Serum C‐reactive protein in the prediction of cardiovascular diseases: Overview of the latest clinical studies and public health practice

Cardiovascular disease is the most common cause of morbidity and mortality globally. Epidemiological studies using high‐sensitivity assays for serum C‐reactive protein have shown a consistent association between cardiovascular disease risk and serum C‐reactive protein concentrations. C‐reactive protein is a biomarker for inflammation, and has been established in clinical practice as an independent risk factor for cardiovascular disease events. There is evidence that serum C‐reactive protein is an excellent biomarker of cardiovascular disease and is also an independent and strong predictor of adverse cardiovascular events. Further characterization of the impact and influence of lifestyle exposures and genetic variation on the C‐reactive protein response to cardiovascular disease events may have implications for the therapeutic approaches to reduce cardiovascular disease events. This review summarizes the studies that have examined the association between serum C‐reactive protein and the risk of cardiovascular disease. We also discuss the impact of independent factors and C‐reactive protein genetic polymorphisms on baseline plasma C‐reactive protein levels.     

Rate of resistance evolution and polymorphism in long‐ and short‐lived hosts

Recent theoretical work has shown that long‐lived hosts are expected to evolve higher equilibrium levels of disease resistance than shorter‐lived hosts, but questions of how longevity affects the rate of resistance evolution and the maintenance of polymorphism remain unanswered. Conventional wisdom suggests that adaptive evolution should occur more slowly in long‐lived organisms than in short‐lived organisms. However, the opposite may be true for the evolution of disease‐resistance traits where exposure to disease, and therefore the strength of selection for resistance increases with longevity. In a single locus model of innate resistance to a frequency‐dependent, sterilizing disease, longer lived hosts evolved resistance more rapidly than short‐lived hosts. Moreover, resistance in long‐lived hosts could only be polymorphic for more costly and more extreme resistance levels than short‐lived hosts. The increased rate of evolution occurred in spite of longer generation times because longer‐lived hosts had both a longer period of exposure to disease as well as higher disease prevalence. Qualitatively similar results were found when the model was extended to mortality‐inducing diseases, or to density‐dependent transmission modes. Our study shows that the evolutionary dynamics of host resistance is determined by more than just levels of resistance and cost, but is highly sensitive to the life‐history traits of the host.     

MCLPMDA: A novel method for miRNA‐disease association prediction based on matrix completion and label propagation

MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.     

Prevalence,incidence and mortality from cardiovascular disease in patients with pooled and specific severe mental illness: a large‐scale meta‐analysis of 3,211,768 patients and 113,383,368 controls

People with severe mental illness (SMI) – schizophrenia, bipolar disorder and major depressive disorder – appear at risk for cardiovascular disease (CVD), but a comprehensive meta‐analysis is lacking. We conducted a large‐scale meta‐analysis assessing the prevalence and incidence of CVD; coronary heart disease; stroke, transient ischemic attack or cerebrovascular disease; congestive heart failure; peripheral vascular disease; and CVD‐related death in SMI patients (N=3,211,768) versus controls (N=113,383,368) (92 studies). The pooled CVD prevalence in SMI patients (mean age 50 years) was 9.9% (95% CI: 7.4‐13.3). Adjusting for a median of seven confounders, patients had significantly higher odds of CVD versus controls in cross‐sectional studies (odds ratio, OR=1.53, 95% CI: 1.27‐1.83; 11 studies), and higher odds of coronary heart disease (OR=1.51, 95% CI: 1.47‐1.55) and cerebrovascular disease (OR=1.42, 95% CI: 1.21‐1.66). People with major depressive disorder were at increased risk for coronary heart disease, while those with schizophrenia were at increased risk for coronary heart disease, cerebrovascular disease and congestive heart failure. Cumulative CVD incidence in SMI patients was 3.6% (95% CI: 2.7‐5.3) during a median follow‐up of 8.4 years (range 1.8‐30.0). Adjusting for a median of six confounders, SMI patients had significantly higher CVD incidence than controls in longitudinal studies (hazard ratio, HR=1.78, 95% CI: 1.60‐1.98; 31 studies). The incidence was also higher for coronary heart disease (HR=1.54, 95% CI: 1.30‐1.82), cerebrovascular disease (HR=1.64, 95% CI: 1.26‐2.14), congestive heart failure (HR=2.10, 95% CI: 1.64‐2.70), and CVD‐related death (HR=1.85, 95% CI: 1.53‐2.24). People with major depressive disorder, bipolar disorder and schizophrenia were all at increased risk of CVD‐related death versus controls. CVD incidence increased with antipsychotic use (p=0.008), higher body mass index (p=0.008) and higher baseline CVD prevalence (p=0.03) in patients vs. controls. Moreover, CVD prevalence (p=0.007), but not CVD incidence (p=0.21), increased in more recently conducted studies. This large‐scale meta‐analysis confirms that SMI patients have significantly increased risk of CVD and CVD‐related mortality, and that elevated body mass index, antipsychotic use, and CVD screening and management require urgent clinical attention.     

Molecular mechanisms of copper metabolism and the role of the Menkes disease protein

Menkes disease is an X‐linked, recessive disorder of copper metabolism that occurs in approximately 1 in 200,000 live births. The condition is characterized by skeletal abnormalities, severe mental retardation, neurologic degeneration, and patient mortality in early childhood. The symptoms of Menkes disease result from a deficiency of serum copper and copper‐dependent enzymes. A candidate gene for the disease has been isolated and designated MNK. The MNK gene codes for a P‐type cation transporting ATPase, based on homology to known P‐type ATPases and in vitro experimentation. cDNA clones of MNK in Menkes patients show diminished or absented hybridization in northern blot experiments. The Menkes protein functions to export excess intracellular copper and activates upon Cu(I) binding to the six metal‐binding repeats in the amino‐terminal domain. The loss of Menkes protein activity blocks the export of dietary copper from the gastrointestinal tract and causes the copper deficiency associated with Menkes disease. Each of the Menkes protein amino‐terminal repeats contains a conserved ‐X‐Met‐X‐Cys‐X‐X‐Cys‐ motif (where X is any amino acid). These metal‐binding repeats are conserved in other cation exporting ATPases involved in metal metabolism and in proteins involved in cellular defense against heavy metals in both prokaryotes and eukaryotes. An overview of copper metabolism in humans and a discussion of our understanding of the molecular basis of cellular copper homeostasis is presented. This forms the basis for a discussion of Menkes disease and the protein deficit in this disease. © 1998 John Wiley & Sons, Inc. J Biochem Toxicol 13: 93–106, 1999     

LuTHy: a double‐readout bioluminescence‐based two‐hybrid technology for quantitative mapping of protein–protein interactions in mammalian cells

Information on protein–protein interactions (PPIs) is of critical importance for studying complex biological systems and developing therapeutic strategies. Here, we present a double‐readout bioluminescence‐based two‐hybrid technology, termed LuTHy, which provides two quantitative scores in one experimental procedure when testing binary interactions. PPIs are first monitored in cells by quantification of bioluminescence resonance energy transfer (BRET) and, following cell lysis, are again quantitatively assessed by luminescence‐based co‐precipitation (LuC). The double‐readout procedure detects interactions with higher sensitivity than traditional single‐readout methods and is broadly applicable, for example, for detecting the effects of small molecules or disease‐causing mutations on PPIs. Applying LuTHy in a focused screen, we identified 42 interactions for the presynaptic chaperone CSPα, causative to adult‐onset neuronal ceroid lipofuscinosis (ANCL), a progressive neurodegenerative disease. Nearly 50% of PPIs were found to be affected when studying the effect of the disease‐causing missense mutations L115R and ?L116 in CSPα with LuTHy. Our study presents a robust, sensitive research tool with high utility for investigating the molecular mechanisms by which disease‐associated mutations impair protein activity in biological systems.     

Estimation of log‐odds ratio from group testing data using Firth correction

We consider the estimation of the prevalence of a rare disease, and the log‐odds ratio for two specified groups of individuals from group testing data. For a low‐prevalence disease, the maximum likelihood estimate of the log‐odds ratio is severely biased. However, Firth correction to the score function leads to a considerable improvement of the estimator. Also, for a low‐prevalence disease, if the diagnostic test is imperfect, the group testing is found to yield more precise estimate of the log‐odds ratio than the individual testing.     

Hsp104 facilitates the endoplasmic‐reticulum–associated degradation of disease‐associated and aggregation‐prone substrates

Misfolded proteins in the endoplasmic reticulum (ER) are selected for ER‐associated degradation (ERAD). More than 60 disease‐associated proteins are substrates for the ERAD pathway due to the presence of missense or nonsense mutations. In yeast, the Hsp104 molecular chaperone disaggregates detergent‐insoluble ERAD substrates, but the spectrum of disease‐associated ERAD substrates that may be aggregation prone is unknown. To determine if Hsp104 recognizes aggregation‐prone ERAD substrates associated with human diseases, we developed yeast expression systems for a hydrophobic lipid‐binding protein, apolipoprotein B (ApoB), along with a chimeric protein harboring a nucleotide‐binding domain from the cystic fibrosis transmembrane conductance regulator (CFTR) into which disease‐causing mutations were introduced. We discovered that Hsp104 facilitates the degradation of ER‐associated ApoB as well as a truncated CFTR chimera in which a premature stop codon corresponds to a disease‐causing mutation. Chimeras containing a wild‐type version of the CFTR domain or a different mutation were stable and thus Hsp104 independent. We also discovered that the detergent solubility of the unstable chimera was lower than the stable chimeras, and Hsp104 helped retrotranslocate the unstable chimera from the ER, consistent with disaggregase activity. To determine why the truncated chimera was unstable, we next performed molecular dynamics simulations and noted significant unraveling of the CFTR nucleotide‐binding domain. Because human cells lack Hsp104, these data indicate that an alternate disaggregase or mechanism facilitates the removal of aggregation‐prone, disease‐causing ERAD substrates in their native environments.     

Disease signatures are robust across tissues and experiments

Meta‐analyses combining gene expression microarray experiments offer new insights into the molecular pathophysiology of disease not evident from individual experiments. Although the established technical reproducibility of microarrays serves as a basis for meta‐analysis, pathophysiological reproducibility across experiments is not well established. In this study, we carried out a large‐scale analysis of disease‐associated experiments obtained from NCBI GEO, and evaluated their concordance across a broad range of diseases and tissue types. On evaluating 429 experiments, representing 238 diseases and 122 tissues from 8435 microarrays, we find evidence for a general, pathophysiological concordance between experiments measuring the same disease condition. Furthermore, we find that the molecular signature of disease across tissues is overall more prominent than the signature of tissue expression across diseases. The results offer new insight into the quality of public microarray data using pathophysiological metrics, and support new directions in meta‐analysis that include characterization of the commonalities of disease irrespective of tissue, as well as the creation of multi‐tissue systems models of disease pathology using public data.     

Rice varieties with resistance to multiple races of Magnaporthe oryzae offer opportunities to manage rice blast in Australia

Rice blast caused by Magnaporthe oryzae is the most destructive disease of rice worldwide. Development of resistant varieties is considered as the most cost‐effective and sustainable way to manage rice blast. However, there remains a lack of knowledge about the resistance of rice varieties to blast disease in Australia. This study was conducted to determine if there was any resistance existing among the rice varieties grown in Australia to M. oryzae isolates from this country that belong to different races. There was a resistant reaction of the variety SHZ‐2 to all the five races of IA‐1, IA‐3, IA‐63, IB‐3 and IB‐59, with a percent disease index (%DI) less than 40. Varieties NTR587, BR‐IRGA‐409, Ceysvoni and Rikuto Norin 20 showed a resistant reaction to races IA‐3, IA‐63, IB‐3 and IB‐59; and the variety Kyeema exhibited a resistant reaction to races IA‐3, IB‐3 and IB‐59. For the races IA‐1 and IB‐59 with more than one isolate, varieties with differential disease reactions across different isolates belonging to the same race were also revealed: five varieties, Langi, Opus, Sherpa, Viet 1 and Topaz, exhibited differential disease reactions to the three IA‐1 isolates; 10 varieties showed differential disease reactions to the four IB‐59 isolates; in addition, the varieties that had differential disease reactions to the IA‐1 isolates also exhibited differential disease reactions to the IB‐59 isolates of race. This study provides valuable resistance sources for breeding programmes to develop rice varieties with resistance to multiple races of M. oryzae in Australia.     

Inferring infection hazard in wildlife populations by linking data across individual and population scales

Our ability to infer unobservable disease‐dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time‐averaged value and are based on population‐level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within‐host processes to FOI is needed. Specifically, within‐host antibody kinetics in wildlife hosts can be short‐lived and produce patterns that are repeatable across individuals, suggesting individual‐level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population‐level FOI signal can be recovered from individual‐level antibody kinetics, despite substantial individual‐level variation. In addition to improving inference, the cross‐scale quantitative antibody approach we describe can reveal insights into drivers of individual‐based variation in disease response, and the role of poorly understood processes such as secondary infections, in population‐level dynamics of disease.     

Towards an eco‐phylogenetic framework for infectious disease ecology

Identifying patterns and drivers of infectious disease dynamics across multiple scales is a fundamental challenge for modern science. There is growing awareness that it is necessary to incorporate multi‐host and/or multi‐parasite interactions to understand and predict current and future disease threats better, and new tools are needed to help address this task. Eco‐phylogenetics (phylogenetic community ecology) provides one avenue for exploring multi‐host multi‐parasite systems, yet the incorporation of eco‐phylogenetic concepts and methods into studies of host pathogen dynamics has lagged behind. Eco‐phylogenetics is a transformative approach that uses evolutionary history to infer present‐day dynamics. Here, we present an eco‐phylogenetic framework to reveal insights into parasite communities and infectious disease dynamics across spatial and temporal scales. We illustrate how eco‐phylogenetic methods can help untangle the mechanisms of host–parasite dynamics from individual (e.g. co‐infection) to landscape scales (e.g. parasite/host community structure). An improved ecological understanding of multi‐host and multi‐pathogen dynamics across scales will increase our ability to predict disease threats.     

Efficient Inoculation of Rice black‐streaked dwarf virus to Maize Using Laodelphax striatellus Fallen

Maize rough dwarf disease caused by Rice black‐streaked dwarf virus (RBSDV) is the most important disease of maize in China. Although deploying disease resistant hybrids would be the most effective way to control the disease, development of resistant hybrids has been limited by virus transmission rates that are too low for effective screening. An efficient inoculation technique for RBSDV was developed using Laodelphax striatellus Fallen, in which a virus‐free planthopper colony was developed and viruliferous planthoppers were obtained by allowing a 3‐ to 4‐day acquisition access period on RBSDV‐infected wheat plants. Planthoppers were then allowed a 25‐ to 28‐day latent period on wheat seedlings followed by a 3‐day inoculation access period on two‐to‐three‐leaf stage maize seedlings. By 35 days postinoculation, susceptible hybrid ‘Zhengdan 958’, inbred lines of ‘Ye 107’ and ‘Ye 478’ plants showed 100% RBSDV infection with symptoms of stunting plants, darkening leaves and white waxy swellings on underside of leaves. At tasseling stage, average disease indices were from 96.4 to 100.0%. Enzyme‐linked immunosorbent assays were correlated with the presence of symptoms. The high efficiency of RBSDV transmission obtained using this technique provides a reliable procedure to screen for RBSDV resistance in maize.     

Wildlife disease prevalence in human‐modified landscapes

Human‐induced landscape change associated with habitat loss and fragmentation places wildlife populations at risk. One issue in these landscapes is a change in the prevalence of disease which may result in increased mortality and reduced fecundity. Our understanding of the influence of habitat loss and fragmentation on the prevalence of wildlife diseases is still in its infancy. What is evident is that changes in disease prevalence as a result of human‐induced landscape modification are highly variable. The importance of infectious diseases for the conservation of wildlife will increase as the amount and quality of suitable habitat decreases due to human land‐use pressures. We review the experimental and observational literature of the influence of human‐induced landscape change on wildlife disease prevalence, and discuss disease transmission types and host responses as mechanisms that are likely to determine the extent of change in disease prevalence. It is likely that transmission dynamics will be the key process in determining a pathogen's impact on a host population, while the host response may ultimately determine the extent of disease prevalence. Finally, we conceptualize mechanisms and identify future research directions to increase our understanding of the relationship between human‐modified landscapes and wildlife disease prevalence. This review highlights that there are rarely consistent relationships between wildlife diseases and human‐modified landscapes. In addition, variation is evident between transmission types and landscape types, with the greatest positive influence on disease prevalence being in urban landscapes and directly transmitted disease systems. While we have a limited understanding of the potential influence of habitat loss and fragmentation on wildlife disease, there are a number of important areas to address in future research, particularly to account for the variability in increased and decreased disease prevalence. Previous studies have been based on a one‐dimensional comparison between unmodified and modified sites. What is lacking are spatially and temporally explicit quantitative approaches which are required to enable an understanding of the range of key causal mechanisms and the reasons for variability. This is particularly important for replicated studies across different host‐pathogen systems. Furthermore, there are few studies that have attempted to separate the independent effects of habitat loss and fragmentation on wildlife disease, which are the major determinants of wildlife population dynamics in human‐modified landscapes. There is an urgent need to understand better the potential causal links between the processes of human‐induced landscape change and the associated influences of habitat fragmentation, matrix hostility and loss of connectivity on an animal's physiological stress, immune response and disease susceptibility. This review identified no study that had assessed the influence of human‐induced landscape change on the prevalence of a wildlife sexually transmitted disease. A better understanding of the various mechanisms linking human‐induced landscape change and the prevalence of wildlife disease will lead to more successful conservation management outcomes.     

Half the story: Thermal effects on within‐host infectious disease progression in a warming climate

Immune defense is temperature dependent in cold‐blooded vertebrates (CBVs) and thus directly impacted by global warming. We examined whether immunity and within‐host infectious disease progression are altered in CBVs under realistic climate warming in a seasonal mid‐latitude setting. Going further, we also examined how large thermal effects are in relation to the effects of other environmental variation in such a setting (critical to our ability to project infectious disease dynamics from thermal relationships alone). We employed the three‐spined stickleback and three ecologically relevant parasite infections as a “wild” model. To generate a realistic climatic warming scenario we used naturalistic outdoors mesocosms with precise temperature control. We also conducted laboratory experiments to estimate thermal effects on immunity and within‐host infectious disease progression under controlled conditions. As experimental readouts we measured disease progression for the parasites and expression in 14 immune‐associated genes (providing insight into immunophenotypic responses). Our mesocosm experiment demonstrated significant perturbation due to modest warming (+2°C), altering the magnitude and phenology of disease. Our laboratory experiments demonstrated substantial thermal effects. Prevailing thermal effects were more important than lagged thermal effects and disease progression increased or decreased in severity with increasing temperature in an infection‐specific way. Combining laboratory‐determined thermal effects with our mesocosm data, we used inverse modeling to partition seasonal variation in Saprolegnia disease progression into a thermal effect and a latent immunocompetence effect (driven by nonthermal environmental variation and correlating with immune gene expression). The immunocompetence effect was large, accounting for at least as much variation in Saprolegnia disease as the thermal effect. This suggests that managers of CBV populations in variable environments may not be able to reliably project infectious disease risk from thermal data alone. Nevertheless, such projections would be improved by primarily considering prevailing thermal effects in the case of within‐host disease and by incorporating validated measures of immunocompetence.     

Individual variations in infectiousness explain long-term disease persistence in wildlife populations

Viral disease persistence in species without a reservoir host is of importance for public health and disease management. But how can disease persistence be explained? We developed a spatially‐explicit individual‐based model that takes into account both ecological and viral traits as well as variable space to test disease persistence hypotheses under debate. We introduce a novel concept of modeling alternative disease courses at the individual level, causing transient infections or killing infected animals, with the lethally infected having a variable life‐expectancy. We systematically distinguish between disease invasion and persistence. We use classical swine fever (CSF), an economically very important livestock disease in a social host, the wild boar, as a reference system to test and rank the persistence hypotheses under debate. Parameter values for host population demographics and CSF epidemiology reflect current knowledge. Sensitivity analysis of the model parameters revealed that the most important factor for disease persistence is a disease profile with mostly transient, i.e. surviving individuals requiring immunity, and some chronically, long‐term infected animals. Immune individuals can constantly produce susceptible offspring, while some chronically infected individuals act as ‘super spreaders’ in time. Thus, variations in the course of the disease at the individual level are important factors determining persistence, which is usually not taken into account in the prominent measure of epidemiology, i.e. the basic reproductive number R₀, which reflects the ‘reproductive potential’ of the infected sub‐population. We discuss our results with regard to the general issues of modeling epidemics and disease management issues.     

Demographic buffering and compensatory recruitment promotes the persistence of disease in a wildlife population

Demographic buffering allows populations to persist by compensating for fluctuations in vital rates, including disease‐induced mortality. Using long‐term data on a badger (Meles meles Linnaeus, 1758) population naturally infected with Mycobacterium bovis, we built an integrated population model to quantify impacts of disease, density and environmental drivers on survival and recruitment. Badgers exhibit a slow life‐history strategy, having high rates of adult survival with low variance, and low but variable rates of recruitment. Recruitment exhibited strong negative density‐dependence, but was not influenced by disease, while adult survival was density independent but declined with increasing prevalence of diseased individuals. Given that reproductive success is not depressed by disease prevalence, density‐dependent recruitment of cubs is likely to compensate for disease‐induced mortality. This combination of slow life history and compensatory recruitment promotes the persistence of a naturally infected badger population and helps to explain the badger's role as a persistent reservoir of M. bovis.     

Potentials of single‐cell biology in identification and validation of disease biomarkers

Single‐cell biology is considered a new approach to identify and validate disease‐specific biomarkers. However, the concern raised by clinicians is how to apply single‐cell measurements for clinical practice, translate the message of single‐cell systems biology into clinical phenotype or explain alterations of single‐cell gene sequencing and function in patient response to therapies. This study is to address the importance and necessity of single‐cell gene sequencing in the identification and development of disease‐specific biomarkers, the definition and significance of single‐cell biology and single‐cell systems biology in the understanding of single‐cell full picture, the development and establishment of whole‐cell models in the validation of targeted biological function and the figure and meaning of single‐molecule imaging in single cell to trace intra‐single‐cell molecule expression, signal, interaction and location. We headline the important role of single‐cell biology in the discovery and development of disease‐specific biomarkers with a special emphasis on understanding single‐cell biological functions, e.g. mechanical phenotypes, single‐cell biology, heterogeneity and organization of genome function. We have reason to believe that such multi‐dimensional, multi‐layer, multi‐crossing and stereoscopic single‐cell biology definitely benefits the discovery and development of disease‐specific biomarkers.