Efficient mining differential co-expression biclusters in microarray datasets

Background

Biclustering algorithm can find a number of co-expressed genes under a set of experimental conditions. Recently, differential co-expression bicluster mining has been used to infer the reasonable patterns in two microarray datasets, such as, normal and cancer cells.

Methods

In this paper, we propose an algorithm, DECluster, to mine Differential co-Expression biCluster in two discretized microarray datasets. Firstly, DECluster produces the differential co-expressed genes from each pair of samples in two microarray datasets, and constructs a differential weighted undirected sample–sample relational graph. Secondly, the differential biclusters are generated in the above differential weighted undirected sample–sample relational graph. In order to mine maximal differential co-expression biclusters efficiently, we design several pruning techniques for generating maximal biclusters without candidate maintenance.

Results

The experimental results show that our algorithm is more efficient than existing methods. The performance of DECluster is evaluated by empirical p-value and gene ontology, the results show that our algorithm can find more statistically significant and biological differential co-expression biclusters than other algorithms.

Conclusions

Our proposed algorithm can find more statistically significant and biological biclusters in two microarray datasets than the other two algorithms.     

The Effect of Souvenaid on Functional Brain Network Organisation in Patients with Mild Alzheimer’s Disease: A Randomised Controlled Study

Background

Synaptic loss is a major hallmark of Alzheimer’s disease (AD). Disturbed organisation of large-scale functional brain networks in AD might reflect synaptic loss and disrupted neuronal communication. The medical food Souvenaid, containing the specific nutrient combination Fortasyn Connect, is designed to enhance synapse formation and function and has been shown to improve memory performance in patients with mild AD in two randomised controlled trials.

Objective

To explore the effect of Souvenaid compared to control product on brain activity-based networks, as a derivative of underlying synaptic function, in patients with mild AD.

Design

A 24-week randomised, controlled, double-blind, parallel-group, multi-country study.

Participants

179 drug-naïve mild AD patients who participated in the Souvenir II study.

Intervention

Patients were randomised 1∶1 to receive Souvenaid or an iso-caloric control product once daily for 24 weeks.

Outcome

In a secondary analysis of the Souvenir II study, electroencephalography (EEG) brain networks were constructed and graph theory was used to quantify complex brain structure. Local brain network connectivity (normalised clustering coefficient gamma) and global network integration (normalised characteristic path length lambda) were compared between study groups, and related to memory performance.

Results

The network measures in the beta band were significantly different between groups: they decreased in the control group, but remained relatively unchanged in the active group. No consistent relationship was found between these network measures and memory performance.

Conclusions

The current results suggest that Souvenaid preserves the organisation of brain networks in patients with mild AD within 24 weeks, hypothetically counteracting the progressive network disruption over time in AD. The results strengthen the hypothesis that Souvenaid affects synaptic integrity and function. Secondly, we conclude that advanced EEG analysis, using the mathematical framework of graph theory, is useful and feasible for assessing the effects of interventions.

Trial registration

Dutch Trial Register NTR1975.     

Integrating domain similarity to improve protein complexes identification in TAP-MS data

Background

Detecting protein complexes in protein-protein interaction (PPI) networks plays an important role in improving our understanding of the dynamic of cellular organisation. However, protein interaction data generated by high-throughput experiments such as yeast-two-hybrid (Y2H) and tandem affinity-purification/mass-spectrometry (TAP-MS) are characterised by the presence of a significant number of false positives and false negatives. In recent years there has been a growing trend to incorporate diverse domain knowledge to support large-scale analysis of PPI networks.

Methods

This paper presents a new algorithm, by incorporating Gene Ontology (GO) based semantic similarities, to detect protein complexes from PPI networks generated by TAP-MS. By taking co-complex relations in TAP-MS data into account, TAP-MS PPI networks are modelled as bipartite graph, where bait proteins consist of one set of nodes and prey proteins are on the other. Similarities between pairs of bait proteins are computed by considering both the topological features and GO-driven semantic similarities. Bait proteins are then grouped in to sets of clusters based on their pair-wise similarities to produce a set of 'seed' clusters. An expansion process is applied to each 'seed' cluster to recruit prey proteins which are significantly associated with the same set of bait proteins. Thus, completely identified protein complexes are then obtained.

Results

The proposed algorithm has been applied to real TAP-MS PPI networks. Fifteen quality measures have been employed to evaluate the quality of generated protein complexes. Experimental results show that the proposed algorithm has greatly improved the accuracy of identifying complexes and outperformed several state-of-the-art clustering algorithms. Moreover, by incorporating semantic similarity, the proposed algorithm is more robust to noises in the networks.

     

Mucins: A biologically relevant glycan barrier in mucosal protection

Background

The mucins found as components of mucus gel layers at mucosal surfaces throughout the body play roles in protection as part of the defensive barrier on an organ and tissue specific basis.

Scope of the review

The human MUC gene family codes up to 20 known proteins, which can be divided into secreted and membrane-associated forms each with a typical protein domain structure. The secreted mucins are adapted to cross link in order to allow formation of the extended mucin networks found in the secreted mucus gels. The membrane-associated mucins possess membrane specific domains which enable their various biological functions as part of the glycocalyx. All mucins are highly O-glycosylated and this is tissue specific and linked with specific biological functions at these locations. Mucin biology is dynamic and the processes of degradation and turnover are well integrated with biosynthesis to maintain a continuous mucosal protection against all external aggressive forces. Interaction of mucins with microflora plays an important role in normal function. Mucins are modified in a variety of diseases and this may be due to abberant mucin peptide or glycosylation.

Major conclusions

Mucins represent a family of glycoprotein having fundamental roles in mucosal protection and communication with external environment.

General significance

The review emphasises the nature of mucins as glycoproteins and their role in presenting an array of glycan structures at the mucosal cell surface.     

From knockouts to networks: establishing direct cause-effect relationships through graph analysis

Background

Reverse-engineering gene networks from expression profiles is a difficult problem for which a multitude of techniques have been developed over the last decade. The yearly organized DREAM challenges allow for a fair evaluation and unbiased comparison of these methods.

Results

We propose an inference algorithm that combines confidence matrices, computed as the standard scores from single-gene knockout data, with the down-ranking of feed-forward edges. Substantial improvements on the predictions can be obtained after the execution of this second step.

Conclusions

Our algorithm was awarded the best overall performance at the DREAM4 In Silico 100-gene network sub-challenge, proving to be effective in inferring medium-size gene regulatory networks. This success demonstrates once again the decisive importance of gene expression data obtained after systematic gene perturbations and highlights the usefulness of graph analysis to increase the reliability of inference.     

The effects of ferulic acid on β-amyloid fibrillar structures investigated through experimental and computational techniques

Background

Current research has indicated that small natural compounds could interfere with β-amyloid fibril growth and have the ability to disassemble preformed folded structures. Ferulic acid (FA), which possesses both hydrophilic and hydrophobic moieties and binds to peptides/proteins, is a potential candidate against amyloidogenesis. The molecular mechanisms connected to this action have not been elucidated in detail yet.

Methods

Here the effects of FA on preformed fibrils are investigated by means of a concerted experimental–computational approach. Spectroscopic techniques, such as FTIR, fluorescence, size exclusion chromatography and confocal microscopy in combination with molecular dynamics simulations are used to identify those features which play a key role in the destabilization of the aggregates.

Results

Experimental findings highlight that FA has disruptive effects on the fibrils. The computational analysis suggests that dissociation of peptides from the amyloid superstructures could take place along the fibril axis and be primarily determined by the cooperative rupture of the backbone hydrogen bonds and of the Asp-Lys salt bridges.

Conclusion

FA clusters could induce a sort of stabilization and tightening of the fibril structure in the short term and its disruption in the long term, inhibiting further fibril re-assembly through FA screening effects.

General significance

The combination of experimental and computational techniques could be successfully used to identify the disrupting action of FA on preformed Aβ fibrils in water solution.     

Scalable Steady State Analysis of Boolean Biological Regulatory Networks

Background

Computing the long term behavior of regulatory and signaling networks is critical in understanding how biological functions take place in organisms. Steady states of these networks determine the activity levels of individual entities in the long run. Identifying all the steady states of these networks is difficult due to the state space explosion problem.

Methodology

In this paper, we propose a method for identifying all the steady states of Boolean regulatory and signaling networks accurately and efficiently. We build a mathematical model that allows pruning a large portion of the state space quickly without causing any false dismissals. For the remaining state space, which is typically very small compared to the whole state space, we develop a randomized traversal method that extracts the steady states. We estimate the number of steady states, and the expected behavior of individual genes and gene pairs in steady states in an online fashion. Also, we formulate a stopping criterion that terminates the traversal as soon as user supplied percentage of the results are returned with high confidence.

Conclusions

This method identifies the observed steady states of boolean biological networks computationally. Our algorithm successfully reported the G1 phases of both budding and fission yeast cell cycles. Besides, the experiments suggest that this method is useful in identifying co-expressed genes as well. By analyzing the steady state profile of Hedgehog network, we were able to find the highly co-expressed gene pair GL1-SMO together with other such pairs.

Availability

Source code of this work is available at http://bioinformatics.cise.ufl.edu/palSteady.html twocolumnfalse]     

Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning

Background

The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence.

Results

In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes.

Conclusions

The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.     

The Global Sequence Signature algorithm unveils a structural network surrounding heavy chain CDR3 loop in Camelidae variable domains

Background

A large fraction of camelid (camels and llamas) antibodies is composed of heavy chain-only homodimers, able to recognise antigens with their variable domain. Events in somatic assembly and maturation of antibodies such as hypermutations and rearrangement of variable loops (CDRs — complementary determining regions) and selection among a wide range of framework variants are generally considered to be random processes.

Methods

An original algorithmic approach (Global Sequence Signature—GSS) was developed, able to take into account multiple functional and/or local sequence properties to detect scattered evolutionary constraints into sequences.

Results

Using the GSS approach, we show that the length of the main hypervariable loop (CDR3) is linked to the nature of 19 surrounding residues on the scaffold. Surprisingly, the relation between CDR3 size and scaffold residues strongly depends on the considered species, illustrating either significant differences in selection mechanisms or functional constraints during antibody maturation.

Conclusions

Combined with the statistical coupling analysis (SCA) approach at the level of scaffold residues, this study has unravelled a robust interaction network on antibody structure surrounding the CDR3 loop.

General significance

In addition to the general applicability of the GSS algorithm, which can bring together functional and sequence data to locate hot spots of constrained evolution, the relationship between CDR3 and scaffold discussed here should be taken into account in protein engineering when designing antibody libraries.     

Cells as semantic systems

Background

We consider cells as biological systems that process information by means of molecular codes. Many studies analyze cellular information processing exclusively in syntactic terms (e.g., by measuring Shannon entropy of sets of macromolecules), and abstract completely from semantic aspects that are related to the meaning of molecular information.

Methods

This mini-review focusses on semantic aspects of molecular information, particularly on codes that organize the semantic dimension of molecular information. First, a general conceptual framework for describing molecular information is proposed. Second, some examples of molecular codes are presented. Third, a mathematical approach that makes the identification of molecular codes in reaction networks possible, is developed.

Results

By combining a systematic conceptual framework for describing molecular information and a mathematical approach to identify molecular codes, it is possible to give a formally consistent and empirically adequate model of the code-based semantics of molecular information in cells.

General significance

Research on the semantics of molecular information is of great importance particularly to systems biology since molecular codes embedded in systems of interrelated codes govern main traits of cells. Describing cells as semantic systems may thus trigger new experiments and generate new insights into the fundamental processes of cellular information processing. This article is part of a Special Issue entitled Systems Biology of Microorganisms.     

The biochemistry of asthma

Background

Asthma is not one disease. Different patients have biochemically distinct phenotypes.

Scope of review

Biomarker analysis was developed to identify inflammation in the asthmatic airway. It has led to a renewed interest in biochemical abnormalities in the asthmatic airway. The biochemical determinants of asthma heterogeneity are many. Examples include decreased activity of superoxide dismutases; increased activity of eosinophil peroxidase, S-nitrosoglutathione reductase, and arginases; decreased airway pH; and increased levels of asymmetric dimethyl arginine.

Major conclusions

New discoveries suggest that biomarkers such as exhaled nitric oxide reflect complex airway biochemistry. This biochemistry can be informative and therapeutically relevant.

General significance

Improved understanding of airway biochemistry will lead to new tests to identify biochemically unique subpopulations of patients with asthma. It will also likely lead to new, targeted treatments for these specific asthma subpopulations. This article is part of a Special Issue entitled Biochemistry of Asthma.     

In silico and in vitro characterization of anti-amyloidogenic activity of vitamin K3 analogues for Alzheimer's disease

Background

Aggregation of amyloid-beta (Aβ) has been proposed as the main cause of Alzheimer's disease (AD). Vitamin K deficiency has been linked to the pathogenesis of AD. Therefore, 15 synthesized vitamin K3 (VK3) analogues were studied for their anti-amyloidogenic activity.

Methods

Biological and spectroscopic assays were used to characterize the effect of VK3 analogues on amyloidogenic properties of Aβ, such as aggregation, free radical formation, and cell viability. Molecular dynamics simulation was used to calculate the binding affinity and mode of VK3 analogue binding to Aβ.

Results

Both numerical and experimental results showed that several VK3 analogues, including VK3-6, VK3-8, VK3-9, VK3-10, and VK3-224 could effectively inhibit Aβ aggregation and conformational conversion. The calculated inhibition constants were in the μM range for VK3-10, VK3-6, and VK3-9 which was similar to the IC₅₀ of curcumin. Cell viability assays indicated that VK3-9 could effectively reduce free radicals and had a protective effect on cytotoxicity induced by Aβ.

Conclusions

The results clearly demonstrated that VK3 analogues could effectively inhibit Aβ aggregation and protect cells against Aβ induced toxicity. Modified VK3 analogues can possibly be developed as effective anti-amyloidogenic drugs for the treatment of AD.

General significance

VK3 analogues effectively inhibit Aβ aggregation and are highly potent as anti-amyloidogenic drugs for therapeutic treatment of AD.     

Detection and identification of cancerous murine fibroblasts,transformed by murine sarcoma virus in culture,using Raman spectroscopy and advanced statistical methods

Background

Cancer is one of the leading worldwide causes of death. It may be induced by a variety of factors, including carcinogens, radiation, genetic factors, or DNA and RNA viruses. The early detection of cancer is critical for its successful therapy, which can result in complete recovery from some types of cancer.

Methods

Raman spectroscopy has been widely used in medicine and biology. It is a noninvasive, nondestructive, and water-insensitive technique that can detect changes in cells and tissues that are caused by different disorders, such as cancer.In this study, Raman spectroscopy was used for the identification and characterization of murine fibroblast cell lines (NIH/3T3) and malignant fibroblast cells transformed by murine sarcoma virus (NIH-MuSV) cells.

Results

Using principal component analysis and LDA it was possible to differentiate between the NIH/3T3 and NIH-MuSV cells with an 80–85% success rate based on their Raman shift spectra.

Conclusions

The best results for differentiation were achieved from spectra that were obtained from the rich membrane sites.

General significance

Because of its homogeneity and complete control of most factors affecting its growth, cell culture is a preferred model for the detection and identification of specific biomarkers related to cancer transformation or other cellular modifications.     

NetCooperate: a network-based tool for inferring host-microbe and microbe-microbe cooperation

Background

Host-microbe and microbe-microbe interactions are often governed by the complex exchange of metabolites. Such interactions play a key role in determining the way pathogenic and commensal species impact their host and in the assembly of complex microbial communities. Recently, several studies have demonstrated how such interactions are reflected in the organization of the metabolic networks of the interacting species, and introduced various graph theory-based methods to predict host-microbe and microbe-microbe interactions directly from network topology. Using these methods, such studies have revealed evolutionary and ecological processes that shape species interactions and community assembly, highlighting the potential of this reverse-ecology research paradigm.

Results

NetCooperate is a web-based tool and a software package for determining host-microbe and microbe-microbe cooperative potential. It specifically calculates two previously developed and validated metrics for species interaction: the Biosynthetic Support Score which quantifies the ability of a host species to supply the nutritional requirements of a parasitic or a commensal species, and the Metabolic Complementarity Index which quantifies the complementarity of a pair of microbial organisms’ niches. NetCooperate takes as input a pair of metabolic networks, and returns the pairwise metrics as well as a list of potential syntrophic metabolic compounds.

Conclusions

The Biosynthetic Support Score and Metabolic Complementarity Index provide insight into host-microbe and microbe-microbe metabolic interactions. NetCooperate determines these interaction indices from metabolic network topology, and can be used for small- or large-scale analyses. NetCooperate is provided as both a web-based tool and an open-source Python module; both are freely available online at http://elbo.gs.washington.edu/software_netcooperate.html.     

Secreted glyceraldehye-3-phosphate dehydrogenase is a multifunctional autocrine transferrin receptor for cellular iron acquisition

Background

The long held view is that mammalian cells obtain transferrin (Tf) bound iron utilizing specialized membrane anchored receptors. Here we report that, during increased iron demand, cells secrete the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH) which enhances cellular uptake of Tf and iron.

Methods

These observations could be mimicked by utilizing purified GAPDH injected into mice as well as when supplemented in culture medium of model cell lines and primary cell types that play a key role in iron metabolism. Transferrin and iron delivery was evaluated by biochemical, biophysical and imaging based assays.

Results

This mode of iron uptake is a saturable, energy dependent pathway, utilizing raft as well as non-raft domains of the cell membrane and also involves the membrane protein CD87 (uPAR). Tf internalized by this mode is also catabolized.

Conclusions

Our research demonstrates that, even in cell types that express the known surface receptor based mechanism for transferrin uptake, more transferrin is delivered by this route which represents a hidden dimension of iron homeostasis.

General significance

Iron is an essential trace metal for practically all living organisms however its acquisition presents major challenges. The current paradigm is that living organisms have developed well orchestrated and evolved mechanisms involving iron carrier molecules and their specific receptors to regulate its absorption, transport, storage and mobilization. Our research uncovers a hidden and primitive pathway of bulk iron trafficking involving a secreted receptor that is a multifunctional glycolytic enzyme that has implications in pathological conditions such as infectious diseases and cancer.     

Glycan specificity of a testis-specific lectin chaperone calmegin and effects of hydrophobic interactions

Background

Testis-specific chaperone calmegin is required for the generation of normal spermatozoa. Calmegin is known to be a homologue of endoplasmic reticulum (ER) residing lectin chaperone calnexin. Although functional similarity between calnexin and calmegin has been predicted, detailed information concerned with substrate recognition by calmegin, such as glycan specificity, chaperone function and binding affinity, are obscure.

Methods

In this study, biochemical properties of calmegin and calnexin were compared using synthetic glycans and glycosylated or non-glycosylated proteins as substrates.

Results

Whereas their amino acid sequences are quite similar to each other, a certain difference in secondary structures was indicated by circular dichroism (CD) spectrum. While both of them inhibited protein heat-aggregation to a similar extent, calnexin exhibited a higher ability to facilitate protein folding. Similarly to calnexin, calmegin preferentially recognizes monoglucosylated glycans such as Glc₁Man₉GlcNAc₂ (G1M9). While the surface hydrophobicity of calmegin was higher than that of calnexin, calnexin showed stronger binding to substrate. We reasoned that lectin activity, in addition to hydrophobic interaction, contributes to this strong affinity between calnexin and substrate.

Conclusions

Although their similarity in carbohydrate binding specificities is high, there seems to be some differences in the mode of substrate recognition between calmegin and calnexin.

General significance

Properties of calmegin as a lectin-chaperone were revealed in comparison with calnexin.     

A novel ATP-generating machinery to counter nitrosative stress is mediated by substrate-level phosphorylation

Background

It is well-known that elevated amounts of nitric oxide and other reactive nitrogen species (RNS) impact negatively on the tricarboxylic acid (TCA) cycle and oxidative phosphorylation. These perturbations severely compromise O₂-dependent energy production. While bacteria are known to adapt to RNS, a key tool employed by macrophages to combat infections, the exact mechanisms are unknown.

Methods

The bacterium was cultured in a defined mineral medium and cell-free extracts obtained at the same growth phase were utilized for various biochemical studies Blue native polyacrylamide gel electrophoresis followed by in-gel activity assays, high performance liquid chromatography and co-immunoprecipitaton are applied to investigate the effects of RNS on the model microbe Pseudomonas fluorescens.

Results

Citrate is channeled away from the tricarboxylic acid cycle using a novel metabolon consisting of citrate lyase (CL), phosphoenolpyruvate carboxylase (PEPC) and pyruvate phosphate dikinase (PPDK). This metabolic engine comprising three disparate enzymes appears to transiently assemble as a supercomplex aimed at ATP synthesis. The up-regulation in the activities of adenylate kinase (AK) and nucleoside diphosphate kinase (NDPK) ensured the efficacy of this ATP-making machine.

Conclusion

Microbes may escape the effects of nitrosative stress by re-engineering metabolic networks in order to generate and store ATP anaerobically when the electron transport chain is defective.

General significance

The molecular configuration described herein provides further understanding of how metabolism plays a key role in the adaptation to nitrosative stress and reveals novel targets that will inform the development of antimicrobial agents to counter RNS-resistant pathogens.     

Detection of S-nitrosothiols

Background

S-nitrosothiols have been recognized as biologically-relevant products of nitric oxide that are involved in many of the diverse activities of this free radical.

Scope of review

This review serves to discuss current methods for the detection and analysis of protein S-nitrosothiols. The major methods of S-nitrosothiol detection include chemiluminescence-based methods and switch-based methods, each of which comes in various flavors with advantages and caveats.

Major conclusions

The detection of S-nitrosothiols is challenging and prone to many artifacts. Accurate measurements require an understanding of the underlying chemistry of the methods involved and the use of appropriate controls.

General significance

Nothing is more important to a field of research than robust methodology that is generally trusted. The field of S-nitrosation has developed such methods but, as S-nitrosothiols are easy to introduce as artifacts, it is vital that current users learn from the lessons of the past. This article is part of a Special Issue entitled Current methods to study reactive oxygen species - pros and cons and biophysics of membrane proteins. Guest Editor: Christine Winterbourn.