Cellular stress promotes NOD1/2-dependent inflammation via the endogenous metabolite sphingosine-1-phosphate.

Cellular stress has been associated with inflammation, yet precise underlying mechanisms remain elusive. In this study, various unrelated stress inducers were employed to screen for sensors linking altered cellular homeostasis and inflammation. We identified the intracellular pattern recognition receptors NOD1/2, which sense bacterial peptidoglycans, as general stress sensors detecting perturbations of cellular homeostasis. NOD1/2 activation upon such perturbations required generation of the endogenous metabolite sphingosine-1-phosphate (S1P). Unlike peptidoglycan sensing via the leucine-rich repeats domain, cytosolic S1P directly bound to the nucleotide binding domains of NOD1/2, triggering NF-κB activation and inflammatory responses. In sum, we unveiled a hitherto unknown role of NOD1/2 in surveillance of cellular homeostasis through sensing of the cytosolic metabolite S1P. We propose S1P, an endogenous metabolite, as a novel NOD1/2 activator and NOD1/2 as molecular hubs integrating bacterial and metabolic cues.

Innate-like Gene Expression of Lung-Resident Memory CD8+ T Cells during Experimental Human Influenza: A Clinical Study.

Rationale: Suboptimal vaccine immunogenicity and antigenic mismatch, compounded by poor uptake, means that influenza remains a major global disease. T cells recognizing peptides derived from conserved viral proteins could enhance vaccine-induced cross-strain protection. Objectives: To investigate the kinetics, phenotypes, and function of influenza virus-specific CD8+ resident memory T (Trm) cells in the lower airway and infer the molecular pathways associated with their response to infection in vivo. Methods: Healthy volunteers, aged 18-55, were inoculated intranasally with influenza A/California/4/09(H1N1). Blood, upper airway, and (in a subgroup) lower airway samples were obtained throughout infection. Symptoms were assessed by using self-reported diaries, and the nasal viral load was assessed by using quantitative PCR. T-cell responses were analyzed by using a three-color FluoroSpot assay, flow cytometry with MHC I-peptide tetramers, and RNA sequencing, with candidate markers being confirmed by using the immunohistochemistry results for endobronchial biopsy specimens. Measurements and Main Results: After challenge, 57% of participants became infected. Preexisting influenza-specific CD8+ T cells in blood correlated strongly with a reduced viral load, which peaked at Day 3. Influenza-specific CD8+ T cells in BAL fluid were highly enriched and predominantly expressed the Trm markers CD69 and CD103. Comparison between preinfection CD8+ T cells in BAL fluid and blood by using RNA sequencing revealed 3,928 differentially expressed genes, including all major Trm-cell markers. However, gene set enrichment analysis of BAL-fluid CD8+ T cells showed primarily innate cell-related pathways and, during infection, included upregulation of innate chemokines (Cxcl1, Cxcl10, and Cxcl16) that were also expressed by CD8+ cells in bronchial tissues. Conclusions: CD8+ Trm cells in the human lung display innate-like gene and protein expression that demonstrates blurred divisions between innate and adaptive immunity. Clinical study registered with www.clinicaltrials.gov (NCT02755948).

Platelets Restrict the Oxidative Burst in Phagocytes and Facilitate Primary Progressive Tuberculosis.

Rationale: Platelets are generated in the capillaries of the lung, control hemostasis, and display immunological functions. Tuberculosis primarily affects the lung, and patients show platelet changes and hemoptysis. A role of platelets in immunopathology of pulmonary tuberculosis requires careful assessment.Objectives: To identify the dynamics and interaction partners of platelets in the respiratory tissue and establish their impact on the outcome of pulmonary tuberculosis.Methods: Investigations were primarily performed in murine models of primary progressive pulmonary tuberculosis, by analysis of mouse strains with variable susceptibility to Mycobacterium tuberculosis infection using platelet depletion and delivery of antiplatelet drugs.Measurements and Main Results: Platelets were present at the site of infection and formed aggregates with different myeloid subsets during experimental tuberculosis. Such aggregates were also detected in patients with tuberculosis. Platelets were detrimental during the early phase of infection, and this effect was uncoupled from their canonical activation. Platelets left lung cell dynamics and patterns of antimycobacterial T-cell responses unchanged but hampered antimicrobial defense by restricting production of reactive oxygen species in lung-residing myeloid cells.Conclusions: Platelets are detrimental in primary progressive pulmonary tuberculosis, orchestrate lung immunity by modulating innate immune responsiveness, and may be amenable to new interventions for this deadly disease.

Kras mutations and PU.1 promoter methylation are new pathways in murine radiation-induced AML.

Therapy-related and more specifically radiotherapy-associated acute myeloid leukaemia (AML) is a well-recognized potential complication of cytotoxic therapy for the treatment of a primary cancer. The CBA mouse model is used to study radiation leukaemogenesis mechanisms with Sfpi1/PU.1 deletion and point mutation already identified as driving events during AML development. To identify new pathways, we analysed 123 mouse radiation-induced AML (rAML) samples for the presence of mutations identified previously in human AML and found three genes to be mutated; Sfpi1 R235 (68%), Flt3-ITD (4%) and Kras G12 (3%), of which G12R was previously unreported. Importantly, a significant decrease in Sfpi1 gene expression is found almost exclusively in rAML samples without an Sfpi1 R235 mutation and is specifically associated with up-regulation of mir-1983 and mir-582-5p. Moreover, this down-regulation of Sfpi1 mRNA is negatively correlated with DNA methylation levels at specific CpG sites upstream of the Sfpi1 transcriptional start site. The down regulation of Sfpi1/PU.1 has also been reported in human AML cases revealing one common pathway of myeloid disruption between mouse and human AML where dysregulation of Sfpi1/PU.1 is a necessary step in AML development.

Gene set enrichment for reproducible science: comparison of CERNO and eight other algorithms.

MOTIVATION: Analysis of gene set (GS) enrichment is an essential part of functional omics studies. Here, we complement the established evaluation metrics of GS enrichment algorithms with a novel approach to assess the practical reproducibility of scientific results obtained from GS enrichment tests when applied to related data from different studies. RESULTS: We evaluated eight established and one novel algorithm for reproducibility, sensitivity, prioritization, false positive rate and computational time. In addition to eight established algorithms, we also included Coincident Extreme Ranks in Numerical Observations (CERNO), a flexible and fast algorithm based on modified Fisher P-value integration. Using real-world datasets, we demonstrate that CERNO is robust to ranking metrics, as well as sample and GS size. CERNO had the highest reproducibility while remaining sensitive, specific and fast. In the overall ranking Pathway Analysis with Down-weighting of Overlapping Genes, CERNO and over-representation analysis performed best, while CERNO and GeneSetTest scored high in terms of reproducibility. AVAILABILITY AND IMPLEMENTATION: tmod package implementing the CERNO algorithm is available from CRAN (cran.r-project.org/web/packages/tmod/index.html) and an online implementation can be found at http://tmod.online/. The datasets analyzed in this study are widely available in the KEGGdzPathwaysGEO, KEGGandMetacoreDzPathwaysGEO R package and GEO repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combining CDKN1A gene expression and genome-wide SNPs in a twin cohort to gain insight into the heritability of individual radiosensitivity.

Individual variability in response to radiation exposure is recognised and has often been reported as important in treatment planning. Despite many efforts to identify biomarkers allowing the identification of radiation sensitive patients, it is not yet possible to distinguish them with certainty before the beginning of the radiotherapy treatment. A comprehensive analysis of genome-wide single-nucleotide polymorphisms (SNPs) and a transcriptional response to ionising radiation exposure in twins have the potential to identify such an individual. In the present work, we investigated SNP profile and CDKN1A gene expression in blood T lymphocytes from 130 healthy Caucasians with a complex level of individual kinship (unrelated, mono- or dizygotic twins). It was found that genetic variation accounts for 66% (95% CI 37-82%) of CDKN1A transcriptional response to radiation exposure. We developed a novel integrative multi-kinship strategy allowing investigating the role of genome-wide polymorphisms in transcriptomic radiation response, and it revealed that rs205543 (ETV6 gene), rs2287505 and rs1263612 (KLF7 gene) are significantly associated with CDKN1A expression level. The functional analysis revealed that rs6974232 (RPA3 gene), involved in mismatch repair (p value = 9.68e-04) as well as in RNA repair (p value = 1.4e-03) might have an important role in that process. Two missense polymorphisms with possible deleterious effect in humans were identified: rs1133833 (AKIP1 gene) and rs17362588 (CCDC141 gene). In summary, the data presented here support the validity of this novel integrative data analysis strategy to provide insights into the identification of SNPs potentially influencing radiation sensitivity. Further investigations in radiation response research at the genomic level should be therefore continued to confirm these findings.

Ranking metrics in gene set enrichment analysis: do they matter?

BACKGROUND: There exist many methods for describing the complex relation between changes of gene expression in molecular pathways or gene ontologies under different experimental conditions. Among them, Gene Set Enrichment Analysis seems to be one of the most commonly used (over 10,000 citations). An important parameter, which could affect the final result, is the choice of a metric for the ranking of genes. Applying a default ranking metric may lead to poor results. METHODS AND RESULTS: In this work 28 benchmark data sets were used to evaluate the sensitivity and false positive rate of gene set analysis for 16 different ranking metrics including new proposals. Furthermore, the robustness of the chosen methods to sample size was tested. Using k-means clustering algorithm a group of four metrics with the highest performance in terms of overall sensitivity, overall false positive rate and computational load was established i.e. absolute value of Moderated Welch Test statistic, Minimum Significant Difference, absolute value of Signal-To-Noise ratio and Baumgartner-Weiss-Schindler test statistic. In case of false positive rate estimation, all selected ranking metrics were robust with respect to sample size. In case of sensitivity, the absolute value of Moderated Welch Test statistic and absolute value of Signal-To-Noise ratio gave stable results, while Baumgartner-Weiss-Schindler and Minimum Significant Difference showed better results for larger sample size. Finally, the Gene Set Enrichment Analysis method with all tested ranking metrics was parallelised and implemented in MATLAB, and is available at https://github.com/ZAEDPolSl/MrGSEA . CONCLUSIONS: Choosing a ranking metric in Gene Set Enrichment Analysis has critical impact on results of pathway enrichment analysis. The absolute value of Moderated Welch Test has the best overall sensitivity and Minimum Significant Difference has the best overall specificity of gene set analysis. When the number of non-normally distributed genes is high, using Baumgartner-Weiss-Schindler test statistic gives better outcomes. Also, it finds more enriched pathways than other tested metrics, which may induce new biological discoveries.

Seeking genetic signature of radiosensitivity--a novel method for data analysis in case of small sample sizes.

BACKGROUND: The identification of polymorphisms and/or genes responsible for an organism's radiosensitivity increases the knowledge about the cell cycle and the mechanism of the phenomena themselves, possibly providing the researchers with a better understanding of the process of carcinogenesis. AIM: The aim of the study was to develop a data analysis strategy capable of discovering the genetic background of radiosensitivity in the case of small sample size studies. RESULTS: Among many indirect measures of radiosensitivity known, the level of radiation-induced chromosomal aberrations was used in the study. Mathematical modelling allowed the transformation of the yield-time curve of radiation-induced chromosomal aberrations into the exponential curve with limited number of parameters, while Gaussian mixture models applied to the distributions of these parameters provided the criteria for mouse strain classification. A detailed comparative analysis of genotypes between the obtained subpopulations of mice followed by functional validation provided a set of candidate polymorphisms that might be related to radiosensitivity. Among 1857 candidate relevant SNPs, that cluster in 28 genes, eight SNPs were detected nonsynonymous (nsSNP) on protein function. Two of them, rs48840878 (gene Msh3) and rs5144199 (gene Cc2d2a), were predicted as having increased probability of a deleterious effect. Additionally, rs48840878 is capable of disordering phosphorylation with 14 PKs. In silico analysis of candidate relevant SNP similarity score distribution among 60 CGD mouse strains allowed for the identification of SEA/GnJ and ZALENDE/EiJ mouse strains (95.26% and 86.53% genetic consistency respectively) as the most similar to radiosensitive subpopulation CONCLUSIONS: A complete step-by-step strategy for seeking the genetic signature of radiosensitivity in the case of small sample size studies conducted on mouse models was proposed. It is shown that the strategy, which is a combination of mathematical modelling, statistical analysis and data mining methodology, allows for the discovery of candidate polymorphisms which might be responsible for radiosensitivity phenomena.

Tetrahydrocannabinol and Cannabidiol for Pain Treatment-An Update on the Evidence.

In light of the current International Association for the Study of Pain (IASP) clinical practice guidelines (CPGs) and the European Society for Medical Oncology (ESMO) guidelines, the topic of cannabinoids in relation to pain remains controversial, with insufficient research presently available. Cannabinoids are an attractive pain management option due to their synergistic effects when administered with opioids, thereby also limiting the extent of respiratory depression. On their own, however, cannabinoids have been shown to have the potential to relieve specific subtypes of chronic pain in adults, although controversies remain. Among these subtypes are neuropathic, musculoskeletal, cancer, and geriatric pain. Another interesting feature is their effectiveness in chemotherapy-induced peripheral neuropathy (CIPN). Analgesic benefits are hypothesized to extend to HIV-associated neuropathic pain, as well as to lower back pain in the elderly. The aim of this article is to provide an up-to-date review of the existing preclinical as well as clinical studies, along with relevant systematic reviews addressing the roles of various types of cannabinoids in neuropathic pain settings. The impact of cannabinoids in chronic cancer pain and in non-cancer conditions, such as multiple sclerosis and headaches, are all discussed, as well as novel techniques of administration and relevant mechanisms of action.

Advancements in bioinformatics and computational biology: 15th annual Polish Bioinformatic Society Symposium.

The Polish Bioinformatic Society (PTBI) Symposium convenes annually at leading Polish Universities, and in 2023, the Silesian University of Technology hosted participants from all over the world. The 15th PTBI Symposium, spanning a 3-day duration and divided into four scientific sessions, gathered around 100 participants and centered on research related to machine learning in biomedicine, RNA structure algorithms, next-generation sequencing methods, and microbiome analysis but was not limited to only those topics. The meeting also recognized outstanding research conducted by young scientists by awarding the best poster and best talk. Finally, the awards for the best PhD, MSc, and BSc thesis in bioinformatics defended in Poland were given. This report summarizes the key highlights and outcomes of the meeting.

miR-122 and miR-21 are Stable Components of miRNA Signatures of Early Lung Cancer after Validation in Three Independent Cohorts.

Several panels of circulating miRNAs have been reported as potential biomarkers of early lung cancer, yet the overlap of components between different panels is limited, and the universality of proposed biomarkers has been minimal across proposed panels. To assess the stability of the diagnostic potential of plasma miRNA signature of early lung cancer among different cohorts, a panel of 24 miRNAs tested in the frame of one lung cancer screening study (MOLTEST-2013, Poland) was validated with material collected in the frame of two other screening studies (MOLTEST-BIS, Poland; and SMAC, Italy) using the same standardized analytical platform (the miRCURY LNA miRNA PCR assay). On analysis of selected miRNAs, two associated with lung cancer development, miR-122 and miR-21, repetitively differentiated healthy participants from individuals with lung cancer. Additionally, miR-144 differentiated controls from cases specifically in subcohorts with adenocarcinoma. Other tested miRNAs did not overlap in the three cohorts. Classification models based on neither a single miRNA nor multicomponent miRNA panels (24-mer and 7-mer) showed classification performance sufficient for a standalone diagnostic biomarker (AUC, 75%, 71%, and 53% in MOLTEST-2013, SMAC, and MOLTEST-BIS, respectively, in the 7-mer model). The performance of classification in the MOLTEST-BIS cohort with the lowest contribution of adenocarcinomas was increased when only this cancer type was considered (AUC, 60% in 7-mer model).

Activin A levels are raised during human tuberculosis and blockade of the activin signaling axis influences murine responses to M. tuberculosis infection.

Activin A strongly influences immune responses; yet, few studies have examined its role in infectious diseases. We measured serum activin A levels in two independent tuberculosis (TB) patient cohorts and in patients with pneumonia and sarcoidosis. Serum activin A levels were increased in TB patients compared to healthy controls, including those with positive tuberculin skin tests, and paralleled severity of disease, assessed by X-ray scores. In pneumonia patients, serum activin A levels were also raised, but in sarcoidosis patients, levels were lower. To determine whether blockade of the activin A signaling axis could play a functional role in TB, we harnessed a soluble activin type IIB receptor fused to human IgG1 Fc, ActRIIB-Fc, as a ligand trap in a murine TB model. The administration of ActRIIB-Fc to Mycobacterium tuberculosis-infected mice resulted in decreased bacterial loads and increased numbers of CD4 effector T cells and tissue-resident memory T cells in the lung. Increased frequencies of tissue-resident memory T cells corresponded with downregulated T-bet expression in lung CD4 and CD8 T cells. Altogether, the results suggest a disease-exacerbating role of ActRIIB signaling pathways. Serum activin A may be useful as a biomarker for diagnostic triage of active TB or monitoring of anti-tuberculosis therapy. IMPORTANCE: Tuberculosis remains the leading cause of death by a bacterial pathogen. The etiologic agent of tuberculosis, Mycobacterium tuberculosis, can remain dormant in the infected host for years before causing disease. Significant effort has been made to identify biomarkers that can discriminate between latently infected and actively diseased individuals. We found that serum levels of the cytokine activin A were associated with increased lung pathology and could discriminate between active tuberculosis and tuberculin skin-test-positive healthy controls. Activin A signals through the ActRIIB receptor, which can be blocked by administration of the ligand trap ActRIIB-Fc, a soluble activin type IIB receptor fused to human IgG1 Fc. In a murine model of tuberculosis, we found that ActRIIB-Fc treatment reduced mycobacterial loads. Strikingly, ActRIIB-Fc treatment significantly increased the number of tissue-resident memory T cells. These results suggest a role for ActRIIB signaling pathways in host responses to Mycobacterium tuberculosis and activin A as a biomarker of ongoing disease.

Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms.

Recent advances in sample preparation and sequencing technology have made it possible to profile the transcriptomes of individual cells using single-cell RNA sequencing (scRNA-Seq). Compared to bulk RNA-Seq data, single-cell data often contain a higher percentage of zero reads, mainly due to lower sequencing depth per cell, which affects mostly measurements of low-expression genes. However, discrepancies between platforms are observed regardless of expression level. Using four paired datasets with multiple samples each, we investigated technical and biological factors that can contribute to this expression shift. Using two separate machine learning models we found that, in addition to expression level, RNA integrity, gene or UTR3 length, and the number of transcripts potentially also influence the occurrence of zeros. These findings could enable the development of novel analytical methods for cross-platform expression shift correction. We also identified genes and biological pathways in our diverse datasets that consistently showed differences when assessed at the single cell versus bulk level to assist in interpreting analysis across transcriptomic platforms. At the gene level, 25 genes (0.12%) were found in all datasets as discordant, but at the pathway level, 7 pathways (2.02%) showed shared enrichment in discordant genes.

Combining Low-Dose Computer-Tomography-Based Radiomics and Serum Metabolomics for Diagnosis of Malignant Nodules in Participants of Lung Cancer Screening Studies.

Radiomics is an emerging approach to support the diagnosis of pulmonary nodules detected via low-dose computed tomography lung cancer screening. Serum metabolome is a promising source of auxiliary biomarkers that could help enhance the precision of lung cancer diagnosis in CT-based screening. Thus, we aimed to verify whether the combination of these two techniques, which provides local/morphological and systemic/molecular features of disease at the same time, increases the performance of lung cancer classification models. The collected cohort consists of 1086 patients with radiomic and 246 patients with serum metabolomic evaluations. Different machine learning techniques, i.e., random forest and logistic regression were applied for each omics. Next, model predictions were combined with various integration methods to create a final model. The best single omics models were characterized by an AUC of 83% in radiomics and 60% in serum metabolomics. The model integration only slightly increased the performance of the combined model (AUC equal to 85%), which was not statistically significant. We concluded that radiomics itself has a good ability to discriminate lung cancer from benign lesions. However, additional research is needed to test whether its combination with other molecular assessments would further improve the diagnosis of screening-detected lung nodules.

Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies.

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar's test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

Gene Set Enrichment Analysis Reveals Individual Variability in Host Responses in Tuberculosis Patients.

Group-aggregated responses to tuberculosis (TB) have been well characterized on a molecular level. However, human beings differ and individual responses to infection vary. We have combined a novel approach to individual gene set analysis (GSA) with the clustering of transcriptomic profiles of TB patients from seven datasets in order to identify individual molecular endotypes of transcriptomic responses to TB. We found that TB patients differ with respect to the intensity of their hallmark interferon (IFN) responses, but they also show variability in their complement system, metabolic responses and multiple other pathways. This variability cannot be sufficiently explained with covariates such as gender or age, and the molecular endotypes are found across studies and populations. Using datasets from a Cynomolgus macaque model of TB, we revealed that transcriptional signatures of different molecular TB endotypes did not depend on TB progression post-infection. Moreover, we provide evidence that patients with molecular endotypes characterized by high levels of IFN responses (IFN-rich), suffered from more severe lung pathology than those with lower levels of IFN responses (IFN-low). Harnessing machine learning (ML) models, we derived gene signatures classifying IFN-rich and IFN-low TB endotypes and revealed that the IFN-low signature allowed slightly more reliable overall classification of TB patients from non-TB patients than the IFN-rich one. Using the paradigm of molecular endotypes and the ML-based predictions allows more precisely tailored treatment regimens, predicting treatment-outcome with higher accuracy and therefore bridging the gap between conventional treatment and precision medicine.

Identification of two novel mutations in human acute myeloid leukemia cases.

Acute myeloid leukemia (AML) is an aggressive cancer that progresses rapidly with a poor prognosis. Cytogenetic analysis provides the most accurate determination of diagnosis and prognosis however, about 42-48% of AML patients have a cytogenetically normal karyotype. Genetic analysis can provide further information and the identification of new mutations could result in improved risk stratification, prognosis and better understanding of the mechanisms of AML leukaemogenesis. In this study, we analyzed genetic alterations in 16 human AML cases by Haloplex sequencing with confirmation of two previously unreported mutations in the genes DNMT3A and RUNX1 by Sanger sequencing or pyrosequencing. The two novel mutations consist of two frameshift mutations identified in two different AML patients and reported as deleterious by bioinformatic analysis. These mutations confirm the exclusion and co-occurrence of specific gene mutation patterns in AML and may provide further information for patient diagnosis and prognosis.

The Lipid Composition of Serum-Derived Small Extracellular Vesicles in Participants of a Lung Cancer Screening Study.

Molecular components of exosomes and other classes of small extracellular vesicles (sEV) present in human biofluids are potential biomarkers with possible applicability in the early detection of lung cancer. Here, we compared the lipid profiles of serum-derived sEV from three groups of lung cancer screening participants: individuals without pulmonary alterations, individuals with benign lung nodules, and patients with screening-detected lung cancer (81 individuals in each group). Extracellular vesicles and particles were purified from serum by size-exclusion chromatography, and a fraction enriched in sEV and depleted of low-density lipoproteins (LDLs) was selected (similar sized vesicles was observed in all groups: 70-100 nm). The targeted mass-spectrometry-based approach enabled the detection of 352 lipids, including 201 compounds used in quantitative analyses. A few compounds, exemplified by Cer(42:1), i.e., a ceramide whose increased plasma/serum level was reported in different pathological conditions, were upregulated in vesicles from cancer patients. On the other hand, the contribution of phosphatidylcholines with poly-unsaturated acyl chains was reduced in vesicles from lung cancer patients. Cancer-related features detected in serum-derived sEV were different than those of the corresponding whole serum. A high heterogeneity of lipid profiles of sEV was observed, which markedly impaired the performance of classification models based on specific compounds (the three-state classifiers showed an average AUC = 0.65 and 0.58 in the training and test subsets, respectively).

Serum Metabolite Profiles in Participants of Lung Cancer Screening Study; Comparison of Two Independent Cohorts.

Serum metabolome is a promising source of molecular biomarkers that could support early detection of lung cancer in screening programs based on low-dose computed tomography. Several panels of metabolites that differentiate lung cancer patients and healthy individuals were reported, yet none of them were validated in the population at high-risk of developing cancer. Here we analyzed serum metabolome profiles in participants of two lung cancer screening studies: MOLTEST-BIS (Poland, n = 369) and SMAC-1 (Italy, n = 93). Three groups of screening participants were included: lung cancer patients, individuals with benign pulmonary nodules, and those without any lung alterations. Concentrations of about 400 metabolites (lipids, amino acids, and biogenic amines) were measured by a mass spectrometry-based approach. We observed a reduced level of lipids, in particular cholesteryl esters, in sera of cancer patients from both studies. Despite several specific compounds showing significant differences between cancer patients and healthy controls within each study, only a few cancer-related features were common when both cohorts were compared, which included a reduced concentration of lysophosphatidylcholine LPC (18:0). Moreover, serum metabolome profiles in both noncancer groups were similar, and differences between cancer patients and both groups of healthy participants were comparable. Large heterogeneity in levels of specific metabolites was observed, both within and between cohorts, which markedly impaired the accuracy of classification models: The overall AUC values of three-state classifiers were 0.60 and 0.51 for the test (MOLTEST) and validation (SMAC) cohorts, respectively. Therefore, a hypothetical metabolite-based biomarker for early detection of lung cancer would require adjustment to lifestyle-related confounding factors that putatively affect the composition of serum metabolome.

Weaker protection against tuberculosis in BCG-vaccinated male 129 S2 mice compared to females.

BCG - the only available vaccine against tuberculosis (TB) - was first given to babies 100 years ago in 1921. While it is effective against TB meningitis and disseminated TB, its efficacy against pulmonary TB is variable, notably in adults and adolescents. TB remains one of the world's leading health problems, with a higher prevalence among men. Male sex is associated with increased susceptibility to Mycobacterium tuberculosis in mice, but sex-specific responses to BCG vaccination have not been examined. In this study we vaccinated TB-susceptible 129 S2 mice with BCG and challenged with low-dose M. tuberculosis H37Rv by aerosol infection. BCG was protective against TB in both sexes, as unvaccinated mice lost weight more rapidly than vaccinated ones and suffered from worse lung pathology. However, female mice were better protected than males, showing lower lung bacterial burdens and less weight loss. Overall, vaccinated female mice had increased numbers of T cells and less myeloid cells in the lungs compared to vaccinated males. Principal component analysis of measured features revealed that mice grouped according to timepoint, sex and vaccination status. The features that had the biggest impact on grouping overall included numbers of CD8 T cells, CD8 central memory T cells and CD4 T effector cells, with neutrophil and CD11b+GR-1- cell numbers having a big impact at day 29. Hierarchical clustering confirmed that the main difference in global immune response was due to mouse sex, with only a few misgrouped mice. In conclusion, we found sex-specific differences in response to M. tuberculosis H37Rv -challenge in BCG-vaccinated 129 S2 mice. This highlights the need to include both male and female mice in preclinical testing of vaccine candidates.