Bioinformatics in Gene and Genome Analysis

Book review: Flow cytometry with plant cells: Analysis of genes, chromosomes and genomes

IntroductionThe emergence and spread of new strains of zoonotic bacteria, such as multidrug resistant (MDR) Salmonella Infantis, represent a growing health risk for humans in and outside Europe due to foodborne infections of poultry meat origin.ObjectivesIn order to understand genome relations of S. Infantis strains from Hungary and from different geographic regions, we performed a comprehensive genome analysis of nine Hungarian and 67 globally selected strains of S. Infantis and 26 Salmonella strains representing 13 non-Infantis serovars.ResultsAnalyses of whole-, and accessory genomes, showed that almost all S. Infantis strains were separated from the non-Infantis serovars. S. Infantis strains from Hungary formed subclusters based on their time of isolation. In whole genome sequence analysis, the Swiss strains of S. Infantis were closely related to each other and clustered together with subclusters of strains from Hungary, Japan, Italy, United States, and Israel. The accessory genome analysis revealed that the Swiss strains were distinct from most of the strains investigated, including the Hungarian ones. Analysis of the cloud genes offered the most detailed insight into the genetic distance and relationship of S. Infantis strains confirming that the Swiss and Hungarian strains belonged to different lineages. As expected, core genome analysis provided the least discriminatory power for analysis of S. Infantis. Genomic sequences of nine strains from Brazil, Israel, Mexico, Nigeria, and Senegal (deposited as S. Infantis) proved to be outliers from the S. Infantis clade. They were predicted to be Salmonella Rissen, Salmonella Ouakarm, Salmonella Kentucky, Salmonella Thompson, and Salmonella enterica subsp. diarizonae.ConclusionAccessory genome of S. Infantis showed the highest diversity suggesting a faster evolution than that of the whole genomes contributing to the emergence of multiple genetic variants of S. Infantis worldwide. Accordingly, in spite of the comprehensive analysis of several genomic characteristics, no epidemiologic links between these S. Infantis strains from different countries could be established. It is also concluded that several strains originally designated as S. Infantis need in databanks reclassification.

A study on amplification of Cry1 gene and genome analysis of local isolate of Bacillus thuringiensis TU1 has been done. Amplification was done for cryI gene by PCR-technique. Genome analysis was performed using pulsed-field gel electrophoresis. Insecticidal specificity assay of the isolate to larvae was done in larvae of Heliothis armigera, Plutella xylostella, Aedes aegypti, and Culex sp. Isolate of commercial strain, B. thuringiensis var kurstaki strain HD-7, was used as control for amplification of cryI gene, genome analysis, and insecticidal specificity assay. The result showed that insecticidal specificity assay of the isolates of both TU1 and commercial have similar spectrum of toxicity to the larvae. Amplification of cry1 gene resulted to a fragment of approximately 550 bp in both TU1 and commercial. Genome profiles of TU1 and commercial strain were similar.

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the “Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project” show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.

Genome-wide analysis of tandem duplicated genes and their contribution to stress resistance in pigeonpea (Cajanus cajan)

Frontiers in molecular mycorrhizal research – genes, loci, dots and spins

Renal cell carcinoma (RCC) is the most common form of kidney cancer, caused by renal epithelial cells. RCC remains to be a challenging public health problem worldwide. Metastases that are resistant to radiotherapy and chemotherapy are the major cause of death from cancer. However, the underlying molecular mechanism regulating the metastasis of RCC is poorly known. Publicly available databases of RCC were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using GEO2R analysis, whereas the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by Gene Set Enrichment Analysis (GSEA) and Metascape. Protein-protein interaction (PPI) network of DEGs was analyzed by STRING online database, and Cytoscape software was used for visualizing PPI network. Survival analysis of hub genes was conducted using GEPIA online database. The expression levels of hub genes were investigated from The Human Protein Atlas online database and GEPIA online database. Finally, the comparative toxicogenomics database (CTD; http://ctdbase.org) was used to identify hub genes associated with tumor or metastasis. We identified 229 DEGs comprising 135 downregulated genes and 94 upregulated genes. Functional analysis revealed that these DEGs were associates with cell recognition, regulation of immune, negative regulation of adaptive immune response, and other functions. And these DEGs mainly related to P53 signaling pathway, cytokine-cytokine receptor interaction, Natural killer cell mediated cytotoxicity, and other pathways are involved. Ten genes were identified as hub genes through module analyses in the PPI network. Finally, survival analysis of 10 hub genes was conducted, which showed that the MMP2 (matrix metallo peptidase 2), DCN, COL4A1, CASR (calcium sensing receptor), GPR4 (G protein-coupled receptor 4), UTS2 (urotensin 2), and LDLR (low density lipoprotein receptor) genes were significant for survival. In this study, the DEGs between RCC and metastatic RCC were analyzed, which assist us in systematically understanding the pathogeny underlying metastasis of RCC. The MMP2, DCN, COL4A1, CASR, GPR4, UTS2, and LDLR genes might be used as potential targets to improve diagnosis and immunotherapy biomarkers for RCC.

BackgroundOvarian neoplasms are the fifth most common cancer affecting the health of women, and they are the most lethal gynecologic malignancies; however, the etiology of ovarian neoplasms remains largely unknown. There is an urgent need to further broaden the understanding of the development mechanism of ovarian neoplasms through in vitro research using different cell lines.Material/MethodsTo screen the differentially expressed genes (DEGs) that may play critical roles in OVDM1 (an ovarian cancer cell line), the public microarray data (GSE70264) were downloaded and screened for DEGs. Then, Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. To screen hub genes, the protein–protein interaction network was constructed. The expression level and survival analysis of hub genes in patients with ovarian neoplasms were also analyzed.ResultsThere were 79 upregulated and 926 downregulated DEGs detected, and the biological processes of the GO analysis were enriched in extracellular matrix organization, extracellular structure organization, and chromosome segregation, whereas, the KEGG pathway analysis was enriched in cell cycle and cell adhesion molecules. The hub gene BIRC5, which might play a key role in ovarian neoplasms, was further screened.ConclusionsThe present study could deepen the understanding of the molecular mechanism of ovarian neoplasms using the OVDM1 cell line, which could be useful in developing clinical treatments of ovarian neoplasms.

Background: Many studies on circular RNAs (circRNAs) have recently been published. However, the function of circRNAs in recurrent implantation failure (RIF) is unknown and remains to be explored. This study aims to determine the regulatory mechanisms of circRNAs in RIF.Methods: Microarray data of RIF circRNA (GSE147442), microRNA (miRNA; GSE71332), and messenger RNA (mRNA; GSE103465) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNA, miRNA, and mRNA. The circRNA–miRNA–mRNA network was constructed by Cytoscape 3.8.0 software, then the protein–protein interaction (PPI) network was constructed by STRING database, and the hub genes were identified by cytoHubba plug-in. The circRNA–miRNA–hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in RIF. Finally, the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the hub genes were performed by clusterProfiler package of Rstudio software, and Reactome Functional Interaction (FI) plug-in was used for reactome analysis to comprehensively analyze the mechanism of hub genes in RIF.Results: A total of eight upregulated differentially expressed circRNAs (DECs), five downregulated DECs, 56 downregulated differentially expressed miRNAs (DEmiRs), 104 upregulated DEmiRs, 429 upregulated differentially expressed genes (DEGs), and 1,067 downregulated DEGs were identified regarding RIF. The miRNA response elements of 13 DECs were then predicted. Seven overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 56 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of seven miRNAs with 1,496 DEGs. The circRNA–miRNA–mRNA network and PPI network were constructed through six circRNAs, seven miRNAs, and 56 mRNAs; and four hub genes (YWHAZ, JAK2, MYH9, and RAP2C) were identified. The circRNA–miRNA–hub gene regulatory subnetwork with nine regulatory axes was formed in RIF. Functional enrichment analysis and reactome analysis showed that these four hub genes were closely related to the biological functions and pathways of RIF.Conclusion: The results of this study provide further understanding of the potential pathogenesis from the perspective of circRNA-related competitive endogenous RNA network in RIF.

Sepsis, a life-threatening disease caused by dysregulated host response to infection, is a major public health problem with a high mortality and morbidity rate. Pyroptosis is a new type of programmed cell death discovered in recent years, which has been proved to play an important role in sepsis. Nevertheless, there is no comprehensive report, which can help researchers get a quick overview and find research hotspots. Thus, we aimed to identify the study status and knowledge structures of pyroptosis in sepsis and summarize the key mechanism of pyroptosis in sepsis. The data were retrieved and downloaded from the WOS database. Software such as VOSviewer was used to analyze these publications. Key genes were picked out by using (https://www.genecards.org) and (http://www.bioinformatics.com). Then, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to performed these key genes. From 2011 to 2021, a total of 299 papers met the search criteria, and the global interest in pyroptosis in sepsis measured by the value of (RRI) has started to increase since 2016. China ranked first in the number of publications, followed by the USA. The journal Frontiers in Immunology published the most relevant articles. Through keyword co-occurrence analysis, the high-frequency subject terms were divided into three clusters like “animal research”, “cell research,” and “molecular research” clusters. “mir,” “aki,” “monocyte,” and “neutrophil” were the newest keywords that may be the hotspot. In addition, a total of 15 genes were identified as hub genes. TNF, IL-1β, AKT1, CASP1, and STAT3 were highly expressed in lung tissues, thymus tissues, and lymphocytes. KEGG analysis indicated that pyroptosis may play a vital role in sepsis via the NOD, PI3K/AKT, and MAPK/JNK pathways. Through the quantitative analysis of the literature on pyroptosis in sepsis, we revealed the current status and hotspots of research in this field and provided some guidance for further studies.

BackgroundAn increasing number of whole viral and bacterial genomes are being sequenced and deposited in public databases. In parallel to the mounting interest in whole genomes, the number of whole genome analyses software tools is also increasing. GeneOrder was originally developed to provide an analysis of genes between two genomes, allowing visualization of gene order and synteny comparisons of any small genomes. It was originally developed for comparing virus, mitochondrion and chloroplast genomes. This is now extended to small bacterial genomes of sizes less than 2 Mb.ResultsGeneOrder3.0 has been developed and validated successfully on several small bacterial genomes (ca. 580 kb to 1.83 Mb) archived in the NCBI GenBank database. It is an updated web-based "on-the-fly" computational tool allowing gene order and synteny comparisons of any two small bacterial genomes. Analyses of several bacterial genomes show that a large amount of gene and genome re-arrangement occurs, as seen with earlier DNA software tools. This can be displayed at the protein level using GeneOrder3.0. Whole genome alignments of genes are presented in both a table and a dot plot. This allows the detection of evolutionary more distant relationships since protein sequences are more conserved than DNA sequences.ConclusionsGeneOrder3.0 allows researchers to perform comparative analysis of gene order and synteny in genomes of sizes up to 2 Mb "on-the-fly." Availability: and .

Analysis of Genes and Genomes

In the phase 3 CheckMate 459 study (NCT02576509), first-line nivolumab (NIVO) treatment demonstrated higher overall response rate (ORR) and a favorable safety profile, but no significant improvement in overall survival (OS) compared with sorafenib (SORA) in patients with advanced hepatocellular carcinoma (HCC). Here, we report exploratory biomarker analyses from the trial with a potential to identify patients who may benefit from NIVO. Archival or fresh tumor samples were collected before treatment and subjected to whole exome sequencing (WES) (400/730: 55%) for genetic alterations including high microsatellite instability (MSI-H) and WNT/beta-catenin pathway components, as well as whole transcriptome RNA sequencing (469/730: 64%) for analysis of individual genes, inflammation signatures and Gene Set Enrichment Analysis (GSEA). Associations between biomarkers and best overall response (BOR), progression free survival (PFS) and OS were evaluated at a minimum follow-up of 33·6 months. RNA sequencing analysis demonstrated that high Gajewski inflammation gene signature score of ≥0.47 (30% of tumors) was associated with an improvement in BOR (p=0.0043), PFS (p=0.00065) and OS (p=0.0073) within the NIVO arm only. Upon comparison of NIVO versus SORA, only patients whose tumors were inflammation high demonstrated NIVO benefit for BOR (p=0.017), PFS (p=0.0063) and OS (p=0.037). Responses to NIVO were associated with higher expression of CD8A, CD8B, PDCD1 (PD-1) and other inflammation-associated genes within the tumor (false discovery rate, FDR<0.01). GSEA of 50 hallmark gene sets for ORR showed that responders to NIVO and SORA were enriched in similar gene sets such as inflammatory response, interferon alpha/gamma response, E2F targets and G2M checkpoint (FDR<0.01). In contrast, GSEA for OS showed inflammatory response and IL6-JAK-STAT3 signaling gene sets were associated with benefit from NIVO, but shorter OS for SORA (FDR<0.01). WES analysis demonstrated that the prevalence of MSI-H was low (12/400: 3%) and insufficient to confirm relationship to clinical outcomes (no radiographic responses observed in 9 NIVO- and 3 SORA-treated patients). Alterations within beta-catenin gene were not associated with survival outcomes for NIVO. Overall, patients with advanced HCC whose tumors had high Gajewski inflammation gene signature demonstrated improved outcomes with NIVO versus SORA. NIVO responders had higher expression of individual inflammation-associated genes. Expression of inflammatory response and IL6-JAK-STAT3 signaling gene sets showed association with longer OS for NIVO, but not SORA. These exploratory analyses support further studies of tumor inflammation-related biomarkers to identify patients most likely to benefit from PD-1 inhibition in advanced HCC. Citation Format: Jaclyn Neely, Jin Yao, Masatoshi Kudo, Richard S. Finn, Bruno Sangro, Ignacio Melero, Anthony El-Khoueiry, Marina Tschaika, Damir Begic, Ashwin Sama, Parul Doshi, Thomas Yau, Robin Kate Kelley. Genomic and transcriptomic analyses related to the clinical efficacy of first-line nivolumab in advanced hepatocellular carcinoma from the phase 3 CheckMate 459 trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2145.

BackgroundPeri-implantitis is the main cause of failure of implant treatment, and there is little research on its molecular mechanism. This study aimed to identify key biomarkers and immune infiltration of peri-implantitis using a bioinformatics method.Material/MethodsThree Gene Ontology (GO) gene expression profiles were selected from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by the LIMMA package, and functional correlations of DEGs were analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Information on immune-related genes was obtained from ImmPort (https://www.immport.org) and InnateDB (http://www.innatedb.com). Immune-related DEGs were screened by least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE). The single-sample Gene Set Enrichment Analysis algorithm was used to analyze immune cell infiltration in gingival tissue between peri-implantitis and normal controls. Finally, results of bioinformatics analysis were verified by qPCR.ResultsA total of 398 DEGs were identified, of which 96 were immune-related. Enrichment analysis showed these genes were enriched in inflammatory response, leucocyte chemotaxis, immune response-regulating signaling pathway, and cell activation. Seven key genes were selected by LASSO and SVM-RFE. Receiver operating characteristic curve results showed these genes had excellent diagnostic efficacy. Results of qPCR showed significant differences in the expression of these genes.ConclusionsDifferences in key genes and immune infiltration between peri-implantitis and gingival tissues of normal controls may provide new insights into the development of peri-implantitis. Elucidating the difference in immune infiltration between peri-implantitis tissues and normal tissues will help to understand the development of peri-implantitis.

Banana Fusarium wilt is a devastating banana disease caused by Fusarium oxysporum f. sp. cubense (Foc), seriously threatening the global banana industry. However, knowledge of the molecular mechanisms of pathogenicity and the interaction between the host and Foc is limited. In this study, we confi rmed the changes in gene expression and pathways in the Cavendish banana variety “Brazilian” during early infection with Foc1 and Foc4 by comparative transcriptomic analysis, including pathogenesisrelated protein genes, leucine-rich repeat type genes, and plant hormone genes. In the early stages of Foc inoculation, more differentially expressed genes were identifi ed in roots infected with Foc1 than in those infected with Foc4. According to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, bananas with Foc1 infection caused differential accumulation and expression of various defence-related compounds and genes at the early stage of infection. In contrast, changes in the expression of defence-related genes in the early stages of Foc4 infection were small or weak and were not apparent until 24 h post-infection. Therefore, plant immune responses may occur at this stage of infection. This study could help clarify the interaction between banana plants and Foc and develop control measures for banana Fusarium wilt.