Aim. The current study aims to investigate changes in the expression of proprotein convertases (PCs) genes during the development of lung tumors. PCs are a family of highly specific subtilisin-like serine endopeptidases of mammals that process precursors of various proteins and peptides. Nine genes encoding PCs have been identified in the human genome that are essential for normal body functioning. Additionally, PCs can activate proteins involved in carcinogenesis, as well as the expression levels of their genes were shown to correlate with tumor aggressiveness and patient survival. We previously evaluated the expression of all nine PC genes using quantitative PCR on paired samples of lung tumor and adjacent normal tissue. For the first time, we identified four distinct patterns of PCs gene expression change in tumor tissue, which we have called scenarios. For three of them, covering more than two thirds of the samples, a dominant change in the expression of one PC gene in the tumor tissue was shown. These results may indicate the existence of a limited number of possible options for changes in PCs gene expression during the malignant transformation of lung cells. However, these results need to be confirmed using expanded cohort of tumor samples. Materials and methods. To confirm our previous findings, we analyzed the expression of PCs genes using modern methods of mathematical statistics and data from three previously published studies, which evaluated gene expression through high-throughput RNA sequencing in paired tumor and normal tissue samples from 194 patients with non-small cell lung cancer. Results. Our meta-analysis confirmed that the changes in PCs gene expression in lung tumor tissue compared to adjacent normal tissue follow a limited set of possible scenarios, each having a unique profile of PCs gene expression. Conclusions. The reasons for implementing each scenario may be linked to the origin of tumors, their mutation status, characteristics of the tumor microenvironment, and other factors. Correspondingly, these scenarios may correlate, for example, with tumor aggressiveness and resistance to therapy, and therefore may potentially be used to choose the treatment approach and/or to predict the course of the disease. However, this issue requires further research.

Pancreatic cancer (PC) is the sixth leading cause of cancer-related deaths worldwide. Patients with pancreatic ductal adenocarcinoma (PDAC), the most common type of PC, have a 5-year survival rate of approximately 10%. This low survival rate is mainly attributed to late-stage diagnoses and the lack of robust screening methods. Several serum proteins have been proposed as potential PDAC biomarkers, but they have not been introduced into clinical practice due to their low sensitivity and specificity. Therefore, the identification of new PDAC biomarkers remains highly important, and multiple reaction monitoring (MRM), a highly accurate mass spectrometry (MS) technique, can be used for this purpose. Using MRM MS analysis, we estimated the concentrations of 103 proteins in peripheral blood plasma from 132 participants: patients with newly diagnosed PDAC at different stages and healthy individuals. We identified six proteins that were differentially presented between healthy controls and patients with PDAC at all stages (adjusted p-value < 0.01), and that were associated with survival rates for 23 months. A developed cross-validated model based on these six proteins showed an average accuracy of 90% in distinguishing between early-stage PDAC and healthy controls (AUC = 0.933). However, further research is needed to implement this model in clinical practice.

The Severe Acute Respiratory Syndrome-related Coronavirus 2 (SARS-CoV-2), a causative agent of the COVID-19 disease, has been constantly evolving since its first identification. Mutations that are embedded in the viral genomic RNA affect the properties of the virus and lead to the emergence of new variants. During the COVID-19 pandemic, the World Health Organization has identified more than ten variants of the SARS-CoV-2 virus. Five of these—Alpha, Beta, Gamma, Delta, and Omicron—were classified as variants of concern (VOCs), as they caused significant outbreaks of the disease. Additionally, two progeny variants of Omicron, designated JN.1 and KS.1, are still causing new waves of infections. Due to the emergence of various SARS-CoV-2 variants, in some cases, it has become important to identify a particular variant in a sample. Here, we have developed a multiplexed probe-based real-time PCR system for the identification of SARS-CoV-2 VOCs (Alpha, Beta, Gamma, Delta, Omicron B.1.1.529/BA.1, and Omicron BA.2), as well as modern Omicron variants JN.1 and KS.1. The sensitivity and specificity of the PCR system have been tested using isolated viral genomes and RNA preparations from human nasopharyngeal swabs. The system allows for rapid identification of coronavirus variants in the cryopreserved and fresh samples.

Lung inflammation, pneumonia, is an acute respiratory disease of varying etiology that has recently drawn much attention during the COVID-19 pandemic as lungs are among the main targets for SARS-CoV-2. Multiple other etiological agents are associated with pneumonias. Here, we describe a newly-recognized pathology, namely abnormal lipid depositions in the lungs of patients who died from COVID-19 as well as from non-COVID-19 pneumonias. Our analysis of both semi-thin and Sudan III-stained lung specimens revealed extracellular and intracellular lipid depositions irrespective of the pneumonia etiology. Most notably, lipid depositions were located within vessels adjacent to inflamed regions, where they apparently interfere with the blood flow. Structurally, the lipid droplets in the inflamed lung tissue were homogeneous and lacked outer membranes as assessed by electron microscopy. Morphometric analysis of lipid droplet deposition area allowed us to distinguish the non-pneumonia control lung specimens from the macroscopically intact area of the pneumonia lung and from the inflamed area of the pneumonia lung. Our measurements revealed a gradient of lipid deposition towards the inflamed region. The pattern of lipid distribution proved universal for all pneumonias. Finally, lipid metabolism in the lung tissue was assessed by the fatty acid analysis and by expression of genes involved in lipid turnover. Chromato-mass spectrometry revealed that unsaturated fatty acid content was elevated at inflammation sites compared to that in control non-inflamed lung tissue from the same individual. The expression of genes involved in lipid metabolism was altered in pneumonia, as shown by qPCR and in silico RNA-seq analysis. Thus, pneumonias of various etiologies are associated with specific lipid abnormalities; therefore, lipid metabolism can be considered to be a target for new therapeutic strategies.

BACKGROUND:Protease 3C (3Cpro) is the only protease encoded in the human hepatitis A virus genome and is considered as a potential target for antiviral drugs due to its critical role in the viral life cycle. Additionally, 3Cpro has been identified as a potent inducer of ferroptosis, a newly described type of cell death. Therefore, studying the molecular mechanism of 3Cpro functioning can provide new insights into viral-host interaction and the biological role of ferroptosis. However, such studies require a reliable technique for producing the functionally active recombinant enzyme. OBJECTIVE:Here, we expressed different modified forms of 3Cpro with a hexahistidine tag on the N- or C-terminus to investigate the applicability of immobilized metal Ion affinity chromatography (IMAC) for producing 3Cpro. METHODS:We expressed the proteins in Escherichia coli and purified them using IMAC, followed by gel permeation chromatography. The enzymatic activity of the produced proteins was assayed using a specific chromogenic substrate. RESULTS:Our findings showed that the introduction and position of the hexahistidine tag did not affect the activity of the enzyme. However, the yield of the target protein was highest for the variant with seven C-terminal residues replaced by a hexahistidine sequence. CONCLUSION:We demonstrated the applicability of our approach for producing recombinant, enzymatically active 3Cpro.

Atherosclerotic plaques are sites of chronic inflammation with diverse cell contents and complex immune signaling. Plaque progression and destabilization are driven by the infiltration of immune cells and the cytokines that mediate their interactions. Here, we attempted to compare the systemic cytokine profiles in the blood plasma of patients with atherosclerosis and the local cytokine production, using ex vivo plaque explants from the same patients. The developed method of 41-plex xMAP data normalization allowed us to differentiate twenty-two cytokines produced by the plaque that were not readily detectable in free circulation and six cytokines elevated in blood plasma that may have other sources than atherosclerotic plaque. To verify the xMAP data on the putative atherogenesis-driving chemokines MCP-1 (CCL2), MIP-1α (CCL3), MIP-1β (CCL4), RANTES (CCL5), and fractalkine (CX3CL1), qPCR was performed. The MIP1A (CCL3), MIP1B (CCL4), FKN (CX3CL1), and MCP1 (CCL2) genes were expressed at high levels in the plaques, whereas RANTES (CCL5) was almost absent. The expression patterns of the chemokines were restricted to the plaque cell types: the MCP1 (CCL2) gene was predominantly expressed in endothelial cells and monocytes/macrophages, MIP1A (CCL3) in monocytes/macrophages, and MIP1B (CCL4) in monocytes/macrophages and T cells. RANTES (CCL5) was restricted to T cells, while FKN (CX3CL1) was not differentially expressed. Taken together, our data indicate a plaque-specific cytokine production profile that may be a useful tool in atherosclerosis studies.

Approach and proof of concept. A, Hypotheses: proteins encoded by L1 are expected to be recognized as tumor-associated antigens and become targets for antibody response. This response is stimulated by the cGAS-STING-mediated induction of inflammation. Circulating Abs to L1 antigens can be used as cancer biomarkers if detected by anti-ORF1p and anti-ORF2p immunoassays. Comparison of antibody levels in healthy individuals (N = 137) and patients with cancer (N = 331) in the assays for anti-ORF1p (B), anti-ORF2p (C), and p53 (D). The significance of differences is assessed by Mann–Whitney U test. E, Spearman correlation between anti-ORF1p IgG titers and anti-p53 IgG signals in serum samples of patients with cancer (N = 331). All P value less than 0.05 is considered significant.

AbstractLong interspersed nuclear element-1 (LINE-1 or L1), the most abundant family of autonomous retrotransposons occupying over 17% of human DNA, is epigenetically silenced in normal tissues by the mechanisms involving p53 but is frequently derepressed in cancer, suggesting that L1-encoded proteins may act as tumor-associated antigens recognized by the immune system. In this study, we established an immunoassay to detect circulating autoantibodies against L1 proteins in human blood. Using this assay in >2,800 individuals with or without cancer, we observed significantly higher IgG titers against L1-encoded ORF1p and ORF2p in patients with lung, pancreatic, ovarian, esophageal, and liver cancers than in healthy individuals. Remarkably, elevated levels of anti–ORF1p-reactive IgG were observed in patients with cancer with disease stages 1 and 2, indicating that the immune response to L1 antigens can occur in the early phases of carcinogenesis. We concluded that the antibody response against L1 antigens could contribute to the diagnosis and determination of immunoreactivity of tumors among cancer types that frequently escape early detection.Significance:The discovery of autoantibodies against antigens encoded by L1 retrotransposons in patients with five poorly curable cancer types has potential implications for the detection of an ongoing carcinogenic process and tumor immunoreactivity.

Supplementary Table S8 shows the results of linear regression analysis: association between ORF1p IgG titers and individual cancer types (stages 1-2) relative to healthy subjects after adjustment for age.

Supplementary Methods include synthesis and purification of recombinant L1 antigens, immunoassays for L1 antigens and peptides derived from ORF1p and ORF2p,and Western immunoblotting protocols

Supplementary Figure S9 shows comparison of anti-ORF1p IgG titers in cancer patients with or without exposure to anti‐cancer therapies.

Supplementary Figure S11 shows comparison of anti‐ORF1p, anti‐ORF2p and anti‐p53 IgG in blood samples from patients with SLE and healthy individuals.

Supplementary Figure S6 shows correlation of anti‐ORF1p and anti‐ORF2p IgG titers in patients with five cancer types and healthy individuals.

Anti-ORF1p IgG response in serum samples from patients with five cancer types. A, Selection of cancers for analysis in anti-ORF1p immunoassay. Sector sizes are proportional to the incidence of these cancers in the United States in 2021 (https://seer.cancer.gov/statfacts/). Color code indicates a 5-year survival. “S” stands for the existence of approved cancer screening protocols. B, Detection of anti-ORF1p IgG titers in serum samples of patients with the indicated cancer types of all stages. Sample size: ovary (N = 979), esophagus (N = 377), lung (N = 907), pancreas (N = 124), liver (N = 217) cancers and healthy (N = 352). C, The same as panel B; only samples from patients with cancer stages 1 and 2 are shown. Sample size: ovary (N = 193), esophagus (N = 79), lung (N = 90), pancreas (N = 40), and liver (N = 67) cancers and healthy (N = 352). The gray areas in B and C mark samples below the anti-ORF1p IgG titer threshold of 450. Statistics were calculated by Dunn multiple comparison test with adjusted P value for anti-ORF1p IgG titers. All P value less than 0.05 is considered significant. D, Anti-ORF1p immunoassay threshold determination. E, Spearman r correlation analysis of anti-ORF1p IgG titers and age among healthy individuals (20–89 years of age) or patients with cancer (18–95 years of age).

Supplementary Figure S7 shows the ROC curves indicating specificity and sensitivity of anti‐ORF1p immunoassay for 5 cancer types.

Supplementary Table S7 shows the results of linear regression analysis of association between ORF1p IgG titers and individual cancer types relative to healthy subjects after adjustment for age.

Supplementary Table S6 shows results of logistic regression analysis of anti-ORF1 IgG titers in cancer patients relative to healthy individuals after adjusting for age.

Supplementary Table S5 shows the distribution of age between cancer and healthy subjects.