Apoptosis regulatory protein-protein interaction demonstrates hierarchical scale-free fractal network

SLC22A17 as a Cell Death-Linked Regulator of Tight Junctions in Cerebral Ischemia

BACKGROUND

Beyond neuronal injury, cell death pathways may also contribute to vascular injury after stroke. We examined protein networks linked to major cell death pathways and identified SLC22A17 (solute carrier family 22 member 17) as a novel mediator that regulates endothelial tight junctions after ischemia and inflammatory stress.

METHODS

Protein-protein interactions and brain enrichment analyses were performed using STRING, Cytoscape, and a human tissue-specific expression RNA-seq database. In vivo experiments were performed using mouse models of transient focal cerebral ischemia. Human stroke brain tissues were used to detect SLC22A17 by immunostaining. In vitro experiments were performed using human brain endothelial cultures subjected to inflammatory stress. Immunostaining and Western blot were used to assess responses in SLC22A17 and endothelial tight junctional proteins. Water content, dextran permeability, and electrical resistance assays were used to assess edema and blood-brain barrier (BBB) integrity. Gain and loss-of-function studies were performed using lentiviral overexpression of SLC22A17 or short interfering RNA against SLC22A17, respectively.

RESULTS

Protein-protein interaction analysis showed that core proteins from apoptosis, necroptosis, ferroptosis, and autophagy cell death pathways were closely linked. Among the 20 proteins identified in the network, the iron-handling solute carrier SLC22A17 emerged as the mediator enriched in the brain. After cerebral ischemia in vivo, endothelial expression of SLC22A17 increases in both human and mouse brains along with BBB leakage. In human brain endothelial cultures, short interfering RNA against SLC22A17 prevents TNF-α (tumor necrosis factor alpha)-induced ferroptosis and downregulation in tight junction proteins and disruption in transcellular permeability. Notably, SLC22A17 could repress the transcription of tight junctional genes. Finally, short interfering RNA against SLC22A17 ameliorates BBB leakage in a mouse model of focal cerebral ischemia.

CONCLUSIONS

Using a combination of cell culture, human stroke samples, and mouse models, our data suggest that SLC22A17 may play a role in the control of BBB function after cerebral ischemia. These findings may offer a novel mechanism and target for ameliorating BBB injury and edema after stroke.

Selectively advantageous instability in biotic and pre-biotic systems and implications for evolution and aging

Rules of biology typically involve conservation of resources. For example, common patterns such as hexagons and logarithmic spirals require minimal materials, and scaling laws involve conservation of energy. Here a relationship with the opposite theme is discussed, which is the selectively advantageous instability (SAI) of one or more components of a replicating system, such as the cell. By increasing the complexity of the system, SAI can have benefits in addition to the generation of energy or the mobilization of building blocks. SAI involves a potential cost to the replicating system for the materials and/or energy required to create the unstable component, and in some cases, the energy required for its active degradation. SAI is well-studied in cells. Short-lived transcription and signaling factors enable a rapid response to a changing environment, and turnover is critical for replacement of damaged macromolecules. The minimal gene set for a viable cell includes proteases and a nuclease, suggesting SAI is essential for life. SAI promotes genetic diversity in several ways. Toxin/antitoxin systems promote maintenance of genes, and SAI of mitochondria facilitates uniparental transmission. By creating two distinct states, subject to different selective pressures, SAI can maintain genetic diversity. SAI of components of synthetic replicators favors replicator cycling, promoting emergence of replicators with increased complexity. Both classical and recent computer modeling of replicators reveals SAI. SAI may be involved at additional levels of biological organization. In summary, SAI promotes replicator genetic diversity and reproductive fitness, and may promote aging through loss of resources and maintenance of deleterious alleles.

A computational biology approach to identify potential protein biomarkers and drug targets for sporadic amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by the loss of upper and lower motor neurons. The sporadic ALS (sALS) is a multigenic disorder and the complex mechanisms underlying its onset are still not fully delineated. Despite the recent scientific advancements, certain aspects of ALS pathogenic targets need to be yet clarified. The aim of the presented study is to identify potential genetic biomarkers and drug targets for sALS, by analysing gene expression profiles, presented in the publicly available GSE68605 dataset, of motor neurons cells obtained from sALS patients. We used different computational approaches including differential expression analysis, protein network mapping, candidate protein biomarker (CPB) identification, elucidation of the role of functional modules, and molecular docking analysis. The resultant top ten up- and downregulated genes were further used to construct protein-protein interaction network (PPIN). The PPIN analysis resulted in identifying four CPBs (namely RIOK2, AKT1, CTNNB1, and TNF) that commonly overlapped with one another in network parameters (degree, bottleneck and maximum neighbourhood component). The RIOK2 protein emerged as a potential mediator of top five functional modules that are associated with RNA binding, lipoprotein particle receptor binding in pre-ribosome, and interferon, cytokine-mediated signaling pathway. Furthermore, molecular docking analysis revealed that cyclosporine exhibited the highest binding affinity (-8.6 kJ/mol) with RIOK2, and surpassed the FDA-approved ALS drugs, such as riluzole and edaravone. This suggested that cyclosporine may serve as a promising candidate for targeting RIOK2 downregulation observed in sALS patients. In order to validate our computational results, it is suggested that in vitro and in vivo studies may be conducted in future to provide a more detailed understanding of ALS diagnosis, prognosis, and therapeutic intervention.

Computational study of the motor neuron protein KIF5A to identify nsSNPs, bioactive compounds, and its key regulators

Introduction: Kinesin family member 5A (KIF5A) is a motor neuron protein expressed in neurons and involved in anterograde transportation of organelles, proteins, and RNA. Variations in the KIF5A gene that interfere with axonal transport have emerged as a distinguishing feature in several neurodegenerative disorders, including hereditary spastic paraplegia (HSP10), Charcot-Marie-Tooth disease type 2 (CMT2), and Amyotrophic Lateral Sclerosis (ALS). Methods: In this study, we implemented a computational structural and systems biology approach to uncover the role of KIF5A in ALS. Using the computational structural biology method, we explored the role of non-synonymous Single Nucleotide Polymorphism (nsSNPs) in KIF5A. Further, to identify the potential inhibitory molecule against the highly destabilizing structure variant, we docked 24 plant-derived phytochemicals involved in ALS. Results: We found KIF5AS291F variant showed the most structure destabilizing behavior and the phytocompound "epigallocatechin gallate" showed the highest binding affinity (-9.0 Kcal/mol) as compared to wild KIF5A (-8.4 Kcal/mol). Further, with the systems biology approach, we constructed the KIF5A protein-protein interaction (PPI) network to identify the associated Kinesin Families (KIFs) proteins, modules, and their function. We also constructed a transcriptional and post-transcriptional regulatory network of KIF5A. With the network topological parameters of PPIN (Degree, Bottleneck, Closeness, and MNC) using CytoHubba and computational knock-out experiment using Network Analyzer, we found KIF1A, 5B, and 5C were the significant proteins. The functional modules were highly enriched with microtubule motor activity, chemical synaptic transmission in neurons, GTP binding, and GABA receptor activity. In regulatory network analysis, we found KIF5A post-transcriptionally down-regulated by miR-107 which is further transcriptionally up-regulated by four TFs (HIF1A, PPARA, SREBF1, and TP53) and down-regulated by three TFs (ZEB1, ZEB2, and LIN28A). Discussion: We concluded our study by finding a crucial variant of KIF5A and its potential therapeutic target (epigallocatechin gallate) and KIF5A associated significant genes with important regulators which could decrypt the novel therapeutics in ALS and other neurodegenerative diseases.

Combinatorial Network of Transcriptional and miRNA Regulation in Colorectal Cancer

Colorectal cancer is one of the leading causes of cancer-associated mortality across the worldwide. One of the major challenges in colorectal cancer is the understanding of the regulatory mechanisms of biological molecules. In this study, we aimed to identify novel key molecules in colorectal cancer by using a computational systems biology approach. We constructed the colorectal protein–protein interaction network which followed hierarchical scale-free nature. We identified TP53, CTNBB1, AKT1, EGFR, HRAS, JUN, RHOA, and EGF as bottleneck-hubs. The HRAS showed the largest interacting strength with functional subnetworks, having strong correlation with protein phosphorylation, kinase activity, signal transduction, and apoptotic processes. Furthermore, we constructed the bottleneck-hubs’ regulatory networks with their transcriptional (transcription factor) and post-transcriptional (miRNAs) regulators, which exhibited the important key regulators. We observed miR-429, miR-622, and miR-133b and transcription factors (EZH2, HDAC1, HDAC4, AR, NFKB1, and KLF4) regulates four bottleneck-hubs (TP53, JUN, AKT1 and EGFR) at the motif level. In future, biochemical investigation of the observed key regulators could provide further understanding about their role in the pathophysiology of colorectal cancer.

KISL: knowledge-injected semi-supervised learning for biological co-expression network modules

The exploration of important biomarkers associated with cancer development is crucial for diagnosing cancer, designing therapeutic interventions, and predicting prognoses. The analysis of gene co-expression provides a systemic perspective on gene networks and can be a valuable tool for mining biomarkers. The main objective of co-expression network analysis is to discover highly synergistic sets of genes, and the most widely used method is weighted gene co-expression network analysis (WGCNA). With the Pearson correlation coefficient, WGCNA measures gene correlation, and uses hierarchical clustering to identify gene modules. The Pearson correlation coefficient reflects only the linear dependence between variables, and the main drawback of hierarchical clustering is that once two objects are clustered together, the process cannot be reversed. Hence, readjusting inappropriate cluster divisions is not possible. Existing co-expression network analysis methods rely on unsupervised methods that do not utilize prior biological knowledge for module delineation. Here we present a method for identification of outstanding modules in a co-expression network using a knowledge-injected semi-supervised learning approach (KISL), which utilizes apriori biological knowledge and a semi-supervised clustering method to address the issue existing in the current GCN-based clustering methods. To measure the linear and non-linear dependence between genes, we introduce a distance correlation due to the complexity of the gene-gene relationship. Eight RNA-seq datasets of cancer samples are used to validate its effectiveness. In all eight datasets, the KISL algorithm outperformed WGCNA when comparing the silhouette coefficient, Calinski-Harabasz index and Davies-Bouldin index evaluation metrics. According to the results, KISL clusters had better cluster evaluation values and better gene module aggregation. Enrichment analysis of the recognition modules demonstrated their effectiveness in discovering modular structures in biological co-expression networks. In addition, as a general method, KISL can be applied to various co-expression network analyses based on similarity metrics. Source codes for the KISL and the related scripts are available online at https://github.com/Mowonhoo/KISL.git.

The Protein-Protein Interaction Network of Hereditary Parkinsonism Genes Is a Hierarchical Scale-Free Network

PURPOSE

Hereditary parkinsonism genes consist of causative genes of familial Parkinson's disease (PD) with a locus symbol prefix (PARK genes) and hereditary atypical parkinsonian disorders that present atypical features and limited responsiveness to levodopa (non-PARK genes). Although studies have shown that hereditary parkinsonism genes are related to idiopathic PD at the phenotypic, gene expression, and genomic levels, no study has systematically investigated connectivity among the proteins encoded by these genes at the protein-protein interaction (PPI) level.

MATERIALS AND METHODS

Topological measurements and physical interaction enrichment were performed to assess PPI networks constructed using some or all the proteins encoded by hereditary parkinsonism genes (n=96), which were curated using the Online Mendelian Inheritance in Man database and literature.

RESULTS

Non-PARK and PARK genes were involved in common functional modules related to autophagy, mitochondrial or lysosomal organization, catecholamine metabolic process, chemical synapse transmission, response to oxidative stress, neuronal apoptosis, regulation of cellular protein catabolic process, and vesicle-mediated transport in synapse. The hereditary parkinsonism proteins formed a single large network comprising 51 nodes, 83 edges, and three PPI pairs. The probability of degree distribution followed a power-law scaling behavior, with a degree exponent of 1.24 and a correlation coefficient of 0.92. LRRK2 was identified as a hub gene with the highest degree of betweenness centrality; its physical interaction enrichment score was 1.28, which was highly significant.

CONCLUSION

Both PARK and non-PARK genes show high connectivity at the PPI and biological functional levels.

Distance correlation application to gene co-expression network analysis

BACKGROUND

To construct gene co-expression networks, it is necessary to evaluate the correlation between different gene expression profiles. However, commonly used correlation metrics, including both linear (such as Pearson's correlation) and monotonic (such as Spearman's correlation) dependence metrics, are not enough to observe the nature of real biological systems. Hence, introducing a more informative correlation metric when constructing gene co-expression networks is still an interesting topic.

RESULTS

In this paper, we test distance correlation, a correlation metric integrating both linear and non-linear dependence, with other three typical metrics (Pearson's correlation, Spearman's correlation, and maximal information coefficient) on four different arrays (macrophage and liver) and RNA-seq (cervical cancer and pancreatic cancer) datasets. Among all the metrics, distance correlation is distribution free and can provide better performance on complex relationships and anti-outlier. Furthermore, distance correlation is applied to Weighted Gene Co-expression Network Analysis (WGCNA) for constructing a gene co-expression network analysis method which we named Distance Correlation-based Weighted Gene Co-expression Network Analysis (DC-WGCNA). Compared with traditional WGCNA, DC-WGCNA can enhance the result of enrichment analysis and improve the module stability.

CONCLUSIONS

Distance correlation is better at revealing complex biological relationships between gene profiles compared with other correlation metrics, which contribute to more meaningful modules when analyzing gene co-expression networks. However, due to the high time complexity of distance correlation, the implementation requires more computer memory.

Protein network analysis to prioritize key genes in amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a fatal disease, progressive nature characterizes by loss of both upper and lower motor neuron functions. One of the major challenge is to understand the mechanism of ALS multifactorial nature. We aimed to explore some key genes related to ALS through bioinformatics methods for its therapeutic intervention. Here, we applied a systems biology approach involving experimentally validated 148 ALS-associated proteins and construct ALS protein-protein interaction network (ALS-PPIN). The network was further statistically analysed and identified bottleneck-hubs. The network is also subjected to identify modules which could have similar functions. The interaction between the modules and bottleneck-hubs provides the functional regulatory role of the ALS mechanism. The ALS-PPIN demonstrated a hierarchical scale-free nature. We identified 17 bottleneck-hubs, in which CDC5L, SNW1, TP53, SOD1, and VCP were the high degree nodes (hubs) in ALS-PPIN. CDC5L was found to control highly cluster modules and play a vital role in the stability of the overall network followed by SNW1, TP53, SOD1, and VCP. HSPA5 and HSPA8 acting as a common connector for CDC5L and TP53 bottleneck-hubs. The functional and disease association analysis showed ALS has a strong correlation with mRNA processing, protein deubiquitination, and neoplasms, nervous system, immune system disease classes. In the future, biochemical investigation of the observed bottleneck-hubs and their interacting partners could provide a further understanding of their role in the pathophysiology of ALS.

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Amyotrophic Lateral Sclerosis protein-protein interaction network (ALS-PPIN) followed a hierarchical scale-free nature.

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We identified 17 bottleneck-hubs in the ALS-PPIN.

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Among bottleneck-hubs we found CDC5L, SNW1, TP53, SOD1, and VCP were the high degree nodes (hubs) in the ALS-PPIN.

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CDC5L is the effective communicator with all five modules in the ALS-PPIN and followed by SNW1 and TP53.

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Modules are highly associated with various disease classes like neoplasms, nervous systems and others.

XDeathDB: a visualization platform for cell death molecular interactions

Lots of cell death initiator and effector molecules, signalling pathways and subcellular sites have been identified as key mediators in both cell death processes in cancer. The XDeathDB visualization platform provides a comprehensive cell death and their crosstalk resource for deciphering the signaling network organization of interactions among different cell death modes associated with 1461 cancer types and COVID-19, with an aim to understand the molecular mechanisms of physiological cell death in disease and facilitate systems-oriented novel drug discovery in inducing cell deaths properly. Apoptosis, autosis, efferocytosis, ferroptosis, immunogenic cell death, intrinsic apoptosis, lysosomal cell death, mitotic cell death, mitochondrial permeability transition, necroptosis, parthanatos, and pyroptosis related to 12 cell deaths and their crosstalk can be observed systematically by the platform. Big data for cell death gene-disease associations, gene-cell death pathway associations, pathway-cell death mode associations, and cell death-cell death associations is collected by literature review articles and public database from iRefIndex, STRING, BioGRID, Reactom, Pathway's commons, DisGeNET, DrugBank, and Therapeutic Target Database (TTD). An interactive webtool, XDeathDB, is built by web applications with R-Shiny, JavaScript (JS) and Shiny Server Iso. With this platform, users can search specific interactions from vast interdependent networks that occur in the realm of cell death. A multilayer spectral graph clustering method that performs convex layer aggregation to identify crosstalk function among cell death modes for a specific cancer. 147 hallmark genes of cell death could be observed in detail in these networks. These potential druggable targets are displayed systematically and tailoring networks to visualize specified relations is available to fulfil user-specific needs. Users can access XDeathDB for free at https://pcm2019.shinyapps.io/XDeathDB/ .

In Vivo Biological Evaluation of Ethyl 4-(7-hydroxy-4-methyl-2-oxoquinolin-1- ylamino)-coumarin-3-carboxylate as an Antitumor Agent

BACKGROUND

Hybridization of coumarin moiety with additional antitumor pharmacophores is an auspicious stratagem to afford precious therapeutic interference for the medication of cancer.

OBJECTIVE

The present study aimed to evaluate the antitumor activity of ethyl 4-(7-hydroxy-4-methyl-2- oxoquinolin-1-ylamino)-coumarin-3-carboxylate against Ehrlich Ascites Carcinoma (EAC) cells in the peritoneal cavity of female mice.

METHODOLOGY

Molecular docking was used to predict the binding between the test compound and the receptor of breast cancer mutant 3HB5-oxidoreductase, as well as the viability of tumor cells and life span prolongation. The total anti-oxidant capacity was evaluated in the liver and kidneys. Serum alanine transaminase, aspartate transaminase, albumin, total protein, creatinine, and urea were estimated. The concentrations of Bcl-2 and Bax were measured in the liver and kidney tissues. Histopathological examination of the liver and kidney tissues was also carried out.

RESULTS

EAC-bearing mice injected with the test compound showed a highly significant decrease in tumor cell viability by 100%, compared to the EAC control. Also, it exhibited significant anti-oxidant and apoptotic agents through the results of total anti-oxidant capacity and apoptosis assays. Confirmed by histological examination, the results of the liver and kidney function tests revealed that the test compound had no harmful effect on either of the organs. The docking investigation disclosed an auspicious interaction between the test compound and the receptor (3HB5). To confirm these results, correlations between different parameters were carried out. It was found that there were significant positive and negative correlations between the parameters.

CONCLUSION

Hybrid molecules containing coumarin and quinolinone exhibited a potential antitumor effect against EAC cells by the induction of apoptosis and anti-oxidant activities. Results of liver and kidney function tests and the histopathological study revealed that the administration of the test compound nullified most of the pathological alterations induced by EAC cells in mice. Based on these findings, the test compound can be developed as an effective chemotherapeutic agent.

Identification of the Key Regulators of Spina Bifida Through Graph-Theoretical Approach

Spina Bifida (SB) is a congenital spinal cord malformation. Efforts to discern the key regulators (KRs) of the SB protein-protein interaction (PPI) network are requisite for developing its successful interventions. The architecture of the SB network, constructed from 117 manually curated genes was found to self-organize into a scale-free fractal state having a weak hierarchical organization. We identified three modules/motifs consisting of ten KRs, namely, TNIP1, TNF, TRAF1, TNRC6B, KMT2C, KMT2D, NCOA3, TRDMT1, DICER1, and HDAC1. These KRs serve as the backbone of the network, they propagate signals through the different hierarchical levels of the network to conserve the network’s stability while maintaining low popularity in the network. We also observed that the SB network exhibits a rich-club organization, the formation of which is attributed to our key regulators also except for TNIP1 and TRDMT1. The KRs that were found to ally with each other and emerge in the same motif, open up a new dimension of research of studying these KRs together. Owing to the multiple etiology and mechanisms of SB, a combination of several biomarkers is expected to have higher diagnostic accuracy for SB as compared to using a single biomarker. So, if all the KRs present in a single module/motif are targetted together, they can serve as biomarkers for the diagnosis of SB. Our study puts forward some novel SB-related genes that need further experimental validation to be considered as reliable future biomarkers and therapeutic targets.

Comparative Genomics and Integrated Network Approach Unveiled Undirected Phylogeny Patterns, Co-mutational Hot Spots, Functional Cross Talk, and Regulatory Interactions in SARS-CoV-2

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has resulted in 92 million cases in a span of 1 year. The study focuses on understanding population-specific variations attributing its high rate of infections in specific geographical regions particularly in the United States. Rigorous phylogenomic network analysis of complete SARS-CoV-2 genomes (245) inferred five central clades named a (ancestral), b, c, d, and e (subtypes e1 and e2). Clade d and subclade e2 were found exclusively comprised of U.S. strains. Clades were distinguished by 10 co-mutational combinations in Nsp3, ORF8, Nsp13, S, Nsp12, Nsp2, and Nsp6. Our analysis revealed that only 67.46% of single nucleotide polymorphism (SNP) mutations were at the amino acid level. T1103P mutation in Nsp3 was predicted to increase protein stability in 238 strains except for 6 strains which were marked as ancestral type, whereas co-mutation (P409L and Y446C) in Nsp13 were found in 64 genomes from the United States highlighting its 100% co-occurrence. Docking highlighted mutation (D614G) caused reduction in binding of spike proteins with angiotensin-converting enzyme 2 (ACE2), but it also showed better interaction with the TMPRSS2 receptor contributing to high transmissibility among U.S. strains. We also found host proteins, MYO5A, MYO5B, and MYO5C, that had maximum interaction with viral proteins (nucleocapsid [N], spike [S], and membrane [M] proteins). Thus, blocking the internalization pathway by inhibiting MYO5 proteins which could be an effective target for coronavirus disease 2019 (COVID-19) treatment. The functional annotations of the host-pathogen interaction (HPI) network were found to be closely associated with hypoxia and thrombotic conditions, confirming the vulnerability and severity of infection. We also screened CpG islands in Nsp1 and N conferring the ability of SARS-CoV-2 to enter and trigger zinc antiviral protein (ZAP) activity inside the host cell.

IMPORTANCE In the current study, we presented a global view of mutational pattern observed in SARS-CoV-2 virus transmission. This provided a who-infect-whom geographical model since the early pandemic. This is hitherto the most comprehensive comparative genomics analysis of full-length genomes for co-mutations at different geographical regions especially in U.S. strains. Compositional structural biology results suggested that mutations have a balance of opposing forces affecting pathogenicity suggesting that only a few mutations are effective at the translation level. Novel HPI analysis and CpG predictions elucidate the proof of concept of hypoxia and thrombotic conditions in several patients. Thus, the current study focuses the understanding of population-specific variations attributing a high rate of SARS-CoV-2 infections in specific geographical regions which may eventually be vital for the most severely affected countries and regions for sharp development of custom-made vindication strategies.

Systematic Analysis of Protein-Protein and Gene-Environment Interactions to Decipher the Cognitive Mechanisms of Autism Spectrum Disorder

Autism spectrum disorder (ASD), a heterogeneous neurodevelopmental disorder resulting from both genetic and environmental risk factors, is manifested by deficits in cognitive function. Elucidating the cognitive disorder-relevant biological mechanisms may open up promising therapeutic approaches. In this work, we mined ASD cognitive phenotype proteins to construct and analyze protein-protein and gene-environment interaction networks. Incorporating the protein-protein interaction (PPI), human cognition proteins, and connections of autism-cognition proteins enabled us to generate an autism-cognition network (ACN). With the topological analysis of ACN, important proteins, highly clustered modules, and 3-node motifs were identified. Moreover, the impact of environmental exposures in cognitive impairment was investigated through chemicals that target the cognition-related proteins. Functional enrichment analysis of the ACN-associated modules and chemical targets revealed biological processes involved in the cognitive deficits of ASD. Among the 17 identified hub-bottlenecks in the ACN, PSD-95 was recognized as an important protein through analyzing the module and motif interactions. PSD-95 and its interacting partners constructed a cognitive-specific module. This hub-bottleneck interacted with the 89 cognition-related 3-node motifs. The identification of gene-environment interactions indicated that most of the cognitive-related proteins interact with bisphenol A (BPA) and valproic acid (VPA). Moreover, we detected significant expression changes of 56 cognitive-specific genes using four ASD microarray datasets in the GEO database, including GSE28521, GSE26415, GSE18123 and GSE29691. Our outcomes suggest future endeavors for dissecting the PSD-95 function in ASD and evaluating the various environmental conditions to discover possible mechanisms of the different levels of cognitive impairment.

Identification of novel candidate genes in heterotaxy syndrome patients with congenital heart diseases by whole exome sequencing

Heterotaxy syndrome (HS) involves dysfunction of multiple systems resulting from abnormal left-right (LR) body patterning. Most HS patients present with complex congenital heart diseases (CHD), the disability and mortality of HS patients are extremely high. HS has great heterogeneity in phenotypes and genotypes, which have rendered gene discovery challenging. The aim of this study was to identify novel genes that underlie pathogenesis of HS patients with CHD. Whole exome sequencing was performed in 25 unrelated HS cases and 100 healthy controls; 19 nonsynonymous variants in 6 novel candidate genes (FLNA, ITGA1, PCNT, KIF7, GLI1, KMT2D) were identified. The functions of candidate genes were further analyzed in zebrafish model by CRISPR/Cas9 technique. Genome-editing was successfully introduced into the gene loci of flna, kmt2d and kif7, but the phenotypes were heterogenous. Disruption of each gene disturbed normal cardiac looping while kif7 knockout had a more prominent effect on liver budding and pitx2 expression. Our results revealed three potential HS pathogenic genes with probably different molecular mechanisms.

Hamiltonian energy as an efficient approach to identify the significant key regulators in biological networks

The topological characteristics of biological networks enable us to identify the key nodes in terms of modularity. However, due to a large size of the biological networks with many hubs and functional modules across intertwined layers within the network, it often becomes difficult to accomplish the task of identifying potential key regulators. We use for the first time a generalized formalism of Hamiltonian Energy (HE) with a recursive approach. The concept, when applied to the Apoptosis Regulatory Gene Network (ARGN), helped us identify 11 Motif hubs (MHs), which influenced the network up to motif levels. The approach adopted allowed to classify MHs into 5 significant motif hubs (S-MHs) and 6 non-significant motif hubs (NS-MHs). The significant motif hubs had a higher HE value and were considered as high-active key regulators; while the non-significant motif hubs had a relatively lower HE value and were considered as low-active key regulators, in network control mechanism. Further, we compared the results of the HE analyses with the topological characterization, after subjecting to the three conditions independently: (i) removing all MHs, (ii) removing only S-MHs, and (iii) removing only NS-MHs from the ARGN. This procedure allowed us to cross-validate the role of 5 S-MHs, NFk-B1, BRCA1, CEBPB, AR, and POU2F1 as the potential key regulators. The changes in HE calculations further showed that the removal of 5 S-MHs could cause perturbation at all levels of the network, a feature not discernible by topological analysis alone.

Exploring novel key regulators in breast cancer network

The breast cancer network constructed from 70 experimentally verified genes is found to follow hierarchical scale free nature with heterogeneous modular organization and diverge leading hubs. The topological parameters (degree distributions, clustering co-efficient, connectivity and centralities) of this network obey fractal rules indicating absence of centrality lethality rule, and efficient communication among the components. From the network theoretical approach, we identified few key regulators out of large number of leading hubs, which are deeply rooted from top to down of the network, serve as backbone of the network, and possible target genes. However, p53, which is one of these key regulators, is found to be in low rank and keep itself at low profile but directly cross-talks with important genes BRCA2 and BRCA3. The popularity of these hubs gets changed in unpredictable way at various levels of organization thus showing disassortive nature. The local community paradigm approach in this network shows strong correlation of nodes in majority of modules/sub-modules (fast communication among nodes) and weak correlation of nodes only in few modules/sub-modules (slow communication among nodes) at various levels of network organization.

Disentangling Interactions in the Microbiome: A Network Perspective

Microbiota are now widely recognized as being central players in the health of all organisms and ecosystems, and subsequently have been the subject of intense study. However, analyzing and converting microbiome data into meaningful biological insights remain very challenging. In this review, we highlight recent advances in network theory and their applicability to microbiome research. We discuss emerging graph theoretical concepts and approaches used in other research disciplines and demonstrate how they are well suited for enhancing our understanding of the higher-order interactions that occur within microbiomes. Network-based analytical approaches have the potential to help disentangle complex polymicrobial and microbe-host interactions, and thereby further the applicability of microbiome research to personalized medicine, public health, environmental and industrial applications, and agriculture.

Identification of key regulators and their controlling mechanism in a combinatorial apoptosis network: a systems biology approach

NFKB1, SP1 and hsa-let-7a, were identified as key regulators of apoptosis, by network theory through probability of signal propagation, hub-removal and motif analysis.