Towards a holistic, yet gene-centered analysis of gene expression profiles: a case study of human lung cancers

Is Cancer Reversible? Rethinking Carcinogenesis Models-A New Epistemological Tool

A growing number of studies shows that it is possible to induce a phenotypic transformation of cancer cells from malignant to benign. This process is currently known as "tumor reversion". However, the concept of reversibility hardly fits the current cancer models, according to which gene mutations are considered the primary cause of cancer. Indeed, if gene mutations are causative carcinogenic factors, and if gene mutations are irreversible, how long should cancer be considered as an irreversible process? In fact, there is some evidence that intrinsic plasticity of cancerous cells may be therapeutically exploited to promote a phenotypic reprogramming, both in vitro and in vivo. Not only are studies on tumor reversion highlighting a new, exciting research approach, but they are also pushing science to look for new epistemological tools capable of better modeling cancer.

Reimagining Cancer: Moving from the Cellular to the Tissue Level

The current universally accepted explanation of cancer origin and behavior, the somatic mutation theory, is cell-centered and rooted in perturbation of gene function independent of the external environmental context. However, tumors consist of various epithelial and stromal cell populations temporally and spatially organized into an integrated neoplastic community, and they can have properties similar to normal tissues. Accordingly, we review specific normal cellular and tissue traits and behaviors with adaptive temporal and spatial self-organization that result in ordered patterns and structures. A few recent theories have described these tissue-level cancer behaviors, invoking a conceptual shift from the cellular level and highlighting the need for methodologic approaches based on the analysis of complex systems. We propose extending the analytical approach of regulatory networks to the tissue level and introduce the concept of "cancer attractors." These concepts require reevaluation of cancer imaging and investigational approaches and challenge the traditional reductionist approach of cancer molecular biology.

Endotype Transitions During the Acute Phase of Pediatric Septic Shock Reflect Changing Risk and Treatment Response

OBJECTIVE

We previously identified septic shock endotypes A and B based on 100 genes reflecting adaptive immunity and glucocorticoid receptor signaling. The endotypes differ with respect to outcome and corticosteroid responsiveness. We determined whether endotypes change during the initial 3 days of illness, and whether changes are associated with outcomes.

DESIGN

Observational cohort study including existing and newly enrolled participants.

SETTING

Multiple PICUs.

PATIENTS

Children with septic shock.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We measured the 100 endotyping genes at day 1 and day 3 of illness in 375 patients. We determined if endotype assignment changes over time, and whether changing endotype is associated with corticosteroid response and outcomes. We used multivariable logistic regression to adjust for illness severity, age, and comorbidity burden. Among the 132 subjects assigned to endotype A on day 1, 56 (42%) transitioned to endotype B by day 3. Among 243 subjects assigned to endotype B on day 1, 77 (32%) transitioned to endotype A by day 3. Assignment to endotype A on day 1 was associated with increased odds of mortality. This risk was modified by the subsequent day 3 endotype assignment. Corticosteroids were associated with increased risk of mortality among subjects who persisted as endotype A.

CONCLUSIONS

A substantial proportion of children with septic shock transition endotypes during the acute phase of illness. The risk of poor outcome and the response to corticosteroids change with changes in endotype assignment. Patients persisting as endotype A are at highest risk of poor outcomes.

Evidence of Endotypes in Pediatric Acute Hypoxemic Respiratory Failure Caused by Sepsis

OBJECTIVES

Subclassification based on clinical or biologic commonalities (endotypes) is one approach to reduce heterogeneity in acute hypoxemic respiratory failure. In adults, biomarker-defined endotypes of respiratory failure have been described, with differential outcome profiles and response to therapy. To date, no studies have tested whether endotypes exist in pediatric acute hypoxemic respiratory failure, although messenger RNA expression-based endotypes have been described in pediatric sepsis. The aim of the present study was to test whether endotypes identified in pediatric sepsis are applicable to pediatric acute hypoxemic respiratory failure.

DESIGN

Secondary analysis of a previously reported microarray-based study of pediatric sepsis.

SETTING

Multiple PICUs in the United States.

PATIENTS

Sixty-seven children with acute hypoxemic respiratory failure caused by sepsis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of the larger septic shock cohort, 67 met eligibility for acute hypoxemic respiratory failure. Twenty-three subjects were assigned to endotype A, and 44 to endotype B. Subjects assigned to endotype A had over four-fold greater unadjusted 28-day mortality, and nearly three-fold greater rates of complicated course. The association with mortality (odds ratio, 8.0; 95% CI, 1.6-41.0) and complicated course (odds ratio, 4.2; 95% CI, 1.2-14.9) persisted after adjustment for age, severity of illness, and PaO2/FIO2.

CONCLUSIONS

Applying a previously reported endotyping strategy in children with septic shock identified endotypes of pediatric acute hypoxemic respiratory failure secondary to sepsis, with differential risk for poor outcomes. To our knowledge, this is the first demonstration of endotypes in pediatric respiratory failure. Our results support an investigation into using transcriptomics to identify messenger RNA-based endotypes in a dedicated, well-defined acute hypoxemic respiratory failure cohort.

Circular RNAs profiling in the cystathionine-β-synthase mutant mouse reveals novel gene targets for hyperhomocysteinemia induced ocular disorders

Cystathionine-β-synthase (CBS) gene encodes L-serine hydrolyase which catalyzes β-reaction to condense serine with homocysteine (Hcy) by pyridoxal-5'-phosphate helps to form cystathionine which in turn is converted to cysteine. CBS resides at the intersection of transmethylation, transsulfuration, and remethylation pathways, thus lack of CBS fundamentally blocks Hcy degradation; an essential step in glutathione synthesis. Redox homeostasis, free-radical detoxification and one-carbon metabolism (Methionine-Hcy-Folate cycle) require CBS and its deficiency leads to hyperhomocysteinemia (HHcy) causing retinovascular thromboembolism and eye-lens dislocation along with vascular cognitive impairment and dementia. HHcy results in retinovascular, coronary, cerebral and peripheral vessels' dysfunction and how it causes metabolic dysregulation predisposing patients to serious eye conditions remains unknown. HHcy orchestrates inflammation and redox imbalance via epigenetic remodeling leading to neurovascular pathologies. Although circular RNAs (circRNAs) are dominant players regulating their parental genes' expression dynamics, their importance in ocular biology has not been appreciated. Progress in gene-centered analytics via improved microarray and bioinformatics are enabling dissection of genomic pathways however there is an acute under-representation of circular RNAs in ocular disorders. This study undertook circRNAs' analysis in the eyes of CBS deficient mice identifying a pool of 12532 circRNAs, 74 exhibited differential expression profile, _∼27% were down-regulated while most were up-regulated (_∼73%). Findings also revealed several microRNAs that are specific to each circRNA suggesting their roles in HHcy induced ocular disorders. Further analysis of circRNAs helped identify novel parental genes that seem to influence certain eye disease phenotypes.

A liquid biopsy for bronchopulmonary/lung carcinoid diagnosis

No effective blood biomarker exists to detect and clinically manage bronchopulmonary (BP) neuroendocrine tumors (NET). We developed a blood-based 51 NET-specific transcript set for diagnosis and monitoring and evaluated clinical performance metrics. It accurately diagnosed the tumor and differentiated stable from progressive disease as determined by RECIST criteria. Gene expression was evaluated in: a) publicly available BPNET transcriptomes (GSE35679); b) two BPNET cell-lines; and c) BPNET tissue with paired blood (n = 7). Blood gene expression was assessed in 194 samples including controls, benign lung diseases, malignant lung diseases and small bowel NETs. A separate validation study in 25 age- and gender-matched BPNETs/controls was performed. Gene expression measured by real-time PCR was scored (0–100%; normal: < 14%). Regression analyses, Principal Component Analysis (PCA), hierarchical clustering, Fisher's and non-parametric evaluations were undertaken. All 51 genes were identified in BPNET transcriptomes, tumor samples and cell-lines. Significant correlations were evident between paired tumor and blood (R2:0.63–0.91, p < 0.001). PCA and hierarchical clustering identified blood gene expression was significantly different between lung cancers and benign diseases, including BPNETs. Gene expression was highly correlated (R²: 0.91, p = 1.7 × 10-¹⁵) between small bowel and BPNET. For validation, all 25 BPNETs were positive compared to 20% controls (p < 0.0001). Scores were significantly elevated (p < 0.0001) in BPNETs (57 ± 28%) compared to controls (4 ± 5%). BPNETs with progressive disease (85 ± 11%) exhibited higher scores than stable disease (32 ± 7%, p < 0.0001). Blood measurements accurately diagnosed bronchopulmonary carcinoids, distinguishing stable from progressive disease. This marker panel will have clinical utility as a diagnostic liquid biopsy able to define disease activity and progression in real-time.

Stem Cell Differentiation Stage Factors from Zebrafish Embryo: A Novel Strategy to Modulate the Fate of Normal and Pathological Human (Stem) Cells

In spite of the growing body of evidence on the biology of the Zebrafish embryo and stem cells, including the use of Stem Cell Differentiation Stage Factors (SCDSFs) taken from Zebrafish embryo to impact cancer cell dynamics, comparatively little is known about the possibility to use these factors to modulate the homeostasis of normal human stem cells or to modulate the behavior of cells involved in different pathological conditions. In the present review we recall in a synthetic way the most important researches about the use of SCDSFs in reprogramming cancer cells and in modulating the high speed of multiplication of keratinocytes which is characteristic of some pathological diseases like psoriasis. Moreover we add here the results about the capability of SCDSFs in modulating the homeostasis of human adiposederived stem cells (hASCs) isolated from a fat tissue obtained with a novel-non enzymatic method and device. In addition we report the data not yet published about a first protein analysis of the SCDSFs and about their role in a pathological condition like neurodegeneration.

Developing a clinically feasible personalized medicine approach to pediatric septic shock

RATIONALE

Using microarray data, we previously identified gene expression-based subclasses of septic shock with important phenotypic differences. The subclass-defining genes correspond to adaptive immunity and glucocorticoid receptor signaling. Identifying the subclasses in real time has theranostic implications, given the potential for immune-enhancing therapies and controversies surrounding adjunctive corticosteroids for septic shock.

OBJECTIVES

To develop and validate a real-time subclassification method for septic shock.

METHODS

Gene expression data for the 100 subclass-defining genes were generated using a multiplex messenger RNA quantification platform (NanoString nCounter) and visualized using gene expression mosaics. Study subjects (n = 168) were allocated to the subclasses using computer-assisted image analysis and microarray-based reference mosaics. A gene expression score was calculated to reduce the gene expression patterns to a single metric. The method was tested prospectively in a separate cohort (n = 132).

MEASUREMENTS AND MAIN RESULTS

The NanoString-based data reproduced two septic shock subclasses. As previously, one subclass had decreased expression of the subclass-defining genes. The gene expression score identified this subclass with an area under the curve of 0.98 (95% confidence interval [CI95] = 0.96-0.99). Prospective testing of the subclassification method corroborated these findings. Allocation to this subclass was independently associated with mortality (odds ratio = 2.7; CI95 = 1.2-6.0; P = 0.016), and adjunctive corticosteroids prescribed at physician discretion were independently associated with mortality in this subclass (odds ratio = 4.1; CI95 = 1.4-12.0; P = 0.011).

CONCLUSIONS

We developed and tested a gene expression-based classification method for pediatric septic shock that meets the time constraints of the critical care environment, and can potentially inform therapeutic decisions.

Personalized Disease Phenotypes from Massive OMICs Data

Application of new high-throughput technologies in molecular medicine collects massive data for hundreds to thousands of persons in large cohort studies by characterizing the phenotype of each individual on a personalized basis. The chapter aims at increasing our understanding of disease genesis and progression and to improve diagnosis and treatment. New methods are needed to handle such “big data.” Machine learning enables one to recognize and to visualize complex data patterns and to make decisions potentially relevant for diagnosis and treatment. The authors address these tasks by applying the method of self-organizing maps and present worked examples from different disease entities of the colon ranging from inflammation to cancer.

Time-course human urine proteomics in space-flight simulation experiments

BACKGROUND

Long-term space travel simulation experiments enabled to discover different aspects of human metabolism such as the complexity of NaCl salt balance. Detailed proteomics data were collected during the Mars105 isolation experiment enabling a deeper insight into the molecular processes involved.

RESULTS

We studied the abundance of about two thousand proteins extracted from urine samples of six volunteers collected weekly during a 105-day isolation experiment under controlled dietary conditions including progressive reduction of salt consumption. Machine learning using Self Organizing maps (SOM) in combination with different analysis tools was applied to describe the time trajectories of protein abundance in urine. The method enables a personalized and intuitive view on the physiological state of the volunteers. The abundance of more than one half of the proteins measured clearly changes in the course of the experiment. The trajectory splits roughly into three time ranges, an early (week 1-6), an intermediate (week 7-11) and a late one (week 12-15). Regulatory modes associated with distinct biological processes were identified using previous knowledge by applying enrichment and pathway flow analysis. Early protein activation modes can be related to immune response and inflammatory processes, activation at intermediate times to developmental and proliferative processes and late activations to stress and responses to chemicals.

CONCLUSIONS

The protein abundance profiles support previous results about alternative mechanisms of salt storage in an osmotically inactive form. We hypothesize that reduced NaCl consumption of about 6 g/day presumably will reduce or even prevent the activation of inflammatory processes observed in the early time range of isolation. SOM machine learning in combination with analysis methods of class discovery and functional annotation enable the straightforward analysis of complex proteomics data sets generated by means of mass spectrometry.

Differential expression of the nuclear-encoded mitochondrial transcriptome in pediatric septic shock

Introduction

Increasing evidence supports a role for mitochondrial dysfunction in organ injury and immune dysregulation in sepsis. Although differential expression of mitochondrial genes in blood cells has been reported for several diseases in which bioenergetic failure is a postulated mechanism, there are no data about the blood cell mitochondrial transcriptome in pediatric sepsis.

Methods

We conducted a focused analysis using a multicenter genome-wide expression database of 180 children ≤10 years of age with septic shock and 53 healthy controls. Using total RNA isolated from whole blood within 24 hours of PICU admission for septic shock, we evaluated 296 nuclear-encoded mitochondrial genes using a false discovery rate of 1%. A series of bioinformatic approaches were applied to compare differentially expressed genes across previously validated gene expression-based subclasses (groups A, B, and C) of pediatric septic shock.

Results

In total, 118 genes were differentially regulated in subjects with septic shock compared to healthy controls, including 48 genes that were upregulated and 70 that were downregulated. The top scoring canonical pathway was oxidative phosphorylation, with general downregulation of the 51 genes corresponding to the electron transport system (ETS). The top two gene networks were composed primarily of mitochondrial ribosomal proteins highly connected to ETS complex I, and genes encoding for ETS complexes I, II, and IV that were highly connected to the peroxisome proliferator activated receptor (PPAR) family. There were 162 mitochondrial genes differentially regulated between groups A, B, and C. Group A, which had the highest maximum number of organ failures and mortality, exhibited a greater downregulation of mitochondrial genes compared to groups B and C.

Conclusions

Based on a focused analysis of a pediatric septic shock transcriptomic database, nuclear-encoded mitochondrial genes were differentially regulated early in pediatric septic shock compared to healthy controls, as well as across genotypic and phenotypic distinct pediatric septic shock subclasses. The nuclear genome may be an important mechanism contributing to alterations in mitochondrial bioenergetic function and outcomes in pediatric sepsis.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-014-0623-9) contains supplementary material, which is available to authorized users.

Corticosteroids are associated with repression of adaptive immunity gene programs in pediatric septic shock

RATIONALE

Corticosteroids are prescribed commonly for patients with septic shock, but their use remains controversial and concerns remain regarding side effects.

OBJECTIVES

To determine the effect of adjunctive corticosteroids on the genomic response of pediatric septic shock.

METHODS

We retrospectively analyzed an existing transcriptomic database of pediatric septic shock. Subjects receiving any formulation of systemic corticosteroids at the time of blood draw for microarray analysis were classified in the septic shock corticosteroid group. We compared normal control subjects (n = 52), a septic shock no corticosteroid group (n = 110), and a septic shock corticosteroid group (n = 70) using analysis of variance. Genes differentially regulated between the no corticosteroid group and the corticosteroid group were analyzed using Ingenuity Pathway Analysis.

MEASUREMENTS AND MAIN RESULTS

The two study groups did not differ with respect to illness severity, organ failure burden, mortality, or mortality risk. There were 319 gene probes differentially regulated between the no corticosteroid group and the corticosteroid group. These genes corresponded predominately to adaptive immunity-related signaling pathways, and were down-regulated relative to control subjects. Notably, the degree of down-regulation was significantly greater in the corticosteroid group, compared with the no corticosteroid group. A similar pattern was observed for genes corresponding to the glucocorticoid receptor signaling pathway.

CONCLUSIONS

Administration of corticosteroids in pediatric septic shock is associated with additional repression of genes corresponding to adaptive immunity. These data should be taken into account when considering the benefit to risk ratio of adjunctive corticosteroids for septic shock.

Analysis of microRNA expression using machine learning

The systematic analysis of miRNA expression and its potential mRNA targets constitutes a basal objective in miRNA research in addition to miRNA gene detection and miRNA target prediction. In this chapter we address methodical issues of miRNA expression analysis using self-organizing maps (SOM), a neural network machine learning algorithm with strong visualization and second-level analysis capabilities widely used to categorize large-scale, high-dimensional data. We shortly review selected experimental and theoretical aspects of miRNA expression analysis. Then, the protocol of our SOM method is outlined with special emphasis on miRNA/mRNA coexpression. The method allows extracting differentially expressed RNA transcripts, their functional context, and also characterization of global properties of expression states and profiles. In addition to the separate study of miRNA and mRNA expression landscapes, we propose the combined analysis of both entities using a covariance SOM.

Portraying the Expression Landscapes of B-CellLymphoma-Intuitive Detection of Outlier Samples and of Molecular Subtypes

We present an analytic framework based on Self-Organizing Map (SOM) machine learning to study large scale patient data sets. The potency of the approach is demonstrated in a case study using gene expression data of more than 200 mature aggressive B-cell lymphoma patients. The method portrays each sample with individual resolution, characterizes the subtypes, disentangles the expression patterns into distinct modules, extracts their functional context using enrichment techniques and enables investigation of the similarity relations between the samples. The method also allows to detect and to correct outliers caused by contaminations. Based on our analysis, we propose a refined classification of B-cell Lymphoma into four molecular subtypes which are characterized by differential functional and clinical characteristics.

Genome-wide expression profiling in pediatric septic shock

For nearly a decade, our research group has had the privilege of developing and mining a multicenter, microarray-based, genome-wide expression database of critically ill children (≤10 y of age) with septic shock. Using bioinformatic and systems biology approaches, the expression data generated through this discovery-oriented, exploratory approach have been leveraged for a variety of objectives, which are reviewed here. Fundamental observations include widespread repression of gene programs corresponding to the adaptive immune system and biologically significant differential patterns of gene expression across developmental age groups. The data have also identified gene expression-based subclasses of pediatric septic shock having clinically relevant phenotypic differences. The data have also been leveraged for the discovery of novel therapeutic targets, as well as for the discovery and development of novel stratification and diagnostic biomarkers. Almost a decade of genome-wide expression profiling in pediatric septic shock is now demonstrating tangible results. The studies have progressed from an initial discovery-oriented and exploratory phase to a new phase in which the data are being translated and applied to address several areas of clinical need.

Physarum polycephalum mutants in the photocontrol of sporulation display altered patterns in the correlated expression of developmentally regulated genes

Physarum polycephalum is a lower eukaryote belonging to the amoebozoa group of organisms that forms macroscopic, multinucleate plasmodial cells during its developmental cycle. Plasmodia can exit proliferative growth and differentiate by forming fruiting bodies containing mononucleate, haploid spores. This process, called sporulation, is controlled by starvation and visible light. To genetically dissect the regulatory control of the commitment to sporulation, we have isolated plasmodial mutants that are altered in the photocontrol of sporulation in a phenotypic screen of N-ethyl-N-nitrosourea (ENU) mutagenized cells. Several non-sporulating mutants were analyzed by measuring the light-induced change in the expression pattern of a set of 35 genes using GeXP multiplex reverse transcription-polymerase chain reaction with RNA isolated from individual plasmodial cells. Mutants showed altered patterns of differentially regulated genes in response to light stimulation. Some genes clearly displayed pairwise correlation in terms of their expression level as measured in individual plasmodial cells. The pattern of pairwise correlation differed in various mutants, suggesting that different upstream regulators were disabled in the different mutants. We propose that patterns of pairwise correlation in gene expression might be useful to infer the underlying gene regulatory network.

Whole blood genome-wide gene expression profile in males after prolonged wakefulness and sleep recovery

Although the specific functions of sleep have not been completely elucidated, the literature has suggested that sleep is essential for proper homeostasis. Sleep loss is associated with changes in behavioral, neurochemical, cellular, and metabolic function as well as impaired immune response. Using high-resolution microarrays we evaluated the gene expression profiles of healthy male volunteers who underwent 60 h of prolonged wakefulness (PW) followed by 12 h of sleep recovery (SR). Peripheral whole blood was collected at 8 am in the morning before the initiation of PW (Baseline), after the second night of PW, and one night after SR. We identified over 500 genes that were differentially expressed. Notably, these genes were related to DNA damage and repair and stress response, as well as diverse immune system responses, such as natural killer pathways including killer cell lectin-like receptors family, as well as granzymes and T-cell receptors, which play important roles in host defense. These results support the idea that sleep loss can lead to alterations in molecular processes that result in perturbation of cellular immunity, induction of inflammatory responses, and homeostatic imbalance. Moreover, expression of multiple genes was downregulated following PW and upregulated after SR compared with PW, suggesting an attempt of the body to re-establish internal homeostasis. In silico validation of alterations in the expression of CETN3, DNAJC, and CEACAM genes confirmed previous findings related to the molecular effects of sleep deprivation. Thus, the present findings confirm that the effects of sleep loss are not restricted to the brain and can occur intensely in peripheral tissues.

Interleukin-27 is a novel candidate diagnostic biomarker for bacterial infection in critically ill children

Introduction

Differentiating between sterile inflammation and bacterial infection in critically ill patients with fever and other signs of the systemic inflammatory response syndrome (SIRS) remains a clinical challenge. The objective of our study was to mine an existing genome-wide expression database for the discovery of candidate diagnostic biomarkers to predict the presence of bacterial infection in critically ill children.

Methods

Genome-wide expression data were compared between patients with SIRS having negative bacterial cultures (n = 21) and patients with sepsis having positive bacterial cultures (n = 60). Differentially expressed genes were subjected to a leave-one-out cross-validation (LOOCV) procedure to predict SIRS or sepsis classes. Serum concentrations of interleukin-27 (IL-27) and procalcitonin (PCT) were compared between 101 patients with SIRS and 130 patients with sepsis. All data represent the first 24 hours of meeting criteria for either SIRS or sepsis.

Results

Two hundred twenty one gene probes were differentially regulated between patients with SIRS and patients with sepsis. The LOOCV procedure correctly predicted 86% of the SIRS and sepsis classes, and Epstein-Barr virus-induced gene 3 (EBI3) had the highest predictive strength. Computer-assisted image analyses of gene-expression mosaics were able to predict infection with a specificity of 90% and a positive predictive value of 94%. Because EBI3 is a subunit of the heterodimeric cytokine, IL-27, we tested the ability of serum IL-27 protein concentrations to predict infection. At a cut-point value of ≥5 ng/ml, serum IL-27 protein concentrations predicted infection with a specificity and a positive predictive value of >90%, and the overall performance of IL-27 was generally better than that of PCT. A decision tree combining IL-27 and PCT improved overall predictive capacity compared with that of either biomarker alone.

Conclusions

Genome-wide expression analysis has provided the foundation for the identification of IL-27 as a novel candidate diagnostic biomarker for predicting bacterial infection in critically ill children. Additional studies will be required to test further the diagnostic performance of IL-27.

The microarray data reported in this article have been deposited in the Gene Expression Omnibus under accession number GSE4607.

Genetics and genomics in pediatric septic shock

OBJECTIVES

Pediatric septic shock continues to be an important public health problem. Several investigative groups have applied genetic and genomic approaches as a means of identifying novel pathways and therapeutic targets, discovery of sepsis-related biomarkers, and identification of septic shock subclasses. This review will highlight studies in pediatric sepsis with a focus on gene association studies and genome-wide expression profiling.

DATA SOURCES

A summary of published literature involving gene association and expression profiling studies specifically involving pediatric sepsis and septic shock.

SUMMARY

Several polymorphisms of genes broadly involved in inflammation, immunity, and coagulation have been linked with susceptibility to sepsis, or outcome of sepsis in children. Many of these studies involve meningococcemia, and the strongest association involves a functional polymorphism of the plasminogen activator inhibitor-1 promoter region and meningococcal sepsis. Expression profiling studies in pediatric septic shock have identified zinc supplementation and inhibition of matrix metalloproteinase-8 activity as potential, novel therapeutic approaches in sepsis. Studies focused on discovery of sepsis-related biomarkers have identified interleukin-8 as a robust outcome biomarker in pediatric septic shock. Additional studies have demonstrated the feasibility and clinical relevance of gene expression-based subclassification of pediatric septic shock.

CONCLUSIONS

Pediatric sepsis and septic shock are increasingly being studied by genetic and genomic approaches and the accumulating data hold the promise of enhancing our future approach to this ongoing clinical problem.

Clinical review: sepsis and septic shock--the potential of gene arrays

Over the past decade several investigators have applied microarray technology and related bioinformatic approaches to clinical sepsis and septic shock, thus allowing for an assessment of how, or if, this branch of genomic medicine has meaningfully impacted the field of sepsis research. The ability to simultaneously and efficiently measure the steady-state mRNA abundance of thousands of transcripts from a given tissue source (that is, 'transcriptomics') has provided an unprecedented opportunity to gain a broader, genome-level 'picture' of complex and heterogeneous clinical syndromes such as sepsis. A trancriptomic approach to sepsis and septic shock is technically challenging on multiple levels, but nonetheless modest, tangible advances are being realized. These include a genome-level understanding of the complexity of sepsis and septic shock, identification of novel candidate pathways and targets for potential intervention, discovery of novel, candidate diagnostic and stratification biomarkers, and the ability to stratify patients into clinically relevant, expression-based subclasses. The challenges moving forward include robust validation studies, standardization of technical approaches, standardization and further development of analytical algorithms, and large-scale collaborations.

Validation of a gene expression-based subclassification strategy for pediatric septic shock

OBJECTIVE

Septic shock heterogeneity has important implications for clinical trial implementation and patient management. We previously addressed this heterogeneity by identifying three putative subclasses of children with septic shock based exclusively on a 100-gene expression signature. Here we attempted to prospectively validate the existence of these gene expression-based subclasses in a validation cohort.

DESIGN

Prospective observational study involving microarray-based bioinformatics.

SETTING

Multiple pediatric intensive care units in the United States.

PATIENTS

Separate derivation (n = 98) and validation (n = 82) cohorts of children with septic shock.

INTERVENTIONS

None other than standard care.

MEASUREMENTS AND MAIN RESULTS

Gene expression mosaics of the 100 class-defining genes were generated for 82 individual patients in the validation cohort. Using computer-based image analysis, patients were classified into one of three subclasses ("A," "B," or "C") based on color and pattern similarity relative to reference mosaics generated from the original derivation cohort. After subclassification, the clinical database was mined for phenotyping. Subclass A patients had higher illness severity relative to subclasses B and C as measured by maximal organ failure, fewer intensive care unit-free days, and a higher Pediatric Risk of Mortality score. Patients in subclass A were characterized by repression of genes corresponding to adaptive immunity and glucocorticoid receptor signaling. Separate subclass assignments were conducted by 21 individual clinicians using visual inspection. The consensus classification of the clinicians had modest agreement with the computer algorithm.

CONCLUSIONS

We have validated the existence of subclasses of children with septic shock based on a biologically relevant, 100-gene expression signature. The subclasses have relevant clinical differences.

Expression cartography of human tissues using self organizing maps

Background

Parallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.

Results

The method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.

Conclusions

The SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'.

The influence of developmental age on the early transcriptomic response of children with septic shock

Septic shock is a frequent and costly problem among patients in the pediatric intensive care unit (PICU) and is associated with high mortality and devastating survivor morbidity. Genome-wide expression patterns can provide molecular granularity of the host response and offer insight into why large variations in outcomes exist. We derived whole-blood genome-wide expression patterns within 24 h of PICU admission from children with septic shock. We compared the transcriptome between septic shock developmental-age groups defined as neonates (≤ 28 d, n = 17), infants (1 month to 1 year, n = 62), toddlers (2-5 years, n = 54) and school-age (≥ 6 years, n = 47) and age-matched controls. Direct intergroup comparisons demonstrated profound changes in neonates, relative to older children. Neonates with septic shock demonstrated reduced expression of genes representing key pathways of innate and adaptive immunity. In contrast to the largely upregulated transcriptome in all other groups, neonates exhibited a predominantly downregulated transcriptome when compared with controls. Neonates and school-age subjects had the most uniquely regulated genes relative to controls. Age-specific studies of the host response are necessary to identify developmentally relevant translational opportunities that may lead to improved sepsis outcomes.

Fractal analysis in a systems biology approach to cancer

Cancer is a highly complex disease due to the disruption of tissue architecture. Thus, tissues, and not individual cells, are the proper level of observation for the study of carcinogenesis. This paradigm shift from a reductionist approach to a systems biology approach is long overdue. Indeed, cell phenotypes are emergent modes arising through collective non-linear interactions among different cellular and microenvironmental components, generally described by "phase space diagrams", where stable states (attractors) are embedded into a landscape model. Within this framework, cell states and cell transitions are generally conceived as mainly specified by gene-regulatory networks. However, the system's dynamics is not reducible to the integrated functioning of the genome-proteome network alone; the epithelia-stroma interacting system must be taken into consideration in order to give a more comprehensive picture. Given that cell shape represents the spatial geometric configuration acquired as a result of the integrated set of cellular and environmental cues, we posit that fractal-shape parameters represent "omics" descriptors of the epithelium-stroma system. Within this framework, function appears to follow form, and not the other way around.

Human stem cell cultures from cleft lip/palate patients show enrichment of transcripts involved in extracellular matrix modeling by comparison to controls

Nonsyndromic cleft lip and palate (NSCL/P) is a complex disease resulting from failure of fusion of facial primordia, a complex developmental process that includes the epithelial-mesenchymal transition (EMT). Detection of differential gene transcription between NSCL/P patients and control individuals offers an interesting alternative for investigating pathways involved in disease manifestation. Here we compared the transcriptome of 6 dental pulp stem cell (DPSC) cultures from NSCL/P patients and 6 controls. Eighty-seven differentially expressed genes (DEGs) were identified. The most significant putative gene network comprised 13 out of 87 DEGs of which 8 encode extracellular proteins: ACAN, COL4A1, COL4A2, GDF15, IGF2, MMP1, MMP3 and PDGFa. Through clustering analyses we also observed that MMP3, ACAN, COL4A1 and COL4A2 exhibit co-regulated expression. Interestingly, it is known that MMP3 cleavages a wide range of extracellular proteins, including the collagens IV, V, IX, X, proteoglycans, fibronectin and laminin. It is also capable of activating other MMPs. Moreover, MMP3 had previously been associated with NSCL/P. The same general pattern was observed in a further sample, confirming involvement of synchronized gene expression patterns which differed between NSCL/P patients and controls. These results show the robustness of our methodology for the detection of differentially expressed genes using the RankProd method. In conclusion, DPSCs from NSCL/P patients exhibit gene expression signatures involving genes associated with mechanisms of extracellular matrix modeling and palate EMT processes which differ from those observed in controls. This comparative approach should lead to a more rapid identification of gene networks predisposing to this complex malformation syndrome than conventional gene mapping technologies.

Toward a clinically feasible gene expression-based subclassification strategy for septic shock: proof of concept

OBJECTIVE

To develop a clinically feasible stratification strategy for pediatric septic shock, using gene expression mosaics and a 100-gene signature representing the first 24 hrs of admission to the pediatric intensive care unit.

DESIGN

Prospective, observational study involving microarray-based bioinformatics.

SETTING

Multiple pediatric intensive care units in the United States.

PATIENTS

Ninety-eight children with septic shock.

INTERVENTIONS

None other than standard care.

MEASUREMENTS AND MAIN RESULTS

Patients were classified into three previously published, genome-wide, expression-based subclasses (subclasses A, B, and C) having clinically relevant phenotypic differences. The class-defining 100-gene signature was depicted for each individual patient, using mosaics generated by the Gene Expression Dynamics Inspector (GEDI). Composite mosaics were generated representing the average expression patterns for each of the three subclasses. Nine individual clinicians served as blinded evaluators. Each evaluator was shown the 98 individual patient mosaics and asked to classify each patient into one of the three subclasses, using the composite mosaics as the reference point. The respective sensitivities, specificities, positive predictive values, and negative predictive values of the subclassification strategy were ≥ 4% across the three subclasses. The classification strategy also generated positive likelihood ratios of ≥ 6.8 and negative likelihood ratios of ≤ .2 across the three subclasses. The κ coefficient across all possible interevaluator comparisons was 0.81.

CONCLUSIONS

We have provided initial evidence (proof of concept) for a clinically feasible and robust stratification strategy for pediatric septic shock based on a 100-gene signature and gene expression mosaics.

Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective

Cell lineage commitment and differentiation are governed by a complex gene regulatory network. Disruption of these processes by inappropriate regulatory signals and by mutational rewiring of the network can lead to tumorigenesis. Cancer cells often exhibit immature or embryonic traits and dysregulated developmental genes can act as oncogenes. However, the prevailing paradigm of somatic evolution and multi-step tumorigenesis, while useful in many instances, offers no logically coherent reason for why oncogenesis recapitulates ontogenesis. The formal concept of "cancer attractors", derived from an integrative, complex systems approach to gene regulatory network may provide a natural explanation. Here we present the theory of attractors in gene network dynamics and review the concept of cell types as attractors. We argue that cancer cells are trapped in abnormal attractors and discuss this concept in the light of recent ideas in cancer biology, including cancer genomics and cancer stem cells, as well as the implications for differentiation therapy.

Regulation by transcription factors in bacteria: beyond description

Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema.

Analysis of metagene portraits reveals distinct transitions during kidney organogenesis

Organogenesis is a multistage process, but it has been difficult, by conventional analysis, to separate stages and identify points of transition in developmentally complex organs or define genetic pathways that regulate pattern formation. We performed a detailed time-series examination of global gene expression during kidney development and then represented the resulting data as self-organizing maps (SOMs), which reduced more than 30,000 genes to 650 metagenes. Further clustering of these maps identified potential stages of development and suggested points of stability and transition during kidney organogenesis that are not obvious from either standard morphological analyses or conventional microarray clustering algorithms. We also performed entropy calculations of SOMs generated for each day of development and found correlations with morphometric parameters and expression of candidate genes that may help in orchestrating the transitions between stages of kidney development, as well as macro- and micropatterning of the organ.

Radiation metabolomics. 1. Identification of minimally invasive urine biomarkers for gamma-radiation exposure in mice

Gamma-radiation exposure has both short- and long-term adverse health effects. The threat of modern terrorism places human populations at risk for radiological exposures, yet current medical countermeasures to radiation exposure are limited. Here we describe metabolomics for gamma-radiation biodosimetry in a mouse model. Mice were gamma-irradiated at doses of 0, 3 and 8 Gy (2.57 Gy/min), and urine samples collected over the first 24 h after exposure were analyzed by ultra-performance liquid chromatography-time-of-flight mass spectrometry (UPLC-TOFMS). Multivariate data were analyzed by orthogonal partial least squares (OPLS). Both 3- and 8-Gy exposures yielded distinct urine metabolomic phenotypes. The top 22 ions for 3 and 8 Gy were analyzed further, including tandem mass spectrometric comparison with authentic standards, revealing that N-hexanoylglycine and beta-thymidine are urinary biomarkers of exposure to 3 and 8 Gy, 3-hydroxy-2-methylbenzoic acid 3-O-sulfate is elevated in urine of mice exposed to 3 but not 8 Gy, and taurine is elevated after 8 but not 3 Gy. Gene Expression Dynamics Inspector (GEDI) self-organizing maps showed clear dose-response relationships for subsets of the urine metabolome. This approach is useful for identifying mice exposed to gamma radiation and for developing metabolomic strategies for noninvasive radiation biodosimetry in humans.

Metabolomics

Metabolomics is the systematic identification and quantitation of all metabolites in a given organism or biological sample. The enhanced resolution provided by nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), along with powerful chemometric software, allows the simultaneous determination and comparison of thousands of chemical entities, which has lead to an expansion of small molecule biochemistry studies in bacteria, plants, and mammals. Continued development of these analytical platforms will accelerate the widespread use of metabolomics and allow further integration of small molecules into systems biology. Here, recent studies using metabolomics in xenobiotic metabolism and genetically modified mice are highlighted.