Immunogenic cell death-led discovery of COVID-19 biomarkers and inflammatory infiltrates

Immunogenic cell death (ICD) serves a critical role in regulating cell death adequate to activate an adaptive immune response, and it is associated with various inflammation-related diseases. However, the specific role of ICD-related genes in COVID-19 remains unclear. We acquired COVID-19-related information from the GEO database and a total of 14 ICD-related differentially expressed genes (DEGs) were identified. These ICD-related DEGs were closely associated with inflammation and immune activity. Afterward, CASP1, CD4, and EIF2AK3 among the 14 DEGs were selected as feature genes based on LASSO, Random Forest, and SVM-RFE algorithms, which had reliable diagnostic abilities. Moreover, functional enrichment analysis indicated that these feature genes may have a potential role in COVID-19 by being involved in the regulation of immune response and metabolism. Further CIBERSORT analysis demonstrated that the variations in the immune microenvironment of COVID-19 patients may be correlated with CASP1, CD4, and EIF2AK3. Additionally, 33 drugs targeting 3 feature genes had been identified, and the ceRNA network demonstrated a complicated regulative association based on these feature genes. Our work identified that CASP1, CD4, and EIF2AK3 were diagnostic genes of COVID-19 and correlated with immune activity. This study presents a reliable diagnostic signature and offers an overview to investigate the mechanism of COVID-19.

Women's Respiratory Movements during Spontaneous Breathing and Physical Fitness: A Cross-Sectional, Correlational Study

BACKGROUND

Abdominal/diaphragmatic breathing exercises are popular worldwide and have been proven to be beneficial for physical performance. Is abdominal motion (AM) during spontaneous breathing correlated with physical fitness? The present study aimed to answer this question.

METHODS

434 women (aged 20-59) were enrolled and participated in respiration tests using two respiration belts (one was tied at the height of the xiphoid and another at the navel) to detect AM and thoracic motion (TM). They also performed physical fitness tests to measure body size, muscular strength, muscular power, muscular endurance, balance, flexibility, reaction time, and cardiorespiratory endurance.

RESULTS

All the correlation coefficients between respiratory movements (AM, TM, AM + TM, AM/(AM + TM)) and physical fitness outcomes were less than 0.4/-0.4. Only AM and muscular power (countermovement jump height) had a weak correlation, with a correlation coefficient close to 0.4 in the 20-29-year age group (r_s = 0.398, p = 0.011, n = 40).

CONCLUSIONS

Women's respiratory movements during spontaneous breathing were not correlated with physical fitness. Future studies may focus on the relationship between AM and countermovement jump height in young women with a larger sample size and using ultrasound to directly test the excursion of the diaphragm.

Tight gene co-expression in BCB positive cattle oocytes and their surrounding cumulus cells

BACKGROUND

Cytoplasmic and nuclear maturation of oocytes, as well as interaction with the surrounding cumulus cells, are important features relevant to the acquisition of developmental competence.

METHODS

Here, we utilized Brilliant cresyl blue (BCB) to distinguish cattle oocytes with low activity of the enzyme Glucose-6-Phosphate Dehydrogenase, and thus separated fully grown (BCB positive) oocytes from those in the growing phase (BCB negative). We then analyzed the developmental potential of these oocytes, mitochondrial DNA (mtDNA) copy number in single oocytes, and investigated the transcriptome of single oocytes and their surrounding cumulus cells of BCB positive versus BCB negative oocytes.

RESULTS

The BCB positive oocytes were twice as likely to produce a blastocyst in vitro compared to BCB- oocytes (P < 0.01). We determined that BCB negative oocytes have 1.3-fold more mtDNA copies than BCB positive oocytes (P = 0.004). There was no differential transcript abundance of genes expressed in oocytes, however, 172 genes were identified in cumulus cells with differential transcript abundance (FDR < 0.05) based on the BCB staining of their oocyte. Co-expression analysis between oocytes and their surrounding cumulus cells revealed a subset of genes whose co-expression in BCB positive oocytes (n = 75) and their surrounding cumulus cells (n = 108) compose a unique profile of the cumulus-oocyte complex.

CONCLUSIONS

If oocytes transition from BCB negative to BCB positive, there is a greater likelihood of producing a blastocyst, and a reduction of mtDNA copies, but there is no systematic variation of transcript abundance. Cumulus cells present changes in transcript abundance, which reflects in a dynamic co-expression between the oocyte and cumulus cells.

Unraveling the occurrence of contaminants of emerging concern in groundwater from urban setting: A combined multidisciplinary approach and self-organizing maps

In recent decades, changes in human behavior and new technologies have introduced thousands of new compounds into the environment called "contaminants of emerging concern" (CEC). These compounds have been detected in different environmental compartments such as soil, surface water, air, and groundwater. The presence of these contaminants in groundwater may pose risks to human health when used as potable water. In some urban areas in Brazil, groundwater is normally consumed without previous treatment. This study aimed to use statistical analysis by self-organizing maps (SOM) to evaluate the trends of CEC in urban groundwater systems. A total of 23 CEC compounds including pesticides, pharmaceuticals, and hormones were determined in groundwater samples using solid phase extraction and liquid chromatography-mass spectrometry. The CEC most frequently detected were atrazine and degradation products, fipronil, simazine, tebuconazole, hexazinone, and caffeine in concentrations up to 300 ng L^-1. All studied compounds were detected in groundwater at least in one sample. Patterns in the data through SOM have shown a strong positive correlation between atrazine, hexazinone, simazine, tebuthiuron, 2-hydroxyatrazine, and 17β-estradiol. The hormones estrone and testosterone also show a positive correlation due to their similar chemical properties. On the other hand, caffeine was detected in 90% of the samples, likely due to a population habit of taking daily a hot drink made of yerba mate associated with low rates of treated domestic sewage in the study area.

Item-Level Psychometric Analysis of the Psychosocial Processes at Work Scale (PROPSIT) in Workers

The structural attributes and correlates of items have an effect on their composite scores and exploring them strengthens the content validity of a measure adapted to another context. The objective of this study was to evaluate the item properties of a measure of psychosocial work factors (PWFs). Data were collected through a web platform from 188 Peruvian working adults (men = 101, 50.5%) holding various professions and jobs. The instrument was the Psychosocial Processes at Work Scale (PROPSIT), adapted for the Peruvian context. The distributional characteristics, the efficiency of its response options and its correlates with engagement, occupational self-efficacy, general stress and psychological distress (explored with a coefficient of maximum information and another of monotonic association) were analyzed. It was found that the items were asymmetrically distributed, without statistical normality and with a response tendency at low (for psychosocial risk factors (PSRFs)) and medium (favorable psychosocial resources) levels. The number of efficient response options was lower (approximately five options) than the original structure (seven options). The monotonic associations with gender and age were essentially zero and theoretically converged with the external constructs, except for some items related to job demands. The contributions of the results to the content validity of the PROPSIT and the orientation of working hypotheses about PROPSIT item constructs and measures of work effects are discussed.

COVID-19 Biomarkers Recognition & Classification Using Intelligent Systems

Background:

SARS-CoV-2 has paralyzed mankind due to its high transmissibility and its associated mortality, causing millions of infections and deaths worldwide. The search for gene expression biomarkers from the host transcriptional response to infection may help understand the underlying mechanisms by which the virus causes COVID-19. This research proposes a smart methodology integrating different RNA-Seq datasets from SARS-CoV-2, other respiratory diseases, and healthy patients.

Methods:

The proposed pipeline exploits the functionality of the ‘KnowSeq’ R/Bioc package, integrating different data sources and attaining a significantly larger gene expression dataset, thus endowing the results with higher statistical significance and robustness in comparison with previous studies in the literature. A detailed preprocessing step was carried out to homogenize the samples and build a clinical decision system for SARS-CoV-2. It uses machine learning techniques such as feature selection algorithm and supervised classification system. This clinical decision system uses the most differentially expressed genes among different diseases (including SARS-Cov-2) to develop a four-class classifier.

Results:

The multiclass classifier designed can discern SARS-CoV-2 samples, reaching an accuracy equal to 91.5%, a mean F1-Score equal to 88.5%, and a SARS-CoV-2 AUC equal to 94% by using only 15 genes as predictors. A biological interpretation of the gene signature extracted reveals relations with processes involved in viral responses.

Conclusion:

This work proposes a COVID-19 gene signature composed of 15 genes, selected after applying the feature selection ‘minimum Redundancy Maximum Relevance’ algorithm. The integration among several RNA-Seq datasets was a success, allowing for a considerable large number of samples and therefore providing greater statistical significance to the results than previous studies. Biological interpretation of the selected genes was also provided.

Heterogeneous Gene Expression Cross-Evaluation of Robust Biomarkers Using Machine Learning Techniques Applied to Lung Cancer

Background:

Nowadays, gene expression analysis is one of the most promising pillars for understanding and uncovering the mechanisms underlying the development and spread of cancer. In this sense, Next Generation Sequencing technologies, such as RNA-Seq, are currently leading the market due to their precision and cost. Nevertheless, there is still an enormous amount of non-analyzed data obtained from older technologies, such as Microarray, which could still be useful to extract relevant knowledge.

Methods:

Throughout this research, a complete machine learning methodology to cross-evaluate the compatibility between both RNA-Seq and Microarray sequencing technologies is described and implemented. In order to show a real application of the designed pipeline, a lung cancer case study is addressed by considering two detected subtypes: adenocarcinoma and squamous cell carcinoma. Transcriptomic datasets considered for our study have been obtained from the public repositories NCBI/GEO, ArrayExpress and GDC-Portal. From them, several gene experiments have been carried out with the aim of finding gene signatures for these lung cancer subtypes, linked to both transcriptomic technologies. With these DEGs selected, intelligent predictive models capable of classifying new samples belonging to these cancer subtypes have been developed.

Results:

The predictive models built using one technology are capable of discerning samples from a different technology. The classification results are evaluated in terms of accuracy, F1-score and ROC curves along with AUC. Finally, the biological information of the gene sets obtained and their relationship with lung cancer are reviewed, encountering strong biological evidence linking them to the disease.

Conclusion:

Our method has the capability of finding strong gene signatures which are also independent of the transcriptomic technology used to develop the analysis. In addition, our article highlights the potential of using heterogeneous transcriptomic data to increase the amount of samples for the studies, increasing the statistical significance of the results.

Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality

The study of the interdependence relationships of the variables of an examined system is of great importance and remains a challenging task. There are two distinct cases of interdependence. In the first case, the variables evolve in synchrony, connections are undirected and the connectivity is examined based on symmetric measures, such as correlation. In the second case, a variable drives another one and they are connected with a causal relationship. Therefore, directed connections entail the determination of the interrelationships based on causality measures. The main open question that arises is the following: can symmetric correlation measures or directional causality measures be applied to infer the connectivity network of an examined system? Using simulations, we demonstrate the performance of different connectivity measures in case of contemporaneous or/and temporal dependencies. Results suggest the sensitivity of correlation measures when temporal dependencies exist in the data. On the other hand, causality measures do not spuriously indicate causal effects when data present only contemporaneous dependencies. Finally, the necessity of introducing effective instantaneous causality measures is highlighted since they are able to handle both contemporaneous and causal effects at the same time. Results based on instantaneous causality measures are promising; however, further investigation is required in order to achieve an overall satisfactory performance.

Analysis of Electromagnetic Coupling Characteristics of Balise Transmission System Based on Digital Twin

The balise transmission system (BTS) is an automatic identification system for vehicle–ground communication based on radio frequency identification (RFID) technology. The electromagnetic coupling characteristics of BTS have a very important effect on the transmission quality of the uplink telegram signals. However, signal transmission problems of BTS often occur due to unreasonable installation mode or parameter setting. In order to solve these problems, it is necessary to fully discuss the electromagnetic coupling characteristics of the BTS. In this paper, the transmission process of energy and data between the onboard antenna unit and the balise was analyzed using digital twin technology. A high-precision dynamic electromagnetic coupling model of the BTS was established from four aspects of three-dimensional structure, physical properties, behavior patterns, and rule restrictions. Then the accuracy of the model was verified by experiments. Finally, the influence of typical parameters on the uplink signal is calculated and analyzed quantitatively. The results showed that compared with other factors discussed in this paper, the vertical distance and the installation mode had greater effects on the uplink signal. These results can be used to guide the engineering installation and related optimization of the BTS.

Network Modeling of Complex Data Sets

We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.

Molecular alterations during larval development of Haemonchus contortus in vitro are under tight post-transcriptional control

In this study, we explored the molecular alterations in the developmental switch from the L3 to the exsheathed L3 (xL3) and to the L4 stage of Haemonchus contortus in vitro using an integrated transcriptomic, proteomic and bioinformatic approach. Totals of 9,754 mRNAs, 88 microRNAs (miRNAs) and 1,591 proteins were identified, and 6,686 miRNA-mRNA pairs inferred in all larval stages studied. Approximately 16% of transcripts in the combined transcriptome (representing all three larval stages) were expressed as proteins, and there were positive correlations (r = 0.39-0.44) between mRNA transcription and protein expression in the three distinct developmental stages of the parasite. Of the predicted targets, 1,019 (27.0%) mRNA transcripts were expressed as proteins, and there was a negative correlation (r = -0.60 to -0.50) in the differential mRNA transcription and protein expression between developmental stages upon pairwise comparison. The changes in transcription (mRNA and miRNA) and protein expression from the free-living to the parasitic life cycle phase of H. contortus related to enrichments in biological pathways associated with metabolism (e.g., carbohydrate and lipid degradation, and amino acid metabolism), environmental information processing (e.g., signal transduction, signalling molecules and interactions) and/or genetic information processing (e.g., transcription and translation). Specifically, fatty acid degradation, steroid hormone biosynthesis and the Rap1 signalling pathway were suppressed, whereas transcription, translation and protein processing in the endoplasmic reticulum were upregulated during the transition from the free-living L3 to the parasitic xL3 and L4 stages of the nematode in vitro. Dominant post-transcriptional regulation was inferred to elicit these changes, and particular miRNAs (e.g., hco-miR-34 and hco-miR-252) appear to play roles in stress responses and/or environmental adaptations during developmental transitions of H. contortus. Taken together, these integrated results provide a comprehensive insight into the developmental biology of this important parasite at the molecular level in vitro. The approach applied here to H. contortus can be readily applied to other parasitic nematodes.

Identifying network-based biomarkers of complex diseases from high-throughput data

In this work, we review the main available computational methods of identifying biomarkers of complex diseases from high-throughput data. The emerging omics techniques provide powerful alternatives to measure thousands of molecules in cells in parallel manners. The generated genomic, transcriptomic, proteomic, metabolomic and phenomic data provide comprehensive molecular and cellular information for detecting critical signals served as biomarkers by classifying disease phenotypic states. Networks are often employed to organize these profiles in the identification of biomarkers to deal with complex diseases in diagnosis, prognosis and therapy as well as mechanism deciphering from systematic perspectives. Here, we summarize some representative network-based bioinformatics methods in order to highlight the importance of computational strategies in biomarker discovery.

Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models

In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure-Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model. Verification of the relationships between descriptors and toxicity or other activity in the QSAR model has a vital role in understanding the mechanisms of action. The well-known phrase "correlation does not imply causation" reflects insight statistically correlated with the endpoint descriptor may not cause the emergence of this endpoint. Hence, paradigmatic shifts must be undertaken when moving from traditional statistical correlation analysis to causal analysis of multivariate data. Methods of causal discovery have been applied for broader physical insight into mechanisms of action and interpretation of the developed nano-QSAR models. Previously developed nano-QSAR models for toxicity of 17 nano-sized metal oxides towards E. coli bacteria have been validated by means of the causality criteria. Using the descriptors confirmed by the causal technique, we have developed new models consistent with the straightforward causal-reasoning account. It was proven that causal inference methods are able to provide a more robust mechanistic interpretation of the developed nano-QSAR models.

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.

Vitamin D receptor, an important transcription factor associated with aldosterone-producing adenoma

OBJECTIVE

To explore the endocrine mechanisms of aldosterone-producing adenoma (APA) by using the microarray expression profiles of normal and APA samples.

METHODS

The gene expression profile GSE8514 was downloaded from Gene Expression Omnibus database, including samples from normal adrenals (n = 5) and APAs (n = 10). The differentially expressed genes (DEGs) were identified by samr package and endocrine DEGs were obtained according to Clinical Genome Database. Then, functional enrichment analysis of screened DEGs was performed by DAVID (Database for Annotation, Visualization and Integrated Discovery). Finally, a regulatory network was constructed to screen endocrine genes related with adrenal dysfunction and pathway enrichment analysis for the constructed network was performed.

RESULTS

A total of 2149 DEGs were identified including 379 up- and 1770 down-regulated genes. And 26 endocrine genes were filtered from the DEGs. Furthermore, the down-regulated DEGs are mainly related to protein kinase cascade, response to molecule of bacterial origin, response to lipopolysaccharide, cellular macromolecule catabolic process and macromolecule catabolic process, while the up-regulated DEGs are related with regulation of ion transport. The target genes of VDR (vitamin D receptor), one of the three endocrine genes differentially expressed in the regulatory network, were endocrine genes including CYP24A1 (25-hydroxyvitamin D-24-hydroxylase) and PTH (parathyroid hormone). Three pathways may be associated with APA pathogenesis including cytokine-cytokine receptor interaction, pathways in cancer and autoimmune thyroid disease.

CONCLUSION

The VDR is the most significant transcription factor and related endocrine genes might play important roles in the endocrine mechanisms of APA.

Gene expression analysis of zebrafish melanocytes, iridophores, and retinal pigmented epithelium reveals indicators of biological function and developmental origin

In order to facilitate understanding of pigment cell biology, we developed a method to concomitantly purify melanocytes, iridophores, and retinal pigmented epithelium from zebrafish, and analyzed their transcriptomes. Comparing expression data from these cell types and whole embryos allowed us to reveal gene expression co-enrichment in melanocytes and retinal pigmented epithelium, as well as in melanocytes and iridophores. We found 214 genes co-enriched in melanocytes and retinal pigmented epithelium, indicating the shared functions of melanin-producing cells. We found 62 genes significantly co-enriched in melanocytes and iridophores, illustrative of their shared developmental origins from the neural crest. This is also the first analysis of the iridophore transcriptome. Gene expression analysis for iridophores revealed extensive enrichment of specific enzymes to coordinate production of their guanine-based reflective pigment. We speculate the coordinated upregulation of specific enzymes from several metabolic pathways recycles the rate-limiting substrate for purine synthesis, phosphoribosyl pyrophosphate, thus constituting a guanine cycle. The purification procedure and expression analysis described here, along with the accompanying transcriptome-wide expression data, provide the first mRNA sequencing data for multiple purified zebrafish pigment cell types, and will be a useful resource for further studies of pigment cell biology.

Clustering of High Throughput Gene Expression Data

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics - clustering gene expression data - to the operations research community.

Evaluation of gene association methods for coexpression network construction and biological knowledge discovery

BACKGROUND

Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical.

METHODS AND RESULTS

In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods.

CONCLUSIONS

We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

Comparison of WTC dust size on macrophage inflammatory cytokine release in vivo and in vitro

Background

The WTC collapse exposed over 300,000 people to high concentrations of WTC-PM; particulates up to ∼50 mm were recovered from rescue workers’ lungs. Elevated MDC and GM-CSF independently predicted subsequent lung injury in WTC-PM-exposed workers. Our hypotheses are that components of WTC dust strongly induce GM-CSF and MDC in AM; and that these two risk factors are in separate inflammatory pathways.

Methodology/Principal Findings

Normal adherent AM from 15 subjects without WTC-exposure were incubated in media alone, LPS 40 ng/mL, or suspensions of WTC-PM_10–53 or WTC-PM_2.5 at concentrations of 10, 50 or 100 µg/mL for 24 hours; supernatants assayed for 39 chemokines/cytokines. In addition, sera from WTC-exposed subjects who developed lung injury were assayed for the same cytokines. In the in vitro studies, cytokines formed two clusters with GM-CSF and MDC as a result of PM_10–53 and PM_2.5. GM-CSF clustered with IL-6 and IL-12(p70) at baseline, after exposure to WTC-PM_10–53 and in sera of WTC dust-exposed subjects (n = 70) with WTC lung injury. Similarly, MDC clustered with GRO and MCP-1. WTC-PM_10–53 consistently induced more cytokine release than WTC-PM_2.5 at 100 µg/mL. Individual baseline expression correlated with WTC-PM-induced GM-CSF and MDC.

Conclusions

WTC-PM_10–53 induced a stronger inflammatory response by human AM than WTC-PM_2.5. This large particle exposure may have contributed to the high incidence of lung injury in those exposed to particles at the WTC site. GM-CSF and MDC consistently cluster separately, suggesting a role for differential cytokine release in WTC-PM injury. Subject-specific response to WTC-PM may underlie individual susceptibility to lung injury after irritant dust exposure.

Tumor and reproductive traits are linked by RNA metabolism genes in the mouse ovary: a transcriptome-phenotype association analysis

BACKGROUND

The link between reproductive life history and incidence of ovarian tumors is well known. Periods of reduced ovulations may confer protection against ovarian cancer. Using phenotypic data available for mouse, a possible association between the ovarian transcriptome, reproductive records and spontaneous ovarian tumor rates was investigated in four mouse inbred strains. NIA15k-DNA microarrays were employed to obtain expression profiles of BalbC, C57BL6, FVB and SWR adult ovaries.

RESULTS

Linear regression analysis with multiple-test control (adjusted p ≤ 0.05) resulted in ovarian tumor frequency (OTF) and number of litters (NL) as the top-correlated among five tested phenotypes. Moreover, nearly one-hundred genes were coincident between these two traits and were decomposed in 76 OTF(-) NL(+) and 20 OTF(+) NL(-) genes, where the plus/minus signs indicate the direction of correlation. Enriched functional categories were RNA-binding/mRNA-processing and protein folding in the OTF(-) NL(+) and the OTF(+) NL(-) subsets, respectively. In contrast, no associations were detected between OTF and litter size (LS), the latter a measure of ovulation events in a single estrous cycle.

CONCLUSION

Literature text-mining pointed to post-transcriptional control of ovarian processes including oocyte maturation, folliculogenesis and angiogenesis as possible causal relationships of observed tumor and reproductive phenotypes. We speculate that repetitive cycling instead of repetitive ovulations represent the actual link between ovarian tumorigenesis and reproductive records.

The impact of measurement errors in the identification of regulatory networks

BACKGROUND

There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise.

RESULTS

This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data.

CONCLUSIONS

Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.