Charlotte Tumescheit

Introduction: Biological and chemical ontologies are structured representations that can be used to map entities such as metabolites onto broader chemical and biological categories. Enrichment analysis applied to such ontologies identifies classes whose representation within a set deviates from an expected background distribution, helping to interpret complex metabolomics measurements. Objectives: This study aimed to develop a tool for ChEBI (Chemical Entities of Biological Interest) ontology-based enrichment analysis, named ChEBI-N (ChEBI eNrichment). It is a successor of the previous tool BiNChE, with new features that broaden its applicability. Methods: We reimplemented BiNChE and extended it to accept SMILES or InChI strings as well as ChEBI IDs. These broaden applicability to datasets with entities that are not yet present in ChEBI. We also constructed organism-specific background populations. Results: We demonstrate the tool on three published metabolomics datasets covering wine, cheese, and human plasma samples. ChEBI-N correctly highlighted relevant enriched classes in all three datasets, such as flavonoids in the wine dataset and fatty acid classes in the cheese dataset. Comparing organism-specific and full-ChEBI backgrounds shows that restricted backgrounds reduce the inflated significance of generic classes, although the most enriched classes remain largely consistent. Conclusion: ChEBI-N is an extended successor to BiNChE for ontology-based enrichment analysis of metabolomics data, offering a hypothesis-generating complement to existing metabolomics analysis pipelines. Availability: ChEBI-N is freely available at chebin.hastingslab.org or as a Python package (pypi.org/project/chebin), with source code at github.com/ontology-tools/chebin, archived at Zenodo (doi:10.5281/zenodo.23264219) and Software Heritage (swh:1:rev: 133a41792704eb7f1db746f36a5ae97e9247b2b7).

Recent advances in the field of machine learning have shown that integration of expert knowledge improves performances, in particular for complex domains such as biology. Bio-ontologies offer a rich source of curated biological knowledge that can be harnessed to this end. Here, we describe an intuitive and generalisable approach to embed the knowledge contained in a classification hierarchy derived from a bio-ontology into a machine learning model as an intermediate training step between general-purpose pre-training and task-specific fine-tuning in a process that we call ‘ontology pre-training’. We show that this approach leads to an improvement in predictive performance and a reduction in training time for a broad range of prediction tasks relevant to understanding metabolite functions in living systems, using a range of datasets derived from MoleculeNet. We see the biggest improvement for regression tasks, e.g. prediction of lipophilicity and aqueous solubility of molecules, and a robust improvement for most classification tasks. Our approach can be adapted for a wide range of knowledge sources, models and prediction tasks.

The gender knowledge gap in medicine, particularly regarding menstruation and disorders such as endometriosis, often results in delayed diagnoses and inadequate care. Many menstruating individuals report dismissal of debilitating symptoms, driving them to seek information and support on online platforms such as TikTok and YouTube. This study leverages social media to identify key topics reflecting lived experiences and needs to bridge this knowledge gap. Using a novel pipeline, we analysed video comments using BERTopic and the Llama 3.1 model. Key topics, including emotional support, educational guidance, and community validation, were consistent with prior research. This study underscores the potential of social media and large language models to inform inclusive menstrual health research, revealing unique insights regarding the menstruation experiences and needs of underrepresented and historically overlooked individuals such as those with irregular cycles.

Viruses are the cause of a considerable burden to human, animal and plant health, while on the other hand playing an important role in regulating entire ecosystems. The power of new sequencing technologies combined with new tools for processing “Big Data” offers unprecedented opportunities to answer fundamental questions in virology. Virologists have an urgent need for virus-specific bioinformatics tools. These developments have led to the formation of the European Virus Bioinformatics Center, a network of experts in virology and bioinformatics who are joining forces to enable extensive exchange and collaboration between these research areas. The EVBC strives to provide talented researchers with a supportive environment free of gender bias, but the gender gap in science, especially in math-intensive fields such as computer science, persists. To bring more talented women into research and keep them there, we need to highlight role models to spark their interest, and we need to ensure that female scientists are not kept at lower levels but are given the opportunity to lead the field. Here we showcase the work of the EVBC and highlight the achievements of some outstanding women experts in virology and viral bioinformatics.

Despite advances in resolving the structures of multi-pass membrane proteins, little is known about the native folding pathways of these complex structures. Using single-molecule magnetic tweezers, we here report a folding pathway of purified human glucose transporter 3 (GLUT3) reconstituted within synthetic lipid bilayers. The N-terminal major facilitator superfamily (MFS) fold strictly forms first, serving as a structural template for its C-terminal counterpart. We found polar residues comprising the conduit for glucose molecules present major folding challenges. The endoplasmic reticulum membrane protein complex facilitates insertion of these hydrophilic transmembrane helices, thrusting GLUT3’s microstate sampling toward folded structures. Final assembly between the N- and C-terminal MFS folds depends on specific lipids that ease desolvation of the lipid shells surrounding the domain interfaces. Sequence analysis suggests that this asymmetric folding propensity across the N- and C-terminal MFS folds prevails for metazoan sugar porters, revealing evolutionary conflicts between foldability and functionality faced by many multi-pass membrane proteins.

The arterivirus porcine reproductive and respiratory syndrome virus (PRRSV) causes significant economic losses to the swine industry worldwide. Here we apply ribosome profiling (RiboSeq) and parallel RNA sequencing (RNASeq) to characterise the transcriptome and translatome of both species of PRRSV and to analyse the host response to infection. We calculated programmed ribosomal frameshift (PRF) efficiency at both sites on the viral genome. This revealed the nsp2 PRF site as the second known example where temporally regulated frameshifting occurs, with increasing −2 PRF efficiency likely facilitated by accumulation of the PRF-stimulatory viral protein, nsp1β. Surprisingly, we find that PRF efficiency at the canonical ORF1ab frameshift site also increases over time, in contradiction of the common assumption that RNA structure-directed frameshift sites operate at a fixed efficiency. This has potential implications for the numerous other viruses with canonical PRF sites. Furthermore, we discovered several highly translated additional viral ORFs, the translation of which may be facilitated by multiple novel viral transcripts. For example, we found a highly expressed 125-codon ORF overlapping nsp12, which is likely translated from novel subgenomic RNA transcripts that overlap the 3′ end of ORF1b. Similar transcripts were discovered for both PRRSV-1 and PRRSV-2, suggesting a potential conserved mechanism for temporally regulating expression of the 3′-proximal region of ORF1b. We also identified a highly translated, short upstream ORF in the 5′ UTR, the presence of which is highly conserved amongst PRRSV-2 isolates. These findings reveal hidden complexity in the gene expression programmes of these important nidoviruses.

Highly accurate structure prediction methods are generating an avalanche of publicly available protein structures. Searching through these structures is becoming the main bottleneck in their analysis. Foldseek enables fast and sensitive comparisons of large structure sets. It reaches sensitivities similar to state-of-the-art structural aligners while being at least 20,000 times faster. Foldseek is free open-source software available at foldseek.com and as a webserver at search.foldseek.com. Contact soeding{at}mpinat.mpg.de , martin.steinegger{at}snu.ac.kr

Porcine reproductive and respiratory syndrome virus (PRRSV) is an arterivirus which causes significant economic losses to the swine industry worldwide. Here, we use ribosome profiling (RiboSeq) and parallel RNA sequencing (RNASeq) to characterise the transcriptome and translatome of both species of PRRSV and analyse the host response to infection. We quantified viral gene expression over a timecourse of infection, and calculated the efficiency of programmed ribosomal frameshifting (PRF) at both sites on the viral genome. At the nsp2 frameshift site (a rare example of protein-stimulated frameshifting), −2 PRF efficiency increases over time, likely facilitated by accumulation of the PRF- stimulatory viral protein (nsp1β) during infection. This marks arteriviruses as the second example of temporally regulated PRF. Surprisingly, we also found PRF efficiency at the canonical ORF1ab frameshift site increases over time, in apparent contradiction of the common assumption that RNA structure-directed frameshift sites operate at a fixed efficiency. This has potential implications for the numerous other viruses with canonical PRF sites. Furthermore, we discovered several highly translated additional viral ORFs, the translation of which may be facilitated by multiple novel viral transcripts. For example, we found a 125-codon ORF overlapping nsp12, which is expressed as highly as nsp12 itself at late stages of replication, and is likely translated from novel subgenomic (sg) RNA transcripts that overlap the 3′ end of ORF1b. Similar transcripts were discovered for both PRRSV-1 and PRRSV- 2, suggesting a potential conserved mechanism for temporal regulation of expression of the 3′-proximal region of ORF1b. In addition, we identified a highly translated, short upstream ORF (uORF) in the 5′ UTR, the presence of which is highly conserved amongst PRRSV-2 isolates. This is the first application of RiboSeq to arterivirus-infected cells, and reveals new features which add to the complexity of gene expression programmes in this important family of nidoviruses.

Background Throughout biology, multiple sequence alignments (MSAs) form the basis of much investigation into biological features and relationships. These alignments are at the heart of many bioinformatics analyses. However, sequences in MSAs are often incomplete or very divergent, which leads to poorly aligned regions or large gaps in alignments. This slows down computation and can impact conclusions without being biologically relevant. Therefore, cleaning the alignment by removing these regions can substantially improve analyses. Results We present a comprehensive, user-friendly MSA trimming tool with multiple visualisation options. Our highly customisable command line tool aims to give intervention power to the user by offering various options, and outputs graphical representations of the alignment before and after processing to give the user a clear overview of what has been removed. The main functionalities of the tool include removing regions of low coverage due to insertions, removing gaps, cropping poorly aligned sequence ends and removing sequences that are too divergent or too short. The thresholds for each function can be specified by the user and parameters can be adjusted to each individual MSA. CIAlign is complementary to existing alignment trimming tools, with an emphasis on solving specific and common alignment problems and on providing transparency to the user. Conclusion CIAlign effectively removes poorly aligned regions and sequences from MSAs and provides novel visualisation options. This tool can be used to improve the alignment quality for further analysis and processing. The tool is aimed at anyone who wishes to automatically clean up parts of an MSA and those requiring a new, accessible way for visualising large MSAs.

A metagenomics approach was used to detect novel and recognized RNA viruses in mosquitoes from the Yucatan Peninsula of Mexico. A total of 1359 mosquitoes of 7 species and 5 genera (Aedes, Anopheles, Culex, Mansonia and Psorophora) were sorted into 37 pools, homogenized and inoculated onto monolayers of Aedes albopictus (C6/36) cells. A second blind passage was performed and then total RNA was extracted and analysed by RNA-seq. Two novel viruses, designated Uxmal virus and Mayapan virus, were identified. Uxmal virus was isolated from three pools of Aedes (Ochlerotatus) taeniorhynchus and phylogenetic data indicate that it should be classified within the recently proposed taxon Negevirus. Mayapan virus was recovered from two pools of Psorophora ferox and is most closely related to unclassified Nodaviridae-like viruses. Two recognized viruses were also detected: Culex flavivirus (family Flaviviridae) and Houston virus (family Mesoniviridae), with one and two isolates being recovered, respectively. The in vitro host ranges of all four viruses were determined by assessing their replicative abilities in cell lines of avian, human, monkey, hamster, murine, lepidopteran and mosquito (Aedes, Anopheles and Culex) origin, revealing that all viruses possess vertebrate replication-incompetent phenotypes. In conclusion, we report the isolation of both novel and recognized RNA viruses from mosquitoes collected in Mexico, and add to the growing plethora of viruses discovered recently through the use of metagenomics.