The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes.

BACKGROUND: Metagenomic data can shed light on animal-microbiome relationships and the functional potential of these communities. Over the past years, the generation of metagenomics data has increased exponentially, and so has the availability and reusability of data present in public repositories. However, identifying which datasets and associated metadata are available is not straightforward. We created the Animal-Associated Metagenome Metadata Database (AnimalAssociatedMetagenomeDB - AAMDB) to facilitate the identification and reuse of publicly available non-human, animal-associated metagenomic data, and metadata. Further, we used the AAMDB to (i) annotate common and scientific names of the species; (ii) determine the fraction of vertebrates and invertebrates; (iii) study their biogeography; and (iv) specify whether the animals were wild, pets, livestock or used for medical research. RESULTS: We manually selected metagenomes associated with non-human animals from SRA and MG-RAST.  Next, we standardized and curated 51 metadata attributes (e.g., host, compartment, geographic coordinates, and country). The AAMDB version 1.0 contains 10,885 metagenomes associated with 165 different species from 65 different countries. From the collected metagenomes, 51.1% were recovered from animals associated with medical research or grown for human consumption (i.e., mice, rats, cattle, pigs, and poultry). Further, we observed an over-representation of animals collected in temperate regions (89.2%) and a lower representation of samples from the polar zones, with only 11 samples in total. The most common genus among invertebrate animals was Trichocerca (rotifers). CONCLUSION: Our work may guide host species selection in novel animal-associated metagenome research, especially in biodiversity and conservation studies. The data available in our database will allow scientists to perform meta-analyses and test new hypotheses (e.g., host-specificity, strain heterogeneity, and biogeography of animal-associated metagenomes), leveraging existing data. The AAMDB WebApp is a user-friendly interface that is publicly available at https://webapp.ufz.de/aamdb/ .

Corrigendum: The fate of sulfonamide resistance genes and anthropogenic pollution marker intI1 after discharge of wastewater into a pristine river stream.

[This corrects the article DOI: 10.3389/fmicb.2023.1058350.].

The fate of sulfonamide resistance genes and anthropogenic pollution marker intI1 after discharge of wastewater into a pristine river stream.

Introduction: Currently there are sparse regulations regarding the discharge of antibiotics from wastewater treatment plants (WWTP) into river systems, making surface waters a latent reservoir for antibiotics and antibiotic resistance genes (ARGs). To better understand factors that influence the fate of ARGs in the environment and to foster surveillance of antibiotic resistance spreading in such habitats, several indicator genes have been proposed, including the integrase gene intI1 and the sulfonamide resistance genes sul1 and sul2. Methods: Here we used quantitative PCR and long-read nanopore sequencing to monitor the abundance of these indicator genes and ARGs present as class 1 integron gene cassettes in a river system from pristine source to WWTP-impacted water. ARG abundance was compared with the dynamics of the microbial communities determined via 16S rRNA gene amplicon sequencing, conventional water parameters and the concentration of sulfamethoxazole (SMX), sulfamethazine (SMZ) and sulfadiazine (SDZ). Results: Our results show that WWTP effluent was the principal source of all three sulfonamides with highest concentrations for SMX (median 8.6 ng/l), and of the indicator genes sul1, sul2 and intI1 with median relative abundance to 16S rRNA gene of 0.55, 0.77 and 0.65%, respectively. Downstream from the WWTP, water quality improved constantly, including lower sulfonamide concentrations, decreasing abundances of sul1 and sul2 and lower numbers and diversity of ARGs in the class 1 integron. The riverine microbial community partially recovered after receiving WWTP effluent, which was consolidated by a microbiome recovery model. Surprisingly, the relative abundance of intI1 increased 3-fold over 13 km of the river stretch, suggesting an internal gene multiplication. Discussion: We found no evidence that low amounts of sulfonamides in the aquatic environment stimulate the maintenance or even spread of corresponding ARGs. Nevertheless, class 1 integrons carrying various ARGs were still present 13 km downstream from the WWTP. Therefore, limiting the release of ARG-harboring microorganisms may be more crucial for restricting the environmental spread of antimicrobial resistance than attenuating ng/L concentrations of antibiotics.

Machine learning-assisted identification of bioindicators predicts medium-chain carboxylate production performance of an anaerobic mixed culture.

BACKGROUND: The ability to quantitatively predict ecophysiological functions of microbial communities provides an important step to engineer microbiota for desired functions related to specific biochemical conversions. Here, we present the quantitative prediction of medium-chain carboxylate production in two continuous anaerobic bioreactors from 16S rRNA gene dynamics in enriched communities. RESULTS: By progressively shortening the hydraulic retention time (HRT) from 8 to 2 days with different temporal schemes in two bioreactors operated for 211 days, we achieved higher productivities and yields of the target products n-caproate and n-caprylate. The datasets generated from each bioreactor were applied independently for training and testing machine learning algorithms using 16S rRNA genes to predict n-caproate and n-caprylate productivities. Our dataset consisted of 14 and 40 samples from HRT of 8 and 2 days, respectively. Because of the size and balance of our dataset, we compared linear regression, support vector machine and random forest regression algorithms using the original and balanced datasets generated using synthetic minority oversampling. Further, we performed cross-validation to estimate model stability. The random forest regression was the best algorithm producing more consistent results with median of error rates below 8%. More than 90% accuracy in the prediction of n-caproate and n-caprylate productivities was achieved. Four inferred bioindicators belonging to the genera Olsenella, Lactobacillus, Syntrophococcus and Clostridium IV suggest their relevance to the higher carboxylate productivity at shorter HRT. The recovery of metagenome-assembled genomes of these bioindicators confirmed their genetic potential to perform key steps of medium-chain carboxylate production. CONCLUSIONS: Shortening the hydraulic retention time of the continuous bioreactor systems allows to shape the communities with desired chain elongation functions. Using machine learning, we demonstrated that 16S rRNA amplicon sequencing data can be used to predict bioreactor process performance quantitatively and accurately. Characterizing and harnessing bioindicators holds promise to manage reactor microbiota towards selection of the target processes. Our mathematical framework is transferrable to other ecosystem processes and microbial systems where community dynamics is linked to key functions. The general methodology used here can be adapted to data types of other functional categories such as genes, transcripts, proteins or metabolites. Video Abstract.

HumanMetagenomeDB: a public repository of curated and standardized metadata for human metagenomes.

Metagenomics became a standard strategy to comprehend the functional potential of microbial communities, including the human microbiome. Currently, the number of metagenomes in public repositories is increasing exponentially. The Sequence Read Archive (SRA) and the MG-RAST are the two main repositories for metagenomic data. These databases allow scientists to reanalyze samples and explore new hypotheses. However, mining samples from them can be a limiting factor, since the metadata available in these repositories is often misannotated, misleading, and decentralized, creating an overly complex environment for sample reanalysis. The main goal of the HumanMetagenomeDB is to simplify the identification and use of public human metagenomes of interest. HumanMetagenomeDB version 1.0 contains metadata of 69 822 metagenomes. We standardized 203 attributes, based on standardized ontologies, describing host characteristics (e.g. sex, age and body mass index), diagnosis information (e.g. cancer, Crohn's disease and Parkinson), location (e.g. country, longitude and latitude), sampling site (e.g. gut, lung and skin) and sequencing attributes (e.g. sequencing platform, average length and sequence quality). Further, HumanMetagenomeDB version 1.0 metagenomes encompass 58 countries, 9 main sample sites (i.e. body parts), 58 diagnoses and multiple ages, ranging from just born to 91 years old. The HumanMetagenomeDB is publicly available at https://webapp.ufz.de/hmgdb/.