An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus

Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study

Nowadays, anti-TNF therapy remarkably improves the medical management of ulcerative colitis (UC), but approximately 40 % of patients do not respond to this treatment. In this study, we used 79 anti-TNF-naive patients with moderate-to-severe UC from four cohorts to discover alternative therapeutic targets and develop a personalized medicine approach that can diagnose UC non-responders (UCN) prior to receiving anti-TNF therapy. To this end, two microarray data series were integrated to create a discovery cohort with 35 UC samples. A comprehensive gene expression and functional analysis was performed and identified 313 significantly altered genes, among which IL6 and INHBA were highlighted as overexpressed genes in the baseline mucosal biopsies of UCN, whose cooperation may lead to a decrease in the Tregs population. Besides, screening the abundances of immune cell subpopulations showed neutrophils' accumulation increasing the inflammation. Furthermore, the correlation of KRAS signaling activation with unresponsiveness to anti-TNF mAb was observed using network analysis. Using 50x repeated 10-fold cross-validation LASSO feature selection and a stack ensemble machine learning algorithm, a five-mRNA prognostic panel including IL13RA2, HCAR3, CSF3, INHBA, and MMP1 was introduced that could predict the response of UC patients to anti-TNF antibodies with an average accuracy of 95.3 %. The predictive capacity of the introduced biomarker panel was also validated in two independent cohorts (44 UC patients). Moreover, we presented a distinct immune cell landscape and gene signature for UCN to anti-TNF drugs and further studies should be considered to make this predictive biomarker panel and therapeutic targets applicable in the clinical setting.

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns

Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced. Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores. Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers. Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

The SELP, CD93, IL2RG, and VAV1 Genes Associated with Atherosclerosis May Be Potential Diagnostic Biomarkers for Psoriasis

Purpose

Psoriasis and atherosclerosis are immunometabolic diseases. This study aimed to integrate bioinformatics and updated public resources to find potential biological markers associated with atherosclerosis that can cause psoriasis.

Patients and Methods

Microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened, and functional enrichment analysis was performed. We identified psoriasis and atherosclerosis common immune-related genes (PA-IRGs) by overlapping immune-related genes (IRGs) with genes in the module most associated with psoriasis and atherosclerosis obtained by weighted gene co-expression network analysis (WGCNAs). Receiver operating characteristic (ROC) was conducted to evaluate the predictive ability. The skin expression levels of diagnostic biomarkers were further verified by immunohistochemical staining. CIBERSORT, single-sample gene set enrichment analysis (ssGSEA), and Pearson's correlation analysis were applied to evaluate immune and lipid metabolism relationships in psoriatic tissues. In addition, a lincRNA-miRNA-mRNA network was constructed to find the pathogenesis in which diagnostic markers may be involved.

Results

Four PA-IRGs (SELP, CD93, IL2RG, and VAV1) demonstrated the optimal diagnostic value, with an AUC above 0.8. The immune cell infiltration analysis showed that dendritic resting cells, NK cell activation, neutrophils, macrophages M2, macrophages M0, and B-cell memory were highly abundant in psoriasis. Immune response analysis showed that TNF family members, chemokine receptors, interferons, natural killer cells, and TGF-β family members might be involved in psoriasis. Diagnostic biomarkers are strongly associated with various infiltrating immune cells, immune responses, and lipid metabolism. A lincRNA-miRNA-mRNA regulatory network consisting of 31 lincRNAs and 23 miRNAs was constructed. LINC00662 is involved in modulating four diagnostic biomarkers.

Conclusion

This study identified atherosclerosis-related genes SELP, CD93, VAV1, and IL2RG as potential psoriasis diagnostic markers. Provide novel insights into the possible regulatory mechanisms involved in psoriasis.

Aberrant B-Cell Activation in Systemic Lupus Erythematosus

Background

B lymphocytes (B cells) are essential in humoral response, and their activation is an important first step for the production of antibodies. However, aberrant B-cell activation is common in the development and progression of autoimmune diseases including systemic lupus erythematosus (SLE), which is characterized by the generation of superfluous autoantibodies. SLE exhibits clinical manifestation such as excessive inflammation and tissue damage. This review aims to summarize the recent emerging studies on aberrant B-cell activation and the associated concurrent therapeutic targets in SLE.

Summary

Aberrant B-cell activation is closely associated with the pathogenesis of SLE. Among a variety of mechanisms, dysregulations of B-cell receptor (BCR), toll-like receptor (TLR), and B-cell activating factor receptor (BAFF-R) pathways are the common and dominating factors involved in aberrant B-cell activation. These aberrant signaling transductions play diverse and integrated roles in the development and the pathogenesis of SLE. Therapies targeting aberrant B-cell activation have shown promising efficacy in achieving the clinical alleviation of SLE, suggesting the discovery of new drug targets from these aberrant signaling pathways is imminent. Here, an integrated survey or review of published high-throughput sequencing database covering RNAs of B cells from SLE versus criteria-matched healthy controls highlights that reported signaling molecules in BCR pathway (VAV2, PLC-γ2), TLR pathway (TLR9, P105, IRF7, TAB1), and BAFF-R pathway (SDF-1α) are attitudinally upregulated in SLE patients. This review thus suggests the concurrent and future therapeutic targets and potential biomarkers in both basic and clinical studies of SLE.

Key Messages

This review focuses on core B-cell signaling pathways, discussing the progress in the role of aberrant B-cell activation during the pathogenesis of SLE. This review also highlights the signaling molecules from published studies and database for the possible prevention and treatment targets serving the future clinical treatments of SLE.

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.

Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis

BACKGROUND

Regardless of improvements in controlling the COVID-19 pandemic, the lack of comprehensive insight into SARS-COV-2 pathogenesis is still a sophisticated challenge. In order to deal with this challenge, we utilized advanced bioinformatics and machine learning algorithms to reveal more characteristics of SARS-COV-2 pathogenesis and introduce novel host response-based diagnostic biomarker panels.

METHODS

In the present study, eight published RNA-Seq datasets related to whole-blood (WB) and nasopharyngeal (NP) swab samples of patients with COVID-19, other viral and non-viral acute respiratory illnesses (ARIs), and healthy controls (HCs) were integrated. To define COVID-19 meta-signatures, Gene Ontology and pathway enrichment analyses were applied to compare COVID-19 with other similar diseases. Additionally, CIBERSORTx was executed in WB samples to detect the immune cell landscape. Furthermore, the optimum WB- and NP-based diagnostic biomarkers were identified via all the combinations of 3 to 9 selected features and the 2-phases machine learning (ML) method which implemented k-fold cross validation and independent test set validation.

RESULTS

The host gene meta-signatures obtained for SARS-COV-2 infection were different in the WB and NP samples. The gene ontology and enrichment results of the WB dataset represented the enhancement in inflammatory host response, cell cycle, and interferon signature in COVID-19 patients. Furthermore, NP samples of COVID-19 in comparison with HC and non-viral ARIs showed the significant upregulation of genes associated with cytokine production and defense response to the virus. In contrast, these pathways in COVID-19 compared to other viral ARIs were strikingly attenuated. Notably, immune cell proportions of WB samples altered in COVID-19 versus HC. Moreover, the optimum WB- and NP-based diagnostic panels after two phases of ML-based validation included 6 and 8 markers with an accuracy of 97% and 88%, respectively.

CONCLUSIONS

Based on the distinct gene expression profiles of WB and NP, our results indicated that SARS-COV-2 function is body-site-specific, although according to the common signature in WB and NP COVID-19 samples versus controls, this virus also induces a global and systematic host response to some extent. We also introduced and validated WB- and NP-based diagnostic biomarkers using ML methods which can be applied as a complementary tool to diagnose the COVID-19 infection from non-COVID cases.

Association of microRNA-34a rs2666433 (A/G) Variant with Systemic Lupus Erythematosus in Female Patients: A Case-Control Study

Several microRNAs (miRNAs) are associated with autoimmune disease susceptibility and phenotype, including systemic lupus erythematosus (SLE). We aimed to explore for the first time the role of the miRNA-34a gene (MIR34A) rs2666433A > G variant in SLE risk and severity. A total of 163 adult patients with SLE and matched controls were recruited. Real-Time allelic discrimination PCR was applied for genotyping. Correlation with disease activity and clinic-laboratory data was done. The rs2666433 variant conferred protection against SLE development under heterozygous [A/G vs. G/G; OR = 0.57, 95%CI = 0.34-0.95], homozygous [A/A vs. G/G; OR = 0.52, 95%CI = 0.29-0.94], dominant [A/G + A/A vs. GG; OR = 0.55, 95%CI = 0.35-0.88], and log-additive [OR = 0.71, 95%CI = 0.53-0.95] models. Data stratification by sex revealed a significant association with SLE development in female participants under heterozygous/homozygous models (p-interaction = 0.004). There was no clear demarcation between SLE patients carrying different genotypes regarding the disease activity index or patients stratified according to lupus nephritis. Enrichment analysis confirmed the implication of MIR34A in the SLE pathway by targeting several genes related to SLE etiopathology. In conclusion, although the MIR34A rs2666433 variant conferred protection against developing SLE disease in the study population, it showed no association with disease activity. Replication studies in other populations are warranted.

Association of microRNA 17 host gene variant (rs4284505) with susceptibility and severity of systemic lupus erythematosus

Objective

MicroRNAs are large family clusters of small noncoding RNAs that implicated in genetic and epigenetic regulation of several immunological processes and pathways. As an epigenetic modifier, the microRNA 17‐92 cluster host gene (MIR17HG) has been shown to regulate the expression of genes involved in systemic lupus erythematosus (SLE) pathway. This study aimed to explore the association of MIR17HG (rs4284505; A>G) variant with SLE development and phenotype in a sample of the Eastern Mediterranean population.

Methods

A total of 326 participants (163 patients with SLE and 163 healthy controls) were enrolled in this study. The different genotypes of the MIR17HG (rs4284505) variant were characterized using the TaqMan real‐time polymerase chain reaction technique. Association with the available clinical and laboratory data, including the systemic lupus erythematosus disease activity index (SLEDAI), was also executed.

Results

The MIR17HG (rs4284505) variant showed a protective effect against developing SLE under heterozygote (A/G vs A/A; odds ratio [OR] = 0.10, 95% confidence interval [CI] = 0.05‐0.20, P < 0.001) and dominant (A/G+G/G vs A/A; OR = 0.39, 95% CI = 0.25‐0.61, P < .001) models. This association was consistent even after SLE stratified by lupus nephritis. In contrast, rs4284505 (G/G) genotype conferred increased susceptibility to SLE (G/G vs A/A+A/G; OR = 2.15, 95% CI = 1.31‐3.53, P = .002). Moreover, the rs4284505 variant showed a statistically significant association with mucocutaneous lesions and SLEDAI scores (all P < .05).

Conclusion

This study is the first one to explore that the MIR17HG rs4284505 is associated with SLE risk; (A/G) genotype conferred a protective effect, while the (G/G) genotype showed increased susceptibility to SLE and association with the disease severity in the study population.