Journal of Translational Medicine

The Tasmanian devil (Sarcophilus harrisii) population has undergone a major decline in the wild due to the epidemics of two transmissible cancers known as devil facial tumours (DFT1 and DFT2). A multipronged conservation strategy is in place, but a vaccine that prevents devils from developing devil facial tumour disease would be a major step towards recovering the wild devil population. Critical to an effective DFT vaccine are target antigens. Putative non-coding regions of genomes have been identified as potential tumour-specific antigens for human cancer. These non-coding regions include long terminal repeats of endogenous retroviral elements. We identified 11,193 ERV LTR transcripts that were expressed in DFT1 or DFT2 transcriptomes but not in healthy tissue samples. Using a proteogenomic approach, we identified 33 ERV LTR peptides unique to DFT1 and/or DFT2 immunopeptidomes; four of these were validated in subsequent screens with synthetic peptides. Our study shows the potential for ERV LTRs as novel vaccine targets against DFT1 and DFT2. This method can be applied to other species for the development of cancer vaccine targets that may be shared across tumour types. summaryERV LTRs are present in the Tasmanian devil genome and devil facial tumour cell transcriptomes and immunopeptidome.

Psoriasis is an inflammatory skin disorder driven by abnormal immune activation that promotes excessive proliferation and accelerated turnover of epidermal keratinocytes. IL-17 and TNF pathways are well known in psoriasis, but the other mechanisms that keep the disease active and link it to systemic comorbidities are not yet fully understood. A combined transcriptomic and systems biology framework was applied to map regulatory circuits in psoriatic lesions and to identify phytochemical candidates capable of multi-target modulation for topical intervention. Differential gene expression between lesional and healthy skin was analyzed, followed by pathway enrichment, upstream regulator inference, protein-protein interaction network, and chemical-gene interaction mapping. This integrative strategy revealed a transcriptional landscape dominated by type I/III interferon signaling, antiviral and antimicrobial responses, immune metabolic dysregulation, and transcriptional hubs centered on AP-1 and CREB1. Several genes and upstream regulators not previously associated with psoriasis were identified within inflammatory and cell migration-related modules, indicating unexplored regulatory layers in disease control. Network-guided chemical prioritization and direction-of-effect filtering highlighted seven phytochemicals (mahanine, atractylon, protopine, annomontine, taraxasterol, tricin, and tamarixetin) with multi-target activity across key disease axes. ADMET-based screening suggested protopine and atractylon as favorable candidates for topical delivery, while synergy modeling supported flavonoid-alkaloid combination designs. This multi-layered approach provides mechanistically informed phytochemicals targeting the IL-17/TNF-interferon-AP-1/CREB1-COX-2/MMP9 axis in psoriasis. Experimental validation in keratinocyte and organotypic skin models will be required to determine whether these compounds, individually or in combination, can effectively restore psoriatic signaling in vivo.

This paper presents the Neurobiome Navigator, an AI-powered, highly interactive, and easily navigable application designed to help users explore the complex relationships between the human microbiome and Parkinsons disease (PD). The app focuses on the gut microbiome and the oral microbiome, known to have a strong relationship with PD, as well as impulse control disorders (ICD), a significant non-motor symptom of PD. The system integrates the MINERVA (Microbiome Network Research and Visualization Atlas) knowledge graph with supplemental scientific literature to deliver evidence-based insights. Built using an AI agentic workflow including Streamlit, Neo4j, and LangChain, the application enables users to submit structured survey responses (e.g., oral health, impulse control) or free-text queries. Data retrieval methods include Neo4j graph queries and semantic vector search. Retrieved content is synthesized by a large language model (GPT-4o) and an agentic pipeline to generate personalized, non-clinical suggestions. By bridging advanced AI capabilities with an accessible interface, the Neurobiome Navigator aims to empower users to make informed lifestyle decisions that can potentially improve quality of life, particularly in managing non-motor symptoms of PD. The platform enhances understanding of microbiome-PD connections by presenting complex scientific relationships in an interactive, visually engaging, and easily navigable format. Through personalized insights, dynamic charts, and exploratory tools, it transforms dense biomedical data into actionable, user-friendly guidance, making the learning process both informative and enjoyable.

Current treatments are ineffective to cure or prevent occurrences of autoimmune psoriasis and psoriatic cardiovascular disease/CVD. Psoriasis is associated with deregulated expressions of human endogenous retroviruses (ERVs) variants. ERV transcripts and proteins are detected in lesioned biopsies--without assembled viral particles--in addition to antibody and T-cell responses against ERV-K dUTPase. In persons living with HIV-1, manifestations of psoriasis are exacerbated variably. These may depend on multiple factors, differences in ERVs expressions, subtypes of HIV-1, and/or epigenetics. This article represents a quantitative risk assessment and meta-analysis approach with an attempt to assess causality. We surmise that mutated ERVs trigger aberrant proliferation and differentiation of keratinocytes, which in turn induce proinflammatory polarization. Independent risk factors and/or covariates with a range of relative risk/RR ratios appear to significantly impact the development of autoimmune psoriasis or immune intolerance, plausibly through ERVs genes activity. Given the antihypertensive drugs potential in psoriasis development, a probable role in promising either ERVs activation or perturbations in epigenetic factors is questionable. Although the correlational nature of the data based on RR ratios prevents making robust conclusions, we reckon that the likelihood of attributable risk factors for certain antihypertensive drugs may stem from their pleiotropic effects or potentials for inducing ERV-mediated dysregulation of keratinocytes and/or endothelial cells. These findings expand our knowledge regarding ERV activations and HIV-1, antihypertensive drugs use, and incidents of psoriatic disease, and call for exploring cell-specific therapies aimed at blocking or reversing mutated ERVs gene activity toward attaining stable remissions in psoriasis and associated CVD.

Atopic dermatitis (AD) is a chronic inflammatory disease sustained by dysregulated T cell activity. The OX40/OX40L pathway drives effector and memory T cell proliferation, survival, and cytokine production, making it a key therapeutic target. STAR-0310, a novel anti-OX40 antibody, binds a noncanonical epitope that sterically blocks receptor trimerization without inducing agonism. Structural and functional studies demonstrated a dual mechanism: prevention of new OX40/OX40L interactions and efficient disruption of pre-formed complexes, outperforming comparator antibodies. The pure antagonism and complex disruption capacity of STAR-0310 support its clinical evaluation (NCT06782477) as a differentiated OX40-targeted therapy for AD. HighlightsO_LINovel binding mechanism: STAR-0310 engages OX40 distal to the OX40L site, sterically blocking receptor trimerization without inducing agonism. C_LIO_LIDual action: Prevents formation of new OX40/OX40L complexes and efficiently disrupts pre-formed complexes sustaining inflammation. C_LIO_LIDifferentiation from competitors: Achieves greater efficiency in complex disruption compared with rocatinlimab and IMG-007 with no partial agonist activity. C_LIO_LIClinical potential: Pure antagonist profile supports ongoing evaluation of STAR-0310 (NCT06782477) as a best-in-class OX40 therapy for atopic dermatitis. C_LI

BackgroundSkin aging is multifactorial, and finished multi-ingredient oral beauty supplements require dedicated clinical evaluation because their effects cannot be inferred from individual ingredient data alone. ObjectiveTo explore, in a 56-day single-arm open-label study, whether daily oral intake of NatureU(R) Mind Care BeautyU Caps is associated with within-participant changes in crows-feet wrinkle count (primary endpoint), stratum corneum hydration (secondary endpoint), and additional exploratory skin-aging parameters in adult women. MethodsA single-center, open-label, single-arm exploratory study enrolled 33 healthy women aged 36-56 years; 31 completed the protocol and were included in the completer efficacy analysis. Participants took one capsule orally once daily for 56 consecutive days. Assessments were performed at D0, D28 and D56 using PRIMOS CR, Corneometer CM 825, Cutometer MPA580, Glossymeter, Colorimeter CL400, Mexameter MX18, VISIA CR, DermaScan and a structured self-assessment. ResultsPRIMOS CR crows-feet wrinkle count fell from 965 {+/-} 334 at D0 to 514 {+/-} 171 at D56 (within-participant change -46.74%; nominal P = 0.001). Corneometer hydration rose from 44.3 {+/-} 7.8 to 70.3 {+/-} 9.9 (+58.69%; nominal P = 0.001). Exploratory parameters (other wrinkle metrics, elasticity, gloss, ITA{degrees}, melanin, spots, dermal thickness/density) generally moved in directions consistent with the primary signal. No adverse reactions were reported. ConclusionIn this open-label, single-arm exploratory study, daily NatureU(R) Mind Care BeautyU Caps was associated with within-participant reductions in crows-feet wrinkle count and increases in stratum corneum hydration over 56 days. Findings are hypothesis-generating; randomized placebo-controlled trials are required.

INTRODUCTIONIdiopathic pulmonary fibrosis (IPF) is a progressive and irreversible lung disease with a poor prognosis. While pirfenidone and nintedanib offer some benefits, they cannot cure IPF. Nintedanib inhibits various proliferative pathways and has antifibrotic effects, but its molecular mechanisms and impact on the lung transcriptome in vivo remain unclear. This study aims to evaluate nintedanibs transcriptomic profile in a rat model of bleomycin-induced lung fibrosis. METHODOLOGY/PRINCIPAL FINDINGSLung fibrosis was induced by two intratracheal administrations of bleomycin. Nintedanib protocol included three weeks of daily oral treatments beginning seven days after the first bleomycin dose. Left lungs were processed for histological evaluation using an automated fibrosis quantification system and the Ashcroft Score, while the right lungs were used for RNA sequencing to conduct differential expression and correlation network analysis (WGCNA). WGCNA modules were examined by cell and pathway enrichment analysis. Lipid peroxidation was assessed through the measurement of malondialdehyde in right lung lysates. Bleomycin induced significant fibrotic lesions, as confirmed by the histological evaluations. Nintedanib reduced fibrotic lesion size by about 15% and decreased severe Ashcroft scores. When compared to controls, the number of differentially expressed genes decreased from over 2000 to barely more than 400 after nintedanib treatment. WGCNA identified two gene clusters correlated to histological parameters, with nintedanib-treated animals showing gene expression levels similar to control animals. One cluster was associated with mesenchymal cells and extracellular matrix-related pathways, in line with the known anti-fibrotic effect of nintedanib. The second cluster, involving principally macrophages, was related to lipid metabolism, potentially uncovering a new mechanistic role of nintedanib in modulating lung fibrosis. CONCLUSIONS/SIGNIFICANCEThe mechanisms involving macrophages and lipid metabolism, influenced by nintedanib in this study, may open new research directions to better inquire the role of this cellular type in tissue repair and pathological lung fibrosis.

BackgroundMultiple sclerosis (MS) is the most common inflammatory disease of the central nervous system in young adulthood leading to disability and early retirement. Ketone-based diets improve the disease course in MS animal models and health outcomes in different pilot studies of neurodegenerative diseases. MethodsWe enrolled 105 individuals with relapsing-remitting MS (RRMS) in an 18-month, randomized, controlled study, and randomized them into 1) standard healthy diet (SD) as recommended by the German Nutrition Society, 2) fasting diet (FD) with 7-day fasts every 6 months with intermittent fasting at 6 of 7 days a week or 3) ketogenic diet (KD) with 20-40 g carbohydrates per day. Primary outcome was the number of new MRI lesions after 18 months in the KD and FD compared to SD and compared to baseline. Secondary outcomes included further MRI outcomes, disease biomarkers as well as metabolic, and clinical MS outcomes. ResultsEighty-one participants completed the study. The primary endpoint number of new T2 lesions after 18 months did not change in any of the groups (SD 0 (0-(-1)), FD 0 (2-0), KD 0 (2-0)). Compared to baseline, in the FD group, Neurofilament light chain (NfL) - concentrations were lower at 9 months (-1.94 pg/mL, p = 0.042) and depressive symptoms improved slightly at 18 months (p = 0.079). In the KD group, cognition improved at 18 months (symbol digit modalities test +3.7, p = 0.020). Cardiometabolic risk markers (body mass index, abdominal fat, blood lipids, adipokines, blood pressure) improved in all three groups at 9 months differently and were partially associated with clinical outcomes in the FD and KD group. ConclusionDietary interventions may stabilize RRMS disease course and improve cardiometabolic risk factors, cognition, and depressive symptoms, providing valuable complementary treatment options. Trial registrationClinicalTrials.gov, NCT03508414. Retrospectively registered on 25 April 2018.

Long-COVID (LC) is a serious clinical condition characterised by debilitating fatigue together with a wide array of symptoms that significantly reduce the quality of life of patients. Currently no holistic or even symptom specific treatment options are available, likely due to both a lack of insight into the disease processes that drive LC symptoms and an extreme heterogeneity in patients profiles. We characterised patients and post infection controls, with respect to their immunological profiles with a non-exhaustive panel of biomarkers rationalised based on their potential role in driving symptoms. We observed that the patients symptoms could be grouped into 4 clusters suggesting possible stratification. Systemic inflammation persisted and did not normalize over time in LC. This was not related to persistent SARS-CoV-2 infection, as the presence of circulating N-protein was detected similarly in both patients and controls. No obvious deviation in B-cells and monocytes profiles were observed with minor changes for NK-cells (CD62L+/CD16+/HLA-DR+). Major changes affected CD4+T-cells (and to a lesser extent CD8+T-cells) with respect to exhaustion (PD1+/LAG3+/CD44+), regulation (Treg) and differentiation (naive/memory-CD62L+). Several candidate biomarkers (cytokines, microRNAs, phosphate metabolism) were present more frequently in LC at high levels and provided information on underlying disease processes. While frequencies of candidate autoantibody+ participants were not different, levels of some antibodies were higher in LC. Yet none of these candidates stood out as a universal biomarker for LC, with the exception of CRP (73% cases), and loss of Treg (50%). However, we confirmed that several overlapping underlying aetiologies may be involved in this complex disease. Specific groups of biomarkers also associated with the 4 cluster of patients. Although to be taken with caution due to small numbers, 3 biomarkers discriminated controls from patients (Treg/CD4+PD1+/CD4+CD161+), others were associated with symptoms recovery (low IL10/IL12/IL4 and high miR766) or deterioration (high CD4+CD38+/ CD8+naiveCD62L+/low IL2) over 12 months. This study provides rational for developing targeted therapeutic strategies as well as biomarkers to stratify LC patients most likely to respond.

AO_SCPLOWBSTRACTC_SCPLOWLung cancer is characterized by profound intratumoral and inter-patient heterogeneity, spanning histological subtypes, molecular landscapes, and the tumor microenvironment. While multi-omics integration is essential for capturing this complexity, leveraging these data to explicitly define survival-associated subpopulations remains a significant challenge. In this study, we developed NeuroMDAVIS-FS, an unsupervised deep learning framework designed to stratify lung cancer patients by survival risk, and identify molecular determinants underlying improved clinical outcomes. Using the CPTAC cohort, we integrated genomic (CNV), transcriptomic (RNA-seq), and proteomic profiles to extract modality-specific features. Candidate biomarkers were validated through Kaplan- Meier (KM) survival analysis and univariate Cox proportional hazards (CoxPH) regression. A final multivariate CoxPH model effectively stratified patients into high-risk and low-risk cohorts (Kaplan Meier p-value < 0.001). Notably, the integration of these molecular features with baseline clinical models significantly enhanced prognostic accuracy, improving the concordance index by 43.79% in LUAD, 31.05% in LSCC, and 23.76% across the pan-lung cancer cohort. These results demonstrate that NeuroMDAVIS-FS identifies robust, biologically relevant features that surpass traditional clinical variables in predicting patient outcomes, offering a scalable path for precision oncology.

Fibrosis drives pathology in the chronic autoimmune disease systemic sclerosis (SSc), which has the highest case fatality rate of any systemic autoimmune disease with no validated biomarkers or curative treatments. Our prior work has shown that CD206+ macrophages and dermal fibroblasts engage in cooperative mechanisms of inflammatory and fibrotic activation in SSc. Here, we designed a targeted immunotherapeutic approach to eliminate CD206+ macrophages using chimeric antigen receptor (CAR) T cells. We demonstrate that systemic delivery of a single dose of anti-CD206 CAR T cells restores dermal white adipose tissue (DWAT) in vivo. Notably, loss of subcutaneous fat is a well-recognized but poorly understood aspect of SSc pathogenesis that precedes the development of fibrosis and is driven by changes in lineage commitment of adipose-derived stem cells (ADSCs). Using snRNA-seq and a newly-developed in vitro co-culture model, we report that CD206high macrophages mediate ADSC shift from adipocytic to fibrotic activation in part through an IL-6-dependent mechanism. This report implicates a novel function for macrophages in the regulation of early SSc pathogenesis and is the first to establish the therapeutic efficacy of using CAR T cell immunotherapy to target macrophages in the treatment of SSc skin disease.

BackgroundSequelae of Coronavirus disease 2019 (COVID-19) were investigated by both patient-initiated and academic initiatives. Patients subjective illness perceptions might differ from physicians clinical assessment results. Herein, we explored factors influencing patients perception during COVID-19 recovery. MethodsParticipants of the prospective observation CovILD study with persistent somatic symptoms or cardiopulmonary findings at the clinical follow-up one year after COVID-19 were analyzed (n = 74). Explanatory variables included baseline demographic and comorbidity data, COVID-19 course and one-year follow-up data of persistent somatic symptoms, physical performance, lung function testing (LFT), chest computed tomography (CT) and trans-thoracic echocardiography (TTE). Factors affecting illness perception (Brief Illness Perception Questionnaire, BIPQ) were identified by penalized multi-parameter regression and unsupervised clustering. ResultsIn modeling, 47% of overall illness perception variance at one year after COVID-19 was attributed to fatigue intensity, reduced physical performance, hair loss and baseline respiratory comorbidity. Overall illness perception was independent of LFT results, pulmonary lesions in CT or heart abnormality in TTE. As identified by clustering, persistent somatic symptom count, fatigue, diminished physical performance, dyspnea, hair loss and sleep problems at the one-year follow-up and severe acute COVID-19 were associated with the BIPQ domains of concern, emotional representation, complaints, disease timeline and consequences. ConclusionPersistent somatic symptoms rather than clinical assessment results, revealing lung and heart abnormalities, impact on severity and quality of illness perception at one year after COVID-19 and may foster unhelpful coping mechanisms. Besides COVID-19 severity, individual illness perception should be taken into account when allocating rehabilitation and psychological therapy resources. Study registrationClinicalTrials.gov: NCT04416100.

Image-based machine learning holds great promise for facilitating clinical care, however the datasets often used for model training differ from the interventional clinical trial-based findings frequently used to inform treatment guidelines. Here, we draw on longitudinal imaging of psoriasis patients undergoing treatment in the Ultima 2 clinical trial (NCT02684357), including 2,700 body images with psoriasis area severity index (PASI) annotations by uniformly trained dermatologists. An image-processing workflow integrating clinical photos of multiple body regions into one model pipeline was developed, which we refer to as the One-Step PASI framework due to its simultaneous body detection, lesion detection, and lesion severity classification. Group-stratified cross-validation was performed with 145 deep convolutional neural network models combined in an ensemble learning architecture. The highest-performing model demonstrated a mean absolute error of 3.3, Lins concordance correlation coefficient of 0.86, and Pearson correlation coefficient of 0.90 across a wide range of PASI scores comprising disease classifications of clear skin, mild, and moderate-to-severe disease. Within-person, time-series analysis of model performance demonstrated that PASI predictions closely tracked the trajectory of physician scores from severe to clear skin without systematically over or underestimating PASI scores or percent changes from baseline. This study demonstrates the potential of image processing and deep learning to translate otherwise inaccessible clinical trial data into accurate, extensible machine learning models to assess therapeutic efficacy.

BackgroundIdiopathic membranous nephropathy (IMN) is a predominant cause of nephrotic syndrome among adults. Existing drugs are ineffective in about one-third of IMN patients, and the high recurrence rate makes them far from satisfactory. Therefore, it is imperative to find new therapeutic targets for membranous nephropathy. Circulating inflammatory proteins in plasma have been found to be related to the disease and prognosis of IMN patients, yet the causal relationship between them still remains unclear. A better understanding of the inflammatory response of IMN can help us better understand the occurrence of IMN, as well as a good way to find new therapeutic targets. In this study, we aim to use proteome-wide Mendelian Randomization and colocalization analysis to identify plasma inflammatory proteins as potential therapeutic targets for IMN. MethodsWe selected the genetic instrumental variables (IVs) of 91 plasma inflammatory protein quantitative trait locus (pQTL) data obtained from 14824 European population samples by Zhao JH et al. in 2023 as exposure factors. The outcome variable was obtained from the Genome-Wide Association Study (GWAS) data on IMN, which involved 2150 cases and 5829 controls across five European cohorts. To investigate the associations between inflammatory proteins and IMN risk, we conducted a two-sample bi-directional MR analysis, sensitivity analysis, Bayesian colocalization, phenotype scanning, and analysis of the Protein-Protein Interaction (PPI) network. ResultsThe MR analysis uncovered 2 inflammatory factors associated with IMN. TNF-beta [(Tumor Necrosis Factor-beta) (IVW, OR=1.483, 95%CI=1.186-1.853, P=0.0005, PFDR=0.046)] was associated with an increased risk of IMN. IL-5 [(Interleukin-5) (IVW, OR=0.482, 95%CI=0.302-0.770, P=0.002, PFDR=0.097)] was associated with protective effects against IMN. After False Discovery Rate multiple correction and sensitivity analysis, they remain significant. None of these proteins exhibited a reverse causal relationship. Bayesian colocalization analysis provided evidence that TNF-beta share variants with IMN [posterior probability of hypothesis 4 (PPH4) = 0.88]. Utilizing the PPI network, we identified several proteins associated with the previously mentioned inflammatory proteins. Notably, TNF-beta and IL-5 were found to be linked to Nuclear Factor Kappa B Subunit 1 (NFKB1). ConclusionsOur exhaustive analysis suggests a causative impact of TNF-beta and IL-5 levels on the genetically predisposed risk of IMN. These proteins hold potential as promising therapeutic targets for IMN treatment, thus necessitating further clinical investigation.

Influenza A (H1N1) is an acute respiratory infection, while Guillain-Barre syndrome (GBS) is an autoimmune peripheral neuropathy that can occur as a post-infectious complication. Clinical evidence suggests a potential link between H1N1 infection and subsequent GBS development, implying common immunopathological mechanisms. To explore this, we integrated bioinformatics and systems biology approaches to analyze transcriptome data from H1N1 and GBS patients in the GEO database, and 32 common differentially expressed genes (DEGs) were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed key functional associations for these DEGs. Protein-protein interaction (PPI) network analysis highlighted TLR4, TNF, and ITGAM as central hub genes, uncovering potential shared molecular pathways between H1N1 and GBS. Furthermore, analysis of hub gene interactions with microRNAs (miRNAs), transcription factors (TFs), and related diseases facilitated the prediction of potential therapeutic drugs. Molecular docking simulations were performed to validate predicted drug interactions with the hub gene products. Collectively, these findings delineate shared molecular mechanisms and provide insights for targeted therapy in patients developing GBS post-H1N1 infection.

PurposeTo determine whether genetic predisposition to various skin diseases influences the risk of non-small cell lung cancer (NSCLC) through Mendelian randomization (MR). MethodsSingle nucleotide polymorphisms (SNPs) associated with 26 skin diseases were extracted from the FinnGen R11 dataset and underwent rigorous quality control. To evaluate the association between these skin diseases and the risk of non-small cell lung cancer (NSCLC), we applied several analytical methods, including inverse-variance weighted (IVW), MR-Egger regression, weighted median, Simple mode, and Weighted mode. The robustness of the findings was further supported by assessing SNP heterogeneity with the Cochran Q test and evaluating horizontal pleiotropy using the MR-Egger intercept test. ResultsOur study revealed that genetically predicted dermatitis herpetiformis (DH) was significantly associated with an elevated risk of squamous cell carcinoma of the lung (SCC). Acne was nominally linked to an increased risk of SCC. Additionally, rhinophyma (RHN), hidradenitis suppurativa (HS), and DH were nominally associated with a higher risk of adenocarcinoma of the lung (ADC). Of the remaining 22 skin diseases analyzed, 7 lacked sufficient instrumental variables to meet inclusion criteria. The other 15 skin diseases showed no statistically significant association with NSCLC. ConclusionThis study ultimately analyzed the relationship between 19 skin diseases and NSCLC at the genetic level, while 7 other skin diseases could not be analyzed due to insufficient instrumental variables. Dermatitis herpetiformis and acne were associated with an increased risk of squamous cell carcinoma of the lung. Additionally, rhinophyma, hidradenitis suppurativa, and dermatitis herpetiformis were associated with an increased risk of adenocarcinoma of the lung.

BackgroundPost-acute sequelae of SARS-CoV-2 infection (PASC) affects millions globally, yet the molecular mechanisms underlying acute COVID-19 and its chronic sequelae remain poorly understood. MethodsWe performed an integrative transcriptomic analysis of three independent RNA-seq datasets, capturing the complete COVID-19 pathophysiology from health through acute severe infection to post-acute sequelae and mortality (n=142 total samples). We implemented a containerized analytical pipeline from data download, quantification, differential gene expression to uniformly process these three RNA-seq datasets. ResultsOur analysis reveals striking molecular dichotomies contrasting disease phases with profound clinical implications. Acute severe/critical COVID-19 reveals predominant enrichment of TNF- signaling via NF-{kappa}B pathways (normalized enrichment score >2.5, FDR <0.001), reflecting a cytokine storm pathophysiology characterized by rapid inflammatory developments involving IL-6, TNF-, and anti-apoptotic responses. In contrast, PASC patients exhibit dominant enrichment of Myc Targets V1 and Oxidative Phosphorylation pathways (NES >2.2, FDR <0.005), indicating important shifts toward cellular adaptation. Pathway signature analysis identifies core differentially expressed genes that reliably distinguish disease phases, thereby offering objective biomarkers for precision diagnosis and monitoring. ConclusionsThese findings establish a comprehensive molecular framework distinguishing acute inflammatory from chronic metabolic COVID-19 phases, with potential clinical applicability. TNF-/NF-{kappa}B pathway signatures identify patients at risk for severe disease progression, while Myc/OXPHOS signatures allow objective PASC diagnosis, addressing current reliance on subjective and eliminative diagnosis. This integrative analytical framework has utility beyond COVID-19, offering an applicable approach for precision medicine implementation across other diseases processes. Clinical SignificanceThis study transforms COVID-19 from a symptom-based to a molecularly-defined disease spectrum, enabling precision diagnosis, prognostic monitoring, classification, and targeted therapeutic possibilities based on pathway-specific biomarkers rather than subjective clinical assessments.

BK polyomavirus-associated nephropathy (BKPyVAN) is a serious complication of kidney transplantation. Numerous kidney diseases such as BKPyVAN have been shown to cause mitochondrial dysfunction. This study aims to identify key mitochondria-related genes in BKPyVAN. We merged two datasets, GSE72925 and GSE47199, to form a training set after batch-effect removal. Hub mitochondria-related genes in BKPyVAN were identified using bioinformatics tools. The functional information of the hub genes was analyzed using gene set enrichment analysis (GSEA). A mouse model of polyomavirus (MPyV) infection was established to verify the expression levels of B-cell lymphoma 2-related protein A1 (BCL2A1), Caspase-3 (CASP3), and threonine synthase like 1 (THNSL1) in BKPyVAN. We identified nine mitochondria-related genes that were differentially expressed between BKPyVAN and stable graft samples and correlated with BKPyVAN onset. Among these, three genes (THNSL1, BCL2A1, and CASP3) were identified as robust mitochondria-related genes in BKPyVAN. THNSL1 and BCL2A1 were upregulated and CASP3 was downregulated in BKPyVAN samples. Moreover, THNSL1 and BCL2A1 were upregulated and CASP3 was downregulated in MPyV kidney tissue samples. These genes showed a significant diagnostic value for BKPyVAN. GSEA revealed the potential involvement of these genes in the immune pathways. Additionally, we found correlations between these genes and immune cell infiltration in BKPyVAN patients. Our study identified three robust biomarkers (THNSL1, BCL2A1 and CASP3) for BKPyVAN that might be potential targets for diagnosis and treatment. These biomarkers may be involved in immune pathways and show a significant correlation with immune cell infiltration, suggesting their critical role in BKPyVAN pathogenesis.

BackgroundThe causal relationship between Multiple Sclerosis (MS) and Gastric Cancer (GC) remains unclear despite reports suggesting that MS, an autoimmune disease, may contribute to the development of various tumors. MethodsMendelian Randomization (MR) analysis was employed to explore the potential causal relationship between MS and GC. Subsequently, the GEO database was utilized to identify differentially expressed genes (DEGs) that are commonly associated with both MS and GC, thereby revealing the shared molecular mechanisms underlying these two diseases. ResultsThe MR analysis indicated that MS significantly increased the risk of GC, demonstrating a positive causal effect. However, reverse analysis from GC to MS did not reveal any significant causal relationships. The sensitivity analysis supported the evidence of a positive causal effect of MS on GC. Transcriptomic data analysis identified shared DEGs between MS and GC, particularly those involved in immune regulation, stromal formation, and cell migration, suggesting that these genes operate through similar biological pathways in both diseases. ConclusionThese findings underscore the intricate interplay between autoimmune disorders and gastrointestinal malignancies, offering potential molecular targets for the personalized management of MS and GC prevention.

Long-COVID-19 manifests as a multisystemic condition with varied symptoms lingering beyond three weeks of acute SARS-CoV-2 infection, though its underlying mechanisms remain elusive. Aiming to decipher the long-term molecular impacts of COVID-19, we conducted a transcriptomic analysis on PBMCs from 1-year post-covid patients, including individuals without pneumonia (NP, n=10), those with severe pneumonia (SP, n=11), and healthy controls (C, n=13). Our extensive RNA sequencing revealed 4843 differentially expressed genes (DEGs) and 1056 differentially expressed long non-coding RNAs (DElncRNAs) in "C vs NP," 1651 DEGs and 577 DElncRNAs in "C vs SP," 954 DEGs and 148 DElncRNAs in "NP vs SP," with 291 DEGs and 70 DElncRNAs shared across all groups. We identified 14 hub genes from 291 DEGs, with functional enrichment analysis showing upregulated DEGs mainly linked to inflammation and osteoclast differentiation, and downregulated DEGs to viral infections and immune responses. These hub genes play central roles in inflammatory and immune processes and are significantly associated with pneumonitis and diverse lung diseases. Investigations revealed unique immune cell signatures across DEG categories, associating upregulated DEGs with neutrophils and monocytes, and downregulated DEGs with CD4 memory effector T cells. Analysis of 14 hub genes showed notable upregulation in the no pneumonia group versus healthy controls, displaying complex patterns in the severe pneumonia group. Our study uncovered potential idiopathic pulmonary fibrosis signals in Long-COVID-19 patients PBMC transcriptome, highlighting the urgency for thorough monitoring and extended research to understand COVID-19s lasting effects. This study sheds light on COVID-19s transcriptomic changes and potential lasting effects, guiding future research and therapeutic approaches for Long-COVID-19.