Naturwissenschaften. 2025 Jan 8;112(1):5. doi: 10.1007/s00114-024-01950-4.

ABSTRACT

Physiological or genetic assays and computational modeling are valuable tools for understanding animals' visual discrimination capabilities. Yet sometimes, the results generated by these methods appear not to jive with other aspects of an animal's appearance or natural history, and behavioral confirmatory tests are warranted. Here we examine the peculiar case of a male jumping spider that displays red, black, white, and UV color patches during courtship despite the fact that, according to microspectrophotometry and color vision modeling, they are unlikely able to discriminate red from black. To test whether some optical or neurological component could have been missed using these methods, we conduct mate choice experiments. Some females are presented with a choice between males with their red leg coloration painted over with either red or black paint, while other females are presented with a choice between males with the same coloration painted over by either red or white paint. This latter pairing of red and white males should have been easily distinguishable to the spiders and served as a control to ensure our experimental setup was conducive to natural mating behavior. Red males were more likely to mate than white males (P = 0.035), whereas red and black males had identical mating success (P = 1.0). This suggests that previous physiological and computational work on these spiders was correct in concluding that they are unable to discriminate between red and black. Any functional significance of displaying both colors, rather than only black, remains unresolved.

PMID:39775916 | DOI:10.1007/s00114-024-01950-4

Bioinformatics. 2025 Jan 7:btaf005. doi: 10.1093/bioinformatics/btaf005. Online ahead of print.

ABSTRACT

MOTIVATION: The identification of biologically meaningful domains is a central step in the analysis of spatial transcriptomic data.

RESULTS: Following Occam's razor, we show that a simple PCA-based algorithm for unsupervised spatial domain identification rivals the performance of ten competing state-of-the-art methods across six single-cell spatial transcriptomic datasets. Our reductionist approach, NichePCA, provides researchers with intuitive domain interpretation and excels in execution speed, robustness, and scalability.

AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/imsb-uke/nichepca.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:39775801 | DOI:10.1093/bioinformatics/btaf005

Int J Comput Assist Radiol Surg. 2025 Jan 7. doi: 10.1007/s11548-024-03310-z. Online ahead of print.

ABSTRACT

PURPOSE: Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We propose to make lung fissure segmentation more efficient by using geometric deep learning (GDL) on sparse point clouds.

METHODS: We abstract image data with sparse keypoint (KP) clouds. We train GDL models to segment the point cloud, comparing three major paradigms of models (PointNets, graph convolutional networks (GCNs), and PointTransformers). From the sparse point segmentations, 3D meshes of the objects are reconstructed to obtain a dense surface. The state-of-the-art Poisson surface reconstruction (PSR) makes up most of the time in our pipeline. Therefore, we propose an efficient point cloud to mesh autoencoder (PC-AE) that deforms a template mesh to fit a point cloud in a single forward pass. Our pipeline is evaluated extensively and compared to the 3D-CNN gold standard nnU-Net on diverse clinical and pathological data.

RESULTS: GCNs yield the best trade-off between inference time and accuracy, being 21 × faster with only 1.4 × increased error over the nnU-Net. Our PC-AE also achieves a favorable trade-off, being 3 × faster at 1.5 × the error compared to the PSR.

CONCLUSION: We present a KP-based fissure segmentation pipeline that is more efficient than 3D-CNNs and can greatly speed up large-scale analyses. A novel PC-AE for efficient mesh reconstruction from sparse point clouds is introduced, showing promise not only for fissure segmentation. Source code is available on https://github.com/kaftanski/fissure-segmentation-IJCARS.

PMID:39775630 | DOI:10.1007/s11548-024-03310-z

Eur J Clin Microbiol Infect Dis. 2025 Jan 7. doi: 10.1007/s10096-024-05030-3. Online ahead of print.

ABSTRACT

PURPOSE: We designed and tested a point of care test panel to detect E.coli and antibiotic susceptibility in urine samples from patients at the point of care in the urological department. The aim of this approach is to facilitate choosing an appropriate antibiotic for urinary tract infections (UTI) at first presentation in the context of increasing antibiotic resistance in uropathogens worldwide.

METHODS: We analyzed 162 E.coli isolates from samples from a university urological department to determine phenotypic and genotypic resistance data. With this data we created customized LAMP (loop-mediated isothermal amplification) panels for a commercial machine with which to detect and possibly quantify E.coli and six antibiotic resistance determinants. In a second step we tested these panel(s) for diagnostic accuracy on 1596 urine samples and compared with routine microbiological culture.

RESULTS: E.coli was detected with 95.4% sensitivity and 96.1% specificity. Dynamics of the LAMP amplification could be used to gauge bacterial loads in the samples. Antibiotic sensitivity was detected with good negative (sensitive) predictive values: ampicillin 92.8%, ampicillin/sulbactam 96.4%, cefuroxime 92.8%, cefotaxime 97.8%, trimethoprim/sulfamethoxazole 96.5%, ciprofloxacin 96.8%.

CONCLUSION: The LAMP panel provided E.coli detection and sensitivity information within one hour and thus could principally guide initial antibiotic therapy upon patients presenting with UTI. The panel helps to select initial adequate antibiotic therapy as well as providing diagnostic stewardship. Follow up investigations will expand the test system to other uropathogens.

PMID:39775368 | DOI:10.1007/s10096-024-05030-3

PLoS Comput Biol. 2024 Dec 30;20(12):e1012653. doi: 10.1371/journal.pcbi.1012653. Online ahead of print.

ABSTRACT

Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies. Many methods exist that reconstruct cancer phylogenies using point mutations detected with bulk DNA sequencing. However, these methods become inaccurate when reconstructing phylogenies with more than 30 mutations, or, in some cases, fail to recover a phylogeny altogether. Here, we introduce Orchard, a cancer phylogeny reconstruction algorithm that is fast and accurate using up to 1000 mutations. Orchard samples without replacement from a factorized approximation of the posterior distribution over phylogenies, a novel result derived in this paper. Each factor in this approximate posterior corresponds to a conditional distribution for adding a new mutation to a partially built phylogeny. Orchard optimizes each factor sequentially, generating a sequence of incrementally larger phylogenies that ultimately culminate in a complete tree containing all mutations. Our evaluations demonstrate that Orchard outperforms state-of-the-art cancer phylogeny reconstruction methods in reconstructing more plausible phylogenies across 90 simulated cancers and 14 B-progenitor acute lymphoblastic leukemias (B-ALLs). Remarkably, Orchard accurately reconstructs cancer phylogenies using up to 1,000 mutations. Additionally, we demonstrate that the large and accurate phylogenies reconstructed by Orchard are useful for identifying patterns of somatic mutations and genetic variations among distinct cancer cell subpopulations.

PMID:39775053 | DOI:10.1371/journal.pcbi.1012653

Nat Commun. 2025 Jan 7;16(1):441. doi: 10.1038/s41467-024-55593-0.

ABSTRACT

New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could improve our understanding of viral biology and highlight new variants that warrant further study. Here we develop a generic, analytical epidemiological model to infer the transmission effects of mutations from genomic surveillance data. Applying our model to SARS-CoV-2 data across many regions, we find multiple mutations that substantially affect the transmission rate, both within and outside the Spike protein. The mutations that we infer to have the largest effects on transmission are strongly supported by experimental evidence from prior studies. Importantly, our model detects lineages with increased transmission even at low frequencies. As an example, we infer significant transmission advantages for the Alpha, Delta, and Omicron variants shortly after their appearances in regional data, when they comprised only around 1-2% of sample sequences. Our model thus facilitates the rapid identification of variants and mutations that affect transmission from genomic surveillance data.

PMID:39774959 | DOI:10.1038/s41467-024-55593-0

Nat Commun. 2025 Jan 7;16(1):461. doi: 10.1038/s41467-025-55872-4.

ABSTRACT

The dual challenges of global population explosion and environmental deterioration represent major hurdles for 21st Century agriculture culminating in an unprecedented demand for food security. In this Review, we revisit historical concepts of plasticity and canalization before integrating them with contemporary studies of genotype-environment interactions (G×E) that are currently being carried out at the genome-wide level. In doing so we address both fundamental questions regarding G×E and potential strategies to best secure yields in both current and future climate scenarios.

PMID:39774717 | DOI:10.1038/s41467-025-55872-4

J Exp Bot. 2025 Jan 8:eraf005. doi: 10.1093/jxb/eraf005. Online ahead of print.

ABSTRACT

The complex gene regulatory landscape underlying early flower development in Arabidopsis has been extensively studied through transcriptome profiling, and gene networks controlling floral organ development have been derived from the analyses of genome wide binding of key transcription factors. In contrast, the dynamic nature of the proteome during the flower development process is much less understood. In this study, we characterized the floral proteome at different stages during early flower development and correlated it with unbiased transcript expression data. Shotgun proteomics and transcript profiling were conducted using an APETALA1-based floral induction system. A specific analysis pipeline to process the time-course proteomics data was developed. In total, 8,924 proteins and 23,069 transcripts were identified. Co-expression analysis revealed that RNA-protein pairs clustered in various expression pattern modules. An overall positive correlation between RNA and protein level changes was observed, but subgroups of RNA/protein pairs with anticorrelated gene expression changes were also identified and found to be enriched in hormone responsive pathways. In addition, the RNA-seq dataset reported here further expanded the identification of genes whose expression changes during early flower development, and its combination with previously published AP1 ChIP-seq datasets allowed the identification of additional AP1 direct and high-confidence targets.

PMID:39774695 | DOI:10.1093/jxb/eraf005

Anesth Analg. 2024 Nov 19. doi: 10.1213/ANE.0000000000007293. Online ahead of print.

ABSTRACT

BACKGROUND: Delirium after cardiac surgery is common, morbid, and costly, but may be prevented with risk stratification and targeted intervention. In this study, we aimed to identify protein biomarkers and develop a predictive model for postoperative delirium in older patients undergoing cardiac surgery.

METHODS: SomaScan analysis of 1305 proteins in the plasma from 57 older adults undergoing cardiac surgery requiring cardiopulmonary bypass was conducted to define delirium-specific protein signatures at baseline (preoperative baseline timepoint [PREOP]) and postoperative day 2 (POD2). Selected proteins were validated in 115 patients using the Enzyme-Linked Lectin Assay (ELLA) multiplex immunoassay platform. Proteins were combined with clinical and demographic variables to build multivariable models that estimate the risk of postoperative delirium and bring light to the underlying pathophysiology.

RESULTS: Of the 115 patients, 21 (18.3%) developed delirium after surgery. The SomaScan proteome screening evidenced differential expression of 115 and 85 proteins in delirious patients compared to nondelirious preoperatively and at POD2, respectively (P < .05). Following biological and methodological criteria, 12 biomarker candidates (Tukey's fold change [|tFC|] >1.4, Benjamini-Hochberg [BH]-P < .01) were selected for ELLA multiplex validation. Statistical analyses of model fit resulted in the combination of age, sex, and 3 proteins (angiopoietin-2; C-C motif chemokine 5; and metalloproteinase inhibitor 1; area under the curve [AUC] = 0.829) as the best performing predictive model for delirium. Analyses of pathways showed that delirium-associated proteins are involved in inflammation, glial dysfunction, vascularization, and hemostasis.

CONCLUSIONS: Our results support the identification of patients at higher risk of developing delirium after cardiac surgery using a multivariable model that combines demographic and physiological features, also bringing light to the role of immune and vascular dysregulation as underlying mechanisms.

PMID:39774401 | DOI:10.1213/ANE.0000000000007293

PLoS Comput Biol. 2025 Jan 7;21(1):e1012736. doi: 10.1371/journal.pcbi.1012736. Online ahead of print.

ABSTRACT

The denitrifying bacterium Thauera sp. MZ1T, a common member of microbial communities in wastewater treatment facilities, can produce different compounds from a range of carbon (C) and nitrogen (N) sources under aerobic and anaerobic conditions. In these different conditions, Thauera modifies its metabolism to produce different compounds that influence the microbial community. In particular, Thauera sp. MZ1T produces different exopolysaccharides with floc-forming properties, impacting the physical disposition of wastewater consortia and the efficiency of nutrient assimilation by the microbial community. Under N-limiting conditions, Thauera sp. MZ1T decreases its growth rate and accelerates the accumulation of polyhydroxyalkanoate-related (PHA) compounds including polyhydroxybutyrate (PHB), which plays a fundamental role as C and energy storage in this β-proteobacterium. However, the metabolic mechanisms employed by Thauera sp. MZ1T to assimilate and catabolize many of the different C and N sources under aerobic and anaerobic conditions remain unknown. Systems biology approaches such as genome-scale metabolic modeling have been successfully used to unveil complex metabolic mechanisms for various microorganisms. Here, we developed a comprehensive metabolic model (M-model) for Thauera sp. MZ1T (iThauera861), consisting of 1,744 metabolites, 2,384 reactions, and 861 genes. We validated the model experimentally using over 70 different C and N sources under both aerobic and anaerobic conditions. iThauera861 achieved a prediction accuracy of 95% for growth on various C and N sources and close to 85% for assimilation of aromatic compounds under denitrifying conditions. The M-model was subsequently deployed to determine the effects of substrates, oxygen presence, and the C:N ratio on the production of PHB and exopolysaccharides (EPS), showing the highest polymer yields are achieved with nucleotides and amino acids under aerobic conditions. This comprehensive M-model will help reveal the metabolic processes by which this ubiquitous species influences communities in wastewater treatment systems and natural environments.

PMID:39774301 | DOI:10.1371/journal.pcbi.1012736

J Clin Sleep Med. 2025 Jan 7. doi: 10.5664/jcsm.11524. Online ahead of print.

ABSTRACT

STUDY OBJECTIVES: Onera Health has developed the first wireless, patch-based, type-II PSG system, the Onera Sleep Test System (STS), to allow studies to be performed unattended at the patient's home or in any bed at a medical facility. The goal of this multicenter study was to validate data collected from the patch-based PSG to a traditional PSG for sleep staging and AHI.

METHODS: Simultaneous traditional PSG and patch-based PSG study data were obtained in a sleep laboratory from 206 participants with a suspected sleep disorder recruited from 7 clinical sites. Blinded, randomized scoring of the traditional PSG and patch-based PSG recordings was completed according to The AASM Manual for the Scoring of Sleep and Associated Events, version 2.6 criteria by three independent scorers.

RESULTS: Concordance correlation coefficients were high between the patch-based device and traditional PSG across essential sleep and respiratory variables - TST (0.87); Wake (0.84); NREM (0.80); N1 (0.72); N2 (0.71); N3 (0.64); REM (0.80) and AHI (0.94). There was substantial agreement between epoch sleep staging scored on the patch-based device and traditional PSG (average Cohen's kappa of 0.62 ± 0.13 across all scorers).

CONCLUSIONS: The patch-based type-II PSG had a similar performance on sleep staging and respiratory variables when compared to Traditional PSG, thus making it possible to use the patch-based PSG for a routine PSG study. These results open the possibility of performing unattended PSG studies efficiently and accurately outside the sleep laboratory improving access to high quality sleep assessments for patients with sleep disorders.

CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Identifier: NCT05310708.

PMID:39773950 | DOI:10.5664/jcsm.11524

BMC Bioinformatics. 2025 Jan 7;26(1):4. doi: 10.1186/s12859-024-06027-7.

ABSTRACT

In cell line perturbation experiments, a collection of cells is perturbed with external agents and responses such as protein expression measured. Due to cost constraints, only a small fraction of all possible perturbations can be tested in vitro. This has led to the development of computational models that can predict cellular responses to perturbations in silico. A central challenge for these models is to predict the effect of new, previously untested perturbations that were not used in the training data. Here we propose causal structural equations for modeling how perturbations effect cells. From this model, we derive two estimators for predicting responses: a Linear Regression (LR) estimator and a causal structure learning estimator that we term Causal Structure Regression (CSR). The CSR estimator requires more assumptions than LR, but can predict the effects of drugs that were not applied in the training data. Next we present Cellbox, a recently proposed system of ordinary differential equations (ODEs) based model that obtained the best prediction performance on a Melanoma cell line perturbation data set (Yuan et al. in Cell Syst 12:128-140, 2021). We derive analytic results that show a close connection between CSR and Cellbox, providing a new causal interpretation for the Cellbox model. We compare LR and CSR/Cellbox in simulations, highlighting the strengths and weaknesses of the two approaches. Finally we compare the performance of LR and CSR/Cellbox on the benchmark Melanoma data set. We find that the LR model has comparable or slightly better performance than Cellbox.

PMID:39773352 | DOI:10.1186/s12859-024-06027-7

Sci Transl Med. 2025 Jan 8;17(780):eadl2103. doi: 10.1126/scitranslmed.adl2103. Epub 2025 Jan 8.

ABSTRACT

Several antibiotic candidates are in development against Gram-positive bacterial pathogens, but their long-term utility is unclear. To investigate this issue, we studied the laboratory evolution of resistance to antibiotics that have not yet reached the market. We found that, with the exception of compound SCH79797, antibiotic resistance generally readily evolves in Staphylococcus aureus. Cross-resistance was detected between such candidates and antibiotics currently in clinical use, including vancomycin, daptomycin, and the promising antibiotic candidate teixobactin. These patterns were driven by overlapping molecular mechanisms through mutations in regulatory systems. In particular, teixobactin-resistant bacteria displayed clinically relevant multidrug resistance and retained their virulence in an invertebrate infection model, raising concerns. More generally, we demonstrate that putative resistance mutations against candidate antibiotics are already present in natural bacterial populations. Therefore, antibiotic resistance in nature may evolve readily from the selection of preexisting genetic variants. Our work highlights the importance of predicting future evolution of resistance to antibiotic candidates at an early stage of drug development.

PMID:39772773 | DOI:10.1126/scitranslmed.adl2103

J Proteome Res. 2025 Jan 7. doi: 10.1021/acs.jproteome.4c00160. Online ahead of print.

ABSTRACT

Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods is often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecific binding, their results can be filtered using negative controls and computational modeling. However, such filtering does not effectively detect false-positive interactions when IP-MS/MS is performed on human plasma samples. Therein, proteins cannot be overexpressed or inhibited, and existing modeling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS/MS, which leverages negative controls that include antibodies targeting proteins not expected to be present in human plasma. A set of negative controls used for false positive interaction modeling is first constructed. MAGPIE then assesses the reliability of PPIs detected in IP-MS/MS experiments using antibodies that target known plasma proteins. When applied to five IP-MS/MS experiments as a proof of concept, our algorithm identified 68 PPIs with an FDR of 20.77%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool and identified known and predicted PPIs. Our approach provides an unprecedented ability to detect human plasma PPIs, which enables a better understanding of biological processes in plasma.

PMID:39772751 | DOI:10.1021/acs.jproteome.4c00160

ACS Appl Mater Interfaces. 2025 Jan 7. doi: 10.1021/acsami.4c17482. Online ahead of print.

ABSTRACT

Enveloped viruses, such as flaviviruses and coronaviruses, are pathogens of significant medical concern that cause severe infections in humans. Some photosensitizers are known to possess virucidal activity against enveloped viruses, targeting their lipid bilayer. Here we report a series of halogenated difluoroboron-dipyrromethene (BODIPYs) photosensitizers with strong virus-inactivating activity. Our structure-activity relationship analysis revealed that BODIPY scaffolds with a heavy halogen atom demonstrate significant efficacy against both tick-borne encephalitis virus (TBEV; Flaviviridae family) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; Coronaviridae family) along with high singlet oxygen quantum yields. Moreover, select compounds also inactivated other enveloped viruses, such as herpes simplex virus type 1 and monkeypox virus. The nature and length of the alkyl side chain notably influenced the virus-inactivating activity of BODIPY molecules. Furthermore, molecular dynamics studies highlighted the critical importance of the positioning of the chromophore moiety within the lipid bilayer. As membrane-targeting photosensitizers, BODIPYs interact directly with virus particles, causing damage to the viral envelope membranes. Thus, TBEV pretreated with BODIPY was completely noninfective for lab mice. Consequently, BODIPY-based photosensitizers hold potential either as broad-spectrum virus-inactivating antivirals against a variety of phylogenetically unrelated enveloped viruses or as potent inactivators of viruses for the development of vaccines for preventing life-threatening emerging viral diseases.

PMID:39772406 | DOI:10.1021/acsami.4c17482

STAR Protoc. 2025 Jan 7;6(1):103517. doi: 10.1016/j.xpro.2024.103517. Online ahead of print.

ABSTRACT

Recently, studies have emerged exploring the potential application of fecal microbiota transplantation (FMT) in pre-clinical settings. Here, we present a protocol for FMT for mice housed in a specific pathogen-free (SPF) facility. We describe steps for sample collection, microaerophilic processing of freshly collected fecal pellets, and administration through oral gavage. We then detail procedures for the engraftment of the bacterial community. This protocol focuses on age- and gender-matched, healthy donor mice using a mobile and cost-effective alternative to an anoxic cabinet.

PMID:39772388 | DOI:10.1016/j.xpro.2024.103517

Viruses. 2024 Dec 17;16(12):1926. doi: 10.3390/v16121926.

ABSTRACT

This study investigated a library of known and novel glycyrrhizic acid (GL) conjugates with amino acids and dipeptide esters, as inhibitors of the DENV NS2B-NS3 protease. We utilized docking algorithms to evaluate the interactions of these GL derivatives with key residues (His51, Asp75, Ser135, and Gly153) within 10 Å of the DENV-2 NS2B-NS3 protease binding pocket (PDB ID: 2FOM). It was found that compounds 11 and 17 exhibited unique binding patterns, forming hydrogen bonds with Asp75, Tyr150, and Gly153. Based on the molecular docking data, conjugates 11 with L-glutamic acid dimethyl ester, 17 with β-alanine ethyl ester, and 19 with aminoethantic acid methyl ester were further demonstrated as potent inhibitors of DENV-2 NS3 protease, with IC50 values below 1 μM, using NS3-mediated cleavage assay. Compound 11 was the most potent, with EC50 values of 0.034 μM for infectivity, 0.042 μM for virus yield, and a selective index over 2000, aligning with its strong NS3 protease inhibition. Compound 17 exhibited better NS3 protease inhibition than compound 19 but showed weaker effects on infectivity and virus yield. While all compounds strongly inhibited viral infectivity post-entry, compound 19 also blocked viral entry. This study provided valuable insights into the interactions between active GL derivatives and DENV-2 NS2B-NS3 protease, offering a comprehensive framework for identifying lead compounds for further drug optimization and design as NS2B-NS3 protease inhibitors against DENV.

PMID:39772233 | DOI:10.3390/v16121926

Viruses. 2024 Dec 5;16(12):1885. doi: 10.3390/v16121885.

ABSTRACT

Viromics studies are allowing us to understand not only the enormous diversity of the virosphere, but also the potential threat posed by the emerging viruses. Regarding the latter, the main concern lies in monitoring the presence of RNA viruses, but the zoonotic potential of some DNA viruses, on which we have focused in the present study, should also be highlighted. For this purpose, we analyzed 160 fecal samples from 14 species of small terrestrial mammals, 9 of them belonging to the order Rodentia. This allowed us to identify a total of 25 complete or near-complete genomes belonging to the families Papillomaviridae, Polyomaviridae, Adenoviridae, Circoviridae, and Genomoviridae, 18 of which could be considered new species or types. Our results provide a significant increase in the number of complete genomes of DNA viruses of European origin with zoonotic potential in databases, which are at present under-represented compared to RNA viruses. In addition, the characterization of whole genomes is of relevance for the further study of the evolutionary forces governing virus adaptation, such as recombination, which may play an important role in cross-species transmission.

PMID:39772193 | DOI:10.3390/v16121885

Vaccines (Basel). 2024 Dec 6;12(12):1377. doi: 10.3390/vaccines12121377.

ABSTRACT

Background/Objectives: Poxviruses are large DNA viruses that replicate in the host cytoplasm without a nuclear phase. As vaccine vectors, they can package and express large recombinant cassettes from different positions of their genomic core region. We present a comparison between wildtype modified vaccinia Ankara (MVA) and isolate CR19, which has significantly expanded inverted terminal repeats (ITRs). With this expansion, a site in wildtype MVA, called deletion site (DS) IV, has been duplicated at both ends of the genome and now occupies an almost central position in the newly formed ITRs. Methods: We inserted various reporter genes into this site and found that the ITRs can be used for transgene expression. However, ITRs are genomic structures that can rapidly adapt to selective pressure through transient duplication and contraction. To test the potential utility of insertions into viral telomers, we inserted a factor from the cellular innate immune system that interferes with viral replication as an example of a difficult transgene. Results: A site almost in the centre of the ITRs can be used for transgene expression, and both sides are mirrored into identical copies. The example of a challenging transgene, tetherin, proved to be surprisingly efficient in selecting candidate vectors against the large background of parental viruses. Conclusions: Insertion of transgenes into ITRs automatically doubles the gene doses. The functionalisation of viruses with tetherin may accelerate the identification and generation of recombinant vectors for personalised medicine and pandemic preparedness.

PMID:39772039 | DOI:10.3390/vaccines12121377

Polymers (Basel). 2024 Dec 21;16(24):3573. doi: 10.3390/polym16243573.

ABSTRACT

Polyurethane materials, widely used in indoor environments, occasionally exhibit unpleasant odors. An important source of polyurethane odorants is polyether polyols. Previous studies identified odorous 2-ethyldimethyl-1,3,6-trioxocanes in polyurethane materials and polyols but did not investigate the odor activity of the individual isomers. In the present work, an isomer mixture of the precursor dipropylene glycol was fractionated through preparative high-performance liquid chromatography. After the conversion to the corresponding trioxocanes, gas chromatography-olfactometry analyses revealed that just one positional isomer, namely 2-ethyl-4,7-dimethyl-1,3,6-trioxocane, was odor active. Moreover, we observed clear differences in the odor threshold concentrations among its stereoisomers. Only two out of eight isomers displayed an odor, both with an earthy smell and one being approximately 60 times more potent than the other. These insights contribute to a better understanding of polyurethane odor on a molecular level and provide a basis for effective odor control.

PMID:39771426 | DOI:10.3390/polym16243573