Integrating pan-genome with metagenome for microbial community profiling

Biofilm-associated proteins: from the gut biofilms to neurodegeneration

Human microbiota form a biofilm with substantial consequences for health and disease. Numerous studies have indicated that microbial communities produce functional amyloids as part of their biofilm extracellular scaffolds. The overlooked interplay between bacterial amyloids and the host may have detrimental consequences for the host, including neurodegeneration. This work gives an overview of the biofilm-associated amyloids expressed by the gut microbiota and their potential role in neurodegeneration. It discusses the biofilm-associated proteins (BAPs) of the gut microbiota, maps the amyloidogenic domains of these proteins, and analyzes the presence of bap genes within accessory genomes linked with transposable elements. Furthermore, the evidence supporting the existence of amyloids in the gut are presented. Finally, it explores the potential interactions between BAPs and α-synuclein, extending the literature on amyloid cross-kingdom interactions. Based on these findings, this study propose that BAP amyloids act as transmissible catalysts, facilitating the misfolding, accumulation, and spread of α-synuclein aggregates. This review contributes to the understanding of complex interactions among the microbiota, transmissible elements, and host, which is crucial for developing novel therapeutic approaches to combat microbiota-related diseases and improve overall health outcomes.

Metagenomic approaches and opportunities in arid soil research

Arid soils present unique challenges and opportunities for studying microbial diversity and bioactive potential due to the extreme environmental conditions they bear. This review article investigates soil metagenomics as an emerging tool to explore complex microbial dynamics and unexplored bioactive potential in harsh environments. Utilizing advanced metagenomic techniques, diverse microbial populations that grow under extreme conditions such as high temperatures, salinity, high pH levels, and exposure to metals and radiation can be studied. The use of extremophiles to discover novel natural products and biocatalysts emphasizes the role of functional metagenomics in identifying enzymes and secondary metabolites for industrial and pharmaceutical purposes. Metagenomic sequencing uncovers a complex network of microbial diversity, offering significant potential for discovering new bioactive compounds. Functional metagenomics, connecting taxonomic diversity to genetic capabilities, provides a pathway to identify microbes' mechanisms to synthesize valuable secondary metabolites and other bioactive substances. Contrary to the common perception of desert soil as barren land, the metagenomic analysis reveals a rich diversity of life forms adept at extreme survival. It provides valuable findings into their resilience and potential applications in biotechnology. Moreover, the challenges associated with metagenomics in arid soils, such as low microbial biomass, high DNA degradation rates, and DNA extraction inhibitors and strategies to overcome these issues, outline the latest advancements in extraction methods, high-throughput sequencing, and bioinformatics. The importance of metagenomics for investigating diverse environments opens the way for future research to develop sustainable solutions in agriculture, industry, and medicine. Extensive studies are necessary to utilize the full potential of these powerful microbial communities. This research will significantly improve our understanding of microbial ecology and biotechnology in arid environments.

Integrated proteome and pangenome analysis revealed the variation of microalga Isochrysis galbana and associated bacterial community to 2,6-Di-tert-butyl-p-cresol (BHT) stress

The phenolic antioxidant 2,6-Di-tert-butyl-p-cresol (BHT) has been detected in various environments and is considered a potential threat to aquatic organisms. Algal-bacterial interactions are crucial for maintaining ecosystem balance and elemental cycling, but their response to BHT remains to be investigated. This study analyzed the physiological and biochemical responses of the microalga Isochrysis galbana and the changes of associated bacterial communities under different concentrations of BHT stress. Results showed that the biomass of I. galbana exhibited a decreasing trend with increasing BHT concentrations up to 40 mg/L. The reduction in chlorophyll, carotenoid, and soluble protein content of microalgal cells was also observed under BHT stress. The production of malondialdehyde and the activities of superoxide dismutase, peroxidase, and catalase were further determined. Scanning electron microscopy analysis revealed that BHT caused surface rupture of the algal cells and loss of intracellular nutrients. Proteomic analysis demonstrated the upregulation of photosynthesis and citric acid cycle pathways as a response to BHT stress. Additionally, BHT significantly increased the relative abundance of specific bacteria in the phycosphere, including Marivita, Halomonas, Marinobacter, and Alteromonas. Further experiments confirmed that these bacteria had the ability to utilize BHT as the sole carbon resource for growth, and genes related to the degradation of phenolic compounds were detected through pangenome analysis.

Pan-genomics: Insight into the Functional Genome, Applications, Advancements, and Challenges

A pan-genome is a compilation of the common and unique genomes found in a given species. It incorporates the genetic information from all of the genomes sampled, producing a big and diverse set of genetic material. Pan-genomic analysis has various advantages over typical genomics research. It creates a vast and varied spectrum of genetic material by combining the genetic data from all the sampled genomes. Comparing pan-genomics analysis to conventional genomic research, there are a number of benefits. Although the most recent era of pan-genomic studies has used cutting-edge sequencing technology to shed fresh light on biological variety and improvement, the potential uses of pan-genomics in improvement have not yet been fully realized. Pan-genome research in various organisms has demonstrated that missing genetic components and the detection of significant Structural Variants (SVs) can be investigated using pan-genomic methods. Many individual-specific sequences have been linked to biological adaptability, phenotypic, and key economic attributes. This study aims to focus on how pangenome analysis uncovers genetic differences in various organisms, including human, and their effects on phenotypes, as well as how this might help us comprehend the diversity of species. The review also concentrated on potential problems and the prospects for future pangenome research.

The Raphidiopsis (= Cylindrospermopsis) raciborskii pangenome updated: Two new metagenome-assembled genomes from the South American clade

Two Raphidiopsis (=Cylindrospermopsis) raciborskii metagenome-assembled genomes (MAGs) were recovered from two freshwater metagenomic datasets sampled in 2011 and 2012 in Pampulha Lake, a hypereutrophic, artificial, shallow reservoir, located in the city of Belo Horizonte (MG), Brazil. Since the late 1970s, the lake has undergone increasing eutrophication pressure, due to wastewater input, leading to the occurrence of frequent cyanobacterial blooms. The major difference observed between PAMP2011 and PAMP2012 MAGs was the lack of the saxitoxin gene cluster in PAMP2012, which also presented a smaller genome, while PAMP2011 presented the complete sxt cluster and all essential proteins and clusters. The pangenome analysis was performed with all Raphidiopsis/Cylindrospermopsis genomes available at NCBI to date, with the addition of PAMP2011 and PAMP2012 MAGs (All33 subset), but also without the South American strains (noSA subset), and only among the South American strains (SA10 and SA8 subsets). We observed a substantial increase in the core genome size for the 'noSA' subset, in comparison to 'All33' subset, and since the core genome reflects the closeness among the pangenome members, the results strongly suggest that the conservation level of the essential gene repertoire seems to be affected by the geographic origin of the strains being analyzed, supporting the existence of a distinct SA clade. The Raphidiopsis pangenome comprised a total of 7943 orthologous protein clusters, and the two new MAGs increased the pangenome size by 11%. The pangenome based phylogenetic relationships among the 33 analyzed genomes showed that the SA genomes clustered together with 99% bootstrap support, reinforcing the metabolic particularity of the Raphidiopsis South American clade, related to its saxitoxin producing unique ability, while also indicating a different evolutionary history due to its geographic isolation.

The Pan-Genomic Analysis of Corynebacterium striatum Revealed its Genetic Characteristics as an Emerging Multidrug-Resistant Pathogen

Corynebacterium striatum is a Gram-positive bacterium that is straight or slightly curved and non-spore-forming. Although it was originally believed to be a part of the normal microbiome of human skin, a growing number of studies have identified it as a cause of various chronic diseases, bacteremia, and respiratory infections. However, despite its increasing importance as a pathogen, the genetic characteristics of the pathogen population, such as genomic characteristics and differences, the types of resistance genes and virulence factors carried by the pathogen and their distribution in the population are poorly understood. To address these knowledge gaps, we conducted a pan-genomic analysis of 314 strains of C. striatum isolated from various tissues and geographic locations. Our analysis revealed that C. striatum has an open pan-genome, comprising 5692 gene families, including 1845 core gene families, 2362 accessory gene families, and 1485 unique gene families. We also found that C. striatum exhibits a high degree of diversity across different sources, but strains isolated from skin tissue are more conserved. Furthermore, we identified 53 drug resistance genes and 42 virulence factors by comparing the strains to the drug resistance gene database (CARD) and the pathogen virulence factor database (VFDB), respectively. We found that these genes and factors are widely distributed among C. striatum, with 77.7% of strains carrying 2 or more resistance genes and displaying primary resistance to aminoglycosides, tetracyclines, lincomycin, macrolides, and streptomycin. The virulence factors are primarily associated with pathogen survival within the host, iron uptake, pili, and early biofilm formation. In summary, our study provides insights into the population diversity, resistance genes, and virulence factors ofC. striatum from different sources. Our findings could inform future research and clinical practices in the diagnosis, prevention, and treatment of C. striatum-associated diseases.

Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference

A pangenome is a collection of the common and unique genomes that are present in a given species. It combines the genetic information of all the genomes sampled, resulting in a large and diverse range of genetic material. Pangenomic analysis offers several advantages compared to traditional genomic research. For example, a pangenome is not bound by the physical constraints of a single genome, so it can capture more genetic variability. Thanks to the introduction of the concept of pangenome, it is possible to use exceedingly detailed sequence data to study the evolutionary history of two different species, or how populations within a species differ genetically. In the wake of the Human Pangenome Project, this review aims at discussing the advantages of the pangenome around human genetic variation, which are then framed around how pangenomic data can inform population genetics, phylogenetics, and public health policy by providing insights into the genetic basis of diseases or determining personalized treatments, targeting the specific genetic profile of an individual. Moreover, technical limitations, ethical concerns, and legal considerations are discussed.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Hypersaline Lake Urmia: a potential hotspot for microbial genomic variation

Lake Urmia located in Iran is a hypersaline environment with a salinity of about 27% (w/v). Metagenomic analyses of water samples collected from six locations in the lake exhibited a microbial community dominated by representatives of the family Haloferacaceae (69.8%), mainly those affiliated to only two genera, Haloquadratum (59.3%) and Halonotius (9.1%). Similar to other hypersaline lakes, the bacterial community was dominated by Salinibacter ruber (23.3%). Genomic variation analysis by inspecting single nucleotide variations (SNVs) and insertions/deletions (INDELs) exhibited a high level of SNVs and insertions, most likely through transformation for abundant taxa in the Lake Urmia community. We suggest that the extreme conditions of Lake Urmia and specifically its high ionic concentrations could potentially increase the SNVs and insertions, which can consequently hamper the assembly and genome reconstruction from metagenomic reads of Lake Urmia.

High-quality pan-genome of Escherichia coli generated by excluding confounding and highly similar strains reveals an association between unique gene clusters and genomic islands

The pan-genome analysis of bacteria provides detailed insight into the diversity and evolution of a bacterial population. However, the genomes involved in the pan-genome analysis should be checked carefully, as the inclusion of confounding strains would have unfavorable effects on the identification of core genes, and the highly similar strains could bias the results of the pan-genome state (open versus closed). In this study, we found that the inclusion of highly similar strains also affects the results of unique genes in pan-genome analysis, which leads to a significant underestimation of the number of unique genes in the pan-genome. Therefore, these strains should be excluded from pan-genome analysis at the early stage of data processing. Currently, tens of thousands of genomes have been sequenced for Escherichia coli, which provides an unprecedented opportunity as well as a challenge for pan-genome analysis of this classical model organism. Using the proposed strategies, a high-quality E. coli pan-genome was obtained, and the unique genes was extracted and analyzed, revealing an association between the unique gene clusters and genomic islands from a pan-genome perspective, which may facilitate the identification of genomic islands.

ODGI: understanding pangenome graphs

MOTIVATION

Pangenome graphs provide a complete representation of the mutual alignment of collections of genomes. These models offer the opportunity to study the entire genomic diversity of a population, including structurally complex regions. Nevertheless, analyzing hundreds of gigabase-scale genomes using pangenome graphs is difficult as it is not well-supported by existing tools. Hence, fast and versatile software is required to ask advanced questions to such data in an efficient way.

RESULTS

We wrote Optimized Dynamic Genome/Graph Implementation (ODGI), a novel suite of tools that implements scalable algorithms and has an efficient in-memory representation of DNA pangenome graphs in the form of variation graphs. ODGI supports pre-built graphs in the Graphical Fragment Assembly format. ODGI includes tools for detecting complex regions, extracting pangenomic loci, removing artifacts, exploratory analysis, manipulation, validation and visualization. Its fast parallel execution facilitates routine pangenomic tasks, as well as pipelines that can quickly answer complex biological questions of gigabase-scale pangenome graphs.

AVAILABILITY AND IMPLEMENTATION

ODGI is published as free software under the MIT open source license. Source code can be downloaded from https://github.com/pangenome/odgi and documentation is available at https://odgi.readthedocs.io. ODGI can be installed via Bioconda https://bioconda.github.io/recipes/odgi/README.html or GNU Guix https://github.com/pangenome/odgi/blob/master/guix.scm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ODGI: understanding pangenome graphs

MOTIVATION

Pangenome graphs provide a complete representation of the mutual alignment of collections of genomes. These models offer the opportunity to study the entire genomic diversity of a population, including structurally complex regions. Nevertheless, analyzing hundreds of gigabase-scale genomes using pangenome graphs is difficult as it is not well-supported by existing tools. Hence, fast and versatile software is required to ask advanced questions to such data in an efficient way.

RESULTS

We wrote Optimized Dynamic Genome/Graph Implementation (ODGI), a novel suite of tools that implements scalable algorithms and has an efficient in-memory representation of DNA pangenome graphs in the form of variation graphs. ODGI supports pre-built graphs in the Graphical Fragment Assembly format. ODGI includes tools for detecting complex regions, extracting pangenomic loci, removing artifacts, exploratory analysis, manipulation, validation and visualization. Its fast parallel execution facilitates routine pangenomic tasks, as well as pipelines that can quickly answer complex biological questions of gigabase-scale pangenome graphs.

AVAILABILITY AND IMPLEMENTATION

ODGI is published as free software under the MIT open source license. Source code can be downloaded from https://github.com/pangenome/odgi and documentation is available at https://odgi.readthedocs.io. ODGI can be installed via Bioconda https://bioconda.github.io/recipes/odgi/README.html or GNU Guix https://github.com/pangenome/odgi/blob/master/guix.scm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Metagenomics and artificial intelligence in the context of human health

Human microbiome is ubiquitous, dynamic, and site-specific consortia of microbial communities. The pathogenic nature of microorganisms within human tissues has led to an increase in microbial studies. Characterization of genera, like Streptococcus, Cutibacterium, Staphylococcus, Bifidobacterium, Lactococcus and Lactobacillus through culture-dependent and culture-independent techniques has been reported. However, due to the unique environment within human tissues, it is difficult to culture these microorganisms making their molecular studies strenuous. MGs offer a gateway to explore and characterize hidden microbial communities through a culture-independent mode by direct DNA isolation. By function and sequence-based MGs, Scientists can explore the mechanistic details of numerous microbes and their interaction with the niche. Since the data generated from MGs studies is highly complex and multi-dimensional, it requires accurate analytical tools to evaluate and interpret the data. Artificial intelligence (AI) provides the luxury to automatically learn the data dimensionality and ease its complexity that makes the disease diagnosis and disease response easy, accurate and timely. This review provides insight into the human microbiota and its exploration and expansion through MG studies. The review elucidates the significance of MGs in studying the changing microbiota during disease conditions besides highlighting the role of AI in computational analysis of MG data.

Genomics and Informatics, Conjoined Tools Vital for Understanding and Protecting Plant Health

Genomics' impact on crop production continuously expands. The number of sequenced plant and microbial species and strains representing diverse populations of individual species rapidly increases thanks to the advent of next-generation sequencing technologies. Their genomic blueprints revealed candidate genes involved in various functions and processes crucial for crop health and helped in understanding how the sequenced organisms have evolved at the genome level. Functional genomics quickly translates these blueprints into a detailed mechanistic understanding of how such functions and processes work and are regulated; this understanding guides and empowers efforts to protect crops from diverse biotic and abiotic threats. Metagenome analyses help identify candidate microbes crucial for crop health and uncover how microbial communities associated with crop production respond to environmental conditions and cultural practices, presenting opportunities to enhance crop health by judiciously configuring microbial communities. Efficient conversion of disparate types of massive genomics data into actionable knowledge requires a robust informatics infrastructure supporting data preservation, analysis, and sharing. This review starts with an overview of how genomics came about and has quickly transformed life science. We illuminate how genomics and informatics can be applied to investigate various crop health-related problems using selected studies. We end the review by noting why community empowerment via crowdsourcing is crucial to harnessing genomics to protect global food and nutrition security without continuously expanding the environmental footprint of crop production.

Assessment of Hydrocarbon Degradation Potential in Microbial Communities in Arctic Sea Ice

The anthropogenic release of oil hydrocarbons into the cold marine environment is an increasing concern due to the elevated usage of sea routes and the exploration of new oil drilling sites in Arctic areas. The aim of this study was to evaluate prokaryotic community structures and the genetic potential of hydrocarbon degradation in the metagenomes of seawater, sea ice, and crude oil encapsulating the sea ice of the Norwegian fjord, Ofotfjorden. Although the results indicated substantial differences between the structure of prokaryotic communities in seawater and sea ice, the crude oil encapsulating sea ice (SIO) showed increased abundances of many genera-containing hydrocarbon-degrading organisms, including Bermanella, Colwellia, and Glaciecola. Although the metagenome of seawater was rich in a variety of hydrocarbon degradation-related functional genes (HDGs) associated with the metabolism of n-alkanes, and mono- and polyaromatic hydrocarbons, most of the normalized gene counts were highest in the clean sea ice metagenome, whereas in SIO, these counts were the lowest. The long-chain alkane degradation gene almA was detected from all the studied metagenomes and its counts exceeded ladA and alkB counts in both sea ice metagenomes. In addition, almA was related to the most diverse group of prokaryotic genera. Almost all 18 good- and high-quality metagenome-assembled genomes (MAGs) had diverse HDGs profiles. The MAGs recovered from the SIO metagenome belonged to the abundant taxa, such as Glaciecola, Bermanella, and Rhodobacteracea, in this environment. The genera associated with HDGs were often previously known as hydrocarbon-degrading genera. However, a substantial number of new associations, either between already known hydrocarbon-degrading genera and new HDGs or between genera not known to contain hydrocarbon degraders and multiple HDGs, were found. The superimposition of the results of comparing HDG associations with taxonomy, the HDG profiles of MAGs, and the full genomes of organisms in the KEGG database suggest that the found relationships need further investigation and verification.

Thermostable Cellulases / Xylanases From Thermophilic and Hyperthermophilic Microorganisms: Current Perspective

The bioconversion of lignocellulose into monosaccharides is critical for ensuring the continual manufacturing of biofuels and value-added bioproducts. Enzymatic degradation, which has a high yield, low energy consumption, and enhanced selectivity, could be the most efficient and environmentally friendly technique for converting complex lignocellulose polymers to fermentable monosaccharides, and it is expected to make cellulases and xylanases the most demanded industrial enzymes. The widespread nature of thermophilic microorganisms allows them to proliferate on a variety of substrates and release substantial quantities of cellulases and xylanases, which makes them a great source of thermostable enzymes. The most significant breakthrough of lignocellulolytic enzymes lies in lignocellulose-deconstruction by enzymatic depolymerization of holocellulose into simple monosaccharides. However, commercially valuable thermostable cellulases and xylanases are challenging to produce in high enough quantities. Thus, the present review aims at giving an overview of the most recent thermostable cellulases and xylanases isolated from thermophilic and hyperthermophilic microbes. The emphasis is on recent advancements in manufacturing these enzymes in other mesophilic host and enhancement of catalytic activity as well as thermostability of thermophilic cellulases and xylanases, using genetic engineering as a promising and efficient technology for its economic production. Additionally, the biotechnological applications of thermostable cellulases and xylanases of thermophiles were also discussed.

Mechanisms Underlying Host Range Variation in Flavivirus: From Empirical Knowledge to Predictive Models

Preventing and controlling epidemics caused by vector-borne viruses are particularly challenging due to their diverse pool of hosts and highly adaptive nature. Many vector-borne viruses belong to the Flavivirus genus, whose members vary greatly in host range and specificity. Members of the Flavivirus genus can be categorized to four main groups: insect-specific viruses that are maintained solely in arthropod populations, mosquito-borne viruses and tick-borne viruses that are transmitted to vertebrate hosts by mosquitoes or ticks via blood feeding, and those with no-known vector. The mosquito-borne group encompasses the yellow fever, dengue, and West Nile viruses, all of which are globally spread and cause severe morbidity in humans. The Flavivirus genus is genetically diverse, and its members are subject to different host-specific and vector-specific selective constraints, which do not always align. Thus, understanding the underlying genetic differences that led to the diversity in host range within this genus is an important aspect in deciphering the mechanisms that drive host compatibility and can aid in the constant arms-race against viral threats. Here, we review the phylogenetic relationships between members of the genus, their infection bottlenecks, and phenotypic and genomic differences. We further discuss methods that utilize these differences for prediction of host shifts in flaviviruses and can contribute to viral surveillance efforts.