Methods and approaches in the topology-based analysis of biological pathways

Neurodegenerative disease pathways are perturbed in patients with cancer who self-report cognitive changes and anxiety: A pathway impact analysis

BACKGROUND

Cancer-related cognitive impairment (CRCI) and anxiety co-occur in patients with cancer. Little is known about mechanisms for the co-occurrence of these two symptoms. The purposes of this secondary analysis were to evaluate for perturbed pathways associated with the co-occurrence of self-reported CRCI and anxiety in patients with low versus high levels of these two symptoms and to identify potential mechanisms for the co-occurrence of CRCI and anxiety using biological processes common across any perturbed neurodegenerative disease pathways.

METHODS

Patients completed the Attentional Function Index and the Spielberger State-Trait Anxiety Inventory six times over two cycles of chemotherapy. Based on findings from a previous latent profile analysis, patients were grouped into none versus both high levels of these symptoms. Gene expression was quantified, and pathway impact analyses were performed. Signaling pathways for evaluation were defined with the Kyoto Encyclopedia of Genes and Genomes database.

RESULTS

A total of 451 patients had data available for analysis. Approximately 85.0% of patients were in the none class and 15.0% were in the both high class. Pathway impact analyses identified five perturbed pathways related to neurodegenerative diseases (i.e., amyotrophic lateral sclerosis, Huntington disease, Parkinson disease, prion disease, and pathways of neurodegeneration-multiple diseases). Apoptosis, mitochondrial dysfunction, oxidative stress, and endoplasmic reticulum stress were common biological processes across these pathways.

CONCLUSIONS

This study is the first to describe perturbations in neurodegenerative disease pathways associated with CRCI and anxiety in patients receiving chemotherapy. These findings provide new insights into potential targets for the development of mechanistically based interventions.

Estimating Enzyme Expression and Metabolic Pathway Activity in Borreliella-Infected and Uninfected Mice

Evaluating changes in metabolic pathway activity is essential for studying disease mechanisms and developing new treatments, with significant benefits extending to human health. Here, we propose EMPathways2, a maximum likelihood pipeline that is based on the expectation-maximization algorithm, which is capable of evaluating enzyme expression and metabolic pathway activity level. We first estimate enzyme expression from RNA-seq data that is used for simultaneous estimation of pathway activity levels using enzyme participation levels in each pathway. We implement the novel pipeline to RNA-seq data from several groups of mice, which provides a deeper look at the biochemical changes occurring as a result of bacterial infection, disease, and immune response. Our results show that estimated enzyme expression, pathway activity levels, and enzyme participation levels in each pathway are robust and stable across all samples. Estimated activity levels of a significant number of metabolic pathways strongly correlate with the infected and uninfected status of the respective rodent types.

RCPA: An Open-Source R Package for Data Processing, Differential Analysis, Consensus Pathway Analysis, and Visualization

Identifying impacted pathways is important because it provides insights into the biology underlying conditions beyond the detection of differentially expressed genes. Because of the importance of such analysis, more than 100 pathway analysis methods have been developed thus far. Despite the availability of many methods, it is challenging for biomedical researchers to learn and properly perform pathway analysis. First, the sheer number of methods makes it challenging to learn and choose the correct method for a given experiment. Second, computational methods require users to be savvy with coding syntax, and comfortable with command-line environments, areas that are unfamiliar to most life scientists. Third, as learning tools and computational methods are typically implemented only for a few species (i.e., human and some model organisms), it is difficult to perform pathway analysis on other species that are not included in many of the current pathway analysis tools. Finally, existing pathway tools do not allow researchers to combine, compare, and contrast the results of different methods and experiments for both hypothesis testing and analysis purposes. To address these challenges, we developed an open-source R package for Consensus Pathway Analysis (RCPA) that allows researchers to conveniently: (1) download and process data from NCBI GEO; (2) perform differential analysis using established techniques developed for both microarray and sequencing data; (3) perform both gene set enrichment, as well as topology-based pathway analysis using different methods that seek to answer different research hypotheses; (4) combine methods and datasets to find consensus results; and (5) visualize analysis results and explore significantly impacted pathways across multiple analyses. This protocol provides many example code snippets with detailed explanations and supports the analysis of more than 1000 species, two pathway databases, three differential analysis techniques, eight pathway analysis tools, six meta-analysis methods, and two consensus analysis techniques. The package is freely available on the CRAN repository. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing Affymetrix microarrays Basic Protocol 2: Processing Agilent microarrays Support Protocol: Processing RNA sequencing (RNA-Seq) data Basic Protocol 3: Differential analysis of microarray data (Affymetrix and Agilent) Basic Protocol 4: Differential analysis of RNA-Seq data Basic Protocol 5: Gene set enrichment analysis Basic Protocol 6: Topology-based (TB) pathway analysis Basic Protocol 7: Data integration and visualization.

Adult Human, but Not Rodent, Spermatogonial Stem Cells Retain States with a Foetal-like Signature

Spermatogenesis involves a complex process of cellular differentiation maintained by spermatogonial stem cells (SSCs). Being critical to male reproduction, it is generally assumed that spermatogenesis starts and ends in equivalent transcriptional states in related species. Based on single-cell gene expression profiling, it has been proposed that undifferentiated human spermatogonia can be subclassified into four heterogenous subtypes, termed states 0, 0A, 0B, and 1. To increase the resolution of the undifferentiated compartment and trace the origin of the spermatogenic trajectory, we re-analysed the single-cell (sc) RNA-sequencing libraries of 34 post-pubescent human testes to generate an integrated atlas of germ cell differentiation. We then used this atlas to perform comparative analyses of the putative SSC transcriptome both across human development (using 28 foetal and pre-pubertal scRNA-seq libraries) and across species (including data from sheep, pig, buffalo, rhesus and cynomolgus macaque, rat, and mouse). Alongside its detailed characterisation, we show that the transcriptional heterogeneity of the undifferentiated spermatogonial cell compartment varies not only between species but across development. Our findings associate 'state 0B' with a suppressive transcriptomic programme that, in adult humans, acts to functionally oppose proliferation and maintain cells in a ready-to-react state. Consistent with this conclusion, we show that human foetal germ cells-which are mitotically arrested-can be characterised solely as state 0B. While germ cells with a state 0B signature are also present in foetal mice (and are likely conserved at this stage throughout mammals), they are not maintained into adulthood. We conjecture that in rodents, the foetal-like state 0B differentiates at birth into the renewing SSC population, whereas in humans it is maintained as a reserve population, supporting testicular homeostasis over a longer reproductive lifespan while reducing mutagenic load. Together, these results suggest that SSCs adopt differing evolutionary strategies across species to ensure fertility and genome integrity over vastly differing life histories and reproductive timeframes.

Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.

Robustness evaluations of pathway activity inference methods on gene expression data

BACKGROUND

With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches.

RESULTS

Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets.

CONCLUSION

However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Anxiety in oncology outpatients is associated with perturbations in pathways identified in anxiety focused network pharmacology research

PURPOSE

Evaluate for perturbed signaling pathways associated with subgroups of patients with low versus high levels of state anxiety. These pathways were compared to the pathways identified across eight network pharmacology studies of the anxiolytic effect(s) of a variety of compounds.

METHODS

Adult outpatients had a diagnosis of breast, gastrointestinal, gynecological, or lung cancer; had received chemotherapy within the preceding four weeks; and were scheduled to receive at least two additional cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct anxiety profiles based on Spielberger State Anxiety Inventory scores that were obtained six times over two cycles of chemotherapy. Blood samples were processed using RNA sequencing (i.e., RNA-seq sample, n = 244) and microarray (i.e., microarray sample; n = 256) technologies. Pathway perturbations were assessed using pathway impact analysis. Fisher's combined probability method was used to combine test results using a false discovery rate of 0.01.

RESULTS

In the RNA-seq sample, 62.3% and 37.7% of the patients were in the low- and high-anxiety classes, respectively. In the microarray sample, 61.3% and 38.7% were in the low and high-anxiety classes, respectively. Forty-one perturbed signaling pathways were identified. Eight of these pathways were common to those identified in the network pharmacology studies.

CONCLUSIONS

Findings increase our knowledge of the molecular mechanisms that underlie anxiety in patients receiving chemotherapy. This study provides initial insights into how anxiety in patients with cancer may share common mechanisms with anxiety in patients with other clinical conditions.

A novel approach for predicting upstream regulators (PURE) that affect gene expression

External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely important in many studies. Furthermore, the ability to correctly infer CDTs that can revert the gene expression changes induced by a given disease phenotype is a crucial step in drug repurposing. We present an approach for Predicting Upstream REgulators (PURE) designed to tackle this challenge. PURE can correctly infer a CDT from the measured expression changes in a given phenotype, as well as correctly identify drugs that could revert disease-induced gene expression changes. We compared the proposed approach with four classical approaches as well as with the causal analysis used in Ingenuity Pathway Analysis (IPA) on 16 data sets (1 rat, 5 mouse, and 10 human data sets), involving 8 chemicals or drugs. We assessed the results based on the ability to correctly identify the CDT as indicated by its rank. We also considered the number of false positives, i.e. CDTs other than the correct CDT that were reported to be significant by each method. The proposed approach performed best in 11 out of the 16 experiments, reporting the correct CDT at the very top 7 times. IPA was the second best, reporting the correct CDT at the top 5 times, but was unable to identify the correct CDT at all in 5 out of the 16 experiments. The validation results showed that our approach, PURE, outperformed some of the most popular methods in the field. PURE could effectively infer the true CDTs responsible for the observed gene expression changes and could also be useful in drug repurposing applications.

STARGATE-X: a Python package for statistical analysis on the REACTOME network

Many important aspects of biological knowledge at the molecular level can be represented by pathways. Through their analysis, we gain mechanistic insights and interpret lists of interesting genes from experiments (usually omics and functional genomic experiments). As a result, pathways play a central role in the development of bioinformatics methods and tools for computing predictions from known molecular-level mechanisms. Qualitative as well as quantitative knowledge about pathways can be effectively represented through biochemical networks linking the biochemical reactions and the compounds (e.g., proteins) occurring in the considered pathways. So, repositories providing biochemical networks for known pathways play a central role in bioinformatics and in systems biology. Here we focus on Reactome, a free, comprehensive, and widely used repository for biochemical networks and pathways. In this paper, we: (1) introduce a tool StARGate-X (STatistical Analysis of the Reactome multi-GrAph Through nEtworkX) to carry out an automated analysis of the connectivity properties of Reactome biochemical reaction network and of its biological hierarchy (i.e., cell compartments, namely, the closed parts within the cytosol, usually surrounded by a membrane); the code is freely available at https://github.com/marinoandrea/stargate-x; (2) show the effectiveness of our tool by providing an analysis of the Reactome network, in terms of centrality measures, with respect to in- and out-degree. As an example of usage of StARGate-X, we provide a detailed automated analysis of the Reactome network, in terms of centrality measures. We focus both on the subgraphs induced by single compartments and on the graph whose nodes are the strongly connected components. To the best of our knowledge, this is the first freely available tool that enables automatic analysis of the large biochemical network within Reactome through easy-to-use APIs (Application Programming Interfaces).

Metabolomics of asthma, COPD, and asthma-COPD overlap: an overview

The two common progressive lung diseases, asthma and chronic obstructive pulmonary disease (COPD), are the leading causes of morbidity and mortality worldwide. Asthma-COPD overlap, referred to as ACO, is another complex pulmonary disease that manifests itself with features of both asthma and COPD. The disease has no clear diagnostic or therapeutic guidelines, thereby making both diagnosis and treatment challenging. Though a number of studies on ACO have been documented, gaps in knowledge regarding the pathophysiologic mechanism of this disorder exist. Addressing this issue is an urgent need for improved diagnostic and therapeutic management of the disease. Metabolomics, an increasingly popular technique, reveals the pathogenesis of complex diseases and holds promise in biomarker discovery. This comprehensive narrative review, comprising 99 original research articles in the last five years (2017-2022), summarizes the scientific advances in terms of metabolic alterations in patients with asthma, COPD, and ACO. The analytical tools, nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS), commonly used to study the expression of the metabolome, are discussed. Challenges frequently encountered during metabolite identification and quality assessment are highlighted. Bridging the gap between phenotype and metabotype is envisioned in the future.

MAPKAPK2-centric transcriptome profiling reveals its major role in governing molecular crosstalk of IGFBP2, MUC4, and PRKAR2B during HNSCC pathogenesis

Transcriptome analysis of head and neck squamous cell carcinoma (HNSCC) has been pivotal to comprehending the convoluted biology of HNSCC tumors. MAPKAPK2 or MK2 is a critical modulator of the mRNA turnover of crucial genes involved in HNSCC progression. However, MK2-centric transcriptome profiles of tumors are not well known. This study delves into HNSCC progression with MK2 at the nexus to delineate the biological relevance and intricate crosstalk of MK2 in the tumor milieu. We performed next-generation sequencing-based transcriptome profiling of HNSCC cells and xenograft tumors to ascertain mRNA expression profiles in MK2-wild type and MK2-knockdown conditions. The findings were validated using gene expression assays, immunohistochemistry, and transcript turnover studies. Here, we identified a pool of crucial MK2-regulated candidate genes by annotation and differential gene expression analyses. Regulatory network and pathway enrichment revealed their significance and involvement in the HNSCC pathogenesis. Additionally, 3'-UTR-based filtering recognized important MK2-regulated downstream target genes and validated them by nCounter gene expression assays. Finally, immunohistochemistry and transcript stability studies revealed the putative role of MK2 in regulating the transcript turnover of IGFBP2, MUC4, and PRKAR2B in HNSCC. Conclusively, MK2-regulated candidate genes were identified in this study, and their plausible involvement in HNSCC pathogenesis was elucidated. These genes possess investigative values as targets for diagnosis and therapeutic interventions for HNSCC.

Perturbations in Neuroinflammatory Pathways Are Associated With a Worst Pain Profile in Oncology Patients Receiving Chemotherapy

Unrelieved pain occurs in 55% of cancer patients. Identification of molecular mechanisms for pain may provide insights into therapeutic targets. Purpose was to evaluate for perturbations in neuroinflammatory pathways between oncology patients with and without severe pain. Worst pain severity was rated using a 0 to 10 numeric rating scale six times over two cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct pain profiles. Pathway impact analyses were performed in two independent samples using gene expression data obtained from RNA sequencing (n = 192) and microarray (n = 197) technologies. Fisher's combined probability test was used to identify significantly perturbed pathways between None versus the Severe pain classes. In the RNA sequencing and microarray samples, 62.5% and 56.3% of patients were in the Severe pain class, respectively. Nine perturbed pathways were related to neuroinflammatory mechanisms (i.e., retrograde endocannabinoid signaling, gamma-aminobutyric acid synapse, glutamatergic synapse, Janus kinase-signal transducer and activator of transcription signaling, phagosome, complement and coagulation cascades, cytokine-cytokine receptor interaction, chemokine signaling, calcium signaling). First study to identify perturbations in neuroinflammatory pathways associated with severe pain in oncology outpatients. Findings suggest that complex neuroimmune interactions are involved in the maintenance of chronic pain conditions. Perspective: In this study that compared oncology patients with none versus severe pain, nine perturbed neuroinflammatory pathways were identified. Findings suggest that complex neuroimmune interactions are involved in the maintenance of persistent pain conditions.

Sleep disturbance is associated with perturbations in immune-inflammatory pathways in oncology outpatients undergoing chemotherapy

OBJECTIVE/BACKGROUND

Sleep disturbance is a common problem in patients receiving chemotherapy. Purpose was to evaluate for perturbations in immune-inflammatory pathways between oncology patients with low versus very high levels of sleep disturbance.

PATIENTS/METHODS

Sleep disturbance was evaluated using the General Sleep Disturbance Scale six times over two cycles of chemotherapy. Latent profile analysis was used to identify subgroups of patients with distinct sleep disturbance profiles. Pathway impact analyses were performed in two independent samples using gene expression data obtained from RNA sequencing (n = 198) and microarray (n = 162) technologies. Fisher's combined probability test was used to identify significantly perturbed pathways between Low versus Very High sleep disturbance classes.

RESULTS

In the RNA sequencing and microarray samples, 59.1% and 51.9% of patients were in the Very High sleep disturbance class, respectively. Thirteen perturbed pathways were related to immune-inflammatory mechanisms (i.e., endocytosis, phagosome, antigen processing and presentation, natural killer cell mediated cytotoxicity, cytokine-cytokine receptor interaction, apoptosis, neutrophil extracellular trap formation, nucleotide-binding and oligomerization domain-like receptor signaling, Th17 cell differentiation, intestinal immune network for immunoglobulin A production, T-cell receptor signaling, complement and coagulation cascades, and tumor necrosis factor signaling).

CONCLUSIONS

First study to identify perturbations in immune-inflammatory pathways associated with very high levels of sleep disturbance in oncology outpatients. Findings suggest that complex immune-inflammatory interactions underlie sleep disturbance.

SEMgsa: topology-based pathway enrichment analysis with structural equation models

Background

Pathway enrichment analysis is extensively used in high-throughput experimental studies to gain insight into the functional roles of pre-defined subsets of genes, proteins and metabolites. Methods that leverages information on the topology of the underlying pathways outperform simpler methods that only consider pathway membership, leading to improved performance. Among all the proposed software tools, there’s the need to combine high statistical power together with a user-friendly framework, making it difficult to choose the best method for a particular experimental environment.

Results

We propose SEMgsa, a topology-based algorithm developed into the framework of structural equation models. SEMgsa combine the SEM p values regarding node-specific group effect estimates in terms of activation or inhibition, after statistically controlling biological relations among genes within pathways. We used SEMgsa to identify biologically relevant results in a Coronavirus disease (COVID-19) RNA-seq dataset (GEO accession: GSE172114) together with a frontotemporal dementia (FTD) DNA methylation dataset (GEO accession: GSE53740) and compared its performance with some existing methods. SEMgsa is highly sensitive to the pathways designed for the specific disease, showing low p values (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$< 0.001$$\end{document}<0.001) and ranking in high positions, outperforming existing software tools. Three pathway dysregulation mechanisms were used to generate simulated expression data and evaluate the performance of methods in terms of type I error followed by their statistical power. Simulation results confirm best overall performance of SEMgsa.

Conclusions

SEMgsa is a novel yet powerful method for identifying enrichment with regard to gene expression data. It takes into account topological information and exploits pathway perturbation statistics to reveal biological information. SEMgsa is implemented in the R package SEMgraph, easily available at https://CRAN.R-project.org/package=SEMgraph.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04884-8.

Connecting omics signatures and revealing biological mechanisms with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.

Comparing Bayesian-Based Reconstruction Strategies in Topology-Based Pathway Enrichment Analysis

The development of high-throughput omics technologies has enabled the quantification of vast amounts of genes and gene products in the whole genome. Pathway enrichment analysis (PEA) provides an intuitive solution for extracting biological insights from massive amounts of data. Topology-based pathway analysis (TPA) represents the latest generation of PEA methods, which exploit pathway topology in addition to lists of differentially expressed genes and their expression profiles. A subset of these TPA methods, such as BPA, BNrich, and PROPS, reconstruct pathway structures by training Bayesian networks (BNs) from canonical biological pathways, providing superior representations that explain causal relationships between genes. However, these methods have never been compared for their differences in the PEA and their different topology reconstruction strategies. In this study, we aim to compare the BN reconstruction strategies of the BPA, BNrich, PROPS, Clipper, and Ensemble methods and their PEA and performance on tumor and non-tumor classification based on gene expression data. Our results indicate that they performed equally well in distinguishing tumor and non-tumor samples (AUC > 0.95) yet with a varying ranking of pathways, which can be attributed to the different BN structures resulting from the different cyclic structure removal strategies. This can be clearly seen from the reconstructed JAK-STAT networks by different strategies. In a nutshell, BNrich, which relies on expert intervention to remove loops and cyclic structures, produces BNs that best fit the biological facts. The plausibility of the Clipper strategy can also be partially explained by intuitive biological rules and theorems. Our results may offer an informed reference for the proper method for a given data analysis task.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

Background

With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem.

Results

We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet can also be used to find explicitly multi-omics subgraphs which we demonstrate by presenting subgraphs with consistent methylation-transcription patterns. DeRegNet is freely available as open-source software.

Conclusion

The proposed algorithmic framework and its available implementation can serve as a valuable heuristic hypothesis generation tool contextualizing omics data within biomolecular networks.

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/.

Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase

Abstract

Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we compared predictions of perturbation effects based on Reactome pathways against published empirical observations. Ten cancer-relevant Reactome pathways, representing diverse biological processes such as signal transduction, cell division, DNA repair and transcriptional regulation, were selected for testing. For each pathway, root input nodes and key pathway outputs were defined. We then used pathway-diagram-derived logic graphs to predict, either by inspection by biocurators or using a novel algorithm MP-BioPath, the effects of bidirectional perturbations (upregulation/activation or downregulation/inhibition) of single root inputs on the status of key outputs. These predictions were then compared to published empirical tests. In total, 4968 test cases were analyzed across 10 pathways, of which 847 were supported by published empirical findings. Out of the 847 test cases, curators’ predictions agreed with the experimental evidence in 670 and disagreed in 177 cases, resulting in ∼81% overall accuracy. MP-BioPath predictions agreed with experimental evidence for 625 and disagreed for 222 test cases, resulting in ∼75% overall accuracy. The expected accuracy of random guessing was 33%. Per-pathway accuracy did not correlate with the number of pathway edges nor the number of pathway nodes but varied across pathways, ranging from 56% (curator)/44% (MP-BioPath) for ‘Mitotic G1 phase and G1/S transition’ to 100% (curator)/94% (MP-BioPath) for ‘RAF/MAP kinase cascade’. This study highlights the potential of pathway databases such as Reactome in modeling genetic perturbations, promoting standardization of experimental pathway activity readout and supporting hypothesis-driven research by revealing relationships between pathway inputs and outputs that have not yet been directly experimentally tested.

Database URL

www.reactome.org

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

Functional Enrichment Analysis of Regulatory Elements

Statistical methods for enrichment analysis are important tools to extract biological information from omics experiments. Although these methods have been widely used for the analysis of gene and protein lists, the development of high-throughput technologies for regulatory elements demands dedicated statistical and bioinformatics tools. Here, we present a set of enrichment analysis methods for regulatory elements, including CpG sites, miRNAs, and transcription factors. Statistical significance is determined via a power weighting function for target genes and tested by the Wallenius noncentral hypergeometric distribution model to avoid selection bias. These new methodologies have been applied to the analysis of a set of miRNAs associated with arrhythmia, showing the potential of this tool to extract biological information from a list of regulatory elements. These new methods are available in GeneCodis 4, a web tool able to perform singular and modular enrichment analysis that allows the integration of heterogeneous information.

Differences in peripheral immune system gene expression in frontotemporal degeneration

ABSTRACT

The peripheral immune system has a key pathophysiologic role in Frontotemporal degeneration (FTD). We sought a comprehensive transcriptome-wide evaluation of gene expression alterations unique to the peripheral immune system in FTD compared to healthy controls and amyotrophic lateral sclerosis.Nineteen subjects with FTD with 19 matched healthy controls and 9 subjects with amyotrophic lateral sclerosis underwent isolation of peripheral blood mononuclear cells (PBMCs) which then underwent bulk ribonucleic acid sequencing.There was increased expression in genes associated with CD19+ B-cells, CD4+ T-cells, and CD8+ T-cells in FTD participants compared to healthy controls. In contrast, there was decreased expression in CD33+ myeloid cells, CD14+ monocytes, BDCA4+ dendritic cells, and CD56+ natural killer cells in FTD and healthy controls. Additionally, there was decreased expression is seen in associated with 2 molecular processes: autophagy with phagosomes and lysosomes, and protein processing/export. Significantly downregulated in PBMCs of FTD subjects were genes involved in antigen processing and presentation as well as lysosomal lumen formation compared to healthy control PBMCs.Our findings that the immune signature based on gene expression in PBMCs of FTD participants favors adaptive immune cells compared to innate immune cells. And decreased expression in genes associated with phagosomes and lysosomes in PBMCs of FTD participants compared to healthy controls.

Axl regulated survival/proliferation network and its therapeutic intervention in mouse models of glomerulonephritis

BACKGROUND

Lupus nephritis (LN) is the most common and serious complication of systemic lupus erythematosus (SLE). LN pathogenesis is not fully understood. Axl receptor tyrosine kinase is upregulated and contributes to the pathogenic progress in LN. We have reported that Axl disruption attenuates nephritis development in mice.

METHODS

In this study, we analyzed the gene expression profiles with RNA-seq using renal cortical samples from nephritic mice. Axl-KO mice were bred onto a B6.lpr spontaneous lupus background, and renal disease development was followed and compared to the Axl-sufficient B6.lpr mice. Finally, anti-glomerular basement membrane (anti-GBM) Ab-induced nephritic mice were treated with Axl small molecule inhibitor, R428, at different stages of nephritis development. Blood urine nitrogen levels and renal pathologies were evaluated.

RESULTS

Transcriptome analysis revealed that renal Axl activation contributed to cell proliferation, survival, and motility through regulation of the Akt, c-Jun, and actin pathways. Spontaneous lupus-prone B6.lpr mice with Axl deficiency showed significantly reduced kidney damages and decreased T cell infiltration compared to the renal damage and T cell infiltration in Axl-sufficient B6.lpr mice. The improved kidney function was independent of autoAb production. Moreover, R428 significantly reduced anti-GBM glomerulonephritis at different stages of GN development compared to the untreated nephritic control mice. R428 administration reduced inflammatory cytokine (IL-6) production, T cell infiltration, and nephritis disease activity.

CONCLUSIONS

Results from this study emphasize the important role of Axl signaling in LN and highlight Axl as an attractive target in LN.

Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar's test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.

Cancer-related cognitive impairment is associated with perturbations in inflammatory pathways

Cancer-related cognitive impairment (CRCI) is a significant problem for patients receiving chemotherapy. While a growing amount of pre-clinical and clinical evidence suggests that inflammatory mechanisms underlie CRCI, no clinical studies have evaluated for associations between CRCI and changes in gene expression. Therefore, the purpose of this study was to evaluate for differentially expressed genes and perturbed inflammatory pathways across two independent samples of patients with cancer who did and did not report CRCI. The Attentional Function Index (AFI) was the self-report measure used to assess CRCI. AFI scores of <5 and of >7.5 indicate low versus high levels of cognitive function, respectively. Of the 185 patients in Sample 1, 49.2% had an AFI score of <5 and 50.8% had an AFI score of >7.5. Of the 158 patients in Sample 2, 50.6% had an AFI score of <5 and 49.4% had an AFI score of >7.5. Data from 182 patients in Sample 1 were analyzed using RNA-seq. Data from 158 patients in Sample 2 were analyzed using microarray. Twelve KEGG signaling pathways were significantly perturbed between the AFI groups, five of which were signaling pathways related to inflammatory mechanisms (e.g., cytokine-cytokine receptor interaction, tumor necrosis factor signaling). This study is the first to describe perturbations in inflammatory pathways associated with CRCI. Findings highlight the role of cytokines both in terms of cytokine-specific pathways, as well as pathways involved in cytokine production and cytokine activation. These findings have the potential to identify new targets for therapeutics and lead to the development of interventions to improve cognition in patients with cancer.

Deep Learning Analyses to Delineate the Molecular Remodeling Process after Myocardial Infarction

Specific proteins and processes have been identified in post-myocardial infarction (MI) pathological remodeling, but a comprehensive understanding of the complete molecular evolution is lacking. We generated microarray data from swine heart biopsies at baseline and 6, 30, and 45 days after infarction to feed machine-learning algorithms. We cross-validated the results using available clinical and experimental information. MI progression was accompanied by the regulation of adipogenesis, fatty acid metabolism, and epithelial-mesenchymal transition. The infarct core region was enriched in processes related to muscle contraction and membrane depolarization. Angiogenesis was among the first morphogenic responses detected as being sustained over time, but other processes suggesting post-ischemic recapitulation of embryogenic processes were also observed. Finally, protein-triggering analysis established the key genes mediating each process at each time point, as well as the complete adverse remodeling response. We modeled the behaviors of these genes, generating a description of the integrative mechanism of action for MI progression. This mechanistic analysis overlapped at different time points; the common pathways between the source proteins and cardiac remodeling involved IGF1R, RAF1, KPCA, JUN, and PTN11 as modulators. Thus, our data delineate a structured and comprehensive picture of the molecular remodeling process, identify new potential biomarkers or therapeutic targets, and establish therapeutic windows during disease progression.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.

The therapeutic effects of Prunella vulgaris against fluoride-induced oxidative damage by using the metabolomics method

Fluoride is considered as one of the most ubiquitous environmental pollutants. Numerous studies have linked reactive oxygen species (ROS)-dependent oxidative damage with fluoride intoxication, which could be prevented by antioxidants. However, the metabolomic changes induced by ROS disruptions in fluoride intoxication are yet unknown. The present study aimed to provide novel mechanistic insights into the fluoride-induced oxidative damage and to investigate the potential protective effects of ethanolic extract of Prunella vulgaris (natural antioxidant, PV) against fluoride-induced oxidative damage. The serum biochemical indicators related to fluoride-induced oxidative damage, such as lipid peroxidation parameter, inflammation and marker enzymes in the liver increased significantly in the fluoride-treated group, while antioxidant enzymes were decreased. However, PV treatment restored the level of these biochemical indicators, indicating satisfactory antioxidant, anti-inflammatory, and hepatoprotective potential of PV. The metabolomics analysis in the serum was performed by liquid chromatography-mass spectroscopy, whereas the fluoride treatment caused severe metabolic disorders in rats, which could be improved by PV. The differential metabolites screened by multivariate analysis after fluoride and PV treatment, were organic acids, fatty acids, and lipids. These differential metabolites represented disorders of glyoxylate and dicarboxylate metabolism and the citrate cycle (TCA) according to metabolic pathway analysis in fluoride treatment rats. Interestingly, the result of metabolic pathway analysis of post-treatment with PV was consistent with that of fluoride treatment, indicating that the energy metabolism plays a major role in the progress of fluoride-induced oxidative damage, as well as the therapeutic effect of PV. These findings provided a theoretical basis for understanding the mechanism underlying metabolic disorders of fluoride toxicity and the effect of PV.

CPA: a web-based platform for consensus pathway analysis and interactive visualization

In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.

Transcriptomic Analysis of Peripheral Monocytes upon Fingolimod Treatment in Relapsing Remitting Multiple Sclerosis Patients

Fingolimod (FTY), a second-line oral drug approved for relapsing remitting Multiple Sclerosis (RRMS) acts in preventing lymphocyte migration outside lymph nodes; moreover, several lines of evidence suggest that it also inhibits myeloid cell activation. In this study, we investigated the transcriptional changes induced by FTY in monocytes in order to better elucidate its mechanism of action. CD14⁺ monocytes were collected from 24 RRMS patients sampled at baseline and after 6 months of treatment and RNA profiles were obtained through next-generation sequencing. We conducted pathway and sub-paths analysis, followed by centrality analysis of cell-specific interactomes on differentially expressed genes (DEGs). We investigated also the predictive role of baseline monocyte transcription profile in influencing the response to FTY therapy. We observed a marked down-regulation effect (60 down-regulated vs. 0 up-regulated genes). Most of the down-regulated DEGs resulted related with monocyte activation and migration like IL7R, CCR7 and the Wnt signaling mediators LEF1 and TCF7. The involvement of Wnt signaling was also confirmed by subpaths analyses. Furthermore, pathway and network analyses showed an involvement of processes related to immune function and cell migration. Baseline transcriptional profile of the HLA class II gene HLA-DQA1 and HLA-DPA1 were associated with evidence of disease activity after 2 years of treatment. Our data support the evidence that FTY induces major transcriptional changes in monocytes, mainly regarding genes involved in cell trafficking and immune cell activation. The baseline transcriptional levels of genes associated with antigen presenting function were associated with disease activity after 2 years of FTY treatment.

Popularity and performance of bioinformatics software: the case of gene set analysis

Background

Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. "Popularity" is estimated according to each paper's citation counts, while "performance" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies.

Results

Regarding popularity, data is collected into an online open database ("GSARefDB") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (“GSA-BenchmarKING”). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods.

Conclusions

The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04124-5.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.

SIGNAL: A web-based iterative analysis platform integrating pathway and network approaches optimizes hit selection from genome-scale assays

Hit selection from high-throughput assays remains a critical bottleneck in realizing the potential of omic-scale studies in biology. Widely used methods such as setting of cutoffs, prioritizing pathway enrichments, or incorporating predicted network interactions offer divergent solutions yet are associated with critical analytical trade-offs. The specific limitations of these individual approaches and the lack of a systematic way by which to integrate their rankings have contributed to limited overlap in the reported results from comparable genome-wide studies and costly inefficiencies in secondary validation efforts. Using comparative analysis of parallel independent studies as a benchmark, we characterize the specific complementary contributions of each approach and demonstrate an optimal framework to integrate these methods. We describe selection by iterative pathway group and network analysis looping (SIGNAL), an integrated, iterative approach that uses both pathway and network methods to optimize gene prioritization. SIGNAL is accessible as a rapid user-friendly web-based application (https://signal.niaid.nih.gov). A record of this paper's transparent peer review is included in the Supplemental information.

A Pathway Analysis Approach Using Petri Net

Perturbation in the normal function of the cell signaling pathways often leads to diseases. One of the factors that help understand the mechanism of diseases is the precise identification and investigation of perturbed signaling pathways. Pathway analysis methods have been developed as their purpose is to identify perturbed signaling pathways in given conditions. Among these methods, some consider the pathways topologies in their analysis, which are referred to as topology-based methods. Most of the topology-based methods used simple graph-based models to incorporate topology in their analysis, which have some limitations. We describe a new Pathway Analysis method using Petri net (PAPet) that uses the Petri net to model the signaling pathways and then propose an algorithm to measure the perturbation on a given pathway under a given condition. Modeling with Petri net has some advantages and could overcome the shortcomings of the simple graph-based models. We illustrate the capabilities of the proposed method using sensitivity, prioritization, mean reciprocal rank, and false-positive rate metrics on 36 real datasets from various diseases. The results of comparing PAPet with five pathway analysis methods FoPA, PADOG, GSEA, CePa and SPIA show that PAPet is the best one that provides a good compromise between all metrics. In addition, the results of applying methods to gene expression profiles in normal and Pancreatic Ductal Adenocarcinoma cancer (PDAC) samples show that the PAPet method achieves the best rank among others in finding the pathways that have been previously reported for PDAC. The PAPet method is available at https://github.com/fmansoori/PAPET.

Predicting mechanism of action of cellular perturbations with pathway activity signatures

MOTIVATION

Misregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures (TSeses) a difficult problem.

RESULTS

We developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the Library of Integrated Network-based Cellular Signature TSes of genetic perturbations of pathway genes and the TS of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only the TS of the perturbation fail.

AVAILABILITY AND IMPLEMENTATION

Open source R package paslincs is available at https://github.com/uc-bd2k/paslincs.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

GeneWalk identifies relevant gene functions for a biological context using network representation learning

A bottleneck in high-throughput functional genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Gene Ontology (GO) enrichment methods provide insight at the gene set level. Here, we introduce GeneWalk ( github.com/churchmanlab/genewalk ) that identifies individual genes and their relevant functions critical for the experimental setting under examination. After the automatic assembly of an experiment-specific gene regulatory network, GeneWalk uses representation learning to quantify the similarity between vector representations of each gene and its GO annotations, yielding annotation significance scores that reflect the experimental context. By performing gene- and condition-specific functional analysis, GeneWalk converts a list of genes into data-driven hypotheses.

PAFway: pairwise associations between functional annotations in biological networks and pathways

Motivation

Large gene networks can be dense and difficult to interpret in a biologically meaningful way.

Results

Here, we introduce PAFway, which estimates pairwise associations between functional annotations in biological networks and pathways. It answers the biological question: do genes that have a specific function tend to regulate genes that have a different specific function? The results can be visualized as a heatmap or a network of biological functions. We apply this package to reveal associations between functional annotations in an Arabidopsis thaliana gene network.

Availability and implementation

PAFway is submitted to CRAN. Currently available here: https://github.com/ezer/PAFway.

Supplementary information

Supplementary data are available at Bioinformatics online.

MAGPEL: an autoMated pipeline for inferring vAriant-driven Gene PanEls from the full-length biomedical literature

In spite of the efforts in developing and maintaining accurate variant databases, a large number of disease-associated variants are still hidden in the biomedical literature. Curation of the biomedical literature in an effort to extract this information is a challenging task due to: (i) the complexity of natural language processing, (ii) inconsistent use of standard recommendations for variant description, and (iii) the lack of clarity and consistency in describing the variant-genotype-phenotype associations in the biomedical literature. In this article, we employ text mining and word cloud analysis techniques to address these challenges. The proposed framework extracts the variant-gene-disease associations from the full-length biomedical literature and designs an evidence-based variant-driven gene panel for a given condition. We validate the identified genes by showing their diagnostic abilities to predict the patients' clinical outcome on several independent validation cohorts. As representative examples, we present our results for acute myeloid leukemia (AML), breast cancer and prostate cancer. We compare these panels with other variant-driven gene panels obtained from Clinvar, Mastermind and others from literature, as well as with a panel identified with a classical differentially expressed genes (DEGs) approach. The results show that the panels obtained by the proposed framework yield better results than the other gene panels currently available in the literature.

Gene Set Analysis: Challenges, Opportunities, and Future Research

Gene set analysis methods are widely used to provide insight into high-throughput gene expression data. There are many gene set analysis methods available. These methods rely on various assumptions and have different requirements, strengths and weaknesses. In this paper, we classify gene set analysis methods based on their components, describe the underlying requirements and assumptions for each class, and provide directions for future research in developing and evaluating gene set analysis methods.

Revisiting the use of graph centrality models in biological pathway analysis

The use of graph theory models is widespread in biological pathway analyses as it is often desired to evaluate the position of genes and proteins in their interaction networks of the biological systems. In this article, we argue that the common standard graph centrality measures do not sufficiently capture the informative topological organizations of the pathways, and thus, limit the biological inference. While key pathway elements may appear both upstream and downstream in pathways, standard directed graph centralities attribute significant topological importance to the upstream elements and evaluate the downstream elements as having no importance.We present a directed graph framework, Source/Sink Centrality (SSC), to address the limitations of standard models. SSC separately measures the importance of a node in the upstream and the downstream of a pathway, as a sender and a receiver of biological signals, and combines the two terms for evaluating the centrality. To validate SSC, we evaluate the topological position of known human cancer genes and mouse lethal genes in their respective KEGG annotated pathways and show that SSC-derived centralities provide an effective framework for associating higher positional importance to the genes with higher importance from a priori knowledge. While the presented work challenges some of the modeling assumptions in the common pathway analyses, it provides a straight-forward methodology to extend the existing models. The SSC extensions can result in more informative topological description of pathways, and thus, more informative biological inference.

Alterations in Patterns of Gene Expression and Perturbed Pathways in the Gut-Brain Axis Are Associated With Chemotherapy-Induced Nausea

CONTEXT

Despite current advances in antiemetic treatments, approximately 50% of oncology patients experience chemotherapy-induced nausea (CIN).

OBJECTIVES

The purpose of this study was to evaluate for differentially expressed genes and perturbed pathways associated with the gut-brain axis (GBA) across two independent samples of oncology patients who did and did not experience CIN.

METHODS

Oncology patients (n = 735) completed study questionnaires in the week before their second or third cycle of chemotherapy. CIN occurrence was assessed using the Memorial Symptom Assessment Scale. Gene expression analyses were performed in two independent samples using ribonucleic acid sequencing (Sample 1, n = 357) and microarray (Sample 2, n = 352) methodologies. Fisher's combined probability method was used to determine genes that were differentially expressed and pathways that were perturbed between the two nausea groups across both samples.

RESULTS

CIN was reported by 63.6% of the patients in Sample 1 and 48.9% of the patients in Sample 2. Across the two samples, 703 genes were differentially expressed, and 37 pathways were found to be perturbed between the two CIN groups. We identified nine perturbed pathways that are involved in mechanisms associated with alterations in the GBA (i.e., mucosal inflammation, disruption of gut microbiome).

CONCLUSION

Persistent CIN remains a significant clinical problem. Our study is the first to identify novel GBA-related pathways associated with the occurrence of CIN. Our findings warrant confirmation and suggest directions for future clinical studies to decrease CIN occurrence.

Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology in breast cancer survivors with chronic paclitaxel-induced peripheral neuropathy

Background

The major dose-limiting toxicity of paclitaxel, one of the most commonly used drugs to treat breast cancer, is peripheral neuropathy (paclitaxel-induced peripheral neuropathy). Paclitaxel-induced peripheral neuropathy, which persists into survivorship, has a negative impact on patient’s mood, functional status, and quality of life. Currently, no interventions are available to treat paclitaxel-induced peripheral neuropathy. A critical barrier to the development of efficacious interventions is the lack of understanding of the mechanisms that underlie paclitaxel-induced peripheral neuropathy. While data from preclinical studies suggest that disrupting cytoskeleton- and axon morphology-related processes are a potential mechanism for paclitaxel-induced peripheral neuropathy, clinical evidence is limited. The purpose of this study in breast cancer survivors was to evaluate whether differential gene expression and co-expression patterns in these pathways are associated with paclitaxel-induced peripheral neuropathy.

Methods

Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology were identified between survivors who received paclitaxel and did (n = 25) or did not (n = 25) develop paclitaxel-induced peripheral neuropathy.

Results

Pathway impact analysis identified four significantly perturbed cytoskeleton- and axon morphology-related signaling pathways. Weighted gene co-expression network analysis identified three co-expression modules. One module was associated with paclitaxel-induced peripheral neuropathy group membership. Functional analysis found that this module was associated with four signaling pathways and two ontology annotations related to cytoskeleton and axon morphology.

Conclusions

This study, which is the first to apply systems biology approaches using circulating whole blood RNA-seq data in a sample of breast cancer survivors with and without chronic paclitaxel-induced peripheral neuropathy, provides molecular evidence that cytoskeleton- and axon morphology-related mechanisms identified in preclinical models of various types of neuropathic pain including chemotherapy-induced peripheral neuropathy are found in breast cancer survivors and suggests pathways and a module of genes for validation and as potential therapeutic targets.

Detecting qualitative changes in biological systems

Currently, most diseases are diagnosed only after significant disease-associated transformations have taken place. Here, we propose an approach able to identify when systemic qualitative changes in biological systems happen, thus opening the possibility for therapeutic interventions before the occurrence of symptoms. The proposed method exploits knowledge from biological networks and longitudinal data using a system impact analysis. The method is validated on eight biological phenomena, three synthetic datasets and five real datasets, for seven organisms. Most importantly, the method accurately detected the transition from the control stage (benign) to the early stage of hepatocellular carcinoma on an eight-stage disease dataset.

Identification of Protein Complexes by Using a Spatial and Temporal Active Protein Interaction Network

The rapid development of proteomics and high-throughput technologies has produced a large amount of Protein-Protein Interaction (PPI) data, which makes it possible for considering dynamic properties of protein interaction networks (PINs) instead of static properties. Identification of protein complexes from dynamic PINs becomes a vital scientific problem for understanding cellular life in the post genome era. Up to now, plenty of models or methods have been proposed for the construction of dynamic PINs to identify protein complexes. However, most of the constructed dynamic PINs just focus on the temporal dynamic information and thus overlook the spatial dynamic information of the complex biological systems. To address the limitation of the existing dynamic PIN analysis approaches, in this paper, we propose a new model-based scheme for the construction of the Spatial and Temporal Active Protein Interaction Network (ST-APIN) by integrating time-course gene expression data and subcellular location information. To evaluate the efficiency of ST-APIN, the commonly used classical clustering algorithm MCL is adopted to identify protein complexes from ST-APIN and the other three dynamic PINs, NF-APIN, DPIN, and TC-PIN. The experimental results show that, the performance of MCL on ST-APIN outperforms those on the other three dynamic PINs in terms of matching with known complexes, sensitivity, specificity, and f-measure. Furthermore, we evaluate the identified protein complexes by Gene Ontology (GO) function enrichment analysis. The validation shows that the identified protein complexes from ST-APIN are more biologically significant. This study provides a general paradigm for constructing the ST-APINs, which is essential for further understanding of molecular systems and the biomedical mechanism of complex diseases.