A systems biology approach to the study of cisplatin drug resistance in ovarian cancers

PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis

Background: Understanding how cells and tissues respond to stress factors and perturbations during disease processes is crucial for developing effective prevention, diagnosis, and treatment strategies. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cells and exploration of cell heterogeneity, shedding light on cell differentiation/maturation and functional differences. Recent advancements in multimodal sequencing technologies have focused on improving access to cell-specific subgroups for functional genomics analysis. To facilitate the functional annotation of cell groups and characterization of molecular mechanisms underlying cell trajectories, we introduce the Pathways, Annotated Gene Lists, and Gene Signatures Electronic Repository for Single-Cell Functional Genomics Analysis (PAGER-scFGA). Results: We have developed PAGER-scFGA, which integrates cell functional annotations and gene-set enrichment analysis into popular single-cell analysis pipelines such as Scanpy. Using differentially expressed genes (DEGs) from pairwise cell clusters, PAGER-scFGA infers cell functions through the enrichment of potential cell-marker genesets. Moreover, PAGER-scFGA provides pathways, annotated gene lists, and gene signatures (PAGs) enriched in specific cell subsets with tissue compositions and continuous transitions along cell trajectories. Additionally, PAGER-scFGA enables the construction of a gene subcellular map based on DEGs and allows examination of the gene functional compartments (GFCs) underlying cell maturation/differentiation. In a real-world case study of mouse natural killer (mNK) cells, PAGER-scFGA revealed two major stages of natural killer (NK) cells and three trajectories from the precursor stage to NK T-like mature stage within blood, spleen, and bone marrow tissues. As the trajectories progress to later stages, the DEGs exhibit greater divergence and variability. However, the DEGs in different trajectories still interact within a network during NK cell maturation. Notably, PAGER-scFGA unveiled cell cytotoxicity, exocytosis, and the response to interleukin (IL) signaling pathways and associated network models during the progression from precursor NK cells to mature NK cells. Conclusion: PAGER-scFGA enables in-depth exploration of functional insights and presents a comprehensive knowledge map of gene networks and GFCs, which can be utilized for future studies and hypothesis generation. It is expected to become an indispensable tool for inferring cell functions and detecting molecular mechanisms within cell trajectories in single-cell studies. The web app (accessible at https://au-singlecell.streamlit.app/) is publicly available.

DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features

SUMMARY

In biology, graph layout algorithms can reveal comprehensive biological contexts by visually positioning graph nodes in their relevant neighborhoods. A layout software algorithm/engine commonly takes a set of nodes and edges and produces layout coordinates of nodes according to edge constraints. However, current layout engines normally do not consider node, edge or node-set properties during layout and only curate these properties after the layout is created. Here, we propose a new layout algorithm, distance-bounded energy-field minimization algorithm (DEMA), to natively consider various biological factors, i.e., the strength of gene-to-gene association, the gene's relative contribution weight and the functional groups of genes, to enhance the interpretation of complex network graphs. In DEMA, we introduce a parameterized energy model where nodes are repelled by the network topology and attracted by a few biological factors, i.e., interaction coefficient, effect coefficient and fold change of gene expression. We generalize these factors as gene weights, protein-protein interaction weights, gene-to-gene correlations and the gene set annotations-four parameterized functional properties used in DEMA. Moreover, DEMA considers further attraction/repulsion/grouping coefficient to enable different preferences in generating network views. Applying DEMA, we performed two case studies using genetic data in autism spectrum disorder and Alzheimer's disease, respectively, for gene candidate discovery. Furthermore, we implement our algorithm as a plugin to Cytoscape, an open-source software platform for visualizing networks; hence, it is convenient. Our software and demo can be freely accessed at http://discovery.informatics.uab.edu/dema.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PAGER Web APP: An Interactive, Online Gene Set and Network Interpretation Tool for Functional Genomics

Functional genomics studies have helped researchers annotate differentially expressed gene lists, extract gene expression signatures, and identify biological pathways from omics profiling experiments conducted on biological samples. The current geneset, network, and pathway analysis (GNPA) web servers, e.g., DAVID, EnrichR, WebGestaltR, or PAGER, do not allow automated integrative functional genomic downstream analysis. In this study, we developed a new web-based interactive application, "PAGER Web APP", which supports online R scripting of integrative GNPA. In a case study of melanoma drug resistance, we showed that the new PAGER Web APP enabled us to discover highly relevant pathways and network modules, leading to novel biological insights. We also compared PAGER Web APP's pathway analysis results retrieved among PAGER, EnrichR, and WebGestaltR to show its advantages in integrative GNPA. The interactive online web APP is publicly accessible from the link, https://aimed-lab.shinyapps.io/PAGERwebapp/.

Network-based expression analysis reveals key genes related to glucocorticoid resistance in infant acute lymphoblastic leukemia

PURPOSE

Despite vast improvements that have been made in the treatment of children with acute lymphoblastic leukemia (ALL), the majority of infant ALL patients (~80 %, < 1 year of age) that carry a chromosomal translocation involving the mixed lineage leukemia (MLL) gene shows a poor response to chemotherapeutic drugs, especially glucocorticoids (GCs), which are essential components of all current treatment regimens. Although addressed in several studies, the mechanism(s) underlying this phenomenon have remained largely unknown. A major drawback of most previous studies is their primary focus on individual genes, thereby neglecting the putative significance of inter-gene correlations. Here, we aimed at studying GC resistance in MLL-rearranged infant ALL patients by inferring an associated module of genes using co-expression network analysis. The implications of newly identified candidate genes with associations to other well-known relevant genes from the same module, or with associations to known transcription factor or microRNA interactions, were substantiated using literature data.

METHODS

A weighted gene co-expression network was constructed to identify gene modules associated with GC resistance in MLL-rearranged infant ALL patients. Significant gene ontology (GO) terms and signaling pathways enriched in relevant modules were used to provide guidance towards which module(s) consisted of promising candidates suitable for further analysis.

RESULTS

Through gene co-expression network analysis a novel set of genes (module) related to GC-resistance was identified. The presence in this module of the S100 and ANXA genes, both well-known biomarkers for GC resistance in MLL-rearranged infant ALL, supports its validity. Subsequent gene set net correlation analyses of the novel module provided further support for its validity by showing that the S100 and ANXA genes act as 'hub' genes with potentially major regulatory roles in GC sensitivity, but having lost this role in the GC resistant phenotype. The detected module implicates new genes as being candidates for further analysis through associations with known GC resistance-related genes.

CONCLUSIONS

From our data we conclude that available systems biology approaches can be employed to detect new candidate genes that may provide further insights into drug resistance of MLL-rearranged infant ALL cases. Such approaches complement conventional gene-wise approaches by taking putative functional interactions between genes into account.

Pathway and network analysis in proteomics

Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics.

Ethics and Privacy Considerations for Systems Biology Applications in Predictive and Personalized Medicine

Integrative analysis and modeling of the omics data using systems biology have led to growing interests in the development of predictive and personalized medicine. Personalized medicine enables future physicians to prescribe the right drug to the right patient at the right dosage, by helping them link each patient’s genotype to their specific disease conditions. This chapter shares technological, ethical, and social perspectives on emerging personalized medicine applications. First, it examines the history and research trends of pharmacogenomics, systems biology, and personalized medicine. Next, it presents bioethical concerns that arise from dealing with the increasing accumulation of biological samples in many biobanking projects today. Lastly, the chapter describes growing concerns over patient privacy when large amount of individuals’ genetic data and clinical data are managed electronically and accessible online.

A new approach to construct pathway connected networks and its application in dose responsive gene expression profiles of rat liver regulated by 2,4DNT

Abstract

Background

Military and industrial activities have lead to reported release of 2,4-dinitrotoluene (2,4DNT) into soil, groundwater or surface water. It has been reported that 2,4DNT can induce toxic effects on humans and other organisms. However the mechanism of 2,4DNT induced toxicity is still unclear. Although a series of methods for gene network construction have been developed, few instances of applying such technology to generate pathway connected networks have been reported.

Results

Microarray analyses were conducted using liver tissue of rats collected 24h after exposure to a single oral gavage with one of five concentrations of 2,4DNT. We observed a strong dose response of differentially expressed genes after 2,4DNT treatment. The most affected pathways included: long term depression, breast cancer regulation by stathmin1, WNT Signaling; and PI3K signaling pathways. In addition, we propose a new approach to construct pathway connected networks regulated by 2,4DNT. We also observed clear dose response pathway networks regulated by 2,4DNT.

Conclusions

We developed a new method for constructing pathway connected networks. This new method was successfully applied to microarray data from liver tissue of 2,4DNT exposed animals and resulted in the identification of unique dose responsive biomarkers in regards to affected pathways.

Discovery of pathway biomarkers from coupled proteomics and systems biology methods

Background

Breast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.

Results

In this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 “activated” plasma proteins were present in several cancer pathways, including ‘Complement and coagulation cascades’, ‘Regulation of actin cytoskeleton’, and ‘Focal adhesion’, and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.

Conclusions

We presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.

Unraveling human complexity and disease with systems biology and personalized medicine

We are all perplexed that current medical practice often appears maladroit in curing our individual illnesses or disease. However, as is often the case, a lack of understanding, tools and technologies are the root cause of such situations. Human individuality is an often-quoted term but, in the context of human biology, it is poorly understood. This is compounded when there is a need to consider the variability of human populations. In the case of the former, it is possible to quantify human complexity as determined by the 35,000 genes of the human genome, the 1-10 million proteins (including antibodies) and the 2000-3000 metabolites of the human metabolome. Human variability is much more difficult to assess, since many of the variables, such as the definition of race, are not even clearly agreed on. In order to accommodate human complexity, variability and its influence on health and disease, it is necessary to undertake a systematic approach. In the past decade, the emergence of analytical platforms and bioinformatics tools has led to the development of systems biology. Such an approach offers enormous potential in defining key pathways and networks involved in optimal human health, as well as disease onset, progression and treatment. The tools and technologies now available in systems biology analyses offer exciting opportunities to exploit the emerging areas of personalized medicine. In this article, we discuss the current status of human complexity, and how systems biology and personalized medicine can impact at the individual and population level.

HAPPI: an online database of comprehensive human annotated and predicted protein interactions

Background

Human protein-protein interaction (PPIs) data are the foundation for understanding molecular signalling networks and the functional roles of biomolecules. Several human PPI databases have become available; however, comparisons of these datasets have suggested limited data coverage and poor data quality. Ongoing collection and integration of human PPIs from different sources, both experimentally and computationally, can enable disease-specific network biology modelling in translational bioinformatics studies.

Results

We developed a new web-based resource, the Human Annotated and Predicted Protein Interaction (HAPPI) database, located at . The HAPPI database was created by extracting and integrating publicly available protein interaction databases, including HPRD, BIND, MINT, STRING, and OPHID, using database integration techniques. We designed a unified entity-relationship data model to resolve semantic level differences of diverse concepts involved in PPI data integration. We applied a unified scoring model to give each PPI a measure of its reliability that can place each PPI at one of the five star rank levels from 1 to 5. We assessed the quality of PPIs contained in the new HAPPI database, using evolutionary conserved co-expression pairs called "MetaGene" pairs to measure the extent of MetaGene pair and PPI pair overlaps. While the overall quality of the HAPPI database across all star ranks is comparable to the overall qualities of HPRD or IntNetDB, the subset of the HAPPI database with star ranks between 3 and 5 has a much higher average quality than all other human PPI databases. As of summer 2008, the database contains 142,956 non-redundant, medium to high-confidence level human protein interaction pairs among 10,592 human proteins. The HAPPI database web application also provides …” should be “The HAPPI database web application also provides hyperlinked information of genes, pathways, protein domains, protein structure displays, and sequence feature maps for interactive exploration of PPI data in the database.

Conclusion

HAPPI is by far the most comprehensive public compilation of human protein interaction information. It enables its users to fully explore PPI data with quality measures and annotated information necessary for emerging network biology studies.

Sensitization of squamous cell carcinoma to cisplatin induced killing by natural agents

Cisplatin resistance is a major problem in the successful treatment of squamous cell carcinoma (SCC). In the present study we showed, for the first time, that the constitutive activation of NF-kappaB partly contributes to cisplatin resistance and that the inactivation of NF-kappaB by natural agents [G2535 (isoflavone mixture containing genistein and diadzein), 3,3'-diindolylmethane (Bioresponse BR-DIM referred to as B-DIM)] could overcome this resistance, resulting in the inhibition of cell growth and induction of apoptosis, which might be an useful strategy for achieving better treatment outcome in patients diagnosed with cisplatin-resistant tumors of SCC.

Semantic web for integrated network analysis in biomedicine

The Semantic Web technology enables integration of heterogeneous data on the World Wide Web by making the semantics of data explicit through formal ontologies. In this article, we survey the feasibility and state of the art of utilizing the Semantic Web technology to represent, integrate and analyze the knowledge in various biomedical networks. We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herb-drug interactions analysis.

Matrix metalloproteinase-7 and -13 expression associate to cisplatin resistance in head and neck cancer cell lines

Concomitant chemoradiotherapy is a common treatment for advanced head and neck squamous cell carcinomas (HNSCC). Cisplatin is the backbone of chemotherapy regimens used to treat HNSCC. Therefore, the aim of this study was to identify predictive markers for cisplatin treatment outcome in HNSCC. The intrinsic cisplatin sensitivity (ICS) was determined in a panel of tumour cell lines. From this panel, one sensitive and two resistant cell lines were selected for comparative transcript profiling using microarray analysis. The enrichment of Gene Ontology (GO) categories in sensitive versus resistant cell lines were assessed using the Gene Ontology Tree Machine bioinformatics tool. In total, 781 transcripts were found to be differentially expressed and 11 GO categories were enriched. Transcripts contributing to this enrichment were further analyzed using Ingenuity Pathway Analysis (IPA) for identification of key regulator genes. IPA recognized 20 key regulator genes of which five were differentially expressed in sensitive versus resistant cell lines. The mRNA level of these five genes was further assessed in a panel of 25 HNSCC cell lines using quantitative real-time PCR. Among these key regulators, MMP-7 and MMP-13 are implicated as potential biomarkers of ICS. Taken together, genome-wide transcriptional analysis identified single genes, GO categories as well as molecular networks that are differentially expressed in HNSCC cell lines with different ICS. Furthermore, two novel predictive biomarkers for cisplatin resistance, MMP-7 and MMP-13, were identified.

Proteome analysis of multidrug resistance of human oral squamous carcinoma cells using CD147 silencing

There is a correlation between the multidrug-resistance (MDR) of cancer cells and their enhanced invasive or metastatic potential. We studied the expression of CD147, a plasma membrane glycoprotein that plays a key role in tumor metastasis by stimulating the production of matrix metalloproteinases (MMPs), in sensitive human oral squamous KB and MDR derivative KB/V cells. Reverse transcription-PCR and flow cytometric analysis revealed that KB/V cells expressed CD147 at significantly higher levels than their parental KB cells. Using stable RNA interference, we succeeded in establishing a CD147 knock-down KB/V cell line (KB/VsiCD147). MTT colorimetric assay showed an increase in the chemosensitivity to vincristine (VCR), all transretinoic acid (ATRA), taxol, and 5-fluorouracil (5-Fu) of KB/VsiCD147 cells. Proteome analysis of KB, KB/V, and KB/VsiCD147 cell lines identified 21 differently expressed proteins. The enhanced expression of representative active proteins, GRP75 and CyPA, was confirmed by Western blotting and RT-PCR. In addition, pretreatment of KB/V cells with a CyPA-binding immunosuppressive drug, cyclosporine A (CsA), enhanced their chemosensitivity to VCR and 5-Fu. We document an abundance of molecules that interact with CD147 in the MDR of human oral squamous carcinoma cells. Additional studies are needed to investigate these novel target proteins of CD147.

Predicting antibiotic resistance

The treatment of bacterial infections is increasingly complicated because microorganisms can develop resistance to antimicrobial agents. This article discusses the information that is required to predict when antibiotic resistance is likely to emerge in a bacterial population. Indeed, the development of the conceptual and methodological tools required for this type of prediction represents an important goal for microbiological research. To this end, we propose the establishment of methodological guidelines that will allow researchers to predict the emergence of resistance to a new antibiotic before its clinical introduction.