Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes

Hereditary Hyperferritinemia

Ferritin is a ubiquitous protein that is present in most tissues as a cytosolic protein. The major and common role of ferritin is to bind Fe²⁺, oxidize it and sequester it in a safe form in the cell, and to release iron according to cellular needs. Ferritin is also present at a considerably low proportion in normal mammalian sera and is relatively iron poor compared to tissues. Serum ferritin might provide a useful and convenient method of assessing the status of iron storage, and its measurement has become a routine laboratory test. However, many additional factors, including inflammation, infection, metabolic abnormalities, and malignancy-all of which may elevate serum ferritin-complicate interpretation of this value. Despite this long history of clinical use, fundamental aspects of the biology of serum ferritin are still unclear. According to the high number of factors involved in regulation of ferritin synthesis, secretion, and uptake, and in its central role in iron metabolism, hyperferritinemia is a relatively common finding in clinical practice and is found in a large spectrum of conditions, both genetic and acquired, associated or not with iron overload. The diagnostic strategy to reveal the cause of hyperferritinemia includes family and personal medical history, biochemical and genetic tests, and evaluation of liver iron by direct or indirect methods. This review is focused on the forms of inherited hyperferritinemia with or without iron overload presenting with normal transferrin saturation, as well as a step-by-step approach to distinguish these forms to the acquired forms, common and rare, of isolated hyperferritinemia.

Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers

Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for and development of label-free methodologies to classify cells is strong and its impact on precision medicine is relevant. Towards this end, high-throughput techniques for cell mechanical phenotyping have been proposed to get a multidimensional biophysical characterization of single cells. With this motivation, our goal here is to investigate the extent to which an unsupervised machine learning methodology, which is applied exclusively on morpho-rheological markers obtained by real-time deformability and fluorescence cytometry (RT-FDC), can address the difficult task of providing label-free discrimination of reticulocytes from mature red blood cells. We focused on this problem, since the characterization of reticulocytes (their percentage and cellular features) in the blood is vital in multiple human disease conditions, especially bone-marrow disorders such as anemia and leukemia. Our approach reports promising label-free results in the classification of reticulocytes from mature red blood cells, and it represents a step forward in the development of high-throughput morpho-rheological-based methodologies for the computational categorization of single cells. Besides, our methodology can be an alternative but also a complementary method to integrate with existing cell-labelling techniques.

Mathematical and computational models for pain: a systematic review

OBJECTIVE

There is no single prevailing theory of pain that explains its origin, qualities, and alleviation. Although many studies have investigated various molecular targets for pain management, few have attempted to examine the etiology or working mechanisms of pain through mathematical or computational model development. In this systematic review, we identified and classified mathematical and computational models for characterizing pain.

METHODS

The databases queried were Science Direct and PubMed, yielding 560 articles published prior to January 1st, 2020. After screening for inclusion of mathematical or computational models of pain, 31 articles were deemed relevant.

RESULTS

Most of the reviewed articles utilized classification algorithms to categorize pain and no-pain conditions. We found the literature heavily focused on the application of existing models or machine learning algorithms to identify the presence or absence of pain, rather than to explore features of pain that may be used for diagnostics and treatment.

CONCLUSIONS

Although understudied, the development of mathematical models may augment the current understanding of pain by providing directions for testable hypotheses of its underlying mechanisms. Additional focus is needed on developing models that seek to understand the underlying mechanisms of pain, as this could potentially lead to major breakthroughs in its treatment.

Optimisation of the coalescent hyperbolic embedding of complex networks

Several observations indicate the existence of a latent hyperbolic space behind real networks that makes their structure very intuitive in the sense that the probability for a connection is decreasing with the hyperbolic distance between the nodes. A remarkable network model generating random graphs along this line is the popularity-similarity optimisation (PSO) model, offering a scale-free degree distribution, high clustering and the small-world property at the same time. These results provide a strong motivation for the development of hyperbolic embedding algorithms, that tackle the problem of finding the optimal hyperbolic coordinates of the nodes based on the network structure. A very promising recent approach for hyperbolic embedding is provided by the noncentered minimum curvilinear embedding (ncMCE) method, belonging to the family of coalescent embedding algorithms. This approach offers a high-quality embedding at a low running time. In the present work we propose a further optimisation of the angular coordinates in this framework that seems to reduce the logarithmic loss and increase the greedy routing score of the embedding compared to the original version, thereby adding an extra improvement to the quality of the inferred hyperbolic coordinates.

Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome

The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in early phase and rely principally on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques with nonlinear ones to unveil hidden patterns that remain unseen by linear embedding. Then, we prove the advantages to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of bacterial network re-organizations which emerge from perturbations induced by a medical treatment (PPIs) or an infectious state (H. pylori). Finally, we show how to build bacteria-metabolite multilayer networks that can deepen our understanding of the metabolite pathways significantly associated to the perturbed microbial communities.

Multiview: a software package for multiview pattern recognition methods

SUMMARY

Multiview datasets are the norm in bioinformatics, often under the label multi-omics. Multiview data are gathered from several experiments, measurements or feature sets available for the same subjects. Recent studies in pattern recognition have shown the advantage of using multiview methods of clustering and dimensionality reduction; however, none of these methods are readily available to the extent of our knowledge. Multiview extensions of four well-known pattern recognition methods are proposed here. Three multiview dimensionality reduction methods: multiview t-distributed stochastic neighbour embedding, multiview multidimensional scaling and multiview minimum curvilinearity embedding, as well as a multiview spectral clustering method. Often they produce better results than their single-view counterparts, tested here on four multiview datasets.

AVAILABILITY AND IMPLEMENTATION

R package at the B2SLab site: http://b2slab.upc.edu/software-and-tutorials/ and Python package: https://pypi.python.org/pypi/multiview.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Framework for improving outcome prediction for acute to chronic low back pain transitions

Clinical practice guidelines and the Federal Pain Research Strategy (United States) have recently highlighted research priorities to lessen the public health impact of low back pain (LBP). It may be necessary to improve existing predictive approaches to meet these research priorities for the transition from acute to chronic LBP. In this article, we first present a mapping review of previous studies investigating this transition and, from the characterization of the mapping review, present a predictive framework that accounts for limitations in the identified studies. Potential advantages of implementing this predictive framework are further considered. These advantages include (1) leveraging routinely collected health care data to improve prediction of the development of chronic LBP and (2) facilitating use of advanced analytical approaches that may improve prediction accuracy. Furthermore, successful implementation of this predictive framework in the electronic health record would allow for widespread testing of accuracy resulting in validated clinical decision aids for predicting chronic LBP development.

Prediction of comorbid diseases using weighted geometric embedding of human interactome

BACKGROUND

Comorbidity is the phenomenon of two or more diseases occurring simultaneously not by random chance and presents great challenges to accurate diagnosis and treatment. As an effort toward better understanding the genetic causes of comorbidity, in this work, we have developed a computational method to predict comorbid diseases. Two diseases sharing common genes tend to increase their comorbidity. Previous work shows that after mapping the associated genes onto the human interactome the distance between the two disease modules (subgraphs) is correlated with comorbidity.

METHODS

To fully incorporate structural characteristics of interactome as features into prediction of comorbidity, our method embeds the human interactome into a high dimensional geometric space with weights assigned to the network edges and uses the projection onto different dimension to "fingerprint" disease modules. A supervised machine learning classifier is then trained to discriminate comorbid diseases versus non-comorbid diseases.

RESULTS

In cross-validation using a benchmark dataset of more than 10,000 disease pairs, we report that our model achieves remarkable performance of ROC score = 0.90 for comorbidity threshold at relative risk RR = 0 and 0.76 for comorbidity threshold at RR = 1, and significantly outperforms the previous method and the interactome generated by annotated data. To further incorporate prior knowledge pathways association with diseases, we weight the protein-protein interaction network edges according to their frequency of occurring in those pathways in such a way that edges with higher frequency will more likely be selected in the minimum spanning tree for geometric embedding. Such weighted embedding is shown to lead to further improvement of comorbid disease prediction.

CONCLUSION

The work demonstrates that embedding the two-dimension planar graph of human interactome into a high dimensional geometric space allows for characterizing and capturing disease modules (subgraphs formed by the disease associated genes) from multiple perspectives, and hence provides enriched features for a supervised classifier to discriminate comorbid disease pairs from non-comorbid disease pairs more accurately than based on simply the module separation.

CSF levels of apolipoprotein C1 and autotaxin found to associate with neuropathic pain and fibromyalgia

Objective

Neuropathic pain and fibromyalgia are two common and poorly understood chronic pain conditions that lack satisfactory treatments, cause substantial suffering and societal costs. Today, there are no biological markers on which to base chronic pain diagnoses, treatment choices or to understand the pathophysiology of pain for the individual patient. This study aimed to investigate cerebrospinal fluid (CSF) protein profiles potentially associated with fibromyalgia and neuropathic pain.

Methods

CSF samples were collected from 25 patients with neuropathic pain (two independent sets, n=14 patients for discovery, and n=11 for verification), 40 patients with fibromyalgia and 134 controls without neurological disease from two different populations. CSF protein profiling of 55 proteins was performed using antibody suspension bead array technology.

Results

We found increased levels of apolipoprotein C1 (APOC1) in CSF of neuropathic pain patients compared to controls and there was a trend for increased levels also in fibromyalgia patients. In addition, levels of ectonucleotide pyrophosphatase family member 2 (ENPP2, also referred to as autotaxin) were increased in the CSF of fibromyalgia patients compared to all other groups including patients with neuropathic pain.

Conclusion

The increased levels of APOC1 and ENPP2 found in neuropathic pain and fibromyalgia patients may shed light on the underlying mechanisms of these conditions. Further investigation is required to elucidate their role in maintaining pain and other main symptoms of these disorders.

Controlling distinct signaling states in cultured cancer cells provides a new platform for drug discovery

Cancer cells can switch between signaling pathways to regulate growth under different conditions. In the tumor microenvironment, this likely helps them evade therapies that target specific pathways. We must identify all possible states and utilize them in drug screening programs. One such state is characterized by expression of the transcription factor Hairy and Enhancer of Split 3 (HES3) and sensitivity to HES3 knockdown, and it can be modeled in vitro. Here, we cultured 3 primary human brain cancer cell lines under 3 different culture conditions that maintain low, medium, and high HES3 expression and characterized gene regulation and mechanical phenotype in these states. We assessed gene expression regulation following HES3 knockdown in the HES3-high conditions. We then employed a commonly used human brain tumor cell line to screen Food and Drug Administration (FDA)-approved compounds that specifically target the HES3-high state. We report that cells from multiple patients behave similarly when placed under distinct culture conditions. We identified 37 FDA-approved compounds that specifically kill cancer cells in the high-HES3-expression conditions. Our work reveals a novel signaling state in cancer, biomarkers, a strategy to identify treatments against it, and a set of putative drugs for potential repurposing.-Poser, S. W., Otto, O., Arps-Forker, C., Ge, Y., Herbig, M., Andree, C., Gruetzmann, K., Adasme, M. F., Stodolak, S., Nikolakopoulou, P., Park, D. M., Mcintyre, A., Lesche, M., Dahl, A., Lennig, P., Bornstein, S. R., Schroeck, E., Klink, B., Leker, R. R., Bickle, M., Chrousos, G. P., Schroeder, M., Cannistraci, C. V., Guck, J., Androutsellis-Theotokis, A. Controlling distinct signaling states in cultured cancer cells provides a new platform for drug discovery.

Comprehensive and quantitative analysis of white and brown adipose tissue by shotgun lipidomics

Objective

Shotgun lipidomics enables an extensive analysis of lipids from tissues and fluids. Each specimen requires appropriate extraction and processing procedures to ensure good coverage and reproducible quantification of the lipidome. Adipose tissue (AT) has become a research focus with regard to its involvement in obesity-related pathologies. However, the quantification of the AT lipidome is particularly challenging due to the predominance of triacylglycerides, which elicit high ion suppression of the remaining lipid classes.

Methods

We present a new and validated method for shotgun lipidomics of AT, which tailors the lipid extraction procedure to the target specimen and features high reproducibility with a linear dynamic range of at least 4 orders of magnitude for all lipid classes.

Results

Utilizing this method, we observed tissue-specific and diet-related differences in three AT types (brown, gonadal, inguinal subcutaneous) from lean and obese mice. Brown AT exhibited a distinct lipidomic profile with the greatest lipid class diversity and responded to high-fat diet by altering its lipid composition, which shifted towards that of white AT. Moreover, diet-induced obesity promoted an overall remodeling of the lipidome, where all three AT types featured a significant increase in longer and more unsaturated triacylglyceride and phospholipid species.

Conclusions

The here presented method facilitates reproducible systematic lipidomic profiling of AT and could be integrated with further –omics approaches used in (pre-) clinical research, in order to advance the understanding of the molecular metabolic dynamics involved in the pathogenesis of obesity-associated disorders.

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Validated shotgun lipidomics method of AT covering 300 lipids of 20 classes and linear dynamic range of 4 orders of magnitude.

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Increase of longer and more unsaturated triacylglycerides and phospholipids in brown and white AT under high-fat diet.

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Differences in the lipidomes of gonadal, subcutaneous and brown AT.

Ceruloplasmin replacement therapy ameliorates neurological symptoms in a preclinical model of aceruloplasminemia

Aceruloplasminemia is a monogenic disease caused by mutations in the ceruloplasmin gene that result in loss of protein ferroxidase activity. Ceruloplasmin plays a role in iron homeostasis, and its activity impairment leads to iron accumulation in liver, pancreas, and brain. Iron deposition promotes diabetes, retinal degeneration, and progressive neurodegeneration. Current therapies mainly based on iron chelation, partially control systemic iron deposition but are ineffective on neurodegeneration. We investigated the potential of ceruloplasmin replacement therapy in reducing the neurological pathology in the ceruloplasmin-knockout (CpKO) mouse model of aceruloplasminemia. CpKO mice were intraperitoneal administered for 2 months with human ceruloplasmin that was able to enter the brain inducing replacement of the protein levels and rescue of ferroxidase activity. Ceruloplasmin-treated mice showed amelioration of motor incoordination that was associated with diminished loss of Purkinje neurons and reduced brain iron deposition, in particular in the choroid plexus. Computational analysis showed that ceruloplasmin-treated CpKO mice share a similar pattern with wild-type animals, highlighting the efficacy of the therapy. These data suggest that enzyme replacement therapy may be a promising strategy for the treatment of aceruloplasminemia.

Network embedding in biomedical data science

Owning to the rapid development of computer technologies, an increasing number of relational data have been emerging in modern biomedical research. Many network-based learning methods have been proposed to perform analysis on such data, which provide people a deep understanding of topology and knowledge behind the biomedical networks and benefit a lot of applications for human healthcare. However, most network-based methods suffer from high computational and space cost. There remain challenges on handling high dimensionality and sparsity of the biomedical networks. The latest advances in network embedding technologies provide new effective paradigms to solve the network analysis problem. It converts network into a low-dimensional space while maximally preserves structural properties. In this way, downstream tasks such as link prediction and node classification can be done by traditional machine learning methods. In this survey, we conduct a comprehensive review of the literature on applying network embedding to advance the biomedical domain. We first briefly introduce the widely used network embedding models. After that, we carefully discuss how the network embedding approaches were performed on biomedical networks as well as how they accelerated the downstream tasks in biomedical science. Finally, we discuss challenges the existing network embedding applications in biomedical domains are faced with and suggest several promising future directions for a better improvement in human healthcare.

Morphogenetic degeneracies in the actomyosin cortex

One of the great challenges in biology is to understand the mechanisms by which morphogenetic processes arise from molecular activities. We investigated this problem in the context of actomyosin-based cortical flow in C. elegans zygotes, where large-scale flows emerge from the collective action of actomyosin filaments and actin binding proteins (ABPs). Large-scale flow dynamics can be captured by active gel theory by considering force balances and conservation laws in the actomyosin cortex. However, which molecular activities contribute to flow dynamics and large-scale physical properties such as viscosity and active torque is largely unknown. By performing a candidate RNAi screen of ABPs and actomyosin regulators we demonstrate that perturbing distinct molecular processes can lead to similar flow phenotypes. This is indicative for a ‘morphogenetic degeneracy’ where multiple molecular processes contribute to the same large-scale physical property. We speculate that morphogenetic degeneracies contribute to the robustness of bulk biological matter in development.

Lipidomics in Major Depressive Disorder

Omic sciences coupled with novel computational approaches such as machine intelligence offer completely new approaches to major depressive disorder (MDD) research. The complexity of MDD's pathophysiology is being integrated into studies examining MDD's biology within the omic fields. Lipidomics, as a late-comer among other omic fields, is increasingly being recognized in psychiatric research because it has allowed the investigation of global lipid perturbations in patients suffering from MDD and indicated a crucial role of specific patterns of lipid alterations in the development and progression of MDD. Combinatorial lipid-markers with high classification power are being developed in order to assist MDD diagnosis, while rodent models of depression reveal lipidome changes and thereby unveil novel treatment targets for depression. In this systematic review, we provide an overview of current breakthroughs and future trends in the field of lipidomics in MDD research and thereby paving the way for precision medicine in MDD.

Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy

Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent self-organizing maps of neuronal networks, swarm intelligence and Minimum Curvilinear Embedding, a cluster structure was found in the input data space comprising serum concentrations of d = 43 different lipid-markers of various classes. The structure coincided largely with the clinical diagnosis, indicating that the data provide a basis for the creation of a biomarker (classifier). This was subsequently assessed using supervised machine-learning, implemented as random forests and computed ABC analysis-based feature selection. Bayesian statistics-based biomarker creation was used to map the diagnostic classes of either MS patients (n = 102) or healthy subjects (n = 301). Eight lipid-markers passed the feature selection and comprised GluCerC16, LPA20:4, HETE15S, LacCerC24:1, C16Sphinganine, biopterin and the endocannabinoids PEA and OEA. A complex classifier or biomarker was developed that predicted MS at a sensitivity, specificity and accuracy of approximately 95% in training and test data sets, respectively. The present successful application of serum lipid marker concentrations to MS data is encouraging for further efforts to establish an MS biomarker based on serum lipidomics.

Reward-enhanced encoding improves relearning of forgotten associations

Research on human memory has shown that monetary incentives can enhance hippocampal memory consolidation and thereby protect memory traces from forgetting. However, it is not known whether initial reward may facilitate the recovery of already forgotten memories weeks after learning. Here, we investigated the influence of monetary reward on later relearning. Nineteen healthy human participants learned object-location associations, for half of which we offered money. Six weeks later, most of these associations had been forgotten as measured by a test of declarative memory. Yet, relearning in the absence of any reward was faster for the originally rewarded associations. Thus, associative memories encoded in a state of monetary reward motivation may persist in a latent form despite the failure to retrieve them explicitly. Alternatively, such facilitation could be analogous to the renewal effect observed in animal conditioning, whereby a reward-associated cue can reinstate anticipatory arousal, which would in turn modulate relearning. This finding has important implications for learning and education, suggesting that even when learned information is no longer accessible via explicit retrieval, the enduring effects of a past prospect of reward could facilitate its recovery.

A computational framework for complex disease stratification from multiple large-scale datasets

BACKGROUND

Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states.

METHODS

The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification.

RESULTS

We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes.

CONCLUSIONS

This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

Machine learning meets complex networks via coalescent embedding in the hyperbolic space

Physicists recently observed that realistic complex networks emerge as discrete samples from a continuous hyperbolic geometry enclosed in a circle: the radius represents the node centrality and the angular displacement between two nodes resembles their topological proximity. The hyperbolic circle aims to become a universal space of representation and analysis of many real networks. Yet, inferring the angular coordinates to map a real network back to its latent geometry remains a challenging inverse problem. Here, we show that intelligent machines for unsupervised recognition and visualization of similarities in big data can also infer the network angular coordinates of the hyperbolic model according to a geometrical organization that we term "angular coalescence." Based on this phenomenon, we propose a class of algorithms that offers fast and accurate "coalescent embedding" in the hyperbolic circle even for large networks. This computational solution to an inverse problem in physics of complex systems favors the application of network latent geometry techniques in disciplines dealing with big network data analysis including biology, medicine, and social science.

Evolutionary analysis and interaction prediction for protein-protein interaction network in geometric space

Prediction of protein-protein interaction (PPI) remains a central task in systems biology. With more PPIs identified, forming PPI networks, it has become feasible and also imperative to study PPIs at the network level, such as evolutionary analysis of the networks, for better understanding of PPI networks and for more accurate prediction of pairwise PPIs by leveraging the information gained at the network level. In this work we developed a novel method that enables us to incorporate evolutionary information into geometric space to improve PPI prediction, which in turn can be used to select and evaluate various evolutionary models. The method is tested with cross-validation using human PPI network and yeast PPI network data. The results show that the accuracy of PPI prediction measured by ROC score is increased by up to 14.6%, as compared to a baseline without using evolutionary information. The results also indicate that our modified evolutionary model DANEOsf—combining a gene duplication/neofunctionalization model and scale-free model—has a better fitness and prediction efficacy for these two PPI networks. The improved PPI prediction performance may suggest that our DANEOsf evolutionary model can uncover the underlying evolutionary mechanism for these two PPI networks better than other tested models. Consequently, of particular importance is that our method offers an effective way to select evolutionary models that best capture the underlying evolutionary mechanisms, evaluating the fitness of evolutionary models from the perspective of PPI prediction on real PPI networks.

Reovirus infection triggers inflammatory responses to dietary antigens and development of celiac disease

Viral infections have been proposed to elicit pathological processes leading to the initiation of T helper 1 (TH1) immunity against dietary gluten and celiac disease (CeD). To test this hypothesis and gain insights into mechanisms underlying virus-induced loss of tolerance to dietary antigens, we developed a viral infection model that makes use of two reovirus strains that infect the intestine but differ in their immunopathological outcomes. Reovirus is an avirulent pathogen that elicits protective immunity, but we discovered that it can nonetheless disrupt intestinal immune homeostasis at inductive and effector sites of oral tolerance by suppressing peripheral regulatory T cell (pTreg) conversion and promoting TH1 immunity to dietary antigen. Initiation of TH1 immunity to dietary antigen was dependent on interferon regulatory factor 1 and dissociated from suppression of pTreg conversion, which was mediated by type-1 interferon. Last, our study in humans supports a role for infection with reovirus, a seemingly innocuous virus, in triggering the development of CeD.

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

Clear differences in cerebrospinal fluid proteome between women with chronic widespread pain and healthy women - a multivariate explorative cross-sectional study

Introduction

Frequent chronic local pain can develop into chronic widespread pain (CWP). The spread of pain is correlated with pain intensity, anxiety, and depression, conditions that ultimately lead to a poor quality of life. Knowledge is incomplete about CWP’s etiology, although it has been suggested that both central hyperexcitability and/or a combination with peripheral factors may be involved. Cerebrospinal fluid (CSF) could act as a mirror for the central nervous system as proteins are signal substances that activate the formation of algesics and control nociceptive processes. To this end, this study investigates the CSF protein expression in women with CWP and in female healthy controls.

Materials and methods

This study included 12 female patients with CWP diagnosed according to the American College of Rheumatology criteria with 13 healthy age- and sex-matched pain-free subjects. All subjects went through a clinical examination and answered a health questionnaire that registered sociodemographic and anthropometric data, pain characteristics, psychological status, and quality of life rating. CSF was collected by lumbar puncture from each subject. Two-dimensional gel electrophoresis in combination with mass spectrometry was used to analyze the CSF proteome. This study identifies proteins that significantly discriminate between the two groups using multivariate data analysis (MVDA) (i.e., orthogonal partial least squares discriminant analysis [OPLS-DA]).

Results

There were no clinically significant levels of psychological distress and catastrophization presented in subjects with CWP. MVDA revealed a highly significant OPLS-DA model where 48 proteins from CSF explained 91% (R²) of the variation and with a prediction of 90% (Q²). The highest discriminating proteins were metabolic, transport, stress, and inflammatory.

Conclusion

The highest discriminating proteins (11 proteins), according to the literature, are involved in apoptotic regulations, anti-inflammatory and anti-oxidative processes, the immune system, and endogenous repair. The results of this explorative study may indicate the presence of neuro-inflammation in the central nervous system of CWP patients. Future studies should be larger and control for confounders and determine which alterations are unspecific/general and which are specific changes.

Improved prediction of missing protein interactome links via anomaly detection

Interactomes such as Protein interaction networks have many undiscovered links between entities. Experimental verification of every link in these networks is prohibitively expensive, and therefore computational methods to direct the search for possible links are of great value. The problem of finding undiscovered links in a network is also referred to as the link prediction problem. A popular approach for link prediction has been to formulate it as a binary classification problem in which class labels indicate the existence or absence of a link (we refer to these as positive links or negative links respectively) between a pair of nodes in the network. Researchers have successfully applied such supervised classification techniques to determine the presence of links in protein interaction networks. However, it is quite common for protein-protein interaction (PPI) networks to have a large proportion of undiscovered links. Thus, a link prediction approach could incorrectly treat undiscovered positive links as negative links, thereby introducing a bias in the learning. In this paper, we propose to denoise the class of negative links in the training data via a Gaussian process anomaly detector. We show that this significantly reduces the noise due to mislabelled negative links and improves the resulting link prediction accuracy. We evaluate the approach by introducing synthetic noise into the PPI networks and measuring how accurately we can reconstruct the original PPI networks using classifiers trained on both noisy and denoised data. Experiments were performed with five different PPI network datasets and the results indicate a significant reduction in bias due to label noise, and more importantly, a significant improvement in the accuracy of detecting missing links via classification.

Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?

The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named epitopological learning.

Gender, Contraceptives and Individual Metabolic Predisposition Shape a Healthy Plasma Lipidome

Lipidomics of human blood plasma is an emerging biomarker discovery approach that compares lipid profiles under pathological and physiologically normal conditions, but how a healthy lipidome varies within the population is poorly understood. By quantifying 281 molecular species from 27 major lipid classes in the plasma of 71 healthy young Caucasians whose 35 clinical blood test and anthropometric indices matched the medical norm, we provided a comprehensive, expandable and clinically relevant resource of reference molar concentrations of individual lipids. We established that gender is a major lipidomic factor, whose impact is strongly enhanced by hormonal contraceptives and mediated by sex hormone-binding globulin. In lipidomics epidemiological studies should avoid mixed-gender cohorts and females taking hormonal contraceptives should be considered as a separate sub-cohort. Within a gender-restricted cohort lipidomics revealed a compositional signature that indicates the predisposition towards an early development of metabolic syndrome in ca. 25% of healthy male individuals suggesting a healthy plasma lipidome as resource for early biomarker discovery.

Texture analysis in gel electrophoresis images using an integrative kernel-based approach

Texture information could be used in proteomics to improve the quality of the image analysis of proteins separated on a gel. In order to evaluate the best technique to identify relevant textures, we use several different kernel-based machine learning techniques to classify proteins in 2-DE images into spot and noise. We evaluate the classification accuracy of each of these techniques with proteins extracted from ten 2-DE images of different types of tissues and different experimental conditions. We found that the best classification model was FSMKL, a data integration method using multiple kernel learning, which achieved AUROC values above 95% while using a reduced number of features. This technique allows us to increment the interpretability of the complex combinations of textures and to weight the importance of each particular feature in the final model. In particular the Inverse Difference Moment exhibited the highest discriminating power. A higher value can be associated with an homogeneous structure as this feature describes the homogeneity; the larger the value, the more symmetric. The final model is performed by the combination of different groups of textural features. Here we demonstrated the feasibility of combining different groups of textures in 2-DE image analysis for spot detection.

Co-transcriptomic Analysis by RNA Sequencing to Simultaneously Measure Regulated Gene Expression in Host and Bacterial Pathogen

Intramacrophage pathogens subvert antimicrobial defence pathways using various mechanisms, including the targeting of host TLR-mediated transcriptional responses. Conversely, TLR-inducible host defence mechanisms subject intramacrophage pathogens to stress, thus altering pathogen gene expression programs. Important biological insights can thus be gained through the analysis of gene expression changes in both the host and the pathogen during an infection. Traditionally, research methods have involved the use of qPCR, microarrays and/or RNA sequencing to identify transcriptional changes in either the host or the pathogen. Here we describe the application of RNA sequencing using samples obtained from in vitro infection assays to simultaneously quantify both host and bacterial pathogen gene expression changes, as well as general approaches that can be undertaken to interpret the RNA sequencing data that is generated. These methods can be used to provide insights into host TLR-regulated transcriptional responses to microbial challenge, as well as pathogen subversion mechanisms against such responses.

Image Pretreatment Tools I: Algorithms for Map Denoising and Background Subtraction Methods

One of the critical steps in two-dimensional electrophoresis (2-DE) image pre-processing is the denoising, that might aggressively affect either spot detection or pixel-based methods. The Median Modified Wiener Filter (MMWF), a new nonlinear adaptive spatial filter, resulted to be a good denoising approach to use in practice with 2-DE. MMWF is suitable for global denoising, and contemporary for the removal of spikes and Gaussian noise, being its best setting invariant on the type of noise. The second critical step rises because of the fact that 2-DE gel images may contain high levels of background, generated by the laboratory experimental procedures, that must be subtracted for accurate measurements of the proteomic optical density signals. Here we discuss an efficient mathematical method for background estimation, that is suitable to work even before the 2-DE image spot detection, and it is based on the 3D mathematical morphology (3DMM) theory.

Nonlinear Dimensionality Reduction by Minimum Curvilinearity for Unsupervised Discovery of Patterns in Multidimensional Proteomic Data

Dimensionality reduction is largely and successfully employed for the visualization and discrimination of patterns, hidden in multidimensional proteomics datasets. Principal component analysis (PCA), which is the preferred approach for linear dimensionality reduction, may present serious limitations, in particular when samples are nonlinearly related, as often occurs in several two-dimensional electrophoresis (2-DE) datasets. An aggravating factor is that PCA robustness is impaired when the number of samples is small in comparison to the number of proteomic features, and this is the case in high-dimensional proteomic datasets, including 2-DE ones. Here, we describe the use of a nonlinear unsupervised learning machine for dimensionality reduction called minimum curvilinear embedding (MCE) that was successfully applied to different biological samples datasets. In particular, we provide an example where we directly compare MCE performance with that of PCA in disclosing neuropathic pain patterns, hidden in a multidimensional proteomic dataset.

Multivariate proteomic analysis of the cerebrospinal fluid of patients with peripheral neuropathic pain and healthy controls - a hypothesis-generating pilot study

Pain medicine lacks objective biomarkers to guide diagnosis and treatment. Combining two-dimensional gel proteomics with multivariate data analysis by projection, we exploratively analyzed the cerebrospinal fluid of eleven patients with severe peripheral neuropathic pain due to trauma and/or surgery refractory to conventional treatment and eleven healthy controls. Using orthogonal partial least squares discriminant analysis, we identified a panel of 36 proteins highly discriminating between the two groups. Due to a possible confounding effect of age, a new model with age as outcome variable was computed for patients (n=11), and four out of 36 protein spots were excluded due to a probable influence of age. Of the 32 remaining proteins, the following seven had the highest discriminatory power between the two groups: an isoform of angiotensinogen (upregulated in patients), two isoforms of alpha-1-antitrypsin (downregulated in patients), three isoforms of haptoglobin (upregulated in patients), and one isoform of pigment epithelium-derived factor (downregulated in patients). It has recently been hypothesized that the renin–angiotensin system may play a role in the pathophysiology of neuropathic pain, and a clinical trial of an angiotensin II receptor antagonist was recently published. It is noteworthy that when searching for neuropathic pain biomarkers with a purely explorative methodology, it was indeed a renin–angiotensin system protein that had the highest discriminatory power between patients and controls in the present study. The results from this hypothesis-generating pilot study have to be confirmed in larger, hypothesis-driven studies with age-matched controls, but the present study illustrates the fruitfulness of combining proteomics with multivariate data analysis in hypothesis-generating pain biomarker studies in humans.

Comparative Proteomics for the Evaluation of Protein Expression and Modifications in Neurodegenerative Diseases

Together with hypothesis-driven approaches, high-throughput differential proteomic analysis performed primarily not only in human cerebrospinal fluid and serum but also on protein content of other tissues (blood cells, muscles, peripheral nerves, etc.) has been used in the last years to investigate neurodegenerative diseases. Even if the goal for these analyses was mainly the discovery of neurodegenerative disorders biomarkers, the characterization of specific posttranslational modifications (PTMs) and the differential protein expression resulted in being very informative to better define the pathological mechanisms. In this chapter are presented and discussed the positive aspects and challenges of the outcomes of some of our investigations on neurological and neurodegenerative disease, in order to highlight the important role of protein PTMs studies in proteomics-based approaches.

Highlighting nonlinear patterns in population genetics datasets

Detecting structure in population genetics and case-control studies is important, as it exposes phenomena such as ecoclines, admixture and stratification. Principal Component Analysis (PCA) is a linear dimension-reduction technique commonly used for this purpose, but it struggles to reveal complex, nonlinear data patterns. In this paper we introduce non-centred Minimum Curvilinear Embedding (ncMCE), a nonlinear method to overcome this problem. Our analyses show that ncMCE can separate individuals into ethnic groups in cases in which PCA fails to reveal any clear structure. This increased discrimination power arises from ncMCE's ability to better capture the phylogenetic signal in the samples, whereas PCA better reflects their geographic relation. We also demonstrate how ncMCE can discover interesting patterns, even when the data has been poorly pre-processed. The juxtaposition of PCA and ncMCE visualisations provides a new standard of analysis with utility for discovering and validating significant linear/nonlinear complementary patterns in genetic data.

The co-transcriptome of uropathogenic Escherichia coli-infected mouse macrophages reveals new insights into host-pathogen interactions

Urinary tract infections (UTI) are among the most common infections in humans. Uropathogenic Escherichia coli (UPEC) can invade and replicate within bladder epithelial cells, and some UPEC strains can also survive within macrophages. To understand the UPEC transcriptional programme associated with intramacrophage survival, we performed host–pathogen co‐transcriptome analyses using RNA sequencing. Mouse bone marrow‐derived macrophages (BMMs) were challenged over a 24 h time course with two UPEC reference strains that possess contrasting intramacrophage phenotypes: UTI89, which survives in BMMs, and 83972, which is killed by BMMs. Neither of these strains caused significant BMM cell death at the low multiplicity of infection that was used in this study. We developed an effective computational framework that simultaneously separated, annotated and quantified the mammalian and bacterial transcriptomes. Bone marrow‐derived macrophages responded to the two UPEC strains with a broadly similar gene expression programme. In contrast, the transcriptional responses of the UPEC strains diverged markedly from each other. We identified UTI89 genes up‐regulated at 24 h post‐infection, and hypothesized that some may contribute to intramacrophage survival. Indeed, we showed that deletion of one such gene (pspA) significantly reduced UTI89 survival within BMMs. Our study provides a technological framework for simultaneously capturing global changes at the transcriptional level in co‐cultures, and has generated new insights into the mechanisms that UPEC use to persist within the intramacrophage environment.

GeoChip-based insights into the microbial functional gene repertoire of marine sponges (high microbial abundance, low microbial abundance) and seawater

The GeoChip 4.2 gene array was employed to interrogate the microbial functional gene repertoire of sponges and seawater collected from the Red Sea and the Mediterranean. Complementary amplicon sequencing confirmed the microbial community composition characteristic of high microbial abundance (HMA) and low microbial abundance (LMA) sponges. By use of GeoChip, altogether 20,273 probes encoding for 627 functional genes and representing 16 gene categories were identified. Minimum curvilinear embedding analyses revealed a clear separation between the samples. The HMA/LMA dichotomy was stronger than any possible geographic pattern, which is shown here for the first time on the level of functional genes. However, upon inspection of individual genes, very few specific differences were discernible. Differences were related to microbial ammonia oxidation, ammonification, and archaeal autotrophic carbon fixation (higher gene abundance in sponges over seawater) as well as denitrification and radiation-stress-related genes (lower gene abundance in sponges over seawater). Except for few documented specific differences the functional gene repertoire between the different sources appeared largely similar. This study expands previous reports in that functional gene convergence is not only reported between HMA and LMA sponges but also between sponges and seawater.

Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

MOTIVATION

Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.

METHODS

Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.

RESULTS

We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.

CONCLUSION

Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules.

AVAILABILITY

https://sites.google.com/site/carlovittoriocannistraci/home.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Exploitation of genetic interaction network topology for the prediction of epistatic behavior

Genetic interaction (GI) detection impacts the understanding of human disease and the ability to design personalized treatment. The mapping of every GI in most organisms is far from complete due to the combinatorial amount of gene deletions and knockdowns required. Computational techniques to predict new interactions based only on network topology have been developed in network science but never applied to GI networks. We show that topological prediction of GIs is possible with high precision and propose a graph dissimilarity index that is able to provide robust prediction in both dense and sparse networks. Computational prediction of GIs is a strong tool to aid high-throughput GI determination. The dissimilarity index we propose in this article is able to attain precise predictions that reduce the universe of candidate GIs to test in the lab.

Questing for Circadian Dependence in ST-Segment–Elevation Acute Myocardial Infarction

Rationale:

Four monocentric studies reported that circadian rhythms can affect left ventricular infarct size after ST-segment–elevation acute myocardial infarction (STEMI).

Objective:

To further validate the circadian dependence of infarct size after STEMI in a multicentric and multiethnic population.

Methods and Results:

We analyzed a prospective cohort of subjects with first STEMI from the First Acute Myocardial Infarction study that enrolled 1099 patients (ischemic time <6 hours) in Italy, Scotland, and China. We confirmed a circadian variation of STEMI incidence with an increased morning incidence (from 6:00 am till noon). We investigated the presence of circadian dependence of infarct size plotting the peak creatine kinase against time onset of ischemia. In addition, we studied the patients from the 3 countries separately, including 624 Italians; all patients were treated with percutaneous coronary intervention. We adopted several levels of analysis with different inclusion criteria consistent with previous studies. In all the analyses, we did not find a clear-cut circadian dependence of infarct size after STEMI.

Conclusions:

Although the circadian dependence of infarct size supported by previous studies poses an intriguing hypothesis, we were unable to converge toward their conclusions in a multicentric and multiethnic setting. Parameters that vary as a function of latitude could potentially obscure the circadian variations observed in monocentric studies. We believe that, to assess whether circadian rhythms can affect the infarct size, future study design should not only include larger samples but also aim to untangle the molecular time–dynamic mechanisms underlying such a relation.

Pivotal role of the muscle-contraction pathway in cryptorchidism and evidence for genomic connections with cardiomyopathy pathways in RASopathies

BACKGROUND

Cryptorchidism is the most frequent congenital disorder in male children; however the genetic causes of cryptorchidism remain poorly investigated. Comparative integratomics combined with systems biology approach was employed to elucidate genetic factors and molecular pathways underlying testis descent.

METHODS

Literature mining was performed to collect genomic loci associated with cryptorchidism in seven mammalian species. Information regarding the collected candidate genes was stored in MySQL relational database. Genomic view of the loci was presented using Flash GViewer web tool (http://gmod.org/wiki/Flashgviewer/). DAVID Bioinformatics Resources 6.7 was used for pathway enrichment analysis. Cytoscape plug-in PiNGO 1.11 was employed for protein-network-based prediction of novel candidate genes. Relevant protein-protein interactions were confirmed and visualized using the STRING database (version 9.0).

RESULTS

The developed cryptorchidism gene atlas includes 217 candidate loci (genes, regions involved in chromosomal mutations, and copy number variations) identified at the genomic, transcriptomic, and proteomic level. Human orthologs of the collected candidate loci were presented using a genomic map viewer. The cryptorchidism gene atlas is freely available online: http://www.integratomics-time.com/cryptorchidism/. Pathway analysis suggested the presence of twelve enriched pathways associated with the list of 179 literature-derived candidate genes. Additionally, a list of 43 network-predicted novel candidate genes was significantly associated with four enriched pathways. Joint pathway analysis of the collected and predicted candidate genes revealed the pivotal importance of the muscle-contraction pathway in cryptorchidism and evidence for genomic associations with cardiomyopathy pathways in RASopathies.

CONCLUSIONS

The developed gene atlas represents an important resource for the scientific community researching genetics of cryptorchidism. The collected data will further facilitate development of novel genetic markers and could be of interest for functional studies in animals and human. The proposed network-based systems biology approach elucidates molecular mechanisms underlying co-presence of cryptorchidism and cardiomyopathy in RASopathies. Such approach could also aid in molecular explanation of co-presence of diverse and apparently unrelated clinical manifestations in other syndromes.

The evolution of ultraconserved elements with different phylogenetic origins

Background

Ultraconserved elements of DNA have been identified in vertebrate and invertebrate genomes. These elements have been found to have diverse functions, including enhancer activities in developmental processes. The evolutionary origins and functional roles of these elements in cellular systems, however, have not yet been determined.

Results

Here, we identified a wide range of ultraconserved elements common to distant species, from primitive aquatic organisms to terrestrial species with complicated body systems, including some novel elements conserved in fruit fly and human. In addition to a well-known association with developmental genes, these DNA elements have a strong association with genes implicated in essential cell functions, such as epigenetic regulation, apoptosis, detoxification, innate immunity, and sensory reception. Interestingly, we observed that ultraconserved elements clustered by sequence similarity. Furthermore, species composition and flanking genes of clusters showed lineage-specific patterns. Ultraconserved elements are highly enriched with binding sites to developmental transcription factors regardless of how they cluster.

Conclusion

We identified large numbers of ultraconserved elements across distant species. Specific classes of these conserved elements seem to have been generated before the divergence of taxa and fixed during the process of evolution. Our findings indicate that these ultraconserved elements are not the exclusive property of higher modern eukaryotes, but rather transmitted from their metazoan ancestors.

Identification and Predictive Value of Interleukin-6 + Interleukin-10 + and Interleukin-6 − Interleukin-10 + Cytokine Patterns in ST-Elevation Acute Myocardial Infarction

Rationale:

At the onset of ST-elevation acute myocardial infarction (STEMI), patients can present with very high circulating interleukin-6 (IL-6 + ) levels or very low-IL-6 – levels.

Objective:

We compared these 2 groups of patients to understand whether it is possible to define specific STEMI phenotypes associated with outcome based on the cytokine response.

Methods and Results:

We compared 109 patients with STEMI in the top IL-6 level (median, 15.6 pg/mL; IL-6 + STEMI) with 96 in the bottom IL-6 level (median, 1.7 pg/mL; IL-6 − STEMI) and 103 matched controls extracted from the multiethnic First Acute Myocardial Infarction study. We found minimal clinical differences between IL-6 + STEMI and IL-6 − STEMI. We assessed the inflammatory profiles of the 2 STEMI groups and the controls by measuring 18 cytokines in blood samples. We exploited clustering analysis algorithms to infer the functional modules of interacting cytokines. IL-6 + STEMI patients were characterized by the activation of 2 modules of interacting signals comprising IL-10, IL-8, macrophage inflammatory protein-1α, and C-reactive protein, and monocyte chemoattractant protein-1, macrophage inflammatory protein-1β, and monokine induced by interferon-γ. IL-10 was increased both in IL-6 + STEMI and IL-6 − STEMI patients compared with controls. IL-6 + IL-10 + STEMI patients had an increased risk of systolic dysfunction at discharge and an increased risk of death at 6 months in comparison with IL-6 − IL-10 + STEMI patients. We combined IL-10 and monokine induced by interferon-γ (derived from the 2 identified cytokine modules) with IL-6 in a formula yielding a risk index that outperformed any single cytokine in the prediction of systolic dysfunction and death.

Conclusions:

We have identified a characteristic circulating inflammatory cytokine pattern in STEMI patients, which is not related to the extent of myocardial damage. The simultaneous elevation of IL-6 and IL-10 levels distinguishes STEMI patients with worse clinical outcomes from other STEMI patients. These observations could have potential implications for risk-oriented patient stratification and immune-modulating therapies.

Supporting regenerative medicine by integrative dimensionality reduction

OBJECTIVE

The assessment of the developmental potential of stem cells is a crucial step towards their clinical application in regenerative medicine. It has been demonstrated that genome-wide expression profiles can predict the cellular differentiation stage by means of dimensionality reduction methods. Here we show that these techniques can be further strengthened to support decision making with i) a novel strategy for gene selection; ii) methods for combining the evidence from multiple data sets.

METHODS

We propose to exploit dimensionality reduction methods for the selection of genes specifically activated in different stages of differentiation. To obtain an integrated predictive model, the expression values of the selected genes from multiple data sets are combined. We investigated distinct approaches that either aggregate data sets or use learning ensembles.

RESULTS

We analyzed the performance of the proposed methods on six publicly available data sets. The selection procedure identified a reduced subset of genes whose expression values gave rise to an accurate stage prediction. The assessment of predictive accuracy demonstrated a high quality of predictions for most of the data integration methods presented.

CONCLUSION

The experimental results highlighted the main potentials of proposed approaches. These include the ability to predict the true staging by combining multiple training data sets when this could not be inferred from a single data source, and to focus the analysis on a reduced list of genes of similar predictive performance.

Acclimation increases freezing stress response of Arabidopsis thaliana at proteome level

This study used 2DE to investigate how Arabidopsis thaliana modulates protein levels in response to freezing stress after sub-lethal exposure at -10°C, both in cold-acclimated and in non-acclimated plants. A map was implemented in which 62 spots, corresponding to 44 proteins, were identified. Twenty-two spots were modulated upon treatments, and the corresponding proteins proved to be related to photosynthesis, energy metabolism, and stress response. Proteins demonstrated differences between control and acclimation conditions. Most of the acclimation-responsive proteins were either not further modulated or they were down-modulated by freezing treatment, indicating that the levels reached during acclimation were sufficient to deal with freezing. Anabolic metabolism appeared to be down-regulated in favor of catabolic metabolism. Acclimated plants and plants submitted to freezing after acclimation showed greater reciprocal similarity in protein profiles than either showed when compared both to control plants and to plants frozen without acclimation. The response of non-acclimated plants was aimed at re-modulating photosynthetic apparatus activity, and at increasing the levels of proteins with antioxidant-, molecular chaperone-, or post-transcriptional regulative functions. These changes, even less effective than the acclimation strategy, might allow the injured plastids to minimize the production of non-useful metabolites and might counteract photosynthetic apparatus injuries.

Ceruloplasmin oxidation, a feature of Parkinson's disease CSF, inhibits ferroxidase activity and promotes cellular iron retention

Parkinson's disease is a neurodegenerative disorder characterized by oxidative stress and CNS iron deposition. Ceruloplasmin is an extracellular ferroxidase that regulates cellular iron loading and export, and hence protects tissues from oxidative damage. Using two-dimensional electrophoresis, we investigated ceruloplasmin patterns in the CSF of human Parkinson's disease patients. Parkinson's disease ceruloplasmin profiles proved more acidic than those found in healthy controls and in other human neurological diseases (peripheral neuropathies, amyotrophic lateral sclerosis, and Alzheimer's disease); degrees of acidity correlated with patients' pathological grading. Applying an unsupervised pattern recognition procedure to the two-dimensional electrophoresis images, we identified representative pathological clusters. In vitro oxidation of CSF in two-dimensional electrophoresis generated a ceruloplasmin shift resembling that observed in Parkinson's disease and co-occurred with an increase in protein carbonylation. Likewise, increased protein carbonylation was observed in Parkinson's disease CSF, and the same modification was directly identified in these samples on ceruloplasmin. These results indicate that ceruloplasmin oxidation contributes to pattern modification in Parkinson's disease. From the functional point of view, ceruloplasmin oxidation caused a decrease in ferroxidase activity, which in turn promotes intracellular iron retention in neuronal cell lines as well as in primary neurons, which are more sensitive to iron accumulation. Accordingly, the presence of oxidized ceruloplasmin in Parkinson's disease CSF might be used as a marker for oxidative damage and might provide new insights into the underlying pathological mechanisms.