Large-scale Bioinformatics Analysis Research Articles

199 Large-scale bioinformatic analysis reveals cancer specific B cell responses with opposing effects on survival

199 Large-scale bioinformatic analysis reveals cancer specific B cell responses with opposing effects on survival

The structural basis of hyperpromiscuity in a core combinatorial network of type II toxin–antitoxin and related phage defense systems

Toxin-antitoxin (TA) systems are a large group of small genetic modules found in prokaryotes and their mobile genetic elements. Type II TAs are encoded as bicistronic (two-gene) operons that encode two proteins: a toxin and a neutralizing antitoxin. Using our tool NetFlax (standing for Network-FlaGs for toxins and antitoxins), we have performed a large-scale bioinformatic analysis of proteinaceous TAs, revealing interconnected clusters constituting a core network of TA-like gene pairs. To understand the structural basis of toxin neutralization by antitoxins, we have predicted the structures of 3,419 complexes with AlphaFold2. Together with mutagenesis and functional assays, our structural predictions provide insights into the neutralizing mechanism of the hyperpromiscuous Panacea antitoxin domain. In antitoxins composed of standalone Panacea, the domain mediates direct toxin neutralization, while in multidomain antitoxins the neutralization is mediated by other domains, such as PAD1, Phd-C, and ZFD. We hypothesize that Panacea acts as a sensor that regulates TA activation. We have experimentally validated 16 NetFlax TA systems and used domain annotations and metabolic labeling assays to predict their potential mechanisms of toxicity (such as membrane disruption, and inhibition of cell division or protein synthesis) as well as biological functions (such as antiphage defense). We have validated the antiphage activity of a RosmerTA system encoded by Gordonia phage Kita, and used fluorescence microscopy to confirm its predicted membrane-depolarizing activity. The interactive version of the NetFlax TA network that includes structural predictions can be accessed at http://netflax.webflags.se/.

Cuproptosis engages in c-Myc-mediated breast cancer stemness

BackgroundIntra-tumoral heterogeneity (ITH) is a distinguished hallmark of cancer, and cancer stem cells (CSCs) contribute to this malignant characteristic. Therefore, it is of great significance to investigate and even target the regulatory factors driving intra-tumoral stemness. c-Myc is a vital oncogene frequently overexpressed or amplified in various cancer types, including breast cancer. Our previous study indicated its potential association with breast cancer stem cell (BCSC) biomarkers.MethodsIn this research, we performed immunohistochemical (IHC) staining on sixty breast cancer surgical specimens for c-Myc, CD44, CD24, CD133 and ALDH1A1. Then, we analyzed transcriptomic atlas of 1533 patients with breast cancer from public database.ResultsIHC staining indicated the positive correlation between c-Myc and BCSC phenotype. Then, we used bioinformatic analysis to interrogate transcriptomics data of 1533 breast cancer specimens and identified an intriguing link among c-Myc, cancer stemness and copper-induced cell death (also known as “cuproptosis”). We screened out cuproptosis-related characteristics that predicts poor clinical outcomes and found that the pro-tumoral cuproptosis-based features were putatively enriched in MYC-targets and showed a significantly positive correlation with cancer stemness.ConclusionIn addition to previous reports on its oncogenic roles, c-Myc showed significant correlation to stemness phenotype and copper-induced cell toxicity in breast cancer tissues. Moreover, transcriptomics data demonstrated that pro-tumoral cuproptosis biomarkers had putative positive association with cancer stemness. This research combined clinical samples with large-scale bioinformatic analysis, covered description and deduction, bridged classic oncogenic mechanisms to innovative opportunities, and inspired the development of copper-based nanomaterials in targeting highly heterogeneous tumors.

Census of exposed aggregation-prone regions in proteomes.

Loss of solubility usually leads to the detrimental elimination of protein function. In some cases, the protein aggregation is also required for beneficial functions. Given the duality of this phenomenon, it remains a fundamental question how natural selection controls the aggregation. The exponential growth of genomic sequence data and recent progress with in silico predictors of the aggregation allows approaching this problem by a large-scale bioinformatics analysis. Most of the aggregation-prone regions are hidden within the 3D structure, rendering them inaccessible for the intermolecular interactions responsible for aggregation. Thus, the most realistic census of the aggregation-prone regions requires crossing aggregation prediction with information about the location of the natively unfolded regions. This allows us to detect so-called 'exposed aggregation-prone regions' (EARs). Here, we analyzed the occurrence and distribution of the EARs in 76 reference proteomes from the three kingdoms of life. For this purpose, we used a bioinformatics pipeline, which provides a consensual result based on several predictors of aggregation. Our analysis revealed a number of new statistically significant correlations about the presence of EARs in different organisms, their dependence on protein length, cellular localizations, co-occurrence with short linear motifs and the level of protein expression. We also obtained a list of proteins with the conserved aggregation-prone sequences for further experimental tests. Insights gained from this work led to a deeper understanding of the relationship between protein evolution and aggregation.

Pan-Cancer Analysis of the Immunological Role of PDIA5: A Potential Target for Immunotherapy

The aberrant protein disulfide isomerase A5 (PDIA5) expression was relevant to the poor prognosis of patients with human cancers. However, its relationship with the epigenetic and genetic alterations and its effect on tumor immunity is still lacking. In the present study, we comprehensively analyzed the immune infiltration role of PDIA5 in human cancers based on large-scale bioinformatics analyses and in vitro experiments. Obvious DNA methylation and moderate alteration frequency of PDIA5 were observed in human cancers. The expression level of PDIA5 was significantly correlated with infiltrated immune cells, immune pathways, and other immune signatures. We found that cancer cells and macrophages exhibited high PDIA5 expression in human cancers using the single-cell RNA sequencing analysis. We also demonstrated the interaction between PDIA5 and immune cells in glioblastoma multiforme (GBM). Multiplex immunofluorescence staining showed the upregulated expression level of PDIA5 and the increased number of M2 macrophage markers-CD163 positive cells in pan-cancer samples. Notably, PDIA5 silencing resulted in upregulated expression of PD-L1 and SPP1 in U251 cells. Silencing of PDIA5 in hepG2 cells, U251 cells, and PC3 cells contributed to a decline in their ability of proliferation, clone formation, and invasion and inhibited the migration of cocultured M2 macrophages. Additionally, PDIA5 also displayed predictive value in the immunotherapy response of both murine and human cancer cohorts. Overall, our findings indicated that PDIA5 might be a potential target for immunotherapies in cancers.

Antigen Presentation Machinery Signature-Derived CALR Mediates Migration, Polarization of Macrophages in Glioma and Predicts Immunotherapy Response.

Immunogenicity, influenced by tumor antigenicity and antigen presenting efficiency, critically determines the effectiveness of immune checkpoint inhibitors. The role of immunogenicity has not been fully elucidated in gliomas. In this study, a large-scale bioinformatics analysis was performed to analyze the prognostic value and predictive value of antigen presentation machinery (APM) signature in gliomas. ssGSEA algorithm was used for development of APM signature and LASSO regression analysis was used for construction of APM signature-based risk score. APM signature and risk score showed favorable performance in stratifying survival and predicting tumorigenic factors of glioma patients. APM signature and risk score were also associated with different genomic features in both training cohort TCGA and validating cohort CGGA. Furthermore, APM signature-based risk score was independently validated in three external cohorts and managed to predict immunotherapy response. A prognostic nomogram was constructed based on risk score. Risk score-derived CALR was found to mediate the invasion and polarization of macrophages based on the coculture of HMC3 and U251 cells. CALR could significantly predict immunotherapy response. In conclusion, APM signature and APM signature-based risk score could help promote the clinical management of gliomas.

PBK/TOPK Is a Favorable Prognostic Biomarker Correlated with Antitumor Immunity in Colon Cancers.

Immune checkpoint inhibitor therapy has proven efficacy in a subset of colon cancer patients featuring a deficient DNA mismatch repair system or a high microsatellite instability profile. However, there is high demand for more effective biomarkers to expand the colon cancer population responding to ICI therapy. PBK/TOPK, a serine/threonine kinase, plays a role in cell cycle regulation and mitotic progression. Here, we investigated the correlation between PBK/TOPK expression and tumor immunity and its prognostic value in colon cancer. Based on large-scale bioinformatics analysis, we discovered that elevated PBK/TOPK expression predicted a favorable outcome in patients with colon cancer and was positively associated with immune infiltration levels of CD8+ T cells, CD4+ T cells, natural killer cells, and M1 macrophages. In contrast, a negative correlation was found between PBK/TOPK expression and immune suppressor cells, including regulatory T cells and M2 macrophages. Furthermore, the expression of PBK/TOPK was correlated with the expression of T-cell cytotoxicity genes in colon cancer. Additionally, high PBK/TOPK expression was associated with mutations in DNA damage repair genes, and thus with increased tumor mutation and neoantigen burden. These findings suggest that PBK/TOPK may serve as a prognostic and predictive biomarker for immunotherapy in colon cancer.

Identification of catabolic pathway for 1-deoxy-D-sorbitol in Bacillus licheniformis

1-Deoxy-D-sorbitol, the 1-deoxy analogue of D-sorbitol, has been detected in human urine as well as in natural herbs and spices. Although there are sporadic reports about 1-deoxy-D-sorbitol dehydrogenase, the complete catabolic pathway of 1-deoxy-D-sorbitol remains unsolved. Informed by the promiscuous activities of fructose-6-phosphate aldolase (FSA) which is involved in the sorbitol (glucitol) utilization (gut) operon and guided by the large scale bioinformatics analysis, we predicted and then experimentally verified the gut operon encoded by Bacillus licheniformis ATCC14580 is responsible for the catabolism of both D-sorbitol and 1-deoxy-D-sorbitol by in vitro activity assays of pathway enzymes, in vivo growth phenotypes, and transcriptomic studies. Moreover, the phylogenetic distribution analysis suggests that the D-sorbitol and 1-deoxy-D-sorbitol catabolic gene cluster is mostly conserved in members of Firmicutes phylum.

Prostaglandin D2 synthase/prostaglandin D2/TWIST2 signaling inhibits breast cancer proliferation.

Though the past few years have witnessed exciting achievements in targeted and immunotherapeutic treatments of all breast cancer subtypes, yet the decline in breast cancer mortality has been slowed, urging the need for further expanding options of high-quality treatments. Prostaglandin D2 synthase (PTGDS)/prostaglandin D2 (PGD2) play important roles in a variety of cancer types and show tissue-specificity, however, there are limited relevant reports in breast cancer. Therefore, the aims of the present study were to investigate the effects of PTGDS/PGD2 in breast cancer by large-scale bioinformatic analysis and in vitro experiments conducted on human breast cancer cell lines. Results of our study indicated that patients with high levels of PTGDS expression showed a reduced potential of tumor proliferation. PGD2 treatment significantly inhibited the proliferation and migration of breast cancer cells, which was mediated by the reduced expression of TWIST2. Overexpression of TWIST2 reversed the inhibitory effects of PGD2 on breast cancer cell proliferation. These results provided the novel evidence that PTGDS may play a significant role in modulating breast cancer growth, with implications for its potential use in treating breast cancer.

PDIA5 is Correlated With Immune Infiltration and Predicts Poor Prognosis in Gliomas.

Gliomas are the most common and lethal primary malignant tumor of the brain. Routine treatment including surgical resection, chemotherapy, and radiotherapy produced limited therapeutic effect, while immunotherapy targeting the glioma microenvironment has offered a novel therapeutic option. PDIA5 protein is the member of PDI family, which is highly expressed in glioma and participates in glioma progression. Based on large-scale bioinformatics analysis, we discovered that PDIA5 expression level is upregulated in aggressive gliomas, with high PDIA5 expression predicting poor clinical outcomes. We also observed positive correlation between PDIA5 and immune infiltrating cells, immune related pathways, inflammatory activities, and other immune checkpoint members. Patients with high PDIA5 high-expression benefited from immunotherapies. Additionally, immunohistochemistry revealed that PDIA5 and macrophage biomarker CD68 were upregulated in high-grade gliomas, and patients with low PDIA5 level experienced favorable outcomes among 33 glioma patients. Single cell RNA sequencing exhibited that PDIA5 was in high level presenting in neoplastic cells and macrophages. Cell transfection and co-culture of glioma cells and macrophages revealed that PDIA5 in tumor cells mediated macrophages exhausting. Altogether, our findings indicate that PDIA5 overexpression is associated with immune infiltration in gliomas, and may be a promising therapeutic target for glioma immunotherapy.

Evolution of isoprenyl diphosphate synthase-like terpene synthases in fungi

Terpene synthases (TPSs) and trans-isoprenyl diphosphate synthases (IDSs) are among the core enzymes for creating the enormous diversity of terpenoids. Despite having no sequence homology, TPSs and IDSs share a conserved “α terpenoid synthase fold” and a trinuclear metal cluster for catalysis, implying a common ancestry with TPSs hypothesized to evolve from IDSs anciently. Here we report on the identification and functional characterization of novel IDS-like TPSs (ILTPSs) in fungi that evolved from IDS relatively recently, indicating recurrent evolution of TPSs from IDSs. Through large-scale bioinformatic analyses of fungal IDSs, putative ILTPSs that belong to the geranylgeranyl diphosphate synthase (GGDPS) family of IDSs were identified in three species of Melampsora. Among the GGDPS family of the two Melampsora species experimentally characterized, one enzyme was verified to be bona fide GGDPS and all others were demonstrated to function as TPSs. Melampsora ILTPSs displayed kinetic parameters similar to those of classic TPSs. Key residues underlying the determination of GGDPS versus ILTPS activity and functional divergence of ILTPSs were identified. Phylogenetic analysis implies a recent origination of these ILTPSs from a GGDPS progenitor in fungi, after the split of Melampsora from other genera within the class of Pucciniomycetes. For the poplar leaf rust fungus Melampsora larici-populina, the transcripts of its ILTPS genes were detected in infected poplar leaves, suggesting possible involvement of these recently evolved ILTPS genes in the infection process. This study reveals the recurrent evolution of TPSs from IDSs since their ancient occurrence and points to the possibility of a wide distribution of ILTPS genes in three domains of life.

Watchdog 2.0: New developments for reusability, reproducibility, and workflow execution.

BackgroundAdvances in high-throughput methods have brought new challenges for biological data analysis, often requiring many interdependent steps applied to a large number of samples. To address this challenge, workflow management systems, such as Watchdog, have been developed to support scientists in the (semi-)automated execution of large analysis workflows.ImplementationHere, we present Watchdog 2.0, which implements new developments for module creation, reusability, and documentation and for reproducibility of analyses and workflow execution. Developments include a graphical user interface for semi-automatic module creation from software help pages, sharing repositories for modules and workflows, and a standardized module documentation format. The latter allows generation of a customized reference book of public and user-specific modules. Furthermore, extensive logging of workflow execution, module and software versions, and explicit support for package managers and container virtualization now ensures reproducibility of results. A step-by-step analysis protocol generated from the log file may, e.g., serve as a draft of a manuscript methods section. Finally, 2 new execution modes were implemented. One allows resuming workflow execution after interruption or modification without rerunning successfully executed tasks not affected by changes. The second one allows detaching and reattaching to workflow execution on a local computer while tasks continue running on computer clusters.ConclusionsWatchdog 2.0 provides several new developments that we believe to be of benefit for large-scale bioinformatics analysis and that are not completely covered by other competing workflow management systems. The software itself, module and workflow repositories, and comprehensive documentation are freely available at https://www.bio.ifi.lmu.de/watchdog.

An ABC Transporter Drives Medulloblastoma Pathogenesis by Regulating Sonic Hedgehog Signaling

Mutations in Sonic hedgehog (SHH) signaling promote aberrant proliferation and tumor growth. SHH-medulloblastoma (MB) is among the most frequent brain tumors in children less than 3 years of age. Although key components of the SHH pathway are well-known, we hypothesized that new disease-modifying targets of SHH-MB might be identified from large-scale bioinformatics and systems biology analyses. Using a data-driven systems biology approach, we built a MB-specific interactome. The ATP-binding cassette transporter ABCC4 was identified as a modulator of SHH-MB. Accordingly, increased ABCC4 expression correlated with poor overall survival in patients with SHH-MB. Knockdown of ABCC4 expression markedly blunted the constitutive activation of the SHH pathway secondary to Ptch1 or Sufu insufficiency. In human tumor cell lines, ABCC4 knockdown and inhibition reduced full-length GLI3 levels. In a clinically relevant murine SHH-MB model, targeted ablation of Abcc4 in primary tumors significantly reduced tumor burden and extended the lifespan of tumor-bearing mice. These studies reveal ABCC4 as a potent SHH pathway regulator and a new candidate to target with the potential to improve SHH-MB therapy. SIGNIFICANCE: These findings identify ABCC4 transporter as a new target in SHH-MB, prompting the development of inhibitors or the repurporsing of existing drugs to target ABCC4.

A heterodimer of evolved designer-recombinases precisely excises a human genomic DNA locus.

Site-specific recombinases (SSRs) such as the Cre/loxP system are useful genome engineering tools that can be repurposed by altering their DNA-binding specificity. However, SSRs that delete a natural sequence from the human genome have not been reported thus far. Here, we describe the generation of an SSR system that precisely excises a 1.4 kb fragment from the human genome. Through a streamlined process of substrate-linked directed evolution we generated two separate recombinases that, when expressed together, act as a heterodimer to delete a human genomic sequence from chromosome 7. Our data indicates that designer-recombinases can be generated in a manageable timeframe for precision genome editing. A large-scale bioinformatics analysis suggests that around 13% of all human protein-coding genes could be targetable by dual designer-recombinase induced genomic deletion (dDRiGD). We propose that heterospecific designer-recombinases, which work independently of the host DNA repair machinery, represent an efficient and safe alternative to nuclease-based genome editing technologies.

Identification of TAF1, SAT1, and ARHGEF9 as DNA methylation biomarkers for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths worldwide. More than 90% of primary HCC is HCC. Hepatitis C virus (HCV) infection and alcohol consumption have been widely accepted as two major risk factors for developing HCC. Herein, we aimed to identify DNA methylation genes related to both HCV infection and alcohol consumption. In this study, we identified methylation genes that were associated with the risk of HCV infection and alcohol consumption, respectively, by a large-scale bioinformatic analysis. Through PPI network analysis, we revealed the associations between the two types of genes and found six hub genes-TAF1, SAT1, Phospholipase C-beta 2, FGD1, ARHGAP4, and ARHGEF9-that may be associated with both HCV infection and alcohol consumption. Gene Ontology enrichment analysis was used to analyze the function which these genes in the network enriched. Among them, TAF1, SAT1, and ARHGEF9 were methylated genes that have been found to be related to tumor progression in HCC patients. Through independent data sets, we verified the methylation pattern of these six genes in HCC samples that had both HCV infection and alcohol consumption risks. Furthermore, we found that three of the six methylated genes were also associated with the prognosis of HCC patients. To summarize, we identified six hub genes that were associated with both HCV infection and alcohol consumption in the progress of HCC. The six methylation genes that might play an important role in both HCV infection and alcohol consumption would be potential therapy targets for HCC.

Clustermq enables efficient parallelization of genomic analyses.

MotivationHigh performance computing (HPC) clusters play a pivotal role in large-scale bioinformatics analysis and modeling. For the statistical computing language R, packages exist to enable a user to submit their analyses as jobs on HPC schedulers. However, these packages do not scale well to high numbers of tasks, and their processing overhead quickly becomes a prohibitive bottleneck.ResultsHere we present clustermq, an R package that can process analyses up to three orders of magnitude faster than previously published alternatives. We show this for investigating genomic associations of drug sensitivity in cancer cell lines, but it can be applied to any kind of parallelizable workflow.Availability and implementationThe package is available on CRAN and https://github.com/mschubert/clustermq. Code for performance testing is available at https://github.com/mschubert/clustermq-performance.Supplementary information Supplementary data are available at Bioinformatics online.

4-Hydroxy-tetrahydrodipicolinate reductase from Neisseria gonorrhoeae – structure and interactions with coenzymes and substrate analog

Neisseria gonorrhoeae, an obligate human pathogen, is a leading cause of communicable diseases globally. Due to rapid development of drug resistance, the rate of successfully curing gonococcal infections is rapidly decreasing. Hence, research is being directed toward finding alternative drugs or drug targets to help eradicate these infections. 4-Hydroxy-tetrahydrodipicolinate reductase (DapB), an important enzyme in the meso-diaminopimelate pathway, is a promising target for the development of new antibiotics. This manuscript describes the first structure of DapB from N. gonorrhoeae determined at 1.85 Å. This enzyme uses NAD(P)H as cofactor. Details of the interactions of the enzyme with its cofactors and a substrate analog/inhibitor are discussed. A large scale bioinformatics analysis of DapBs' sequences is also described.

SciApps: a cloud-based platform for reproducible bioinformatics workflows.

MotivationThe rapid accumulation of both sequence and phenotype data generated by high-throughput methods has increased the need to store and analyze data on distributed storage and computing systems. Efficient data management across these heterogeneous systems requires a workflow management system to simplify the task of analysis through automation and make large-scale bioinformatics analyses accessible and reproducible.ResultsWe developed SciApps, a web-based platform for reproducible bioinformatics workflows. The platform is designed to automate the execution of modular Agave apps and support execution of workflows on local clusters or in a cloud. Two workflows, one for association and one for annotation, are provided as exemplar scientific use cases.Availability and implementation https://www.sciapps.org Supplementary information Supplementary data are available at Bioinformatics online.

Sensing the cilium, digital capture of ciliary data for comparative genomics investigations

BackgroundCilia are specialized, hair-like structures that project from the cell bodies of eukaryotic cells. With increased understanding of the distribution and functions of various types of cilia, interest in these organelles is accelerating. To effectively use this great expansion in knowledge, this information must be made digitally accessible and available for large-scale analytical and computational investigation. Capture and integration of knowledge about cilia into existing knowledge bases, thus providing the ability to improve comparative genomic data analysis, is the objective of this work.MethodsWe focused on the capture of information about cilia as studied in the laboratory mouse, a primary model of human biology. The workflow developed establishes a standard for capture of comparative functional data relevant to human biology. We established the 310 closest mouse orthologs of the 302 human genes defined in the SYSCILIA Gold Standard set of ciliary genes. For the mouse genes, we identified biomedical literature for curation and used Gene Ontology (GO) curation paradigms to provide functional annotations from these publications.ResultsEmploying a methodology for comprehensive capture of experimental data about cilia genes in structured, digital form, we established a workflow for curation of experimental literature detailing molecular function and roles of cilia proteins starting with the mouse orthologs of the human SYSCILIA gene set. We worked closely with the GO Consortium ontology development editors and the SYSCILIA Consortium to improve the representation of ciliary biology within the GO. During the time frame of the ontology improvement project, we have fully curated 134 of these 310 mouse genes, resulting in an increase in the number of ciliary and other experimental annotations.ConclusionsWe have improved the GO annotations available for mouse genes orthologous to the human genes in the SYSCILIA Consortium’s Gold Standard set. In addition, ciliary terminology in the GO itself was improved in collaboration with GO ontology developers and the SYSCILIA Consortium. These improvements to the GO terms for the functions and roles of ciliary proteins, along with the increase in annotations of the corresponding genes, enhance the representation of ciliary processes and localizations and improve access to these data during large-scale bioinformatic analyses.

Functional Characterization of Non-coding RNAs Through Genomic Data Fusion.

Acting as a class of regulators of gene expression, miRNAs play crucial roles in various biological processes, such as cell proliferation, differentiation, and apoptosis. Increasing evidence indicates that dysregulation of miRNA expression and function is related to the pathogenesis of many human diseases. Here, we applied an integration strategy to identify conserved miRNA co-expression relationships and constructed a miRNA co-expression network based on human and mouse miRNA expression data. We performed large-scale bioinformatics analyses of conserved miRNA co-expression relationships and their functional links and confirmed that these conserved co-expressed miRNA relationships in the network tend to be functionally relevant. Co-expressed miRNA pairs regulated by common TFs are significantly enriched within the same miRNA clusters and/or miRNA families. Mapping well-known disease miRNAs to the network, we identified three miRNA sub-networks that are highly related to cancer risk. Furthermore, we observed that these conserved co-expressed miRNA sub-networks cooperatively regulate cancer-related functions through synergistically repressing crucial components of these processes. Our results suggest that co-expressed miRNAs assist to drive the initiation and progression of cancer in a cooperative manner.