Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer.

BACKGROUND: The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health. METHODS: In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116). RESULTS: According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29). CONCLUSIONS: Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.

BACKGROUND: P4 medicine (predict, prevent, personalize, and participate) is a new approach to diagnosing and predicting diseases on a patient-by-patient basis. For the prevention and treatment of diseases, prediction plays a fundamental role. One of the intelligent strategies is the design of deep learning models that can predict the state of the disease using gene expression data. RESULTS: We create an autoencoder deep learning model called DeeP4med, including a Classifier and a Transferor that predicts cancer's gene expression (mRNA) matrix from its matched normal sample and vice versa. The range of the F1 score of the model, depending on tissue type in the Classifier, is from 0.935 to 0.999 and in Transferor from 0.944 to 0.999. The accuracy of DeeP4med for tissue and disease classification was 0.986 and 0.992, respectively, which performed better compared to seven classic machine learning models (Support Vector Classifier, Logistic Regression, Linear Discriminant Analysis, Naive Bayes, Decision Tree, Random Forest, K Nearest Neighbors). CONCLUSIONS: Based on the idea of DeeP4med, by having the gene expression matrix of a normal tissue, we can predict its tumor gene expression matrix and, in this way, find effective genes in transforming a normal tissue into a tumor tissue. Results of Differentially Expressed Genes (DEGs) and enrichment analysis on the predicted matrices for 13 types of cancer showed a good correlation with the literature and biological databases. This led that by using the gene expression matrix, to train the model with features of each person in a normal and cancer state, this model could predict diagnosis based on gene expression data from healthy tissue and be used to identify possible therapeutic interventions for those patients.

Exploring Co-occurrence patterns and microbial diversity in the lung microbiome of patients with non-small cell lung cancer.

BACKGROUND: It has been demonstrated in the literature that a dysbiotic microbiome could have a negative impact on the host immune system and promote disease onset or exacerbation. Co-occurrence networks have been widely adopted to identify biomarkers and keystone taxa in the pathogenesis of microbiome-related diseases. Despite the promising results that network-driven approaches have led to in various human diseases, there is a dearth of research pertaining to key taxa that contribute to the pathogenesis of lung cancer. Therefore, our primary goal in this study is to explore co-existing relationships among members of the lung microbial community and any potential gained or lost interactions in lung cancer. RESULTS: Using integrative and network-based approaches, we integrated four studies assessing the microbiome of lung biopsies of cancer patients. Differential abundance analyses showed that several bacterial taxa are different between tumor and tumor-adjacent normal tissues (FDR adjusted p-value < 0.05). Four, fifteen, and twelve significantly different associations were found at phylum, family, and genus levels. Diversity analyses suggested reduced alpha diversity in the tumor microbiome. However, beta diversity analysis did not show any discernible pattern between groups. In addition, four distinct modules of bacterial families were detected by the DBSCAN clustering method. Finally, in the co-occurrence network context, Actinobacteria, Firmicutes, Bacteroidetes, and Chloroflexi at the phylum level and Bifidobacterium, Massilia, Sphingobacterium, and Ochrobactrum at the genus level showed the highest degree of rewiring. CONCLUSIONS: Despite the absence of statistically significant differences in the relative abundance of certain taxa between groups, it is imperative not to overlook them for further exploration. This is because they may hold pivotal central roles in the broader network of bacterial taxa (e.g., Bifidobacterium and Massilia). These findings emphasize the importance of a network analysis approach for studying the lung microbiome since it could facilitate identifying key microbial taxa in lung cancer pathogenesis. Relying exclusively on differentially abundant taxa may not be enough to fully grasp the complex interplay between lung cancer and the microbiome. Therefore, a network-based approach can offer deeper insights and a more comprehensive understanding of the underlying mechanisms.

A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data.

Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and data integration techniques are powerful tools to achieve this goal. This study aimed to introduce a machine learning method to integrate three types of biological data, and investigate the performance of two other methods, in identifying the survival dependency of patients. The data included TCGA RNA-seq gene expression, DNA methylation, and clinical data from 368 patients with colon cancer also we use an independent external validation data set, containing 232 samples. Three methods including, hyper-parameter optimized autoencoders (HPOAE), normal autoencoder, and penalized principal component analysis (PPCA) were used for simultaneous data integration and estimation under a COX hazards model. The HPOAE was thought to outperform other methods. The HPOAE had the Log Rank Mantel-Cox value of 14.27 ± 2, and a Breslow-Generalized Wilcoxon value of 13.13 ± 1. Ten miRNA, 11 methylated genes, and 28 mRNA all by (importance of marginal cutoff > 0.95) were identified. The study demonstrated that hsa-miR-485-5p targets both ZMYM1 and tp53, the latter of which has been previously associated with cancer in numerous studies. Furthermore, compared to other methods, the HPOAE exhibited a greater capacity for identifying survival subgroups and the genes associated with them in patients with colon cancer. However, all of the results were obtained by computational methods, and clinical and experimental studies are needed to validate these results.

Spinal Muscular Atrophy with Progressive Myoclonic Epilepsy (SMA-PME): three new cases and review of the mutational spectrum.

BACKGROUND: Spinal muscular atrophy (SMA) could be classified as 5q and non-5q, based on the chromosomal location of causative genes. A rare form of non-5q SMA is an autosomal-recessive condition called spinal muscular atrophy with progressive myoclonic epilepsy (SMA-PME), phenotypically characterized by myoclonic and generalized seizures with progressive neurological deterioration. SMA-PME is a clinically heterogeneous disorder that arises from biallelic pathogenic variants in ASAH1 gene. METHODS: Following clinical and primary laboratory assessments, whole-exome sequencing was performed to detect the disease-causing variants in three cases of SMA-PME from different families. Also, Multiplex ligation-dependent probe amplification (MLPA) was employed for determining the copy numbers of SMN1 and SMN2 genes to rule out 5q SMA. RESULTS: Exome sequencing revealed two different homozygous missense mutations (c.109 C > A [p.Pro37Thr] or c.125 C > T [p.Thr42Met]) in exon 2 of the ASAH1 gene in the affected members of the families. Sanger sequencing of the other family members showed the expected heterozygous carriers. In addition, no clinically relevant variant was identified in patients by MLPA. CONCLUSION: This study describes two different ASAH1 mutations and the clinical picture of 3 SMA-PME patients. In addition, previously reported mutations have been reviewed. This study could help to fortify the database of this rare disease with more clinical and genomic data.

The effect of caring intervention (physical activity, diet and counseling) on gestational diabetes for pregnant women with metabolic syndrome.

OBJECTIVE: Metabolic syndrome and its indexes is one of the critical health problems during pregnancy. This study aimed to examine the effects of a care intervention program on pregnancy outcome in pregnant women with Metabolic Syndrome during 2017-2018. METHODS: This randomized clinical trial study was conducted in 120 singleton pregnant women with metabolic syndrome. The participants were selected using purposive sampling method from pregnant women referred to two health centers of Babol. They were randomly divided in two control and intervention groups (60 samples in each group). Intervention group received one motivational interview session for 120 min, two consultation sessions with expert for nutritional recommendations based on Nutrition Guidelines of Ministry of Health for Pregnant Women. They were also theoretically and practically provided by three training sessions for physical activity and pregnancy exercises combining pelvic floor muscles, bodybuilding, muscle strengthening, stretching, and relaxation and walking activities. To follow up on the intervention, the researcher made a phone call with the participants in intervention group every 10 days to two weeks. Pregnancy outcomes were assessed using SPSS software. RESULTS: The obtained results showed that there was a significant difference between the two groups in maternal complications including gestational diabetes, hospitalization due to gestational diabetes, nutritional diet for gestational diabetes, pregnancy weight gain and 2-hour post-prandial blood glucose test (p ≤ .01). There was no significant difference in terms of demographic, midwifery and metabolic syndrome indices between the intervention and control groups. CONCLUSION: The results of the study indicated that intervention program improved the maternal pregnancy outcome such as gestational diabetes and weight gain during the pregnancy in the intervention group. This program had no adverse effects for the mother who is consistent with pregnancy health objectives.

A systematic approach introduced novel targets in rectal cancer by considering miRNA/mRNA interactions in response to radiotherapy.

BACKGROUND: The discovery of miRNA/mRNA interactions in several biological samples prompted the researchers to explore new biomarkers in tumors. OBJECTIVE: We aimed to investigate the interactions of miRNA/mRNA in response to radiotherapy in the plasma samples of rectal cancer patients. METHODS: Five microarray datasets related to cancerous and non-cancerous individuals were first used to construct networks. The databases of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were applied to analyze pathway enrichment. The plasma samples were then collected from 55 patients with recently diagnosed rectal cancer and 10 healthy subjects. For radiotherapy courses, the patients have consecutively received 30 sessions of local radiation for six weeks. At last, the expression of selected genes and miRNAs was experimentally measured before and after radiotherapy by qPCR, and the protein levels of the target genes were measured by ELISA assay. We evaluated the therapeutic responses based on the tumor regression grade of the Dworak classification. RESULTS: We identified 5 up-regulated and 5 down-regulated miRNAs and 8 up-regulated and 3 down-regulated genes of the databases. There was a significant increase in tumor suppressor miRNAs, including miR-101-3p, miR-145-5p, miR-26a-5p, miR-34a-5p, and a significant decrease in oncomiRs, including miR-221-3p and miR-17-5p, after radiotherapy compared to the pre-treatment. Moreover, the up-regulated miR-17-5p and miR-221-5p and the down-regulated miR-101-3p and miR-145-5p were directly related to rectal cancer through the interaction with the Wnt, RAS, PI3K, and TGF-ß signaling pathways. An analysis of receiver operating characteristics showed that miRNAs 221, 17, and 23 were response-related in locally advanced rectal cancer patients. CONCLUSIONS: It seems that monitoring the miRNA/mRNA interactions during radiotherapy can be an appropriate diagnostic tool to track the recovery process and respond to standard therapies.

Evaluation of molecular apoptosis signaling pathways and its correlation with EBV viral load in SLE patients using systems biology approach.

BACKGROUND: Considerable evidence supports that SLE could be related to apoptotic cells and EBV infection. OBJECTIVE: The aim of this study was to identify the transcriptional signature of EBV infection in SLE patients for survey of the molecular apoptosis signaling pathways. METHODS: The PBMCs gene expression profiles of healthy control and SLE patients were obtained from GEO. Functional annotation and signaling pathway enrichment were carried out using DAVID, KEGG. To validate bioinformatics analysis the changes in genes expression of some of obtained genes, Real time PCR was performed on PBMCs from 28 SLE patients and 18 controls. RESULTS: We found that mean viral load was 6013 ± 390.1 copy/µg DNA from PBMCs in all patients. QRT-PCR results showed that the expression of the DUSP1 and LAMP3 genes which had most changes in the logFC among 4 candidate genes, increased significantly in comparison with control. The consistent expression of LMP2 as viral latency gene involve in apoptosis signaling pathways was detected in SLE patients with EBV viral load and some controls. CONCLUSIONS: The study indicated that some cellular genes may have an important role in pathogenesis of SLE through apoptosis signaling pathways. Beside, EBV infection as an environmental risk factor for SLE may affect the dysfunction of apoptosis.

Identification of common microRNA between COPD and non-small cell lung cancer through pathway enrichment analysis.

BACKGROUND: Different factors have been introduced which influence the pathogenesis of chronic obstructive pulmonary disease (COPD) and non-small cell lung cancer (NSCLC). COPD as an independent factor is involved in the development of lung cancer. Moreover, there are certain resemblances between NSCLC and COPD, such as growth factors, activation of intracellular pathways, as well as epigenetic factors. One of the best approaches to understand the possible shared pathogenesis routes between COPD and NSCLC is to study the biological pathways that are activated. MicroRNAs (miRNAs) are critical biomolecules that implicate the regulation of several biological and cellular processes. As such, the main goal of this study was to use a systems biology approach to discover common dysregulated miRNAs between COPD and NSCLC, one that targets most genes within common enriched pathways. RESULTS: To reconstruct the miRNA-pathways for each disease, we used the microarray miRNA expression data. Then, we employed "miRNA set enrichment analysis" (MiRSEA) to identify the most significant joint miRNAs between COPD and NSCLC based on the enrichment scores. Overall, our study revealed the involvement of the targets of miRNAs (such as has-miR-15b, hsa-miR-106a, has-miR-17, has-miR-103, and has-miR-107) in the most important common biological pathways. CONCLUSIONS: According to the promising results of the pathway analysis, the identified miRNAs can be utilized as the new potential signatures for therapy through understanding the molecular mechanisms of both diseases.

Candidate Molecular Biomarkers for the Non- Invasive Detection of Colorectal Cancer using Gene Expression Profiling.

BACKGROUND & OBJECTIVE: olorectal Cancer (CRC) is the third most common cancer after prostate (breast in women) and lung cancer; it is also the third cause of cancer deaths reported in both men and women in 2020. Currently, the most commonly used diagnostic tools for CRC are colonoscopy, serological methods, and other imaging techniques. Despite the benefits and abilities of these methods, each of them has disadvantages that reduce its functionality and acceptance. The aim of this study was identifying specific and non-invasive genetic biomarkers to diagnose colorectal cancer. METHODS: In this study, changes in the expression of HLTF and SEPT9 genes were evaluated by Real Time PCR in blood and tissue samples of CRC patients. A total of 100 samples (50 Blood and 50 Tissue samples) were evaluated with a definite diagnosis of CRC in Firoozgar Hspital, Tehran, Iran, in 2018. The QPCR method was used to compare the expression of candidate genes between the patients group and control group in both samples. Sensitivity and specificity of the test were examined using ROC curve analysis. RESULTS: The results showed a significant down-regulation in the expression of both selected genes in tissue and peripheral blood in the various stages of the CRC. The sensitivity and specifity of both genes was about 80%. CONCLUSION: The findings showed that the two candidate genes can be suggested as specific biomarkers for diagnosis of CRC using the peripheral blood as a non-invasive method. For a definite conclusion, more research is needed.

DNA methylation association with stage progression of head and neck squamous cell carcinoma.

Head and Neck Squamous Cell Carcinoma (HNSCC) is the sixth most common cancer worldwide, which accounts for approximately 6% of all cases and is responsible for an estimated 2% of all cancer deaths. Despite progress in the treatment of squamous cell carcinomas, survival rates remain low. It is a fact that epigenetic modifications have numerous associations with biological processes and complex diseases such as cancer. Hence, a more systematic approach is needed to provide potential screening targets and have an effective therapy method. This study developed a workflow to analyze HM450 methylation arrays with mRNA expression profiles that identified novel signatures of epigenetic regulators for tumor progression. We identified differentially expressed genes and differentially methylated regions and the correlation between associated genes to identify epigenetic modifications underlying regulation roles. We have taken the differentiation direction of expressions into account during the integration of gene expression and DNA methylation modification to detect epigenetic regulators of core genes of tumor-stage progression. Enrichment analysis of selected key genes provides better insight into their functionality. Thus, we have investigated gene copy number alteration and mutations to filter differentially expressed genes, including some members of the fibroblast growth factor family and cyclin-dependent kinase inhibitor family with other potential known regulators. Our analysis has revealed the list of 61 commercial methylation probes positively correlated with 31 differentially expressed genes, which can be associated with HNSC metastasis stages. Most of these genes have already reported potential epigenetic regulators, and their role in cancer progression was studied. We suggest these selected probes of DNA methylation as potential targets of the epigenetic regulators in revealed genes that have displayed significant genetic and epigenetic modification behavior during cancer stage progression and tumor metastasis.

Immunoinformatic design of a COVID-19 subunit vaccine using entire structural immunogenic epitopes of SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) is an acute pneumonic disease, with no prophylactic or specific therapeutical solution. Effective and rapid countermeasure against the spread of the disease's associated virus, SARS-CoV-2, requires to incorporate the computational approach. In this study, we employed various immunoinformatics tools to design a multi-epitope vaccine polypeptide with the highest potential for activating the human immune system against SARS-CoV-2. The initial epitope set was extracted from the whole set of viral structural proteins. Potential non-toxic and non-allergenic T-cell and B-cell binding and cytokine inducing epitopes were then identified through a priori prediction. Selected epitopes were bound to each other with appropriate linkers, followed by appending a suitable adjuvant to increase the immunogenicity of the vaccine polypeptide. Molecular modelling of the 3D structure of the vaccine construct, docking, molecular dynamics simulations and free energy calculations confirmed that the vaccine peptide had high affinity for Toll-like receptor 3 binding, and that the vaccine-receptor complex was highly stable. As our vaccine polypeptide design captures the advantages of structural epitopes and simultaneously integrates precautions to avoid relevant side effects, it is suggested to be promising for elicitation of an effective and safe immune response against SARS-CoV-2 in vivo.

An efficient hybrid feature selection method to identify potential biomarkers in common chronic lung inflammatory diseases.

Asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious lung inflammatory diseases. The understanding of the pathogenesis mechanism and the identification of potential prognostic biomarkers of these diseases can provide the patients with more efficient treatments. In this study, an efficient hybrid feature selection method was introduced in order to extract informative genes. We implemented an ontology-based ranking approach on differentially expressed genes following a wrapper method. The examination of the different gene ontologies and their combinations motivated us to propose a biological functional-based method to improve the performance of further wrapper methods. The results identified: TOM1L1, SRSF1, and GIT2 in asthma; CHCHD4, PAIP2, CRLF3, UBQLN4, TRAK1, PRELID1, VAMP4, CCM2, and APBB1IP in COPD; and TUFT1, GAB2, B4GALNT1, TNFRSF17, PRDM8, and SETDB2 in IPF as the potential biomarkers. The proposed method can be used to identify hub genes in other high-throughput datasets.

Role of dimethyl fumarate in the treatment of glioblastoma multiforme: A review article.

Glioblastoma multiforme (GBM), the most frequent malignant and aggressive primary brain tumor, is characterized by genetically unstable heterogeneous cells, diffused growth pattern, microvascular proliferation, and resistance to chemotherapy. Extensive investigations are being carried out to identify the molecular origin of resistance to chemo- and radio-therapy in GBM and find novel targets for therapy to improve overall survival rate. Dimethyl fumarate (DMF) has been shown to be a safe drug with limited short and long-term side effects, and fumaric acid esters (FAEs), including DMF, present both anti-oxidative and anti-inflammatory activity in different cell types and tissues. DMF has also anti-tumoral and neuroprotective effects and so it could be repurposed in the treatment of this invasive tumor in the future. Here, we have reviewed DMF pharmacokinetics and different mechanisms by which DMF could have therapeutic effects on GBM.

Survival analysis and functional annotation of long non-coding RNAs in lung adenocarcinoma.

Long non-coding RNAs (lncRNAs) are a subclass of non-protein coding transcripts that are involved in several regulatory processes and are considered as potential biomarkers for almost all cancer types. This study aims to investigate the prognostic value of lncRNAs for lung adenocarcinoma (LUAD), the most prevalent subtype of lung cancer. To this end, the processed data of The Cancer Genome Atlas LUAD were retrieved from GEPIA and circlncRNAnet databases, matched with each other and integrated with the analysis results of a non-small cell lung cancer plasma RNA-Seq study. Then, the data were filtered in order to separate the differentially expressed lncRNAs that have a prognostic value for LUAD. Finally, the selected lncRNAs were functionally annotated using a bioinformatic and systems biology approach. Accordingly, we identified 19 lncRNAs as the novel LUAD prognostic lncRNAs. Also, based on our results, all 19 lncRNAs might be involved in lung cancer-related biological processes. Overall, we suggested several novel biomarkers and drug targets which could help early diagnosis, prognosis and treatment of LUAD patients.

LncRNA and mRNA integration network reconstruction reveals novel key regulators in esophageal squamous-cell carcinoma.

Many experimental and computational studies have identified key protein coding genes in initiation and progression of esophageal squamous cell carcinoma (ESCC). However, the number of researches that tried to reveal the role of long non-coding RNAs (lncRNAs) in ESCC has been limited. LncRNAs are one of the important regulators of cancers which are transcribed dominantly in the genome and in various conditions. The main goal of this study was to use a systems biology approach to predict novel lncRNAs as well as protein coding genes associated with ESCC and assess their prognostic values. By using microarray expression data for mRNAs and lncRNAs from a large number of ESCC patients, we utilized "Weighted Gene Co-expression Network Analysis" (WGCNA) method to make a big coding-non-coding gene co-expression network, and discovered important functional modules. Gene set enrichment and pathway analysis revealed major biological processes and pathways involved in these modules. After selecting some protein coding genes involved in biological processes and pathways related to cancer, we used "LncTar", a computational tool to predict potential interactions between these genes and lncRNAs. By combining interaction results with Pearson correlations, we introduced some novel lncRNAs with putative key regulatory roles in the network. Survival analysis with Kaplan-Meier estimator and Log-rank test statistic confirmed that most of the introduced genes are associated with poor prognosis in ESCC. Overall, our study reveals novel protein coding genes and lncRNAs associated with ESCC, along with their predicted interactions. Based on the promising results of survival analysis, these genes can be used as good estimators of patients' survival, or even can be analyzed further as new potential signatures or targets for the therapy of ESCC disease.

A potential clinical significance of DAB2IP and SPRY2 transcript variants in prostate cancer.

Deregulation of key signaling pathways is one of the primary phenomena in carcinogenesis. DAB2IP and SPRY2 are regulatory elements, which act as feedback inhibitors of receptor tyrosine kinases signaling in mitogen-activated protein kinase pathway. These elements have also been implicated in the pathophysiology of cancer. Therefore, this study is aimed to investigate the expression of all known splice variants of DAB2IP and SPRY2 in prostate tissue. Fresh Prostate tissue samples (50 prostate cancer/ matched normal tissue and 30 BPH) were collected and total RNA was extracted followed by cDNA synthesis. The expression of DAB2IP and SPRY2 transcript variants were evaluated using RT-PCR and quantitative Real-time PCR. The results indicated significant down-regulation of DAB2IP transcript variant 1 in cancerous tissues compared to paired normal tissues (P = 0.001) as well as SPRY2 transcript variant 2 in cancerous tissues in comparison with the normal counterparts and BPH (P = 0.008 and P = 0.025, respectively). In addition, there was a significant negative correlation between DAB2IP.1 and SPRY2.2 expression with PSA levels in prostate cancer (P = 0.039 ρ =-0.24 and P = 0.045 ρ =-0.3, respectively). Interestingly, the down-regulation of DAB2IP.1 mRNA and SPRY2.2 mRNA was positively correlated in tumor samples (P = 0.002 ρ = 0.434). For the first time, this experiment highlights the deregulation of DAB2IP and SPRY2 transcript variants in human prostate cancer. The present study confirms and extends the previous reports through indicating transcript-specific down-regulation and significant association of DAB2IP and SPRY2 in prostate tumorigenesis.

A lentiviral vaccine expressing KMP11-HASPB fusion protein increases immune response to Leishmania major in BALB/C.

Hydrophilic acylated surface protein B (HASPB) is an immunogenic Leishmania-specific protein that antibodies are produced against it in the sera of Leishmania-infected individuals. Kinetoplastid membrane protein 11 (KMP11) is another Leishmania antigen and considered as the suitable candidate for vaccine development Leishmaniasis. It is a highly conserved surface protein expressed in both promastigotes and amastigotes. In this study, KMP11 and HASPB coding sequences were cloned into a pCDH-cGFP lentiviral vector as a fusion protein to be used as a DNA vaccine against L. major. The KMP11-HASPB fusion protein was successfully expressed as evidenced by RT-PCR and Western blot assays. The effect of the vaccine was determined by evaluating the level of IFN-γ, IL-10, IgG1, and IgG2a performed using ELISA as well as determining the parasite load after challenge with L. major in vaccinated mice. The results revealed that IFN-γ, IL-10, IgG1, and IgG2a significantly increased after vaccination using KMP11-HASPB-expressing lentiviruses in BALB/c mice. It is noteworthy that the level of IFN-γ and IgG2a was higher than that of IL-10 and IgG1, respectively, which indicates the activation Th1 cells, macrophages, and cellular immunity. Moreover, the parasite load in the spleen and lymph node of vaccinated mice after challenge was significantly lower than that of controls.

Design and construction of a new recombinant fusion protein (2b2t+EPC1) and its assessment for serodiagnosis of cystic echinococcosis.

The immunodiagnostic tests for cystic echinococcosis (CE) are mostly serological tests based on ELISA that use hydatid cyst antigens for primary screening because of its simple preparation and availability. The challenge to develop new serological methods (as compared to those based on the hydatid cyst fluid antigens) to meet the gold standard remains. Appropriate sources of antigenic material are necessary for application to improve the efficacy of immunodiagnostic tests at a population level. In the current study, a fusion protein containing the coding sequence of antigen B2t and two sequences of EPC1 antigen with some modifications was reconstructed. Using bioinformatics tools, these sequences were joined together by applying the sequence of a rigid α-helix-forming linker to obtain an appropriate structure of a fusion protein. Synthetic recombinant fusion protein was expressed using pET28a as a vector and evaluated by indirect ELISA test for sera from patients with hepatic CE and other parasitic infections. The sensitivity of the fusion protein was lower (88.46%) than the available ELISA kit (96.15%). However, the differences in sensitivity were not statistically significant as compared to the recombinant fusion peptide with the commercial kit (p = 0.269). The specificity of the recombinant fusion protein (95.45%) was not significantly lower than the commercial kit (96.59%; p = 1.000). Moreover, surprisingly there was no difference in the cross-reactivity values of performance between the recombinant-ELISA and commercial kit. The positive and negative predictive values of the recombinant antigen were achieved as 92% and 93.33%, respectively, while for the commercial kit, they were obtained as 94.33% and 97.70%, respectively. In conclusion, as an early evaluation of these antigens the performance of our recombinant fusion protein in ELISA is relatively promising. Although, it seemed that this peptide with specific antigenic epitopes might be more appropriate for the serological evaluation of CE by use of bioinformatics tools, our findings showed that cross-reactions and a negative reaction could occur in clinical performance. This fusion protein may have utility for diagnosis in humans, but further evaluation is needed using the WHO ultrasound classification for CE.

MicroRNA expression in serum samples of sulfur mustard veterans as a diagnostic gateway to improve care.

Sulfur mustard is a vesicant chemical warfare agent, which has been used during Iraq-Iran-war. Many veterans and civilians still suffer from long-term complications of sulfur mustard exposure, especially in their lung. Although the lung lesions of these patients are similar to Chronic Obstructive Pulmonary Disease (COPD), there are some differences due to different etiology and clinical care. Less is known on the molecular mechanism of sulfur mustard patients and specific treatment options. microRNAs are master regulators of many biological pathways and proofed to be stable surrogate markers in body fluids. Based on that microRNA expression for serum samples of sulfur mustard patients were examined, to establish specific microRNA patterns as a basis for diagnostic use and insight into affected molecular pathways. Patients were categorized based on their long-term complications into three groups and microRNA serum levels were measured. The differentially regulated microRNAs and their corresponding gene targets were identified. Cell cycle arrest, ageing and TGF-beta signaling pathways showed up to be the most deregulated pathways. The candidate microRNA miR-143-3p could be validated on all individual patients. In a ROC analysis miR-143-3p turned out to be a suitable diagnostic biomarker in the mild and severe categories of patients. Further microRNAs which might own a link to the biology of the sulfur mustard patients are miR-365a-3p, miR-200a-3p, miR-663a. miR-148a-3p, which showed up only in a validation study, might be linked to the airway complications of the sulfur mustard patients. All the other candidate microRNAs do not directly link to COPD phenotype or lung complications. In summary the microRNA screening study characterizes several molecular differences in-between the clinical categories of the sulfur mustard exposure groups and established some useful microRNA biomarkers. qPCR raw data is available via the Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110797.