Robert Rosen in the age of systems biology

The widespread use of the term Systems Biology (SB) signals a welcome recognition that organisms must be understood as integrated systems. Although just what this is taken to mean varies from one group to another, it generally implies a focus on biological functions and processes rather than on biological parts and a reliance on mathematical modeling to arrive at an understanding of these biological processes based on biological observations or measurements. SB, thus, falls directly in the line of reflection carried out by Robert Rosen throughout his work. In the present article, we briefly introduce the various currents of SB and then point out several ways Rosen's work can be used to avoid certain pitfalls associated with the use of dynamical systems models for the study of complex systems, as well as to inspire a productive path forward based on loosely organized cooperation among dispersed laboratories.     

Organellar proteomics to create the cell map

The elucidation of a complete, accurate, and permanent representation of the proteome of the mammalian cell may be achievable piecemeal by an organellar based approach. The small volume of organelles assures high protein concentrations. Providing isolated organelles are homogenous, this assures reliable protein characterization within the sensitivity and dynamic range limits of current mass spec based analysis. The stochastic aspect of peptide selection by tandem mass spectrometry for sequence determination by fragmentation is dealt with by multiple biological replicates as well as by prior protein separation on 1-D gels. Applications of this methodology to isolated synaptic vesicles, clathrin coated vesicles, endosomes, phagosomes, endoplasmic reticulum, and Golgi apparatus, as well as Golgi-derived COPI vesicles, have led to mechanistic insight into the identity and function of these organelles.     

Cell wall integrity maintenance in plants: Lessons to be learned from yeast?

The plant cell wall is involved in different biological processes like cell morphogenesis and response to biotic/abiotic stress. Functional integrity of the wall is apparently being maintained during these processes by changing structure/composition and coordinating cell wall with cellular metabolism. In S.cerevisiae a well-characterized mechanism exists that is maintaining functional integrity of yeast the cell wall during similar processes. During the last years it has become obvious that plants have evolved a mechanism to monitor and maintain functional integrity of their cell walls. However, our understanding of the mechanism is rather limited. The available evidence suggests that similar signaling cascades may be involved and particular protein activities may be conserved between plants and yeast. Here we review the available evidence briefly and highlight similarities between yeast and plants that could help us to understand the mode of action of the signaling cascades maintaining plant cell wall integrity.     

Evaluation of accuracy and applicability of protein models: retrospective analysis of biological and biomedical predictions

In order to study protein function and activity structural data is required. Since experimental structures are available for just a small fraction of all known protein sequences, computational methods such as protein modelling can provide useful information. Over the last few decades we have predicted, with homology modelling methods, the structures for numerous proteins. In this study we assess the structural quality and validity of the biological and medical interpretations and predictions made based on the models. All the models had correct scaffolding and were ranked at least as correct or good by numerical evaluators even though the sequence identity with the template was as low as 8%. The biological explanations made based on models were well in line with experimental structures and other experimental studies. Retrospective analysis of homology models indicates the power of protein modelling when made carefully from sequence alignment to model building and refinement. Modelling can be applied to studying and predicting different kinds of biological phenomena and according to our results it can be done so with success.     

Membrane lysis during biological membrane fusion: collateral damage by misregulated fusion machines

In the canonical model of membrane fusion, the integrity of the fusing membranes is never compromised, preserving the identity of fusing compartments. However, recent molecular simulations provided evidence for a pathway to fusion in which holes in the membrane evolve into a fusion pore. Additionally, two biological membrane fusion models—yeast cell mating and in vitro vacuole fusion—have shown that modifying the composition or altering the relative expression levels of membrane fusion complexes can result in membrane lysis. The convergence of these findings showing membrane integrity loss during biological membrane fusion suggests new mechanistic models for membrane fusion and the role of membrane fusion complexes.     

How advancement in biological network analysis methods empowers proteomics

Proteomics provides important information--that may not be inferable from indirect sources such as RNA or DNA--on key players in biological systems or disease states. However, it suffers from coverage and consistency problems. The advent of network-based analysis methods can help in overcoming these problems but requires careful application and interpretation. This review considers briefly current trends in proteomics technologies and understanding the causes of critical issues that need to be addressed--i.e., incomplete data coverage and inter-sample inconsistency. On the coverage issue, we argue that holistic analysis based on biological networks provides a suitable background on which more robust models and interpretations can be built upon; and we introduce some recently developed approaches. On consistency, group-based approaches based on identified clusters, as well as on properly integrated pathway databases, are particularly useful. Despite that protein interactions and pathway networks are still largely incomplete, given proper quality checks, applications and reasonably sized data sets, they yield valuable insights that greatly complement data generated from quantitative proteomics.     

Relations between fatty acids and oestrogen binding properties of pure rat alpha 1-foetoprotein.

A delipidation procedure based on treatment with charcoal at pH 3 has been applied to highly purified rat alpha 1-foetoprotein preparations. The oestrogen binding properties of the delipidated proteins have been studied with an equilibrium dialysis technique, and compared with the properties of the untreated foetal protein, as well as those of preparations reconstituted from the defatted alpha 1-foetoprotein and the removed lipids. An important increase has been evidenced for the binding levels of oestrone, oestradiol-17 beta and diethylstilboestrol by the delipidated alpha 1-foetoprotein. A reversal of this effect has been obtained by incubating the delipidated protein either with the lipids extracted from the purified alpha 1-foetoprotein or with a potent competitor of the rat alpha 1-foetoprotein-oestrogen interaction, designated as 'L', previously demonstrated and isolated from whole rat sera, and tentatively characterized as a mixture of fatty acids. Scatchard analysis of the oestrone and oestradiol-17 beta binding parameters show that the enhanced fixation of the hormones after defatting is primarily due to a two-fold increase of the apparent number of binding sites/mol alpha 1-foetoprotein. The results are interpreted in terms of the probable, at least partial, identity between the lipids closely associated with the pure alpha 1-foetoprotein and the fatty acid mixture 'L' isolated from whole sera. The possible biological role of complex interplay between oestrophilic alpha 1-foetoproteins, phenolsteroids and fatty acids in the control of oestrogen levels during development is discussed briefly.     

Protein chips for high-throughput doping screening in athletes

The biological significance of protein interactions, their method of generation and reliability is briefly reviewed. Protein interaction networks adopt a scale-free topology that explains their error tolerance or vulnerability, depending on whether hubs or peripheral proteins are attacked. Networks also allow the prediction of protein function from their interaction partners and therefore, the formulation of analytical hypotheses. Comparative network analysis predicts interactions for distantly related species based on conserved interactions, even if sequences are only weakly conserved. Finally, the medical relevance of protein interaction analysis is discussed and the necessity for data integration is emphasized.     

What do we learn from high-throughput protein interaction data?

The biological significance of protein interactions, their method of generation and reliability is briefly reviewed. Protein interaction networks adopt a scale-free topology that explains their error tolerance or vulnerability, depending on whether hubs or peripheral proteins are attacked. Networks also allow the prediction of protein function from their interaction partners and therefore, the formulation of analytical hypotheses. Comparative network analysis predicts interactions for distantly related species based on conserved interactions, even if sequences are only weakly conserved. Finally, the medical relevance of protein interaction analysis is discussed and the necessity for data integration is emphasized.     

Expression and characterization of human proinsulin fused to thioredoxin in Escherichia coli

Native proinsulin (PI) belongs to the class of the difficult-to-express proteins in Escherichia coli. Problems mainly arise due to its high proteolytic decay and troubles to reproduce the native disulphide pattern. In the present study, human PI was produced in E. coli as a fusion thioredoxin protein (Trx-PI). Such chimeric protein was obtained from the intracellular soluble fraction, and it was purified in one step by affinity chromatography on immobilized phenylarsine oxide. Trx-PI was also recovered from inclusion bodies and purified by anion exchange chromatography. The product identity and integrity were verified by mass analysis (22,173.5 Da) and mapping with Staphylococcus aureus V8 protease. Native PI folding was evaluated by biochemical and also by immunochemical analysis using specific sera from PI antibody-positive diabetic patients that recognise conformational discontinue epitopes. Dose-response curves showed identity between standard PI and Trx-PI. Moreover, surface plasmon resonance technique verified the correct conformation of the recombinant protein. The biochemical and immunochemical assays demonstrated the integrity of the chimera and the epitopes involved in the interaction with antibodies. In conclusion, it was possible to obtain with high-yield purified human PI as a fusion protein in E. coli and useful for analytical purposes.     

A proteome catalog of Drosophila melanogaster: an essential resource for targeted quantitative proteomics

Proteomic analyses are critically important for systems biology because important aspects related to the structure, function and control of biological systems are only amenable by direct protein measurements. It has become apparent that the current proteomics technologies are unlikely to allow routine, quantitative measurements of whole proteomes. We have therefore suggested and largely implemented a two-step strategy for quantitative proteome analysis. In a first step, the discovery phase, the proteome observable by mass spectrometry is extensively analyzed. The resulting proteome catalog can then be used to select peptides specific to only one protein, so-called proteotypic peptides (PTPs). It represents the basis to realize sensitive, robust and reproducible measurements based on targeted mass spectrometry of these PTPs in a subsequent scoring phase. In this Extra View we describe the need for such proteome catalogs and their multiple benefits for catalyzing the shift towards targeted quantitative proteomic analysis and beyond. We use the Insulin signaling cascade as a representative example to illustrate the limitations of currently used proteomics approaches for the specific analysis of individual pathway components, and describe how the recently published Drosophila proteome catalog already helped to overcome many of these limitations.     

Critical Examination of the Colloidal Particle Model of Globular Proteins

Recent studies of globular protein solutions have uniformly adopted a colloidal view of proteins as particles, a perspective that neglects the polymeric primary structure of these biological macromolecules, their intrinsic flexibility, and their ability to sample a large configurational space. While the colloidal perspective often serves as a useful idealization in many cases, the macromolecular identity of proteins must reveal itself under thermodynamic conditions in which the native state is no longer stable, such as denaturing solvents and high protein concentrations where macromolecules tend to have screened excluded volume, charge, and hydrodynamic interactions. Under extreme pH conditions, charge repulsion interactions within the protein chain can overcome the attractive hydrogen-bonding interactions, holding it in its native globular state. Conformational changes can therefore be expected to have great significance on the shear viscosity and other rheological properties of protein solutions. These changes are not envisioned in conventional colloidal protein models and we have initiated an investigation of the scattering and rheological properties of model proteins. We initiate this effort by considering bovine serum albumin because it is a globular protein whose solution properties have also been extensively investigated as a function of pH, temperature, ionic strength, and concentration. As we anticipated, near-ultraviolet circular dichroism measurements and intrinsic viscosity measurements clearly indicate that the bovine serum albumin tertiary structure changes as protein concentration and pH are varied. Our findings point to limited validity of the colloidal protein model and to the need for further consideration and quantification of the effects of conformational changes on protein solution viscosity, protein association, and the phase behavior. Small-angle Neutron Scattering measurements have allowed us to assess how these conformational changes influence protein size, shape, and interprotein interaction strength.     

Characterisation of recombinant unglycosylated human serum transferrin purified from Saccharomyces cerevisiae

Structural identity between a recombinant transferrin mutant (N413Q, N611Q) secreted from Saccharomyces cerevisiae and the native protein was shown by CD analysis and immunodiffusion assays against anti-hSTf. The ability of the recombinant protein to bind iron was confirmed by urea–PAGE and EPR analysis of the iron-saturated protein revealed the characteristic holo-transferrin spectrum, indicating conservation of both iron-binding sites. The integrity of the unglycosylated recombinant protein indicates that such protein could be a valuable tool not only for structure–function characterisation but also crystallisation assays. In addition, the recombinant transferrin was found to be as effective as native transferrin as a growth factor in cell culture medium.     

Clinical pharmacology aspects of development and application of biosimilar antibodies

Due to the microheterogeneity of the macromolecules produced in biological systems, only the high degree of similarity but not the structural identity of the follow-on biological active agents can be proven. In case of monoclonal antibodies (mAbs) the proof of similarity is much more difficult due to the large molecular weight, complicated structure and complex biological effects of the IgG molecule. The identity of the amino acid sequence is the basic requirement of biosimilarity. Smaller changes in the carbohydrate side chains attached to the protein molecule can be accepted if they do not cause significant functional changes. On the other hand, the alteration of some carbohydrate side chain(s) located at strategically important sites might significantly influence the biological properties of the mAbs. Besides the antigen binding the pharmacological effects of the antibodies depend on several other effector functions related to the Fc fragment such as antibody-dependent cell-mediated cytotoxicity (ADCC), complement-dependent cytotoxicity (CDC), and phagocytosis. Hence several biological functions must be measured and evaluated in their totality for stating the degree of biosimilarity. According to the concordant strict requirements of the European Medicines Agency (EMA) and the American Food and Drug Administration (FDA), the proof of similar efficacy and safety must be based on the results obtained in the most sensitive and suitable in vitro and in vivo experimental designs. The fundamental proofs are provided by the comparison of the pharmacokinetic and pharmacodynamic measurements, as well as the similarity of the dose-effect relationships. These observations are complemented by the comparative clinical therapeutic trials which eventually support the similar therapeutic use of the follow-on medicinal products in the clinical practice. The final judgement of biosimilarity is a case-by-case decision based on the joint evaluation of all the data available.     

MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets

Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology ('MCAM') employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems.     

Reproducibility of mass spectrometry based protein profiles for diagnosis of breast cancer across clinical studies: a systematic review

Serum protein profiling by mass spectrometry has achieved attention as a promising technology in oncoproteomics. We performed a systematic review of published reports on protein profiling as a diagnostic tool for breast cancer. The MEDLINE, EMBASE, and COCHRANE databases were searched for original studies reporting discriminatory protein peaks for breast cancer as either protein identity or as m/ z values in the period from January 1995 to October 2006. To address the important aspect of reproducibility of mass spectrometry data across different clinical studies, we compared the published lists of potential discriminatory peaks with those peaks detected in an original MALDI MS protein profiling study performed by our own research group. A total of 20 protein/peptide profiling studies were eligible for inclusion in the systematic review. Only 3 reports included information on protein identity. Although the studies revealed a considerable heterogeneity in relation to experimental design, biological variation, preanalytical conditions, methods of computational data analysis, and analytical reproducibility of profiles, we found that 45% of peaks previously reported to correlate with breast cancer were also detected in our experimental study. Furthermore, 25% of these redetected peaks also showed a significant difference between cases and controls in our study. Thus, despite known problems related to reproducibility, we were able to demonstrate overlap in peaks between clinical studies indicating some convergence toward a set of common discriminating, reproducible peaks for breast cancer. These peaks should be further characterized for identification of the protein identity and validated as biomarkers for breast cancer.     

What is river health?

1. Traditionally the assessment of river water quality has been based solely on the measurement of physical, chemical and some biological characteristics. While these measurements may be efficient for regulating effluent discharges and protecting humans, they are not very useful for large-scale management of catchments or for assessing whether river ecosystems are being protected. 2. Measurements of aquatic biota, to identify structural or functional integrity of ecosystems, have recently gained acceptance for river assessment. Empirical evidence from studies of river ecosystems under stress suggests that a small group of biological ecosystem-level indicators can assess river condition. However, physical and chemical features of the environment affect these indicators, the structure and function of which may be changed by human activities. 3. The term ‘river health’, applied to the assessment of river condition, is often seen as being analogous with human health, giving many a sense of understanding. Unfortunately, the meaning of ‘river health’ remains obscure. It is not clear what aspects of river health sets of ecosystem-level indicators actually identify, nor how physical, chemical and biological characteristics may be integrated into measures rather than just observations of cause and effect. 4. Increased examination of relationships between environmental variables that affect aquatic biota, such as habitat structure, flow regime, energy sources, water quality and biotic interactions and biological condition, are required in the study of river health.