Kombucha is a multispecies microbial ecosystem mainly composed of acetic acid bacteria and osmophilic acid-tolerant yeasts, which is used to produce a probiotic drink. Furthermore, Kombucha Mutualistic Community (KMC) has been recently proposed to be used during long space missions as both a living functional fermented product to improve astronauts' health and an efficient source of bacterial nanocellulose. In this study, we compared KMC structure and functions before and after samples were exposed to the space/Mars-like environment outside the International Space Station in order to investigate the changes related to their re-adaptation to Earth-like conditions by shotgun metagenomics, using both diversity and functional analyses of Community Ecology and Complex Networks approach. Our study revealed that the long-term exposure to space/Mars-like conditions on low Earth orbit may disorganize the KMC to such extent that it will not restore the initial community structure; however, KMC core microorganisms of the community were maintained. Nonetheless, there were no significant differences in the community functions, meaning that the KMC communities are ecologically resilient. Therefore, despite the extremely harsh conditions, key KMC species revived and provided the community with the genetic background needed to survive long periods of time under extraterrestrial conditions.

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A multitude of factors, such as drug misuse, lack of strong regulatory measures, improper sewage disposal, and low-quality medicine and medications, have been attributed to the emergence of drug resistant microbes. The emergence and outbreaks of multidrug resistance to last-line antibiotics has become quite common. This is further fueled by the slow rate of drug development and the lack of effective resistome surveillance systems. In this review, we provide insights into the recent advances made in computational approaches for the surveillance of antibiotic resistomes, as well as experimental formulation of combinatorial drugs. We explore the multiple roles of antibiotics in nature and the current status of combinatorial and adjuvant-based antibiotic treatments with nanoparticles, phytochemical, and other non-antibiotics based on synergetic effects. Furthermore, advancements in machine learning algorithms could also be applied to combat the spread of antibiotic resistance. Development of resistance to new antibiotics is quite rapid. Hence, we review the recent literature on discoveries of novel antibiotic resistant genes though shotgun and expression-based metagenomics. To decelerate the spread of antibiotic resistant genes, surveillance of the resistome is of utmost importance. Therefore, we discuss integrative applications of whole-genome sequencing and metagenomics together with machine learning models as a means for state-of-the-art surveillance of the antibiotic resistome. We further explore the interactions and negative effects between antibiotics and microbiomes upon drug administration.

Turritopsis nutricula (T. nutricula) is the one of the known reported organisms that can revert its life cycle to the polyp stage even after becoming sexually mature, defining itself as the only immortal organism in the animal kingdom. Therefore, the animal is having prime importance in basic biological, aging, and biomedical researches. However, till date, the genome of this organism has not been sequenced and even there is no molecular phylogenetic study to reveal its close relatives. Here, using phylogenetic analysis based on available 16s rRNA gene and protein sequences of Cytochrome oxidase subunit-I (COI or COX1) of T. nutricula, we have predicted the closest relatives of the organism. While we found Nemopsis bachei could be closest organism based on COX1 gene sequence; T. dohrnii may be designated as the closest taxon to T. nutricula based on rRNA. Moreover, we have figured out four species that showed similar root distance based on COX1 protein sequence.

Nature has its ways of resolving imbalances in its environment and microorganisms are one of the best tools of nature to eliminate toxic pollutants. The process of eliminating pollutants using microbes is termed Bioremediation. Metagenomics is a strategic approach for analysing microbial communities at a genomic level. It is one of the best technological upgradation to bioremediation. Identification and screening of metagenomes from the polluted environments are crucial in a metagenomic study. This chapter emphasizes recent multiple case studies explaining the approaches of metagenomics in bioremediation in different contaminated environments such as soil, water etc. The second section explains different sequences and function-based metagenomic strategies and tools starting from providing a detailed view of metagenomic screening, FACS, and multiple advanced metagenomic sequencing strategies dealing with the prevalent metagenomes in bioremediation and giving a list of different widespread metagenomic organisms and their respective projects. Eventually, we have provided a detailed view of different major bioinformatic tools and datasets most prevalently used in metagenomic data analysis and processing during metagenomic bioremediation.

A pure environment gives a quality of life on earth. In ancient times, it was believed that people on earth had an unlimited abundance of land and resources today, however, the resources in the world show a greater or lesser degree of our carelessness and negligence in using them. In many parts of globe, the problems associated with contaminated sites are now growing up. The actual cause of this scenario is result from past industrial activities when awareness of the health and environmental effects connected with the production, use, and disposal of hazardous substances were less well recognized than today. It became a global complication when the estimated number of contaminated sites became significant. There are several traditional methods which have been applied to overcome this inconvenience. From the list of ideas which have been applied the best ones are to completely demolish the pollutants if possible, or at least to transform them to innoxious substances. Bioremediation is an option that utilizes microbes to remove many contaminants from the environment by a diversity of enzymatic processes. However, it will not always be suitable as the range of contaminants on which it is effective is limited, the time scales involved are relatively long, and the residual contaminant levels achievable may not always be appropriate. we attempted to assist by providing information how the bioremediation is linked with cutting edge sciences like genomics, transcriptomics, proteomics, interactomics and bioinformatics.Some new techniques in molecular biology particularly genetic engineering, transcriptomics, proteomics and interactomics offer remarkable promise as tools to study the mechanisms involved in regulation of mineralization pathways. The strategies need to be refined in which transcriptomics and proteomics data are combined together in order to understand the mineralization process in a meaningful way.These techniques show great promise in their ability to predict organisms metabolism in contaminated environments and to predict the microbial assisted attenuation of contaminants to accelerate bioremediation. Bioinformatics technology has been developed to identify and analyse various components of cells

Lung cancer and pulmonary tuberculosis caused by Mycobacterium are two major causes of deaths worldwide. Tuberculosis linked lung cancer is known. However, the precise molecular mechanism of Mycobacterium associated increased risk of lung cancer is not understood. We report 45 common human miRNAs deregulated in both pulmonary tuberculosis and lung cancer. We show that sRNA_1096 and sRNA_1414 from M. tuberculosis have sequence homology with human mir-21. Hence, the potential role of these three small non-coding RNAs in rifampicin resistance in pulmonary tuberculosis is implied. Further, the linking of sRNA_1096 and sRNA_1414 from M. tuberculosis with the host lung tumorigenesis is inferred. Nonetheless, further analysis and validation is required to associate these three non-coding RNAs with Mycobacterium associated increased risk of lung cancer.

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