Next Article in Journal
Danofloxacin Treatment Alters the Diversity and Resistome Profile of Gut Microbiota in Calves
Next Article in Special Issue
Microbiomics in Collusion with the Nervous System in Carcinogenesis: Diagnosis, Pathogenesis and Treatment
Previous Article in Journal
Identification of Lactobacillus Strains Capable of Fermenting Fructo-Oligosaccharides and Inulin
Previous Article in Special Issue
High Throughput Analysis Reveals Changes in Gut Microbiota and Specific Fecal Metabolomic Signature in Hematopoietic Stem Cell Transplant Patients
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Microbiome Studies from Saudi Arabia over the Last 10 Years: Achievements, Gaps, and Future Directions

by
Khalid J. Alzahrani
Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi Arabia
Microorganisms 2021, 9(10), 2021; https://doi.org/10.3390/microorganisms9102021
Submission received: 22 August 2021 / Revised: 13 September 2021 / Accepted: 16 September 2021 / Published: 24 September 2021
(This article belongs to the Special Issue Microbiota: From the Environment to Humans)

Abstract

:
In the past ten years, microbiome studies have shown tremendous potentiality for implementation of understanding microbiome structures and functions of various biomes and application of this knowledge for human betterment. Saudi Arabia is full of geographical, ecological, ethnical, and industrial diversities and scientific capacities. Therefore, there is a great potential in Saudi Arabia to conduct and implement microbiome-based research and applications. However, there is no review available on where Saudi Arabia stands with respect to global microbiome research trends. This review highlights the metagenome-assisted microbiome research from Saudi Arabia compared to the global focuses on microbiome research. Further, it also highlights the gaps and areas that should be focused on by Saudi microbiome researchers and the possible initiatives to be taken by Saudi government and universities. This literature review shows that the global trends of microbiome research cover a broad spectrum of human and animal health conditions and diseases, environmental and antimicrobial resistance surveillance, surveillance of food and food processing, production of novel industrial enzymes and bioactive pharmaceutical products, and space applications. However, Saudi microbiome studies are mostly confined to very few aspects of health (human and animal) and environment/ecology in last ten years, without much application. Therefore, Saudi Arabia should focus more on applied microbiome research through government, academic, and industry initiatives and global cooperation to match the global trends.

1. Introduction

In past 15 years, remarkable advancements have been accomplished in the field of metagenomics due to the availability of low-cost sequencing technologies and high-end analytical software. Metagenomic approaches have been applied to understand core microbiota, networks and interactions of microbial communities, host–environment interactions and their effect on spatial and temporal changes of microbiota, prediction of functional phenotypes, and co-evolution of the host microbe, etc. of a microbial ecosystem [1]. Metagenome-based information has been successfully applied for the betterment of plant, animal, and human health; improvement of agricultural productivity; and monitoring of ecosystems and environments, among others [1,2]. Similar to metagenome studies, meta-transcriptome, meta-proteome, and meta-metabolome can also be studied for any microbiota [3]. However, metagenome studies so far have been given the most priority and gene array/panels; 16S rRNA gene amplicon, 18S rRNA gene amplicon, whole metagenome shotgun (WMS), and metagenomic next-generation sequencing (mNGS) are most common molecular technologies used in metagenome studies, followed by the use of several bioinformatic tools to analysis the metagenome data for specific purposes [4,5]. Saudi Arabia is full of distinct climatic regions such as wetlands, deserts, seas, etc. [6,7] and is now facing increased non-communicable disease risk factors in its population due to diversity in demography and socio-economic structure [8,9]. Therefore, microbiome-based research outcomes, specifically in the area of health and the environment, could be important for Saudi Arabia to assess the upcoming health- and environment-associated challenges and to develop proper management strategies.
In this review, first an overview on global trends of metagenome-based microbiome studies is discussed (Table 1), followed by literature search specific to Saudi Arabian metagenome-based microbiome studies (Table 2) to present the microbiome studies that have been so far carried out by Saudi scientists. Finally, the gaps in Saudi studies are highlighted and future directions are discussed.

2. Global Trends of Metagenome Studies

Metagenome-based microbiome studies have been applicable to many fields, including human health, agriculture, plant pathology, biotechnology, food science, antimicrobial resistance, environmental monitoring, marine biology, astrobiology, etc., to name a few [1]. Table 1 provides an overview of the global focus on metagenome-based microbiome studies.

2.1. Global Metagenome Studies on Human Health

Genomes of the microbes and host are called hologenomes and the host–microbiota interactions are one of the major aspects of metagenome studies towards improvement of human health. Studies on diversity and function of healthy human microbiomes started in 2012 by the Human Microbiome Project Consortium [10]. The gut and oral microbiota can be a signature of ethnicity [11,12]. A change of gut microbiota directly correlates with diet and various diseases [13]. Gut microbiota dysbiosis is associated with several diseases including Parkinson’s disease, Alzheimer’s disease, hypertension, atherosclerosis, obesity, diabetes, non-alcoholic fatty liver disease, inflammatory bowel disease, and colorectal cancer, among others [14]. Similarly, dysbiosis of oral microbiota can be a sign of periodontitis, dental caries, oral cancer, esophageal cancer, pancreatic cancer, cystic fibrosis, cardiovascular disease, rheumatoid arthritis, Alzheimer’s disease, diabetes, etc. [15]. Vaginal microbiome composition indicates normal and various gynecological complications [16,17]. Clinical metagenomics have also been successfully applied for infectious disease diagnosis. Array or panel technologies have been used to diagnose pathogens for gastrointestinal diseases from stool samples [18], encephalitis or meningitis from cerebrospinal fluids [19], and respiratory tract infection from lower respiratory tract samples [20,21]. The mNGS technology has been used to diagnose pneumonia [22], sepsis [23], and encephalitis [24]. Therefore, restoration of normal microbiome is a potential therapy for various diseases associated with microbiome dysbiosis, including cognitive impairments [25,26,27]. Similarly, microbiome engineering has also been suggested to improve domestic animal health [28,29]. There are several examples and Table 1 provides an overview.

2.2. Global Metagenome Studies on Environment

2.2.1. Metagenome for Environment and Ecology Surveillance

Metagenome-assisted analysis of environmental microbial communities can be employed as a biosensor for monitoring biodiversity and environmental management [30]. The Earth Microbiome Project (www.earthmicrobiome.org) was initiated during 2010 to construct the microbial and microbiome map of the earth. Similarly, the Tara Oceans consortium is investigating the marine ecosystems, microbiome diversity, and microbiome–environment interactions at the genetic, morphological, and functional level of oceans [31,32]. A lot of metagenomic studies have been conducted so far on the environment and ecology. To mention a few, it was reported that the microbiota differs among human-occupied homes, and this microbiome could be unique for each family [33]. The Tongue River sediment metagenome shows a highly enriched microbial ecosystem, and the microbial community structure and functions change in response to anthropogenic drivers near towns, coal and methane by-products [34]. Processes of anaerobic hydrocarbon degradation have been explained by a bacterial metagenomic study of sub-tidal sediments from polar and sub-polar coasts [35]. Rhodanobacter and Rhodocyclaceae are sensors for the presence of uranium, and Oceanospirillales is a good indicator for oil contamination [36]. Metagenome-based analysis of microbial diversity and ecology of soil, river, lake, seashore, mangrove, ocean water and sediments could indicate the metabolic architecture of the specific microbiome and could also be a marker for water quality, various pollutants, and chemical contaminations [37,38,39,40,41,42] (Table 1). Metagenome of water sediment can also be used for public health risk assessment [43].

2.2.2. Metagenome for Surveillance Antimicrobial Resistance

In today’s scenario, antimicrobial resistance (AMR) is a global health and environmental issue. Metagenome can be a valuable tool to identify and monitor antimicrobial resistance genes (ARGs). Metagenome-based analysis reveals that water treatment plants (urban wastewater and sewage) area hotspot for ARGs [44,45]. Metagenome analysis of mangrove microbiome and glacial lake sediments also show the presence of ARGs [38,46]. A comparative metagenomics analysis shows that gut microbiomes of humans and mammals carry the widest diversity of ARGs compared to the metagenome samples from water, soil, plants, and insects [47,48]. Using the metagenome approach, several ARGs have also been identified in ready-to-eat foods [49], fecal microbiota [50], merine fish [51], and dairy and beef production wastes [52] (Table 1). Apart from the known ARGs, metagenome approaches have also been successfully applied to identify novel kanamycin and ceftazidime resistance genes [53].

2.3. Global Metagenome Studies on Other Aspects

2.3.1. Metagenome for Food Monitoring

Pathogenic microbial contamination is one of biggest problems in the food industry with food safety. Whole metagenome sequencing (WMS) has been used to detect E. coli that produce Shiga toxins in spinach [54]. Pathogenic E. coli and Klebsiella pneumoniae strains were identified from Ghanaian fermented milk product Nunu samples by Walsh et al. [55]. WMS showed that Lactobacillus, Leuconostoc, and Weissella were the predominant genera in Mexican Cotija cheese [56]. Diagnosis of foodborne outbreaks can also be monitored by WMS [57]. L. monocytogenes that were responsible for a Listeriosis outbreak were identified from ice cream samples using WMS [58]. Microbiome analysis of a beef processing chain showed the presence of production stage-specific shifts of food pathogens such as: S. enterica, E. coli, and C. botulinum [59] (Table 1). The uncultured food-spoiling bacteria Thermus thermophilus that is associated with cheese pinking spoilage was identified using the metagenome approach [60]. Metagenome has also been used to understand the food fermentation process [61] and how the microbiota and specific microbial species of fermented foods improve health [62]. Therefore, metagenomics can be a highly useful tool to assess, monitor, and improve food and food industries.

2.3.2. Metagenome for Industrial Applications

Application of metagenomics has tremendous industrial potentiality. Metagenomics-based functional screening of environmental microbiome is an important trend in industrials, with biotechnologies to identify bacterial strains producing ideal biocatalysts, elusive antimicrobial metabolites, and novel industrial enzymes [63] (Table 1). So far, several industry-grade novel cellulases, proteases, lipases, and bleomycin resistance dioxygenase enzymes have been successfully screened and produced using metagenome approaches. Functional metagenomics have been used to discover novel enzymes for food and pharmaceutical industries. Such novel enzymes include low pH thermo-stable alpha-amylase, thermo-stable esterase, cold-active lipase, alkaline-stable family IV lipase, protease-insensitive feruloyl esterase, and cold-active beta-galactosidase, to name a few. Important metagenome-assisted discoveries of novel bioactive and biosynthetic pathways include pederin, biotin, vibrioferrin, Borregomycin A and B encoded by bor pathway, serine protease inhibitor, salt-tolerance genes and acid resistance genes. Similarly, several novel antimicrobials, anti-infectives, and antimicrobial resistance genes have also been identified through metagenome approaches. Some examples include turbomycin A and B, chitinase with chitobiosidase activity, lactonases, LuxR/LuxI genes, bacterial NAHLase, carboxylesterase, kanamycin, and ceftazidime resistance genes [53,64]. Metagenomics have also been used to discover novel endoglucanases for production of second-generation biofuel [65].

2.3.3. Microbiome and Astrobiology

Applications of metagenomics are not only restricted to humans, industries, or the earth, but also beyond the earth’s atmosphere. In the last few years, metagenomics has been applied towards safety and space exploration to understand the taxonomic and functional characteristics of microbiomes in extreme conditions and anoxic sites [66]. Metagenomics study has revealed how the minimal genomes and genome plasticity of Pseudomonas can thrive under severe nutrient stress conditions [67]. Corynebacterium ihumii GD7 was identified by WMS as the dominant species at the International Space Station (ISS) [68]. Spacecraft assembly cleanrooms and ISS microbiomes also showed several pathogenic bacteria, including Acinetobacter baumannii, Haemophilus influenzae, Klebsiella pneumoniae, Salmonella enterica, Shigella sonnei, Staphylococcus aureus, Yersinia frederiksenii, and Aspergillus lentulus, with several ARGs and cobalt-zinc-cadmium resistance genes [69,70]. Simulated long space flight travel shows increased dominance of Bacteroides and Prevotella in the astronaut’s gut microbiome [71]. It was also revealed through metagenome analysis that transmission of microbes to ISS or an astronaut’s microbiome happened, and vice versa [72,73]. Metagenomic analysis has similarly been used to understand how and which bacterial community members of the kombucha (a drink) mutualistic community (KMC) survive under a Mars-like environment at ISS [74]. Researchers also have shown how the most dominant species, K. oboediens, of space-exposed KMC retains its robustness in cellulose production through its intact cellulose-producing bcs operon [75]. Metagenome-based approaches have also been used to isolate and characterize the novel strain Kalamiella piersonii gen. nov., sp. nov available only at ISS [76] (Table 1). Therefore, a lot of metagenome applications are possible towards understanding the space environment and exploration.

3. Literature Search Criteria and Article Selection to Review Metagenome-Assisted Microbiome Studies from Saudi Arabia

To retrieve literature on Saudi Arabia’s microbiome-related publications, the PubMed literature database (www.pubmed.ncbi.nlm.nih.gov) was searched with the advanced search option using keywords: “Microbiome” (Title/Abstract) OR “microbiota” (Title/Abstract) OR “metagenome” (Title/Abstract) OR “16SrRNA” (Title/Abstract) OR “amplicon” (Title/Abstract) AND “Saudi Arabia” (Title/Abstract). Articles written in English and published from June 2011 to July 2021 were considered. Each retrieved article was manually scanned, and the technology-related articles were excluded, while microbiome studies as per global trends were considered for this review. Following this search criteria, 121 articles were collected, out of which 38 were found to be relevant for this review. When we used the same keywords without “Saudi Arabia” (Title/Abstract), 75,391 papers were found. When the selected articles were classified as per the global trends of microbiome studies, a mismatch was observed. Unlike the global trends, the Saudi Arabian studies were aligned to only two major global trends: human and animal health (13 articles) and the environment (11 articles). However, three articles on camel parasites and one article on date palm pests were found. Additionally, 10 articles were found to describe novel isolates. However, for most of these studies the metagenome approach was not used. Table 2 represents the metagenome-based microbiome studies by Saudi researchers in the last 10 years.

4. Saudi Arabian Microbiome Studies on Human Health

Saudi Arabian metagenome studies are limited to obesity, diabetes, autism, infections, and pregnancy (Table 2). Yasir et al. [77] reported significant abundance of Firmicutes in fecal microbiota of obese Saudis as compared to their normal weight controls. The gut microbiome analysis by Angelakis et al. showed less species richness and biodiversity in Saudi obese subjects as compared to Amazonians and Polynesians. However, Lactobacillus sp. abundance is more prevalent in Saudis than the Polynesians [78]. Two new strains of Bacillus jeddahensis sp. nov. (JCE(T) and Oceanobacillus jeddahense sp. nov. (S5T) were isolated from a stool specimen of young obese patients by Bittar et al. [79] and Khelaifia et al. [80], respectively. Recently, Kieu et al. reported the presence of new bacterial species Clostridium culturomicium strain CL-6T and Clostridium jeddahitimonense strain CL-2T in the gut microbiota of an obese man from Saudi Arabia [81]. Using sub-gingival samples and 16S rRNA-based analysis, Al-Obaida et al. reported the presence of Aggregatibacter actinomycetemcomitans (43%) and Capnocytophaga ochracea (100%) in diabetic patients from Saudi Arabia [82]. Abdulhaq et al. studied the tongue microbiota of children with autism spectrum disorder (ASD) in Jazan city and noted that there was no significant difference in the microbiota compared to that of healthy children [83]. Analysis of bacterial metagenome from blood samples of Saudi Arabian blood cancer patients with bloodstream infections showed the predominance of Gram-negative bacteria (82%) in which E. coli and K. pneumonia were higher [84]. Based on amplicon and metagenome sequencing, various bacteria associated with pneumonia, namely Acinetobacter baumannii, Pseudomonas aeruginosa and Streptococcus pneumonia, and several ARGs were reported to be present in the oropharyngeal swabs, tracheal aspirates, and throat swabs samples of Saudi patients infected with Middle East respiratory syndrome CoV (MERS-CoV) [85]. 16S amplicon sequencing and the culturomics-based gut microbiota of pregnant and non- pregnant Saudi females were studied by Khan et al. [86]. Both the groups showed the presence of Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. However, a sharp decline of Bacteroidetes was noticed during the first trimester of pregnant women. On the other hand, relative abundance of butyrate-producing bacteria (e.g., Faecalibacterium spp. and Eubacterium spp.) were found in pregnant women, whereas Prevotella copri was significantly higher in non-pregnant females. The researchers also noted the presence of antimicrobial resistant genes (ARGs) in pregnant women that could be unfavorable for both mother and fetal health [86] (Table 2). Al-Moaleem et al. showed that the oral microbiome of khat- and non-khat-chewing Saudi people from Jazan region predomantly had Lactobacillus and Veillonella spp. in both the groups. Lactobacillus was found to be higher in khat chewers, which was associated with prosthetic materials [87]. Badger-Emeka et al. reported that different doses of vitamin D can influence the body weight and gut microbial colonies in mice. The authors found decreased gut Salmonella/Shigella and E. coli colonies under low- and normal-dose vitamin D. However, P. aeruginosa was significantly decreased under high vitamin D doses [88].

5. Saudi Arabian Microbiome Studies on Animal Health

Camel is an important animal in Arabian deserts, and ticks (a blood feeder that can transmit a wide range of microbes and pathogens) are one of the most important parasites of camel. Using 16S rRNA amplicon analysis of ticks from the camels of Hofuf city, Elbir et al. reported 17 microbial species under 114 genera. The camel ticks of this region predominantly showed abundance of phylum such as Proteobacteria (98%), Firmicutes (1.38%), Actinobacteria (0.36%), and Bacteroidetes (0.17%). The researchers also identified the bacterial pathogen H. pylori in their tick samples [89]. Elbir et al. in another study also reported species diversity among Francisella-like endosymbionts (FLEs) and non-specialized circulation of FLEs among 151 H. dromedarii ticks that were collected from 33 camels from 13 different locations in Saudi Arabia [90]. Alreshidi et al. carried out a similar study involving 200 ticks obtained from healthy camels in the Al Khotha and Al Gayed regions of Hail province. Their massive 16S rDNA sequencing-based metagenome analysis revealed the presence of several distinct microbial communities from two locations. Proteobacteria (61.3%) and Firmicutes (31.2%) mostly dominated the ticks from Al Khotha region and Proteobacteria (81.2%) and Firmicutes (9.2%) were predominant in ticks from the Al Gayed [91]. Such data and findings may have great veterinary and medical importance. Except for these studies, no other metagenome-based microbiome studies by Saudi Arabian authors were found on human or animal health (Table 2).

6. Saudi Arabian Microbiome Studies on the Environment

Almost all environmental microbiome studies by Saudi scientists are to report microbial diversity in various ecological samples of the country (Table 2). Alzubaidy et al. first reported the microbial diversity of rhizosphere microbiome (of Avicennia marina) from the Red Sea. The authors observed a predominance of Proteobacteria, Bacteroidetes, and Firmicutes in their study [92]. Al-Quwaie et al. reported that the soil rhizosphere microbiota varies depend on the desert wild plants, such as Calotropis procera and Senna alexandrina. Streptococcus sobrinus, Veillonella parvula, and Sphingomonas genus are enriched in the rhizosphere of S. alexandrina. High abundance of Pseudomonas stutzeri and Virgibacillus koreensis in the soil rhizosphere of desert wild plants C. procera provides saline resistance. Marinobacter, Porticoccus, and Alcanivorax genera that are present only in the rhizosphere of C. procera protect the plants from pathogen infections [93]. Using 16S RNA sequencing, Yasir et al. analyzed the soil bacterial community along the south-western highlands, which is susceptible to environmental changes. They identified 33 different phyla, among them Proteobacteria, Actinobacteria, and Acidobacteria were most dominant [94]. Moussa et al. analyzed soil samples from four different locations in the Mecca region and reported 460 fungal species that belong to 133 genera, 58 families, 33 orders, 13 classes, and 4 phyla [103]. In another study, Saudi Arabian dust storms showed relatively low abundance of Actinobacteria and high abundance of Proteobacteria when compared with other dust storms in other countries [95]. However, these two studies did not use the metagenomic approach. Yasir et al. studied the bacterial diversity in sediment samples from six hot springs of Saudi Arabia and reported that the most abundant species were Bacillus and Brevibacillus [96]. The same research group analyzed microbial communities in mat samples from two hot springs from Al Aridhah and found that the Chloroflexus was the most dominant taxa among the diverse group of bacteria identified [97] (Table 2). In both the studies, the authors used 16S RNA sequencing. Li et al. analyzed the microbial communities in managed aquifer recharge (MAR) systems obtained from Taif River (Taif, Saudi Arabia) and South Platte River (Colorado), in which heterotropic Proteobacteria were found to be dominant. The authors inferred that the addition of labile-dissolved organic carbon could influence the composition and/or metabolism of these microbial communities [98]. Several genera related to opportunistic pathogens (e.g., Acinetobacter, Aeromonas, Arcobacter, Legionella, Mycobacterium, Neisseria, Pseudomonas, and Streptococcus) were reported in chlorinated effluent of local wastewater collected from the Thuwal area of Saudi Arabia [99]. Mineralization of crude oil requires organic transformation by bacteria. Albokari et al. reported microbial communities of crude oil and oil sludge samples obtained from Saudi ARAMCO Oil Company [101]. The authors noted prevalence of Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Clostridia, Spingobacteria, and Flavobacteria in sludge samples and Bacillus, Clostridia, and Gammaproteobacteria in crude oil. They also pointed out that Bacilli is the most dominant taxa in crude oil and Flavobacteria is the most dominant taxa in oil sludge samples [101] (Table 2). Bibi et al. investigated three marine sponges belonging to the species of Pione vastifica, Siphonochalina siphonella, and Suberea mollis collected from the Red Sea in Jeddah. They identified large diverse communities in S. mollis with 105 OTUs belonging to the phylum Proteobacteria and concluded that the abundance of Proteobacteria in sponges may have ecological significance and may be used for environmental monitoring [100] (Table 2).

7. Saudi Arabian Microbiome Studies on Other Aspects

7.1. Studies on Plant Pests

Date palm is an important economic fruit crop in Saudi Arabia. Date palm root borer Oryctes agamemnon causes significant loss of crop productivity [104]. El-Sayed and Ibrahim studied the endosymbiotic bacterial communities of O. agamemnon larval mid-gut metagenome and reported the presence of 11 major operational taxonomic units (OTUs), such as Photobacterium, Vibrio, Allomonas, Shewanella, Cellulomonas, and Citrobacter. The endosymbiotic bacterial community found predominantly consisted of Vibrionaceae, uncultured bacteria, Enterobacteriaceae, Shewanellaceae, and Cellulomonadaceae. The authors concluded that the presence of these bacteria may play a role in digestion and other developmental functions of O. agamemnon larvae. However, the authors did not conclude the role of the identified bacteria in host and pest interactions [102].

7.2. Bacterial Novel Strains Isolated from Various Parts of Saudi Arabia

Due to the presence of both sea and desert, the climate of Saudi Arabia can be a source of novel microbes of various importance. Sefrji et al. isolated novel Mangrovivirga cuniculi gen. nov., sp of the Mangrovivirgaceae family from a bioturbated mangrove sediment on the Saudi Arabian Red Sea coast [105]. The same research group also reported another novel bacterium Kaustia mangrovi gen. nov., sp. nov of the Parvibaculaceae family from the Red Sea mangrove sediments [106]. Rotting et al. isolated a novel extremophile bacterium species Streptomyces jeddahensis sp. nov. that can tolerate 50 °C from the desert soil near Jeddah [107]. Similarly, novel halotolerant bacteria Siccirubricoccus deserti gen. nov., sp. nov. [108], Sphingomonas jeddahensis sp. nov. [109], Microbacterium album sp. nov. and Microbacterium deserti sp. nov. [110], Georgenia alba sp. nov [111]. Georgenia deserti sp. nov [112] were also isolated from various desert samples in Saudi Arabia. Gamma- and UV-radiation-resistant novel Deinococcus saudiensis sp. Nov was also isolated from the desert soil of Yanbual Bahr [113]. The genus Streptomyces, showing anti-blood cancer activity, was identified from the actinobacterial isolates that were collected from the Al-Jouf desert of Saudi Arabia [114]. However, none of these novel strains were isolated using a microbiome or metagenome approach, and in most cases the utility of these bacteria is unknown.

8. Conclusions and Future Direction

The global trend of most microbiome studies has focused on understanding the interactions, functional characterization, and implementation of a microbiome towards improvement of human and animal health, the environment and ecology surveillance, and surveillance of antimicrobial resistance and food chain ecology. Additionally, microbiomes and metagenomics also have important industrial applications and astrobiology. Metagenome-based microbiomes have been successfully implemented for diagnosis and to understand disease mechanisms and treatment strategies (microbiome restoration) for various diseases including several cancers and infectious, metabolic, cardio-vascular, neurological, inflammatory, and gynecological diseases. The second global trend of microbiome studies mainly focused on understanding microbial biodiversity of various environmental conditions, such as air, soil, rivers, lakes, seashore, mangroves, ocean water, etc. The environmental microbiome studies also help in developing biosensors for water quality, pollution, and ecological surveillance. Similarly, environmental microbiome analysis through metagenomics can be an important tool to monitor the global crisis of antimicrobial drug resistance. Metagenomics is also an inevitable tool to monitor food processing setup environments and food quality. The technology has prime importance for diagnosing foodborne pathogens and diseases. Microbiomes and metagenome are successfully used to identify novel biocatalysts, antimicrobial metabolites, and enzymes for industrial production and various industrial uses. Even the microbiome analysis using metagenome is a key tool now for astrobiolocial experiments and space exploration.
As compared to the global trends of microbiome studies discussed above, the initiatives from Saudia Arabia on microbiome-based research are lagging behind. While there are more than 75,391 articles published on microbiomes and metagenomics by the global community in last 10 years, Saudi Arabian authors have published only 121 articles and out of which only 27 articles have used microbiome analysis using the metagenome approach. While the global focus of microbiome studies is on (i) human and animal health, (ii) environment and ecology surveillance, (iii) surveillance antimicrobial resistance, (iv) food and food process monitoring, (v) industrial applications, and (vi) space biology/astrobiology, most of the Saudi Arabian studies are restricted to (i) human and animal health (13 articles) and (ii) environmental microbiomes (11 articles). Additionally, while metagenomics have been used in several cancers and infectious, metabolic, cardio-vascular, neurological, inflammatory, and gynecological diseases globally, the Saudi studies are restricted to only two metabolic diseases (obesity and diabetes), two infections (bloodstream infections and MERS-CoV), and one gynecological condition (pregnancy). The environmental microbiome studies are also minimal in Saudi Arabia (only 11 studies), and are only to understand microbial diversity; however, the oil microbiome study is very unique. Other unique microbiome studies from Saudi Arabia include tick metagenomics (parasites of camels) and metagenomics of date palm root borer O. agamemnon. Although Saudi scientists have isolated several novel bacterial strains from various deserts in the country, they probably have not used a microbiome and metagenomics approach.
Therefore, to match the global trends, Saudi researchers need to focus on the untouched areas, such as application of metagenomics in various predominant non-communicable diseases. Dysbiosis of the gut microbiota is associated with several gastrointestinal (ulcerative colitis, Crohn’s disease, irritable bowel syndrome) and extra-gastrointestinal diseases (diabetes, obesity, autoimmune disorders, Parkinson’s disease, autism, multiple sclerosis, infections with multidrug-resistant bacteria, multiple organ failure, etc.) and fecal microbiota transplantation (FMT) is one potential therapy for these conditions [115,116] (Table 1). However, no research has been conducted so far in Saudi Arabia on FMT and the clinical microbiome researchers should focus on this aspect. Similarly, other important areas such as antimicrobial drug resistance, food microbiomes (for example, isolating novel probiotic bacteria from the local food resources in Saudi Arabia), industrial novel product development, and space biology also need to be explored. Importantly for microbiome research, international initiatives and cooperation between countries [117], such as the Earth Microbiome Project (www.earthmicrobiome.org) and Tara Oceans consortium [31,32], as well as university initiatives such as the USF microbiome initiative, Vanderbilt microbiome initiative, etc. are required. As per the available literature, Saudi Arabia has so far not taken any international initiative at the government, university, or non-government level to explore microbiome research. Therefore, to be in the global race of microbiome R&D, the government should make a task force to identify the opportunities in microbiome research in Saudi Arabia. Then, by individual initiative (for instance, the Saudi Microbiome Project) or collaboration with suitable partner countries, allocate funds to the universities/research institutes to conduct the research. Alternatively, the Saudi universities and industries could also come forward with their own ideas and take specific microbiome initiatives, like the initiatives taken by universities from the USA and other countries.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The author declares no conflict of interest.

References

  1. Berg, G.; Rybakova, D.; Fischer, D.; Cernava, T.; Vergès, M.-C.C.; Charles, T.; Chen, X.; Cocolin, L.; Eversole, K.; Corral, G.H.; et al. Microbiome definition re-visited: Old concepts and new challenges. Microbiome 2020, 8, 103. [Google Scholar] [CrossRef]
  2. Cullen, C.M.; Aneja, K.K.; Beyhan, S.; Cho, C.E.; Woloszynek, S.; Convertino, M.; McCoy, S.J.; Zhang, Y.; Anderson, M.Z.; Alvarez-Ponce, D.; et al. Emerging Priorities for Microbiome Research. Front. Microbiol. 2020, 11, 136. [Google Scholar] [CrossRef] [Green Version]
  3. Kumar Awasthi, M.; Ravindran, B.; Sarsaiya, S.; Chen, H.; Wainaina, S.; Singh, E.; Liu, T.; Kumar, S.; Pandey, A.; Singh, L.; et al. Metagenomics for taxonomy profiling: Tools and approaches. Bioengineered 2020, 11, 356–374. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Roumpeka, D.D.; Wallace, R.J.; Escalettes, F.; Fotheringham, I.; Watson, M. A Review of Bioinformatics Tools for Bio-Prospecting from Metagenomic Sequence Data. Front. Genet. 2017, 8, 23. [Google Scholar] [CrossRef] [Green Version]
  5. Escobar-Zepeda, A.; Godoy-Lozano, E.E.; Raggi, L.; Segovia, L.; Merino, E.; Gutiérrez-Rios, R.M.; Juarez, K.; Licea-Navarro, A.F.; Pardo-Lopez, L.; Sanchez-Flores, A. Analysis of sequencing strategies and tools for taxonomic annotation: Defining standards for progressive metagenomics. Sci. Rep. 2018, 8, 12034. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Hasanean, H.; Almazroui, M. Rainfall: Features and Variations over Saudi Arabia, A Review. Climate 2015, 3, 578–626. [Google Scholar] [CrossRef] [Green Version]
  7. Al-Obaid, S.; Samraoui, B.; Thomas, J.; El-Serehy, H.A.; Alfarhan, A.H.; Schneider, W.; O’Connell, M. An overview of wetlands of Saudi Arabia: Values, threats, and perspectives. Ambio 2016, 46, 98–108. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Herzallah, H.K.; Antonisamy, B.R.; Shafee, M.H.; Al-Otaibi, S.T. Temporal trends in the incidence and demographics of cancers, communicable diseases, and non-communicable diseases in Saudi Arabia over the last decade. Saudi Med. J. 2019, 40, 277–286. [Google Scholar] [CrossRef]
  9. Al-Hanawi, M.K.; Keetile, M. Socio-Economic and Demographic Correlates of Non-communicable Disease Risk Factors Among Adults in Saudi Arabia. Front. Med. 2021, 8, 605912. [Google Scholar] [CrossRef] [PubMed]
  10. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 2012, 486, 207–214. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Gaulke, C.A.; Sharpton, T.J. The influence of ethnicity and geography on human gut microbiome composition. Nat. Med. 2018, 24, 1495–1496. [Google Scholar] [CrossRef]
  12. Premaraj, T.S.; Vella, R.; Chung, J.; Lin, Q.; Panier, H.; Underwood, K.; Premaraj, S.; Zhou, Y. Ethnic variation of oral microbiota in children. Sci. Rep. 2020, 10, 14788. [Google Scholar] [CrossRef] [PubMed]
  13. Wilson, A.S.; Koller, K.R.; Ramaboli, M.C.; Nesengani, L.T.; Ocvirk, S.; Chen, C.; Flanagan, C.A.; Sapp, F.R.; Merritt, Z.T.; Bhatti, F.; et al. Diet and the Human Gut Microbiome: An International Review. Dig. Dis. Sci. 2020, 65, 723–740. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Chen, Y.; Zhou, J.; Wang, L. Role and Mechanism of Gut Microbiota in Human Disease. Front. Cell. Infect. Microbiol. 2021, 11, 625913. [Google Scholar] [CrossRef] [PubMed]
  15. Willis, J.R.; Gabaldón, T. The Human Oral Microbiome in Health and Disease: From Sequences to Ecosystems. Microorganisms 2020, 8, 308. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Anahtar, M.N.; Gootenberg, D.B.; Mitchell, C.M.; Kwon, D.S. Cervicovaginal Microbiota and Reproductive Health: The Virtue of Simplicity. Cell Host Microbe 2018, 23, 159–168. [Google Scholar] [CrossRef] [Green Version]
  17. Chen, X.; Lu, Y.; Chen, T.; Li, R. The Female Vaginal Microbiome in Health and Bacterial Vaginosis. Front. Cell. Infect. Microbiol. 2021, 11, 631972. [Google Scholar] [CrossRef]
  18. Khare, R.; Espy, M.J.; Cebelinski, E.; Boxrud, D.; Sloan, L.M.; Cunningham, S.A.; Pritt, B.S.; Patel, R.; Binnicker, M.J. Comparative evaluation of two commercial multiplex panels for detection of gastrointestinal pathogens by use of clinical stool specimens. J. Clin. Microbiol. 2014, 52, 3667–3673. [Google Scholar] [CrossRef] [Green Version]
  19. Leber, A.L.; Everhart, K.; Balada-Llasat, J.-M.; Cullison, J.; Daly, J.; Holt, S.; Lephart, P.; Salimnia, H.; Schreckenberger, P.C.; DesJarlais, S.; et al. Multicenter Evaluation of BioFire FilmArray Meningitis/Encephalitis Panel for Detection of Bacteria, Viruses, and Yeast in Cerebrospinal Fluid Specimens. J. Clin. Microbiol. 2016, 54, 2251–2261. [Google Scholar] [CrossRef] [Green Version]
  20. Ruggiero, P.; McMillen, T.; Tang, Y.-W.; Babady, N.E. Evaluation of the BioFire FilmArray respiratory panel and the GenMark eSensor respiratory viral panel on lower respiratory tract specimens. J. Clin. Microbiol. 2014, 52, 288–290. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Tang, Y.-W.; Gonsalves, S.; Sun, J.Y.; Stiles, J.; Gilhuley, K.A.; Mikhlina, A.; Dunbar, S.A.; Babady, N.E.; Zhang, H. Clinical Evaluation of the Luminex NxTAG Respiratory Pathogen Panel. J. Clin. Microbiol. 2016, 54, 1912–1914. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Schlaberg, R.; Queen, K.; Simmon, K.; Tardif, K.; Stockmann, C.; Flygare, S.; Kennedy, B.; Voelkerding, K.; Bramley, A.; Zhang, J.; et al. Viral Pathogen Detection by Metagenomics and Pan-Viral Group Polymerase Chain Reaction in Children with Pneumonia Lacking Identifiable Etiology. J. Infect. Dis. 2017, 215, 1407–1415. [Google Scholar] [CrossRef] [PubMed]
  23. Blauwkamp, T.A.; Thair, S.; Rosen, M.J.; Blair, L.; Lindner, M.S.; Vilfan, I.D.; Kawli, T.; Christians, F.C.; Venkatasubrahmanyam, S.; Wall, G.D.; et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nat. Microbiol. 2019, 4, 663–674. [Google Scholar] [CrossRef] [PubMed]
  24. Miller, S.; Naccache, S.N.; Samayoa, E.; Messacar, K.; Arevalo, S.; Federman, S.; Stryke, D.; Pham, E.; Fung, B.; Bolosky, W.J.; et al. Laboratory validation of a clinical metagenomic sequencing assay for pathogen detection in cerebrospinal fluid. Genome Res. 2019, 29, 831–842. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Wong, A.C.; Levy, M. New Approaches to Microbiome-Based Therapies. mSystems 2019, 4, e00122-19. [Google Scholar] [CrossRef] [Green Version]
  26. Barrientos-Durán, A.; Fuentes-López, A.; de Salazar, A.; Plaza-Díaz, J.; García, F. Reviewing the Composition of Vaginal Microbiota: Inclusion of Nutrition and Probiotic Factors in the Maintenance of Eubiosis. Nutrients 2020, 12, 419. [Google Scholar] [CrossRef] [Green Version]
  27. Sun, Y.; Baptista, L.C.; Roberts, L.M.; Jumbo-Lucioni, P.; McMahon, L.L.; Buford, T.W.; Carter, C.S. The Gut Microbiome as a Therapeutic Target for Cognitive Impairment. J. Gerontol. A Biol. Sci. Med. Sci. 2020, 75, 1242–1250. [Google Scholar] [CrossRef]
  28. Mueller, U.G.; Sachs, J.L. Engineering Microbiomes to Improve Plant and Animal Health. Trends Microbiol. 2015, 23, 606–617. [Google Scholar] [CrossRef] [Green Version]
  29. Peixoto, R.S.; Harkins, D.M.; Nelson, K.E. Advances in Microbiome Research for Animal Health. Annu. Rev. Anim. Biosci. 2021, 9, 289–311. [Google Scholar] [CrossRef] [PubMed]
  30. Garlapati, D.; Charankumar, B.; Ramu, K.; Madeswaran, P.; Ramana Murthy, M.V. A review on the applications and recent advances in environmental DNA (eDNA) metagenomics. Rev. Environ. Sci. Biotechnol. 2019, 18, 389–411. [Google Scholar] [CrossRef]
  31. Karsenti, E.; Acinas, S.G.; Bork, P.; Bowler, C.; De Vargas, C.; Raes, J.; Sullivan, M.; Arendt, D.; Benzoni, F.; Claverie, J.-M.; et al. Tara Oceans Consortium. A holistic approach to marine eco-systems biology. PLoS Biol. 2011, 9, e1001177. [Google Scholar] [CrossRef] [PubMed]
  32. Gorsky, G.; Bourdin, G.; Lombard, F.; Pedrotti, M.L.; Audrain, S.; Bin, N.; Boss, E.; Bowler, C.; Cassar, N.; Caudan, L.; et al. Expanding Tara Oceans Protocols for Underway, Ecosystemic Sampling of the Ocean-Atmosphere Interface During Tara Pacific Expedition (2016–2018). Front. Mar. Sci. 2019, 6, 750. [Google Scholar] [CrossRef] [Green Version]
  33. Lax, S.; Smith, D.P.; Hampton-Marcell, J.; Owens, S.M.; Handley, K.M.; Scott, N.M.; Gibbons, S.M.; Larsen, P.; Shogan, B.D.; Weiss, S.; et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 2014, 345, 1048–1052. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Gibbons, S.M.; Jones, E.; Bearquiver, A.; Blackwolf, F.; Roundstone, W.; Scott, N.; Hooker, J.; Madsen, R.; Coleman, M.L.; Gilbert, J.A. Human and environmental impacts on river sediment microbial communities. PLoS ONE 2014, 9, e97435. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Espínola, F.; Dionisi, H.M.; Borglin, S.; Brislawn, C.J.; Jansson, J.K.; Mac Cormack, W.P.; Carroll, J.; Sjöling, S.; Lozada, M. Metagenomic Analysis of Subtidal Sediments from Polar and Subpolar Coastal Environments Highlights the Relevance of Anaerobic Hydrocarbon Degradation Processes. Microb. Ecol. 2018, 75, 123–139. [Google Scholar] [CrossRef] [PubMed]
  36. Techtmann, S.M.; Hazen, T.C. Metagenomic applications in environmental monitoring and bioremediation. J. Ind. Microbiol. Biotechnol. 2016, 43, 1345–1354. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Köchling, T.; Sanz, J.L.; Galdino, L.; Florencio, L.; Kato, M.T. Impact of pollution on the microbial diversity of a tropical river in an urbanized region of northeastern Brazil. Int. Microbiol. 2017, 20, 11–24. [Google Scholar] [CrossRef]
  38. Imchen, M.; Kumavath, R.; Barh, D.; Vaz, A.; Góes-Neto, A.; Tiwari, S.; Ghosh, P.; Wattam, A.R.; Azevedo, V. Comparative mangrove metagenome reveals global prevalence of heavy metals and antibiotic resistome across different ecosystems. Sci. Rep. 2018, 8, 11187. [Google Scholar] [CrossRef]
  39. Zhuang, M.; Sanganyado, E.; Li, P.; Liu, W. Distribution of microbial communities in metal-contaminated nearshore sediment from Eastern Guangdong, China. Environ. Pollut. 2019, 250, 482–492. [Google Scholar] [CrossRef]
  40. Acinas, S.G.; Sánchez, P.; Salazar, G.; Cornejo-Castillo, F.M.; Sebastián, M.; Logares, R.; Royo-Llonch, M.; Paoli, L.; Sunagawa, S.; Hingamp, P.; et al. Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Commun. Biol. 2021, 4, 604. [Google Scholar] [CrossRef]
  41. Bourhane, Z.; Lanzén, A.; Cagnon, C.; Said, O.B.; Mahmoudi, E.; Coulon, F.; Atai, E.; Borja, A.; Cravo-Laureau, C.; Duran, R. Microbial diversity alteration reveals biomarkers of contamination in soil-river-lake continuum. J. Hazard. Mater. 2021, 421, 126789. [Google Scholar] [CrossRef]
  42. Wang, Y.; Liao, S.; Gai, Y.; Liu, G.; Jin, T.; Liu, H.; Gram, L.; Strube, M.L.; Fan, G.; Sahu, S.K.; et al. Metagenomic Analysis Reveals Microbial Community Structure and Metabolic Potential for Nitrogen Acquisition in the Oligotrophic Surface Water of the Indian Ocean. Front. Microbiol. 2021, 12, 518865. [Google Scholar] [CrossRef]
  43. Suttner, B.; Johnston, E.R.; Orellana, L.H.; Rodriguez-R, L.M.; Hatt, J.K.; Carychao, D.; Carter, M.Q.; Cooley, M.B.; Konstantinidis, K.T. Metagenomics as a Public Health Risk Assessment Tool in a Study of Natural Creek Sediments Influenced by Agricultural and Livestock Runoff: Potential and Limitations. Appl. Environ. Microbiol. 2020, 86, e02525-19. [Google Scholar] [CrossRef] [PubMed]
  44. Hendriksen, R.S.; Munk, P.; Njage, P.; van Bunnik, B.; McNally, L.; Lukjancenko, O.; Röder, T.; Nieuwenhuijse, D.; Pedersen, S.K.; Kjeldgaard, J.; et al. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat. Commun. 2019, 10, 1124. [Google Scholar] [CrossRef] [PubMed]
  45. Yadav, S.; Kapley, A. Antibiotic resistance: Global health crisis and metagenomics. Biotechnol. Rep. 2021, 29, e00604. [Google Scholar] [CrossRef] [PubMed]
  46. Filipic, B.; Novovic, K.; Studholme, D.J.; Malesevic, M.; Mirkovic, N.; Kojic, M.; Jovcic, B. Shotgun metagenomics reveals differences in antibiotic resistance genes among bacterial communities in Western Balkans glacial lakes sediments. J. Water Health 2020, 18, 383–397. [Google Scholar] [CrossRef]
  47. Hu, Y.; Yang, X.; Qin, J.; Lu, N.; Cheng, G.; Wu, N.; Pan, Y.; Li, J.; Zhu, L.; Wang, X.; et al. Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota. Nat. Commun. 2013, 4, 2151. [Google Scholar] [CrossRef] [Green Version]
  48. Fitzpatrick, D.; Walsh, F. Antibiotic resistance genes across a wide variety of metagenomes. FEMS Microbiol. Ecol. 2016, 92, fiv168. [Google Scholar] [CrossRef] [PubMed]
  49. Li, Y.; Cao, W.; Liang, S.; Yamasaki, S.; Chen, X.; Shi, L.; Ye, L. Metagenomic characterization of bacterial community and antibiotic resistance genes in representative ready-to-eat food in southern China. Sci. Rep. 2020, 10, 15175. [Google Scholar] [CrossRef]
  50. Auguet, O.T.; Niehus, R.; Gweon, H.S.; Berkley, J.A.; Waichungo, J.; Njim, T.; Edgeworth, J.D.; Batra, R.; Chau, K.; Swann, J.; et al. Population-level faecal metagenomic profiling as a tool to predict antimicrobial resistance in Enterobacterales isolates causing invasive infections: An exploratory study across Cambodia, Kenya, and the UK. EClinicalMedicine 2021, 36, 100910. [Google Scholar] [CrossRef]
  51. Naik, O.A.; Shashidhar, R.; Rath, D.; Bandekar, J.R.; Rath, A. Characterization of multiple antibiotic resistance of culturable microorganisms and metagenomic analysis of total microbial diversity of marine fish sold in retail shops in Mumbai, India. Environ. Sci. Pollut. Res. Int. 2018, 25, 6228–6239. [Google Scholar] [CrossRef]
  52. Noyes, N.R.; Yang, X.; Linke, L.M.; Magnuson, R.J.; Cook, S.R.; Zaheer, R.; Yang, H.; Woerner, D.R. Characterization of the resistome in manure, soil and wastewater from dairy and beef production systems. Sci. Rep. 2016, 6, 24645. [Google Scholar] [CrossRef]
  53. Coughlan, L.M.; Cotter, P.D.; Hill, C.; Alvarez-Ordóñez, A. Biotechnological applications of functional metagenomics in the food and pharmaceutical industries. Front. Microbiol. 2015, 6, 672. [Google Scholar] [CrossRef] [Green Version]
  54. Leonard, S.R.; Mammel, M.K.; Lacher, D.W.; Elkins, C.A. Application of metagenomic sequencing to food safety: Detection of Shiga Toxin-producing Escherichia coli on fresh bagged spinach. Appl. Environ. Microbiol. 2015, 81, 8183–8191. [Google Scholar] [CrossRef] [Green Version]
  55. Walsh, A.M.; Crispie, F.; Daari, K.; O’Sullivan, O.; Martin, J.C.; Arthur, C.T.; Claesson, M.J.; Scott, K.P.; Cotter, P. D Strain-Level Metagenomic Analysis of the Fermented Dairy Beverage Nunu Highlights Potential Food Safety Risks. Appl. Environ. Microbiol. 2017, 83, e01144-17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Escobar-Zepeda, A.; Sanchez-Flores, A.; Quirasco Baruch, M. Metagenomic analysis of a Mexican ripened cheese reveals a unique complex microbiota. Food Microbiol. 2016, 57, 116–127. [Google Scholar] [CrossRef]
  57. Huang, A.D.; Luo, C.; Pena-Gonzalez, A.; Weigand, M.R.; Tarr, C.L.; Konstantinidis, K.T. Metagenomics of Two Severe Foodborne Outbreaks Provides Diagnostic Signatures and Signs of Coinfection Not Attainable by Traditional Methods. Appl. Environ. Microbiol. 2017, 83, e02577-16. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Ottesen, A.; Ramachandran, P.; Reed, E.; White, J.R.; Hasan, N.; Subramanian, P.; Ryan, G.; Jarvis, K.; Grim, C.; Daquiqan, N.; et al. Enrichment dynamics of Listeria monocytogenes and the associated microbiome from naturally contaminated ice cream linked to a listeriosis outbreak. BMC Microbiol. 2016, 16, 275. [Google Scholar] [CrossRef] [PubMed]
  59. Yang, X.; Noyes, N.R.; Doster, E.; Martin, J.N.; Linke, L.M.; Magnuson, R.J.; Yang, H.; Geornaras, I.; Woerner, D.R.; Jones, K.L.; et al. Use of Metagenomic Shotgun Sequencing Technology to Detect Foodborne Pathogens within the Microbiome of the Beef Production Chain. Appl. Environ. Microbiol. 2016, 82, 2433–2443. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Quigley, L.; O’Sullivan, D.J.; Daly, D.; O’Sullivan, O.; Burdikova, Z.; Vana, R.; Beresford, T.P.; Ross, R.P.; Fitzgerald, G.F.; McSweeney, P.L.H.; et al. Thermus and the Pink Discoloration Defect in Cheese. mSystems 2016, 1, e00023-16. [Google Scholar] [CrossRef] [Green Version]
  61. De Filippis, F.; Parente, E.; Ercolini, D. Metagenomics insights into food fermentations. Microb. Biotechnol. 2017, 10, 91–102. [Google Scholar] [CrossRef] [PubMed]
  62. Leech, J.; Cabrera-Rubio, R.; Walsh, A.M.; Macori, G.; Walsh, C.J.; Barton, W.; Finnegan, L.; Crispie, F.; O’Sullivan, O.; Claesson, M.J.; et al. Fermented-Food Metagenomics Reveals Substrate-Associated Differences in Taxonomy and Health-Associated and Antibiotic Resistance Determinants. mSystems 2020, 5, e00522-20. [Google Scholar] [CrossRef]
  63. Lorenz, P.; Eck, J. Metagenomics and industrial applications. Nat. Rev. Microbiol. 2005, 3, 510–516. [Google Scholar] [CrossRef] [PubMed]
  64. Prayogo, F.A.; Budiharjo, A.; Kusumaningrum, H.P.; Wijanarka, W.; Suprihadi, A.; Nurhayati, N. Metagenomic applications in exploration and development of novel enzymes from nature: A review. J. Genet. Eng. Biotechnol. 2020, 18, 39. [Google Scholar] [CrossRef] [PubMed]
  65. Pabbathi, N.P.P.; Velidandi, A.; Tavarna, T.; Gupta, S.; Raj, R.S.; Gandam, P.K.; Baadhe, R.R. Role of metagenomics in prospecting novel endoglucanases, accentuating functional metagenomics approach in second-generation biofuel production: A review. Biomass Convers. Biorefin. 2021, 7, 1–28. [Google Scholar] [CrossRef]
  66. Bashir, A.K.; Wink, L.; Duller, S.; Schwendner, P.; Cockell, C.; Rettberg, P.; Mahnert, A.; Beblo-Vranesevic, K.; Bohmeier, M.; Rabbow, E.; et al. Taxonomic and functional analyses of intact microbial communities thriving in extreme, astrobiology-relevant, anoxic sites. Microbiome 2021, 9, 50. [Google Scholar] [CrossRef]
  67. Peimbert, M.; Alcaraz, L.D.; Bonilla-Rosso, G.; Olmedo-Alvarez, G.; García-Oliva, F.; Segovia, L.; Eguiarte, L.E.; Souza, V. Comparative metagenomics of two microbial mats at Cuatro Ciénegas Basin I: Ancient lessons on how to cope with an environment under severe nutrient stress. Astrobiology 2012, 12, 648–658. [Google Scholar] [CrossRef] [PubMed]
  68. Be, N.A.; Avila-Herrera, A.; Allen, J.E.; Singh, N.; Checinska Sielaff, A.; Jaing, C.; Venkateswaran, K. Whole metagenome profiles of particulates collected from the International Space Station. Microbiome 2017, 5, 81. [Google Scholar] [CrossRef]
  69. Bashir, M.; Ahmed, M.; Weinmaier, T.; Ciobanu, D.; Ivanova, N.; Pieber, T.R.; Vaishampayan, P.A. Functional Metagenomics of Spacecraft Assembly Cleanrooms: Presence of Virulence Factors Associated with Human Pathogens. Front. Microbiol. 2016, 7, 1321. [Google Scholar] [CrossRef] [Green Version]
  70. Singh, N.K.; Wood, J.M.; Karouia, F.; Venkateswaran, K. Succession and persistence of microbial communities and antimicrobial resistance genes associated with International Space Station environmental surfaces. Microbiome 2018, 6, 204. [Google Scholar] [CrossRef] [Green Version]
  71. Mardanov, A.V.; Babykin, M.M.; Beletsky, A.V.; Grigoriev, A.I.; Zinchenko, V.V.; Kadnikov, V.V.; Kirpichnikov, M.P.; Mazur, A.M.; Nedoluzhko, A.V.; Novikova, N.D.; et al. Metagenomic Analysis of the Dynamic Changes in the Gut Microbiome of the Participants of the MARS-500 Experiment, Simulating Long Term Space Flight. Acta Nat. 2013, 5, 116–125. [Google Scholar] [CrossRef]
  72. Avila-Herrera, A.; Thissen, J.; Urbaniak, C.; Be, N.A.; Smith, D.J.; Karouia, F.; Mehta, S.; Venkateswaran, K.; Jaing, C. Crewmember microbiome may influence microbial composition of ISS habitable surfaces. PLoS ONE 2020, 15, e0231838. [Google Scholar] [CrossRef] [PubMed]
  73. Lee, M.D.; O’Rourke, A.; Lorenzi, H.; Bebout, B.M.; Dupont, C.L.; Everroad, R.C. Reference-guided metagenomics reveals genome-level evidence of potential microbial transmission from the ISS environment to an astronaut’s microbiome. iScience 2021, 24, 102114. [Google Scholar] [CrossRef]
  74. Góes-Neto, A.; Kukharenko, O.; Orlovska, I.; Podolich, O.; Imchen, M.; Kumavath, R.; Kato, R.B.; Carvalho, D.S.; Tiwari, S.; Brenig, B.; et al. Shotgun metagenomic analysis of kombucha mutualistic community exposed to Mars-like environment outside the International Space Station. Environ. Microbiol. 2021, 23, 3727–3742. [Google Scholar] [CrossRef]
  75. Orlovska, I.; Podolich, O.; Kukharenko, O.; Zaets, I.; Reva, O.; Khirunenko, L.; Zmejkoski, D.; Rogalsky, S.; Barh, D.; Tiwari, S.; et al. Bacterial Cellulose Retains Robustness but Its Synthesis Declines After Exposure to a Mars-like Environment Simulated Outside the International Space Station. Astrobiology 2021, 21, 706–717. [Google Scholar] [CrossRef] [PubMed]
  76. Singh, N.K.; Wood, J.M.; Mhatre, S.S.; Venkateswaran, K. Metagenome to phenome approach enables isolation and genomics characterization of Kalamiella piersonii gen. nov.; sp. nov. from the International Space Station. Appl. Microbiol. Biotechnol. 2019, 103, 4483–4497. [Google Scholar] [CrossRef]
  77. Yasir, M.; Angelakis, E.; Bibi, F.; Azhar, E.I.; Bachar, D.; Lagier, J.-C.; Gaborit, B.; Hassan, A.M.; Jiman-Fatani, A.A.; Alshali, K.Z.; et al. Comparison of the gut microbiota of people in France and Saudi Arabia. Nutr. Diabetes 2015, 5, e153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Angelakis, E.; Bachar, D.; Yasir, M.; Musso, D.; Djossou, F.; Melenotte, C.; Robert, C.; Davoust, B.; Gaborit, B.; Azhar, E.I.; et al. Comparison of the gut microbiota of obese individuals from different geographic origins. New Microbes New Infect. 2018, 27, 40–47. [Google Scholar] [CrossRef]
  79. Bittar, F.; Bibi, F.; Ramasamy, D.; Lagier, J.-C.; Azhar, E.I.; Jiman-Fatani, A.A.; Al-Ghamdi, A.K.; Nguyen, T.T.; Yasir, M.; Fournier, P.-E.; et al. Non contiguous-finished genome sequence and description of Bacillus jeddahensis sp. nov. Stand. Genom. Sci. 2015, 10, 47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Khelaifia, S.; Lagier, J.-C.; Bibi, F.; Azhar, E.I.; Croce, O.; Padmanabhan, R.; Jiman-Fatani, A.A.; Yasir, M.; Robert, C.; Andrieu, C.; et al. Microbial Culturomics to Map Halophilic Bacterium in Human Gut: Genome Sequence and Description of Oceanobacillus jeddahense sp. nov. Omics 2016, 20, 248–258. [Google Scholar] [CrossRef] [PubMed]
  81. Kieu, H.T.; Garrigou, N.; Fadlane, A.; Brechard, L.; Armstrong, N.; Decloquement, P.; Yasir, M.; Azhar, E.I.; Al-Masaudi, S.B.; Lagier, J.-C.; et al. Clostridium culturomicium sp. nov. and Clostridium jeddahitimonense sp. nov.; novel members of the Clostridium genus isolated from the stool of an obese Saudi Arabian. Curr. Microbiol. 2021, 74, 3586–3595. [Google Scholar] [CrossRef]
  82. Al-Obaida, M.I.; Al-Nakhli, A.K.M.; Arif, I.A.; Faden, A.; Al-Otaibi, S.; Al-Eid, B.; Ekhzaimy, A.; Khan, H.A. Molecular identification and diversity analysis of dental bacteria in diabetic and non-diabetic females from Saudi Arabia. Saudi J. Biol. Sci. 2020, 27, 358–362. [Google Scholar] [CrossRef]
  83. Abdulhaq, A.; Halboub, E.; Homeida, H.E.; Kumar Basode, V.; Ghzwani, A.H.; Zain, K.A.; Baraniya, D.; Chen, T.; Al-Hebshi, N.N. Tongue microbiome in children with autism spectrum disorder. J. Oral. Microbiol. 2021, 13, 1936434. [Google Scholar] [CrossRef]
  84. Alzahrani, F.M.; Al-Amri, A.; Shaikh, S.S.; Alomar, A.I.; Acharya, S.; Aldossary, M.A.; Hassan, F.M. Direct DNA Sequencing-Based Analysis of Microbiota Associated with Hematological Malignancies in the Eastern Province of Saudi Arabia. BioMed Res. Int. 2021, 2021, 4202019. [Google Scholar] [CrossRef]
  85. Aljabr, W.; Alruwaili, M.; Penrice-Randal, R.; Alrezaihi, A.; Harrison, A.J.; Ryan, Y.; Bentley, E.; Jones, B.; Alhatlani, B.Y.; AlShahrani, D.; et al. Amplicon and Metagenomic Analysis of Middle East Respiratory Syndrome (MERS) Coronavirus and the Microbiome in Patients with Severe MERS. mSphere 2021, 21, e0021921. [Google Scholar] [CrossRef]
  86. Khan, I.; Yasir, M.; Farman, M.; Kumosani, T.; AlBasri, S.F.; Bajouh, O.S.; Azhar, E.I. Evaluation of gut bacterial community composition and antimicrobial resistome in pregnant and non-pregnant women from Saudi population. Infect. Drug Resist. 2019, 12, 1749–1761. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Al Moaleem, M.M.; Porwal, A.; Al Ahmari, N.M.; Shariff, M.; Homeida, H.; Khalid, A. Khat Chewing Induces a Floral Shift in Dental Material-Associated Microbiota: A Preliminary Study. Med. Sci. Monit. 2020, 26, e918219. [Google Scholar] [CrossRef] [PubMed]
  88. Badger-Emeka, L.I.; AlJaziri, Z.Y.; Almulhim, C.F.; Aldrees, A.S.; AlShakhs, Z.H.; AlAithan, R.I.; Alothman, F.A. Vitamin D Supplementation in Laboratory-Bred Mice: An In Vivo Assay on Gut Microbiome and Body Weight. Microbiol. Insights 2020, 13, 1178636120945294. [Google Scholar] [CrossRef] [PubMed]
  89. Elbir, H.; Almathen, F.; Alhumam, N.A. A glimpse of the bacteriome of Hyalomma dromedarii ticks infesting camels reveals human Helicobacter pylori pathogen. J. Infect. Dev. Ctries. 2019, 13, 1001–1012. [Google Scholar] [CrossRef]
  90. Elbir, H.; Almathen, F.; Elnahas, A. Low genetic diversity among Francisella-like endosymbionts within different genotypes of Hyalomma dromedarii ticks infesting camels in Saudi Arabia. Vet. World 2020, 13, 1462–1472. [Google Scholar] [CrossRef]
  91. Alreshidi, M.M.; Veettil, V.N.; Noumi, E.; Campo, R.D.; Snoussi, M. Description of microbial diversity associated with ticks Hyalomma dromedarii (Acari: Ixodidae) isolated from camels in Hail region (Saudi Arabia) using massive sequencing of 16S rDNA. Bioinformation 2020, 16, 602–610. [Google Scholar] [CrossRef] [PubMed]
  92. Alzubaidy, H.; Essack, M.; Malas, T.B.; Bokhari, A.; Motwalli, O.; Kamanu, F.K.; Jamhor, S.A.; Mokhtar, N.A.; Antunes, A.; Simões, M.F.; et al. Rhizosphere microbiome metagenomics of gray mangroves (Avicennia marina) in the Red Sea. Gene 2016, 576, 626–636. [Google Scholar] [CrossRef] [Green Version]
  93. Al-Quwaie, D.A. Bacterial community dynamics with rhizosphere of Calotropis procera and Senna alexandrina desert plants in Saudi Arabia. Bioinformation 2020, 16, 567–578. [Google Scholar] [CrossRef]
  94. Yasir, M.; Azhar, E.I.; Khan, I.; Bibi, F.; Baabdullah, R.; Al-Zahrani, I.A.; Al-Ghamdi, A.K. Composition of soil microbiome along elevation gradients in southwestern highlands of Saudi Arabia. BMC Microbiol. 2015, 15, 65. [Google Scholar] [CrossRef] [Green Version]
  95. Gat, D.; Mazar, Y.; Cytryn, E.; Rudich, Y. Origin-Dependent Variations in the Atmospheric Microbiome Community in Eastern Mediterranean Dust Storms. Environ. Sci. Technol. 2017, 51, 6709–6718. [Google Scholar] [CrossRef]
  96. Yasir, M.; Qureshi, A.K.; Khan, I.; Bibi, F.; Rehan, M.; Khan, S.B.; Azhar, E.I. Culturomics-Based Taxonomic Diversity of Bacterial Communities in the Hot Springs of Saudi Arabia. Omics 2019, 23, 17–27. [Google Scholar] [CrossRef]
  97. Yasir, M.; Qureshi, A.K.; Srinivasan, S.; Ullah, R.; Bibi, F.; Rehan, M.; Khan, S.B.; Azhar, E.I. Domination of Filamentous Anoxygenic Phototrophic Bacteria and Prediction of Metabolic Pathways in Microbial Mats from the Hot Springs of Al Aridhah. Folia Biol. 2020, 66, 24–35. [Google Scholar]
  98. Li, D.; Sharp, J.O.; Saikaly, P.E.; Ali, S.; Alidina, M.; Alarawi, M.S.; Keller, S.; Hoppe-Jones, C.; Drewes, J.E. Dissolved organic carbon influences microbial community composition and diversity in managed aquifer recharge systems. Appl. Environ. Microbiol. 2012, 78, 6819–6828. [Google Scholar] [CrossRef] [Green Version]
  99. Al-Jassim, N.; Ansari, M.I.; Harb, M.; Hong, P.Y. Removal of bacterial contaminants and antibiotic resistance genes by conventional wastewater treatment processes in Saudi Arabia: Is the treated wastewater safe to reuse for agricultural irrigation? Water Res. 2015, 73, 277–290. [Google Scholar] [CrossRef] [Green Version]
  100. Bibi, F.; Alvi, S.A.; Al-Sofyani, A.; Naseer, M.I.; Yasir, M.; Azhar, E.I. Pyrosequencing reveals sponge specific bacterial communities in marine sponges of Red Sea, Saudi Arabia. Saudi J. Biol. Sci. 2020, 27, 67–73. [Google Scholar] [CrossRef]
  101. Albokari, M.; Mashhour, I.; Alshehri, M.; Boothman, C.; Al-Enezi, M. Characterization of microbial communities in heavy crude oil from Saudi Arabia. Ann. Microbiol. 2015, 65, 95–104. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  102. El-Sayed, W.S.; Ibrahim, R.A. Diversity and phylogenetic analysis of endosymbiotic bacteria of the date palm root borer Oryctes agamemnon (Coleoptera: Scarabaeidae). BMC Microbiol. 2015, 15, 88. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  103. Moussa, T.A.; Al-Zahrani, H.S.; Almaghrabi, O.A.; Sabry, N.M.; Fuller, M.P. Metagenomic analysis of fungal taxa inhabiting Mecca region, Saudi Arabia. Genom. Data 2016, 9, 126–127. [Google Scholar] [CrossRef] [Green Version]
  104. Soltani, R.; Lkbel, C.; Hamouda, M.H.B. Descriptive study of damage caused by the rhinoceros beetle, Oryctes agamemnon, and its influence on date palm oases of Rjim Maatoug, Tunisia. J. Insect Sci. 2008, 8, 57. [Google Scholar] [CrossRef] [Green Version]
  105. Sefrji, F.O.; Michoud, G.; Marasco, R.; Merlino, G.; Daffonchio, D. Mangrovivirga cuniculi gen. nov.; sp. nov.; a moderately halophilic bacterium isolated from bioturbated Red Sea mangrove sediment, and proposal of the novel family Mangrovivirgaceae fam. nov. Int. J. Syst. Evol. Microbiol. 2021, 71, 004866. [Google Scholar] [CrossRef]
  106. Sefrji, F.O.; Marasco, R.; Michoud, G.; Seferji, K.A.; Merlino, G.; Daffonchio, D. Kaustia mangrovi gen. nov.; sp. nov. isolated from Red Sea mangrove sediments belongs to the recently proposed Parvibaculaceae family within the order Rhizobiales. Int. J. Syst. Evol. Microbiol. 2021, 71, 004806. [Google Scholar] [CrossRef]
  107. Röttig, A.; Atasayar, E.; Meier-Kolthoff, J.P.; Spröer, C.; Schumann, P.; Schauer, J.; Steinbüchel, A. Streptomyces jeddahensis sp. nov.; an oleaginous bacterium isolated from desert soil. Int. J. Syst. Evol. Microbiol. 2017, 67, 1676–1682. [Google Scholar] [CrossRef] [PubMed]
  108. Yang, Z.W.; Salam, N.; Hua, Z.S.; Liu, B.B.; Han, M.X.; Fang, B.Z.; Wang, D.; Xiao, M.; Hozzein, W.N.; Li, W.J. Siccirubricoccus deserti gen. nov.; sp. nov.; a proteobacterium isolated from a desert sample. Int. J. Syst. Evol. Microbiol. 2017, 67, 4862–4867. [Google Scholar] [CrossRef] [PubMed]
  109. Wübbeler, J.H.; Oppermann-Sanio, F.B.; Ockenfels, A.; Röttig, A.; Osthaar-Ebker, A.; Verbarg, S.; Poehlein, A.; Madkour, M.H.; Al-Ansari, A.M.; Almakishah, N.H.; et al. Sphingomonas jeddahensis sp. nov.; isolated from Saudi Arabian desert soil. Int. J. Syst. Evol. Microbiol. 2017, 67, 4057–4063. [Google Scholar] [CrossRef]
  110. Yang, Z.W.; Salam, N.; Mohany, M.; Chinnathambi, A.; Alharbi, S.A.; Xiao, M.; Hozzein, W.N.; Li, W.J. Microbacterium album sp. nov. and Microbacterium deserti sp. nov.; two halotolerant actinobacteria isolated from desert soil. Int. J. Syst. Evol. Microbiol. 2018, 68, 217–222. [Google Scholar] [CrossRef]
  111. Li, L.Y.; Yang, Z.W.; Asem, M.D.; Salam, N.; Xiao, M.; Alkhalifah, D.H.M.; Hozzein, W.N.; Nie, G.X.; Li, W.J. Georgenia alba sp. nov.; a novel halotolerant actinobacterium isolated from a desert sand sample. Antonie Van Leeuwenhoek 2019, 112, 203–209. [Google Scholar] [CrossRef] [PubMed]
  112. Hozzein, W.N.; Yang, Z.W.; Alharbi, S.A.; Alsakkaf, W.A.A.; Asem, M.D.; Xiao, M.; Salam, N.; Li, W.J. Georgenia deserti sp. nov.; a halotolerant actinobacterium isolated from a desert sample. Int. J. Syst. Evol. Microbiol. 2018, 68, 1135–1139. [Google Scholar] [CrossRef]
  113. Hussain, F.; Khan, I.U.; Habib, N.; Xian, W.D.; Hozzein, W.N.; Zhang, Z.D.; Zhi, X.Y.; Li, W.J. Deinococcus saudiensis sp. nov.; isolated from desert. Int. J. Syst. Evol. Microbiol. 2016, 66, 5106–5111. [Google Scholar] [CrossRef] [PubMed]
  114. Almuhayawi, M.S.; Mohamed, M.S.M.; Abdel-Mawgoud, M.; Selim, S.; Al Jaouni, S.K.; AbdElgawad, H. Bioactive Potential of Several Actinobacteria Isolated from Microbiologically Barely Explored Desert Habitat, Saudi Arabia. Biology 2021, 10, 235. [Google Scholar] [CrossRef] [PubMed]
  115. Wang, J.-W.; Kuo, C.-H.; Kuo, F.-C.; Wang, Y.-K.; Hsu, W.-H.; Yu, F.-J.; Hu, H.-M.; Hsu, P.-I.; Wang, J.-Y.; Wu, D.C. Fecal microbiota transplantation: Review and update. J. Formos Med. Assoc. 2019, 118, S23–S31. [Google Scholar] [CrossRef]
  116. Ooijevaar, R.E.; Terveer, E.M.; Verspaget, H.W.; Kuijper, E.J.; Keller, J.J. Clinical Application and Potential of Fecal Microbiota Transplantation. Annu. Rev. Med. 2019, 70, 335–351. [Google Scholar] [CrossRef] [Green Version]
  117. Dubilier, N.; McFall-Ngai, M.; Zhao, L. Microbiology: Create a global microbiome effort. Nature 2015, 526, 631–634. [Google Scholar] [CrossRef] [Green Version]
Table 1. An overview of the global focus on metagenome-based microbiome studies.
Table 1. An overview of the global focus on metagenome-based microbiome studies.
Broad Areas of Global InterestSampleOutcome of the Metagenome StudyRef.
Metagenome studies on human healthDisease diagnosis and management
Gut microbiotaMicrobiota composition indicates Parkinson’s disease, Alzheimer’s disease, hypertension, cognitive impairments, atherosclerosis, obesity, diabetes, non-alcoholic fatty liver disease, inflammatory bowel disease, colorectal cancer[14,25,26,27]
Oral microbiotaMicrobiota composition indicates Periodontitis, dental caries, oral cancer, esophageal cancer, pancreatic cancer, cystic fibrosis, cardiovascular disease, rheumatoid arthritis, Alzheimer’s disease, diabetes[15]
Vaginal microbiomeMicrobiota composition indicates female reproductive health[16,17]
Cerebrospinal fluidsMicrobiota composition indicates encephalitis, meningitis[19]
Respiratory tractMicrobiota composition indicates respiratory tract infection[20,21]
Metagenome studies on environmentAntimicrobial resistance
Water treatment plantsAbundance and diversity of antimicrobial resistance genes found[44,45]
Mangrove and glacial lakes sedimentsAbundance and diversity of antimicrobial resistance genes found[38,46]
Ready-to-eat foodsAntimicrobial resistance genes detected[49]
Fecal microbiotaAntimicrobial resistance genes detected[50]
Beef production wastes Antimicrobial resistance genes detected[52]
Environmental monitoring
Human-occupied homeMicrobiome uniquely differs for each family [33]
Tongue River sedimentMicrobial community indicates presence of town waste and methane by-products [34]
Soil microbiome Presence of Rhodanobacter and Rhodocyclaceae indicates presence of uranium[36]
Soil, river, lake, seashore, mangrove, ocean water, and sedimentsMicrobial community indicates water quality, various pollutants, and chemical contaminations in respective biomes[37,38,39,40,41,42]
Metagenome studies on other aspectsFood monitoring
Mexican Cotija cheesePredominant genera are: Lactobacillus, Leuconostoc, and Weissella[56]
Ice cream samples Identification of L. monocytogenes, the causal organism for Listeriosis outbreak [58]
Beef processing wastePresence of S. enterica, E. coli, and C. botulinum [59]
Fermentation samples Microbial community indicates stage of fermentation and the microbes that are beneficial to health[61,62]
Industrial applications
Environmental samplesIdentification of bacterial strains producing novel biocatalysts, antimicrobial metabolites, and industrial enzymes[53,63,64,65]
Astrobiology
International Space Station (ISS)Corynebacterium ihumii GD7 is the dominant species in ISS[68]
International Space Station (ISS)Identification of novel strain Kalamiella piersonii gen. nov., sp. nov in ISS[76]
Spacecraft assembly cleanroom samplesIdentification of several pathogenic bacteria, antimicrobial resistance genes, and metal resistance genes[69,70]
Kombucha mutualistic communityEnable the understanding of how and which bacterial community members survive under a Mars-like environment [74,75]
Table 2. Metagenome-based microbiome studies from Saudi Arabia in the last 10 years.
Table 2. Metagenome-based microbiome studies from Saudi Arabia in the last 10 years.
Broad Areas of Global InterestSpecific Area of StudySampleOutcome of the Metagenome StudyRef.
Metagenome studies on human healthObesityFecal microbiotaAbundance of Firmicutes in obese cases[77]
Gut microbiotaAbundance of Lactobacillus sp. in obese samples[78]
Fecal microbiotaIsolation of Bacillus jeddahensis sp. nov. (JCE(T)) and Oceanobacillus jeddahense sp. nov. (S5T) from obese samples[79,80]
PregnancyGut microbiotaAbundance of Faecalibacterium spp. and Eubacterium spp. in pregnant women[86]
DiabetesSub-gingival samplesAggregatibacter actinomycetemcomitans and Capnocytophaga ochracea are predominant in diabetic samples[82]
Autism Spectrum Disorder (ASD)Tongue microbiotaAbundance of Actinomyces odontolyticus and Actinomyces lingnae are increased and Campylobacter concisus and Streptococcus vestibularis are decreased in ASD[83]
Bloodstream infectionsBlood cancer patient’s blood sampleAbundance of E. coli and K. pneumonia in the samples [84]
MERS-CoVOropharyngeal and throat swabsDominance of Acinetobacter baumannii, Pseudomonas aeruginosa, Streptococcus pneumonia, and several ARGs [85]
Metagenome studies on animal healthVitamin D deficiencyMice gut microbiotaDecline of P. aeruginosa abandance under high vitamin D dose [88]
Microbial diversityCamel parasite ticksAbundance of Proteobacteria[89,91]
Metagenome studies on environmentMicrobial diversityRhizosphere microbiome, Red SeaPredominance of Proteobacteria, Bacteroidetes, and Firmicutes[92]
Rhizosphere microbiota desertPredominance of Pseudomonas stutzeri and Virgibacillus koreensis provide saline resistance in desert plant[93]
Rhizosphere microbiota desertMarinobacter, Porticoccus, and Alcanivorax genera provide pathogen resistance in desert plant[93]
South-western highlandsPredominance of Proteobacteria, Actinobacteria, and Acidobacteria[94]
Dust stormPredominance of Proteobacteria and decline of Actinobacteria [95]
Hot spring sedimentsDominance of Bacillus, Chloroflexus, and Brevibacillus [96,97]
Taif River waterDominance of Proteobacteria[98]
local waste water treatment plantDominance of several opportunistic pathogens[99]
Red sea marine spongeDominance of Proteobacteria in sponges may be a biosensor for environmental monitoring[100]
Oil samplesPrevalence of Bacilli in crude oil and Flavobacteria in oil sludge[101]
O. agamemnon larval mid-gutPresence of Enterobacteriaceae, Shewanellaceae, and Cellulomonadaceae[102]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Alzahrani, K.J. Microbiome Studies from Saudi Arabia over the Last 10 Years: Achievements, Gaps, and Future Directions. Microorganisms 2021, 9, 2021. https://doi.org/10.3390/microorganisms9102021

AMA Style

Alzahrani KJ. Microbiome Studies from Saudi Arabia over the Last 10 Years: Achievements, Gaps, and Future Directions. Microorganisms. 2021; 9(10):2021. https://doi.org/10.3390/microorganisms9102021

Chicago/Turabian Style

Alzahrani, Khalid J. 2021. "Microbiome Studies from Saudi Arabia over the Last 10 Years: Achievements, Gaps, and Future Directions" Microorganisms 9, no. 10: 2021. https://doi.org/10.3390/microorganisms9102021

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop