Characterizing gene sets with FuncAssociate

EnsemBic: An effective ensemble of biclustering to identify potential biomarkers of esophageal squamous cell carcinoma

The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity of genetic associations. To validate the effectiveness and robustness of EnsemBic, we apply three well-known biclustering techniques, viz. Laplace Prior, iBBiG, and xMotif to implement EnsemBic and have been compared using different leading parameters. It is observed that the EnsemBic outperforms its competing algorithms in several prominent functional and biological measures. Next, the biclusters obtained from EnsemBic are used to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC) by exploring topological and biological relevance with reference to the elite genes, attained from genecards. Finally, we discover that the genes F2RL3, APPL1, CALM1, IFNGR1, LPAR1, ANGPT2, ARPC2, CGN, CLDN7, ATP6V1C2, CEACAM1, FTL, PLAU,PSMB4, and EPHB2 carry both the topological and biological significance of previously established ESCC elite genes. Therefore, we declare the aforementioned genes as potential biomarkers of ESCC.

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.

MEM-FET: Essential protein prediction using membership feature and machine learning approach

Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab. The results of observation in wet lab always ensure high reliability and accuracy in the biological ground. Essential protein prediction using computational approaches is an alternative choice in research. It proves its significance rapidly in day-to-day life as well as reduces the experimental cost of wet lab effectively. Existing computational methods were implemented using Protein interaction networks (PPIN), Sequence, Gene Expression Dataset (GED), Gene Ontology (GO), Orthologous groups, and Subcellular localized datasets. Machine learning has diverse categories of features that enable to model and predict essential macromolecules of understudied organisms. A novel methodology MEM-FET (membership feature) is predicted based on features, that is, edge clustering coefficient, Average clustering coefficient, subcellular localization, and Gene Ontology within a compartment of common neighbors. The accuracy (ACC) values of the predicted true positive (TP) essential proteins are 0.79, 0.74, 0.78, and 0.71 for YHQ, YMIPS, YDIP, and YMBD datasets. An enriched set of essential proteins are also predicted using the MEM-FET algorithm. Ensemble ML also validated the proposed model with an accuracy of 60%. It has been predicted that MEM-FET algorithms outperform other existing algorithms with an ACC value of 80% for the yeast dataset.

Respiratory Effects of Traffic-Related Air Pollution: A Randomized, Crossover Analysis of Lung Function, Airway Metabolome, and Biomarkers of Airway Injury

BACKGROUND

Exposure to traffic-related air pollution (TRAP) has been associated with increased risks of respiratory diseases, but the biological mechanisms are not yet fully elucidated.

OBJECTIVES

Our aim was to evaluate the respiratory responses and explore potential biological mechanisms of TRAP exposure in a randomized crossover trial.

METHODS

We conducted a randomized crossover trial in 56 healthy adults. Each participant was exposed to high- and low-TRAP exposure sessions by walking in a park and down a road with high traffic volume for 4 h in random order. Respiratory symptoms and lung function, including forced expiratory volume in the first second (FEV 1 ), forced vital capacity (FVC), the ratio of FEV 1 to FVC, and maximal mid-expiratory flow (MMEF), were measured before and after each exposure session. Markers of 8-isoprostane, tumor necrosis factor- α (TNF- α ), and ezrin in exhaled breath condensate (EBC), and surfactant proteins D (SP-D) in serum were also measured. We used linear mixed-effects models to estimate the associations, adjusted for age, sex, body mass index, meteorological condition, and batch (only for biomarkers). Liquid chromatography-mass spectrometry was used to profile the EBC metabolome. Untargeted metabolome-wide association study (MWAS) analysis and pathway enrichment analysis using mummichog were performed to identify critical metabolomic features and pathways associated with TRAP exposure.

RESULTS

Participants had two to three times higher exposure to traffic-related air pollutants except for fine particulate matter while walking along the road compared with in the park. Compared with the low-TRAP exposure at the park, high-TRAP exposure at the road was associated with a higher score of respiratory symptoms [2.615 (95% CI: 0.605, 4.626), p = 1.2 × 10 - 2 ] and relatively lower lung function indicators [- 0.075 L (95% CI: - 0.138 , - 0.012 ), p = 2.1 × 10 - 2 ] for FEV 1 and - 0.190 L / s (95% CI: - 0.351 , - 0.029 ; p = 2.4 × 10 - 2 ) for MMEF]. Exposure to TRAP was significantly associated with changes in some, but not all, biomarkers, particularly with a 0.494 -ng / mL (95% CI: 0.297, 0.691; p = 9.5 × 10 - 6 ) increase for serum SP-D and a 0.123 -ng / mL (95% CI: - 0.208 , - 0.037 ; p = 7.2 × 10 - 3 ) decrease for EBC ezrin. Untargeted MWAS analysis revealed that elevated TRAP exposure was significantly associated with perturbations in 23 and 32 metabolic pathways under positive- and negative-ion modes, respectively. These pathways were most related to inflammatory response, oxidative stress, and energy use metabolism.

CONCLUSIONS

This study suggests that TRAP exposure might lead to lung function impairment and respiratory symptoms. Possible underlying mechanisms include lung epithelial injury, inflammation, oxidative stress, and energy metabolism disorders. https://doi.org/10.1289/EHP11139.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data

BACKGROUND

Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices.

RESULTS

We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

MM-CCNB: Essential protein prediction using MAX-MIN strategies and compartment of common neighboring approach

BACKGROUND AND OBJECTIVE

Proteins are indispensable for the flow of the life of living organisms. Protein pairs in interaction exhibit more functional activities than individuals. These activities have been considered an essential measure in predicting their essentiality. Neighborhood approaches have been used frequently in the prediction of essentiality scores. All paired neighbors of the essential proteins are nominated for the suitable candidate seeds for prediction. Still now Jaccard's coefficient is limited to predicting functions, homologous groups, sequence analysis, etc. It really motivate us to predict essential proteins efficiently using different computational approaches.

METHODS

In our work, we proposed modified Jaccard's coefficient to predict essential proteins. We have proposed a novel methodology for predicting essential proteins using MAX-MIN strategies and modified Jaccard's coefficient approach.

RESULTS

The performance of our proposed methodology has been analyzed for Saccharomyces cerevisiae datasets with an accuracy of more than 80%. It has been observed that the proposed algorithm is outperforms with an accuracy of 0.78, 0.74, 0.79, and 0.862 for YDIP, YMIPS, YHQ, and YMBD datasets respectivly.

CONCLUSIONS

There are several computational approaches in the existing state-of-art model of essential protein prediction. It has been noted that our predicted methodology outperforms other existing models viz. different centralities, local interaction density combined with protein complexes, modified monkey algorithm and ortho_sim_loc methods.

A comprehensive survey of the approaches for pathway analysis using multi-omics data integration

Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.

Prolonged FOS activity disrupts a global myogenic transcriptional program by altering 3D chromatin architecture in primary muscle progenitor cells

BACKGROUND

The AP-1 transcription factor, FBJ osteosarcoma oncogene (FOS), is induced in adult muscle satellite cells (SCs) within hours following muscle damage and is required for effective stem cell activation and muscle repair. However, why FOS is rapidly downregulated before SCs enter cell cycle as progenitor cells (i.e., transiently expressed) remains unclear. Further, whether boosting FOS levels in the proliferating progeny of SCs can enhance their myogenic properties needs further evaluation.

METHODS

We established an inducible, FOS expression system to evaluate the impact of persistent FOS activity in muscle progenitor cells ex vivo. We performed various assays to measure cellular proliferation and differentiation, as well as uncover changes in RNA levels and three-dimensional (3D) chromatin interactions.

RESULTS

Persistent FOS activity in primary muscle progenitor cells severely antagonizes their ability to differentiate and form myotubes within the first 2 weeks in culture. RNA-seq analysis revealed that ectopic FOS activity in muscle progenitor cells suppressed a global pro-myogenic transcriptional program, while activating a stress-induced, mitogen-activated protein kinase (MAPK) transcriptional signature. Additionally, we observed various FOS-dependent, chromosomal re-organization events in A/B compartments, topologically associated domains (TADs), and genomic loops near FOS-regulated genes.

CONCLUSIONS

Our results suggest that elevated FOS activity in recently activated muscle progenitor cells perturbs cellular differentiation by altering the 3D chromosome organization near critical pro-myogenic genes. This work highlights the crucial importance of tightly controlling FOS expression in the muscle lineage and suggests that in states of chronic stress or disease, persistent FOS activity in muscle precursor cells may disrupt the muscle-forming process.

Liver X receptor-agonist treatment rescues degeneration in a Drosophila model of hereditary spastic paraplegia

Hereditary spastic paraplegias (HSPs) are a group of inherited, progressive neurodegenerative conditions characterised by prominent lower-limb spasticity and weakness, caused by a length-dependent degeneration of the longest corticospinal upper motor neurons. While more than 80 spastic paraplegia genes (SPGs) have been identified, many cases arise from mutations in genes encoding proteins which generate and maintain tubular endoplasmic reticulum (ER) membrane organisation. The ER-shaping proteins are essential for the health and survival of long motor neurons, however the mechanisms by which mutations in these genes cause the axonopathy observed in HSP have not been elucidated. To further develop our understanding of the ER-shaping proteins, this study outlines the generation of novel in vivo and in vitro models, using CRISPR/Cas9-mediated gene editing to knockout the ER-shaping protein ADP-ribosylation factor-like 6 interacting protein 1 (ARL6IP1), mutations in which give rise to the HSP subtype SPG61. Loss of Arl6IP1 in Drosophila results in progressive locomotor deficits, emulating a key aspect of HSP in patients. ARL6IP1 interacts with ER-shaping proteins and is required for regulating the organisation of ER tubules, particularly within long motor neuron axons. Unexpectedly, we identified physical and functional interactions between ARL6IP1 and the phospholipid transporter oxysterol-binding protein-related protein 8 in both human and Drosophila model systems, pointing to a conserved role for ARL6IP1 in lipid homeostasis. Furthermore, loss of Arl6IP1 from Drosophila neurons results in a cell non-autonomous accumulation of lipid droplets in axonal glia. Importantly, treatment with lipid regulating liver X receptor-agonists blocked lipid droplet accumulation, restored axonal ER organisation, and improved locomotor function in Arl6IP1 knockout Drosophila. Our findings indicate that disrupted lipid homeostasis contributes to neurodegeneration in HSP, identifying a potential novel therapeutic avenue for the treatment of this disorder.

MOET: a web-based gene set enrichment tool at the Rat Genome Database for multiontology and multispecies analyses

Biological interpretation of a large amount of gene or protein data is complex. Ontology analysis tools are imperative in finding functional similarities through overrepresentation or enrichment of terms associated with the input gene or protein lists. However, most tools are limited by their ability to do ontology-specific and species-limited analyses. Furthermore, some enrichment tools are not updated frequently with recent information from databases, thus giving users inaccurate, outdated or uninformative data. Here, we present MOET or the Multi-Ontology Enrichment Tool (v.1 released in April 2019 and v.2 released in May 2021), an ontology analysis tool leveraging data that the Rat Genome Database (RGD) integrated from in-house expert curation and external databases including the National Center for Biotechnology Information (NCBI), Mouse Genome Informatics (MGI), The Kyoto Encyclopedia of Genes and Genomes (KEGG), The Gene Ontology Resource, UniProt-GOA, and others. Given a gene or protein list, MOET analysis identifies significantly overrepresented ontology terms using a hypergeometric test and provides nominal and Bonferroni corrected P-values and odds ratios for the overrepresented terms. The results are shown as a downloadable list of terms with and without Bonferroni correction, and a graph of the P-values and number of annotated genes for each term in the list. MOET can be accessed freely from https://rgd.mcw.edu/rgdweb/enrichment/start.html.

An Algorithm to Mine Therapeutic Motifs for Cancer from Networks of Genetic Interactions

Study of pairwise genetic interactions, such as mutually exclusive mutations, has led to understanding of underlying mechanisms in cancer. Investigation of various combinatorial motifs within networks of such interactions can lead to deeper insights into its mutational landscape and inform therapy development. One such motif called the Between-Pathway Model (BPM) represents redundant or compensatory pathways that can be therapeutically exploited. Finding such BPM motifs is challenging since most formulations require solving variants of the NP-complete maximum weight bipartite subgraph problem. In this paper we design an algorithm based on Integer Linear Programming (ILP) to solve this problem. In our experiments, our approach outperforms the best previous method to mine BPM motifs. Further, our ILP-based approach allows us to easily model additional application-specific constraints. We illustrate this advantage through a new application of BPM motifs that can potentially aid in finding combination therapies to combat cancer.

Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data

Background

Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications of scRNA-seq data analysis is the identification of new cell types and cell states. Deep neural networks (DNNs) are among the best methods to address this problem. However, this performance comes with the trade-off for a lack of interpretability in the results. In this work we propose an intelligible pathway-driven neural network to correctly solve cell-type related problems at single-cell resolution while providing a biologically meaningful representation of the data.

Results

In this study, we explored the deep neural networks constrained by several types of prior biological information, e.g. signaling pathway information, as a way to reduce the dimensionality of the scRNA-seq data. We have tested the proposed biologically-based architectures on thousands of cells of human and mouse origin across a collection of public datasets in order to check the performance of the model. Specifically, we tested the architecture across different validation scenarios that try to mimic how unknown cell types are clustered by the DNN and how it correctly annotates cell types by querying a database in a retrieval problem. Moreover, our approach demonstrated to be comparable to other less interpretable DNN approaches constrained by using protein-protein interactions gene regulation data. Finally, we show how the latent structure learned by the network could be used to visualize and to interpret the composition of human single cell datasets.

Conclusions

Here we demonstrate how the integration of pathways, which convey fundamental information on functional relationships between genes, with DNNs, that provide an excellent classification framework, results in an excellent alternative to learn a biologically meaningful representation of scRNA-seq data. In addition, the introduction of prior biological knowledge in the DNN reduces the size of the network architecture. Comparative results demonstrate a superior performance of this approach with respect to other similar approaches. As an additional advantage, the use of pathways within the DNN structure enables easy interpretability of the results by connecting features to cell functionalities by means of the pathway nodes, as demonstrated with an example with human melanoma tumor cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00285-4.

Dietary palmitic acid promotes a prometastatic memory via Schwann cells

Fatty acid uptake and altered metabolism constitute hallmarks of metastasis^1,2, yet evidence of the underlying biology, as well as whether all dietary fatty acids are prometastatic, is lacking. Here we show that dietary palmitic acid (PA), but not oleic acid or linoleic acid, promotes metastasis in oral carcinomas and melanoma in mice. Tumours from mice that were fed a short-term palm-oil-rich diet (PA), or tumour cells that were briefly exposed to PA in vitro, remained highly metastatic even after being serially transplanted (without further exposure to high levels of PA). This PA-induced prometastatic memory requires the fatty acid transporter CD36 and is associated with the stable deposition of histone H3 lysine 4 trimethylation by the methyltransferase Set1A (as part of the COMPASS complex (Set1A/COMPASS)). Bulk, single-cell and positional RNA-sequencing analyses indicate that genes with this prometastatic memory predominantly relate to a neural signature that stimulates intratumoural Schwann cells and innervation, two parameters that are strongly correlated with metastasis but are aetiologically poorly understood^3,4. Mechanistically, tumour-associated Schwann cells secrete a specialized proregenerative extracellular matrix, the ablation of which inhibits metastasis initiation. Both the PA-induced memory of this proneural signature and its long-term boost in metastasis require the transcription factor EGR2 and the glial-cell-stimulating peptide galanin. In summary, we provide evidence that a dietary metabolite induces stable transcriptional and chromatin changes that lead to a long-term stimulation of metastasis, and that this is related to a proregenerative state of tumour-activated Schwann cells.

POPBic: Pathway-Based Order Preserving Biclustering Algorithm Towards the Analysis of Gene Expression Data

To understand the underlying biological mechanisms of gene expression data, it is important to discover the groups of genes that have similar expression patterns under certain subsets of conditions. Biclustering algorithms have been effective in analyzing large-scale gene expression data. Recently, traditional biclustering has been improved by introducing biological knowledge along with the expression data during the biclustering process. In this paper, we propose the Pathway-based Order Preserving Biclustering (POPBic) algorithm by incorporating Kyoto Encyclopedia of Genes and Genomes (KEGG) based on the hypothesis that two genes sharing similar pathways are likely to be similar. The basic principle of the POPBic approach is to apply the concept of Longest Common Subsequence between a pair of genes which have a high number of common pathways. The algorithm identifies the expression patterns from data using two major steps: (i) selection of significant seed genes and (ii) extraction of biclusters. We performe exhaustive experimentation with the POPBic algorithm using synthetic dataset to evaluate the bicluster model, finding its robustness in the presence of noise and identifying overlapping biclusters. We demonstrate that POPBic is able to discover biologically significant biclusters for four cancer microarray gene expression datasets. POPBic has been found to perform consistently well in comparison to its closest competitors.

Ortho_Sim_Loc: Essential protein prediction using orthology and priority-based similarity approach

Proteins are the essential macro-molecules of living organism. But all proteins cannot be considered as essential in different relevant studies. Essentiality of a protein is thus computed by computation methods rather than biological experiments which in turn save both time and effort. Different computational approaches are already predicted to select essential proteins successfully with different biological significances by researchers. Most of the experimental approaches return higher false negative outcomes with respect to others. In order to retain the prediction accuracy level, a novel methodology "Ortho_Sim_Loc"has been proposed which is a combined approach of Orthology, Similarity (using clustering and priority based GO-Annotation) and Subcellular localization. Ortho_Sim_Loc can predict enriched functional set essential proteins. The predicted results are validated with other existing methods like different centrality measures, LIDC. The validation results exhibits better performance of Ortho_Sim_Loc in compare to other existing computational approaches.

Muscle Proteomic and Transcriptomic Profiling of Healthy Aging and Metabolic Syndrome in Men

(1) Background: Aging is associated with a progressive decline in muscle mass and function. Aging is also a primary risk factor for metabolic syndrome, which further alters muscle metabolism. However, the molecular mechanisms involved remain to be clarified. Herein we performed omic profiling to decipher in muscle which dominating processes are associated with healthy aging and metabolic syndrome in old men. (2) Methods: This study included 15 healthy young, 15 healthy old, and 9 old men with metabolic syndrome. Old men were selected from a well-characterized cohort, and each vastus lateralis biopsy was used to combine global transcriptomic and proteomic analyses. (3) Results: Over-representation analysis of differentially expressed genes (ORA) and functional class scoring of pathways (FCS) indicated that healthy aging was mainly associated with upregulations of apoptosis and immune function and downregulations of glycolysis and protein catabolism. ORA and FCS indicated that with metabolic syndrome the dominating biological processes were upregulation of proteolysis and downregulation of oxidative phosphorylation. Proteomic profiling matched 586 muscle proteins between individuals. The proteome of healthy aging revealed modifications consistent with a fast-to-slow transition and downregulation of glycolysis. These transitions were reduced with metabolic syndrome, which was more associated with alterations in NADH/NAD+ shuttle and β-oxidation. Proteomic profiling further showed that all old muscles overexpressed protein chaperones to preserve proteostasis and myofiber integrity. There was also evidence of aging-related increases in reactive oxygen species but better detoxifications of cytotoxic aldehydes and membrane protection in healthy than in metabolic syndrome muscles. (4) Conclusions: Most candidate proteins and mRNAs identified herein constitute putative muscle biomarkers of healthy aging and metabolic syndrome in old men.

Rank-preserving biclustering algorithm: a case study on miRNA breast cancer

Effective biomarkers aid in the early diagnosis and monitoring of breast cancer and thus play an important role in the treatment of patients suffering from the disease. Growing evidence indicates that alteration of expression levels of miRNA is one of the principal causes of cancer. We analyze breast cancer miRNA data to discover a list of biclusters as well as breast cancer miRNA biomarkers which can help to understand better this critical disease and take important clinical decisions for treatment and diagnosis. In this paper, we propose a pattern-based parallel biclustering algorithm termed Rank-Preserving Biclustering (RPBic). The key strategy is to identify rank-preserved rows under a subset of columns based on a modified version of all substrings common subsequence (ALCS) framework. To illustrate the effectiveness of the RPBic algorithm, we consider synthetic datasets and show that RPBic outperforms relevant biclustering algorithms in terms of relevance and recovery. For breast cancer data, we identify 68 biclusters and establish that they have strong clinical characteristics among the samples. The differentially co-expressed miRNAs are found to be involved in KEGG cancer related pathways. Moreover, we identify frequency-based biomarkers (hsa-miR-410, hsa-miR-483-5p) and network-based biomarkers (hsa-miR-454, hsa-miR-137) which we validate to have strong connectivity with breast cancer. The source code and the datasets used can be found at http://agnigarh.tezu.ernet.in/~rosy8/Bioinformatics_RPBic_Data.rar . Graphical Abstract.

Mitochondrial bioenergetic deficits in C9orf72 amyotrophic lateral sclerosis motor neurons cause dysfunctional axonal homeostasis

Axonal dysfunction is a common phenotype in neurodegenerative disorders, including in amyotrophic lateral sclerosis (ALS), where the key pathological cell-type, the motor neuron (MN), has an axon extending up to a metre long. The maintenance of axonal function is a highly energy-demanding process, raising the question of whether MN cellular energetics is perturbed in ALS, and whether its recovery promotes axonal rescue. To address this, we undertook cellular and molecular interrogation of multiple patient-derived induced pluripotent stem cell lines and patient autopsy samples harbouring the most common ALS causing mutation, C9orf72. Using paired mutant and isogenic expansion-corrected controls, we show that C9orf72 MNs have shorter axons, impaired fast axonal transport of mitochondrial cargo, and altered mitochondrial bioenergetic function. RNAseq revealed reduced gene expression of mitochondrially encoded electron transport chain transcripts, with neuropathological analysis of C9orf72-ALS post-mortem tissue importantly confirming selective dysregulation of the mitochondrially encoded transcripts in ventral horn spinal MNs, but not in corresponding dorsal horn sensory neurons, with findings reflected at the protein level. Mitochondrial DNA copy number was unaltered, both in vitro and in human post-mortem tissue. Genetic manipulation of mitochondrial biogenesis in C9orf72 MNs corrected the bioenergetic deficit and also rescued the axonal length and transport phenotypes. Collectively, our data show that loss of mitochondrial function is a key mediator of axonal dysfunction in C9orf72-ALS, and that boosting MN bioenergetics is sufficient to restore axonal homeostasis, opening new potential therapeutic strategies for ALS that target mitochondrial function.

Actionable Cytopathogenic Host Responses of Human Alveolar Type 2 Cells to SARS-CoV-2

Human transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causative pathogen of the COVID-19 pandemic, exerts a massive health and socioeconomic crisis. The virus infects alveolar epithelial type 2 cells (AT2s), leading to lung injury and impaired gas exchange, but the mechanisms driving infection and pathology are unclear. We performed a quantitative phosphoproteomic survey of induced pluripotent stem cell-derived AT2s (iAT2s) infected with SARS-CoV-2 at air-liquid interface (ALI). Time course analysis revealed rapid remodeling of diverse host systems, including signaling, RNA processing, translation, metabolism, nuclear integrity, protein trafficking, and cytoskeletal-microtubule organization, leading to cell cycle arrest, genotoxic stress, and innate immunity. Comparison to analogous data from transformed cell lines revealed respiratory-specific processes hijacked by SARS-CoV-2, highlighting potential novel therapeutic avenues that were validated by a high hit rate in a targeted small molecule screen in our iAT2 ALI system.

Determining crucial genes associated with COVID-19 based on COPD Findings✶,✶✶

Genes act in groups known as gene modules, which accomplish different cellular functions in the body. The modular nature of gene networks was used in this study to detect functionally enriched modules in samples obtained from COPD patients. We analyzed modules extracted from COPD samples and identified crucial genes associated with the disease COVID-19. We also extracted modules from a COVID-19 dataset and analyzed a suspected set of genes that may be associated with this deadly disease. We used information available for two other viruses that cause SARS and MERS because their physiology is similar to that of the COVID-19 virus. We report several crucial genes associated with COVID-19: RPA2, POLD4, MAPK8, IRF7, JUN, NFKB1, NFKBIA, CD40LG, FASLG, ICAM1, LIFR, STAT2 and CCR1. Most of these genes are related to the immune system and respiratory organs, which emphasizes the fact that COPD weakens this system and makes patients more susceptible to developing severe COVID-19.

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Association of respiratory disease COPD (Chronic Obstructive Pulmonary Disease) with COVID-19.

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Discuss the resemblance between SARS, MERS and COVID-19 causal agents.

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Uses gene module analysis and pathway information to determine the role of genes which may be associated with COVID-19.

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Few interesting genes were found which might be potentially useful in designing drugs targeting COVID-19.

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

Immunostimulatory efficacy and protective potential of putative TgERK7 protein in mice experimentally infected by Toxoplasma gondii

The extracellular signal-regulated kinases (ERKs) serve as important determinants of cellular signal transduction pathways, and hence may play important roles during infections. Previous work suggested that putative ERK7 of Toxoplasma gondii is required for efficient intracellular replication of the parasite. However, the antigenic and immunostimulatory properties of TgERK7 protein remain unknown. The objective of this study was to produce a recombinant TgERK7 protein in vitro and to evaluate its effect on the induction of humoral and T cell-mediated immune responses against T. gondii infection in BALB/c mice. Immunization using TgERK7 mixed with Freund's adjuvants significantly increased the ratio of CD3e⁺CD4⁺ T/CD3e⁺CD8a⁺ T lymphocytes in spleen and elevated serum cytokines (IFN-γ, IL-2, IL-4, IL-10, IL-12p70, IL-23, MCP-1, and TNF-α) in immunized mice compared to control mice. On the contrary, immunization did not induce high levels of serum IgG antibodies. Five predicted peptides of TgERK7 were synthesized and conjugated with KLH and used to analyze the antibody specificity in the sera of immunized mice. We detected a progressive increase in the antibody level only against TgERK7 peptide A (DEVDKHVLRKYD). Antibody raised against this peptide significantly decreased intracellular proliferation of T. gondii in vitro, suggesting that peptide A can potentially induce a protective antibody response. We also showed that immunization improved the survival rate of mice challenged with a virulent strain and significantly reduced the parasite cyst burden within the brains of chronically infected mice. Our data show that TgERK7-based immunization induced TgERK7 peptide A-specific immune responses that can impart protective immunity against T. gondii infection. The therapeutic potential of targeting ERK7 signaling pathway for future toxoplasmosis treatment is warranted.

Differences in plasma metabolites related to Alzheimer's disease, APOE ε4 status, and ethnicity

INTRODUCTION

We investigated metabolites in plasma to capture systemic biochemical changes associated with Alzheimer's disease (AD).

METHODS

Metabolites in plasma were measured in 59 AD cases and 60 healthy participants of African American (AA), Caribbean Hispanic (CH), and non-Hispanic white (NHW) ancestry using untargeted liquid-chromatography-based ultra-high-resolution mass spectrometry. Metabolite differences between AD and healthy, ethnic groups and apolipoprotein E gene (APOE) ε4 status were analyzed. Untargeted network analysis identified pathways enriched in AD-associated metabolites.

RESULTS

A total of 5929 annotated metabolites were measured. Partial least squares discriminant analysis (PLS-DA) inferred that AD clustered separately from healthy controls (area under the curve [AUC] = 0.9816); discriminating pathways included glycerophospholipid, sphingolipid, and non-essential amino acid (alanine, aspartate, glutamate) metabolism. Metabolic features in AA clustered differently from CH and NHW (AUC = 0.9275), and differed between APOE ε4 carriers and non-carriers (AUC = 0.9972).

DISCUSSION

Metabolites, specifically lipids, were associated with AD, APOE ε4, and ethnic group. Metabolite profiling can identify perturbed AD pathways, but genetic and ancestral background need to be considered.

Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges

Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.

Genetic interaction mapping and exon-resolution functional genomics with a hybrid Cas9-Cas12a platform

Systematic mapping of genetic interactions (GIs) and interrogation of the functions of sizable genomic segments in mammalian cells represent important goals of biomedical research. To advance these goals, we present a CRISPR (clustered regularly interspaced short palindromic repeats)-based screening system for combinatorial genetic manipulation that employs coexpression of CRISPR-associated nucleases 9 and 12a (Cas9 and Cas12a) and machine-learning-optimized libraries of hybrid Cas9-Cas12a guide RNAs. This system, named Cas Hybrid for Multiplexed Editing and screening Applications (CHyMErA), outperforms genetic screens using Cas9 or Cas12a editing alone. Application of CHyMErA to the ablation of mammalian paralog gene pairs reveals extensive GIs and uncovers phenotypes normally masked by functional redundancy. Application of CHyMErA in a chemogenetic interaction screen identifies genes that impact cell growth in response to mTOR pathway inhibition. Moreover, by systematically targeting thousands of alternative splicing events, CHyMErA identifies exons underlying human cell line fitness. CHyMErA thus represents an effective screening approach for GI mapping and the functional analysis of sizable genomic regions, such as alternative exons.

The Firre locus produces a trans-acting RNA molecule that functions in hematopoiesis

RNA has been classically known to play central roles in biology, including maintaining telomeres, protein synthesis, and in sex chromosome compensation. While thousands of long noncoding RNAs (lncRNAs) have been identified, attributing RNA-based roles to lncRNA loci requires assessing whether phenotype(s) could be due to DNA regulatory elements, transcription, or the lncRNA. Here, we use the conserved X chromosome lncRNA locus Firre, as a model to discriminate between DNA- and RNA-mediated effects in vivo. We demonstrate that (i) Firre mutant mice have cell-specific hematopoietic phenotypes, and (ii) upon exposure to lipopolysaccharide, mice overexpressing Firre exhibit increased levels of pro-inflammatory cytokines and impaired survival. (iii) Deletion of Firre does not result in changes in local gene expression, but rather in changes on autosomes that can be rescued by expression of transgenic Firre RNA. Together, our results provide genetic evidence that the Firre locus produces a trans-acting lncRNA that has physiological roles in hematopoiesis.

Genome-wide analysis of Homo sapiens, Arabidopsis thaliana, and Saccharomyces cerevisiae reveals novel attributes of tail-anchored membrane proteins

BACKGROUND

Tail-anchored membrane proteins (TAMPs) differ from other integral membrane proteins, because they contain a single transmembrane domain at the extreme carboxyl-terminus and are therefore obliged to target to membranes post-translationally. Although 3-5% of all transmembrane proteins are predicted to be TAMPs only a small number are well characterized.

RESULTS

To identify novel putative TAMPs across different species, we used TAMPfinder software to identify 859, 657 and 119 putative TAMPs in human (Homo sapiens), plant (Arabidopsis thaliana), and yeast (Saccharomyces cerevisiae), respectively. Bioinformatics analyses of these putative TAMP sequences suggest that the list is highly enriched for authentic TAMPs. To experimentally validate the software predictions several human and plant proteins identified by TAMPfinder that were previously uncharacterized were expressed in cells and visualized at subcellular membranes by fluorescence microscopy and further analyzed by carbonate extraction or by bimolecular fluorescence complementation. With the exception of the pro-apoptotic protein harakiri, which is, peripherally bound to the membrane this subset of novel proteins behave like genuine TAMPs. Comprehensive bioinformatics analysis of the generated TAMP datasets revealed previously unappreciated common and species-specific features such as the unusual size distribution of and the propensity of TAMP proteins to be part of larger complexes. Additionally, novel features of the amino acid sequences that anchor TAMPs to membranes were also revealed.

CONCLUSIONS

The findings in this study more than double the number of predicted annotated TAMPs and provide new insights into the common and species-specific features of TAMPs. Furthermore, the list of TAMPs and annotations provide a resource for further investigation.

Identifying significantly impacted pathways: a comprehensive review and assessment

BACKGROUND

Many high-throughput experiments compare two phenotypes such as disease vs. healthy, with the goal of understanding the underlying biological phenomena characterizing the given phenotype. Because of the importance of this type of analysis, more than 70 pathway analysis methods have been proposed so far. These can be categorized into two main categories: non-topology-based (non-TB) and topology-based (TB). Although some review papers discuss this topic from different aspects, there is no systematic, large-scale assessment of such methods. Furthermore, the majority of the pathway analysis approaches rely on the assumption of uniformity of p values under the null hypothesis, which is often not true.

RESULTS

This article presents the most comprehensive comparative study on pathway analysis methods available to date. We compare the actual performance of 13 widely used pathway analysis methods in over 1085 analyses. These comparisons were performed using 2601 samples from 75 human disease data sets and 121 samples from 11 knockout mouse data sets. In addition, we investigate the extent to which each method is biased under the null hypothesis. Together, these data and results constitute a reliable benchmark against which future pathway analysis methods could and should be tested.

CONCLUSION

Overall, the result shows that no method is perfect. In general, TB methods appear to perform better than non-TB methods. This is somewhat expected since the TB methods take into consideration the structure of the pathway which is meant to describe the underlying phenomena. We also discover that most, if not all, listed approaches are biased and can produce skewed results under the null.

Evolution-based screening enables genome-wide prioritization and discovery of DNA repair genes

DNA repair is critical for genome stability and is maintained through conserved pathways. Traditional genome-wide mammalian screens are both expensive and laborious. However, computational approaches circumvent these limitations and are a powerful tool to identify new DNA repair factors. By analyzing the evolutionary relationships between genes in the major DNA repair pathways, we uncovered functional relationships between individual genes and identified partners. Here we ranked 17,487 mammalian genes for coevolution with 6 distinct DNA repair pathways. Direct comparison to genetic screens for homologous recombination or Fanconi anemia factors indicates that our evolution-based screen is comparable, if not superior, to traditional screening approaches. Demonstrating the utility of our strategy, we identify a role for the DNA damage-induced apoptosis suppressor (DDIAS) gene in double-strand break repair based on its coevolution with homologous recombination. DDIAS knockdown results in DNA double-strand breaks, indicated by ATM kinase activation and 53BP1 foci induction. Additionally, DDIAS-depleted cells are deficient for homologous recombination. Our results reveal that evolutionary analysis is a powerful tool to uncover novel factors and functional relationships in DNA repair.

Signatures of adaptive divergence among populations of an avian species of conservation concern

Understanding the genetic underpinning of adaptive divergence among populations is a key goal of evolutionary biology and conservation. Gunnison sage-grouse (Centrocercus minimus) is a sagebrush obligate species with a constricted range consisting of seven discrete populations, each with distinctly different habitat and climatic conditions. Though geographically close, populations have low levels of natural gene flow resulting in relatively high levels of differentiation. Here, we use 15,033 SNP loci in genomic outlier analyses, genotype-environment association analyses, and gene ontology enrichment tests to examine patterns of putatively adaptive genetic differentiation in an avian species of conservation concern. We found 411 loci within 5 kbp of 289 putative genes associated with biological functions or pathways that were overrepresented in the assemblage of outlier SNPs. The identified gene set was enriched for cytochrome P450 gene family members (CYP4V2, CYP2R1, CYP2C23B, CYP4B1) and could impact metabolism of plant secondary metabolites, a critical challenge for sagebrush obligates. Additionally, the gene set was also enriched with members potentially involved in antiviral response (DEAD box helicase gene family and SETX). Our results provide a first look at local adaption for isolated populations of a single species and suggest adaptive divergence in multiple metabolic and biochemical pathways may be occurring. This information can be useful in managing this species of conservation concern, for example, to identify unique populations to conserve, avoid translocation or release of individuals that may swamp locally adapted genetic diversity, or guide habitat restoration efforts.

Computational Network Analysis Identifies Evolutionarily Conserved miRNA Gene Interactions Potentially Regulating Immune Response in Bovine Trypanosomosis

Bovine trypanosomosis is a devastating disease that causes huge economic loss to the global cattle industry on a yearly basis. Selection of accurate biomarkers are important in early disease diagnosis and treatment. Of late, micro-RNAs (miRNAs) are becoming the most useful biomarkers for both infectious and non-infectious diseases in humans, but this is not the case in animals. miRNAs are non-coding RNAs that regulate gene expression through binding to the 3'-, 5'-untranslated regions (UTR) or coding sequence (CDS) region of one or more target genes. The molecular identification of miRNAs that regulates the expression of immune genes responding to bovine trypanosomosis is poorly defined, as is the possibility that these miRNAs could serve as potential biomarkers for disease diagnosis and treatment currently unknown. To this end, we utilized in silico tools to elucidate conserved miRNAs regulating immune response genes during infection, in addition to cataloging significant genes. Based on the p value of 1.77E-32, we selected 25 significantly expressed immune genes. Using prediction analysis, we identified a total of 4,251 bovine miRNAs targeting these selected genes across the 3'UTR, 5'UTR and CDS regions. Thereafter, we identified candidate miRNAs based on the number of gene targets and their abundance at the three regions. In all, we found the top 13 miRNAs that are significantly conserved targeting 7 innate immune response genes, including bta-mir-2460, bta-mir-193a, bta-mir-2316, and bta-mir-2456. Our gene ontology analysis suggests that these miRNAs are involved in gene silencing, cellular protein modification process, RNA-induced silencing complex, regulation of humoral immune response mediated by circulating immunoglobulin and negative regulation of chronic inflammatory response, among others. In conclusion, this study identifies specific miRNAs that may be involved in the regulation of gene expression during bovine trypanosomosis. These miRNAs have the potential to be used as biomarkers in the animal and veterinary research community to facilitate the development of tools for early disease diagnosis/detection, drug targeting, and the rational design of drugs to facilitate disease treatment.

The Set1 complex is dimeric and acts with Jhd2 demethylation to convey symmetrical H3K4 trimethylation

In this study, Choudhury et al. report that yeast Set1C/COMPASS is dimeric and, consequently, symmetrically trimethylates histone 3 Lys4 (H3K4me3) on promoter nucleosomes. This presents a new paradigm for the establishment of epigenetic detail, in which dimeric methyltransferase and monomeric demethylase cooperate to eliminate asymmetry and focus symmetrical H3K4me3 onto selected nucleosomes.

Epigenetic modifications can maintain or alter the inherent symmetry of the nucleosome. However, the mechanisms that deposit and/or propagate symmetry or asymmetry are not understood. Here we report that yeast Set1C/COMPASS (complex of proteins associated with Set1) is dimeric and, consequently, symmetrically trimethylates histone 3 Lys4 (H3K4me3) on promoter nucleosomes. Mutation of the dimer interface to make Set1C monomeric abolished H3K4me3 on most promoters. The most active promoters, particularly those involved in the oxidative phase of the yeast metabolic cycle, displayed H3K4me2, which is normally excluded from active promoters, and a subset of these also displayed H3K4me3. In wild-type yeast, deletion of the sole H3K4 demethylase, Jhd2, has no effect. However, in monomeric Set1C yeast, Jhd2 deletion increased H3K4me3 levels on the H3K4me2 promoters. Notably, the association of Set1C with the elongating polymerase was not perturbed by monomerization. These results imply that symmetrical H3K4 methylation is an embedded consequence of Set1C dimerism and that Jhd2 demethylates asymmetric H3K4me3. Consequently, rather than methylation and demethylation acting in opposition as logic would suggest, a dimeric methyltransferase and monomeric demethylase cooperate to eliminate asymmetry and focus symmetrical H3K4me3 onto selected nucleosomes. This presents a new paradigm for the establishment of epigenetic detail.

Computational prediction of the bioactivity potential of proteomes based on expert knowledge

Advances in the field of genome sequencing have enabled a comprehensive analysis and annotation of the dynamics of the protein inventory of cells. This has been proven particularly rewarding for microbial cells, for which the majority of proteins are already accessible to analysis through automatic metagenome annotation. The large-scale in silico screening of proteomes and metaproteomes is key to uncover bioactivities of translational, clinical and biotechnological interest, and to help assign functions to certain proteins, such as those predicted as hypothetical. This work introduces a new method for the prediction of the bioactivity potential of proteomes/metaproteomes, supporting the discovery of functionally relevant proteins based on prior knowledge. This methodology complements functional annotation enrichment methods by allowing the assignment of functions to proteins annotated as hypothetical/putative/uncharacterised, as well as and enabling the detection of specific bioactivities and the recovery of proteins from defined taxa. This work shows how the new method can be applied to screen proteome and metaproteome sets to obtain predictions of clinical or biotechnological interest based on reference datasets. Notably, with this methodology, the large information files obtained after DNA sequencing or protein identification experiments can be associated for translational purposes that, in cases such as antibiotic-resistance pathogens or foodborne diseases, may represent changes in how these important and global health burdens are approached in the clinical practice. Finally, the Sequence-based Expert-driven pRoteome bioactivity Prediction EnvironmENT, a public Web service implemented in Scala functional programming style, is introduced as means to ensure broad access to the method as well as to discuss main implementation issues, such as modularity, extensibility and interoperability.

An Integrative Systems Biology and Experimental Approach Identifies Convergence of Epithelial Plasticity, Metabolism, and Autophagy to Promote Chemoresistance

The evolution of therapeutic resistance is a major cause of death for cancer patients. The development of therapy resistance is shaped by the ecological dynamics within the tumor microenvironment and the selective pressure of the host immune system. These selective forces often lead to evolutionary convergence on pathways or hallmarks that drive progression. Thus, a deeper understanding of the evolutionary convergences that occur could reveal vulnerabilities to treat therapy-resistant cancer. To this end, we combined phylogenetic clustering, systems biology analyses, and molecular experimentation to identify convergences in gene expression data onto common signaling pathways. We applied these methods to derive new insights about the networks at play during transforming growth factor-β (TGF-β)-mediated epithelial–mesenchymal transition in lung cancer. Phylogenetic analyses of gene expression data from TGF-β-treated cells revealed convergence of cells toward amine metabolic pathways and autophagy during TGF-β treatment. Knockdown of the autophagy regulatory, ATG16L1, re-sensitized lung cancer cells to cancer therapies following TGF-β-induced resistance, implicating autophagy as a TGF-β-mediated chemoresistance mechanism. In addition, high ATG16L expression was found to be a poor prognostic marker in multiple cancer types. These analyses reveal the usefulness of combining evolutionary and systems biology methods with experimental validation to illuminate new therapeutic vulnerabilities for cancer.

Characterization of Proteome Variation During Modern Maize Breeding

The success of modern maize breeding has been demonstrated by remarkable increases in productivity with tremendous modification of agricultural phenotypes over the last century. Although the underlying genetic changes of the maize adaptation from tropical to temperate regions have been extensively studied, our knowledge is limited regarding the accordance of protein and mRNA expression levels accompanying such adaptation. Here we conducted an integrative analysis of proteomic and transcriptomic changes in a maize association panel. The minimum extent of correlation between protein and RNA levels suggests that variation in mRNA expression is often not indicative of protein expression at a population scale. This is corroborated by the observation that mRNA- and protein-based coexpression networks are relatively independent of each other, and many pQTLs arise without the presence of corresponding eQTLs. Importantly, compared with transcriptome, the subtypes categorized by the proteome show a markedly high accuracy to resemble the genomic subpopulation. These findings suggest that proteome evolved under a greater evolutionary constraint than transcriptome during maize adaptation from tropical to temperate regions. Overall, the integrated multi-omics analysis provides a functional context to interpret gene expression variation during modern maize breeding.

A common neighbor based technique to detect protein complexes in PPI networks

Detection of protein complexes by analyzing and understanding PPI networks is an important task and critical to all aspects of cell biology. We present a technique called PROtein COmplex DEtection based on common neighborhood (PROCODE) that considers the inherent organization of protein complexes as well as the regions with heavy interactions in PPI networks to detect protein complexes. Initially, the core of the protein complexes is detected based on the neighborhood of PPI network. Then a merging strategy based on density is used to attach proteins and protein complexes to the core-protein complexes to form biologically meaningful structures. The predicted protein complexes of PROCODE was evaluated and analyzed using four PPI network datasets out of which three were from budding yeast and one from human. Our proposed technique is compared with some of the existing techniques using standard benchmark complexes and PROCODE was found to match very well with actual protein complexes in the benchmark data. The detected complexes were at par with existing biological evidence and knowledge.

Graph coloring for extracting discriminative genes in cancer data

BACKGROUND AND OBJECTIVE

The major difficulty of the analysis of the input gene expression data in a microarray-based approach for an automated diagnosis of cancer is the large number of genes (high dimensionality) with many irrelevant genes (noise) compared to the very small number of samples. This research study tackles the dimensionality reduction challenge in this area.

METHODS

This research study introduces a dimension-reduction technique termed graph coloring approach (GCA) for microarray data-based cancer classification based on analyzing the absolute correlation between gene-gene pairs and partitioning genes into several hubs using graph coloring. GCA starts by a gene-selection step in which top relevant genes are selected using a biserial correlation. Each time, a gene from an ordered list of top relevant genes is selected as the hub gene (representative) and redundant genes are added to its group; the process is repeated recursively for the remaining genes. A gene is considered redundant if its absolute correlation with the hub gene is greater than a controlling threshold. A suitable range for the threshold is estimated by computing a percentage graph for the absolute correlation between gene-gene pairs. Each value in the estimated range for the threshold can efficiently produce a new feature subset.

RESULTS

GCA achieved significant improvement over several existing techniques in terms of higher accuracy and a smaller number of features. Also, genes selected by this method are relevant genes according to the information stored in scientific repositories.

CONCLUSIONS

The proposed dimension-reduction technique can help biologists accurately predict cancer in several areas of the body.

Computational Methods to Investigate the Impact of miRNAs on Pathways

Pathway analysis is a wide class of methods allowing to determine the alteration of functional processes in complex diseases. However, biological pathways are still partial, and knowledge coming from posttranscriptional regulators has started to be considered in a systematic way only recently. Here we will give a global and updated view of the main pathway and subpathway analysis methodologies, focusing on the improvements obtained through the recent introduction of microRNAs as regulatory elements in these frameworks.

Proximity-dependent biotinylation by TurboID to identify protein-protein interaction networks in yeast

The use of proximity-dependent biotinylation assays coupled to mass spectrometry (PDB-MS) has changed the field of protein-protein interaction studies. Yet, despite the recurrent and successful use of BioID-based protein-protein interactions screening in mammalian cells, the implementation of PDB-MS in yeast has not been effective. Here we report a simple and rapid approach in yeast to effectively screen for proximal and interacting proteins in their natural cellular environment by using TurboID, a recently described version of the BirA biotin ligase. Using the protein arginine methyltransferase Rmt3 and the RNA exosome subunits, Rrp6 and Dis3, the application of PDB-MS in yeast by using TurboID was able to recover protein-protein interactions previously identified using other biochemical approaches and provided new complementary information for a given protein bait. The development of a rapid and effective PDB assay that can systematically analyze protein-protein interactions in living yeast cells opens the way for large-scale proteomics studies in this powerful model organism.

GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

Background

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists’ capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter-gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets.

Results

In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets’ ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets.

Conclusions

Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0642-2) contains supplementary material, which is available to authorized users.

The pervasive effects of recombinant Fasciola gigantica Ras-related protein Rab10 on the functions of goat peripheral blood mononuclear cells

BACKGROUND

Fasciola gigantica-induced immunomodulation is a major hurdle faced by the host for controlling infection. Here, we elucidated the role of F. gigantica Ras-related protein Rab10 (FgRab10) in the modulation of key functions of peripheral blood mononuclear cells (PBMCs) of goats.

METHODS

We cloned and expressed recombinant FgRab10 (rFgRab10) protein and examined its effects on several functions of goat PBMCs. Protein interactors of rFgRab10 were predicted in silico by querying the databases Intact, String, BioPlex and BioGrid. In addition, a total energy analysis of each of the identified interactions was also conducted. Gene Ontology (GO) enrichment analysis was carried out using FuncAssociate 3.0.

RESULTS

The FgRab10 gene (618 bp), encodes 205-amino-acid residues with a molecular mass of ~23 kDa, had complete nucleotide sequence homology with F. hepatica Ras family protein gene (PIS87503.1). The rFgRab10 protein specifically cross-reacted with anti-Fasciola antibodies as shown by Western blot and immunofluorescence analysis. This protein exhibited multiple effects on goat PBMCs, including increased production of cytokines [interleukin-2 (IL-2), IL-4, IL-10, transforming growth factor beta (TGF-β) and interferon gamma (IFN-γ)] and total nitric oxide (NO), enhancing apoptosis and migration of PBMCs, and promoting the phagocytic ability of monocytes. However, it significantly inhibited cell proliferation. Homology modelling revealed 63% identity between rFgRab10 and human Rab10 protein (Uniprot ID: P61026). Protein interaction network analysis revealed more stabilizing interactions between Rab proteins geranylgeranyltransferase component A 1 (CHM) and Rab proteins geranylgeranyltransferase component A 2 (CHML) and rFgRab10 protein. Gene Ontology analysis identified RabGTPase mediated signaling as the most represented pathway.

CONCLUSIONS

rFgRab10 protein exerts profound influences on various functions of goat PBMCs. This finding may help explain why F. gigantica is capable of provoking recognition by host immune cells, less capable of destroying this successful parasite.

"Super Gene Set" Causal Relationship Discovery from Functional Genomics Data

In this article, we present a computational framework to identify "causal relationships" among super gene sets. For "causal relationships," we refer to both stimulatory and inhibitory regulatory relationships, regardless of through direct or indirect mechanisms. For super gene sets, we refer to "pathways, annotated lists, and gene signatures," or PAGs. To identify causal relationships among PAGs, we extend the previous work on identifying PAG-to-PAG regulatory relationships by further requiring them to be significantly enriched with gene-to-gene co-expression pairs across the two PAGs involved. This is achieved by developing a quantitative metric based on PAG-to-PAG Co-expressions (PPC), which we use to infer the likelihood that PAG-to-PAG relationships under examination are causal-either stimulatory or inhibitory. Since true causal relationships are unknown, we approximate the overall performance of inferring causal relationships with the performance of recalling known r-type PAG-to-PAG relationships from causal PAG-to-PAG inference, using a functional genomics benchmark dataset from the GEO database. We report the area-under-curve (AUC) performance for both precision and recall being 0.81. By applying our framework to a myeloid-derived suppressor cells (MDSC) dataset, we further demonstrate that this framework is effective in helping build multi-scale biomolecular systems models with new insights on regulatory and causal links for downstream biological interpretations.

Systems-level analysis of risk genes reveals the modular nature of schizophrenia

Schizophrenia (SCZ) is a complex mental disorder with high heritability. Genetic studies (especially recent genome-wide association studies) have identified many risk genes for schizophrenia. However, the physical interactions among the proteins encoded by schizophrenia risk genes remain elusive and it is not known whether the identified risk genes converge on common molecular networks or pathways. Here we systematically investigated the network characteristics of schizophrenia risk genes using the high-confidence protein-protein interactions (PPI) from the human interactome. We found that schizophrenia risk genes encode a densely interconnected PPI network (P = 4.15 × 10^-31). Compared with the background genes, the schizophrenia risk genes in the interactome have significantly higher degree (P = 5.39 × 10^-11), closeness centrality (P = 7.56 × 10^-11), betweeness centrality (P = 1.29 × 10^-11), clustering coefficient (P = 2.22 × 10^-2), and shorter average shortest path length (P = 7.56 × 10^-11). Based on the densely interconnected PPI network, we identified 48 hub genes and 4 modules formed by highly interconnected schizophrenia genes. We showed that the proteins encoded by schizophrenia hub genes have significantly more direct physical interactions. Gene ontology (GO) analysis revealed that cell adhesion, cell cycle, immune system response, and GABR-receptor complex categories were enriched in the modules formed by highly interconnected schizophrenia risk genes. Our study reveals that schizophrenia risk genes encode a densely interconnected molecular network and demonstrates the modular nature of schizophrenia.

CASS: A distributed network clustering algorithm based on structure similarity for large-scale network

As the size of networks increases, it is becoming important to analyze large-scale network data. A network clustering algorithm is useful for analysis of network data. Conventional network clustering algorithms in a single machine environment rather than a parallel machine environment are actively being researched. However, these algorithms cannot analyze large-scale network data because of memory size issues. As a solution, we propose a network clustering algorithm for large-scale network data analysis using Apache Spark by changing the paradigm of the conventional clustering algorithm to improve its efficiency in the Apache Spark environment. We also apply optimization approaches such as Bloom filter and shuffle selection to reduce memory usage and execution time. By evaluating our proposed algorithm based on an average normalized cut, we confirmed that the algorithm can analyze diverse large-scale network datasets such as biological, co-authorship, internet topology and social networks. Experimental results show that the proposed algorithm can develop more accurate clusters than comparative algorithms with less memory usage. Furthermore, we confirm the proposed optimization approaches and the scalability of the proposed algorithm. In addition, we validate that clusters found from the proposed algorithm can represent biologically meaningful functions.

An application of the Shapley value to the analysis of co-expression networks

We study the problem of identifying relevant genes in a co-expression network using a (cooperative) game theoretic approach. The Shapley value of a cooperative game is used to asses the relevance of each gene in interaction with the others, and to stress the role of nodes in the periphery of a co-expression network for the regulation of complex biological pathways of interest. An application of the method to the analysis of gene expression data from microarrays is presented, as well as a comparison with classical centrality indices. Finally, making further assumptions about the a priori importance of genes, we combine the game theoretic model with other techniques from cluster analysis.

Gli3 controls the onset of cortical neurogenesis by regulating the radial glial cell cycle through Cdk6 expression

The cerebral cortex contains an enormous number of neurons, allowing it to perform highly complex neural tasks. Understanding how these neurons develop at the correct time and place and in accurate numbers constitutes a major challenge. Here, we demonstrate a novel role for Gli3, a key regulator of cortical development, in cortical neurogenesis. We show that the onset of neuron formation is delayed in Gli3 conditional mouse mutants. Gene expression profiling and cell cycle measurements indicate that shortening of the G1 and S phases in radial glial cells precedes this delay. Reduced G1 length correlates with an upregulation of the cyclin-dependent kinase gene Cdk6, which is directly regulated by Gli3. Moreover, pharmacological interference with Cdk6 function rescues the delayed neurogenesis in Gli3 mutant embryos. Overall, our data indicate that Gli3 controls the onset of cortical neurogenesis by determining the levels of Cdk6 expression, thereby regulating neuronal output and cortical size.