Relative effect size-based profiles as an alternative to differentiation analysis in multi-species single-cell transcriptional studies.

Combining data from experiments on multispecies studies provides invaluable contributions to the understanding of basic disease mechanisms and pathophysiology of pathogens crossing species boundaries. The task of multispecies gene expression analysis, however, is often challenging given annotation inconsistencies and in cases of small sample sizes due to bias caused by batch effects. In this work we aim to demonstrate that an alternative approach to standard differential expression analysis in single cell RNA-sequencing (scRNA-seq) based on effect size profiles is suitable for the fusion of data from small samples and multiple organisms. The analysis pipeline is based on effect size metric profiles of samples in specific cell clusters. The effect size substitutes standard differentiation analyses based on p-values and profiles identified based on these effect size metrics serve as a tool to link cell type clusters between the studied organisms. The algorithms were tested on published scRNA-seq data sets derived from several species and subsequently validated on own data from human and bovine peripheral blood mononuclear cells stimulated with Mycobacterium tuberculosis. Correlation of the effect size profiles between clusters allowed for the linkage of human and bovine cell types. Moreover, effect size ratios were used to identify differentially regulated genes in control and stimulated samples. The genes identified through effect size profiling were confirmed experimentally using qPCR. We demonstrate that in situations where batch effects dominate cell type variation in single cell small sample size multispecies studies, effect size profiling is a valid alternative to traditional statistical inference techniques.

Transcriptomic Profile of Mouse Brain Ageing in Early Developmental Stages.

Ageing is a continuous process that can cause neurodevelopmental changes in the body. Several studies have examined its effects, but few have focused on how time affects biological processes in the early stages of brain development. As studying the changes that occur in the early stages of life is important to prevent age-related neurological and psychiatric disorders, we aim to focus on these changes. The transcriptomic markers of ageing that are common to the analysed brain regions of C57Bl/6J mice were identified after conducting two-way ANOVA tests and effect size analysis on the time courses of gene expression profiles in various mouse brain regions. A total of 16,374 genes (59.9%) significantly changed their expression level, among which 7600 (27.8%) demonstrated tissue-dependent differences only, and 1823 (6.7%) displayed time-dependent and tissue-independent responses. Focusing on genes with at least a large effect size gives the list of potential biomarkers 12,332 (45.1%) and 1670 (6.1%) genes, respectively. There were 305 genes that exhibited similar significant time response trends (independently of the brain region). Samples from an 11-day-old mouse embryo validated the identified early-stage brain ageing markers. The overall functional analysis revealed tRNA and rRNA processing in the mitochondrion and contact activation system (CAS), as well as the kallikrein/kinin system (KKS), together with clotting cascade and defective factor F9 activation being affected by ageing. Most ageing-related pathways were significantly enriched, especially those that are strongly connected to development processes and neurodegenerative diseases.

Venom Immunotherapy Does Not Affect Survival of Patients with Malignant Tumor in Poland.

Background: Allergen immunotherapy (AIT) is a well-established and efficient method of causative treatment for allergic rhinitis, asthma and insect venom allergy. Traditionally, a recent history of malignant neoplasm is regarded as a contraindication to AIT due to concerns that AIT might stimulate tumor growth. However, there are no data confirming that the silencing of the Th2 response affects prognosis in cancer. Objectives: The aim of this study was to investigate frequency of malignant tumors in patients undergoing AIT and the association between AIT and cancer-related mortality. Patients and Methods: A group of 2577 patients with insect venom allergy undergoing AIT in 10 Polish allergology centers was screened in the Polish National Cancer Registry. Data on cancer type, diagnosis time and patients' survival were collected and compared with the general population. Results: In the study group, 86 cases of malignancies were found in 85 patients (3.3% of the group). The most common were breast (19 cases), lung (9 cases), skin (8 cases), colon and prostate cancers (5 cases each). There were 21 cases diagnosed before AIT, 38 during and 27 after completing AIT. Laplace's crude incidence rate was 159.5/100,000/year (general population rate: 260/100,000/year). During follow-up, 13 deaths related to cancer were revealed (15% of patients with cancer). Laplace's cancer mortality rate was 37.3/100,000/year (general population rate: 136.8/100,000/year). Conclusions: Malignancy was found in patients undergoing immunotherapy less often than in the general population. Patients with cancer diagnosed during or after AIT did not show a lower survival rate, which suggests that AIT does not affect the prognosis.

Glycemic control in children with type 1 diabetes treated with the advanced hybrid closed loop system 2-year prospective, observational, two-center study.

Background and aims: MiniMed 780G is the first Advanced Hybrid Closed Loop (AHCL) system in Poland, approved in the EU in 2020. To date, observations of glycemic control up to 12 months have been published. This study aimed to analyze glycemic control and anthropometric parameters in children and adolescents with type 1 diabetes (T1D) after two years of using the AHCL system. Materials and methods: We prospectively collected anthropometric data, pump, and continuous glucose records of fifty T1D children (9.9 ± 2.4 years, 24 (48%) boys, T1D for 3.9 ± 2.56 years) using an AHCL system. We compared the two-week AHCL records obtained after AHCL enrollment with data 6, 12, and 24 months after starting AHCL. Results: Time in range (70-180 mg/dl) and BMI z-score did not change during the 2 years of observation (p>0.05). The percentage of autocorrection in total daily insulin increased significantly (p<0.005). Conclusion: Glycemic control in the investigated group of children with T1D treated with the AHCL system for 2 years remained stable. Children in this group maintained weight and optimal metabolic control, most likely due to autocorrection boluses.

Epigenetic signature of ionizing radiation in therapy-related AML patients.

Therapy-related acute myeloid leukaemia (t-AML) is a late side effect of previous chemotherapy (ct-AML) and/or radiotherapy (rt-AML) or immunosuppressive treatment. t-AMLs, which account for ∼10-20 % of all AML cases, are extremely aggressive and have a poor prognosis compared to de novo AML. Our hypothesis is that exposure to radiation causes genome-wide epigenetic changes in rt-AML. An epigenome-wide association study was undertaken, measuring over 850K methylation sites across the genome from fifteen donors (five healthy, five de novo, and five t-AMLs). The study predominantly focussed on 94K sites that lie in CpG-rich gene promoter regions. Genome-wide hypomethylation was discovered in AML, primarily in intergenic regions. Additionally, genes specific to AML were identified with promoter hypermethylation. A two-step validation was conducted, both internally, using pyrosequencing to measure methylation levels in specific regions across fifteen primary samples, and externally, with an additional eight AML samples. We demonstrated that the MEST and GATA5 gene promoters, which were previously identified as tumour suppressors, were noticeably hypermethylated in rt-AML, as opposed to other subtypes of AML and control samples. These may indicate the epigenetic involvement in the development of rt-AML at the molecular level and could serve as potential targets for drug therapy in rt-AML.

Transcriptional Inflammatory Signature in Healthy Donors and Different Radiotherapy Cancer Patients.

Cancer and ionizing radiation exposure are associated with inflammation. To identify a set of radiation-specific signatures of inflammation-associated genes in the blood of partially exposed radiotherapy patients, differential expression of 249 inflammatory genes was analyzed in blood samples from cancer patients and healthy individuals. The gene expression analysis on a cohort of 63 cancer patients (endometrial, head and neck, and prostate cancer) before and during radiotherapy (24 h, 48 h, ~1 week, ~4-8 weeks, and 1 month after the last fraction) identified 31 genes and 15 up- and 16 down-regulated genes. Transcription variability under normal conditions was determined using blood drawn on three separate occasions from four healthy donors. No difference in inflammatory expression between healthy donors and cancer patients could be detected prior to radiotherapy. Remarkably, repeated sampling of healthy donors revealed an individual endogenous inflammatory signature. Next, the potential confounding effect of concomitant inflammation was studied in the blood of seven healthy donors taken before and 24 h after a flu vaccine or ex vivo LPS (lipopolysaccharide) treatment; flu vaccination was not detected at the transcriptional level and LPS did not have any effect on the radiation-induced signature identified. Finally, we identified a radiation-specific signature of 31 genes in the blood of radiotherapy patients that were common for all cancers, regardless of the immune status of patients. Confirmation via MQRT-PCR was obtained for BCL6, MYD88, MYC, IL7, CCR4 and CCR7. This study offers the foundation for future research on biomarkers of radiation exposure, radiation sensitivity, and radiation toxicity for personalized radiotherapy treatment.

Short- and long-term effects of radiation exposure at low dose and low dose rate in normal human VH10 fibroblasts.

Introduction: Experimental studies complement epidemiological data on the biological effects of low doses and dose rates of ionizing radiation and help in determining the dose and dose rate effectiveness factor. Methods: Human VH10 skin fibroblasts exposed to 25, 50, and 100 mGy of 137Cs gamma radiation at 1.6, 8, 12 mGy/h, and at a high dose rate of 23.4 Gy/h, were analyzed for radiation-induced short- and long-term effects. Two sample cohorts, i.e., discovery (n = 30) and validation (n = 12), were subjected to RNA sequencing. The pool of the results from those six experiments with shared conditions (1.6 mGy/h; 24 h), together with an earlier time point (0 h), constituted a third cohort (n = 12). Results: The 100 mGy-exposed cells at all abovementioned dose rates, harvested at 0/24 h and 21 days after exposure, showed no strong gene expression changes. DMXL2, involved in the regulation of the NOTCH signaling pathway, presented a consistent upregulation among both the discovery and validation cohorts, and was validated by qPCR. Gene set enrichment analysis revealed that the NOTCH pathway was upregulated in the pooled cohort (p = 0.76, normalized enrichment score (NES) = 0.86). Apart from upregulated apical junction and downregulated DNA repair, few pathways were consistently changed across exposed cohorts. Concurringly, cell viability assays, performed 1, 3, and 6 days post irradiation, and colony forming assay, seeded just after exposure, did not reveal any statistically significant early effects on cell growth or survival patterns. Tendencies of increased viability (day 6) and reduced colony size (day 21) were observed at 12 mGy/h and 23.4 Gy/min. Furthermore, no long-term changes were observed in cell growth curves generated up to 70 days after exposure. Discussion: In conclusion, low doses of gamma radiation given at low dose rates had no strong cytotoxic effects on radioresistant VH10 cells.

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.