You are viewing the site in preview mode

Skip to main content
Download PDF

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

Environmental Microbiome volume 20, Article number: 25 (2025) Cite this article

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.

Introduction

Keeping up with the global trend of urbanization, more than 35% of the overall Indian population has migrated to urban settings [1], which consequently has confined the population to live within the closed spaces of building infrastructures. This rapid urbanization has led people to spend most of their time in indoor environments such as homes or workspace buildings [2]. People within specific age categories, such as elderly people, young children, and people with impairments, spend much more time indoors. Pandemics such as the recent global COVID-19 outbreak further restrict people to their homes.

In addition to humans and pets, houses function as a place of dwelling for a wide variety of microorganisms [3]. The impact of the house microbiome on the health and well-being of human occupants is substantial and multifaceted and can manifest in direct or indirect ways. Pathogenic and opportunistic pathogenic microorganisms are components of building microbiomes and can spread infection through aerosols or fomites to occupants [4]. House dust has been shown to contain infectious bacterial and fungal species on floor surfaces [5]. Children and toddlers who frequently crawl or play on floor surfaces risk coming into contact with these microbes, either directly through contact or by inhaling aerosolized dust that can cause infection [6]. Poorly ventilated and damp indoor settings could accumulate these aerosols, potentially facilitating the spread of infectious diseases and allergies [7]. Understanding how to minimize infectious microbial components in building microbiomes by implementing appropriate practices and regular cleaning of hotspots and high-frequency contact surfaces is essential. Certain microorganisms in homes may lead to the emergence of allergic illnesses [8], while exposure to the microbiome in early life is also necessary to develop a robust immune system [9]. The microbiome of the surrounding environment, including the microbiome of built and outdoor environments, is known to influence gut microbiome diversity, which plays a significant role in health and disease [10, 11]. It is essential to learn about the role of the house microbiome and its interactions with residents.

Previous research has elucidated the factors that play a role in determining indoor microbial diversity, such as geography [4], building design, cleaning habits [12], and human occupancy [13]. Buildings with varying functionalities also influence the microbiome of the built environment [14], in which microbiota variation was identified in different building types, such as apartment houses, hospitals, and university buildings. Surface type is a significant influencing factor of indoor microbiome diversity [15]. Physiological factors such as moisture, temperature, exposure to sunlight, heat, and ventilation (HVAC) are the other critical conditions for the growth, transmission, and succession of microbial communities inside buildings [16]. The microbiome’s constant influx and efflux occur through ventilation [16], the water system and occupancy of human residents, and other anthropogenic factors across houses [17, 18]. Some microorganisms may find a particular location or surface type optimal for their growth and survival and may proliferate in those specific niches [19]. The transmission of microbes across different locations in buildings and humans is a two-way process. Microbes from different house locations can be transferred to the human body by touching contact surfaces and breathing; however, human skin shedding and breathing are likewise responsible for transferring the human microbiome to the built environment [20, 21].

Previous house microbiome studies indicate that the diversity of the microbiome varies among houses and is majorly influenced by the residents and the surfaces from where samples were collected. A large proportion of the house microbiome is reported to be composed of human-associated microorganisms [22], while some studies concluded that it is derived from outdoor air [23]. In most studies, Proteobacteria dominated the microbiome inside the homes [22, 23]. Findings suggest that the indoor microbial diversity significantly changes with factors such as the location of the residence, presence of indoor plants and pets, crop, animal farming, and season of sample collection [23,24,25]. Farming houses had significantly higher diverse microbiome than non-farming houses [25]. A significant difference was observed in the diversity of house microbiomes of allergic and non-allergic people [26].

There are no documented house microbiome studies from the Indian subcontinent despite the region’s markedly diverse geographic and climatic conditions, population ethnicities, way of life, and architectural styles. New Delhi is one of India’s most populous cities and is home to individuals of many different ethnic backgrounds and a wide range of building types. The present study is the first to analyze 90 samples from 9 locations in 10 Indian houses in New Delhi city by leveraging shotgun metagenomics to characterize microbial diversity. A total of > 1400 bacterial species and various viruses and fungi were identified. The house microbiome is composed of several potential pathogens and opportunistic pathogens and carries a reservoir of antimicrobial resistance genes.

Materials and methods

Sample collection

This study recruited five houses, each with tiled or cemented floors, for sample collection (Fig. 1). The houses were located in Sukhdev Vihar, Sarai Jullena, Akshardham, Mayur Vihar, Shakarpur, and Hauz Khas, New Delhi, India. Each house was occupied by 2 to 4 residents. The floors and other surfaces were regularly cleaned. The samples were collected one day after the regular cleaning, in the forenoon before the next routine cleaning of the sampled locations. All the samples for DNA extraction and plating were collected from the houses between November 2020 and February 2021. Floor samples were collected from the foyer (F), bedroom (B), drawing room (D), shower area (SA), toilet area (TA), and kitchen. Additionally, samples were also collected from kitchen slabs (KSL), kitchen sinks (KS), and bathroom sinks (BS). Samples were collected wearing sterile latex gloves and masks. Sterile cotton-tipped swabs [Puritan, V.W.R.] pre-moistened with saline (0.15 M NaCl, 0.1% Tween 20) were used for swabbing the locations. Four swabs were used for 30 × 30 cm2 at each location and stored in 50 ml sterile falcon tubes. The samples were then brought to the laboratory for processing and used on the same day for DNA extraction and plating. Duplicate samples were collected from nearby areas and stored at -80 °C for later use, if required. Swabs moistened with saline were used as collection controls. For colony-forming units (cfu) counts, the four swab samples were added to 10 ml of phosphate buffer saline (PBS) solution in a falcon tube. The falcon tubes were kept at room temperature for 30 min and vortexed intermittently. Two appropriate dilutions of the samples were prepared for plating. Plating was done on L.B. agar media in replicates and incubated at 37 oC for 24 h. The final cfu/ml value was calculated as an average of the two replicates. A two-tailed, unpaired t-test with unequal variance was used to check for significant differences between cfu counts of sample groups.

Fig. 1
figure 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

Full size image

DNA extraction and sequencing

The metagenomics DNA was isolated from the swab samples using a PowerSoil DNA isolation kit (Qiagen, Germany). The samples were processed inside a class II Biosafety hood. DNA isolation was performed according to the manufacturer’s protocol, with the following modifications. The 4 sample swabs from each location were dipped in 5 ml of saline solution for 30 min with intermittent vortexing; to the pellet obtained after centrifugation (7000 rpm for 10 min at room temperature), 60 µl of C1 solution was added, and the cell suspension was transferred to a power bead tube, followed by the addition of 40 µl of lysozyme (10 mg/ml) and incubation for 30 min at 37 °C. After the incubation, 40 µl of proteinase K (10 mg/ml) was added, and the tubes were further incubated for 90 min at 60 °C. The quality and concentration of the DNA were estimated using a Nanodrop. The sequencing libraries were prepared with an Illumina DNA Prep kit according to the manufacturer’s protocol. The IDT Illumina DNA/RNA UD Indices Set A was utilized to index the samples. The concentration of the libraries was determined using a Qubit dsDNA H.S. assay, and the average size of the insert was estimated using Bioanalyzer (Agilent Technologies). Shotgun metagenome sequencing was performed on the Illumina NovaSeq platform using the S1 Reagent Kit v1.5 (300 cycles) to obtain 150 bp paired-end reads. The data generated in concatenated base call format was converted to fastq format using bcl2fastq2 (v2.2.0); demultiplexing and removal of adapters were also performed simultaneously.

Data processing and analysis

The average Phred score of all the reads obtained from NovaSeq 6000 platform (Illumina) was more than 30. Trimmomatic (v 0.39) was used for trimming the bases with lower quality scores with a sliding window of 4:20, and reads with less than 70 bp length were discarded [27]. The read quality was analyzed using FastQC before and after trimming [28]. The reads were subsampled to 15,166,428 according to the sample with the least number of reads using the reformat.sh script of the BBMap suite [29].

Taxonomic profiling was performed using MetaPhlAn4 (version 4.1.1) [30]. Paired-end FASTQ reads were provided as input to MetaPhlAn4. Differentially abundant features for microbial diversity were identified using the ANCOM-BC2 (version 2.6.0) package using default parameters [31]. ViromeScan was used to identify viruses in the house microbiome [32]. The ARGs-OAP pipeline [33] was used to detect and quantify the antibiotic-resistance genes in all the samples. The abundance of 22 broad categories of antibiotic resistance genes was identified from the raw metagenomic reads using the Structured Antibiotic Resistance Genes Database (SARG-DB) Differentially abundant ARG categories were identified using ANCOM-II [34].

α and β diversity were calculated using bacterial community profiles at the species level, and α diversity was estimated by calculating Shannon, Simpson, and Inverse Simpson diversity indices for richness and Pielou’s Index for evenness using functions from the Vegan package [35]. The Bray‒Curtis distance was plotted using principal coordinate analysis (PCoA). To analyze the similarities between samples, we used analysis of similarities (ANOSIM), and to estimate the differences in composition, we used PERmutational Multivariate ANalysis of VAriance (PERMANOVA). The R package “vegan” version 2.5-7 was used in RStudio 2022.02.1 [36].

Identification of potential pathogens and opportunistic pathogens in the floor microbiome was performed based on string matching with “tidyverse” version 1.3.1 in RStudio [37]. Two pathogen lists were used: an in-house list containing 659 unique community-acquired pathogenic organisms compiled from multiple sources, a U.K.-approved list of biological agents (150 pathogens) [38], the National Institute of Allergy and Infectious Diseases (NIAID) Emerging pathogens (56 pathogens) [39], the Pathosystems Resource Integration Center (PATRIC) database (146 pathogens) [40], the Virulence Factor Database (VFDB, 57 pathogens) [41], the Indian Priority Pathogen List (IPPL, ten pathogens) [42], and a list of 200 pathogens used by Chen et al. [43]. The second list was prepared using CDC NHSN Pathogen codes [44] and included 1998 unique organisms at the species level; this list included organisms with the potential to cause healthcare-associated infections and was filtered based on SNOMED preferred terms. Virulence factors for the read data of each sample were identified using MetaVF toolkit [45].

Results

Bacterial diversity in Indian houses

A total of 1409 bacterial species belonging to 530 genera, representing 16 phyla, were identified from the total shotgun metagenomic data of 90 samples. At the phylum level, Proteobacteria (mean relative abundance [M.R.A], 59.04%) and Actinobacteria (32.86%) were present in all the samples. Firmicutes (2.81%) were predominantly present in the foyer, bedroom, and drawing room samples; Bacteroidetes (2.99%) was present in all the samples except for the bathroom sink sample; and Deinococcus-Thermus (1.99%) was more prevalent in the foyer, bedroom and drawing room samples (Fig. 2). Overall, Proteobacteria was the most predominant phylum in the house microbiome however, Actinobacteria (48.16%) dominated the foyer samples, and both were present in almost equal abundance in the bedroom and kitchen floors. In the kitchen sink and toilet areas, Proteobacteria (average ~ 79%) dominated the other phyla by a large proportion. The most abundant species present in more than 80 samples of the house microbiome was P. marcusii (6.36%), followed by M. osloensis (4.87%), Qipengyuania sp YIMB01966 (2.42%), P. sphaerophysae (2.37%), M. aurum (2.35%) and (Fig. S1, Table S1). Other abundant species with prevalence less than 80 included K. palustris (5.6%) and Paracoccus SGB99481 (5.06%).

Fig. 2
figure 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)

Full size image

Locations in Indian houses drive bacterial diversity and composition

The species detected in each sample ranged from 52 to 307 (Table S2). The number of species detected in the foyer (average of 10 samples = 246), bedroom (199), and drawing room (198) samples was nearly two-fold higher than that in bathroom samples (Fig. S2, Table S3). The lowest number of species was identified in samples from the shower area (83) and toilet area (93).

Variations in Shannon diversity index based on location were statistically significant (ANOVA p-value = 6.83e-06 ***). The Shannon index values indicated that the foyer (average Shannon index = 3.8) and bathroom sink (average Shannon index = 2.5) were the most and least diverse locations, respectively (Fig. 3A). None of the alpha diversity indices indicated substantial variation between the houses (Fig. S3, Table 1). Post-hoc analysis using Tukey’s HSD test grouped the kitchen floor, kitchen slab, kitchen sink, and bathroom sink to be completely different from the foyer, bedroom and drawing room, shower area, and toilet area, indicating the difference in diversity based on richness and abundance of species (Table S4).

Fig. 3
figure 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

Full size image
Table 1 Alpha diversity of the house microbiome was calculated for the observed species, Shannon diversity index, Simpson, and inverse Simpson diversity indices. Evenness was measured using Pielou’s evenness index. The difference in diversity indices was calculated using ANOVA, and the table below represents the obtained p-values for each variable factor
Full size table

In contrast, the kitchen floor and bathroom sink had the lowest evenness index, indicating a more homogeneous microbiome where some species dominate the community (Table S3). Although the drawing room had more species than the kitchen floor and bathroom sink, it also had the highest evenness index. This indicates that the drawing room had a heterogeneous microbiome. The bacterial community composition significantly differed based on the location of the samples (PERMANOVA; p-value 0.001***; R2-value 0.20) (Fig. S4). Pairwise comparisons of the locations revealed that the foyer, bedroom, and drawing room locations significantly differed from the other 6 locations (Fig. S5A). Although we found a higher bacterial diversity in the foyer, bedroom, and drawing room locations, a significantly lower number of bacterial colony-forming units (cfu/ml) were observed at these locations. The bacterial count varied significantly based on each location (ANOVA p-value 0.00131**, Fig. S6) and overall surface type (ANOVA p-value 0.000248***) and did not vary based on the house (ANOVA p-value 0.113). The average bacterial count was highest in the kitchen sink samples (1.7 × 106 cfu/ml) and lowest in the drawing room (1.0 × 104 cfu/ml) (Table S5). The bacterial count in the two floor surface types, cemented and tiled, did not vary significantly in each location.

Grouping the locations in three major zones (foyer, bedroom, and drawing room as the living area; the shower area, toilet area, and bathroom sink as the bathroom; and kitchen floor, kitchen slab, and kitchen sink as the kitchen) presented statistically significant differences across the number of species observed, Shannon diversity index (Fig. 3B) and Pielou’s evenness index among the three groups (Table 1). A clear distinction in distribution was observed between samples from living area and bathroom and kitchen (PERMANOVA, p-value = 0.001*** Fig. 4A). In contrast, the bathroom and kitchen samples display more similarity (Fig. S7) but still have statistically significant differences in composition (PERMANOVA, p-value = 0.024*) (Fig. 4A, Fig. S5B).

Fig. 4
figure 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

Full size image

The differential abundance analysis of the microbial community across the three location groups reveals significant variations in species distribution. Species such as Lysobacter sp. CJ11, Pseudoxanthomonas mexicana, and Acinetobacter SGB66365 were more abundant in the bathroom, as indicated by their negative log fold change (LFC) values (Fig. S8). On the other hand, K. palustris and Micrococcus endophyticus were more prevalent in the living area, as shown by their positive LFC values (Fig. S8). These species were found to be statistically significant and pass the ANCOM-BC2 sensitivity test.

Inhabitant-associated microorganisms can become part of the house microbiome due to skin shedding and contact. To test this hypothesis, we searched for human skin-associated bacterial genera in the total microbiome data. Skin-associated genera such as Cutibacterium, Staphylococcus, Corynebacterium, Streptococcus, and Micrococcus [46] were significantly more abundant in the living area than in the bathroom and kitchen (Fig. S9).

Surface type influences bacterial diversity

The surface type significantly affected the composition of the bacterial communities. The Shannon index varied considerably depending on surface type (Fig. 3C; Table 1). PCoA based on surface type showed significant differences (Fig. 4B, PERMANOVA; p-value 0.002). All of the alpha and beta diversity analyses showed that most of the bacterial compositions on the cemented and tiled surfaces were similar. However, both were significantly different from ceramic and steel surfaces (Fig. S5C). The granite surface type has significant difference from ceramic surface types only. The difference observed in bacterial composition based on surface type was mainly due to the use of steel and ceramic surfaces, both of which are sinks and have very different uses and conditions than other surfaces (Fig. S5C). No species were found to be differentially abundant in any of the surface types.

Archaea, fungi, and viruses in the India house microbiome

Three archaeal species were detected in the microbiome data: GGB27181_SGB39400 (Methanobacteriacea family), GGB27265_SGB75289 (Methanotrichaceae family), and GGB41707_SGB58791(Euryachaeota phylum) in 10 samples, with a mean relative abundance less than 0.001% each (Fig. S10). MetaPhlAn4 detected 5 fungal species belonging to 3 different genera: Candida (2 species), Malassezia (2 species) and Cladosporium (1 species) (Fig. S11).

ViromeScan identified a total of 474 DNA viruses across all the samples. The most prevalent viral species across all 90 samples, along with their respective relative abundances, include Pandoravirus dulcis (7.9%), Pandoravirus salinus (6.9%), Cyprinid herpesvirus 3 (4.5%), Ectocarpus siliculosus virus 1 (3.9%), Macacine herpesvirus 1 (3.7%), Cyprinid herpesvirus 1 (2.7%), Caviid herpesvirus 2 (2.7%), Pandoravirus inopinatum (2.2%), Tupaiid herpesvirus 1 (2.0%), and Aureococcus anophagefferens virus (1.0%) (Fig. 5).

Fig. 5
figure 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)

Full size image

Potential pathogens in indian houses

A total of 62 bacterial species from the floor microbiome were identified as putatively pathogenic or opportunistic pathogens compared with the compiled community-acquired pathogen list (Fig. 6). Acinetobacter junii was the most prevalent bacterial pathogen present in 69 samples of the house microbiome. Three potentially pathogenic bacterial species were present in more than 0.5% of the M.R.A. in the present study, namely, A. junii, Acinetobacter calcoaceticus, and E. coli. Other important potential pathogens include Strenotrophomonas maltophilia and Klebsiella pneumoniae, which are present in 17 and 9 samples, respectively. None of the pathogenic species were identified in fifteen samples. With a collective abundance of 77.5%, sample A9 from the bedroom location had the highest abundance of potentially pathogenic organisms. The number of putative pathogenic species varied significantly based on location (ANOVA; p-value < 5.55 e-10***); the number of putative pathogenic species was highest in the drawing room, bedroom, and foyer locations, and the number of putative pathogenic species was lowest in the bathroom sink, toilet area, and shower area (Fig. S12A). The average abundance of putative pathogens differed significantly based on location (ANOVA; p-value = 0.0114*) (Fig. S12B) but not based on house, surface type, or location (Table S6).

Fig. 6
figure 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)

Full size image

Apart from the bacterial species, one of the viral species Molluscum contagiosum virus (1.35%), was detected as pathogenic when matched with the U.K. Approved list of biological agents. Only one fungal species Candida parapsilosis was identified as potential pathogen.

When we used the CDC NHSN pathogen code as the reference for identifying the pathogens, 229 of the 1409 bacterial species identified could be considered putative pathogens or opportunistic pathogens, accounting for 16% of the taxonomic species detected. The top 50 putative pathogens included Acinetobacter calcoaceticus, Mycobacterium gordonae, and Moraxella osloensis (Fig. S13). The number of putative pathogenic species varied significantly based on location (ANOVA; p-value = 3.42e-16***); the number of putative pathogenic species was highest in the drawing room, bedroom, and foyer locations, and the number of putative pathogenic species was lowest in the shower area, toilet area and bathroom sink (Fig. S14A, Table S7). The average abundance of putative pathogens differed significantly based on location (ANOVA p-value = 0.00211**) but not based on house or surface type (Fig. S14B, Table S7). The bedroom samples had the highest percentage (35.3%), and the kitchen slab had the least abundance of putative pathogens (12.8%) (Fig. S14B). The ESKAPE pathogens, namely, Enterococcus faecium, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and 8 of the Enterobacter species, were detected in the house microbiome (Fig. S15). A total of 311 virulence factors were detected for 40 different bacterial species including A. baumannii, Aeromonas hydrophila, Bacillus cereus, E. faecalis, E. faecium, E. coli, K. pneumoniae, P. aeruginosa and Vibrio cholerae by MetaVF toolkit (Fig. S16).

Multidrug resistance genes dominate the Indian house resistome

The increase in antimicrobial resistance in pathogens is a global problem and a matter of vigilance. The acquisition of resistance can involve the transfer of antibiotic-resistance genes among bacteria from the environment, wildlife, and humans. Pathogens are acquiring antibiotic resistance faster than ever due to the overuse of antibiotics and an increasing amount of antibiotic pollution in the environment. Antimicrobial resistance genes in the Indian house microbiome were identified from the shotgun metagenomics data. Twenty-two resistance gene types with 669 subtypes were identified from the total metagenomic reads using the ARG-OAP pipeline with the SARG-DB as a reference database. Among the resistance genes, multidrug resistance genes were the most dominant in all the samples, with an average of 56% relative abundance, followed by aminoglycoside (11.1%) and bacitracin (5.6%). All the samples from different locations contained resistance genes for aminoglycoside, bacitracin, beta-lactam, chloramphenicol, fosmidomycin, M.L.S., multidrug, quinolone, sulfonamide, tetracycline and unclassified ARGs (Fig. 7). Beta-lactam was found to be differentially abundant in bedroom among all the locations (Fig. S17A) and in living area among the location groups (Fig. S17B). Bacitracin was more abundant in the bathroom (Fig. S17C).

Fig. 7
figure 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)

Full size image

The multidrug transporter was the most abundant subtype (11.4%), followed by mexF (MRA 5.85%) and bacA (MRA 5.63%), which encode resistance to multidrug and bacitracin, respectively. Of the 669 subtypes detected, 387 subtypes (of 902 in the database) were of the beta-lactam resistance type, followed by 74 multidrug subtypes and 44 M.L.S. resistance subtypes. All 43 subtypes of tetracycline were detected in the resistome. The core resistome included 28 subtypes detected in all the samples. These 28 subtypes accounted for 63.7% of the total ARGs abundance in the study. Eighteen of these subtypes were multidrug-resistant. Most of the core resistome genes use the efflux of antibiotics as a mechanism of action. Other resistance mechanisms include antibiotic target replacement, antibiotic target protection, antibiotic target alteration, antibiotic inactivation, and reduced permeability to antibiotics against the drug classes across all the samples. All the multidrug, M.L.S., and fosmidomycin subtypes confer resistance via an antibiotic efflux mechanism.

Alpha and beta diversity metrics were calculated based on the relative abundance of ARGs subtypes. The Shannon diversity index varied significantly across location (ANOVA; p-value 9.51e-10 ***) and surface type (ANOVA; p-value 0.00444 **) but not across the houses (Fig. S18). The ARGs diversity showed many similarities with the taxonomic diversity, with higher diversity occurring in the foyer, bedroom, and drawing room locations. Compared with the other surface types, the floor surface type cemented and tiled samples had higher and almost equal Shannon diversity. The Foyer, bedroom, and drawing room samples had significantly different ARGs subtype compositions from those of the other locations (PERMANOVA; p-value 0.001); this difference was evident from the PCoA (Fig. 8A) and the pairwise comparison between samples from each location (Fig. S19). The location groups formed three different clusters, and each group seemed different from the others (Fig. 8B, PERMANOVA; p value = 0.001). This variation was more pronounced between the living area with bathrooms and kitchen groups, while less variation was observed between the latter (as depicted by the p-value heatmap).

Fig. 8
figure 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)

Full size image

Based on a literature survey [47], 14 AMR subtype families and 50 crucial ARG subtypes were identified to be most discussed in the past 30 years. Of the listed ARG subtypes, 25 were detected in the house microbiome data, including aadA (aminoglycoside resistance) and sul1 (sulfonamide resistance), which were present in all the samples, sul2 (sulfonamide resistance) in 87, and floR (florfenicol/chloramphenicol resistance) in 86 samples. The aadA genes were particularly more abundant in the shower and toilet areas (Kruskalwallis; p-value 9.56e-04) but was sporadically more abundant in one sample each from the bathroom sink and kitchen sink. mecA (methicillin resistance), TEM-1 (beta-lactam), tetW, tetO, and tetM (tetracycline resistance) were significantly more abundant in living areas.

Discussion

Proteobacteria and Actinobacteria composed the core microbiome of the Indian houses (59% and 32%, respectively), clearly dominating other phyla. Previously, Proteobacteria was found to be dominant in crop farming houses (36%) [25], in-home site samples from Cincinnati (up to 50%) [48], and around the U.S.A [22]. However, several other groups have reported Actinobacteria and Firmicutes as dominant phyla in their studies of the indoor microbiome [26, 49, 50]. Another study identified Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes as the key phyla of the home microbiome even though the houses were present in two different bioclimatic zones in the U.K. and Greece [3].

The three location groups (living area, kitchen area, and bathroom area) had distinct microbial compositions according to species richness and abundance, which was confirmed by alpha-beta diversity analyses. Firmicutes were differentially abundant in the living area compared to other locations, possibly due to the more significant amount of time spent and a higher load of skin and hair shed from the residents. Notably, E. coli was more abundant and prevalent on the floors of the living area group, which is an odd finding given that it was expected to be more prevalent on the toilet and bathroom floors. A previous study that examined the cfu of the E. coli bacterium as a measure of fecal contamination on house floors (at the kitchen and entrance) revealed that floors of houses had higher fecal contamination than the bathroom areas [51]. Thus, E. coli on the floors of the living areas of Indian houses could be attributed to fecal contamination, possibly from aerosols from the attached toilets or dust from outside. These hypotheses need further evaluation in a future study to determine the cause of the higher prevalence and abundance of E. coli in living area samples.

The most abundant species in the house microbiome was P. marcusii, which dominated the toilet area and kitchen slab microbiota in particular and Paracoccus was the most abundant genera in the house microbiome. P. marcusii has been reported to be present as the most prevalent species in the floor of the spacecraft assembly facility [52]. Several studies have shown the Paracoccus to be one of the top abundant genera in the house microbiome [3, 53]. These findings underscore the recurring presence of Paracoccus in indoor environments and its potential role in shaping the microbiome of residential spaces. M. osloensis, an opportunistic pathogen, was the most prevalent bacterial species in the Indian house samples. Previously, Moraxellaceae was reported as the most abundant family in farming houses [25] and one of the most abundant families in house dust [3]. It is also reported to be the dominant species in studies delineating the microbiome of kitchen surfaces and kitchen sponges. A study of the microbiome of kitchen surfaces revealed that unique cleaning methods further increased the abundance of M. osloensis [54]. A study described this species exists at high frequencies in various living environments [55]. The high prevalence of M. osloensis in this study (86 samples) can be attributed to its high survival capability under different environmental conditions. Although this organism is highly abundant and prevalent in various built environment microbiomes, it should be acknowledged that it is also part of the normal human skin microbiome and the upper human respiratory tract [56]. M. osloensis is also known to cause infections such as bacteremia, neonatal ophthalmia, endophthalmitis, meningitis, endocarditis, and peritonitis, mostly in immunocompromised patients but sometimes in individuals with no previous medical history [57,58,59]. There are case studies from India reporting that M. osloensis causes bacteremia and diarrhea in immunocompetent and immunocompromised patients [60]. The microbial diversity within the house was not evenly distributed, and the foyer, bedroom, and drawing room (living area) locations had significantly higher bacterial richness and evenness than the other locations. When microbial diversity was compared across the houses, no significant variation was observed according to the alpha or beta diversity. Antithetically, a study assessing the microbial communities across ten houses in the U.S. reported a substantial difference in the composition of the microbiota among the houses included in the study. The samples from the same location in different homes differed more than those from various locations within the same home [22]. The study showed that the house microbiome differed from home to home and shared more species from the occupants’ skin microbiome in each home [22]. The study included homes of people from diverse ethnicities, while this study included people from a single ethnicity and found no difference between the houses; instead, the microbial diversity varied with location within the houses. Moreover, samples from the same location in different houses exhibited similar microbial compositions.

The distinction between the locations was more pronounced when the locations within each house were grouped according to living area (foyer, bedroom, or drawing room), kitchen (kitchen sink, kitchen floor, or kitchen slab), or bathroom (bathroom sink, shower area, or toilet area). Alpha and beta-diversity analyses confirmed the differences in microbial composition across the location groups. The Shannon diversity index and the number of species were significantly higher in the living area than in the bathroom and kitchen. The living area samples differed in terms of bacterial composition from the bathroom and kitchen in the beta-diversity principal coordinate analysis People tend to spend more time in their living area, making the living area the most anthropogenically impacted area. Factors such as human skin shedding, skin-surface contact, and respiration might result in the detection of human-associated bacteria in living area floor samples.

The metagenomic samples from the houses were collected with the hypothesis that the samples from two categories of houses (tiled and cemented floors) would have distinct microbial communities. The results confirmed that the cement and tile surface types did not significantly influence the microbial communities across the houses. Alpha and beta diversity did not vary substantially between the two groups, suggesting that the microbiota composition did not change with the type of flooring in the houses. Although the surface type was a factor that influenced the bacterial composition, the steel (kitchen sinks) and ceramic (bathroom sinks) samples exhibited the most differences. The sinks have higher exposure to water than floors, and the variations in the diversity observed may suggest that the usage of the surface types influences the diversity. The surface usage pattern has been shown to influence the office microbiome [61].

Many species detected in the study were potentially pathogenic and opportunistic pathogens, including ESKAPE pathogens and others such as E. coli, A. junii, and A. calcoaceticus. The high number of potential pathogenic species in the house microbiome raises concern about possible risks to occupants. Some opportunistic pathogens were present at high prevalence and abundance.

A nontuberculous mycobacterium species, M. gordonae, was detected at high abundance in two of the bathroom sink samples and was found in 10 other samples. Nontuberculous mycobacteria are common in the environment and can be present in soil, dust, and water [62]. Many studies have reported this bacterium as an emerging opportunistic pathogen in immunocompromised patients, causing infection in the respiratory tract. Most studies describing infections caused by M. gordonae have reported the source to be contaminated water in hospitals [63]. Microbiome studies of drinking water treatment systems have shown contamination with nontuberculous bacteria such as M. gordonae. Several cases have also detected the presence of this organism with antibiotic resistance genes for aminoglycoside (aac(2′)–I) in drinking water of house water purifiers in China and explained its potential to cause failure in treatment with antibiotics and threaten human health [64]. Another pathogenic bacterium, Stenotrophomonas maltophilia, was detected in our study and has emerged as an important opportunistic pathogen in debilitated hosts. It causes pneumonia and bloodstream infections, and severe cases of infection with difficulty in treatment have been reported in immunosuppressed patients [65]. Its resistance to broad-spectrum β-lactam, aminoglycoside, and carbapenem antibiotics is what distinguishes it and is linked to increased morbidity and death in immunocompromised people [66]. S. maltophilia has also been found in controlled built environments such as hospitals and house dust samples, and its presence is correlated with the moisture content of buildings [67, 68]. The bedrooms and drawing rooms were identified with M. globosa and M. restricta, which are considered the causative fungal species for dandruff, eczema, and other human skin-related diseases [69]. One of the house microbiome samples was detected to harbor mupapillomavirus-2 at a very high relative abundance, which is known to cause plantar warts in humans.

A framework for improving hygiene practices at home and in daily life was presented to increase population resilience to the spread of infectious diseases [70]. This finding implies that by identifying and sanitizing areas at increased risk of exposure, targeted hygiene measures could limit exposure to pathogens. The current study extensively analyzed different floors and other house surfaces to determine the presence and abundance of pathogens. The floors of the drawing room, bedroom, and foyer area show the presence of potentially pathogenic organisms. Another interesting finding of this study is that the floors of the toilet and shower areas of the houses had the lowest presence and abundance of potential pathogenic organisms. However, these areas are perceived to contain more pathogenic bacteria. This observation may be explained by the fact that these places are frequently washed out by water and cleaned with strong disinfectants in Indian houses. The identification of potential pathogens in different areas of the household is intriguing. However, whether this contributes to infection risk is dependent on multiple factors that can be ascertained using microbial risk assessment.

Our research delineates different types of antimicrobial resistance genes in metagenomic data conferring resistance to various antibiotic drug classes. The most abundant were multidrug resistance genes, aminoglycosides, bacitracin, tetracycline, and beta-lactamases. Many subtypes of ARGs detected in house microbiome data are among those reported clinically relevant, such as tetX, OXA, MCR, SHVs, and Icr-Mo [47]. The presence of more than 600 subtypes of ARGs in the present data indicates the potential risk that house microbial community harbors. ARGs profiling of the residences could reflect the AMR burden on urban cities. Maamar et al. 2020 highlighted the potential of the built environment to facilitate the spread of antibiotic resistance through microbial interactions within the microbial community and horizontal gene transfer. Additionally, it was highlighted that stressors in the built environment might facilitate genetic material flow and the retention of genes associated with antibiotic resistance [71]. Cleaner-built environments with reduced microbial diversity are associated with increased antibiotic-resistance genes [72]. It was also observed that private residences and places of employment, frequently overlooked in studies on antibiotic resistance, can act as ARGs reservoirs and aid in the spread of these pathogens [72]. It is crucial to investigate the likelihood of ARGs transmission in other organisms and to further connect ARGs with the possible bacteria carrying these genes. The resistome of the house microbiome could help in further understanding the influence of anthropogenic factors on the transmission of antimicrobial resistance carried by the house microbial communities.

One of the ways through which the built environment microbiome could affect human health is by colonization of the pathogenic organisms on the human body either by touching the house surfaces or inhaling through the respiratory tract. This later results in bacterial infections depending upon the health status of the people in contact. Since the samples for this study were taken during the global pandemic, it may be assumed that the microbial diversity in Indian dwellings may be even higher in normal conditions because of the limited movement of residents or visitors across homes. Residents followed a more stringent cleaning regime, which may have decreased the influx of microbes from external sources and cleaner surfaces. More such exhaustive research is indeed required for making bio-informed decisions for the development of housing infrastructures based on an understanding of the microbiome of the building and its impact on human health [73].

This research reveals for the first time the microbiome of Indian houses and that it is chiefly influenced by the locations in the houses. The living areas have a distinct abundance of bacteria-associated bacteria associated with the human skin microbiota, probably owing to anthropogenic activities such as the shedding of human skin. A substantial proportion of the bacterial microbiota is identified as potentially pathogenic and opportunistic pathogens, and the microbial community of the houses harbors a wide diversity of antimicrobial resistance genes, indicating potential risks for residents. Hence, regularly cleaning and disinfection of floors and surfaces is critical for maintaining a healthy home environment and reducing potential health hazards related to the house microbiome. The present research paves the way for further investigations and understanding of the impacts of the complex interactions between the housing infrastructure, microbiome, and human occupants.

Data availability

“Sequence data and metadata for the samples in the study are deposited at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (S.R.A.) under the accession number PRJNA1053252 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1053252)”.

Abbreviations

ARGs:

Antimicrobial Resistance Genes

AMR:

Antimicrobial Resistance

MRA:

Mean Relative Abundance

MLS:

Macrolide Lincosamide Streptogramin

References

  1. World Bank Open Data. 2022. https://data.worldbank.org/indicator/SP.URB.TOTL?locations=IN. Accessed 15 Oct 2023.

  2. Klepeis NE, Nelson WC, Ott WR, Robinson JP, Tsang AM, Switzer P, et al. The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. J Expo Anal Environ Epidemiol. 2001;11(3):231–52.

    Article  CAS  PubMed  Google Scholar 

  3. Thompson JR, Argyraki A, Bashton M, Bramwell L, Crown M, Hursthouse AS et al. Bacterial diversity in House Dust: characterization of a core indoor microbiome. Front Environ Sci. 2021;9.

  4. Eames I, Tang JW, Li Y, Wilson P. Airborne transmission of disease in hospitals. J R Soc Interface. 2009;6:S697–702.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Chauhan BV, Higgins Jones D, Banerjee G, Agrawal S, Sulaiman IM, Jia C, et al. Indoor bacterial and fungal burden in moldy versus non-moldy homes: a case study employing advanced sequencing techniques in a US metropolitan area. Pathogens. 2023;1(8):1006.

    Article  Google Scholar 

  6. Hyytiäinen HK, Jayaprakash B, Kirjavainen PV, Saari SE, Holopainen R, Keskinen J, et al. Crawling-induced floor dust resuspension affects the microbiota of the infant breathing zone. Microbiome. 2018;2(1):25.

    Article  Google Scholar 

  7. Ege MJ, Mayer M, Normand A-C, Genuneit J, Cookson MD, Phil D, et al. Exposure to environmental microorganisms and childhood asthma. N Engl J Med. 2011;364:701–9.

    Article  CAS  PubMed  Google Scholar 

  8. Cooley JD, Wong WC, Jumper CA, Straus DC. Correlation between the presence of certain fungi and sick building syndrome. Occup Environ Med. 1998;55(9):579–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Stein MM, Hrusch CL, Gozdz J, Igartua C, Pivniouk V, Murray SE, et al. Innate immunity and asthma risk in amish and hutterite farm children. Arch Argent Pediatr. 2017;115:e49–50.

    Google Scholar 

  10. Gacesa R, Kurilshikov A, Vich Vila A, Sinha T, Klaassen MAY, Bolte LA, et al. Environmental factors shaping the gut microbiome in a Dutch population. Nature. 2022;604:732–9.

    Article  CAS  PubMed  Google Scholar 

  11. Mills M, Lee S, Piperata BA, Garabed R, Choi B, Lee J. Household environment and animal fecal contamination are critical modifiers of the gut microbiome and resistome in young children from rural Nicaragua. Microbiome. 2023;15(1):207.

    Article  Google Scholar 

  12. Chen CH, Tu CC, Kuo HY, Zeng RF, Yu CS, Lu HHS, et al. Dynamic change of surface microbiota with different environmental cleaning methods between two wards in a hospital. Appl Microbiol Biotechnol. 2017;101:771–81.

    Article  CAS  PubMed  Google Scholar 

  13. Lax S, Sangwan N, Smith D, Larsen P, Handley KM, Richardson M, et al. Bacterial colonization and succession in a newly opened hospital. Sci Transl Med. 2017;24(391):eaah6500.

    Article  Google Scholar 

  14. Adams RI, Bateman AC, Bik HM, Meadow JF. Microbiota of the indoor environment: a meta-analysis. Microbiome. 2015;3:49.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hsu T, Joice R, Vallarino J, Abu-Ali G, Hartmann EM, Shafquat A, et al. Urban transit system microbial communities differ by surface type and interaction with humans and the environment. mSystems. 2016;28(3):e00018–16.

    Google Scholar 

  16. Hoisington AJ, Brenner LA, Kinney KA, Postolache TT, Lowry CA. The microbiome of the built environment and mental health. Microbiome. 2015;17:3:60.

    Article  Google Scholar 

  17. Cao L, Yang L, Swanson CS, Li S, He Q. Comparative analysis of impact of human occupancy on indoor microbiomes. Front Environ Sci Eng. 2021;15:89.

    Article  PubMed  Google Scholar 

  18. Rai S, Singh DK, Kumar A. Microbial, environmental and anthropogenic factors influencing the indoor microbiome of the built environment. J Basic Microbiol. 2021;61:267–92.

    Article  PubMed  Google Scholar 

  19. Sharpe T, McGill G, Dancer SJ, King MF, Fletcher L, Noakes CJ. Influence of ventilation use and occupant behaviour on surface microorganisms in contemporary social housing. Sci Rep. 2020;16(1):11841.

    Article  Google Scholar 

  20. Greene C, Vadlamudi G, Eisenberg M, Foxman B, Koopman J, Xi C. Fomite-fingerpad transfer efficiency (pick-up and deposit) of Acinetobacter baumannii - With and without a latex glove. Am J Infect Control. 2015;43:928–34.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hoisington AJ, Stamper CE, Bates KL, Stanislawski MA, Flux MC, Postolache TT, et al. Human microbiome transfer in the built environment differs based on occupants, objects, and buildings. Sci Rep. 2023;20(1):6446.

    Article  Google Scholar 

  22. Lax S, Smith DP, Hampton-Marcell J, Owens SM, Handley KM, Scott NM et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science (1979). 2014;345:1048–52.

  23. Miletto M, Lindow SE. Relative and contextual contribution of different sources to the composition and abundance of indoor air bacteria in residences. Microbiome. 2015;3:61.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Mahnert A, Moissl-Eichinger C, Berg G. Microbiome interplay: plants alter microbial abundance and diversity within the built environment. Front Microbiol. 2015;6:887.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lee MK, Carnes MU, Butz N, Azcarate-Peril MA, Richards M, Umbach DM, et al. Exposures related to house dust microbiota in a U.S. farming population. Environ Health Perspect. 2018;1(6):067001.

    Article  Google Scholar 

  26. Loo EXL, Chew LJM, Zulkifli AB, Ta LDH, Kuo IC, Goh A, et al. Comparison of microbiota and allergen profile in house dust from homes of allergic and non-allergic subjects- results from the GUSTO study. World Allergy Organ J. 2018;5(1):37.

    Article  Google Scholar 

  27. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010.

  29. Bushnell B. BBMap: a fast, accurate, splice-aware aligner. 2014.

  30. Blanco-Míguez A, Beghini F, Cumbo F, McIver LJ, Thompson KN, Zolfo M, et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat Biotechnol. 2023;41:1633–44.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lin H, Peddada S, Das. Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures. Nat Methods. 2024;21:83–91.

    Article  CAS  PubMed  Google Scholar 

  32. Rampelli S, Soverini M, Turroni S, Quercia S, Biagi E, Brigidi P, et al. ViromeScan: a new tool for metagenomic viral community profiling. BMC Genomics. 2016;17:165.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Yang Y, Jiang X, Chai B, Ma L, Li B, Zhang A, et al. ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database. Bioinformatics. 2016;32:2346–51.

    Article  CAS  PubMed  Google Scholar 

  34. Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015;29:26:27663.

    Google Scholar 

  35. Oksanen J, Roeland Kindt P, Legendre, Bob O’Hara M, Henry H, Stevens et al. Maintainer Jari Oksanen,. The vegan package. Community ecology package. 2007;10:631–7.

  36. Ushey K, Allaire J, Wickham H, Ritchie G, rstudioapi. Safely Access the RStudio API. R. 2022.

  37. Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4:1686.

    Article  Google Scholar 

  38. The approved list of biological agents: advisory committee on dangerous pathogens. 2023. https://www.hse.gov.uk/pubns/misc208.htm

  39. NIAID Emerging Infectious Disease/Pathogens| NIH: National Institute of Allergy and Infectious Diseases. 2018. https://www.niaid.nih.gov/research/emerging-infectious-diseases-pathogens. Accessed 15 Oct 2023.

  40. Snyder EE, Kampanya N, Lu J, Nordberg EK, Karur HR, Shukla M, et al. PATRIC: the VBI PathoSystems Resource Integration Center. Nucleic Acids Res. 2007;35:D401–6.

    Article  CAS  PubMed  Google Scholar 

  41. Chen L, Yang J, Yu J, Yao Z, Sun L, Shen Y, et al. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res. 2005;33:D325–8.

    Article  CAS  PubMed  Google Scholar 

  42. Sharma A. Dec. Indian priority pathogen list to guide research, discovery, and development of new antibiotics in India. https://dbtindia.gov.in/sites/default/files/IPPL_final.pdf. Accessed 22 2023.

  43. Chen Q, An X, Li H, Su J, Ma Y, Zhu YG. Long-term field application of sewage sludge increases the abundance of antibiotic resistance genes in soil. Environ Int. 2016;92–93:1–10.

    Article  PubMed  Google Scholar 

  44. Data Validation Resources| NHSN| CDC. 2022. https://www.cdc.gov/nhsn/validation/2022.html. Accessed 15 Oct 2023.

  45. Dong W, Fan X, Guo Y, Wang S, Jia S, Lv N, et al. An expanded database and analytical toolkit for identifying bacterial virulence factors and their associations with chronic diseases. Nat Commun. 2024;15:8084.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Nygaard AB, Charnock C. Longitudinal development of the dust microbiome in a newly opened Norwegian kindergarten. Microbiome. 2018;6:159.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Zhuang M, Achmon Y, Cao Y, Liang X, Chen L, Wang H, et al. Distribution of antibiotic resistance genes in the environment. Environ Pollut. 2021;15:285:117402.

    Article  Google Scholar 

  48. Niemeier-Walsh C, Ryan PH, Meller J, Ollberding NJ, Adhikari A, Indugula R, et al. The mycobiomes and bacteriomes of sputum, saliva, and home dust. Indoor Air. 2021;31:357–68.

    Article  CAS  PubMed  Google Scholar 

  49. Vandenborght L-E, Enaud R, Urien C, Coron N, Girodet P-O, Berger P, et al. Type 2-high asthma is associated with a specific indoor mycobiome and microbiome. J Allergy Clin Immunol. 2021;147:1296–305.

    Article  CAS  PubMed  Google Scholar 

  50. Hanson B, Jackson L, Zhou Y. Characterization of the bacterial and fungal microbiome in indoor dust and outdoor air samples: a pilot study. Environ Sci Process Impacts. 2016;15(6):713–24.

    Article  Google Scholar 

  51. Exum NG, Olórtegui MP, Yori PP, Davis MF, Heaney CD, Kosek M, et al. Floors and toilets: association of floors and sanitation practices with fecal contamination in Peruvian Amazon peri-urban households. Environ Sci Technol. 2016;50:7373–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Minich JJ, Zhu Q, Janssen S, Hendrickson R, Amir A, Vetter R, et al. KatharoSeq enables high-throughput Microbiome Analysis from Low-Biomass Samples. mSystems. 2018;13(3):e00218–17.

    Google Scholar 

  53. Ding LJ, Zhou XY, Zhu YG. Microbiome and antibiotic resistome in household dust from Beijing, China. Environ Int. 2020;139:105702.

    Article  PubMed  Google Scholar 

  54. Cardinale M, Kaiser D, Lueders T, Schnell S, Egert M. Microbiome analysis and confocal microscopy of used kitchen sponges reveal massive colonization by Acinetobacter, Moraxella and Chryseobacterium species. Sci Rep. 2017;7:5791.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Kubota H, Mitani A, Niwano Y, Takeuchi K, Tanaka A, Yamaguchi N, et al. Moraxella species are primarily responsible for generating malodor in laundry. Appl Environ Microbiol. 2012;78:3317–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Berger U, Falsen E. Uber die Artenverteilung Von Moraxella und moraxella-iihnhchen Keimen Im Nasopharynx Gesunder Erwachsener distribution of species of Moraxella and Moraxella-like organisms in the nasopharynx of healthy human adults. Med Microbiol Immunol. 1976;162:239–49.

    Article  CAS  PubMed  Google Scholar 

  57. Shah SS, Ruth A, Coffin SE. Infection due to Moraxella osloensis: case report and review of the literature. Clin Infect Dis. 2000;30(1):179–81.

    Article  CAS  PubMed  Google Scholar 

  58. Berrocal AM, Scott IU, Miller D, Flynn HW. Endophthalmitis caused by Moraxella species. Am J Ophthalmol. 2001;132:788–90.

    Article  CAS  PubMed  Google Scholar 

  59. Purohit P, Sadek SAL, Tak K, Emara M, Bafna R, Al-Obaid IA, et al. Moraxella osloensis Bacteremia in an Immunocompchild Child. J Pediatr Infect Dis. 2020;15:107–9.

    Article  Google Scholar 

  60. Veerabathini BC, Manthani K, Gandhi S. An unusual case of Moraxella osleonsis bacteremia in an immunocompetent patient with SARS-CoV-2 infection. Cureus. 2020;31(8):e10154.

    Google Scholar 

  61. Chase J, Fouquier J, Zare M, Sonderegger DL, Knight R, Kelley ST, et al. Geography and location are the primary drivers of office microbiome composition. mSystems. 2016;1. https://doiorg.publicaciones.saludcastillayleon.es/10.1128/msystems.00022.

  62. Jeon D. Infection source and epidemiology of nontuberculous mycobacterial lung disease. Tuberc Respir Dis. 2019;82:94–101.

    Article  Google Scholar 

  63. Fujita J, Nanki N, Negayama K, Tsutsui S, Taminato T, Ishida T. Nosocomial contamination by Mycobacterium gordonae in hospital water supply and super-oxidized water. J Hosp Infect. 2002;51:65–8.

    Article  PubMed  Google Scholar 

  64. Ma L, Li B, Zhang T. New insights into antibiotic resistome in drinking water and management perspectives: a metagenomic based study of small-sized microbes. Water Res. 2019;152:191–201.

    Article  CAS  PubMed  Google Scholar 

  65. Looney WJ, Narita M, Mühlemann K. Stenotrophomonas maltophilia: an emerging opportunist human pathogen. Lancet Infect Dis. 2009;9:312–23.

    Article  CAS  PubMed  Google Scholar 

  66. Brooke JS. New strategies against Stenotrophomonas maltophilia: a serious worldwide intrinsically drug-resistant opportunistic pathogen. Expert Rev Anti Infect Ther. 2014;12:1–4.

    Article  CAS  PubMed  Google Scholar 

  67. Ryan RP, Monchy S, Cardinale M, Taghavi S, Crossman L, Avison MB, et al. The versatility and adaptation of bacteria from the genus Stenotrophomonas. Nat Rev Microbiol. 2009;7:514–25.

    Article  CAS  PubMed  Google Scholar 

  68. Brooke JS. Stenotrophomonas maltophania: An emerging global opportunistic pathogen. Clin Microbiol Rev. 2012;25:2–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Saunders CW, Scheynius A, Heitman J. Malassezia fungi are specialized to live on skin and associated with dandruff, eczema, and other skin diseases. PLoS Pathog. 2012;8(6):e1002701.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Bloomfield SF, Ackerley LM. Developing resilience against the threat of infectious diseases and anti-microbial resistance: putting targeted hygiene into practice in home and everyday lives. Public Health Pract. 2023;5:100362.

    Article  Google Scholar 

  71. Maamar S, Ben, Glawe AJ, Brown TK, Hellgeth N, Hu J, Wang JP et al. Mobilizable antibiotic resistance genes are present in dust microbial communities. PLoS Pathog. 2020;16.

  72. Mahnert A, Moissl-Eichinger C, Zojer M, Bogumil D, Mizrahi I, Rattei T, et al. Man-made microbial resistances in built environments. Nat Commun. 2019;10:968.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Bruno A, Fumagalli S, Ghisleni G, Labra M. The microbiome of the built environment: the nexus for urban regeneration for the cities of tomorrow. Microorganisms. 2022;10(12):2311.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the director, CSIR-IGIB, for his constant support. We thank Dr. Vinod Scaria for his support and help during this project. This work was sponsored by Reckitt (India) Pvt Ltd. through Projects CLP0030 and CNP0011. V.M.H. acknowledges junior research fellowship support from CSIR, India. We also thank Dr. Lipi Thukral and Dr. Vivek T. Natarajan for their valuable suggestions on the manuscript.

Funding

Reckitt (India) Pvt Ltd. funded this work under grant # CLP0030 and CNP0011.

Author information

Authors and Affiliations

  1. CSIR-Institute of Genomics and Integrative Biology, New Delhi, India

    Saraswati Awasthi, Vikas M Hiremath, Sonam Nain, Pooja Rose, Ashutosh Choudhury & Rakesh Sharma

  2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India

    Saraswati Awasthi, Vikas M Hiremath, Sonam Nain & Rakesh Sharma

  3. Reckitt (India) Ltd, Research & Development, Gurgaon, India

    Shweta Malik & Ritika Grover

  4. Reckitt Benckiser L.L.C., Global Research and Development for Lysol and Dettol, Montvale, NJ, 07645, USA

    Vanita Srinivasan

  5. Present address: Source Bioscience, Endeavour House, Cambridge, UK

    Sonam Nain

  6. Present address: Department of Molecular Genetics, Erasmus University Medical Centre, Erasmus MC Cancer Institute, Rotterdam, Netherlands

    Ashutosh Choudhury

Authors
  1. Saraswati Awasthi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Vikas M Hiremath
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Sonam Nain
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Shweta Malik
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Vanita Srinivasan
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Pooja Rose
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Ashutosh Choudhury
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Ritika Grover
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Rakesh Sharma
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

S.A. and V.M.H.: literature search, data analyses, and interpretation, writing-original draft & editing; S.N.: literature search, Illumina library preparation, and sequencing & editing; S.M., VS, and R.G: conceptualization & editing; P.R. and A.C.: sample collection, c.f.u. Count and D.N.A. extraction R.S.: literature search, conceptualization, methodology, funding acquisition, supervision, analysis, writing & editing.

Corresponding author

Correspondence to Rakesh Sharma.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All the authors reviewed the content of the paper and agreed to its publication.

Competing interests

This work was sponsored by Reckitt (India) Ltd, Research & Development, Gurgaon, India, to RS. SM and RG are affiliated with Reckitt (India) Ltd, Research & Development, Gurgaon, India, and VS with Reckitt Benckiser L.L.C., Global Research and Development for Lysol and Dettol, Montvale, NJ 07645, U.S.A. Reckitt is a manufacturer of hygiene, health, and nutrition brands.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Awasthi, S., Hiremath, V.M., Nain, S. et al. Microbial landscape of Indian homes: the microbial diversity, pathogens and antimicrobial resistome in urban residential spaces. Environmental Microbiome 20, 25 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40793-025-00684-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40793-025-00684-8

Keywords

Environmental Microbiome

ISSN: 2524-6372