Mumame: A software tool for quantifying gene-specific point-mutations in shotgun metagenomic data

Metagenomics has emerged as a central technique for studying the structure and function of microbial communities. Often the functional analysis is restricted to classification into broad functional categories. However, important phenotypic differences, such as resistance to antibiotics, are often the result of just one or a few point mutations in otherwise identical sequences. Bioinformatic methods for metagenomic analysis have generally been poor at accounting for this fact, resulting in a somewhat limited picture of important aspects of microbial communities. Here, we address this problem by providing a software tool called Mumame, which can distinguish between wildtype and mutated sequences in shotgun metagenomic data and quantify their relative abundances. We demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets. We also identified that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than for most other applications of shotgun metagenomics. Mumame is freely available online (http://microbiology.se/software/mumame)

The structure and diversity of human, animal and environmental resistomes

Background:Antibiotic resistance genes (ARGs) are widespread but cause problems only when present in pathogens. Environments where selection and transmission of antibiotic resistance frequently take place are likely to be characterized by high abundance and diversity of horizontally transferable ARGs. Large-scale quantitative data on ARGs is, however, lacking for most types of environments, including humans and animals, as is data on resistance genes to potential co-selective agents, such as biocides and metals. This paucity prevents efficient identification of risk environments.Results:We provide a comprehensive characterization of resistance genes, mobile genetic elements (MGEs) and bacterial taxonomic compositions for 864 metagenomes from humans (n = 350), animals (n = 145) and external environments (n = 369), all deeply sequenced using Illumina technology. Environment types showed clear differences in both resistance profiles and bacterial community compositions. Human and animal microbial communities were characterized by limited taxonomic diversity and low abundance and diversity of biocide/metal resistance genes and MGEs but a relatively high abundance of ARGs. In contrast, external environments showed consistently high taxonomic diversity which in turn was linked to high diversity of both biocide/metal resistance genes and MGEs. Water, sediment and soil generally carried low relative abundance and few varieties of known ARGs, whereas wastewater/sludge was on par with the human gut. The environments with the largest relative abundance and/or diversity of ARGs, including genes encoding resistance to last resort antibiotics, were those subjected to industrial antibiotic pollution and a limited set of deeply sequenced air samples from a Beijing smog event.Conclusions:Our study identifies air and antibiotic-polluted environments as under-investigated transmission routes and reservoirs for antibiotic resistance. The high taxonomic and genetic diversity of external environments supports the hypothesis that these also form vast sources of unknown resistance genes, with potential to be transferred to pathogens in the future

Environmental factors influencing the development and spread of antibiotic resistance

Antibiotic resistance and its wider implications present us with a growing healthcare crisis. Recent research points to the environment as an important component for the transmission of resistant bacteria and in the emergence of resistant pathogens. However, a deeper understanding of the evolutionary and ecological processes that lead to clinical appearance of resistance genes is still lacking, as is knowledge of environmental dispersal barriers. This calls for better models of how resistance genes evolve, are mobilized, transferred and disseminated in the environment. Here, we attempt to define the ecological and evolutionary environmental factors that contribute to resistance development and transmission. Although mobilization of resistance genes likely occurs continuously, the great majority of such genetic events do not lead to the establishment of novel resistance factors in bacterial populations, unless there is a selection pressure for maintaining them or their fitness costs are negligible. To enable preventative measures it is therefore critical to investigate under what conditions and to what extent environmental selection for resistance takes place. In addition, understanding dispersal barriers is not only key to evaluate risks, but also to prevent resistant pathogens, as well as novel resistance genes, from reaching humans

The complete mitochondrial genome of the copepod Calanus glacialis

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Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes

BACKGROUND: Bacterial communities in humans, animals, and the external environment maintain a large collection of antibiotic resistance genes (ARGs). However, few of these ARGs are well-characterized and thus established in existing resistance gene databases. In contrast, the remaining latent ARGs are typically unknown and overlooked in most sequencing-based studies. Our view of the resistome and its diversity is therefore incomplete, which hampers our ability to assess risk for promotion and spread of yet undiscovered resistance determinants. RESULTS: A reference database consisting of both established and latent ARGs (ARGs not present in current resistance gene repositories) was created. By analyzing more than 10,000 metagenomic samples, we showed that latent ARGs were more abundant and diverse than established ARGs in all studied environments, including the human- and animal-associated microbiomes. The pan-resistomes, i.e., all ARGs present in an environment, were heavily dominated by latent ARGs. In comparison, the core-resistome, i.e., ARGs that were commonly encountered, comprised both latent and established ARGs. We identified several latent ARGs shared between environments and/or present in human pathogens. Context analysis of these genes showed that they were located on mobile genetic elements, including conjugative elements. We, furthermore, identified that wastewater microbiomes had a surprisingly large pan- and core-resistome, which makes it a potentially high-risk environment for the mobilization and promotion of latent ARGs. CONCLUSIONS: Our results show that latent ARGs are ubiquitously present in all environments and constitute a diverse reservoir from which new resistance determinants can be recruited to pathogens. Several latent ARGs already had high mobile potential and were present in human pathogens, suggesting that they may constitute emerging threats to human health. We conclude that the full resistome-including both latent and established ARGs-needs to be considered to properly assess the risks associated with antibiotic selection pressures. Video Abstract

Approximate reasoning with fuzzy-syllogistic systems

The well known Aristotelian syllogistic system consists of 256 moods. We have found earlier that 136 moods are distinct in terms of equal truth ratios that range in τ=[0,1]. The truth ratio of a particular mood is calculated by relating the number of true and false syllogistic cases the mood matches. A mood with truth ratio is a fuzzy-syllogistic mood. The introduction of (n-1) fuzzy existential quantifiers extends the system to fuzzy-syllogistic systems nS, 1<n, of which every fuzzy-syllogistic mood can be interpreted as a vague inference with a generic truth ratio that is determined by its syllogistic structure. We experimentally introduce the logic of a fuzzy-syllogistic ontology reasoner that is based on the fuzzy-syllogistic systems nS. We further introduce a new concept, the relative truth ratio rτ=[0,1] that is calculated based on the cardinalities of the syllogistic cases

Development of early life gut resistome and mobilome across gestational ages and microbiota-modifying treatments

Background: Gestational age (GA) and associated level of gastrointestinal tract maturation are major factors driving the initial gut microbiota composition in preterm infants. Besides, compared to term infants, premature infants often receive antibiotics to treat infections and probiotics to restore optimal gut microbiota. How GA, antibiotics, and probiotics modulate the microbiota\u27s core characteristics, gut resistome and mobilome, remains nascent. Methods: We analysed metagenomic data from a longitudinal observational study in six Norwegian neonatal intensive care units to describe the bacterial microbiota of infants of varying GA and receiving different treatments. The cohort consisted of probiotic-supplemented and antibiotic-exposed extremely preterm infants (n = 29), antibiotic-exposed very preterm (n = 25), antibiotic-unexposed very preterm (n = 8), and antibiotic-unexposed full-term (n = 10) infants. The stool samples were collected on days of life 7, 28, 120, and 365, and DNA extraction was followed by shotgun metagenome sequencing and bioinformatical analysis. Findings: The top predictors of microbiota maturation were hospitalisation length and GA. Probiotic administration rendered the gut microbiota and resistome of extremely preterm infants more alike to term infants on day 7 and ameliorated GA-driven loss of microbiota interconnectivity and stability. GA, hospitalisation, and both microbiota-modifying treatments (antibiotics and probiotics) contributed to an elevated carriage of mobile genetic elements in preterm infants compared to term controls. Finally, Escherichia coli was associated with the highest number of antibiotic-resistance genes, followed by Klebsiella pneumoniae and Klebsiella aerogenes. Interpretation: Prolonged hospitalisation, antibiotics, and probiotic intervention contribute to dynamic alterations in resistome and mobilome, gut microbiota characteristics relevant to infection risk. Funding: Odd-Berg Group, Northern Norway Regional Health Authority

Microbial and human transcriptome in vaginal fluid at midgestation: Association with spontaneous preterm delivery

Background Intrauterine infection and inflammation caused by microbial transfer from the vagina are believed to be important factors causing spontaneous preterm delivery (PTD). Multiple studies have examined the relationship between the cervicovaginal microbiome and spontaneous PTD with divergent results. Most studies have applied a DNA-based assessment, providing information on the microbial composition but not transcriptional activity. A transcriptomic approach was applied to investigate differences in the active vaginal microbiome and human transcriptome at midgestation between women delivering spontaneously preterm versus those delivering at term. Methods Vaginal swabs were collected in women with a singleton pregnancy at 18 + 0 to 20 + 6 gestational weeks. For each case of spontaneous PTD (delivery &lt;37 + 0 weeks) two term controls were randomized (39 + 0 to 40 + 6 weeks). Vaginal specimens were subject to sequencing of both human and microbial RNA. Microbial reads were taxonomically classified using Kraken2 and RefSeq as a reference. Statistical analyses were performed using DESeq2. GSEA and HUMAnN3 were used for pathway analyses. Results We found 17 human genes to be differentially expressed (false discovery rate, FDR &lt; 0.05) in the preterm group (n = 48) compared to the term group (n = 96). Gene expression of kallikrein-2 (KLK2), KLK3 and four isoforms of metallothioneins 1 (MT1s) was higher in the preterm group (FDR &lt; 0.05). We found 11 individual bacterial species to be differentially expressed (FDR &lt; 0.05), most with a low occurrence. No statistically significant differences in bacterial load, diversity or microbial community state types were found between the groups. Conclusions In our mainly white population, primarily bacterial species of low occurrence were differentially expressed at midgestation in women who delivered preterm versus at term. However, the expression of specific human transcripts including KLK2, KLK3 and several isoforms of MT1s was higher in preterm cases. This is of interest, because these genes may be involved in critical inflammatory pathways associated with spontaneous PTD

Introducing ribosomal tandem repeat barcoding for fungi

Sequence comparison and analysis of the various ribosomal genetic markers are the dominant molecular methods for identification and description of fungi. However, new environmental fungal lineages known only from DNA data reveal significant gaps in our sampling of the fungal kingdom in terms of both taxonomy and marker coverage in the reference sequence databases. To facilitate the integration of reference data from all of the ribosomal markers, we present three sets of general primers that allow for amplification of the complete ribosomal operon from the ribosomal tandem repeats. The primers cover all ribosomal markers: ETS, SSU, ITS1, 5.8S, ITS2, LSU and IGS. We coupled these primers successfully with third-generation sequencing (PacBio and Nanopore sequencing) to showcase our approach on authentic fungal herbarium specimens (Basidiomycota), aquatic chytrids (Chytridiomycota) and a poorly understood lineage of early diverging fungi (Nephridiophagidae). In particular, we were able to generate high-quality reference data with Nanopore sequencing in a high-throughput manner, showing that the generation of reference data can be achieved on a regular desktop computer without the involvement of any large-scale sequencing facility. The quality of the Nanopore generated sequences was 99.85%, which is comparable with the 99.78% accuracy described for Sanger sequencing. With this work, we hope to stimulate the generation of a new comprehensive standard of ribosomal reference data with the ultimate aim to close the huge gaps in our reference datasets

Minimal selective concentrations of tetracycline in complex aquatic bacterial biofilms

Selection pressure generated by antibiotics released into the environment could enrich for antibiotic resistance genes and antibiotic resistant bacteria, thereby increasing the risk for transmission to humans and animals. Tetracyclines comprise an antibiotic class of great importance to both human and animal health. Accordingly, residues of tetracycline are commonly detected in aquatic environments. To assess if tetracycline pollution in aquatic environments promotes development of resistance, we determined minimal selective concentrations (MSCs) in biofilms of complex aquatic bacterial communities using both phenotypic and genotypic assays. Tetracycline significantly increased the relative abundance of resistant bacteria at 10 μg/L, while specific tet genes (tetA and tetG) increased significantly at the lowest concentration tested (1 μg/L). Taxonomic composition of the biofilm communities was altered with increasing tetracycline concentrations. Metagenomic analysis revealed a concurrent increase of several tet genes and a range of other genes providing resistance to different classes of antibiotics (e.g. cmlA, floR, sul1, and mphA), indicating potential for co-selection. Consequently, MSCs for the tet genes of ≤ 1 μg/L suggests that current exposure levels in e.g. sewage treatment plants could be sufficient to promote resistance. The methodology used here to assess MSCs could be applied in risk assessment of other antibiotics as well