Massive human co-expression network and its medical applications

Molecular Docking and Molecular Dynamics Studies Reveal Secretory Proteins as Novel Targets of Temozolomide in Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is a tumor of glial origin and is the most malignant, aggressive and prevalent type, with the highest mortality rate in adult brain cancer. Surgical resection of the tumor followed by Temozolomide (TMZ) therapy is currently available, but the development of resistance to TMZ is a common limiting factor in effective treatment. The present study investigated the potential interactions of TMZ with several secretory proteins involved in various molecular and cellular processes in GBM. Automated docking studies were performed using AutoDock 4.2, which showed an encouraging binding affinity of TMZ towards all targeted proteins, with the strongest interaction and binding affinity with GDF1 and SLIT1, followed by NPTX1, CREG2 and SERPINI, among the selected proteins. Molecular dynamics (MD) simulations of protein-ligand complexes were performed via CABS-flex V2.0 and the iMOD server to evaluate the root-mean-square fluctuations (RMSFs) and measure protein stability, respectively. The results showed that docked models were more flexible and stable with TMZ, suggesting that it may be able to target putative proteins implicated in gliomagenesis that may impact radioresistance. However, additional in vitro and in vivo investigations can ascertain the potential of the selected proteins to serve as novel targets for TMZ for GBM treatment.

Cloning and Functional Analysis of Rat Tweety-Homolog 1 Gene Promoter

Tweety-homolog 1 protein (Ttyh1) is abundantly expressed in neurons in the healthy brain, and its expression is induced under pathological conditions. In hippocampal neurons in vitro, Ttyh1 was implicated in the regulation of primary neuron morphology. However, the mechanisms that underlie transcriptional regulation of the Ttyh1 gene in neurons remain elusive. The present study sought to identify the promoter of the Ttyh1 gene and functionally characterize cis-regulatory elements that are potentially involved in the transcriptional regulation of Ttyh1 expression in rat dissociated hippocampal neurons in vitro. We cloned a 592 bp rat Ttyh1 promoter sequence and designed deletion constructs of the transcription factors specificity protein 1 (Sp1), E2F transcription factor 3 (E2f3), and achaete-scute homolog 1 (Ascl1) that were fused upstream of a luciferase reporter gene in pGL4.10[luc2]. The luciferase reporter gene assay showed the possible involvement of Ascl1, Sp1, and responsive cis-regulatory elements in Ttyh1 expression. These findings provide novel information about Ttyh1 gene regulation in neurons.

Cardiac risk stratification in cancer patients: A longitudinal patient-patient network analysis

BACKGROUND

Cardiovascular disease is a leading cause of death in general population and the second leading cause of mortality and morbidity in cancer survivors after recurrent malignancy in the United States. The growing awareness of cancer therapy-related cardiac dysfunction (CTRCD) has led to an emerging field of cardio-oncology; yet, there is limited knowledge on how to predict which patients will experience adverse cardiac outcomes. We aimed to perform unbiased cardiac risk stratification for cancer patients using our large-scale, institutional electronic medical records.

METHODS AND FINDINGS

We built a large longitudinal (up to 22 years' follow-up from March 1997 to January 2019) cardio-oncology cohort having 4,632 cancer patients in Cleveland Clinic with 5 diagnosed cardiac outcomes: atrial fibrillation, coronary artery disease, heart failure, myocardial infarction, and stroke. The entire population includes 84% white Americans and 11% black Americans, and 59% females versus 41% males, with median age of 63 (interquartile range [IQR]: 54 to 71) years old. We utilized a topology-based K-means clustering approach for unbiased patient-patient network analyses of data from general demographics, echocardiogram (over 25,000), lab testing, and cardiac factors (cardiac). We performed hazard ratio (HR) and Kaplan-Meier analyses to identify clinically actionable variables. All confounding factors were adjusted by Cox regression models. We performed random-split and time-split training-test validation for our model. We identified 4 clinically relevant subgroups that are significantly correlated with incidence of cardiac outcomes and mortality. Among the 4 subgroups, subgroup I (n = 625) has the highest risk of de novo CTRCD (28%) with an HR of 3.05 (95% confidence interval (CI) 2.51 to 3.72). Patients in subgroup IV (n = 1,250) had the worst survival probability (HR 4.32, 95% CI 3.82 to 4.88). From longitudinal patient-patient network analyses, the patients in subgroup I had a higher percentage of de novo CTRCD and a worse mortality within 5 years after the initiation of cancer therapies compared to long-time exposure (6 to 20 years). Using clinical variable network analyses, we identified that serum levels of NT-proB-type Natriuretic Peptide (NT-proBNP) and Troponin T are significantly correlated with patient's mortality (NT-proBNP > 900 pg/mL versus NT-proBNP = 0 to 125 pg/mL, HR = 2.95, 95% CI 2.28 to 3.82, p < 0.001; Troponin T > 0.05 μg/L versus Troponin T ≤ 0.01 μg/L, HR = 2.08, 95% CI 1.83 to 2.34, p < 0.001). Study limitations include lack of independent cardio-oncology cohorts from different healthcare systems to evaluate the generalizability of the models. Meanwhile, the confounding factors, such as multiple medication usages, may influence the findings.

CONCLUSIONS

In this study, we demonstrated that the patient-patient network clustering methodology is clinically intuitive, and it allows more rapid identification of cancer survivors that are at greater risk of cardiac dysfunction. We believed that this study holds great promise for identifying novel cardiac risk subgroups and clinically actionable variables for the development of precision cardio-oncology.

iTRAQ comparison of proteomic profiles of endometrial receptivity

Endometrial receptivity is a limiting step in human reproduction. A disruption in the development of endometrial receptivity is responsible for recurrent implantation failures (RIF) of endometrial origin. To understand the molecular mechanisms behind the endometrial receptivity process, we used the isobaric tag for relative and absolute quantitation (iTRAQ) method to compare three different endometrial statuses: fertile women, intrauterine device (IUD) carriers, and RIF patients. Overall, iTRAQ allowed identified 1889 non-redundant proteins. Of these, 188 were differentially expressed proteins (DEP) (p-value < .05). Pairwise comparisons revealed 133 significant DEP in fertile vs. IUD carriers and 158 DEP in RIF vs. IUD carriers. However, no DEP were identified between fertile and RIF patients. Western blot validation of three DEP involved in endometrial receptivity (plastin 2, lactotransferrin, and lysozyme) confirmed our iTRAQ results. Moreover, functional KEGG enrichment revealed that complement and coagulation cascades and peroxisome were the two most significant pathways for the RIF vs. IUD comparison and ribosome and spliceosome for the fertile vs. IUD comparison, as possible important pathways involved in the endometrial receptivity acquisition. The lack of DEP between fertile and RIF patient endometria suggest that idiopathic RIF may not have an endometrial origin, with other as-yet-unknown factors involved. SIGNIFICANCE: A pilot study where a comparison of the endometrial protein profile from women with different endometrial receptive grade (fertile women, IUD carriers and RIF patients) during the same period of time (overlapping with the window of implantation) of a hormone replacement therapy was performed using a high-throughput proteomic technique. This approach lead us to better understand the molecular mechanisms undergoing endometrial receptivity, a time-limiting step to achieve pregnancy in humans. Moreover, the number of samples per group (10 Fertile women, 10 IUD carriers and 8 RIF patients) according to the methodology here employed (8plex iTRAQ), give more robustness to our results. Our findings confirm that an IUD introduces numerous changes in the endometrial protein profile when compared to fertile and RIF endometria, revealing some key proteins involved in endometrial receptivity. Finding no significant differences between Fertile and RIF patient endometria could suggest that other as-yet-unknown factors could be involved in the etiology of idiopathic RIF.

Tweety-Homolog 1 Drives Brain Colonization of Gliomas

Early and progressive colonization of the healthy brain is one hallmark of diffuse gliomas, including glioblastomas. We recently discovered ultralong (>10 to hundreds of microns) membrane protrusions [tumor microtubes (TMs)] extended by glioma cells. TMs have been associated with the capacity of glioma cells to effectively invade the brain and proliferate. Moreover, TMs are also used by some tumor cells to interconnect to one large, resistant multicellular network. Here, we performed a correlative gene-expression microarray and in vivo imaging analysis, and identified novel molecular candidates for TM formation and function. Interestingly, these genes were previously linked to normal CNS development. One of the genes scoring highest in tests related to the outgrowth of TMs was tweety-homolog 1 (TTYH1), which was highly expressed in a fraction of TMs in mice and patients. Ttyh1 was confirmed to be a potent regulator of normal TM morphology and of TM-mediated tumor-cell invasion and proliferation. Glioma cells with one or two TMs were mainly responsible for effective brain colonization, and Ttyh1 downregulation particularly affected this cellular subtype, resulting in reduced tumor progression and prolonged survival of mice. The remaining Ttyh1-deficient tumor cells, however, had more interconnecting TMs, which were associated with increased radioresistance in those small tumors. These findings imply a cellular and molecular heterogeneity in gliomas regarding formation and function of distinct TM subtypes, with multiple parallels to neuronal development, and suggest that Ttyh1 might be a promising target to specifically reduce TM-associated brain colonization by glioma cells in patients.SIGNIFICANCE STATEMENT In this report, we identify tweety-homolog 1 (Ttyh1), a membrane protein linked to neuronal development, as a potent driver of tumor microtube (TM)-mediated brain colonization by glioma cells. Targeting of Ttyh1 effectively inhibited the formation of invasive TMs and glioma growth, but increased network formation by intercellular TMs, suggesting a functional and molecular heterogeneity of the recently discovered TMs with potential implications for future TM-targeting strategies.

Identification and Network-Enabled Characterization of Auxin Response Factor Genes in Medicago truncatula

The Auxin Response Factor (ARF) family of transcription factors is an important regulator of environmental response and symbiotic nodulation in the legume Medicago truncatula. While previous studies have identified members of this family, a recent spurt in gene expression data coupled with genome update and reannotation calls for a reassessment of the prevalence of ARF genes and their interaction networks in M. truncatula. We performed a comprehensive analysis of the M. truncatula genome and transcriptome that entailed search for novel ARF genes and the co-expression networks. Our investigation revealed 8 novel M. truncatula ARF (MtARF) genes, of the total 22 identified, and uncovered novel gene co-expression networks as well. Furthermore, the topological clustering and single enrichment analysis of several network models revealed the roles of individual members of the MtARF family in nitrogen regulation, nodule initiation, and post-embryonic development through a specialized protein packaging and secretory pathway. In summary, this study not just shines new light on an important gene family, but also provides a guideline for identification of new members of gene families and their functional characterization through network analyses.

Large-scale gene co-expression network as a source of functional annotation for cattle genes

Background

Genome sequencing and subsequent gene annotation of genomes has led to the elucidation of many genes, but in vertebrates the actual number of protein coding genes are very consistent across species (~20,000). Seven years after sequencing the cattle genome, there are still genes that have limited annotation and the function of many genes are still not understood, or partly understood at best. Based on the assumption that genes with similar patterns of expression across a vast array of tissues and experimental conditions are likely to encode proteins with related functions or participate within a given pathway, we constructed a genome-wide Cattle Gene Co-expression Network (CGCN) using 72 microarray datasets that contained a total of 1470 Affymetrix Genechip Bovine Genome Arrays that were retrieved from either NCBI GEO or EBI ArrayExpress.

Results

The total of 16,607 probe sets, which represented 11,397 genes, with unique Entrez ID were consolidated into 32 co-expression modules that contained between 29 and 2569 probe sets. All of the identified modules showed strong functional enrichment for gene ontology (GO) terms and Reactome pathways. For example, modules with important biological functions such as response to virus, response to bacteria, energy metabolism, cell signaling and cell cycle have been identified. Moreover, gene co-expression networks using “guilt-by-association” principle have been used to predict the potential function of 132 genes with no functional annotation. Four unknown Hub genes were identified in modules highly enriched for GO terms related to leukocyte activation (LOC509513), RNA processing (LOC100848208), nucleic acid metabolic process (LOC100850151) and organic-acid metabolic process (MGC137211). Such highly connected genes should be investigated more closely as they likely to have key regulatory roles.

Conclusions

We have demonstrated that the CGCN and its corresponding regulons provides rich information for experimental biologists to design experiments, interpret experimental results, and develop novel hypothesis on gene function in this poorly annotated genome. The network is publicly accessible at http://www.animalgenome.org/cgi-bin/host/reecylab/d.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3176-2) contains supplementary material, which is available to authorized users.

Mining kidney toxicogenomic data by using gene co-expression modules

Background

Acute kidney injury (AKI) caused by drug and toxicant ingestion is a serious clinical condition associated with high mortality rates. We currently lack detailed knowledge of the underlying molecular mechanisms and biological networks associated with AKI. In this study, we carried out gene co-expression analyses using DrugMatrix—a large toxicogenomics database with gene expression data from rats exposed to diverse chemicals—and identified gene modules associated with kidney injury to probe the molecular-level details of this disease.

Results

We generated a comprehensive set of gene co-expression modules by using the Iterative Signature Algorithm and found distinct clusters of modules that shared genes and were associated with similar chemical exposure conditions. We identified two module clusters that showed specificity for kidney injury in that they 1) were activated by chemical exposures causing kidney injury, 2) were not activated by other chemical exposures, and 3) contained known AKI-relevant genes such as Havcr1, Clu, and Tff3. We used the genes in these AKI-relevant module clusters to develop a signature of 30 genes that could assess the potential of a chemical to cause kidney injury well before injury actually occurs. We integrated AKI-relevant module cluster genes with protein-protein interaction networks and identified the involvement of immunoproteasomes in AKI. To identify biological networks and processes linked to Havcr1, we determined genes within the modules that frequently co-express with Havcr1, including Cd44, Plk2, Mdm2, Hnmt, Macrod1, and Gtpbp4. We verified this procedure by showing that randomized data did not identify Havcr1 co-expression genes and that excluding up to 10 % of the data caused only minimal degradation of the gene set. Finally, by using an external dataset from a rat kidney ischemic study, we showed that the frequently co-expressed genes of Havcr1 behaved similarly in a model of non-chemically induced kidney injury.

Conclusions

Our study demonstrated that co-expression modules and co-expressed genes contain rich information for generating novel biomarker hypotheses and constructing mechanism-based molecular networks associated with kidney injury.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3143-y) contains supplementary material, which is available to authorized users.

The QQS orphan gene of Arabidopsis modulates carbon and nitrogen allocation in soybean

The genome of each species contains as high as 8% of genes that are uniquely present in that species. Little is known about the functional significance of these so-called species specific or orphan genes. The Arabidopsis thaliana gene Qua-Quine Starch (QQS) is species specific. Here, we show that altering QQS expression in Arabidopsis affects carbon partitioning to both starch and protein. We hypothesized QQS may be conserved in a feature other than primary sequence, and as such could function to impact composition in another species. To test the potential of QQS in affecting composition in an ectopic species, we introduced QQS into soybean. Soybean T1 lines expressing QQS have up to 80% decreased leaf starch and up to 60% increased leaf protein; T4 generation seeds from field-grown plants contain up to 13% less oil, while protein is increased by up to 18%. These data broaden the concept of QQS as a modulator of carbon and nitrogen allocation, and demonstrate that this species-specific gene can affect the seed composition of an agronomic species thought to have diverged from Arabidopsis 100 million years ago.

Coming of age: orphan genes in plants

Sizable minorities of protein-coding genes from every sequenced eukaryotic and prokaryotic genome are unique to the species. These so-called ‘orphan genes’ may evolve de novo from non-coding sequence or be derived from older coding material. They are often associated with environmental stress responses and species-specific traits or regulatory patterns. However, difficulties in studying genes where comparative analysis is impossible, and a bias towards broadly conserved genes, have resulted in underappreciation of their importance. We review here the identification, possible origins, evolutionary trends, and functions of orphans with an emphasis on their role in plant biology. We exemplify several evolutionary trends with an analysis of Arabidopsis thaliana and present QQS as a model orphan gene.

Breaking free from the chains of pathway annotation: de novo pathway discovery for the analysis of disease processes

Interpreting the biological implications of high-throughput experiments such as gene-expression studies, genome-wide association studies and large-scale sequencing studies is not trivial. Gene-set and pathway analyses are useful tools to support the interpretation of such experiments, but rely on curated pathways or gene sets. The recent development of de novo pathway discovery methods aims to overcome this limitation. This article provides an overview of the methods currently available and reviews the advantages and challenges of this approach. In detail, it highlights the particular issues of de novo pathway discovery based on genome-wide association studies data, for which multiple different strategies have been proposed.

A global approach to analysis and interpretation of metabolic data for plant natural product discovery

Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publicly available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis of metabolomics data, and methods for integrating these datasets with transcriptomic data to create hypotheses concerning specialized metabolisms that generate the diversity in natural product chemistry. We discuss the importance of close collaborations among biologists, chemists, computer scientists and statisticians throughout the development of such integrated metabolism-centric databases and software.

BirdsEyeView (BEV): graphical overviews of experimental data

Background

Analyzing global experimental data can be tedious and time-consuming. Thus, helping biologists see results as quickly and easily as possible can facilitate biological research, and is the purpose of the software we describe.

Results

We present BirdsEyeView, a software system for visualizing experimental transcriptomic data using different views that users can switch among and compare. BirdsEyeView graphically maps data to three views: Cellular Map (currently a plant cell), Pathway Tree with dynamic mapping, and Gene Ontology http://www.geneontology.org Biological Processes and Molecular Functions. By displaying color-coded values for transcript levels across different views, BirdsEyeView can assist users in developing hypotheses about their experiment results.

Conclusions

BirdsEyeView is a software system available as a Java Webstart package for visualizing transcriptomic data in the context of different biological views to assist biologists in investigating experimental results. BirdsEyeView can be obtained from http://metnetdb.org/MetNet_BirdsEyeView.htm.