Evidence map›Paper›PMID 41437386›Full record

ArticleMicrobiome2025

Genome-resolved surveillance and predictive ecological risk modeling of urban microbiomes.

Suleiman Aminu, AbdulAziz Ascandari, Morad M Mokhtar, Achraf El Allali, Rachid Benhida, Rachid Daoud

Abstract read
In one paragraph

Article in Microbiome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Suleiman AminuCollege of Chemical Sciences and Engineering (CCSE), Chemical and Biochemical Sciences (CBS), Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, Benguerir, 43150, Morocco. Suleiman.aminu@um6p.ma.
AbdulAziz AscandariCollege of Chemical Sciences and Engineering (CCSE), Chemical and Biochemical Sciences (CBS), Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, Benguerir, 43150, Morocco.
Morad M MokhtarCollege of Chemical Sciences and Engineering (CCSE), Chemical and Biochemical Sciences (CBS), Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, Benguerir, 43150, Morocco.
Achraf El AllaliBioinformatics Laboratory, College of Computing, Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, Benguerir, 43150, Morocco.
Rachid BenhidaCollege of Chemical Sciences and Engineering (CCSE), Chemical and Biochemical Sciences (CBS), Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, Benguerir, 43150, Morocco.
Rachid DaoudCollege of Chemical Sciences and Engineering (CCSE), Chemical and Biochemical Sciences (CBS), Mohammed VI Polytechnic University (UM6P), Lot 660, Hay Moulay Rachid, Benguerir, 43150, Morocco. rachid.daoud@um6p.ma.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHuman-built environment microbiomes mediate pathogen persistence and antimicrobial resistance (AMR) circulation, yet their ecological organization and resilience remain poorly quantified. Hospitals, sewage systems, ambulances, and public transport form interconnected microbial networks where contamination potential and compositional stability define biosurveillance risk. Understanding these dynamics requires genome-resolved frameworks capable of linking community composition to ecological behavior.

methodsWe analyzed 767 publicly available Illumina metagenomes from four urban environments using the GRUMB workflow. Quality-filtered reads were assembled into 10,834 metagenome-assembled genomes (MAGs) and dereplicated into 1542 species-level representatives. Functional annotation with CARD and VFDB identified ARG- and VF-carrying species, producing a genome-resolved abundance matrix used for ecological and predictive modeling. Alpha and beta diversity, indicator taxa, and prevalence were assessed in R, while machine learning (Random Forest, scikit-learn) achieved a nested cross-validation balanced accuracy of 0.97 ± 0.01. Synthetic donor-recipient simulations (α = 0-1) implemented in Python modeled compositional blending, entropy-based uncertainty, and Minimal Detectable Contamination (MDC) thresholds.

resultsMicrobial communities exhibited strong environment-specific structure (PERMANOVA R

conclusionsThis genome-resolved and simulation-driven framework reveals a directional microbial continuum across urban infrastructures governed by dominance, resilience, and clinical connectivity. Hospital sewage functions as a microbial donor, while hospital environments act as ecological stabilizers anchoring built-environment microbiomes. These findings advance biosurveillance from descriptive profiling to predictive ecological modeling, offering quantitative metrics for risk-informed infrastructure design. Video Abstract.

Indexed as

BacteriaMicrobiotaCitiesGenome, BacterialHumansMachine LearningMetagenomeMetagenomicsSewageSewageAntimicrobial resistanceBiosurveillanceBuilt-environment microbiomeEcological resilienceEntropyGenome-resolved metagenomicsMachine learningSynthetic contamination modeling

Identifiers

PMID41437386
PMCPMC12837002

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.