Microbial landscape of Indian homes: the microbial diversity, pathogens and antimicrobial resistome in urban residential spaces

Abstract

Background: Urban dwellings serve as complex and diverse microbial community niches. Interactions and impact of house microbiome on the health of the inhabitants need to be clearly defined. Therefore, it is critical to understand the diversity of the house microbiota, the presence and abundance of potential pathogens, and antimicrobial resistance.

Results: Shotgun metagenomics was used to analyze the samples collected from 9 locations in 10 houses in New Delhi, India. The microbiota includes more than 1409 bacterial, 5 fungal, and 474 viral species en masse. The most prevalent bacterial species were Moraxella osloensis, Paracoccus marcusii, Microbacterium aurum, Qipengyuania sp YIMB01966, and Paracoccus sphaerophysae, which were detected in at least 80 samples. The location was the primary factor influencing the microbiome diversity in the Indian houses. The overall diversity of different houses did not differ significantly from each other. The surface type influenced the microbial community, but the microbial diversity on the cemented and tiled floors did not vary significantly. A substantial fraction of the bacterial species were potentially pathogenic or opportunistic pathogens, including the ESKAPE pathogens. Escherichia coli was relatively more abundant in bedroom, foyer, and drawing room locations. Analysis of the house microbiome antimicrobial resistome revealed 669 subtypes representing 22 categories of antimicrobial resistance genes, with multidrug resistance genes being the most abundant, followed by aminoglycoside genes.

Conclusions: This study provides the first insight into the microbiomes of houses in New Delhi, showing that these houses have diverse microbiomes and that the location within the house significantly influences the microbiota. The presence of potential pathogens and a repertoire of antimicrobial resistance genes reflect possible health risks, as these could lead to infectious disease transmission. This study builds a framework for understanding the microbial diversity of houses in terms of geographical location, environment, building design, cleaning habits, and impact on the health of occupants.

Keywords: Antimicrobial resistance genes; Built environment; Floor; House microbiome; Locations; Microbiome; Potential pathogens.

Fig. 1

Study Design. The house microbiome study was conducted across ten houses in New Delhi, India. The houses were divided into two categories based on the type of flooring: 5 houses with cemented floors and five houses with tiled floors. Ninety samples were collected from 9 distinct locations in each apartment. These samples were subjected to DNA isolation and shotgun metagenome sequencing. Further bioinformatics analyses were conducted for taxonomic profiling, ARGs, and Pathogen detection

Fig. 2

The bacterial phylum composition of all the house samples. The house samples primarily contained Proteobacteria, Actinobacteria, and Firmicutes, which had relatively greater abundances in the foyer, bedroom, and drawing room locations. The bacterial phyla with less than 3% relative abundance were pooled as others. The samples are grouped based on their locations, abbreviated F (foyer), B (bedroom), D (drawing room), SA (shower area), TA (toilet area), BS (bathroom sink), KF (kitchen floor), KSL (kitchen slab) and KS (kitchen sink)

Fig. 3

Shannon diversity variations. (A) Shannon diversity across different locations of the houses. (B) Shannon diversity across different location groups. (C) Shannon diversity across different surface types of the houses. The locations are abbreviated as F: foyer, B: bedroom, D: drawing room, SA: shower area, TA: toilet area, BS: bathroom sink, KF: kitchen floor, KSL: kitchen slab and KS: kitchen sink. The location groups are abbreviated as Living Area (foyer, drawing room, and bedroom), Bathroom (toilet area, shower area, and bathroom sink), and Kitchen (Kitchen floor, kitchen slab, and kitchen sink). The surface types are abbreviated as C: cemented, T: tiled, G: granite, S: steel, and Cr: ceramic. The box plots represent the interquartile range, with the dark line representing the group’s median

Fig. 4

Beta diversity variations. (A) Principal coordinate analysis of samples grouped as location groups, and (B) surface types, based on the Bray–Curtis dissimilarity between the samples. Each point represents a sample. The amount of variance explained by each axis is denoted by the axis label. The location groups are abbreviated as Living Area (foyer, drawing room, and bedroom), Bathroom (toilet area, shower area, and bathroom sink), and Kitchen (Kitchen floor, kitchen slab, and kitchen sink). The surface types are abbreviated as C: cemented, T: tiled, G: granite, S: steel, and Cr: ceramic

Fig. 5

Viral diversity across the house samples. Viruses were identified using ViromeScan; the top 20 viruses were represented, and the rest were pooled as others. The locations are abbreviated as F (foyer), B (bedroom), D (drawing room), SA (shower area), TA (toilet area), BS (bathroom sink), KF (kitchen floor), KSL (kitchen slab) and KS (kitchen sink)

Fig. 6

Potential pathogens in house microbiome data. The top 50 potential pathogens and opportunistic pathogens in the house microbiome were identified based on the compiled pathogenic list (A). The top 50 species were plotted based on percentile scores calculated using the relative abundance of the species across all the samples. The locations are abbreviated as F (foyer), B (bedroom), D (drawing room), SA (shower area), TA (toilet area), BS (bathroom sink), KF (kitchen floor), KSL (kitchen slab) and KS (kitchen sink)

Fig. 7

The composition of antibiotic resistance gene (ARGs) types across all the samples. The samples are grouped based on their location. ARGs types with less than 5% mean relative abundance were pooled as others. The locations are abbreviated as F (foyer), B (bedroom), D (drawing room), SA (shower area), TA (toilet area), BS (bathroom sink), KF (kitchen floor), KSL (kitchen slab) and KS (kitchen sink)

Fig. 8

Principal coordinate analysis based on the composition of the antibiotic resistance genes subtypes. PCoA was carried out using the Bray‒Curtis distance between the samples. The samples are labeled based on their location (A) and location group (B). The locations are abbreviated as F (foyer), B (bedroom), D (drawing room), SA (shower area), TS (toilet area), BS (bathroom sink), KF (kitchen floor), KSL (kitchen slab) and KS (kitchen sink)