Adjunctive pascolizumab in rifampicin-susceptible pulmonary tuberculosis: proof-of-concept, partially-randomised, double-blind, placebo-controlled, dose-escalation trial.

BACKGROUND: Interleukin-4 (IL-4), increased in tuberculosis infection, may impair bacterial killing. Blocking IL-4 confers benefit in animal models. We evaluated safety and efficacy of pascolizumab (humanised anti-IL-4 monoclonal antibody) as adjunctive tuberculosis treatment. METHODS: Participants with rifampicin-susceptible pulmonary tuberculosis received a single intravenous infusion of pascolizumab or placebo; and standard 6-month tuberculosis treatment. Pascolizumab dose increased in successive cohorts: [1] non-randomised 0.05 mg/kg (n = 4); [2] non-randomised 0.5 mg/kg (n = 4); [3] randomised 2.5 mg/kg (n = 9) or placebo (n = 3); [4] randomised 10 mg/kg (n = 9) or placebo (n = 3). Co-primary safety outcome was study-drug-related grade 4 or serious adverse event (G4/SAE); in all cohorts (1-4). Co-primary efficacy outcome was week-8 sputum culture time-to-positivity (TTP); in randomised cohorts (3-4) combined. RESULTS: Pascolizumab levels exceeded IL-4 50% neutralising dose for 8 weeks in 78-100% of participants in cohorts 3-4. There were no study-drug-related G4/SAEs. Median week-8 TTP was 42 days in pascolizumab and placebo groups (p = 0.185). Rate of TTP increase was greater with pascolizumab (difference from placebo 0.011 [95% Bayesian credible interval 0.006 to 0.015] log10TTP/day. CONCLUSIONS: There was no evidence to suggest blocking IL-4 was unsafe. Preliminary efficacy findings are consistent with animal models. This supports further investigation of adjunctive anti-IL-4 interventions for tuberculosis in larger phase 2 trials.

Variability of the Plasma Lipidome and Subclinical Coronary Atherosclerosis.

OBJECTIVE: While the risk of acute coronary events has been associated with biological variability of circulating cholesterol, the association with variability of other atherogenic lipids remains less understood. We evaluated the longitudinal variability of 284 lipids and investigated their association with asymptomatic coronary atherosclerosis. Approach and Results: Circulating lipids were extracted from fasting blood samples of 83 community-sampled symptom-free participants (age 41-75 years), collected longitudinally over 6 months. Three types of coronary plaque volume (calcified, lipid-rich, and fibrotic) were quantified using computed tomography coronary angiogram. We first deconvoluted between-subject (CVg) and within-subject (CVw) lipid variabilities. We then tested whether the mean lipid abundance was different across groups categorized by Framingham risk score and plaques phenotypes (lipid-rich, fibrotic, and calcified). Finally, we investigated whether visit-to-visit variability of each lipid was associated with plaque burden. Most lipids (72.5%) exhibited higher CVg than CVw. Among the lipids (n=145) with 1.2-fold higher CVg than CVw, 26 species including glycerides and ceramides were significantly associated with Framingham risk score and the 3 plaque phenotypes (false discovery rate <0.05). In an exploratory analysis of person-specific visit-to-visit variability without multiple testing correction, high variability of 3 lysophospholipids (lysophosphatidylethanolamines 16:0, 18:0, and lysophosphatidylcholine O-18:1) was associated with lipid-rich and fibrotic (noncalcified) plaque volume while high variability of diacylglycerol 18:1_20:0, triacylglycerols 52:2, 52:3, and 52:4, ceramide d18:0/20:0, dihexosylceramide d18:1/16:0, and sphingomyelin 36:3 was associated with calcified plaque volume. CONCLUSIONS: High person-specific longitudinal variation of specific nonsterol lipids is associated with the burden of subclinical coronary atherosclerosis. Larger studies are needed to confirm these exploratory findings.

Exploiting Interdata Relationships in Next-generation Proteomics Analysis.

Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called "integromics." We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets.

EBprotV2: A Perseus Plugin for Differential Protein Abundance Analysis of Labeling-Based Quantitative Proteomics Data.

We present EBprotV2, a Perseus plugin for peptide-ratio-based differential protein abundance analysis in labeling-based proteomics experiments. The original version of EBprot models the distribution of log-transformed peptide-level ratios as a Gaussian mixture of differentially abundant proteins and nondifferentially abundant proteins and computes the probability score of differential abundance for each protein based on the reproducible magnitude of peptide ratios. However, the fully parametric model can be inflexible, and its R implementation is time-consuming for data sets containing a large number of peptides (e.g., >100 000). The new tool built in the C++ language is not only faster in computation time but also equipped with a flexible semiparametric model that handles skewed ratio distributions better. We have also developed a Perseus plugin for EBprotV2 for easy access to the tool. In addition, the tool now offers a new submodule (MakeGrpData) to transform label-free peptide intensity data into peptide ratio data for group comparisons and performs differential abundance analysis using mixture modeling. This approach is especially useful when the label-free data have many missing peptide intensity data points.

Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress.

The relative importance of regulation at the mRNA versus protein level is subject to ongoing debate. To address this question in a dynamic system, we mapped proteomic and transcriptomic changes in mammalian cells responding to stress induced by dithiothreitol over 30 h. Specifically, we estimated the kinetic parameters for the synthesis and degradation of RNA and proteins, and deconvoluted the response patterns into common and unique to each regulatory level using a new statistical tool. Overall, the two regulatory levels were equally important, but differed in their impact on molecule concentrations. Both mRNA and protein changes peaked between two and eight hours, but mRNA expression fold changes were much smaller than those of the proteins. mRNA concentrations shifted in a transient, pulse-like pattern and returned to values close to pre-treatment levels by the end of the experiment. In contrast, protein concentrations switched only once and established a new steady state, consistent with the dominant role of protein regulation during misfolding stress. Finally, we generated hypotheses on specific regulatory modes for some genes.

Plasma fatty acids, oxylipins, and risk of myocardial infarction: the Singapore Chinese Health Study.

We aimed to examine the prospective association between plasma FAs, oxylipins, and risk of acute myocardial infarction (AMI) in a Singapore Chinese population. A nested case-control study with 744 incident AMI cases and 744 matched controls aged 47-83 years was conducted within the Singapore Chinese Health Study. Nineteen plasma FAs and 12 oxylipins were quantified using MS. These were grouped into 12 FA clusters and 5 oxylipin clusters using hierarchical clustering, and their associations with AMI risk were assessed. Long-chain n-3 FAs [odds ratio (OR) = 0.67 per SD increase, 95% confidence interval (CI): 0.53-0.84, P < 0.001] and stearic acid (OR = 0.65, 95% CI: 0.44-0.97, P = 0.03) were inversely associated with AMI risk, whereas arachidonic acid (AA) was positively associated with AMI risk (OR = 1.25, 95% CI: 1.03-1.52, P = 0.02) in the multivariable model with adjustment for other FAs. Further adjustment for oxylipins did not substantially change these associations. An inverse association was observed between AA-derived oxylipin, thromboxane (TX)B2, and AMI risk (OR = 0.81, 95% CI: 0.71-0.93, P = 0.003). Circulating long-chain n-3 FAs and stearic acid were associated with a lower and AA was associated with a higher AMI risk in this Chinese population. The association between the oxylipin TXB2 and AMI requires further research.

EBprot: Statistical analysis of labeling-based quantitative proteomics data.

Labeling-based proteomics is a powerful method for detection of differentially expressed proteins (DEPs). The current data analysis platform typically relies on protein-level ratios, which is obtained by summarizing peptide-level ratios for each protein. In shotgun proteomics, however, some proteins are quantified with more peptides than others, and this reproducibility information is not incorporated into the differential expression (DE) analysis. Here, we propose a novel probabilistic framework EBprot that directly models the peptide-protein hierarchy and rewards the proteins with reproducible evidence of DE over multiple peptides. To evaluate its performance with known DE states, we conducted a simulation study to show that the peptide-level analysis of EBprot provides better receiver-operating characteristic and more accurate estimation of the false discovery rates than the methods based on protein-level ratios. We also demonstrate superior classification performance of peptide-level EBprot analysis in a spike-in dataset. To illustrate the wide applicability of EBprot in different experimental designs, we applied EBprot to a dataset for lung cancer subtype analysis with biological replicates and another dataset for time course phosphoproteome analysis of EGF-stimulated HeLa cells with multiplexed labeling. Through these examples, we show that the peptide-level analysis of EBprot is a robust alternative to the existing statistical methods for the DE analysis of labeling-based quantitative datasets. The software suite is freely available on the Sourceforge website http://ebprot.sourceforge.net/. All MS data have been deposited in the ProteomeXchange with identifier PXD001426 (http://proteomecentral.proteomexchange.org/dataset/PXD001426/).

The Pittsburgh Sleep Quality Index in a multi-ethnic Asian population contains a three-factor structure.

PURPOSE: The Pittsburgh Sleep Quality Index (PSQI) is a widely used measure for assessing sleep impairment. Although it was developed as a unidimensional instrument, there is much debate that it contains multidimensional latent constructs. We examined the dimensionality of the underlying factor structure of PSQI in Singapore, a rapidly industrialising Asian country with multi-ethnicities representing the Chinese, Malays and Indians. METHODS: The PSQI was administered through an interviewer-based questionnaire in two separate population-based cross-sectional surveys. An explanatory factor analysis (EFA) was first used to explore the underlying construct of the PSQI in both studies. Then, a confirmatory factor analysis (CFA) was conducted to evaluate an optimal factor model by comparing against other possible models identified in EFA. RESULTS: There are three correlated yet distinguishable factors that account for an individual's sleep experience from the same best-fit model obtained in both studies: perceived sleep quality, daily disturbances and sleep efficiency. Our three-factor structure of PSQI is superior to the originally intended unidimensional model. Our model also shows the best-fit indices when compared to the previously reported single-factor, two-factor and three-factor (by Cole et al.) models in a multi-ethnic Asian population. CONCLUSION: There is strong evidence that the PSQI contains a three-factor rather than a unidimensional structure in a multi-ethnic Asian population. Scoring the PSQI along their multidimensional perspectives may provide a more accurate understanding of the relationship between sleep impairment and health conditions rather than using a single global score.

An integrated signature of extracellular matrix proteins and a diastolic function imaging parameter predicts post-MI long-term outcomes.

Background: Patients suffering from acute myocardial infarction (AMI) are at risk of secondary outcomes including major adverse cardiovascular events (MACE) and heart failure (HF). Comprehensive molecular phenotyping and cardiac imaging during the post-discharge time window may provide cues for risk stratification for the outcomes. Materials and methods: In a prospective AMI cohort in New Zealand (N = 464), we measured plasma proteins and lipids 30 days after hospital discharge and inferred a unified partial correlation network with echocardiographic variables and established clinical biomarkers (creatinine, c-reactive protein, cardiac troponin I and natriuretic peptides). Using a network-based data integration approach (iOmicsPASS+), we identified predictive signatures of long-term secondary outcomes based on plasma protein, lipid, imaging markers and clinical biomarkers and assessed the prognostic potential in an independent cohort from Singapore (N = 190). Results: The post-discharge levels of plasma proteins and lipids showed strong correlations within each molecular type, reflecting concerted homeostatic regulation after primary MI events. However, the two molecular types were largely independent with distinct correlation structures with established prognostic imaging parameters and clinical biomarkers. To deal with massively correlated predictive features, we used iOmicsPASS + to identify subnetwork signatures of 211 and 189 data features (nodes) predictive of MACE and HF events, respectively (160 overlapping). The predictive features were primarily imaging parameters, including left ventricular and atrial parameters, tissue Doppler parameters, and proteins involved in extracellular matrix (ECM) organization, cell differentiation, chemotaxis, and inflammation. The network signatures contained plasma protein pairs with area-under-the-curve (AUC) values up to 0.74 for HF prediction in the validation cohort, but the pair of NT-proBNP and fibulin-3 (EFEMP1) was the best predictor (AUC = 0.80). This suggests that there were a handful of plasma proteins with mechanistic and functional roles in predisposing patients to the secondary outcomes, although they may be weaker prognostic markers than natriuretic peptides individually. Among those, the diastolic function parameter (E/e' - an indicator of left ventricular filling pressure) and two ECM proteins, EFEMP1 and follistatin-like 3 (FSTL3) showed comparable performance to NT-proBNP and outperformed left ventricular measures as benchmark prognostic factors for post-MI HF. Conclusion: Post-discharge levels of E/e', EFEMP1 and FSTL3 are promising complementary markers of secondary adverse outcomes in AMI patients.

Plasma Metabolome and Lipidome Associations with Type 2 Diabetes and Diabetic Nephropathy.

We conducted untargeted metabolomics analysis of plasma samples from a cross-sectional case-control study with 30 healthy controls, 30 patients with diabetes mellitus and normal renal function (DM-N), and 30 early diabetic nephropathy (DKD) patients using liquid chromatography-mass spectrometry (LC-MS). We employed two different modes of MS acquisition on a high-resolution MS instrument for identification and semi-quantification, and analyzed data using an advanced multivariate method for prioritizing differentially abundant metabolites. We obtained semi-quantification data for 1088 unique compounds (~55% lipids), excluding compounds that may be either exogenous compounds or treated as medication. Supervised classification analysis over a confounding-free partial correlation network shows that prostaglandins, phospholipids, nucleotides, sugars, and glycans are elevated in the DM-N and DKD patients, whereas glutamine, phenylacetylglutamine, 3-indoxyl sulfate, acetylphenylalanine, xanthine, dimethyluric acid, and asymmetric dimethylarginine are increased in DKD compared to DM-N. The data recapitulate the well-established plasma metabolome changes associated with DM-N and suggest uremic solutes and oxidative stress markers as the compounds indicating early renal function decline in DM patients.

iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery.

Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while others use biological networks to propagate differential expression signals and find consensus signatures. However, few approaches directly consider molecular interaction as a data feature, the essential linker between different omics data sets. The increasing availability of genome-scale interactome data connecting different molecular levels motivates a new class of methods to extract interactive signals from multiomics data. Here we developed iOmicsPASS, a tool to search for predictive subnetworks consisting of molecular interactions within and between related omics data types in a supervised analysis setting. Based on user-provided network data and relevant omics data sets, iOmicsPASS computes a score for each molecular interaction, and applies a modified nearest shrunken centroid algorithm to the scores to select densely connected subnetworks that can accurately predict each phenotypic group. iOmicsPASS detects a sparse set of predictive molecular interactions without loss of prediction accuracy compared to alternative methods, and the selected network signature immediately provides mechanistic interpretation of the multiomics profile representing each sample group. Extensive simulation studies demonstrate clear benefit of interaction-level modeling. iOmicsPASS analysis of TCGA/CPTAC breast cancer data also highlights new transcriptional regulatory network underlying the basal-like subtype as positive protein markers, a result not seen through analysis of individual omics data.

Polyubiquitin Chains Linked by Lysine Residue 48 (K48) Selectively Target Oxidized Proteins In Vivo.

Aims: Ubiquitin is a highly conserved protein modifier that heavily accumulates during the oxidative stress response. Here, we investigated the role of the ubiquitination system, particularly at the linkage level, in the degradation of oxidized proteins. The function of ubiquitin in the removal of oxidized proteins remains elusive because of the wide range of potential targets and different roles that polyubiquitin chains play. Therefore, we describe in detail the dynamics of the K48 ubiquitin response as the canonical signal for protein degradation. We identified ubiquitin targets and defined the relationship between protein ubiquitination and oxidation during the stress response. Results: Combining oxidized protein isolation, linkage-specific ubiquitination screens, and quantitative proteomics, we found that K48 ubiquitin accumulated at both the early and late phases of the stress response. We further showed that a fraction of oxidized proteins are conjugated with K48 ubiquitin. We identified ∼750 ubiquitinated proteins and ∼400 oxidized proteins that were modified during oxidative stress, and around half of which contain both modifications. These proteins were highly abundant and function in translation and energy metabolism. Innovation and Conclusion: Our work showed for the first time that K48 ubiquitin modifies a large fraction of oxidized proteins, demonstrating that oxidized proteins can be targeted by the ubiquitin/proteasome system. We suggest that oxidized proteins that rapidly accumulate during stress are subsequently ubiquitinated and degraded during the late phase of the response. This delay between oxidation and ubiquitination may be necessary for reprogramming protein dynamics, restoring proteostasis, and resuming cell growth.

Plasma Protein and MicroRNA Biomarkers of Insulin Resistance: A Network-Based Integrative -Omics Analysis.

Although insulin resistance (IR) is a key pathophysiologic condition underlying various metabolic disorders, impaired cellular glucose uptake is one of many manifestations of metabolic derangements in the human body. To study the systems-wide molecular changes associated with obesity-dependent IR, we integrated information on plasma proteins and microRNAs in eight obese insulin-resistant (OIR, HOMA-IR > 2.5) and nine lean insulin-sensitive (LIS, HOMA-IR < 1.0) normoglycemic males. Of 374 circulating miRNAs we profiled, 65 species increased and 73 species decreased in the OIR compared to the LIS subjects, suggesting that the overall balance of the miRNA secretome is shifted in the OIR subjects. We also observed that 40 plasma proteins increased and 4 plasma proteins decreased in the OIR subjects compared to the LIS subjects, and most proteins are involved in metabolic and endocytic functions. We used an integrative -omics analysis framework called iOmicsPASS to link differentially regulated miRNAs with their target genes on the TargetScan map and the human protein interactome. Combined with tissue of origin information, the integrative analysis allowed us to nominate obesity-dependent and obesity-independent protein markers, along with potential sites of post-transcriptional regulation by some of the miRNAs. We also observed the changes in each -omics platform that are not linked by the TargetScan map, suggesting that proteins and microRNAs provide orthogonal information for the progression of OIR. In summary, our integrative analysis provides a network of elevated plasma markers of OIR and a global shift of microRNA secretome composition in the blood plasma.

Metabolic Remodeling during Liver Regeneration.

Liver disease is linked to a decreased capacity of hepatocytes to divide. In addition, cellular metabolism is important for tissue homeostasis and regeneration. Since metabolic changes are a hallmark of liver disease, we investigated the connections between metabolism and cell division. We determined global metabolic changes at different stages of liver regeneration using a combination of integrated transcriptomic and metabolomic analyses with advanced functional redox in vivo imaging. Our data indicate that blocking hepatocyte division during regeneration leads to mitochondrial dysfunction and downregulation of oxidative pathways. This resulted in an increased redox ratio and hyperactivity of alanine transaminase allowing the production of alanine and α-ketoglutarate from pyruvate when mitochondrial functions are impaired. Our data suggests that during liver regeneration, cell division leads to hepatic metabolic remodeling. Moreover, we demonstrate that hepatocytes are equipped with a flexible metabolic machinery able to adapt dynamically to changes during tissue regeneration.

Factors influencing infection by pandemic influenza A(H1N1)pdm09 over three epidemic waves in Singapore.

INTRODUCTION: Previous influenza pandemics had second and on occasion third waves in many countries that were at times more severe than the initial pandemic waves. OBJECTIVE: This study aims to determine the seroepidemiology of successive waves of H1N1pdm09 infections in Singapore and the overall risks of infection. METHODS: We performed a cohort study amongst 838 adults, with blood samples provided upon recruitment and at 5 points from 2009 to 2011 and tested by haemagglutination inhibition (HI) with A/California/7/2009 (H1N1pdm09). Surveys on key demographic and clinical information were conducted at regular intervals, and associations between seroconversion and these variables were investigated. RESULTS: After the initial wave from June to September 2009, second and third waves occurred from November 2009 to February 2010 and April to June 2010, respectively. Seroconversion was 13·5% during the first wave and decreased to 6·2% and 6·8% in subsequent waves. Across the three waves, the elderly and those with higher starting HI titres were at lower risk of seroconversion, while those with larger households were at greater risk. Those with higher starting HI titres were also less likely to have an acute respiratory infection. CONCLUSIONS: The second and third waves in Singapore had lower serological attack rates than the first wave. The elderly and those with higher HI titres had lower risk, while those in larger households had higher risk of seroconversion.