Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry

EHMT2 Suppresses ARRB1 Transcription and Activates the Hedgehog Signaling to Promote Malignant Phenotype and Stem Cell Property in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) represents the primary subtype of head and neck squamous cell carcinoma (HNSCC), characterized by a high morbidity and mortality rate. Although previous studies have established specific correlations between euchromatic histone lysine methyltransferase 2 (EHMT2), a histone lysine methyltransferase, and the malignant phenotype of OSCC cells, its biological functions in OSCC remain largely unknown. This study, grounded in bioinformatics predictions, aims to clarify the influence of EHMT2 on the malignant behavior of OSCC cells and delve into the underlying mechanisms. EHMT2 exhibited high expression in OSCC tissues and demonstrated an association with poor patient outcomes. Artificial EHMT2 silencing in OSCC cells, achieved through lentiviral vector infection, significantly inhibited colony formation, migration, invasion, and cell survival. Regarding the mechanism, EHMT2 was found to bind the promoter of arrestin beta 1 (ARRB1), thereby suppressing its transcription through H3K9me2 modification. ARRB1, in turn, was identified as a negative regulator of the Hedgehog pathway, leading to a reduction in the proteins GLI1 and PTCH1. Cancer stem cells (CSCs) were enriched through repeated sphere formation assays in two OSCC cell lines. EHMT2 was found to activate the Hedgehog pathway, thus promoting sphere formation, migration and invasion, survival, and tumorigenic activity of the OSCC-CSCs. Notably, these effects were counteracted by the additional overexpression of ARRB1. In conclusion, this study provides novel evidence suggesting that EHMT2 plays specific roles in enhancing stem cell properties in OSCC by modulating the ARRB1-Hedgehog signaling cascade.

What if amoxicillin/clavulanic acid reduces the cisplatin anticancer impact on oral cancer treatment?

Antibiotics-chemotherapeutics combination have become on the table for many cancer treatments. For this reason, we thought that further progress and development of studies to support chemotherapeutic approaches with the use of antibiotics may be beneficial in the clinical field. Cell lines (SCC-15, HTB-41, and MRC-5) were treated with 5-100 μM/ml concentrations of cisplatin (cisp) and amoxicillin/clavulanic acid (amx/cla) with combination (amx/cla-cisp) and alone in three different incubation periods. The all-cells viability was examined with WST-1 and apoptotic activity of the drugs were investigated via cell death ELISA assay kit. The cytotoxic impact of the 100 μM amx/cla-cisp combination was found to be reduced by up to 21.8%, which was significant given that the cytotoxic effect of only cisplatin therapy was 86.1%. Because our findings demonstrated that solo amx/cla therapy have almost no impact on proliferation or death, we focused on the amx/cla-cisp combination effect. It was found that the amx/cla-cisp combination has reduced the apoptotic fragment when comparing with the solely cisp-treated cells. Due to amx/cla-cisp combination on both cells but significantly on SCC-15 recovered the sole cisplatin effect, we believe that there might be a second thought when prescribing antibiotics while treating cancer patients. Not only the antibiotic's type but also the cancer type might interact to lessen the chemotherapeutic agent's impact which is clinically a dilemma to focus on.

Bioinformatics in Early Cancer Detection

Bioinformatics is a pretty recent branch of biology that encompasses the use of algebraic, analytic, and computing approaches to the processing and interpretation of biological information. A wide term, "bioinformatics" refers to the use of digital technology to study biological processes using high-dimensional data collected from many resources. The design and testing of the software tools required to evaluate the information are the core of bioinformatics research, which is conducted in great portions in silico and typically involves the synthesis of new learning from available data. Early diagnosis of cancer results in improved prognosis, but at the same time, it is difficult to conform to diagnosis at a very early stage. The use of DNA microarrays and proteomics studies for large-scale gene expression research has advanced technology, thus elevating the significance of bioinformatics tools. In today's research, wet experimentation and the application of bioinformatics analytics go side by side. Molecular profiling of tumor biopsies is becoming more and more crucial to both cancer research and the treatment of cancer.

Comparison of manual and automated digital image analysis systems for quantification of cellular protein expression

OBJECTIVE

Quantifying protein expression in immunohistochemically stained histological slides is an important tool for oncologic research. The use of computer-aided evaluation of IHC-stained slides significantly contributes to objectify measurements. Manual digital image analysis (mDIA) requires a user-dependent annotation of the region of interest (ROI). Others have built-in machine learning algorithms with automated digital image analysis (aDIA) and can detect the ROIs automatically. We aimed to investigate the agreement between the results obtained by aDIA and those derived from mDIA systems.

METHODS

We quantified chromogenic intensity (CI) and calculated the positive index (PI) in cohorts of tissue microarrays (TMA) using mDIA and aDIA. To consider the different distributions of staining within cellular sub-compartments and different tumor architecture our study encompassed nuclear and cytoplasmatic stainings in adenocarcinomas and squamous cell carcinomas.

RESULTS

Within all cohorts, we were able to show a high correlation between mDIA and aDIA for the CI (p<0.001) along with high agreement for the PI. Moreover, we were able to show that the cell detections of the programs were comparable as well and both proved to be reliable when compared to manual counting.

CONCLUSION

mDIA and aDIA show a high correlation in acquired IHC data. Both proved to be suitable to stratify patients for evaluation with clinical data. As both produce the same level of information, aDIA might be preferable as it is time-saving, can easily be reproduced, and enables regular and efficient output in large studies in a reasonable time period.

Ontologies and Knowledge Graphs in Oncology Research

The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.

Methylation-Driven Gene PLAU as a Potential Prognostic Marker for Differential Thyroid Carcinoma

Purpose: Aberrant DNA methylation plays a crucial role in the tumorigenesis of differentiated thyroid cancer (DTC); nevertheless, the factors leading to the local and regional recurrence of DTC are not well understood. This study aimed to establish the connection between DNA methylation-driven genes and the recurrence of DTC.

Methods: RNA sequencing profiles and DNA methylation profiles of DTC were downloaded from The Cancer Genome Atlas (TCGA) database. Combined application of the methylmix R package and univariate Cox regression analyses were used to screen and distinguish prognosis-related methylation-driven genes. Multivariate Cox regression analyses were utilized to identify the target genes that were closely associated with the recurrence of DTC. Then, correlations between the expression levels of the target genes and the clinicopathological features were verified, as well as their potential biological functions.

Results: A total of 168 Methylation-driven genes were differentially expressed in thyroid cancer, among which 10 genes (GSTO2, GSTM5, GSTM1, GPX7, FGF2, LIF, PLAU, BCL10, SHARPIN and TNFRSF1A) were identified as Hub genes. We selected PLAU for further analysis because PLAU was most strongly correlated with DTC recurrence and the DNA methylation levels of PLAU were closely associated with multiple clinicopathological features of DTC. PLAU was significantly upregulated in DTC, and patients with a high expression level of PLAU had a higher risk of recurrence (p < 0.05). Functional predictions suggested that PLAU-related genes were mainly involved in the regulation of immune-related signaling pathways. Moreover, the mRNA level of PLAU was found to be positively correlated with the cell markers of neutrophils and dendritic cells. In addition, we found that two DNA methylation sites (cg06829584, cg19399285) were associated with abnormal expression of PLAU in DTC.

Conclusion: The methylation-driven gene PLAU is an independent risk factor for the recurrence of DTC and it functions as an oncogene through the regulation of immune-related signaling pathways, which offers new insight into the molecular mechanisms of thyroid cancer and provides new possibilities for individualized treatment of thyroid cancer patients.

Integrated multiplex network based approach for hub gene identification in oral cancer

Background: The incidence of Oral Cancer (OC) is high in Asian countries, which goes undetected at its early stage. The study of genetics, especially genetic networks holds great promise in this endeavor. Hub genes in a genetic network are prominent in regulating the whole network structure of genes. Thus identification of such genes related to specific cancer types can help in reducing the gap in OC prognosis. Methods: Traditional study of network biology is unable to decipher the inter-dependencies within and across diverse biological networks. Multiplex network provides a powerful representation of such systems and encodes much richer information than isolated networks. In this work, we focused on the entire multiplex structure of the genetic network integrating the gene expression profile and DNA methylation profile for OC. Further, hub genes were identified by considering their connectivity in the multiplex structure and the respective protein-protein interaction (PPI) network as well. Results: 46 hub genes were inferred in our approach with a high prediction accuracy (96%), outstanding Matthews coefficient correlation value (93%) and significant biological implications. Among them, genes PIK3CG, PIK3R5, MYH7, CDC20 and CCL4 were differentially expressed and predominantly enriched in molecular cascades specific to OC. Conclusions: The identified hub genes in this work carry ontological signatures specific to cancer, which may further facilitate improved understanding of the tumorigenesis process and the underlying molecular events. Result indicates the effectiveness of our integrated multiplex network approach for hub gene identification. This work puts an innovative research route for multi-omics biological data analysis.

An integrated approach for identification of a panel of candidate genes arbitrated for invasion and metastasis in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is known for its aggressiveness associated with poor prognosis. The molecular mechanisms underlying the invasion and metastasis are still poorly understood. An improved understanding of these mechanisms shall precede the development of new diagnostic tools and targeted therapies. We report an integrated approach using bioinformatics to predict candidate genes, coupled with proteomics and immunohistochemistry for validating their presence and involvement in OSCC pathways heralding invasion and metastasis. Four genes POSTN, TNC, CAV1 and FSCN1 were identified. A protein–protein interaction network analysis teamed with pathway analysis led us to propose the role of the identified genes in invasion and metastasis in OSCC. Further analyses of archived FFPE blocks of various grades of oral cancer was carried out using TMT-based mass spectrometry and immunohistochemistry. Results of this study expressed a strong communiqué and interrelationship between these candidate genes. This study emphasizes the significance of a molecular biomarker panel as a diagnostic tool and its correlation with the invasion and metastatic pathway of OSCC. An insight into the probable association of CAF's and these biomarkers in the evolution and malignant transformation of OSCC further magnifies the molecular-biological spectrum of OSCC tumour microenvironment.

miR-146a Overexpression in Oral Squamous Cell Carcinoma Potentiates Cancer Cell Migration and Invasion Possibly via Targeting HTT

Huntingtin (HTT) is one of the target genes of miR-146-a and regulates various cancer cell activities. This study aims to explore the miR-146a expression pattern in oral squamous cell carcinoma (OSCC) and its role and mechanism in OSCC progression and metastasis via targeting the HTT gene. OSCC tissue and non-cancerous matched tissue (NCMT) were obtained from 14 patients. OSCC cell lines and normal HOK cells were used to analyze migration and invasion assay. OSCC-induced miR-146a knockout mice (B6.Cg-Mir146tm1.1Bal) model was developed. Transwell cell migration/invasion and scratch wound assays were used to investigate the OSCC cell migration and invasion in vitro. Kaplan-Meier survival analysis was used to investigate the association of HTT expression patterns in cancer tissue with patient survival percentage and duration. Pearson's correlation analysis tested the association between miR-146a and HTT expression in OSCC tissues. miR-146a mimic and inhibitor transfection were performed to overexpress and knockdown the miR-146a in OSCC cells, respectively. miR-146a expression was highly upregulated in OSCC tissues and OSCC cell lines. Cancer cell migration/invasion was enhanced in miR-146a overexpressed cells and reduced in mi-R146a knockdowned cells. HTT expression was reduced in OSCC tissues and cell lines compared to NCMT and HOK cells, respectively. HTT expression was downregulated in miR-146a overexpressed OSCC cells and upregulated in miR-146a knockdowned OSCC cells. The expression pattern of miR-146a in OSCC cell lines and tissues was inversely correlated with HTT expression. Prediction of miRNA target analysis showed that HTT possesses the binding sites for miR-146a. HTT overexpression in OSCC tissues was associated with patients' higher survival percentage and duration. HTT knockdown in OSCC cells enhanced miR-146a expression and cell migration/invasion. Inducing OSCC in miR-146a knockout mice increased the HTT expression in tongue tissue and alleviated the cancer aggressiveness and epithelial damage. Overexpressed miR-146a in OSCC targets the HTT gene and enhances cancer cell migration/invasion unraveling the possible role of HTT in miR146a-mediated OSCC cell migration and invasion.

Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection

Simple Summary

Our study aims to develop a novel quantitative analysis method that can increase the oral cancer detection rate for screening oral cancer. We used two different optical techniques, a light-based detection technique (VELScope) and a vibrational spectroscopic technique (Raman spectroscopy). First, we analyzed and evaluated the performance of these two techniques individually using PCA–LDA, and PCA–QDA classifiers. The PCA–LDA of Raman spectroscopy had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while the region of interests on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. Afterward, we combined both techniques and evaluated their performance. The combination of two optical techniques can differentiate the cancer and normal groups with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. The main advantage of our study is that we can confirm our results by using two different techniques that are completely independent of each other. That is the reason that the combination of two techniques can increase the sensitivity and specificity.

Abstract

In this study, we developed a novel quantitative analysis method to enhance the detection capability for oral cancer screening. We combined two different optical techniques, a light-based detection technique (visually enhanced lesion scope) and a vibrational spectroscopic technique (Raman spectroscopy). Materials and methods: Thirty-five oral cancer patients who went through surgery were enrolled. Thirty-five cancer lesions and thirty-five control samples with normal oral mucosa (adjacent to the cancer lesion) were analyzed. Thirty-five autofluorescence images and 70 Raman spectra were taken from 35 cancer and 35 control group cryopreserved samples. The normalized intensity and heterogeneity of the 70 regions of interest (ROIs) were calculated along with 70 averaged Raman spectra. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to differentiate the cancer and control groups (normal). The classifications rates were validated using two different validation methods, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Results: The cryopreserved normal and tumor tissues were differentiated using the PCA–LDA and PCA–QDA models. The PCA–LDA of Raman spectroscopy (RS) had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while ROIs on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. The combination of two optical techniques differentiated cancer and normal group with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. Conclusion: In this study, we combined the data of two different optical techniques. Furthermore, PCA–LDA and PCA–QDA quantitative analysis models were used to differentiate tumor and normal groups, creating a complementary pathway for efficient tumor diagnosis. The error rates of RS and VELcope analysis were 17.10% and 10%, respectively, which was reduced to 3% when the two optical techniques were combined.

EMDIP: An Entropy Measure to Discover Important Proteins in PPI networks

Discovering important proteins in Protein-Protein Interaction (PPI) networks has attracted a lot of attention in recent years. Most of the previous work applies different network centrality measures such as Closeness, Betweenness, PageRank and many others to discover the most influential proteins in PPI networks. Although entropy is a well-known graph-based method in computer science, according to our knowledge, it is not used in the biology domain for this purpose. In this paper, first, we annotate the human PPI network with available annotation data. Second, we introduce a new concept called annotation-context that describes each protein according to annotation data of its neighbors. Third, we apply an entropy measure to discover proteins with varied annotation-context. Empirical results indicate that our proposed method succeeded in (1) differentiating essential and non-essential proteins in PPI networks with annotation data; (2) outperforming centrality measures in the task of discovering essential nodes; (3) predicting new annotated proteins based on existing annotation data.

Network-based identification of signature genes KLF6 and SPOCK1 associated with oral submucous fibrosis

The molecular mechanism of oral submucous fibrosis (OSF) is yet to be fully elucidated. The identification of reliable signature genes to screen patients with a high risk of OSF and to provide oral cancer surveillance is therefore required. The present study produced a filtering criterion based on network characteristics and principal component analysis, and identified the genes that were involved in OSF prognosis. Two gene expression datasets were analyzed using meta-analysis, the results of which revealed 1,176 biologically significant genes. A co-expression network was subsequently constructed and weighted gene modules were detected. The pathway and functional enrichment analyses of the present study allowed for the identification of modules 1 and 2, and their respective genes, SPARC (osteonectin), cwcv and kazal like domain proteoglycan 1 (SPOCK1) and kruppel like factor 6 (KLF6), which were involved in the occurrence of OSF. The results revealed that both genes had a prominent role in epithelial to mesenchymal transition during OSF progression. The genes identified in the present study require further exploration and validation within clinical settings to determine their roles in OSF.

Oral lichen planus interactome reveals CXCR4 and CXCL12 as candidate therapeutic targets

Today, we face difficulty in generating new hypotheses and understanding oral lichen planus due to the large amount of biomedical information available. In this research, we have used an integrated bioinformatics approach assimilating information from data mining, gene ontologies, protein–protein interaction and network analysis to predict candidate genes related to oral lichen planus. A detailed pathway analysis led us to propose two promising therapeutic targets: the stromal cell derived factor 1 (CXCL12) and the C-X-C type 4 chemokine receptor (CXCR4). We further validated our predictions and found that CXCR4 was upregulated in all oral lichen planus tissue samples. Our bioinformatics data cumulatively support the pathological role of chemokines and chemokine receptors in oral lichen planus. From a clinical perspective, we suggest a drug (plerixafor) and two therapeutic targets for future research.

Multiclass classification of autofluorescence images of oral cavity lesions based on quantitative analysis

Background

Oral cancer is one of the most common diseases globally. Conventional oral examination and histopathological examination are the two main clinical methods for diagnosing oral cancer early. VELscope is an oral cancer-screening device that exploited autofluorescence. It yields inconsistent results when used to differentiate between normal, premalignant and malignant lesions. We develop a new method to increase the accuracy of differentiation.

Materials and methods

Five samples (images) of each of 21 normal mucosae, as well as 31 premalignant and 16 malignant lesions of the tongue and buccal mucosa were collected under both white light and autofluorescence (VELscope, 400-460 nm wavelength). The images were developed using an iPod (Apple, Atlanta Georgia, USA).

Results

The normalized intensity and standard deviation of intensity were calculated to classify image pixels from the region of interest (ROI). Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) classifiers were used. The performance of both of the classifiers was evaluated with respect to accuracy, precision, and recall. These parameters were used for multiclass classification. The accuracy rate of LDA with un-normalized data was increased by 2% and 14% and that of QDA was increased by 16% and 25% for the tongue and buccal mucosa, respectively.

Conclusion

The QDA algorithm outperforms the LDA classifier in the analysis of autofluorescence images with respect to all of the standard evaluation parameters.

Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection

Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer.

TCGA based integrated genomic analyses of ceRNA network and novel subtypes revealing potential biomarkers for the prognosis and target therapy of tongue squamous cell carcinoma

OBJECTIVES

The study aimed to investigate the ceRNA network in biological development of Tongue Squamous Cell Carcinoma (TSCC) and to identify novel molecular subtypes of TSCC to screen potential biomarkers for target therapy and prognosis by using integrated genomic analysis based on The Cancer Genome Atlas (TCGA) database.

MATERIAL AND METHODS

Data on gene expressions were downloaded from TCGA and GEO database. Differentially expressed RNAs(DERNAs) were shown by DESeq2 package in R. Functional enrichment analysis of DEmRNAs was performed using clusterprofilers in R. PPI network was established by referring to String website. Survival analysis of DERNAs was carried out by survival package in R. Interactions among mRNAs, miRNAs and lncRNAs were obtained from Starbase v3.0 and used to construct ceRNA network. Consensus Cluster Plus package was applied to identify molecular subtypes. All key genes were validated by comparing them with GEO microarray data. Statistical analyses of clinical features among different subtypes were performed using SPSS 22.0.

RESULTS

A total of 2907 mRNAs (1366 up-regulated and 1541 down-regulated), 191miRNAs (98 up-regulated and 93 down-regulated) and 1831 lncRNAs (1151 up-regulated and 680 down-regulated) were identified from tumor and normal tissues. A ceRNA network was successfully constructed and 15 DEmRNAs, 1 DEmiRNA, 2 DElncRNAs associated with prognosis were employed. Furthermore, we firstly identified 2 molecular subtypes, basal and differentiated, and found that differentiated subtype consumed less alcohol and was related to a better overall survival.

CONCLUSION

The study constructed a ceRNA network and identified molecular subtypes of TSCC, and our findings provided a novel insight into this intractable cancer and potential therapeutic targets and prognostic indicators.

Ranking Cancer Proteins by Integrating PPI Network and Protein Expression Profiles

Proteomics, the large-scale analysis of proteins, is contributing greatly to understanding gene function in the postgenomic era. However, disease protein ranking using shotgun proteomics data has not been fully evaluated. In this study, we prioritized disease-related proteins by integrating the protein-protein interaction (PPI) network and protein differential expression profiles from colon and rectal cancer (CRC) or breast cancer (BC) proteomics. We applied Local Ranking (LR) and Global Ranking (GR) methods in network with three kinds of protein sets as a priori knowledge, which were known disease proteins (KDPs) that were collected from the Online Mendelian Inheritance in Man (OMIM) database, differentially expressed proteins (DEPs), and the collection of KDPs and their direct neighborhood with differential expression (eKDPs). The cross-validations showed that GR method outperformed LR method while using eKDPs as the initial training showed significantly higher accuracy compared to using the other two a priori sets. And then we validated the top ranked proteins using RNAi-based loss-of-function screens in the DepMap database. The results showed that 75% of top 20 proteins in CRC are necessary for tumor survival. In summary, the network-based Global Ranking with protein differential expression can efficiently prioritize cancer-related proteins and discover new candidate cancer genes or proteins.

Plausibility of the zebrafish embryos/larvae as an alternative animal model for autism: A comparison study of transcriptome changes

Autism spectrum disorder (ASD) is a serious neurodevelopmental disorder characterized by impaired or abnormal social interaction and communication and by restricted and repetitive behaviour. ASD is highly prevalent in Asia, Europe, and the United States, and the frequency of ASD is growing each year. Recent epidemiological studies have indicated that ASD may be caused or triggered by exposure to chemicals in the environment, such as those in the air or water. Thus, toxicological studies are needed to examine chemicals that might be implicated. However, the experimental efficiency of existing experimental models is limited, and many models represent challenges in terms of animal welfare. Thus, alternative ASD animal models are necessary. To address this, we examined the efficacy of the zebrafish embryo/larva as an alternative model of ASD. Specifically, we exposed zebrafish to valproic acid (0, 12.5, 25, 50, or 100 μM), which is a chemical known to induce autism-like effects. We then analysed subsequent developmental, behavioural, and transcriptomic changes. We found that 100 μM and 50 μM valproic acid decreased the hatching rate and locomotor activity of zebrafish embryos/larvae. Transcriptomic analysis revealed significant alterations in a number of genes associated with autism, such as adsl, mbd5, shank3, and tsc1b. Additionally, we found changes in gene ontology that were also reported in previous studies. Our findings indicate that zebrafish embryos/larvae and humans with ASD might have common physiological pathways, indicating that this animal model may represent an alternative tool for examining the causes of and potential treatments for this illness.

Identification and Validation of Fibroblast Growth Factor 12 Gene as a Novel Potential Biomarker in Esophageal Cancer Using Cancer Genomic Datasets

Esophageal squamous cell carcinoma (ESCC) has a complex, multifactorial etiology in which environmental, geographical, and genetic factors play major roles. It is the second most common cancer among men and the fourth most common among women in India, with a particularly high prevalence in Northeast India. In this study, an integrative in silico [DAVID, NCG5.0, Oncomine, Cancer Cell Line Encyclopedia, and The Cancer Genome Atlas (TCGA)] approach was used to identify the potential biomarkers by using the available three genomic datasets on ESCC from Northeast India followed by its in vitro functional validation. Fibroblast Growth Factor 12 (FGF12) gene was overexpressed in ESCC. The upregulation of FGF12 was also observed on ESCC of TCGA OncoPrint portal, whereas very low expression of FGF12 gene was mapped in normal esophageal tissue on the GTEx database. Silencing of FGF12 showed significant inhibition in activity of tumor cell proliferation, colony formation, and cell migration. The upregulation of FGF12 showed significantly reduced survival in ESCC patients. The protein interaction analysis of FGF12 found the binding with MAPK8IP2 and MAPK13. High expression of FGF12 along with MAPK8IP2, and MAPK13 proteins correlate with poor survival in ESCC patients. Tissue microarray also showed expression of these proteins in patients with ESCC. These results indicate that FGF12 has a potential role in ESCC and suggest that cancer genomic datasets with application of in silico approaches are instrumental for biomarker discovery research broadly and specifically, for the identification of FGF12 as a putative biomarker in ESCC.

The Biological Properties and Potential Interacting Proteins of d-Alanyl-d-alanine Ligase A from Mycobacterium tuberculosis

(1) Background: d-alanine-d-alanine ligase (DdlA), an effective target for drug development to combat against Mycobacterium tuberculosis (Mtb), which threatens human health globally, supplies a substrate of d-alanyl-d-alanine for peptidoglycan crosslinking by catalyzing the dimerization of two d-alanines. To obtain a better understanding of DdlA profiles and develop a colorimetric assay for high-throughput inhibitor screening, we focused on explicating and characterizing Tb-DdlA. (2) Methods and Results: Rv2981c (ddlA) was expressed in Escherichia coli, and the purified Tb-DdlA was identified using (anti)-polyhistidine antibody followed by DdlA activity confirmation by measuring the released orthophosphate via colorimetric assay and the yielded d-alanyl-d-alanine through high performance thin layer chromatography (HP-TLC). The kinetic assays on Tb-DdlA indicated that Tb-DdlA exhibited a higher affinity to ATP (K_mATP: 50.327 ± 4.652 μmol/L) than alanine (K_mAla: 1.011 ± 0.094 mmol/L). A colorimetric assay for Tb-DdlA activity was developed for high-throughput screening of DdlA inhibitors in this study. In addition, we presented an analysis on Tb-DdlA interaction partners by pull-down assay and MS/MS. Eight putative interaction partners of Tb-DdlA were identified. (3) Conclusions: Our dataset provided a valuable resource for exploring Tb-DdlA biology, and developed an easy colorimetric assay for screening of Tb-DdlA inhibitors.