Vector competence of Swedish Culex pipiens mosquitoes for Japanese encephalitis virus

BACKGROUND

Japanese encephalitis virus (JEV) is an emerging mosquito-borne Orthoflavivirus that poses a significant public health risk in many temperate and tropical regions in Asia. Since the climate in some endemic countries is similar to temperate climates observed in Europe, understanding the role of specific mosquito species in the transmission of JEV is essential for predicting and effectively controlling the potential for the introduction and establishment of JEV in Europe.

METHODS

This study aimed to investigate the vector competence of colonized Culex pipiens biotype molestus mosquitoes for JEV. The mosquitoes were initially collected from the field in southern Sweden. The mosquitoes were offered a blood meal containing the Nakayama strain of JEV (genotype III), and infection rates, dissemination rates, and transmission rates were evaluated at 14, 21, and 28 days post-feeding.

RESULTS

The study revealed that colonized Swedish Cx. pipiens are susceptible to JEV infection, with a stable infection rate of around 10% at all timepoints. However, the virus was only detected in the legs of one mosquito at 21 days post-feeding, and no mosquito saliva contained JEV.

CONCLUSIONS

Overall, this research shows that Swedish Cx. pipiens can become infected with JEV, and emphasizes the importance of further understanding of the thresholds and barriers for JEV dissemination in mosquitoes.

Comparative genomics of Ascetosporea gives new insight into the evolutionary basis for animal parasitism in Rhizaria

BACKGROUND

Ascetosporea (Endomyxa, Rhizaria) is a group of unicellular parasites infecting aquatic invertebrates. They are increasingly being recognized as widespread and important in marine environments, causing large annual losses in invertebrate aquaculture. Despite their importance, little molecular data of Ascetosporea exist, with only two genome assemblies published to date. Accordingly, the evolutionary origin of these parasites is unclear, including their phylogenetic position and the genomic adaptations that accompanied the transition from a free-living lifestyle to parasitism. Here, we sequenced and assembled three new ascetosporean genomes, as well as the genome of a closely related amphizoic species, to investigate the phylogeny, origin, and genomic adaptations to parasitism in Ascetosporea.

RESULTS

Using a phylogenomic approach, we confirm the monophyly of Ascetosporea and show that Paramyxida group with Mikrocytida, with Haplosporida being sister to both groups. We report that the genomes of these parasites are relatively small (12-36 Mb) and gene-sparse (~ 2300-5200 genes), while containing surprisingly high amounts of non-coding sequence (~ 70-90% of the genomes). Performing gene-tree aware ancestral reconstruction of gene families, we demonstrate extensive gene losses at the origin of parasitism in Ascetosporea, primarily of metabolic functions, and little gene gain except on terminal branches. Finally, we highlight some functional gene classes that have undergone expansions during evolution of the group.

CONCLUSIONS

We present important new genomic information from a lineage of enigmatic but important parasites of invertebrates and illuminate some of the genomic innovations accompanying the evolutionary transition to parasitism in this lineage. Our results and data provide a genetic basis for the development of control measures against these parasites.

Iron oxide nanoparticles for treatment and diagnosis of chronic inflammatory diseases: A systematic review

Chronic inflammatory conditions are among the most prevalent diseases worldwide. Several debilitating diseases such as atherosclerosis, inflammatory bowel disease, rheumatoid arthritis, and Alzheimer's are linked to chronic inflammation. These conditions often develop into complex and fatal conditions, making early detection and treatment of chronic inflammation crucial. Current diagnostic methods show high variability and do not account for disease heterogeneity and disease-specific proinflammatory markers, often delaying the disease detection until later stages. Furthermore, existing treatment strategies, including high-dose anti-inflammatory and immunosuppressive drugs, have significant side effects and an increased risk of infections. In recent years, superparamagnetic iron oxide nanoparticles (SPIONs) have shown tremendous biomedical potential. SPIONs can function as imaging modalities for magnetic resonance imaging, and as therapeutic agents due to their magnetic hyperthermia capability. Furthermore, the surface functionalization of SPIONs allows the detection of specific disease biomarkers and targeted drug delivery. This systematic review explores the utility of SPIONs against chronic inflammatory disorders, focusing on their dual role as diagnostic and therapeutic agents. We extracted studies indexed in the Web of Science database from the last 10 years (2013-2023), and applied systematic inclusion criteria. This resulted in a final selection of 38 articles, which were analyzed for nanoparticle characteristics, targeted diseases, in vivo and in vitro models used, and the efficacy of the therapeutic or diagnostic modalities. The results revealed that ultrasmall SPIONs are excellent for imaging arterial and neuronal inflammation. Furthermore, novel therapies using SPIONs loaded with chemotherapeutic drugs show promise in the treatment of inflammatory diseases. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.

Phase Identification and Discovery of an Elusive Polymorph of Drug-Polymer Inclusion Complex Using Automated 3D Electron Diffraction

3D electron diffraction (3D ED) has shown great potential in crystal structure determination in materials, small organic molecules, and macromolecules. In this work, an automated, low-dose and low-bias 3D ED protocol has been implemented to identify six phases from a multiple-phase melt-crystallisation product of an active pharmaceutical ingredient, griseofulvin (GSF). Batch data collection under low-dose conditions using a widely available commercial software was combined with automated data analysis to collect and process over 230 datasets in three days. Accurate unit cell parameters obtained from 3D ED data allowed direct phase identification of GSF Forms III, I and the known GSF inclusion complex (IC) with polyethylene glycol (PEG) (GSF-PEG IC-I), as well as three minor phases, namely GSF Forms II, V and an elusive new phase, GSF-PEG IC-II. Their structures were then directly determined by 3D ED. Furthermore, we reveal how the stabilities of the two GSF-PEG IC polymorphs are closely related to their crystal structures. These results demonstrate the power of automated 3D ED for accurate phase identification and direct structure determination of complex, beam-sensitive crystallisation products, which is significant for drug development where solid form screening is crucial for the overall efficacy of the drug product.

Rapid and Bifunctional Chemoselective Metabolome Analysis of Liver Disease Plasma Using the Reagent 4-Nitrophenyl-2H-azirine

Primary sclerosing cholangitis (PSC) is a chronic inflammatory disease of the bile ducts that has been associated with diverse metabolic carboxylic acids. Mass spectrometric techniques are the method of choice for their analysis. However, the broad investigation of this metabolite class remains challenging. Derivatization of carboxylic acids represents a strategy to overcome these limitations but available methods suffer from diverse analytical challenges. Herein, we have designed a novel strategy introducing 4-nitrophenyl-2H-azirine as a new chemoselective moiety for the first time for carboxylic acid metabolites. This moiety was selected as it rapidly forms a stable amide bond and also generates a new ketone, which can be analyzed by our recently developed quant-SCHEMA method specific for carbonyl metabolites. Optimization of this new method revealed a high reproducibility and robustness, which was utilized to validate 102 metabolic carboxylic acids using authentic synthetic standard conjugates in human plasma samples including nine metabolites that were newly detected. Using this sequential analysis of the carbonyl- and carboxylic acid-metabolomes revealed alterations of the ketogenesis pathway, which demonstrates the vast benefit of our unique methodology. We anticipate that the developed azirine moiety with rapid functional group transformation will find broad application in diverse chemical biology research fields.

Effects of microgravity on neural crest stem cells

Exposure to microgravity (μg) results in a range of systemic changes in the organism, but may also have beneficial cellular effects. In a previous study we detected increased proliferation capacity and upregulation of genes related to proliferation and survival in boundary cap neural crest stem cells (BC) after MASER14 sounding rocket flight compared to ground-based controls. However, whether these changes were due to μg or hypergravity was not clarified. In the current MASER15 experiment BCs were exposed simultaneously to μg and 1 g conditions provided by an onboard centrifuge. BCs exposed to μg displayed a markedly increased proliferation capacity compared to 1 g on board controls, and genetic analysis of BCs harvested 5 h after flight revealed an upregulation, specifically in μg-exposed BCs, of Zfp462 transcription factor, a key regulator of cell pluripotency and neuronal fate. This was associated with alterations in exosome microRNA content between μg and 1 g exposed MASER15 specimens. Since the specimens from MASER14 were obtained for analysis with 1 week's delay, we examined whether gene expression and exosome content were different compared to the current MASER15 experiments, in which specimens were harvested 5 h after flight. The overall pattern of gene expression was different and Zfp462 expression was down-regulated in MASER14 BC μg compared to directly harvested specimens (MASER15). MicroRNA exosome content was markedly altered in medium harvested with delay compared to directly collected samples. In conclusion, our analysis indicates that even short exposure to μg alters gene expression, leading to increased BC capacity for proliferation and survival, lasting for a long time after μg exposure. With delayed harvest of specimens, a situation which may occur due to special post-flight circumstances, the exosome microRNA content is modified compared to fast specimen harvest, and the direct effects from μg exposure may be partially attenuated, whereas other effects can last for a long time after return to ground conditions.

PACMan: A software package for automated single-cell chlorophyll fluorometry

Microalgae, small photosynthetic unicells, are of great interest to ecology, ecotoxicology and biotechnology and there is a growing need to investigate the ability of cells to photosynthesize under variable conditions. Current strategies involve hand-operated pulse-amplitude-modulated (PAM) chlorophyll fluorimeters, which can provide detailed insights into the photophysiology of entire populations- or individual cells of microalgae but are typically limited in their throughput. To increase the throughput of a commercially available MICROSCOPY-PAM system, we present the PAM Automation Control Manager ('PACMan'), an open-source Python software package that automates image acquisition, microscopy stage control and the triggering of external hardware components. PACMan comes with a user-friendly graphical user interface and is released together with a stand-alone tool (PAMalysis) for the automated calculation of per-cell maximum quantum efficiencies (= Fv /Fm ). Using these two software packages, we successfully tracked the photophysiology of >1000 individual cells of green algae (Chlamydomonas reinhardtii) and dinoflagellates (genus Symbiodiniaceae) within custom-made microfluidic devices. Compared to the manual operation of MICROSCOPY-PAM systems, this represents a 10-fold increase in throughput. During experiments, PACMan coordinated the movement of the microscope stage and triggered the MICROSCOPY-PAM system to repeatedly capture high-quality image data across multiple positions. Finally, we analyzed single-cell Fv /Fm with the manufacturer-supplied software and PAMalysis, demonstrating a median difference <0.5% between both methods. We foresee that PACMan, and its auxiliary software package will help increase the experimental throughput in a range of microalgae studies currently relying on hand-operated MICROSCOPY-PAM technologies.

Whole genome case-control study of central nervous system toxicity due to antimicrobial drugs

A genetic predisposition to central nervous system (CNS) toxicity induced by antimicrobial drugs (antibiotics, antivirals, antifungals, and antiparasitic drugs) has been suspected. Whole genome sequencing of 66 cases and 833 controls was performed to investigate whether antimicrobial drug-induced CNS toxicity was associated with genetic variation. The primary objective was to test whether antimicrobial-induced CNS toxicity was associated with seventeen efflux transporters at the blood-brain barrier. In this study, variants or structural elements in efflux transporters were not significantly associated with CNS toxicity. Secondary objectives were to test whether antimicrobial-induced CNS toxicity was associated with genes over the whole genome, with HLA, or with structural genetic variation. Uncommon variants in and close to three genes were significantly associated with CNS toxicity according to a sequence kernel association test combined with an optimal unified test (SKAT-O). These genes were LCP1 (q = 0.013), RETSAT (q = 0.013) and SFMBT2 (q = 0.035). Two variants were driving the LCP1 association: rs6561297 (p = 1.15x10-6, OR: 4.60 [95% CI: 2.51-8.46]) and the regulatory variant rs10492451 (p = 1.15x10-6, OR: 4.60 [95% CI: 2.51-8.46]). No common genetic variant, HLA-type or structural variation was associated with CNS toxicity. In conclusion, CNS toxicity due to antimicrobial drugs was associated with uncommon variants in LCP1, RETSAT and SFMBT2.

Ancient reindeer mitogenomes reveal island-hopping colonisation of the Arctic archipelagos

Climate warming at the end of the last glacial period had profound effects on the distribution of cold-adapted species. As their range shifted towards northern latitudes, they were able to colonise previously glaciated areas, including remote Arctic islands. However, there is still uncertainty about the routes and timing of colonisation. At the end of the last ice age, reindeer/caribou (Rangifer tarandus) expanded to the Holarctic region and colonised the archipelagos of Svalbard and Franz Josef Land. Earlier studies have proposed two possible colonisation routes, either from the Eurasian mainland or from Canada via Greenland. Here, we used 174 ancient, historical and modern mitogenomes to reconstruct the phylogeny of reindeer across its whole range and to infer the colonisation route of the Arctic islands. Our data shows a close affinity among Svalbard, Franz Josef Land and Novaya Zemlya reindeer. We also found tentative evidence for positive selection in the mitochondrial gene ND4, which is possibly associated with increased heat production. Our results thus support a colonisation of the Eurasian Arctic archipelagos from the Eurasian mainland and provide some insights into the evolutionary history and adaptation of the species to its High Arctic habitat.

Whole-genome resequencing facilitates the development of a 50K single nucleotide polymorphism genotyping array for Scots pine (Pinus sylvestris L.) and its transferability to other pine species

Scots pine (Pinus sylvestris L.) is one of the most widespread and economically important conifer species in the world. Applications like genomic selection and association studies, which could help accelerate breeding cycles, are challenging in Scots pine because of its large and repetitive genome. For this reason, genotyping tools for conifer species, and in particular for Scots pine, are commonly based on transcribed regions of the genome. In this article, we present the Axiom Psyl50K array, the first single nucleotide polymorphism (SNP) genotyping array for Scots pine based on whole-genome resequencing, that represents both genic and intergenic regions. This array was designed following a two-step procedure: first, 192 trees were sequenced, and a 430K SNP screening array was constructed. Then, 480 samples, including haploid megagametophytes, full-sib family trios, breeding population, and range-wide individuals from across Eurasia were genotyped with the screening array. The best 50K SNPs were selected based on quality, replicability, distribution across the draft genome assembly, balance between genic and intergenic regions, and genotype-environment and genotype-phenotype associations. Of the final 49 877 probes tiled in the array, 20 372 (40.84%) occur inside gene models, while the rest lie in intergenic regions. We also show that the Psyl50K array can yield enough high-confidence SNPs for genetic studies in pine species from North America and Eurasia. This new genotyping tool will be a valuable resource for high-throughput fundamental and applied research of Scots pine and other pine species.

QComics: Recommendations and Guidelines for Robust, Easily Implementable and Reportable Quality Control of Metabolomics Data

The implementation of quality control strategies is crucial to ensure the reproducibility, accuracy, and meaningfulness of metabolomics data. However, this pivotal step is often overlooked within the metabolomics workflow and frequently relies on the use of nonstandardized and poorly reported protocols. To address current limitations in this respect, we have developed QComics, a robust, easily implementable and reportable method for monitoring and controlling data quality. The protocol operates in various sequential steps aimed to (i) correct for background noise and carryover, (ii) detect signal drifts and "out-of-control" observations, (iii) deal with missing data, (iv) remove outliers, (v) monitor quality markers to identify samples affected by improper collection, preprocessing, or storage, and (vi) assess overall data quality in terms of precision and accuracy. Notably, this tool considers important issues often neglected along quality control, such as the need of separately handling missing values and truly absent data to avoid losing relevant biological information, as well as the large impact that preanalytical factors may elicit on metabolomics results. Altogether, the guidelines compiled in QComics might contribute to establishing gold standard recommendations and best practices for quality control within the metabolomics community.

Non-invasive imaging of sympathetic innervation of the pancreas in individuals with type 2 diabetes

AIMS/HYPOTHESIS

Compromised pancreatic sympathetic innervation has been suggested as a factor involved in both immune-mediated beta cell destruction and endocrine dysregulation of pancreatic islets. To further explore these intriguing findings, new techniques for in vivo assessment of pancreatic innervation are required. This is a retrospective study that aimed to investigate whether the noradrenaline (norepinephrine) analogue 11C-hydroxy ephedrine (11C-HED) could be used for quantitative positron emission tomography (PET) imaging of the sympathetic innervation of the human pancreas.

METHODS

In 25 individuals with type 2 diabetes and 64 individuals without diabetes, all of whom had previously undergone 11C-HED-PET/CT because of pheochromocytoma or paraganglioma (or suspicion thereof), the 11C-HED standardised uptake value (SUVmean), 11C-HED specific binding index (SBI), pancreatic functional volume (FV, in ml), functional neuronal volume (FNV, calculated as SUVmean × FV), specific binding index with functional volume (SBI FV, calculated as SBI × FV) and attenuation on CT (HU) were investigated in the entire pancreas, and additionally in six separate anatomical pancreatic regions.

RESULTS

Generally, 11C-HED uptake in the pancreas was high, with marked individual variation, suggesting variability in sympathetic innervation. Moreover, pancreatic CT attenuation (HU) (p<0.001), 11C-HED SBI (p=0.0049) and SBI FV (p=0.0142) were lower in individuals with type 2 diabetes than in individuals without diabetes, whereas 11C-HED SUVmean (p=0.15), FV (p=0.73) and FNV (p=0.30) were similar.

CONCLUSIONS/INTERPRETATION

We demonstrate the feasibility of using 11C-HED-PET for non-invasive assessment of pancreatic sympathetic innervation in humans. These findings warrant further prospective evaluation, especially in individuals with theoretical defects in pancreatic sympathetic innervation, such as those with type 1 diabetes.

Rapid Metabolic Profiling of 1 μL Crude Cerebrospinal Fluid by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Can Differentiate De Novo Parkinson's Disease

Parkinson's disease (PD) is a highly prevalent neurodegenerative disorder affecting the motor system. However, the correct diagnosis of PD and atypical parkinsonism may be difficult with high clinical uncertainty. There is an urgent need to identify reliable biomarkers using high-throughput, molecular-specific methods to improve current diagnostics. Here, we present a matrix-assisted laser desorption/ionization mass spectrometry imaging method that requires minimal sample preparation and only 1 μL of crude cerebrospinal fluid (CSF). The method enables analysis of hundreds of samples in a single experiment while simultaneously detecting numerous metabolites with subppm mass accuracy. To test the method, we analyzed CSF samples from 12 de novo PD patients (that is, newly diagnosed and previously untreated) and 12 age-matched controls. Within the identified molecules, we found neurotransmitters and their metabolites such as γ-aminobutyric acid, 3-methoxytyramine, homovanillic acid, serotonin, histamine, amino acids, and metabolic intermediates. Limits of detection were estimated for multiple neurotransmitters with high linearity (R2 > 0.99) and sensitivity (as low as 16 pg/μL). Application of multivariate classification led to a highly significant (P < 0.001) model of PD prediction with a 100% classification rate, which was further thoroughly validated with a permutation test and univariate analysis. Molecules related to the neuromelanin pathway were found to be significantly increased in the PD group, indicated by their elevated relative intensities compared to the control group. Our method enables rapid detection of PD-related biomarkers in low sample volumes and could serve as a valuable tool in the development of robust PD diagnostics.

Protein-Decorated Microbubbles for Ultrasound-Mediated Cell Surface Manipulation

Delivering cargo to the cell membranes of specific cell types in the body is a major challenge for a range of treatments, including immunotherapy. This study investigates employing protein-decorated microbubbles (MBs) and ultrasound (US) to "tag" cellular membranes of interest with a specific protein. Phospholipid-coated MBs were produced and functionalized with a model protein using a metallochelating complex through an NTA(Ni) and histidine residue interaction. Successful "tagging" of the cellular membrane was observed using microscopy in adherent cells and was promoted by US exposure. Further modification of the MB surface to enable selective binding to target cells was then achieved by functionalizing the MBs with a targeting protein (transferrin) that specifically binds to a receptor on the target cell membrane. Attachment and subsequent transfer of material from MBs functionalized with transferrin to the target cells significantly increased, even in the absence of US. This work demonstrates the potential of these MBs as a platform for the noninvasive delivery of proteins to the surface of specific cell types.

Copy number variations and their effect on the plasma proteome

Structural variations, including copy number variations (CNVs), affect around 20 million bases in the human genome and are common causes of rare conditions. CNVs are rarely investigated in complex disease research because most CNVs are not targeted on the genotyping arrays or the reference panels for genetic imputation. In this study, we characterize CNVs in a Swedish cohort (N = 1,021) using short-read whole-genome sequencing (WGS) and use long-read WGS for validation in a subcohort (N = 15), and explore their effect on 438 plasma proteins. We detected 184,182 polymorphic CNVs and identified 15 CNVs to be associated with 16 proteins (P < 8.22×10-10). Of these, 5 CNVs could be perfectly validated using long-read sequencing, including a CNV which was associated with measurements of the osteoclast-associated immunoglobulin-like receptor (OSCAR) and located upstream of OSCAR, a gene important for bone health. Two other CNVs were identified to be clusters of many short repetitive elements and another represented a complex rearrangement including an inversion. Our findings provide insights into the structure of common CNVs and their effects on the plasma proteome, and highlights the importance of investigating common CNVs, also in relation to complex diseases.

Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes

BACKGROUND

Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis.

METHODS

A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity).

RESULTS

Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B.

CONCLUSIONS

The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.

Coevolution-Driven Method for Efficiently Simulating Conformational Changes in Proteins Reveals Molecular Details of Ligand Effects in the β2AR Receptor

With the advent of AI-powered structure prediction, the scientific community is inching closer to solving protein folding. An unresolved enigma, however, is to accurately, reliably, and deterministically predict alternative conformational states that are crucial for the function of, e.g., transporters, receptors, or ion channels where conformational cycling is innately coupled to protein function. Accurately discovering and exploring all conformational states of membrane proteins has been challenging due to the need to retain atomistic detail while enhancing the sampling along interesting degrees of freedom. The challenges include but are not limited to finding which degrees of freedom are relevant, how to accelerate the sampling along them, and then quantifying the populations of each micro- and macrostate. In this work, we present a methodology that finds relevant degrees of freedom by combining evolution and physics through machine learning and apply it to the conformational sampling of the β2 adrenergic receptor. In addition to predicting new conformations that are beyond the training set, we have computed free energy surfaces associated with the protein's conformational landscape. We then show that the methodology is able to quantitatively predict the effect of an array of ligands on the β2 adrenergic receptor activation through the discovery of new metastable states not present in the training set. Lastly, we also stake out the structural determinants of activation and inactivation pathway signaling through different ligands and compare them to functional experiments to validate our methodology and potentially gain further insights into the activation mechanism of the β2 adrenergic receptor.

Development and validation of a quantitative Proximity Extension Assay instrument with 21 proteins associated with cardiovascular risk (CVD-21)

BACKGROUND

Treatment of cardiovascular diseases (CVD) is a substantial burden to healthcare systems worldwide. New tools are needed to improve precision of treatment by optimizing the balance between efficacy, safety, and cost. We developed a high-throughput multi-marker decision support instrument which simultaneously quantifies proteins associated with CVD.

METHODS AND FINDINGS

Candidate proteins independently associated with different clinical outcomes were selected from clinical studies by the screening of 368 circulating biomarkers. We then custom-designed a quantitative PEA-panel with 21 proteins (CVD-21) by including recombinant antigens as calibrator samples for normalization and absolute quantification of the proteins. The utility of the CVD-21 tool was evaluated in plasma samples from a case-control cohort of 4224 patients with chronic coronary syndrome (CCS) using multivariable Cox regression analyses and machine learning techniques. The assays in the CVD-21 tool gave good precision and high sensitivity with lower level of determination (LOD) between 0.03-0.7 pg/ml for five of the biomarkers. The dynamic range for the assays was sufficient to accurately quantify the biomarkers in the validation study except for troponin I, which in the modeling was replaced by high-sensitive cardiac troponin T (hs-TnT). We created seven different multimarker models, including a reference model with NT-proBNP, hs-TnT, GDF-15, IL-6, and cystatin C and one model with only clinical variables, for the comparison of the discriminative value of the CVD-21 tool. All models with biomarkers including hs-TnT provided similar discrimination for all outcomes, e.g. c-index between 0.68-0.86 and outperformed models using only clinical variables. Most important prognostic biomarkers were MMP-12, U-PAR, REN, VEGF-D, FGF-23, TFF3, ADM, and SCF.

CONCLUSIONS

The CVD-21 tool is the very first instrument which with PEA simultaneously quantifies 21 proteins with associations to different CVD. Novel pathophysiologic and prognostic information beyond that of established biomarkers were identified by a number of proteins.

Inhibition of CDK12 elevates cancer cell dependence on P-TEFb by stimulation of RNA polymerase II pause release

P-TEFb and CDK12 facilitate transcriptional elongation by RNA polymerase II. Given the prominence of both kinases in cancer, gaining a better understanding of their interplay could inform the design of novel anti-cancer strategies. While down-regulation of DNA repair genes in CDK12-targeted cancer cells is being explored therapeutically, little is known about mechanisms and significance of transcriptional induction upon inhibition of CDK12. We show that selective targeting of CDK12 in colon cancer-derived cells activates P-TEFb via its release from the inhibitory 7SK snRNP. In turn, P-TEFb stimulates Pol II pause release at thousands of genes, most of which become newly dependent on P-TEFb. Amongst the induced genes are those stimulated by hallmark pathways in cancer, including p53 and NF-κB. Consequently, CDK12-inhibited cancer cells exhibit hypersensitivity to inhibitors of P-TEFb. While blocking P-TEFb triggers their apoptosis in a p53-dependent manner, it impedes cell proliferation irrespective of p53 by preventing induction of genes downstream of the DNA damage-induced NF-κB signaling. In summary, stimulation of Pol II pause release at the signal-responsive genes underlies the functional dependence of CDK12-inhibited cancer cells on P-TEFb. Our study establishes the mechanistic underpinning for combinatorial targeting of CDK12 with either P-TEFb or the induced oncogenic pathways in cancer.

Complete Mitochondrial DNA Genome Variation in the Swedish Population

The development of complete mitochondrial genome (mitogenome) reference data for inclusion in publicly available population databases is currently underway, and the generation of more high-quality mitogenomes will only enhance the statistical power of this forensically useful locus. To characterize mitogenome variation in Sweden, the mitochondrial DNA (mtDNA) reads from the SweGen whole genome sequencing (WGS) dataset were analyzed. To overcome the interference from low-frequency nuclear mtDNA segments (NUMTs), a 10% variant frequency threshold was applied for the analysis. In total, 934 forensic-quality mitogenome haplotypes were characterized. Almost 45% of the SweGen haplotypes belonged to haplogroup H. Nearly all mitogenome haplotypes (99.1%) were assigned to European haplogroups, which was expected based on previous mtDNA studies of the Swedish population. There were signature northern Swedish and Finnish haplogroups observed in the dataset (e.g., U5b1, W1a), consistent with the nuclear DNA analyses of the SweGen data. The complete mitogenome analysis resulted in high haplotype diversity (0.9996) with a random match probability of 0.15%. Overall, the SweGen mitogenomes provide a large mtDNA reference dataset for the Swedish population and also contribute to the effort to estimate global mitogenome haplotype frequencies.

Label-Free Quantitative Thermal Proteome Profiling Reveals Target Transcription Factors with Activities Modulated by MC3R Signaling

Thermal proteome profiling with label-free quantitation using ion-mobility-enhanced LC-MS offers versatile data sets, providing information on protein differential expression, thermal stability, and the activities of transcription factors. We developed a multidimensional data analysis workflow for label-free quantitative thermal proteome profiling (TPP) experiments that incorporates the aspects of gene set enrichment analysis, differential protein expression analysis, and inference of transcription factor activities from LC-MS data. We applied it to study the signaling processes downstream of melanocortin 3 receptor (MC3R) activation by endogenous agonists derived from the proopiomelanocortin prohormone: ACTH, α-MSH, and γ-MSH. The obtained information was used to map signaling pathways downstream of MC3R and to deduce transcription factors responsible for cellular response to ligand treatment. Using our workflow, we identified differentially expressed proteins and investigated their thermal stability. We found in total 298 proteins with altered thermal stability, resulting from MC3R activation. Out of these, several proteins were transcription factors, indicating them as being downstream target regulators that take part in the MC3R signaling cascade. We found transcription factors CCAR2, DDX21, HMGB2, SRSF7, and TET2 to have altered thermal stability. These apparent target transcription factors within the MC3R signaling cascade play important roles in immune responses. Additionally, we inferred the activities of the transcription factors identified in our data set. This was done with Bayesian statistics using the differential expression data we obtained with label-free quantitative LC-MS. The inferred transcription factor activities were validated in our bioinformatic pipeline by the phosphorylated peptide abundances that we observed, highlighting the importance of post-translational modifications in transcription factor regulation. Our multidimensional data analysis workflow allows for a comprehensive characterization of the signaling processes downstream of MC3R activation. It provides insights into protein differential expression, thermal stability, and activities of key transcription factors. All proteomic data generated in this study are publicly available at DOI: 10.6019/PXD039945.

Imaging of fibrogenesis in the liver by [18F]TZ-Z09591, an Affibody molecule targeting platelet derived growth factor receptor β

BACKGROUND

Platelet-derived growth factor receptor beta (PDGFRβ) is a receptor overexpressed on activated hepatic stellate cells (aHSCs). Positron emission tomography (PET) imaging of PDGFRβ could potentially allow the quantification of fibrogenesis in fibrotic livers. This study aims to evaluate a fluorine-18 radiolabeled Affibody molecule ([18F]TZ-Z09591) as a PET tracer for imaging liver fibrogenesis.

RESULTS

In vitro specificity studies demonstrated that the trans-Cyclooctenes (TCO) conjugated Z09591 Affibody molecule had a picomolar affinity for human PDGFRβ. Biodistribution performed on healthy rats showed rapid clearance of [18F]TZ-Z09591 through the kidneys and low liver background uptake. Autoradiography (ARG) studies on fibrotic livers from mice or humans correlated with histopathology results. Ex vivo biodistribution and ARG revealed that [18F]TZ-Z09591 binding in the liver was increased in fibrotic livers (p = 0.02) and corresponded to binding in fibrotic scars.

CONCLUSIONS

Our study highlights [18F]TZ-Z09591 as a specific tracer for fibrogenic cells in the fibrotic liver, thus offering the potential to assess fibrogenesis clearly.

Strain-specific metabarcoding reveals rapid evolution of copper tolerance in populations of the coastal diatom Skeletonema marinoi

Phytoplankton have short generation times, flexible reproduction strategies, large population sizes and high standing genetic diversity, traits that should facilitate rapid evolution under directional selection. We quantified local adaptation of copper tolerance in a population of the diatom Skeletonema marinoi from a mining-exposed inlet in the Baltic Sea and in a non-exposed population 100 km away. We hypothesized that mining pollution has driven evolution of elevated copper tolerance in the impacted population of S. marinoi. Assays of 58 strains originating from sediment resting stages revealed no difference in the average tolerance to copper between the two populations. However, variation within populations was greater at the mining site, with three strains displaying hyper-tolerant phenotypes. In an artificial evolution experiment, we used a novel intraspecific metabarcoding locus to track selection and quantify fitness of all 58 strains during co-cultivation in one control and one toxic copper treatment. As expected, the hyper-tolerant strains enabled rapid evolution of copper tolerance in the mining-exposed population through selection on available strain diversity. Within 42 days, in each experimental replicate a single strain dominated (30%-99% abundance) but different strains dominated the different treatments. The reference population developed tolerance beyond expectations primarily due to slowly developing plastic response in one strain, suggesting that different modes of copper tolerance are present in the two populations. Our findings provide novel empirical evidence that standing genetic diversity of phytoplankton resting stage allows populations to evolve rapidly (20-50 generations) and flexibly on timescales relevant for seasonal bloom progressions.

OME-Zarr: a cloud-optimized bioimaging file format with international community support

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.

Stratified genetic analysis reveals sex differences in MPO-ANCA-associated vasculitis

OBJECTIVE

To identify and genetically characterize subgroups of patients with ANCA-associated vasculitides (AAV) based on sex and ANCA subtype.

METHODS

A previously established SNP dataset derived from DNA sequencing of 1853 genes and genotyping of 1088 Scandinavian cases with AAV and 1589 controls was stratified for sex and ANCA subtype and analysed for association with five top AAV SNPs. rs9274619, a lead variant at the HLA-DQB1/HLA-DQA2 locus previously associated with AAV positive for myeloperoxidase (MPO)-ANCA, was analysed for association with the cumulative disease involvement of ten different organ systems.

RESULTS

rs9274619 showed a significantly stronger association to MPO-ANCA-positive females than males [P = 2.0 × 10-4, OR = 2.3 (95% CI 1.5, 3.5)], whereas proteinase 3 (PR3)-ANCA-associated variants rs1042335, rs9277341 (HLA-DPB1/A1) and rs28929474 (SERPINA1) were equally associated with females and males with PR3-ANCA. In MPO-ANCA-positive cases, carriers of the rs9274619 risk allele were more prone to disease engagement of eyes [P = 0.021, OR = 11 (95% CI 2.2, 205)] but less prone to pulmonary involvement [P = 0.026, OR = 0.52 (95% CI 0.30, 0.92)]. Moreover, AAV with both MPO-ANCA and PR3-ANCA was associated with the PR3-ANCA lead SNP rs1042335 [P = 0.0015, OR = 0.091 (95% CI 0.0022, 0.55)] but not with rs9274619.

CONCLUSIONS

Females and males with MPO-ANCA-positive AAV differ in genetic predisposition to disease, suggesting at least partially distinct disease mechanisms between the sexes. Double ANCA-positive AAV cases are genetically similar to PR3-ANCA-positive cases, providing clues to the clinical follow-up and treatment of these patients.

Apilactobacillus kunkeei releases RNA-associated membrane vesicles and proteinaceous nanoparticles

Extracellularly released particles, including membrane vesicles, have increasingly been recognized as important for bacterial community functions and host-interaction processes, but their compositions and functional roles differ between species and also between strains of the same species. In this study, we have determined the composition of membrane vesicles and protein particles identified in the cell-free pellets of two strains of Apilactobacillus kunkeei, a defensive symbiont of honeybees. The membrane vesicles were separated from the extracellular particles using density gradient ultracentrifugation. The peaks of the RNA and protein distributions were separated from each other and the highest concentration of RNA was observed in the fractions that contained the membrane vesicles while the highest protein concentration coincided with the fractions that contained extracellular particles. A comparative proteomics analysis by LC-MS/MS showed that 37 proteins with type-I signal peptides were consistently identified across the fractionated samples obtained from the cell-free pellets, of which 29 were orthologs detected in both strains. Functional predictions of the extracellular proteins revealed the presence of glycoside hydrolases, glycosyltransferases, giant proteins and peptidases. The extracellular transcriptomes mapped to a broad set of genes with a similar functional profile as the whole cell transcriptome. This study provides insights into the composition of membrane vesicles and extracellular proteins of a bee-associated symbiont.

Immobilized Enzymes on Magnetic Beads for Separate Mass Spectrometric Investigation of Human Phase II Metabolite Classes

The human body has evolved to remove xenobiotics through a multistep clearance process. Non-endogenous metabolites are converted through a series of phase I and different phase II enzymes into compounds with higher hydrophilicity. These compounds are important for diverse research fields such as toxicology, nutrition, biomarker discovery, doping control, and microbiome metabolism. One of the challenges in these research fields has been the investigation of the two major phase II modifications, sulfation and glucuronidation, and the corresponding unconjugated aglycon independently. We have now developed a new methodology utilizing an immobilized arylsulfatase and an immobilized β-glucuronidase to magnetic beads for treatment of human urine samples. The enzyme activities remained the same compared to the enzyme in solution. The separate mass spectrometric investigation of each metabolite class in a single sample was successfully applied to obtain the dietary glucuronidation and sulfation profile of 116 compounds. Our new chemical biology strategy provides a new tool for the investigation of metabolites in biological samples with the potential for broad-scale application in metabolomics, nutrition, and microbiome studies.

Nascent evolution of recombination rate differences as a consequence of chromosomal rearrangements

Reshuffling of genetic variation occurs both by independent assortment of chromosomes and by homologous recombination. Such reshuffling can generate novel allele combinations and break linkage between advantageous and deleterious variants which increases both the potential and the efficacy of natural selection. Here we used high-density linkage maps to characterize global and regional recombination rate variation in two populations of the wood white butterfly (Leptidea sinapis) that differ considerably in their karyotype as a consequence of at least 27 chromosome fissions and fusions. The recombination data were compared to estimates of genetic diversity and measures of selection to assess the relationship between chromosomal rearrangements, crossing over, maintenance of genetic diversity and adaptation. Our data show that the recombination rate is influenced by both chromosome size and number, but that the difference in the number of crossovers between karyotypes is reduced as a consequence of a higher frequency of double crossovers in larger chromosomes. As expected from effects of selection on linked sites, we observed an overall positive association between recombination rate and genetic diversity in both populations. Our results also revealed a significant effect of chromosomal rearrangements on the rate of intergenic diversity change between populations, but limited effects on polymorphisms in coding sequence. We conclude that chromosomal rearrangements can have considerable effects on the recombination landscape and consequently influence both maintenance of genetic diversity and efficiency of selection in natural populations.

Chemoselective bicyclobutane-based mass spectrometric detection of biological thiols uncovers human and bacterial metabolites

Sulfur is an essential element of life. Thiol-containing metabolites in all organisms are involved in the regulation of diverse biological processes. Especially, the microbiome produces bioactive metabolites or biological intermediates of this compound class. The analysis of thiol-containing metabolites is challenging due to the lack of specific tools, making these compounds difficult to investigate selectively. We have now developed a new methodology comprising bicyclobutane for chemoselective and irreversible capturing of this metabolite class. We utilized this new chemical biology tool immobilized onto magnetic beads for the investigation of human plasma, fecal samples, and bacterial cultures. Our mass spectrometric investigation detected a broad range of human, dietary and bacterial thiol-containing metabolites and we even captured the reactive sulfur species cysteine persulfide in both fecal and bacterial samples. The described comprehensive methodology represents a new mass spectrometric strategy for the discovery of bioactive thiol-containing metabolites in humans and the microbiome.

The spatial landscape of gene expression isoforms in tissue sections

In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, we introduce spatial isoform transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity using long-read sequencing. We show in mouse brain how SiT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching of Plp1 gene between different layers of the olfactory bulb, and the use of external single-cell data allows the nomination of cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that are independently validated by in situ sequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource (https://www.isomics.eu), where isoform expression and RNA editing can be visualized in a spatial context.

RNA interactome capture in Escherichia coli globally identifies RNA-binding proteins

RNA-binding proteins (RPBs) are deeply involved in fundamental cellular processes in bacteria and are vital for their survival. Despite this, few studies have so far been dedicated to direct and global identification of bacterial RBPs. We have adapted the RNA interactome capture (RIC) technique, originally developed for eukaryotic systems, to globally identify RBPs in bacteria. RIC takes advantage of the base pairing potential of poly(A) tails to pull-down RNA-protein complexes. Overexpressing poly(A) polymerase I in Escherichia coli drastically increased transcriptome-wide RNA polyadenylation, enabling pull-down of crosslinked RNA-protein complexes using immobilized oligo(dT) as bait. With this approach, we identified 169 putative RBPs, roughly half of which are already annotated as RNA-binding. We experimentally verified the RNA-binding ability of a number of uncharacterized RBPs, including YhgF, which is exceptionally well conserved not only in bacteria, but also in archaea and eukaryotes. We identified YhgF RNA targets in vivo using CLIP-seq, verified specific binding in vitro, and reveal a putative role for YhgF in regulation of gene expression. Our findings present a simple and robust strategy for RBP identification in bacteria, provide a resource of new bacterial RBPs, and lay the foundation for further studies of the highly conserved RBP YhgF.

Assessing compatibility and viral fitness between poultry-adapted H9N2 and wild bird-derived neuraminidases

Exchange of viral segments between one or more influenza virus subtypes can contribute to a shift in virulence and adaptation to new hosts. Among several influenza subtypes, H9N2 is widely circulating in poultry populations worldwide and has the ability to infect humans. Here, we studied the reassortant compatibility between chicken H9N2 with N1-N9 gene segments of wild bird origin, either with an intact or truncated stalk. Naturally occurring amino acid deletions in the NA stalk of the influenza virus can lead to increased virulence in both mallard ducks and chickens. Our findings show extended genetic compatibility between chicken H9Nx gene segments and the wild-bird NA with and without 20 amino acid stalk deletion. Replication kinetics in avian, mammalian and human cell lines revealed that parental chH9N2 and rH9N6 viruses with intact NA-stalk replicated significantly better in avian DF1 cells compared to human A549 cells. After introducing a stalk deletion, an enhanced preference for replication in mammalian and human cell lines could be observed for rH9N2_Δ(H6), rH9N6_Δ and rH9N9_Δ compared to the parental chH9N2 virus. This highlights the potential emergence of novel viruses with variable phenotypic traits, warranting the continuous monitoring of H9N2 and co-circulating subtypes in avian hosts.

Magnetoresponsive fluorescent core-shell nanoclusters for biomedical applications

Nowadays, superparamagnetic iron oxide nanoparticles (SPIONs) have a dominant role in many subfields of biomedicine. Owing to their peculiar properties, they can be employed for magnetic separation, drug delivery, diagnostics, and hyperthermia treatments. However, these magnetic nanoparticles (NPs) suffer from low unit magnetization due to size constraints (up to 20-30 nm) to exhibit superparamagnetic character. In this work, we have designed and synthesized superparamagnetic nanoclusters (SP-NCs) with diameters of up to 400 nm with high unit magnetization for enhanced loading capacity. These were synthesized with conventional or microwave-assisted solvothermal methods, in the presence of either of the two biomolecules (citrate or l-lysine) as the capping agent. Primary particle size, SP-NC size, surface chemistry, and the resultant magnetic properties were observed to be significantly influenced by the choice of synthesis route and capping agent. Selected SP-NCs were then coated with a fluorophore-doped silica shell to provide fluorescence properties, in the near-infrared spectrum region, while silica provided high chemical and colloidal stability. Heating efficiency studies were performed under alternating magnetic field on the synthesized SP-NCs, highlighting their potential in hyperthermia treatment. We envision that their enhanced magnetically-active content, fluorescence, magnetic property, and heating efficiency will pave the way to more effective uses in biomedical applications.

Novel and unusual genes for nitrogen and metal cycling in Planctomycetota- and KSB1-affiliated metagenome-assembled genomes reconstructed from a marine subsea tunnel

The Oslofjord subsea road tunnel is a unique environment in which the typically anoxic marine deep subsurface is exposed to oxygen. Concrete biodeterioration and steel corrosion in the tunnel have been linked to the growth of iron- and manganese-oxidizing biofilms in areas of saline water seepage. Surprisingly, previous 16S rRNA gene surveys of biofilm samples revealed microbial communities dominated by sequences affiliated with nitrogen-cycling microorganisms. This study aimed to identify microbial genomes with metabolic potential for novel nitrogen- and metal-cycling reactions, representing biofilm microorganisms that could link these cycles and play a role in concrete biodeterioration. We reconstructed 33 abundant, novel metagenome-assembled genomes (MAGs) affiliated with the phylum Planctomycetota and the candidate phylum KSB1. We identified novel and unusual genes and gene clusters in these MAGs related to anaerobic ammonium oxidation, nitrite oxidation, and other nitrogen-cycling reactions. Additionally, 26 of 33 MAGs also had the potential for iron, manganese, and arsenite cycling, suggesting that bacteria represented by these genomes might couple these reactions. Our results expand the diversity of microorganisms putatively involved in nitrogen and metal cycling, and contribute to our understanding of potential biofilm impacts on built infrastructure.

A quantitative model for human neurovascular coupling with translated mechanisms from animals

Neurons regulate the activity of blood vessels through the neurovascular coupling (NVC). A detailed understanding of the NVC is critical for understanding data from functional imaging techniques of the brain. Many aspects of the NVC have been studied both experimentally and using mathematical models; various combinations of blood volume and flow, local field potential (LFP), hemoglobin level, blood oxygenation level-dependent response (BOLD), and optogenetics have been measured and modeled in rodents, primates, or humans. However, these data have not been brought together into a unified quantitative model. We now present a mathematical model that describes all such data types and that preserves mechanistic behaviors between experiments. For instance, from modeling of optogenetics and microscopy data in mice, we learn cell-specific contributions; the first rapid dilation in the vascular response is caused by NO-interneurons, the main part of the dilation during longer stimuli is caused by pyramidal neurons, and the post-peak undershoot is caused by NPY-interneurons. These insights are translated and preserved in all subsequent analyses, together with other insights regarding hemoglobin dynamics and the LFP/BOLD-interplay, obtained from other experiments on rodents and primates. The model can predict independent validation-data not used for training. By bringing together data with complementary information from different species, we both understand each dataset better, and have a basis for a new type of integrative analysis of human data.

Occurrence of late-apoptotic symptoms in porcine preimplantation embryos upon exposure of oocytes to perfluoroalkyl substances (PFASs) under in vitro meiotic maturation

The objectives of this study were to evaluate the effect of perfluoroalkyl substances on early embryonic development and apoptosis in blastocysts using a porcine in vitro model. Porcine oocytes (N = 855) collected from abattoir ovaries were subjected to perfluorooctane sulfonic acid (PFOS) (0.1 μg/ml) and perfluorohexane sulfonic acid (PFHxS) (40 μg/ml) during in vitro maturation (IVM) for 45 h. The gametes were then fertilized and cultured in vitro, and developmental parameters were recorded. After 6 days of culture, resulting blastocysts (N = 146) were stained using a terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay and imaged as stacks using confocal laser scanning microscopy. Proportion of apoptotic cells as well as total numbers of nuclei in each blastocyst were analyzed using objective image analysis. The experiment was run in 9 replicates, always with a control present. Effects on developmental parameters were analyzed using logistic regression, and effects on apoptosis and total numbers of nuclei were analyzed using linear regression. Higher cell count was associated with lower proportion of apoptotic cells, i.e., larger blastocysts contained less apoptotic cells. Upon PFAS exposure during IVM, PFHxS tended to result in higher blastocyst rates on day 5 post fertilization (p = 0.07) and on day 6 post fertilization (p = 0.05) as well as in higher apoptosis rates in blastocysts (p = 0.06). PFHxS resulted in higher total cell counts in blastocysts (p = 0.002). No effects attributable to the concentration of PFOS used here was seen. These findings add to the evidence that some perfluoroalkyl substances may affect female reproduction. More studies are needed to better understand potential implications for continued development as well as for human health.

Mechanistic model for human brain metabolism and its connection to the neurovascular coupling

The neurovascular and neurometabolic couplings (NVC and NMC) connect cerebral activity, blood flow, and metabolism. This interconnection is used in for instance functional imaging, which analyses the blood-oxygen-dependent (BOLD) signal. The mechanisms underlying the NVC are complex, which warrants a model-based analysis of data. We have previously developed a mechanistically detailed model for the NVC, and others have proposed detailed models for cerebral metabolism. However, existing metabolic models are still not fully utilizing available magnetic resonance spectroscopy (MRS) data and are not connected to detailed models for NVC. Therefore, we herein present a new model that integrates mechanistic modelling of both MRS and BOLD data. The metabolic model covers central metabolism, using a minimal set of interactions, and can describe time-series data for glucose, lactate, aspartate, and glutamate, measured after visual stimuli. Statistical tests confirm that the model can describe both estimation data and predict independent validation data, not used for model training. The interconnected NVC model can simultaneously describe BOLD data and can be used to predict expected metabolic responses in experiments where metabolism has not been measured. This model is a step towards a useful and mechanistically detailed model for cerebral blood flow and metabolism, with potential applications in both basic research and clinical applications.

Reply: U-BIOPRED/BIOAIR proteins: inflammation or infection?

We thank D.L. Hahn and W. Webley for their interesting interpretation of the data in our original article, in which we reported on plasma proteins associated with asthma severity in two independent cohorts, U-BIOPRED and BIOAIR [1].

Protein profiles in plasma remained associated with asthma severity in the European asthma cohort U-BIOPRED when controlling for previous history of respiratory infections https://bit.ly/3rdEL0O

Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

Integrated experimental-computational analysis of a HepaRG liver-islet microphysiological system for human-centric diabetes research

Microphysiological systems (MPS) are powerful tools for emulating human physiology and replicating disease progression in vitro. MPS could be better predictors of human outcome than current animal models, but mechanistic interpretation and in vivo extrapolation of the experimental results remain significant challenges. Here, we address these challenges using an integrated experimental-computational approach. This approach allows for in silico representation and predictions of glucose metabolism in a previously reported MPS with two organ compartments (liver and pancreas) connected in a closed loop with circulating medium. We developed a computational model describing glucose metabolism over 15 days of culture in the MPS. The model was calibrated on an experiment-specific basis using data from seven experiments, where HepaRG single-liver or liver-islet cultures were exposed to both normal and hyperglycemic conditions resembling high blood glucose levels in diabetes. The calibrated models reproduced the fast (i.e. hourly) variations in glucose and insulin observed in the MPS experiments, as well as the long-term (i.e. over weeks) decline in both glucose tolerance and insulin secretion. We also investigated the behaviour of the system under hypoglycemia by simulating this condition in silico, and the model could correctly predict the glucose and insulin responses measured in new MPS experiments. Last, we used the computational model to translate the experimental results to humans, showing good agreement with published data of the glucose response to a meal in healthy subjects. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

Microphysiological systems (MPS) are powerful tools to unravel biological knowledge underlying disease. MPS provide a physiologically relevant, human-based in vitro setting, which can potentially yield better translatability to humans than current animal models and traditional cell cultures. However, mechanistic interpretation and extrapolation of the experimental results to human outcome remain significant challenges. In this study, we confront these challenges using an integrated experimental-computational approach. We present a computational model describing glucose metabolism in a previously reported MPS integrating liver and pancreas. This MPS supports a homeostatic feedback loop between HepaRG/HHSteC spheroids and pancreatic islets, and allows for detailed investigations of mechanisms underlying type 2 diabetes in humans. We show that the computational model captures the complex dynamics of glucose-insulin regulation observed in the system, and can provide mechanistic insight into disease progression features, such as insulin resistance and β-cell dynamics. Furthermore, the computational model can explain key differences in temporal dynamics between MPS and human responses, and thus provides a tool for translating experimental insights into human outcome. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

Markov state modelling reveals heterogeneous drug-inhibition mechanism of Calmodulin

Calmodulin (CaM) is a calcium sensor which binds and regulates a wide range of target-proteins. This implicitly enables the concentration of calcium to influence many downstream physiological responses, including muscle contraction, learning and depression. The antipsychotic drug trifluoperazine (TFP) is a known CaM inhibitor. By binding to various sites, TFP prevents CaM from associating to target-proteins. However, the molecular and state-dependent mechanisms behind CaM inhibition by drugs such as TFP are largely unknown. Here, we build a Markov state model (MSM) from adaptively sampled molecular dynamics simulations and reveal the structural and dynamical features behind the inhibitory mechanism of TFP-binding to the C-terminal domain of CaM. We specifically identify three major TFP binding-modes from the MSM macrostates, and distinguish their effect on CaM conformation by using a systematic analysis protocol based on biophysical descriptors and tools from machine learning. The results show that depending on the binding orientation, TFP effectively stabilizes features of the calcium-unbound CaM, either affecting the CaM hydrophobic binding pocket, the calcium binding sites or the secondary structure content in the bound domain. The conclusions drawn from this work may in the future serve to formulate a complete model of pharmacological modulation of CaM, which furthers our understanding of how these drugs affect signaling pathways as well as associated diseases.

Calmodulin (CaM) is a calcium-sensing protein which makes other proteins dependent on the surrounding calcium concentration by binding to these proteins. Such protein-protein interactions with CaM are vital for calcium to control many physiological pathways within the cell. The antipsychotic drug trifluoperazine (TFP) inhibits CaM’s ability to bind and regulate other proteins. Here, we use molecular dynamics simulations together with Markov state modeling and machine learning to understand the structural and dynamical features by which TFP bound to the one domain of CaM prevents association to other proteins. We find that TFP encourages CaM to adopt a conformation that is like the one stabilized in absence of calcium: depending on the binding orientation of TFP, the drug indeed either affects the CaM hydrophobic binding pocket, the calcium binding sites or the secondary structure content in the domain. Understanding TFP binding is a first step towards designing better drugs targeting CaM.

Recall of preexisting cross-reactive B cell memory after Omicron BA.1 breakthrough infection

Understanding immune responses after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) breakthrough infection will facilitate the development of next-generation vaccines. Here, we profiled spike (S)-specific B cell responses after Omicron/BA.1 infection in messenger RNA-vaccinated donors. The acute antibody response was characterized by high levels of somatic hypermutation and a bias toward recognition of ancestral SARS-CoV-2 strains, suggesting the early activation of vaccine-induced memory B cells. BA.1 breakthrough infection induced a shift in B cell immunodominance hierarchy from the S2 subunit, which is highly conserved across SARS-CoV-2 variants of concern (VOCs), and toward the antigenically variable receptor binding domain (RBD). A large proportion of RBD-directed neutralizing antibodies isolated from BA.1 breakthrough infection donors displayed convergent sequence features and broadly recognized SARS-CoV-2 VOCs. Together, these findings provide insights into the role of preexisting immunity in shaping the B cell response to heterologous SARS-CoV-2 variant exposure.

Using multimarker screening to identify biomarkers associated with cardiovascular death in patients with atrial fibrillation

AIMS

Atrial fibrillation (AF) is associated with higher mortality. Biomarkers may improve the understanding of key pathophysiologic processes in AF that lead to death. Using a new multiplex analytic technique, we explored the association between 268 biomarkers and cardiovascular (CV) death in anticoagulated patients with AF.

METHODS AND RESULTS

A case-cohort design with 1.8- to 1.9-year follow-up. The identification cohort included 517 cases and 4057 randomly selected patients from ARISTOTLE. The validation cohort included 277 cases and 1042 randomly selected controls from RE-LY. Plasma collected at randomization was analysed with conventional immunoassays and the OLINK proximity extension assay panels: CVDII, CVDIII, and Inflammation. Association between biomarkers and CV death was evaluated using Random Survival Forest, Boruta, and adjusted Cox-regression analyses. The biomarkers most strongly and consistently associated with CV death were as follows (hazard ratio for inter-quartile comparison [95% CI]): N-terminal pro-B-type natriuretic peptide [NT-proBNP; 1.63 (1.37-1.93)], cardiac troponin T [cTnT-hs; 1.60 (1.35-1.88)], interleukin-6 [IL-6; 1.29 (1.13-1.47)], growth differentiation factor-15 [GDF-15; 1.30 (1.10-1.53)], fibroblast growth factor 23 [FGF-23; 1.21 (1.10-1.33)], urokinase receptor [uPAR; 1.38 (1.16-1.64)], trefoil factor 3 [TFF3; 1.27 (1.10-1.46)], tumour necrosis factor receptor 1 [TNFR1; 1.21 (1.01-1.45)], TNF-related apoptosis-inducing ligand receptor 2 [TRAILR2; 1.18 (1.04-1.34)], and cathepsin L1 [CTSL1; 1.22 (1.07-1.39)].

CONCLUSION

In this comprehensive screening of 268 biomarkers in anticoagulated patients with AF, the underlying mechanisms most strongly associated with CV death were cardiorenal dysfunction (NT-proBNP, cTnT-hs, CTSL1, TFF3), oxidative stress (GDF-15), inflammation (IL-6, GDF-15), calcium balance, vascular and renal dysfunction (FGF-23), fibrinolysis (suPAR), and apoptosis (TNFR1, TRAILR2). These findings provide novel insights into pathophysiologic aspects associated with CV death in AF.

CLINICALTRIALS.GOV IDENTIFIER

NCT00412984 and NCT00262600.

Hyperthermia-Induced In Situ Drug Amorphization by Superparamagnetic Nanoparticles in Oral Dosage Forms

Superparamagnetic iron oxide nanoparticles (SPIONs) generate heat upon exposure to an alternating magnetic field (AMF), which has been studied for hyperthermia treatment and triggered drug release. This study introduces a novel application of magnetic hyperthermia to induce amorphization of a poorly aqueous soluble drug, celecoxib, in situ in tablets for oral administration. Poor aqueous solubility of many drug candidates is a major hurdle in oral drug development. A novel approach to overcome this challenge is in situ amorphization of crystalline drugs. This method facilitates amorphization by molecular dispersion of the drug in a polymeric network inside a tablet, circumventing the physical instability encountered during the manufacturing and storage of conventional amorphous solid dispersions. However, the current shortcomings of this approach include low drug loading, toxicity of excipients, and drug degradation. Here, doped SPIONs produced by flame spray pyrolysis are compacted with polyvinylpyrrolidone and celecoxib and exposed to an AMF in solid state. A design of experiments approach was used to investigate the effects of SPION composition (Zn_0.5Fe_2.5O₄ and Mn_0.5Fe_2.5O₄), doped SPION content (10-20 wt %), drug load (30-50 wt %), and duration of AMF (3-15 min) on the degree of drug amorphization. The degree of amorphization is strongly linked to the maximum tablet temperature achieved during the AMF exposure (r = 0.96), which depends on the SPION composition and content in the tablets. Complete amorphization is achieved with 20 wt % Mn_0.5Fe_2.5O₄ and 30 wt % celecoxib in the tablets that reached the maximum temperature of 165.2 °C after 15 min of AMF exposure. Furthermore, manganese ferrite exhibits no toxicity in human intestinal Caco-2 cell lines. The resulting maximum solubility of in situ amorphized celecoxib is 5 times higher than that of crystalline celecoxib in biorelevant intestinal fluid. This demonstrates the promising capability of SPIONs as enabling excipients to magnetically induce amorphization in situ in oral dosage forms.

MAIT cell compartment characteristics are associated with the immune response magnitude to the BNT162b2 mRNA anti-SARS-CoV-2 vaccine

Mucosa-associated invariant T (MAIT) cells are unconventional T cells with innate-like capacity to rapidly respond to microbial infection via MR1-restricted antigen recognition. Emerging evidence indicate that they can also act as rapid sensors of viral infection via innate cytokine activation. However, their possible role in the immune response to mRNA vaccination is unknown. Here, we evaluated the involvement of MAIT cells in individuals vaccinated with the BNT162b2 mRNA SARS-CoV-2 vaccine. MAIT cell levels, phenotype and function in circulation were preserved and unperturbed through day 35 post-vaccination in healthy donor (HD) vaccinees, as well as people living with HIV (PLWH) or with primary immunodeficiency (PID). Unexpectedly, pre-vaccination and post-vaccination levels of MAIT cells correlated positively with the magnitude of the SARS-CoV-2 spike protein-specific CD4 T cell and antibody responses in the HD vaccinees. This pattern was largely preserved in the PID group, but less so in the PLWH group. Furthermore, in the HD vaccinees levels of MAIT cell activation and cytolytic potential correlated negatively to the adaptive antigen-specific immune responses. These findings indicate an unexpected association between MAIT cell compartment characteristics and the immune response magnitude to the BNT162b2 mRNA vaccine.

A Study Protocol for Validation and Implementation of Whole-Genome and -Transcriptome Sequencing as a Comprehensive Precision Diagnostic Test in Acute Leukemias

Background

Whole-genome sequencing (WGS) and whole-transcriptome sequencing (WTS), with the ability to provide comprehensive genomic information, have become the focal point of research interest as novel techniques that can support precision diagnostics in routine clinical care of patients with various cancer types, including hematological malignancies. This national multi-center study, led by Genomic Medicine Sweden, aims to evaluate whether combined application of WGS and WTS (WGTS) is technically feasible and can be implemented as an efficient diagnostic tool in patients with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). In addition to clinical impact assessment, a health-economic evaluation of such strategy will be performed.

Methods and Analysis

The study comprises four phases (i.e., retrospective, prospective, real-time validation, and follow-up) including approximately 700 adult and pediatric Swedish AML and ALL patients. Results of WGS for tumor (90×) and normal/germline (30×) samples as well as WTS for tumors only will be compared to current standard of care diagnostics. Primary study endpoints are diagnostic efficiency and improved diagnostic yield. Secondary endpoints are technical and clinical feasibility for routine implementation, clinical utility, and health-economic impact.

Discussion

Data from this national multi-center study will be used to evaluate clinical performance of the integrated WGTS diagnostic workflow compared with standard of care. The study will also elucidate clinical and health-economic impacts of a combined WGTS strategy when implemented in routine clinical care.

Clinical Trial Registration

[https://doi.org/10.1186/ISRCTN66987142], identifier [ISRCTN66987142].

Prognosis in moderate-severe traumatic brain injury in a Swedish cohort and external validation of the IMPACT models

Background

A major challenge in management of traumatic brain injury (TBI) is to assess the heterogeneity of TBI pathology and outcome prediction. A reliable outcome prediction would have both great value for the healthcare provider, but also for the patients and their relatives. A well-known prediction model is the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic calculator. The aim of this study was to externally validate all three modules of the IMPACT calculator on TBI patients admitted to Uppsala University hospital (UUH).

Method

TBI patients admitted to UUH are continuously enrolled into the Uppsala neurointensive care unit (NICU) TBI Uppsala Clinical Research (UCR) quality register. The register contains both clinical and demographic data, radiological evaluations, and outcome assessments based on the extended Glasgow outcome scale extended (GOSE) performed at 6 months to 1 year. In this study, we included 635 patients with severe TBI admitted during 2008–2020. We used IMPACT core parameters: age, motor score, and pupillary reaction.

Results

The patients had a median age of 56 (range 18–93), 142 female and 478 male. Using the IMPACT Core model to predict outcome resulted in an AUC of 0.85 for mortality and 0.79 for unfavorable outcome. The CT module did not increase AUC for mortality and slightly decreased AUC for unfavorable outcome to 0.78. However, the lab module increased AUC for mortality to 0.89 but slightly decreased for unfavorable outcome to 0.76. Comparing the predicted risk to actual outcomes, we found that all three models correctly predicted low risk of mortality in the surviving group of GOSE 2–8. However, it produced a greater variance of predicted risk in the GOSE 1 group, denoting general underprediction of risk. Regarding unfavorable outcome, all models once again underestimated the risk in the GOSE 3–4 groups, but correctly predicts low risk in GOSE 5–8.

Conclusions

The results of our study are in line with previous findings from centers with modern TBI care using the IMPACT model, in that the model provides adequate prediction for mortality and unfavorable outcome. However, it should be noted that the prediction is limited to 6 months outcome and not longer time interval.

Identification of electroporation sites in the complex lipid organization of the plasma membrane

The plasma membrane of a biological cell is a complex assembly of lipids and membrane proteins, which tightly regulate transmembrane transport. When a cell is exposed to strong electric field, the membrane integrity becomes transiently disrupted by formation of transmembrane pores. This phenomenon termed electroporation is already utilized in many rapidly developing applications in medicine including gene therapy, cancer treatment, and treatment of cardiac arrhythmias. However, the molecular mechanisms of electroporation are not yet sufficiently well understood; in particular, it is unclear where exactly pores form in the complex organization of the plasma membrane. In this study, we combine coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis to identify how formation of pores depends on the local lipid organization. We show that pores do not form homogeneously across the membrane, but colocalize with domains that have specific features, the most important being high density of polyunsaturated lipids. We further show that knowing the lipid organization is sufficient to reliably predict poration sites with machine learning. Additionally, by analysing poration kinetics with Bayesian survival analysis we show that poration does not depend solely on local lipid arrangement, but also on membrane mechanical properties and the polarity of the electric field. Finally, we discuss how the combination of atomistic and coarse-grained molecular dynamics simulations, machine learning methods, and Bayesian survival analysis can guide the design of future experiments and help us to develop an accurate description of plasma membrane electroporation on the whole-cell level. Achieving this will allow us to shift the optimization of electroporation applications from blind trial-and-error approaches to mechanistic-driven design.