Exploring Taxonomic and Genetic Relationships in the Pinus mugo Complex Using Genome Skimming Data

Genome skimming is a novel approach that enables obtaining large-scale genomic information based on high-copy DNA fractions from shallow whole-genome sequencing. The simplicity of this method, low analysis costs, and large amounts of generated data have made it widely used in plant research, including species identification, especially in the case of protected or endangered taxa. This task is particularly difficult in the case of closely related taxa. The Pinus mugo complex includes several dozen closely related taxa occurring in the most important mountain ranges in Europe. The taxonomic rank, origin, or distribution of many of these taxa have been debated for years. In this study, we used genome skimming and multilocus DNA barcoding approaches to obtain different sequence data sets and also to determine their genetic diversity and suitability for distinguishing closely related taxa in the Pinus mugo complex. We generated seven different data sets, which were then analyzed using three discrimination methods, i.e., tree based, distance based, and assembling species by automatic partitioning. Genetic diversity among populations and taxa was also investigated using haplotype network analysis and principal coordinate analysis. The proposed data set based on divergence hotspots is even twenty-times more variable than the other analyzed sets and improves the phylogenetic resolution of the Pinus mugo complex. In light of the obtained results, Pinus × rhaetica does not belong to the Pinus mugo complex and should not be identified with either Pinus uliginosa or Pinus rotundata. It seems to represent a fixed hybrid or introgressant between Pinus sylvestris and Pinus mugo. In turn, Pinus mugo and Pinus uncinata apparently played an important role in the origins of Pinus uliginosa and Pinus rotundata.

Inference of drowning sites using bacterial composition and random forest algorithm

Diagnosing the drowning site is a major challenge in forensic practice, particularly when corpses are recovered from flowing rivers. Recently, forensic experts have focused on aquatic microorganisms, including bacteria, which can enter the bloodstream during drowning and may proliferate in corpses. The emergence of 16S ribosomal RNA gene (16S rDNA) amplicon sequencing has provided a new method for analyzing bacterial composition and has facilitated the development of forensic microbiology. We propose that 16S rDNA amplicon sequencing could be a useful tool for inferring drowning sites. Our study found significant differences in bacterial composition in different regions of the Guangzhou section of the Pearl River, which led to differences in bacteria of drowned rabbit lungs at different drowning sites. Using the genus level of bacteria in the lung tissue of drowned rabbits, we constructed a random forest model that accurately predicted the drowning site in a test set with 100% accuracy. Furthermore, we discovered that bacterial species endemic to the water were not always present in the corresponding drowned lung tissue. Our findings demonstrate the potential of a random forest model based on bacterial genus and composition in drowned lung tissues for inferring drowning sites.

Current understanding of plant-microbe interaction through the lenses of multi-omics approaches and their benefits in sustainable agriculture

The success of sustainable agricultural practices has now become heavily dependent on the interactions between crop plants and their associated microbiome. Continuous advancement in high throughput sequencing platforms, omics-based approaches, and gene editing technologies has remarkably accelerated this area of research. It has enabled us to characterize the interactions of plants with associated microbial communities more comprehensively and accurately. Furthermore, the genomic and post-genomic era has significantly refined our perspective toward the complex mechanisms involved in those interactions, opening new avenues for efficiently deploying the knowledge in developing sustainable agricultural practices. This review focuses on our fundamental understanding of plant-microbe interactions and the contribution of existing multi-omics approaches, including those under active development and their tremendous success in unraveling different aspects of the complex network between plant hosts and microbes. In addition, we have also discussed the importance of sustainable and eco-friendly agriculture and the associated outstanding challenges ahead.

Effect of Increasing C/N Ratio on Performance and Microbial Community Structure in a Membrane Bioreactor with a High Ammonia Load

Herein, the responses of the operational performance of a membrane bioreactor (MBR) with a high ammonium-nitrogen (NH4+-N) load and microbial community structure to increasing carbon to nitrogen (C/N) ratios were studied. Variation in the influent C/N ratio did not affect the removal efficiencies of chemical oxygen demand (COD) and NH4+-N but gradually abated the ammonia oxidization activity of sludge. The concentration of the sludge in the reactor at the end of the process increased four-fold compared with that of the seed sludge, ensuring the stable removal of NH4+-N. The increasing influent COD concentration resulted in an elevated production of humic acids in soluble microbial product (SMP) and accelerated the rate of membrane fouling. High-throughput sequencing analysis showed that the C/N ratio had selective effects on the microbial community structure. In the genus level, Methyloversatilis, Subsaxibacter, and Pseudomonas were enriched during the operation. However, the relative abundance of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) involved in nitrification declined gradually and were decreased by 86.54 and 90.17%, respectively, with influent COD increasing from 0 to 2000 mg/L. The present study offers a more in-depth insight into the control strategy of the C/N ratio in the operation of an MBR with a high NH4+-N load.

EMA-amplicon-based sequencing informs risk assessment analysis of water treatment systems

Illumina amplicon-based sequencing was coupled with ethidium monoazide bromide (EMA) pre-treatment to monitor the total viable bacterial community and subsequently identify and prioritise the target organisms for the health risk assessment of the untreated rainwater and rainwater treated using large-volume batch solar reactor prototypes installed in an informal settlement and rural farming community. Taxonomic assignments indicated that Legionella and Pseudomonas were the most frequently detected genera containing opportunistic bacterial pathogens in the untreated and treated rainwater at both sites. Additionally, Mycobacterium, Clostridium sensu stricto and Escherichia/Shigella displayed high (≥80%) detection frequencies in the untreated and/or treated rainwater samples at one or both sites. Numerous exposure scenarios (e.g. drinking, cleaning) were subsequently investigated and the health risk of using untreated and solar reactor treated rainwater in developing countries was quantified based on the presence of L. pneumophila, P. aeruginosa and E. coli. The solar reactor prototypes were able to reduce the health risk associated with E. coli and P. aeruginosa to below the 1 × 10^-4 annual benchmark limit for all the non-potable uses of rainwater within the target communities (exception of showering for E. coli). However, the risk associated with intentional drinking of untreated or treated rainwater exceeded the benchmark limit (E. coli and P. aeruginosa). Additionally, while the solar reactor treatment reduced the risk associated with garden hosing and showering based on the presence of L. pneumophila, the risk estimates for both activities still exceeded the annual benchmark limit. The large-volume batch solar reactor prototypes were thus able to reduce the risk posed by the target bacteria for non-potable activities rainwater is commonly used for in water scarce regions of sub-Saharan Africa. This study highlights the need to assess water treatment systems in field trials using QMRA.

Detection of the endangered European weather loach (Misgurnus fossilis) via water and sediment samples: Testing multiple eDNA workflows

The European weather loach (Misgurnus fossilis) is classified as highly endangered in several countries of Central Europe. Populations of M. fossilis are predominantly found in ditches with low water levels and thick sludge layers and are thus hard to detect using conventional fishing methods. Therefore, environmental DNA (eDNA) monitoring appears particularly relevant for this species. In previous studies, M. fossilis was surveyed following eDNA water sampling protocols, which were not optimized for this species. Therefore, we created two full factorial study designs to test six different eDNA workflows for sediment samples and twelve different workflows for water samples. We used qPCR to compare the threshold cycle (C t) values of the different workflows, which indicate the target DNA amount in the sample, and spectrophotometry to quantify and compare the total DNA amount inside the samples. We analyzed 96 water samples and 48 sediment samples from a pond with a known population of M. fossilis. We tested several method combinations for long-term sample preservation, DNA capture, and DNA extraction. Additionally, we analyzed the DNA yield of samples from a ditch with a natural M. fossilis population monthly over one year to determine the optimal sampling period. Our results showed that the long-term water preservation method commonly used for eDNA surveys of M. fossilis did not lead to optimal DNA yields, and we present a valid long-term sample preservation alternative. A cost-efficient high salt DNA extraction led to the highest target DNA yields and can be used for sediment and water samples. Furthermore, we were able to show that in a natural habitat of M. fossilis, total and target eDNA were higher between June and September, which implies that this period is favorable for eDNA sampling. Our results will help to improve the reliability of future eDNA surveys of M. fossilis.

A coliform-targeted metagenomic method facilitating human exposure estimates to Escherichia coli-borne antibiotic resistance genes

Antimicrobial resistance and the spread of antibiotic resistance genes (ARGs) pose a threat to human health. Community-acquired infections resistant to treatment with first-line antibiotics are increasing, and there are few studies investigating environmental exposures and transmission. Our objective is to develop a novel targeted metagenomic method to quantify the abundance and diversity of ARGs in a faecal indicator bacterium, and to estimate human exposure to resistant bacteria in a natural environment. Sequence data from Escherichia coli metagenomes from 13 bathing waters in England were analysed using the ARGs Online Analysis Pipeline to estimate the abundance and diversity of resistance determinants borne by this indicator bacterium. These data were averaged over the 13 sites and used along with data on the levels of E. coli in English bathing waters in 2016 and estimates of the volume of water that water users typically ingest in an average session of their chosen activityto quantify the numbers of ARGs that water users ingest. Escherichia coli in coastal bathing waters were found to harbour on average 1.24 ARGs per cell. Approximately 2.5 million water sports sessions occurred in England in 2016 that resulted in water users ingesting at least 100 E. coli-borne ARGs.

Calculation and application of Sb toxicity coefficient for potential ecological risk assessment

The potential ecological risk index (RI) is a diagnostic tool for pollution control which integrate the concentration of heavy metals with ecological effect, environmental effect and toxicity. However, the lack of toxicity coefficients for specific heavy metals limits its widespread use. In this study, we calculated the toxicity coefficient (=7) for antimony (Sb) based on Hakanson's principles, thus broadening the range of potential applications of this risk assessment tool. Taking the case of Xikuangshan (XKS), the largest Sb mine in the world, we predicted the potential ecological risk factor (E_rⁱ) of Sb for sediment and soil. This was then compared with the enrichment factor (EF) and index of geoaccumulation (I_geo). Results showed that Sb shared the similar pollution categories regardless of E_rⁱ, EF or I_geo indexes was used indicating the appropriateness of the determined toxicity coefficient. Regression analysis results further demonstrated that E_rⁱ was in agreement with bioavailable concentrations (Diffusive Gradient in Thin Films and Community Bureau of Reference extraction concentrations), particularly in sediments. This means that E_rⁱ is a reliable and logical index for evaluating Sb pollution in sediments within aquatic environments and in soils within terrestrial environments.

Amplicon-based taxonomic characterization of bacteria in urban and peri-urban roof-harvested rainwater stored in tanks

Overall, 26% of Australian households use rainwater tanks as a source of potable and nonpotable water. Limited information is available on the total bacterial communities in tank water. Therefore, identification of dominant bacterial communities, diversity, and their distribution is important in understanding the microbial quality of tank water. In this study, the abundance and diversity of bacterial communities in 88 tank water samples collected from the urban areas of Brisbane (n=44) and the peri-urban center of Currumbin (n=44) in Southeast Queensland, Australia were determined using amplicon-based Illumina next-generation sequencing. In addition, the SourceTracker program was used to identify the sources of fecal contamination in tank water samples. Sequence reads were also analyzed to detect potential bacterial pathogenic genera in the tank water samples collected. Differences in sample coverage, alpha diversity, and richness did not differ significantly between the Brisbane and Currumbin tank water samples. Comamonadaceae and Planctomycetaceae were the most abundant families in all tank water samples. Curvibacter was the most abundant genus in all tank water samples. SourceTracker revealed that around 34% (Brisbane) and 43% (Currumbin) of tank water samples had a signature for bird fecal contamination. The potential opportunistic pathogenic genera including Burkholderia, Chromobacterium, Clostridium, Legionella, Mycobacterium, Nocardia, and Pseudomonas were most prevalent in tank water samples. Next-generation sequencing can be used as an initial screening tool to identify a wide array of potential pathogenic genera in tank water samples followed by quantifying specific pathogen(s) of interest using more sensitive molecular assays such as quantitative PCR (qPCR).

CoMeta: classification of metagenomes using k-mers

Nowadays, the study of environmental samples has been developing rapidly. Characterization of the environment composition broadens the knowledge about the relationship between species composition and environmental conditions. An important element of extracting the knowledge of the sample composition is to compare the extracted fragments of DNA with sequences derived from known organisms. In the presented paper, we introduce an algorithm called CoMeta (Classification of metagenomes), which assigns a query read (a DNA fragment) into one of the groups previously prepared by the user. Typically, this is one of the taxonomic rank (e.g., phylum, genus), however prepared groups may contain sequences having various functions. In CoMeta, we used the exact method for read classification using short subsequences (k-mers) and fast program for indexing large set of k-mers. In contrast to the most popular methods based on BLAST, where the query is compared with each reference sequence, we begin the classification from the top of the taxonomy tree to reduce the number of comparisons. The presented experimental study confirms that CoMeta outperforms other programs used in this context. CoMeta is available at https://github.com/jkawulok/cometa under a free GNU GPL 2 license.

Random whole metagenomic sequencing for forensic discrimination of soils

Here we assess the ability of random whole metagenomic sequencing approaches to discriminate between similar soils from two geographically distinct urban sites for application in forensic science. Repeat samples from two parklands in residential areas separated by approximately 3 km were collected and the DNA was extracted. Shotgun, whole genome amplification (WGA) and single arbitrarily primed DNA amplification (AP-PCR) based sequencing techniques were then used to generate soil metagenomic profiles. Full and subsampled metagenomic datasets were then annotated against M5NR/M5RNA (taxonomic classification) and SEED Subsystems (metabolic classification) databases. Further comparative analyses were performed using a number of statistical tools including: hierarchical agglomerative clustering (CLUSTER); similarity profile analysis (SIMPROF); non-metric multidimensional scaling (NMDS); and canonical analysis of principal coordinates (CAP) at all major levels of taxonomic and metabolic classification. Our data showed that shotgun and WGA-based approaches generated highly similar metagenomic profiles for the soil samples such that the soil samples could not be distinguished accurately. An AP-PCR based approach was shown to be successful at obtaining reproducible site-specific metagenomic DNA profiles, which in turn were employed for successful discrimination of visually similar soil samples collected from two different locations.

Metagenomic search strategies for interactions among plants and multiple microbes

Plants harbor multiple microbes. Metagenomics can facilitate understanding of the significance, for the plant, of the microbes, and of the interactions among them. However, current approaches to metagenomic analysis of plants are computationally time consuming. Efforts to speed the discovery process include improvement of computational speed, condensing the sequencing reads into smaller datasets before BLAST searches, simplifying the target database of BLAST searches, and flipping the roles of metagenomic and reference datasets. The latter is exemplified by the e-probe diagnostic nucleic acid analysis approach originally devised for improving analysis during plant quarantine.

Is metagenomics resolving identification of functions in microbial communities?

We are coming up on the tenth anniversary of the broad use of the method involving whole metagenome shotgun sequencing, referred to as metagenomics. The application of this approach has definitely revolutionized microbiology and the related fields, including the realization of the importance of the human microbiome. As such, metagenomics has already provided a novel outlook on the complexity and dynamics of microbial communities that are an important part of the biosphere of the planet. Accumulation of massive amounts of sequence data also caused a surge in the development of bioinformatics tools specially designed to provide pipelines for data analysis and visualization. However, a critical outlook into the field is required to appreciate what could be and what has currently been gained from the massive sequence databases that are being generated with ever-increasing speed.