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Are biofilms secretly lurking in your cooling water systems?

Water has excellent heat transfer efficiency, which is the driving force behind medium to large commercial and manufacturing facilities’ preference for water-cooled systems over their air-cooled counterparts. Ensuring optimal ambient air temperatures for guest comfort and precise process temperature control, water-cooled systems play a crucial role in various industries. 

Microbiological fouling is an ongoing concern in open recirculating cooling water systems. Biofouling can lead to corrosion damage, losses in heat transfer efficiency, and Legionella development. By detecting, eradicating, and monitoring biofilms using modern DNA tools, effective biocontrol methods will achieve superior results at lower cost.

Credit: H.Wang (1)

Key Underlying Principles

Legionella is a parasitic organism that requires the presence of certain amoeba and protozoa host organisms in a cooling water system to survive and thrive. Legionella infects and multiplies in host organisms until such point that they lyse, exploding out of the host often in thousands of new Legionella organisms, which then search for new hosts and repeat the process.

Legionella hosts require the presence of biofilms (slime layers) which can form in cooling water systems and adhere to metal surfaces. Biofilms have a diverse ecology, providing food for protozoa and amoeba. Biofilms facilitate Microbial Influenced Corrosion (MIC) and are more thermally insulating than calcium carbonate scale. When biofilms form on heat exchange surfaces they can cause significant increases in energy costs. Biofilms provide a layer of protection from biocides and disinfectants, enabling more resistant and resilient microbial communities.

Credit: B. Hayes/NIST

Case Study: DNA Insights into Cooling Water Biofilms

Microbe Detectives analyzed 3 cooling water samples using next gen 16S and 18S DNA sequencing and qPCR for an experienced industrial water treatment company and specialist in treating cooling water systems. Each sample was collected from a different cooling water system operating during a warm summer day and treated to prevent scaling, biofouling, and MIC.

Microbial control was described as poor for sample #1, excellent for sample #2, and good for sample #3. Summary bio-characteristics of these samples are provided in the below table.

Estimated Unique Identities

209 unique genera were observed in sample #1, 216 in sample #2, and 150 in sample #3, measured as Operational Taxonomic Units (OTUs).

Estimated Quantities

The largest estimated quantity of microbes observed was in sample #1 with ~39 million 16S and 18S gene copies/mL. Approximately 220 thousand were observed in sample #2, and 514 thousand in sample #3. These values correlate with the descriptions provided by the customer about the degree of microbial control in each sample.

Shannon Diversity Index (SDI)

SDI biodiversity observed was similar across the samples in the mid-to-high 2’s, on a scale of 0 – 5, with 5 representing the highest possible biodiversity value. This is the first clue that there may be more to the story. I would expect to see a greater difference in biodiversity between excellent microbial control of a cooling water system and poor control. Specifically, excellent microbial control should normally correlate to a lower SDI biodiversity value compared to a poorly controlled system. There are always site specific considerations to account for, however, as a good microbe detective, this provides reason to dig further.

% Relative Abundance of Kingdoms

  • Bacteria (59% – 71%) were the primary kingdom observed in all three samples, with a % rel. abundance of 71%, 59%, and 69% for samples #1, #2, and #3 respectively. The ecology of bulk recirculating cooling water with good to excellent microbial control should mainly consist of bacteria. Higher life forms are indicative of biofilms.
  • Protista (13% – 22%) were the second most abundant kingdom observed in all three samples, ranging from 13% to 22% rel. abundance. Protista is the kingdom of known Legionella hosts Acanthamoeba, Naegleria, and Vermamoeba (2). This discovery provided more evidence that biofilms may be lurking undetected in all three cooling water systems. In fact, the % rel. abundance of Protista in sample #2 (22%) was the highest observed. This is conflicted with the description of excellent microbial control for this sample. The % rel. abundance of Protista observed in sample #3 (13%) was about the same as in sample #1 (14%). Sample #3 was described as having good microbial control.
  • Fungi (9% – 11%) were the third most abundant kingdom observed in all three samples, with similar rel. abundance ranging from 9% to 11%.
  • Animalia and Plantae (2% – 6%) were the fourth and fifth most abundant kingdoms observed in all three samples, ranging from 2% to 6%.

The presence of Legionella hosts and other Eukarya are an indication of biofilm(s) lurking in the cooling water system.

Legionella was detected by a standard culture test and by 16S DNA sequencing in all three samples. Est. quantities of Legionella were about 7,300 in sample #1, 300 in #2, and 6,600 in #3. Legionella hosts, specifically Vermamoeba, were observed in all 3 samples. Est. quantities were about 500 in sample #1; 70 in #2, and 90 in #3 (18S gene copies/mL). 

Other Biofilm Indicators

As shown in the above table, slime forming and corrosion associated microbes were observed in all three samples. Slime formers help produce biofilms. MIC microbes often thrive underneath biofilms and cause underdeposit corrosion. The best way to interpret this data is by monitoring trends and observing results. In general, the goal is to minimize or eliminate slime forming and MIC microbes from the cooling water system.

Key Performance Indicators (KPIs) of Biofim Removal

Shannon Diversity Index (SDI)

  1. One way to verify biofilm removal from open recirculating cooling water systems is to first measure the SDI of the bulk recirculating water prior to shutdown for scheduled maintanance.
  2. During this scheduled maintenance, thoroughly review the system for potential biofilm locations. Look for biofilms in deadlegs, any strainers or filters, and any low or no-flow areas.
  3. Locate and completely remove the biofilm(s) from the system.
  4. Return the cooling system to operation and apply a strong biocontrol treatment to provide further assurance that the system has been properly restored.
  5. Once returned to normal treatment operation, re-measure the SDI of bulk recirculating water. You should find SDI values to be lower. Those values can then be used as an indicator or benchmark for excellent microbial control of the cooling water system when biofilms are known to have been eradicated.

Direct Measurement of Legionella Hosts

Generally speaking, if biofilms do not develop, Legionella hosts are not likely to develop because they need the diverse ecology of microbes and nutrients provided by biofilms to survive and thrive. If Legionella hosts do not develop in the system, Legionella are not likely to develop in the system.

Therefore, by detecting and tracking Legionella hosts, you are one step closer to a more predictive indicator of biofilm and Legionella development.

The Take Away

This data demonstrates that Legionella hosts can secretly develop in a cooling water system, even when microbial control is believed to be excellent. As demonstrated, if Legionella hosts are present, Legionella bacteria are likely to be present. Making things worse, underdeposit corrosion beneath biofilms, and loss of heat transfer efficiency are also threats lurking in the system.

By detecting, eradicating, and monitoring Legionella hosts and biofilms using modern DNA tools, effective biocontrol methods will achieve superior results at lower cost.

By applying modern tools such as DNA, cooling tower owners, operators, and service professionals, are in a much stronger position to detect and eradicate biofilms and prevent them from wreaking havoc.

Contact us today to learn more.

References

  1. ACS Appl. Bio Mater. 2023, 6, 8, 3213–3220, Publication Date: July 10, 2023, Copyright © 2023, American Chemical Society
  2. Boamah DK, Zhou G, Ensminger AW, O’Connor TJ. From Many Hosts, One Accidental Pathogen: The Diverse Protozoan Hosts of Legionella. Front Cell Infect Microbiol. 2017 Nov 30;7:477. doi: 10.3389/fcimb.2017.00477. PMID: 29250488; PMCID: PMC5714891.
  3. Chadee, Amanda and Skovhus, Torben Lund. “Linking Microbiologically Influenced Corrosion to Microbiological Activity UsingMolecular Microbiological Methods.” Materials Performance, May 2020.
  4. “Mapping the microbiome of… ” The Forefront, University of Chicago, November 2017.
  5. Thompson, , Sanders, J., McDonald, D. et al. “A communal catalogue reveals Earth’s multiscale microbial diversity.” Nature, November 2017.
  6. Keele, “Using eDNA to test for pathogens in reused water.” U.S. Department of the Interior, Bureau of Reclamation, September 2016.
  7. Ghylin, “DNA based microbial analysis detects and locates potential contamination in distribution systems.” Journal AWWA, March 2014.
Performance problems with dairy farm anaerobic digester linked to overly dominant Fungi

Performance problems with dairy farm anaerobic digester linked to overly dominant Fungi

Performance problems with dairy farm anaerobic digester linked to overly dominant Fungi

Biogas production from anaerobic digestion of organic waste is a biotechnological process involving complex bacterial, archaeal and likely fungal communities. The presence and function of active fungi in anaerobic digesters have been barely investigated (1). In this project, Aspergillus fungi were observed at a surprisingly dominant abundance in a Jersey cow dairy farm anaerobic digestion process that had been experiencing persistent, unresolvable foaming challenges. This raised suspicions by the customer’s fermentation engineers about the potential for Aspergillus to be contributing to the foaming problem, as well as negatively impacting biogas yields.

Background

A medium size dairy farm had been experiencing persistant, unresolvable foaming problems with their anaerobic digester for more than a year. They called on Microbe Detectives to see if the root cause could be microbiological. The selected diagnostic strategy had three parts.

Part 1

One sample of biosolids was collected with a specialized DNA analysis sample collection kit, from the influent, acidogenesis phase, and methanogenesis phase of the anaerobic digestion process. Each sample was analyzed with DNA amplicon sequencing of the 16S and 18S rRNA genes, identifying nearly all Bacteria, Archaea, and Eukarya in each sample at the genus level or above and their relative percentage abundance.

Part 2

A second set of samples was collected from a Holstein cow dairy farm anaerobic digestion process that was owned and operated by the same farmer. One sample of biosolids was collected from the acidogenesis phase, and methanogenesis phase. Each sample was collected and analyzed using the same DNA methods applied in Part 1. The Holstein dairy farm anaerobic digester was reported by the farmer to be performing well, and did not have a foaming issue.

Part 3

The data collected from Part 1 and Part 2 where then compared to see what we could discover, and potentially link to the foaming problem, warranting further investigation.

Top 10 Most Abundant Microbes

Farm A Anaerobic Digester, Bacteria and Archaea

Lachnospiraceae were the most abundant bacteria and archaea observed in the influent, acidogenesis phase, and methanogenesis phase. Lachnospiraceae are a family of obligately anaerobic, spore-forming bacteria that ferment diverse plant polysaccharides to short-chain fatty acids (butyrate, acetate) and alcohols (ethanol) (2). These bacteria are in the order Eubacteriales and are known to be the most abundant microbe group in the rumen, or second stomach, of dairy cows (3). Lachnoclostridum are genera members of the Lachnospiraceae family. Together, the % rel. abundance observed was 52% in the influent biosolids, 51% in the acidogenesis phase, and 9% in the methanogenesis phase.

Farm A Anaerobic Digester, Eukarya

Aspergillus was observed to be the most abundant eukaryotic microbe in the influent (47%), acidogenesis phase (49%), and methanogenesis phase (58%). It was notable that Aspergillus was observed to increase in abundance in methanogenesis to 58% rel. abundance, as shown below. This clearly demonstrated its dominance among eukaryotic microbes.

Farm B Anaerobic Digester

Our analysis of the top 10 most abundant microbes observed in the anaerobic digester at Farm B mainly focused on searching for notable differences in the microbiota, as compared to the anaerobic digester at Farm A. As shown below, a notable differentiating characteristic observed was a more diverse ecology and lack of a dominating eukaryotic microbe group, as compared to the anaerobic digester at Farm A.

Comparison of Eukarya % Abundance

The differentiating characteristics of the microbiota observed in the anaerobic digesters were also revealed by comparing the % rel. abundance of eukarya kingdoms observed. Specifically, in the methanogenesis phase of the anaerobic digester at Farm A, Fungi % rel. abundance was a dominant 74%. In the methanogenesis phase of the anaerobic digester at Farm B, a much more even distribution of the kingdoms was observed, where Fungi represented only 38% rel. abundance.

Comparison of Archaea % Abundance

Further evidence of key differences between the microbiota observed in the anaerobic digester at Farm A compared to Farm B was revealed by comparing % rel. abundance of archaea. All methanogens are members of the Archaea domain. These microbe groups play a key role in decomposing organics and producing renewable energy in the form of methane biogas.

As shown below, the % relative abundance of Archaea observed were significantly greater in the anaerobic digester at Farm B, 5.3% versus 0.7% in the acidogenesis phase, and 12.3% versus 2.6% in the methanogenesis phase. These are significant differences that indicate superior biogas production yields at Farm B.

Comparison of Methanogen % Abundance

A comparison of methanogen % relative abundance confirmed the differences with Archaea abundance. Methanogen % rel. abundance observed in the methanogenesis phase at Farm B was nearly five times greater than in the methanogenesis phase at Farm A, as illustrated below.

The Takeaway

In reviewing this data with Malcolm Fabiyi, PhD, an expert in anaerobic digestion and bioaugmentation, we were able to shed further light as follows:

  • The DNA data showed that Farm A had been extensively overrun by Aspergillus.
  • In the affected farm, the population of methanogens was depressed by the Fungi. The implication is that a route for methanogen seeding was significantly reduced, and as a result, archaea mediated processes, especially methanogenesis were compromised. 
  • Aspergillus sp. are known to generate biosurfactants which can affect the surface tension of digestate and enhance foaming tendency.
  • Foaming can occur as a result of an accumulation of fermentation products (VFAs, CO2, H2, etc.) due to reduced capacity for conversion of these intermediates to methane by hydrogenotrophic (CO2 + H2) and acetotrophic methanogens (convert VFAs to CH44 and CO2).
  • As proteinaceous matter gets degraded during fermentation, digester alkalinity increases and the combination of alkali and VFA can lead to foam formation.
  • Other mechanisms that could have contributed to foaming includes poor mixing, and excessive gas accumulation (e.g., CO2)

References

  1. Langer SG, Gabris C, Einfalt D, Wemheuer B, Kazda M, Bengelsdorf FR. Different response of bacteria, archaea and fungi to process parameters in nine full-scale anaerobic digesters. Microb Biotechnol. 2019 Nov;12(6):1210-1225. doi: 10.1111/1751-7915.13409. Epub 2019 Apr 17. PMID: 30995692; PMCID: PMC6801161.
  2. Boutard, M; Cerisy, T (13 November 2014). “Functional Diversity of Carbohydrate-Active Enzymes Enabling a Bacterium to Ferment Plant Biomass”. PLOS Genetics. 10 (11): e1004773. doi:10.1371/journal.pgen.1004773. PMC 4230839. PMID 25393313
  3. Seshadri, R; Leahy, SC (19 March 2018). “Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection”. Nature Biotechnology. 36 (4): 359–367. doi:10.1038/nbt.4110. PMC 6118326. PMID 29553575
Are biofilms secretly lurking in your cooling water systems?

Are biofilms secretly lurking in your cooling water systems?

Are biofilms secretly lurking in your cooling water systems?

Water has excellent heat transfer efficiency, which is the driving force behind medium to large commercial and manufacturing facilities’ preference for water-cooled systems over their air-cooled counterparts. Ensuring optimal ambient air temperatures for guest comfort and precise process temperature control, water-cooled systems play a crucial role in various industries. 

Microbiological fouling is an ongoing concern in open recirculating cooling water systems. Biofouling can lead to corrosion damage, losses in heat transfer efficiency, and Legionella development. By detecting, eradicating, and monitoring biofilms using modern DNA tools, effective biocontrol methods will achieve superior results at lower cost.

Credit: H.Wang (1)

Key Underlying Principles

Legionella is a parasitic organism that requires the presence of certain amoeba and protozoa host organisms in a cooling water system to survive and thrive. Legionella infects and multiplies in host organisms until such point that they lyse, exploding out of the host often in thousands of new Legionella organisms, which then search for new hosts and repeat the process.

Legionella hosts require the presence of biofilms (slime layers) which can form in cooling water systems and adhere to metal surfaces. Biofilms have a diverse ecology, providing food for protozoa and amoeba. Biofilms facilitate Microbial Influenced Corrosion (MIC) and are more thermally insulating than calcium carbonate scale. When biofilms form on heat exchange surfaces they can cause significant increases in energy costs. Biofilms provide a layer of protection from biocides and disinfectants, enabling more resistant and resilient microbial communities.

Credit: B. Hayes/NIST

Example DNA Insights in 3 Cooling Water Samples

Microbe Detectives analyzed 3 cooling water samples using next gen 16S and 18S DNA sequencing and qPCR for an experienced industrial water treatment company and specialist in treating cooling water systems. Each sample was collected from a different cooling water system operating during a warm summer day and treated to prevent scaling, biofouling, and MIC.

Microbial control was described as poor for sample #1, excellent for sample #2, and good for sample #3. Summary bio-characteristics of these samples are provided in the below table.

% Relative Abundance of Microbe Group Domains

Archaea: 0.0%

No archaea were detected in any of the samples.

Bacteria: 90% – 99%

Bacteria were the primary domain observed in all three samples, with a % relative abundance of 99%, 92%, and 90% for samples #1, #2, and #3 respectively, measured as 16S gene copies/mL. The ecology of bulk recirculating cooling water with good to excellent microbial control should mainly, if not entirely, consist of bacteria. Higher life forms are indicative of biofilms.

Eukarya: 1% – 10%

Eukarya are higher life forms that rely on bacteria, archaea, and nutrients to survive and thrive in cooling water systems. Their presence in cooling water systems is generally indicative of the presence of biofilms. There are four kingdoms of Eukarya including Protists, Fungi, Plants, and Animals. Eukarya were detected in all three samples, with an estimated % rel. abundance ranging from 1% to 10%.

% Relative Abundance of Eukarya

The % rel. abundance of Eukarya groups observed is summarized in the below chart. Protista is the kingdom of known Legionella hosts Acanthamoeba, Naegleria, and Vermamoeba (2). The observed presence of Protista in all three samples was the indicator that biofilm may be present.

Identities: 150 to 216 Estimated Unique Genera

209 unique genera were observed in sample #1, 216 in sample #2, and 150 in sample #3, measured as Operational Taxonomic Units (OTUs).

Quantities: 0.2 to 39 Million Estimated

The largest estimated quantity of microbes observed was in sample #1 with ~39 million 16S and 18S gene copies/mL. Approximately 220 thousand were observed in sample #2, and 514 thousand in sample #3. These values correlate with the descriptions provided by the customer about the degree of microbial control in each sample.

Biodiversity: 2.4 to 2.9 (Range = 0 – 5)

SDI biodiversity observed was similar across the samples in the mid-to-high 2’s, on a scale of 0 – 5, with 5 representing the highest possible biodiversity value. This was the second clue that there may be more to the story. A greater difference in biodiversity between excellent microbial control of a cooling water system and poor control was expected. Specifically, excellent microbial control was expected to correlate to a lower SDI biodiversity value compared to a poorly controlled system. There are always site specific considerations to account for.

The presence of Legionella hosts and other Eukarya are an indication of biofilm(s) lurking in the cooling water system.

Legionella was detected by a standard culture test and by 16S DNA sequencing in all three samples. Est. quantities of Legionella were about 7,300 in sample #1, 300 in #2, and 6,600 in #3. Legionella hosts, specifically Vermamoeba, were observed in all 3 samples. Est. quantities were about 500 in sample #1; 70 in #2, and 90 in #3 (18S gene copies/mL). 

Other Biofilm Indicators

As shown in the above table, slime forming and corrosion associated microbes were observed in all three samples. Slime formers help produce biofilms. MIC microbes often thrive underneath biofilms and cause underdeposit corrosion. The best way to interpret this data is by monitoring trends and observing results. In general, the goal is to minimize or eliminate slime forming and MIC microbes from the cooling water system.

Key Performance Indicators (KPIs) of Biofim Removal

Shannon Diversity Index (SDI)

  1. One way to verify biofilm removal from open recirculating cooling water systems is to first measure the SDI of the bulk recirculating water prior to shutdown for scheduled maintanance.
  2. During this scheduled maintenance, thoroughly review the system for potential biofilm locations in deadlegs, strainers, filters, and any low or no-flow areas.
  3. Locate and completely remove the biofilm(s) from the system.
  4. Return the cooling system to operation and apply a strong biocontrol treatment to provide further assurance that the system has been properly restored.
  5. Once returned to normal treatment operation, re-measure the SDI of bulk recirculating water. You should find SDI values to be lower. Those values can be used as an indicator for excellent microbial control of the cooling water system when biofilms are known to have been eradicated.

Direct Measurement of Legionella Hosts

Generally speaking, if biofilms do not develop, Legionella hosts are not likely to develop because they need the diverse ecology of microbes and nutrients provided by biofilms to survive and thrive. If Legionella hosts do not develop in the system, Legionella are not likely to develop in the system.

Therefore, by detecting and tracking Legionella hosts, you are one step closer to a more predictive indicator of biofilm and Legionella development.

The Takeaway

This data demonstrates that Legionella hosts can secretly develop in a cooling water system, even when microbial control is believed to be excellent. As demonstrated, if Legionella hosts are present, Legionella bacteria are likely to be present. Making things worse, underdeposit corrosion beneath biofilms, and loss of heat transfer efficiency are also threats lurking in the system.

By detecting, eradicating, and monitoring Legionella hosts and biofilms using modern DNA tools, effective biocontrol methods will achieve superior results at lower cost.

References

  1. ACS Appl. Bio Mater. 2023, 6, 8, 3213–3220, Publication Date: July 10, 2023, Copyright © 2023, American Chemical Society
  2. Boamah DK, Zhou G, Ensminger AW, O’Connor TJ. From Many Hosts, One Accidental Pathogen: The Diverse Protozoan Hosts of Legionella. Front Cell Infect Microbiol. 2017 Nov 30;7:477. doi: 10.3389/fcimb.2017.00477. PMID: 29250488; PMCID: PMC5714891.
  3. Chadee, Amanda and Skovhus, Torben Lund. “Linking Microbiologically Influenced Corrosion to Microbiological Activity UsingMolecular Microbiological Methods.” Materials Performance, May 2020.
  4. “Mapping the microbiome of… ” The Forefront, University of Chicago, November 2017.
  5. Thompson, , Sanders, J., McDonald, D. et al. “A communal catalogue reveals Earth’s multiscale microbial diversity.” Nature, November 2017.
  6. Keele, “Using eDNA to test for pathogens in reused water.” U.S. Department of the Interior, Bureau of Reclamation, September 2016.
  7. Ghylin, “DNA based microbial analysis detects and locates potential contamination in distribution systems.” Journal AWWA, March 2014.

Fecal contamination observed near wastewater plant along Florida’s coast was from non-human sources

Fecal contamination observed near wastewater plant along Florida’s coast was from non-human sources

Fecal contamination observed near wastewater plant along Florida’s coast was from non-human sources

A municipal wastewater treatment plant (City WWTP) fielded complaints regarding fecal matter on local beaches. In review of the potential sources of fecal contamination with Microbe Detectives (MD), and MD technical advisor Alison Ling, PhD, three had been contemplated including animal feces, human feces from the City’s homeless population, and the City WWTP.  The objectives of this sampling and analysis program were to:

  • Evaluate the presence/absence of overall fecal contamination and human fecal contamination in samples collected from stormwater outfalls.
  • Evaluate the relative contribution of human fecal contamination in samples collected.
  • Evaluate how presence/absence and relative contribution of human fecal contamination change with time.

Fecal Coliform Measurements

Fecal coliform and similar culture-based microbiology testing methods count how many bacteria grow on a specific type of food under specific laboratory conditions. As such, the term “fecal coliform” is misleading in that it describes bacteria that grow in a certain way rather than bacteria of a specific origin. Fecal coliform tests, including Standard Method 9222D, account for bacteria that can be present in animal feces as well as naturally occurring environmental bacteria. These methods were developed to indicate whether fecal contamination of any original exists in drinking water sources (typically very low in bacterial counts).  They routinely detect bacteria that are not fecal in origin, including bacteria common in plant materials and industrial effluents not associated with sewage (Reference 1).

Bacteria that proliferate during a controlled fecal coliform test can be found naturally in numerous environmental settings, including soils, sediments, algae, and lake water columns.  These “indigenous” fecal coliforms can be present in natural waters, even in cold climates.  When those waters are sampled, the fecal coliform laboratory method can sometimes indicate that fecal coliforms are present, in effect presenting a false positive (Reference 2).

The EPA states that “fecal coliform” test results do not meet the World Health Organization criteria for effective fecal indicators.  The EPA recommended in 1986 that states use either E. coli or enterococci tests instead of fecal coliforms to set water quality criteria, as fecal coliforms can present false positives with no association to fecal pollution (References 2, 3).

Molecular methods using qPCR Bacteroides spp. Markers

Due to these well documented method deficiencies, there has been an emphasis in recent years on the development of molecular biology methods to measure specific types of bacteria associated with fecal contamination rather than relying on culture-dependent methods with high false positive rates.  In addition to being more precise (only about 1% of bacteria can be grown in the lab), these molecular biology methods are less subject to false positives.  Specific methods used for evaluating fecal contamination are designed to measure specific types of bacteria that are prevalent in animal guts, but they are not good at growing in the environment, including Prevotella and Bacteroides genera.  Bacteriodes, specifically, include several types that evolve with the host animal and thus present host-specific targets for analysis.  These targets have been used to track the source of fecal contamination to different sources, including bovine, waterfowl, and human wastes (Reference 5).  Bacteriodes also have the benefit of comprising a significant portion of the bacteria present in human guts, and are 1,000 times more abundant in feces than fecal coliforms (References 2, 6).  Numerous studies have shown that Bacteriodes concentrations were better predictors of human pathogens than fecal coliforms (References 2, 7).

Bacteriodes spp. bacteria can be detected and quantified using quantitative polymerase chain reaction (qPCR) of the 16S ribosomal RNA gene (rRNA).  The 16S rRNA gene is the industry standard for identifying what types of microorganisms are present in a given sample, because it falls in the sweet spot of being conserved enough to be present in all organisms but variant enough to detect differences between specific species.  The qPCR method uses specific “primers” segments of DNA that bookend a targeted portion of the gene.  The primers are designed to be specific to the targeted organism (in this case Bacteroides spp.).  When combined with extracted DNA from the environmental sample and DNA replication enzymes and supplies, the qPCR process replicates DNA from only that target and back-calculates the initial concentration of the targeted DNA fragment.

Specific qPCR primers for general Bacteriodes (indicating fecal contamination) and host-specific Bacteriodes species and strains (Bacteriodes spp.) have been developed to assist with fecal source tracking efforts in environmental settings (References 5, 7). The HF183 marker targets a subset of Bacteriodes bacteria that evolved specifically with humans and is thus only present in human guts. HF183 was developed in 2005 (Reference 8) and an updated version of the assay is generally considered the most accurate method for detecting human fecal contamination (References 5, 9).

Samples collected during the first and fifth sampling events occurred after a rain event. All other samples were collected in the absence of rain before or during sampling. Fecal coliforms were analyzed by the City WWTP according to their methods. Host specific analysis for fecal bacteria were analyzed using molecular biology methods that target DNA of specific host-associated gut bacteria, specifically Bacteroides spp. (general fecal origin) and HF183 (human fecal origin).  The method involves collection of water in DNA-free containers, overnight shipment to the lab, filtration, DNA extraction, and quantitative polymerase chain reaction (qPCR) using primers developed for the specific targets.

Sampling Steps

Microbiology methods are especially sensitive to contamination from other materials. Samples were collected by authorized personnel at the City of Client WWTP, avoiding disturbance to the ground surface near or under the water close to the sampling location. The following steps were followed during sample collection.

  • Each sample container was submerged in the water at the outfall to fill the container. A one liter of water per sample was collected using sterile bottles provided by Microbe Detectives.  The inside of the bottle cap or rim of the sample bottle was not touched with anything.  If touched, the sample bottle was discarded and a new one was used.
  • The required volume of salt water was collected for each sample for fecal coliform analysis.
  • Gloves were used while sampling and were replaced often.  If gloves touched dirt or skin or clothing, they were replaced with new, clean gloves straight from the box.
  • Each sample bottle was clearly labelled with the sample name, Sample ID, date and time collected.
  • The labels on the bottles were covered with clear tape to protect them from getting wet and rubbing off from the ice and shipping.  “Bacteriodes spp.” and “HF183” were recorded on the COC for the host-specific fecal markers.

Findings

The below table summarizes test results from the first five rounds of sampling.

CT value reflects the threshold cycle number for a qPCR assay, which is the cycle number where the measured fluorescence exceeds a set threshold.  Smaller CT values correspond to larger concentrations of the target gene (Reference 11).

The below table compares results of the fecal coliform test versus the qPCR Bacteriodes spp test on the basis of whether fecal associated microbes were detected or not detected. A comparison of results is summarized by sampling event, and by sample location. The results confirm the stated testing inaccuracies of the fecal coliform test that is summarized above, as compared to the more accurate qPCR Bacteriodes spp test.

Takeaways

Results from the first five rounds of molecular testing suggested the following main takeaways:

  • Human-specific fecal contamination (Human Specific HF183) was not detected at any of the four sites during the five sampling events.
  • Samples from all four sites tested positive for fecal contamination using the molecular method (for Bacteroides spp.)
  • Positive fecal coliform results did not always correspond to positive fecal marker detection, suggesting that some fecal coliform tests may be subject to false positives.  Specifically, false positives were observed in the first sampling event (1 of 4) and the fifth sampling event (4 of 4). 
  • Positive fecal coliform and negative molecular results suggested a potential false positive fecal coliform test.  False positives on fecal coliform tests are a common concern and can be caused by naturally occurring bacteria from soil or other sources that can also grow on fecal coliform media.
  • In the third sampling event, molecular fecal markers were detected despite no detection of fecal coliforms in 3 of 4 sample locations.  This likely reflects the higher sensitivity of the molecular method over the culture-based fecal coliform method, and therefore is a false negative result.

References

  1. Doyle, MP and Erickson, MC. Closing the door on the fecal coliform assay. Microbe. 2006. Vol. 1, 4.
  2. US Environmental Protection Agency. Assessment of Fecal Indicators in Ambient Waters. 2015.
  3. World Health Organization. Guidelines for drinking-water quality. Recommendations – First addendum to the third edition. Geneva, Switzerland: World Health Organization.
  4. Zhang, Y. & Liu, WT (2019). The application of molecular tools to study the drinking water microbiome – Current understanding and future needs, Critical Reviews in Environmental Science and Technology, 49:13,  1188-1235,  DOI: 10.1080 /10643389.2019.1571351
  5. Ahmed, W, Hughes, B and Harwood, V. Current status of marker genes of Bacteroides and related taka for identifying sewage pollution in environmental waters. Water. s.l. : 8, 2016. Vol. 6.
  6. King, CH, et al. Baseline human gut microbiota profile in healthy people and standard reporting template. PLoS ONE. 2019.
  7. Savichtcheva, O, Okayama, N and Okabe, S. Relationship between Bacteroides 16S rRNA genetic markers and presence of bacterial enteric pathogens and fecal indicators. Water Research. 2007. Vol. 41.
  8. Seurinck, S, et al. Detection and quantification of the human-specific HF183 Bacteroides 16S rRNA genetic marker with real-time PCR for assessment of human fecal pollution in freshwater. Environmental Microbiology. 2005. Vol. 7, 2.
  9. Green, HC, et al. Improved HF183 quantitative real-time PCR assay for characterization of human fecal pollution in ambient surface water samples. Applied and Environmental Microbiology. 2014. Vol. 80, 10.
  10. Gronewold, AD and Wolpert , RL. Modeling the relationship between most probable number (MPN) and colony-forming unit (CFU) estimates of fecal coliform concentration. Water Research. 2008. Vol. 42, 13.

Insights to major upset at wastewater plant revealed

Insights to major upset at wastewater plant revealed

Insights to major upset at wastewater treatment plant revealed

A 10 MGD municipal wastewater treatment plant in the southwest US experienced a major plant upset and called Microbe Detectives to help diagnose the problem. Three Mixed Liquor Suspended Solids (MLSS) samples were collected from the aeration basin and were analyzed using 16S and 18S rRNA sequencing methodology, identifying all Bacteria, Archaea, and Eukarya present in the sample and the % relative abundance of each.

Problem #1 – Total Nitrogen (TN) removal plummeted

Prior to the upset, effluent daily average total nitrogen (TN) was consistently maintained < 6 mg/L. After the plant upset, TN persistently increased up to 18 mg/L, which well exceeded the discharge limits.

Notable observations

  1. The % relative abundance of tracked microbes with nitrogen removal capabilities dropped to less than half the amount observed prior to the upset (~45% down to ~20%). This is illustrated above.
  2. Thaurea, a well known Nitrate Reducing Bacteria (NRB) in wastewater treatment, was nearly wiped out (from ~10% to ~0.01%). NRBs are referred to as denitrifiers. Denitrification is a microbial facilitated process where nitrate (NO3−) is reduced to Nitrite (NO2-) and ultimately molecular nitrogen (N2). Certain species of Thaurea are known to complete nitrogen removal via simultaneous nitrification and denitrification (1).
  3. Dechloromonas, also known to have denitrification capabilities was also nearly wiped out (from ~12% to 0.04%).

Problem #2 – Final effluent clarity plummeted

The second major problem was that the daily average turbidity, measured as Nephelometric Turbidity Units (NTUs), of final effluent increased by a factor greater than 10X, up from <0.5 to 5.7 NTUs, falling outside of permit requirements.

Notable observations

  1. The % rel. abundance observed of Zoogloea plummeted after the upset, down from 8% rel. abundance to <0.1%. If Zoogloea are at a proper abundance they are known to be beneficial floc-forming bacteria due to their generation of extracellular polysaccharides (EPS) which is key to gravitational effluent-and-sludge separation and clear effluent (2). 
  2. The rel. abundance of Candidatus Microthrix was also nearly eliminated, down from ~1.2% to <0.01%. The Midas Field Guide recognizes Ca. Microthrix as filamentous organisms which are commonly associated with problematic bulking and foaming in nutrient removal plants. In this case, effluent clarity substantially decreased when their % rel. abundance was observed at trace (<0.01%) levels.

Other problems

Phosphorus Accumulating Organisms (PAOs) % rel. abundance dropped significantly

The % rel. abundance observed of two well known PAOs plummeted after the upset. Dechloromonas, dropped from ~12% to ~0.04%. Rhodocyclus, dropped from ~5% to 0.2%.

At the time, Phosphorus was not regulated in the discharge permit. The plant was anticipating new phosphorus limits when the permit is renewed.

Biological diversity dropped 32%

Biological diversity in wastewater treatment generally is associated with system resiliency. The higher the diversity, the more resilient or resistant to system upsets. Biological diversity observed, measured as Shannon’s Diversity Index, dropped ~32% after the upset, from a value of 3.8 (on a scale of 0 to 5), to 2.6.

Diagnostic clues to potential sources of the upset

The largest observed increases in % rel. abundance after the upset were generally fecal associated bacteria

Citrobacter

The largest observed increase in % rel. abundance after the plant upset was with Citrobacter, with a ~29% increase. Citrobacter spp. primarily are inhabitants of the intestinal tract of mammals and other vertebrates. Their isolation from environmental sources such as water and soil likely is the result of fecal excretion.(3)

Pantoea

The second largest increase observed was with Pantoea with a ~27% increase. As a member of the Order Enterobacterales, Pantoea is commonly associated with human gut bacteria.

Klebsiella

The third largest increase observed was with Klebsiella, with a ~9% increase. Klebsiella is a type of gram-negative bacteria that can cause different types of healthcare-associated infections. Increasingly, Klebsiella bacteria have developed antimicrobial resistance, most recently to the class of antibiotics known as carbapenems. Klebsiella bacteria are normally found in the human intestines and in the human stool (feces) and are commonly associated with healthcare facilities. (4)

Raoultella and Faecalibacterium

The fourth and fifth largest increases observed after the upset were with Raoultella (up ~2%) and Faecalibacterium (up ~2%). Both are commonly associated with fecal as well.

The prime suspect

Microbes with the largest increase in relative abundance levels after the plant upset were all fecal associated. Therefore, the root cause of the upset was likely a large, unplanned discharge of human waste (feces). Industrial dischargers are not indicated. More specifically, since Klebsiella is commonly associated with healthcare facilities, it may be a worthwhile activity to consider healthcare facilities that discharge to the wastewater treatment plant as possible sources.

References

  1. Wang Q, He J. Complete nitrogen removal via simultaneous nitrification and denitrification by a novel phosphate accumulating Thauera sp. strain SND5. Water Res. 2020 Oct 15;185:116300. doi: 10.1016/j.watres.2020.116300. Epub 2020 Aug 13. PMID: 32823196.
  2. Weixing An, Feng Guo, Yulong Song, Na Gao, Shijie Bai, Jingcheng Dai, Hehong Wei, Liping Zhang, Dianzhen Yu, Ming Xia, Ying Yu, Ming Qi, Chunyuan Tian, Haofeng Chen, Zhenbin Wu, Tong Zhang, Dongru Qiu, Comparative genomics analyses on EPS biosynthesis genes required for floc formation of Zoogloea resiniphila and other activated sludge bacteria, Water Research, Volume 102, 2016,Pages 494-504, ISSN 0043-1354, https://doi.org/10.1016/j.watres.2016.06.058.Stella Antonara,
  3. Monica I. Ardura, 141 – Citrobacter Species,
    Editor(s): Sarah S. Long, Charles G. Prober, Marc Fischer, Principles and Practice of Pediatric Infectious Diseases (Fifth Edition), Elsevier, 2018, ISBN 9780323401814, https://doi.org/10.1016/B978-0-323-40181-4.00141-9. (https://www.sciencedirect.com/science/article/pii/B9780323401814001419)
  4. Centers for Disease Control and Prevention, website (https://www.cdc.gov/klebsiella/about/index.html)