The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a leading cause of death in adults that may have origins in early lung development. It is a complex disease, influenced by multiple factors including genetic variants and environmental factors. Maternal smoking during pregnancy may influence the risk for diseases during adulthood, potentially through epigenetic modifications including methylation.

METHODS

In this work, we explore the fetal origins of COPD by utilizing lung DNA methylation marks associated with in utero smoke (IUS) exposure, and evaluate the network relationships between methylomic and transcriptomic signatures associated with adult lung tissue from former smokers with and without COPD. To identify potential pathobiological mechanisms that may link fetal lung, smoke exposure and adult lung disease, we study the interactions (physical and functional) of identified genes using protein-protein interaction networks.

RESULTS

We build IUS-exposure and COPD modules, which identify connected subnetworks linking fetal lung smoke exposure to adult COPD. Studying the relationships and connectivity among the different modules for fetal smoke exposure and adult COPD, we identify enriched pathways, including the AGE-RAGE and focal adhesion pathways.

CONCLUSIONS

The modules identified in our analysis add new and potentially important insights to understanding the early life molecular perturbations related to the pathogenesis of COPD. We identify AGE-RAGE and focal adhesion as two biologically plausible pathways that may reveal lung developmental contributions to COPD. We were not only able to identify meaningful modules but were also able to study interconnections between smoke exposure and lung disease, augmenting our knowledge about the fetal origins of COPD.

A proteomic survival predictor for COVID-19 patients in intensive care

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.

Systems Approaches to Treatment Response to Imatinib in Severe Asthma: A Pilot Study

There is an acute need for advances in pharmacologic therapies and a better understanding of novel drug targets for severe asthma. Imatinib, a tyrosine kinase inhibitor, has been shown to improve forced expiratory volume in 1 s (FEV1) in a clinical trial of patients with severe asthma. In a pilot study, we applied systems biology approaches to epithelium gene expression from these clinical trial patients treated with imatinib to better understand lung function response with imatinib treatment. Bronchial brushings from ten imatinib-treated patient samples and 14 placebo-treated patient samples were analyzed. We used personalized perturbation profiles (PEEPs) to characterize gene expression patterns at the individual patient level. We found that strong responders-patients with greater than 20% increase in FEV1-uniquely shared multiple downregulated mitochondrial-related pathways. In comparison, weak responders (5-10% FEV1 increase), and non-responders to imatinib shared none of these pathways. The use of PEEP highlights its potential for application as a systems biology tool to develop individual-level approaches to predicting disease phenotypes and response to treatment in populations needing innovative therapies. These results support a role for mitochondrial pathways in airflow limitation in severe asthma and as potential therapeutic targets in larger clinical trials.

Computing irreversible minimal cut sets in genome-scale metabolic networks via flux cone projection

MOTIVATION

Minimal cut sets (MCSs) for metabolic networks are sets of reactions which, if they are removed from the network, prevent a target reaction from carrying flux. To compute MCSs different methods exist, which may fail to find sufficiently many MCSs for larger genome-scale networks.

RESULTS

Here we introduce irreversible minimal cut sets (iMCSs). These are MCSs that consist of irreversible reactions only. The advantage of iMCSs is that they can be computed by projecting the flux cone of the metabolic network on the set of irreversible reactions, which usually leads to a smaller cone. Using oriented matroid theory, we show how the projected cone can be computed efficiently and how this can be applied to find iMCSs even in large genome-scale networks.

AVAILABILITY AND IMPLEMENTATION

Software is freely available at https://sourceforge.net/projects/irreversibleminimalcutsets/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[Medical ID and emergency apps: A useful tool in emergency situations or a waste of time?]

Hintergrund

Um in Notfallsituationen eine effektive Versorgung des Patienten zu gewährleisten, sind Informationen über Vorerkrankungen und bisherige Medikamenteneinnahme essenziell. Daher haben Smartphonehersteller entsprechende Anwendungssoftware (App) entwickelt, auf die im Notfall zugegriffen werden kann, um jene Informationen zu erhalten. Ziel der Studie war es herauszufinden, ob Notfall-Apps von Smartphoneinhabern aktiv genutzt werden und ob diese in Notfallsituationen von den behandelnden Notärzten eingesehen werden.

Methode

Zur Datenerhebung wurde eine anonymisierte Umfrage über das Nutzungsverhalten der Notfall-Apps bei verunfallten Patienten der unfallchirurgischen Ambulanz eines universitären Maximalversorgers über einen Zeitraum von 3 Monaten durchgeführt. Parallel fand eine Befragung von Notärzten verschiedener Standorte zu ihren beruflichen Erfahrungen mit den Apps statt.

Ergebnisse

Insgesamt wurden 192 Patienten und 103 Notärzte befragt. Die Notfall-Apps waren 45 % (n = 79) der Befragten nicht bekannt; nur bei 10 % (n = 19) der Befragten war die App mit Daten hinterlegt. Weiterhin zeigte sich, dass insgesamt 21 % (n = 41) der Personen einen Notizzettel mit Vorerkrankungen und Medikamenten bei sich trugen. Von den Befragten Ärzten gaben 42 % (n = 44) an, schon einmal von der App gehört zu haben; nur 6 % (n = 5) durchsuchten jedoch routinemäßig bei nichtansprechbaren Patienten das Smartphone. Erfolgreich genutzt wurde die App bisher nur von 14 % der Ärzte (n = 14).

Schlussfolgerung

Aufgrund der geringen Bekanntheit erscheint es in zeitkritischen Situationen nicht empfehlenswert, das Smartphone der Patienten nach Notfall-Apps zu durchsuchen. Bei Patienten über 55 ist es zurzeit erfolgsversprechender, die Brieftasche nach Informationen zu Vorerkrankungen zu kontrollieren.

Finding MEMo: minimum sets of elementary flux modes

Metabolic network reconstructions are widely used in computational systems biology for in silico studies of cellular metabolism. A common approach to analyse these models are elementary flux modes (EFMs), which correspond to minimal functional units in the network. Already for medium-sized networks, it is often impossible to compute the set of all EFMs, due to their huge number. From a practical point of view, this might also not be necessary because a subset of EFMs may already be sufficient to answer relevant biological questions. In this article, we study MEMos or minimum sets of EFMs that can generate all possible steady-state behaviours of a metabolic network. The number of EFMs in a MEMo may be by several orders of magnitude smaller than the total number of EFMs. Using MEMos, we can compute generating sets of EFMs in metabolic networks where the whole set of EFMs is too large to be enumerated.

[Intramedullary injection with tethered cord : Case report of a rare complication during spinal anesthesia]

Although very rare, severe neurological complications can occur when undergoing spinal anesthesia. This report describes and analyses a case of spinal injury due to an undiagnosed tethered cord (TC) during spinal anesthesia for a cesarean section of a 31-year-old woman expecting twins. As a consequence of spinal dysraphism during embryogenesis, an atypically low conus level can occur and increase the risk of injury during neuraxial anesthesia, especially in the absence of symptoms. Injuries can be caused by mechanical trauma from direct needle injury, hematoma or neurotoxicity from local anesthetics. Special attention should therefore be paid to frequent symptoms, such as a hairy nevus on the back, deformities of the feet or bladder and bowels, voiding and micturition dysfunction in order to reduce the risk of complications.

A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks

Background

Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement.

Results

Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways.

Conclusions

Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1412-z) contains supplementary material, which is available to authorized users.

Pulse oximetry during intraaortic balloon pump application

BACKGROUND

Pulse oximeters are multiple used devices in anaesthesiology and intensive care medicine and must provide reliable data during various conditions of signal interference, including light, motion and reduced perfusion. The aim of this study was to evaluate the reliability of different new-generation pulse oximeters during intraaortic balloon pump (IABP) therapy.

METHODS

In the experimental setting, the validity of three pulse oximetry technologies (Masimo Radical 7, Nellcor N-600 and Datex Ohmeda TruSat) was evaluated in patients with IABP treatment. Arterial blood gas analysis (BGA-SaO2) data were compared with the pulse oximetric values (SpO2) during 1:1, 1:2 and 1:3 support ratio.

RESULTS

The mean differences (bias) during 1:1, 1:2 and 1:3 IABP support between BGA-SaO2 and Datex-SpO2 were 3.38% [95% confidence intervals (CI):±1.39%], 1.41% (95% CI 1.14%) and 2.10% (95% CI:±0.94%), respectively. Between BGA-SaO2 and Nellcor-SpO2, a bias of 0.77% (95% CI:±0.46%), 0.85% (95% CI:±0.40%) and 0.59% (95% CI:±0.38%) was found. In the comparison of BGA-SaO2 and Masimo-SpO2, a bias of 0.58% (95% CI:±0.56%), 0.19% (95% CI:±0.40%) and -0.01% (95% CI:±0.43%) was found, respectively.

CONCLUSIONS

In patients with IABP support, the pulse oximetric values of the Masimo Radical 7 are accurate in 1:2 and 1:3 support ratio compared with blood gas analysis. In these support ratios, the Masimo Radical 7 is superior to the Nellcor N-600. The Datex Ohmeda TruSat showed a significant difference between the measured pulse oximetric values and blood gas analysis in all support ratios.

Phylogenetic star contraction applied to Asian and Papuan mtDNA evolution

In the past decade, mitochondrial DNA (mtDNA) of 826 representative East Asians and Papuans has been typed by high-resolution (14-enzyme) restriction fragment length polymorphism (RFLP) analysis. Compared with mtDNA control region sequencing, RFLP typing of the complete human mitochondrial DNA generally yields a cleaner phylogeny, the nodes of which can be dated assuming a molecular clock. We present here a novel star contraction algorithm which rigorously identifies starlike nodes (clusters) diagnostic of prehistoric demographic expansions. Applied to the Asian and Papuan data, we date the out-of-Africa migration of the ancestral mtDNA types that founded all Eurasian (including Papuan) lineages at 54,000 years. While the proto-Papuan mtDNA continued expanding at this time along a southern route to Papua New Guinea, the proto-Eurasian mtDNA appears to have drifted genetically and does not show any comparable demographic expansion until 30,000 years ago. By this time, the East Asian, Indian, and European mtDNA pools seem to have separated from each other, as postulated by the weak Garden of Eden model. The east Asian expansion entered America about 25,000 years ago, but was then restricted on both sides of the Pacific to more southerly latitudes during the Last Glacial Maximum around 20,000 years ago, coinciding with a chronological gap in our expansion dates. Repopulation of northern Asian latitudes occurred after the Last Glacial Maximum, obscuring the ancestral Asian gene pool of Amerinds.

An annotated mtDNA database

We have compiled a database of mitochondrial DNA (mtDNA) control region, hypervariable regions 1 (HVR1) and 2 (HVR2) sequences of a total of 14,138 individuals compiled from 103 mtDNA publications before 1 January 2000, 13 data sets published in 2000 and 2001 and 2 unpublished data sets of Iraqi Kurds and Indians from Kerala. By contacting the authors and by other means, we have confirmed and corrected sequence errors, eliminated duplications and harmonised the sequence format. These changes affected all but 26 of the 116 publications. Furthermore, we have implemented a geographic information system ("mtradius") which searches for closest matches to a given mtDNA control region sequence and displays them on a geographic map. A potential application is to estimate a chance matching probability when a forensic stain and a suspect have an identical mtDNA sequence: we suggest that the geographic area with the highest frequency of closely related mtDNA sequence types may be used to define a reference population to give the suspect the maximum benefit of doubt in accordance with the ceiling principle.

Median-joining networks for inferring intraspecific phylogenies

Reconstructing phylogenies from intraspecific data (such as human mitochondrial DNA variation) is often a challenging task because of large sample sizes and small genetic distances between individuals. The resulting multitude of plausible trees is best expressed by a network which displays alternative potential evolutionary paths in the form of cycles. We present a method ("median joining" [MJ]) for constructing networks from recombination-free population data that combines features of Kruskal's algorithm for finding minimum spanning trees by favoring short connections, and Farris's maximum-parsimony (MP) heuristic algorithm, which sequentially adds new vertices called "median vectors", except that our MJ method does not resolve ties. The MJ method is hence closely related to the earlier approach of Foulds, Hendy, and Penny for estimating MP trees but can be adjusted to the level of homoplasy by setting a parameter epsilon. Unlike our earlier reduced median (RM) network method, MJ is applicable to multistate characters (e.g., amino acid sequences). An additional feature is the speed of the implemented algorithm: a sample of 800 worldwide mtDNA hypervariable segment I sequences requires less than 3 h on a Pentium 120 PC. The MJ method is demonstrated on a Tibetan mitochondrial DNA RFLP data set.

[Multifactorial etiology of idiopathic Parkinson disease. A case-control study]

The etiology of Parkinson's disease (PD) is currently thought to be due to an interplay of genetic and environmental factors. We examined (1) familial prevalences of PD and essential tremor (ET) using a control population and personal investigation; (2) comorbidity and possible traits of a altered premorbid lifestyle in PD were also investigated. Sixty-six PD patients with disease onset after 40 and before 70 years of life and 72 age- and sex-matched hospital controls with chronic diseases underwent neurologic examination and a structured interview. Allegedly symptomatic relatives were investigated personally. For the time prior to 40 years of age, PD patients reported significantly less frequent cigarette smoking overall (Odds ratio = OR 0.37); significantly less frequently a smoking duration of more than 2 years (OR 0.42); significantly less frequently an amount of more than 10 cigarettes smoked per diem (OR 0.24); they did report, however, more frequently an onset for smoking before the age of 20 (OR 3.56). Place of residence and source of drinking water during the first 15 years of life, and number of moves and journeys outside Europe before 40th year of life were not significantly different. PD patients demonstrated an increased risk of a 1st or 2nd degree relative with PD (OR 7.14) or ET (OR 3.62) compared to the control families. Congenital or perinatal abnormalities, and comorbid diseases did not suggest greater risk for PD. Stepwise logistic regression analysis yielded three variables that separated PD patients and controls most significantly, though not completely: "smoking more than 10 cigarettes per diem", "additional family members with PD", "additional family members with ET".