Biology subjects

Brejnrod, A. D.

Publications and source records attributed to Brejnrod, A. D..

8 recordsLinked to original sources

bioRxiv2021

A supervised fingerprint-based strategy to connect natural product mass spectrometry fragmentation data to their biosynthetic gene clusters

Microbial specialized metabolites are an important source of and inspiration for many pharmaceutical, biotechnological products and play key roles in ecological processes. However, most bioactivity-guided isolation and identification methods widely employed in metabolite discovery programs do not explore the full biosynthetic potential of an organism. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical compound class). Our pipeline offers an efficient method to link biosynthetic genes to known, analogous, or cryptic metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired data sets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra to their corresponding previously experimentally validated biosynthetic genes (i.e., via NMR or genetic engineering). Finally, we demonstrated that this new approach is a substantial step towards making in silico (and even de novo) structure predictions for peptidic metabolites and a glycosylated terpene. Altogether, we conclude that NPOmix minimizes the need for culturing and facilitates specialized metabolite isolation and structure elucidation based on integrative omics mining. SignificanceThe pace of natural product discovery has remained relatively constant over the last two decades. At the same time, there is an urgent need to find new therapeutics to fight antibiotic-resistant bacteria, cancer, tropical parasites, pathogenic viruses, and other severe diseases. Here, we introduce a new machine learning algorithm that can efficiently connect metabolites to their biosynthetic genes. Our Natural Products Mixed Omics (NPOmix) tool provides access to genomic information for bioactivity, class, (partial) structure, and stereochemistry predictions to prioritize relevant metabolite products and facilitate their structural elucidation. Our approach can be applied to biosynthetic genes from bacteria (used in this study), fungi, algae, and plants where (meta)genomes are paired with corresponding mass fragmentation data.

bioinformatics↗

bioRxiv2021

Multi-omics profiling of Earth's biomes reveals that microbial and metabolite composition are shaped by the environment

As our understanding of the structure and diversity of the microbial world grows, interpreting its function is of critical interest for understanding and managing the many systems microbes influence. Despite advances in sequencing, lack of standardization challenges comparisons among studies that could provide insight into the structure and function of microbial communities across multiple habitats on a planetary scale. Technical variation among distinct studies without proper standardization of approaches prevents robust meta-analysis. Here, we present a multi-omics, meta-analysis of a novel, diverse set of microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry), centering our description on relationships and co-occurrences of microbially-related metabolites and microbial taxa across environments. Standardized protocols and analytical methods for characterizing microbial communities, including assessment of molecular diversity using untargeted metabolomics, facilitate identification of shared microbial and metabolite features, permitting us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of a community resource that will become more valuable with time. To provide examples of applying this database, we outline important ecological questions that can be addressed, and test the hypotheses that every microbe and metabolite is everywhere, but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment. The relative abundances of microbially-related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner, and highlight the power of certain chemistry - in particular terpenoids - in distinguishing Earths environments.

ecology↗

bioRxiv2021

Metagenomic evidence for co-occurrence of antibiotic, biocide and metal resistancegenes in pigs

Antibiotic-resistant pathogens constitute an escalating public health concern. Hence a better understanding of the underlying processes responsible for this expansion is urgently needed. Co-selection of heavy metals/biocides and antibiotic resistance genes (ARGs) has been suggested as one potential mechanism promoting the proliferation of antimicrobial resistance (AMR). This paper aims to elucidate this interplay and exploit differences in antibiotic usage to infer patterns of co-selection by the non-antibiotic factors metals and biocides in the context of pig farming. We examined 278 gut metagenomes from pigs with continuous antibiotic exposure, only at weaning and at no exposure. Metals as growth promoters and biocides as disinfectants are currently used with little restrictions in stock farming. The pigs under continuous antibiotic exposure displayed the highest co-occurrence of ARGs and other genetic elements while the pigs under limited use of antibiotics still showed abundant co-occurrences. Pathogens belonging to Enterobacteriaceae displayed increased co-occurrence phenomena, suggesting that this maintenance is not a random selection process from a mobilized pool but pertains to specific phylogenetic clades. These results suggest that metals and biocides displayed strong selective pressures on ARGs exerted by intensive farming, regardless of the current use of antibiotics. HighlightsO_LIA comprehensive gut microbiome metagenomics analysis of 278 pigs C_LIO_LICo-selection phenomena were investigated via co-occurrence patterns as a proxy C_LIO_LITwenty-seven types of co-occurrences involving 131 resistance genes were detected C_LIO_LIRegardless of use of antibiotics, AMR can be maintained by co-occurrence with MRGs/BRGs C_LIO_LIMaintenance of AMR is not a random selection process but pertains to specific phylogenetic clades C_LI

bioinformatics↗

bioRxiv2020

Stability of the vaginal microbiota during pregnancy and its importance for early infant colonization

Early life microbiota has been linked to the development of chronic inflammatory diseases. It has been hypothesized that maternal vaginal microbiota is an important initial seeding source and therefore can have lifelong effects on disease risk. To understand maternal vaginal microbiotas role in seeding the childs microbiota and the extent of delivery mode-dependent transmission, we studied 700 mother-child dyads from the COPSAC2010 cohort. The maternal vaginal microbiota was evaluated in the third trimester and compared with the childrens fecal and airway microbiota. The vaginal samples displayed known stable community state types and only 1:6 changed over time. Only one OTU was significantly transferred to childrens fecal compartment, but an inflated number had positive transfer odds. A few taxonomic families showed early transfer enrichment to vaginally-born children, indicating vertical transfer, while half of the observed transfer effects were delivery mode independent enrichment with attenuating strength over time, indicating a common reservoir.

bioinformatics↗

bioRxiv2020

The microbiome of captive hamadryas baboon

Hamadryas baboon is a highly social primate that lives in complex multilevel societies exhibiting a wide range of group behaviors akin to humans. Here, we report the comprehensive 16s rRNA gene analyses of group-living baboon microbiota across different body sites. Additionally, we compared the baboon and human microbiome of the oral cavity, gut and vagina. Our analyses show that the baboon microbiome is distinct from the human and baboon cohabitants share similar microbial profiles in multiple body sites. The oral, gut and vagina shared more bacterial ASVs in group-living baboons than in humans. The shared ASVs in baboons had significantly positive correlations, suggesting a potential bacterial exchange throughout the body. No significant differences in baboon gut microbiome composition within the maternity line and between maternity lines were detected, suggesting that the offspring acquire their gut microbiota primarily through bacterial exchange among cohabitants. Besides, Lactobacillus was not so predominant in baboon vagina as in the human vagina but was the most abundant genus in baboon gut. These data and findings can form the basis of future microbiome studies in baboons and be used as a reference to research where the microbiome is expected to impact human modeling with baboons.

microbiology↗

bioRxiv2019

Implementations of the chemical structural and compositional similarity metric in R and Python

MotivationTandem mass spectrometry (MS/MS) has the potential to substantially improve metabolomics by acquiring spectra of fragmented ions. These fragmentation spectra can be represented as a molecular network, by measuring cosine distances between them, thus identifying signals from the same or similar molecules. Metrics that enable comparison between pairs of samples based on their metabolite profiles are in great need. Taking inspiration from the successful phylogeny-aware beta-diversity measures used in microbiome research, integrating chemical similarity information about the features in addition to their abundances could lead to better insights when comparing metabolite profiles. Chemical Structural and Compositional Similarity (CSCS) is a recently published similarity metric comparing the full set of signals and their chemical similarity between two samples. Efficient, scalable and easily accessible implementations of this algorithm is currently lacking. Here, we present an easily accessible and scalable implementation of CSCS in both python and R, including a version not weighted by intensity information. ResultsWe provide a new implementation of the CSCS algorithm that is over 300 times faster than the published implementation in R, making the algorithm suitable for large-scale metabolomics applications. We also show that adding chemical information enriches existing methods. Furthermore, the R implementation includes functions for exporting molecular networks directly from the mass spectral molecular networking platform GNPS for ease of use for downstream applications. Contactbrejnrod@sund.ku.dk Availabilitygithub.com/askerdb/rCSCS, github.com/askerdb/pyCSCS

bioinformatics↗

bioRxiv2018

In vitro community synergy between bacterial soil isolates can be facilitated by pH stabilisation of the environment

Composition and development of naturally occurring microbial communities is defined by a complex interplay between the community and the surrounding environment and by interactions between community members. Intriguingly, these interactions can in some cases cause community synergies where the community is able to outperform it single species constituents. However, the underlying mechanisms driving community interactions are often unknown and difficult to identify due to high community complexity. Here we show how pH stabilisation of the environment through the metabolic activity of specific community members acts as a positive inter-species interaction driving in vitro community synergy in a model consortium of four co-isolated soil bacteria: Microbacterium oxydans, Xanthomonas retroflexus, Stenotrophomonas rhizophila and Paenibacillus amylolyticus. Using micro-sensor pH measurements to show how individual species change the local pH micro-environment, and how co-cultivation leads to a stabilised pH regime over time. Specifically, in vitro acid production from Paenibacillus amylolyticus and alkali production primarily from Xanthomonas retroflexus lead to an overall pH stabilisation of the local environment over time, which in turn resulted in enhanced community growth. This specific type of interspecies interaction was found to be highly dependent on media type and media concentration, however similar pH drift from the individual species could be observed across media variants.\n\nImportanceWe show that in vitro metabolic activity of individual members of a synthetic, co- isolated model community presenting community synergistic growth arises through the inter-species interaction of pH stabilization of the community micro-environment. The observed inter-species interaction is highly media specific and most pronounced under high nutrient availability. This adds to the growing diversity of identified community interactions leading to enhanced community growth.

microbiology↗

bioRxiv2018

DAtest: a framework for choosing differential abundance or expression method

DAtest is an R package for directly comparing different statistical methods for differential abundance and expression analysis on a dataset of interest; be it data from RNA-seq, proteomics, metabolomics or a microbial marker-gene survey. A myriad of statistical methods exists for conducting these analyses, and with this tool we give the analyst an empirical foundation for choosing a method suitable for a specific dataset. The package supports categorical and quantitative variables, paired/block experimental designs, and the inclusion of covariates. It is freely available at GitHub: https://github.com/Russel88/DAtest along with detailed instructions.

bioinformatics↗