Ni, and Pb, respectively (Supplementary Figure 4).

C = O group in metal-unloaded biomass biofilm of *B. thuringiensis* ENSW401 at the position of 1,645.89 cm⁻¹ was shifted to 1,636.01, 1,630.25, and 1,636.94 cm⁻¹ in response to Cu, Ni, and Pb, respectively (Figure 8B). The COO⁻ group that appeared at 1,546.68 cm⁻¹ in metal-unloaded biomass biofilm was moved to 1,549.05, 1,546.04, and 1,545.85 cm⁻¹, challenging to Cu, Ni, and Pb, respectively. Nevertheless, phosphate groups and P–O of the (C–PO₂⁻³) moiety at 1,084.50 cm⁻¹ in metal-unloaded biomass biofilm were shifted to 1,084.38, 1,084.50, and

1,045.32 cm⁻¹ in response to Cu, Ni, and Pb, respectively. Thus, carbonyl, carboxyl, and phosphate groups might play a vital role in the biosorption of Cu, Ni, and Pb.

## DISCUSSION

### Environmental Conditions Affect Bacterial Biofilm Formation

In this study, it was observed that environmental cues play a vital role in biofilm formation (Figure 1), production of curli and cellulose (Figures 2–4), and biosorption of Cu, Ni, and Pb (Figures 5–7) by *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401. *E. asburiae* ENSD102 and *Vitreoscilla* sp. ENSG301 produced the SAL biofilms both at pH 4 (Figure 1E) and pH 8 (Figure 1F), while *E. ludwigii* ENSH201 and *B. thuringiensis* ENSW401 constructed the SAL biofilms only at pH 8 (Figure 1F). However, all these bacterial strains developed the AL biofilm at pH 7 (Figure 1D). Furthermore, only *Vitreoscilla* sp. ENSG301 and *B. thuringiensis* ENSW401, but not other bacterial strains, generated the SAL biofilms in high osmotic condition (Figure 1G). Thus, temperature, pH, and osmolarity and bacterial strains might play an important role in SAL and AL biofilm formation. Numerous researchers have differentiated AL and SAL biofilm genetically, enzymatically, and based on cultural conditions (Friedman and Kolter, 2004; Yap et al., 2005; Haque et al., 2012). Bacterial biofilm formation in different bacterial strains was shown to be influenced by the pH (Hostacká et al., 2010; Ramli et al., 2012; Nguyen et al., 2014; Zhou et al., 2014; Haque et al., 2017), high osmolarity (Lebeer et al., 2007; Hou et al., 2014; Kavamura and de Melo, 2014; Haque et al., 2017), and oxygen tension (Gerstel and Römling, 2001; Bjergbæk et al., 2006; Ahn and Burne, 2007; Liang et al., 2010; Wu et al., 2013). Except temperature (Mosharaf et al., 2018), effect of pH, osmolarity, and oxygen tension on biofilm formation by *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 was not studied by any other contemporary researches.
<figure><img src="image_93.png" /><figcaption>**FIGURE 8** | The IR spectra of metal-unloaded and metal-loaded (100 mg I⁻¹ of Cu, Ni, or Pb) biomass biofilms of *Vitreoscilla* sp. ENSG301 (A) and *B. thuringiensis* ENSW401 (B) at pH 7 and at 28°C. In each case, first line represents the unloaded biomass biofilm (i.e., control) followed by Cu, Ni, and Pb, respectively.</figcaption></figure>

## Bacterial Biofilm Formation as Affected by Nutritional Factors

In ¹/₂-strength SOBG media, all these bacterial strains generated thin and fragile AL biofilms (Figure 1H) as compared to those of regular SOBG media (Figure 1D). Shen et al. (2018) reported that at higher nutrient concentrations, biofilms are thicker and denser than under nutrient-poor conditions. Bacterial biofilm formation was also shown to be activated by different divalent cations (such as Mg²⁺ and Ca²⁺) through their effect on electro-static interactions (Patrauchan et al., 2005). Among the divalent cations, Ca²⁺ impacts the mechanical properties of biofilms, as well as cross linkers (Patrauchan et al., 2005). On the other hand, Mg²⁺ increased initial attachment in *P. fluorescens* (Song and Leff, 2006) by reducing the repulsive force between the negatively charged bacterial and substratum surfaces and between negative functional groups of the polysaccharides (Koechler et al., 2015). Haque et al. (2012) have reported that a low concentration (10 µM) of Mg²⁺ increases the AL biofilm formation of *Dickeya dadantii* 3937 in a PhoP-PhoQ-dependent manner. AL biofilm formation controlled by the CytR homolog in *Pectobacterium carotovorum* subsp. *carotovorum* PC1 was also shown to be increased by the different divalent cations, e.g., Mg²⁺, Ca²⁺, Cu²⁺, Zn²⁺, and Mn²⁺ (Haque et al., 2017). In this study, *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 also produced the dense and stout AL biofilms by addition of different divalent cations (0.009 M) including Mg²⁺, Ca²⁺, Cu²⁺, and Zn²⁺ (data not shown). Thus, not only environmental cues but also nutritional conditions might play a crucial role in the formation of a thick and robust AL biofilm.

## Environmental and Nutritional Cues Affect Curli and Cellulose Expression in Bacteria

Protein filaments, known as curli, and cellulose played a pivotal role in AL biofilm/pellicle formation in *E. coli* (Prigent-Combaret et al., 2000) and *Salmonella enterica* serovar Enteritidis (White et al., 2003). The production of curli in *Enterobacteriaceae* was shown to be regulated by temperature. For example, curli in *Salmonella* usually is visible under 30°C (Gerstel and Römling, 2003; Bokranz et al., 2005), but some strains such as *S. typhimurium* can express at 37°C (Olsén et al., 1998). Conversely, in clinical isolates of the *E. coli*, the expression of the curli at 37°C is a rarely visible phenomenon (Barnhart and Chapman, 2006). In the present study (in Congo red binding assays), *E. asburiae* ENSD102 and *B. thuringiensis* ENSW401 produced the curli both at 28 and 37°C (Figures 2A,B), while *E. ludwigii* ENSH201 expressed only curli at 28°C (Figure 2A). Curli was not synthesized by *Vitreoscilla* sp. ENSG301 both at 28°C (Figure 2A) and 37°C (Figure 2B), while this bacterial strain generated the curli at pH 4 at 28°C (Figure 2C). The production of cellulose was also found to be dramatically reduced by high temperature (i.e., 37°C), pH 8, high osmolarity, and anaerobic conditions in *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 (Figures 3B-D, 4). The expression of the curli fimbriae and cellulose in *Salmonella* spp. is most intense at temperatures under 30°C, in low osmolarity, limited availability of nutrients, and aerobic conditions (Gerstel and Römling, 2003; Solomon et al., 2005; Steenackers et al., 2012). Thus, a variation of biofilm formation in *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301,
A. *lwoffii* ENSG302, and *B. thuringiensis* ENSW401 in response to environmental and nutritional conditions might be due to differential expression of curli and cellulose.

## Mechanisms of Heavy Metal Tolerance in Bacteria

The determination of MTC in different heavy metals is of particular interest, when bacterial strains are applied for biosorption. In this study, the MTC of Cu, Ni, and Pb was remarkably higher in *B. thuringiensis* ENSW401 as compared to other bacterial strains tested (Table 2). *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, and *A. lwoffii* ENSG302 are Gram-negative bacteria, while *B. thuringiensis* ENSW401 is a Gram-positive bacterium. Generally, the cell wall of both types encompass a peptidoglycan layer that is rich in carboxylate groups and completely surrounds the cell. The peptidoglycan layer of Gram-positive bacterium is thicker (three layers) than the peptidoglycan layer of Gram-negative bacterium (two layers). Thus, cell wall structure might play a pivotal role in MTC of Cu, Ni, and Pb in these bacterial strains. Roane et al. (2009) have reported that (i) binding of metals to extracellular materials of the bacterial cells immobilizes the metals and prevents them from entering into the bacterial cells, (ii) several bacterial strains produced siderophore (iron-complexing, low-molecular-weight organic compounds) complexes that increase metal tolerance, (iii) bacterial strains also generated biosurfactant (excreted from the bacterial cells) complexes of metals that are non-toxic to the cells, and (iv) numerous plasmid-encoded genes [e.g., *cusCBA* (resistance to copper), *cnrCBA* (cobalt-nickel resistance), and *pbrA* (encoding lead resistance)] conferred higher levels of metal tolerance in different bacteria. Thus, several mechanisms might be used in these bacterial strains for the tolerance to Cu, Ni, and Pb.

## Mechanisms of Heavy Metal Toxicity in Bacteria

The growth of *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 was severely inhibited with the increase of the concentration of Cu, Ni, and Pb after MTC (data not shown). Bacterial growth, morphological characteristics, and biochemical processes were reported to be disrupted due to toxicity of heavy metals (Roane et al., 2009). Koechler et al. (2015) stated that high concentrations of heavy metals including Cu, Ni, and Pb directly or indirectly generate reactive oxygen species (ROS) upon reacting with DNA, resulting in damaged bases or strand breaks, lipid peroxidation, or protein modification. Roane et al. (2009) reported that metals bind to many cellular ligands and displace essential metals from their native binding sites due to ionic interactions. Moreover, they have shown that metals affect the oxidative phosphorylation and membrane permeability. Some metals can inhibit cellular activity because they present a structural homology with enzyme substrates leading to the metal toxicity. Heavy metal can also cause ion imbalance by adhering to the cell surface and entering through ion channels or transmembrane carriers (Chen et al., 2014).

Therefore, future studies should focus on the mechanism of the toxicity of *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, and *B. thuringiensis* ENSW401 in response to higher concentrations of Cu, Ni, and Pb.

## Biofilm Formation in Relation to Heavy Metal Uptake in Bacteria

Biofilm production in *E. asburiae* ENSD102, *Vitreoscilla* sp. ENSG301, and *A. lwoffii* ENSG302 was reported to be affected by 500 to 2,000 mg l⁻¹ of CuSO₄·5H₂O, Pb(NO₃)₂, or NiCl₂ (Mosharaf et al., 2018). In this study, biofilm production was not remarkably varied in *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 in response to 12.5 to 200 mg l⁻¹ Cu, Ni, or Pb (Supplementary Figure 1). Accordingly, biosorption capacity was not significantly varied in these bacterial strains in response to 12.5, 25, and 50 mg l⁻¹ Cu, Ni, and Pb (Figure 5). Interestingly, only *Vitreoscilla* sp. ENSG301 and *B. thuringiensis* ENSW401 completely removed (100%) Cu and Ni at an initial concentration of 12.5 mg l⁻¹, while all these bacterial strains totally removed (100%) Pb at initial concentrations of 12.5 and 25 mg l⁻¹ at pH 7 and 28°C. However, *Vitreoscilla* sp. ENSG301 and *B. thuringiensis* ENSW401 removed much more Cu, Ni, or Pb as compared to *E. asburiae* ENSD102, *E. ludwigii* ENSH201, and *A. lwoffii* ENSG302 in response to 100, 150, and 200 mg l⁻¹ (Figure 5). Thus, biosorption might be dependent on both concentrations of the heavy metals (Cu, Ni, and Pb) and bacterial strains. At present, various ordinances, laws, rules, acts, and policies have been made to control environmental pollution in Bangladesh. The Department of Environment of Bangladesh also set safety limit of different heavy metals in industrial effluent [DoE (Department of Environment), 2008]. The World Health Organization [WHO (World Health Organization), 2017], European Union [European Union (EU), 2002], United States Environmental Protection Agency [USEPA (United States Environmental Protection Agency), 2012], and Bangladesh [DoE (Department of Environment), 2008] prescribed the maximum acceptable concentrations at (mg l⁻¹) 2.0, 0.5, 0.2, and 0.5, respectively, for Cu; 0.02, 0.5, 0.2, and 1.0, respectively, for Ni; and 0.01, 0.5, 0.05, and 0.10, respectively, for Pb. Thus, treated wastewater by these bacterial strains at an initial concentration of 12.5 mg l⁻¹ for Cu and 12.5 and 25.0 mg l⁻¹ for both Ni and Pb are within the safety limit set by the abovementioned organizations, while concentrations of Cu, Ni, and Pb are higher than the maximum acceptable values in all other treated samples.

## Cellular Structure, EPS, and Enzymes on Metal Biosorption in Bacteria

Bacterial cellular structure, EPS, and extracellular enzyme play a vital role in metal biosorption. Both Gram-positive and Gram-negative bacterial cell wall contain peptidoglycan that determines the metal-binding capability. However, teichoic acids are present only in Gram-positive bacteria that provide an overall negative charge to the cell wall, due to the presence of phosphodiester bonds. On the other hand, lipopolysaccharides (LPS) are only present in Gram-negative bacteria that confer
an overall negative charge to the cell wall of Gram-negative bacteria. The anionic functional groups present in the cell wall of Gram-positive and Gram-negative bacteria are the key contents primarily responsible for the anionic character and metal-binding or biosorption capacity of the cell wall (Moat et al., 2002). Notably, bacterial biofilm EPS also play a key role in metal biosorption (Pal and Paul, 2008; Li and Yu, 2014). Among the contents of the EPS, proteins form complexes with heavy metal ions (Mejäre and Bülow, 2001), while polysaccharides cross-link with metals (Li and Yu, 2014). Biofilm EPS matrix has abundant chemical functional groups such as amino, carboxyl, carboxylate, phosphate, and hydroxyl groups (Mosharaf et al., 2018). It was reported that negatively charged functional groups present in the EPS matrix formed organometallic complexes with multivalent metal cations via electrostatic interactions and subsequent metal removal (Gutnick and Bach, 2000). Numerous extracellular enzymes (e.g., protease, peptidase, endo-cellulase, α-glucosidase, β-glucosidase, peroxidase, etc.) have been detected in bacterial biofilms (Flemming and Wingender, 2010). Many of them were reported to degrade the contents of EPS (Flemming and Wingender, 2010) and detoxify the heavy metals (Pal and Paul, 2008). However, extracellular enzymes synthesized by *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, and *B. thuringiensis* ENSW401 and their involvement in detoxification of heavy metals are yet to be examined.

## Physicochemical Conditions Alter Metal Biosorption in Bacteria

Removal of heavy metals from aqueous solution by bacteria is a complex process due to the effect of different physicochemical factors such as initial metal concentration, temperature, pH, time, ionic strength, and metal chemistry (Gabr et al., 2008; Hassan et al., 2009). In this study, increasing the concentrations of Cu, Ni, and Pb decreased the metal biosorption (Figure 5). Higher metal biosorption at lower concentrations of heavy metals was reported to be due to the availability of free metal-binding sites, while lower metal biosorption at higher concentrations is due to lack of free metal-binding sites (Kaduková and Virčíková, 2005; Oves et al., 2013; Kirova et al., 2015).

Cellulose-based materials including cellulose gels, cellulose composites, cellulose derivatives, functionalized cellulose, and nano-crystaline cellulose are widely used for the adsorption of heavy metals from wastewater (Jamshaid et al., 2017). *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 were shown to produce nanocellulose that is amorphous in nature (Mosharaf et al., 2018). In this study, cellulose production (Figure 4) and biosorption of Cu, Ni, and Pb by these bacterial strains were dramatically reduced at 37°C as compared with 28°C (Figure 6). Bacterial cellulose production was shown to be linked with biosorption of metals (Teitzel and Parsek, 2003; Li and Yu, 2014). Thus, reduction of the removal of Cu, Ni, and Pb by these bacterial strains at 37°C might be due to a lower production of cellulose. Redha (2020) has shown that metal biosorption is not highly affected in temperatures ranging from 20 to 35°C.

On the other hand, Taşar et al. (2014) have reported that increasing temperature from 20 to 40°C decreases the surface activity of biosorbents such as peanut shells leading to decreased biosorption of Pb.

In this study, pH levels regulated the biosorption of Cu, Ni, and Pb (Figure 7). The difference in metal biosorption at different pH was associated with the effect of both the chemistry of the functional groups and the chemistry of metal ions (Wei et al., 2016). At low pH, functional groups present in the biofilm EPS tightly bound with hydronium ions leading to restricting the binding of metal cations due to repulsive force. Conversely, with increasing pH, various functional groups including carbonyl, carboxyl, phosphate, and amino start experiencing negative charges due to deprotonation leading to binding with metal cations and thus increasing the biosorption capacity (Oves et al., 2013; Abdi and Kazemi, 2015). In this study, the chemical functional groups present in the biofilm EPS of *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 were determined using FTIR in both metal-loaded and -unloaded (control) samples at an initial Cu, Ni, and Pb concentration of 100 mg l⁻¹, pH 7, and at 28°C. Several functional groups including -OH, -NH, -CH, C = O, COO-, and P-O were detected in the samples (Figures 8A,B and Supplementary Figures 2-4). FTIR results revealed that Cu, Ni, and Pb could be sorbed by carbonyl, carboxyl, and phosphate groups of *E. asburiae* ENSD102, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401, while phosphate groups and P-O of the (C-PO₂⁻³) moiety have no role in Cu removal by *E. ludwigii* ENSH201. The chemical functional groups in metal-loaded biofilm EPS in *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 were not reported by any other contemporary researches.

## Future Perspective and Scale Up

Future studies should focus on the biosorption of heavy metal from real wastewater by these bacterial strains. The mechanisms involved in metal toxicity in these bacterial strains should be studied. Extracellular enzymes produced in the EPS matrix of these bacterial strains and their role in the detoxification of heavy metals should also be examined. Nevertheless, genetic engineering tools should be used to construct the engineered *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 with higher metal sorption capacity. For scale up, large amounts of biomass biofilm can be produced in less expensive growth media by using these bacterial strains. Currently, bacterial biofilm biomass is being used in different types of bioreactors including fixed bed reactor, packed bed reactor, and fluidized bed reactor to remove heavy metals from wastewater. Thus, biomass biofilm produced by *E. asburiae* ENSD102, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 can be utilized in the *ex situ* conditions for different engineered bioreactor systems. This will require an interdisciplinary approach with the integration of metallurgical, chemical, mathematical, and civil engineering skills along with
sorption and wastewater treatment plan to combat heavy metal pollution from the aquatic environment. The last but not the least, in order to get the best out of the results obtained and to get this technology used for heavy metal pollution mitigation, it needs to be integrated into the policy simultaneously by the concerned government and international donor agencies.

## CONCLUSION

Biofilm formation and biofilm matrix compounds such as curli and cellulose production in *E. asburiae* ENSD102, *E. ludwigii* ENSH201, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401 were remarkably affected by different environmental and nutritional conditions. Only *Vitreoscilla* sp. ENSG301 and *B. thuringiensis* ENSW401 completely removed (100%) Cu and Ni at an initial concentration of 12.5 mg l⁻¹, while all these strains totally removed (100%) Pb at initial concentrations of 12.5 and 25 mg l⁻¹ at pH 7 and 28°C. FTIR study showed that Cu, Ni, and Pb could be sorbed by carbonyl, carboxyl, and phosphate groups of the biomass biofilms of *E. asburiae* ENSD102, *Vitreoscilla* sp. ENSG301, *A. lwoffii* ENSG302, and *B. thuringiensis* ENSW401, while phosphate groups and P-O of the (C-PO₂⁻³) moiety have no role in Cu removal by *E. ludwigii* ENSH201. Thus, all these bacterial strains can be utilized in biosorption of heavy metals from wastewater.

## DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

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## AUTHOR CONTRIBUTIONS

MMH conceived the idea, developed the methodologies, conducted the experiments, wrote the manuscript, and collected the research fund. MKM and MZHT conducted the experiments. MAH and MKA performed FTIR and AAS, respectively. All authors read the manuscript and approved for publication.

## FUNDING

This project was supported by the Bangladesh Bureau of Educational Information and Statistics (Project ID: LS2018751) and Ministry of Education, Government of the People's Republic of Bangladesh to MMH.

## ACKNOWLEDGMENTS

We would like to thank Dr. Ziban Chandra Das, Department of Gynecology, Obstetrics and Reproductive Health for supplying sheep blood for hemolytic test.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2021.615113/full#supplementary-material

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