Exploration of blood-derived coding and non-coding RNA diagnostic immunological panels for COVID-19 through a co-expressed-based machine learning procedure

Exploring genetic signatures of obesity: hub genes and miRNAs unveiled through comprehensive bioinformatic analysis

Objectives

Adipogenesis, the process of fat accumulation in adipose tissue, is closely linked to obesity, a condition characterized by excessive fat storage. Genetic factors significantly contribute to an individual's susceptibility to adipogenesis and the development of obesity.

Methods

In this study, we conducted a comprehensive bioinformatic analysis, including Weighted Gene Co-expression Analysis, differentially expressed gene analysis, and protein-protein interaction analysis, to identify hub genes and miRNAs associated with obesity.

Results

Our findings highlight the potential involvement of genes such as ATP5F1A, FN1, CCl2, RPS14, and RPS16, as well as miRNAs including hsa-miR-6844, hsa-miR-4528, hsa-miR-3686, hsa-miR-3124-3p, hsa-miR-381-3p, and hsa-miR-300 in obesity.

Conclusions

The findings from this study contribute to the growing knowledge of adipogenesis and obesity genetics, and provide potential biomarkers for further investigation and translation into clinical or research applications.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01490-8.

Revealing disease subtypes and heterogeneity in common variable immunodeficiency through transcriptomic analysis

Common Variable Immunodeficiency (CVID) is a primary immunodeficiency characterized by reduced levels of specific immunoglobulins, resulting in frequent infections, autoimmune disorders, increased cancer risk, and diminished antibody production despite an adequate B cell count. With its clinical manifestations being highly variable, the classification of CVID, including the widely recognized Freiburg classification, is primarily based on clinical symptoms and genetic variations. Our study aims to refine the classification of CVID by analyzing transcriptomics data to identify distinct disease subtypes. We utilized the GSE51405 dataset, examining transcriptomic profiles from 30 CVID patients without complications. Employing a combination of clustering techniques-KMeans, hierarchical agglomerative clustering, spectral clustering, and Gaussian Mixture models-and differential gene expression analysis with R's limma package, we integrated molecular findings with demographic data (age and gender) through correlation analysis and identified common genes among clusters. Three distinct clusters of CVID patients were identified using KMeans, Agglomerative Clustering, and Gaussian Mixture Models, highlighting the disease's heterogeneity. Differential expression analysis unveiled 31 genes with variable expression levels across these clusters. Notably, nine genes (EIF5A, RPL21, ANP32A, DTX3L, NCF2, CDC42EP3, CHP1, FOLR3, and DEFA4) exhibited consistent differential expression across all clusters, independent of demographic factors. The study recommends categorizing patients based on the four genes, NCF2, CHP1, FOLR3, and DEFA4-as they may assist in prognostic prediction. Transcriptomic analysis of common variable immunodeficiency (CVID) patients identified three distinct clusters based on gene expression, independent of age and gender. Nine differentially expressed genes were identified across these clusters, suggesting potential biomarkers for CVID subtype classification. These findings highlight the genetic heterogeneity of CVID and provide novel insights into disease classification and potential personalized treatment approaches.

SARS-CoV-2 remodels the landscape of small non-coding RNAs with infection time and symptom severity

The COVID-19 pandemic caused by the coronavirus SARS-CoV-2 has significantly impacted global health, stressing the necessity of basic understanding of the host response to this viral infection. In this study, we investigated how SARS-CoV-2 remodels the landscape of small non-coding RNAs (sncRNA) from a large collection of nasopharyngeal swab samples taken at various time points from patients with distinct symptom severity. High-throughput RNA sequencing analysis revealed a global alteration of the sncRNA landscape, with abundance peaks related to species of 21-23 and 32-33 nucleotides. Host-derived sncRNAs, including microRNAs (miRNAs), transfer RNA-derived small RNAs (tsRNAs), and small nucleolar RNA-derived small RNAs (sdRNAs) exhibited significant differential expression in infected patients compared to controls. Importantly, miRNA expression was predominantly down-regulated in response to SARS-CoV-2 infection, especially in patients with severe symptoms. Furthermore, we identified specific tsRNAs derived from Glu- and Gly-tRNAs as major altered elements upon infection, with 5' tRNA halves being the most abundant species and suggesting their potential as biomarkers for viral presence and disease severity prediction. Additionally, down-regulation of C/D-box sdRNAs and altered expression of tinyRNAs (tyRNAs) were observed in infected patients. These findings provide valuable insights into the host sncRNA response to SARS-CoV-2 infection and may contribute to the development of further diagnostic and therapeutic strategies in the clinic.

Effect of the SARS-CoV-2 Delta-associated G15U mutation on the s2m element dimerization and its interactions with miR-1307-3p

The s2m, a highly conserved 41-nt hairpin structure in the SARS-CoV-2 genome, serves as an attractive therapeutic target that may have important roles in the virus life cycle or interactions with the host. However, the conserved s2m in Delta SARS-CoV-2, a previously dominant variant characterized by high infectivity and disease severity, has received relatively less attention than that of the original SARS-CoV-2 virus. The focus of this work is to identify and define the s2m changes between Delta and SARS-CoV-2 and the subsequent impact of those changes upon the s2m dimerization and interactions with the host microRNA miR-1307-3p. Bioinformatics analysis of the GISAID database targeting the s2m element reveals a >99% correlation of a single nucleotide mutation at the 15th position (G15U) in Delta SARS-CoV-2. Based on 1H NMR spectroscopy assignments comparing the imino proton resonance region of s2m and the s2m G15U at 19°C, we show that the U15-A29 base pair closes, resulting in a stabilization of the upper stem without overall secondary structure deviation. Increased stability of the upper stem did not affect the chaperone activity of the viral N protein, as it was still able to convert the kissing dimers formed by s2m G15U into a stable duplex conformation, consistent with the s2m reference. However, we show that the s2m G15U mutation drastically impacts the binding of host miR-1307-3p. These findings demonstrate that the observed G15U mutation alters the secondary structure of s2m with subsequent impact on viral binding of host miR-1307-3p, with potential consequences on immune responses.

Gene biomarkers and classifiers for various subtypes of HTLV-1-caused ATLL cancer identified by a combination of differential gene co‑expression and support vector machine algorithms

Adult T-cell leukemia/lymphoma (ATLL) is pathogen-caused cancer that is progressed after the infection by human T-cell leukemia virus type 1. Four significant subtypes comprising acute, lymphoma, chronic, and smoldering have been identified for this cancer. However, there are no trustworthy prognostic biomarkers for these subtypes. We utilized a combination of two powerful network-based and machine-learning algorithms including differential co-expressed genes (DiffCoEx) and support vector machine-recursive feature elimination with cross-validation (SVM-RFECV) methods to categorize disparate ATLL subtypes from asymptomatic carriers (ACs). The results disclosed the significant involvement of CBX6, CNKSR1, and MAX in chronic, MYH10 and P2RY1 in acute, C22orf46 and HNRNPA0 in smoldering subtypes. These genes also can classify each ATLL subtype from AC carriers. The integration of the results of two powerful algorithms led to the identification of reliable gene classifiers and biomarkers for diverse ATLL subtypes.

Potential miRNA-gene interactions determining progression of various ATLL cancer subtypes after infection by HTLV-1 oncovirus

BACKGROUND

Adult T-cell Leukemia/Lymphoma (ATLL) is a rapidly progressing type of T-cell non-Hodgkin lymphoma that is developed after the infection by human T-cell leukemia virus type 1 (HTLV-1). It could be categorized into four major subtypes, acute, lymphoma, chronic, and smoldering. These different subtypes have some shared clinical manifestations, and there are no trustworthy biomarkers for diagnosis of them.

METHODS

We applied weighted-gene co-expression network analysis to find the potential gene and miRNA biomarkers for various ATLL subtypes. Afterward, we found reliable miRNA-gene interactions by identifying the experimentally validated-target genes of miRNAs.

RESULTS

The outcomes disclosed the interactions of miR-29b-2-5p and miR-342-3p with LSAMP in ATLL_acute, miR-575 with UBN2, miR-342-3p with ZNF280B, and miR-342-5p with FOXRED2 in ATLL_chronic, miR-940 and miR-423-3p with C6orf141, miR-940 and miR-1225-3p with CDCP1, and miR-324-3p with COL14A1 in ATLL_smoldering. These miRNA-gene interactions determine the molecular factors involved in the pathogenesis of each ATLL subtype and the unique ones could be considered biomarkers.

CONCLUSION

The above-mentioned miRNAs-genes interactions are suggested as diagnostic biomarkers for different ATLL subtypes.

Immunologic Profile of Severe COVID-19 Patients in Alborz Province, Iran

Background: The coronavirus disease 2019 (COVID-19) pandemic has prompted researchers to look for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogenicity in depth. Immune system dysregulation was one of the major mechanisms in its pathogenesis. The evidence regarding the levels of interferons (IFNs) and pro- and anti-inflammatory cytokines in COVID-19 patients is not well-established. Objectives: Therefore, this study evaluated the expression level of type-I, II, III IFNs, along with interleukin-1 (IL-1), interleukin-6 (IL-6), interleukin-10 (IL-10), and FOXP3 genes in patients with severe COVID-19 to provide additional insights regarding the regulation of these cytokines during COVID-19 infection. Methods: Peripheral blood mononuclear cells were isolated from two groups, including severe COVID-19 patients and healthy controls. Ribonucleic acid was extracted to evaluate the expression level of IFN-a, IFN-b, IFN-g, IFN-la, IL-1, IL-6, IL-10, and FOXP3 genes using real-time polymerase chain reaction. The correlations between the expression levels of these genes were also assessed. Results: A total of 40 samples were divided into two groups, with each group consisting of 20 samples. When comparing the severe COVID-19 group to the controls, the expression levels of IFN-g, tumor necrosis factor-alpha (TNF-α), IL-6, and IL-10 genes were significantly higher in the severe COVID-19 group. The two groups had no significant differences in IFN-a, IFN-b, IFN-la, IL-1, and FOXP3 expression. The correlation analysis revealed a negative correlation between type I and type III IFNs (i.e., IFN-a and IFN-la) and pro-inflammatory cytokines (i.e., IL-1 and IL-10). Conclusions: This study suggests the possible upregulation of IFN-g, IL-6, IL-10, and TNF-α during SARS-CoV-2 pathogenicity. The preliminary findings of this study and those reported previously show that the levels of IFNs and pro- and anti-inflammatory cytokines are not uniformly expressed among all COVID-19 patients and might differ as the disease progresses to the severe stage.

Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features

The persistent impact of the COVID-19 pandemic and heterogeneity in disease manifestations point to a need for innovative approaches to identify drivers of immune pathology and predict whether infected patients will present with mild/moderate or severe disease. We have developed a novel iterative machine learning pipeline that utilizes gene enrichment profiles from blood transcriptome data to stratify COVID-19 patients based on disease severity and differentiate severe COVID cases from other patients with acute hypoxic respiratory failure. The pattern of gene module enrichment in COVID-19 patients overall reflected broad cellular expansion and metabolic dysfunction, whereas increased neutrophils, activated B cells, T-cell lymphopenia, and proinflammatory cytokine production were specific to severe COVID patients. Using this pipeline, we also identified small blood gene signatures indicative of COVID-19 diagnosis and severity that could be used as biomarker panels in the clinical setting.

Identification of novel miRNAs potentially involved in the pathogenesis of adult T-cell leukemia/lymphoma using WGCNA followed by RT-qPCR test of hub genes

BACKGROUND

Adult T-cell Lymphoma/Leukemia (ATLL) is characterized by the malignant proliferation of T-cells in Human T-Lymphotropic Virus Type 1 and a high mortality rate. Considering the emerging roles of microRNAs (miRNAs) in various malignancies, the analysis of high-throughput miRNA data employing computational algorithms helps to identify potential biomarkers.

METHODS

Weighted gene co-expression network analysis was utilized to analyze miRNA microarray data from ATLL and healthy uninfected samples. To identify miRNAs involved in the progression of ATLL, module preservation analysis was used. Subsequently, based on the target genes of the identified miRNAs, the STRING database was employed to construct protein-protein interaction networks (PPIN). Real-time quantitative PCR was also performed to validate the expression of identified hub genes in the PPIN network.

RESULTS

After constructing co-expression modules and then performing module preservation analysis, four out of 15 modules were determined as ATLL-specific modules. Next, the hub miRNA including hsa-miR-18a-3p, has-miR-187-5p, hsa-miR-196a-3p, and hsa-miR-346 were found as hub miRNAs. The protein-protein interaction networks were constructed for the target genes of each hub miRNA and hub genes were identified. Among them, UBB, RPS15A, and KMT2D were validated by Reverse-transcriptase PCR in ATLL patients.

CONCLUSION

The results of the network analysis of miRNAs and their target genes revealed the major players in the pathogenesis of ATLL. Further studies are required to confirm the role of these molecular factors and to discover their potential benefits as treatment targets and diagnostic biomarkers.

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Data from the England National Health Services Heart Disease Prediction Cohort was used. After comparison of model metrics for XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting, XGBoost was used as the machine-learning model of choice in this study. Boost-strap simulation (N = 10,000) was used to empirically derive the distribution of model metrics and covariate Gain statistics. SHapely Additive exPlanations (SHAP) to provide explanations to machine-learning output and simulation to evaluate the variance of model accuracy metrics. For the XGBoost modeling method, we observed (through 10,000 completed simulations) that the AUROC ranged from 0.771 to 0.947, a difference of 0.176, the balanced accuracy ranged from 0.688 to 0.894, a 0.205 difference, the sensitivity ranged from 0.632 to 0.939, a 0.307 difference, and the specificity ranged from 0.595 to 0.944, a 0.394 difference. Among 10,000 simulations completed, we observed that the gain for Angina ranged from 0.225 to 0.456, a difference of 0.231, for Cholesterol ranged from 0.148 to 0.326, a difference of 0.178, for maximum heart rate (MaxHR) ranged from 0.081 to 0.200, a range of 0.119, and for Age ranged from 0.059 to 0.157, difference of 0.098. Use of simulations to empirically evaluate the variability of model metrics and explanatory algorithms to observe if covariates match the literature are necessary for increased transparency, reliability, and utility of machine learning methods. These variance statistics, combined with model accuracy statistics can help researchers identify the best model for a given dataset.

Effect of the SARS-CoV-2 Delta-associated G15U mutation on the s2m element dimerization and its interactions with miR-1307-3p

The stem loop 2 motif (s2m), a highly conserved 41-nucleotide hairpin structure in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome, serves as an attractive therapeutic target that may have important roles in the virus life cycle or interactions with the host. However, the conserved s2m in Delta SARS-CoV-2, a previously dominant variant characterized by high infectivity and disease severity, has received relatively less attention than that of the original SARS-CoV-2 virus. The focus of this work is to identify and define the s2m changes between Delta and SARS-CoV-2 and subsequent impact of those changes upon the s2m dimerization and interactions with the host microRNA miR-1307-3p. Bioinformatics analysis of the GISAID database targeting the s2m element reveals a greater than 99% correlation of a single nucleotide mutation at the 15 th position (G15U) in Delta SARS-CoV-2. Based on 1 H NMR assignments comparing the imino proton resonance region of s2m and the G15U at 19°C, we find that the U15-A29 base pair closes resulting in a stabilization of the upper stem without overall secondary structure deviation. Increased stability of the upper stem did not affect the chaperone activity of the viral N protein, as it was still able to convert the kissing dimers formed by s2m G15U into a stable duplex conformation, consistent with the s2m reference. However, we find that the s2m G15U mutation drastically reduces the binding affinity of the host miR-1307-3p. These findings demonstrate that the observed G15U mutation alters the secondary structure of s2m with subsequent impact on viral binding of host miR-1307-3p, with potential consequences on the immune response.

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.