Construction of m6A-Related Gene Prediction Model and Subtype Analysis in Non-Obstructive Azoospermia Based on Bioinformatics.

PURPOSE: Non-obstructive azoospermia (NOA) is a severe and common cause of male infertility. Currently, the most reliable predictor of sperm retrieval success in NOA is histopathology, but preoperative testicular biopsy often increases the difficulty of sperm retrieval surgery. This study aims to explore the characteristics of N6-methyladenosine (m6A) modification in NOA patients and investigate the potential biomarkers and molecular mechanisms for pathological diagnosis and treatment of NOA using m6A-related genes. METHODS: NOA-related datasets were downloaded from the GEO database. Based on the results of LASSO regression analysis, a prediction model was established from differentially expressed m6A-related genes, and the predictive performance of the model was evaluated using ROC curves. Cluster analysis was performed based on differentially expressed m6A-related genes to evaluate the differences in different m6A modification patterns in terms of differentially expressed genes (DEGs), biological features, and immune features. RESULTS: There were significant differences in eight m6A-related genes between NOA samples and healthy controls. The ROC curves showed excellent predictive performance for the diagnostic models constructed with ALKBH5 and FTO. DEGs of two m6A modification subtypes indicated the influence of m6A-related genes in the biological processes of mitosis and meiosis in NOA patients, and there were significant immune differences between the two subtypes. CONCLUSION: The NOA pathological diagnostic models constructed with FTO and ALKBH5 have good predictive ability. We have identified two different m6A modification subtypes, which may help predict sperm retrieval success rate and treatment selection in NOA patients.

[Bioinformatic analysis of immune-related transcription factors in diabetic kidney disease].

Objective To identify immune-related transcription factors (TFs) in renal glomeruli and tubules from diabetic kidney disease (DKD) patients by bioinformatics analysis. Methods Gene expression datasets from GEO (GSE30528, GSE30529) and RNA sequencing (RNA-seq) data from the Karolinska Kidney Research Center were used. Gene set enrichment analysis (GSEA) was conducted to examine differences in immune-related gene expression in the glomeruli and tubules (DKD) patients. To identify immune-related genes (IRGs) and TFs, differential expression analysis was carried out using the Limma and DESeq2 software packages. Key immune-related TFs were pinpointed through co-expression analysis. The interaction network between TFs and IRGs was constructed using the STRING database and Cytoscape software. Furthermore, the Nephroseq database was employed to investigate the correlation between the identified TFs and clinical-pathological features. Results When compared to normal control tissues, significant differences in the expression of immune genes were observed in both the glomeruli and tubules of individuals with Diabetic Kidney Disease (DKD). Through differential and co-expression analysis, 50 immune genes and 9 immune-related transcription factors (TFs) were identified in the glomeruli. In contrast, 131 immune response genes (IRGs) and 41 immune-related TFs were discovered in the renal tubules. The protein-protein interaction (PPI) network highlighted four key immune-related TFs for the glomeruli: Interferon regulatory factor 8 (IRF8), lactotransferrin (LTF), CCAAT/enhancer binding protein alpha (CEBPA), and Runt-related transcription factor 3 (RUNX3). For the renal tubules, the key immune-related TFs were FBJ murine osteosarcoma viral oncogene homolog B (FOSB), nuclear receptor subfamily 4 group A member 1 (NR4A1), IRF8, and signal transducer and activator of transcription 1 (STAT1). These identified TFs demonstrated a significant correlation with the glomerular filtration rate (GFR), highlighting their potential importance in the pathology of DKD. Conclusion Bioinformatics analysis identifies potential genes associated with DKD pathogenesis and immune dysregulation. Further validation of the expression and function of these genes may contribute to immune-based therapeutic research for DKD.

Advances in Computational Biology for Diagnosing Neurodegenerative Diseases: A Comprehensive Review.

The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial intelligence has fundamentally changed the diagnostic picture by providing effective and early means of identifying these crippling illnesses. As a subset of computational intelligence, machine-learning algorithms have become very effective tools for the analysis of large datasets that include genetic, imaging, and clinical data. Moreover, multi-modal data integration, which includes information from brain imaging (MRI, PET scans), genetic profiles, and clinical evaluations, is made easier by computational intelligence. A thorough knowledge of the course of the illness is made possible by this consolidative method, which also facilitates the creation of predictive models for early medical evaluation and outcome prediction. Furthermore, there has been a great deal of promise shown by the use of artificial intelligence to neuroimaging analysis. Sophisticated image processing methods combined with machine learning algorithms make it possible to identify functional and structural anomalies in the brain, which often act as early indicators of neurodegenerative diseases. This chapter examines how computational intelligence plays a critical role in improving the diagnosis of neurodegenerative diseases such as Parkinson's, Alzheimer's, etc. To sum up, computational intelligence provides a revolutionary approach for improving the identification of neurodegenerative illnesses. In the battle against these difficult disorders, embracing and improving these computational techniques will surely pave the path for more individualized therapy and more therapies that are successful.

Labeled-free quantitative proteomic analysis of cervical squamous cell carcinoma identifies potential protein biomarkers.

Background: Cervical cancer remains a prevalent cancer among women, and reliance on surgical and radio-chemical therapies can irreversibly affect patients' life span and quality of life. Thus, early diagnosis and further exploration into the pathogenesis of cervical cancer are crucial. Mass spectrometry technology is widely applied in clinical practice and can be used to further investigate the protein alterations during the onset of cervical cancer. Methods: Employing labeled-free quantitative proteomics technology and bioinformatics tools, we analyzed and compared the differential protein expression profiles between normal cervical squamous cell tissues and cervical squamous cell cancer tissues. GEPIA is an online website for analyzing the RNA sequencing expression data of tumor and normal tissue data from the TCGA and the GTEx databases. This approach aided in identifying qualitative and quantitative changes in key proteins related to the progression of cervical cancer. Results: Compared to normal samples, a total of 562 differentially expressed proteins were identified in cervical cancer samples, including 340 up-regulated and 222 down-regulated proteins. Gene ontology functional annotation, and KEGG pathway, and enrichment analysis revealed that the differentially expressed proteins mainly participated in metabolic pathways, spliceosomes, regulation of the actin cytoskeleton, and focal adhesion signaling pathways. Specifically, desmoplakin (DSP), protein phosphatase 1, regulatory (inhibitor) subunit 13 like (PPP1R13L) and ANXA8 may be involved in cervical tumorigenesis by inhibiting apoptotic signal transmission. Moreover, we used GEPIA database to validate the expression of DSP, PPP1R13L and ANXA8 in human cancers and normal cervix. Conclusion: In this study, we identified 562 differentially expressed proteins, and there were three proteins expressed higher in the cervical cancer tissues. The functions and signaling pathways of these differentially expressed proteins lay a theoretical foundation for elucidating the molecular mechanisms of cervical cancer.

Multi-omics analysis and experimental validation of the value of monocyte-associated features in prostate cancer prognosis and immunotherapy.

Background: Monocytes play a critical role in tumor initiation and progression, with their impact on prostate adenocarcinoma (PRAD) not yet fully understood. This study aimed to identify key monocyte-related genes and elucidate their mechanisms in PRAD. Method: Utilizing the TCGA-PRAD dataset, immune cell infiltration levels were assessed using CIBERSORT, and their correlation with patient prognosis was analyzed. The WGCNA method pinpointed 14 crucial monocyte-related genes. A diagnostic model focused on monocytes was developed using a combination of machine learning algorithms, while a prognostic model was created using the LASSO algorithm, both of which were validated. Random forest and gradient boosting machine singled out CCNA2 as the most significant gene related to prognosis in monocytes, with its function further investigated through gene enrichment analysis. Mendelian randomization analysis of the association of HLA-DR high-expressing monocytes with PRAD. Molecular docking was employed to assess the binding affinity of CCNA2 with targeted drugs for PRAD, and experimental validation confirmed the expression and prognostic value of CCNA2 in PRAD. Result: Based on the identification of 14 monocyte-related genes by WGCNA, we developed a diagnostic model for PRAD using a combination of multiple machine learning algorithms. Additionally, we constructed a prognostic model using the LASSO algorithm, both of which demonstrated excellent predictive capabilities. Analysis with random forest and gradient boosting machine algorithms further supported the potential prognostic value of CCNA2 in PRAD. Gene enrichment analysis revealed the association of CCNA2 with the regulation of cell cycle and cellular senescence in PRAD. Mendelian randomization analysis confirmed that monocytes expressing high levels of HLA-DR may promote PRAD. Molecular docking results suggested a strong affinity of CCNA2 for drugs targeting PRAD. Furthermore, immunohistochemistry experiments validated the upregulation of CCNA2 expression in PRAD and its correlation with patient prognosis. Conclusion: Our findings offer new insights into monocyte heterogeneity and its role in PRAD. Furthermore, CCNA2 holds potential as a novel targeted drug for PRAD.

CCR7 and CD48 as Predicted Targets in Acute Rejection Related to M1 Macrophage after Pediatric Kidney Transplantation.

Background: Kidney transplantation (KT) is the best treatment for end-stage renal disease. Although long and short-term survival rates for the graft have improved significantly with the development of immunosuppressants, acute rejection (AR) remains a major risk factor attacking the graft and patients. The innate immune response plays an important role in rejection. Therefore, our objective is to determine the biomarkers of congenital immunity associated with AR after KT and provide support for future research. Materials and Methods: A differential expression genes (DEGs) analysis was performed based on the dataset GSE174020 from the NCBI gene Expression Synthesis Database (GEO) and then combined with the GSE5099 M1 macrophage-related gene identified in the Molecular Signatures Database. We then identified genes in DEGs associated with M1 macrophages defined as DEM1Gs and performed gene ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis. Cibersort was used to analyze the immune cell infiltration during AR. At the same time, we used the protein-protein interaction (PPI) network and Cytoscape software to determine the key genes. Dataset, GSE14328 derived from pediatric patients, GSE138043 and GSE9493 derived from adult patients, were used to verify Hub genes. Additional verification was the rat KT model, which was used to perform HE staining, immunohistochemical staining, and Western Blot. Hub genes were searched in the HPA database to confirm their expression. Finally, we construct the interaction network of transcription factor (TF)-Hub genes and miRNA-Hub genes. Results: Compared to the normal group, 366 genes were upregulated, and 423 genes were downregulated in the AR group. Then, 106 genes related to M1 macrophages were found among these genes. GO and KEGG enrichment analysis showed that these genes are mainly involved in cytokine binding, antigen binding, NK cell-mediated cytotoxicity, activation of immune receptors and immune response, and activation of the inflammatory NF-κB signaling pathway. Two Hub genes, namely CCR7 and CD48, were identified by PPI and Cytoscape analysis. They have been verified in external validation sets, originated from both pediatric patients and adult patients, and animal experiments. In the HPA database, CCR7 and CD48 are mainly expressed in T cells, B cells, macrophages, and tissues where these immune cells are distributed. In addition to immunoinfiltration, CD4+T, CD8+T, NK cells, NKT cells, and monocytes increased significantly in the AR group, which was highly consistent with the results of Hub gene screening. Finally, we predicted that 19 TFs and 32 miRNAs might interact with the Hub gene. Conclusions: Through a comprehensive bioinformatic analysis, our findings may provide predictive and therapeutic targets for AR after KT.

PlasmidHunter: accurate and fast prediction of plasmid sequences using gene content profile and machine learning.

Plasmids are extrachromosomal DNA found in microorganisms. They often carry beneficial genes that help bacteria adapt to harsh conditions. Plasmids are also important tools in genetic engineering, gene therapy, and drug production. However, it can be difficult to identify plasmid sequences from chromosomal sequences in genomic and metagenomic data. Here, we have developed a new tool called PlasmidHunter, which uses machine learning to predict plasmid sequences based on gene content profile. PlasmidHunter can achieve high accuracies (up to 97.6%) and high speeds in benchmark tests including both simulated contigs and real metagenomic plasmidome data, outperforming other existing tools.

SGCP: a spectral self-learning method for clustering genes in co-expression networks.

BACKGROUND: A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules. RESULTS: We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines. CONCLUSION: Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

Identification and expression analysis of Sox family genes in echinoderms.

The Sox gene family, a collection of transcription factors widely distributed throughout the animal kingdom, plays a crucial role in numerous developmental processes. Echinoderms occupy a pivotal position in many research fields, such as neuroscience, sex determination and differentiation, and embryonic development. However, to date, no comprehensive study has been conducted to characterize and analyze Sox genes in echinoderms. In the present study, the evolution and expression of Sox family genes across 11 echinoderms were analyzed using bioinformatics methods. The results revealed a total of 70 Sox genes, with counts ranging from 5 to 8 across different echinoderms. Phylogenetic analysis revealed that the identified Sox genes could be categorized into seven distinct classes: the SoxB1 class, SoxB2 class, SoxC class, SoxD class, SoxE class, SoxF class and SoxH class. Notably, the SoxB1, SoxB2, and SoxF genes were ubiquitously present in all the echinoderms studied, which suggests that these genes may be conserved in echinoderms. The spatiotemporal expression patterns observed for Sox genes in the three echinoderms indicated that various Sox members perform distinct functional roles. Notably, SoxB1 is likely involved in echinoderm ovary development, while SoxH may play a crucial role in testis development in starfish and sea cucumber. In general, the present investigation provides a molecular foundation for exploring the Sox gene in echinoderms, providing a valuable resource for future phylogenetic and genomic studies.

Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods.

BACKGROUND: Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions. RESULTS: In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers. CONCLUSIONS: Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.

Streamlining heterologous expression of top carbonic anhydrases in Escherichia coli: bioinformatic and experimental approaches.

BACKGROUND: Carbonic anhydrase (CA) enzymes facilitate the reversible hydration of CO2 to bicarbonate ions and protons. Identifying efficient and robust CAs and expressing them in model host cells, such as Escherichia coli, enables more efficient engineering of these enzymes for industrial CO2 capture. However, expression of CAs in E. coli is challenging due to the possible formation of insoluble protein aggregates, or inclusion bodies. This makes the production of soluble and active CA protein a prerequisite for downstream applications. RESULTS: In this study, we streamlined the process of CA expression by selecting seven top CA candidates and used two bioinformatic tools to predict their solubility for expression in E. coli. The prediction results place these enzymes in two categories: low and high solubility. Our expression of high solubility score CAs (namely CA5-SspCA, CA6-SazCAtrunc, CA7-PabCA and CA8-PhoCA) led to significantly higher protein yields (5 to 75 mg purified protein per liter) in flask cultures, indicating a strong correlation between the solubility prediction score and protein expression yields. Furthermore, phylogenetic tree analysis demonstrated CA class-specific clustering patterns for protein solubility and production yields. Unexpectedly, we also found that the unique N-terminal, 11-amino acid segment found after the signal sequence (not present in its homologs), was essential for CA6-SazCA activity. CONCLUSIONS: Overall, this work demonstrated that protein solubility prediction, phylogenetic tree analysis, and experimental validation are potent tools for identifying top CA candidates and then producing soluble, active forms of these enzymes in E. coli. The comprehensive approaches we report here should be extendable to the expression of other heterogeneous proteins in E. coli.

Identification and validation of miR-29b-3p and LIN7A as important diagnostic markers for bone non-union by WGCNA.

Bone non-union is a common fracture complication that can severely impact patient outcomes, yet its mechanism is not fully understood. This study used differential analysis and weighted co-expression network analysis (WGCNA) to identify susceptibility modules and hub genes associated with fracture healing. Two datasets, GSE125289 and GSE213891, were downloaded from the GEO website, and differentially expressed miRNAs and genes were analysed and used to construct the WGCNA network. Gene ontology (GO) analysis of the differentially expressed genes showed enrichment in cytokine and inflammatory factor secretion, phagocytosis, and trans-Golgi network regulation pathways. Using bioinformatic site prediction and crossover gene search, miR-29b-3p was identified as a regulator of LIN7A expression that may negatively affect fracture healing. Potential miRNA-mRNA interactions in the bone non-union mechanism were explored, and miRNA-29-3p and LIN7A were identified as biomarkers of skeletal non-union. The expression of miRNA-29b-3p and LIN7A was verified in blood samples from patients with fracture non-union using qRT-PCR and ELISA. Overall, this study identified characteristic modules and key genes associated with fracture non-union and provided insight into its molecular mechanisms. Downregulated miRNA-29b-3p was found to downregulate LIN7A protein expression, which may affect the healing process after fracture in patients with bone non-union. These findings may serve as a prognostic biomarker and potential therapeutic target for bone non-union.

An integrated comparative genomics, subtractive proteomics and immunoinformatics framework for the rational design of a Pan-Salmonella multi-epitope vaccine.

Salmonella infections pose a significant global public health concern due to the substantial expenses associated with monitoring, preventing, and treating the infection. In this study, we explored the core proteome of Salmonella to design a multi-epitope vaccine through Subtractive Proteomics and immunoinformatics approaches. A total of 2395 core proteins were curated from 30 different isolates of Salmonella (strain NZ CP014051 was taken as reference). Utilizing the subtractive proteomics approach on the Salmonella core proteome, Curlin major subunit A (CsgA) was selected as the vaccine candidate. csgA is a conserved gene that is related to biofilm formation. Immunodominant B and T cell epitopes from CsgA were predicted using numerous immunoinformatics tools. T lymphocyte epitopes had adequate population coverage and their corresponding MHC alleles showed significant binding scores after peptide-protein based molecular docking. Afterward, a multi-epitope vaccine was constructed with peptide linkers and Human Beta Defensin-2 (as an adjuvant). The vaccine could be highly antigenic, non-toxic, non-allergic, and have suitable physicochemical properties. Additionally, Molecular Dynamics Simulation and Immune Simulation demonstrated that the vaccine can bind with Toll Like Receptor 4 and elicit a robust immune response. Using in vitro, in vivo, and clinical trials, our findings could yield a Pan-Salmonella vaccine that might provide protection against various Salmonella species.

Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes.

The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach. We analyzed patient esophageal and colorectal carcinomas and found that reads from distinct genera tend to co-occur in the same host cells, testifying to possible intracellular polymicrobial interactions. Microbial reads are disproportionately abundant within myeloid cells that up-regulate proinflammatory cytokines like IL1Β and CXCL8, while infected tumor cells up-regulate antigen processing and presentation pathways. These results show that myeloid cells with bacteria engulfed are a major source of bacterial RNA within the tumor microenvironment (TME) and may inflame the TME and influence immunotherapy response.

An Immunoinformatic-Based In Silico Identification on the Creation of a Multiepitope-Based Vaccination Against the Nipah Virus.

The zoonotic viruses pose significant threats to public health. Nipah virus (NiV) is an emerging virus transmitted from bats to humans. The NiV causes severe encephalitis and acute respiratory distress syndrome, leading to high mortality rates, with fatality rates ranging from 40% to 75%. The first emergence of the disease was found in Malaysia in 1998-1999 and later in Bangladesh, Cambodia, Timor-Leste, Indonesia, Singapore, Papua New Guinea, Vietnam, Thailand, India, and other South and Southeast Asian nations. Currently, no specific vaccines or antiviral drugs are available. The potential advantages of epitope-based vaccines include their ability to elicit specific immune responses while minimizing potential side effects. The epitopes have been identified from the conserved region of viral proteins obtained from the UniProt database. The selection of conserved epitopes involves analyzing the genetic sequences of various viral strains. The present study identified two B cell epitopes, seven cytotoxic T lymphocyte (CTL) epitopes, and seven helper T lymphocyte (HTL) epitope interactions from the NiV proteomic inventory. The antigenic and physiological properties of retrieved protein were analyzed using online servers ToxinPred, VaxiJen v2.0, and AllerTOP. The final vaccine candidate has a total combined coverage range of 80.53%. The tertiary structure of the constructed vaccine was optimized, and its stability was confirmed with the help of molecular simulation. Molecular docking was performed to check the binding affinity and binding energy of the constructed vaccine with TLR-3 and TLR-5. Codon optimization was performed in the constructed vaccine within the Escherichia coli K12 strain, to eliminate the danger of codon bias. However, these findings must require further validation to assess their effectiveness and safety. The development of vaccines and therapeutic approaches for virus infection is an ongoing area of research, and it may take time before effective interventions are available for clinical use.

Design patterns for the construction of computational biological models.

Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models.

Single cell RNA-sequencing in uveal melanoma: advances in heterogeneity, tumor microenvironment and immunotherapy.

Uveal melanoma (UM) is a highly aggressive and fatal tumor in the eye, and due the special biology of UM, immunotherapy showed little effect in UM patients. To improve the efficacy of immunotherapy for UM patients is of great clinical importance. Single-cell RNA sequencing(scRNA-seq) provides a critical perspective for deciphering the complexity of intratumor heterogeneity and tumor microenvironment(TME). Combing the bioinformatics analysis, scRNA-seq could help to find prognosis-related molecular indicators, develop new therapeutic targets especially for immunotherapy, and finally to guide the clinical treatment options.

Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification.

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

Combination of computational techniques and RNAi reveal targets in Anopheles gambiae for malaria vector control.

Increasing reports of insecticide resistance continue to hamper the gains of vector control strategies in curbing malaria transmission. This makes identifying new insecticide targets or alternative vector control strategies necessary. CLassifier of Essentiality AcRoss EukaRyote (CLEARER), a leave-one-organism-out cross-validation machine learning classifier for essential genes, was used to predict essential genes in Anopheles gambiae and selected predicted genes experimentally validated. The CLEARER algorithm was trained on six model organisms: Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Schizosaccharomyces pombe, and employed to identify essential genes in An. gambiae. Of the 10,426 genes in An. gambiae, 1,946 genes (18.7%) were predicted to be Cellular Essential Genes (CEGs), 1716 (16.5%) to be Organism Essential Genes (OEGs), and 852 genes (8.2%) to be essential as both OEGs and CEGs. RNA interference (RNAi) was used to validate the top three highly expressed non-ribosomal predictions as probable vector control targets, by determining the effect of these genes on the survival of An. gambiae G3 mosquitoes. In addition, the effect of knockdown of arginase (AGAP008783) on Plasmodium berghei infection in mosquitoes was evaluated, an enzyme we computationally inferred earlier to be essential based on chokepoint analysis. Arginase and the top three genes, AGAP007406 (Elongation factor 1-alpha, Elf1), AGAP002076 (Heat shock 70kDa protein 1/8, HSP), AGAP009441 (Elongation factor 2, Elf2), had knockdown efficiencies of 91%, 75%, 63%, and 61%, respectively. While knockdown of HSP or Elf2 significantly reduced longevity of the mosquitoes (p<0.0001) compared to control groups, Elf1 or arginase knockdown had no effect on survival. However, arginase knockdown significantly reduced P. berghei oocytes counts in the midgut of mosquitoes when compared to LacZ-injected controls. The study reveals HSP and Elf2 as important contributors to mosquito survival and arginase as important for parasite development, hence placing them as possible targets for vector control.

Identification of core genes shared by ischemic stroke and myocardial infarction using an integrated approach.

BACKGROUND: Both ischemic stroke (IS) and myocardial infarction (MI) are caused by vascular occlusion that results in ischemia. While there may be similarities in their mechanisms, the potential relationship between these 2 diseases has not been comprehensively analyzed. Therefore, this study explored the commonalities in the pathogenesis of IS and MI. METHODS: Datasets for IS (GSE58294, GSE16561) and MI (GSE60993, GSE61144) were downloaded from the Gene Expression Omnibus database. Transcriptome data from each of the 4 datasets were analyzed using bioinformatics, and the differentially expressed genes (DEGs) shared between IS and MI were identified and subsequently visualized using a Venn diagram. A protein-protein interaction (PPI) network was constructed using the Interacting Gene Retrieval Tool database, and identification of key core genes was performed using CytoHubba. Gene Ontology (GO) term annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the shared DEGs were conducted using prediction and network analysis methods, and the functions of the hub genes were determined using Metascape. RESULTS: The analysis revealed 116 and 1321 DEGs in the IS and MI datasets, respectively. Of the 75 DEGs shared between IS and MI, 56 were upregulated and 19 were downregulated. Furthermore, 15 core genes - S100a12, Hp, Clec4d, Cd163, Mmp9, Ormdl3, Il2rb, Orm1, Irak3, Tlr5, Lrg1, Clec4e, Clec5a, Mcemp1, and Ly96 - were identified. GO enrichment analysis of the DEGs showed that they were mainly involved in the biological functions of neutrophil degranulation, neutrophil activation during immune response, and cytokine secretion. KEGG analysis showed enrichment in pathways pertaining to Salmonella infection, Legionellosis, and inflammatory bowel disease. Finally, the core gene-transcription factor, gene-microRNA, and small-molecule relationships were predicted. CONCLUSION: These core genes may provide a novel theoretical basis for the diagnosis and treatment of IS and MI.