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.

New insights into extracellular and intracellular redox status in COVID-19 patients.

BACKGROUND: The imbalance of redox homeostasis induces hyper-inflammation in viral infections. In this study, we explored the redox system signature in response to SARS-COV-2 infection and examined the status of these extracellular and intracellular signatures in COVID-19 patients. METHOD: The multi-level network was constructed using multi-level data of oxidative stress-related biological processes, protein-protein interactions, transcription factors, and co-expression coefficients obtained from GSE164805, which included gene expression profiles of peripheral blood mononuclear cells (PBMCs) from COVID-19 patients and healthy controls. Top genes were designated based on the degree and closeness centralities. The expression of high-ranked genes was evaluated in PBMCs and nasopharyngeal (NP) samples of 30 COVID-19 patients and 30 healthy controls. The intracellular levels of GSH and ROS/O2• - and extracellular oxidative stress markers were assayed in PBMCs and plasma samples by flow cytometry and ELISA. ELISA results were applied to construct a classification model using logistic regression to differentiate COVID-19 patients from healthy controls. RESULTS: CAT, NFE2L2, SOD1, SOD2 and CYBB were 5 top genes in the network analysis. The expression of these genes and intracellular levels of ROS/O2• - were increased in PBMCs of COVID-19 patients while the GSH level decreased. The expression of high-ranked genes was lower in NP samples of COVID-19 patients compared to control group. The activity of extracellular enzymes CAT and SOD, and the total oxidant status (TOS) level were increased in plasma samples of COVID-19 patients. Also, the 2-marker panel of CAT and TOS and 3-marker panel showed the best performance. CONCLUSION: SARS-COV-2 disrupts the redox equilibrium in immune cells and the upper respiratory tract, leading to exacerbated inflammation and increased replication and entrance of SARS-COV-2 into host cells. Furthermore, utilizing markers of oxidative stress as a complementary validation to discriminate COVID-19 from healthy controls, seems promising.

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.

Inhibition of epithelial SHH signaling exerts a dual protective effect against inflammation and epithelial-mesenchymal transition in inflammatory bowel disease.

Inflammatory bowel disease (IBD) is a debilitating and incurable inflammatory disorder. Despite its increasing prevalence, the underlying pathogenic mechanisms of IBD have not been fully clarified. In addition to the regulatory role of Sonic Hedgehog (SHH) signaling in the maintenance of gut homeostasis, its involvement in development of inflammatory disorders and organ fibrosis has also been reported. Here, we investigated the role of SHH signaling in IBD and examined the molecular mechanisms targeted by the SHH signaling blockade. In addition to increased inflammatory responses and induced Epithelial-mesenchymal transition (EMT) process, SHH signaling activity also increased in active lesions of IBD patients. These findings were similar to what was observed in the LPS-induced Caco2-RAW264.7 co-culture model. Inhibition of SHH signaling in the intestinal epithelial cells using SHH inhibitors influenced inflammatory responses through decreased expression of inflammatory cytokines. Moreover, treatment of differentiated Caco2 cells with SHH signaling inhibitors prevented the overexpression of EMT markers and downregulation of epithelial adherens and tight junctions in inflammatory conditions. This study demonstrated that the inhibition of SHH signaling by small molecules might have therapeutic benefit in IBD, and provided compelling experimental evidence that SHH signaling inhibitors can impose anti-inflammatory effects in intestinal epithelial cells while preserving their epithelial characteristics by restricting the induction of EMT.

Y chromosome is moving out of sex determination shadow.

Although sex hormones play a key role in sex differences in susceptibility, severity, outcomes, and response to therapy of different diseases, sex chromosomes are also increasingly recognized as an important factor. Studies demonstrated that the Y chromosome is not a 'genetic wasteland' and can be a useful genetic marker for interpreting various male-specific physiological and pathophysiological characteristics. Y chromosome harbors male­specific genes, which either solely or in cooperation with their X-counterpart, and independent or in conjunction with sex hormones have a considerable impact on basic physiology and disease mechanisms in most or all tissues development. Furthermore, loss of Y chromosome and/or aberrant expression of Y chromosome genes cause sex differences in disease mechanisms. With the launch of the human proteome project (HPP), the association of Y chromosome proteins with pathological conditions has been increasingly explored. In this review, the involvement of Y chromosome genes in male-specific diseases such as prostate cancer and the cases that are more prevalent in men, such as cardiovascular disease, neurological disease, and cancers, has been highlighted. Understanding the molecular mechanisms underlying Y chromosome-related diseases can have a significant impact on the prevention, diagnosis, and treatment of diseases.

RNA Sequencing of CD4+ T Cells in Relapsing-Remitting Multiple Sclerosis Patients at Relapse: Deciphering the Involvement of Novel genes and Pathways.

CD4+ T cells are known as a noteworthy potential modulator of inflammation in multiple sclerosis (MS). In the current study, we investigated the transcriptome profile of CD4+ T cells in patients with relapsing-remitting MS (RRMS) at the relapse phase. We performed RNA sequencing of CD4+ T cells isolated from four relapsing-remitting MS (RRMS) patients at the relapse phase and four age- and sex-matched healthy controls. The edgeR statistical method was employed to determine differentially expressed genes (DEGs). Gene set enrichment analysis was subsequently performed. Applying a physical interaction network, genes with higher degrees were selected as hub genes. A total of 1278 and 1034 genes were defined at significantly higher or lower levels, respectively, in CD4+ T cells of RRMS patients at the relapse phase as compared with healthy controls. The top up- and downregulated genes were JAML and KDM3A. The detected DEGs were remarkable on chromosomes 1 and 2, respectively. The DEGs were mainly enriched in the pathways "regulation of transcription, DNA-templated," "regulation of B cell receptor signaling pathway," "protein phosphorylation," "epidermal growth factor receptor signaling pathway," and "positive regulation of neurogenesis." Moreover, 16 KEGG pathways mostly associated with the immune system and viral infections were enriched. In the constructed physical interaction networks, UBA52 and TP53 were shown to be the most highly ranked hub genes among upregulated and downregulated genes, respectively. By applying global transcriptome profiling of CD4+ T cells, we deciphered the involvement of several novel genes and pathways in MS pathogenesis. The present results must be confirmed by in vivo and in vitro studies.

Interplay between SARS-CoV-2 and human long non-coding RNAs.

The long non-coding RNAs (lncRNAs) play a critical regulatory role in the host response to the viral infection. However, little is understood about the transcriptome architecture, especially lncRNAs pattern during the SARS-CoV-2 infection. In the present study, using publicly available RNA sequencing data of bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) samples from COVID-19 patients and healthy individuals, three interesting findings highlighted: (a) More than half of the interactions between lncRNAs-PCGs of BALF samples established by three trans-acting lncRNAs (HOTAIRM1, PVT1 and AL392172.1), which also exhibited the high affinity for binding to the SARS-CoV-2 genome, suggesting the major regulatory role of these lncRNAs during the SARS-CoV-2 infection. (b) lncRNAs of MALAT1 and NEAT1 are possibly contributed to the inflammation development in the SARS-CoV-2 infected cells. (c) In contrast to the 3' part of the SARS-CoV-2 genome, the 5' part can interact with many human lncRNAs. Therefore, the mRNA-based vaccines will not show any side effects because of the off-label interactions with the human lncRNAs. Overall, the putative functionalities of lncRNAs can be promising to design the non-coding RNA-based drugs and to inspect the efficiency of vaccines to overcome the current pandemic.

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

BACKGROUND: A comprehensive intuition of the systemic lupus erythematosus (SLE), as a complex and multifactorial disease, is a biological challenge. Dealing with this challenge needs employing sophisticated bioinformatics algorithms to discover the unknown aspects. This study aimed to underscore key molecular characteristics of SLE pathogenesis, which may serve as effective targets for therapeutic intervention. METHODS: In the present study, the human peripheral blood mononuclear cell (PBMC) microarray datasets (n = 6), generated by three platforms, which included SLE patients (n = 220) and healthy control samples (n = 135) were collected. Across each platform, we integrated the datasets by cross-platform normalization (CPN). Subsequently, through BNrich method, the structures of Bayesian networks (BNs) were extracted from KEGG-indexed SLE, TCR, and BCR signaling pathways; the values of the node (gene) and edge (intergenic relationships) parameters were estimated within each integrated datasets. Parameters with the FDR < 0.05 were considered significant. Finally, a mixture model was performed to decipher the signaling pathway alterations in the SLE patients compared to healthy controls. RESULTS: In the SLE signaling pathway, we identified the dysregulation of several nodes involved in the (1) clearance mechanism (SSB, MACROH2A2, TRIM21, H2AX, and C1Q gene family), (2) autoantigen presentation by MHCII (HLA gene family, CD80, IL10, TNF, and CD86), and (3) end-organ damage (FCGR1A, ELANE, and FCGR2A). As a remarkable finding, we demonstrated significant perturbation in CD80 and CD86 to CD28, CD40LG to CD40, C1QA and C1R to C2, and C1S to C4A edges. Moreover, we not only replicated previous studies regarding alterations of subnetworks involved in TCR and BCR signaling pathways (PI3K/AKT, MAPK, VAV gene family, AP-1 transcription factor) but also distinguished several significant edges between genes (PPP3 to NFATC gene families). Our findings unprecedentedly showed that different parameter values assign to the same node based on the pathway topology (the PIK3CB parameter values were 1.7 in TCR vs - 0.5 in BCR signaling pathway). CONCLUSIONS: Applying the BNrich as a hybridized network construction method, we highlight under-appreciated systemic alterations of SLE, TCR, and BCR signaling pathways in SLE. Consequently, having such a systems biology approach opens new insights into the context of multifactorial disorders.

2015 Estimation of Hospitals Safety from Disasters in I.R.Iran: The Results from the Assessment of 421 Hospitals.

BACKGROUND AND OBJECTIVE: Iran's health system has developed a Farsi edition of the Hospital Safety Index (HSI) and has integrated the related assessment program into the health information system. This article presents the results of the 2015 estimation of hospital safety from disasters in I.R.Iran using HSI. METHODS: We analyzed data from 421 hospitals that had submitted a complete HSI assessment form on the Ministry of Health and Medical Education Portal System. Data collection was based on the self-assessments of the hospital disaster committees. HSI includes 145 items categorized in three components including, structural, non-structural and functional capacity. For each item, safety status was categorized into three levels: not safe (0), average safety (1) and high safety (2). A normalized scoring scheme on a 100-point scale was developed. Hospitals were classified to three safety classes according to their normalized total score: low (≤34.0), average (34.01-66.0) and high (>66.0). RESULTS: The average score of all safety components were 43.0 out of 100 (± 11.0). Eighty-two hospitals (19.4%) were classified as not safe, and 339 hospitals (80.6%) were classified in the average safety category. No hospital was placed in the high safety category. Average safety scores were 41.0, 47.0, and 42.0 for functional capacity, non-structural safety, and structural safety respectively. The average safety score increased between 2012 and 2015, from 34.0 to 43.0. CONCLUSIONS: Hospital safety in the event of disasters has improved in Iran in recent years and more hospitals have joined the HSI program. This is a result of continuous efforts invested in capacity building programs and promotion of the 2012 HSI estimation. The HSI should be maintained to monitor the progress of Iran's health system in regards to hospital safety in the case of disasters. It is recommended that WHO continue advocacy of HSI, establish a HSI monitoring system, and add it to country profiles on WHO website.

Hospitals safety from disasters in I.R.iran: the results from assessment of 224 hospitals.

BACKGROUND AND OBJECTIVE: Iran's hospitals have been considerably affected by disasters during last decade. To address this, health system of I.R.Iran has taken an initiative to assess disaster safety of the hospitals using an adopted version of Hospital Safety Index (HSI). This article presents the results of disaster safety assessment in 224 Iran's hospitals. METHODS: A self-assessment approach was applied to assess the disaster safety in 145 items categorized in 3 components including structural, non-structural and functional capacity. For each item, safety level was categorized to 3 levels: not safe (0), average safe (1) and high safe (2). A raw score was tallied for each safety component and its elements by a simple sum of all the corresponding scores. All scores were normalized on a 100 point scale. Hospitals were classified to three safety classes according to their normalized total score: low (≤34.0), average (34.01-66.0) and high (>66.0). RESULTS: The average score of all safety components were 32.4 out of 100 (± 12.7 SD). 122 hospitals (54.5%) were classified as low safe and 102 hospitals (45.5%) were classified as average safe. No hospital was placed in the high safe category. Average safety scores out of 100 were 27.3 (±14.2 SD) for functional capacity, 36.0 (±13.9 SD) for non-structural component and 36.0 (±19.0 SD) for structural component. Neither the safety classes nor the scores of safety components were significantly associated with types of hospitals in terms of affiliation, function and size (P>0.05). CONCLUSIONS: To enhance the hospitals safety for disaster in Iran, we recommend: 1) establishment of a national committee for hospital safety in disasters; 2) supervision on implementation of the safety standards in construction of new hospitals; 3) enhancement of functional readiness and safety of non-structural components while structural retrofitting of the existing hospitals is being taken into consideration, whenever is cost-effective; 4) considering the disaster safety status as the criteria for licensing and accreditation of the hospitals. Key words: Hospital, safety, disaster, emergency, Iran Correspondence to: Ali Ardalan MD, PhD. Tehran University of Medical Sciences, Harvard Humanitarian Initiative, Email: aardalan@tums.ac.ir, ardalan@hsph.harvard.edu.