Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme

<p>Abstract</p> <p>Background</p> <p>Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the <it>S. cerevisiae </it>interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied.</p> <p>Results</p> <p>We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI) weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database.</p> <p>Conclusions</p> <p>The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting.</p

GIBA: a clustering tool for detecting protein complexes

Background: During the last years, high throughput experimental methods have been developed which generate large datasets of protein - protein interactions (PPIs). However, due to the experimental methodologies these datasets contain errors mainly in terms of false positive data sets and reducing therefore the quality of any derived information. Typically these datasets can be modeled as graphs, where vertices represent proteins and edges the pairwise PPIs, making it easy to apply automated clustering methods to detect protein complexes or other biological significant functional groupings. Methods: In this paper, a clustering tool, called GIBA (named by the first characters of its developers' nicknames), is presented. GIBA implements a two step procedure to a given dataset of protein-protein interaction data. First, a clustering algorithm is applied to the interaction data, which is then followed by a filtering step to generate the final candidate list of predicted complexes. Results: The efficiency of GIBA is demonstrated through the analysis of 6 different yeast protein interaction datasets in comparison to four other available algorithms. We compared the results of the different methods by applying five different performance measurement metrices. Moreover, the parameters of the methods that constitute the filter have been checked on how they affect the final results. Conclusion: GIBA is an effective and easy to use tool for the detection of protein complexes out of experimentally measured protein - protein interaction networks. The results show that GIBA has superior prediction accuracy than previously published methods

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p

Effect of Neutralizing Monoclonal Antibody Treatment on Early Trajectories of Virologic and Immunologic Biomarkers in Patients Hospitalized With COVID-19

BACKGROUND: Neutralizing monoclonal antibodies (nmAbs) failed to show clear benefit for hospitalized patients with coronavirus disease 2019 (COVID-19). Dynamics of virologic and immunologic biomarkers remain poorly understood. METHODS: Participants enrolled in the Therapeutics for Inpatients with COVID-19 trials were randomized to nmAb versus placebo. Longitudinal differences between treatment and placebo groups in levels of plasma nucleocapsid antigen (N-Ag), anti-nucleocapsid antibody, C-reactive protein, interleukin-6, and D-dimer at enrollment, day 1, 3, and 5 were estimated using linear mixed models. A 7-point pulmonary ordinal scale assessed at day 5 was compared using proportional odds models. RESULTS: Analysis included 2149 participants enrolled between August 2020 and September 2021. Treatment resulted in 20% lower levels of plasma N-Ag compared with placebo (95% confidence interval, 12%-27%; P \u3c .001), and a steeper rate of decline through the first 5 days (P \u3c .001). The treatment difference did not vary between subgroups, and no difference was observed in trajectories of other biomarkers or the day 5 pulmonary ordinal scale. CONCLUSIONS: Our study suggests that nmAb has an antiviral effect assessed by plasma N-Ag among hospitalized patients with COVID-19, with no blunting of the endogenous anti-nucleocapsid antibody response. No effect on systemic inflammation or day 5 clinical status was observed. CLINICAL TRIALS REGISTRATION: NCT04501978

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system

Update in Viral Infections in the Intensive Care Unit.

The advent of highly sensitive molecular diagnostic techniques has improved our ability to detect viral pathogens leading to severe and often fatal infections that require admission to the Intensive Care Unit (ICU). Viral infections in the ICU have pleomorphic clinical presentations including pneumonia, acute respiratory distress syndrome, respiratory failure, central or peripheral nervous system manifestations, and viral-induced shock. Besides de novo infections, certain viruses fall into latency and can be reactivated in both immunosuppressed and immunocompetent critically ill patients. Depending on the viral strain, transmission occurs either directly through contact with infectious materials and large droplets, or indirectly through suspended air particles (airborne transmission of droplet nuclei). Many viruses can efficiently spread within hospital environment leading to in-hospital outbreaks, sometimes with high rates of mortality and morbidity, thus infection control measures are of paramount importance. Despite the advances in detecting viral pathogens, limited progress has been made in antiviral treatments, contributing to unexpectedly high rates of unfavorable outcomes. Herein, we review the most updated data on epidemiology, common clinical features, diagnosis, pathogenesis, treatment and prevention of severe community- and hospital-acquired viral infections in the ICU settings

Update in Viral Infections in the Intensive Care Unit.

The advent of highly sensitive molecular diagnostic techniques has improved our ability to detect viral pathogens leading to severe and often fatal infections that require admission to the Intensive Care Unit (ICU). Viral infections in the ICU have pleomorphic clinical presentations including pneumonia, acute respiratory distress syndrome, respiratory failure, central or peripheral nervous system manifestations, and viral-induced shock. Besides de novo infections, certain viruses fall into latency and can be reactivated in both immunosuppressed and immunocompetent critically ill patients. Depending on the viral strain, transmission occurs either directly through contact with infectious materials and large droplets, or indirectly through suspended air particles (airborne transmission of droplet nuclei). Many viruses can efficiently spread within hospital environment leading to in-hospital outbreaks, sometimes with high rates of mortality and morbidity, thus infection control measures are of paramount importance. Despite the advances in detecting viral pathogens, limited progress has been made in antiviral treatments, contributing to unexpectedly high rates of unfavorable outcomes. Herein, we review the most updated data on epidemiology, common clinical features, diagnosis, pathogenesis, treatment and prevention of severe community- and hospital-acquired viral infections in the ICU settings

Update in Viral Infections in the Intensive Care Unit

The advent of highly sensitive molecular diagnostic techniques has improved our ability to detect viral pathogens leading to severe and often fatal infections that require admission to the Intensive Care Unit (ICU). Viral infections in the ICU have pleomorphic clinical presentations including pneumonia, acute respiratory distress syndrome, respiratory failure, central or peripheral nervous system manifestations, and viral-induced shock. Besides de novo infections, certain viruses fall into latency and can be reactivated in both immunosuppressed and immunocompetent critically ill patients. Depending on the viral strain, transmission occurs either directly through contact with infectious materials and large droplets, or indirectly through suspended air particles (airborne transmission of droplet nuclei). Many viruses can efficiently spread within hospital environment leading to in-hospital outbreaks, sometimes with high rates of mortality and morbidity, thus infection control measures are of paramount importance. Despite the advances in detecting viral pathogens, limited progress has been made in antiviral treatments, contributing to unexpectedly high rates of unfavorable outcomes. Herein, we review the most updated data on epidemiology, common clinical features, diagnosis, pathogenesis, treatment and prevention of severe community- and hospital-acquired viral infections in the ICU settings

Insights into CD24 and Exosome Physiology and Potential Role in View of Recent Advances in COVID-19 Therapeutics: A Narrative Review

Cluster of differentiation (CD) 24, a long-known protein with multifaceted functions, has gained attention as a possible treatment for Coronavirus Disease 19 (COVID-19) due to its known anti-inflammatory action. Extracellular vesicles (EVs), such as exosomes and microvesicles, may serve as candidate drug delivery platforms for novel therapeutic approaches in COVID-19 and various other diseases due to their unique characteristics. In the current review, we describe the physiology of CD24 and EVs and try to elucidate their role, both independently and as a combination, in COVID-19 therapeutics. CD24 may act as an important immune regulator in diseases with complex physiologies characterized by excessive inflammation. Very recent data outline a possible therapeutic role not only in COVID-19 but also in other similar disease states, e.g., acute respiratory distress syndrome (ARDS) and sepsis where immune dysregulation plays a key pathophysiologic role. On the other hand, CD24, as well as other therapeutic molecules, can be administered with the use of exosomes, exploiting their unique characteristics to create a novel drug delivery platform as outlined in recent clinical efforts. The implications for human therapeutics in general are huge with regard to pharmacodynamics, pharmacokinetics, safety, and efficacy that will be further elucidated in future randomized controlled trials (RCTs)

New Insights into the Fluid Management in Patients with Septic Shock

The importance of fluid resuscitation therapy during the early stages of sepsis management is a well-established principle. Current Surviving Sepsis Campaign (SSC) guidelines recommend the early administration of intravenous crystalloid fluids for sepsis-related hypotension or hyperlactatemia due to tissue hypoperfusion, within the first 3 h of resuscitation and suggest using balanced solutions (BSs) instead of normal saline (NS) for the management of patients with sepsis or septic shock. Studies comparing BS versus NS administration in septic patients have demonstrated that BSs are associated with better outcomes including decreased mortality. After initial resuscitation, fluid administration has to be judicious in order to avoid fluid overload, which has been associated with increased mortality, prolonged mechanical ventilation, and worsening of acute kidney injury. The “one size fits all” approach may be “convenient” but it should be avoided. Personalized fluid management, based on patient-specific hemodynamic indices, provides the foundations for better patient outcomes in the future. Although there is a consensus on the need for adequate fluid therapy in sepsis, the type, the amount of administered fluids, and the ideal fluid resuscitation strategy remain elusive. Well-designed large randomized controlled trials are certainly needed to compare fluid choices specifically in the septic patient, as there is currently limited evidence of low quality. This review aims to summarize the physiologic principles and current scientific evidence regarding fluid management in patients with sepsis, as well as to provide a comprehensive overview of the latest data on the optimal fluid administration strategy in sepsis