Functional implications of glycans and their curation: insights from the workshop held at the 16th Annual International Biocuration Conference in Padua, Italy.

Dynamic changes in protein glycosylation impact human health and disease progression. However, current resources that capture disease and phenotype information focus primarily on the macromolecules within the central dogma of molecular biology (DNA, RNA, proteins). To gain a better understanding of organisms, there is a need to capture the functional impact of glycans and glycosylation on biological processes. A workshop titled "Functional impact of glycans and their curation" was held in conjunction with the 16th Annual International Biocuration Conference to discuss ongoing worldwide activities related to glycan function curation. This workshop brought together subject matter experts, tool developers, and biocurators from over 20 projects and bioinformatics resources. Participants discussed four key topics for each of their resources: (i) how they curate glycan function-related data from publications and other sources, (ii) what type of data they would like to acquire, (iii) what data they currently have, and (iv) what standards they use. Their answers contributed input that provided a comprehensive overview of state-of-the-art glycan function curation and annotations. This report summarizes the outcome of discussions, including potential solutions and areas where curators, data wranglers, and text mining experts can collaborate to address current gaps in glycan and glycosylation annotations, leveraging each other's work to improve their respective resources and encourage impactful data sharing among resources. Database URL: https://wiki.glygen.org/Glycan_Function_Workshop_2023.

Navigating the maze of mass spectra: a machine-learning guide to identifying diagnostic ions in O-glycan analysis.

Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotating topologies or linkages. Diagnostic fragments are often only known informally among practitioners or stem from individual studies, with unclear validity or generalizability, causing annotation heterogeneity and hampering new analysts. Drawing on a curated set of 237,000 O-glycomics spectra, we here present a rule-based machine learning workflow to uncover quantifiably valid and generalizable diagnostic fragments. This results in fragmentation rules to robustly distinguish common O-glycan isomers for reduced glycans in negative ion mode. We envision this resource to improve glycan annotation accuracy and concomitantly make annotations more transparent and homogeneous across analysts.

Predicting glycan structure from tandem mass spectrometry via deep learning.

Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry (MS/MS) data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 500,000 annotated MS/MS spectra, here we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw liquid chromatography-MS/MS data in seconds (top-1 accuracy: 90.3%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb . We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.

Decoding glycomics with a suite of methods for differential expression analysis.

Glycomics, the comprehensive profiling of all glycan structures in samples, is rapidly expanding to enable insights into physiology and disease mechanisms. However, glycan structure complexity and glycomics data interpretation present challenges, especially for differential expression analysis. Here, we present a framework for differential glycomics expression analysis. Our methodology encompasses specialized and domain-informed methods for data normalization and imputation, glycan motif extraction and quantification, differential expression analysis, motif enrichment analysis, time series analysis, and meta-analytic capabilities, synthesizing results across multiple studies. All methods are integrated into our open-source glycowork package, facilitating performant workflows and user-friendly access. We demonstrate these methods using dedicated simulations and glycomics datasets of N-, O-, lipid-linked, and free glycans. Differential expression tests here focus on human datasets and cancer vs. healthy tissue comparisons. Our rigorous approach allows for robust, reliable, and comprehensive differential expression analyses in glycomics, contributing to advancing glycomics research and its translation to clinical and diagnostic applications.

GlycoDraw: a python implementation for generating high-quality glycan figures.

Glycans are essential to all scales of biology, with their intricate structures being crucial for their biological functions. The structural complexity of glycans is communicated through simplified and unified visual representations according to the Symbol Nomenclature for Glycans (SNFGs) guidelines adopted by the community. Here, we introduce GlycoDraw, a Python-native implementation for high-throughput generation of high-quality, SNFG-compliant glycan figures with flexible display options. GlycoDraw is released as part of our glycan analysis ecosystem, glycowork, facilitating integration into existing workflows by enabling fully automated annotation of glycan-related figures and thus assisting the analysis of e.g. differential abundance data or glycomics mass spectra.

Interband cascade laser absorption of hydrogen chloride for high-temperature thermochemical analysis of fire-resistant polymer reactivity.

A mid-infrared absorption spectroscopy technique has been developed to quantitatively and spatially resolve gas temperature and molecular abundance of $ ^1{{\rm H}^{35}}{\rm Cl} $1H35Cl in the high-temperature pyrolysis and oxidation layers of chlorinated polymers. Two transitions in the R-branch of the fundamental vibrational band of HCl near 3.34 µm are selected due to their relative strength and spectral isolation from other combustion products at elevated temperatures, and they are probed using a distributed feedback interband cascade laser. A scanned-wavelength direct absorption method is employed with a Voigt line-fitting routine to recover projected absorbance areas across the exit plane of a cylindrical polymeric slab, wherein the gaseous core comprises the axisymmetric reaction layer. Tikhonov-regularized Abel inversion of the projected absorbance areas yields line-integrated radial absorption coefficients, from which a two-line ratio is used to infer a radially resolved temperature between the gas core and solid polymer surface. The method is demonstrated to provide insights into the gas-phase combustion physics that lead to the formation of toxicants when a fire-resistant polymer, polyvinyl chloride (PVC), is burned in the presence of oxygen. Two-dimensional images were generated by assembling multiple tomographic reconstructions for several cylinder lengths. The quantitative images highlight the technique's ability to characterize the thermochemical evolution and toxicant formation during the burning of a halogenated polymer fuel.

Rhodococcus phenolicus sp. nov., a novel bioprocessor isolated actinomycete with the ability to degrade chlorobenzene, dichlorobenzene and phenol as sole carbon sources.

The aerobic degradation of phenol, chlorobenzene and dichlorobenzene as a sole carbon source has been observed in bacterial Gram-positive strain G2PT isolated from a wastewater bioprocessor. Cells display branching mycelia fragmenting into rod and coccoid elements when grown on TSA. Aerial hyphae formation occurs when grown on phenol and chlorinated aromatics as the sole carbon source. Growth was observed at up to 0.75% phenol as a sole carbon source, indicating a strong tolerance for the compound. The 16S rRNA gene sequence shares the greatest similarity with members of the Rhodococcus genus, with the closest shared nucleotide identity of 98% with the aromatic toxin degrading bacteria Rhodococcus zopfii DSM 44108T. Neighbor-joining and parsimony analysis of Corynebacterineae 16S rRNA gene sequences consistently places strain G2PT in a clade shared with R. zopfii within the Rhodococcus rhodochrous subclade. Based on a unique polyphasic profile involving phenotypic, ribosomal DNA sequence analysis, DNA-DNA hybridization, mol% DNA G+C content and fatty acid composition, G2PT is proposed to represent a previously uncharacterized, novel species in the genus Rhodococcus. The name Rhodococcus phenolicus is proposed for the isolate with the type strain G2PT (= DSM 44812) (= NRRL B-24323) [corrected]

Alcaligenes faecalis subsp. phenolicus subsp. nov. a phenol-degrading, denitrifying bacterium isolated from a graywater bioprocessor.

A Gram (-) coccobacillary bacterium, J(T), was isolated from a graywater bioprocessor. 16S rRNA and biochemical analysis has revealed strain J(T) closely resembles Alcaligenes faecalis ATCC 8750T and A. faecalis subsp. parafaecalis DSM 13975T, but is a distinct, previously uncharacterized isolate. Strain J(T), along with the type strain of A. faecalis and its previously described subspecies share the ability to aerobically degrade phenol. The degradation rates of phenol for strain J(T) and reference phenol degrading bacteria were determined by photometrically measuring the change in optical density when grown on 0.1% phenol as the sole carbon source, followed by addition of Gibb's reagent to measure depletion of substrate. The phenol degradation rates of strain J(T) was found to exceed that of the phenol hydroxylase group III bacterium Pseudomonas pseudoalcaligenes, with isolate J(T) exhibiting a doubling time of 4.5 h. The presence of the large subunit of the multicomponent phenol hydroxylase gene in strain J(T) was confirmed by PCR. The presence of the nirK nitrite reductase gene as demonstrated by PCR as well as results obtained from nitrite media indicated denitrification at least to N2O. Based on phenotypic, phylogenetic, fatty acid analysis and results from DNA DNA hybridization, we propose assigning a novel subspecies of Alcaligenes faecalis, to be named Alcaligenes faecalis subsp. phenolicus with the type strain J(T) (= DSM 16503) (= NRRL B-41076).

A mammalian cell regulatory agent, CeReS-18, inhibits yeast cell proliferation but not bacterial replication.

A cell regulatory sialoglycopeptide, CeReS-18, purified from intact bovine cerebral cortex cells, has exhibited the capability of reversibly inhibiting cellular DNA synthesis and the proliferation of a wide array of mammalian cells. In the present study, the effect of CeReS-18 on the proliferation of bacterial ( Bacillus cereus and Escherichia coli) and yeast ( Saccharomyces cerevisiae and Schizosaccharomyces pombe) cells was investigated. The results showed that replication and viability of the bacterial cells were not affected by CeReS-18 at any concentration tested, including 15-fold higher than that used for inhibiting mouse 3T3 cell proliferation. In contrast to bacterial cells, CeReS-18 was able to inhibit the replication of yeast cells, in a concentration-dependent, reversible manner, and the addition of calcium to the culture medium could abrogate the inhibitory effect of CeReS-18. A cytotoxic effect of CeReS-18 on both yeast cell species was observed when it was applied at higher concentrations.

Assessment of bioaerosols in swine barns by filtration and impaction.

Bioaerosol concentrations inside one naturally ventilated and one mechanically ventilated swine finishing barn were assessed by sampling air using membrane filtration and impaction (six-stage Andersen sampler), and assayed by culture method. The barns, located on the same commercial farm in northeast Kansas, did not show any significant difference (p > 0.05) in concentrations of total and respirable airborne microorganisms. The overall mean total concentrations inside the two barns were 6.6 x 10(4) colony forming units (CFU)/m3 (SD = 3.8 x 10(4) CFU/m3 as measured by filtration and 8.6 x 10(4) CFU/m3 (SD = 5.1 x 10(4) CFU/m3) by impaction. The overall mean respirable concentrations were 9.0 x 10(3) CFU/m3 (SD = 4.1 x 10(3) CFU/m3) measured by filtration and 2.8 x 10(4) CFU/m3 (SD = 2.2 x 10(4) CFU/m3) by impaction. Total and respirable CFU concentrations measured by impaction were significantly (p < 0.05) higher than that by filtration. The persistent strains of microorganisms were various species of the following genera: Staphylococcus, Pseudomonas, Bacillus, Listeria, Enterococcus, Nocardia, Lactobacillus, and Penicillium. It appears that filtration sampling can be used for a qualitative survey of bioaerosols in swine barns while the Andersen sampler is suitable for both quantitative and qualitative assessments.