The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics

A Review of Web-Based Metagenomics Platforms for Analysing Next-Generation Sequence Data

Metagenomics has now evolved as a promising technology for understanding the microbial population in the environment. By metagenomics, a number of extreme and complex environment has been explored for their microbial population. Using this technology, researchers have brought out novel genes and their potential characteristics, which have robust applications in food, pharmaceutical, scientific research, and other biotechnological fields. A sequencing platform can provide a sequence of microbial populations in any given environment. The sequence needs to be analysed computationally to derive meaningful information. It is presumed that only bioinformaticians with extensive computational skills can process the sequencing data till the downstream end. However, numerous open-source software and online servers are available to analyse the metagenomic data developed for a biologist with less computational skills. This review is focused on bioinformatics tools such as Galaxy, CSI-NGS portal, ANASTASIA and SHAMAN, EBI- metagenomics, IDseq, and MG-RAST for analysing metagenomic data.

Strain-level characterization of foodborne pathogens without culture enrichment for outbreak investigation using shotgun metagenomics facilitated with nanopore adaptive sampling

Introduction

Shotgun metagenomics has previously proven effective in the investigation of foodborne outbreaks by providing rapid and comprehensive insights into the microbial contaminant. However, culture enrichment of the sample has remained a prerequisite, despite the potential impact on pathogen detection resulting from the growth competition. To circumvent the need for culture enrichment, we explored the use of adaptive sampling using various databases for a targeted nanopore sequencing, compared to shotgun metagenomics alone.

Methods

The adaptive sampling method was first tested on DNA of mashed potatoes mixed with DNA of a Staphylococcus aureus strain previously associated with a foodborne outbreak. The selective sequencing was used to either deplete the potato sequencing reads or enrich for the pathogen sequencing reads, and compared to a shotgun sequencing. Then, living S. aureus were spiked at 105 CFU into 25 g of mashed potatoes. Three DNA extraction kits were tested, in combination with enrichment using adaptive sampling, following whole genome amplification. After data analysis, the possibility to characterize the contaminant with the different sequencing and extraction methods, without culture enrichment, was assessed.

Results

Overall, the adaptive sampling outperformed the shotgun sequencing. While the use of a host removal DNA extraction kit and targeted sequencing using a database of foodborne pathogens allowed rapid detection of the pathogen, the most complete characterization was achieved when using solely a database of S. aureus combined with a conventional DNA extraction kit, enabling accurate placement of the strain on a phylogenetic tree alongside outbreak cases.

Discussion

This method shows great potential for strain-level analysis of foodborne outbreaks without the need for culture enrichment, thereby enabling faster investigations and facilitating precise pathogen characterization. The integration of adaptive sampling with metagenomics presents a valuable strategy for more efficient and targeted analysis of microbial communities in foodborne outbreaks, contributing to improved food safety and public health.

Predicting feed efficiency of Angus steers using the gastrointestinal microbiome

Microbial composition of the gastrointestinal tracts is an important factor affecting the variation in feed efficiency in ruminants. Several studies have investigated the composition of the ruminal and fecal microbiotas, as well as their impacts on feed efficiency and digestion. In addition, next-generation DNA sequencing techniques have allowed us to gain a better understanding of such microbiomes. In this study, the beef cattle microbiome data were analyzed using both a multivariate and a univariate approach and the results were compared. Moreover, a statistical procedure to classify calves in two groups with extreme Residual Feed Intake (RFI) values, using their microbiota profile, was developed. Both fecal and ruminal samples were collected from 63 Angus steers at two different time points for evaluation of their microbiomes: at the beginning and at the end of the feedlot. An additional fecal sample was collected at weaning. A total of 149 and 119 bacterial families (BFs) were retrieved from the ruminal and fecal samples, respectively. A Canonical Discriminant Analysis (CDA) was used to investigate whether BFs were able to distinguish between rumen and fecal samples. A sub-sample of 28 steers was divided in two groups based on their feed efficiency status: positive or negative for RFI. Fecal samples collected at weaning were used to assign the positive and negative RFI animals to their corresponding groups using both Stepwise Discriminant Analysis and CDA. Results revealed that CDA was able to distinguish between rumen and fecal samples. Peptostreptococcaceae was the family most associated with the fecal samples, whereas Prevotellaceae the most associated with the ruminal samples. The CDA using 19 BFs selected from the stepwise was able to correctly assign all animals to the proper RFI groups (negative or positive). Rhizobiaceae was the family most associated with negative RFI, whereas Comamonadacea was the family most linked with positive RFI. The results from this study showed that the multivariate approach can be used to improve microbiome data analysis, as well as to predict feed efficiency in beef cattle using information derived from the fecal microbiome.

Recent advances in applying omic technologies for studying acetic acid bacteria in industrial vinegar production: A comprehensive review

Vinegar and related bioproducts containing acetic acid as the main component are among the most appreciated fermented foodstuffs in numerous European and Asian countries because of their exceptional organoleptic and bio-healthy properties. Regarding the acetification process and obtaining of final products, there is still a lack of knowledge on fundamental aspects, especially those related to the study of biodiversity and metabolism of the present microbiota. In this context, omic technologies currently allow for the massive analysis of macromolecules and metabolites for the identification and characterization of these microorganisms working in their natural media without the need for isolation. This review approaches comprehensive research on the application of omic tools for the identification of vinegar microbiota, mainly acetic acid bacteria, with subsequent emphasis on the study of the microbial diversity, behavior, and key molecular strategies used by the predominant groups throughout acetification. The current omics tools are enabling both the finding of new vinegar microbiota members and exploring underlying strategies during the elaboration process. The species Komagataeibacter europaeus may be a model organism for present and future research in this industry; moreover, the development of integrated meta-omic analysis may facilitate the achievement of numerous of the proposed milestones. This work might provide useful guidance for the vinegar industry establishing the first steps towards the improvement of the acetification conditions and the development of new products with sensory and bio-healthy profiles adapted to the agri-food market.

Taxonomic resolution of different 16S rRNA variable regions varies strongly across plant-associated bacteria

Plant-microbiome research plays a pivotal role in understanding the relationships between plants and their associated microbial communities, with implications for agriculture and ecosystem dynamics. Metabarcoding analysis on variable regions of the 16S ribosomal RNA (rRNA) gene remains the dominant technology to study microbiome diversity in this field. However, the choice of the targeted variable region might affect the outcome of the microbiome studies. In our in silico analysis, we have evaluated whether the targeted variable region has an impact on taxonomic resolution in 16 plant-related microbial genera. Through a comparison of 16S rRNA gene variable regions with whole-genome data, our findings suggest that the V1-V3 region is generally a more suitable option than the widely used V3-V4 region for targeting microbiome analysis in plant-related genera. However, sole reliance on one region could introduce detection biases for specific genera. Thus, we are suggesting that while transitioning to full-length 16S rRNA gene and whole-genome sequencing for plant-microbiome analysis, the usage of genus-specific variable regions can achieve more precise taxonomic assignments. More broadly, our approach provides a blueprint to identify the most discriminating variable regions of the 16S rRNA gene for genera of interest.

Rumen multi-omics addressing diet-host-microbiome interplay in farm animals: a review

Continuous improvement in the living standards of developing countries, calls for an urgent need of high quality meat and dairy products. The farm animals have a micro-ecosystem in gastro-intestinal tract, comprising of a wide variety of flora and fauna which converts roughages and agricultural byproducts as well as nutrient rich concentrate sources into the useful products such as volatile fatty acids and microbial crude proteins. The microbial diversity changes according to composition of the feed, host species/breed and host's individual genetic makeup. From culture methods to next-generation sequencing technologies, the knowledge has emerged a lot to know-how of microbial world viz. their identification, enzymatic activities and metabolites which are the keys of ruminant's successful existence. The structural composition of ruminal community revealed through metagenomics can be elaborated by metatranscriptomics and metabolomics through deciphering their functional role in metabolism and their responses to the external and internal stimuli. These highly sophisticated analytical tools have made possible to correlate the differences in the feed efficiency, nutrients utilization and methane emissions to their rumen microbiome. The comprehensively understood rumen microbiome will enhance the knowledge in the fields of animal nutrition, biotechnology and climatology through deciphering the significance of each and every domain of residing microbial entity. The present review undertakes the recent investigations regarding rumen multi-omics viz. taxonomic and functional potential of microbial populations, host-diet-microbiome interactions and correlation with metabolic dynamics.

Metagenomics and metatranscriptomics as potential driving forces for the exploration of diversity and functions of micro-eukaryotes in soil

Micro-eukaryotes are ubiquitous and play vital roles in diverse ecological systems, yet their diversity and functions are scarcely known. This may be due to the limitations of formerly used conventional culture-based methods. Metagenomics and metatranscriptomics are enabling to unravel the genomic, metabolic, and phylogenetic diversity of micro-eukaryotes inhabiting in different ecosystems in a more comprehensive manner. The in-depth study of structural and functional characteristics of micro-eukaryote community residing in soil is crucial for the complete understanding of this major ecosystem. This review provides a deep insight into the methodologies employed under these approaches to study soil micro-eukaryotic organisms. Furthermore, the review describes available computational tools, pipelines, and database sources and their manipulation for the analysis of sequence data of micro-eukaryotic origin. The challenges and limitations of these approaches are also discussed in detail. In addition, this review summarizes the key findings of metagenomic and metatranscriptomic studies on soil micro-eukaryotes. It also highlights the exploitation of these methods to study the structural as well as functional profiles of soil micro-eukaryotic community and to screen functional eukaryotic protein coding genes for biotechnological applications along with the future perspectives in the field.

Exploring microbial worlds: a review of whole genome sequencing and its application in characterizing the microbial communities

The classical microbiology techniques have inherent limitations in unraveling the complexity of microbial communities, necessitating the pivotal role of sequencing in studying the diversity of microbial communities. Whole genome sequencing (WGS) enables researchers to uncover the metabolic capabilities of the microbial community, providing valuable insights into the microbiome. Herein, we present an overview of the rapid advancements achieved thus far in the use of WGS in microbiome research. There was an upsurge in publications, particularly in 2021 and 2022 with the United States, China, and India leading the metagenomics research landscape. The Illumina platform has emerged as the widely adopted sequencing technology, whereas a significant focus of metagenomics has been on understanding the relationship between the gut microbiome and human health where distinct bacterial species have been linked to various diseases. Additionally, studies have explored the impact of human activities on microbial communities, including the potential spread of pathogenic bacteria and antimicrobial resistance genes in different ecosystems. Furthermore, WGS is used in investigating the microbiome of various animal species and plant tissues such as the rhizosphere microbiome. Overall, this review reflects the importance of WGS in metagenomics studies and underscores its remarkable power in illuminating the variety and intricacy of the microbiome in different environments.

Soil as a transdisciplinary research catalyst: from bioprospecting to biorespecting

The vast microbial biodiversity of soils is beginning to be observed and understood by applying modern DNA sequencing techniques. However, ensuring this potentially valuable information is used in a fair and equitable way remains a challenge. Here, we present a public engagement project that explores this topic through collaborative research of soil microbiomes at six urban locations using nanopore-based DNA sequencing. The project brought together researchers from the disciplines of synthetic biology, environmental humanities and microbial ecology, as well as school students aged 14-16 years old, to gain a broader understanding of views on the use of data from the environment. Discussions led to the transformation of 'bioprospecting', a metaphor with extractive connotations which is often used to frame environmental DNA sequencing studies, towards a more collaborative approach-'biorespecting'. This shift in terminology acknowledges that genetic information contained in soil arises as a result of entire ecosystems, including the people involved in its creation. Therefore, any use of sequence information should be accountable to the ecosystems from which it arose. As knowledge can arise from ecosystems and communities, science and technology should acknowledge this link and reciprocate with care and benefit-sharing to help improve the wellbeing of future generations.

Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types

Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.

Microbiome variations among age classes and diets of captive Asian elephants (Elephas maximus) in Thailand using full-length 16S rRNA nanopore sequencing

Asian elephant (Elephas maximus) is the national symbol of Thailand and linked to Thai history and culture for centuries. The elephant welfare improvement is one of the major components to achieve sustainable captive management. Microbiome inhabiting digestive tracts have been shown with symbiotic relations to host health. This work provided high-resolution microbiome profiles of 32 captive elephants at a species level by utilizing full-length 16S rRNA gene nanopore sequencing. Eleven common uncultured bacterial species were found across elephants fed with solid food including uncultured bacterium Rikenellaceae RC9 gut group, Kiritimatiellae WCHB1-41, Phascolarctobacterium, Oscillospiraceae NK4A214 group, Christensenellaceae R-7 group, Oribacterium, Oscillospirales UCG-010, Lachnospiraceae, Bacteroidales F082, uncultured rumen Rikenellaceae RC9 gut group, and Lachnospiraceae AC2044 group. We observed microbiome shifts along the age classes of baby (0-2 years), juvenile (2-10 years), and adult (> 10 years). Interestingly, we found distinct microbiome profiles among adult elephants fed with a local palm, Caryota urens, as a supplement. Potential beneficial microbes have been revealed according to the age classes and feed diets. The retrieved microbiome data could be provided as good baseline microbial profiles for monitoring elephant health, suggesting further studies towards dietary selection suitable for each age class and the use of local supplementary diets.

Single-cell analysis and spatial resolution of the gut microbiome

Over the past decade it has become clear that various aspects of host physiology, metabolism, and immunity are intimately associated with the microbiome and its interactions with the host. Specifically, the gut microbiome composition and function has been shown to play a critical role in the etiology of different intestinal and extra-intestinal diseases. While attempts to identify a common pattern of microbial dysbiosis linked with these diseases have failed, multiple studies show that bacterial communities in the gut are spatially organized and that disrupted spatial organization of the gut microbiome is often a common underlying feature of disease pathogenesis. As a result, focus over the last few years has shifted from analyzing the diversity of gut microbiome by sequencing of the entire microbial community, towards understanding the gut microbiome in spatial context. Defining the composition and spatial heterogeneity of the microbiome is critical to facilitate further understanding of the gut microbiome ecology. Development in single cell genomics approach has advanced our understanding of microbial community structure, however, limitations in approaches exist. Single cell genomics is a very powerful and rapidly growing field, primarily used to identify the genetic composition of microbes. A major challenge is to isolate single cells for genomic analyses. This review summarizes the different approaches to study microbial genomes at single-cell resolution. We will review new techniques for microbial single cell sequencing and summarize how these techniques can be applied broadly to answer many questions related to the microbiome composition and spatial heterogeneity. These methods can be used to fill the gaps in our understanding of microbial communities.

Data-driven decision-making for precision diagnosis of digestive diseases

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.

Unveiling the microbial diversity and functional dynamics of Shiv Kund, Sohna hot spring, India through a shotgun metagenomics approach

In this research, we examined the microbial diversity in Sohna hot spring, Haryana, India using shotgun metagenome sequencing based on the Illumina Hiseq 4000 sequencing technology. The raw sequence data from metagenomic paired-end libraries were analysed for taxonomic classification, diversity, and functional annotation using MG-RAST online server. The results showed the presence of total of 57 phyla, 931 genera, and 2068 species, predominantly occupied by Moraxellaceae (Gammaproteobacteria). However, at the species level, we reported the presence of some representative pathogenic taxa, such as Acinetobacter baumannii and Moraxella osloensis. The functional annotation predicted at various levels based on SEED-based subsystem, KEGG ortholog identity (KO), Cluster of Orthologous Groups (COGs) database identified the predominance of genes associated with primary and secondary metabolism along with a crucial role in environmental and genetic signals, cellular communication, and cell signalling. Comparative Genome Analysis (CGA) using The Pathosystem Resource Integration Centre (PATRIC) tool based on genome annotation and assembly of the metagenomic libraries for representative taxon Acinetobacter baumannii (NCBI tax id:470) characterized the reads with a unique genome identifier of 470.20380 (A. baumannii DDLJ4) which is evolutionary closer to A. baumannii ATCC 470.17978 400667.7. In addition, the CARD database results about the presence of potential AMR pathotypes and the prevalence of adeABC, adeIJK, abeM gene-specific clusters that function as multidrug efflux pumps. Overall, the results provided a comprehensive insight into virulence and anti-microbial resistance mechanism and could be useful for developing potential drug targets against the possible AMR pathotypes.

Effect of endophytic diazotroph Enterobacter roggenkampii ED5 on nitrogen-metabolism-related microecology in the sugarcane rhizosphere at different nitrogen levels

Sugarcane is an important sugar and energy crop worldwide, requiring a large amount of nitrogen (N). However, excessive application of synthetic N fertilizer causes environmental pollution in farmland. Endophytic nitrogen-fixing bacteria (ENFB) provide N nutrition for plants through biological N fixation, thus reducing the need for chemical fertilizers. The present study investigated the effect of the N-fixing endophytic strain Enterobacter roggenkampii ED5 on phytohormone indole-3-acetic acid (IAA), N-metabolism enzyme activities, microbial community compositions, and N cycle genes in sugarcane rhizosphere soil at different N levels. Three levels of 15N-urea, such as low N (0 kg/ha), medium N (150 kg/ha), and high N (300 kg/ha), were applied. The results showed that, after inoculating strain ED5, the IAA content in sugarcane leaves was significantly increased by 68.82% under low N condition at the seedling stage (60 days). The nitrate reductase (NR) activity showed a downward trend. However, the glutamine synthase (GS) and NADH-glutamate dehydrogenase (NADH-GDH) activities were significantly enhanced compared to the control under the high N condition, and the GS and NR genes had the highest expression at 180 and 120 days, respectively, at the low N level. The total N content in the roots, stems, and leaves of sugarcane was higher than the control. The 15N atom % excess of sugarcane decreased significantly under medium N condition, indicating that the medium N level was conducive to N fixation in strain ED5. Metagenome analysis of sugarcane rhizosphere soil exhibited that the abundance of N-metabolizing microbial richness was increased under low and high N conditions after inoculation of strain ED5 at the genus level, while it was increased at the phylum level only under the low N condition. The LefSe (LDA > 2, p < 0.05) found that the N-metabolism-related differential microorganisms under the high N condition were higher than those under medium and low N conditions. It was also shown that the abundance of nifDHK genes was significantly increased after inoculation of ED5 at the medium N level, and other N cycle genes had high abundance at the high N level after inoculation of strain ED5. The results of this study provided a scientific reference for N fertilization in actual sugarcane production.

Chronic kidney disease and gut microbiota

Chronic kidney disease (CKD) refers to a range of various pathophysiological processes correlated with abnormal renal function and a progressive loss in GFR. Just as dysbiosis and altered pathology of the gut are accompanied with hypertension, which is a significant CKD risk factor. Gut dysbiosis in CKD patients is associated with an elevated levels of uremic toxins, which in turn increases the CKD progression. According to research results, the gut-kidney axis has a role in the formation of kidney stones, also in IgAN. A number of researchers have categorized the gut microbiota as enterotypes, and others, skeptical of theory of enterotypes, have suggested biomarkers to describe taxa that related to lifestyle, nutrition, and disease status. Metabolome-microbiome studies have been used to investigate the interactions of host-gut microbiota in terms of the involvement of metabolites in these interactions and are yielded promising results. The correlation between gut microbiota and CKD requires further multi-omic researches. Also, with regard to systems biology, studies on the communication network of proteins and transporters such as SLC and ABC, can help us achieve a deeper understanding of the gut-liver-kidney axis communication and can thus provide promising new horizons in the treatment of CKD patients. Probiotic-based treatment is an approach to reduce uremic poisoning, which is accomplished by swallowing microbes those can catalyze URS in the gut. If further comprehensive studies are carried out, we will know about the probiotics impact in slowing the renal failure progression and reducing inflammatory markers.

Disease-associated gut microbiome and critical metabolomic alterations in patients with colorectal cancer

BACKGROUND

Gut microbiota plays a significant role in the colorectal cancer (CRC) process. Ectopic colonization of multiple oral bacteria is reportedly associated with CRC pathogenesis and progression, but the details remain unclear.

METHODS

We enrolled a cohort of 50 CRC patients and 52 healthy controls from an East China population. Taxonomic and functional analysis of the fecal microbiota were performed using 16S rDNA (50 + 52 samples) and shotgun metagenomic sequencing (8 + 6 samples), respectively, with particular attention paid to gut-colonized oral bacteria.

RESULTS AND CONCLUSIONS

The results showed more detected bacterial species but lower species evenness within the samples from CRC patients. To determine the specific bacteria enriched in each group, we analyzed their possible protective, carcinogenic, or opportunistic roles in the CRC process. Among the ectopic oral bacteria, we observed a significant increase in the abundance of Fusobacterium and decreased abundance of Prevotella and Ruminococcus in the CRC group. Main differences in the functional composition of these two groups were related to energy metabolism and biosynthesis, especially the glycolytic pathway. Furthermore, we validated the colonization of Fusobacterium nucleatum subsp. animalis within CRC tissues and studied its impact on the host intestinal epithelium and tumor cells. With high selectivity for cancerous tissues, this subspecies promoted CRC cell proliferation and induced potential DNA damage.

Gut and urinary microbiota: the causes and potential treatment measures of renal cell carcinoma

Mounting evidence suggests that the gut microbiota plays a crucial role in the development and treatment of various cancers. Recent research on the urinary microbiota challenges the long-standing belief that urine is sterile, as urinary microbiota has been implicated in the development of bladder and prostate cancers, similar to the role of gut microbiota in cancer development. Although the precise involvement of microbiota in the proliferation and differentiation of renal cell carcinoma (RCC) remains unclear, dysbiosis is considered one possible mechanism by which microbiota may contribute to RCC development and treatment. This review summarizes potential mechanisms by which gut microbiota may contribute to the development of RCC, and provides evidence for the involvement of urinary microbiota in RCC. We also explore the role of gut microbiota in RCC treatment and propose that the composition of gut microbiota could serve as a predictive marker for the potential efficacy of immune checkpoint inhibitors (ICIs) in RCC patients. Additionally, evidence suggests that modulating the abundance and distribution of microbiota can enhance the therapeutic effects of drugs, suggesting that microbiota may serve as a promising adjuvant therapy for RCC. Overall, we believe that further investigation into the gut and urinary microbiome of RCC patients could yield valuable insights and strategies for the prevention and personalized treatment of RCC.

Replicated life-history patterns and subsurface origins of the bacterial sister phyla Nitrospirota and Nitrospinota

The phyla Nitrospirota and Nitrospinota have received significant research attention due to their unique nitrogen metabolisms important to biogeochemical and industrial processes. These phyla are common inhabitants of marine and terrestrial subsurface environments and contain members capable of diverse physiologies in addition to nitrite oxidation and complete ammonia oxidation. Here, we use phylogenomics and gene-based analysis with ancestral state reconstruction and gene-tree-species-tree reconciliation methods to investigate the life histories of these two phyla. We find that basal clades of both phyla primarily inhabit marine and terrestrial subsurface environments. The genomes of basal clades in both phyla appear smaller and more densely coded than the later-branching clades. The extant basal clades of both phyla share many traits inferred to be present in their respective common ancestors, including hydrogen, one-carbon, and sulfur-based metabolisms. Later-branching groups, namely the more frequently studied classes Nitrospiria and Nitrospinia, are both characterized by genome expansions driven by either de novo origination or laterally transferred genes that encode functions expanding their metabolic repertoire. These expansions include gene clusters that perform the unique nitrogen metabolisms that both phyla are most well known for. Our analyses support replicated evolutionary histories of these two bacterial phyla, with modern subsurface environments representing a genomic repository for the coding potential of ancestral metabolic traits.

Mining metagenomes reveals diverse antibiotic biosynthetic genes in uncultured microbial communities

Pathogens resistant to antimicrobials form a significant threat to public health worldwide. Tackling multidrug-resistant pathogens via screening metagenomic libraries has become a common approach for the discovery of new antibiotics from uncultured microorganisms. This study focuses on capturing nonribosomal peptide synthase (NRPS) gene clusters implicated in the synthesis of many natural compounds of industrial relevance. A NRPS PCR assay was used to screen 2976 Escherichia coli clones in a soil metagenomic library to target NRPS genes. DNA extracts from 4 clones were sequenced and subjected to bioinformatic analysis to identify NRPS domains, their phylogeny, and substrate specificity.Successfully, 17 NRPS-positive hits with a biosynthetic potential were identified. DNA sequencing and BLAST analysis confirmed that NRPS protein sequences shared similarities with members of the genus Delftia in the Proteobacteria taxonomic position. Multiple alignment and phylogenetic analysis demonstrated that clones no. 15cd35 and 15cd37 shared low bootstrap values (54%) and were distantly far from close phylogenetic neighbors. Additionally, NRPS domain substrate specificity has no hits with the known ones; hence, they are more likely to use different substrates to produce new diverse antimicrobials. Further analysis confirmed that the NRPS hits resemble several transposon elements from other bacterial taxa, confirming its diversity. We confirmed that the analyses of the soil metagenomic library revealed a diverse set of NRPS related to the genus Delftia. An in-depth understanding of those positive NRPS hits is a crucial step for genetic manipulation of NRPS, shedding light on alternative novel antimicrobial compounds that can be used in drug discovery and hence supports the pharmaceutical sector.

Rhometa: Population recombination rate estimation from metagenomic read datasets

Prokaryotic evolution is influenced by the exchange of genetic information between species through a process referred to as recombination. The rate of recombination is a useful measure for the adaptive capacity of a prokaryotic population. We introduce Rhometa (https://github.com/sid-krish/Rhometa), a new software package to determine recombination rates from shotgun sequencing reads of metagenomes. It extends the composite likelihood approach for population recombination rate estimation and enables the analysis of modern short-read datasets. We evaluated Rhometa over a broad range of sequencing depths and complexities, using simulated and real experimental short-read data aligned to external reference genomes. Rhometa offers a comprehensive solution for determining population recombination rates from contemporary metagenomic read datasets. Rhometa extends the capabilities of conventional sequence-based composite likelihood population recombination rate estimators to include modern aligned metagenomic read datasets with diverse sequencing depths, thereby enabling the effective application of these techniques and their high accuracy rates to the field of metagenomics. Using simulated datasets, we show that our method performs well, with its accuracy improving with increasing numbers of genomes. Rhometa was validated on a real S. pneumoniae transformation experiment, where we show that it obtains plausible estimates of the rate of recombination. Finally, the program was also run on ocean surface water metagenomic datasets, through which we demonstrate that the program works on uncultured metagenomic datasets.

Dysbiosis of Oral Microbiota and Metabolite Profiles Associated with Type 2 Diabetes Mellitus

Several previous studies have shown that oral microbial disorders may be closely related to the occurrence and development of type 2 diabetes mellitus (T2DM). However, whether the function of oral microorganisms and their metabolites have changed in patients with T2DM who have not suffered from any oral diseases has not been reported. We performed metagenomic analyses and nontargeted metabolic analysis of saliva and supragingival plaque samples from patients with T2DM who have not suffered any oral diseases and normal controls. We found that periodontal pathogens such as Porphyromonas gingivalis and Prevotella melaninogenica were significantly enriched, while the abundances of dental caries pathogens such as Streptococcus mutans and Streptococcus sobrinus were not significantly different in patients with T2DM compared to those in normal controls. Metabolomic analyses showed that the salivary levels of cadaverine and L-(+)-leucine of patients with T2DM were significantly higher than those of normal controls, while the supragingival plaque levels of N-acetyldopamine and 3,4-dimethylbenzoic acid in patients with T2DM were significantly higher than those in the normal controls. Additionally, we identified the types of oral microorganisms related to the changes in the levels of circulating metabolites, and the oral microorganisms were involved in the dysregulation of harmful metabolites such as cadaverine and n, n-dimethylarginine. Overall, our study first described the changes in the composition of oral microorganisms and their metabolites in patients with T2DM who have not suffered any oral diseases, which will provide a direct basis for finding oral biomarkers for early warning of oral diseases in T2DM. IMPORTANCE The incidence of oral diseases in type 2 diabetic patients might increase, and the severity might also be more serious. At present, the relationship between oral microorganisms and type 2 diabetes mellitus (T2DM) has become a hot topic in systemic health research. However, whether the function of oral microorganisms and their metabolites have changed in patients with T2DM who have not suffered from any oral diseases has not been reported. We found that even if the oral condition of T2DM is healthy, their oral microbes and metabolites have changed, thus increasing the risk of periodontal disease. Our study first described the changes in the composition of oral microorganisms and their metabolites in T2DM who have not suffered any oral diseases and revealed the correlation between oral microorganisms and their metabolites, which will provide a direct basis for finding oral biomarkers for early warning of oral diseases in patients with T2DM.

Phylogeny analysis of whole protein-coding genes in metagenomic data detected an environmental gradient for the microbiota

Environmental factors affect the growth of microorganisms and therefore alter the composition of microbiota. Correlative analysis of the relationship between metagenomic composition and the environmental gradient can help elucidate key environmental factors and establishment principles for microbial communities. However, a reasonable method to quantitatively compare whole metagenomic data and identify the primary environmental factors for the establishment of microbiota has not been reported so far. In this study, we developed a method to compare whole proteomes deduced from metagenomic shotgun sequencing data, and quantitatively display their phylogenetic relationships as metagenomic trees. We called this method Metagenomic Phylogeny by Average Sequence Similarity (MPASS). We also compared one of the metagenomic trees with dendrograms of environmental factors using a comparison tool for phylogenetic trees. The MPASS method correctly constructed metagenomic trees of simulated metagenomes and soil and water samples. The topology of the metagenomic tree of samples from the Kirishima hot springs area in Japan was highly similarity to that of the dendrograms based on previously reported environmental factors for this area. The topology of the metagenomic tree also reflected the dynamics of microbiota at the taxonomic and functional levels. Our results strongly suggest that MPASS can successfully classify metagenomic shotgun sequencing data based on the similarity of whole protein-coding sequences, and will be useful for the identification of principal environmental factors for the establishment of microbial communities. Custom Perl script for the MPASS pipeline is available at https://github.com/s0sat/MPASS.

Generation of a high quality library of bioactive filamentous actinomycetes from extreme biomes using a culture-based bioprospecting strategy

Introduction

Filamentous actinomycetes, notably members of the genus Streptomyces, remain a rich source of new specialized metabolites, especially antibiotics. In addition, they are also a valuable source of anticancer and biocontrol agents, biofertilizers, enzymes, immunosuppressive drugs and other biologically active compounds. The new natural products needed for such purposes are now being sought from extreme habitats where harsh environmental conditions select for novel strains with distinctive features, notably an ability to produce specialized metabolites of biotechnological value.

Methods

A culture-based bioprospecting strategy was used to isolate and screen filamentous actinomycetes from three poorly studied extreme biomes. Actinomycetes representing different colony types growing on selective media inoculated with environmental suspensions prepared from high-altitude, hyper-arid Atacama Desert soils, a saline soil from India and from a Polish pine forest soil were assigned to taxonomically predictive groups based on characteristic pigments formed on oatmeal agar. One hundred and fifteen representatives of the colour-groups were identified based on 16S rRNA gene sequences to determine whether they belonged to validly named or to putatively novel species. The antimicrobial activity of these isolates was determined using a standard plate assay. They were also tested for their capacity to produce hydrolytic enzymes and compounds known to promote plant growth while representative strains from the pine forest sites were examined to determine their ability to inhibit the growth of fungal and oomycete plant pathogens.

Results

Comparative 16S rRNA gene sequencing analyses on isolates representing the colour-groups and their immediate phylogenetic neighbours showed that most belonged to either rare or novel species that belong to twelve genera. Representative isolates from the three extreme biomes showed different patterns of taxonomic diversity and characteristic bioactivity profiles. Many of the isolates produced bioactive compounds that inhibited the growth of one or more strains from a panel of nine wild strains in standard antimicrobial assays and are known to promote plant growth. Actinomycetes from the litter and mineral horizons of the pine forest, including acidotolerant and acidophilic strains belonging to the genera Actinacidiphila, Streptacidiphilus and Streptomyces, showed a remarkable ability to inhibit the growth of diverse fungal and oomycete plant pathogens.

Discussion

It can be concluded that selective isolation and characterization of dereplicated filamentous actinomyctes from several extreme biomes is a practical way of generating high quality actinomycete strain libraries for agricultural, industrial and medical biotechnology.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Tool and techniques study to plant microbiome current understanding and future needs: an overview

Microorganisms are present in the universe and they play role in beneficial and harmful to human life, society, and environments. Plant microbiome is a broad term in which microbes are present in the rhizo, phyllo, or endophytic region and play several beneficial and harmful roles with the plant. To know of these microorganisms, it is essential to be able to isolate purification and identify them quickly under laboratory conditions. So, to improve the microbial study, several tools and techniques such as microscopy, rRNA, or rDNA sequencing, fingerprinting, probing, clone libraries, chips, and metagenomics have been developed. The major benefits of these techniques are the identification of microbial community through direct analysis as well as it can apply in situ. Without tools and techniques, we cannot understand the roles of microbiomes. This review explains the tools and their roles in the understanding of microbiomes and their ecological diversity in environments.

Metagenomic-based surveillance systems for antibiotic resistance in non-clinical settings

The success of antibiotics as a therapeutic agent has led to their ineffectiveness. The continuous use and misuse in clinical and non-clinical areas have led to the emergence and spread of antibiotic-resistant bacteria and its genetic determinants. This is a multi-dimensional problem that has now become a global health crisis. Antibiotic resistance research has primarily focused on the clinical healthcare sectors while overlooking the non-clinical sectors. The increasing antibiotic usage in the environment - including animals, plants, soil, and water - are drivers of antibiotic resistance and function as a transmission route for antibiotic resistant pathogens and is a source for resistance genes. These natural compartments are interconnected with each other and humans, allowing the spread of antibiotic resistance via horizontal gene transfer between commensal and pathogenic bacteria. Identifying and understanding genetic exchange within and between natural compartments can provide insight into the transmission, dissemination, and emergence mechanisms. The development of high-throughput DNA sequencing technologies has made antibiotic resistance research more accessible and feasible. In particular, the combination of metagenomics and powerful bioinformatic tools and platforms have facilitated the identification of microbial communities and has allowed access to genomic data by bypassing the need for isolating and culturing microorganisms. This review aimed to reflect on the different sequencing techniques, metagenomic approaches, and bioinformatics tools and pipelines with their respective advantages and limitations for antibiotic resistance research. These approaches can provide insight into resistance mechanisms, the microbial population, emerging pathogens, resistance genes, and their dissemination. This information can influence policies, develop preventative measures and alleviate the burden caused by antibiotic resistance.

Longitudinal fecal microbiome and metabolite data demonstrate rapid shifts and subsequent stabilization after an abrupt dietary change in healthy adult dogs

Background

Diet has a large influence on gut microbiota diversity and function. Although previous studies have investigated the effect of dietary interventions on the gut microbiome, longitudinal changes in the gut microbiome, microbial functions, and metabolite profiles post dietary interventions have been underexplored. How long these outcomes require to reach a steady-state, how they relate to one another, and their impact on host physiological changes are largely unknown. To address these unknowns, we collected longitudinal fecal samples following an abrupt dietary change in healthy adult beagles (n = 12, age: 5.16 ± 0.87 year, BW: 13.37 ± 0.68 kg) using a crossover design. All dogs were fed a kibble diet (control) from d1-14, and then fed that same diet supplemented with fiber (HFD) or a protein-rich canned diet (CD) from d15-27. Fresh fecal samples were collected on d13, 16, 20, 24, and 27 for metabolite and microbiome assessment. Fecal microbial diversity and composition, metabolite profiles, and microbial functions dramatically diverged and stabilized within a few days (2 d for metabolites; 6 d for microbiota) after dietary interventions. Fecal acetate, propionate, and total short-chain fatty acids increased after change to HFD, while fecal isobutyrate, isovalerate, total branched-chain fatty acids, phenol, and indole increased after dogs consumed CD. Relative abundance of ~ 100 bacterial species mainly belonging to the Firmicutes, Proteobacteria, and Actinobacteria phyla increased in HFD. These shifts in gut microbiome diversity and composition were accompanied by functional changes. Transition to HFD led to increases in the relative abundance of KEGG orthology (KO) terms related to starch and sucrose metabolism, fatty acid biosynthesis, and amino sugar and nucleotide sugar metabolism, while transition to CD resulted in increased relative abundance of KO terms pertaining to inositol phosphate metabolism and sulfur metabolism. Significant associations among fecal microbial taxa, KO terms, and metabolites were observed, allowing for high-accuracy prediction of diet group by random forest analysis.

Conclusions

Longitudinal sampling and a multi-modal approach to characterizing the gastrointestinal environment allowed us to demonstrate how drastically and quickly dietary changes impact the fecal microbiome and metabolite profiles of dogs following an abrupt dietary change and identify key microbe-metabolite relationships that allowed for treatment prediction.

Multi-Omics Approaches in Colorectal Cancer Screening and Diagnosis, Recent Updates and Future Perspectives

Colorectal cancer (CRC) is common Cancer as well as the third leading cause of mortality around the world; its exact molecular mechanism remains elusive. Although CRC risk is significantly correlated with genetic factors, the pathophysiology of CRC is also influenced by external and internal exposures and their interactions with genetic factors. The field of CRC research has recently benefited from significant advances through Omics technologies for screening biomarkers, including genes, transcripts, proteins, metabolites, microbiome, and lipidome unbiasedly. A promising application of omics technologies could enable new biomarkers to be found for the screening and diagnosis of CRC. Single-omics technologies cannot fully understand the molecular mechanisms of CRC. Therefore, this review article aims to summarize the multi-omics studies of Colorectal cancer, including genomics, transcriptomics, proteomics, microbiomics, metabolomics, and lipidomics that may shed new light on the discovery of novel biomarkers. It can contribute to identifying and validating new CRC biomarkers and better understanding colorectal carcinogenesis. Discovering biomarkers through multi-omics technologies could be difficult but valuable for disease genotyping and phenotyping. That can provide a better knowledge of CRC prognosis, diagnosis, and treatments.

Metabarcoding assessment of fungal diversity in brown algae and sponges of Mauritius

Marine fungi are largely associated with second most inhabitants of the marine ecosystem such as sponges and algae. They are important colonizers and play vital ecological roles, such as nutrient cycling, organic matter decomposition, and symbiosis with other organisms. High throughput sequencing methods have been used successfully to reveal unknown fungal communities associated with a number of hosts particularly in the marine environment. However, the diversity of marine fungi associated with sponges and brown algae in Mauritius remains largely unknown. Traditional methods based on culturing do not provide reliable estimate of fungal diversity as only those that are able to grow under laboratory conditions are dominant; in addition, a large proportion of fungi, cultured in vitro remain most of the time unidentifiable, given that there are no sporulating structures to be examined morphologically. To overcome these limitations, we employed Illumina sequencing to unravel fungi species present in the sponges, Iotrochota sp. and Biemna sp. and the brown algae Turbinaria conoides, Sargassum pfeifferae, and Sargassum obovatum, collected from the north of Mauritius. Diversity analyses revealed that Biemna sp. had the highest diversity from the sampled sponges with fungi from 24 orders being recovered while from brown algae; Turbinaria conoides had the highest diversity with recovery of fungal taxa of the orders Botryosphaeriales, Chaetothyriales, Eurotiales, Hypocreales, and Mucorales with the latter four orders being common in both sampled algae and sponges. Beta diversity analyses revealed clustering only in the algae, Turbinaria conoides, and Sargassum pfeifferae and not in the co-occurring sponges, indicating that sampling location did not have much influence on fungal diversity. Our findings provide the first amplicon sequencing based insights of the fungal communities associated with macro-algae and sponges in Mauritius and supplements research on the fungal community existing in the oceans around the world.

Diagnostic Methods of Common Intestinal Protozoa: Current and Future Immunological and Molecular Methods

Intestinal protozoan infection is a persisting public health problem affecting the populations of developing countries in the tropical and subtropical regions. The diagnosis of intestinal protozoa remains a challenge especially in developing countries due to a shortage of laboratory facilities, limited health funding, and the remoteness of communities. Despite still being widely used, conventional diagnoses using microscopy and staining methods pose important limitations, particularly due to their low sensitivities and specificities. The selection of diagnostic methods needs to be carefully considered based on the objective of examination, availability of resources, and the expected parasite to be found. In this review, we describe various immunodiagnosis and molecular diagnostic methods for intestinal protozoa infection, including their advantages, disadvantages, and suitability for different settings, with a focus on Entamoeba histolytica, Giardia duodenalis, and Cryptosporidium spp.

Simultaneous monitoring of SARS-CoV-2 and bacterial profiles from the air of hospital environments with COVID-19-affected patients

The SARS-CoV-2 presence and the bacterial community profile in air samples collected at the Intensive Care Unit (ICU) of the Operational Unit of Infectious Diseases of Santa Caterina Novella Hospital in Galatina (Lecce, Italy) have been evaluated in this study. Air samplings were performed in different rooms of the ICU ward with and without COVID-19 patients. No sample was found positive to SARS-CoV-2, according to Allplex 2019-nCoV Assay. The airborne bacterial community profiles determined by the 16S rRNA gene metabarcoding approach up to the species level were characterized by richness and biodiversity indices, Spearman correlation coefficients, and Principal Coordinate Analysis. Pathogenic and non-pathogenic bacterial species, also detected in outdoor air samples, were found in all collected indoor samples. Staphylococcus pettenkoferi, Corynebacterium tuberculostearicum, and others coagulase-negative staphylococci, detected at high relative abundances in all the patients' rooms, were the most abundant pathogenic species. The highest mean relative abundance of S. pettenkoferi and C. tuberculostearicum suggested that they were likely the main pathogens of COVID-19 patients at the ICU ward of this study. The identification of nosocomial pathogens representing potential patients' risks in ICU COVID-19 rooms and the still controversial airborne transmission of the SARS-CoV-2 are the main contributions of this study.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10453-022-09754-7.

Compositional Data Analysis of 16S rRNA Gene Sequencing Results from Hospital Airborne Microbiome Samples

The compositional analysis of 16S rRNA gene sequencing datasets is applied to characterize the bacterial structure of airborne samples collected in different locations of a hospital infection disease department hosting COVID-19 patients, as well as to investigate the relationships among bacterial taxa at the genus and species level. The exploration of the centered log-ratio transformed data by the principal component analysis via the singular value decomposition has shown that the collected samples segregated with an observable separation depending on the monitoring location. More specifically, two main sample clusters were identified with regards to bacterial genera (species), consisting of samples mostly collected in rooms with and without COVID-19 patients, respectively. Human pathogenic genera (species) associated with nosocomial infections were mostly found in samples from areas hosting patients, while non-pathogenic genera (species) mainly isolated from soil were detected in the other samples. Propionibacterium acnes, Staphylococcus pettenkoferi, Corynebacterium tuberculostearicum, and jeikeium were the main pathogenic species detected in COVID-19 patients' rooms. Samples from these locations were on average characterized by smaller richness/evenness and diversity than the other ones, both at the genus and species level. Finally, the ρ metrics revealed that pairwise positive associations occurred either between pathogenic or non-pathogenic taxa.

Sustainable strategies for combating hydrocarbon pollution: Special emphasis on mobil oil bioremediation

The global rise in industrialization and vehicularization has led to the increasing trend in the use of different crude oil types. Among these mobil oil has major application in automobiles and different machines. The combustion of mobil oil renders a non-usable form that ultimately enters the environment thereby causing problems to environmental health. The aliphatic and aromatic hydrocarbon fraction of mobil oil has serious human and environmental health hazards. These components upon interaction with soil affect its fertility and microbial diversity. The recent advancement in the omics approach viz. metagenomics, metatranscriptomics and metaproteomics has led to increased efficiency for the use of microbial based remediation strategy. Additionally, the use of biosurfactants further aids in increasing the bioavailability and thus biodegradation of crude oil constituents. The combination of more than one approach could serve as an effective tool for efficient reduction of oil contamination from diverse ecosystems. To the best of our knowledge only a few publications on mobil oil have been published in the last decade. This systematic review could be extremely useful in designing a micro-bioremediation strategy for aquatic and terrestrial ecosystems contaminated with mobil oil or petroleum hydrocarbons that is both efficient and feasible. The state-of-art information and future research directions have been discussed to address the issue efficiently.

Carotenoid Biosynthesis: Genome-Wide Profiling, Pathway Identification in Rhodotorula glutinis X-20, and High-Level Production

Rhodotorula glutinis, as a member of the family Sporidiobolaceae, is of great value in the field of biotechnology. However, the evolutionary relationship of R. glutinis X-20 with Rhodosporidiobolus, Sporobolomyces, and Rhodotorula are not well understood, and its metabolic pathways such as carotenoid biosynthesis are not well resolved. Here, genome sequencing and comparative genome techniques were employed to improve the understanding of R. glutinis X-20. Phytoene desaturase (crtI) and 15-cis-phytoene synthase/lycopene beta-cyclase (crtYB), key enzymes in carotenoid pathway from R. glutinis X-20 were more efficiently expressed in S. cerevisiae INVSc1 than in S. cerevisiae CEN.PK2-1C. High yielding engineered strains were obtained by using synthetic biology technology constructing carotenoid pathway in S. cerevisiae and optimizing the precursor supply after fed-batch fermentation with palmitic acid supplementation. Genome sequencing analysis and metabolite identification has enhanced the understanding of evolutionary relationships and metabolic pathways in R. glutinis X-20, while heterologous construction of carotenoid pathway has facilitated its industrial application.

Metagenomic Analysis Using Phylogenetic Placement-A Review of the First Decade

Phylogenetic placement refers to a family of tools and methods to analyze, visualize, and interpret the tsunami of metagenomic sequencing data generated by high-throughput sequencing. Compared to alternative (e. g., similarity-based) methods, it puts metabarcoding sequences into a phylogenetic context using a set of known reference sequences and taking evolutionary history into account. Thereby, one can increase the accuracy of metagenomic surveys and eliminate the requirement for having exact or close matches with existing sequence databases. Phylogenetic placement constitutes a valuable analysis tool per se, but also entails a plethora of downstream tools to interpret its results. A common use case is to analyze species communities obtained from metagenomic sequencing, for example via taxonomic assignment, diversity quantification, sample comparison, and identification of correlations with environmental variables. In this review, we provide an overview over the methods developed during the first 10 years. In particular, the goals of this review are 1) to motivate the usage of phylogenetic placement and illustrate some of its use cases, 2) to outline the full workflow, from raw sequences to publishable figures, including best practices, 3) to introduce the most common tools and methods and their capabilities, 4) to point out common placement pitfalls and misconceptions, 5) to showcase typical placement-based analyses, and how they can help to analyze, visualize, and interpret phylogenetic placement data.

Characterization of carbapenem resistance in environmental samples and Acinetobacter spp. isolates from wastewater and river water in Poland

The aim of this study was to analyze the prevalence of carbapenem resistance genes in Acinetobacter spp. isolated from wastewater in a municipal WWTP and to determine their spread from treated wastewater to river water with the use of conventional and molecular microbiology methods (qualitative and quantitative PCR and metagenomic analysis). Samples of untreated and treated wastewater and samples of river water obtained upstream and downstream from the wastewater discharge point were collected in 3 seasons (February, June, and September) of 2019. Acinetobacter spp. isolates were obtained by the culture method on the CHROMagar™ Acinetobacter medium. Additionally, environmental DNA was extracted from the samples for metagenomic and qPCR analyses. The presence of beta-lactam resistance genes (Ambler class B and D), insertion sequence ISAba1, and class I, II, and III integron-integrase genes was determined, and the bacterial taxonomic structure and wastewater and river samples was analyzed. Out of the 301 isolates obtained on the CHROMagar™ Acinetobacter medium, 258 belonged to the genus Acinetobacter, including 21 isolates that were identified as Acinetobacter baumannii. The highest number of Acinetobacter spp. and A. baumannii isolates were obtained from wastewater and river water samples collected in June and September. The ISAba1/bla_OXA-51 complex was identified in 13 isolates, which confirms the occurrence of carbapenem-resistance isolates in the analyzed samples. The number of Acinetobacter isolates carrying antibiotic resistance genes (ARGs) increased in river water samples collected downstream from the wastewater discharge point (48 out of 258 isolates - 18.6%) compared to river water samples collected upstream from the wastewater discharge point (34 out of 258 isolates - 13.2%), which suggests that WWTP is a source of pollution in the natural environment. The conducted research provides evidence that bacteria of the genus Acinetobacter may spread alarming beta-lactam resistance in the environment and, therefore, pose a serious epidemiological threat.

Library Preparation and Sequencing Platform Introduce Bias in Metagenomic-Based Characterizations of Microbiomes

Metagenomics is increasingly used to describe microbial communities in biological specimens. Ideally, the steps involved in the processing of the biological specimens should not change the microbiome composition in a way that it could lead to false interpretations of inferred microbial community composition. Common steps in sample preparation include sample collection, storage, DNA isolation, library preparation, and DNA sequencing. Here, we assess the effect of three library preparation kits and two DNA sequencing platforms. Of the library preparation kits, one involved a PCR step (Nextera), and two were PCR free (NEXTflex and KAPA). We sequenced the libraries on Illumina HiSeq and NextSeq platforms. As example microbiomes, two pig fecal samples and two sewage samples of which aliquots were stored at different storage conditions (immediate processing and storage at -80°C) were assessed. All DNA isolations were performed in duplicate, totaling 80 samples, excluding controls. We found that both library preparation and sequencing platform had systematic effects on the inferred microbial community composition. The different sequencing platforms introduced more variation than library preparation and freezing the samples. The results highlight that all sample processing steps need to be considered when comparing studies. Standardization of sample processing is key to generating comparable data within a study, and comparisons of differently generated data, such as in a meta-analysis, should be performed cautiously. IMPORTANCE Previous research has reported effects of sample storage conditions and DNA isolation procedures on metagenomics-based microbiome composition; however, the effect of library preparation and DNA sequencing in metagenomics has not been thoroughly assessed. Here, we provide evidence that library preparation and sequencing platform introduce systematic biases in the metagenomic-based characterization of microbial communities. These findings suggest that library preparation and sequencing are important parameters to keep consistent when aiming to detect small changes in microbiome community structure. Overall, we recommend that all samples in a microbiome study are processed in the same way to limit unwanted variations that could lead to false conclusions. Furthermore, if we are to obtain a more holistic insight from microbiome data generated around the world, we will need to provide more detailed sample metadata, including information about the different sample processing procedures, together with the DNA sequencing data at the public repositories.

Machine Learning and Deep Learning Applications in Metagenomic Taxonomy and Functional Annotation

Shotgun sequencing of environmental DNA (i.e., metagenomics) has revolutionized the field of environmental microbiology, allowing the characterization of all microorganisms in a sequencing experiment. To identify the microbes in terms of taxonomy and biological activity, the sequenced reads must necessarily be aligned on known microbial genomes/genes. However, current alignment methods are limited in terms of speed and can produce a significant number of false positives when detecting bacterial species or false negatives in specific cases (virus, plasmids, and gene detection). Moreover, recent advances in metagenomics have enabled the reconstruction of new genomes using de novo binning strategies, but these genomes, not yet fully characterized, are not used in classic approaches, whereas machine and deep learning methods can use them as models. In this article, we attempted to review the different methods and their efficiency to improve the annotation of metagenomic sequences. Deep learning models have reached the performance of the widely used k-mer alignment-based tools, with better accuracy in certain cases; however, they still must demonstrate their robustness across the variety of environmental samples and across the rapid expansion of accessible genomes in databases.

Microbiomes of biohydrogen production from dark fermentation of industrial wastes: current trends, advanced tools and future outlook

Biohydrogen production through dark fermentation is very attractive as a solution to help mitigate the effects of climate change, via cleaner bioenergy production. Dark fermentation is a process where organic substrates are converted into bioenergy, driven by a complex community of microorganisms of different functional guilds. Understanding of the microbiomes underpinning the fermentation of organic matter and conversion to hydrogen, and the interactions among various distinct trophic groups during the process, is critical in order to assist in the process optimisations. Research in biohydrogen production via dark fermentation is currently advancing rapidly, and various microbiology and molecular biology tools have been used to investigate the microbiomes. We reviewed here the different systems used and the production capacity, together with the diversity of the microbiomes used in the dark fermentation of industrial wastes, with a special emphasis on palm oil mill effluent (POME). The current challenges associated with biohydrogen production were also included. Then, we summarised and discussed the different molecular biology tools employed to investigate the intricacy of the microbial ecology associated with biohydrogen production. Finally, we included a section on the future outlook of how microbiome-based technologies and knowledge can be used effectively in biohydrogen production systems, in order to maximise the production output.

Nanopore sequencing of RNA and cDNA molecules in Escherichia coli

High-throughput sequencing dramatically changed our view of transcriptome architectures and allowed for ground-breaking discoveries in RNA biology. Recently, sequencing of full-length transcripts based on the single-molecule sequencing platform from Oxford Nanopore Technologies (ONT) was introduced and is widely used to sequence eukaryotic and viral RNAs. However, experimental approaches implementing this technique for prokaryotic transcriptomes remain scarce. Here, we present an experimental and bioinformatic workflow for ONT RNA-seq in the bacterial model organism Escherichia coli, which can be applied to any microorganism. Our study highlights critical steps of library preparation and computational analysis and compares the results to gold standards in the field. Furthermore, we comprehensively evaluate the applicability and advantages of different ONT-based RNA sequencing protocols, including direct RNA, direct cDNA, and PCR-cDNA. We find that (PCR)-cDNA-seq offers improved yield and accuracy compared to direct RNA sequencing. Notably, (PCR)-cDNA-seq is suitable for quantitative measurements and can be readily used for simultaneous and accurate detection of transcript 5' and 3' boundaries, analysis of transcriptional units, and transcriptional heterogeneity. In summary, based on our comprehensive study, we show nanopore RNA-seq to be a ready-to-use tool allowing rapid, cost-effective, and accurate annotation of multiple transcriptomic features. Thereby nanopore RNA-seq holds the potential to become a valuable alternative method for RNA analysis in prokaryotes.

Pollen Monitoring by Optical Microscopy and DNA Metabarcoding: Comparative Study and New Insights

Environmental samples collected in Brindisi (Italy) by a Hirst-type trap and in Lecce (Italy) by a PM10 sampler were analysed by optical microscopy and DNA-metabarcoding, respectively, to identify airborne pollen and perform an exploratory study, highlighting the benefits and limits of both sampling/detection systems. The Hirst-type trap/optical-microscopy system allowed detecting pollen on average over the full bloom season, since whole pollen grains, whose diameter vary within 10–100 μm, are required for morphological detection with optical microscopy. Conversely, pollen fragments with an aerodynamic diameter ≤10 μm were collected in Lecce by the PM10 sampler. Pollen grains and fragments are spread worldwide by wind/atmospheric turbulences and can age in the atmosphere, but aerial dispersal, aging, and long-range transport of pollen fragments are favoured over those of whole pollen grains because of their smaller size. Twenty-four Streptophyta families were detected in Lecce throughout the sampling year, but only nine out of them were in common with the 21 pollen families identified in Brindisi. Meteorological parameters and advection patterns were rather similar at both study sites, being only 37 km apart in a beeline, but their impact on the sample taxonomic structure was different, likely for the different pollen sampling/detection systems used in the two monitoring areas.

Oceanic Crustal Fluid Single Cell Genomics Complements Metagenomic and Metatranscriptomic Surveys With Orders of Magnitude Less Sample Volume

Fluids circulating through oceanic crust play important roles in global biogeochemical cycling mediated by their microbial inhabitants, but studying these sites is challenged by sampling logistics and low biomass. Borehole observatories installed at the North Pond study site on the western flank of the Mid-Atlantic Ridge have enabled investigation of the microbial biosphere in cold, oxygenated basaltic oceanic crust. Here we test a methodology that applies redox-sensitive fluorescent molecules for flow cytometric sorting of cells for single cell genomic sequencing from small volumes of low biomass (approximately 103 cells ml-1) crustal fluid. We compare the resulting genomic data to a recently published paired metagenomic and metatranscriptomic analysis from the same site. Even with low coverage genome sequencing, sorting cells from less than one milliliter of crustal fluid results in similar interpretation of dominant taxa and functional profiles as compared to 'omics analysis that typically filter orders of magnitude more fluid volume. The diverse community dominated by Gammaproteobacteria, Bacteroidetes, Desulfobacterota, Alphaproteobacteria, and Zetaproteobacteria, had evidence of autotrophy and heterotrophy, a variety of nitrogen and sulfur cycling metabolisms, and motility. Together, results indicate fluorescence activated cell sorting methodology is a powerful addition to the toolbox for the study of low biomass systems or at sites where only small sample volumes are available for analysis.

Abiotic Stress and Belowground Microbiome: The Potential of Omics Approaches

Nowadays, the worldwide agriculture is experiencing a transition process toward more sustainable production, which requires the reduction of chemical inputs and the preservation of microbiomes’ richness and biodiversity. Plants are no longer considered as standalone entities, and the future of agriculture should be grounded on the study of plant-associated microorganisms and all their potentiality. Moreover, due to the climate change scenario and the resulting rising incidence of abiotic stresses, an innovative and environmentally friendly technique in agroecosystem management is required to support plants in facing hostile environments. Plant-associated microorganisms have shown a great attitude as a promising tool to improve agriculture sustainability and to deal with harsh environments. Several studies were carried out in recent years looking for some beneficial plant-associated microbes and, on the basis of them, it is evident that Actinomycetes and arbuscular mycorrhizal fungi (AMF) have shown a considerable number of positive effects on plants’ fitness and health. Given the potential of these microorganisms and the effects of climate change, this review will be focused on their ability to support the plant during the interaction with abiotic stresses and on multi-omics techniques which can support researchers in unearthing the hidden world of plant–microbiome interactions. These associated microorganisms can increase plants’ endurance of abiotic stresses through several mechanisms, such as growth-promoting traits or priming-mediated stress tolerance. Using a multi-omics approach, it will be possible to deepen these mechanisms and the dynamic of belowground microbiomes, gaining fundamental information to exploit them as staunch allies and innovative weapons against crop abiotic enemies threatening crops in the ongoing global climate change context.

A Metagenomics Investigation of Intergenerational Effects of Non-nutritive Sweeteners on Gut Microbiome

To identify possible mechanisms by which maternal consumption of non-nutritive sweeteners increases obesity risk in offspring, we reconstructed the major alterations in the cecal microbiome of 3-week-old offspring of obese dams consuming high fat/sucrose (HFS) diet with or without aspartame (5-7 mg/kg/day) or stevia (2-3 mg/kg/day) by shotgun metagenomic sequencing (n = 36). High throughput 16S rRNA gene sequencing (n = 105) was performed for dams, 3- and 18-week-old offspring. Maternal consumption of sweeteners altered cecal microbial composition and metabolism of propionate/lactate in their offspring. Offspring daily body weight gain, liver weight and body fat were positively correlated to the relative abundance of key microbes and enzymes involved in succinate/propionate production while negatively correlated to that of lactose degradation and lactate production. The altered propionate/lactate production in the cecum of weanlings from aspartame and stevia consuming dams implicates an altered ratio of dietary carbohydrate digestion, mainly lactose, in the small intestine vs. microbial fermentation in the large intestine. The reconstructed microbiome alterations could explain increased offspring body weight and body fat. This study demonstrates that intense sweet tastants have a lasting and intergenerational effect on gut microbiota, microbial metabolites and host health.

Exploring the universal healthy human gut microbiota around the World

The human gut holds a special place in the study of different microbial environments due to growing evidence that the gut microbiota is related to host health. However, despite extensive research, there is still a lack of knowledge about the core taxa forming the gut microbiota and, moreover, available information is biased towards western microbiomes in both genome databases and most core taxa studies. To tackle these limitations, we tested a database enrichment strategy and analyzed public datasets of whole-genome shotgun data, generated from 545 fecal samples, comprising three gradients of westernization. The NT database was selected as a baseline of biological diversity, subsequently being combined with various studies of interest related to the human microbiota. This enrichment strategy made it possible to improve classification capacity, compared to the original unenriched database, regarding the various lifestyles and populations studied. The effects of incomplete-taxonomy metagenome-assembled genomes on genome database enrichment were also examined, revealing that, while they are helpful, they should be used with caution depending on the taxonomic level of interest. Moreover, in terms of high prevalence, the core analysis revealed a conserved set of bacterial taxa in the healthy human gut microbiota worldwide, despite apparent lifestyle differences. Such taxa show a set of traits, metabolic roles, and ancestral status, making them suitable candidates for a hypothetical phylogenetic core of mutualistic microorganisms co-evolving with the human species.

Resource recovery of lignocellulosic biomass waste into lactic acid - Trends to sustain cleaner production

Biomass waste generation concerns regulatory authorities to develop novel methods to sustain biotransformation processes. Particularly, lactic acid (LA) is a bulk commodity chemical used in diverse industries and holds a growing global market demand. Recently, lignocellulosic waste biomass is preferred for LA bio-production because of its non-edible and inexpensive nature. However, the information about new pretreatment methods for lignocellulosic feedstock, and novel strains capable to produce LA through fermentation is limited. Therefore, this review highlights the advancement of pretreatments methods of lignocellulosic biomass and biotransformation. Herein, we first briefly explored the main sources of lignocellulosic waste biomass, then we explored their latest advances in pretreatment processes particularly supercritical fluid extraction, and microwave-assisted extraction. Approaches for bioconversion were also analyzed, such as consolidated bioprocessing (CBP), simultaneous saccharification and fermentation (SSF), separate hydrolysis fermentation (SHF), among other alternatives. Also, new trends and approaches were documented, such as metagenomics to find novel strains of microorganisms and the use of recombinant strategies for the creation of new strains. Finally, we developed a holistic and sustainable perspective based on novel microbial ecology tools such as next-gen sequencing, bioinformatics, and metagenomics. All these shed light on the needs to culture powerful microbial isolates, co-cultures, and mixed consortia to improve fermentation processes with the aim of optimizing cultures and feedstock pretreatments.

Decoding the roles of extremophilic microbes in the anaerobic environments: Past, Present, and Future

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The inaccessible extreme environments harbor a large majority of anaerobic microbes which remain unknown.

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Anaerobic microbes are used in a variety of industrial applications.

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In the future, metagenomic-assisted techniques can be used to identify novel anaerobic microbes from the unexplored extreme environments.

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Genetic engineering can be used to enhance the efficiency of anaerobic microbes for various processes.

The genome of an organism is directly or indirectly correlated with its environment. Consequently, different microbes have evolved to survive and sustain themselves in a variety of environments, including unusual anaerobic environments. It is believed that their genetic material could have played an important role in the early evolution of their existence in the past. Presently, out of the uncountable number of microbes found in different ecosystems we have been able to discover only one percent of the total communities. A large majority of the microbial populations exists in the most unusual and extreme environments. For instance, many anaerobic bacteria are found in the gastrointestinal tract of humans, soil, and hydrothermal vents. The recent advancements in Metagenomics and Next Generation Sequencing technologies have improved the understanding of their roles in these environments. Presently, anaerobic bacteria are used in various industries associated with biofuels, fermentation, production of enzymes, vaccines, vitamins, and dairy products. This broad applicability brings focus to the significant contribution of their genomes in these functions. Although the anaerobic microbes have become an irreplaceable component of our lives, a major and important section of such anaerobic microbes still remain unexplored. Therefore, it can be said that unlocking the role of the microbial genomes of the anaerobes can be a noteworthy discovery not just for mankind but for the entire biosystem as well.

Amplicon-based next-generation sequencing of eukaryotic nuclear ribosomal genes (metabarcoding) for the detection of single-celled parasites in human faecal samples

Comprehensive detection and differentiation of intestinal protists mostly rely on DNA-based methods. Here, we evaluated next-generation sequencing of eukaryotic nuclear ribosomal genes (metabarcoding) for the detection and differentiation of intestinal eukaryotic protists in the stool of healthy Tunisian individuals.

Thirty-six faecal DNA samples previously evaluated by microscopy and ameboid species-specific PCRs were tested. The hypervariable regions V3-V4 and V3-V5 of the 18S rRNA gene were amplified using three universal eukaryotic primer sets and sequenced using Illumina®MiSeq sequencing. In addition, real-time PCR assays were used to detect Dientamoeba fragilis, Giardia duodenalis, and Cryptosporidium spp.

The metabarcoding assay detected Blastocystis (subtypes 1, 2, and 3) and archamoebid species and subtypes (Entamoeba dispar, Entamoeba hartmanni, Entamoeba coli RL1 and RL2, Endolimax nana, Iodamoeba bütschlii RL1) in 27 (75%) and 22 (61%) of the 36 stool samples, respectively. Meanwhile, the assay had limited sensitivity for flagellates as evidenced by the fact that no Giardia-specific reads were found in any of the five Giardia-positive samples included, and Dientamoeba-specific reads were observed only in 3/13 D. fragilis-positive samples. None of the samples were positive for Cryptosporidium by any of the methods. In conclusion, a large variety of intestinal eukaryotic protists were detected and differentiated at species and subtype level; however, limited sensitivity for common flagellates was observed.