PLoS Comput Biol. 2023 Sep 6;19(9):e1011448. doi: 10.1371/journal.pcbi.1011448. Online ahead of print.

ABSTRACT

African horse sickness is an equine orbivirus transmitted by Culicoides Latreille biting midges. In the last 80 years, it has caused several devastating outbreaks in the equine population in Europe, the Far and Middle East, North Africa, South-East Asia, and sub-Saharan Africa. The disease is endemic in South Africa; however, a unique control area has been set up in the Western Cape where increased surveillance and control measures have been put in place. A deterministic metapopulation model was developed to explore if an outbreak might occur, and how it might develop, if a latently infected horse was to be imported into the control area, by varying the geographical location and months of import. To do this, a previously published ordinary differential equation model was developed with a metapopulation approach and included a vaccinated horse population. Outbreak length, time to peak infection, number of infected horses at the peak, number of horses overall affected (recovered or dead), re-emergence, and Rv (the basic reproduction number in the presence of vaccination) were recorded and displayed using GIS mapping. The model predictions were compared to previous outbreak data to ensure validity. The warmer months (November to March) had longer outbreaks than the colder months (May to September), took more time to reach the peak, and had a greater total outbreak size with more horses infected at the peak. Rv appeared to be a poor predictor of outbreak dynamics for this simulation. A sensitivity analysis indicated that control measures such as vaccination and vector control are potentially effective to manage the spread of an outbreak, and shortening the vaccination window to July to September may reduce the risk of vaccine-associated outbreaks.

PMID:37672554 | DOI:10.1371/journal.pcbi.1011448

PLoS Comput Biol. 2023 Sep 6;19(9):e1011424. doi: 10.1371/journal.pcbi.1011424. Online ahead of print.

ABSTRACT

Chronic Pseudomonas aeruginosa (Pa) lung infections are the leading cause of mortality among cystic fibrosis (CF) patients; therefore, the eradication of new-onset Pa lung infections is an important therapeutic goal that can have long-term health benefits. The use of early antibiotic eradication therapy (AET) has been shown to clear the majority of new-onset Pa infections, and it is hoped that identifying the underlying basis for AET failure will further improve treatment outcomes. Here we generated machine learning models to predict AET outcomes based on pathogen genomic data. We used a nested cross validation design, population structure control, and recursive feature selection to improve model performance and showed that incorporating population structure control was crucial for improving model interpretation and generalizability. Our best model, controlling for population structure and using only 30 recursively selected features, had an area under the curve of 0.87 for a holdout test dataset. The top-ranked features were generally associated with motility, adhesion, and biofilm formation.

PMID:37672526 | DOI:10.1371/journal.pcbi.1011424

Mol Biol Cell. 2023 Sep 6:mbcE23030103. doi: 10.1091/mbc.E23-03-0103. Online ahead of print.

ABSTRACT

The invasive potential of glioblastoma cells is attributed to large changes in pressure and volume, driven by diverse elements, including the cytoskeleton and ion co-transporters. However, how the cell actuates changes in pressure and volume in confinement, and how these changes contribute to invasive motion is unclear. Here, we inhibited SPAK activity, with known impacts on the cytoskeleton and co-transporter activity and explored its role on the migration of glioblastoma cells in confining microchannels to model invasive spread through brain tissue. First, we found that confinement altered cell shape, inducing a transition in morphology that resembled droplet interactions with a capillary vessel, from 'wetting' (more adherent) at low confinement, to 'non-wetting' (less adherent) at high confinement. This transition was marked by a change from negative to positive pressure by the cells to the confining walls, and an increase in migration speed. Second, we found that the SPAK pathway impacted the migration speed in different ways dependent upon the extent of wetting. For non-wetting cells, SPAK inhibition increased cell surface tension and co-transporter activity. By contrast, for wetting cells, it also reduced myosin II and YAP phosphorylation. In both cases, membrane-to-cortex attachment is dramatically reduced. Thus, our results suggest that SPAK inhibition differentially coordinates co-transporter and cytoskeleton-induced forces, to impact glioblastoma migration depending on the extent of confinement. [Media: see text] [Media: see text].

PMID:37672340 | DOI:10.1091/mbc.E23-03-0103

Elife. 2023 Sep 6;12:e82914. doi: 10.7554/eLife.82914. Online ahead of print.

ABSTRACT

The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of Marr and Albus. In these models, granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli. However, recent observations of dense, low-dimensional activity across granule cells have called into question the role of sparse coding in these neurons. Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classical theories. Our results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.

PMID:37671785 | DOI:10.7554/eLife.82914

J Cachexia Sarcopenia Muscle. 2023 Sep 6. doi: 10.1002/jcsm.13320. Online ahead of print.

ABSTRACT

BACKGROUND: Radiation-induced muscle pathology, characterized by muscle atrophy and fibrotic tissue accumulation, is the most common debilitating late effect of therapeutic radiation exposure particularly in juvenile cancer survivors. In healthy muscle, fibro/adipogenic progenitors (FAPs) are required for muscle maintenance and regeneration, while in muscle pathology FAPs are precursors for exacerbated extracellular matrix deposition. However, the role of FAPs in radiation-induced muscle pathology has not previously been explored.

METHODS: Four-week-old Male CBA or C57Bl/6J mice received a single dose (16 Gy) of irradiation (IR) to a single hindlimb with the shielded contralateral limb (CLTR) serving as a non-IR control. Mice were sacrificed 3, 7, 14 (acute IR response), and 56 days post-IR (long-term IR response). Changes in skeletal muscle morphology, myofibre composition, muscle niche cellular dynamics, DNA damage, proliferation, mitochondrial respiration, and metabolism and changes in progenitor cell fate where assessed.

RESULTS: Juvenile radiation exposure resulted in smaller myofibre cross-sectional area, particularly in type I and IIA myofibres (P < 0.05) and reduced the proportion of type I myofibres (P < 0.05). Skeletal muscle fibrosis (P < 0.05) was evident at 56 days post-IR. The IR-limb had fewer endothelial cells (P < 0.05) and fibro-adipogenic progenitors (FAPs) (P < 0.05) at 56 days post-IR. Fewer muscle satellite (stem) cells were detected at 3 and 56 days in the IR-limb (P < 0.05). IR induced FAP senescence (P < 0.05), increased their fibrogenic differentiation (P < 0.01), and promoted their glycolytic metabolism. Further, IR altered the FAP secretome in a manner that impaired muscle satellite (stem) cell differentiation (P < 0.05) and fusion (P < 0.05).

CONCLUSIONS: Our study suggests that following juvenile radiation exposure, FAPs contribute to long-term skeletal muscle atrophy and fibrosis. These findings provide rationale for investigating FAP-targeted therapies to ameliorate the negative late effects of radiation exposure in skeletal muscle.

PMID:37671686 | DOI:10.1002/jcsm.13320

Cell Rep Methods. 2023 Aug 2;3(8):100547. doi: 10.1016/j.crmeth.2023.100547. eCollection 2023 Aug 28.

ABSTRACT

Single-cell-resolved systems biology methods, including omics- and imaging-based measurement modalities, generate a wealth of high-dimensional data characterizing the heterogeneity of cell populations. Representation learning methods are routinely used to analyze these complex, high-dimensional data by projecting them into lower-dimensional embeddings. This facilitates the interpretation and interrogation of the structures, dynamics, and regulation of cell heterogeneity. Reflecting their central role in analyzing diverse single-cell data types, a myriad of representation learning methods exist, with new approaches continually emerging. Here, we contrast general features of representation learning methods spanning statistical, manifold learning, and neural network approaches. We consider key steps involved in representation learning with single-cell data, including data pre-processing, hyperparameter optimization, downstream analysis, and biological validation. Interdependencies and contingencies linking these steps are also highlighted. This overview is intended to guide researchers in the selection, application, and optimization of representation learning strategies for current and future single-cell research applications.

PMID:37671013 | PMC:PMC10475795 | DOI:10.1016/j.crmeth.2023.100547

Front Endocrinol (Lausanne). 2023 Aug 21;14:1272700. doi: 10.3389/fendo.2023.1272700. eCollection 2023.

NO ABSTRACT

PMID:37670881 | PMC:PMC10476518 | DOI:10.3389/fendo.2023.1272700

Front Plant Sci. 2023 Aug 21;14:1221644. doi: 10.3389/fpls.2023.1221644. eCollection 2023.

ABSTRACT

In Canada, the length of the frost-free season necessitates planting crops as early as possible to ensure that the plants have enough time to reach full maturity before they are harvested. Early planting carries inherent risks of cold water imbibition (specifically less than 4°C) affecting seed germination. A marker dataset developed for a previously identified Canadian soybean GWAS panel was leveraged to investigate the effect of cold water imbibition on germination. Seed from a panel of 137 soybean elite cultivars, grown in the field at Ottawa, ON, over three years, were placed on filter paper in petri dishes and allowed to imbibe water for 16 hours at either 4°C or 20°C prior to being transferred to a constant 20°C. Observations on seed germination, defined as the presence of a 1 cm radicle, were done from day two to seven. A three-parameter exponential rise to a maximum equation (3PERM) was fitted to estimate germination, time to the one-half maximum germination, and germination uniformity for each cultivar. Genotype-by-sequencing was used to identify SNPs in 137 soybean lines, and using genome-wide association studies (GWAS - rMVP R package, with GLM, MLM, and FarmCPU as methods), haplotype block analysis, and assumed linkage blocks of ±100 kbp, a threshold for significance was established using the qvalue package in R, and five significant SNPs were identified on chromosomes 1, 3, 4, 6, and 13 for maximum germination after cold water imbibition. Percent of phenotypic variance explained (PVE) and allele substitution effect (ASE) eliminated two of the five candidate SNPs, leaving three QTL regions on chromosomes 3, 6, and 13 (Chr3-3419152, Chr6-5098454, and Chr13-29649544). Based on the gene ontology (GO) enrichment analysis, 14 candidate genes whose function is predicted to include germination and cold tolerance related pathways were identified as candidate genes. The identified QTLs can be used to select future soybean cultivars tolerant to cold water imbibition and mitigate risks associated with early soybean planting.

PMID:37670866 | PMC:PMC10476531 | DOI:10.3389/fpls.2023.1221644

iScience. 2023 Jul 31;26(9):107499. doi: 10.1016/j.isci.2023.107499. eCollection 2023 Sep 15.

ABSTRACT

Given the importance of gut microbial communities for human health, we may want to ensure their stability in terms of species composition and function. Here, we built a mathematical model of a simplified gut composed of two connected patches where species and metabolites can flow from an upstream patch, allowing upstream species to affect downstream species' growth. First, we found that communities in our model are more stable if they assemble through species invasion over time compared to combining a set of species from the start. Second, downstream communities are more stable when species invade the downstream patch less frequently than the upstream patch. Finally, upstream species that have positive effects on downstream species can further increase downstream community stability. Despite it being quite abstract, our model may inform future research on designing more stable microbial communities or increasing the stability of existing ones.

PMID:37670791 | PMC:PMC10475486 | DOI:10.1016/j.isci.2023.107499

Front Syst Biol. 2022;2:910243. doi: 10.3389/fsysb.2022.910243. Epub 2022 Jul 18.

ABSTRACT

Inborn errors of immunity (IEI) are monogenic disorders that can cause diverse symptoms, including recurrent infections, autoimmunity and malignancy. While many factors have contributed, the increased availability of next-generation sequencing has been central in the remarkable increase in identification of novel monogenic IEI over the past years. Throughout this phase of disease discovery, it has also become evident that a given gene variant does not always yield a consistent phenotype, while variants in seemingly disparate genes can lead to similar clinical presentations. Thus, it is increasingly clear that the clinical phenotype of an IEI patient is not defined by genetics alone, but is also impacted by a myriad of factors. Accordingly, we need methods to amplify our current diagnostic algorithms to better understand mechanisms underlying the variability in our patients and to optimize treatment. In this review, we will explore how systems immunology can contribute to optimizing both diagnosis and treatment of IEI patients by focusing on identifying and quantifying key dysregulated pathways. To improve mechanistic understanding in IEI we must deeply evaluate our rare IEI patients using multimodal strategies, allowing both the quantification of altered immune cell subsets and their functional evaluation. By studying representative controls and patients, we can identify causative pathways underlying immune cell dysfunction and move towards functional diagnosis. Attaining this deeper understanding of IEI will require a stepwise strategy. First, we need to broadly apply these methods to IEI patients to identify patterns of dysfunction. Next, using multimodal data analysis, we can identify key dysregulated pathways. Then, we must develop a core group of simple, effective functional tests that target those pathways to increase efficiency of initial diagnostic investigations, provide evidence for therapeutic selection and contribute to the mechanistic evaluation of genetic results. This core group of simple, effective functional tests, targeting key pathways, can then be equitably provided to our rare patients. Systems biology is thus poised to reframe IEI diagnosis and therapy, fostering research today that will provide streamlined diagnosis and treatment choices for our rare and complex patients in the future, as well as providing a better understanding of basic immunology.

PMID:37670772 | PMC:PMC10477056 | DOI:10.3389/fsysb.2022.910243

Nucleic Acids Res. 2023 Sep 6:gkad721. doi: 10.1093/nar/gkad721. Online ahead of print.

ABSTRACT

Molecular structures are often fitted into cryo-EM maps by flexible fitting. When this requires large conformational changes, identifying rigid bodies can help optimize the model-map fit. Tools for identifying rigid bodies in protein structures exist, however an equivalent for nucleic acid structures is lacking. With the increase in cryo-EM maps containing RNA and progress in RNA structure prediction, there is a need for such tools. We previously developed RIBFIND, a program for clustering protein secondary structures into rigid bodies. In RIBFIND2, this approach is extended to nucleic acid structures. RIBFIND2 can identify biologically relevant rigid bodies in important groups of complex RNA structures, capturing a wide range of dynamics, including large rigid-body movements. The usefulness of RIBFIND2-assigned rigid bodies in cryo-EM model refinement was demonstrated on three examples, with two conformations each: Group II Intron complexed IEP, Internal Ribosome Entry Site and the Processome, using cryo-EM maps at 2.7-5 Å resolution. A hierarchical refinement approach, performed on progressively smaller sets of RIBFIND2 rigid bodies, was clearly shown to have an advantage over classical all-atom refinement. RIBFIND2 is available via a web server with structure visualization and as a standalone tool.

PMID:37670532 | DOI:10.1093/nar/gkad721

Brief Bioinform. 2023 Sep 5:bbad309. doi: 10.1093/bib/bbad309. Online ahead of print.

ABSTRACT

A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guided Multi-Omics Network inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent.

PMID:37670505 | DOI:10.1093/bib/bbad309

Adv Biol (Weinh). 2023 Sep 5:e2300233. doi: 10.1002/adbi.202300233. Online ahead of print.

ABSTRACT

Extracellular vesicles (EVs) are highly sought after as a source of biomarkers for disease detection and monitoring. Tumor EV isolation, processing, and evaluation from biofluids is convoluted by EV heterogeneity and biological contaminants and is limited by technical processing efficacy. This study rigorously compares common bulk EV isolation workflows (size exclusion chromatography, SEC; membrane affinity, MA) alongside downstream RNA extraction protocols to investigate molecular analyte recovery. EV integrity and recovery is evaluated using a variety of technologies to quantify total intact EVs, total and surface proteins, and RNA purity and recovery. A comprehensive evaluation of each analyte is performed, with a specific emphasis on maintaining user (n = 2), biological (n = 3), and technical replicates (n≥3) under in vitro conditions. Subsequent study of tumor EV spike-in into healthy donor plasma samples is performed to further validate biofluid-derived EV purity and isolation for clinical application. Results show that EV surface integrity is considerably preserved in eluates from SEC-derived EVs, but RNA recovery and purity, as well as bulk protein isolation, is significantly improved in MA-isolated EVs. This study concludes that EV isolation and RNA extraction pipelines govern recovered analyte integrity, necessitating careful selection of processing modality to enhance recovery of the analyte of interest.

PMID:37670402 | DOI:10.1002/adbi.202300233

Sci Rep. 2023 Sep 5;13(1):14650. doi: 10.1038/s41598-023-41850-7.

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a clear threat to humanity. It has infected over 200 million and killed 4 million people worldwide, and infections continue with no end in sight. To control the pandemic, multiple effective vaccines have been developed, and global vaccinations are in progress. However, the virus continues to mutate. Even when full vaccine coverage is achieved, vaccine-resistant mutants will likely emerge, thus requiring new annual vaccines against drifted variants analogous to influenza. A complimentary solution to this problem could be developing antiviral drugs that inhibit SARS CoV-2 and its drifted variants. Host defense peptides represent a potential source for such an antiviral as they possess broad antimicrobial activity and significant diversity across species. We screened the cathelicidin family of peptides from 16 different species for antiviral activity and identified a wild boar peptide derivative that inhibits SARS CoV-2. This peptide, which we named Yongshi and means warrior in Mandarin, acts as a viral entry inhibitor. Following the binding of SARS-CoV-2 to its receptor, the spike protein is cleaved, and heptad repeats 1 and 2 multimerize to form the fusion complex that enables the virion to enter the cell. A deep learning-based protein sequence comparison algorithm and molecular modeling suggest that Yongshi acts as a mimetic to the heptad repeats of the virus, thereby disrupting the fusion process. Experimental data confirm the binding of Yongshi to the heptad repeat 1 with a fourfold higher affinity than heptad repeat 2 of SARS-CoV-2. Yongshi also binds to the heptad repeat 1 of SARS-CoV-1 and MERS-CoV. Interestingly, it inhibits all drifted variants of SARS CoV-2 that we tested, including the alpha, beta, gamma, delta, kappa and omicron variants.

PMID:37670110 | DOI:10.1038/s41598-023-41850-7

Nat Commun. 2023 Sep 5;14(1):5404. doi: 10.1038/s41467-023-41255-0.

NO ABSTRACT

PMID:37670013 | DOI:10.1038/s41467-023-41255-0

Sci Rep. 2023 Sep 5;13(1):14614. doi: 10.1038/s41598-023-41738-6.

ABSTRACT

Camptothecin is an important anticancer alkaloid produced by particular plant species. No suitable synthetic route has been established for camptothecin production yet, imposing a stress on plant-based production systems. Endophytes associated with these camptothecin-producing plants have been reported to also produce camptothecin and other high-value phytochemicals. A previous study identified a fungal endophyte Alternaria burnsii NCIM 1409, isolated from Nothapodytes nimmoniana, to be a sustainable producer of camptothecin. Our study provides key insights on camptothecin biosynthesis in this recently discovered endophyte. The whole genome sequence of A. burnsii NCIM 1409 was assembled and screened for biosynthetic gene clusters. Comparative studies with related fungi supported the identification of candidate genes involved in camptothecin synthesis and also helped to understand some aspects of the endophyte's defense against the toxic effects of camptothecin. No evidence for horizontal gene transfer of the camptothecin biosynthetic genes from the host plant to the endophyte was detected suggesting an independent evolution of the camptothecin biosynthesis in this fungus.

PMID:37670002 | DOI:10.1038/s41598-023-41738-6

Nat Commun. 2023 Sep 5;14(1):5096. doi: 10.1038/s41467-023-40523-3.

ABSTRACT

Studying animal societies needs detailed observation of many individuals, but technological advances offer new opportunities in this field. Here, we present a state-of-the-art drone observation of a multilevel herd of Przewalski's horses, consisting of harems (one-male, multifemale groups). We track, in high spatio-temporal resolution, the movements of 238 individually identified horses on drone videos, and combine movement analyses with demographic data from two decades of population monitoring. Analysis of collective movements reveals how the structure of the herd's social network is related to kinship and familiarity of individuals. The network centrality of harems is related to their age and how long the harem stallions have kept harems previously. Harems of genetically related stallions are closer to each other in the network, and female exchange is more frequent between closer harems. High movement similarity of females from different harems predicts becoming harem mates in the future. Our results show that only a few minutes of fine-scale movement tracking combined with high throughput data driven analysis can reveal the structure of a society, reconstruct past group dynamics and predict future ones.

PMID:37669934 | DOI:10.1038/s41467-023-40523-3

Front Neuroendocrinol. 2023 Sep 3:101101. doi: 10.1016/j.yfrne.2023.101101. Online ahead of print.

ABSTRACT

Androgens' pleiotropic actions in promoting sex differences present not only a challenge to providing a comprehensive account of their function, but also an opportunity to gain insights by comparing androgenic actions across organ systems. Although often overlooked by neuroscientists, skeletal muscle is another androgen-responsive organ system which shares with the nervous system properties of electrochemical excitability, behavioral relevance, and remarkable capacity for adaptive plasticity. Here we review androgenic regulation of mitogenic plasticity in skeletal muscle with the goal of identifying areas of interest to those researching androgenic mechanisms mediating sexual differentiation of neurogenesis. We use an organizational-activational framework to relate broad areas of similarity and difference between androgen effects on mitogenesis in muscle and brain throughout the lifespan, from early organogenesis, through pubertal organization, adult activation, and aging. The focus of the review is androgenic regulation of muscle-specific stem cells (satellite cells), which share with neural stem cells essential functions in development, plasticity, and repair, albeit with distinct, muscle-specific features. Also considered are areas of paracrine and endocrine interaction between androgen action on muscle and nervous system, including mediation of neural plasticity of innervating and distal neural populations by muscle-produced trophic factors.

PMID:37669703 | DOI:10.1016/j.yfrne.2023.101101

J Proteome Res. 2023 Sep 5. doi: 10.1021/acs.jproteome.3c00341. Online ahead of print.

ABSTRACT

Cross-linking and mass spectrometry (XL-MS) workflows are increasingly popular techniques for generating low-resolution structural information about interacting biomolecules. xQuest is an established software package for analysis of protein-protein XL-MS data, supporting stable isotope-labeled cross-linking reagents. Resultant paired peaks in mass spectra aid sensitivity and specificity of data analysis. The recently developed cross-linking of isotope-labeled RNA and mass spectrometry (CLIR-MS) approach extends the XL-MS concept to protein-RNA interactions, also employing isotope-labeled cross-link (XL) species to facilitate data analysis. Data from CLIR-MS experiments are broadly compatible with core xQuest functionality, but the required analysis approach for this novel data type presents several technical challenges not optimally served by the original xQuest package. Here we introduce RNxQuest, a Python package extension for xQuest, which automates the analysis approach required for CLIR-MS data, providing bespoke, state-of-the-art processing and visualization functionality for this novel data type. Using functions included with RNxQuest, we evaluate three false discovery rate control approaches for CLIR-MS data. We demonstrate the versatility of the RNxQuest-enabled data analysis pipeline by also reanalyzing published protein-RNA XL-MS data sets that lack isotope-labeled RNA. This study demonstrates that RNxQuest provides a sensitive and specific data analysis pipeline for detection of isotope-labeled XLs in protein-RNA XL-MS experiments.

PMID:37669508 | DOI:10.1021/acs.jproteome.3c00341

Proc Natl Acad Sci U S A. 2023 Sep 12;120(37):e2304722120. doi: 10.1073/pnas.2304722120. Epub 2023 Sep 5.

ABSTRACT

Crimean-Congo hemorrhagic fever (CCHF) caused by CCHF virus (CCHFV) is one of the epidemic-prone diseases prioritized by the World Health Organisation as public health emergency with an urgent need for accelerated research. The trajectory of host response against CCHFV is multifarious and remains unknown. Here, we reported the temporal spectrum of pathogenesis following the CCHFV infection using genome-wide blood transcriptomics analysis followed by advanced systems biology analysis, temporal immune-pathogenic alterations, and context-specific progressive and postinfection genome-scale metabolic models (GSMM) on samples collected during the acute (T0), early convalescent (T1), and convalescent-phase (T2). The interplay between the retinoic acid-inducible gene-I-like/nucleotide-binding oligomerization domain-like receptor and tumor necrosis factor signaling governed the trajectory of antiviral immune responses. The rearrangement of intracellular metabolic fluxes toward the amino acid metabolism and metabolic shift toward oxidative phosphorylation and fatty acid oxidation during acute CCHFV infection determine the pathogenicity. The upregulation of the tricarboxylic acid cycle during CCHFV infection, compared to the noninfected healthy control and between the severity groups, indicated an increased energy demand and cellular stress. The upregulation of glycolysis and pyruvate metabolism potentiated energy generation through alternative pathways associated with the severity of the infection. The downregulation of metabolic processes at the convalescent phase identified by blood cell transcriptomics and single-cell type proteomics of five immune cells (CD4+ and CD8+ T cells, CD14+ monocytes, B cells, and NK cells) potentially leads to metabolic rewiring through the recovery due to hyperactivity during the acute phase leading to post-viral fatigue syndrome.

PMID:37669378 | DOI:10.1073/pnas.2304722120