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Baltoumas FA, Karatzas E, Paez-Espino D, Venetsianou NK, Aplakidou E, Oulas A, Finn RD, Ovchinnikov S, Pafilis E, Kyrpides NC, Pavlopoulos GA. Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters. FRONTIERS IN BIOINFORMATICS 2023; 3:1157956. [PMID: 36959975 PMCID: PMC10029925 DOI: 10.3389/fbinf.2023.1157956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023] Open
Abstract
Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.
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Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
- *Correspondence: Fotis A. Baltoumas, ; Nikos C. Kyrpides, ; Georgios A. Pavlopoulos,
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - David Paez-Espino
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
| | - Nefeli K. Venetsianou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
| | - Anastasis Oulas
- The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Robert D. Finn
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, United Kingdom
| | - Sergey Ovchinnikov
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, United States
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece
| | - Nikos C. Kyrpides
- Lawrence Berkeley National Laboratory, DOE Joint Genome Institute, Berkeley, CA, United States
- *Correspondence: Fotis A. Baltoumas, ; Nikos C. Kyrpides, ; Georgios A. Pavlopoulos,
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC “Alexander Fleming”, Vari, Greece
- Center of New Biotechnologies and Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Athens, Greece
- Hellenic Army Academy, Vari, Greece
- *Correspondence: Fotis A. Baltoumas, ; Nikos C. Kyrpides, ; Georgios A. Pavlopoulos,
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Tian C, Qiu M, Lv H, Yue F, Zhou F. Quantitative Proteomic Analysis of Serum Reveals MST1 as a Potential Candidate Biomarker in Spontaneously Diabetic Cynomolgus Monkeys. ACS OMEGA 2022; 7:46702-46716. [PMID: 36570245 PMCID: PMC9774375 DOI: 10.1021/acsomega.2c05663] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
The prevalence of type 2 diabetes (T2DM) is increasing globally, creating essential demands for T2DM animal models for the study of disease pathogenesis, prevention, and therapy. A non-human primate model such as cynomolgus monkeys can develop T2DM spontaneously in an age-dependent way similar to humans. In this study, a data-independent acquisition-based quantitative proteomics strategy was employed to investigate the serum proteomic profiles of spontaneously diabetic cynomolgus monkeys compared with healthy controls. The results revealed significant differences in protein abundances. A total of 95 differentially expressed proteins (DEPs) were quantitatively identified in the current study, among which 31 and 64 proteins were significantly upregulated and downregulated, respectively. Bioinformatic analysis revealed that carbohydrate digestion and absorption was the top enriched pathway by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction network analysis demonstrated that MST1 was identified as the most connected protein in the network and could be considered as the hub protein. MST1 was significantly and inversely associated with FSG and HbA1c. Furthermore, recent lines of evidence also indicate that MST1 acts as a crucial regulator in regulating hepatic gluconeogenesis to maintain metabolic homeostasis while simultaneously suppressing the inflammatory processes. In conclusion, our study provides novel insights into serum proteome changes in spontaneously diabetic cynomolgus monkeys and points out that the dysregulation of several DEPs may play an important role in the pathogenesis of T2DM.
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Affiliation(s)
- Chaoyang Tian
- Key
Laboratory of Biomedical Engineering of Hainan Province, School of
Biomedical Engineering, Hainan University, Haikou, Hainan 570228, China
- One
Health Institute, Hainan University, Haikou, Hainan 570228, China
| | - Mingyin Qiu
- Animal
Experiment Department, Hainan Jingang Biotech
Co., Ltd., Haikou, Hainan 571100, China
| | - Haizhou Lv
- Animal
Experiment Department, Hainan Jingang Biotech
Co., Ltd., Haikou, Hainan 571100, China
| | - Feng Yue
- Key
Laboratory of Biomedical Engineering of Hainan Province, School of
Biomedical Engineering, Hainan University, Haikou, Hainan 570228, China
- One
Health Institute, Hainan University, Haikou, Hainan 570228, China
| | - Feifan Zhou
- Key
Laboratory of Biomedical Engineering of Hainan Province, School of
Biomedical Engineering, Hainan University, Haikou, Hainan 570228, China
- One
Health Institute, Hainan University, Haikou, Hainan 570228, China
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Anatolou D, Dovrolis N, Ragia G, Kolios G, Manolopoulos VG. Unpacking COVID-19 Systems Biology in Lung and Whole Blood with Transcriptomics and miRNA Regulators. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:608-621. [PMID: 36269619 DOI: 10.1089/omi.2022.0104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
COVID-19 is a systemic disease affecting tissues and organs, including and beyond the lung. Apart from the current pandemic context, we also have vastly inadequate knowledge of consequences of repeated exposures to SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the virus causing COVID-19, in multiple organ systems and the whole organism scales when the disease evolves from a pandemic to an endemic state. This calls for a systems biology and systems medicine approach and unpacking the effects of COVID-19 in lung as well as other tissues. We report here original findings from transcriptomics analyses and differentially expressed genes (DEGs) in lung samples from 60 patients and 27 healthy controls, and in whole blood samples from 255 patients and 103 healthy individuals. A total of 11 datasets with RNA-seq transcriptomic data were obtained from the Gene Expression Omnibus and the European Nucleotide Archive. The identified DEGs were used to construct protein interaction and functional networks and to identify related pathways and miRNAs. We found 35 DEGs common between lung and the whole blood, and importantly, 2 novel genes, namely CYP1B1 and TNFAIP6, which have not been previously implicated with COVID-19. We also identified four novel miRNA potential regulators, hsa-mir-192-5p, hsa-mir-221-3p, hsa-mir-4756-3p, and hsa-mir-10a-5p, implicated in lung or other diseases induced by coronaviruses. In summary, these findings offer new molecular leads and insights to unpack COVID-19 systems biology in a whole organism context and might inform future antiviral drug, diagnostics, and vaccine discovery efforts.
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Affiliation(s)
- Dimitra Anatolou
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
- Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Nikolas Dovrolis
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
- Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Georgia Ragia
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
- Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - George Kolios
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
- Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
| | - Vangelis G Manolopoulos
- Laboratory of Pharmacology, Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
- Individualised Medicine & Pharmacological Research Solutions Center (IMPReS), Alexandroupolis, Greece
- Clinical Pharmacology Unit, Academic General Hospital of Alexandroupolis, Alexandroupolis, Greece
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54
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Nucleotide-based genetic networks: Methods and applications. J Biosci 2022. [PMID: 36226367 PMCID: PMC9554864 DOI: 10.1007/s12038-022-00290-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Genomic variations have been acclaimed as among the key players in understanding the biological mechanisms behind migration, evolution, and adaptation to extreme conditions. Due to stochastic evolutionary forces, the frequency of polymorphisms is affected by changes in the frequency of nearby polymorphisms in the same DNA sample, making them connected in terms of evolution. This article presents all the ingredients to understand the cumulative effects and complex behaviors of genetic variations in the human mitochondrial genome by analyzing co-occurrence networks of nucleotides, and shows key results obtained from such analyses. The article emphasizes recent investigations of these co-occurrence networks, describing the role of interactions between nucleotides in fundamental processes of human migration and viral evolution. The corresponding co-mutation-based genetic networks revealed genetic signatures of human adaptation in extreme environments. This article provides the methods of constructing such networks in detail, along with their graph-theoretical properties, and applications of the genomic networks in understanding the role of nucleotide co-evolution in evolution of the whole genome.
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55
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Lai JW, Cheong KH. A comprehensive framework for preference aggregation Parrondo's paradox. CHAOS (WOODBURY, N.Y.) 2022; 32:103107. [PMID: 36319284 DOI: 10.1063/5.0101321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
Individuals can make choices for themselves that are beneficial or detrimental to the entire group. Consider two losing choices that some individuals have to make on behalf of the group. Is it possible that the losing choices combine to give a winning outcome? We show that it is possible through a variant of Parrondo's paradox-the preference aggregation Parrondo's paradox (PAPP). This new variant of Parrondo's paradox makes use of an aggregate rule that combines with a decision-making heuristic that can be applied to individuals or parts of the social group. The aim of this work is to discuss this PAPP framework and exemplify it on a social network. This work enhances existing research by constructing a feedback loop that allows individuals in the social network to adapt its behavior according to the outcome of the Parrondo's games played.
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Affiliation(s)
- Joel Weijia Lai
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, S487372 Singapore
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, S487372 Singapore
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56
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Smart ECG Biosensor Design with an Improved ANN Performance Based on the Taguchi Optimizer. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9090482. [PMID: 36135028 PMCID: PMC9495665 DOI: 10.3390/bioengineering9090482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022]
Abstract
This paper aims to design a smart biosensor to predict electrocardiogram (ECG) signals in a specific auscultation site from other ECG signals measured from other measurement sites. The proposed design is based on a hybrid architecture using the Artificial Neural Networks (ANNs) model and Taguchi optimizer to avoid the ANN issues related to hyperparameters and to improve its accuracy. The proposed approach aims to optimize the number and type of inputs to be considered for the ANN model. Indeed, different combinations are considered in order to find the optimal input combination for the best prediction quality. By identifying the factors that influence a model’s prediction and their degree of importance via the modified Taguchi optimizer, the developed biosensor improves the prediction accuracy of ECG signals collected from different auscultation sites compared to the ANN-based biosensor. Based on an actual database, the simulation results show that this improvement is significant; it can reach more than 94% accuracy.
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57
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Centurion VB, Campanaro S, Basile A, Treu L, Oliveira VM. Microbiome structure in biofilms from a volcanic island in Maritime Antarctica investigated by genome-centric metagenomics and metatranscriptomics. Microbiol Res 2022; 265:127197. [PMID: 36174355 DOI: 10.1016/j.micres.2022.127197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 09/08/2022] [Accepted: 09/13/2022] [Indexed: 10/14/2022]
Abstract
Antarctica is the coldest and driest continent on Earth, characterized by polyextreme environmental conditions, where species adapted form complex networks of interactions. Microbial communities growing in these harsh environments can form biofilms that help the associated species to survive and thrive. A rich body of knowledge describes environmental biofilm communities; however, most studies have focused on dominant community members rather than functional complexity and metabolic potential. To overcome these limitations, the present study used genome-centric metagenomics to describe two biofilm samples subjected to different temperature collected in Deception Island, Maritime Antarctica. The results unraveled a complex biofilm microbiome represented by 180 metagenome-assembled genomes. The potential metabolic interactions were investigated using metabolic flux balance analysis and revealed that purple bacteria are the community members with the highest correlations with other bacteria. Due to their predicted mixotrophic behavior, they may play a crucial role in the microbiome, likely supporting the heterotrophic species in biofilms. Metatranscriptomics results revealed that the chaperone system and proteins counteracting ROS and toxic compounds have a major role in maintaining bacterial cell homeostasis in sediments of volcanic origin.
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Affiliation(s)
- V B Centurion
- Microbial Resources Division, Research Center for Chemistry, Biology, and Agriculture (CPQBA), State University of Campinas - UNICAMP, Paulínia, SP CEP 13081-970, Brazil; Biology Institute, State University of Campinas - UNICAMP, Campinas, SP CEP 13083-862, Brazil.
| | - S Campanaro
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padua, Italy; CRIBI Biotechnology Center, University of Padova, 35131 Padua, Italy.
| | - A Basile
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padua, Italy.
| | - L Treu
- Department of Biology, University of Padova, Via U. Bassi 58/b, 35121 Padua, Italy.
| | - V M Oliveira
- Microbial Resources Division, Research Center for Chemistry, Biology, and Agriculture (CPQBA), State University of Campinas - UNICAMP, Paulínia, SP CEP 13081-970, Brazil.
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58
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Loske P, Schelter BO. Inferring the underlying multivariate structure from bivariate networks with highly correlated nodes. Sci Rep 2022; 12:12486. [PMID: 35864116 PMCID: PMC9304421 DOI: 10.1038/s41598-022-16296-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 07/07/2022] [Indexed: 11/09/2022] Open
Abstract
Complex systems are often described mathematically as networks. Inferring the actual interactions from observed dynamics of the nodes of the networks is a challenging inverse task. It is crucial to distinguish direct and indirect interactions to allow for a robust identification of the underlying network. If strong and weak links are simultaneously present in the observed network, typical multivariate approaches to address this challenge fail. By means of correlation and partial correlation, we illustrate the challenges that arise and demonstrate how to overcome these. The challenge of strong and weak links translates into ill-conditioned matrices that need to be inverted to obtain the partial correlations, and therefore the correct network topology. Our novel procedure enables robust identification of multivariate network topologies in the presence of highly correlated processes. In applications, this is crucial to avoid erroneous conclusions about network structures and characteristics. Our novel approach applies to other types of interaction measures between processes in a network.
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Affiliation(s)
- Philipp Loske
- Aberdeen Biomedical Imaging Center, University of Aberdeen, Foresterhill, Aberdeen, UK.
| | - Bjoern O Schelter
- TauRx Therapeutics Ltd., Aberdeen, UK
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, UK
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KrishnaPriya S, Omer S, Banerjee S, Karunagaran D, Suraishkumar GK. An integrated approach to understand fluid shear stress-driven and reactive oxygen species-mediated metastasis of colon adenocarcinoma through mRNA-miRNA-lncRNA-circRNA networks. Mol Genet Genomics 2022; 297:1353-1370. [PMID: 35831469 DOI: 10.1007/s00438-022-01924-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 07/01/2022] [Indexed: 11/28/2022]
Abstract
Development of colon adenocarcinoma (COAD) metastasis involves several mediators including fluid shear stress (FSS), intracellular ROS levels, and non-coding RNAs. In our present study, we identified and investigated the role of regulatory non-coding RNA molecules specifically involved in COAD metastasis and their association with FSS and ROS. Interactions between the mRNAs associated with FSS and ROS, the corresponding microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) in COAD metastasis were used to generate the mRNA-miRNA-lncRNA-circRNA network. Experimental validation of the identified RNA hubs using quantitative real-time PCR demonstrated a direct effect of the FSS on their expression levels in cancer cells. FSS resulted in the downregulation of HMGA1 and RAN, as well as the upregulation of HSP90AA1, PMAIP1 and BIRC5. Application of shear stress also led to downregulation of hsa-miR-26b-5p and hsa-miR-34a-5p levels in HCT116 cells. Further, functional enrichment and survival analysis of the significant miRNAs, as well as the OncoPrint and the survival analyses of the selected mRNAs were performed. Subsequently, their functional role was also corroborated with existing literature. Ten significant miRNA hubs were identified, out of which hsa-miR-17-5p and hsa-miR-20a-5p were found to interact with lncRNA (CCAT2) while hsa-miR-335 was found to interact with four circRNAs. Fifteen significant miRNAs were identified in 10 different modules suggesting their importance in FSS and ROS-mediated COAD metastasis. Finally, 10 miRNAs and 3 mRNAs associated with FSS and/or ROS were identified as significant overall survival markers; 33 mRNAs were also identified as metastasis-free survival markers whereas 15 mRNAs showed > 10% gene alterations in TCGA-COAD data and may serve as promising therapeutic biomarkers in the COAD metastasis.
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Affiliation(s)
- Siluveru KrishnaPriya
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600036, India
| | - Sonal Omer
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600036, India
| | - Satarupa Banerjee
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600036, India. .,School of Bioscience and Technology, Vellore Institute of Technology, Vellore, India.
| | - Devarajan Karunagaran
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600036, India
| | - G K Suraishkumar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, 600036, India
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60
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Limbic covariance network alterations in patients with transient global amnesia. J Neurol 2022; 269:5954-5962. [PMID: 35809126 DOI: 10.1007/s00415-022-11263-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/21/2022] [Accepted: 06/27/2022] [Indexed: 10/17/2022]
Abstract
BACKGROUND We compared limbic structure volumes and graph theory parameters of the limbic covariance network between patients with transient global amnesia (TGA) and healthy controls, and between patients with single and recurrent TGA events. METHODS We retrospectively enrolled 122 patients with TGA (single event, n = 107; recurrent events, n = 15) and 50 healthy controls who underwent three-dimensional T1-weighted MRI imaging of the brain. Volumetric analysis of the subcortical limbic structures, including the hippocampus, amygdala, thalamus, mammillary body, hypothalamus, basal forebrain, septal nuclei, fornix, and nucleus accumbens, was performed. We examined the limbic covariance network using a graph theory. RESULTS Limbic structure volumes did not differ between patients with TGA and healthy controls, and between patients with a single event and those with recurrent events. However, the radius of the limbic covariance network was significantly greater in patients with TGA than in healthy controls (6.595 vs. 4.564, p = 0.040). Furthermore, the radius, diameter, eccentricity, and characteristics path length were greater (4.066 vs. 2.000, p = 0.009; 7.062 vs. 3.645, p = 0.029; 5.633 vs. 2.774, p = 0.013; 3.373 vs. 1.688, p = 0.004; respectively), whereas the average strength, global efficiency, local efficiency, mean clustering coefficient, transitivity, and small-worldness index were lower (5.595 vs. 10.831, p = 0.004; 0.350 vs. 0.642, p = 0.002; 0.531 vs. 1.724, p = 0.004; 0.304 vs. 0.624, p = 0.006; 0.456 vs. 0.935, p = 0.003; 0.913 vs. 0.993, p = 0.017; respectively), in patients with recurrent events than in those with a single event. CONCLUSION The limbic covariance network shows significant alterations in patients with TGA, as well as differences between patients with recurrent events and those with a single event. These findings suggest that changes in the limbic covariance network could be related to the pathogenesis of TGA.
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Approaches in Gene Coexpression Analysis in Eukaryotes. BIOLOGY 2022; 11:biology11071019. [PMID: 36101400 PMCID: PMC9312353 DOI: 10.3390/biology11071019] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 06/28/2022] [Accepted: 07/04/2022] [Indexed: 11/22/2022]
Abstract
Simple Summary Genes whose expression levels rise and fall similarly in a large set of samples, may be considered coexpressed. Gene coexpression analysis refers to the en masse discovery of coexpressed genes from a large variety of transcriptomic experiments. The type of biological networks that studies gene coexpression, known as Gene Coexpression Networks, consist of an undirected graph depicting genes and their coexpression relationships. Coexpressed genes are clustered in smaller subnetworks, the predominant biological roles of which can be determined through enrichment analysis. By studying well-annotated gene partners, the attribution of new roles to genes of unknown function or assumption for participation in common metabolic pathways can be achieved, through a guilt-by-association approach. In this review, we present key issues in gene coexpression analysis, as well as the most popular tools that perform it. Abstract Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.
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Pogrmic-Majkic K, Samardzija Nenadov D, Tesic B, Fa Nedeljkovic S, Kokai D, Stanic B, Andric N. Mapping DEHP to the adverse outcome pathway network for human female reproductive toxicity. Arch Toxicol 2022; 96:2799-2813. [PMID: 35790550 PMCID: PMC9352620 DOI: 10.1007/s00204-022-03333-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 06/15/2022] [Indexed: 01/09/2023]
Abstract
Adverse outcome pathways (AOPs) and AOP networks are tools for mechanistic presentation of toxicological effects across different levels of biological organization. These tools are used to better understand how chemicals impact human health. In this study, a four-step workflow was used to derive the AOP network of human female reproductive toxicity (HFRT-AOP) from five AOPs available in the AOP-Wiki and ten AOPs obtained from the literature. Standard network analysis identified key events (KEs) that are point of convergence and divergence, upstream and downstream KEs, and bottlenecks across the network. To map di-(2-ethylhexyl) phthalate (DEHP) to the HFRT-AOP network, we extracted DEHP target genes and proteins from the Comparative Toxicogenomic and the CompTox Chemicals Dashboard databases. Enriched GO terms analysis was used to identify relevant biological processes in the ovary that are DEHP targets, whereas screening of scientific literature was performed manually and automatically using AOP-helpFinder. We combined this information to map DEHP to HFRT-AOP network to provide insight on the KEs and system-level perturbations caused by this endocrine disruptor and the emergent paths. This approach can enable better understanding of the toxic mechanism of DEHP-induced human female reproductive toxicity and reveal potential novel DEHP female reproductive targets for experimental studies.
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Affiliation(s)
- Kristina Pogrmic-Majkic
- Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovica 2, 21000, Novi Sad, Serbia.
| | - Dragana Samardzija Nenadov
- Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovica 2, 21000, Novi Sad, Serbia
| | - Biljana Tesic
- Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovica 2, 21000, Novi Sad, Serbia
| | - Svetlana Fa Nedeljkovic
- Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovica 2, 21000, Novi Sad, Serbia
| | - Dunja Kokai
- Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovica 2, 21000, Novi Sad, Serbia
| | - Bojana Stanic
- Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovica 2, 21000, Novi Sad, Serbia
| | - Nebojsa Andric
- Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovica 2, 21000, Novi Sad, Serbia
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Barman RK, Mukhopadhyay A, Maulik U, Das S. A network biology approach to identify crucial host targets for COVID-19. Methods 2022; 203:108-115. [PMID: 35364279 PMCID: PMC8960288 DOI: 10.1016/j.ymeth.2022.03.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 03/09/2022] [Accepted: 03/27/2022] [Indexed: 12/23/2022] Open
Abstract
The ongoing global pandemic of COVID-19, caused by SARS-CoV-2 has killed more than 5.9 million individuals out of ∼43 million confirmed infections. At present, several parts of the world are encountering the 3rd wave. Mass vaccination has been started in several countries but they are less likely to be broadly available for the current pandemic, repurposing of the existing drugs has drawn highest attention for an immediate solution. A recent publication has mapped the physical interactions of SARS-CoV-2 and human proteins by affinity-purification mass spectrometry (AP-MS) and identified 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Here, we taken a network biology approach and constructed a human protein-protein interaction network (PPIN) with the above SARS-CoV-2 targeted proteins. We utilized a combination of essential network centrality measures and functional properties of the human proteins to identify the critical human targets of SARS-CoV-2. Four human proteins, namely PRKACA, RHOA, CDK5RAP2, and CEP250 have emerged as the best therapeutic targets, of which PRKACA and CEP250 were also found by another group as potential candidates for drug targets in COVID-19. We further found candidate drugs/compounds, such as guanosine triphosphate, remdesivir, adenosine monophosphate, MgATP, and H-89 dihydrochloride that bind the target human proteins. The urgency to prevent the spread of infection and the death of diseased individuals has prompted the search for agents from the pool of approved drugs to repurpose them for COVID-19. Our results indicate that host targeting therapy with the repurposed drugs may be a useful strategy for the treatment of SARS-CoV-2 infection.
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Affiliation(s)
- Ranjan Kumar Barman
- Division of Virology, ICMR-National Institute of Cholera and Enteric Diseases, Kolkata 700010, India; Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
| | - Anirban Mukhopadhyay
- Department of Computer Science and Engineering, University of Kalyani, Kalyani 741235, West Bengal, India
| | - Ujjwal Maulik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
| | - Santasabuj Das
- Division of Clinical Medicine, ICMR-National Institute of Cholera and Enteric Diseases, Kolkata 700010, India; ICMR-National Institute of Occupational Health, Ahmedabad 380016, India.
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64
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Learning software requirements syntax: An unsupervised approach to recognize templates. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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65
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Heacock ML, Lopez AR, Amolegbe SM, Carlin DJ, Henry HF, Trottier BA, Velasco ML, Suk WA. Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:7544-7552. [PMID: 35549252 PMCID: PMC9227711 DOI: 10.1021/acs.est.1c08383] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Indexed: 05/21/2023]
Abstract
Environmental health sciences (EHS) span many diverse disciplines. Within the EHS community, the National Institute of Environmental Health Sciences Superfund Research Program (SRP) funds multidisciplinary research aimed to address pressing and complex issues on how people are exposed to hazardous substances and their related health consequences with the goal of identifying strategies to reduce exposures and protect human health. While disentangling the interrelationships that contribute to environmental exposures and their effects on human health over the course of life remains difficult, advances in data science and data sharing offer a path forward to explore data across disciplines to reveal new insights. Multidisciplinary SRP-funded teams are well-positioned to examine how to best integrate EHS data across diverse research domains to address multifaceted environmental health problems. As such, SRP supported collaborative research projects designed to foster and enhance the interoperability and reuse of diverse and complex data streams. This perspective synthesizes those experiences as a landscape view of the challenges identified while working to increase the FAIR-ness (Findable, Accessible, Interoperable, and Reusable) of EHS data and opportunities to address them.
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Affiliation(s)
- Michelle L. Heacock
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
- . Tel: 984-287-3267
| | | | - Sara M. Amolegbe
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
| | - Danielle J. Carlin
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
| | - Heather F. Henry
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
| | - Brittany A. Trottier
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
| | | | - William A. Suk
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
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66
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MotieGhader H, Tabrizi-Nezhadi P, Deldar Abad Paskeh M, Baradaran B, Mokhtarzadeh A, Hashemi M, Lanjanian H, Jazayeri SM, Maleki M, Khodadadi E, Nematzadeh S, Kiani F, Maghsoudloo M, Masoudi-Nejad A. Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis. Sci Rep 2022; 12:9417. [PMID: 35676421 PMCID: PMC9177601 DOI: 10.1038/s41598-022-13719-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/16/2022] [Indexed: 12/14/2022] Open
Abstract
Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.
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67
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Ahmed MM, Tazyeen S, Ali R, Alam A, Imam N, Malik MZ, Ali S, Ishrat R. Network centrality approaches used to uncover and classify most influential nodes with their related miRNAs in cardiovascular diseases. GENE REPORTS 2022. [DOI: 10.1016/j.genrep.2022.101555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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68
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Constraint-Based, Score-Based and Hybrid Algorithms to Construct Bayesian Gene Networks in the Bovine Transcriptome. Animals (Basel) 2022; 12:ani12101305. [PMID: 35625151 PMCID: PMC9138007 DOI: 10.3390/ani12101305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/10/2022] [Accepted: 05/10/2022] [Indexed: 11/20/2022] Open
Abstract
Simple Summary In this study, we investigated and compared six different Bayesian network algorithms from three different categories to identify hub genes critical to gene expression networks activated in response to progesterone in the bovine uterus. We observed many common hub genes identified between constraint-based algorithms (CBAs) and hybrid algorithms (HAs), while it appeared that score-based algorithm (SBA) methods led to more accurate and relevant predictions of core genes. The results revealed that the identification of hub genes was affected by the type of network reconstruction and by the subsequently used topological parameters. Two identified genes known to have roles during pregnancy are ISG15 and DGAT2. The identified hub genes are associated with biological processes such as amino acid metabolism, hormonal signaling pathways and the immune system. Our analysis revealed a role for miRNAs in the regulation of this system. The biological and physiological roles (enzymatic and hormonal effects) of unannotated identified hub genes should be functionally validated by further studies. Abstract Bayesian gene networks are powerful for modelling causal relationships and incorporating prior knowledge for making inferences about relationships. We used three algorithms to construct Bayesian gene networks around genes expressed in the bovine uterus and compared the efficacies of the algorithms. Dataset GSE33030 from the Gene Expression Omnibus (GEO) repository was analyzed using different algorithms for hub gene expression due to the effect of progesterone on bovine endometrial tissue following conception. Six different algorithms (grow-shrink, max-min parent children, tabu search, hill-climbing, max-min hill-climbing and restricted maximum) were compared in three higher categories, including constraint-based, score-based and hybrid algorithms. Gene network parameters were estimated using the bnlearn bundle, which is a Bayesian network structure learning toolbox implemented in R. The results obtained indicated the tabu search algorithm identified the highest degree between genes (390), Markov blankets (25.64), neighborhood sizes (8.76) and branching factors (4.38). The results showed that the highest number of shared hub genes (e.g., proline dehydrogenase 1 (PRODH), Sam-pointed domain containing Ets transcription factor (SPDEF), monocyte-to-macrophage differentiation associated 2 (MMD2), semaphorin 3E (SEMA3E), solute carrier family 27 member 6 (SLC27A6) and actin gamma 2 (ACTG2)) was seen between the hybrid and the constraint-based algorithms, and these genes could be recommended as central to the GSE33030 data series. Functional annotation of the hub genes in uterine tissue during progesterone treatment in the pregnancy period showed that the predicted hub genes were involved in extracellular pathways, lipid and protein metabolism, protein structure and post-translational processes. The identified hub genes obtained by the score-based algorithms had a role in 2-arachidonoylglycerol and enzyme modulation. In conclusion, different algorithms and subsequent topological parameters were used to identify hub genes to better illuminate pathways acting in response to progesterone treatment in the bovine uterus, which should help with our understanding of gene regulatory networks in complex trait expression.
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69
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Elastic network modeling of cellular networks unveils sensor and effector genes that control information flow. PLoS Comput Biol 2022; 18:e1010181. [PMID: 35639793 PMCID: PMC9216591 DOI: 10.1371/journal.pcbi.1010181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 06/22/2022] [Accepted: 05/07/2022] [Indexed: 12/03/2022] Open
Abstract
The high-level organization of the cell is embedded in indirect relationships that connect distinct cellular processes. Existing computational approaches for detecting indirect relationships between genes typically consist of propagating abstract information through network representations of the cell. However, the selection of genes to serve as the source of propagation is inherently biased by prior knowledge. Here, we sought to derive an unbiased view of the high-level organization of the cell by identifying the genes that propagate and receive information most effectively in the cell, and the indirect relationships between these genes. To this aim, we adapted a perturbation-response scanning strategy initially developed for identifying allosteric interactions within proteins. We deployed this strategy onto an elastic network model of the yeast genetic interaction profile similarity network. This network revealed a superior propensity for information propagation relative to simulated networks with similar topology. Perturbation-response scanning identified the major distributors and receivers of information in the network, named effector and sensor genes, respectively. Effectors formed dense clusters centrally integrated into the network, whereas sensors formed loosely connected antenna-shaped clusters and contained genes with previously characterized involvement in signal transduction. We propose that indirect relationships between effector and sensor clusters represent major paths of information flow between distinct cellular processes. Genetic similarity networks for fission yeast and human displayed similarly strong propensities for information propagation and clusters of effector and sensor genes, suggesting that the global architecture enabling indirect relationships is evolutionarily conserved across species. Our results demonstrate that elastic network modeling of cellular networks constitutes a promising strategy to probe the high-level organization and cooperativity in the cell.
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70
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Panditrao G, Bhowmick R, Meena C, Sarkar RR. Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects. J Biosci 2022. [PMID: 36210749 PMCID: PMC9018971 DOI: 10.1007/s12038-022-00253-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.
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Affiliation(s)
- Gauri Panditrao
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Rupa Bhowmick
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
| | - Chandrakala Meena
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
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71
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Khojasteh H, Khanteymoori A, Olyaee MH. Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features. Sci Rep 2022; 12:5867. [PMID: 35393450 PMCID: PMC8988119 DOI: 10.1038/s41598-022-08574-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 03/03/2022] [Indexed: 01/04/2023] Open
Abstract
SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein–protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures.
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Affiliation(s)
- Hakimeh Khojasteh
- Department of Computer Engineering, University of Zanjan, Zanjan, Iran
| | | | - Mohammad Hossein Olyaee
- Department of Computer Engineering, Engineering Faculty, University of Gonabad, Zanjan, Gonabad, Iran
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72
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Issahaku AR, Aljoundi A, Soliman ME. Establishing the mutational effect on the binding susceptibility of AMG510 to KRAS switch II binding pocket: Computational insights. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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73
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Darling: A Web Application for Detecting Disease-Related Biomedical Entity Associations with Literature Mining. Biomolecules 2022; 12:biom12040520. [PMID: 35454109 PMCID: PMC9028073 DOI: 10.3390/biom12040520] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/24/2022] [Accepted: 03/28/2022] [Indexed: 12/15/2022] Open
Abstract
Finding, exploring and filtering frequent sentence-based associations between a disease and a biomedical entity, co-mentioned in disease-related PubMed literature, is a challenge, as the volume of publications increases. Darling is a web application, which utilizes Name Entity Recognition to identify human-related biomedical terms in PubMed articles, mentioned in OMIM, DisGeNET and Human Phenotype Ontology (HPO) disease records, and generates an interactive biomedical entity association network. Nodes in this network represent genes, proteins, chemicals, functions, tissues, diseases, environments and phenotypes. Users can search by identifiers, terms/entities or free text and explore the relevant abstracts in an annotated format.
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74
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Goździejewska AM, Kruk M. Zooplankton network conditioned by turbidity gradient in small anthropogenic reservoirs. Sci Rep 2022; 12:3938. [PMID: 35273316 PMCID: PMC8913641 DOI: 10.1038/s41598-022-08045-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 03/02/2022] [Indexed: 11/10/2022] Open
Abstract
Water turbidity can significantly influence interspecific interactions in aquatic ecosystems. We tested the hypothesis that the turbidity gradient significantly differentiates the dynamics, significance and type of relationships in the structure of zooplankton communities colonizing mine pit reservoirs. The interactions between zooplankton species were evaluated by network graph analysis for three water turbidity classes: high turbidity (HT), moderate turbidity (MT) and low turbidity (LT). The HT network was most cohesive, and it was controlled by taxa grazing on various food sources within one ecological niche (Polyarthra longiremis, Brachionus angularis, Cyclops vicinus, Codonella cratera) and the positive and negative relationships between them were balanced. The MT biocenotic network was composed of three sub-networks connected by nodes with high communication attributes (Polyarthra vulgaris, Bosmina longirostris, C. vicinus), and antagonistic interactions (predation and competition) were less important. The LT network was most heterogeneous, and Daphnia cuculllata exerted the strongest influence on the network's structure by forming numerous positive (coexistence with predators) and negative (interference competition with microphagous rotifers) interspecific relationships. The study provides new information about the ecology of aquatic ecosystems, that are disturbed by changes in water turbidity.
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Affiliation(s)
- Anna Maria Goździejewska
- Faculty of Geoengineering, University of Warmia and Mazury, Oczapowskiego 5, 10-719, Olsztyn, Poland.
| | - Marek Kruk
- Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn, Słoneczna 54, 10-710, Olsztyn, Poland
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Tebby C, Gao W, Delp J, Carta G, van der Stel W, Leist M, Jennings P, van de Water B, Bois FY. A quantitative AOP of mitochondrial toxicity based on data from three cell lines. Toxicol In Vitro 2022; 81:105345. [PMID: 35278637 DOI: 10.1016/j.tiv.2022.105345] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/22/2022] [Accepted: 03/07/2022] [Indexed: 11/30/2022]
Abstract
Adverse Outcome Pathways (AOPs) are increasingly used to support the integration of in vitro data in hazard assessment for chemicals. Quantitative AOPs (qAOPs) use mathematical models to describe the relationship between key events (KEs). In this paper, data obtained in three cell lines, LHUMES, HepG2 and RPTEC/TERT1, using similar experimental protocols, was used to calibrate a qAOP of mitochondrial toxicity for two chemicals, rotenone and deguelin. The objectives were to determine whether the same qAOP could be used for the three cell types, and to test chemical-independence by cross-validation with a dataset obtained on eight other chemicals in LHUMES cells. Repeating the calibration approach for both chemicals in three cell lines highlighted various practical difficulties. Even when the same readouts of KEs are measured, the mathematical functions used to describe the key event relationships may not be the same. Cross-validation in LHUMES cells was attempted by estimating chemical-specific potency at the molecular initiating events and using the rest of the calibrated qAOP to predict downstream KEs: toxicity of azoxystrobin, carboxine, mepronil and thifluzamide was underestimated. Selection of most relevant readouts and accurate characterization of the molecular initiating event for cross-validation are critical when designing in vitro experiments targeted at calibrating qAOPs.
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Affiliation(s)
- Cleo Tebby
- Experimental Toxicology and Modeling (TEAM) Unit, Ineris, rue Jacques Taffanel, 60550 Verneuil-en-Halatte, France.
| | - Wang Gao
- Experimental Toxicology and Modeling (TEAM) Unit, Ineris, rue Jacques Taffanel, 60550 Verneuil-en-Halatte, France; Applied Mathematics Laboratory (LMAC EA 2222), Université Technologique de Compiègne (UTC), 57 avenue de Landshut, 60203 Compiègne Cedex, France
| | - Johannes Delp
- In Vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Universitaetsstr. 10, 78464, Konstanz, Germany; Cooperative Doctorate College InViTe, University of Konstanz, Konstanz, Germany
| | - Giada Carta
- Division of Molecular and Computational Toxicology, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Wanda van der Stel
- Division of Drug Discovery and Safety, Leiden Academic Centre of Drug Research, Leiden University, Leiden, the Netherlands
| | - Marcel Leist
- In Vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Universitaetsstr. 10, 78464, Konstanz, Germany
| | - Paul Jennings
- Division of Molecular and Computational Toxicology, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Bob van de Water
- Division of Drug Discovery and Safety, Leiden Academic Centre of Drug Research, Leiden University, Leiden, the Netherlands
| | - Frederic Y Bois
- Experimental Toxicology and Modeling (TEAM) Unit, Ineris, rue Jacques Taffanel, 60550 Verneuil-en-Halatte, France; Certara, Simcyp, Sheffield, United Kingdom
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76
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Rosolen RR, Aono AH, Almeida DA, Ferreira Filho JA, Horta MAC, De Souza AP. Network Analysis Reveals Different Cellulose Degradation Strategies Across Trichoderma harzianum Strains Associated With XYR1 and CRE1. Front Genet 2022; 13:807243. [PMID: 35281818 PMCID: PMC8912865 DOI: 10.3389/fgene.2022.807243] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 02/04/2022] [Indexed: 12/24/2022] Open
Abstract
Trichoderma harzianum, whose gene expression is tightly controlled by the transcription factors (TFs) XYR1 and CRE1, is a potential candidate for hydrolytic enzyme production. Here, we performed a network analysis of T. harzianum IOC-3844 and T. harzianum CBMAI-0179 to explore how the regulation of these TFs varies between these strains. In addition, we explored the evolutionary relationships of XYR1 and CRE1 protein sequences among Trichoderma spp. The results of the T. harzianum strains were compared with those of Trichoderma atroviride CBMAI-0020, a mycoparasitic species. Although transcripts encoding carbohydrate-active enzymes (CAZymes), TFs, transporters, and proteins with unknown functions were coexpressed with cre1 or xyr1, other proteins indirectly related to cellulose degradation were identified. The enriched GO terms describing the transcripts of these groups differed across all strains, and several metabolic pathways with high similarity between both regulators but strain-specific differences were identified. In addition, the CRE1 and XYR1 subnetworks presented different topology profiles in each strain, likely indicating differences in the influences of these regulators according to the fungi. The hubs of the cre1 and xyr1 groups included transcripts not yet characterized or described as being related to cellulose degradation. The first-neighbor analyses confirmed the results of the profile of the coexpressed transcripts in cre1 and xyr1. The analyses of the shortest paths revealed that CAZymes upregulated under cellulose degradation conditions are most closely related to both regulators, and new targets between such signaling pathways were discovered. Although the evaluated T. harzianum strains are phylogenetically close and their amino acid sequences related to XYR1 and CRE1 are very similar, the set of transcripts related to xyr1 and cre1 differed, suggesting that each T. harzianum strain used a specific regulation strategy for cellulose degradation. More interestingly, our findings may suggest that XYR1 and CRE1 indirectly regulate genes encoding proteins related to cellulose degradation in the evaluated T. harzianum strains. An improved understanding of the basic biology of fungi during the cellulose degradation process can contribute to the use of their enzymes in several biotechnological applications and pave the way for further studies on the differences across strains of the same species.
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Affiliation(s)
- Rafaela Rossi Rosolen
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
- Graduate Program in Genetics and Molecular Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Alexandre Hild Aono
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
- Graduate Program in Genetics and Molecular Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Déborah Aires Almeida
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
- Graduate Program in Genetics and Molecular Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Jaire Alves Ferreira Filho
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
- Graduate Program in Genetics and Molecular Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | | | - Anete Pereira De Souza
- Center for Molecular Biology and Genetic Engineering (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
- Department of Plant Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
- *Correspondence: Anete Pereira De Souza,
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77
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Cummings MJ, Bakamutumaho B, Price A, Owor N, Kayiwa J, Namulondo J, Byaruhanga T, Muwanga M, Nsereko C, Sameroff S, Tokarz R, Wong W, Shah SS, Larsen MH, Lipkin WI, Lutwama JJ, O’Donnell MR. Multidimensional analysis of the host response reveals prognostic and pathogen-driven immune subtypes among adults with sepsis in Uganda. Crit Care 2022; 26:36. [PMID: 35130948 PMCID: PMC8822787 DOI: 10.1186/s13054-022-03907-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/26/2022] [Indexed: 12/24/2022] Open
Abstract
Background The global burden of sepsis is concentrated in sub-Saharan Africa, where severe infections disproportionately affect young, HIV-infected adults and high-burden pathogens are unique. In this context, poor understanding of sepsis immunopathology represents a crucial barrier to development of locally-effective treatment strategies. We sought to determine inter-individual immunologic heterogeneity among adults hospitalized with sepsis in a sub-Saharan African setting, and characterize associations between immune subtypes, infecting pathogens, and clinical outcomes. Methods Among a prospective observational cohort of 288 adults hospitalized with suspected sepsis in Uganda, we applied machine learning methods to 14 soluble host immune mediators, reflective of key domains of sepsis immunopathology (innate and adaptive immune activation, endothelial dysfunction, fibrinolysis), to identify immune subtypes in randomly-split discovery (N = 201) and internal validation (N = 87) sub-cohorts. In parallel, we applied similar methods to whole-blood RNA-sequencing data from a consecutive subset of patients (N = 128) to identify transcriptional subtypes, which we characterized using biological pathway and immune cell-type deconvolution analyses. Results Unsupervised clustering consistently identified two immune subtypes defined by differential activation of pro-inflammatory innate and adaptive immune pathways, with transcriptional evidence of concomitant CD56(-)/CD16( +) NK-cell expansion, T-cell exhaustion, and oxidative-stress and hypoxia-induced metabolic and cell-cycle reprogramming in the hyperinflammatory subtype. Immune subtypes defined by greater pro-inflammatory immune activation, T-cell exhaustion, and metabolic reprogramming were consistently associated with a high-prevalence of severe and often disseminated HIV-associated tuberculosis, as well as more extensive organ dysfunction, worse functional outcomes, and higher 30-day mortality. Conclusions Our results highlight unique host- and pathogen-driven features of sepsis immunopathology in sub-Saharan Africa, including the importance of severe HIV-associated tuberculosis, and reinforce the need to develop more biologically-informed treatment strategies in the region, particularly those incorporating immunomodulation. Supplementary Information The online version contains supplementary material available at 10.1186/s13054-022-03907-3.
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78
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Verma RK, Kalyakulina A, Mishra A, Ivanchenko M, Jalan S. Role of mitochondrial genetic interactions in determining adaptation to high altitude human population. Sci Rep 2022; 12:2046. [PMID: 35132109 PMCID: PMC8821606 DOI: 10.1038/s41598-022-05719-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 12/17/2021] [Indexed: 12/13/2022] Open
Abstract
Physiological and haplogroup studies performed to understand high-altitude adaptation in humans are limited to individual genes and polymorphic sites. Due to stochastic evolutionary forces, the frequency of a polymorphism is affected by changes in the frequency of a near-by polymorphism on the same DNA sample making them connected in terms of evolution. Here, first, we provide a method to model these mitochondrial polymorphisms as "co-mutation networks" for three high-altitude populations, Tibetan, Ethiopian and Andean. Then, by transforming these co-mutation networks into weighted and undirected gene-gene interaction (GGI) networks, we were able to identify functionally enriched genetic interactions of CYB and CO3 genes in Tibetan and Andean populations, while NADH dehydrogenase genes in the Ethiopian population playing a significant role in high altitude adaptation. These co-mutation based genetic networks provide insights into the role of different set of genes in high-altitude adaptation in human sub-populations.
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Affiliation(s)
- Rahul K Verma
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, India
| | - Alena Kalyakulina
- Department of Applied Mathematics and Centre of Bioinformatics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Ankit Mishra
- Complex Systems Lab, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, India
| | - Mikhail Ivanchenko
- Department of Applied Mathematics and Centre of Bioinformatics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Laboratory of Systems Medicine of Healthy Aging and Department of Applied Mathematics, Lobachevsky University, Nizhny Novgorod, Russia
| | - Sarika Jalan
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, India. .,Complex Systems Lab, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, India.
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79
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Schessner JP, Voytik E, Bludau I. A practical guide to interpreting and generating bottom-up proteomics data visualizations. Proteomics 2022; 22:e2100103. [PMID: 35107884 DOI: 10.1002/pmic.202100103] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 12/22/2021] [Accepted: 01/20/2022] [Indexed: 11/10/2022]
Abstract
Mass-spectrometry based bottom-up proteomics is the main method to analyze proteomes comprehensively and the rapid evolution of instrumentation and data analysis has made the technology widely available. Data visualization is an integral part of the analysis process and it is crucial for the communication of results. This is a major challenge due to the immense complexity of MS data. In this review, we provide an overview of commonly used visualizations, starting with raw data of traditional and novel MS technologies, then basic peptide and protein level analyses, and finally visualization of highly complex datasets and networks. We specifically provide guidance on how to critically interpret and discuss the multitude of different proteomics data visualizations. Furthermore, we highlight Python-based libraries and other open science tools that can be applied for independent and transparent generation of customized visualizations. To further encourage programmatic data visualization, we provide the Python code used to generate all data Figures in this review on GitHub (https://github.com/MannLabs/ProteomicsVisualization). This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Julia Patricia Schessner
- Max-Planck-Institute of Biochemistry, Department of Proteomics and Signal Transduction, Planegg, Germany
| | - Eugenia Voytik
- Max-Planck-Institute of Biochemistry, Department of Proteomics and Signal Transduction, Planegg, Germany
| | - Isabell Bludau
- Max-Planck-Institute of Biochemistry, Department of Proteomics and Signal Transduction, Planegg, Germany
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80
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Bonidia RP, Domingues DS, Sanches DS, de Carvalho ACPLF. MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors. Brief Bioinform 2022; 23:bbab434. [PMID: 34750626 PMCID: PMC8769707 DOI: 10.1093/bib/bbab434] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/18/2021] [Accepted: 09/20/2021] [Indexed: 12/24/2022] Open
Abstract
One of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable of extracting numerical information from biological sequences have been reported in the literature. However, many of these techniques are not available in existing packages, such as mathematical descriptors. This paper presents a new package, MathFeature, which implements mathematical descriptors able to extract relevant numerical information from biological sequences, i.e. DNA, RNA and proteins (prediction of structural features along the primary sequence of amino acids). MathFeature makes available 20 numerical feature extraction descriptors based on approaches found in the literature, e.g. multiple numeric mappings, genomic signal processing, chaos game theory, entropy and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages. To ensure that our descriptors are robust and to assess their relevance, experimental results are presented in nine case studies. According to these results, the features extracted by MathFeature showed high performance (0.6350-0.9897, accuracy), both applying only mathematical descriptors, but also hybridization with well-known descriptors in the literature. Finally, through MathFeature, we overcame several studies in eight benchmark datasets, exemplifying the robustness and viability of the proposed package. MathFeature has advanced in the area by bringing descriptors not available in other packages, as well as allowing non-experts to use feature extraction techniques.
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Affiliation(s)
- Robson P Bonidia
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13566-590, Brazil
| | - Douglas S Domingues
- Group of Genomics and Transcriptomes in Plants, Institute of Biosciences, São Paulo State University (UNESP), Rio Claro 13506-900, Brazil
| | - Danilo S Sanches
- Department of Computer Science, Federal University of Technology - Paraná, UTFPR, Cornélio Procópio 86300-000, Brazil
| | - André C P L F de Carvalho
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos 13566-590, Brazil
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81
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Yadav AK, Shukla R, Singh TR. Topological parameters, patterns, and motifs in biological networks. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00012-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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82
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Redhu N, Thakur Z. Network biology and applications. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00024-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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83
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The Genomic Physics of COVID-19 Pathogenesis and Spread. Cells 2021; 11:cells11010080. [PMID: 35011641 PMCID: PMC8750765 DOI: 10.3390/cells11010080] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 12/19/2021] [Accepted: 12/23/2021] [Indexed: 12/11/2022] Open
Abstract
Coronavirus disease (COVID-19) spreads mainly through close contact of infected persons, but the molecular mechanisms underlying its pathogenesis and transmission remain unknown. Here, we propose a statistical physics model to coalesce all molecular entities into a cohesive network in which the roadmap of how each entity mediates the disease can be characterized. We argue that the process of how a transmitter transforms the virus into a recipient constitutes a triad unit that propagates COVID-19 along reticulate paths. Intrinsically, person-to-person transmissibility may be mediated by how genes interact transversely across transmitter, recipient, and viral genomes. We integrate quantitative genetic theory into hypergraph theory to code the main effects of the three genomes as nodes, pairwise cross-genome epistasis as edges, and high-order cross-genome epistasis as hyperedges in a series of mobile hypergraphs. Charting a genome-wide atlas of horizontally epistatic hypergraphs can facilitate the systematic characterization of the community genetic mechanisms underlying COVID-19 spread. This atlas can typically help design effective containment and mitigation strategies and screen and triage those more susceptible persons and those asymptomatic carriers who are incubation virus transmitters.
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84
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Zhao X, Li J, Liu Z, Powers S. Combinatorial CRISPR/Cas9 Screening Reveals Epistatic Networks of Interacting Tumor Suppressor Genes and Therapeutic Targets in Human Breast Cancer. Cancer Res 2021; 81:6090-6105. [PMID: 34561273 PMCID: PMC9762330 DOI: 10.1158/0008-5472.can-21-2555] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/02/2021] [Accepted: 09/22/2021] [Indexed: 01/07/2023]
Abstract
The majority of cancers are driven by multiple genetic alterations, but how these changes collaborate during tumorigenesis remains largely unknown. To gain mechanistic insights into tumor-promoting genetic interactions among tumor suppressor genes (TSG), we conducted combinatorial CRISPR screening coupled with single-cell transcriptomic profiling in human mammary epithelial cells. As expected, different driver gene alterations in mammary epithelial cells influenced the repertoire of tumor suppressor alterations capable of inducing tumor formation. More surprisingly, TSG interaction networks were comprised of numerous cliques-sets of three or four genes such that each TSG within the clique showed oncogenic cooperation with all other genes in the clique. Genetic interaction profiling indicated that the predominant cooperating TSGs shared overlapping functions rather than distinct or complementary functions. Single-cell transcriptomic profiling of CRISPR double knockouts revealed that cooperating TSGs that synergized in promoting tumorigenesis and growth factor independence showed transcriptional epistasis, whereas noncooperating TSGs did not. These epistatic transcriptional changes, both buffering and synergistic, affected expression of oncogenic mediators and therapeutic targets, including CDK4, SRPK1, and DNMT1. Importantly, the epistatic expression alterations caused by dual inactivation of TSGs in this system, such as PTEN and TP53, were also observed in patient tumors, establishing the relevance of these findings to human breast cancer. An estimated 50% of differentially expressed genes in breast cancer are controlled by epistatic interactions. Overall, our study indicates that transcriptional epistasis is a central aspect of multigenic breast cancer progression and outlines methodologies to uncover driver gene epistatic networks in other human cancers. SIGNIFICANCE: This study provides a roadmap for moving beyond discovery and development of therapeutic strategies based on single driver gene analysis to discovery based on interactions between multiple driver genes.See related commentary by Fong et al., p. 6078.
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Affiliation(s)
- Xiaoyu Zhao
- Department of Pathology and Cancer Center, Renaissance School of Medicine, Stony Brook, New York
- Molecular and Cellular Biology Graduate Program, Stony Brook University, Stony Brook, New York
| | - Jinyu Li
- Department of Pathology and Cancer Center, Renaissance School of Medicine, Stony Brook, New York
| | - Zhimin Liu
- Molecular and Cellular Biology Graduate Program, Stony Brook University, Stony Brook, New York
- Department of Biochemistry, Stony Brook University, Stony Brook, New York
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
- Janssen Research & Development Data Science, Titusville, New Jersey
| | - Scott Powers
- Department of Pathology and Cancer Center, Renaissance School of Medicine, Stony Brook, New York.
- Molecular and Cellular Biology Graduate Program, Stony Brook University, Stony Brook, New York
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York
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85
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Leysen H, Walter D, Christiaenssen B, Vandoren R, Harputluoğlu İ, Van Loon N, Maudsley S. GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease. Int J Mol Sci 2021; 22:ijms222413387. [PMID: 34948182 PMCID: PMC8708147 DOI: 10.3390/ijms222413387] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 02/06/2023] Open
Abstract
GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.
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Affiliation(s)
- Hanne Leysen
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Deborah Walter
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Bregje Christiaenssen
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Romi Vandoren
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - İrem Harputluoğlu
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
- Department of Chemistry, Middle East Technical University, Çankaya, Ankara 06800, Turkey
| | - Nore Van Loon
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Stuart Maudsley
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
- Correspondence:
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86
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Montaldo C, Messina F, Abbate I, Antonioli M, Bordoni V, Aiello A, Ciccosanti F, Colavita F, Farroni C, Najafi Fard S, Giombini E, Goletti D, Matusali G, Rozera G, Rueca M, Sacchi A, Piacentini M, Agrati C, Fimia GM, Capobianchi MR, Lauria FN, Ippolito G. Multi-omics approach to COVID-19: a domain-based literature review. J Transl Med 2021; 19:501. [PMID: 34876157 PMCID: PMC8649311 DOI: 10.1186/s12967-021-03168-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 11/23/2021] [Indexed: 12/15/2022] Open
Abstract
Background Omics data, driven by rapid advances in laboratory techniques, have been generated very quickly during the COVID-19 pandemic. Our aim is to use omics data to highlight the involvement of specific pathways, as well as that of cell types and organs, in the pathophysiology of COVID-19, and to highlight their links with clinical phenotypes of SARS-CoV-2 infection. Methods The analysis was based on the domain model, where for domain it is intended a conceptual repository, useful to summarize multiple biological pathways involved at different levels. The relevant domains considered in the analysis were: virus, pathways and phenotypes. An interdisciplinary expert working group was defined for each domain, to carry out an independent literature scoping review. Results The analysis revealed that dysregulated pathways of innate immune responses, (i.e., complement activation, inflammatory responses, neutrophil activation and degranulation, platelet degranulation) can affect COVID-19 progression and outcomes. These results are consistent with several clinical studies. Conclusions Multi-omics approach may help to further investigate unknown aspects of the disease. However, the disease mechanisms are too complex to be explained by a single molecular signature and it is necessary to consider an integrated approach to identify hallmarks of severity. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03168-8.
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Affiliation(s)
- Chiara Montaldo
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Francesco Messina
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Isabella Abbate
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Manuela Antonioli
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Veronica Bordoni
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Alessandra Aiello
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Fabiola Ciccosanti
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Francesca Colavita
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Chiara Farroni
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Saeid Najafi Fard
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Emanuela Giombini
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Delia Goletti
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Giulia Matusali
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Gabriella Rozera
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Martina Rueca
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Alessandra Sacchi
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Mauro Piacentini
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy.,Dept. Biology, University of Rome Tor Vergata, Via della Ricerca scientifica 1, Rome, Italy
| | - Chiara Agrati
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Gian Maria Fimia
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy.,Dept. Molecular Medicine, Sapienza University of Rome, 00185, Rome, Italy
| | - Maria Rosaria Capobianchi
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy.
| | - Francesco Nicola Lauria
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Giuseppe Ippolito
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
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Sonsungsan P, Chantanakool P, Suratanee A, Buaboocha T, Comai L, Chadchawan S, Plaimas K. Identification of Key Genes in 'Luang Pratahn', Thai Salt-Tolerant Rice, Based on Time-Course Data and Weighted Co-expression Networks. FRONTIERS IN PLANT SCIENCE 2021; 12:744654. [PMID: 34925399 PMCID: PMC8675607 DOI: 10.3389/fpls.2021.744654] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/01/2021] [Indexed: 05/13/2023]
Abstract
Salinity is an important environmental factor causing a negative effect on rice production. To prevent salinity effects on rice yields, genetic diversity concerning salt tolerance must be evaluated. In this study, we investigated the salinity responses of rice (Oryza sativa) to determine the critical genes. The transcriptomes of 'Luang Pratahn' rice, a local Thai rice variety with high salt tolerance, were used as a model for analyzing and identifying the key genes responsible for salt-stress tolerance. Based on 3' Tag-Seq data from the time course of salt-stress treatment, weighted gene co-expression network analysis was used to identify key genes in gene modules. We obtained 1,386 significantly differentially expressed genes in eight modules. Among them, six modules indicated a significant correlation within 6, 12, or 48h after salt stress. Functional and pathway enrichment analysis was performed on the co-expressed genes of interesting modules to reveal which genes were mainly enriched within important functions for salt-stress responses. To identify the key genes in salt-stress responses, we considered the two-state co-expression networks, normal growth conditions, and salt stress to investigate which genes were less important in a normal situation but gained more impact under stress. We identified key genes for the response to biotic and abiotic stimuli and tolerance to salt stress. Thus, these novel genes may play important roles in salinity tolerance and serve as potential biomarkers to improve salt tolerance cultivars.
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Affiliation(s)
- Pajaree Sonsungsan
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok, Thailand
| | - Pheerawat Chantanakool
- Center of Excellence in Environment and Plant Physiology, Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Apichat Suratanee
- Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
| | - Teerapong Buaboocha
- Molecular Crop Research Unit, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Luca Comai
- Department of Plant Biology, College of Biological Sciences, College of Biological Sciences, University of California, Davis, Davis, CA, United States
| | - Supachitra Chadchawan
- Center of Excellence in Environment and Plant Physiology, Department of Botany, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
| | - Kitiporn Plaimas
- Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
- Advanced Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand
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88
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Vesterinen M, Perälä T, Kuparinen A. The effect of fish life-history structures on the topologies of aquatic food webs. FOOD WEBS 2021. [DOI: 10.1016/j.fooweb.2021.e00213] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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89
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Morgan R, da Silveira WA, Kelly RC, Overton I, Allott EH, Hardiman G. Long non-coding RNAs and their potential impact on diagnosis, prognosis, and therapy in prostate cancer: racial, ethnic, and geographical considerations. Expert Rev Mol Diagn 2021; 21:1257-1271. [PMID: 34666586 DOI: 10.1080/14737159.2021.1996227] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
INTRODUCTION Advances in high-throughput sequencing have greatly advanced our understanding of long non-coding RNAs (lncRNAs) in a relatively short period of time. This has expanded our knowledge of cancer, particularly how lncRNAs drive many important cancer phenotypes via their regulation of gene expression. AREAS COVERED Men of African descent are disproportionately affected by PC in terms of incidence, morbidity, and mortality. LncRNAs could serve as biomarkers to differentiate low-risk from high-risk diseases. Additionally, they may represent therapeutic targets for advanced and castrate-resistant cancer. We review current research surrounding lncRNAs and their association with PC. We discuss how lncRNAs can provide new insights and diagnostic biomarkers for African American men. Finally, we review advances in computational approaches that predict the regulatory effects of lncRNAs in cancer. EXPERT OPINION PC diagnostic biomarkers that offer high specificity and sensitivity are urgently needed. PC specific lncRNAs are compelling as diagnostic biomarkers owing to their high tissue and tumor specificity and presence in bodily fluids. Recent studies indicate that PCA3 clinical utility might be restricted to men of European descent. Further work is required to develop lncRNA biomarkers tailored for men of African descent.
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Affiliation(s)
- Rebecca Morgan
- Faculty of Medicine, Health and Life Sciences, School of Biological Sciences, Queen's University Belfast, Belfast, UK.,Institute for Global Food Security (IGFS), Queen's University Belfast, Belfast, UK
| | - Willian Abraham da Silveira
- Faculty of Medicine, Health and Life Sciences, School of Biological Sciences, Queen's University Belfast, Belfast, UK.,Institute for Global Food Security (IGFS), Queen's University Belfast, Belfast, UK
| | - Ryan Christopher Kelly
- Faculty of Medicine, Health and Life Sciences, Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Ian Overton
- Faculty of Medicine, Health and Life Sciences, Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Emma H Allott
- Institute for Global Food Security (IGFS), Queen's University Belfast, Belfast, UK.,Faculty of Medicine, Health and Life Sciences, Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.,Department of Histopathology and Morbid Anatomy, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin, Ireland
| | - Gary Hardiman
- Faculty of Medicine, Health and Life Sciences, School of Biological Sciences, Queen's University Belfast, Belfast, UK.,Institute for Global Food Security (IGFS), Queen's University Belfast, Belfast, UK.,Department of Medicine, Medical University of South Carolina (MUSC), Charleston, South Carolina
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90
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Guo MG, Sosa DN, Altman RB. Challenges and opportunities in network-based solutions for biological questions. Brief Bioinform 2021; 23:6438103. [PMID: 34849568 PMCID: PMC8769687 DOI: 10.1093/bib/bbab437] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/09/2021] [Accepted: 09/22/2021] [Indexed: 11/28/2022] Open
Abstract
Network biology is useful for modeling complex biological phenomena; it has attracted attention with the advent of novel graph-based machine learning methods. However, biological applications of network methods often suffer from inadequate follow-up. In this perspective, we discuss obstacles for contemporary network approaches—particularly focusing on challenges representing biological concepts, applying machine learning methods, and interpreting and validating computational findings about biology—in an effort to catalyze actionable biological discovery.
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Affiliation(s)
- Margaret G Guo
- Stanford Program in Biomedical Informatics, Stanford University, Stanford, CA, USA.,Program in Epithelial Biology, Stanford University, Stanford, CA, USA
| | - Daniel N Sosa
- Stanford Program in Biomedical Informatics, Stanford University, Stanford, CA, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Genetics, Stanford University, Stanford, CA, USA
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91
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Mechanical couplings of protein backbone and side chains exhibit scale-free network properties and specific hotspots for function. Comput Struct Biotechnol J 2021; 19:5309-5320. [PMID: 34765086 PMCID: PMC8554173 DOI: 10.1016/j.csbj.2021.09.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/02/2021] [Accepted: 09/05/2021] [Indexed: 11/23/2022] Open
Abstract
Statistical learning from protein dynamics unravels rigidities in interaction network. Backbone and side-chain mechanical couplings exhibit scale-free network properties. Graphical depiction of network rigidities captures sequence co-evolution patterns. Functional sites at secondary structure peripheries are mechanical hotspots. Our rigidity scores are compelling metrics for residue biological significance.
A backbone-side-chain elastic network model (bsENM) is devised in this contribution to decipher the network of molecular interactions during protein dynamics. The chemical details in 5 μs all-atom molecular dynamics (MD) simulation are mapped onto the bsENM spring constants by self-consistent iterations. The elastic parameters obtained by this structure-mechanics statistical learning are then used to construct inter-residue rigidity graphs for the chemical components in protein amino acids. A key discovery is that the mechanical coupling strengths of both backbone and side chains exhibit heavy-tailed distributions and scale-free network properties. In both rat trypsin and PDZ3 proteins, the statistically prominent modes of rigidity graphs uncover the sequence-specific coupling patterns and mechanical hotspots. Based on the contributions to graphical modes, our residue rigidity scores in backbone and side chains are found to be very useful metrics for the biological significance. Most functional sites have high residue rigidity scores in side chains while the biologically important glycines are generally next to mechanical hotspots. Furthermore, prominent modes in the rigidity graphs involving side chains oftentimes coincide with the co-evolution patterns due to evolutionary restraints. The bsENM specifically devised to resolve the protein chemical character thus provides useful means for extracting functional information from all-atom MD.
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92
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Panditrao G, Ganguli P, Sarkar RR. Delineating infection strategies of Leishmania donovani secretory proteins in Human through host-pathogen protein Interactome prediction. Pathog Dis 2021; 79:6408463. [PMID: 34677584 DOI: 10.1093/femspd/ftab051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 10/20/2021] [Indexed: 12/11/2022] Open
Abstract
Interactions of Leishmania donovani secretory virulence factors with the host proteins and their interplay during the infection process in humans is poorly studied in Visceral Leishmaniasis. Lack of a holistic study of pathway level de-regulations caused due to these virulence factors leads to a poor understanding of the parasite strategies to subvert the host immune responses, secure its survival inside the host and further the spread of infection to the visceral organs. In this study, we propose a computational workflow to predict host-pathogen protein interactome of L.donovani secretory virulence factors with human proteins combining sequence-based Interolog mapping and structure-based Domain Interaction mapping techniques. We further employ graph theoretical approaches and shortest path methods to analyze the interactome. Our study deciphers the infection paths involving some unique and understudied disease-associated signaling pathways influencing the cellular phenotypic responses in the host. Our statistical analysis based in silico knockout study unveils for the first time UBC, 1433Z and HS90A mediator proteins as potential immunomodulatory candidates through which the virulence factors employ the infection paths. These identified pathways and novel mediator proteins can be effectively used as possible targets to control and modulate the infection process further aiding in the treatment of Visceral Leishmaniasis.
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Affiliation(s)
- Gauri Panditrao
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune 411008, Maharashtra, India
| | - Piyali Ganguli
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune 411008, Maharashtra, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune 411008, Maharashtra, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, Uttar Pradesh, India
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93
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Thomas JP, Modos D, Korcsmaros T, Brooks-Warburton J. Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease. Front Genet 2021; 12:760501. [PMID: 34745229 PMCID: PMC8566351 DOI: 10.3389/fgene.2021.760501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/08/2021] [Indexed: 12/22/2022] Open
Abstract
Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.
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Affiliation(s)
- John P Thomas
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
- Department of Gastroenterology, Norfolk and Norwich University Hospital, Norwich, United Kingdom
| | - Dezso Modos
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Tamas Korcsmaros
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Johanne Brooks-Warburton
- Department of Gastroenterology, Lister Hospital, Stevenage, United Kingdom
- Department of Clinical, Pharmaceutical and Biological Sciences, University of Hertfordshire, Hatfield, United Kingdom
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94
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Agapito G, Cannataro M. Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data. BMC Bioinformatics 2021; 22:376. [PMID: 34592927 PMCID: PMC8482563 DOI: 10.1186/s12859-021-04297-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 07/06/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Pathway enrichment analysis (PEA) is a well-established methodology for interpreting a list of genes and proteins of interest related to a condition under investigation. This paper aims to extend our previous work in which we introduced a preliminary comparative analysis of pathway enrichment analysis tools. We extended the earlier work by providing more case studies, comparing BiP enrichment performance with other well-known PEA software tools. METHODS PEA uses pathway information to discover connections between a list of genes and proteins as well as biological mechanisms, helping researchers to overcome the problem of explaining biological entity lists of interest disconnected from the biological context. RESULTS We compared the results of BiP with some existing pathway enrichment analysis tools comprising Centrality-based Pathway Enrichment, pathDIP, and Signaling Pathway Impact Analysis, considering three cancer types (colorectal, endometrial, and thyroid), for a total of six datasets (that is, two datasets per cancer type) obtained from the The Cancer Genome Atlas and Gene Expression Omnibus databases. We measured the similarities between the overlap of the enrichment results obtained using each couple of cancer datasets related to the same cancer. CONCLUSION As a result, BiP identified some well-known pathways related to the investigated cancer type, validated by the available literature. We also used the Jaccard and meet-min indices to evaluate the stability and the similarity between the enrichment results obtained from each couple of cancer datasets. The obtained results show that BiP provides more stable enrichment results than other tools.
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Affiliation(s)
- Giuseppe Agapito
- Department of Legal, Economic and Social Sciences, University "Magna Graecia", Catanzaro, Italy. .,Data Analytics Research Center, University "Magna Graecia", Catanzaro, Italy.
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, University "Magna Graecia", Catanzaro, Italy. .,Data Analytics Research Center, University "Magna Graecia", Catanzaro, Italy.
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95
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Azim R, Wang S. Cell-specific gene association network construction from single-cell RNA sequence. Cell Cycle 2021; 20:2248-2263. [PMID: 34530677 DOI: 10.1080/15384101.2021.1978265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
The recent development of a high throughput single-cell RNA sequence devises the opportunity to study entire transcriptomes in the smallest detail. It also leads to the characterization of molecules and subtypes of a cell. Cancer epigenetics induced not only from individual molecules but also from the dysfunction of the system and the coupling effect of genes. While rapid advances are being made in the development of tools for single-cell RNA-seq data analysis, few slants are noticed in the potential advantages of single-cell network construction.Here, we used network perturbation theory with significant analysis to develop a cell-specific network that provides an insight into gene-gene association based on molecular expressions in a single-cell resolution. Besides, using this method, we can characterize each cell by inspecting how genes are connected and can identify the hub genes using network degree theory. Pathway & Gene enrichment analysis of the identified cell-specific high network degree genes supported the effectiveness of this method. This method could be beneficial for personalized drug design and even therapeutics.
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Affiliation(s)
- Riasat Azim
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, P.R. China
| | - Shulin Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, P.R. China
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96
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Tal O, Bartuv R, Vetcos M, Medina S, Jiang J, Freilich S. NetCom: A Network-Based Tool for Predicting Metabolic Activities of Microbial Communities Based on Interpretation of Metagenomics Data. Microorganisms 2021; 9:microorganisms9091838. [PMID: 34576734 PMCID: PMC8468097 DOI: 10.3390/microorganisms9091838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/08/2021] [Accepted: 08/18/2021] [Indexed: 12/13/2022] Open
Abstract
The study of microbial activity can be viewed as a triangle with three sides: environment (dominant resources in a specific habitat), community (species dictating a repertoire of metabolic conversions) and function (production and/or utilization of resources and compounds). Advances in metagenomics enable a high-resolution description of complex microbial communities in their natural environments and support a systematic study of environment-community-function associations. NetCom is a web-tool for predicting metabolic activities of microbial communities based on network-based interpretation of assembled and annotated metagenomics data. The algorithm takes as an input, lists of differentially abundant enzymatic reactions and generates the following outputs: (i) pathway associations of differently abundant enzymes; (ii) prediction of environmental resources that are unique to each treatment, and their pathway associations; (iii) prediction of compounds that are produced by the microbial community, and pathway association of compounds that are treatment-specific; (iv) network visualization of enzymes, environmental resources and produced compounds, that are treatment specific (2 and 3D). The tool is demonstrated on metagenomic data from rhizosphere and bulk soil samples. By predicting root-specific activities, we illustrate the relevance of our framework for forecasting the impact of soil amendments on the corresponding microbial communities. NetCom is available online.
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Affiliation(s)
- Ofir Tal
- Newe Ya’ar Research Center, Institute of Plant Sciences, The Agricultural Research Organization, Ramat Yishay 30095, Israel; (O.T.); (R.B.); (M.V.); (S.M.)
| | - Rotem Bartuv
- Newe Ya’ar Research Center, Institute of Plant Sciences, The Agricultural Research Organization, Ramat Yishay 30095, Israel; (O.T.); (R.B.); (M.V.); (S.M.)
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Faculty of Agriculture, The Hebrew University of Jerusalem, Rehovot 7628604, Israel
| | - Maria Vetcos
- Newe Ya’ar Research Center, Institute of Plant Sciences, The Agricultural Research Organization, Ramat Yishay 30095, Israel; (O.T.); (R.B.); (M.V.); (S.M.)
| | - Shlomit Medina
- Newe Ya’ar Research Center, Institute of Plant Sciences, The Agricultural Research Organization, Ramat Yishay 30095, Israel; (O.T.); (R.B.); (M.V.); (S.M.)
| | - Jiandong Jiang
- Department of Microbiology, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China;
| | - Shiri Freilich
- Newe Ya’ar Research Center, Institute of Plant Sciences, The Agricultural Research Organization, Ramat Yishay 30095, Israel; (O.T.); (R.B.); (M.V.); (S.M.)
- Correspondence:
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97
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Khan AH, Smith DJ. Cost-Effective Mapping of Genetic Interactions in Mammalian Cells. Front Genet 2021; 12:703738. [PMID: 34434222 PMCID: PMC8381747 DOI: 10.3389/fgene.2021.703738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/13/2021] [Indexed: 11/23/2022] Open
Abstract
Comprehensive maps of genetic interactions in mammalian cells are daunting to construct because of the large number of potential interactions, ~ 2 × 108 for protein coding genes. We previously used co-inheritance of distant genes from published radiation hybrid (RH) datasets to identify genetic interactions. However, it was necessary to combine six legacy datasets from four species to obtain adequate statistical power. Mapping resolution was also limited by the low density PCR genotyping. Here, we employ shallow sequencing of nascent human RH clones as an economical approach to constructing interaction maps. In this initial study, 15 clones were analyzed, enabling construction of a network with 225 genes and 2,359 interactions (FDR < 0.05). Despite its small size, the network showed significant overlap with the previous RH network and with a protein-protein interaction network. Consumables were ≲$50 per clone, showing that affordable, high quality genetic interaction maps are feasible in mammalian cells.
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Affiliation(s)
- Arshad H Khan
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Desmond J Smith
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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98
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Baltoumas FA, Zafeiropoulou S, Karatzas E, Koutrouli M, Thanati F, Voutsadaki K, Gkonta M, Hotova J, Kasionis I, Hatzis P, Pavlopoulos GA. Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review. Biomolecules 2021; 11:1245. [PMID: 34439912 PMCID: PMC8391349 DOI: 10.3390/biom11081245] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.
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Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Sofia Zafeiropoulou
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Mikaela Koutrouli
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Foteini Thanati
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Kleanthi Voutsadaki
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Maria Gkonta
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Joana Hotova
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Ioannis Kasionis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Pantelis Hatzis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
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Abstract
Herein, we wanted to explore the molecular landscape of mucosal melanoma from different sites and identify potential molecular targets for future therapy. Mucosal melanomas (N = 40) from different sites (conjunctiva, sinonasal cavity, rectum, and vagina) were investigated. Targeted next-generation sequencing along with Nanostring gene expression profiling was performed. Genetically, conjunctival melanoma was characterized by BRAF-V600E (30%) and NF1 mutations (17%). Mucosal melanomas at nonsun-exposed sites harbored alterations in NRAS, KIT, NF1, along with atypical BRAF mutations. When comparing the gene expression profile of conjunctival melanoma and nonsun-exposed mucosal melanoma, 41 genes were found to be significantly deregulated. Programmed death-ligand 1 (PD-L1) presented a significant sixfold upregulation in conjunctival melanoma compared to the other mucosal melanomas. While melanomas of the sinonasal cavity, vagina, and rectum are molecularly similar, conjunctival melanoma is characterized by a higher frequency of BRAF-V600E mutations and differential expression of several genes involved in the immune response.
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100
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de Godoi RS, Almerão MP, da Silva FR. In silico evaluation of the antidiabetic activity of natural compounds from Hovenia dulcis Thunberg. J Herb Med 2021. [DOI: 10.1016/j.hermed.2020.100349] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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