A dependency graph approach for the analysis of differential gene expression profiles

Prognostic factors of in-hospital complications after hip fracture surgery: a scoping review

INTRODUCTION

To examine prognostic factors that influence complications after hip fracture surgery. To summarize proposed underlying mechanisms for their influence.

METHODS

We reported according to the Preferred Reporting Items for Systematic Review and Meta-Analysis Scoping Review extension. We searched MEDLINE, Embase, CINAHL, AgeLine, Cochrane Library, and reference lists of retrieved studies for studies of prognostic factor/s of postoperative in-hospital medical complication/s among patients 50 years and older treated surgically for non-pathological closed hip fracture, published in English on January 2008-January 2018. We excluded studies of surgery type or in-hospital medications. Screening was duplicated by two independent reviewers. One reviewer completed the extraction with accuracy checks by the second reviewer. We summarized the extent, nature, and proposed underlying mechanisms for the prognostic factors of complications narratively and in a dependency graph.

RESULTS

We identified 44 prognostic factors of in-hospital complications after hip fracture surgery from 56 studies. Of these, we identified 7 patient factors-dehydration, anemia, hypotension, heart rate variability, pressure risk, nutrition, and indwelling catheter use; and 7 process factors-time to surgery, anesthetic type, transfusion strategy, orthopedic versus geriatric/co-managed care, multidisciplinary care pathway, and potentially modifiable during index hospitalization. We identified underlying mechanisms for 15 of 44 factors. The reported association between 12 prognostic factors and complications was inconsistent across studies.

CONCLUSIONS

Most factors were reported by one study with no proposed underlying mechanism for their influence. Where reported by more than one study, there was inconsistency in reported associations and the conceptualization of complications differed, limiting comparison across studies. It is therefore not possible to be certain whether intervening on these factors would reduce the rate of complications after hip fracture surgery.

Mortality by Timing of Hip Fracture Surgery: Factors and Relationships at Play

In hip fracture care, it is disputed whether mortality worsens when surgery is delayed. This knowledge gap matters when hospital managers seek to justify resource allocation for prioritizing access to one procedure over another. Uncertainty over the surgical timing-death association leads to either surgical prioritization without benefit or the underuse of expedited surgery when it could save lives. The discrepancy in previous findings results in part from differences between patients who happened to undergo surgery at different times. Such differences may produce the statistical association between surgical timing and death in the absence of a causal relationship. Previous observational studies attempted to adjust for structure, process, and patient factors that contribute to death, but not for relationships between structure and process factors, or between patient and process factors. In this article, we (1) summarize what is known about the factors that influence, directly or indirectly, both the timing of surgery and the occurrence of death; (2) construct a dependency graph of relationships among these factors based explicitly on the existing literature; (3) consider factors with a potential to induce covariation of time to surgery and the occurrence of death, directly or through the network of relationships, thereby explaining a putative surgical timing-death association; and (4) show how age, sex, dependent living, fracture type, hospital type, surgery type, and calendar period can influence both time to surgery and occurrence of death through chains of dependencies. We conclude by discussing how these results can inform the allocation of surgical capacity to prevent the avoidable adverse consequences of delaying hip fracture surgery.

Vascular endothelial growth factor A as predictive marker for mTOR inhibition in relapsing high-grade serous ovarian cancer

Background

Development of resistance against first line drug therapy including cisplatin and paclitaxel in high-grade serous ovarian cancer (HGSOC) presents a major challenge. Identifying drug candidates breaking resistance, ideally combined with predictive biomarkers allowing precision use are needed for prolonging progression free survival of ovarian cancer patients.

Modeling of molecular processes driving drug resistance in tumor tissue further combined with mechanism of action of drugs provides a strategy for identification of candidate drugs and associated predictive biomarkers.

Results

Consolidation of transcriptomics profiles and biomedical literature mining results provides 1242 proteins linked with ovarian cancer drug resistance. Integrating this set on a protein interaction network followed by graph segmentation results in a molecular process model representation of drug resistant HGSOC embedding 409 proteins in 24 molecular processes. Utilizing independent transcriptomics profiles with follow-up data on progression free survival allows deriving molecular biomarker-based classifiers for predicting recurrence under first line therapy. Biomarkers of specific relevance are identified in a molecular process encapsulating TGF-beta, mTOR, Jak-STAT and Neurotrophin signaling. Mechanism of action molecular model representations of cisplatin and paclitaxel embed the very same signaling components, and specifically proteins afflicted with the activation status of the mTOR pathway become evident, including VEGFA. Analyzing mechanism of action interference of the mTOR inhibitor sirolimus shows specific impact on the drug resistance signature imposed by cisplatin and paclitaxel, further holding evidence for a synthetic lethal interaction to paclitaxel mechanism of action involving cyclin D1.

Conclusions

Stratifying drug resistant high grade serous ovarian cancer via VEGFA, and specifically treating with mTOR inhibitors in case of activation of the pathway may allow adding precision for overcoming resistance to first line therapy.

Molecular disease presentation in diabetic nephropathy

Diabetic nephropathy, as the most prevalent chronic disease of the kidney, has also become the primary cause of end-stage renal disease with the incidence of kidney disease in type 2 diabetics continuously rising. As with most chronic diseases, the pathophysiology is multifactorial with a number of deregulated molecular processes contributing to disease manifestation and progression. Current therapy mainly involves interfering in the renin-angiotensin-aldosterone system using angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers. Better understanding of molecular processes deregulated in the early stages and progression of disease hold the key for development of novel therapeutics addressing this complex disease. With the advent of high-throughput omics technologies, researchers set out to systematically study the disease on a molecular level. Results of the first omics studies were mainly focused on reporting the highest deregulated molecules between diseased and healthy subjects with recent attempts to integrate findings of multiple studies on the level of molecular pathways and processes. In this review, we will outline key omics studies on the genome, transcriptome, proteome and metabolome level in the context of DN. We will also provide concepts on how to integrate findings of these individual studies (i) on the level of functional processes using the gene-ontology vocabulary, (ii) on the level of molecular pathways and (iii) on the level of phenotype molecular models constructed based on protein-protein interaction data.

Protein interactome of muscle invasive bladder cancer

Muscle invasive bladder carcinoma is a complex, multifactorial disease caused by disruptions and alterations of several molecular pathways that result in heterogeneous phenotypes and variable disease outcome. Combining this disparate knowledge may offer insights for deciphering relevant molecular processes regarding targeted therapeutic approaches guided by molecular signatures allowing improved phenotype profiling. The aim of the study is to characterize muscle invasive bladder carcinoma on a molecular level by incorporating scientific literature screening and signatures from omics profiling. Public domain omics signatures together with molecular features associated with muscle invasive bladder cancer were derived from literature mining to provide 286 unique protein-coding genes. These were integrated in a protein-interaction network to obtain a molecular functional map of the phenotype. This feature map educated on three novel disease-associated pathways with plausible involvement in bladder cancer, namely Regulation of actin cytoskeleton, Neurotrophin signalling pathway and Endocytosis. Systematic integration approaches allow to study the molecular context of individual features reported as associated with a clinical phenotype and could potentially help to improve the molecular mechanistic description of the disorder.

Molecular models of the cardiorenal syndrome

Molecular profiling techniques have provided extensive sets of molecular features characterizing clinical phenotypes, but further extrapolation to mechanistic molecular models of disease pathophysiology faces major challenges. Here, we describe a computational procedure for delineating molecular disease models utilizing omics profiles, and exemplify the methodology on aspects of the cardiorenal syndrome describing the clinical association of declining kidney function and increased cardiovascular event rates. Individual molecular features as well as selected molecular processes were identified as linking cardiovascular and renal pathology as a combination of cross-organ mediators and common pathophysiology. The molecular characterization of the disease presents as a set of molecular processes together with their interactions, composing a molecular disease model of the cardiorenal syndrome. Integrating omics profiles describing aspects of cardiovascular disease and respective profiles for advanced chronic kidney disease on molecular interaction networks, computation of disease term-specific subgraphs, and complemented by subgraph segmentation allowed delineation of disease term-specific molecular models, at their intersection providing contributors to cardiorenal pathology. Building such molecular disease models allows in a generic way to integrate multi-omics sources for generating comprehensive sets of molecular processes, on such basis providing rationale for biomarker panel selection for further characterizing clinical phenotypes.

Integrated analysis of transcriptomic and proteomic data

Until recently, understanding the regulatory behavior of cells has been pursued through independent analysis of the transcriptome or the proteome. Based on the central dogma, it was generally assumed that there exist a direct correspondence between mRNA transcripts and generated protein expressions. However, recent studies have shown that the correlation between mRNA and Protein expressions can be low due to various factors such as different half lives and post transcription machinery. Thus, a joint analysis of the transcriptomic and proteomic data can provide useful insights that may not be deciphered from individual analysis of mRNA or protein expressions. This article reviews the existing major approaches for joint analysis of transcriptomic and proteomic data. We categorize the different approaches into eight main categories based on the initial algorithm and final analysis goal. We further present analogies with other domains and discuss the existing research problems in this area.

Nonlinear dependence in the discovery of differentially expressed genes

Microarray data are used to determine which genes are active in response to a changing cell environment. Genes are “discovered” when they are significantly differentially expressed in the microarray data collected under the differing conditions. In one prevalent approach, all genes are assumed to satisfy a null hypothesis, ℍ₀, of no difference in expression. A false discovery (type 1 error) occurs when ℍ₀ is incorrectly rejected. The quality of a detection algorithm is assessed by estimating its number of false discoveries, 𝔉. Work involving the second-moment modeling of the z-value histogram (representing gene expression differentials) has shown significantly deleterious effects of intergene expression correlation on the estimate of 𝔉. This paper suggests that nonlinear dependencies could likewise be important. With an applied emphasis, this paper extends the “moment framework” by including third-moment skewness corrections in an estimator of 𝔉. This estimator combines observed correlation (corrected for sampling fluctuations) with the information from easily identifiable null cases. Nonlinear-dependence modeling reduces the estimation error relative to that of linear estimation. Third-moment calculations involve empirical densities of 3 × 3 covariance matrices estimated using very few samples. The principle of entropy maximization is employed to connect estimated moments to 𝔉 inference. Model results are tested with BRCA and HIV data sets and with carefully constructed simulations.

Linking molecular feature space and disease terms for the immunosuppressive drug rapamycin

Next to development of novel drugs also drug repositioning appears promising for tackling unmet clinical needs. Here Omics provided the ground for novel analysis strategies for linking drug and disease by integrating profiles on the molecular as well as the clinical data level. We developed a workflow for linking drugs and diseases for identifying repositioning options, and exemplify the procedure for the immunosuppressive drug rapamycin. Our strategy rests on delineating a drug-specific molecular profile by combining Omics data reflecting the drug's impact on the cellular status as well as drug-associated molecular features extracted from the scientific literature. For rapamycin the respective profile held 905 unique molecular features reflecting defined molecular processes as identified by molecular pathway and process enrichment analysis. Literature mining identified 419 diseases significantly associated with this rapamycin molecular feature list, and transforming the significance of gene-disease associations into a continuous score allowed us to compute ROC and precision-recall for comparing this disease list with diseases already undergoing clinical trials utilizing rapamycin. The AUC of this assignment was computed as 0.84, indicating excellent recovery of relevant disease terms solely based on the drug molecular feature profile. We verified relevant indications by comparing molecular feature sets characteristic for the identified diseases to the drug molecular feature profile, demonstrating highly significant overlaps. The presented workflow allowed positive identification of diseases associated with rapamycin utilizing the drug-specific molecular feature profile, and may be well applicable to other drugs of interest.

Detection of changes in gene regulatory patterns, elicited by perturbations of the Hsp90 molecular chaperone complex, by visualizing multiple experiments with an animation

Background

To make sense out of gene expression profiles, such analyses must be pushed beyond the mere listing of affected genes. For example, if a group of genes persistently display similar changes in expression levels under particular experimental conditions, and the proteins encoded by these genes interact and function in the same cellular compartments, this could be taken as very strong indicators for co-regulated protein complexes. One of the key requirements is having appropriate tools to detect such regulatory patterns.

Results

We have analyzed the global adaptations in gene expression patterns in the budding yeast when the Hsp90 molecular chaperone complex is perturbed either pharmacologically or genetically. We integrated these results with publicly accessible expression, protein-protein interaction and intracellular localization data. But most importantly, all experimental conditions were simultaneously and dynamically visualized with an animation. This critically facilitated the detection of patterns of gene expression changes that suggested underlying regulatory networks that a standard analysis by pairwise comparison and clustering could not have revealed.

Conclusions

The results of the animation-assisted detection of changes in gene regulatory patterns make predictions about the potential roles of Hsp90 and its co-chaperone p23 in regulating whole sets of genes. The simultaneous dynamic visualization of microarray experiments, represented in networks built by integrating one's own experimental with publicly accessible data, represents a powerful discovery tool that allows the generation of new interpretations and hypotheses.

Mapping of molecular pathways, biomarkers and drug targets for diabetic nephropathy

PURPOSE

For diseases with complex phenotype such as diabetic nephropathy (DN), integration of multiple Omics sources promises an improved description of the disease pathophysiology, being the basis for novel diagnostics and therapy, but equally important personalization aspects.

EXPERIMENTAL DESIGN

Molecular features on DN were retrieved from public domain Omics studies and by mining scientific literature, patent text and clinical trial specifications. Molecular feature sets were consolidated on a human protein interaction network and interpreted on the level of molecular pathways in the light of the pathophysiology of the disease and its clinical context defined as associated biomarkers and drug targets.

RESULTS

About 1000 gene symbols each could be assigned to the pathophysiological description of DN and to the clinical context. Direct feature comparison showed minor overlap, whereas on the level of molecular pathways, the complement and coagulation cascade, PPAR signaling, and the renin-angiotensin system linked the disease descriptor space with biomarkers and targets.

CONCLUSION AND CLINICAL RELEVANCE

Only the combined molecular feature landscapes closely reflect the clinical implications of DN in the context of hypertension and diabetes. Omics data integration on the level of interaction networks furthermore provides a platform for identification of pathway-specific biomarkers and therapy options.

Integrative analysis of -omics data and histologic scoring in renal disease and transplantation: renal histogenomics

The histologic scoring of renal biopsies is still the gold standard for renal disease classification. The Banff classification scheme and the chronic allograft damage index are histopathologic scoring schemes widely used in renal transplantation. The determination of genome-wide gene expression profiles in human renal biopsies has the potential to serve as independent validation data sets and also provide a more precise evaluation of the functional status behind the visible morphologic alterations. It is expected that results from high-throughput-omics experiments will lead to improved classification schemes in the near future as also discussed at recent Banff meetings. In this review we give an overview on-omics studies, focusing on the association of molecular changes on the transcript as well as on the protein level and morphologic scoring schemes in renal disease and transplantation.

Computational analysis workflows for Omics data interpretation

Progress in experimental procedures has led to rapid availability of Omics profiles. Various open-access as well as commercial tools have been developed for storage, analysis, and interpretation of transcriptomics, proteomics, and metabolomics data. Generally, major analysis steps include data storage, retrieval, preprocessing, and normalization, followed by identification of differentially expressed features, functional annotation on the level of biological processes and molecular pathways, as well as interpretation of gene lists in the context of protein-protein interaction networks. In this chapter, we discuss a sequential transcriptomics data analysis workflow utilizing open-source tools, specifically exemplified on a gene expression dataset on familial hypercholesterolemia.

Concept and application of a computational vaccinology workflow

BACKGROUND

The last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders.

RESULTS

We introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage.

CONCLUSION

Based on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges.

Synthetic lethal hubs associated with vincristine resistant neuroblastoma

Chemotherapy of cancer experiences a number of shortcomings including development of drug resistance. This fact also holds true for neuroblastoma utilizing chemotherapeutics as vincristine. We performed a comparative analysis of molecular and cellular mechanisms associated with vincristine resistance utilizing cell line as well as human tissue data. Differential gene expression analysis revealed molecular features, processes and pathways afflicted with drug resistance mechanisms in general, and specifically with vincristine significantly involving actin associated features. However, specific mode of resistance as well as underlying genotype of parental, vincristine sensitive cells apparently exhibited significant heterogeneity. No consensus profile for vincristine resistance could be derived, but resistance-associated changes on the level of individual neuroblastoma cell lines as well as individual patient profiles became clearly evident. Based on these prerequisites we utilized the concept of synthetic lethality aimed at identifying hub proteins which when inhibited promise to induce cell death due to a synthetic lethal interaction with down-regulated, chemoresistance associated features. Our screening procedure identified synthetic lethal hub proteins afflicted with actin associated processes holding synthetic lethal interactions to down-regulated features individually found in all chemoresistant cell lines tested, therefore promising an improved therapeutic window. Verification of such synthetic lethal hub candidates in human neuroblastoma tissue expression profiles indicated the feasibility of this screening approach for addressing vincristine resistance in neuroblastoma.

Integrative bioinformatics analysis of proteins associated with the cardiorenal syndrome

The cardiorenal syndrome refers to the coexistence of kidney and cardiovascular disease, where cardiovascular events are the most common cause of death in patients with chronic kidney disease. Both, cardiovascular as well as kidney diseases have been extensively analyzed on a molecular level, resulting in molecular features and associated processes indicating a cross-talk of the two disease etiologies on a pathophysiological level. In order to gain a comprehensive picture of molecular factors contributing to the bidirectional interplay between kidney and cardiovascular system, we mined the scientific literature for molecular features reported as associated with the cardiorenal syndrome, resulting in 280 unique genes/proteins. These features were then analyzed on the level of molecular processes and pathways utilizing various types of protein interaction networks. Next to well established molecular features associated with the renin-angiotensin system numerous proteins involved in signal transduction and cell communication were found, involving specific molecular functions covering receptor binding with natriuretic peptide receptor and ligands as well known example. An integrated analysis of identified features pinpointed a protein interaction network involving mediators of hemodynamic change and an accumulation of features associated with the endothelin and VEGF signaling pathway. Some of these features may function as novel therapeutic targets.