Optimising Wastewater Sample Processing for Multiple Pathogen Detection Article Swipe
Miss Shannon Fitz
,
Debora Akortia
,
Richard Larbi
,
Richard Owusu
,
Gifty Nkrumah
,
Dr Joyce Akello
,
Catherine Troman
,
Dr Jaspreet Mahindroo
,
Dr Anton Spader
,
Dr Ben Bellekom
,
Piya Rajendra
,
Professor Yaw Adu-Sarkodie
,
Dr Ellis Owusu-Dabo
,
Dr Nicholas Grassly
,
Dr Dilip Abraham
,
Prabhakar Mohan
,
Dr Michael Owusu
,
David E. Shaw
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.ijid.2024.107385
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.ijid.2024.107385
Introduction: Environmental surveillance (ES) provides valuable insight into emergence, transmission, and spread of infectious diseases, yet can be expensive to perform. The simultaneous detection of multiple pathogens in wastewater can make the process more cost-effective, yet the majority of protocols for ES are often optimised for the detection of single pathogens. Relatively little work has been done comparing concentration, genomic extraction and detection methods that can support a range of pathogens. Methods: In this project we compared five concentration methods and six extraction kits for detection of Hepatitis E, Vibrio cholerae, Salmonella Typhi, and enteroviruses in wastewater. The sensitivity of these methods for pathogen detection was measured by qPCR, PCR, and nanopore sequencing. Target organisms or appropriate surrogates were spiked into London wastewater and each trial method was conducted in triplicate. Optimal workflows were established consisting of wastewater concentration, extraction, and amplicon-based Nanopore sequencing. Results: Following these preliminary tests with spiked samples, Nanotrap® Microbiome “A” Particles and polyethylene-glycol (PEG) precipitation coupled with the MagMAX Wastewater Ultra Nucleic Acid Isolation Kit were taken forward for 6-month field trials performed at Kwame Nkrumah University of Science and Technology (Kumasi, Ghana) and Christian Medical College Vellore (India). In London, these methods showed consistent high recovery across pathogens in spiked wastewater with average recovery rates for nOPV2 and typhi of 3.08% and 45.87% (Ceres) and 3.29% and 100% (PEG). The Wastewater Kit performed well in extracting RNA/DNA across the organisms, recovering 72.27% (cholera surrogate), 100% (typhi), and 88.2% (nOPV2) of the spike compared to the optimal extraction kit for each organism. Discussion: Over the field trial, 40 samples will be collected per month in each country, using both grab and Moore swabs. Methods are being further refined in the field, with initial results indicating detection of strain markers for V. cholera, and S. Typhi. Further analysis will allow comparison of the sensitivity of grab and Moore swab for detecting both viruses and bacteria. Expected Outcome: This study aims to provide proof of principle data for cost effective surveillance of multiple pathogens in LMICs with the development of streamlined, integrated protocols. Ideally these protocols can then be adopted by public health bodies or other surveillance programs to provide added value to existing surveillance. Conclusion: Both Nanotrap Microbiome ''A'' Particles and PEG precipitation used in conjunction with the MagMAX Wastewater Kit showed promise for multi-pathogen detection in wastewater. Continued refinement of these methods throughout ongoing field trials holds the potential to offer significant utility in infectious disease surveillance.
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Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ijid.2024.107385
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- OA Status
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All OpenAlex metadata
Raw OpenAlex JSON
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https://doi.org/10.1016/j.ijid.2024.107385Digital Object Identifier
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Optimising Wastewater Sample Processing for Multiple Pathogen DetectionWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-02-28Full publication date if available
- Authors
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Miss Shannon Fitz, Debora Akortia, Richard Larbi, Richard Owusu, Gifty Nkrumah, Dr Joyce Akello, Catherine Troman, Dr Jaspreet Mahindroo, Dr Anton Spader, Dr Ben Bellekom, Piya Rajendra, Professor Yaw Adu-Sarkodie, Dr Ellis Owusu-Dabo, Dr Nicholas Grassly, Dr Dilip Abraham, Prabhakar Mohan, Dr Michael Owusu, David E. ShawList of authors in order
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https://doi.org/10.1016/j.ijid.2024.107385Publisher landing page
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https://doi.org/10.1016/j.ijid.2024.107385Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.ijid.2024.107385Direct OA link when available
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Wastewater, Sample (material), Pathogen, Environmental science, Biology, Environmental engineering, Microbiology, Chemistry, ChromatographyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.can | 16, 29, 65, 349 |
| abstract_inverted_index.for | 40, 45, 84, 102, 173, 211, 254, 294, 313, 330, 387 |
| abstract_inverted_index.has | 54 |
| abstract_inverted_index.kit | 253 |
| abstract_inverted_index.per | 267 |
| abstract_inverted_index.six | 81 |
| abstract_inverted_index.the | 31, 36, 46, 162, 234, 246, 250, 259, 284, 306, 340, 381, 402 |
| abstract_inverted_index.was | 105, 127 |
| abstract_inverted_index.yet | 15, 35 |
| abstract_inverted_index.(ES) | 3 |
| abstract_inverted_index.100% | 223, 240 |
| abstract_inverted_index.Acid | 167 |
| abstract_inverted_index.Both | 369 |
| abstract_inverted_index.Over | 258 |
| abstract_inverted_index.PCR, | 109 |
| abstract_inverted_index.This | 321 |
| abstract_inverted_index.aims | 323 |
| abstract_inverted_index.been | 55 |
| abstract_inverted_index.both | 273, 315 |
| abstract_inverted_index.cost | 331 |
| abstract_inverted_index.data | 329 |
| abstract_inverted_index.done | 56 |
| abstract_inverted_index.each | 124, 255, 270 |
| abstract_inverted_index.five | 77 |
| abstract_inverted_index.grab | 274, 309 |
| abstract_inverted_index.high | 200 |
| abstract_inverted_index.into | 7, 120 |
| abstract_inverted_index.kits | 83 |
| abstract_inverted_index.make | 30 |
| abstract_inverted_index.more | 33 |
| abstract_inverted_index.swab | 312 |
| abstract_inverted_index.that | 64 |
| abstract_inverted_index.then | 350 |
| abstract_inverted_index.this | 73 |
| abstract_inverted_index.used | 377 |
| abstract_inverted_index.well | 229 |
| abstract_inverted_index.were | 118, 133, 170 |
| abstract_inverted_index.will | 264, 302 |
| abstract_inverted_index.with | 149, 161, 207, 286, 339, 380 |
| abstract_inverted_index.work | 53 |
| abstract_inverted_index.''A'' | 372 |
| abstract_inverted_index.(PEG) | 158 |
| abstract_inverted_index.3.08% | 216 |
| abstract_inverted_index.3.29% | 221 |
| abstract_inverted_index.88.2% | 243 |
| abstract_inverted_index.Kwame | 179 |
| abstract_inverted_index.LMICs | 338 |
| abstract_inverted_index.Moore | 276, 311 |
| abstract_inverted_index.Ultra | 165 |
| abstract_inverted_index.added | 363 |
| abstract_inverted_index.allow | 303 |
| abstract_inverted_index.being | 280 |
| abstract_inverted_index.field | 175, 260, 399 |
| abstract_inverted_index.holds | 401 |
| abstract_inverted_index.month | 268 |
| abstract_inverted_index.nOPV2 | 212 |
| abstract_inverted_index.offer | 405 |
| abstract_inverted_index.often | 43 |
| abstract_inverted_index.other | 358 |
| abstract_inverted_index.proof | 326 |
| abstract_inverted_index.qPCR, | 108 |
| abstract_inverted_index.range | 68 |
| abstract_inverted_index.rates | 210 |
| abstract_inverted_index.spike | 247 |
| abstract_inverted_index.study | 322 |
| abstract_inverted_index.taken | 171 |
| abstract_inverted_index.tests | 148 |
| abstract_inverted_index.these | 100, 146, 196, 347, 395 |
| abstract_inverted_index.trial | 125 |
| abstract_inverted_index.typhi | 214 |
| abstract_inverted_index.using | 272 |
| abstract_inverted_index.value | 364 |
| abstract_inverted_index.(PEG). | 224 |
| abstract_inverted_index.45.87% | 218 |
| abstract_inverted_index.72.27% | 237 |
| abstract_inverted_index.Ghana) | 187 |
| abstract_inverted_index.London | 121 |
| abstract_inverted_index.MagMAX | 163, 382 |
| abstract_inverted_index.Target | 113 |
| abstract_inverted_index.Typhi, | 92 |
| abstract_inverted_index.Typhi. | 299 |
| abstract_inverted_index.Vibrio | 89 |
| abstract_inverted_index.across | 202, 233 |
| abstract_inverted_index.bodies | 356 |
| abstract_inverted_index.field, | 285 |
| abstract_inverted_index.health | 355 |
| abstract_inverted_index.little | 52 |
| abstract_inverted_index.method | 126 |
| abstract_inverted_index.public | 354 |
| abstract_inverted_index.showed | 198, 385 |
| abstract_inverted_index.single | 49 |
| abstract_inverted_index.spiked | 119, 150, 205 |
| abstract_inverted_index.spread | 11 |
| abstract_inverted_index.strain | 292 |
| abstract_inverted_index.swabs. | 277 |
| abstract_inverted_index.trial, | 261 |
| abstract_inverted_index.trials | 176, 400 |
| abstract_inverted_index.(Ceres) | 219 |
| abstract_inverted_index.(nOPV2) | 244 |
| abstract_inverted_index.6-month | 174 |
| abstract_inverted_index.College | 191 |
| abstract_inverted_index.Further | 300 |
| abstract_inverted_index.Ideally | 346 |
| abstract_inverted_index.London, | 195 |
| abstract_inverted_index.Medical | 190 |
| abstract_inverted_index.Methods | 278 |
| abstract_inverted_index.Nkrumah | 180 |
| abstract_inverted_index.Nucleic | 166 |
| abstract_inverted_index.Optimal | 131 |
| abstract_inverted_index.RNA/DNA | 232 |
| abstract_inverted_index.Science | 183 |
| abstract_inverted_index.Vellore | 192 |
| abstract_inverted_index.adopted | 352 |
| abstract_inverted_index.average | 208 |
| abstract_inverted_index.coupled | 160 |
| abstract_inverted_index.disease | 410 |
| abstract_inverted_index.forward | 172 |
| abstract_inverted_index.further | 281 |
| abstract_inverted_index.genomic | 59 |
| abstract_inverted_index.initial | 287 |
| abstract_inverted_index.insight | 6 |
| abstract_inverted_index.markers | 293 |
| abstract_inverted_index.methods | 63, 79, 101, 197, 396 |
| abstract_inverted_index.ongoing | 398 |
| abstract_inverted_index.optimal | 251 |
| abstract_inverted_index.process | 32 |
| abstract_inverted_index.project | 74 |
| abstract_inverted_index.promise | 386 |
| abstract_inverted_index.provide | 325, 362 |
| abstract_inverted_index.refined | 282 |
| abstract_inverted_index.results | 288 |
| abstract_inverted_index.samples | 263 |
| abstract_inverted_index.support | 66 |
| abstract_inverted_index.utility | 407 |
| abstract_inverted_index.viruses | 316 |
| abstract_inverted_index.“A” | 154 |
| abstract_inverted_index.(India). | 193 |
| abstract_inverted_index.(Kumasi, | 186 |
| abstract_inverted_index.(cholera | 238 |
| abstract_inverted_index.(typhi), | 241 |
| abstract_inverted_index.Expected | 319 |
| abstract_inverted_index.Methods: | 71 |
| abstract_inverted_index.Nanopore | 142 |
| abstract_inverted_index.Nanotrap | 370 |
| abstract_inverted_index.Outcome: | 320 |
| abstract_inverted_index.Results: | 144 |
| abstract_inverted_index.analysis | 301 |
| abstract_inverted_index.cholera, | 296 |
| abstract_inverted_index.compared | 76, 248 |
| abstract_inverted_index.country, | 271 |
| abstract_inverted_index.existing | 366 |
| abstract_inverted_index.majority | 37 |
| abstract_inverted_index.measured | 106 |
| abstract_inverted_index.multiple | 25, 335 |
| abstract_inverted_index.nanopore | 111 |
| abstract_inverted_index.pathogen | 103 |
| abstract_inverted_index.perform. | 20 |
| abstract_inverted_index.programs | 360 |
| abstract_inverted_index.provides | 4 |
| abstract_inverted_index.recovery | 201, 209 |
| abstract_inverted_index.samples, | 151 |
| abstract_inverted_index.valuable | 5 |
| abstract_inverted_index.Christian | 189 |
| abstract_inverted_index.Continued | 392 |
| abstract_inverted_index.Following | 145 |
| abstract_inverted_index.Hepatitis | 87 |
| abstract_inverted_index.Isolation | 168 |
| abstract_inverted_index.Particles | 155, 373 |
| abstract_inverted_index.bacteria. | 318 |
| abstract_inverted_index.cholerae, | 90 |
| abstract_inverted_index.collected | 266 |
| abstract_inverted_index.comparing | 57 |
| abstract_inverted_index.conducted | 128 |
| abstract_inverted_index.detecting | 314 |
| abstract_inverted_index.detection | 23, 47, 62, 85, 104, 290, 389 |
| abstract_inverted_index.diseases, | 14 |
| abstract_inverted_index.effective | 332 |
| abstract_inverted_index.expensive | 18 |
| abstract_inverted_index.optimised | 44 |
| abstract_inverted_index.organism. | 256 |
| abstract_inverted_index.organisms | 114 |
| abstract_inverted_index.pathogens | 26, 203, 336 |
| abstract_inverted_index.performed | 177, 228 |
| abstract_inverted_index.potential | 403 |
| abstract_inverted_index.principle | 328 |
| abstract_inverted_index.protocols | 39, 348 |
| abstract_inverted_index.workflows | 132 |
| abstract_inverted_index.Microbiome | 153, 371 |
| abstract_inverted_index.Nanotrap® | 152 |
| abstract_inverted_index.Relatively | 51 |
| abstract_inverted_index.Salmonella | 91 |
| abstract_inverted_index.Technology | 185 |
| abstract_inverted_index.University | 181 |
| abstract_inverted_index.Wastewater | 164, 226, 383 |
| abstract_inverted_index.comparison | 304 |
| abstract_inverted_index.consistent | 199 |
| abstract_inverted_index.consisting | 135 |
| abstract_inverted_index.emergence, | 8 |
| abstract_inverted_index.extracting | 231 |
| abstract_inverted_index.extraction | 60, 82, 252 |
| abstract_inverted_index.indicating | 289 |
| abstract_inverted_index.infectious | 13, 409 |
| abstract_inverted_index.integrated | 344 |
| abstract_inverted_index.organisms, | 235 |
| abstract_inverted_index.pathogens. | 50, 70 |
| abstract_inverted_index.protocols. | 345 |
| abstract_inverted_index.recovering | 236 |
| abstract_inverted_index.refinement | 393 |
| abstract_inverted_index.surrogates | 117 |
| abstract_inverted_index.throughout | 397 |
| abstract_inverted_index.wastewater | 28, 122, 137, 206 |
| abstract_inverted_index.Conclusion: | 368 |
| abstract_inverted_index.Discussion: | 257 |
| abstract_inverted_index.appropriate | 116 |
| abstract_inverted_index.conjunction | 379 |
| abstract_inverted_index.development | 341 |
| abstract_inverted_index.established | 134 |
| abstract_inverted_index.extraction, | 139 |
| abstract_inverted_index.preliminary | 147 |
| abstract_inverted_index.sensitivity | 98, 307 |
| abstract_inverted_index.sequencing. | 112, 143 |
| abstract_inverted_index.significant | 406 |
| abstract_inverted_index.surrogate), | 239 |
| abstract_inverted_index.triplicate. | 130 |
| abstract_inverted_index.wastewater. | 96, 391 |
| abstract_inverted_index.simultaneous | 22 |
| abstract_inverted_index.streamlined, | 343 |
| abstract_inverted_index.surveillance | 2, 333, 359 |
| abstract_inverted_index.Environmental | 1 |
| abstract_inverted_index.Introduction: | 0 |
| abstract_inverted_index.concentration | 78 |
| abstract_inverted_index.enteroviruses | 94 |
| abstract_inverted_index.precipitation | 159, 376 |
| abstract_inverted_index.surveillance. | 367, 411 |
| abstract_inverted_index.transmission, | 9 |
| abstract_inverted_index.amplicon-based | 141 |
| abstract_inverted_index.concentration, | 58, 138 |
| abstract_inverted_index.multi-pathogen | 388 |
| abstract_inverted_index.cost-effective, | 34 |
| abstract_inverted_index.polyethylene-glycol | 157 |
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| institutions_distinct_count | 18 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
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| sustainable_development_goals[0].display_name | Clean water and sanitation |
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| citation_normalized_percentile.is_in_top_10_percent | False |