Pre-pandemic Metabolic Correlates of COVID-19 Severity and Long COVID Incidence in People Living with HIV.

Host metabolic dysregulation, especially in tryptophan metabolism, is intricately linked to COVID-19 severity and its post-acute sequelae (Long COVID). People living with HIV (PLWH) experience similar metabolic dysregulation and face an increased risk of developing Long COVID. However, whether pre-existing HIV-associated metabolic dysregulations contribute in predisposing PLWH to severe COVID-19 outcomes remains underexplored. Analyzing pre-pandemic samples from PLWH with documented post-infection outcomes, we found specific metabolic alterations, including increased tryptophan catabolism, predicting an elevated risk of severe COVID-19 and the incidence of Long COVID. These alterations warrant further investigation for their potential prognostic and mechanistic significance in determining COVID-19 complications.

Interlaboratory comparison of Pseudomonas aeruginosa phage susceptibility testing.

Standardized approaches to phage susceptibility testing (PST) are essential to inform selection of phages for study in patients with bacterial infections. There is no reference standard for assessing bacterial susceptibility to phage. We compared agreement between PST performed at three centers: two centers using a liquid assay standardized between the sites with the third, a plaque assay. Four Pseudomonas aeruginosa phages: PaWRA01ø11 (EPa11), PaWRA01ø39 (EPa39), PaWRA02ø83 (EPa83), PaWRA02ø87 (EPa87), and a cocktail of all four phages were tested against 145 P. aeruginosa isolates. Comparisons were made within measurements at the two sites performing the liquid assay and between these two sites. Agreement was assessed based on coverage probability (CP8), total deviation index, concordance correlation coefficient (CCC), measurement accuracy, and precision. For the liquid assay, there was satisfactory agreement among triplicate measurements made on different days at site 1, and high agreement based on accuracy and precision between duplicate measurements made on the same run at site 2. There was fair accuracy between measurements of the two sites performing the liquid assay, with CCCs below 0.6 for all phages tested. When compared to the plaque assay (performed once at site 3), there was less agreement between results of the liquid and plaque assays than between the two sites performing the liquid assay. Similar findings to the larger group were noted in the subset of 46 P. aeruginosa isolates from cystic fibrosis. Results of this study suggest that reproducibility of PST methods needs further development.

A machine learning-based method for prediction of macrocyclization patterns of polyketides and non-ribosomal peptides.

MOTIVATION: Even though genome mining tools have successfully identified large numbers of non-ribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) biosynthetic gene clusters (BGCs) in bacterial genomes, currently no tool can predict the chemical structure of the secondary metabolites biosynthesized by these BGCs. Lack of algorithms for predicting complex macrocyclization patterns of linear PK/NRP biosynthetic intermediates has been the major bottleneck in deciphering the final bioactive chemical structures of PKs/NRPs by genome mining. RESULTS: Using a large dataset of known chemical structures of macrocyclized PKs/NRPs, we have developed a machine learning (ML) algorithm for distinguishing the correct macrocyclization pattern of PKs/NRPs from the library of all theoretically possible cyclization patterns. Benchmarking of this ML classifier on completely independent datasets has revealed ROC-AUC and PR-AUC values of 0.82 and 0.81, respectively. This cyclization prediction algorithm has been used to develop SBSPKSv3, a genome mining tool for completely automated prediction of macrocyclized structures of NRPs/PKs. SBSPKSv3 has been extensively benchmarked on a dataset of over 100 BGCs with known PKs/NRPs products. AVAILABILITY AND IMPLEMENTATION: The macrocyclization prediction pipeline and all the datasets used in this study are freely available at http://www.nii.ac.in/sbspks3.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RiPPMiner: a bioinformatics resource for deciphering chemical structures of RiPPs based on prediction of cleavage and cross-links.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) constitute a rapidly growing class of natural products with diverse structures and bioactivities. We have developed RiPPMiner, a novel bioinformatics resource for deciphering chemical structures of RiPPs by genome mining. RiPPMiner derives its predictive power from machine learning based classifiers, trained using a well curated database of more than 500 experimentally characterized RiPPs. RiPPMiner uses Support Vector Machine to distinguish RiPP precursors from other small proteins and classify the precursors into 12 sub-classes of RiPPs. For classes like lanthipeptide, cyanobactin, lasso peptide and thiopeptide, RiPPMiner can predict leader cleavage site and complex cross-links between post-translationally modified residues starting from genome sequences. RiPPMiner can identify correct cross-link pattern in a core peptide from among a very large number of combinatorial possibilities. Benchmarking of prediction accuracy of RiPPMiner on a large lanthipeptide dataset indicated high sensitivity, specificity, accuracy and precision. RiPPMiner also provides interfaces for visualization of the chemical structure, downloading of simplified molecular-input line-entry system and searching for RiPPs having similar sequences or chemical structures. The backend database of RiPPMiner provides information about modification system, precursor sequence, leader and core sequence, modified residues, cross-links and gene cluster for more than 500 experimentally characterized RiPPs. RiPPMiner is available at http://www.nii.ac.in/rippminer.html.

SBSPKSv2: structure-based sequence analysis of polyketide synthases and non-ribosomal peptide synthetases.

Genome guided discovery of novel natural products has been a promising approach for identification of new bioactive compounds. SBSPKS web-server has been a valuable resource for analysis of polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS) gene clusters. We have developed an updated version - SBSPKSv2 which is based on comprehensive analysis of sequence, structure and secondary metabolite chemical structure data from 311 experimentally characterized PKS/NRPS gene clusters with known biosynthetic products. A completely new feature of SBSPKSv2 is the inclusion of features for search in chemical space. It allows the user to compare the chemical structure of a given secondary metabolite to the chemical structures of biosynthetic intermediates and final products. For identification of catalytic domains, SBSPKS now uses profile based searches, which are computationally faster and have high sensitivity. HMM profiles have also been added for a number of new domains and motif information has been used for distinguishing condensation (C), epimerization (E) and cyclization (Cy) domains of NRPS. In summary, the new and updated SBSPKSv2 is a versatile tool for genome mining and analysis of polyketide and non-ribosomal peptide biosynthetic pathways in chemical space. The server is available at: http://www.nii.ac.in/sbspks2.html.

RiPPMiner-Genome: A Web Resource for Automated Prediction of Crosslinked Chemical Structures of RiPPs by Genome Mining.

RiPPMiner-Genome is a unique bioinformatics resource for identifying Biosynthetic Gene Clusters (BGC) for RiPPs (Ribosomally Synthesized and Post-translationally Modified Peptides) and automated prediction of crosslinked chemical structures of RiPPs starting from genomic sequences. It is a major update of the RiPPMiner webserver, which used only peptide sequence of RiPP precursors as input for predicting RiPP class and crosslinked chemical structures. Other major improvements are, machine learning (ML) based identification of correct RiPP precursor peptide from among multiple small ORFs (Open Reading Frames) in a BGC, prediction of the cleavage site and cross-links in thiopeptides and identification of non-crosslinked modified residues in lanthipeptides. It has been benchmarked on a dataset of 204 experimentally characterized RiPP BGCs. RiPPMiner-Genome also facilitates visualization of the RiPP BGCs and depiction of the chemical structure of crosslinked RiPP. It also has an interface for searching characterized RiPPs, similar to the predicted core peptide sequence or crosslinked chemical structure.