Biology subjects

Dorrestein, P. C.

Publications and source records attributed to Dorrestein, P. C..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Co-Occurrence Network Analysis Reveals The Alterations Of The Skin Microbiome And Metabolome In Atopic Dermatitis Patients

Skin microbiome can be altered in patients with Atopic Dermatitis (AD). An understanding of the changes from healthy to atopic skin can help develop new targets for better treatments and identify specific microbial or molecular biomarkers. This study investigates the skin microbiome and metabolome of healthy subjects and lesion (ADL) and non-lesion (ADNL) of AD patients by 16S rRNA gene sequencing and mass spectrometry, respectively. Samples from AD patients showed alterations in the diversity and composition of the skin microbiome. Staphylococcus species, especially S. aureus, were significantly increased in the ADL group. Metabolomic profiles were also different between the groups. Dipeptide-derived are more abundant in ADL, which may be related to skin inflammation. Co-occurrence network analysis was applied to integrate the microbiome and metabolomics data and revealed higher co-occurrence of metabolites and bacteria in healthy and ADNL compared to ADL. S. aureus co-occurred with dipeptide-derived in ADL, while phytosphingosine-derived compounds showed co-occurrences with commensal bacteria, e.g. Paracoccus sp., Pseudomonas sp., Prevotella bivia, Lactobacillus iners, Anaerococcus sp., Micrococcus sp., Corynebacterium ureicelerivorans, Corynebacterium massiliense, Streptococcus thermophilus, and Roseomonas mucosa, in healthy and ADNL groups. Therefore, these findings provide valuable insights into how AD affects the human skin metabolome and microbiome. ImportanceThis study provides valuable insight into changes in the skin microbiome and associated metabolomic profiles. It also identifies new therapeutic targets that may be useful for developing personalized treatments for individuals with atopic dermatitis based on their unique skin microbiome and metabolic profiles.

microbiology↗

bioRxiv2022

Trachymyrmex septentrionalis ants promote fungus garden hygiene using Trichoderma-derived metabolite cues

Fungus-growing ants depend on a fungal mutualist that can fall prey to fungal pathogens. This mutualist is cultivated by these ants in structures called fungus gardens. Ants exhibit weeding behaviors that keep their fungus gardens healthy by physically removing compromised pieces. However, how ants detect diseases of their fungus gardens is unknown. Here, we applied the logic of Kochs postulates using environmental fungal community gene sequencing, fungal isolation, and laboratory infection experiments to establish Trichoderma spp. as previously unrecognized pathogens of Trachymyrmex septentrionalis fungus gardens. Our environmental data showed that Trichoderma are the most abundant non-cultivar fungi in wild T. septentrionalis fungus gardens. We further determined that metabolites produced by Trichoderma induce an ant weeding response that mirrors their response to live Trichoderma. Combining ant behavioral experiments with bioactivity-guided fractionation and statistical prioritization of metabolites in Trichoderma extracts demonstrated that T. septentrionalis ants weed in response to peptaibols, a specific class of secondary metabolites known to be produced by Trichoderma fungi. Similar assays conducted using purified peptaibols, including the two new peptaibols trichokindins VIII and IX, suggested that weeding is likely induced by peptaibols as a class rather than by a single peptaibol metabolite. In addition to their presence in laboratory experiments, we detected peptaibols in wild fungus gardens. Our combination of environmental data and laboratory infection experiments strongly support that peptaibols act as chemical cues of Trichoderma pathogenesis in T. septentrionalis fungus gardens. Significance StatementAn extended defense response may exist in any relationship where one partner benefits from defending a mutualistic partner. Such a response is observed in the fungus-growing ant symbiosis, where ants must identify and remove pathogens of their symbiotic fungus gardens. Here we describe the fungal pathogen Trichoderma and its associated metabolites, which induce Trachymyrmex septentrionalis ant weeding behavior. Ants removed fungus garden pieces inoculated with Trichoderma spores or peptaibol-rich Trichoderma extracts, and peptaibols as a class cued ant defensive behavior, allowing T. septentrionalis to differentiate healthy from diseased fungus gardens. Extended defense responses mediated by chemical cues may be underappreciated mechanisms that structure symbiotic interactions.

microbiology↗

bioRxiv2022

Improving annotation propagation on molecular networks through random walks: Introducing ChemWalker

Annotation of the mass signals is still the biggest bottleneck for the untargeted mass spectrometry analysis of complex mixtures. Molecular networks are being increasingly adopted by the mass spectrometry community as a tool to annotate large scale experiments. We have previously shown that the process of propagating annotations from spectral library matches on molecular networks can be automated using Network Annotation Propagation (NAP). One of the limitations of NAP is that the information for the spectral matches is only propagated locally, to the first neighbor of a spectral match. Here we show that annotation propagation can be expanded to nodes not directly connected to spectral matches using random walks on graphs, introducing the ChemWalker python library. Similarly to NAP, ChemWalker relies on combinatorial in silico fragmentation results, performed by MetFrag, searching biologically relevant databases. Departing from the combination of a spectral network and the structural similarity among candidate structures, we have used MetFusion Scoring function to create a weight function, producing a weighted graph. This graph was subsequently used by the random walk to calculate the probability of walking through a set of candidates, departing from seed nodes (represented by spectral library matches). This approach allowed the information propagation to nodes not directly connected to the spectral library match. Compared to NAP, ChemWalker has a series of improvements, on running time, scalability and maintainability and is available as a stand alone python package. ChemWalker is freely available at https://github.com/computational-chemical-biology/ChemWalker.

bioinformatics↗

bioRxiv2022

Time of Sample Collection Critical for Microbiome Replicability

Although many aspects of microbiome studies have been standardized to improve experimental replicability, none account for how the daily diurnal fluctuations in the gut lumen cause dynamic changes in 16S amplicon sequencing. Here we show that sample collection time affects the conclusions drawn from microbiome studies and are larger than the effect size of a daily experimental intervention or dietary changes. The timing of divergence of the microbiome composition between experimental and control groups are unique to each experiment. Sample collection times as short as only four hours apart lead to vastly different conclusions. Lack of consistency in the time of sample collection may explain poor cross-study replicability in microbiome research. Without looking at other data, the impact on other fields is unknown but potentially significant. One-Sentence SummaryIf we are not controlling for host circadian rhythm time in microbiome studies when performing experiments, it is like trying to measure sea level rise while not knowing that tides or waves exist.

systems biology↗

bioRxiv2022

MS2Prop: A machine learning model that directly predicts chemical properties from mass spectrometry data for novel compounds

Mass spectrometry (MS) is a fundamental analytical tool for the study of complex molecular mixtures and in natural products drug discovery and metabolomics specifically, due to its high sensitivity, specificity, and throughput. A major challenge, however, is the lack of structurally annotated mass spectra for these applications. This deficiency is particularly acute for analyses conducted on extracts or fractions that are largely chemically undefined. This work describes the use of mass spectral data in a fundamentally different manner than structure determination; to predict properties or activities of structurally unknown compounds without the need for defined or deduced chemical structure using a machine learning (ML) model, MS2Prop. The models predictive accuracy and scalability is benchmarked against commonly used methods and its performance demonstrated in a natural products drug discovery setting. A new cheminformatic subdiscipline, quantitative spectra-activity relationships (QSpAR), using spectra rather than chemical structure as input, is proposed to describe this approach and to distinguish it from structure based quantitative methods.

bioinformatics↗

bioRxiv2022

Unified and standardized mass spectrometry data processing in Python using spectrum_utils

spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications, to spectrum plotting from online data repositories, and assisting data analysis tasks for dozens of other projects. Here we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.

bioinformatics↗

bioRxiv2022

Comparison of Cosine, Modified Cosine, and Neutral Loss Based Spectral Alignment For Discovery of Structurally Related Molecules

Spectrum alignment of tandem mass spectrometry (MS/MS) data using the modified cosine similarity and subsequent visualization as molecular networks have been demonstrated to be a useful strategy to discover analogs of molecules from untargeted MS/MS-based metabolomics experiments. Recently, a neutral loss matching approach has been introduced as an alternative to MS/MS-based molecular networking, with an implied performance advantage in finding analogs that cannot be discovered using existing MS/MS spectrum alignment strategies. To comprehensively evaluate the scoring properties of neutral loss matching, the cosine similarity, and the modified cosine similarity, similarity measures of 955,228 peptide MS/MS spectrum pairs and 10 million small molecule MS/MS spectrum pairs were compared. This comparative analysis revealed that the modified cosine similarity outperformed neutral loss matching and the cosine similarity in all cases. The data further indicated that the performance of MS/MS spectrum alignment depends on the location and type of the modification, as well as the chemical compound class of fragmented molecules.

bioinformatics↗

bioRxiv2022

Open Access Repository-Scale Propagated Nearest Neighbor Suspect Spectral Library for Untargeted Metabolomics

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of public MS/MS spectra. Annotations were propagated based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimers brain phenotype. The nearest neighbor suspect spectral library is openly available through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

bioinformatics↗

bioRxiv2022

An untargeted metabolomics analysis of exogenous chemicals in human milk and transfer to the infant

Human milk is the optimal infant nutrition. However, while human-derived metabolites such as lipids and oligosaccharides in human milk are regularly reported, the presence of exogenous chemicals (such as drugs, food, and synthetic compounds) are often not addressed. To understand the types of exogenous compounds that might be present, human milk (n=996) was analyzed by untargeted metabolomics. This analysis revealed that lifestyle molecules such as medications and their metabolites, food, industrial sources such as plasticizers, cosmetics, microbial molecules, and other personal care products are found in human milk. We provide further evidence that some of these lifestyle molecules are also detectable in the newborns stool. Thus, this study gives important insight into the types of exposures infants receiving human milk might ingest due to the lifestyle choices, exposure, or medical status of the lactating parent.

pharmacology and toxicology↗

bioRxiv2022

Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets

Covalent or non-covalent heterogeneous multimerization of molecules associated with extracts from biological samples analyzed via LC-MS is quite difficult to recognize/annotate and therefore the prevalence of multimerization remains largely unknown. In this study, we utilized 13C labeled and unlabeled Pichia pastoris extracts to recognize heterogeneous multimers. More specifically, between 0.8% and 1.5% of the biologically-derived features detected in our experiments were confirmed to be heteromers, about half of which we could successfully annotate with monomeric partners. Interestingly, we found specific chemical classes such as nucleotides to disproportionately contribute to heteroadducts. Furthermore, we compiled these compounds into the first MS/MS library that included data from heteromultimers to provide a starting point for other labs to improve the annotation of such ions in other metabolomics data sets. Then, the detected heteromers were also searched in publicly accessible LC-MS datasets available in Metabolights, Metabolomics WB, and GNPS/MassIVE to demonstrate that these newly annotated ions are also relevant to other public datasets. Furthermore, in additional datasets (Triticum aestivum, Fusarium graminearum, and Trichoderma reesei) our developed workflow also detected 0.5% to 4.9% of metabolite features to originate from heterodimers, demonstrating heteroadducts to be present in metabolomics studies at a low percentage.

systems biology↗

bioRxiv2021

Non-Invasive Skin Sampling Detects Systemically Administered Drugs in Humans

Clinical testing typically relies on invasive blood draws and biopsies. Alternative methods of sample collection are continually being developed to improve patient experience; swabbing the skin is one of the least invasive sampling methods possible. To show that skin swabs in combination with untargeted mass spectrometry (metabolomics) can be used for non-invasive monitoring of an oral drug, we report the kinetics and metabolism of diphenhydramine in healthy volunteers (n=10) over the course of 24 hours in blood and three regions of the skin. Diphenhydramine and its metabolites were observed on the skin after peak plasma levels, varying by compound and skin location, and is an illustrative example of how systemically administered molecules can be detected on the skin surface. The observation of diphenhydramine directly from the skin supports the hypothesis that both parent drug and metabolites can be qualitatively measured from a simple non-invasive swab of the skin surface. The mechanism of the drug and metabolites pathway to the skins surface remains unknown.

pharmacology and toxicology↗

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

Native Metabolomics Identifies the Rivulariapeptolide Family of Protease Inhibitors

The identity and biological activity of most metabolites still remain unknown. A key bottleneck in the full exploration of this tremendous source of new structures and pharmaceutical activities is the compound purification needed for bioactivity assignments of individual compounds and downstream structure elucidation. To enable bioactivity-focused compound identification from complex mixtures, we developed a scalable native metabolomics approach that integrates non-targeted liquid chromatography tandem mass spectrometry, and simultaneous detection of protein binding via native mass spectrometry. While screening for new protease inhibitors from an environmental cyanobacteria community, native metabolomics revealed 30 cyclodepsipeptides as chymotrypsin binders. Mass spectrometry-guided purification then allowed for the full structure elucidation of four new specialized metabolites via tandem mass spectrometry, chemical derivatization, and nuclear magnetic resonance spectroscopy. Together with the evaluation of biological activities, our results identified the rivulariapeptolides as a family of serine protease inhibitors with nanomolar potency, highlighting native metabolomics as promising approach for drug discovery, chemical ecology, and chemical biology studies.

systems biology↗

bioRxiv2021

Physicochemical and Pharmacokinetic Properties Determining Drug Detection in Skin

Chemicals, including some systemically administered xenobiotics and their biotransformations, can be detected noninvasively using skin swabs and untargeted metabolomics analysis. We sought to understand the principal drivers that determine whether a drug taken orally or systemically is likely to be observed on the epidermis by using a random forest classifier to predict which drugs would be detected on the skin. A variety of molecular descriptors describing calculated properties of drugs, such as measures of volume, electronegativity, bond energy, and electrotopology, were used to train the classifier. The mean area under the ROC curve was 0.71 for predicting drug detection on the epidermis, and the SHapley Additive exPlanations model interpretation technique was used to determine the most relevant molecular descriptors. Based on the analysis of 2,561 FDA approved drugs, we predict that therapeutic drug classes such as nervous system drugs are more likely to be detected on the skin. Detecting drugs and other chemicals noninvasively on the skin using untargeted metabolomics could be a useful clinical advancement in therapeutic drug monitoring, adherence, and health status.

bioinformatics↗

bioRxiv2021

GNPS Dashboard: Collaborative Analysis of Mass Spectrometry Data in the Web Browser

Access to web-based platforms has enabled scientists to perform research remotely. A critical aspect of mass spectrometry data analysis is the inspection, analysis, and visualization of the raw data to validate data quality and confirm statistical observations. We developed the GNPS Dashboard, a web-based data visualization tool, to facilitate synchronous collaborative inspection, visualization, and analysis of private and public mass spectrometry data remotely.

bioinformatics↗

bioRxiv2021

Assigning confidence to structural annotations from mass spectra with COSMIC

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but these libraries are vastly incomplete; in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. As biological interpretation relies on accurate structure annotations, the ability to assign confidence to such annotations is a key outstanding problem. We introduce the COSMIC workflow that combines structure database generation, in silico annotation, and a confidence score consisting of kernel density p-value estimation and a Support Vector Machine with enforced directionality of features. In evaluation, COSMIC annotates a substantial number of hits at small false discovery rates, and outperforms spectral library search for this purpose. To demonstrate that COSMIC can annotate structures never reported before, we annotated twelve novel bile acid conjugates; nine structures were confirmed by manual evaluation and two structures using synthetic standards. Second, we annotated and manually evaluated 315 molecular structures in human samples currently absent from the Human Metabolome Database. Third, we applied COSMIC to 17,400 experimental runs and annotated 1,715 structures with high confidence that were absent from spectral libraries.

bioinformatics↗

bioRxiv2021

Probiotic bacteria and bile acid profile are modulated by prebiotic diet and associate with facilitated diurnal clock/sleep realignment after chronic disruption of rhythms

Chronic disruption of rhythms (CDR) impacts sleep and can result in circadian misalignment of physiological systems, which in turn is associated with increased disease risk. Exposure to repeated or severe stressors also disturbs sleep and diurnal rhythms. Prebiotic nutrients produce favorable changes in gut microbial ecology, the gut metabolome, and reduce several negative impacts of acute severe stressor exposure, including disturbed sleep, core body temperature rhythmicity, and gut microbial dysbiosis. This study tested the hypothesis whether prebiotics can also reduce the negative impacts of CDR by facilitating light/dark realignment of sleep/wake, core body temperature, and locomotor activity; and whether prebiotic-induced changes in bacteria and bile acid profiles are associated with these effects. Male, Sprague Dawley rats were fed diets enriched in prebiotic substrates or calorically matched control chow. After 5 weeks on diet, rats were exposed to CDR (12h light/dark reversal, weekly for 8 weeks) or remained on undisturbed normal light/dark cycles (NLD). Sleep EEG, core body temperature, and locomotor activity were recorded via biotelemetry in freely moving rats. Fecal samples were collected on experimental days -33, 0 (day of onset of CDR), and 42. Taxonomic identification and relative abundances of gut microbes were measured in fecal samples using 16S rRNA gene sequencing and shotgun metagenomics. Fecal primary, bacterially-modified secondary, and conjugated bile acids were measured using liquid chromatography with tandem mass spectrometry (LC-MS/MS). Prebiotic diet produced rapid and stable increases in the relative abundances of Parabacteroides distasonis and Ruminiclostridium 5. Shotgun metagenomics analyses confirmed reliable increases in relative abundances of Parabacteroides distasonis and Clostridium leptum, a member of the Ruminiclostridium genus. Prebiotic diet also modified fecal bile acid profiles; and based on correlational and step-wise regression analyses, Parabacteroides distasonis and Ruminiclostridium 5 were positively associated with each other and negatively associated with secondary and conjugated bile acids. Prebiotic diet, but not CDR, impacted beta diversity. Measures of alpha diversity evenness were decreased by CDR and prebiotic diet prevented that effect. Rats exposed to CDR while eating prebiotic, compared to control diet, more quickly realigned NREM sleep and core body temperature (ClockLab) diurnal rhythms to the altered light/dark cycle. Finally, both cholic acid and Ruminiclostridium 5 prior to CDR were associated with time to realign CBT rhythms to the new light/dark cycle after CDR; whereas both Ruminiclostridium 5 and taurocholic acid prior to CDR were associated with NREM sleep recovery after CDR. These results suggest that ingestion of prebiotic substrates is an effective strategy to increase the relative abundance of health promoting microbes, alter the fecal bile acid profile, and facilitate the recovery and realignment of sleep and diurnal rhythms after circadian disruption.

physiology↗

bioRxiv2021

Large-scale tandem mass spectrum clustering using fast nearest neighbor searching

RationaleAdvanced algorithmic solutions are necessary to process the ever increasing amounts of mass spectrometry data that is being generated. Here we describe the falcon spectrum clustering tool for efficient clustering of millions of MS/MS spectra. Methodsfalcon succeeds in efficiently clustering large amounts of mass spectral data using advanced techniques for fast spectrum similarity searching. First, high-resolution spectra are binned and converted to low-dimensional vectors using feature hashing. Next, the spectrum vectors are used to construct nearest neighbor indexes for fast similarity searching. The nearest neighbor indexes are used to efficiently compute a sparse pairwise distance matrix without having to exhaustively perform all pairwise spectrum comparisons within the relevant precursor mass tolerance. Finally, density-based clustering is performed to group similar spectra into clusters. ResultsSeveral state-of-the-art spectrum clustering tools were evaluated using a large draft human proteome dataset consisting of 25 million spectra, indicating that alternative tools produce clustering results with different characteristics. Notably, falcon generates larger highly pure clusters than alternative tools, leading to a larger reduction in data volume without the loss of relevant information for more efficient downstream processing. Conclusionsfalcon is a highly efficient spectrum clustering tool. It is publicly available as open source under the permissive BSD license at https://github.com/bittremieux/falcon.

bioinformatics↗