Human Cytomegalovirus Oncoprotection across Diverse Populations, Tumor Histologies, and Age Groups: The Relevance for Prospective Vaccinal Therapy

The oncogenicity of the human cytomegalovirus (CMV) is currently being widely debated. Most recently, mounting clinical evidence suggests an anti-cancer effect via CMV-induced T cell-mediated tumor destruction. However, the data were mostly obtained from single-center studies and in vitro experiments. Broad geographic coverage is required to offer a global perspective. Our study examined the correlation between country-specific CMV seroprevalence (across 73 countries) and the age-standardized incidence rate (of 34 invasive tumors). The populations studied were stratified according to decadal age periods as the immunologic effects of CMV seropositivity may depend upon age at initial infection. The International Agency for Research on Cancer of the World Health Organization (IARC WHO) database was used. The multivariate linear regression analysis revealed a worldwide inverse correlation between CMV seroprevalence and the incidences of 62.8% tumors. Notably, this inverse link persists for all cancers combined (Spearman's ρ = -0.732, p < 0.001; β = -0.482, p < 0.001, adjusted R2 = 0.737). An antithetical and significant correlation was also observed in particular age groups for the vast majority of tumors. Our results corroborate the conclusions of previous studies and indicate that this oncopreventive phenomenon holds true on a global scale. It applies to a wide spectrum of cancer histologies, additionally supporting the idea of a common underlying mechanism-CMV-stimulated T cell tumor targeting. Although these results further advance the notion of CMV-based therapies, in-depth investigation of host-virus interactions is still warranted.

MRI Tumor Regression Grade Combined with T2-Weighted Volumetry May Predict Histopathological Response in Locally Advanced Rectal Cancer following Neoadjuvant Chemoradiotherapy-A New Scoring System Proposal

Modern studies focus on the discovery of innovative methods to improve the value of post-treatment magnetic resonance imaging (MRI) in the prediction of pathological responses to preoperative neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). The aim of this study was to assess the potential benefits of combining magnetic resonance tumor regression grade (mrTRG) with T2-weighted volumetry in the prediction of pathological responses to nCRT in LARC. This was a cohort study conducted on patients with histopathologically confirmed LARC in a period from 2020 to 2022. After histopathological verification, all patients underwent initial MRI studies, while the follow-up MRI was performed after nCRT. Tumor characteristics, MRI estimated tumor regression grade (mrTRG) and tumor volumetry were evaluated both initially and at follow-up. All patients were classified into responders and non-responders according to pathological tumor regression grade (pTRG) and mrTRG. A total of 71 patients, mostly male (66.2%) were included in the study. The median tumor volume reduction rate was significantly higher in nCRT-responders compared to non-responders (79.9% vs. 63.3%) (p = 0.003). Based on ROC analysis, optimal cut-off value for tumor volume reduction rate was determined with an area under the curve (AUC) value of 0.724 (p = 0.003). Using the tumor volume reduction rate ≥75% with the addition of response to nCRT according to mrTRG, a new scoring system for prediction of pTRG to preoperative nCRT in LARC was developed. Diagnostic performance of prediction score was tested and the sensitivity, PPV, specificity, and NPV were 81.8%, 56.3%, 71.4%, and 89.7%, respectively. The combination of mrTRG and T2-weighted volumetry increases the MRI-based prediction of pTRG to preoperative nCRT in LARC. The proposed scoring system could aid in distinguishing responders to nCRT, as these patients could benefit from organ-preserving treatment and a "watch and wait" strategy.

Transcriptome from Paired Samples Improves the Power of Comprehensive COVID-19 Host-Viral Characterization

Previous transcriptome profiling studies showed significantly upregulated genes and altered biological pathways in acute COVID-19. However, changes in the transcriptional signatures during a defined time frame are not yet examined and described. The aims of this study included viral metagenomics and evaluation of the total expression in time-matched and tissue-matched paired COVID-19 samples with the analysis of the host splicing profile to reveal potential therapeutic targets. Prospective analysis of paired nasopharyngeal swabs (NPS) and blood (BL) samples from 18 COVID-19 patients with acute and resolved infection performed using Kallisto, Suppa2, Centrifuge, EdgeR, PantherDB, and L1000CDS2 tools. In NPS, we discovered 6 genes with changed splicing and 40 differentially expressed genes (DEG) that yielded 88 altered pathways. Blood samples yielded 15 alternatively spliced genes. Although the unpaired DEG analysis failed, pairing identified 78 genes and 242 altered pathways with meaningful clinical interpretation and new candidate drug combinations with up to 65% overlap. Metagenomics analyses showed SARS-CoV-2 dominance during and after the acute infection, with a significant reduction in NPS (0.008 vs. 0.002, p = 0.019). Even though both NPS and BL give meaningful insights into expression changes, this is the first demonstration of how the power of blood analysis is vastly maximized by pairing. The obtained results essentially showed that pairing is a determinant between a failed and a comprehensive study. Finally, the bioinformatics results prove to be a comprehensive tool for full-action insights, drug development, and infectious disease research when designed properly.

Molecular diagnostics and inhibition of cross‐reactive carbohydrate determinants in Hymenoptera venom allergy

Background

The composition of venom extracts, cross‐reactive carbohydrate determinants (CCD) and the component‐resolved diagnostics (CRD) are important fields of investigation. IgE‐reactivity to CCD complicates the interpretation of IgE to Hymenoptera venoms, especially in patients with multiple‐positivity. We analyzed the clinical importance of CRD and CCD‐inhibition for selection of allergens for venom immunotherapy (VIT).

Methods

In 71 patients, we measured specific IgE (sIgE) to honeybee venom (HBV), wasp venom (WV), hornet venom (HV), CCD, and recombinant allergens: phospholipase A2 (rApi m 1), hyaluronidase (rApi m 2), icarapin (rApi m 10), antigen 5 (rVes v 5), and phospholipase A1 (Immunoblot). In 29/71 HBV/WV/HV/CCD‐positive patients CCD‐inhibition was performed. According to CRD and CCD‐inhibition, we identified true sensitization and defined groups of multiple‐positive patients who needed CCD‐inhibition before starting VIT.

Results

sIgE‐rApi m 1, sIgE‐rApi m 2, and sIgE‐rApi m 10 were detected in 65.7%, 68.4%, and 58%, respectively. In HBV allergic patients, CRD sensitivity was 86.8%. In WV allergic patients, sensitivity of sIgE‐rVes v 5 was 94%. True multiple‐sensitization was found in 44.8% of HBV/WV/HV/CCD‐positive patients after CCD‐inhibition. Patients with multiple venom‐ and CCD‐positivity had more frequent severe allergic reactions (p < 0.001). CCD‐inhibition was helpful in HBV/WV/HV/CCD‐positive patients who were negative to all tested recombinant honeybee allergens. Persistence of HBV‐positivity after CCD‐inhibition requires CRD to other honeybee recombinant allergens.

Conclusion

CRD, using a profile of five most important recombinant allergens and CCD, has a high sensitivity for the diagnosis of venom allergy, especially in patients positive to several venom extracts. CRD and CCD‐inhibition are helpful to reveal the clinically relevant, true sensitization and improve the selection of venoms for long‐lasting VIT.

Buffy Coat DNA Methylation Profile Is Representative of Methylation Patterns in White Blood Cell Types in Normal Pregnancy

Background: We aimed to assess the extent to which the buffy coat DNA methylome is representative of methylation patterns in constitutive white blood cell (WBC) types in normal pregnancy. Methods: A comparison of differential methylation of buffy coat DNA vs DNA isolated from polymorphonuclear (PMN) and lymphocytic fractions was performed for each blood sample obtained within 24 h prior to delivery from 29 normotensive pregnant women. Methylation profiles were obtained using an Illumina Human Methylation 450 BeadChip and CHaMP bioinformatics pipeline. A subset of differentially methylated probes (DMPs) showing discordant methylation were further investigated using statistical modeling and enrichment analysis. Results: The smallest number of DMPs was found between the buffy coat and the PMN fraction (2.96%). Pathway enrichment analysis of the DMPs identified biological pathways involved in the particular leukocyte lineage, consistent with perturbations during isolation. The comparisons between the buffy coat and the isolated fractions as a group using linear modeling yielded a small number of probes (∼29,000) with discordant methylation. Demethylation of probes in the buffy coat compared to derived cell lines was more common and was prevalent in shelf and open sea regions. Conclusion: Buffy coat is representative of methylation patterns in WBC types in normal pregnancy. The differential methylations are consistent with perturbations during isolation of constituent cells and likely originate in vitro due to the physical stress during cell separation and are of no physiological relevance. These findings help the interpretation of DNA methylation profiling in pregnancy and numerous other conditions.

Preeclamptic Women Have Disrupted Placental microRNA Expression at the Time of Preeclampsia Diagnosis: Meta-Analysis

Introduction: Preeclampsia (PE) is a pregnancy-associated, multi-organ, life-threatening disease that appears after the 20th week of gestation. The aim of this study was to perform a systematic review and meta-analysis to determine whether women with PE have disrupted miRNA expression compared to women who do not have PE. Methods: We conducted a systematic review and meta-analysis of studies that reported miRNAs expression levels in placenta or peripheral blood of pregnant women with vs. without PE. Studies published before October 29, 2021 were identified through PubMed, EMBASE and Web of Science. Two reviewers used predefined forms and protocols to evaluate independently the eligibility of studies based on titles and abstracts and to perform full-text screening, data abstraction and quality assessment. Standardized mean difference (SMD) was used as a measure of effect size. Results: 229 publications were included in the systematic review and 53 in the meta-analysis. The expression levels in placenta were significantly higher in women with PE compared to women without PE for miRNA-16 (SMD = 1.51,95%CI = 0.55-2.46), miRNA-20b (SMD = 0.89, 95%CI = 0.33-1.45), miRNA-23a (SMD = 2.02, 95%CI = 1.25-2.78), miRNA-29b (SMD = 1.37, 95%CI = 0.36-2.37), miRNA-155 (SMD = 2.99, 95%CI = 0.83-5.14) and miRNA-210 (SMD = 1.63, 95%CI = 0.69-2.58), and significantly lower for miRNA-376c (SMD = -4.86, 95%CI = -9.51 to -0.20). An increased level of miRNK-155 expression was found in peripheral blood of women with PE (SMD = 2.06, 95%CI = 0.35-3.76), while the expression level of miRNA-16 was significantly lower in peripheral blood of PE women (SMD = -0.47, 95%CI = -0.91 to -0.03). The functional roles of the presented miRNAs include control of trophoblast proliferation, migration, invasion, apoptosis, differentiation, cellular metabolism and angiogenesis. Conclusion: miRNAs play an important role in the pathophysiology of PE. The identification of differentially expressed miRNAs in maternal blood creates an opportunity to define an easily accessible biomarker of PE.

COVID-19 severity determinants inferred through ecological and epidemiological modeling

Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r), is independent of the disease transmission dynamics (i.e., the basic reproduction number), and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate, and multivariate analyses based on machine learning techniques, is used for selecting important predictors. The usefulness of the introduced severity measure and the validity of the approach are confirmed by the fact that, without using prior knowledge from clinical studies, we recover the main significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) significant predictors. The proposed measure is applicable for inferring severity determinants not only of COVID-19 but also of other infectious diseases, and the obtained results may aid a better understanding of the present and future epidemics. Our holistic, systematic investigation of disease severity at the human-environment intersection by epidemiological dynamical modeling and machine learning ecological regressions is aligned with the One Health approach. The obtained results emphasize a syndemic nature of COVID-19 risks.

PM2.5 as a major predictor of COVID-19 basic reproduction number in the USA

Many studies have proposed a relationship between COVID-19 transmissibility and ambient pollution levels. However, a major limitation in establishing such associations is to adequately account for complex disease dynamics, influenced by e.g. significant differences in control measures and testing policies. Another difficulty is appropriately controlling the effects of other potentially important factors, due to both their mutual correlations and a limited dataset. To overcome these difficulties, we will here use the basic reproduction number (R₀) that we estimate for USA states using non-linear dynamics methods. To account for a large number of predictors (many of which are mutually strongly correlated), combined with a limited dataset, we employ machine-learning methods. Specifically, to reduce dimensionality without complicating the variable interpretation, we employ Principal Component Analysis on subsets of mutually related (and correlated) predictors. Methods that allow feature (predictor) selection, and ranking their importance, are then used, including both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost). Through these substantially different approaches, we robustly obtain that PM_2.5 is a major predictor of R₀ in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition. As a rough magnitude estimate, we obtain that a relative change in R₀, with variations in pollution levels observed in the USA, is typically ~30%, which further underscores the importance of pollution in COVID-19 transmissibility.

Inferring the Main Drivers of SARS-CoV-2 Global Transmissibility by Feature Selection Methods

Identifying the main environmental drivers of SARS-CoV-2 transmissibility in the population is crucial for understanding current and potential future outbursts of COVID-19 and other infectious diseases. To address this problem, we concentrate on the basic reproduction number R 0, which is not sensitive to testing coverage and represents transmissibility in an absence of social distancing and in a completely susceptible population. While many variables may potentially influence R 0, a high correlation between these variables may obscure the result interpretation. Consequently, we combine Principal Component Analysis with feature selection methods from several regression-based approaches to identify the main demographic and meteorological drivers behind R 0. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is the most important R 0 predictor at the global level, probably being a good proxy for the overall contact frequency in a population. This main effect is modulated by built-up area per capita (crowdedness in indoor space), onset of infection (likely related to increased awareness of infection risks), net migration, unhealthy living lifestyle/conditions including pollution, seasonality, and possibly BCG vaccination prevalence. Also, we argue that several variables that significantly correlate with transmissibility do not directly influence R 0 or affect it differently than suggested by naïve analysis.

The First Molecular Characterization of Serbian SARS-CoV-2 Isolates From a Unique Early Second Wave in Europe

March 6, 2020 is considered as the official date of the beginning of the COVID-19 epidemic in Serbia. In late spring and early summer 2020, Europe recorded a decline in the rate of SARS-CoV-2 infection and subsiding of the first wave. This trend lasted until the fall, when the second wave of the epidemic began to appear. Unlike the rest of Europe, Serbia was hit by the second wave of the epidemic a few months earlier. Already in June 2020, newly confirmed cases had risen exponentially. As the COVID-19 pandemic is the first pandemic in which there has been instant sharing of genomic information on isolates around the world, the aim of this study was to analyze whole SARS-CoV-2 viral genomes from Serbia, to identify circulating variants/clade/lineages, and to explore site-specific mutational patterns in the unique early second wave of the European epidemic. This analysis of Serbian isolates represents the first publication from Balkan countries, which demonstrates the importance of specificities of local transmission especially when preventive measures differ among countries. One hundred forty-eight different genome variants among 41 Serbian isolates were detected in this study. One unique and seven extremely rare mutations were identified, with locally specific continuous dominance of the 20D clade. At the same time, amino acid substitutions of newly identified variants of concern were found in our isolates from October 2020. Future research should be focused on functional characterization of novel mutations in order to understand the exact role of these variations.

A systems biology approach to COVID-19 progression in population

A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.

How accurate are citations of frequently cited papers in biomedical literature?

Citations are an important, but often overlooked, part of every scientific paper. They allow the reader to trace the flow of evidence, serving as a gateway to relevant literature. Most scientists are aware of citations' errors, but few appreciate the prevalence of these problems. The purpose of the present study was to examine how often frequently cited papers in biomedical scientific literature are cited inaccurately. The study included an active participation of the first authors of included papers; to first-hand verify the citations accuracy. Findings from feasibility study, where we reviewed 1540 articles containing 2526 citations of 14 most cited articles in which the authors were affiliated with the Faculty of Medicine University of Belgrade, were further evaluated for external confirmation in an independent verification set of articles. Verification set included 4912 citations identified in 2995 articles that cited 13 most cited articles published by authors affiliated with the Mayo Clinic Division of Nephrology and Hypertension. A citation was defined as being accurate if the cited article supported or was in accordance with the statement by citing authors. At least one inaccurate citation was found in 11 and 15% of articles in the feasibility study and verification set, respectively, suggesting that inaccurate citations are common in biomedical literature. The most common problem was the citation of nonexistent findings (38.4%), followed by an incorrect interpretation of findings (15.4%). One-fifth of inaccurate citations were due to chains of inaccurate citations. Based on these findings, several actions to reduce citation inaccuracies have been proposed.

Effects of Demographic and Weather Parameters on COVID-19 Basic Reproduction Number

It is hard to overstate the importance of a timely prediction of the COVID-19 pandemic progression. Yet, this is not possible without a comprehensive understanding of environmental factors that may affect the infection transmissibility. Studies addressing parameters that may influence COVID-19 progression relied on either the total numbers of detected cases and similar proxies (which are highly sensitive to the testing capacity, levels of introduced social distancing measures, etc.), and/or a small number of analyzed factors, including analysis of regions that display a narrow range of these parameters. We here apply a novel approach, exploiting widespread growth regimes in COVID-19 detected case counts. By applying nonlinear dynamics methods to the exponential regime, we extract basic reproductive number R0 (i.e., the measure of COVID-19 inherent biological transmissibility), applying to the completely naïve population in the absence of social distancing, for 118 different countries. We then use bioinformatics methods to systematically collect data on a large number of potentially interesting demographics and weather parameters for these countries (where data was available), and seek their correlations with the rate of COVID-19 spread. While some of the already reported or assumed tendencies (e.g., negative correlation of transmissibility with temperature and humidity, significant correlation with UV, generally positive correlation with pollution levels) are also confirmed by our analysis, we report a number of both novel results and those that help settle existing disputes: the absence of dependence on wind speed and air pressure, negative correlation with precipitation; significant positive correlation with society development level (human development index) irrespective of testing policies, and percent of the urban population, but absence of correlation with population density per se. We find a strong positive correlation of transmissibility on alcohol consumption, and the absence of correlation on refugee numbers, contrary to some widespread beliefs. Significant tendencies with health-related factors are reported, including a detailed analysis of the blood type group showing consistent tendencies on Rh factor, and a strong positive correlation of transmissibility with cholesterol levels. Detailed comparisons of obtained results with previous findings, and limitations of our approach, are also provided.

Systematic review supports the role of DNA methylation in the pathophysiology of preeclampsia: a call for analytical and methodological standardization

BACKGROUND

Studies have recently examined the role of epigenetic mechanisms in preeclampsia pathophysiology. One commonly examined epigenetic process is DNA methylation. This heritable epigenetic marker is involved in many important cellular functions. The aim of this study was to establish the association between DNA methylation and preeclampsia and to critically appraise the roles of major study characteristics that can significantly impact the association between DNA methylation and preeclampsia.

MAIN BODY

A systematic review was performed by searching PubMed, Web of Science, and EMBASE for original research articles published over time, until May 31, 2019 in English. Eligible studies compared DNA methylation levels in pregnant women with vs. without preeclampsia. Ninety articles were included. Epigenome-wide studies identified hundreds of differentially methylated places/regions in preeclamptic patients. Hypomethylation was the predominant finding in studies analyzing placental tissue (14/19), while hypermethylation was detected in three studies that analyzed maternal white blood cells (3/3). In candidate gene studies, methylation alterations for a number of genes were found to be associated with preeclampsia. A greater number of differentially methylated genes was found when analyzing more severe preeclampsia (70/82), compared to studies analyzing less severe preeclampsia vs. controls (13/27). A high degree of heterogeneity existed among the studies in terms of methodological study characteristics including design (study design, definition of preeclampsia, control group, sample size, confounders), implementation (biological sample, DNA methylation method, purification of DNA extraction, and validation of methylation), analysis (analytical method, batch effect, genotyping, and gene expression), and data presentation (methylation quantification measure, measure of variability, reporting). Based on the results of this review, we provide recommendations for study design and analytical approach for further studies.

CONCLUSIONS

The findings from this review support the role of DNA methylation in the pathophysiology of preeclampsia. Establishing field-wide methodological and analytical standards may increase value and reduce waste, allowing researchers to gain additional insights into the role of DNA methylation in the pathophysiology of preeclampsia.

Introducing Clinical Informatics Course in Medical School Curricula: Lessons Learned from Medical Faculty University of Belgrade

The healthcare environment in Serbia has changed dramatically over a last two decades, pointing out the necessity of clinical informatics (CI) education for future MDs. Total of 77 students were enrolled and 72 (93.5%) have successfully finished this course during 4 academic years. Mean total score for all students was 83.4 Âś 9.0 points, without difference between genders. We presented blended learning module as an effective way of gaining competences in CI and recommend this course to be required for future MDs.

A Simple Criterion for Inferring CRISPR Array Direction

Inferring transcriptional direction (orientation) of the CRISPR array is essential for many applications, including systematically investigating non-canonical CRISPR/Cas functions. The standard method, CRISPRDirection (embedded within CRISPRCasFinder), fails to predict the orientation (ND predictions) for ∼37% of the classified CRISPR arrays (>2200 loci); this goes up to >70% for the II-B subtype where non-canonical functions were first experimentally discovered. Alternatively, Potential Orientation (also embedded within CRISPRCasFinder), has a much smaller frequency of ND predictions but might have significantly lower accuracy. We propose a novel simple criterion, where the CRISPR array direction is assigned according to the direction of its associated cas genes (Cas Orientation). We systematically assess the performance of the three methods (Cas Orientation, CRISPRDirection, and Potential Orientation) across all CRISPR/Cas subtypes, by a mutual crosscheck of their predictions, and by comparing them to the experimental dataset. Interestingly, CRISPRDirection agrees much better with Cas Orientation than with Potential Orientation, despite CRISPRDirection and Potential Orientation being mutually related – Potential Orientation corresponding to one of six (heterogeneous) predictors employed by CRISPRDirection – and being unrelated to Cas Orientation. We find that Cas Orientation has much higher accuracy compared to Potential Orientation and comparable accuracy to CRISPRDirection – while accurately assigning an orientation to ∼95% of the CRISPR arrays that are non-determined by CRISPRDirection. Cas Orientation is, at the same time, simple to employ, requiring only (routine for prokaryotes) the prediction of the associated protein coding gene direction.