Nine quick tips for pathway enrichment analysis

Combined inhibition of EZH2 and CDK4/6 perturbs endoplasmic reticulum-mitochondrial homeostasis and increases antitumor activity against glioblastoma

He, we show that combined use of the EZH2 inhibitor GSK126 and the CDK4/6 inhibitor abemaciclib synergistically enhances antitumoral effects in preclinical GBM models. Dual blockade led to HIF1α upregulation and CalR translocation, accompanied by massive impairment of mitochondrial function. Basal oxygen consumption rate, ATP synthesis, and maximal mitochondrial respiration decreased, confirming disrupted endoplasmic reticulum-mitochondrial homeostasis. This was paralleled by mitochondrial depolarization and upregulation of the UPR sensors PERK, ATF6α, and IRE1α. Notably, dual EZH2/CDK4/6 blockade also reduced 3D-spheroid invasion, partially inhibited tumor growth in ovo, and led to impaired viability of patient-derived organoids. Mechanistically, this was due to transcriptional changes in genes involved in mitotic aberrations/spindle assembly (Rb, PLK1, RRM2, PRC1, CENPF, TPX2), histone modification (HIST1H1B, HIST1H3G), DNA damage/replication stress events (TOP2A, ATF4), immuno-oncology (DEPDC1), EMT-counterregulation (PCDH1) and a shift in the stemness profile towards a more differentiated state. We propose a dual EZH2/CDK4/6 blockade for further investigation.

Analysis of the Skin and Brain Transcriptome of Normally Pigmented and Pseudo-Albino Southern Flounder (Paralichthys lethostigma) Juveniles to Study the Molecular Mechanisms of Hypopigmentation and Its Implications for Species Survival in the Natural Environment

Southern flounder skin pigmentation is a critical phenotypic characteristic for this species' survival in the natural environment. Normal pigmentation allows rapid changes of color for concealment to capture prey and UV light protection. In contrast, highly visible hypopigmented pseudo-albinos exhibit a compromised immune system and are vulnerable to predation, sensitive to UV exposure, and likely have poor survival in the wild. Skin and brain tissue samples from normally pigmented and hypopigmented individuals were analyzed with next-generation RNA sequencing. A total of 1,589,613 transcripts were used to identify 952,825 genes to assemble a de novo transcriptome, with 99.43% of genes mapped to the assembly. Differential gene expression and gene enrichment analysis of contrasting tissues and phenotypes revealed that pseudo-albino individuals appeared more susceptible to environmental stress, UV light exposure, hypoxia, and osmotic stress. The pseudo-albinos' restricted immune response showed upregulated genes linked to cancer development, signaling and response, skin tissue formation, regeneration, and healing. The data indicate that a modified skin collagen structure likely affects melanocyte differentiation and distribution, generating the pseudo-albino phenotype. In addition, the comparison of the brain transcriptome revealed changes in myelination and melanocyte stem cell activity, which may indicate modified brain function, reduced melanocyte migration, and impaired vision.

Transcriptomic Analysis of Metastatic Uveal Melanoma and Differences in Male and Female Patients

BACKGROUND/AIM

Uveal melanoma is an ocular malignancy whose prognosis severely worsens following metastasis. In order to improve the understanding of molecular physiology of metastatic uveal melanoma, we identified genes and pathways implicated in metastatic vs non-metastatic uveal melanoma.

PATIENTS AND METHODS

A previously published dataset from Gene Expression Omnibus (GEO) was used to identify differentially expressed genes between metastatic and non-metastatic samples as well as to conduct pathway and perturbagen analyses using Gene Set Enrichment Analysis (GSEA), EnrichR, and iLINCS.

RESULTS

In male metastatic uveal melanoma samples, the gene LOC401052 is significantly down-regulated and FHDC1 is significantly up-regulated compared to non-metastatic male samples. In female samples, no significant differently expressed genes were found. Additionally, we identified many significant up-regulated immune response pathways in male metastatic uveal melanoma, including "T cell activation in immune response". In contrast, many top up-regulated female pathways involve iron metabolism, including "heme biosynthetic process". iLINCS perturbagen analysis identified that both male and female samples have similar discordant activity with growth factor receptors, but only female samples have discordant activity with progesterone receptor agonists.

CONCLUSION

Our results from analyzing genes, pathways, and perturbagens demonstrate differences in metastatic processes between sexes.

Overlap in oncogenic and pro-inflammatory pathways associated with areca nut and nicotine exposure

Background

Betel nut/areca nut/Areca catechu is one of the most commonly used psychoactive substance, and is also a major preventable cause of cancer. Unlike other psychoactive substances, such as nicotine, the mechanisms underlying addiction to areca nuts and related oncogenesis remain elusive. Recent reports suggest a possible overlap in the mechanisms of action of nicotine and areca nuts in the human body. Thus, this study aimed to investigate the interactome of human proteins associated with areca nut exposure and the intricate similarities and differences in the effects of the two psychoactive substances on humans.

Methods

A list of proteins associated with areca nut use was obtained from the available literature using terms from Medical Subject Headings (MeSH). Protein-protein interaction (PPI) networks and functional enrichment were analyzed. The results obtained for both psychoactive substances were compared.

Results

Given the limited number of common proteins (36/226, 16%) in the two sets, a substantial overlap (612/1176 nodes, 52%) was observed in the PPI networks, as well as in Gene Ontology. Areca nuts mainly affect signaling pathways through three hub proteins (alpha serine/threonine-protein kinase, tumor protein 53, and interleukin-6), which are common to both psychoactive substances, as well as two unique hub proteins (epidermal growth factor receptor and master regulator of cell cycle entry and proliferative metabolism). Areca nut-related proteins are associated with unique pathways, such as extracellular matrix organization, lipid storage, and metabolism, which are not found in nicotine-associated proteins.

Conclusions

Areca nuts affect regulatory mechanisms, leading to systemic toxicity and oncogenesis. Areca nuts also affect unique pathways that can be studied as potential markers of exposure, as well as targets for anticancer therapeutic agents.

De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide

Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the 'scientific status' of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes.

Genes Differentially Expressed Across Major Arteries Are Enriched in Endothelial Dysfunction-Related Gene Sets: Implications for Relative Inter-artery Atherosclerosis Risk

Atherosclerosis differs across major arteries. Although the biological basis is not fully understood, limited evidence of genetic differences has been documented. This study, therefore, was aimed to identify differentially expressed genes between clinically relevant major arteries and investigate their enrichment in endothelial dysfunction-related gene sets. A bioinformatic analysis of publicly available gene-level read counts for coronary, aortic, and tibial arteries was performed. Differential gene expression was conducted with DeSeq2 at a false discovery rate of 0.05. Differentially expressed genes were then subjected to over-representation analysis and active-subnetwork-oriented enrichment analysis, both at a false discovery rate of 0.005. Enriched terms common to both analyses were categorized for each contrast into immunity/inflammation-, membrane biology-, lipid metabolism-, and coagulation-related terms, and the top differentially expressed genes validated against Swiss Institute of Bioinformatics' Bgee database. There was mostly upregulation of differentially expressed genes for the coronary/tibial and aorta/tibial contrasts, but milder changes for the coronary/aorta contrast. Transcriptomic differences between coronary or aortic versus tibial samples largely involved immunity/inflammation-, membrane biology-, lipid metabolism-, and coagulation-related genes, suggesting potential to modulate endothelial dysfunction and atherosclerosis. These results imply atheroprone coronary and aortic environments compared with tibial artery tissue, which may explain observed relative inter-artery atherosclerosis risk.

Mitotic abnormalities precede microsatellite instability in lynch syndrome-associated colorectal tumourigenesis

BACKGROUND

Lynch syndrome (LS) is one of the most common hereditary cancer syndromes worldwide. Dominantly inherited mutation in one of four DNA mismatch repair genes combined with somatic events leads to mismatch repair deficiency and microsatellite instability (MSI) in tumours. Due to a high lifetime risk of cancer, regular surveillance plays a key role in cancer prevention; yet the observation of frequent interval cancers points to insufficient cancer prevention by colonoscopy-based methods alone. This study aimed to identify precancerous functional changes in colonic mucosa that could facilitate the monitoring and prevention of cancer development in LS.

METHODS

The study material comprised colon biopsy specimens (n = 71) collected during colonoscopy examinations from LS carriers (tumour-free, or diagnosed with adenoma, or diagnosed with carcinoma) and a control group, which included sporadic cases without LS or neoplasia. The majority (80%) of LS carriers had an inherited genetic MLH1 mutation. The remaining 20% included MSH2 mutation carriers (13%) and MSH6 mutation carriers (7%). The transcriptomes were first analysed with RNA-sequencing and followed up with Gorilla Ontology analysis and Reactome Knowledgebase and Ingenuity Pathway Analyses to detect functional changes that might be associated with the initiation of the neoplastic process in LS individuals.

FINDINGS

With pathway and gene ontology analyses combined with measurement of mitotic perimeters from colonic mucosa and tumours, we found an increased tendency to chromosomal instability (CIN), already present in macroscopically normal LS mucosa. Our results suggest that CIN is an earlier aberration than MSI and may be the initial cancer driving aberration, whereas MSI accelerates tumour formation. Furthermore, our results suggest that MLH1 deficiency plays a significant role in the development of CIN.

INTERPRETATION

The results validate our previous findings from mice and highlight early mitotic abnormalities as an important contributor and precancerous marker of colorectal tumourigenesis in LS.

FUNDING

This work was supported by grants from the Jane and Aatos Erkko Foundation, the Academy of Finland (330606 and 331284), Cancer Foundation Finland sr, and the Sigrid Jusélius Foundation. Open access is funded by Helsinki University Library.

Genes adaptability and NOL6 protein inhibition studies of fabricated flavan-3-ols lead skeleton intended to treat breast carcinoma

Breast cancer invasive 2.3 million women worldly and second prominent factor of cancer-related mortality. Finding a new site-specific and safe small molecule is a current need in this field. With the aid of deep learning Algorithms, we analyzed the published big database from cancer CBioportal to find the best target protein. Further, Multi-omics analysis such as enrichment analysis, scores of molecular, RNA biological function at a cellular level, and protein domain were obtained and matched to find the better hit molecules. The gene analysis output shows nucleolar protein 6 plays a significant responsibility in breast carcinoma and 354 natural and synthetic lead molecules are docked inside the active site. Docking result gave the output hit molecule falavan-3-ols with a binding score of -5.325 (Kcal/mol) and interaction analysis illustrates, 13 active amino acids favoring the binding interaction with functional groups of the hit molecule compared to the standard molecule Abemacilib (-2.857 (Kcal/mol)). Best docked complex of flavan-3-ols and NOL6 protein subjected to dynamic simulation 100 ns to study the stability. The results proved that π-π stacked, carbon‑hydrogen and electrostatic interactions are stable throughout the 100 ns simulation. The overall results conclude the hit molecule flavan-3-ol will be a safe and potent lead molecule to generate and treat breast carcinoma patients.

Integration of transcriptomics and metabolomics analysis for unveiling the toxicological profile in the liver of mice exposed to uranium in drinking water

Uranium is a contaminate in the underground water in many regions of the world, which poses health risks to the local populations through drinking water. Although the health hazards of natural uranium have been concerned for decades, the controversies about its detrimental effects continue at present since it is still unclear how uranium interacts with molecular regulatory networks to generate toxicity. Here, we integrate transcriptomic and metabolomic methods to unveil the molecular mechanism of lipid metabolism disorder induced by uranium. Following exposure to uranium in drinking water for twenty-eight days, aberrant lipid metabolism and lipogenesis were found in the liver, accompanied with aggravated lipid peroxidation and an increase in dead cells. Multi-omics analysis reveals that uranium can promote the biosynthesis of unsaturated fatty acids through dysregulating the metabolism of arachidonic acid (AA), linoleic acid, and glycerophospholipid. Most notably, the disordered metabolism of polyunsaturated fatty acids (PUFAs) like AA may contribute to lipid peroxidation induced by uranium, which in turn triggers ferroptosis in hepatocytes. Our findings highlight disorder of lipid metabolism as an essential toxicological mechanism of uranium in the liver, offering insight into the health risks of uranium in drinking water.

An Exploratory Application of Multilayer Networks and Pathway Analysis in Pharmacogenomics

Over the years, network analysis has become a promising strategy for analysing complex system, i.e., systems composed of a large number of interacting elements. In particular, multilayer networks have emerged as a powerful framework for modelling and analysing complex systems with multiple types of interactions. Network analysis can be applied to pharmacogenomics to gain insights into the interactions between genes, drugs, and diseases. By integrating network analysis techniques with pharmacogenomic data, the goal consists of uncovering complex relationships and identifying key genes to use in pathway enrichment analysis to figure out biological pathways involved in drug response and adverse reactions. In this study, we modelled omics, disease, and drug data together through multilayer network representation. Then, we mined the multilayer network with a community detection algorithm to obtain the top communities. After that, we used the identified list of genes from the communities to perform pathway enrichment analysis (PEA) to figure out the biological function affected by the selected genes. The results show that the genes forming the top community have multiple roles through different pathways.

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments and therapies for patients. Bioinformatics and high-throughput biological data coming from different sources can even be more helpful, because each of these different data chunks can provide alternative, complementary information about a specific biological phenomenon, similar to multiple photos of the same subject taken from different angles. In this context, the integration of bioinformatics and high-throughput biological data gets a pivotal role in running a successful bioinformatics study. In the last decades, data originating from proteomics, metabolomics, metagenomics, phenomics, transcriptomics, and epigenomics have been labelled -omics data, as a unique name to refer to them, and the integration of these omics data has gained importance in all biological areas. Even if this omics data integration is useful and relevant, due to its heterogeneity, it is not uncommon to make mistakes during the integration phases. We therefore decided to present these ten quick tips to perform an omics data integration correctly, avoiding common mistakes we experienced or noticed in published studies in the past. Even if we designed our ten guidelines for beginners, by using a simple language that (we hope) can be understood by anyone, we believe our ten recommendations should be taken into account by all the bioinformaticians performing omics data integration, including experts.

Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment

Some scientific studies involve huge amounts of bioinformatics data that cannot be analyzed on personal computers usually employed by researchers for day-to-day activities but rather necessitate effective computational infrastructures that can work in a distributed way. For this purpose, distributed computing systems have become useful tools to analyze large amounts of bioinformatics data and to generate relevant results on virtual environments, where software can be executed for hours or even days without affecting the personal computer or laptop of a researcher. Even if distributed computing resources have become pivotal in multiple bioinformatics laboratories, often researchers and students use them in the wrong ways, making mistakes that can cause the distributed computers to underperform or that can even generate wrong outcomes. In this context, we present here ten quick tips for the usage of Apache Spark distributed computing systems for bioinformatics analyses: ten simple guidelines that, if taken into account, can help users avoid common mistakes and can help them run their bioinformatics analyses smoothly. Even if we designed our recommendations for beginners and students, they should be followed by experts too. We think our quick tips can help anyone make use of Apache Spark distributed computing systems more efficiently and ultimately help generate better, more reliable scientific results.

Interpreting omics data with pathway enrichment analysis

Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.

Klarigi: Characteristic explanations for semantic biomedical data

Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such as biosample gene expression profiles or patient phenotypes, and is useful for a range of tasks including differential diagnosis and causative variant prioritisation. These approaches, however, usually consider only univariate relationships, make limited use of the semantic features of ontologies, and provide limited information and evaluation of the explanatory power of both singular and grouped candidate classes. Moreover, they are not designed to solve the problem of deriving cohesive, characteristic, and discriminatory sets of classes for entity groups. We have developed a new tool, called Klarigi, which introduces multiple scoring heuristics for identification of classes that are both compositional and discriminatory for groups of entities annotated with ontology classes. The tool includes a novel algorithm for derivation of multivariable semantic explanations for entity groups, makes use of semantic inference through live use of an ontology reasoner, and includes a classification method for identifying the discriminatory power of candidate sets, in addition to significance testing apposite to traditional enrichment approaches. We describe the design and implementation of Klarigi, including its scoring and explanation determination methods, and evaluate its use in application to two test cases with clinical significance, comparing and contrasting methods and results with literature-based and enrichment analysis methods. We demonstrate that Klarigi produces characteristic and discriminatory explanations for groups of biomedical entities in two settings. We also show that these explanations recapitulate and extend the knowledge held in existing biomedical databases and literature for several diseases. We conclude that Klarigi provides a distinct and valuable perspective on biomedical datasets when compared with traditional enrichment methods, and therefore constitutes a new method by which biomedical datasets can be explored, contributing to improved insight into semantic data.

Ten quick tips for computational analysis of medical images

Medical imaging is a great asset for modern medicine, since it allows physicians to spatially interrogate a disease site, resulting in precise intervention for diagnosis and treatment, and to observe particular aspect of patients' conditions that otherwise would not be noticeable. Computational analysis of medical images, moreover, can allow the discovery of disease patterns and correlations among cohorts of patients with the same disease, thus suggesting common causes or providing useful information for better therapies and cures. Machine learning and deep learning applied to medical images, in particular, have produced new, unprecedented results that can pave the way to advanced frontiers of medical discoveries. While computational analysis of medical images has become easier, however, the possibility to make mistakes or generate inflated or misleading results has become easier, too, hindering reproducibility and deployment. In this article, we provide ten quick tips to perform computational analysis of medical images avoiding common mistakes and pitfalls that we noticed in multiple studies in the past. We believe our ten guidelines, if taken into practice, can help the computational-medical imaging community to perform better scientific research that eventually can have a positive impact on the lives of patients worldwide.

Eleven quick tips for data cleaning and feature engineering

Applying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient to generate robust and reliable outcomes and results. Before applying any discovery method, preprocessing steps are necessary to prepare the data to the computational analysis. In this framework, data cleaning and feature engineering are key pillars of any scientific study involving data analysis and that should be adequately designed and performed since the first phases of the project. We call "feature" a variable describing a particular trait of a person or an observation, recorded usually as a column in a dataset. Even if pivotal, these data cleaning and feature engineering steps sometimes are done poorly or inefficiently, especially by beginners and unexperienced researchers. For this reason, we propose here our quick tips for data cleaning and feature engineering on how to carry out these important preprocessing steps correctly avoiding common mistakes and pitfalls. Although we designed these guidelines with bioinformatics and health informatics scenarios in mind, we believe they can more in general be applied to any scientific area. We therefore target these guidelines to any researcher or practitioners wanting to perform data cleaning or feature engineering. We believe our simple recommendations can help researchers and scholars perform better computational analyses that can lead, in turn, to more solid outcomes and more reliable discoveries.

Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients’ survival, researchers have invented prognostic genetic signatures: lists of genes that can be used in scientific analyses to predict if a patient will survive or not. In this study, we joined together five different prognostic signatures, each of them related to a specific cancer type, to generate a unique pan-cancer prognostic signature, that contains 207 unique probesets related to 187 unique gene symbols, with one particular probeset present in two cancer type-specific signatures (203072_at related to the MYO1E gene). We applied our proposed pan-cancer signature with the Random Forests machine learning method to 57 microarray gene expression datasets of 12 different cancer types, and analyzed the results. We also compared the performance of our pan-cancer signature with the performances of two alternative prognostic signatures, and with the performances of each cancer type-specific signature on their corresponding cancer type-specific datasets. Our results confirmed the effectiveness of our prognostic pan-cancer signature. Moreover, we performed a pathway enrichment analysis, which indicated an association between the signature genes and a protein-protein interaction analysis, that highlighted PIK3R2 and FN1 as key genes having a fundamental relevance in our signature, suggesting an important role in pan-cancer prognosis for both of them.

A brief survey of tools for genomic regions enrichment analysis

Functional enrichment analysis or pathway enrichment analysis (PEA) is a bioinformatics technique which identifies the most over-represented biological pathways in a list of genes compared to those that would be associated with them by chance. These biological functions are found on bioinformatics annotated databases such as The Gene Ontology or KEGG; the more abundant pathways are identified through statistical techniques such as Fisher’s exact test. All PEA tools require a list of genes as input. A few tools, however, read lists of genomic regions as input rather than lists of genes, and first associate these chromosome regions with their corresponding genes. These tools perform a procedure called genomic regions enrichment analysis, which can be useful for detecting the biological pathways related to a set of chromosome regions. In this brief survey, we analyze six tools for genomic regions enrichment analysis (BEHST, g:Profiler g:GOSt, GREAT, LOLA, Poly-Enrich, and ReactomePA), outlining and comparing their main features. Our comparison results indicate that the inclusion of data for regulatory elements, such as ChIP-seq, is common among these tools and could therefore improve the enrichment analysis results.