Characterization of Biological Aerosols Over Northeast India: A Metagenomic Approach

Binita Pathak Dibrugarh University Ankita Khataniar Dibrugarh University Centre for Studies in Biotechnology: Dibrugarh University Centre for Biotechnology and Bioinformatics Barlin Das Dibrugarh University Sristisri Upadhyaya Dergaon Kamal Dowerah College Ankita Medhi University of Delhi Miranda House Pradip Kumar Bhuyan Dibrugarh University Alak Kumar Buragohain Royal Global University Debajit Borah (  dborah89@gmail.com ) Royal Global University


Introduction
Bioaerosols are the suspension of airborne particles containing living organisms, dead debris or living components of biological origin with or without being attached to non-biological particles and the byproducts of biological activities. About 20-40% of atmospheric aerosols are of biological origin (Jaenicke, 2005). These include a wide range of organic matter with differences in physical and chemical characteristics and surface properties (Georgakopoulos et al., 2009). Bioaerosols are central elements in the development, evolution, and dynamics of ecosystems and play a major role in the interactions between atmosphere, biosphere, climate, and public health. These in uence the Earth's energy budget by both direct and indirect effects and thus in uencing the climate and the hydrological cycle. For example, Pollen grains attract water at relative humidity well below 100% and thus might locally act as CCN in uencing the cloud formation; Decaying vegetation (including associated bacteria) and marine plankton (Jaenicke 2005), Proteins (Scheel et al., 2016) and Fungi can act as e cient ice nuclei.
Bioaerosols signi cantly in uence both indoor and outdoor air quality. These are linked with spreading biological organisms and reproductive materials (Bundy et al. 2009;Perez-Padilla et al. 2009) and many different adverse health effects like infectious diseases, allergies, asthma, cancer, respiratory disorders in human (Kim et al 2017, Oh et al 2020. In the desert region, microbes present in dust pose a major public health issue. Over the years, the eld of aerobiology has explored the abundance, diversity, survival and transport of micro-organisms in the atmosphere. Microbes are probably the most successful and abundant life forms on earth which can survive from the bottom of deep oceans up to the altitude of 50 km in the stratosphere (Sarma et al., 2020), which attracts the Astrobiologists. Variation of drift in air ow may be one of the most important factors that drive microbes from one location to another creating a great diversity in bioaerosols of various places on earth . Even though, bioaerosols provides extreme environment in terms of low nutrients, moisture content, and higher exposure rate to solar radiation etc. which may limit the viability of a large number of microbes, but a fraction of microbial populations are known to be capable of growing and proliferating under such condition (Burrows et al., 2009;Womack et al., 2010). Airborne bacteria represent a high risk to public health. They mostly cause severe breathing related diseases such as asthma, skin rashes, allergies and many more (Arancibia et al., 2002). Therefore, identi cation of various air borne microbes becomes the need of the hour for the prevention and control of various disease-causing microbes. Culture-based identi cation approach of microorganisms from bioaerosols have shown the presence of mostly Gram positive bacteria out of which Streptococcus sp., Staphylococcus sp., Micrpococcus sp., Bacillus sp., and Corynebacterium sp. etc. are the most common all across the globe . On the other hand, Gram negative microbes such as Acenatobacterium sp., Pseusdomonas sp., and Moraxella sp. etc. are also reported worldwide but are less frequent as compared to Gram positive microbes (Andersson et al., 1999). Other living microbial entities present in bioaerosol includes various fungal spores such as Aspergillus sp., Penicillium sp., Cladosporium sp., and Alternaria sp. etc. . Nevertheless, microbes are underexplored by aerobiologists (<99%) due to the limitations of conventional culturing methods of aerosol borne microbes; as growth medium sometime acts as recalcitrant and restrict their proper growth (Hugenholtz et al., 1998). Considering the above limitations, molecular identi cation techniques have become popular nowadays for their accuracy and culture independent approach . In very recent days, metagenome approach of bacterial identi cation becomes a new trend that enables researchers to identify microbial diversities based on DNA based techniques without culturing the microbes (Serrano-Silva and Calderon-Ezquerro 2018). Metagenome based analysis of microbial diversity involves direct isolation of DNA from microbes without culturing them and it provides an insight view of various microbes present in the sample using bioinformatics tools (Yo et al., 2017). However, the success of the process is also largely dependent on the instrument used for the collection of bioaerosol samples and the quantity and quality of the DNA extracted from the collected samples as extraction process demands very high level of precision and expertise. Bioaerosols can be sampled by using a wide range of methods which involves the use of various traditional and modern devices to collect airborne microorganisms both culturable and non-culturable (Yoo et al. 2017). A particular bioaerosol sampler may not be fully su cient to satisfy all the required experimental outcomes such as analyzing real time particle size distribution and complete microbial diversity analysis together at a time but the involvement of metagenome analysis of samples may provide the maximum possible insight into the wide range of microbial diversity in the samples in a cost effective and less labor intensive way. In view of this, metagenome analysis of bioaerosols have become popular all across the globe to get a better insight into the characterization of bioaerosols in terms of exploration of microbial diversity but are very limited till now. Researchers have analyzed bioaerosol samples collected from indoor as well as outdoor environment , above sea , earth's lower atmosphere (Smith et al., 2018), livestock farms (Wang et al., 2019), urban spaces , waste sorting plants (Degois et al., 2017) etc. Considering the Indian perspective, studies on airborne bacterial species through conventional approach is available for some of the regions such as Mumbai (Gangamma 2014), Delhi (Kumar et al., 2013), Bangalore (Nandini and Sivasaktivel 2014) etc. Further, systematic survey on pollen in uence on human health was initiated in India long back at Calcutta in 1873 (Pathak et al., 2020). Later on, researchers have initiated exhaustive studies on airborne pollen types and their concentration. Pollen calendars prepared from systematic measurements of pollen at various places are very useful for clinicians as well as allergic patients to establish chronologic correlation between the concentration of pollen in atmosphere and seasonal allergic symptoms (Singh et al., 1992). As an outcome of these studies, some important pollen and fungal allergens were identi ed, quanti ed and characterized for their allergenic properties from 18 different places in India.
Despite the fact that India is the world's seventh largest country in terms of total land area which covers rain-forests, deserts, grasslands, terrains etc. and northeastern part of the country is considered as one of the major biodiversity hotspots in the world with largely underexplored micro ora and fauna, but only a handful of reports are available on the analysis of microbial diversity in bioaerosols applying metagenome approach (Chakrawarti et al., 2020). Hence, extensive research in this eld is the need of the hour in order to expand the periphery of our knowledge on microbial diversities in bioaerosols.
Additionally North East India is vulnerable to both primary and secondary aerosols of biogenic origin due to high vegetation cover (~66%). Biological sources are found to emit ~ 1150 teragrams of carbon per year globally in the form of VOCs. The majority of VOCs are produced by plants, the main compound being isoprene and the remaining is produced by animals, microbes and fungi (such as molds). But, research on bio-aerosols has not gained due importance so far in North East India, in the context of air pollution, climate impacts and health impacts in spite of the growing concern on various diseases caused by seasonal growth of primary bio-aerosols like bacteria, fungi, pollen, etc.Thus, the present work supplements to new knowledge in this regard on a regional basis over the North-East India.

Materials And Methods
Study Region and prevailing meteorology: Bioaerosol samples were collected from nine discrete locations of Northeast India ( Figure 1) in a campaign mode. The locations were selected based on urbanization, vegetation type and accessibility (Table S1). It is worthwhile to mention here that the tropical forests have been proposed as signi cant sources of bioaerosols (Jaenicke 2005). A full year (2018-2019) of campaign was conducted, covering all the seasons to explore the seasonality in abundance and type of bioaerosols present in the atmosphere and thus to obtain the regional mapping of bioaerosols. All the samples were collected within 6:00 hr to 18:00 hr. The climate of the study region is Subtropical humid in nature and a year is categorized into four seasons: pre-monsoon (March-May), monsoon (June-September), post monsoon (October-November) and winter (December-February). A portable Automatic weather station (PORTLOG AWS, USA) was used for the simultaneous measurement of meteorological parameters such as Relative Humidity, Temperature, wind speed and direction etc. (Table S2).

Bioaerosol sampling and characterization:
A Burkard Pollen sampler was used for the collection of pollens in the designated locations with an intake air ow rate of 10 LPM (Núñez et al., 2016). It contains agreased rotating drum at a speed of 1 rotation per 7 days in order to collect pollens and other aerosols through impaction. These were characterized under uorescence microscope for identi cation based on their physical characteristics such as shape, size and other morphological features using acridine orange which stains the nucleic acids present in the samples.
A portable Bio-Aerosol Spectrometer (GRIMM Aerosol Technik Ainring GmbH & Co.KG., Germany, model 11.A)was used for real time continuous monitoring of airborne particles (non-biological and biological together) in terms of particle count distribution at 31 size intervals (Peters et al., 2006), at a temporal resolution of 5 minutes and ow rate of 1.2 liters/min. The principle of operation of the spectrometer is available elsewhere (Pathak et al., 2020). The particulate matter (PM) concentrations (in μg/m 3 ) measured by the instrument can be further categorized into inhalable matter (PM ≤ 32 μm for the bioaerosol spectrometer, ≤100 μm in general), thoracic (PM ≤10 μm) and alveoli (alveolar, PM ≤ 4 μm), that may be absorbed in the nasal tract; thorax and alveoli respectively of human body (Petavratzi et al., 2005).
A greased glass slide was used as an impactor which collects aerosols in the form of very thin dusty spot which was used for culturing as well as direct extraction of DNA for metagenome analysis. Culturing of microbes from bioaerosol samples was done by overlay method (Aneja, 2010) by gently placing the slides on nutrient and potato dextrose agar followed by incubation at 37 o C for the isolation of bacteria and fungi respectively. The number of bacteria or fungi i.e., the microbial concentration in the samples can be determined in terms of the colony forming unit (CFU) (Srivastava et al., 2012;Lal et al., 2013) which is de ned as: Health risk assessment of isolated microbes in terms of their multidrug resistance pattern and haemolytic ability: The Kirby Bauer's disc diffusion method was used to screen out multidrug resistant microbes present in bioaerosol samples. The bacterial broth of each bacterial isolates was spread plated for overnight on Muller-Hinton agar (g/L: Beef extract-2.00, acid hydrolysate of Casein-17.50, starch-1.50, agar agar-17.00, at pH-7.4±0.2 at 25 o C). Commercially available standard discs impregnated with designated amount of narrow (viz., Vancomycin-30 mcg and Erythromycin-15 mcg) and broad spectrum antibiotic (viz., Chloramphenicol-30 mcg, and Ampicilin-10 mcg) placed as per the prescribed guidelines of Clinical and Laboratory Standards Institute (USA) guidelines (Sweeney 2018). These were then incubated at 37 o C for 24 h. The clear zones around the antibiotic discs were considered as sensitive zones.
Isolated microbes were individually inoculated on blood agar supplemented with commercially available de brinated sheep blood followed by incubation at 37 o C for 24 h to observe their haemolytic ability.
Molecular identi cation of bacteria present in bioaerosol samples: Sample processing: The environmental sample was mixed in Phosphate Buffered Saline solution in order to obtain a homogeneous representation of each organism. DNA was extracted from the suspension using DNeasy Microbial Kit (Qiagen Ltd, Strasse, Germany) following the manufacturer's instructions (Qiagen Ltd, Strasse, Germany).
16S rDNA gene sequencing: Partial 16S rRNA gene sequences were ampli ed from extracted DNA using 16S gene speci c primers for V3-V4 region as per Illumina 16S V3-V4 sequencing kit that also includes addition of adapter overhang nucleotide sequences to the partial 16S rRNA gene-speci c amplicons. The amplicons were further processed by employing the 16S Metagenomic Sequencing Library Preparation Protocol (Part no. PCR products obtained following ampli cation of part of the 16S rRNA gene sequences were puri ed by a magnetic puri cation step involving Agencourt AMPure XP DNA puri cation beads (Beckman Coulter Genomics GmbH, Bernried, Germany) in order to remove primer dimers. The DNA concentration of the ampli ed sequence library was estimated through uorimetric Qubit quanti cation system (Life Technologies). Amplicons were diluted to 4 nM and 5 μL of each diluted DNA amplicon sample was mixed to prepare the pooled nal library. Sequencing was performed using an Illumina MiSeq sequencer with MiSeq Reagent Kit v3 chemicals.
Analysis of 16S rRNA microbial data: The fastq les were processed using MG-RAST (Keegan et al., 2016). Paired-end reads were merged, and quality control implementation allowed the retention of sequences with a length between 400 to 450 bp, mean sequence quality score >30 and with truncation of a sequence at the rst base if a low quality within a rolling 10-bp window was found. Sequences with mismatched forward and/or reverse primers were omitted. 16S rRNA operational taxonomic units (OTUs) were de ned at ≥97% sequence homology using M5NR hits. All reads were classi ed to the lowest possible taxonomic rank using ribosomal RNA databases. Abundance pro le for each OTU was computed from the expanded similarities.
All the 10 Samples were subjected to pre-processing of reads, de-replication, singleton removal, OTU clustering, and chimera ltering. Each OTU's annotation at species level was carried out using scienti cally accepted tools and pipelines i.e., QIIME2 release 2020.6 and Green gene Database (default for QIIME)(g-13-8-99-515-806-nb-classi er) respectively. DADA2 plugin denoising step from QIIME2 converts the overlapping paired-end reads into consensus sequence of V3-V4 region. It nds the overlap for each pair and combines them into a single read. For quality control of the sequences, the DADA2 plugin in Qiime 2 was used to associate erroneous sequence reads with the true biological sequence from which they were derived, thus producing high-quality sequence variant data. Using DADA2, all reads were trimmed to 260 bp, based on the median quality score. In addition, chimeric sequences were detected and excluded from analyses. 16SrRNA Operational Taxonomic Units (OTUs) were picked from the Illumina reads using a closed-reference OTU picking protocol against the Greengenes database (https://data.qiime2.org/2020.6/common/gg-13-8-99-515-806-nb-classi er.qza). In the next step, taxonomy assignments wereassociated with OTUs based on the taxonomy associated with the Green genes reference sequence de ning each OTU.
Data availability: All 10 annotated gene sequences in FASTA format were deposited in NCBI GenBank database and the following accession number SAMN21545970 has been successfully received.

Results And Discussion
The meteorological conditions such as temperature (T), relative humidity (RH), wind speed (km/h) largely varied in all the sample collection sites during different seasons (Table S2). A strong seasonality is observed in the average concentration of bacteria in terms of colony forming unit in air, with highest value in pre-monsoon season (125.24-632.45 CFU/m3) and the lowest was observed during the monsoon (20.83-319.0 CFU/m3) (Figure 2). The concentration of culturable bacterial population in air samples ranges from 45.5 -645.84 CFU/m3 in the post monsoon season and 118.06-458.33 CFU/m3 in winter. During pre-monsoon, minimum bacterial concentration was observed in the sample collected in Guwahati, an urban location and the maximum was observed in Dibru-Saikhowa, a rainforest area with higher vegetation cover. Overall, spatially higher CFU values were observed in the highly vegetated locations: Shillong, Kaziranga, Dibru Saikhowa and Joypur. On the other hand, CFU values are lower in urban and semi urban locations (i.e., Guwahati, Jorhat, Dimapur, Namsai and Dibrugarh) with less vegetation cover (Figure 2). However, in a unit volume of air, existence of these microbial species is minimal and only upto ~0.20% of CFU counts are found in PM10 counts in pre-monsoon season at Dibru-Saikhuwa followed by Joypur and Namsai. PM10 counts were maximum in Dimapur, an urban location in winter reaching up to ~3 Millions and followed by post-monsoon and it lies within 1 million at other locations throughout all the seasons.
Particulate matter (PM), on the other hand is one of the major criteria of pollutants that affects air quality in urban and even rural areas in the world. Among the many pollutants, the impacts of particulate matter (PM) pollution on human health and ecology are the largest. Airborne ne particles smaller than 2.5 μm (PM2.5) are responsible for haze formation, visibility reduction and also affect global radiation budget. Characterizing the emission sources, concentrations, transport patterns, and impacts is particularly di cult in developing countries, where data are scarce, emissions are high, and health impacts are often severe. The rapid industrialization in the developing countries of South and East Asia has signi cantly increased the demand on energy, which in turn is based on combustion of coal or gas and this leads to high level of anthropogenic PM emissions. The health impact assessment is done based on the concentrations of inhalable, thoracic and alveoli (Figure 3). The highest inhalable percentage of particulate matter value was observed for 'Guwahati (53%)' followed by 'Dimapur (52%)' and 'Dibrugarh (51%)' whereas alveolic percentage ranges from 13-32% which indicates very little possibility as health threat, 33-35% for thoracic. Over the highest vegetation covered location Joypur contribution of alveolar particles (89%) is also signi cant and minimum is observed in Dimapur, the least vegetated location. It can be inferred that the thoracic and alveolar particles over Joypur, are contributed signi cantly by bioaerosols as evident from highest CFU values and appreciable fraction of fungal spores, pollens and animal debris. Similar spatial variability during the post monsoon season only was reported earlier by Pathak et al., 2020. Fluorescence microscopy analyses reveal large spatio-temporal heterogeneity in percentage distribution of both biological and non-biological aerosols in the North-East India (Figure 4a-d). The non-biological (non-viable) aerosols are found to vary from 30% (Shillong) -76% (Jorhat) in monsoon season, 65% (Guwahati) -89% (Jorhat) in post monsoon, 37% (Shillong) -73% (Jorhat) in winter and 55% (Dibrugarh) -89% (Jorhat) in pre-monsoon. Spatially, maximum percentage of non-biological aerosols was found in Jorhat, an urban location very close to the national highway. However, the minimum percentage of NBA was observed over Shillong during monsoon and winter while during post monsoon and pre-monsoon it is over Guwahati and Dibrugarh respectively. The aerosols viz. dust, silica particles etc. are categorized as non-biological aerosols, whose concentration is more in urban locations like Guwahati, Jorhat etc than in vegetated location like Kaziranga, Shillong etc. The predominant bioaerosol type found in the bioaerosol samples is pollens followed by fungal spores and animal debris. The existence of aerosols of biological origin in Northeast India is followed respectively by pollens (4-20%), animal debris (1-24%) and fungal spores (1-17%) in different seasons (Figure 4a-d). Pollen grain, a vital part of the owering plant life cycle, is the most important biological component of ambient air. A pollen grain is a specialized structure that encloses the owering plant male gametes whose biological function is to fertilize the female gametophyte. Pollens (15-200µm) are heavier than most bioaerosols. They settle down after heavy rains and high wind speeds. The average pollen count (in terms of percentage) is found to be higher in monsoon, closely followed by winter. Pollen count was the lowest during post monsoon. Spatially, observed pollen concentration was more in Shillong, a highly vegetated location, reaching upto 20% in monsoon as well as in winter. This high pollen concentration may be due to high species diversity resulted from physiographical features, altitudinal variation, abundant rainfall, salubrious monsoonal climate and fertile soil of Shillong. Additionally, it may be due to several endemic plants exist in this locality.
Pollen size ≤ 10 μm contributed more signi cantly in monsoon (92.3%) than in winter (85%) in Shillong (Table S3). Shillong is followed by 40% contribution of total pollens at other vegetated locations Namsai in monsoon and Dibru-Chaikhuwa in winter. In these locations also pollens of size ≤ 10 μm dominate. On the other hand, the semi-urban location Jorhat receives least pollen burden in all seasons except in winter. There, pollens ≤10 µm varied from 75% in pre-monsoon to 97% in winter. Since, pollen concentration does not exhibit a distinct seasonality and owering seasons varies from place to place depending upon type of vegetation, pollen identi cation is essential to explain the spatio-temporal variability elaborately. Animal debris contains insect parts, dead skin debris etc., and possessing strong spatial and temporal variability. It overdue in Kaziranga, the national park in Assam, followed by Joypur, the rainforest and Dibrugarh university campus. Almost similar spatial variability is observed for fungal spores, with more percentage contribution in monsoon and winter compared to rest of the seasons. It's maximum contribution is observed over Kaziranga in both the seasons followed by Joypur, Dimapur and Dibrugarh in monsoon and Joypur, Dimapur and Dibru-Chaikhuwa in winter.
The post-monsoon bioaerosol samples were subjected to metagenome analysis for the determination of overall bacterial biodiversity in all the sampling sites. The overall bacterial CFU count was found maximum during pre-monsoon season as compared to other seasons. Hence it was expected to observe maximum microbial biodiversity in the samples. Summarily, a total of 184 OTUs (bacterial operational taxonomic units) with 28,028 abundance counts was observed at a 99% nucleotide sequence identity across all 10 samples. Quality Control was performed using FastQC tool v0.11.7. Read quality was good with an average of more than 50,000 reads per sample and read length of 300bp. DADA2 plugin denoising step from QIIME2converts the overlapping paired-end reads into consensus sequence of V3-V4 region. It nds the overlap for each pair and combines them into a single read. For quality control of the sequences, the DADA2 plugin in Qiime 2 was usedto associate erroneous sequence reads with the true biological sequence fromwhich they were derived, thus producing high-quality sequence variant data.Using DADA2, all reads were trimmed to 260 bp, based on the median quality score. In addition, chimeric sequences were detected and excluded from analyses. 16S rRNA Operational Taxonomic Units (OTUs) was picked from the Illumina reads using a closed-reference OTU picking protocol against the Greengenes database (https://data.qiime2.org/2020.6/common/gg-13-8-99-515-806-nb-classi er.qza). In the next step, taxonomy assignments were associated withOTUs based on the taxonomy associated with the Greengenes reference sequence de ning each OTU.
High quality reads were taken for further analysis. The bar diagram in Figure 5 shows the taxonomic distribution of the entire bacterial phylum in bioaerosol samples collected from all the designated locations. The maximum number of bacterial genera (n=10) was observed in the samples PM3, PM4, PM5, and PM7 of which Proteobacteria was found to be the most abundant phylum in all the samples.
On the other hand, sample PM6 exhibited the minimum bacterial diversity with a total of 7 different phyla ( Figure 5). These were further extensively analyzed at their phylum, class, order, family, genus and species level. Overall, a total of 7 major phylum, 6 classes, 10 orders, 15 families, 13 genus, and 8 species of prokaryotes were identi ed in the biosersol samples (Figure 6a-f).The pie chart in Figure 6a shows the overall taxonomic distribution at phylum level with 71% of proteobacteria followed by fermicutes (21%), and bacteroidetes (8%). The species level distribution clearly shows the presence of Gammaproteobacteria as the most abundant one with a contribution of 52% followed by Bacilli (21%), Alphaproteobacteria (14%), Betaproteobacteria (5%, Flavobacteria (5%), and Sphingobacteria (3%) (Figure6f). The Krona chart (Figure 7) summarily depicts the taxonomic pro ling OUT of all the microbes analyzed and their abundance in the samples.
Molecular characterization shows presence of the following genus in the bioaerosol samples, viz., Alkanindiges, Bacillus, Bordetella, Brevibacillus, Ignatzschineria, Kerstersia, Myroides, Nitrobacteria, Ochrobactrum, Paenibacillus, Paralcaligenes, Pseudomonas, and Rhodopseudomonas ( Figure 6e). Even though, some of the species belongs to the genus Alkanindiges such as Alkanindiges hongkongensisis known to cause breathing related infections such as asthma which pose a potential threat to human health (Fazlollahi et al., 2018;Abdel-Aziz et al., 2019) but, the current study shows the presence of Alkanindiges illinoisensis most abundantly in all the aerosol samples and is referred to as a potential biosurfactant producing alkane degrading bacteria with non-pathogenic activities (Ron et al., 2010;Bogan et al., 2003;Xu et al., 2018).Nitrobacteria and Pseudomonads are found as the 2 nd and 3 rd most abundant bacterial genus in the bioaerosol samples, out of which most of the species belongs to the genus Nitrobacteria are considered as non-pathogenic in nature and are widely used for augmenting the nitrogen level in soil for enhance agricultural yield (Vayenas et al., 2011;EPA, 2002). On the other hand, various species of Pseudomonads such as Pseudomonas aeruginosa, P. maltophilia, P. uorescens, P. putida, P. cepacia, P. stutzeri, P. maltophilia, and P. putrefaciensetc. are considered as opportunistic pathogens which may cause not only respiratory diseases but also skin diseases, CNS infections, urinary tract infection etc. (Baron S. Galveston, 1996;Moradali et al.,2017).Bacillus was found to be the 4 th most common bacterial genus in 6 bioaerosol samples. It is worth to mention that a large number of species belongs to the genus Bacillus such as Bacillus subtilis, B. thuringiensis, B. cereus, B. anthracisare known as spore forming bacteria with a possibility to disperse their spores through aerosols (Alhazmi 2015;Driscoll et al., 2007;Huszczynski et al., 2020). On the other hand, such species of Bacillus are well known as potential threat to public health causing respiratory, skin, CNS and gastrointestinal tract associated diseases (Alhazmi 2015;Driscoll et al., 2007;Huszczynski et al., 2020). Additionally, the identity of microbes at different taxon levels along with their abundance in individual bioaerosol samplesare shown in Figures 8a-e, which gives a comprehensive snapshot of the data generated from individual samples. Even though, the DNA based metagenomic approach have shown the presence of possible pathogenic bacteria genus such as Bacillus, Pseudomonas, and Alkanindiges etc., but in contrast previous culture dependent approach of bioaerosol characterization for the screening of antibiotic resistant microbes showed no prevalence of such microbes (Pathak et al., 2020). It may also be noted that, acquired antibiotic resistance in the microbes present in environmental samples may pose a possible threat to human health (Morehead and Scarbrough 2018;Naz et al. 2019). Prevalence of antibiotic resistant microbes in aerosol samples collected from various locations with massive anthropogenic activities in India and other countries are widely reported (Brooks et al. 2010;Yadav et al. 2015;Bragoszewska and Biedron 2018;Naz et al. 2019). However, the present work may be considered as the very rst hand comprehensive report on the extensive characterization of biological aerosol samples of Northeast India in terms of inhalable, thoracic, and alveolic particles, determination of their constituents (pollens, fungal spores, animal debris, and non-biological components), and the microbial diversity through DNA based metagenomic approach in all the four seasons in the year. Metagenome approach to determine microbial diversity in aerosol samples has now become one of the very recent trends in aerobiology. But scienti c ndings are still limited and require extensive metagenomic characterization to have a broader view on the microbial biodiversity present in atmospheric aerosols.
Yoo K, Lee TK, Choi EJ, Yang J, Shukla SK, Hwang S, and Park J (2017): Molecular approaches for the detection and monitoring of microbial communities in bioaerosols: a review. J Environ Sci (China); 51: 234-47. Figure 1 The NDVI map of Northeast India in showing nine discrete campaign locations.