GC-MS with Headspace Extraction for Non-Invasive Diagnostics of IBD Dynamics in a Model of DSS-Induced Colitis in Rats.

Inflammatory bowel diseases are extremely common throughout the world. However, in most cases, it is asymptomatic at the initial stage. Therefore, it is important to develop non-invasive diagnostic methods that allow identification of the IBD risks in a timely manner. It is well known that gastrointestinal microbiota secrete volatile compounds (VOCs) and their composition may change in IBD. We propose a non-invasive method to identify the dynamics of IBD development in the acute and remission stage at the level of VOCs in model of dextran sulfate sodium (DSS) with chemically induced colitis measured by headspace GC/MS (HS GC/MS). Methods: VOCs profile was identified using a headspace GC/MS (HS GC/MS). GC/MS data were processed using MetaboAnalyst 5.0 and GraphPad Prism 8.0.1 software. The disease activity index (DAI) and histological method were used to assess intestinal inflammation. The peak of intestinal inflammation activity was reached on day 7, according to the disease activity index. Histological examination data showed changes in the intestine due to different stages of inflammation. As the acute inflammation stage was reached, the metabolomic profile also underwent changes, especially at the short-fatty acids level. A higher relative amounts of acetic acid (p value < 0.025) and lower relative amounts of propanoic acid (p value < 0.0005), butanoic acid (p value < 0.005) and phenol 4-methyl- (p value = 0.053) were observed in DSS7 group on day 7 compared to the control group. In remission stage, disease activity indexes decreased, and the histological picture also improved. But metabolome changes continued despite the withdrawal of the DSS examination. A lower relative amounts of propanoic acid (p value < 0.025), butanoic acid (p value < 0.0005), pentanoic acid (p value < 0.0005), and a significant de-crease of hexanoic acid (p value < 0.0005) relative amounts were observed in the DSS14 group compared to the control group on day 14. A model of DSS-induced colitis in rats was successfully implemented for metabolomic assessment of different stages of inflammation. We demonstrated that the ratios of volatile compounds change in response to DSS before the appearance of standard signs of inflammation, determined by DAI and histological examination. Changes in the volatile metabolome persisted even after visual intestine repair and it confirms the high sensitivity of the microbiota to the damaging effects of DSS. The use of HS GC/MS may be an important addition to existing methods for assessing inflammation at early stages.

Snapper: high-sensitive detection of methylation motifs based on Oxford Nanopore reads.

MOTIVATION: The Oxford Nanopore technology has a great potential for the analysis of methylated motifs in genomes, including whole-genome methylome profiling. However, we found that there are no methylation motifs detection algorithms, which would be sensitive enough and return deterministic results. Thus, the MEME suit does not extract all Helicobacter pylori methylation sites de novo even using the iterative approach implemented in the most up-to-date methylation analysis tool Nanodisco. RESULTS: We present Snapper, a new highly sensitive approach, to extract methylation motif sequences based on a greedy motif selection algorithm. Snapper does not require manual control during the enrichment process and has enrichment sensitivity higher than MEME coupled with Tombo or Nanodisco instruments that was demonstrated on H.pylori strain J99 studied earlier by the PacBio technology and on four external datasets representing different bacterial species. We used Snapper to characterize the total methylome of a new H.pylori strain A45. At least four methylation sites that have not been described for H.pylori earlier were revealed. We experimentally confirmed the presence of a new CCAG-specific methyltransferase and inferred a gene encoding a new CCAAK-specific methyltransferase. AVAILABILITY AND IMPLEMENTATION: Snapper is implemented using Python and is freely available as a pip package named "snapper-ont." Also, Snapper and the demo dataset are available in Zenodo (10.5281/zenodo.10117651).

Evaluation of microRNA Expression Features in Patients with Various Types of Arterial Damage: Thoracic Aortic Aneurysm and Coronary Atherosclerosis.

Circulating serum miRNA are increasingly used as biomarkers and potential treatment targets in several clinical scenarios, including cardiovascular diseases. However, the current data on circulating miRNA in thoracic aorta aneurism (TAA) patients are inconclusive. The aim of the present study is to compare the levels of several circulating miRNA in patients with degenerative TAA, coronary artery disease (CAD), and controls for special profile identification. We have identified several candidates for the role of new biomarkers: miR-143-3p, miR-181-5p, miR-126-3p, miR-126-5p, miR-145-5p, miR-150-5p, and miR-195-5p. MATERIALS AND METHODS: Serum samples of 100 patients were analyzed, including 388 TAA patients scheduled for elective surgery, 67 patients with stable CAD and 17 controls, were used for miRNA isolation and identification. RESULTS: More specific for TAA with very high predictive ability in ROC analysis was an increase in the levels of miR-21-5p, miR-29b-5p, miR-126-5p/-3p, miR-181b-5p, and miR-92a-3p, with the latter microRNA being investigated as a novel potential marker of TAA for the first time. CONCLUSION: TAA and CAD patients demonstrated a significant increase in the levels of circulating miR-126-5p/-3p, miR-181b-5p, and miR-29b-3p. More specific for TAA with very high predictive ability in ROC analysis was an increase in the levels of miR-21-5p, -29b-5p, -126-5p/-3p, 181b-5p, and -92a-3p, with the latter microRNA being investigated as a potential marker of TAA for the first time.

Investigating volatile compounds in the Bacteroides secretome.

Microorganisms and their hosts communicate with each other by secreting numerous components. This cross-kingdom cell-to-cell signaling involves proteins and small molecules, such as metabolites. These compounds can be secreted across the membrane via numerous transporters and may also be packaged in outer membrane vesicles (OMVs). Among the secreted components, volatile compounds (VOCs) are of particular interest, including butyrate and propionate, which have proven effects on intestinal, immune, and stem cells. Besides short fatty acids, other groups of volatile compounds can be either freely secreted or contained in OMVs. As vesicles might extend their activity far beyond the gastrointestinal tract, study of their cargo, including VOCs, is even more pertinent. This paper is devoted to the VOCs secretome of the Bacteroides genus. Although these bacteria are highly presented in the intestinal microbiota and are known to influence human physiology, their volatile secretome has been studied relatively poorly. The 16 most well-represented Bacteroides species were cultivated; their OMVs were isolated and characterized by NTA and TEM to determine particle morphology and their concentration. In order to analyze the VOCs secretome, we propose a headspace extraction with GC-MS analysis as a new tool for sample preparation and analysis of volatile compounds in culture media and isolated bacterial OMVs. A wide range of released VOCs, both previously characterized and newly described, have been revealed in media after cultivation. We identified more than 60 components of the volatile metabolome in bacterial media, including fatty acids, amino acids, and phenol derivatives, aldehydes and other components. We found active butyrate and indol producers among the analyzed Bacteroides species. For a number of Bacteroides species, OMVs have been isolated and characterized here for the first time as well as volatile compounds analysis in OMVs. We observed a completely different distribution of VOC in vesicles compared to the bacterial media for all analyzed Bacteroides species, including almost complete absence of fatty acids in vesicles. This article provides a comprehensive analysis of the VOCs secreted by Bacteroides species and explores new perspectives in the study of bacterial secretomes in relation the intercellular communication.

FunFun: ITS-based functional annotator of fungal communities.

The study of individual fungi and their communities is of great interest to modern biology because they might be both producers of useful compounds, such as antibiotics and organic acids, and pathogens of various diseases. And certain features associated with the functional capabilities of fungi are determined by differences in gene content. Information about gene content is most often taken from the results of functional annotation of the whole genome. However, in practice, whole genome sequencing of fungi is rarely performed. At the same time, usually sequence amplicons of the ITS region to identify fungal taxonomy. But in the case of amplicon sequencing there is no way to perform a functional annotation. Here, we present FunFun, the instrument that allows to evaluate the gene content of an individual fungus or mycobiome from ITS sequencing data. FunFun algorithm based on a modified K-nearest neighbors algorithm. As input, the program can use ITS1, ITS2, or a full-size ITS cluster (ITS1-5.8S-ITS2). FunFun was realized as a pip-installed command line instrument and validated using a shuffle-split approach. The developed instrument can be very useful in the fungal community comparing and estimating functional capabilities of fungi under study. Also, the program can predict with high accuracy the most variable functions.

BioCAT: Search for biosynthetic gene clusters producing nonribosomal peptides with known structure.

Nonribosomal peptides are a class of secondary metabolites synthesized by multimodular enzymes named nonribosomal peptide synthetases and mainly produced by bacteria and fungi. NMR, LC-MS/MS and other analytical methods allow to determine a peptide structure precisely, but it is often not a trivial task to find natural producers of them. There are cases when potential producers should be found among hundreds of strains, for instance, when analyzing metagenomic data. We have developed BioCAT, a tool designed for finding biosynthetic gene clusters which may produce a given nonribosomal peptide when the structure of an interesting nonribosomal peptide has already been found. BioCAT unites the antiSMASH software and the rBAN retrosynthesis tool but some improvements were added to both gene cluster and peptide structure analysis. The main feature of the method is an implementation of a position-specific score matrix to store specificities of nonribosomal peptide synthetase modules, which has increased the alignment sensitivity in comparison with more strict approaches developed earlier. We tested the method on a manually curated nonribosomal peptide producers database and compared it with competing tools GARLIC and Nerpa. Finally, we showed the method's applicability on several external examples.

UniqPy: A tool for estimation of short-chain fatty acids composition by gas-chromatography/mass-spectrometry with headspace extraction.

Short-chain fatty acids are metabolites widely presented in many natural sources, including human feces and blood. Estimation of their composition is a common procedure, usually performed using nuclear magnetic resonance or gas chromatography with a flame ionization detector. However, the commonly used methods often depend on specific sample preparation, such as filtration and homogenization. The gas-chromatography/mass-spectrometry (GC/MS) method with headspace extraction allows sample preparation to be kept to a minimum regardless of the physical state of the sample, which can be potentially useful in metabolomics research of complex natural samples such as blood or feces. In this work, we have demonstrated the applicability of Headspace GC-MS for estimating short chain fatty acid (SCFA) composition. The main problem here is the complex, non-linear dependence between the composition of the compounds in the source phase and the relative pressures in the vapor phase, which are directly measured by this method. We have implemented a thermodynamic model that performs the reverse transformation of relative abundances in the vapor phase to relative concentrations in the liquid phase, and have tested it on some synthetic SCFA mixtures. The developed method is available as a pip package called UniqPy and can be used to describe liquid-vapor equilibrium for any multicomponent system if a sufficient amount of training data is provided. The gas chromatography method with headspace extraction in conjunction with the UniqPy data transformation showed satisfactory quantification accuracy for propionic acid, butyric acid, isobutyric acid, and valeric acid (R-squared > 0.96). The applicability of the method was additionally demonstrated on a series of fecal samples.