Assessment of the key regulatory genes and their Interologs for Turner Syndrome employing network approach

Clinical practice guidelines for the care of girls and women with Turner syndrome

Turner syndrome (TS) affects 50 per 100 000 females. TS affects multiple organs through all stages of life, necessitating multidisciplinary care. This guideline extends previous ones and includes important new advances, within diagnostics and genetics, estrogen treatment, fertility, co-morbidities, and neurocognition and neuropsychology. Exploratory meetings were held in 2021 in Europe and United States culminating with a consensus meeting in Aarhus, Denmark in June 2023. Prior to this, eight groups addressed important areas in TS care: (1) diagnosis and genetics, (2) growth, (3) puberty and estrogen treatment, (4) cardiovascular health, (5) transition, (6) fertility assessment, monitoring, and counselling, (7) health surveillance for comorbidities throughout the lifespan, and (8) neurocognition and its implications for mental health and well-being. Each group produced proposals for the present guidelines, which were meticulously discussed by the entire group. Four pertinent questions were submitted for formal GRADE (Grading of Recommendations, Assessment, Development and Evaluation) evaluation with systematic review of the literature. The guidelines project was initiated by the European Society for Endocrinology and the Pediatric Endocrine Society, in collaboration with members from the European Society for Pediatric Endocrinology, the European Society of Human Reproduction and Embryology, the European Reference Network on Rare Endocrine Conditions, the Society for Endocrinology, and the European Society of Cardiology, Japanese Society for Pediatric Endocrinology, Australia and New Zealand Society for Pediatric Endocrinology and Diabetes, Latin American Society for Pediatric Endocrinology, Arab Society for Pediatric Endocrinology and Diabetes, and the Asia Pacific Pediatric Endocrine Society. Advocacy groups appointed representatives for pre-meeting discussions and the consensus meeting.

Brain-derived neurotrophic factor G196A (rs6265) gene polymorphism increases Turner syndrome susceptibility

Brain-derived neurotrophic factor (BDNF) is a member of the nerve growth factor family. It plays a significant role in the regulation of brain metabolic activity, modification of synaptic efficacy, and enhances neuronal survival. A common naturally occurring allelic variation, i.e. G196A (Val66 Met, rs6265) of the BDNF gene is implicated in neuroplasticity. This study analyzes its expression levels and determines the frequency of BDNF G196A gene polymorphism in women with Turner syndrome (TS) compared to the controls. This case-control study comprised 14 TS patients and 8 healthy individuals. The expression levels of BDNF gene in TS patients were checked by qPCR. For BDNF gene, a dynamic expression range along with the presence of G196A polymorphism was found across all TS patients. The effects of Val66 Met mutation on BDNF protein structure and function were studied by molecular dynamics simulations of wild and mutant (Val66 Met) forms. The analysis of different trajectories generated by simulation showed that there was a significant change in the protein structure due to Val66 Met polymorphism, which might lead to functional impairment. This is first time we are reporting the association of BDNF G196A gene polymorphism with TS risk. Our study suggests that in turner patients, BDNF G196A polymorphism may be an important genetic factor predisposing to neuroplasticity risk and can be exploited as diagnostic/prognostic marker for TS. Further study on a large number of TS samples will prove this point beyond doubts or otherwise enriching the much desired repertoire of personalized medicine.

Assessment of key regulatory genes and identification of possible drug targets for Leprosy (Hansen's disease) using network-based approach

Leprosy is a major health concern and continues to be a source of fear and stigma among people worldwide. Despite remarkable achievements in the treatment, understanding of pathogenesis and transmission, epidemiology of leprosy still remains inadequate. The prolonged incubation period, slow rates of occurrence in those exposed and deceptive clinical presentation pose challenges to develop reliable strategies to stop transmission. Hence, there is a need for improved diagnostics and therapies to prevent mortality caused by leprosy. The objectives of this study are to identify significant genes from protein-protein interactions (PPIs) network of leprosy and to choose the most effective therapeutic targets. Fifty genes related with leprosy were discovered by literature mining. These genes were used to construct a primary network. Leading Eigen Vector method was used to break down the primary network into various sub-networks or communities. It was found that the primary network was divided into many sub-networks at the 6 levels. Seed genes were traced at each level till key regulatory genes were identified. Three seed genes, namely, GNAI3, NOTCH1, and HIF1A, were able to make their way till the final motif stage. These genes along with their interacting partners were considered key regulators of the leprosy network. This study provides leprosy-associated key genes which can lead to improved diagnosis and therapies for leprosy patients.

Organ Abnormalities Caused by Turner Syndrome

Turner syndrome (TS), a genetic disorder due to incomplete dosage compensation of X-linked genes, affects multiple organ systems, leading to hypogonadotropic hypogonadism, short stature, cardiovascular and vascular abnormalities, liver disease, renal abnormalities, brain abnormalities, and skeletal problems. Patients with TS experience premature ovarian failure with a rapid decline in ovarian function caused by germ cell depletion, and pregnancies carry a high risk of adverse maternal and fetal outcomes. Aortic abnormalities, heart defects, obesity, hypertension, and liver abnormalities, such as steatosis, steatohepatitis, biliary involvement, liver cirrhosis, and nodular regenerative hyperplasia, are commonly observed in patients with TS. The SHOX gene plays a crucial role in short stature and abnormal skeletal phenotype in patients with TS. Abnormal structure formation of the ureter and kidney is also common in patients with TS, and a non-mosaic 45,X karyotype is significantly associated with horseshoe kidneys. TS also affects brain structure and function. In this review, we explore various phenotypic and disease manifestations of TS in different organs, including the reproductive system, cardiovascular system, liver, kidneys, brain, and skeletal system.

Identification of New Key Genes and Their Association with Breast Cancer Occurrence and Poor Survival Using In Silico and In Vitro Methods

Breast cancer is one of the most prevalent types of cancer diagnosed globally and continues to have a significant impact on the global number of cancer deaths. Despite all efforts of epidemiological and experimental research, therapeutic concepts in cancer are still unsatisfactory. Gene expression datasets are widely used to discover the new biomarkers and molecular therapeutic targets in diseases. In the present study, we analyzed four datasets using R packages with accession number GSE29044, GSE42568, GSE89116, and GSE109169 retrieved from NCBI-GEO and differential expressed genes (DEGs) were identified. Protein-protein interaction (PPI) network was constructed to screen the key genes. Subsequently, the GO function and KEGG pathways were analyzed to determine the biological function of key genes. Expression profile of key genes was validated in MCF-7 and MDA-MB-231 human breast cancer cell lines using qRT-PCR. Overall expression level and stage wise expression pattern of key genes was determined by GEPIA. The bc-GenExMiner was used to compare expression level of genes among groups of patients with respect to age factor. OncoLnc was used to analyze the effect of expression levels of LAMA2, TIMP4, and TMTC1 on the survival of breast cancer patients. We identified nine key genes, of which COL11A1, MMP11, and COL10A1 were found up-regulated and PCOLCE2, LAMA2, TMTC1, ADAMTS5, TIMP4, and RSPO3 were found down-regulated. Similar expression pattern of seven among nine genes (except ADAMTS5 and RSPO3) was observed in MCF-7 and MDA-MB-231 cells. Further, we found that LAMA2, TMTC1, and TIMP4 were significantly expressed among different age groups of patients. LAMA2 and TIMP4 were found significantly associated and TMTC1 was found less correlated with breast cancer occurrence. We found that the expression level of LAMA2, TIMP4, and TMTC1 was abnormal in all TCGA tumors and significantly associated with poor survival.

Network pharmacology and bioinformatics approach reveals the therapeutic mechanism of action of curcumin in Alzheimer disease

Curcumin is a natural anti-inflammatory and antioxidant substance which plays a major role in reducing the amyloid plaques formation, which is the major cause of Alzheimer's disease (AD). Consequently, a methodical approach was used to select the potential protein targets of curcumin in AD through network pharmacology. In this study, through integrative methods, AD targets of curcumin through SwissTargetPrediction database, STITCH database, BindingDB, PharmMapper, Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM) database were predicted followed by gene enrichment analysis, network construction, network topology, and docking studies. Gene ontology analysis facilitated identification of a list of possible AD targets of curcumin (74 targets genes). The correlation of the obtained targets with AD was analysed by using gene ontology (GO) pathway enrichment analyses and Kyoto Encyclopaedia of Genes and Genomes (KEGG). We have incorporated the applied network pharmacological approach to identify key genes. Furthermore, we have performed molecular docking for analysing the mechanism of curcumin. In order to validate the temporospatial expression of key genes in human central nervous system (CNS), we searched the Human Brain Transcriptome (HBT) dataset. We identified top five key genes namely, PPARγ, MAPK1, STAT3, KDR and APP. Further validated the expression profiling of these key genes in publicly available brain data expression profile databases. In context to a valuable addition in the treatment of AD, this study is concluded with novel insights into the therapeutic mechanisms of curcumin, will ease the treatment of AD with the clinical application of curcumin.

The Changing Face of Turner Syndrome

Turner syndrome (TS) is a condition in females missing the second sex chromosome (45,X) or parts thereof. It is considered a rare genetic condition and is associated with a wide range of clinical stigmata, such as short stature, ovarian dysgenesis, delayed puberty and infertility, congenital malformations, endocrine disorders, including a range of autoimmune conditions and type 2 diabetes, and neurocognitive deficits. Morbidity and mortality are clearly increased compared with the general population and the average age at diagnosis is quite delayed. During recent years it has become clear that a multidisciplinary approach is necessary toward the patient with TS. A number of clinical advances has been implemented, and these are reviewed. Our understanding of the genomic architecture of TS is advancing rapidly, and these latest developments are reviewed and discussed. Several candidate genes, genomic pathways and mechanisms, including an altered transcriptome and epigenome, are also presented.

Effect of Growth Hormone Treatment on the Concentration of Selected Metabolic Markers in Girls With Turner Syndrome

Background

As Turner syndrome (TS) predisposes to obesity and metabolic disorders, and their complications, such as cardiovascular diseases, are the main causes of shortened life expectancy in patients with TS, new metabolic markers that could serve as early predictors of dysmetabolic state are sought.

Objective

Assessment of MMP-1 (matrix metalloproteinase-1), MMP-2 (matrix metalloproteinase-2), MMP-9 (matrix metallopeptidase-9), BDNF (brain-derived neurotrophic factor), GDNF (glial cell line-derived neurotrophic factor), and VEGF (vascular endothelial growth factor) before the onset of growth hormone (GH) therapy and then during GH treatment as well as markers assessment during GH medication in girls with TS to establish marker stability and repeatability, and the impact of GH on markers concentration.

Method

The concentrations of circulating MMP-1, MMP-2, MMP-9, BDNF, GDNF, and VEGF were measured in nine girls with TS before the onset of GH therapy and then after at least 3 months of treatment period. Subsequently, markers concentration was determined in 17 girls during GH medication, with the first determination after at least a 3-month treatment period. The patients' clinical and biochemical phenotypes were determined by weight, height, BMI, total cholesterol, HDL cholesterol, triglycerides, and glucose concentration.

Results

Comparison of markers concentration revealed a significantly higher concentration of MMP-2 in patients undergoing GH treatment (132.1 ± 42.05) than before the onset of therapy (105.0 ± 45.5, p=0.045). The values of the first measurement of VEGF in girls with TS undergoing GH therapy were significantly higher than those during the second measurement (30.9 ± 33.4 vs. 12.5 ± 11.7, p=0.029). There were no statistically significant differences between the measurements of the remaining markers concentration at any stage of the analysis.

Conclusion

Increase in MMP-2 concentration is visible during GH therapy in comparison to the pre-GH period in girls with TS which demands confirmation in subsequent tests. The role of VEGF requires further studies in the context of carbohydrate-lipid disturbances in girls with TS and its association with GH treatment.

Troxerutin-Mediated Complement Pathway Inhibition is a Disease-Modifying Treatment for Inflammatory Arthritis

Troxerutin (TXR) is a phytochemical reported to possess anti-inflammatory and hepatoprotective effects. In this study, we aimed to exploit the antiarthritic properties of TXR using an adjuvant-induced arthritic (AIA) rat model. AIA-induced rats showed the highest arthritis score at the disease onset and by oral administration of TXR (50, 100, and 200 mg/kg body weight), reduced to basal level in a dose-dependent manner. Isobaric tags for relative and absolute quantitative (iTRAQ) proteomics tool were employed to identify deregulated joint homogenate proteins in AIA and TXR-treated rats to decipher the probable mechanism of TXR action in arthritis. iTRAQ analysis identified a set of 434 proteins with 65 deregulated proteins (log₂ case/control≥1.5) in AIA. Expressions of a set of important proteins (AAT, T-kininogen, vimentin, desmin, and nucleophosmin) that could classify AIA from the healthy ones were validated using Western blot analysis. The Western blot data corroborated proteomics findings. In silico protein–protein interaction study of tissue-proteome revealed that complement component 9 (C9), the major building blocks of the membrane attack complex (MAC) responsible for sterile inflammation, get perturbed in AIA. Our dosimetry study suggests that a TXR dose of 200 mg/kg body weight for 15 days is sufficient to bring the arthritis score to basal levels in AIA rats. We have shown the importance of TXR as an antiarthritic agent in the AIA model and after additional investigation, its arthritic ameliorating properties could be exploited for clinical usability.

Network medicine in ovarian cancer: topological properties to drug discovery

Network medicine provides network theoretical tools, methods and properties to study underlying laws governing human interactome to identify disease states and disease complexity leading to drug discovery. Within this framework, we investigated the topological properties of ovarian cancer network (OCN) and the roles of hubs to understand OCN organization to address disease states and complexity. The OCN constructed from the experimentally verified genes exhibits fractal nature in the topological properties with deeply rooted functional communities indicating self-organizing behavior. The network properties at all levels of organization obey one parameter scaling law which lacks centrality lethality rule. We showed that $\langle k\rangle $ can be taken as a scaling parameter, where, power law exponent can be estimated from the ratio of network diameters. The betweenness centrality $C_B$ shows two distinct behaviors one shown by high degree hubs and the other by segregated low degree nodes. The $C_B$ power law exponent is found to connect the exponents of distributions of high and low degree nodes. OCN showed the absence of rich-club formation which leads to the missing of a number of attractors in the network causing formation of weakly tied diverse functional modules to keep optimal network efficiency. In OCN, provincial and connector hubs, which includes identified key regulators, take major responsibility to keep the OCN integrity and organization. Further, most of the key regulators are found to be over expressed and positively correlated with immune infiltrates. Finally, few potential drugs are identified related to the key regulators.

CDK1 and HSP90AA1 Appear as the Novel Regulatory Genes in Non-Small Cell Lung Cancer: A Bioinformatics Approach

Lung cancer is one of the most invasive cancers affecting over a million of the population. Non-small cell lung cancer (NSCLC) constitutes up to 85% of all lung cancer cases, and therefore, it is essential to identify predictive biomarkers of NSCLC for therapeutic purposes. Here we use a network theoretical approach to investigate the complex behavior of the NSCLC gene-regulatory interactions. We have used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, and GSE31210 and meta-analyzed them to find differentially expressed genes (DEGs) and further constructed a protein–protein interaction (PPI) network. We analyzed its topological properties and identified significant modules of the PPI network using cytoscape network analyzer and MCODE plug-in. From the PPI network, top ten genes of each of the six topological properties like closeness centrality, maximal clique centrality (MCC), Maximum Neighborhood Component (MNC), radiality, EPC (Edge Percolated Component) and bottleneck were considered for key regulator identification. We further compared them with top ten hub genes (those with the highest degrees) to find key regulator (KR) genes. We found that two genes, CDK1 and HSP90AA1, were common in the analysis suggesting a significant regulatory role of CDK1 and HSP90AA1 in non-small cell lung cancer. Our study using a network theoretical approach, as a summary, suggests CDK1 and HSP90AA1 as key regulator genes in complex NSCLC network.

Identification of key regulators in Sarcoidosis through multidimensional systems biological approach

Sarcoidosis is a multi-organ disorder where immunology, genetic and environmental factors play a key role in causing Sarcoidosis, but its molecular mechanism remains unclear. Identification of its genetics profiling that regulates the Sarcoidosis network will be one of the main challenges to understand its aetiology. We have identified differentially expressed genes (DEGs) by analyzing the gene expression profiling of Sarcoidosis and compared it with healthy control. Gene set enrichment analysis showed that these DEGs were mainly enriched in the inflammatory response, immune system, and pathways in cancer. Sarcoidosis protein interaction network was constructed by a total of 877 DEGs (up-down) and calculated its network topological properties, which follow hierarchical scale-free fractal nature up to six levels of the organization. We identified a large number of leading hubs that contain six key regulators (KRs) including ICOS, CTLA4, FLT3LG, CD33, GPR29 and ITGA4 are deeply rooted in the network from top to bottom, considering a backbone of the network. We identified the transcriptional factors (TFs) which are closely interacted with KRs. These genes and their TFs regulating the Sarcoidosis network are expected to be the main target for the therapeutic approaches and potential biomarkers. However, experimental validations of KRs needed to confirm their efficacy.

A Bioinformatics Approach to Identifying Potential Biomarkers for Cryptosporidium parvum: A Coccidian Parasite Associated with Fetal Diarrhea

Cryptosporidium parvum (C. parvum) is a protozoan parasite known for cryptosporidiosis in pre-weaned calves. Animals and patients with immunosuppression are at risk of developing the disease, which can cause potentially fatal diarrhoea. The present study aimed to construct a network biology framework based on the differentially expressed genes (DEGs) of C. parvum infected subjects. In this way, the gene expression profiling analysis of C. parvum infected individuals can give us a snapshot of actively expressed genes and transcripts under infection conditions. In the present study, we have analyzed microarray data sets and compared the gene expression profiles of the patients with the different data sets of the healthy control. Using a network medicine approach to identify the most influential genes in the gene interaction network, we uncovered essential genes and pathways related to C. parvum infection. We identified 164 differentially expressed genes (109 up- and 54 down-regulated DEGs) and allocated them to pathway and gene set enrichment analysis. The results underpin the identification of seven significant hub genes with high centrality values: ISG15, MX1, IFI44L, STAT1, IFIT1, OAS1, IFIT3, RSAD2, IFITM1, and IFI44. These genes are associated with diverse biological processes not limited to host interaction, type 1 interferon production, or response to IL-gamma. Furthermore, four genes (IFI44, IFIT3, IFITM1, and MX1) were also discovered to be involved in innate immunity, inflammation, apoptosis, phosphorylation, cell proliferation, and cell signaling. In conclusion, these results reinforce the development and implementation of tools based on gene profiles to identify and treat Cryptosporidium parvum-related diseases at an early stage.

Network-based analysis of key regulatory genes implicated in Type 2 Diabetes Mellitus and Recurrent Miscarriages in Turner Syndrome

The information on the genotype-phenotype relationship in Turner Syndrome (TS) is inadequate because very few specific candidate genes are linked to its clinical features. We used the microarray data of TS to identify the key regulatory genes implicated with TS through a network approach. The causative factors of two common co-morbidities, Type 2 Diabetes Mellitus (T2DM) and Recurrent Miscarriages (RM), in the Turner population, are expected to be different from that of the general population. Through microarray analysis, we identified nine signature genes of T2DM and three signature genes of RM in TS. The power-law distribution analysis showed that the TS network carries scale-free hierarchical fractal attributes. Through local-community-paradigm (LCP) estimation we find that a strong LCP is also maintained which means that networks are dynamic and heterogeneous. We identified nine key regulators which serve as the backbone of the TS network. Furthermore, we recognized eight interologs functional in seven different organisms from lower to higher levels. Overall, these results offer few key regulators and essential genes that we envisage have potential as therapeutic targets for the TS in the future and the animal models studied here may prove useful in the validation of such targets.

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.

Brain Disease Network Analysis to Elucidate the Neurological Manifestations of COVID-19

Although COVID-19 largely causes respiratory complications, it can also lead to various extrapulmonary manifestations resulting in higher mortality and these comorbidities are posing a challenge to the health care system. Reports indicate that 30–60% of patients with COVID-19 suffer from neurological symptoms. To understand the molecular basis of the neurologic comorbidity in COVID-19 patients, we have investigated the genetic association between COVID-19 and various brain disorders through a systems biology-based network approach and observed a remarkable resemblance. Our results showed 123 brain-related disorders associated with COVID-19 and form a high-density disease-disease network. The brain-disease-gene network revealed five highly clustered modules demonstrating a greater complexity of COVID-19 infection. Moreover, we have identified 35 hub proteins of the network which were largely involved in the protein catabolic process, cell cycle, RNA metabolic process, and nuclear transport. Perturbing these hub proteins by drug repurposing will improve the clinical conditions in comorbidity. In the near future, we assumed that in COVID-19 patients, many other neurological manifestations will likely surface. Thus, understanding the infection mechanisms of SARS-CoV-2 and associated comorbidity is a high priority to contain its short- and long-term effects on human health. Our network-based analysis strengthens the understanding of the molecular basis of the neurological manifestations observed in COVID-19 and also suggests drug for repurposing.

Kinless hubs are potential target genes in prostate cancer network

Complex disease networks can be studied successfully using network theoretical approach which helps in finding key disease genes and associated disease modules. We studied prostate cancer (PCa) protein-protein interaction (PPI) network constructed from patients' gene expression datasets and found that the network exhibits hierarchical scale free topology which lacks centrality lethality rule. Knockout experiments of the sets of leading hubs from the network leads to transition from hierarchical (HN) to scale free (SF) topology affecting network integration and organization. This transition, HN → SF, due to removal of significant number of the highest degree hubs, leads to relatively decrease in information processing efficiency, cost effectiveness of signal propagation, compactness, clustering of nodes and energy distributions. A systematic transition from a diassortative PCa PPI network to assortative networks after the removal of top 50 hubs then again reverting to disassortativity nature on further removal of the hubs was also observed indicating the dominance of the largest hubs in PCa network intergration. Further, functional classification of the hubs done by using within module degrees and participation coefficients for PCa network, and leading hubs knockout experiments indicated that kinless hubs serve as the basis of establishing links among constituting modules and heterogeneous nodes to maintain network stabilization. We, then, checked the essentiality of the hubs in the knockout experiment by performing Fisher's exact test on the hubs, and showed that removal of kinless hubs corresponded to maximum lethality in the network. However, excess removal of these hubs essentially may cause network breakdown.

Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach

Background

Identification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance.

Methods

The protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network.

Results

The PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest.

Conclusions

The popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.

Congenital malformation syndromes associated with peripheral neuroblastic tumors: A systematic review

Malformation syndromes with predisposition to peripheral neuroblastic tumors (pNT), including neuroblastoma, ganglioneuroblastoma, and ganglioneuroma, may provide clues to critical mutations influencing pNT development. Our objective was to identify and characterize features of pNT associated with specific malformation syndromes. A systematic review of the literature was performed using MEDLINE, Scopus, and Web of Science. We identified 154 of 1014 papers that met eligibility, comprising 207 cases. The patient's age, tumor histology, and frequency of multiple primary tumors varied by malformation syndrome. Genomic studies and systematized reporting are necessary to elucidate cancer risk and the distinct clinical and biological pNT patterns within syndromes.

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.

Lysine demethylases KDM6A and UTY: The X and Y of histone demethylation

Histone demethylases remove transcriptional repressive marks from histones in the nucleus. KDM6A (also known as UTX) is a lysine demethylase which acts on the trimethylated lysine at position 27 in histone 3. The KDM6A gene is located on the X chromosome but escapes X inactivation even though it is not located in the pseudoautosomal region. There is a homologue of KDM6A on the Y chromosome, known as UTY. UTY was thought to have lost its demethylase activity and to represent a non-functional remnant of the ancestral KDM6A gene. However, results with knockout mice suggest that the gene is expressed and the protein performs some function within the cell. Female mice with homozygous deletion of Kdm6a do not survive, but hemizygous males are viable, attributed to the presence of the Uty gene. KDM6A is mutated in the human condition Kabuki syndrome type 2 (OMIM 300867) and in many cases of cancer. The amino acid sequence of KDM6A has been conserved across animal phyla, although it is only found on the X chromosome in eutherian mammals. In this review, we reanalyse existing data from various sources (protein sequence comparison, evolutionary genetics, transcription factor binding and gene expression analysis) to determine the function, expression and evolution of KDM6A and UTY and show that UTY has a functional role similar to KDM6A in metabolism and development.

Minipuberty and Sexual Dimorphism in the Infant Human Thymus

AIRE expression in thymus is downregulated by estrogen after puberty, what probably renders women more susceptible to autoimmune disorders. Here we investigated the effects of minipuberty on male and female infant human thymic tissue in order to verify if this initial transient increase in sex hormones - along the first six months of life - could affect thymic transcriptional network regulation and AIRE expression. Gene co-expression network analysis for differentially expressed genes and miRNA-target analysis revealed sex differences in thymic tissue during minipuberty, but such differences were not detected in the thymic tissue of infants aged 7-18 months, i.e. the non-puberty group. AIRE expression was essentially the same in both sexes in minipuberty and in non-puberty groups, as assessed by genomic and immunohistochemical assays. However, AIRE-interactors networks showed several differences in all groups regarding gene-gene expression correlation. Therefore, minipuberty and genomic mechanisms interact in shaping thymic sexual dimorphism along the first six months of life.