Hub genes with positive feedbacks function as master switches in developmental gene regulatory networks

Water deficit response in nodulated soybean roots: a comprehensive transcriptome and translatome network analysis

BACKGROUND

Soybean establishes a mutualistic interaction with nitrogen-fixing rhizobacteria, acquiring most of its nitrogen requirements through symbiotic nitrogen fixation. This crop is susceptible to water deficit; evidence suggests that its nodulation status-whether it is nodulated or not-can influence how it responds to water deficit. The translational control step of gene expression has proven relevant in plants subjected to water deficit.

RESULTS

Here, we analyzed soybean roots' differential responses to water deficit at transcriptional, translational, and mixed (transcriptional + translational) levels. Thus, the transcriptome and translatome of four combined-treated soybean roots were analyzed. We found hormone metabolism-related genes among the differentially expressed genes (DEGs) at the translatome level in nodulated and water-restricted plants. Also, weighted gene co-expression network analysis followed by differential expression analysis identified gene modules associated with nodulation and water deficit conditions. Protein-protein interaction network analysis was performed for subsets of mixed DEGs of the modules associated with the plant responses to nodulation, water deficit, or their combination.

CONCLUSIONS

Our research reveals that the stand-out processes and pathways in the before-mentioned plant responses partially differ; terms related to glutathione metabolism and hormone signal transduction (2 C protein phosphatases) were associated with the response to water deficit, terms related to transmembrane transport, response to abscisic acid, pigment metabolic process were associated with the response to nodulation plus water deficit. Still, two processes were common: galactose metabolism and branched-chain amino acid catabolism. A comprehensive analysis of these processes could lead to identifying new sources of tolerance to drought in soybean.

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

Decoding the principle of cell-fate determination for its reverse control

Understanding and manipulating cell fate determination is pivotal in biology. Cell fate is determined by intricate and nonlinear interactions among molecules, making mathematical model-based quantitative analysis indispensable for its elucidation. Nevertheless, obtaining the essential dynamic experimental data for model development has been a significant obstacle. However, recent advancements in large-scale omics data technology are providing the necessary foundation for developing such models. Based on accumulated experimental evidence, we can postulate that cell fate is governed by a limited number of core regulatory circuits. Following this concept, we present a conceptual control framework that leverages single-cell RNA-seq data for dynamic molecular regulatory network modeling, aiming to identify and manipulate core regulatory circuits and their master regulators to drive desired cellular state transitions. We illustrate the proposed framework by applying it to the reversion of lung cancer cell states, although it is more broadly applicable to understanding and controlling a wide range of cell-fate determination processes.

The distribution of fitness effects during adaptive walks using a simple genetic network

The tempo and mode of adaptation depends on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains largely unknown. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution of gene interactions to phenotypic variance. In this study, we model a quantitative trait as the product of a simple gene regulatory network, the negative autoregulation motif. Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and network models. A key expectation from adaptive walk theory is that the distribution of fitness effects of new beneficial mutations is exponential. We found that both models instead harbored distributions with fewer large-effect beneficial alleles than expected. The network model also had a complex and bimodal distribution of fitness effects among all mutations, with a considerable density at deleterious selection coefficients. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can generate complex and multimodal distributions of fitness effects.

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

BACKGROUND

Prompt identification and assessment of the disease are essential for reducing the death rate associated with colorectal cancer (COL). Identifying specific causal or sensitive components, such as coding RNA (cRNA) and non-coding RNAs (ncRNAs), may greatly aid in the early detection of colorectal cancer.

METHODS

For this purpose, we gave natural chemicals obtained from Sparassis latifolia (SLPs) either alone or in conjunction with chemotherapy (5-Fluorouracil to a mouse colorectal tumor model induced by AOM-DSS. The transcription profile of non-coding RNAs (ncRNAs) and their target hub genes was evaluated using qPCR Real-Time, and ELISA techniques.

RESULTS

MSX2, MMP7, ITIH4, and COL1A2 were identified as factors in inflammation and oxidative stress, leading to the development of COL. The hub genes listed, upstream regulatory factors such as lncRNA PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p have been discovered as biomarkers for prognosis and diagnosis of COL. The SLPs and exercise, effectively decreased the size and quantity of tumors.

CONCLUSIONS

This effect may be attributed to the modulation of gene expression levels, including MSX2, MMP7, ITIH4, COL1A2, PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p. Ultimately, SLPs and exercise have the capacity to be regarded as complementing and enhancing chemotherapy treatments, owing to their efficacious components.

Deep learning untangles the resistance mechanism of p53 reactivator in lung cancer cells

Tumor suppressor p53 plays a pivotal role in suppressing cancer, so various drugs has been suggested to upregulate its function. However, drug resistance is still the biggest hurdle to be overcome. To address this, we developed a deep learning model called AnoDAN (anomalous gene detection using generative adversarial networks and graph neural networks for overcoming drug resistance) that unravels the hidden resistance mechanisms and identifies a combinatorial target to overcome the resistance. Our findings reveal that the TGF-β signaling pathway, alongside the p53 signaling pathway, mediates the resistance, with THBS1 serving as a core regulatory target in both pathways. Experimental validation in lung cancer cells confirms the effects of THBS1 on responsiveness to a p53 reactivator. We further discovered the positive feedback loop between THBS1 and the TGF-β pathway as the main source of resistance. This study enhances our understanding of p53 regulation and offers insights into overcoming drug resistance.

Identification of hub genes associated with somatic cell score in dairy cow

CONTEXT

Somatic cell count (SCC) is used as an indicator of udder health. The log transformation of SCC is called somatic cell score (SCS).

AIM

Several QTL and genes have been identified that are associated with SCS. This study aimed to identify the most important genes associated with SCS.

METHODS

This study compiled 168 genes that were reported to be significantly linked to SCS. Pathway analysis and network analysis were used to identify hub genes.

KEY RESULTS

Pathway analysis of these genes identified 73 gene ontology (GO) terms associated with SCS. These GO terms are associated with molecular function, biological processes, and cellular components, and the identified pathways are directly or indirectly linked with the immune system. In this study, a gene network was constructed, and from this network, the 17 hub genes (CD4, CXCL8, TLR4, STAT1, TLR2, CXCL9, CCR2, IGF1, LEP, SPP1, GH1, GHR, VWF, TNFSF11, IL10RA, NOD2, and PDGFRB) associated to SCS were identified. The subnetwork analysis yielded 10 clusters, with cluster 1 containing all identified hub genes (except for the VWF gene).

CONCLUSION

Most hub genes and pathways identified in our study were mainly involved in inflammatory and cytokine responses.

IMPLICATIONS

Result obtained in current study provides knowledge of the genetic basis and biological mechanisms controlling SCS. Therefore, the identified hub genes may be regarded as the main gene for the genomic selection of mastitis resistance.

RNA-Seq-Based Transcriptome Analysis of Nitric Oxide Scavenging Response in Neurospora crassa

While the biological role of naturally occurring nitric oxide (NO) in filamentous fungi has been uncovered, the underlying molecular regulatory networks remain unclear. In this study, we conducted an analysis of transcriptome profiles to investigate the initial stages of understanding these NO regulatory networks in Neurospora crassa, a well-established model filamentous fungus. Utilizing RNA sequencing, differential gene expression screening, and various functional analyses, our findings revealed that the removal of intracellular NO resulted in the differential transcription of 424 genes. Notably, the majority of these differentially expressed genes were functionally linked to processes associated with carbohydrate and amino acid metabolism. Furthermore, our analysis highlighted the prevalence of four specific protein domains (zinc finger C2H2, PLCYc, PLCXc, and SH3) in the encoded proteins of these differentially expressed genes. Through protein-protein interaction network analysis, we identified eight hub genes with substantial interaction connectivity, with mss-4 and gel-3 emerging as possibly major responsive genes during NO scavenging, particularly influencing vegetative growth. Additionally, our study unveiled that NO scavenging led to the inhibition of gene transcription related to a protein complex associated with ribosome biogenesis. Overall, our investigation suggests that endogenously produced NO in N. crassa likely governs the transcription of genes responsible for protein complexes involved in carbohydrate and amino acid metabolism, as well as ribosomal biogenesis, ultimately impacting the growth and development of hyphae.

Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy

Accumulated genetic alterations in cancer cells distort cellular stimulus-response (or input-output) relationships, resulting in uncontrolled proliferation. However, the complex molecular interaction network within a cell implicates a possibility of restoring such distorted input-output relationships by rewiring the signal flow through controlling hidden molecular switches. Here, a system framework of analyzing cellular input-output relationships in consideration of various genetic alterations and identifying possible molecular switches that can normalize the distorted relationships based on Boolean network modeling and dynamics analysis is presented. Such reversion is demonstrated by the analysis of a number of cancer molecular networks together with a focused case study on bladder cancer with in vitro experiments and patient survival data analysis. The origin of reversibility from an evolutionary point of view based on the redundancy and robustness intrinsically embedded in complex molecular regulatory networks is further discussed.

Prediction of anti-microtubular target proteins of tubulins and their interacting proteins using Gene Ontology tools

BACKGROUND

Tubulins are highly conserved globular proteins involved in stabilization of cellular cytoskeletal microtubules during cell cycle. Different isoforms of tubulins are differentially expressed in various cell types, and their protein-protein interactions (PPIs) analysis will help in identifying the anti-microtubular drug targets for cancer and neurological disorders. Numerous web-based PPIs analysis methods are recently being used, and in this paper, I used Gene Ontology (GO) tools, e.g., Stringbase, ProteomeHD, GeneMANIA, and ShinyGO, to identify anti-microtubular target proteins by selecting strongly interacting proteins of tubulins.

RESULTS

I used 6 different human tubulin isoforms (two from each of α-, β-, and γ-tubulin) and found several thousands of node-to-node protein interactions (highest 4956 in GeneMANIA) and selected top 10 strongly interacting node-to-node interactions with highest score, which included 7 tubulin family protein and 6 non-tubulin family proteins (total 13). Functional enrichment analysis indicated a significant role of these 13 proteins in nucleation, polymerization or depolymerization of microtubules, membrane tethering and docking, dorsal root ganglion development, mitotic cycle, and cytoskeletal organization. I found γ-tubulins (TUBG1, TUBGCP4, and TUBBGCP6) were known to contribute majorly for tubulin-associated functions followed by α-tubulin (TUBA1A) and β-tubulins (TUBB AND TUBB3). In PPI results, I found several non-tubular proteins interacting with tubulins, and six of them (HTT, DPYSL2, SKI, UNC5C, NINL, and DDX41) were found closely associated with their functions.

CONCLUSIONS

Increasing number of regulatory proteins and subpopulation of tubulin proteins are being reported with poor understanding in their association with microtubule assembly and disassembly. The functional enrichment analysis of tubulin isoforms using recent GO tools resulted in identification of γ-tubulins playing a key role in microtubule functions and observed non-tubulin family of proteins HTT, DPYSL2, SKI, UNC5C, NINL, and DDX41 strongly interacting functional proteins of tubulins. The present study yields a promising model system using GO tools to narrow down tubulin-associated proteins as a drug target in cancer, Alzheimer's, neurological disorders, etc.

Investigating the Complexity of Gene Co-expression Estimation for Single-cell Data

With the rapid advance of single-cell RNA sequencing (scRNA-seq) technology, understanding biological processes at a more refined single-cell level is becoming possible. Gene co-expression estimation is an essential step in this direction. It can annotate functionalities of unknown genes or construct the basis of gene regulatory network inference. This study thoroughly tests the existing gene co-expression estimation methods on simulation datasets with known ground truth co-expression networks. We generate these novel datasets using two simulation processes that use the parameters learned from the experimental data. We demonstrate that these simulations better capture the underlying properties of the real-world single-cell datasets than previously tested simulations for the task. Our performance results on tens of simulated and eight experimental datasets show that all methods produce estimations with a high false discovery rate potentially caused by high-sparsity levels in the data. Finally, we find that commonly used pre-processing approaches, such as normalization and imputation, do not improve the co-expression estimation. Overall, our benchmark setup contributes to the co-expression estimator development, and our study provides valuable insights for the community of single-cell data analyses.

Robustness in phenotypic plasticity and heterogeneity patterns enabled by EMT networks

Epithelial-mesenchymal plasticity (EMP) is a key arm of cancer metastasis and is observed across many contexts. Cells undergoing EMP can reversibly switch between three classes of phenotypes: epithelial (E), mesenchymal (M), and hybrid E/M. While a large number of multistable regulatory networks have been identified to be driving EMP in various contexts, the exact mechanisms and design principles that enable robustness in driving EMP across contexts are not yet fully understood. Here, we investigated dynamic and structural robustness in EMP networks with regard to phenotypic heterogeneity and plasticity. We use two different approaches to simulate these networks: a computationally inexpensive, parameter-independent continuous state space Boolean model, and an ODE-based parameter-agnostic framework (RACIPE), both of which yielded similar phenotypic distributions. While the latter approach is useful for measurements of plasticity, the former model enabled us to extensively investigate robustness in phenotypic heterogeneity. Using perturbations to network topology and by varying network parameters, we show that multistable EMP networks are structurally and dynamically more robust compared with their randomized counterparts, thereby highlighting their topological hallmarks. These features of robustness are governed by a balance of positive and negative feedback loops embedded in these networks. Using a combination of the number of negative and positive feedback loops weighted by their lengths, we identified a metric that can explain the structural and dynamical robustness of these networks. This metric enabled us to compare networks across multiple sizes, and the network principles thus obtained can be used to identify fragilities in large networks without simulating their dynamics. Our analysis highlights a network topology-based approach to quantify robustness in the phenotypic heterogeneity and plasticity emergent from EMP networks.

Characterization of highly expressed novel hub genes in hepatitis E virus chronicity in rabbits: a bioinformatics and experimental analysis

BACKGROUND

Hepatitis E virus (HEV), which is the leading cause of acute viral hepatitis worldwide, usually causes self-limited infections in common individuals. However, it can lead to chronic infection in immunocompromised individuals and its mechanisms remain unclear. Rabbits are the natural host of HEV, and chronic HEV infections have been observed in rabbits. Therefore, we aimed to investigate potential key genes in HEV chronicity process in rabbits. In this study, both bioinformatics and experimental analysis were performed to deepen the understanding of hub genes in HEV chronic infection in rabbits.

RESULTS

Ninety-four candidate differentially expressed genes (DEGs) and the pathways they enriched were identified to be related with HEV chronicity. A total of 10 hub genes were found by protein-protein interaction (PPI) network construction. Rabbits of group P (n = 4) which showed symptoms of chronic HEV infection were selected to be compared with HEV negative rabbits (group N, n = 6). By detecting the identified hub genes in groups P and N by real-time PCR, we found that the expressions of MX1, OAS2 and IFI44 were significantly higher in group P (P < 0.05).

CONCLUSIONS

In this work, we presented that MX1, OAS2 and IFI44 were significantly upregulated in HEV chronic infected rabbits, indicating that they may be involved in the pathogenesis of HEV chronicity.

Variation in Pleiotropic Hub Gene Expression Is Associated with Interspecific Differences in Head Shape and Eye Size in Drosophila

Revealing the mechanisms underlying the breathtaking morphological diversity observed in nature is a major challenge in Biology. It has been established that recurrent mutations in hotspot genes cause the repeated evolution of morphological traits, such as body pigmentation or the gain and loss of structures. To date, however, it remains elusive whether hotspot genes contribute to natural variation in the size and shape of organs. As natural variation in head morphology is pervasive in Drosophila, we studied the molecular and developmental basis of differences in compound eye size and head shape in two closely related Drosophila species. We show differences in the progression of retinal differentiation between species and we applied comparative transcriptomics and chromatin accessibility data to identify the GATA transcription factor Pannier (Pnr) as central factor associated with these differences. Although the genetic manipulation of Pnr affected multiple aspects of dorsal head development, the effect of natural variation is restricted to a subset of the phenotypic space. We present data suggesting that this developmental constraint is caused by the coevolution of expression of pnr and its cofactor u-shaped (ush). We propose that natural variation in expression or function of highly connected developmental regulators with pleiotropic functions is a major driver for morphological evolution and we discuss implications on gene regulatory network evolution. In comparison to previous findings, our data strongly suggest that evolutionary hotspots are not the only contributors to the repeated evolution of eye size and head shape in Drosophila.

Preliminary Radiogenomic Evidence for the Prediction of Metastasis and Chemotherapy Response in Pediatric Patients with Osteosarcoma Using 18F-FDF PET/CT, EZRIN and KI67

Simple Summary

Pediatric osteosarcoma is one of the most aggressive cancers, and predictions of metastasis and chemotherapy response have a significant impact on pediatric patient survival. Radiogenomics, as methods of analyzing gene expression or image texture features, have previously been used for the diagnosis of chemotherapy responses and metastasis and can reveal the current state of cancer. In this study, we aimed to generate a predictive model using gene expression and ¹⁸F-FDG PET/CT image texture features in pediatric osteosarcoma in relation to metastasis and chemotherapy response. A predictive model using radiogenomics technology that incorporates both imaging features and gene expression can accurately predict metastasis and chemotherapy responses to improve patient outcomes.

Abstract

Chemotherapy response and metastasis prediction play important roles in the treatment of pediatric osteosarcoma, which is prone to metastasis and has a high mortality rate. This study aimed to estimate the prediction model using gene expression and image texture features. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) images of 52 pediatric osteosarcoma patients were used to estimate the machine learning algorithm. An appropriate algorithm was selected by estimating the machine learning accuracy. ¹⁸F-FDG PET/CT images of 21 patients were selected for prediction model development based on simultaneous KI67 and EZRIN expression. The prediction model for chemotherapy response and metastasis was estimated using area under the curve (AUC) maximum image texture features (AUC_max) and gene expression. The machine learning algorithm with the highest test accuracy in chemotherapy response and metastasis was selected using the random forest algorithm. The chemotherapy response and metastasis test accuracy with image texture features was 0.83 and 0.76, respectively. The highest test accuracy and AUC of chemotherapy response with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.89, respectively. The highest test accuracy and AUC of metastasis with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.8, respectively. The metastasis prediction accuracy increased by 10% using radiogenomics data.

As above, so below: Whole transcriptome profiling demonstrates strong molecular similarities between avian dorsal and ventral pallial subdivisions

Over the last two decades, beginning with the Avian Brain Nomenclature Forum in 2000, major revisions have been made to our understanding of the organization and nomenclature of the avian brain. However, there are still unresolved questions on avian pallial organization, particularly whether the cells above the vestigial ventricle represent distinct populations to those below it or similar populations. To test these two hypotheses, we profiled the transcriptomes of the major avian pallial subdivisions dorsal and ventral to the vestigial ventricle boundary using RNA sequencing and a new zebra finch genome assembly containing about 22,000 annotated, complete genes. We found that the transcriptomes of neural populations above and below the ventricle were remarkably similar. Each subdivision in dorsal pallium (Wulst) had a corresponding molecular counterpart in the ventral pallium (dorsal ventricular ridge). In turn, each corresponding subdivision exhibited shared gene co‐expression modules that contained gene sets enriched in functional specializations, such as anatomical structure development, synaptic transmission, signaling, and neurogenesis. These findings are more in line with the continuum hypothesis of avian brain subdivision organization above and below the vestigial ventricle space, with the pallium as a whole consisting of four major cell populations (intercalated pallium, mesopallium, hyper‐nidopallium, and arcopallium) instead of seven (hyperpallium apicale, interstitial hyperpallium apicale, intercalated hyperpallium, hyperpallium densocellare, mesopallium, nidopallium, and arcopallium). We suggest adopting a more streamlined hierarchical naming system that reflects the robust similarities in gene expression, neural connectivity motifs, and function. These findings have important implications for our understanding of overall vertebrate brain evolution.

Transcriptome analysis of placentae reveals HELLP syndrome exhibits a greater extent of placental metabolic dysfunction than preeclampsia

Objective: The association between HELLP syndrome and preeclampsia has been investigated with conflicting conclusions. This study is to investigate the pathogenesis underlying these two diseases by analyzing placental transcriptome.Methods: The gene expression profile downloaded from Gene Expression Omnibus database was analyzed by R language.Results: A total of 573 differentially expressed genes in HELLP syndrome and 358 in preeclampsia were identified, among which 295 were unique to HELLP syndrome. Some metabolism-associated pathways were uniquely enriched in HELLP syndrome.Conclusions: HELLP syndrome exhibits a greater extent of placental metabolic dysfunction than preeclampsia, although these two diseases might share partial pathogenesis.

MicroRNA comparison between poplar and larch provides insight into the different mechanism of wood formation

MiRNA transcriptome analysis of different tissues in poplar and larch suggests variant roles of miRNAs in regulating wood formation between two kinds of phyla. Poplar and larch belong to two different phyla. Both are ecological woody species and major resources for wood-related industrial applications. However, wood properties are different between these two species and the molecular basis is largely unknown. In this study, we performed high-throughput sequencing of microRNAs (miRNAs) in the three tissues, xylem, phloem and leaf of Populus alba × Populus glandulosa and Larix kaempferi. Differentially expressed miRNA (DEmiRNA) analysis identified 85 xylem-specific miRNAs in P. alba × P. glandulosa and 158 xylem-specific miRNAs in L. kaempferi. Among 36 common miRNAs, 12 were conserved between the two species. GO and KEGG analyses of the miRNA target genes showed similar metabolism in two species. Through KEGG and BLASTN, we predicted target genes of xylem differentially expressed (DEmiRNA) in the wood formation-related pathways and located DEmiRNAs in these pathways. A network was built for wood formation-related DEmiRNAs, their target genes and orthologous genes in Arabidopsis thaliana. Comparison of DEmiRNA and target gene annotation between P. alba × P. glandulosa and L. kaempferi suggested the different functions of DEmiRNAs and divergent mechanism in wood formation between two species, providing knowledge to understand wood formation mechanism in gymnosperm and angiosperm woody plants.

Saliva exosomes-derived UBE2O mRNA promotes angiogenesis in cutaneous wounds by targeting SMAD6

Background

Enhancing angiogenesis is critical for accelerating wound healing. Application of different types of exosomes (Exos) to promote angiogenesis represents a novel strategy for enhanced wound repair. Saliva is known to accelerate wound healing, but the underlying mechanisms remain unclear.

Results

Our results have demonstrated that saliva-derived exosomes (saliva-Exos) induce human umbilical vein endothelial cells (HUVEC) proliferation, migration, and angiogenesis in vitro, and promote cutaneous wound healing in vivo. Further experiments documented that Ubiquitin-conjugating enzyme E2O (UBE2O) is one of the main mRNAs of saliva-Exos, and activation of UBE2O has effects similar to those of saliva-Exos, both in vitro and in vivo. Mechanistically, UBE2O decreases the level of SMAD family member 6 (SMAD6), thereby activating bone morphogenetic protein 2 (BMP2), which, in turn, induces angiogenesis.

Conclusions

The present work suggests that administration of saliva-Exos and UBE2O represents a promising strategy for enhancing wound healing through promotion of angiogenesis.

POPDC3 is a potential biomarker for prognosis and radioresistance in patients with head and neck squamous cell carcinoma

Radiotherapy is the primary means of treatment for patients with head and neck squamous cell carcinoma (HNSCC); however, radioresistance-induced recurrence is the primary cause of HNSCC treatment failure. Therefore, identifying specific predictive biomarkers of the response to radiotherapy may improve prognosis. In the present study, to identify the potential candidate genes associated with radioresistance in patients with HNSCC, the microarray datasets GSE9716, GSE61772 and GSE20549 were downloaded from the Gene Expression Omnibus database. The original CEL files were preprocessed using the Affymetrix package and quantile normalization and background correction were conducted using the Core package in Bioconductor. The GSE9716 dataset, consisting of 18 irradiated and 16 non-irradiated samples, was divided into two groups according to their exposure to irradiation: i) Non-irradiation group, which included 8 radioresistant samples and 8 radiosensitive samples; and ii) post-irradiation group, which included 9 radioresistant samples and 9 radiosensitive samples. The two groups were treated as separate datasets and screened. A total of 86 differentially expressed genes (DEGs) were identified in the non-irradiation group and 405 DEGs in the post-irradiation group. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis detected several significant functions associated with the DEGs. In the co-expression analysis, 76 hub genes in the light green module and 917 hub genes with a high connectivity were selected for further analysis. Finally, overlapping the DEGs and hub genes from the two groups yielded a map of 13 shared differentially expressed genes. The 13 genes showed significantly different expression in radioresistant samples compared with the radiosensitive samples before and after irradiation. Out of these genes, popeye domain-containing protein 3 (POPDC3) was highly expressed in the post-irradiation group compared with the non-irradiation group. In survival analysis, high POPDC3 expression correlated with poor a prognosis for patients with HNSCC. The independent prognostic factors were identified using univariate and multivariate Cox analyses based on The Cancer Genome Atlas database. These were incorporated into a nomogram to predict 3- and 5-year overall survival. Receiver operating characteristic curves were used to estimate the accuracy of the nomogram. Together these studies suggest that POPDC3 may serve as a potential predictive biomarker for prognosis and radioresistance of patients with HNSCC as well as clinical diagnosis and treatment of patients.

Stage-Dependent Gene Expression Profiling in Colorectal Cancer

Temporal gene expression profiles have been widely considered to uncover the mechanism of cancer development and progression. Gene expression patterns, however, have been analyzed for limited stages with small samples, without proper data pre-processing, in many cases. With those approaches, it is difficult to unveil the mechanism of cancer development over time. In this study, we analyzed gene expression profiles of two independent colorectal cancer sample datasets, each of which contained 556 and 566 samples, respectively. To find specific gene expression changes according to cancer stage, we applied the linear mixed-effect regression model (LMER) that controls other clinical variables. Based on this methodology, we found two types of gene expression patterns: continuously increasing and decreasing genes as cancer develops. We found that continuously increasing genes are related to the nervous and developmental system, whereas the others are related to the cell cycle and metabolic processes. We further analyzed connected sub-networks related to the two types of genes. From these results, we suggest that the gene expression profile analysis can be used to understand underlying the mechanisms of cancer development such as cancer growth and metastasis. Furthermore, our approach can provide a good guideline for advancing our understanding of cancer developmental processes.

A system view and analysis of essential hypertension

OBJECTIVES

The goal of this study was to investigate genes associated with essential hypertension from a system perspective, making use of bioinformatic tools to gain insights that are not evident when focusing at a detail-based resolution.

METHODS

Using various databases (pathways, Genome Wide Association Studies, knockouts etc.), we compiled a set of about 200 genes that play a major role in hypertension and identified the interactions between them. This enabled us to create a protein-protein interaction network graph, from which we identified key elements, based on graph centrality analysis. Enriched gene regulatory elements (transcription factors and microRNAs) were extracted by motif finding techniques and knowledge-based tools.

RESULTS

We found that the network is composed of modules associated with functions such as water retention, endothelial vasoconstriction, sympathetic activity and others. We identified the transcription factor SP1 and the two microRNAs miR27 (a and b) and miR548c-3p that seem to play a major role in regulating the network as they exert their control over several modules and are not restricted to specific functions. We also noticed that genes involved in metabolic diseases (e.g. insulin) are central to the network.

CONCLUSION

We view the blood-pressure regulation mechanism as a system-of-systems, composed of several contributing subsystems and pathways rather than a single module. The system is regulated by distributed elements. Understanding this mode of action can lead to a more precise treatment and drug target discovery. Our analysis suggests that insulin plays a primary role in hypertension, highlighting the tight link between essential hypertension and diseases associated with the metabolic syndrome.

Bioinformatic analyses reveal the key pathways and genes in the CXCR4 mediated mesenchymal subtype of glioblastoma

Glioblastoma multiforme (GBM) is one of the most lethal types of tumour, despite severe treatment methods. The Cancer Genome Atlas has categorised GBMs into proneural, neural, classical and mesenchymal subtypes; the mesenchymal subgroup has the worst prognosis. CXCR4 has been reported as selectively overexpressed in the mesenchymal subtype and positively associated with MES markers. However, to the best of our knowledge the underlying mechanisms regarding how CXCR4 may regulate mesenchymal GBM are still unknown. The present study aimed to investigate the critical pathways mediated by CXCR4 in mesenchymal GBM using bioinformatic analyses. The results suggested that CXCR4 is a predictor of poor prognosis and may serve as a biomarker of the mesenchymal subtype in patients with GBM. In addition, CXCR4 mediated the mitogen‑activated protein kinase signaling pathway, which was identified specifically in patients with mesenchymal GBM. CXCR4 associated genes or pathways may be a 'basket trial' option for the management of melanoma, prostate cancer and mesenchymal GBM.

A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression

BACKGROUND

Distance based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. An alternative method to examine disease phenotypes is to use pre-defined biological pathways. These pathways have been shown to be perturbed in different ways in different subjects who have similar clinical features. We hypothesize that differences in the expressions of genes in a given pathway are more predictive of differences in biological differences compared to standard approaches and if integrated into clustering analysis will enhance the robustness and accuracy of the clustering method. To examine this hypothesis, we developed a novel computational method to assess the biological differences between samples using gene expression data by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior.

RESULTS

Pre-defined biological pathways were downloaded and genes in each pathway were used to cluster samples using the Gaussian mixture model. The clustering results across different pathways were then summarized to calculate the pathway-based distance score between samples. This method was applied to both simulated and real data sets and compared to the traditional Euclidean distance and another pathway-based clustering method, Pathifier. The results show that the pathway-based distance score performs significantly better than the Euclidean distance, especially when the heterogeneity is low and genes in the same pathways are correlated. Compared to Pathifier, we demonstrated that our approach achieves higher accuracy and robustness for small pathways. When the pathway size is large, by downsampling the pathways into smaller pathways, our approach was able to achieve comparable performance.

CONCLUSIONS

We have developed a novel distance score that represents the biological differences between samples using gene expression data and pre-defined biological pathway information. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both simulated data and real data when compared to traditional methods. It also has comparable or better performance compared to Pathifier.

MicroRNA-17 Suppresses TNF-α Signaling by Interfering with TRAF2 and cIAP2 Association in Rheumatoid Arthritis Synovial Fibroblasts

TNF-α is a major cytokine implicated in rheumatoid arthritis (RA), and its expression is regulated at the transcriptional and posttranscriptional levels. However, the impact of changes in microRNA expression on posttranslational processes involved in TNF-α signaling networks is not well defined in RA. In this study, we evaluated the effect of miR-17, a member of the miR-17-92 cluster, on the TNF-α signaling pathway in human RA synovial fibroblasts (SFs). We demonstrated that miR-17 expression was significantly low in RA serum, SFs, and synovial tissues, as well as in the serum and joints of adjuvant-induced arthritis rats. RNA-sequencing analysis showed modulation of 664 genes by pre-miR-17 in human RA SFs. Ingenuity pathway analysis of RNA-sequencing data identified the ubiquitin proteasome system in the TNF-α signaling pathway as a primary target of miR-17. Western blot analysis confirmed the reduction in TRAF2, cIAP1, cIAP2, USP2, and PSMD13 expression by miR-17 in TNF-α-stimulated RA SFs. Immunoprecipitation assays showed that miR-17 restoration increased the K48-linked polyubiquitination of TRAF2, cIAP1, and cIAP2 in TNF-α-stimulated RA SFs. Thus, destabilization of TRAF2 by miR-17 reduced the ability of TRAF2 to associate with cIAP2, resulting in the downregulation of TNF-α-induced NF-κBp65, c-Jun, and STAT3 nuclear translocation and the production of IL-6, IL-8, MMP-1, and MMP-13 in human RA SFs. In conclusion, this study provides evidence for the role of miR-17 as a negative regulator of TNF-α signaling by modulating the protein ubiquitin processes in RA SFs.

Design principles of stripe-forming motifs: the role of positive feedback

Interpreting a morphogen gradient into a single stripe of gene-expression is a fundamental unit of patterning in early embryogenesis. From both experimental data and computational studies the feed-forward motifs stand out as minimal networks capable of this patterning function. Positive feedback within gene networks has been hypothesised to enhance the sharpness and precision of gene-expression borders, however a systematic analysis has not yet been reported. Here we set out to assess this hypothesis, and find an unexpected result. The addition of positive-feedback can have different effects on two different designs of feed-forward motif– it increases the parametric robustness of one design, while being neutral or detrimental to the other. These results shed light on the abundance of the former motif and especially of mutual-inhibition positive feedback in developmental networks.

Predicting Essential Proteins Based on Weighted Degree Centrality

Essential proteins are vital for an organism's viability under a variety of conditions. There are many experimental and computational methods developed to identify essential proteins. Computational prediction of essential proteins based on the global protein-protein interaction (PPI) network is severely restricted because of the insufficiency of the PPI data, but fortunately the gene expression profiles help to make up the deficiency. In this work, Pearson correlation coefficient (PCC) is used to bridge the gap between PPI and gene expression data. Based on PCC and edge clustering coefficient (ECC), a new centrality measure, i.e., the weighted degree centrality (WDC), is developed to achieve the reliable prediction of essential proteins. WDC is employed to identify essential proteins in the yeast PPI and e-Coli networks in order to estimate its performance. For comparison, other prediction technologies are also performed to identify essential proteins. Some evaluation methods are used to analyze the results from various prediction approaches. The prediction results and comparative analyses are shown in the paper. Furthermore, the parameter λ in the method WDC will be analyzed in detail and an optimal λ value will be found. Based on the optimal λ value, the differentiation of WDC and another prediction method PeC is discussed. The analyses prove that WDC outperforms other methods including DC, BC, CC, SC, EC, IC, NC, and PeC. At the same time, the analyses also mean that it is an effective way to predict essential proteins by means of integrating different data sources.

The effects of MicroRNA transfections on global patterns of gene expression in ovarian cancer cells are functionally coordinated

Background

MicroRNAs (miRNAs) are a class of small RNAs that have been linked to a number of diseases including cancer. The potential application of miRNAs in the diagnostics and therapeutics of ovarian and other cancers is an area of intense interest. A current challenge is the inability to accurately predict the functional consequences of exogenous modulations in the levels of potentially therapeutic miRNAs.

Methods

In an initial effort to systematically address this issue, we conducted miRNA transfection experiments using two miRNAs (miR-7, miR-128). We monitored the consequent changes in global patterns of gene expression by microarray and quantitative (real-time) polymerase chain reaction. Network analysis of the expression data was used to predict the consequence of each transfection on cellular function and these predictions were experimentally tested.

Results

While ~20% of the changes in expression patterns of hundreds to thousands of genes could be attributed to direct miRNA-mRNA interactions, the majority of the changes are indirect, involving the downstream consequences of miRNA-mediated changes in regulatory gene expression. The changes in gene expression induced by individual miRNAs are functionally coordinated but distinct between the two miRNAs. MiR-7 transfection into ovarian cancer cells induces changes in cell adhesion and other developmental networks previously associated with epithelial-mesenchymal transitions (EMT) and other processes linked with metastasis. In contrast, miR-128 transfection induces changes in cell cycle control and other processes commonly linked with cellular replication.

Conclusions

The functionally coordinated patterns of gene expression displayed by different families of miRNAs have the potential to provide clinicians with a strategy to treat cancers from a systems rather than a single gene perspective.

Spatiotemporal network motif reveals the biological traits of developmental gene regulatory networks in Drosophila melanogaster

Background

Network motifs provided a “conceptual tool” for understanding the functional principles of biological networks, but such motifs have primarily been used to consider static network structures. Static networks, however, cannot be used to reveal time- and region-specific traits of biological systems. To overcome this limitation, we proposed the concept of a “spatiotemporal network motif,” a spatiotemporal sequence of network motifs of sub-networks which are active only at specific time points and body parts.

Results

On the basis of this concept, we analyzed the developmental gene regulatory network of the Drosophila melanogaster embryo. We identified spatiotemporal network motifs and investigated their distribution pattern in time and space. As a result, we found how key developmental processes are temporally and spatially regulated by the gene network. In particular, we found that nested feedback loops appeared frequently throughout the entire developmental process. From mathematical simulations, we found that mutual inhibition in the nested feedback loops contributes to the formation of spatial expression patterns.

Conclusions

Taken together, the proposed concept and the simulations can be used to unravel the design principle of developmental gene regulatory networks.

Inferring cell cycle feedback regulation from gene expression data

Feedback control is an important regulatory process in biological systems, which confers robustness against external and internal disturbances. Genes involved in feedback structures are therefore likely to have a major role in regulating cellular processes. Here we rely on a dynamic Bayesian network approach to identify feedback loops in cell cycle regulation. We analyzed the transcriptional profile of the cell cycle in HeLa cancer cells and identified a feedback loop structure composed of 10 genes. In silico analyses showed that these genes hold important roles in system's dynamics. The results of published experimental assays confirmed the central role of 8 of the identified feedback loop genes in cell cycle regulation. In conclusion, we provide a novel approach to identify critical genes for the dynamics of biological processes. This may lead to the identification of therapeutic targets in diseases that involve perturbations of these dynamics.

Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology

Background

In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN).

Results

The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters.

Conclusions

This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.